[2023-12-25 01:42:47,646 INFO train.py line 128 253097] => Loading config ...
[2023-12-25 01:42:47,646 INFO train.py line 130 253097] Save path: exp/s3dis/semseg-pt-v3m1-0-rpe
[2023-12-25 01:42:48,415 INFO train.py line 131 253097] Config:
weight = None
resume = False
evaluate = True
test_only = False
seed = 25326354
save_path = 'exp/s3dis/semseg-pt-v3m1-0-rpe'
num_worker = 24
batch_size = 12
batch_size_val = None
batch_size_test = None
epoch = 3000
eval_epoch = 100
sync_bn = False
enable_amp = True
empty_cache = False
find_unused_parameters = False
mix_prob = 0.8
param_dicts = [dict(keyword='block', lr=0.0006)]
hooks = [
    dict(type='CheckpointLoader'),
    dict(type='IterationTimer', warmup_iter=2),
    dict(type='InformationWriter'),
    dict(type='SemSegEvaluator'),
    dict(type='CheckpointSaver', save_freq=None),
    dict(type='PreciseEvaluator', test_last=False)
]
train = dict(type='DefaultTrainer')
test = dict(type='SemSegTester', verbose=True)
model = dict(
    type='DefaultSegmentorV2',
    num_classes=13,
    backbone_out_channels=64,
    backbone=dict(
        type='PT-v3m1',
        in_channels=6,
        order=['z', 'z-trans', 'hilbert', 'hilbert-trans'],
        stride=(2, 2, 2, 2),
        enc_depths=(2, 2, 2, 6, 2),
        enc_channels=(32, 64, 128, 256, 512),
        enc_num_head=(2, 4, 8, 16, 32),
        enc_patch_size=(128, 128, 128, 128, 128),
        dec_depths=(2, 2, 2, 2),
        dec_channels=(64, 64, 128, 256),
        dec_num_head=(4, 4, 8, 16),
        dec_patch_size=(128, 128, 128, 128),
        mlp_ratio=4,
        qkv_bias=True,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        drop_path=0.3,
        shuffle_orders=True,
        pre_norm=True,
        enable_rpe=True,
        enable_flash=False,
        upcast_attention=True,
        upcast_softmax=True,
        cls_mode=False,
        pdnorm_bn=False,
        pdnorm_ln=False,
        pdnorm_decouple=True,
        pdnorm_adaptive=False,
        pdnorm_affine=True,
        pdnorm_conditions=('ScanNet', 'S3DIS', 'Structured3D')),
    criteria=[
        dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1),
        dict(
            type='LovaszLoss',
            mode='multiclass',
            loss_weight=1.0,
            ignore_index=-1)
    ])
optimizer = dict(type='AdamW', lr=0.006, weight_decay=0.05)
scheduler = dict(
    type='OneCycleLR',
    max_lr=[0.006, 0.0006],
    pct_start=0.05,
    anneal_strategy='cos',
    div_factor=10.0,
    final_div_factor=1000.0)
dataset_type = 'S3DISDataset'
data_root = 'data/s3dis'
data = dict(
    num_classes=13,
    ignore_index=-1,
    names=[
        'ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
        'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter'
    ],
    train=dict(
        type='S3DISDataset',
        split=('Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_6'),
        data_root='data/s3dis',
        transform=[
            dict(type='CenterShift', apply_z=True),
            dict(
                type='RandomDropout',
                dropout_ratio=0.2,
                dropout_application_ratio=0.2),
            dict(
                type='RandomRotate',
                angle=[-1, 1],
                axis='z',
                center=[0, 0, 0],
                p=0.5),
            dict(
                type='RandomRotate',
                angle=[-0.015625, 0.015625],
                axis='x',
                p=0.5),
            dict(
                type='RandomRotate',
                angle=[-0.015625, 0.015625],
                axis='y',
                p=0.5),
            dict(type='RandomScale', scale=[0.9, 1.1]),
            dict(type='RandomFlip', p=0.5),
            dict(type='RandomJitter', sigma=0.005, clip=0.02),
            dict(type='ChromaticAutoContrast', p=0.2, blend_factor=None),
            dict(type='ChromaticTranslation', p=0.95, ratio=0.05),
            dict(type='ChromaticJitter', p=0.95, std=0.05),
            dict(
                type='GridSample',
                grid_size=0.02,
                hash_type='fnv',
                mode='train',
                return_grid_coord=True),
            dict(type='SphereCrop', sample_rate=0.6, mode='random'),
            dict(type='SphereCrop', point_max=204800, mode='random'),
            dict(type='CenterShift', apply_z=False),
            dict(type='NormalizeColor'),
            dict(type='ToTensor'),
            dict(
                type='Collect',
                keys=('coord', 'grid_coord', 'segment'),
                feat_keys=('color', 'normal'))
        ],
        test_mode=False,
        loop=30),
    val=dict(
        type='S3DISDataset',
        split='Area_5',
        data_root='data/s3dis',
        transform=[
            dict(type='CenterShift', apply_z=True),
            dict(
                type='Copy',
                keys_dict=dict(coord='origin_coord',
                               segment='origin_segment')),
            dict(
                type='GridSample',
                grid_size=0.02,
                hash_type='fnv',
                mode='train',
                return_grid_coord=True),
            dict(type='CenterShift', apply_z=False),
            dict(type='NormalizeColor'),
            dict(type='ToTensor'),
            dict(
                type='Collect',
                keys=('coord', 'grid_coord', 'origin_coord', 'segment',
                      'origin_segment'),
                offset_keys_dict=dict(
                    offset='coord', origin_offset='origin_coord'),
                feat_keys=('color', 'normal'))
        ],
        test_mode=False),
    test=dict(
        type='S3DISDataset',
        split='Area_5',
        data_root='data/s3dis',
        transform=[
            dict(type='CenterShift', apply_z=True),
            dict(type='NormalizeColor')
        ],
        test_mode=True,
        test_cfg=dict(
            voxelize=dict(
                type='GridSample',
                grid_size=0.02,
                hash_type='fnv',
                mode='test',
                keys=('coord', 'color', 'normal'),
                return_grid_coord=True),
            crop=None,
            post_transform=[
                dict(type='CenterShift', apply_z=False),
                dict(type='ToTensor'),
                dict(
                    type='Collect',
                    keys=('coord', 'grid_coord', 'index'),
                    feat_keys=('color', 'normal'))
            ],
            aug_transform=[[{
                'type': 'RandomScale',
                'scale': [0.9, 0.9]
            }], [{
                'type': 'RandomScale',
                'scale': [0.95, 0.95]
            }], [{
                'type': 'RandomScale',
                'scale': [1, 1]
            }], [{
                'type': 'RandomScale',
                'scale': [1.05, 1.05]
            }], [{
                'type': 'RandomScale',
                'scale': [1.1, 1.1]
            }],
                           [{
                               'type': 'RandomScale',
                               'scale': [0.9, 0.9]
                           }, {
                               'type': 'RandomFlip',
                               'p': 1
                           }],
                           [{
                               'type': 'RandomScale',
                               'scale': [0.95, 0.95]
                           }, {
                               'type': 'RandomFlip',
                               'p': 1
                           }],
                           [{
                               'type': 'RandomScale',
                               'scale': [1, 1]
                           }, {
                               'type': 'RandomFlip',
                               'p': 1
                           }],
                           [{
                               'type': 'RandomScale',
                               'scale': [1.05, 1.05]
                           }, {
                               'type': 'RandomFlip',
                               'p': 1
                           }],
                           [{
                               'type': 'RandomScale',
                               'scale': [1.1, 1.1]
                           }, {
                               'type': 'RandomFlip',
                               'p': 1
                           }]])))
num_worker_per_gpu = 6
batch_size_per_gpu = 3
batch_size_val_per_gpu = 1
batch_size_test_per_gpu = 1

[2023-12-25 01:42:48,415 INFO train.py line 132 253097] => Building model ...
[2023-12-25 01:42:48,720 INFO train.py line 209 253097] Num params: 46190553
[2023-12-25 01:42:48,890 INFO train.py line 134 253097] => Building writer ...
[2023-12-25 01:42:48,894 INFO train.py line 219 253097] Tensorboard writer logging dir: exp/s3dis/semseg-pt-v3m1-0-rpe
[2023-12-25 01:42:48,894 INFO train.py line 136 253097] => Building train dataset & dataloader ...
[2023-12-25 01:42:48,903 INFO s3dis.py line 55 253097] Totally 204 x 30 samples in ('Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_6') set.
[2023-12-25 01:42:48,903 INFO train.py line 138 253097] => Building val dataset & dataloader ...
[2023-12-25 01:42:48,905 INFO s3dis.py line 55 253097] Totally 68 x 1 samples in Area_5 set.
[2023-12-25 01:42:48,905 INFO train.py line 140 253097] => Building optimize, scheduler, scaler(amp) ...
[2023-12-25 01:42:48,909 INFO optimizer.py line 54 253097] Params Group 1 - lr: 0.006; Params: ['module.seg_head.weight', 'module.seg_head.bias', 'module.backbone.embedding.stem.conv.weight', 'module.backbone.embedding.stem.norm.weight', 'module.backbone.embedding.stem.norm.bias', 'module.backbone.enc.enc1.down.proj.weight', 'module.backbone.enc.enc1.down.proj.bias', 'module.backbone.enc.enc1.down.norm.0.weight', 'module.backbone.enc.enc1.down.norm.0.bias', 'module.backbone.enc.enc2.down.proj.weight', 'module.backbone.enc.enc2.down.proj.bias', 'module.backbone.enc.enc2.down.norm.0.weight', 'module.backbone.enc.enc2.down.norm.0.bias', 'module.backbone.enc.enc3.down.proj.weight', 'module.backbone.enc.enc3.down.proj.bias', 'module.backbone.enc.enc3.down.norm.0.weight', 'module.backbone.enc.enc3.down.norm.0.bias', 'module.backbone.enc.enc4.down.proj.weight', 'module.backbone.enc.enc4.down.proj.bias', 'module.backbone.enc.enc4.down.norm.0.weight', 'module.backbone.enc.enc4.down.norm.0.bias', 'module.backbone.dec.dec3.up.proj.0.weight', 'module.backbone.dec.dec3.up.proj.0.bias', 'module.backbone.dec.dec3.up.proj.1.weight', 'module.backbone.dec.dec3.up.proj.1.bias', 'module.backbone.dec.dec3.up.proj_skip.0.weight', 'module.backbone.dec.dec3.up.proj_skip.0.bias', 'module.backbone.dec.dec3.up.proj_skip.1.weight', 'module.backbone.dec.dec3.up.proj_skip.1.bias', 'module.backbone.dec.dec2.up.proj.0.weight', 'module.backbone.dec.dec2.up.proj.0.bias', 'module.backbone.dec.dec2.up.proj.1.weight', 'module.backbone.dec.dec2.up.proj.1.bias', 'module.backbone.dec.dec2.up.proj_skip.0.weight', 'module.backbone.dec.dec2.up.proj_skip.0.bias', 'module.backbone.dec.dec2.up.proj_skip.1.weight', 'module.backbone.dec.dec2.up.proj_skip.1.bias', 'module.backbone.dec.dec1.up.proj.0.weight', 'module.backbone.dec.dec1.up.proj.0.bias', 'module.backbone.dec.dec1.up.proj.1.weight', 'module.backbone.dec.dec1.up.proj.1.bias', 'module.backbone.dec.dec1.up.proj_skip.0.weight', 'module.backbone.dec.dec1.up.proj_skip.0.bias', 'module.backbone.dec.dec1.up.proj_skip.1.weight', 'module.backbone.dec.dec1.up.proj_skip.1.bias', 'module.backbone.dec.dec0.up.proj.0.weight', 'module.backbone.dec.dec0.up.proj.0.bias', 'module.backbone.dec.dec0.up.proj.1.weight', 'module.backbone.dec.dec0.up.proj.1.bias', 'module.backbone.dec.dec0.up.proj_skip.0.weight', 'module.backbone.dec.dec0.up.proj_skip.0.bias', 'module.backbone.dec.dec0.up.proj_skip.1.weight', 'module.backbone.dec.dec0.up.proj_skip.1.bias'].
[2023-12-25 01:42:48,909 INFO optimizer.py line 54 253097] Params Group 2 - lr: 0.0006; Params: ['module.backbone.enc.enc0.block0.cpe.0.weight', 'module.backbone.enc.enc0.block0.cpe.0.bias', 'module.backbone.enc.enc0.block0.cpe.1.weight', 'module.backbone.enc.enc0.block0.cpe.1.bias', 'module.backbone.enc.enc0.block0.cpe.2.weight', 'module.backbone.enc.enc0.block0.cpe.2.bias', 'module.backbone.enc.enc0.block0.norm1.0.weight', 'module.backbone.enc.enc0.block0.norm1.0.bias', 'module.backbone.enc.enc0.block0.attn.qkv.weight', 'module.backbone.enc.enc0.block0.attn.qkv.bias', 'module.backbone.enc.enc0.block0.attn.proj.weight', 'module.backbone.enc.enc0.block0.attn.proj.bias', 'module.backbone.enc.enc0.block0.attn.rpe.rpe_table', 'module.backbone.enc.enc0.block0.norm2.0.weight', 'module.backbone.enc.enc0.block0.norm2.0.bias', 'module.backbone.enc.enc0.block0.mlp.0.fc1.weight', 'module.backbone.enc.enc0.block0.mlp.0.fc1.bias', 'module.backbone.enc.enc0.block0.mlp.0.fc2.weight', 'module.backbone.enc.enc0.block0.mlp.0.fc2.bias', 'module.backbone.enc.enc0.block1.cpe.0.weight', 'module.backbone.enc.enc0.block1.cpe.0.bias', 'module.backbone.enc.enc0.block1.cpe.1.weight', 'module.backbone.enc.enc0.block1.cpe.1.bias', 'module.backbone.enc.enc0.block1.cpe.2.weight', 'module.backbone.enc.enc0.block1.cpe.2.bias', 'module.backbone.enc.enc0.block1.norm1.0.weight', 'module.backbone.enc.enc0.block1.norm1.0.bias', 'module.backbone.enc.enc0.block1.attn.qkv.weight', 'module.backbone.enc.enc0.block1.attn.qkv.bias', 'module.backbone.enc.enc0.block1.attn.proj.weight', 'module.backbone.enc.enc0.block1.attn.proj.bias', 'module.backbone.enc.enc0.block1.attn.rpe.rpe_table', 'module.backbone.enc.enc0.block1.norm2.0.weight', 'module.backbone.enc.enc0.block1.norm2.0.bias', 'module.backbone.enc.enc0.block1.mlp.0.fc1.weight', 'module.backbone.enc.enc0.block1.mlp.0.fc1.bias', 'module.backbone.enc.enc0.block1.mlp.0.fc2.weight', 'module.backbone.enc.enc0.block1.mlp.0.fc2.bias', 'module.backbone.enc.enc1.block0.cpe.0.weight', 'module.backbone.enc.enc1.block0.cpe.0.bias', 'module.backbone.enc.enc1.block0.cpe.1.weight', 'module.backbone.enc.enc1.block0.cpe.1.bias', 'module.backbone.enc.enc1.block0.cpe.2.weight', 'module.backbone.enc.enc1.block0.cpe.2.bias', 'module.backbone.enc.enc1.block0.norm1.0.weight', 'module.backbone.enc.enc1.block0.norm1.0.bias', 'module.backbone.enc.enc1.block0.attn.qkv.weight', 'module.backbone.enc.enc1.block0.attn.qkv.bias', 'module.backbone.enc.enc1.block0.attn.proj.weight', 'module.backbone.enc.enc1.block0.attn.proj.bias', 'module.backbone.enc.enc1.block0.attn.rpe.rpe_table', 'module.backbone.enc.enc1.block0.norm2.0.weight', 'module.backbone.enc.enc1.block0.norm2.0.bias', 'module.backbone.enc.enc1.block0.mlp.0.fc1.weight', 'module.backbone.enc.enc1.block0.mlp.0.fc1.bias', 'module.backbone.enc.enc1.block0.mlp.0.fc2.weight', 'module.backbone.enc.enc1.block0.mlp.0.fc2.bias', 'module.backbone.enc.enc1.block1.cpe.0.weight', 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[2023-12-25 01:42:48,921 INFO train.py line 144 253097] => Building hooks ...
[2023-12-25 01:42:48,922 INFO misc.py line 213 253097] => Loading checkpoint & weight ...
[2023-12-25 01:42:48,922 INFO misc.py line 250 253097] No weight found at: None
[2023-12-25 01:42:48,922 INFO train.py line 151 253097] >>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>>
[2023-12-25 01:43:22,466 INFO misc.py line 119 253097] Train: [1/100][1/510] Data 25.671 (25.671) Batch 33.286 (33.286) Remain 471:32:54 loss: 3.6836 Lr: 0.00060
[2023-12-25 01:43:23,669 INFO misc.py line 119 253097] Train: [1/100][2/510] Data 0.007 (0.007) Batch 1.205 (1.205) Remain 17:04:26 loss: 3.1615 Lr: 0.00060
[2023-12-25 01:43:24,785 INFO misc.py line 119 253097] Train: [1/100][3/510] Data 0.004 (0.004) Batch 1.111 (1.111) Remain 15:44:02 loss: 3.0007 Lr: 0.00060
[2023-12-25 01:43:32,739 INFO misc.py line 119 253097] Train: [1/100][4/510] Data 0.009 (0.009) Batch 7.959 (7.959) Remain 112:44:18 loss: 2.5699 Lr: 0.00060
[2023-12-25 01:43:33,777 INFO misc.py line 119 253097] Train: [1/100][5/510] Data 0.005 (0.007) Batch 1.039 (4.499) Remain 63:43:33 loss: 2.3207 Lr: 0.00060
[2023-12-25 01:43:34,613 INFO misc.py line 119 253097] Train: [1/100][6/510] Data 0.004 (0.006) Batch 0.835 (3.278) Remain 46:25:38 loss: 2.4242 Lr: 0.00060
[2023-12-25 01:43:35,817 INFO misc.py line 119 253097] Train: [1/100][7/510] Data 0.005 (0.006) Batch 1.201 (2.759) Remain 39:04:24 loss: 2.5416 Lr: 0.00060
[2023-12-25 01:43:36,862 INFO misc.py line 119 253097] Train: [1/100][8/510] Data 0.006 (0.006) Batch 1.046 (2.416) Remain 34:13:16 loss: 2.4224 Lr: 0.00060
[2023-12-25 01:43:37,761 INFO misc.py line 119 253097] Train: [1/100][9/510] Data 0.006 (0.006) Batch 0.901 (2.163) Remain 30:38:38 loss: 2.1058 Lr: 0.00060
[2023-12-25 01:43:56,171 INFO misc.py line 119 253097] Train: [1/100][10/510] Data 0.005 (0.006) Batch 18.410 (4.484) Remain 63:30:56 loss: 2.3846 Lr: 0.00060
[2023-12-25 01:43:57,539 INFO misc.py line 119 253097] Train: [1/100][11/510] Data 0.005 (0.006) Batch 1.364 (4.094) Remain 57:59:22 loss: 2.2887 Lr: 0.00060
[2023-12-25 01:43:58,558 INFO misc.py line 119 253097] Train: [1/100][12/510] Data 0.008 (0.006) Batch 1.016 (3.752) Remain 53:08:38 loss: 1.9781 Lr: 0.00060
[2023-12-25 01:43:59,686 INFO misc.py line 119 253097] Train: [1/100][13/510] Data 0.012 (0.007) Batch 1.130 (3.490) Remain 49:25:42 loss: 2.3094 Lr: 0.00060
[2023-12-25 01:44:00,928 INFO misc.py line 119 253097] Train: [1/100][14/510] Data 0.011 (0.007) Batch 1.248 (3.286) Remain 46:32:29 loss: 2.0362 Lr: 0.00060
[2023-12-25 01:44:02,104 INFO misc.py line 119 253097] Train: [1/100][15/510] Data 0.004 (0.007) Batch 1.175 (3.110) Remain 44:02:57 loss: 2.0339 Lr: 0.00060
[2023-12-25 01:44:03,127 INFO misc.py line 119 253097] Train: [1/100][16/510] Data 0.005 (0.007) Batch 1.024 (2.950) Remain 41:46:30 loss: 2.0320 Lr: 0.00060
[2023-12-25 01:44:04,460 INFO misc.py line 119 253097] Train: [1/100][17/510] Data 0.005 (0.006) Batch 1.334 (2.834) Remain 40:08:22 loss: 1.8792 Lr: 0.00060
[2023-12-25 01:44:05,688 INFO misc.py line 119 253097] Train: [1/100][18/510] Data 0.004 (0.006) Batch 1.224 (2.727) Remain 38:37:07 loss: 1.7057 Lr: 0.00060
[2023-12-25 01:44:06,678 INFO misc.py line 119 253097] Train: [1/100][19/510] Data 0.007 (0.006) Batch 0.994 (2.619) Remain 37:05:03 loss: 1.6854 Lr: 0.00060
[2023-12-25 01:44:07,752 INFO misc.py line 119 253097] Train: [1/100][20/510] Data 0.003 (0.006) Batch 1.074 (2.528) Remain 35:47:49 loss: 1.6770 Lr: 0.00060
[2023-12-25 01:44:09,085 INFO misc.py line 119 253097] Train: [1/100][21/510] Data 0.003 (0.006) Batch 1.321 (2.461) Remain 34:50:48 loss: 2.3742 Lr: 0.00060
[2023-12-25 01:44:10,264 INFO misc.py line 119 253097] Train: [1/100][22/510] Data 0.016 (0.006) Batch 1.191 (2.394) Remain 33:53:58 loss: 1.6669 Lr: 0.00060
[2023-12-25 01:44:11,253 INFO misc.py line 119 253097] Train: [1/100][23/510] Data 0.003 (0.006) Batch 0.989 (2.324) Remain 32:54:14 loss: 1.7375 Lr: 0.00060
[2023-12-25 01:44:12,362 INFO misc.py line 119 253097] Train: [1/100][24/510] Data 0.003 (0.006) Batch 1.106 (2.266) Remain 32:04:55 loss: 1.7929 Lr: 0.00060
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[2023-12-25 01:45:34,323 INFO misc.py line 119 253097] Train: [1/100][77/510] Data 0.026 (0.006) Batch 1.026 (1.751) Remain 24:45:42 loss: 1.2091 Lr: 0.00061
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[2023-12-25 01:45:37,867 INFO misc.py line 119 253097] Train: [1/100][80/510] Data 0.006 (0.006) Batch 1.297 (1.728) Remain 24:26:50 loss: 1.4432 Lr: 0.00061
[2023-12-25 01:45:39,067 INFO misc.py line 119 253097] Train: [1/100][81/510] Data 0.005 (0.006) Batch 1.200 (1.722) Remain 24:21:03 loss: 1.5221 Lr: 0.00061
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[2023-12-25 01:45:43,779 INFO misc.py line 119 253097] Train: [1/100][85/510] Data 0.004 (0.006) Batch 1.266 (1.695) Remain 23:58:26 loss: 1.1967 Lr: 0.00061
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[2023-12-25 01:54:45,029 INFO misc.py line 119 253097] Train: [1/100][443/510] Data 0.005 (0.025) Batch 1.046 (1.546) Remain 21:42:41 loss: 1.0417 Lr: 0.00099
[2023-12-25 01:54:46,220 INFO misc.py line 119 253097] Train: [1/100][444/510] Data 0.009 (0.024) Batch 1.195 (1.545) Remain 21:41:59 loss: 0.9675 Lr: 0.00099
[2023-12-25 01:54:47,470 INFO misc.py line 119 253097] Train: [1/100][445/510] Data 0.005 (0.024) Batch 1.246 (1.545) Remain 21:41:24 loss: 0.8476 Lr: 0.00099
[2023-12-25 01:54:48,500 INFO misc.py line 119 253097] Train: [1/100][446/510] Data 0.009 (0.024) Batch 1.030 (1.543) Remain 21:40:23 loss: 0.9440 Lr: 0.00100
[2023-12-25 01:54:49,613 INFO misc.py line 119 253097] Train: [1/100][447/510] Data 0.009 (0.024) Batch 1.116 (1.542) Remain 21:39:33 loss: 0.8113 Lr: 0.00100
[2023-12-25 01:54:50,859 INFO misc.py line 119 253097] Train: [1/100][448/510] Data 0.005 (0.024) Batch 1.247 (1.542) Remain 21:38:58 loss: 0.9077 Lr: 0.00100
[2023-12-25 01:54:51,944 INFO misc.py line 119 253097] Train: [1/100][449/510] Data 0.004 (0.024) Batch 1.085 (1.541) Remain 21:38:05 loss: 0.8512 Lr: 0.00100
[2023-12-25 01:54:53,147 INFO misc.py line 119 253097] Train: [1/100][450/510] Data 0.003 (0.024) Batch 1.202 (1.540) Remain 21:37:25 loss: 1.0102 Lr: 0.00100
[2023-12-25 01:54:54,439 INFO misc.py line 119 253097] Train: [1/100][451/510] Data 0.005 (0.024) Batch 1.293 (1.539) Remain 21:36:55 loss: 1.1626 Lr: 0.00100
[2023-12-25 01:54:55,643 INFO misc.py line 119 253097] Train: [1/100][452/510] Data 0.004 (0.024) Batch 1.202 (1.539) Remain 21:36:16 loss: 0.8359 Lr: 0.00101
[2023-12-25 01:54:56,694 INFO misc.py line 119 253097] Train: [1/100][453/510] Data 0.006 (0.024) Batch 1.048 (1.538) Remain 21:35:19 loss: 0.6942 Lr: 0.00101
[2023-12-25 01:54:57,899 INFO misc.py line 119 253097] Train: [1/100][454/510] Data 0.009 (0.024) Batch 1.209 (1.537) Remain 21:34:41 loss: 0.9223 Lr: 0.00101
[2023-12-25 01:54:59,131 INFO misc.py line 119 253097] Train: [1/100][455/510] Data 0.005 (0.024) Batch 1.227 (1.536) Remain 21:34:05 loss: 0.7068 Lr: 0.00101
[2023-12-25 01:55:00,344 INFO misc.py line 119 253097] Train: [1/100][456/510] Data 0.009 (0.024) Batch 1.213 (1.535) Remain 21:33:27 loss: 0.8870 Lr: 0.00101
[2023-12-25 01:55:01,508 INFO misc.py line 119 253097] Train: [1/100][457/510] Data 0.010 (0.024) Batch 1.165 (1.535) Remain 21:32:44 loss: 1.0272 Lr: 0.00102
[2023-12-25 01:55:02,598 INFO misc.py line 119 253097] Train: [1/100][458/510] Data 0.008 (0.024) Batch 1.092 (1.534) Remain 21:31:54 loss: 1.2449 Lr: 0.00102
[2023-12-25 01:55:03,601 INFO misc.py line 119 253097] Train: [1/100][459/510] Data 0.006 (0.024) Batch 1.002 (1.532) Remain 21:30:53 loss: 0.9874 Lr: 0.00102
[2023-12-25 01:55:04,822 INFO misc.py line 119 253097] Train: [1/100][460/510] Data 0.007 (0.024) Batch 1.217 (1.532) Remain 21:30:17 loss: 0.9282 Lr: 0.00102
[2023-12-25 01:55:06,107 INFO misc.py line 119 253097] Train: [1/100][461/510] Data 0.012 (0.024) Batch 1.287 (1.531) Remain 21:29:48 loss: 0.8871 Lr: 0.00102
[2023-12-25 01:55:07,192 INFO misc.py line 119 253097] Train: [1/100][462/510] Data 0.009 (0.024) Batch 1.089 (1.530) Remain 21:28:58 loss: 1.1043 Lr: 0.00102
[2023-12-25 01:55:09,947 INFO misc.py line 119 253097] Train: [1/100][463/510] Data 1.608 (0.027) Batch 2.756 (1.533) Remain 21:31:11 loss: 1.8779 Lr: 0.00103
[2023-12-25 01:55:11,257 INFO misc.py line 119 253097] Train: [1/100][464/510] Data 0.005 (0.027) Batch 1.303 (1.532) Remain 21:30:45 loss: 0.7517 Lr: 0.00103
[2023-12-25 01:55:12,162 INFO misc.py line 119 253097] Train: [1/100][465/510] Data 0.010 (0.027) Batch 0.911 (1.531) Remain 21:29:35 loss: 1.1059 Lr: 0.00103
[2023-12-25 01:55:13,374 INFO misc.py line 119 253097] Train: [1/100][466/510] Data 0.004 (0.027) Batch 1.210 (1.530) Remain 21:28:59 loss: 1.0154 Lr: 0.00103
[2023-12-25 01:55:14,326 INFO misc.py line 119 253097] Train: [1/100][467/510] Data 0.006 (0.027) Batch 0.954 (1.529) Remain 21:27:54 loss: 0.9036 Lr: 0.00103
[2023-12-25 01:55:20,511 INFO misc.py line 119 253097] Train: [1/100][468/510] Data 5.172 (0.038) Batch 6.184 (1.539) Remain 21:36:19 loss: 0.7992 Lr: 0.00104
[2023-12-25 01:55:21,634 INFO misc.py line 119 253097] Train: [1/100][469/510] Data 0.004 (0.038) Batch 1.123 (1.538) Remain 21:35:32 loss: 0.8193 Lr: 0.00104
[2023-12-25 01:55:22,792 INFO misc.py line 119 253097] Train: [1/100][470/510] Data 0.004 (0.038) Batch 1.158 (1.537) Remain 21:34:49 loss: 0.4185 Lr: 0.00104
[2023-12-25 01:55:24,506 INFO misc.py line 119 253097] Train: [1/100][471/510] Data 0.005 (0.038) Batch 1.714 (1.538) Remain 21:35:07 loss: 0.8116 Lr: 0.00104
[2023-12-25 01:55:26,868 INFO misc.py line 119 253097] Train: [1/100][472/510] Data 1.387 (0.041) Batch 2.362 (1.540) Remain 21:36:34 loss: 1.1628 Lr: 0.00104
[2023-12-25 01:55:27,974 INFO misc.py line 119 253097] Train: [1/100][473/510] Data 0.006 (0.041) Batch 1.105 (1.539) Remain 21:35:46 loss: 1.1348 Lr: 0.00104
[2023-12-25 01:55:29,191 INFO misc.py line 119 253097] Train: [1/100][474/510] Data 0.006 (0.041) Batch 1.218 (1.538) Remain 21:35:10 loss: 1.2014 Lr: 0.00105
[2023-12-25 01:55:30,298 INFO misc.py line 119 253097] Train: [1/100][475/510] Data 0.004 (0.041) Batch 1.108 (1.537) Remain 21:34:22 loss: 1.4386 Lr: 0.00105
[2023-12-25 01:55:31,306 INFO misc.py line 119 253097] Train: [1/100][476/510] Data 0.003 (0.040) Batch 1.007 (1.536) Remain 21:33:24 loss: 1.3002 Lr: 0.00105
[2023-12-25 01:55:32,558 INFO misc.py line 119 253097] Train: [1/100][477/510] Data 0.004 (0.040) Batch 1.248 (1.535) Remain 21:32:52 loss: 1.0728 Lr: 0.00105
[2023-12-25 01:55:34,468 INFO misc.py line 119 253097] Train: [1/100][478/510] Data 0.904 (0.042) Batch 1.913 (1.536) Remain 21:33:30 loss: 0.8502 Lr: 0.00105
[2023-12-25 01:55:35,683 INFO misc.py line 119 253097] Train: [1/100][479/510] Data 0.006 (0.042) Batch 1.216 (1.536) Remain 21:32:55 loss: 0.8311 Lr: 0.00106
[2023-12-25 01:55:36,855 INFO misc.py line 119 253097] Train: [1/100][480/510] Data 0.005 (0.042) Batch 1.172 (1.535) Remain 21:32:15 loss: 1.0991 Lr: 0.00106
[2023-12-25 01:55:37,937 INFO misc.py line 119 253097] Train: [1/100][481/510] Data 0.004 (0.042) Batch 1.082 (1.534) Remain 21:31:26 loss: 0.8763 Lr: 0.00106
[2023-12-25 01:55:39,146 INFO misc.py line 119 253097] Train: [1/100][482/510] Data 0.004 (0.042) Batch 1.210 (1.533) Remain 21:30:50 loss: 1.0026 Lr: 0.00106
[2023-12-25 01:55:40,225 INFO misc.py line 119 253097] Train: [1/100][483/510] Data 0.003 (0.042) Batch 1.078 (1.532) Remain 21:30:00 loss: 0.8902 Lr: 0.00106
[2023-12-25 01:55:41,451 INFO misc.py line 119 253097] Train: [1/100][484/510] Data 0.005 (0.042) Batch 1.226 (1.532) Remain 21:29:27 loss: 0.7889 Lr: 0.00106
[2023-12-25 01:55:51,172 INFO misc.py line 119 253097] Train: [1/100][485/510] Data 0.003 (0.042) Batch 9.722 (1.549) Remain 21:43:44 loss: 0.6473 Lr: 0.00107
[2023-12-25 01:55:52,295 INFO misc.py line 119 253097] Train: [1/100][486/510] Data 0.003 (0.042) Batch 1.123 (1.548) Remain 21:42:58 loss: 0.9068 Lr: 0.00107
[2023-12-25 01:55:53,508 INFO misc.py line 119 253097] Train: [1/100][487/510] Data 0.003 (0.042) Batch 1.212 (1.547) Remain 21:42:21 loss: 0.7434 Lr: 0.00107
[2023-12-25 01:55:54,755 INFO misc.py line 119 253097] Train: [1/100][488/510] Data 0.004 (0.041) Batch 1.248 (1.546) Remain 21:41:48 loss: 1.3408 Lr: 0.00107
[2023-12-25 01:55:55,988 INFO misc.py line 119 253097] Train: [1/100][489/510] Data 0.003 (0.041) Batch 1.231 (1.546) Remain 21:41:14 loss: 0.8409 Lr: 0.00107
[2023-12-25 01:55:57,279 INFO misc.py line 119 253097] Train: [1/100][490/510] Data 0.005 (0.041) Batch 1.290 (1.545) Remain 21:40:46 loss: 1.0661 Lr: 0.00108
[2023-12-25 01:55:58,379 INFO misc.py line 119 253097] Train: [1/100][491/510] Data 0.006 (0.041) Batch 1.097 (1.544) Remain 21:39:58 loss: 0.7346 Lr: 0.00108
[2023-12-25 01:55:59,629 INFO misc.py line 119 253097] Train: [1/100][492/510] Data 0.008 (0.041) Batch 1.254 (1.544) Remain 21:39:26 loss: 0.6054 Lr: 0.00108
[2023-12-25 01:56:00,622 INFO misc.py line 119 253097] Train: [1/100][493/510] Data 0.004 (0.041) Batch 0.990 (1.543) Remain 21:38:28 loss: 0.8454 Lr: 0.00108
[2023-12-25 01:56:01,676 INFO misc.py line 119 253097] Train: [1/100][494/510] Data 0.008 (0.041) Batch 1.058 (1.542) Remain 21:37:36 loss: 1.0230 Lr: 0.00108
[2023-12-25 01:56:02,986 INFO misc.py line 119 253097] Train: [1/100][495/510] Data 0.005 (0.041) Batch 1.310 (1.541) Remain 21:37:11 loss: 0.7242 Lr: 0.00109
[2023-12-25 01:56:04,180 INFO misc.py line 119 253097] Train: [1/100][496/510] Data 0.005 (0.041) Batch 1.194 (1.540) Remain 21:36:34 loss: 1.0065 Lr: 0.00109
[2023-12-25 01:56:05,293 INFO misc.py line 119 253097] Train: [1/100][497/510] Data 0.005 (0.041) Batch 1.114 (1.539) Remain 21:35:49 loss: 1.1217 Lr: 0.00109
[2023-12-25 01:56:06,442 INFO misc.py line 119 253097] Train: [1/100][498/510] Data 0.004 (0.041) Batch 1.149 (1.539) Remain 21:35:07 loss: 0.5902 Lr: 0.00109
[2023-12-25 01:56:07,555 INFO misc.py line 119 253097] Train: [1/100][499/510] Data 0.004 (0.041) Batch 1.107 (1.538) Remain 21:34:22 loss: 0.8104 Lr: 0.00109
[2023-12-25 01:56:08,670 INFO misc.py line 119 253097] Train: [1/100][500/510] Data 0.010 (0.041) Batch 1.120 (1.537) Remain 21:33:38 loss: 0.6629 Lr: 0.00109
[2023-12-25 01:56:09,742 INFO misc.py line 119 253097] Train: [1/100][501/510] Data 0.005 (0.041) Batch 1.071 (1.536) Remain 21:32:49 loss: 1.0104 Lr: 0.00110
[2023-12-25 01:56:10,727 INFO misc.py line 119 253097] Train: [1/100][502/510] Data 0.006 (0.040) Batch 0.988 (1.535) Remain 21:31:52 loss: 0.8089 Lr: 0.00110
[2023-12-25 01:56:12,007 INFO misc.py line 119 253097] Train: [1/100][503/510] Data 0.003 (0.040) Batch 1.270 (1.534) Remain 21:31:24 loss: 1.0160 Lr: 0.00110
[2023-12-25 01:56:13,126 INFO misc.py line 119 253097] Train: [1/100][504/510] Data 0.013 (0.040) Batch 1.128 (1.534) Remain 21:30:41 loss: 0.7946 Lr: 0.00110
[2023-12-25 01:56:24,579 INFO misc.py line 119 253097] Train: [1/100][505/510] Data 0.004 (0.040) Batch 11.453 (1.553) Remain 21:47:18 loss: 0.7330 Lr: 0.00110
[2023-12-25 01:56:25,762 INFO misc.py line 119 253097] Train: [1/100][506/510] Data 0.004 (0.040) Batch 1.183 (1.553) Remain 21:46:39 loss: 0.9995 Lr: 0.00111
[2023-12-25 01:56:26,917 INFO misc.py line 119 253097] Train: [1/100][507/510] Data 0.003 (0.040) Batch 1.155 (1.552) Remain 21:45:58 loss: 1.0268 Lr: 0.00111
[2023-12-25 01:56:28,103 INFO misc.py line 119 253097] Train: [1/100][508/510] Data 0.003 (0.040) Batch 1.185 (1.551) Remain 21:45:19 loss: 0.8466 Lr: 0.00111
[2023-12-25 01:56:29,226 INFO misc.py line 119 253097] Train: [1/100][509/510] Data 0.004 (0.040) Batch 1.124 (1.550) Remain 21:44:35 loss: 0.5364 Lr: 0.00111
[2023-12-25 01:56:30,384 INFO misc.py line 119 253097] Train: [1/100][510/510] Data 0.003 (0.040) Batch 1.158 (1.550) Remain 21:43:55 loss: 1.3018 Lr: 0.00111
[2023-12-25 01:56:30,385 INFO misc.py line 136 253097] Train result: loss: 1.1823 
[2023-12-25 01:56:30,385 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 01:56:56,707 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.8608 
[2023-12-25 01:56:57,156 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.8807 
[2023-12-25 01:57:02,574 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.8037 
[2023-12-25 01:57:03,160 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 1.0177 
[2023-12-25 01:57:05,132 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.1789 
[2023-12-25 01:57:05,559 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.8366 
[2023-12-25 01:57:06,437 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0725 
[2023-12-25 01:57:06,993 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.9527 
[2023-12-25 01:57:08,800 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.2901 
[2023-12-25 01:57:11,381 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.9428 
[2023-12-25 01:57:12,235 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.9349 
[2023-12-25 01:57:12,663 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.2008 
[2023-12-25 01:57:13,560 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 1.0529 
[2023-12-25 01:57:16,505 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9949 
[2023-12-25 01:57:16,975 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.5741 
[2023-12-25 01:57:17,593 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.7719 
[2023-12-25 01:57:18,301 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 1.0367 
[2023-12-25 01:57:19,711 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.4896/0.5996/0.8192.
[2023-12-25 01:57:19,711 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.8771/0.9484
[2023-12-25 01:57:19,711 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9724/0.9912
[2023-12-25 01:57:19,711 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7308/0.9207
[2023-12-25 01:57:19,711 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0043/0.0949
[2023-12-25 01:57:19,712 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.0000/0.0000
[2023-12-25 01:57:19,712 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.4009/0.4987
[2023-12-25 01:57:19,712 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.2726/0.4213
[2023-12-25 01:57:19,712 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7302/0.8474
[2023-12-25 01:57:19,712 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.7638/0.9593
[2023-12-25 01:57:19,712 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.1807/0.2865
[2023-12-25 01:57:19,713 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.5920/0.6289
[2023-12-25 01:57:19,713 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.4447/0.6706
[2023-12-25 01:57:19,713 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.3959/0.5272
[2023-12-25 01:57:19,714 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 01:57:19,716 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.4896
[2023-12-25 01:57:19,716 INFO misc.py line 165 253097] Currently Best mIoU: 0.4896
[2023-12-25 01:57:19,716 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 01:57:39,594 INFO misc.py line 119 253097] Train: [2/100][1/510] Data 2.892 (2.892) Batch 13.039 (13.039) Remain 182:51:48 loss: 1.1230 Lr: 0.00112
[2023-12-25 01:57:40,601 INFO misc.py line 119 253097] Train: [2/100][2/510] Data 0.003 (0.003) Batch 1.002 (1.002) Remain 14:03:28 loss: 1.0256 Lr: 0.00112
[2023-12-25 01:57:41,748 INFO misc.py line 119 253097] Train: [2/100][3/510] Data 0.009 (0.009) Batch 1.147 (1.147) Remain 16:05:14 loss: 0.9109 Lr: 0.00112
[2023-12-25 01:57:42,690 INFO misc.py line 119 253097] Train: [2/100][4/510] Data 0.009 (0.009) Batch 0.947 (0.947) Remain 13:16:35 loss: 1.1870 Lr: 0.00112
[2023-12-25 01:57:43,836 INFO misc.py line 119 253097] Train: [2/100][5/510] Data 0.005 (0.007) Batch 1.147 (1.047) Remain 14:40:42 loss: 0.8937 Lr: 0.00112
[2023-12-25 01:57:44,788 INFO misc.py line 119 253097] Train: [2/100][6/510] Data 0.003 (0.006) Batch 0.951 (1.015) Remain 14:13:53 loss: 1.0322 Lr: 0.00113
[2023-12-25 01:57:45,831 INFO misc.py line 119 253097] Train: [2/100][7/510] Data 0.004 (0.005) Batch 1.042 (1.022) Remain 14:19:40 loss: 0.8251 Lr: 0.00113
[2023-12-25 01:57:47,012 INFO misc.py line 119 253097] Train: [2/100][8/510] Data 0.005 (0.005) Batch 1.182 (1.054) Remain 14:46:39 loss: 0.7906 Lr: 0.00113
[2023-12-25 01:57:48,228 INFO misc.py line 119 253097] Train: [2/100][9/510] Data 0.003 (0.005) Batch 1.212 (1.080) Remain 15:08:49 loss: 0.6341 Lr: 0.00113
[2023-12-25 01:57:49,167 INFO misc.py line 119 253097] Train: [2/100][10/510] Data 0.007 (0.005) Batch 0.943 (1.061) Remain 14:52:18 loss: 1.0829 Lr: 0.00113
[2023-12-25 01:57:50,388 INFO misc.py line 119 253097] Train: [2/100][11/510] Data 0.003 (0.005) Batch 1.220 (1.081) Remain 15:09:03 loss: 0.7676 Lr: 0.00114
[2023-12-25 01:57:51,449 INFO misc.py line 119 253097] Train: [2/100][12/510] Data 0.004 (0.005) Batch 1.062 (1.078) Remain 15:07:17 loss: 0.7629 Lr: 0.00114
[2023-12-25 01:57:52,710 INFO misc.py line 119 253097] Train: [2/100][13/510] Data 0.004 (0.005) Batch 1.257 (1.096) Remain 15:22:19 loss: 0.8625 Lr: 0.00114
[2023-12-25 01:57:53,709 INFO misc.py line 119 253097] Train: [2/100][14/510] Data 0.008 (0.005) Batch 0.999 (1.087) Remain 15:14:49 loss: 0.5507 Lr: 0.00114
[2023-12-25 01:57:55,027 INFO misc.py line 119 253097] Train: [2/100][15/510] Data 0.008 (0.005) Batch 1.317 (1.107) Remain 15:30:54 loss: 0.8172 Lr: 0.00114
[2023-12-25 01:57:56,221 INFO misc.py line 119 253097] Train: [2/100][16/510] Data 0.008 (0.005) Batch 1.197 (1.114) Remain 15:36:45 loss: 0.8707 Lr: 0.00115
[2023-12-25 01:57:57,394 INFO misc.py line 119 253097] Train: [2/100][17/510] Data 0.005 (0.005) Batch 1.171 (1.118) Remain 15:40:12 loss: 0.7936 Lr: 0.00115
[2023-12-25 01:57:58,546 INFO misc.py line 119 253097] Train: [2/100][18/510] Data 0.007 (0.005) Batch 1.155 (1.120) Remain 15:42:18 loss: 1.3332 Lr: 0.00115
[2023-12-25 01:57:59,624 INFO misc.py line 119 253097] Train: [2/100][19/510] Data 0.004 (0.005) Batch 1.077 (1.118) Remain 15:40:02 loss: 0.9246 Lr: 0.00115
[2023-12-25 01:58:03,643 INFO misc.py line 119 253097] Train: [2/100][20/510] Data 3.075 (0.186) Batch 4.019 (1.288) Remain 18:03:35 loss: 0.6053 Lr: 0.00115
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[2023-12-25 01:58:12,490 INFO misc.py line 119 253097] Train: [2/100][22/510] Data 0.006 (0.167) Batch 7.730 (1.618) Remain 22:41:09 loss: 0.6701 Lr: 0.00116
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[2023-12-25 01:58:22,746 INFO misc.py line 119 253097] Train: [2/100][31/510] Data 0.005 (0.115) Batch 1.015 (1.464) Remain 20:31:30 loss: 0.7473 Lr: 0.00118
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[2023-12-25 01:58:24,954 INFO misc.py line 119 253097] Train: [2/100][33/510] Data 0.004 (0.108) Batch 1.165 (1.440) Remain 20:11:16 loss: 0.8435 Lr: 0.00118
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[2023-12-25 01:58:27,380 INFO misc.py line 119 253097] Train: [2/100][35/510] Data 0.004 (0.101) Batch 1.129 (1.426) Remain 19:59:12 loss: 1.8586 Lr: 0.00118
[2023-12-25 01:58:28,438 INFO misc.py line 119 253097] Train: [2/100][36/510] Data 0.006 (0.098) Batch 1.044 (1.414) Remain 19:49:25 loss: 0.7160 Lr: 0.00119
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[2023-12-25 01:58:30,806 INFO misc.py line 119 253097] Train: [2/100][38/510] Data 0.003 (0.093) Batch 1.211 (1.402) Remain 19:38:42 loss: 0.9371 Lr: 0.00119
[2023-12-25 01:58:34,644 INFO misc.py line 119 253097] Train: [2/100][39/510] Data 0.005 (0.091) Batch 3.838 (1.469) Remain 20:35:35 loss: 0.8348 Lr: 0.00119
[2023-12-25 01:58:35,735 INFO misc.py line 119 253097] Train: [2/100][40/510] Data 0.003 (0.089) Batch 1.092 (1.459) Remain 20:26:59 loss: 0.8619 Lr: 0.00119
[2023-12-25 01:58:36,731 INFO misc.py line 119 253097] Train: [2/100][41/510] Data 0.003 (0.086) Batch 0.996 (1.447) Remain 20:16:42 loss: 1.0093 Lr: 0.00120
[2023-12-25 01:58:37,777 INFO misc.py line 119 253097] Train: [2/100][42/510] Data 0.003 (0.084) Batch 1.046 (1.437) Remain 20:08:01 loss: 0.7928 Lr: 0.00120
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[2023-12-25 01:58:44,564 INFO misc.py line 119 253097] Train: [2/100][44/510] Data 0.003 (0.080) Batch 5.740 (1.532) Remain 21:28:14 loss: 0.9326 Lr: 0.00120
[2023-12-25 01:58:45,805 INFO misc.py line 119 253097] Train: [2/100][45/510] Data 0.003 (0.078) Batch 1.241 (1.525) Remain 21:22:22 loss: 1.4578 Lr: 0.00121
[2023-12-25 01:58:46,793 INFO misc.py line 119 253097] Train: [2/100][46/510] Data 0.003 (0.077) Batch 0.988 (1.513) Remain 21:11:50 loss: 1.0129 Lr: 0.00121
[2023-12-25 01:58:47,764 INFO misc.py line 119 253097] Train: [2/100][47/510] Data 0.003 (0.075) Batch 0.971 (1.500) Remain 21:01:27 loss: 0.7411 Lr: 0.00121
[2023-12-25 01:58:48,936 INFO misc.py line 119 253097] Train: [2/100][48/510] Data 0.004 (0.073) Batch 1.173 (1.493) Remain 20:55:19 loss: 0.7704 Lr: 0.00121
[2023-12-25 01:58:50,218 INFO misc.py line 119 253097] Train: [2/100][49/510] Data 0.003 (0.072) Batch 1.277 (1.488) Remain 20:51:20 loss: 0.5964 Lr: 0.00121
[2023-12-25 01:58:51,304 INFO misc.py line 119 253097] Train: [2/100][50/510] Data 0.008 (0.070) Batch 1.088 (1.480) Remain 20:44:08 loss: 0.6224 Lr: 0.00122
[2023-12-25 01:58:52,381 INFO misc.py line 119 253097] Train: [2/100][51/510] Data 0.006 (0.069) Batch 1.080 (1.472) Remain 20:37:06 loss: 0.6550 Lr: 0.00122
[2023-12-25 01:58:53,473 INFO misc.py line 119 253097] Train: [2/100][52/510] Data 0.004 (0.068) Batch 1.090 (1.464) Remain 20:30:32 loss: 0.5987 Lr: 0.00122
[2023-12-25 01:58:54,579 INFO misc.py line 119 253097] Train: [2/100][53/510] Data 0.006 (0.067) Batch 1.106 (1.457) Remain 20:24:29 loss: 0.5483 Lr: 0.00122
[2023-12-25 01:58:59,360 INFO misc.py line 119 253097] Train: [2/100][54/510] Data 0.006 (0.065) Batch 4.782 (1.522) Remain 21:19:16 loss: 0.7009 Lr: 0.00122
[2023-12-25 01:59:00,472 INFO misc.py line 119 253097] Train: [2/100][55/510] Data 0.005 (0.064) Batch 1.113 (1.514) Remain 21:12:39 loss: 0.8193 Lr: 0.00123
[2023-12-25 01:59:08,247 INFO misc.py line 119 253097] Train: [2/100][56/510] Data 0.003 (0.063) Batch 7.774 (1.632) Remain 22:51:54 loss: 0.7477 Lr: 0.00123
[2023-12-25 01:59:09,489 INFO misc.py line 119 253097] Train: [2/100][57/510] Data 0.004 (0.062) Batch 1.243 (1.625) Remain 22:45:49 loss: 0.7045 Lr: 0.00123
[2023-12-25 01:59:10,545 INFO misc.py line 119 253097] Train: [2/100][58/510] Data 0.003 (0.061) Batch 1.051 (1.614) Remain 22:37:01 loss: 0.5556 Lr: 0.00123
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[2023-12-25 01:59:12,994 INFO misc.py line 119 253097] Train: [2/100][60/510] Data 0.004 (0.059) Batch 1.263 (1.601) Remain 22:25:31 loss: 1.3162 Lr: 0.00124
[2023-12-25 01:59:14,248 INFO misc.py line 119 253097] Train: [2/100][61/510] Data 0.006 (0.058) Batch 1.255 (1.595) Remain 22:20:29 loss: 0.8238 Lr: 0.00124
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[2023-12-25 01:59:17,770 INFO misc.py line 119 253097] Train: [2/100][64/510] Data 0.004 (0.055) Batch 0.976 (1.574) Remain 22:03:00 loss: 1.2411 Lr: 0.00125
[2023-12-25 01:59:19,028 INFO misc.py line 119 253097] Train: [2/100][65/510] Data 0.005 (0.055) Batch 1.255 (1.569) Remain 21:58:39 loss: 0.5393 Lr: 0.00125
[2023-12-25 01:59:20,029 INFO misc.py line 119 253097] Train: [2/100][66/510] Data 0.007 (0.054) Batch 1.005 (1.560) Remain 21:51:06 loss: 1.3059 Lr: 0.00125
[2023-12-25 01:59:21,267 INFO misc.py line 119 253097] Train: [2/100][67/510] Data 0.003 (0.053) Batch 1.232 (1.555) Remain 21:46:46 loss: 0.9610 Lr: 0.00125
[2023-12-25 01:59:22,420 INFO misc.py line 119 253097] Train: [2/100][68/510] Data 0.009 (0.052) Batch 1.157 (1.549) Remain 21:41:36 loss: 0.9171 Lr: 0.00125
[2023-12-25 01:59:26,241 INFO misc.py line 119 253097] Train: [2/100][69/510] Data 0.006 (0.052) Batch 3.822 (1.583) Remain 22:10:31 loss: 0.8652 Lr: 0.00126
[2023-12-25 01:59:27,463 INFO misc.py line 119 253097] Train: [2/100][70/510] Data 0.003 (0.051) Batch 1.222 (1.578) Remain 22:05:58 loss: 0.8944 Lr: 0.00126
[2023-12-25 01:59:28,658 INFO misc.py line 119 253097] Train: [2/100][71/510] Data 0.003 (0.050) Batch 1.195 (1.572) Remain 22:01:12 loss: 0.7441 Lr: 0.00126
[2023-12-25 01:59:29,786 INFO misc.py line 119 253097] Train: [2/100][72/510] Data 0.004 (0.050) Batch 1.128 (1.566) Remain 21:55:46 loss: 1.2161 Lr: 0.00126
[2023-12-25 01:59:31,681 INFO misc.py line 119 253097] Train: [2/100][73/510] Data 0.003 (0.049) Batch 1.888 (1.570) Remain 21:59:36 loss: 0.6248 Lr: 0.00127
[2023-12-25 01:59:32,883 INFO misc.py line 119 253097] Train: [2/100][74/510] Data 0.011 (0.048) Batch 1.207 (1.565) Remain 21:55:16 loss: 0.7571 Lr: 0.00127
[2023-12-25 01:59:34,131 INFO misc.py line 119 253097] Train: [2/100][75/510] Data 0.007 (0.048) Batch 1.250 (1.561) Remain 21:51:34 loss: 1.0438 Lr: 0.00127
[2023-12-25 01:59:35,111 INFO misc.py line 119 253097] Train: [2/100][76/510] Data 0.004 (0.047) Batch 0.980 (1.553) Remain 21:44:51 loss: 0.7330 Lr: 0.00127
[2023-12-25 01:59:36,072 INFO misc.py line 119 253097] Train: [2/100][77/510] Data 0.003 (0.047) Batch 0.961 (1.545) Remain 21:38:06 loss: 1.0158 Lr: 0.00127
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[2023-12-25 01:59:38,220 INFO misc.py line 119 253097] Train: [2/100][79/510] Data 0.004 (0.045) Batch 1.121 (1.533) Remain 21:27:39 loss: 1.3850 Lr: 0.00128
[2023-12-25 01:59:39,305 INFO misc.py line 119 253097] Train: [2/100][80/510] Data 0.003 (0.045) Batch 1.086 (1.527) Remain 21:22:45 loss: 1.2195 Lr: 0.00128
[2023-12-25 01:59:40,558 INFO misc.py line 119 253097] Train: [2/100][81/510] Data 0.003 (0.044) Batch 1.249 (1.523) Remain 21:19:43 loss: 0.8586 Lr: 0.00128
[2023-12-25 01:59:45,496 INFO misc.py line 119 253097] Train: [2/100][82/510] Data 0.007 (0.044) Batch 4.941 (1.566) Remain 21:56:02 loss: 1.2163 Lr: 0.00129
[2023-12-25 01:59:46,496 INFO misc.py line 119 253097] Train: [2/100][83/510] Data 0.005 (0.043) Batch 0.996 (1.559) Remain 21:50:01 loss: 0.5840 Lr: 0.00129
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[2023-12-25 01:59:49,847 INFO misc.py line 119 253097] Train: [2/100][86/510] Data 0.006 (0.042) Batch 1.046 (1.543) Remain 21:36:32 loss: 0.7647 Lr: 0.00129
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[2023-12-25 01:59:52,465 INFO misc.py line 119 253097] Train: [2/100][88/510] Data 0.007 (0.041) Batch 1.156 (1.538) Remain 21:31:52 loss: 0.8221 Lr: 0.00130
[2023-12-25 01:59:53,714 INFO misc.py line 119 253097] Train: [2/100][89/510] Data 0.005 (0.041) Batch 1.250 (1.535) Remain 21:29:02 loss: 0.8543 Lr: 0.00130
[2023-12-25 01:59:54,816 INFO misc.py line 119 253097] Train: [2/100][90/510] Data 0.004 (0.040) Batch 1.102 (1.530) Remain 21:24:50 loss: 1.0631 Lr: 0.00130
[2023-12-25 01:59:55,999 INFO misc.py line 119 253097] Train: [2/100][91/510] Data 0.004 (0.040) Batch 1.183 (1.526) Remain 21:21:29 loss: 0.7974 Lr: 0.00131
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[2023-12-25 01:59:59,333 INFO misc.py line 119 253097] Train: [2/100][94/510] Data 0.004 (0.039) Batch 1.199 (1.512) Remain 21:09:55 loss: 0.7329 Lr: 0.00131
[2023-12-25 02:00:00,496 INFO misc.py line 119 253097] Train: [2/100][95/510] Data 0.007 (0.039) Batch 1.167 (1.508) Remain 21:06:44 loss: 0.6614 Lr: 0.00131
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[2023-12-25 02:08:54,218 INFO misc.py line 119 253097] Train: [2/100][436/510] Data 0.006 (0.102) Batch 1.109 (1.553) Remain 21:35:36 loss: 0.7930 Lr: 0.00223
[2023-12-25 02:08:55,537 INFO misc.py line 119 253097] Train: [2/100][437/510] Data 0.007 (0.102) Batch 1.322 (1.553) Remain 21:35:08 loss: 0.6483 Lr: 0.00224
[2023-12-25 02:08:56,540 INFO misc.py line 119 253097] Train: [2/100][438/510] Data 0.003 (0.102) Batch 0.997 (1.551) Remain 21:34:02 loss: 0.4579 Lr: 0.00224
[2023-12-25 02:08:57,633 INFO misc.py line 119 253097] Train: [2/100][439/510] Data 0.010 (0.101) Batch 1.098 (1.550) Remain 21:33:09 loss: 0.8539 Lr: 0.00224
[2023-12-25 02:08:58,687 INFO misc.py line 119 253097] Train: [2/100][440/510] Data 0.004 (0.101) Batch 1.054 (1.549) Remain 21:32:10 loss: 1.0755 Lr: 0.00225
[2023-12-25 02:08:59,895 INFO misc.py line 119 253097] Train: [2/100][441/510] Data 0.004 (0.101) Batch 1.208 (1.548) Remain 21:31:30 loss: 0.5074 Lr: 0.00225
[2023-12-25 02:09:00,876 INFO misc.py line 119 253097] Train: [2/100][442/510] Data 0.003 (0.101) Batch 0.981 (1.547) Remain 21:30:24 loss: 0.6767 Lr: 0.00225
[2023-12-25 02:09:02,067 INFO misc.py line 119 253097] Train: [2/100][443/510] Data 0.006 (0.100) Batch 1.189 (1.546) Remain 21:29:41 loss: 0.6511 Lr: 0.00225
[2023-12-25 02:09:03,235 INFO misc.py line 119 253097] Train: [2/100][444/510] Data 0.007 (0.100) Batch 1.171 (1.545) Remain 21:28:57 loss: 0.8769 Lr: 0.00226
[2023-12-25 02:09:04,245 INFO misc.py line 119 253097] Train: [2/100][445/510] Data 0.004 (0.100) Batch 1.007 (1.544) Remain 21:27:55 loss: 0.5406 Lr: 0.00226
[2023-12-25 02:09:05,501 INFO misc.py line 119 253097] Train: [2/100][446/510] Data 0.007 (0.100) Batch 1.257 (1.543) Remain 21:27:21 loss: 0.5804 Lr: 0.00226
[2023-12-25 02:09:06,561 INFO misc.py line 119 253097] Train: [2/100][447/510] Data 0.006 (0.100) Batch 1.060 (1.542) Remain 21:26:25 loss: 0.8997 Lr: 0.00227
[2023-12-25 02:09:07,795 INFO misc.py line 119 253097] Train: [2/100][448/510] Data 0.006 (0.099) Batch 1.236 (1.542) Remain 21:25:49 loss: 0.6355 Lr: 0.00227
[2023-12-25 02:09:15,512 INFO misc.py line 119 253097] Train: [2/100][449/510] Data 0.003 (0.099) Batch 7.717 (1.556) Remain 21:37:20 loss: 0.6627 Lr: 0.00227
[2023-12-25 02:09:16,645 INFO misc.py line 119 253097] Train: [2/100][450/510] Data 0.005 (0.099) Batch 1.128 (1.555) Remain 21:36:30 loss: 0.6151 Lr: 0.00228
[2023-12-25 02:09:17,753 INFO misc.py line 119 253097] Train: [2/100][451/510] Data 0.009 (0.099) Batch 1.114 (1.554) Remain 21:35:40 loss: 1.2536 Lr: 0.00228
[2023-12-25 02:09:18,785 INFO misc.py line 119 253097] Train: [2/100][452/510] Data 0.004 (0.099) Batch 1.032 (1.552) Remain 21:34:40 loss: 0.7974 Lr: 0.00228
[2023-12-25 02:09:19,862 INFO misc.py line 119 253097] Train: [2/100][453/510] Data 0.004 (0.098) Batch 1.072 (1.551) Remain 21:33:45 loss: 0.7527 Lr: 0.00229
[2023-12-25 02:09:21,102 INFO misc.py line 119 253097] Train: [2/100][454/510] Data 0.009 (0.098) Batch 1.241 (1.551) Remain 21:33:09 loss: 0.7682 Lr: 0.00229
[2023-12-25 02:09:22,310 INFO misc.py line 119 253097] Train: [2/100][455/510] Data 0.008 (0.098) Batch 1.212 (1.550) Remain 21:32:30 loss: 0.7789 Lr: 0.00229
[2023-12-25 02:09:23,350 INFO misc.py line 119 253097] Train: [2/100][456/510] Data 0.004 (0.098) Batch 1.000 (1.549) Remain 21:31:28 loss: 0.9401 Lr: 0.00229
[2023-12-25 02:09:26,174 INFO misc.py line 119 253097] Train: [2/100][457/510] Data 0.044 (0.098) Batch 2.864 (1.552) Remain 21:33:51 loss: 0.4963 Lr: 0.00230
[2023-12-25 02:09:27,228 INFO misc.py line 119 253097] Train: [2/100][458/510] Data 0.004 (0.097) Batch 1.054 (1.551) Remain 21:32:55 loss: 0.8824 Lr: 0.00230
[2023-12-25 02:09:28,420 INFO misc.py line 119 253097] Train: [2/100][459/510] Data 0.003 (0.097) Batch 1.192 (1.550) Remain 21:32:14 loss: 0.9679 Lr: 0.00230
[2023-12-25 02:09:29,714 INFO misc.py line 119 253097] Train: [2/100][460/510] Data 0.003 (0.097) Batch 1.289 (1.549) Remain 21:31:44 loss: 0.7339 Lr: 0.00231
[2023-12-25 02:09:30,834 INFO misc.py line 119 253097] Train: [2/100][461/510] Data 0.009 (0.097) Batch 1.119 (1.548) Remain 21:30:55 loss: 0.6513 Lr: 0.00231
[2023-12-25 02:09:32,097 INFO misc.py line 119 253097] Train: [2/100][462/510] Data 0.009 (0.097) Batch 1.264 (1.548) Remain 21:30:23 loss: 0.7040 Lr: 0.00231
[2023-12-25 02:09:43,858 INFO misc.py line 119 253097] Train: [2/100][463/510] Data 0.010 (0.096) Batch 11.766 (1.570) Remain 21:48:52 loss: 0.7637 Lr: 0.00232
[2023-12-25 02:09:45,060 INFO misc.py line 119 253097] Train: [2/100][464/510] Data 0.005 (0.096) Batch 1.198 (1.569) Remain 21:48:11 loss: 0.6317 Lr: 0.00232
[2023-12-25 02:09:46,229 INFO misc.py line 119 253097] Train: [2/100][465/510] Data 0.007 (0.096) Batch 1.173 (1.568) Remain 21:47:26 loss: 0.8997 Lr: 0.00232
[2023-12-25 02:09:47,379 INFO misc.py line 119 253097] Train: [2/100][466/510] Data 0.004 (0.096) Batch 1.151 (1.567) Remain 21:46:39 loss: 0.4404 Lr: 0.00233
[2023-12-25 02:09:48,593 INFO misc.py line 119 253097] Train: [2/100][467/510] Data 0.003 (0.096) Batch 1.211 (1.566) Remain 21:45:59 loss: 0.7733 Lr: 0.00233
[2023-12-25 02:09:49,758 INFO misc.py line 119 253097] Train: [2/100][468/510] Data 0.007 (0.095) Batch 1.167 (1.566) Remain 21:45:15 loss: 0.6976 Lr: 0.00233
[2023-12-25 02:09:50,744 INFO misc.py line 119 253097] Train: [2/100][469/510] Data 0.005 (0.095) Batch 0.986 (1.564) Remain 21:44:11 loss: 0.8571 Lr: 0.00234
[2023-12-25 02:09:51,915 INFO misc.py line 119 253097] Train: [2/100][470/510] Data 0.005 (0.095) Batch 1.172 (1.564) Remain 21:43:28 loss: 0.4586 Lr: 0.00234
[2023-12-25 02:09:52,890 INFO misc.py line 119 253097] Train: [2/100][471/510] Data 0.004 (0.095) Batch 0.975 (1.562) Remain 21:42:23 loss: 0.6522 Lr: 0.00234
[2023-12-25 02:09:53,988 INFO misc.py line 119 253097] Train: [2/100][472/510] Data 0.003 (0.095) Batch 1.098 (1.561) Remain 21:41:32 loss: 1.0338 Lr: 0.00234
[2023-12-25 02:09:55,015 INFO misc.py line 119 253097] Train: [2/100][473/510] Data 0.005 (0.094) Batch 1.027 (1.560) Remain 21:40:34 loss: 0.7373 Lr: 0.00235
[2023-12-25 02:09:56,233 INFO misc.py line 119 253097] Train: [2/100][474/510] Data 0.004 (0.094) Batch 1.218 (1.559) Remain 21:39:56 loss: 0.8818 Lr: 0.00235
[2023-12-25 02:09:57,317 INFO misc.py line 119 253097] Train: [2/100][475/510] Data 0.004 (0.094) Batch 1.084 (1.558) Remain 21:39:04 loss: 0.7618 Lr: 0.00235
[2023-12-25 02:10:01,249 INFO misc.py line 119 253097] Train: [2/100][476/510] Data 0.003 (0.094) Batch 3.931 (1.563) Remain 21:43:13 loss: 0.7021 Lr: 0.00236
[2023-12-25 02:10:02,151 INFO misc.py line 119 253097] Train: [2/100][477/510] Data 0.004 (0.094) Batch 0.902 (1.562) Remain 21:42:02 loss: 0.6129 Lr: 0.00236
[2023-12-25 02:10:03,458 INFO misc.py line 119 253097] Train: [2/100][478/510] Data 0.004 (0.094) Batch 1.307 (1.562) Remain 21:41:33 loss: 0.7615 Lr: 0.00236
[2023-12-25 02:10:04,553 INFO misc.py line 119 253097] Train: [2/100][479/510] Data 0.004 (0.093) Batch 1.096 (1.561) Remain 21:40:43 loss: 0.6600 Lr: 0.00237
[2023-12-25 02:10:08,486 INFO misc.py line 119 253097] Train: [2/100][480/510] Data 0.003 (0.093) Batch 3.932 (1.565) Remain 21:44:50 loss: 0.8601 Lr: 0.00237
[2023-12-25 02:10:09,763 INFO misc.py line 119 253097] Train: [2/100][481/510] Data 0.004 (0.093) Batch 1.273 (1.565) Remain 21:44:18 loss: 0.7073 Lr: 0.00237
[2023-12-25 02:10:10,631 INFO misc.py line 119 253097] Train: [2/100][482/510] Data 0.008 (0.093) Batch 0.871 (1.563) Remain 21:43:04 loss: 0.4716 Lr: 0.00238
[2023-12-25 02:10:11,765 INFO misc.py line 119 253097] Train: [2/100][483/510] Data 0.004 (0.093) Batch 1.135 (1.563) Remain 21:42:18 loss: 0.6823 Lr: 0.00238
[2023-12-25 02:10:12,954 INFO misc.py line 119 253097] Train: [2/100][484/510] Data 0.003 (0.092) Batch 1.190 (1.562) Remain 21:41:37 loss: 1.0501 Lr: 0.00238
[2023-12-25 02:10:14,027 INFO misc.py line 119 253097] Train: [2/100][485/510] Data 0.003 (0.092) Batch 1.072 (1.561) Remain 21:40:45 loss: 0.6887 Lr: 0.00239
[2023-12-25 02:10:15,147 INFO misc.py line 119 253097] Train: [2/100][486/510] Data 0.003 (0.092) Batch 1.120 (1.560) Remain 21:39:58 loss: 0.6060 Lr: 0.00239
[2023-12-25 02:10:16,402 INFO misc.py line 119 253097] Train: [2/100][487/510] Data 0.003 (0.092) Batch 1.253 (1.559) Remain 21:39:24 loss: 0.8101 Lr: 0.00239
[2023-12-25 02:10:17,208 INFO misc.py line 119 253097] Train: [2/100][488/510] Data 0.005 (0.092) Batch 0.808 (1.558) Remain 21:38:05 loss: 0.6065 Lr: 0.00239
[2023-12-25 02:10:18,469 INFO misc.py line 119 253097] Train: [2/100][489/510] Data 0.005 (0.091) Batch 1.257 (1.557) Remain 21:37:33 loss: 0.7676 Lr: 0.00240
[2023-12-25 02:10:19,721 INFO misc.py line 119 253097] Train: [2/100][490/510] Data 0.007 (0.091) Batch 1.252 (1.556) Remain 21:37:00 loss: 0.7542 Lr: 0.00240
[2023-12-25 02:10:20,972 INFO misc.py line 119 253097] Train: [2/100][491/510] Data 0.007 (0.091) Batch 1.252 (1.556) Remain 21:36:27 loss: 1.2211 Lr: 0.00240
[2023-12-25 02:10:22,147 INFO misc.py line 119 253097] Train: [2/100][492/510] Data 0.008 (0.091) Batch 1.177 (1.555) Remain 21:35:47 loss: 0.6076 Lr: 0.00241
[2023-12-25 02:10:23,385 INFO misc.py line 119 253097] Train: [2/100][493/510] Data 0.005 (0.091) Batch 1.234 (1.554) Remain 21:35:13 loss: 1.0211 Lr: 0.00241
[2023-12-25 02:10:24,681 INFO misc.py line 119 253097] Train: [2/100][494/510] Data 0.008 (0.091) Batch 1.297 (1.554) Remain 21:34:45 loss: 0.7038 Lr: 0.00241
[2023-12-25 02:10:25,939 INFO misc.py line 119 253097] Train: [2/100][495/510] Data 0.007 (0.090) Batch 1.258 (1.553) Remain 21:34:13 loss: 0.7294 Lr: 0.00242
[2023-12-25 02:10:27,287 INFO misc.py line 119 253097] Train: [2/100][496/510] Data 0.006 (0.090) Batch 1.348 (1.553) Remain 21:33:51 loss: 0.5584 Lr: 0.00242
[2023-12-25 02:10:28,503 INFO misc.py line 119 253097] Train: [2/100][497/510] Data 0.007 (0.090) Batch 1.218 (1.552) Remain 21:33:16 loss: 0.8720 Lr: 0.00242
[2023-12-25 02:10:29,592 INFO misc.py line 119 253097] Train: [2/100][498/510] Data 0.007 (0.090) Batch 1.083 (1.551) Remain 21:32:27 loss: 0.5744 Lr: 0.00243
[2023-12-25 02:10:39,992 INFO misc.py line 119 253097] Train: [2/100][499/510] Data 0.010 (0.090) Batch 10.406 (1.569) Remain 21:47:18 loss: 0.8001 Lr: 0.00243
[2023-12-25 02:10:41,190 INFO misc.py line 119 253097] Train: [2/100][500/510] Data 0.004 (0.090) Batch 1.198 (1.568) Remain 21:46:39 loss: 0.6716 Lr: 0.00243
[2023-12-25 02:10:42,380 INFO misc.py line 119 253097] Train: [2/100][501/510] Data 0.004 (0.089) Batch 1.191 (1.568) Remain 21:45:59 loss: 0.6621 Lr: 0.00244
[2023-12-25 02:10:43,473 INFO misc.py line 119 253097] Train: [2/100][502/510] Data 0.003 (0.089) Batch 1.093 (1.567) Remain 21:45:10 loss: 0.7057 Lr: 0.00244
[2023-12-25 02:10:44,679 INFO misc.py line 119 253097] Train: [2/100][503/510] Data 0.003 (0.089) Batch 1.206 (1.566) Remain 21:44:33 loss: 1.2261 Lr: 0.00244
[2023-12-25 02:10:45,959 INFO misc.py line 119 253097] Train: [2/100][504/510] Data 0.002 (0.089) Batch 1.280 (1.565) Remain 21:44:03 loss: 0.4216 Lr: 0.00244
[2023-12-25 02:10:47,018 INFO misc.py line 119 253097] Train: [2/100][505/510] Data 0.003 (0.089) Batch 1.053 (1.564) Remain 21:43:10 loss: 0.9475 Lr: 0.00245
[2023-12-25 02:10:48,017 INFO misc.py line 119 253097] Train: [2/100][506/510] Data 0.009 (0.089) Batch 1.000 (1.563) Remain 21:42:12 loss: 0.9694 Lr: 0.00245
[2023-12-25 02:10:49,058 INFO misc.py line 119 253097] Train: [2/100][507/510] Data 0.008 (0.088) Batch 1.043 (1.562) Remain 21:41:19 loss: 0.4534 Lr: 0.00245
[2023-12-25 02:10:50,119 INFO misc.py line 119 253097] Train: [2/100][508/510] Data 0.006 (0.088) Batch 1.064 (1.561) Remain 21:40:28 loss: 0.6967 Lr: 0.00246
[2023-12-25 02:10:51,354 INFO misc.py line 119 253097] Train: [2/100][509/510] Data 0.004 (0.088) Batch 1.234 (1.560) Remain 21:39:54 loss: 0.6007 Lr: 0.00246
[2023-12-25 02:10:52,400 INFO misc.py line 119 253097] Train: [2/100][510/510] Data 0.004 (0.088) Batch 1.044 (1.559) Remain 21:39:02 loss: 0.7128 Lr: 0.00246
[2023-12-25 02:10:52,401 INFO misc.py line 136 253097] Train result: loss: 0.8151 
[2023-12-25 02:10:52,401 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 02:11:19,288 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.8914 
[2023-12-25 02:11:19,639 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.8991 
[2023-12-25 02:11:25,371 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.7599 
[2023-12-25 02:11:25,940 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.7991 
[2023-12-25 02:11:27,923 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9787 
[2023-12-25 02:11:28,361 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.8026 
[2023-12-25 02:11:29,238 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.4680 
[2023-12-25 02:11:29,805 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.9219 
[2023-12-25 02:11:31,615 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 2.0589 
[2023-12-25 02:11:33,733 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.7488 
[2023-12-25 02:11:34,591 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.6890 
[2023-12-25 02:11:35,019 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9436 
[2023-12-25 02:11:35,921 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 1.0823 
[2023-12-25 02:11:38,865 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 1.0781 
[2023-12-25 02:11:39,343 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.5619 
[2023-12-25 02:11:39,951 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.9366 
[2023-12-25 02:11:40,651 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 1.2177 
[2023-12-25 02:11:42,053 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.5303/0.6662/0.8257.
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9067/0.9443
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9772/0.9914
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7387/0.8565
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0001
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.0253/0.0253
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.3021/0.3137
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.4319/0.6871
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7450/0.8935
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8631/0.9238
[2023-12-25 02:11:42,053 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6023/0.7972
[2023-12-25 02:11:42,054 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.6368/0.8526
[2023-12-25 02:11:42,054 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.2537/0.8938
[2023-12-25 02:11:42,054 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.4113/0.4809
[2023-12-25 02:11:42,054 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 02:11:42,055 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.5303
[2023-12-25 02:11:42,056 INFO misc.py line 165 253097] Currently Best mIoU: 0.5303
[2023-12-25 02:11:42,056 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 02:11:51,766 INFO misc.py line 119 253097] Train: [3/100][1/510] Data 4.052 (4.052) Batch 5.071 (5.071) Remain 70:23:48 loss: 0.6044 Lr: 0.00247
[2023-12-25 02:11:57,861 INFO misc.py line 119 253097] Train: [3/100][2/510] Data 4.775 (4.775) Batch 6.096 (6.096) Remain 84:38:00 loss: 0.7928 Lr: 0.00247
[2023-12-25 02:12:00,669 INFO misc.py line 119 253097] Train: [3/100][3/510] Data 0.004 (0.004) Batch 2.808 (2.808) Remain 38:58:51 loss: 0.7509 Lr: 0.00247
[2023-12-25 02:12:01,999 INFO misc.py line 119 253097] Train: [3/100][4/510] Data 0.004 (0.004) Batch 1.328 (1.328) Remain 18:26:23 loss: 0.6378 Lr: 0.00248
[2023-12-25 02:12:03,234 INFO misc.py line 119 253097] Train: [3/100][5/510] Data 0.006 (0.005) Batch 1.235 (1.282) Remain 17:47:34 loss: 0.6013 Lr: 0.00248
[2023-12-25 02:12:04,244 INFO misc.py line 119 253097] Train: [3/100][6/510] Data 0.006 (0.005) Batch 1.011 (1.191) Remain 16:32:17 loss: 0.7608 Lr: 0.00248
[2023-12-25 02:12:05,491 INFO misc.py line 119 253097] Train: [3/100][7/510] Data 0.005 (0.005) Batch 1.246 (1.205) Remain 16:43:38 loss: 0.8079 Lr: 0.00249
[2023-12-25 02:12:06,591 INFO misc.py line 119 253097] Train: [3/100][8/510] Data 0.006 (0.005) Batch 1.102 (1.184) Remain 16:26:30 loss: 0.7195 Lr: 0.00249
[2023-12-25 02:12:07,746 INFO misc.py line 119 253097] Train: [3/100][9/510] Data 0.003 (0.005) Batch 1.151 (1.179) Remain 16:21:47 loss: 0.8902 Lr: 0.00249
[2023-12-25 02:12:08,835 INFO misc.py line 119 253097] Train: [3/100][10/510] Data 0.007 (0.005) Batch 1.094 (1.167) Remain 16:11:40 loss: 0.7932 Lr: 0.00250
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[2023-12-25 02:18:52,459 INFO misc.py line 119 253097] Train: [3/100][253/510] Data 0.005 (0.068) Batch 9.969 (1.647) Remain 22:45:08 loss: 0.6391 Lr: 0.00329
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[2023-12-25 02:18:55,613 INFO misc.py line 119 253097] Train: [3/100][256/510] Data 0.004 (0.067) Batch 1.120 (1.640) Remain 22:39:11 loss: 0.9182 Lr: 0.00330
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[2023-12-25 02:19:16,714 INFO misc.py line 119 253097] Train: [3/100][273/510] Data 0.006 (0.063) Batch 1.182 (1.615) Remain 22:17:55 loss: 0.8656 Lr: 0.00336
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[2023-12-25 02:20:58,840 INFO misc.py line 119 253097] Train: [3/100][342/510] Data 3.908 (0.075) Batch 5.201 (1.588) Remain 21:53:21 loss: 1.1097 Lr: 0.00359
[2023-12-25 02:20:59,847 INFO misc.py line 119 253097] Train: [3/100][343/510] Data 0.004 (0.075) Batch 1.007 (1.586) Remain 21:51:55 loss: 0.9212 Lr: 0.00359
[2023-12-25 02:21:01,071 INFO misc.py line 119 253097] Train: [3/100][344/510] Data 0.003 (0.075) Batch 1.224 (1.585) Remain 21:51:00 loss: 0.5016 Lr: 0.00359
[2023-12-25 02:21:02,150 INFO misc.py line 119 253097] Train: [3/100][345/510] Data 0.003 (0.074) Batch 1.079 (1.583) Remain 21:49:45 loss: 0.6354 Lr: 0.00360
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[2023-12-25 02:21:05,632 INFO misc.py line 119 253097] Train: [3/100][348/510] Data 0.005 (0.074) Batch 1.153 (1.580) Remain 21:46:38 loss: 1.2452 Lr: 0.00361
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[2023-12-25 02:21:07,827 INFO misc.py line 119 253097] Train: [3/100][350/510] Data 0.007 (0.073) Batch 0.982 (1.577) Remain 21:44:17 loss: 0.5103 Lr: 0.00361
[2023-12-25 02:21:09,042 INFO misc.py line 119 253097] Train: [3/100][351/510] Data 0.005 (0.073) Batch 1.211 (1.576) Remain 21:43:23 loss: 1.1947 Lr: 0.00362
[2023-12-25 02:21:10,272 INFO misc.py line 119 253097] Train: [3/100][352/510] Data 0.009 (0.073) Batch 1.234 (1.575) Remain 21:42:33 loss: 0.5738 Lr: 0.00362
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[2023-12-25 02:21:15,974 INFO misc.py line 119 253097] Train: [3/100][357/510] Data 0.009 (0.072) Batch 1.274 (1.569) Remain 21:37:21 loss: 0.5483 Lr: 0.00364
[2023-12-25 02:21:17,256 INFO misc.py line 119 253097] Train: [3/100][358/510] Data 0.007 (0.072) Batch 1.285 (1.568) Remain 21:36:39 loss: 0.5363 Lr: 0.00364
[2023-12-25 02:21:18,393 INFO misc.py line 119 253097] Train: [3/100][359/510] Data 0.004 (0.072) Batch 1.137 (1.567) Remain 21:35:38 loss: 0.5675 Lr: 0.00364
[2023-12-25 02:21:19,473 INFO misc.py line 119 253097] Train: [3/100][360/510] Data 0.004 (0.072) Batch 1.080 (1.565) Remain 21:34:28 loss: 0.6511 Lr: 0.00365
[2023-12-25 02:21:20,525 INFO misc.py line 119 253097] Train: [3/100][361/510] Data 0.004 (0.071) Batch 1.044 (1.564) Remain 21:33:15 loss: 0.7346 Lr: 0.00365
[2023-12-25 02:21:21,629 INFO misc.py line 119 253097] Train: [3/100][362/510] Data 0.012 (0.071) Batch 1.111 (1.563) Remain 21:32:11 loss: 0.8091 Lr: 0.00365
[2023-12-25 02:21:33,422 INFO misc.py line 119 253097] Train: [3/100][363/510] Data 0.003 (0.071) Batch 11.794 (1.591) Remain 21:55:39 loss: 0.6130 Lr: 0.00366
[2023-12-25 02:21:34,730 INFO misc.py line 119 253097] Train: [3/100][364/510] Data 0.004 (0.071) Batch 1.300 (1.590) Remain 21:54:58 loss: 0.5072 Lr: 0.00366
[2023-12-25 02:21:35,864 INFO misc.py line 119 253097] Train: [3/100][365/510] Data 0.012 (0.071) Batch 1.135 (1.589) Remain 21:53:54 loss: 0.7790 Lr: 0.00366
[2023-12-25 02:21:36,840 INFO misc.py line 119 253097] Train: [3/100][366/510] Data 0.011 (0.071) Batch 0.982 (1.587) Remain 21:52:29 loss: 0.7890 Lr: 0.00367
[2023-12-25 02:21:38,131 INFO misc.py line 119 253097] Train: [3/100][367/510] Data 0.005 (0.070) Batch 1.291 (1.586) Remain 21:51:47 loss: 0.5499 Lr: 0.00367
[2023-12-25 02:21:39,273 INFO misc.py line 119 253097] Train: [3/100][368/510] Data 0.005 (0.070) Batch 1.139 (1.585) Remain 21:50:45 loss: 0.7316 Lr: 0.00367
[2023-12-25 02:21:41,368 INFO misc.py line 119 253097] Train: [3/100][369/510] Data 0.007 (0.070) Batch 2.098 (1.587) Remain 21:51:53 loss: 0.5353 Lr: 0.00368
[2023-12-25 02:21:42,490 INFO misc.py line 119 253097] Train: [3/100][370/510] Data 0.003 (0.070) Batch 1.122 (1.585) Remain 21:50:48 loss: 0.6555 Lr: 0.00368
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[2023-12-25 02:21:44,958 INFO misc.py line 119 253097] Train: [3/100][372/510] Data 0.011 (0.069) Batch 1.222 (1.583) Remain 21:49:10 loss: 0.7286 Lr: 0.00369
[2023-12-25 02:21:46,110 INFO misc.py line 119 253097] Train: [3/100][373/510] Data 0.008 (0.069) Batch 1.155 (1.582) Remain 21:48:11 loss: 0.4372 Lr: 0.00369
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[2023-12-25 02:21:50,018 INFO misc.py line 119 253097] Train: [3/100][375/510] Data 0.010 (0.069) Batch 2.615 (1.584) Remain 21:49:47 loss: 0.3884 Lr: 0.00370
[2023-12-25 02:21:51,281 INFO misc.py line 119 253097] Train: [3/100][376/510] Data 0.003 (0.069) Batch 1.258 (1.583) Remain 21:49:02 loss: 0.7124 Lr: 0.00370
[2023-12-25 02:21:52,437 INFO misc.py line 119 253097] Train: [3/100][377/510] Data 0.011 (0.069) Batch 1.160 (1.582) Remain 21:48:04 loss: 0.6840 Lr: 0.00370
[2023-12-25 02:21:53,537 INFO misc.py line 119 253097] Train: [3/100][378/510] Data 0.004 (0.068) Batch 1.097 (1.581) Remain 21:46:59 loss: 0.5796 Lr: 0.00371
[2023-12-25 02:21:54,665 INFO misc.py line 119 253097] Train: [3/100][379/510] Data 0.008 (0.068) Batch 1.126 (1.580) Remain 21:45:57 loss: 0.5597 Lr: 0.00371
[2023-12-25 02:21:55,721 INFO misc.py line 119 253097] Train: [3/100][380/510] Data 0.010 (0.068) Batch 1.060 (1.578) Remain 21:44:47 loss: 0.7763 Lr: 0.00371
[2023-12-25 02:21:56,698 INFO misc.py line 119 253097] Train: [3/100][381/510] Data 0.005 (0.068) Batch 0.978 (1.577) Remain 21:43:27 loss: 1.0406 Lr: 0.00372
[2023-12-25 02:21:57,820 INFO misc.py line 119 253097] Train: [3/100][382/510] Data 0.005 (0.068) Batch 1.124 (1.576) Remain 21:42:26 loss: 0.5357 Lr: 0.00372
[2023-12-25 02:22:02,097 INFO misc.py line 119 253097] Train: [3/100][383/510] Data 0.004 (0.068) Batch 4.276 (1.583) Remain 21:48:17 loss: 0.6713 Lr: 0.00372
[2023-12-25 02:22:03,068 INFO misc.py line 119 253097] Train: [3/100][384/510] Data 0.004 (0.068) Batch 0.967 (1.581) Remain 21:46:55 loss: 0.6973 Lr: 0.00373
[2023-12-25 02:22:04,146 INFO misc.py line 119 253097] Train: [3/100][385/510] Data 0.009 (0.067) Batch 1.080 (1.580) Remain 21:45:48 loss: 0.8709 Lr: 0.00373
[2023-12-25 02:22:05,181 INFO misc.py line 119 253097] Train: [3/100][386/510] Data 0.006 (0.067) Batch 1.037 (1.578) Remain 21:44:37 loss: 0.5877 Lr: 0.00373
[2023-12-25 02:22:06,497 INFO misc.py line 119 253097] Train: [3/100][387/510] Data 0.003 (0.067) Batch 1.315 (1.578) Remain 21:44:01 loss: 0.5952 Lr: 0.00374
[2023-12-25 02:22:13,924 INFO misc.py line 119 253097] Train: [3/100][388/510] Data 0.003 (0.067) Batch 7.428 (1.593) Remain 21:56:33 loss: 0.7079 Lr: 0.00374
[2023-12-25 02:22:15,027 INFO misc.py line 119 253097] Train: [3/100][389/510] Data 0.004 (0.067) Batch 1.102 (1.592) Remain 21:55:28 loss: 0.8552 Lr: 0.00374
[2023-12-25 02:22:16,276 INFO misc.py line 119 253097] Train: [3/100][390/510] Data 0.005 (0.067) Batch 1.249 (1.591) Remain 21:54:43 loss: 0.6473 Lr: 0.00375
[2023-12-25 02:22:17,452 INFO misc.py line 119 253097] Train: [3/100][391/510] Data 0.003 (0.066) Batch 1.176 (1.590) Remain 21:53:48 loss: 0.7014 Lr: 0.00375
[2023-12-25 02:22:18,522 INFO misc.py line 119 253097] Train: [3/100][392/510] Data 0.003 (0.066) Batch 1.070 (1.588) Remain 21:52:41 loss: 0.5202 Lr: 0.00375
[2023-12-25 02:22:22,774 INFO misc.py line 119 253097] Train: [3/100][393/510] Data 0.003 (0.066) Batch 4.251 (1.595) Remain 21:58:18 loss: 0.7374 Lr: 0.00376
[2023-12-25 02:22:24,032 INFO misc.py line 119 253097] Train: [3/100][394/510] Data 0.005 (0.066) Batch 1.247 (1.594) Remain 21:57:32 loss: 0.7379 Lr: 0.00376
[2023-12-25 02:22:25,016 INFO misc.py line 119 253097] Train: [3/100][395/510] Data 0.016 (0.066) Batch 0.997 (1.593) Remain 21:56:15 loss: 0.5085 Lr: 0.00376
[2023-12-25 02:22:26,244 INFO misc.py line 119 253097] Train: [3/100][396/510] Data 0.003 (0.066) Batch 1.225 (1.592) Remain 21:55:27 loss: 0.7573 Lr: 0.00377
[2023-12-25 02:22:27,319 INFO misc.py line 119 253097] Train: [3/100][397/510] Data 0.006 (0.065) Batch 1.077 (1.590) Remain 21:54:20 loss: 0.7852 Lr: 0.00377
[2023-12-25 02:22:28,451 INFO misc.py line 119 253097] Train: [3/100][398/510] Data 0.003 (0.065) Batch 1.131 (1.589) Remain 21:53:21 loss: 0.7769 Lr: 0.00377
[2023-12-25 02:22:29,417 INFO misc.py line 119 253097] Train: [3/100][399/510] Data 0.004 (0.065) Batch 0.966 (1.588) Remain 21:52:02 loss: 0.6471 Lr: 0.00378
[2023-12-25 02:22:30,592 INFO misc.py line 119 253097] Train: [3/100][400/510] Data 0.004 (0.065) Batch 1.174 (1.587) Remain 21:51:08 loss: 0.6947 Lr: 0.00378
[2023-12-25 02:22:31,627 INFO misc.py line 119 253097] Train: [3/100][401/510] Data 0.004 (0.065) Batch 1.035 (1.585) Remain 21:49:58 loss: 0.8549 Lr: 0.00378
[2023-12-25 02:22:32,918 INFO misc.py line 119 253097] Train: [3/100][402/510] Data 0.004 (0.065) Batch 1.291 (1.585) Remain 21:49:20 loss: 0.6010 Lr: 0.00378
[2023-12-25 02:22:34,291 INFO misc.py line 119 253097] Train: [3/100][403/510] Data 0.005 (0.065) Batch 1.374 (1.584) Remain 21:48:52 loss: 0.4886 Lr: 0.00379
[2023-12-25 02:22:35,465 INFO misc.py line 119 253097] Train: [3/100][404/510] Data 0.004 (0.064) Batch 1.174 (1.583) Remain 21:48:00 loss: 0.6448 Lr: 0.00379
[2023-12-25 02:22:36,501 INFO misc.py line 119 253097] Train: [3/100][405/510] Data 0.005 (0.064) Batch 1.032 (1.582) Remain 21:46:50 loss: 0.7624 Lr: 0.00379
[2023-12-25 02:22:37,467 INFO misc.py line 119 253097] Train: [3/100][406/510] Data 0.008 (0.064) Batch 0.970 (1.580) Remain 21:45:33 loss: 0.6829 Lr: 0.00380
[2023-12-25 02:22:38,719 INFO misc.py line 119 253097] Train: [3/100][407/510] Data 0.004 (0.064) Batch 1.252 (1.579) Remain 21:44:52 loss: 0.5280 Lr: 0.00380
[2023-12-25 02:22:39,864 INFO misc.py line 119 253097] Train: [3/100][408/510] Data 0.004 (0.064) Batch 1.141 (1.578) Remain 21:43:56 loss: 0.5424 Lr: 0.00380
[2023-12-25 02:22:41,096 INFO misc.py line 119 253097] Train: [3/100][409/510] Data 0.008 (0.064) Batch 1.233 (1.577) Remain 21:43:13 loss: 0.7351 Lr: 0.00381
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[2023-12-25 02:23:07,410 INFO misc.py line 119 253097] Train: [3/100][425/510] Data 0.005 (0.061) Batch 1.027 (1.580) Remain 21:44:54 loss: 0.8149 Lr: 0.00386
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[2023-12-25 02:23:11,878 INFO misc.py line 119 253097] Train: [3/100][429/510] Data 0.008 (0.061) Batch 1.028 (1.576) Remain 21:41:12 loss: 0.8686 Lr: 0.00387
[2023-12-25 02:23:12,970 INFO misc.py line 119 253097] Train: [3/100][430/510] Data 0.004 (0.061) Batch 1.090 (1.574) Remain 21:40:14 loss: 0.7928 Lr: 0.00388
[2023-12-25 02:23:14,180 INFO misc.py line 119 253097] Train: [3/100][431/510] Data 0.007 (0.061) Batch 1.213 (1.574) Remain 21:39:31 loss: 0.5019 Lr: 0.00388
[2023-12-25 02:23:15,273 INFO misc.py line 119 253097] Train: [3/100][432/510] Data 0.004 (0.061) Batch 1.089 (1.572) Remain 21:38:33 loss: 0.9511 Lr: 0.00388
[2023-12-25 02:23:16,396 INFO misc.py line 119 253097] Train: [3/100][433/510] Data 0.007 (0.060) Batch 1.122 (1.571) Remain 21:37:40 loss: 0.5599 Lr: 0.00389
[2023-12-25 02:23:17,667 INFO misc.py line 119 253097] Train: [3/100][434/510] Data 0.009 (0.060) Batch 1.272 (1.571) Remain 21:37:04 loss: 0.8435 Lr: 0.00389
[2023-12-25 02:23:18,936 INFO misc.py line 119 253097] Train: [3/100][435/510] Data 0.007 (0.060) Batch 1.274 (1.570) Remain 21:36:28 loss: 0.4576 Lr: 0.00389
[2023-12-25 02:23:20,191 INFO misc.py line 119 253097] Train: [3/100][436/510] Data 0.002 (0.060) Batch 1.250 (1.569) Remain 21:35:50 loss: 0.7010 Lr: 0.00390
[2023-12-25 02:23:21,367 INFO misc.py line 119 253097] Train: [3/100][437/510] Data 0.007 (0.060) Batch 1.178 (1.568) Remain 21:35:04 loss: 0.6273 Lr: 0.00390
[2023-12-25 02:23:22,394 INFO misc.py line 119 253097] Train: [3/100][438/510] Data 0.006 (0.060) Batch 1.027 (1.567) Remain 21:34:01 loss: 1.1376 Lr: 0.00390
[2023-12-25 02:23:23,744 INFO misc.py line 119 253097] Train: [3/100][439/510] Data 0.006 (0.060) Batch 1.352 (1.567) Remain 21:33:35 loss: 0.5089 Lr: 0.00391
[2023-12-25 02:23:24,949 INFO misc.py line 119 253097] Train: [3/100][440/510] Data 0.003 (0.060) Batch 1.202 (1.566) Remain 21:32:52 loss: 0.8278 Lr: 0.00391
[2023-12-25 02:23:26,199 INFO misc.py line 119 253097] Train: [3/100][441/510] Data 0.006 (0.059) Batch 1.253 (1.565) Remain 21:32:15 loss: 0.7174 Lr: 0.00391
[2023-12-25 02:23:27,407 INFO misc.py line 119 253097] Train: [3/100][442/510] Data 0.005 (0.059) Batch 1.208 (1.564) Remain 21:31:33 loss: 0.5384 Lr: 0.00392
[2023-12-25 02:23:28,503 INFO misc.py line 119 253097] Train: [3/100][443/510] Data 0.004 (0.059) Batch 1.093 (1.563) Remain 21:30:38 loss: 0.7901 Lr: 0.00392
[2023-12-25 02:23:36,402 INFO misc.py line 119 253097] Train: [3/100][444/510] Data 6.711 (0.074) Batch 7.902 (1.578) Remain 21:42:29 loss: 0.4347 Lr: 0.00392
[2023-12-25 02:23:37,579 INFO misc.py line 119 253097] Train: [3/100][445/510] Data 0.005 (0.074) Batch 1.177 (1.577) Remain 21:41:42 loss: 0.9244 Lr: 0.00392
[2023-12-25 02:23:38,816 INFO misc.py line 119 253097] Train: [3/100][446/510] Data 0.004 (0.074) Batch 1.238 (1.576) Remain 21:41:03 loss: 0.3786 Lr: 0.00393
[2023-12-25 02:23:42,221 INFO misc.py line 119 253097] Train: [3/100][447/510] Data 0.004 (0.074) Batch 3.402 (1.580) Remain 21:44:25 loss: 0.6816 Lr: 0.00393
[2023-12-25 02:23:43,236 INFO misc.py line 119 253097] Train: [3/100][448/510] Data 0.007 (0.074) Batch 1.018 (1.579) Remain 21:43:21 loss: 0.6938 Lr: 0.00393
[2023-12-25 02:23:44,416 INFO misc.py line 119 253097] Train: [3/100][449/510] Data 0.004 (0.073) Batch 1.180 (1.578) Remain 21:42:35 loss: 0.4821 Lr: 0.00394
[2023-12-25 02:23:45,477 INFO misc.py line 119 253097] Train: [3/100][450/510] Data 0.003 (0.073) Batch 1.062 (1.577) Remain 21:41:36 loss: 0.3524 Lr: 0.00394
[2023-12-25 02:23:46,495 INFO misc.py line 119 253097] Train: [3/100][451/510] Data 0.003 (0.073) Batch 1.017 (1.576) Remain 21:40:33 loss: 0.4463 Lr: 0.00394
[2023-12-25 02:23:47,555 INFO misc.py line 119 253097] Train: [3/100][452/510] Data 0.004 (0.073) Batch 1.061 (1.574) Remain 21:39:34 loss: 0.5844 Lr: 0.00395
[2023-12-25 02:23:48,715 INFO misc.py line 119 253097] Train: [3/100][453/510] Data 0.004 (0.073) Batch 1.160 (1.573) Remain 21:38:47 loss: 0.4420 Lr: 0.00395
[2023-12-25 02:23:49,918 INFO misc.py line 119 253097] Train: [3/100][454/510] Data 0.004 (0.073) Batch 1.196 (1.573) Remain 21:38:04 loss: 0.5213 Lr: 0.00395
[2023-12-25 02:23:50,978 INFO misc.py line 119 253097] Train: [3/100][455/510] Data 0.010 (0.073) Batch 1.066 (1.571) Remain 21:37:07 loss: 0.5111 Lr: 0.00396
[2023-12-25 02:23:52,178 INFO misc.py line 119 253097] Train: [3/100][456/510] Data 0.003 (0.072) Batch 1.200 (1.571) Remain 21:36:25 loss: 0.5082 Lr: 0.00396
[2023-12-25 02:23:58,932 INFO misc.py line 119 253097] Train: [3/100][457/510] Data 0.005 (0.072) Batch 6.753 (1.582) Remain 21:45:49 loss: 0.5468 Lr: 0.00396
[2023-12-25 02:23:59,858 INFO misc.py line 119 253097] Train: [3/100][458/510] Data 0.005 (0.072) Batch 0.927 (1.581) Remain 21:44:36 loss: 0.6730 Lr: 0.00397
[2023-12-25 02:24:01,078 INFO misc.py line 119 253097] Train: [3/100][459/510] Data 0.004 (0.072) Batch 1.220 (1.580) Remain 21:43:55 loss: 0.6283 Lr: 0.00397
[2023-12-25 02:24:02,208 INFO misc.py line 119 253097] Train: [3/100][460/510] Data 0.003 (0.072) Batch 1.130 (1.579) Remain 21:43:05 loss: 0.4465 Lr: 0.00397
[2023-12-25 02:24:03,306 INFO misc.py line 119 253097] Train: [3/100][461/510] Data 0.003 (0.072) Batch 1.094 (1.578) Remain 21:42:11 loss: 0.5594 Lr: 0.00398
[2023-12-25 02:24:04,495 INFO misc.py line 119 253097] Train: [3/100][462/510] Data 0.008 (0.072) Batch 1.188 (1.577) Remain 21:41:27 loss: 0.6651 Lr: 0.00398
[2023-12-25 02:24:05,705 INFO misc.py line 119 253097] Train: [3/100][463/510] Data 0.009 (0.071) Batch 1.212 (1.576) Remain 21:40:46 loss: 0.8174 Lr: 0.00398
[2023-12-25 02:24:06,585 INFO misc.py line 119 253097] Train: [3/100][464/510] Data 0.006 (0.071) Batch 0.883 (1.575) Remain 21:39:30 loss: 0.6073 Lr: 0.00399
[2023-12-25 02:24:07,668 INFO misc.py line 119 253097] Train: [3/100][465/510] Data 0.003 (0.071) Batch 1.083 (1.574) Remain 21:38:36 loss: 0.7396 Lr: 0.00399
[2023-12-25 02:24:08,951 INFO misc.py line 119 253097] Train: [3/100][466/510] Data 0.004 (0.071) Batch 1.283 (1.573) Remain 21:38:03 loss: 0.9186 Lr: 0.00399
[2023-12-25 02:24:10,081 INFO misc.py line 119 253097] Train: [3/100][467/510] Data 0.004 (0.071) Batch 1.127 (1.572) Remain 21:37:14 loss: 0.6000 Lr: 0.00400
[2023-12-25 02:24:16,609 INFO misc.py line 119 253097] Train: [3/100][468/510] Data 0.007 (0.071) Batch 6.531 (1.583) Remain 21:46:00 loss: 0.5409 Lr: 0.00400
[2023-12-25 02:24:17,618 INFO misc.py line 119 253097] Train: [3/100][469/510] Data 0.004 (0.071) Batch 1.009 (1.581) Remain 21:44:58 loss: 0.5868 Lr: 0.00400
[2023-12-25 02:24:18,725 INFO misc.py line 119 253097] Train: [3/100][470/510] Data 0.003 (0.070) Batch 1.106 (1.580) Remain 21:44:06 loss: 0.8177 Lr: 0.00401
[2023-12-25 02:24:19,761 INFO misc.py line 119 253097] Train: [3/100][471/510] Data 0.005 (0.070) Batch 1.038 (1.579) Remain 21:43:07 loss: 0.5339 Lr: 0.00401
[2023-12-25 02:24:20,950 INFO misc.py line 119 253097] Train: [3/100][472/510] Data 0.003 (0.070) Batch 1.189 (1.578) Remain 21:42:24 loss: 0.5198 Lr: 0.00401
[2023-12-25 02:24:22,042 INFO misc.py line 119 253097] Train: [3/100][473/510] Data 0.003 (0.070) Batch 1.093 (1.577) Remain 21:41:31 loss: 0.4982 Lr: 0.00402
[2023-12-25 02:24:23,217 INFO misc.py line 119 253097] Train: [3/100][474/510] Data 0.004 (0.070) Batch 1.174 (1.577) Remain 21:40:47 loss: 0.4704 Lr: 0.00402
[2023-12-25 02:24:24,560 INFO misc.py line 119 253097] Train: [3/100][475/510] Data 0.005 (0.070) Batch 1.340 (1.576) Remain 21:40:21 loss: 0.6465 Lr: 0.00402
[2023-12-25 02:24:25,689 INFO misc.py line 119 253097] Train: [3/100][476/510] Data 0.007 (0.070) Batch 1.128 (1.575) Remain 21:39:32 loss: 0.4926 Lr: 0.00402
[2023-12-25 02:24:26,980 INFO misc.py line 119 253097] Train: [3/100][477/510] Data 0.008 (0.069) Batch 1.296 (1.574) Remain 21:39:02 loss: 0.4322 Lr: 0.00403
[2023-12-25 02:24:28,122 INFO misc.py line 119 253097] Train: [3/100][478/510] Data 0.005 (0.069) Batch 1.137 (1.574) Remain 21:38:15 loss: 0.9766 Lr: 0.00403
[2023-12-25 02:24:29,227 INFO misc.py line 119 253097] Train: [3/100][479/510] Data 0.008 (0.069) Batch 1.109 (1.573) Remain 21:37:25 loss: 0.7495 Lr: 0.00403
[2023-12-25 02:24:30,486 INFO misc.py line 119 253097] Train: [3/100][480/510] Data 0.004 (0.069) Batch 1.256 (1.572) Remain 21:36:50 loss: 0.7461 Lr: 0.00404
[2023-12-25 02:24:31,620 INFO misc.py line 119 253097] Train: [3/100][481/510] Data 0.007 (0.069) Batch 1.134 (1.571) Remain 21:36:03 loss: 0.5436 Lr: 0.00404
[2023-12-25 02:24:32,664 INFO misc.py line 119 253097] Train: [3/100][482/510] Data 0.006 (0.069) Batch 1.048 (1.570) Remain 21:35:08 loss: 0.6792 Lr: 0.00404
[2023-12-25 02:24:35,495 INFO misc.py line 119 253097] Train: [3/100][483/510] Data 1.504 (0.072) Batch 2.831 (1.573) Remain 21:37:16 loss: 0.6441 Lr: 0.00405
[2023-12-25 02:24:36,718 INFO misc.py line 119 253097] Train: [3/100][484/510] Data 0.003 (0.072) Batch 1.219 (1.572) Remain 21:36:38 loss: 0.4366 Lr: 0.00405
[2023-12-25 02:24:37,962 INFO misc.py line 119 253097] Train: [3/100][485/510] Data 0.007 (0.071) Batch 1.247 (1.571) Remain 21:36:03 loss: 0.6898 Lr: 0.00405
[2023-12-25 02:24:39,069 INFO misc.py line 119 253097] Train: [3/100][486/510] Data 0.003 (0.071) Batch 1.105 (1.570) Remain 21:35:14 loss: 0.6603 Lr: 0.00406
[2023-12-25 02:24:40,011 INFO misc.py line 119 253097] Train: [3/100][487/510] Data 0.006 (0.071) Batch 0.943 (1.569) Remain 21:34:08 loss: 0.6464 Lr: 0.00406
[2023-12-25 02:24:41,286 INFO misc.py line 119 253097] Train: [3/100][488/510] Data 0.005 (0.071) Batch 1.276 (1.568) Remain 21:33:37 loss: 0.6211 Lr: 0.00406
[2023-12-25 02:24:42,566 INFO misc.py line 119 253097] Train: [3/100][489/510] Data 0.003 (0.071) Batch 1.279 (1.568) Remain 21:33:06 loss: 0.8921 Lr: 0.00407
[2023-12-25 02:24:43,895 INFO misc.py line 119 253097] Train: [3/100][490/510] Data 0.005 (0.071) Batch 1.327 (1.567) Remain 21:32:40 loss: 0.6272 Lr: 0.00407
[2023-12-25 02:24:44,816 INFO misc.py line 119 253097] Train: [3/100][491/510] Data 0.006 (0.071) Batch 0.923 (1.566) Remain 21:31:33 loss: 0.5571 Lr: 0.00407
[2023-12-25 02:24:46,094 INFO misc.py line 119 253097] Train: [3/100][492/510] Data 0.005 (0.071) Batch 1.278 (1.565) Remain 21:31:02 loss: 0.6590 Lr: 0.00408
[2023-12-25 02:24:47,262 INFO misc.py line 119 253097] Train: [3/100][493/510] Data 0.005 (0.070) Batch 1.168 (1.564) Remain 21:30:21 loss: 0.5587 Lr: 0.00408
[2023-12-25 02:24:48,367 INFO misc.py line 119 253097] Train: [3/100][494/510] Data 0.004 (0.070) Batch 1.105 (1.564) Remain 21:29:33 loss: 0.4876 Lr: 0.00408
[2023-12-25 02:24:49,624 INFO misc.py line 119 253097] Train: [3/100][495/510] Data 0.004 (0.070) Batch 1.256 (1.563) Remain 21:29:00 loss: 0.5343 Lr: 0.00409
[2023-12-25 02:24:50,856 INFO misc.py line 119 253097] Train: [3/100][496/510] Data 0.004 (0.070) Batch 1.231 (1.562) Remain 21:28:25 loss: 1.0897 Lr: 0.00409
[2023-12-25 02:24:52,071 INFO misc.py line 119 253097] Train: [3/100][497/510] Data 0.005 (0.070) Batch 1.216 (1.562) Remain 21:27:49 loss: 0.7377 Lr: 0.00409
[2023-12-25 02:24:53,075 INFO misc.py line 119 253097] Train: [3/100][498/510] Data 0.005 (0.070) Batch 1.005 (1.560) Remain 21:26:52 loss: 0.9817 Lr: 0.00410
[2023-12-25 02:24:54,000 INFO misc.py line 119 253097] Train: [3/100][499/510] Data 0.004 (0.070) Batch 0.925 (1.559) Remain 21:25:47 loss: 0.5626 Lr: 0.00410
[2023-12-25 02:24:55,091 INFO misc.py line 119 253097] Train: [3/100][500/510] Data 0.003 (0.069) Batch 1.090 (1.558) Remain 21:24:59 loss: 0.9506 Lr: 0.00410
[2023-12-25 02:24:56,100 INFO misc.py line 119 253097] Train: [3/100][501/510] Data 0.004 (0.069) Batch 1.010 (1.557) Remain 21:24:03 loss: 0.7413 Lr: 0.00410
[2023-12-25 02:24:57,255 INFO misc.py line 119 253097] Train: [3/100][502/510] Data 0.003 (0.069) Batch 1.154 (1.556) Remain 21:23:21 loss: 0.5870 Lr: 0.00411
[2023-12-25 02:25:01,242 INFO misc.py line 119 253097] Train: [3/100][503/510] Data 3.031 (0.075) Batch 3.987 (1.561) Remain 21:27:20 loss: 0.4330 Lr: 0.00411
[2023-12-25 02:25:02,344 INFO misc.py line 119 253097] Train: [3/100][504/510] Data 0.005 (0.075) Batch 1.100 (1.560) Remain 21:26:33 loss: 1.0211 Lr: 0.00411
[2023-12-25 02:25:03,439 INFO misc.py line 119 253097] Train: [3/100][505/510] Data 0.005 (0.075) Batch 1.091 (1.559) Remain 21:25:45 loss: 0.7003 Lr: 0.00412
[2023-12-25 02:25:04,432 INFO misc.py line 119 253097] Train: [3/100][506/510] Data 0.008 (0.075) Batch 0.998 (1.558) Remain 21:24:49 loss: 0.7298 Lr: 0.00412
[2023-12-25 02:25:05,579 INFO misc.py line 119 253097] Train: [3/100][507/510] Data 0.004 (0.075) Batch 1.148 (1.557) Remain 21:24:07 loss: 0.5943 Lr: 0.00412
[2023-12-25 02:25:06,650 INFO misc.py line 119 253097] Train: [3/100][508/510] Data 0.002 (0.074) Batch 1.070 (1.556) Remain 21:23:18 loss: 0.9236 Lr: 0.00413
[2023-12-25 02:25:08,966 INFO misc.py line 119 253097] Train: [3/100][509/510] Data 1.239 (0.077) Batch 2.316 (1.558) Remain 21:24:30 loss: 0.9114 Lr: 0.00413
[2023-12-25 02:25:10,153 INFO misc.py line 119 253097] Train: [3/100][510/510] Data 0.003 (0.077) Batch 1.187 (1.557) Remain 21:23:53 loss: 0.5558 Lr: 0.00413
[2023-12-25 02:25:10,153 INFO misc.py line 136 253097] Train result: loss: 0.6915 
[2023-12-25 02:25:10,154 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 02:25:35,470 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6170 
[2023-12-25 02:25:35,816 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.8770 
[2023-12-25 02:25:41,171 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.7522 
[2023-12-25 02:25:41,684 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.9203 
[2023-12-25 02:25:43,656 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0170 
[2023-12-25 02:25:44,077 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.8031 
[2023-12-25 02:25:44,960 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2119 
[2023-12-25 02:25:45,515 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.6437 
[2023-12-25 02:25:47,329 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.5449 
[2023-12-25 02:25:49,446 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4410 
[2023-12-25 02:25:50,301 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.6554 
[2023-12-25 02:25:50,728 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.1256 
[2023-12-25 02:25:51,629 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6080 
[2023-12-25 02:25:54,562 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8583 
[2023-12-25 02:25:55,037 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4166 
[2023-12-25 02:25:55,644 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.7505 
[2023-12-25 02:25:56,343 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.7189 
[2023-12-25 02:25:57,783 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.5638/0.6813/0.8441.
[2023-12-25 02:25:57,783 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9096/0.9439
[2023-12-25 02:25:57,783 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9579/0.9939
[2023-12-25 02:25:57,783 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7806/0.8752
[2023-12-25 02:25:57,783 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0014/0.0437
[2023-12-25 02:25:57,783 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2369/0.2779
[2023-12-25 02:25:57,783 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5154/0.5553
[2023-12-25 02:25:57,784 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.3908/0.4895
[2023-12-25 02:25:57,784 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.6954/0.8218
[2023-12-25 02:25:57,784 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8609/0.8972
[2023-12-25 02:25:57,784 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4848/0.6002
[2023-12-25 02:25:57,784 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.6482/0.7159
[2023-12-25 02:25:57,784 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.3661/0.9017
[2023-12-25 02:25:57,784 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.4816/0.7412
[2023-12-25 02:25:57,784 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 02:25:57,786 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.5638
[2023-12-25 02:25:57,786 INFO misc.py line 165 253097] Currently Best mIoU: 0.5638
[2023-12-25 02:25:57,787 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 02:26:07,277 INFO misc.py line 119 253097] Train: [4/100][1/510] Data 2.679 (2.679) Batch 3.536 (3.536) Remain 48:35:07 loss: 0.8256 Lr: 0.00414
[2023-12-25 02:26:12,353 INFO misc.py line 119 253097] Train: [4/100][2/510] Data 0.437 (0.437) Batch 5.077 (5.077) Remain 69:45:27 loss: 0.6836 Lr: 0.00414
[2023-12-25 02:26:16,780 INFO misc.py line 119 253097] Train: [4/100][3/510] Data 0.003 (0.003) Batch 4.427 (4.427) Remain 60:49:47 loss: 0.7573 Lr: 0.00414
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[2023-12-25 02:27:14,892 INFO misc.py line 119 253097] Train: [4/100][39/510] Data 7.190 (0.288) Batch 8.466 (1.614) Remain 22:09:51 loss: 0.4694 Lr: 0.00426
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[2023-12-25 02:27:31,176 INFO misc.py line 119 253097] Train: [4/100][53/510] Data 0.004 (0.209) Batch 1.222 (1.488) Remain 20:25:24 loss: 0.8487 Lr: 0.00430
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[2023-12-25 02:27:34,459 INFO misc.py line 119 253097] Train: [4/100][56/510] Data 0.003 (0.197) Batch 1.069 (1.466) Remain 20:07:02 loss: 0.4906 Lr: 0.00431
[2023-12-25 02:27:35,744 INFO misc.py line 119 253097] Train: [4/100][57/510] Data 0.004 (0.194) Batch 1.286 (1.462) Remain 20:04:16 loss: 0.8681 Lr: 0.00431
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[2023-12-25 02:29:45,928 INFO misc.py line 119 253097] Train: [4/100][144/510] Data 0.010 (0.186) Batch 1.023 (1.483) Remain 20:19:23 loss: 0.3744 Lr: 0.00457
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[2023-12-25 02:29:51,141 INFO misc.py line 119 253097] Train: [4/100][149/510] Data 0.003 (0.180) Batch 1.165 (1.468) Remain 20:06:54 loss: 0.9582 Lr: 0.00459
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[2023-12-25 02:30:06,041 INFO misc.py line 119 253097] Train: [4/100][161/510] Data 0.004 (0.169) Batch 1.226 (1.451) Remain 19:52:27 loss: 0.5610 Lr: 0.00462
[2023-12-25 02:30:07,057 INFO misc.py line 119 253097] Train: [4/100][162/510] Data 0.005 (0.168) Batch 1.013 (1.448) Remain 19:50:10 loss: 1.1938 Lr: 0.00463
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[2023-12-25 02:30:14,194 INFO misc.py line 119 253097] Train: [4/100][164/510] Data 0.004 (0.166) Batch 6.126 (1.475) Remain 20:11:47 loss: 0.8219 Lr: 0.00463
[2023-12-25 02:30:15,335 INFO misc.py line 119 253097] Train: [4/100][165/510] Data 0.004 (0.165) Batch 1.141 (1.473) Remain 20:10:04 loss: 0.6421 Lr: 0.00463
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[2023-12-25 02:30:31,001 INFO misc.py line 119 253097] Train: [4/100][179/510] Data 0.003 (0.152) Batch 1.042 (1.444) Remain 19:46:37 loss: 0.4826 Lr: 0.00467
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[2023-12-25 02:30:34,634 INFO misc.py line 119 253097] Train: [4/100][182/510] Data 0.009 (0.150) Batch 1.140 (1.441) Remain 19:43:19 loss: 0.4189 Lr: 0.00468
[2023-12-25 02:30:35,867 INFO misc.py line 119 253097] Train: [4/100][183/510] Data 0.006 (0.149) Batch 1.236 (1.439) Remain 19:42:22 loss: 0.4560 Lr: 0.00469
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[2023-12-25 02:30:38,005 INFO misc.py line 119 253097] Train: [4/100][185/510] Data 0.009 (0.147) Batch 1.045 (1.435) Remain 19:38:58 loss: 0.5437 Lr: 0.00469
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[2023-12-25 02:32:06,891 INFO misc.py line 119 253097] Train: [4/100][243/510] Data 5.325 (0.178) Batch 6.436 (1.459) Remain 19:56:51 loss: 0.5186 Lr: 0.00485
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[2023-12-25 02:37:00,033 INFO misc.py line 119 253097] Train: [4/100][422/510] Data 0.007 (0.136) Batch 1.128 (1.535) Remain 20:54:58 loss: 0.6109 Lr: 0.00530
[2023-12-25 02:37:01,275 INFO misc.py line 119 253097] Train: [4/100][423/510] Data 0.006 (0.136) Batch 1.243 (1.535) Remain 20:54:22 loss: 0.5643 Lr: 0.00530
[2023-12-25 02:37:02,520 INFO misc.py line 119 253097] Train: [4/100][424/510] Data 0.005 (0.136) Batch 1.244 (1.534) Remain 20:53:47 loss: 0.4875 Lr: 0.00530
[2023-12-25 02:37:03,505 INFO misc.py line 119 253097] Train: [4/100][425/510] Data 0.007 (0.135) Batch 0.988 (1.533) Remain 20:52:42 loss: 0.5309 Lr: 0.00531
[2023-12-25 02:37:08,812 INFO misc.py line 119 253097] Train: [4/100][426/510] Data 0.003 (0.135) Batch 5.307 (1.541) Remain 20:59:58 loss: 0.6848 Lr: 0.00531
[2023-12-25 02:37:09,939 INFO misc.py line 119 253097] Train: [4/100][427/510] Data 0.003 (0.135) Batch 1.127 (1.540) Remain 20:59:09 loss: 0.3939 Lr: 0.00531
[2023-12-25 02:37:11,027 INFO misc.py line 119 253097] Train: [4/100][428/510] Data 0.004 (0.134) Batch 1.089 (1.539) Remain 20:58:15 loss: 0.6535 Lr: 0.00531
[2023-12-25 02:37:12,269 INFO misc.py line 119 253097] Train: [4/100][429/510] Data 0.003 (0.134) Batch 1.238 (1.539) Remain 20:57:39 loss: 0.2422 Lr: 0.00531
[2023-12-25 02:37:13,533 INFO misc.py line 119 253097] Train: [4/100][430/510] Data 0.006 (0.134) Batch 1.263 (1.538) Remain 20:57:06 loss: 0.6223 Lr: 0.00532
[2023-12-25 02:37:14,763 INFO misc.py line 119 253097] Train: [4/100][431/510] Data 0.008 (0.133) Batch 1.230 (1.537) Remain 20:56:29 loss: 0.4301 Lr: 0.00532
[2023-12-25 02:37:15,765 INFO misc.py line 119 253097] Train: [4/100][432/510] Data 0.008 (0.133) Batch 1.005 (1.536) Remain 20:55:26 loss: 0.5192 Lr: 0.00532
[2023-12-25 02:37:16,918 INFO misc.py line 119 253097] Train: [4/100][433/510] Data 0.004 (0.133) Batch 1.154 (1.535) Remain 20:54:41 loss: 0.9148 Lr: 0.00532
[2023-12-25 02:37:17,715 INFO misc.py line 119 253097] Train: [4/100][434/510] Data 0.004 (0.132) Batch 0.797 (1.533) Remain 20:53:16 loss: 0.3745 Lr: 0.00533
[2023-12-25 02:37:18,602 INFO misc.py line 119 253097] Train: [4/100][435/510] Data 0.003 (0.132) Batch 0.887 (1.532) Remain 20:52:01 loss: 0.5978 Lr: 0.00533
[2023-12-25 02:37:19,846 INFO misc.py line 119 253097] Train: [4/100][436/510] Data 0.003 (0.132) Batch 1.244 (1.531) Remain 20:51:27 loss: 0.6018 Lr: 0.00533
[2023-12-25 02:37:20,975 INFO misc.py line 119 253097] Train: [4/100][437/510] Data 0.004 (0.132) Batch 1.128 (1.530) Remain 20:50:40 loss: 0.3608 Lr: 0.00533
[2023-12-25 02:37:21,983 INFO misc.py line 119 253097] Train: [4/100][438/510] Data 0.003 (0.131) Batch 1.008 (1.529) Remain 20:49:39 loss: 0.7356 Lr: 0.00533
[2023-12-25 02:37:27,780 INFO misc.py line 119 253097] Train: [4/100][439/510] Data 4.891 (0.142) Batch 5.798 (1.539) Remain 20:57:38 loss: 0.7905 Lr: 0.00534
[2023-12-25 02:37:29,046 INFO misc.py line 119 253097] Train: [4/100][440/510] Data 0.297 (0.143) Batch 1.266 (1.538) Remain 20:57:06 loss: 0.4781 Lr: 0.00534
[2023-12-25 02:37:30,246 INFO misc.py line 119 253097] Train: [4/100][441/510] Data 0.003 (0.142) Batch 1.200 (1.538) Remain 20:56:26 loss: 0.4631 Lr: 0.00534
[2023-12-25 02:37:31,162 INFO misc.py line 119 253097] Train: [4/100][442/510] Data 0.003 (0.142) Batch 0.917 (1.536) Remain 20:55:15 loss: 0.4387 Lr: 0.00534
[2023-12-25 02:37:32,291 INFO misc.py line 119 253097] Train: [4/100][443/510] Data 0.003 (0.142) Batch 1.129 (1.535) Remain 20:54:28 loss: 0.5414 Lr: 0.00535
[2023-12-25 02:37:33,384 INFO misc.py line 119 253097] Train: [4/100][444/510] Data 0.003 (0.141) Batch 1.093 (1.534) Remain 20:53:38 loss: 0.4917 Lr: 0.00535
[2023-12-25 02:37:34,478 INFO misc.py line 119 253097] Train: [4/100][445/510] Data 0.003 (0.141) Batch 1.094 (1.533) Remain 20:52:47 loss: 0.8060 Lr: 0.00535
[2023-12-25 02:37:35,430 INFO misc.py line 119 253097] Train: [4/100][446/510] Data 0.004 (0.141) Batch 0.952 (1.532) Remain 20:51:41 loss: 0.8666 Lr: 0.00535
[2023-12-25 02:37:36,545 INFO misc.py line 119 253097] Train: [4/100][447/510] Data 0.003 (0.140) Batch 1.115 (1.531) Remain 20:50:54 loss: 0.6550 Lr: 0.00535
[2023-12-25 02:37:37,808 INFO misc.py line 119 253097] Train: [4/100][448/510] Data 0.003 (0.140) Batch 1.263 (1.530) Remain 20:50:23 loss: 1.0993 Lr: 0.00536
[2023-12-25 02:37:38,925 INFO misc.py line 119 253097] Train: [4/100][449/510] Data 0.003 (0.140) Batch 1.116 (1.529) Remain 20:49:36 loss: 0.4747 Lr: 0.00536
[2023-12-25 02:37:39,889 INFO misc.py line 119 253097] Train: [4/100][450/510] Data 0.006 (0.139) Batch 0.965 (1.528) Remain 20:48:32 loss: 0.4260 Lr: 0.00536
[2023-12-25 02:37:41,146 INFO misc.py line 119 253097] Train: [4/100][451/510] Data 0.005 (0.139) Batch 1.258 (1.528) Remain 20:48:01 loss: 0.5235 Lr: 0.00536
[2023-12-25 02:37:42,356 INFO misc.py line 119 253097] Train: [4/100][452/510] Data 0.004 (0.139) Batch 1.207 (1.527) Remain 20:47:24 loss: 0.4405 Lr: 0.00536
[2023-12-25 02:37:43,459 INFO misc.py line 119 253097] Train: [4/100][453/510] Data 0.006 (0.139) Batch 1.106 (1.526) Remain 20:46:37 loss: 0.2718 Lr: 0.00537
[2023-12-25 02:37:44,682 INFO misc.py line 119 253097] Train: [4/100][454/510] Data 0.004 (0.138) Batch 1.222 (1.525) Remain 20:46:03 loss: 0.4506 Lr: 0.00537
[2023-12-25 02:37:45,832 INFO misc.py line 119 253097] Train: [4/100][455/510] Data 0.004 (0.138) Batch 1.150 (1.524) Remain 20:45:20 loss: 0.6621 Lr: 0.00537
[2023-12-25 02:37:47,086 INFO misc.py line 119 253097] Train: [4/100][456/510] Data 0.004 (0.138) Batch 1.251 (1.524) Remain 20:44:49 loss: 0.8907 Lr: 0.00537
[2023-12-25 02:37:48,302 INFO misc.py line 119 253097] Train: [4/100][457/510] Data 0.008 (0.137) Batch 1.217 (1.523) Remain 20:44:14 loss: 0.4380 Lr: 0.00538
[2023-12-25 02:38:04,626 INFO misc.py line 119 253097] Train: [4/100][458/510] Data 15.019 (0.170) Batch 16.327 (1.556) Remain 21:10:48 loss: 0.6878 Lr: 0.00538
[2023-12-25 02:38:05,750 INFO misc.py line 119 253097] Train: [4/100][459/510] Data 0.003 (0.170) Batch 1.123 (1.555) Remain 21:10:00 loss: 0.5605 Lr: 0.00538
[2023-12-25 02:38:07,056 INFO misc.py line 119 253097] Train: [4/100][460/510] Data 0.004 (0.169) Batch 1.305 (1.554) Remain 21:09:31 loss: 1.0102 Lr: 0.00538
[2023-12-25 02:38:08,076 INFO misc.py line 119 253097] Train: [4/100][461/510] Data 0.005 (0.169) Batch 1.022 (1.553) Remain 21:08:33 loss: 0.8736 Lr: 0.00538
[2023-12-25 02:38:09,201 INFO misc.py line 119 253097] Train: [4/100][462/510] Data 0.003 (0.169) Batch 1.116 (1.552) Remain 21:07:45 loss: 0.8102 Lr: 0.00539
[2023-12-25 02:38:10,422 INFO misc.py line 119 253097] Train: [4/100][463/510] Data 0.012 (0.168) Batch 1.228 (1.551) Remain 21:07:08 loss: 0.7054 Lr: 0.00539
[2023-12-25 02:38:11,530 INFO misc.py line 119 253097] Train: [4/100][464/510] Data 0.005 (0.168) Batch 1.109 (1.550) Remain 21:06:20 loss: 0.5796 Lr: 0.00539
[2023-12-25 02:38:12,566 INFO misc.py line 119 253097] Train: [4/100][465/510] Data 0.006 (0.168) Batch 1.036 (1.549) Remain 21:05:24 loss: 0.9629 Lr: 0.00539
[2023-12-25 02:38:13,657 INFO misc.py line 119 253097] Train: [4/100][466/510] Data 0.005 (0.167) Batch 1.089 (1.548) Remain 21:04:33 loss: 0.5048 Lr: 0.00539
[2023-12-25 02:38:14,843 INFO misc.py line 119 253097] Train: [4/100][467/510] Data 0.007 (0.167) Batch 1.189 (1.548) Remain 21:03:54 loss: 0.5286 Lr: 0.00540
[2023-12-25 02:38:16,061 INFO misc.py line 119 253097] Train: [4/100][468/510] Data 0.003 (0.167) Batch 1.213 (1.547) Remain 21:03:17 loss: 0.4636 Lr: 0.00540
[2023-12-25 02:38:17,254 INFO misc.py line 119 253097] Train: [4/100][469/510] Data 0.008 (0.166) Batch 1.198 (1.546) Remain 21:02:39 loss: 0.8160 Lr: 0.00540
[2023-12-25 02:38:18,396 INFO misc.py line 119 253097] Train: [4/100][470/510] Data 0.004 (0.166) Batch 1.137 (1.545) Remain 21:01:55 loss: 0.7656 Lr: 0.00540
[2023-12-25 02:38:19,369 INFO misc.py line 119 253097] Train: [4/100][471/510] Data 0.008 (0.166) Batch 0.978 (1.544) Remain 21:00:54 loss: 1.0410 Lr: 0.00541
[2023-12-25 02:38:27,348 INFO misc.py line 119 253097] Train: [4/100][472/510] Data 0.003 (0.165) Batch 7.979 (1.558) Remain 21:12:04 loss: 0.5598 Lr: 0.00541
[2023-12-25 02:38:28,451 INFO misc.py line 119 253097] Train: [4/100][473/510] Data 0.004 (0.165) Batch 1.102 (1.557) Remain 21:11:15 loss: 0.6720 Lr: 0.00541
[2023-12-25 02:38:29,542 INFO misc.py line 119 253097] Train: [4/100][474/510] Data 0.004 (0.165) Batch 1.092 (1.556) Remain 21:10:25 loss: 0.5620 Lr: 0.00541
[2023-12-25 02:38:30,416 INFO misc.py line 119 253097] Train: [4/100][475/510] Data 0.003 (0.164) Batch 0.873 (1.554) Remain 21:09:13 loss: 0.5867 Lr: 0.00541
[2023-12-25 02:38:31,635 INFO misc.py line 119 253097] Train: [4/100][476/510] Data 0.004 (0.164) Batch 1.216 (1.554) Remain 21:08:36 loss: 0.5904 Lr: 0.00542
[2023-12-25 02:38:32,512 INFO misc.py line 119 253097] Train: [4/100][477/510] Data 0.007 (0.163) Batch 0.881 (1.552) Remain 21:07:25 loss: 0.4571 Lr: 0.00542
[2023-12-25 02:38:33,585 INFO misc.py line 119 253097] Train: [4/100][478/510] Data 0.004 (0.163) Batch 1.068 (1.551) Remain 21:06:34 loss: 0.7458 Lr: 0.00542
[2023-12-25 02:38:34,790 INFO misc.py line 119 253097] Train: [4/100][479/510] Data 0.011 (0.163) Batch 1.209 (1.550) Remain 21:05:57 loss: 0.8839 Lr: 0.00542
[2023-12-25 02:38:35,814 INFO misc.py line 119 253097] Train: [4/100][480/510] Data 0.005 (0.163) Batch 1.023 (1.549) Remain 21:05:01 loss: 0.5670 Lr: 0.00542
[2023-12-25 02:38:36,796 INFO misc.py line 119 253097] Train: [4/100][481/510] Data 0.006 (0.162) Batch 0.983 (1.548) Remain 21:04:02 loss: 1.5779 Lr: 0.00543
[2023-12-25 02:38:37,765 INFO misc.py line 119 253097] Train: [4/100][482/510] Data 0.004 (0.162) Batch 0.969 (1.547) Remain 21:03:01 loss: 0.5721 Lr: 0.00543
[2023-12-25 02:38:39,032 INFO misc.py line 119 253097] Train: [4/100][483/510] Data 0.003 (0.162) Batch 1.266 (1.546) Remain 21:02:31 loss: 0.4572 Lr: 0.00543
[2023-12-25 02:38:40,224 INFO misc.py line 119 253097] Train: [4/100][484/510] Data 0.005 (0.161) Batch 1.192 (1.546) Remain 21:01:53 loss: 0.4692 Lr: 0.00543
[2023-12-25 02:38:41,424 INFO misc.py line 119 253097] Train: [4/100][485/510] Data 0.004 (0.161) Batch 1.199 (1.545) Remain 21:01:16 loss: 0.7402 Lr: 0.00543
[2023-12-25 02:38:42,399 INFO misc.py line 119 253097] Train: [4/100][486/510] Data 0.005 (0.161) Batch 0.977 (1.544) Remain 21:00:17 loss: 0.7382 Lr: 0.00544
[2023-12-25 02:38:54,510 INFO misc.py line 119 253097] Train: [4/100][487/510] Data 2.297 (0.165) Batch 12.107 (1.566) Remain 21:18:05 loss: 0.2736 Lr: 0.00544
[2023-12-25 02:38:55,778 INFO misc.py line 119 253097] Train: [4/100][488/510] Data 0.008 (0.165) Batch 1.271 (1.565) Remain 21:17:33 loss: 0.5961 Lr: 0.00544
[2023-12-25 02:38:56,696 INFO misc.py line 119 253097] Train: [4/100][489/510] Data 0.005 (0.164) Batch 0.918 (1.564) Remain 21:16:27 loss: 0.5196 Lr: 0.00544
[2023-12-25 02:38:57,790 INFO misc.py line 119 253097] Train: [4/100][490/510] Data 0.005 (0.164) Batch 1.094 (1.563) Remain 21:15:38 loss: 0.6658 Lr: 0.00544
[2023-12-25 02:38:58,957 INFO misc.py line 119 253097] Train: [4/100][491/510] Data 0.005 (0.164) Batch 1.167 (1.562) Remain 21:14:57 loss: 0.4194 Lr: 0.00545
[2023-12-25 02:38:59,951 INFO misc.py line 119 253097] Train: [4/100][492/510] Data 0.004 (0.163) Batch 0.994 (1.561) Remain 21:13:58 loss: 0.4518 Lr: 0.00545
[2023-12-25 02:39:01,176 INFO misc.py line 119 253097] Train: [4/100][493/510] Data 0.004 (0.163) Batch 1.225 (1.560) Remain 21:13:23 loss: 0.6549 Lr: 0.00545
[2023-12-25 02:39:12,235 INFO misc.py line 119 253097] Train: [4/100][494/510] Data 0.004 (0.163) Batch 11.059 (1.579) Remain 21:29:09 loss: 0.6507 Lr: 0.00545
[2023-12-25 02:39:13,419 INFO misc.py line 119 253097] Train: [4/100][495/510] Data 0.003 (0.162) Batch 1.183 (1.579) Remain 21:28:28 loss: 0.8817 Lr: 0.00545
[2023-12-25 02:39:14,458 INFO misc.py line 119 253097] Train: [4/100][496/510] Data 0.006 (0.162) Batch 1.040 (1.577) Remain 21:27:33 loss: 0.6219 Lr: 0.00546
[2023-12-25 02:39:15,777 INFO misc.py line 119 253097] Train: [4/100][497/510] Data 0.004 (0.162) Batch 1.318 (1.577) Remain 21:27:06 loss: 0.7245 Lr: 0.00546
[2023-12-25 02:39:16,872 INFO misc.py line 119 253097] Train: [4/100][498/510] Data 0.005 (0.161) Batch 1.096 (1.576) Remain 21:26:16 loss: 0.4675 Lr: 0.00546
[2023-12-25 02:39:17,795 INFO misc.py line 119 253097] Train: [4/100][499/510] Data 0.005 (0.161) Batch 0.924 (1.575) Remain 21:25:10 loss: 0.6352 Lr: 0.00546
[2023-12-25 02:39:19,028 INFO misc.py line 119 253097] Train: [4/100][500/510] Data 0.003 (0.161) Batch 1.233 (1.574) Remain 21:24:35 loss: 0.4508 Lr: 0.00546
[2023-12-25 02:39:20,064 INFO misc.py line 119 253097] Train: [4/100][501/510] Data 0.004 (0.160) Batch 1.029 (1.573) Remain 21:23:40 loss: 0.5286 Lr: 0.00547
[2023-12-25 02:39:21,173 INFO misc.py line 119 253097] Train: [4/100][502/510] Data 0.010 (0.160) Batch 1.115 (1.572) Remain 21:22:54 loss: 0.4833 Lr: 0.00547
[2023-12-25 02:39:22,441 INFO misc.py line 119 253097] Train: [4/100][503/510] Data 0.004 (0.160) Batch 1.260 (1.571) Remain 21:22:21 loss: 0.8157 Lr: 0.00547
[2023-12-25 02:39:23,711 INFO misc.py line 119 253097] Train: [4/100][504/510] Data 0.012 (0.160) Batch 1.277 (1.571) Remain 21:21:51 loss: 0.5314 Lr: 0.00547
[2023-12-25 02:39:24,908 INFO misc.py line 119 253097] Train: [4/100][505/510] Data 0.005 (0.159) Batch 1.196 (1.570) Remain 21:21:13 loss: 0.6603 Lr: 0.00547
[2023-12-25 02:39:26,021 INFO misc.py line 119 253097] Train: [4/100][506/510] Data 0.007 (0.159) Batch 1.112 (1.569) Remain 21:20:27 loss: 0.7178 Lr: 0.00548
[2023-12-25 02:39:27,151 INFO misc.py line 119 253097] Train: [4/100][507/510] Data 0.007 (0.159) Batch 1.128 (1.568) Remain 21:19:42 loss: 0.6638 Lr: 0.00548
[2023-12-25 02:39:28,392 INFO misc.py line 119 253097] Train: [4/100][508/510] Data 0.008 (0.158) Batch 1.240 (1.568) Remain 21:19:09 loss: 0.3516 Lr: 0.00548
[2023-12-25 02:39:29,307 INFO misc.py line 119 253097] Train: [4/100][509/510] Data 0.009 (0.158) Batch 0.921 (1.566) Remain 21:18:05 loss: 0.6783 Lr: 0.00548
[2023-12-25 02:39:30,346 INFO misc.py line 119 253097] Train: [4/100][510/510] Data 0.003 (0.158) Batch 1.039 (1.565) Remain 21:17:13 loss: 0.6026 Lr: 0.00548
[2023-12-25 02:39:30,347 INFO misc.py line 136 253097] Train result: loss: 0.6171 
[2023-12-25 02:39:30,347 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 02:39:58,242 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6266 
[2023-12-25 02:39:58,605 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.9239 
[2023-12-25 02:40:03,553 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.8315 
[2023-12-25 02:40:04,076 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.9560 
[2023-12-25 02:40:06,055 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.1290 
[2023-12-25 02:40:06,488 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.8149 
[2023-12-25 02:40:07,376 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0502 
[2023-12-25 02:40:07,941 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5560 
[2023-12-25 02:40:09,757 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9574 
[2023-12-25 02:40:11,876 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4249 
[2023-12-25 02:40:12,730 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.6903 
[2023-12-25 02:40:13,151 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.2075 
[2023-12-25 02:40:14,049 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4188 
[2023-12-25 02:40:16,998 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7741 
[2023-12-25 02:40:17,471 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2784 
[2023-12-25 02:40:18,080 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.6781 
[2023-12-25 02:40:18,781 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.9120 
[2023-12-25 02:40:20,255 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.5470/0.6381/0.8440.
[2023-12-25 02:40:20,255 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9187/0.9595
[2023-12-25 02:40:20,255 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9723/0.9883
[2023-12-25 02:40:20,255 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7976/0.9131
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0018/0.0430
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2019/0.2485
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5215/0.6843
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.4985/0.6355
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.6749/0.8930
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.7684/0.7854
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.1382/0.1382
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.4910/0.4999
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6493/0.7286
[2023-12-25 02:40:20,256 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.4769/0.7781
[2023-12-25 02:40:20,256 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 02:40:20,257 INFO misc.py line 165 253097] Currently Best mIoU: 0.5638
[2023-12-25 02:40:20,257 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 02:40:39,501 INFO misc.py line 119 253097] Train: [5/100][1/510] Data 7.145 (7.145) Batch 16.615 (16.615) Remain 225:57:11 loss: 0.6603 Lr: 0.00549
[2023-12-25 02:40:40,438 INFO misc.py line 119 253097] Train: [5/100][2/510] Data 0.005 (0.005) Batch 0.935 (0.935) Remain 12:42:51 loss: 0.4738 Lr: 0.00549
[2023-12-25 02:40:41,621 INFO misc.py line 119 253097] Train: [5/100][3/510] Data 0.009 (0.009) Batch 1.185 (1.185) Remain 16:06:56 loss: 0.6526 Lr: 0.00549
[2023-12-25 02:40:42,814 INFO misc.py line 119 253097] Train: [5/100][4/510] Data 0.004 (0.004) Batch 1.194 (1.194) Remain 16:14:28 loss: 0.9563 Lr: 0.00549
[2023-12-25 02:40:49,609 INFO misc.py line 119 253097] Train: [5/100][5/510] Data 0.004 (0.004) Batch 6.795 (3.995) Remain 54:19:22 loss: 0.7744 Lr: 0.00549
[2023-12-25 02:40:50,828 INFO misc.py line 119 253097] Train: [5/100][6/510] Data 0.003 (0.004) Batch 1.218 (3.069) Remain 41:44:15 loss: 0.4479 Lr: 0.00550
[2023-12-25 02:40:51,767 INFO misc.py line 119 253097] Train: [5/100][7/510] Data 0.003 (0.003) Batch 0.939 (2.537) Remain 34:29:45 loss: 0.7033 Lr: 0.00550
[2023-12-25 02:40:52,737 INFO misc.py line 119 253097] Train: [5/100][8/510] Data 0.003 (0.003) Batch 0.970 (2.223) Remain 30:14:03 loss: 0.5213 Lr: 0.00550
[2023-12-25 02:40:53,779 INFO misc.py line 119 253097] Train: [5/100][9/510] Data 0.004 (0.003) Batch 1.042 (2.027) Remain 27:33:21 loss: 0.6144 Lr: 0.00550
[2023-12-25 02:40:55,083 INFO misc.py line 119 253097] Train: [5/100][10/510] Data 0.004 (0.004) Batch 1.304 (1.923) Remain 26:09:03 loss: 0.6029 Lr: 0.00550
[2023-12-25 02:40:56,148 INFO misc.py line 119 253097] Train: [5/100][11/510] Data 0.005 (0.004) Batch 1.059 (1.815) Remain 24:40:54 loss: 0.8138 Lr: 0.00551
[2023-12-25 02:40:57,271 INFO misc.py line 119 253097] Train: [5/100][12/510] Data 0.011 (0.005) Batch 1.129 (1.739) Remain 23:38:40 loss: 0.5878 Lr: 0.00551
[2023-12-25 02:40:58,414 INFO misc.py line 119 253097] Train: [5/100][13/510] Data 0.003 (0.004) Batch 1.142 (1.679) Remain 22:49:57 loss: 0.4937 Lr: 0.00551
[2023-12-25 02:40:59,461 INFO misc.py line 119 253097] Train: [5/100][14/510] Data 0.004 (0.004) Batch 1.048 (1.622) Remain 22:03:07 loss: 0.5663 Lr: 0.00551
[2023-12-25 02:41:00,591 INFO misc.py line 119 253097] Train: [5/100][15/510] Data 0.004 (0.004) Batch 1.123 (1.580) Remain 21:29:09 loss: 0.6299 Lr: 0.00551
[2023-12-25 02:41:01,939 INFO misc.py line 119 253097] Train: [5/100][16/510] Data 0.011 (0.005) Batch 1.355 (1.563) Remain 21:14:58 loss: 0.5539 Lr: 0.00551
[2023-12-25 02:41:02,936 INFO misc.py line 119 253097] Train: [5/100][17/510] Data 0.003 (0.005) Batch 0.996 (1.522) Remain 20:41:54 loss: 0.7089 Lr: 0.00552
[2023-12-25 02:41:04,157 INFO misc.py line 119 253097] Train: [5/100][18/510] Data 0.005 (0.005) Batch 1.222 (1.502) Remain 20:25:32 loss: 0.4834 Lr: 0.00552
[2023-12-25 02:41:05,202 INFO misc.py line 119 253097] Train: [5/100][19/510] Data 0.005 (0.005) Batch 1.044 (1.474) Remain 20:02:09 loss: 0.7554 Lr: 0.00552
[2023-12-25 02:41:06,183 INFO misc.py line 119 253097] Train: [5/100][20/510] Data 0.017 (0.005) Batch 0.982 (1.445) Remain 19:38:31 loss: 0.8520 Lr: 0.00552
[2023-12-25 02:41:07,159 INFO misc.py line 119 253097] Train: [5/100][21/510] Data 0.005 (0.005) Batch 0.974 (1.419) Remain 19:17:10 loss: 0.5279 Lr: 0.00552
[2023-12-25 02:41:17,544 INFO misc.py line 119 253097] Train: [5/100][22/510] Data 0.007 (0.005) Batch 10.386 (1.891) Remain 25:42:06 loss: 1.0247 Lr: 0.00553
[2023-12-25 02:41:18,651 INFO misc.py line 119 253097] Train: [5/100][23/510] Data 0.004 (0.005) Batch 1.109 (1.852) Remain 25:10:11 loss: 0.4176 Lr: 0.00553
[2023-12-25 02:41:19,738 INFO misc.py line 119 253097] Train: [5/100][24/510] Data 0.003 (0.005) Batch 1.086 (1.815) Remain 24:40:25 loss: 0.4018 Lr: 0.00553
[2023-12-25 02:41:20,791 INFO misc.py line 119 253097] Train: [5/100][25/510] Data 0.004 (0.005) Batch 1.053 (1.780) Remain 24:12:08 loss: 0.3016 Lr: 0.00553
[2023-12-25 02:41:22,050 INFO misc.py line 119 253097] Train: [5/100][26/510] Data 0.003 (0.005) Batch 1.256 (1.758) Remain 23:53:29 loss: 0.4017 Lr: 0.00553
[2023-12-25 02:41:23,218 INFO misc.py line 119 253097] Train: [5/100][27/510] Data 0.007 (0.005) Batch 1.168 (1.733) Remain 23:33:26 loss: 0.4979 Lr: 0.00554
[2023-12-25 02:41:24,375 INFO misc.py line 119 253097] Train: [5/100][28/510] Data 0.006 (0.005) Batch 1.156 (1.710) Remain 23:14:34 loss: 0.7051 Lr: 0.00554
[2023-12-25 02:41:25,591 INFO misc.py line 119 253097] Train: [5/100][29/510] Data 0.007 (0.005) Batch 1.216 (1.691) Remain 22:59:04 loss: 1.0583 Lr: 0.00554
[2023-12-25 02:41:27,826 INFO misc.py line 119 253097] Train: [5/100][30/510] Data 0.008 (0.005) Batch 2.238 (1.711) Remain 23:15:33 loss: 0.7856 Lr: 0.00554
[2023-12-25 02:41:28,872 INFO misc.py line 119 253097] Train: [5/100][31/510] Data 0.005 (0.005) Batch 1.046 (1.688) Remain 22:56:09 loss: 0.8039 Lr: 0.00554
[2023-12-25 02:41:29,893 INFO misc.py line 119 253097] Train: [5/100][32/510] Data 0.004 (0.005) Batch 1.023 (1.665) Remain 22:37:26 loss: 0.7667 Lr: 0.00554
[2023-12-25 02:41:31,153 INFO misc.py line 119 253097] Train: [5/100][33/510] Data 0.003 (0.005) Batch 1.255 (1.651) Remain 22:26:16 loss: 0.4434 Lr: 0.00555
[2023-12-25 02:41:32,238 INFO misc.py line 119 253097] Train: [5/100][34/510] Data 0.008 (0.005) Batch 1.086 (1.633) Remain 22:11:23 loss: 0.5029 Lr: 0.00555
[2023-12-25 02:41:33,586 INFO misc.py line 119 253097] Train: [5/100][35/510] Data 0.007 (0.005) Batch 1.351 (1.624) Remain 22:04:10 loss: 0.7630 Lr: 0.00555
[2023-12-25 02:41:34,767 INFO misc.py line 119 253097] Train: [5/100][36/510] Data 0.003 (0.005) Batch 1.180 (1.610) Remain 21:53:10 loss: 0.6730 Lr: 0.00555
[2023-12-25 02:41:35,768 INFO misc.py line 119 253097] Train: [5/100][37/510] Data 0.006 (0.005) Batch 0.998 (1.592) Remain 21:38:26 loss: 0.6134 Lr: 0.00555
[2023-12-25 02:41:36,879 INFO misc.py line 119 253097] Train: [5/100][38/510] Data 0.009 (0.005) Batch 1.113 (1.579) Remain 21:27:15 loss: 0.5067 Lr: 0.00556
[2023-12-25 02:41:38,130 INFO misc.py line 119 253097] Train: [5/100][39/510] Data 0.007 (0.005) Batch 1.253 (1.570) Remain 21:19:51 loss: 0.3695 Lr: 0.00556
[2023-12-25 02:41:39,413 INFO misc.py line 119 253097] Train: [5/100][40/510] Data 0.004 (0.005) Batch 1.283 (1.562) Remain 21:13:30 loss: 0.6150 Lr: 0.00556
[2023-12-25 02:41:40,455 INFO misc.py line 119 253097] Train: [5/100][41/510] Data 0.004 (0.005) Batch 1.036 (1.548) Remain 21:02:12 loss: 0.6686 Lr: 0.00556
[2023-12-25 02:41:41,660 INFO misc.py line 119 253097] Train: [5/100][42/510] Data 0.010 (0.005) Batch 1.208 (1.539) Remain 20:55:03 loss: 0.6518 Lr: 0.00556
[2023-12-25 02:41:42,789 INFO misc.py line 119 253097] Train: [5/100][43/510] Data 0.008 (0.006) Batch 1.130 (1.529) Remain 20:46:42 loss: 1.0011 Lr: 0.00556
[2023-12-25 02:41:43,804 INFO misc.py line 119 253097] Train: [5/100][44/510] Data 0.006 (0.006) Batch 1.016 (1.517) Remain 20:36:28 loss: 0.7177 Lr: 0.00557
[2023-12-25 02:41:45,066 INFO misc.py line 119 253097] Train: [5/100][45/510] Data 0.005 (0.006) Batch 1.259 (1.511) Remain 20:31:27 loss: 0.3886 Lr: 0.00557
[2023-12-25 02:41:46,183 INFO misc.py line 119 253097] Train: [5/100][46/510] Data 0.007 (0.006) Batch 1.120 (1.501) Remain 20:24:01 loss: 0.4923 Lr: 0.00557
[2023-12-25 02:41:47,241 INFO misc.py line 119 253097] Train: [5/100][47/510] Data 0.005 (0.006) Batch 1.059 (1.491) Remain 20:15:48 loss: 0.5849 Lr: 0.00557
[2023-12-25 02:41:48,190 INFO misc.py line 119 253097] Train: [5/100][48/510] Data 0.003 (0.006) Batch 0.949 (1.479) Remain 20:05:56 loss: 0.5934 Lr: 0.00557
[2023-12-25 02:41:49,364 INFO misc.py line 119 253097] Train: [5/100][49/510] Data 0.004 (0.005) Batch 1.174 (1.473) Remain 20:00:30 loss: 0.4074 Lr: 0.00558
[2023-12-25 02:41:50,434 INFO misc.py line 119 253097] Train: [5/100][50/510] Data 0.003 (0.005) Batch 1.070 (1.464) Remain 19:53:30 loss: 0.6613 Lr: 0.00558
[2023-12-25 02:41:51,467 INFO misc.py line 119 253097] Train: [5/100][51/510] Data 0.004 (0.005) Batch 1.034 (1.455) Remain 19:46:10 loss: 0.8351 Lr: 0.00558
[2023-12-25 02:41:52,627 INFO misc.py line 119 253097] Train: [5/100][52/510] Data 0.003 (0.005) Batch 1.158 (1.449) Remain 19:41:11 loss: 0.5512 Lr: 0.00558
[2023-12-25 02:41:53,859 INFO misc.py line 119 253097] Train: [5/100][53/510] Data 0.006 (0.005) Batch 1.232 (1.445) Remain 19:37:38 loss: 0.5687 Lr: 0.00558
[2023-12-25 02:41:55,054 INFO misc.py line 119 253097] Train: [5/100][54/510] Data 0.005 (0.005) Batch 1.193 (1.440) Remain 19:33:35 loss: 0.5034 Lr: 0.00558
[2023-12-25 02:41:56,225 INFO misc.py line 119 253097] Train: [5/100][55/510] Data 0.007 (0.005) Batch 1.168 (1.435) Remain 19:29:18 loss: 0.3988 Lr: 0.00559
[2023-12-25 02:41:57,269 INFO misc.py line 119 253097] Train: [5/100][56/510] Data 0.010 (0.005) Batch 1.048 (1.427) Remain 19:23:20 loss: 0.6047 Lr: 0.00559
[2023-12-25 02:41:58,362 INFO misc.py line 119 253097] Train: [5/100][57/510] Data 0.006 (0.005) Batch 1.092 (1.421) Remain 19:18:15 loss: 0.4108 Lr: 0.00559
[2023-12-25 02:41:59,402 INFO misc.py line 119 253097] Train: [5/100][58/510] Data 0.007 (0.005) Batch 1.041 (1.414) Remain 19:12:36 loss: 0.5618 Lr: 0.00559
[2023-12-25 02:42:00,496 INFO misc.py line 119 253097] Train: [5/100][59/510] Data 0.006 (0.005) Batch 1.097 (1.409) Remain 19:07:57 loss: 0.5174 Lr: 0.00559
[2023-12-25 02:42:01,537 INFO misc.py line 119 253097] Train: [5/100][60/510] Data 0.003 (0.005) Batch 1.039 (1.402) Remain 19:02:39 loss: 0.4429 Lr: 0.00560
[2023-12-25 02:42:06,023 INFO misc.py line 119 253097] Train: [5/100][61/510] Data 0.004 (0.005) Batch 4.486 (1.455) Remain 19:45:58 loss: 0.3267 Lr: 0.00560
[2023-12-25 02:42:07,186 INFO misc.py line 119 253097] Train: [5/100][62/510] Data 0.004 (0.005) Batch 1.165 (1.450) Remain 19:41:56 loss: 0.5996 Lr: 0.00560
[2023-12-25 02:42:08,445 INFO misc.py line 119 253097] Train: [5/100][63/510] Data 0.003 (0.005) Batch 1.253 (1.447) Remain 19:39:13 loss: 0.4883 Lr: 0.00560
[2023-12-25 02:42:09,739 INFO misc.py line 119 253097] Train: [5/100][64/510] Data 0.008 (0.005) Batch 1.295 (1.445) Remain 19:37:10 loss: 0.6430 Lr: 0.00560
[2023-12-25 02:42:10,828 INFO misc.py line 119 253097] Train: [5/100][65/510] Data 0.007 (0.005) Batch 1.089 (1.439) Remain 19:32:28 loss: 0.5168 Lr: 0.00560
[2023-12-25 02:42:11,851 INFO misc.py line 119 253097] Train: [5/100][66/510] Data 0.008 (0.005) Batch 1.024 (1.432) Remain 19:27:05 loss: 0.6535 Lr: 0.00561
[2023-12-25 02:42:13,701 INFO misc.py line 119 253097] Train: [5/100][67/510] Data 0.006 (0.005) Batch 1.853 (1.439) Remain 19:32:25 loss: 0.5025 Lr: 0.00561
[2023-12-25 02:42:14,558 INFO misc.py line 119 253097] Train: [5/100][68/510] Data 0.005 (0.005) Batch 0.857 (1.430) Remain 19:25:05 loss: 0.7127 Lr: 0.00561
[2023-12-25 02:42:15,682 INFO misc.py line 119 253097] Train: [5/100][69/510] Data 0.004 (0.005) Batch 1.124 (1.425) Remain 19:21:17 loss: 0.7843 Lr: 0.00561
[2023-12-25 02:42:16,889 INFO misc.py line 119 253097] Train: [5/100][70/510] Data 0.005 (0.005) Batch 1.207 (1.422) Remain 19:18:37 loss: 0.4165 Lr: 0.00561
[2023-12-25 02:42:17,856 INFO misc.py line 119 253097] Train: [5/100][71/510] Data 0.005 (0.005) Batch 0.967 (1.415) Remain 19:13:08 loss: 0.4780 Lr: 0.00561
[2023-12-25 02:42:18,962 INFO misc.py line 119 253097] Train: [5/100][72/510] Data 0.005 (0.005) Batch 1.107 (1.411) Remain 19:09:29 loss: 0.4302 Lr: 0.00562
[2023-12-25 02:42:20,114 INFO misc.py line 119 253097] Train: [5/100][73/510] Data 0.003 (0.005) Batch 1.148 (1.407) Remain 19:06:24 loss: 0.5613 Lr: 0.00562
[2023-12-25 02:42:21,148 INFO misc.py line 119 253097] Train: [5/100][74/510] Data 0.011 (0.005) Batch 1.035 (1.402) Remain 19:02:06 loss: 0.8265 Lr: 0.00562
[2023-12-25 02:42:22,318 INFO misc.py line 119 253097] Train: [5/100][75/510] Data 0.007 (0.006) Batch 1.172 (1.399) Remain 18:59:29 loss: 0.4609 Lr: 0.00562
[2023-12-25 02:42:23,611 INFO misc.py line 119 253097] Train: [5/100][76/510] Data 0.005 (0.006) Batch 1.291 (1.397) Remain 18:58:15 loss: 0.6610 Lr: 0.00562
[2023-12-25 02:42:24,448 INFO misc.py line 119 253097] Train: [5/100][77/510] Data 0.007 (0.006) Batch 0.840 (1.390) Remain 18:52:06 loss: 0.3853 Lr: 0.00562
[2023-12-25 02:42:25,596 INFO misc.py line 119 253097] Train: [5/100][78/510] Data 0.005 (0.006) Batch 1.147 (1.386) Remain 18:49:26 loss: 0.7364 Lr: 0.00563
[2023-12-25 02:42:30,055 INFO misc.py line 119 253097] Train: [5/100][79/510] Data 0.005 (0.006) Batch 4.461 (1.427) Remain 19:22:22 loss: 0.7036 Lr: 0.00563
[2023-12-25 02:42:31,250 INFO misc.py line 119 253097] Train: [5/100][80/510] Data 0.007 (0.006) Batch 1.192 (1.424) Remain 19:19:51 loss: 0.7023 Lr: 0.00563
[2023-12-25 02:42:32,546 INFO misc.py line 119 253097] Train: [5/100][81/510] Data 0.006 (0.006) Batch 1.298 (1.422) Remain 19:18:31 loss: 0.8390 Lr: 0.00563
[2023-12-25 02:42:33,713 INFO misc.py line 119 253097] Train: [5/100][82/510] Data 0.004 (0.006) Batch 1.165 (1.419) Remain 19:15:51 loss: 0.8895 Lr: 0.00563
[2023-12-25 02:42:34,942 INFO misc.py line 119 253097] Train: [5/100][83/510] Data 0.006 (0.006) Batch 1.231 (1.417) Remain 19:13:55 loss: 0.5300 Lr: 0.00563
[2023-12-25 02:42:36,004 INFO misc.py line 119 253097] Train: [5/100][84/510] Data 0.004 (0.006) Batch 1.058 (1.412) Remain 19:10:17 loss: 0.4691 Lr: 0.00564
[2023-12-25 02:42:39,272 INFO misc.py line 119 253097] Train: [5/100][85/510] Data 0.009 (0.006) Batch 3.271 (1.435) Remain 19:28:43 loss: 0.9757 Lr: 0.00564
[2023-12-25 02:42:40,293 INFO misc.py line 119 253097] Train: [5/100][86/510] Data 0.007 (0.006) Batch 1.023 (1.430) Remain 19:24:40 loss: 0.8784 Lr: 0.00564
[2023-12-25 02:42:41,444 INFO misc.py line 119 253097] Train: [5/100][87/510] Data 0.003 (0.006) Batch 1.150 (1.426) Remain 19:21:56 loss: 0.8541 Lr: 0.00564
[2023-12-25 02:42:42,401 INFO misc.py line 119 253097] Train: [5/100][88/510] Data 0.003 (0.006) Batch 0.955 (1.421) Remain 19:17:23 loss: 0.4156 Lr: 0.00564
[2023-12-25 02:42:44,469 INFO misc.py line 119 253097] Train: [5/100][89/510] Data 0.006 (0.006) Batch 2.068 (1.428) Remain 19:23:30 loss: 0.3878 Lr: 0.00564
[2023-12-25 02:42:45,492 INFO misc.py line 119 253097] Train: [5/100][90/510] Data 0.007 (0.006) Batch 1.024 (1.424) Remain 19:19:41 loss: 0.5403 Lr: 0.00565
[2023-12-25 02:42:46,673 INFO misc.py line 119 253097] Train: [5/100][91/510] Data 0.005 (0.006) Batch 1.181 (1.421) Remain 19:17:25 loss: 0.4559 Lr: 0.00565
[2023-12-25 02:42:47,819 INFO misc.py line 119 253097] Train: [5/100][92/510] Data 0.005 (0.006) Batch 1.146 (1.418) Remain 19:14:52 loss: 0.5051 Lr: 0.00565
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[2023-12-25 02:42:51,181 INFO misc.py line 119 253097] Train: [5/100][95/510] Data 0.005 (0.006) Batch 0.949 (1.408) Remain 19:06:54 loss: 0.8799 Lr: 0.00565
[2023-12-25 02:42:52,080 INFO misc.py line 119 253097] Train: [5/100][96/510] Data 0.006 (0.006) Batch 0.900 (1.403) Remain 19:02:26 loss: 0.4373 Lr: 0.00566
[2023-12-25 02:42:53,119 INFO misc.py line 119 253097] Train: [5/100][97/510] Data 0.005 (0.006) Batch 1.037 (1.399) Remain 18:59:14 loss: 0.7064 Lr: 0.00566
[2023-12-25 02:42:54,261 INFO misc.py line 119 253097] Train: [5/100][98/510] Data 0.005 (0.006) Batch 1.142 (1.396) Remain 18:57:01 loss: 0.5357 Lr: 0.00566
[2023-12-25 02:42:55,501 INFO misc.py line 119 253097] Train: [5/100][99/510] Data 0.007 (0.006) Batch 1.239 (1.395) Remain 18:55:39 loss: 0.6669 Lr: 0.00566
[2023-12-25 02:42:56,654 INFO misc.py line 119 253097] Train: [5/100][100/510] Data 0.007 (0.006) Batch 1.154 (1.392) Remain 18:53:37 loss: 0.5324 Lr: 0.00566
[2023-12-25 02:42:57,953 INFO misc.py line 119 253097] Train: [5/100][101/510] Data 0.007 (0.006) Batch 1.300 (1.391) Remain 18:52:49 loss: 0.6798 Lr: 0.00566
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[2023-12-25 02:43:48,588 INFO misc.py line 119 253097] Train: [5/100][132/510] Data 5.426 (0.080) Batch 6.688 (1.449) Remain 19:39:29 loss: 0.4496 Lr: 0.00571
[2023-12-25 02:43:49,573 INFO misc.py line 119 253097] Train: [5/100][133/510] Data 0.006 (0.079) Batch 0.986 (1.446) Remain 19:36:33 loss: 0.4297 Lr: 0.00571
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[2023-12-25 02:43:53,210 INFO misc.py line 119 253097] Train: [5/100][136/510] Data 0.007 (0.077) Batch 1.202 (1.440) Remain 19:32:10 loss: 0.6918 Lr: 0.00572
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[2023-12-25 02:43:57,603 INFO misc.py line 119 253097] Train: [5/100][140/510] Data 0.005 (0.075) Batch 0.961 (1.431) Remain 19:23:58 loss: 0.6706 Lr: 0.00572
[2023-12-25 02:43:58,456 INFO misc.py line 119 253097] Train: [5/100][141/510] Data 0.004 (0.075) Batch 0.854 (1.426) Remain 19:20:32 loss: 0.5894 Lr: 0.00573
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[2023-12-25 02:44:24,508 INFO misc.py line 119 253097] Train: [5/100][157/510] Data 0.003 (0.083) Batch 7.119 (1.447) Remain 19:37:13 loss: 0.4822 Lr: 0.00575
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[2023-12-25 02:44:26,839 INFO misc.py line 119 253097] Train: [5/100][159/510] Data 0.009 (0.082) Batch 1.210 (1.444) Remain 19:34:12 loss: 0.3213 Lr: 0.00575
[2023-12-25 02:44:27,818 INFO misc.py line 119 253097] Train: [5/100][160/510] Data 0.009 (0.082) Batch 0.984 (1.441) Remain 19:31:48 loss: 0.5013 Lr: 0.00575
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[2023-12-25 02:44:31,602 INFO misc.py line 119 253097] Train: [5/100][163/510] Data 0.003 (0.080) Batch 1.565 (1.437) Remain 19:28:59 loss: 0.4460 Lr: 0.00576
[2023-12-25 02:44:36,753 INFO misc.py line 119 253097] Train: [5/100][164/510] Data 4.053 (0.105) Batch 5.153 (1.460) Remain 19:47:44 loss: 0.4061 Lr: 0.00576
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[2023-12-25 02:44:51,276 INFO misc.py line 119 253097] Train: [5/100][177/510] Data 0.002 (0.097) Batch 1.182 (1.435) Remain 19:26:34 loss: 0.6699 Lr: 0.00578
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[2023-12-25 02:44:54,510 INFO misc.py line 119 253097] Train: [5/100][180/510] Data 0.003 (0.096) Batch 1.083 (1.429) Remain 19:21:34 loss: 0.4813 Lr: 0.00578
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[2023-12-25 02:47:07,230 INFO misc.py line 119 253097] Train: [5/100][261/510] Data 0.007 (0.067) Batch 1.073 (1.495) Remain 20:13:05 loss: 0.8408 Lr: 0.00587
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[2023-12-25 02:47:22,639 INFO misc.py line 119 253097] Train: [5/100][268/510] Data 6.513 (0.090) Batch 7.734 (1.513) Remain 20:28:04 loss: 0.2986 Lr: 0.00588
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[2023-12-25 02:47:26,131 INFO misc.py line 119 253097] Train: [5/100][271/510] Data 0.003 (0.089) Batch 1.182 (1.509) Remain 20:24:48 loss: 0.5172 Lr: 0.00588
[2023-12-25 02:47:27,232 INFO misc.py line 119 253097] Train: [5/100][272/510] Data 0.008 (0.089) Batch 1.105 (1.508) Remain 20:23:34 loss: 0.5539 Lr: 0.00588
[2023-12-25 02:47:33,066 INFO misc.py line 119 253097] Train: [5/100][273/510] Data 4.587 (0.106) Batch 5.835 (1.524) Remain 20:36:32 loss: 0.2887 Lr: 0.00589
[2023-12-25 02:47:41,066 INFO misc.py line 119 253097] Train: [5/100][274/510] Data 6.721 (0.130) Batch 7.999 (1.548) Remain 20:55:54 loss: 0.3901 Lr: 0.00589
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[2023-12-25 02:51:10,370 INFO misc.py line 119 253097] Train: [5/100][416/510] Data 0.005 (0.093) Batch 1.186 (1.522) Remain 20:31:42 loss: 0.5935 Lr: 0.00598
[2023-12-25 02:51:11,301 INFO misc.py line 119 253097] Train: [5/100][417/510] Data 0.011 (0.092) Batch 0.938 (1.521) Remain 20:30:32 loss: 0.5459 Lr: 0.00598
[2023-12-25 02:51:12,517 INFO misc.py line 119 253097] Train: [5/100][418/510] Data 0.004 (0.092) Batch 1.216 (1.520) Remain 20:29:55 loss: 0.6268 Lr: 0.00598
[2023-12-25 02:51:16,488 INFO misc.py line 119 253097] Train: [5/100][419/510] Data 0.003 (0.092) Batch 3.971 (1.526) Remain 20:34:39 loss: 0.2700 Lr: 0.00598
[2023-12-25 02:51:24,248 INFO misc.py line 119 253097] Train: [5/100][420/510] Data 0.002 (0.092) Batch 7.757 (1.541) Remain 20:46:43 loss: 0.5608 Lr: 0.00598
[2023-12-25 02:51:25,445 INFO misc.py line 119 253097] Train: [5/100][421/510] Data 0.009 (0.092) Batch 1.194 (1.540) Remain 20:46:01 loss: 0.6015 Lr: 0.00598
[2023-12-25 02:51:26,653 INFO misc.py line 119 253097] Train: [5/100][422/510] Data 0.009 (0.091) Batch 1.210 (1.539) Remain 20:45:21 loss: 0.3513 Lr: 0.00598
[2023-12-25 02:51:27,692 INFO misc.py line 119 253097] Train: [5/100][423/510] Data 0.007 (0.091) Batch 1.038 (1.538) Remain 20:44:22 loss: 0.6865 Lr: 0.00598
[2023-12-25 02:51:28,800 INFO misc.py line 119 253097] Train: [5/100][424/510] Data 0.007 (0.091) Batch 1.110 (1.537) Remain 20:43:31 loss: 0.5214 Lr: 0.00598
[2023-12-25 02:51:29,928 INFO misc.py line 119 253097] Train: [5/100][425/510] Data 0.006 (0.091) Batch 1.128 (1.536) Remain 20:42:42 loss: 0.5184 Lr: 0.00599
[2023-12-25 02:51:31,051 INFO misc.py line 119 253097] Train: [5/100][426/510] Data 0.005 (0.091) Batch 1.122 (1.535) Remain 20:41:53 loss: 0.5332 Lr: 0.00599
[2023-12-25 02:51:32,092 INFO misc.py line 119 253097] Train: [5/100][427/510] Data 0.010 (0.090) Batch 1.038 (1.534) Remain 20:40:55 loss: 0.7793 Lr: 0.00599
[2023-12-25 02:51:33,098 INFO misc.py line 119 253097] Train: [5/100][428/510] Data 0.009 (0.090) Batch 1.010 (1.533) Remain 20:39:53 loss: 0.4658 Lr: 0.00599
[2023-12-25 02:51:34,046 INFO misc.py line 119 253097] Train: [5/100][429/510] Data 0.006 (0.090) Batch 0.949 (1.532) Remain 20:38:45 loss: 0.3563 Lr: 0.00599
[2023-12-25 02:51:35,318 INFO misc.py line 119 253097] Train: [5/100][430/510] Data 0.003 (0.090) Batch 1.270 (1.531) Remain 20:38:14 loss: 0.7752 Lr: 0.00599
[2023-12-25 02:51:36,412 INFO misc.py line 119 253097] Train: [5/100][431/510] Data 0.012 (0.090) Batch 1.095 (1.530) Remain 20:37:23 loss: 0.3916 Lr: 0.00599
[2023-12-25 02:51:37,515 INFO misc.py line 119 253097] Train: [5/100][432/510] Data 0.005 (0.089) Batch 1.104 (1.529) Remain 20:36:34 loss: 0.5177 Lr: 0.00599
[2023-12-25 02:51:38,539 INFO misc.py line 119 253097] Train: [5/100][433/510] Data 0.003 (0.089) Batch 1.025 (1.528) Remain 20:35:35 loss: 0.5068 Lr: 0.00599
[2023-12-25 02:51:39,707 INFO misc.py line 119 253097] Train: [5/100][434/510] Data 0.003 (0.089) Batch 1.167 (1.527) Remain 20:34:53 loss: 0.4106 Lr: 0.00599
[2023-12-25 02:51:40,992 INFO misc.py line 119 253097] Train: [5/100][435/510] Data 0.004 (0.089) Batch 1.281 (1.526) Remain 20:34:24 loss: 0.5579 Lr: 0.00599
[2023-12-25 02:51:41,999 INFO misc.py line 119 253097] Train: [5/100][436/510] Data 0.008 (0.089) Batch 1.011 (1.525) Remain 20:33:25 loss: 0.7550 Lr: 0.00599
[2023-12-25 02:51:43,189 INFO misc.py line 119 253097] Train: [5/100][437/510] Data 0.003 (0.089) Batch 1.185 (1.524) Remain 20:32:45 loss: 0.4597 Lr: 0.00599
[2023-12-25 02:51:44,263 INFO misc.py line 119 253097] Train: [5/100][438/510] Data 0.007 (0.088) Batch 1.076 (1.523) Remain 20:31:54 loss: 0.3814 Lr: 0.00599
[2023-12-25 02:51:45,218 INFO misc.py line 119 253097] Train: [5/100][439/510] Data 0.007 (0.088) Batch 0.957 (1.522) Remain 20:30:49 loss: 0.9318 Lr: 0.00599
[2023-12-25 02:51:46,377 INFO misc.py line 119 253097] Train: [5/100][440/510] Data 0.006 (0.088) Batch 1.160 (1.521) Remain 20:30:07 loss: 0.4033 Lr: 0.00599
[2023-12-25 02:51:47,635 INFO misc.py line 119 253097] Train: [5/100][441/510] Data 0.004 (0.088) Batch 1.256 (1.521) Remain 20:29:36 loss: 0.5685 Lr: 0.00599
[2023-12-25 02:51:48,931 INFO misc.py line 119 253097] Train: [5/100][442/510] Data 0.006 (0.088) Batch 1.297 (1.520) Remain 20:29:10 loss: 0.5847 Lr: 0.00599
[2023-12-25 02:51:50,204 INFO misc.py line 119 253097] Train: [5/100][443/510] Data 0.037 (0.087) Batch 1.273 (1.520) Remain 20:28:41 loss: 0.8286 Lr: 0.00599
[2023-12-25 02:51:51,404 INFO misc.py line 119 253097] Train: [5/100][444/510] Data 0.004 (0.087) Batch 1.197 (1.519) Remain 20:28:04 loss: 0.5927 Lr: 0.00599
[2023-12-25 02:51:55,366 INFO misc.py line 119 253097] Train: [5/100][445/510] Data 0.008 (0.087) Batch 3.964 (1.524) Remain 20:32:31 loss: 0.6238 Lr: 0.00599
[2023-12-25 02:51:59,075 INFO misc.py line 119 253097] Train: [5/100][446/510] Data 0.006 (0.087) Batch 3.710 (1.529) Remain 20:36:29 loss: 0.3949 Lr: 0.00599
[2023-12-25 02:52:00,379 INFO misc.py line 119 253097] Train: [5/100][447/510] Data 0.003 (0.087) Batch 1.296 (1.529) Remain 20:36:02 loss: 1.0185 Lr: 0.00599
[2023-12-25 02:52:01,453 INFO misc.py line 119 253097] Train: [5/100][448/510] Data 0.012 (0.087) Batch 1.079 (1.528) Remain 20:35:12 loss: 0.3860 Lr: 0.00599
[2023-12-25 02:52:02,580 INFO misc.py line 119 253097] Train: [5/100][449/510] Data 0.007 (0.086) Batch 1.128 (1.527) Remain 20:34:27 loss: 0.5087 Lr: 0.00599
[2023-12-25 02:52:03,441 INFO misc.py line 119 253097] Train: [5/100][450/510] Data 0.006 (0.086) Batch 0.863 (1.525) Remain 20:33:13 loss: 0.3123 Lr: 0.00599
[2023-12-25 02:52:04,502 INFO misc.py line 119 253097] Train: [5/100][451/510] Data 0.003 (0.086) Batch 1.061 (1.524) Remain 20:32:21 loss: 0.8550 Lr: 0.00599
[2023-12-25 02:52:10,553 INFO misc.py line 119 253097] Train: [5/100][452/510] Data 0.004 (0.086) Batch 6.051 (1.534) Remain 20:40:29 loss: 0.6470 Lr: 0.00599
[2023-12-25 02:52:11,741 INFO misc.py line 119 253097] Train: [5/100][453/510] Data 0.004 (0.086) Batch 1.188 (1.534) Remain 20:39:50 loss: 0.6774 Lr: 0.00599
[2023-12-25 02:52:12,788 INFO misc.py line 119 253097] Train: [5/100][454/510] Data 0.003 (0.085) Batch 1.042 (1.533) Remain 20:38:56 loss: 0.3234 Lr: 0.00599
[2023-12-25 02:52:14,050 INFO misc.py line 119 253097] Train: [5/100][455/510] Data 0.008 (0.085) Batch 1.263 (1.532) Remain 20:38:25 loss: 0.9482 Lr: 0.00599
[2023-12-25 02:52:15,409 INFO misc.py line 119 253097] Train: [5/100][456/510] Data 0.008 (0.085) Batch 1.358 (1.532) Remain 20:38:05 loss: 0.4678 Lr: 0.00599
[2023-12-25 02:52:16,518 INFO misc.py line 119 253097] Train: [5/100][457/510] Data 0.009 (0.085) Batch 1.109 (1.531) Remain 20:37:18 loss: 0.7290 Lr: 0.00599
[2023-12-25 02:52:17,559 INFO misc.py line 119 253097] Train: [5/100][458/510] Data 0.008 (0.085) Batch 1.042 (1.530) Remain 20:36:25 loss: 0.5775 Lr: 0.00599
[2023-12-25 02:52:18,831 INFO misc.py line 119 253097] Train: [5/100][459/510] Data 0.006 (0.085) Batch 1.275 (1.529) Remain 20:35:56 loss: 0.6510 Lr: 0.00599
[2023-12-25 02:52:19,889 INFO misc.py line 119 253097] Train: [5/100][460/510] Data 0.004 (0.084) Batch 1.046 (1.528) Remain 20:35:03 loss: 0.3645 Lr: 0.00599
[2023-12-25 02:52:21,076 INFO misc.py line 119 253097] Train: [5/100][461/510] Data 0.015 (0.084) Batch 1.195 (1.527) Remain 20:34:26 loss: 0.6715 Lr: 0.00600
[2023-12-25 02:52:22,203 INFO misc.py line 119 253097] Train: [5/100][462/510] Data 0.007 (0.084) Batch 1.127 (1.526) Remain 20:33:43 loss: 0.6784 Lr: 0.00600
[2023-12-25 02:52:23,462 INFO misc.py line 119 253097] Train: [5/100][463/510] Data 0.009 (0.084) Batch 1.263 (1.526) Remain 20:33:13 loss: 0.5089 Lr: 0.00600
[2023-12-25 02:52:24,577 INFO misc.py line 119 253097] Train: [5/100][464/510] Data 0.003 (0.084) Batch 1.113 (1.525) Remain 20:32:28 loss: 0.4841 Lr: 0.00600
[2023-12-25 02:52:25,852 INFO misc.py line 119 253097] Train: [5/100][465/510] Data 0.006 (0.084) Batch 1.276 (1.524) Remain 20:32:01 loss: 0.5597 Lr: 0.00600
[2023-12-25 02:52:27,075 INFO misc.py line 119 253097] Train: [5/100][466/510] Data 0.005 (0.083) Batch 1.217 (1.524) Remain 20:31:27 loss: 0.6449 Lr: 0.00600
[2023-12-25 02:52:28,131 INFO misc.py line 119 253097] Train: [5/100][467/510] Data 0.011 (0.083) Batch 1.062 (1.523) Remain 20:30:37 loss: 0.3799 Lr: 0.00600
[2023-12-25 02:52:29,355 INFO misc.py line 119 253097] Train: [5/100][468/510] Data 0.004 (0.083) Batch 1.223 (1.522) Remain 20:30:05 loss: 0.2878 Lr: 0.00600
[2023-12-25 02:52:30,431 INFO misc.py line 119 253097] Train: [5/100][469/510] Data 0.005 (0.083) Batch 1.077 (1.521) Remain 20:29:17 loss: 0.8207 Lr: 0.00600
[2023-12-25 02:52:31,780 INFO misc.py line 119 253097] Train: [5/100][470/510] Data 0.004 (0.083) Batch 1.348 (1.521) Remain 20:28:57 loss: 0.6008 Lr: 0.00600
[2023-12-25 02:52:32,862 INFO misc.py line 119 253097] Train: [5/100][471/510] Data 0.006 (0.083) Batch 1.079 (1.520) Remain 20:28:10 loss: 0.2613 Lr: 0.00600
[2023-12-25 02:52:33,953 INFO misc.py line 119 253097] Train: [5/100][472/510] Data 0.011 (0.082) Batch 1.092 (1.519) Remain 20:27:24 loss: 0.7208 Lr: 0.00600
[2023-12-25 02:52:35,074 INFO misc.py line 119 253097] Train: [5/100][473/510] Data 0.007 (0.082) Batch 1.123 (1.518) Remain 20:26:42 loss: 0.3611 Lr: 0.00600
[2023-12-25 02:52:38,017 INFO misc.py line 119 253097] Train: [5/100][474/510] Data 0.007 (0.082) Batch 2.943 (1.521) Remain 20:29:07 loss: 0.9276 Lr: 0.00600
[2023-12-25 02:52:39,191 INFO misc.py line 119 253097] Train: [5/100][475/510] Data 0.005 (0.082) Batch 1.175 (1.520) Remain 20:28:30 loss: 0.3809 Lr: 0.00600
[2023-12-25 02:52:40,406 INFO misc.py line 119 253097] Train: [5/100][476/510] Data 0.003 (0.082) Batch 1.213 (1.520) Remain 20:27:57 loss: 0.3391 Lr: 0.00600
[2023-12-25 02:52:41,350 INFO misc.py line 119 253097] Train: [5/100][477/510] Data 0.006 (0.082) Batch 0.945 (1.518) Remain 20:26:57 loss: 0.9717 Lr: 0.00600
[2023-12-25 02:52:42,337 INFO misc.py line 119 253097] Train: [5/100][478/510] Data 0.006 (0.081) Batch 0.988 (1.517) Remain 20:26:01 loss: 0.8209 Lr: 0.00600
[2023-12-25 02:52:43,443 INFO misc.py line 119 253097] Train: [5/100][479/510] Data 0.004 (0.081) Batch 1.106 (1.516) Remain 20:25:18 loss: 0.3070 Lr: 0.00600
[2023-12-25 02:52:44,527 INFO misc.py line 119 253097] Train: [5/100][480/510] Data 0.004 (0.081) Batch 1.084 (1.516) Remain 20:24:32 loss: 0.3558 Lr: 0.00600
[2023-12-25 02:52:45,828 INFO misc.py line 119 253097] Train: [5/100][481/510] Data 0.004 (0.081) Batch 1.288 (1.515) Remain 20:24:08 loss: 0.6095 Lr: 0.00600
[2023-12-25 02:52:47,143 INFO misc.py line 119 253097] Train: [5/100][482/510] Data 0.018 (0.081) Batch 1.327 (1.515) Remain 20:23:47 loss: 0.5635 Lr: 0.00600
[2023-12-25 02:52:48,373 INFO misc.py line 119 253097] Train: [5/100][483/510] Data 0.004 (0.081) Batch 1.225 (1.514) Remain 20:23:16 loss: 0.4171 Lr: 0.00600
[2023-12-25 02:52:49,395 INFO misc.py line 119 253097] Train: [5/100][484/510] Data 0.010 (0.081) Batch 1.028 (1.513) Remain 20:22:26 loss: 0.3123 Lr: 0.00600
[2023-12-25 02:52:50,631 INFO misc.py line 119 253097] Train: [5/100][485/510] Data 0.005 (0.080) Batch 1.232 (1.512) Remain 20:21:56 loss: 0.8471 Lr: 0.00600
[2023-12-25 02:52:51,665 INFO misc.py line 119 253097] Train: [5/100][486/510] Data 0.007 (0.080) Batch 1.037 (1.511) Remain 20:21:07 loss: 0.4152 Lr: 0.00600
[2023-12-25 02:52:52,837 INFO misc.py line 119 253097] Train: [5/100][487/510] Data 0.005 (0.080) Batch 1.173 (1.511) Remain 20:20:31 loss: 0.5922 Lr: 0.00600
[2023-12-25 02:52:53,910 INFO misc.py line 119 253097] Train: [5/100][488/510] Data 0.003 (0.080) Batch 1.069 (1.510) Remain 20:19:46 loss: 0.6436 Lr: 0.00600
[2023-12-25 02:52:55,241 INFO misc.py line 119 253097] Train: [5/100][489/510] Data 0.007 (0.080) Batch 1.333 (1.510) Remain 20:19:27 loss: 0.4063 Lr: 0.00600
[2023-12-25 02:52:56,330 INFO misc.py line 119 253097] Train: [5/100][490/510] Data 0.009 (0.080) Batch 1.088 (1.509) Remain 20:18:43 loss: 0.4683 Lr: 0.00600
[2023-12-25 02:52:57,567 INFO misc.py line 119 253097] Train: [5/100][491/510] Data 0.007 (0.079) Batch 1.238 (1.508) Remain 20:18:15 loss: 0.3517 Lr: 0.00600
[2023-12-25 02:52:58,617 INFO misc.py line 119 253097] Train: [5/100][492/510] Data 0.005 (0.079) Batch 1.049 (1.507) Remain 20:17:28 loss: 0.4683 Lr: 0.00600
[2023-12-25 02:52:59,618 INFO misc.py line 119 253097] Train: [5/100][493/510] Data 0.007 (0.079) Batch 1.003 (1.506) Remain 20:16:36 loss: 0.5277 Lr: 0.00600
[2023-12-25 02:53:09,878 INFO misc.py line 119 253097] Train: [5/100][494/510] Data 0.004 (0.079) Batch 10.261 (1.524) Remain 20:30:59 loss: 0.9358 Lr: 0.00600
[2023-12-25 02:53:11,017 INFO misc.py line 119 253097] Train: [5/100][495/510] Data 0.004 (0.079) Batch 1.138 (1.523) Remain 20:30:20 loss: 0.4886 Lr: 0.00600
[2023-12-25 02:53:12,101 INFO misc.py line 119 253097] Train: [5/100][496/510] Data 0.003 (0.079) Batch 1.085 (1.522) Remain 20:29:35 loss: 0.5546 Lr: 0.00600
[2023-12-25 02:53:13,085 INFO misc.py line 119 253097] Train: [5/100][497/510] Data 0.003 (0.079) Batch 0.983 (1.521) Remain 20:28:41 loss: 0.6085 Lr: 0.00600
[2023-12-25 02:53:14,255 INFO misc.py line 119 253097] Train: [5/100][498/510] Data 0.003 (0.078) Batch 1.170 (1.520) Remain 20:28:05 loss: 0.6353 Lr: 0.00600
[2023-12-25 02:53:15,296 INFO misc.py line 119 253097] Train: [5/100][499/510] Data 0.003 (0.078) Batch 1.042 (1.520) Remain 20:27:16 loss: 0.6228 Lr: 0.00600
[2023-12-25 02:53:16,553 INFO misc.py line 119 253097] Train: [5/100][500/510] Data 0.003 (0.078) Batch 1.257 (1.519) Remain 20:26:49 loss: 0.4805 Lr: 0.00600
[2023-12-25 02:53:26,151 INFO misc.py line 119 253097] Train: [5/100][501/510] Data 8.342 (0.095) Batch 9.598 (1.535) Remain 20:39:54 loss: 0.8869 Lr: 0.00600
[2023-12-25 02:53:27,178 INFO misc.py line 119 253097] Train: [5/100][502/510] Data 0.003 (0.095) Batch 1.027 (1.534) Remain 20:39:03 loss: 0.6149 Lr: 0.00600
[2023-12-25 02:53:28,412 INFO misc.py line 119 253097] Train: [5/100][503/510] Data 0.003 (0.094) Batch 1.230 (1.534) Remain 20:38:32 loss: 0.5003 Lr: 0.00600
[2023-12-25 02:53:29,702 INFO misc.py line 119 253097] Train: [5/100][504/510] Data 0.006 (0.094) Batch 1.287 (1.533) Remain 20:38:07 loss: 0.3874 Lr: 0.00600
[2023-12-25 02:53:30,777 INFO misc.py line 119 253097] Train: [5/100][505/510] Data 0.009 (0.094) Batch 1.076 (1.532) Remain 20:37:21 loss: 0.3155 Lr: 0.00600
[2023-12-25 02:53:32,008 INFO misc.py line 119 253097] Train: [5/100][506/510] Data 0.008 (0.094) Batch 1.236 (1.532) Remain 20:36:51 loss: 0.2720 Lr: 0.00600
[2023-12-25 02:53:33,255 INFO misc.py line 119 253097] Train: [5/100][507/510] Data 0.003 (0.094) Batch 1.244 (1.531) Remain 20:36:22 loss: 0.3599 Lr: 0.00600
[2023-12-25 02:53:34,314 INFO misc.py line 119 253097] Train: [5/100][508/510] Data 0.008 (0.093) Batch 1.062 (1.530) Remain 20:35:35 loss: 0.4062 Lr: 0.00600
[2023-12-25 02:53:35,485 INFO misc.py line 119 253097] Train: [5/100][509/510] Data 0.004 (0.093) Batch 1.172 (1.529) Remain 20:34:59 loss: 0.6814 Lr: 0.00600
[2023-12-25 02:53:36,745 INFO misc.py line 119 253097] Train: [5/100][510/510] Data 0.003 (0.093) Batch 1.259 (1.529) Remain 20:34:32 loss: 0.6388 Lr: 0.00600
[2023-12-25 02:53:36,746 INFO misc.py line 136 253097] Train result: loss: 0.5653 
[2023-12-25 02:53:36,746 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 02:54:04,525 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5931 
[2023-12-25 02:54:04,876 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.6802 
[2023-12-25 02:54:09,805 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.7872 
[2023-12-25 02:54:10,328 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.7509 
[2023-12-25 02:54:12,300 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9564 
[2023-12-25 02:54:12,722 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.7548 
[2023-12-25 02:54:13,597 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0280 
[2023-12-25 02:54:14,153 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5703 
[2023-12-25 02:54:15,957 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9395 
[2023-12-25 02:54:18,077 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.5754 
[2023-12-25 02:54:18,930 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.5730 
[2023-12-25 02:54:19,356 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0687 
[2023-12-25 02:54:20,256 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.7600 
[2023-12-25 02:54:23,199 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8349 
[2023-12-25 02:54:23,670 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.7086 
[2023-12-25 02:54:24,277 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.7149 
[2023-12-25 02:54:24,974 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.8588 
[2023-12-25 02:54:26,564 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.5904/0.6755/0.8537.
[2023-12-25 02:54:26,564 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9214/0.9570
[2023-12-25 02:54:26,564 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9743/0.9915
[2023-12-25 02:54:26,564 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7516/0.9321
[2023-12-25 02:54:26,564 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0009/0.0148
[2023-12-25 02:54:26,564 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2245/0.3368
[2023-12-25 02:54:26,564 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5122/0.6796
[2023-12-25 02:54:26,565 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.1957/0.2168
[2023-12-25 02:54:26,565 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7799/0.8357
[2023-12-25 02:54:26,565 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8915/0.9254
[2023-12-25 02:54:26,565 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5968/0.7705
[2023-12-25 02:54:26,565 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.6558/0.7137
[2023-12-25 02:54:26,565 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6270/0.7281
[2023-12-25 02:54:26,565 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5439/0.6797
[2023-12-25 02:54:26,565 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 02:54:26,566 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.5904
[2023-12-25 02:54:26,566 INFO misc.py line 165 253097] Currently Best mIoU: 0.5904
[2023-12-25 02:54:26,567 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 02:54:52,516 INFO misc.py line 119 253097] Train: [6/100][1/510] Data 3.957 (3.957) Batch 20.396 (20.396) Remain 274:29:13 loss: 0.4183 Lr: 0.00600
[2023-12-25 02:54:53,700 INFO misc.py line 119 253097] Train: [6/100][2/510] Data 0.005 (0.005) Batch 1.186 (1.186) Remain 15:57:36 loss: 0.4332 Lr: 0.00600
[2023-12-25 02:54:54,792 INFO misc.py line 119 253097] Train: [6/100][3/510] Data 0.003 (0.003) Batch 1.089 (1.089) Remain 14:39:40 loss: 0.5156 Lr: 0.00600
[2023-12-25 02:54:55,826 INFO misc.py line 119 253097] Train: [6/100][4/510] Data 0.006 (0.006) Batch 1.036 (1.036) Remain 13:56:14 loss: 0.3038 Lr: 0.00600
[2023-12-25 02:54:57,146 INFO misc.py line 119 253097] Train: [6/100][5/510] Data 0.004 (0.005) Batch 1.313 (1.174) Remain 15:48:12 loss: 0.6064 Lr: 0.00600
[2023-12-25 02:54:58,423 INFO misc.py line 119 253097] Train: [6/100][6/510] Data 0.013 (0.008) Batch 1.284 (1.211) Remain 16:17:43 loss: 0.4984 Lr: 0.00600
[2023-12-25 02:54:59,473 INFO misc.py line 119 253097] Train: [6/100][7/510] Data 0.003 (0.007) Batch 1.049 (1.170) Remain 15:44:58 loss: 0.5504 Lr: 0.00600
[2023-12-25 02:55:00,575 INFO misc.py line 119 253097] Train: [6/100][8/510] Data 0.006 (0.007) Batch 1.103 (1.157) Remain 15:34:03 loss: 0.8279 Lr: 0.00600
[2023-12-25 02:55:01,654 INFO misc.py line 119 253097] Train: [6/100][9/510] Data 0.004 (0.006) Batch 1.080 (1.144) Remain 15:23:37 loss: 0.4895 Lr: 0.00600
[2023-12-25 02:55:02,895 INFO misc.py line 119 253097] Train: [6/100][10/510] Data 0.003 (0.006) Batch 1.240 (1.158) Remain 15:34:41 loss: 0.5252 Lr: 0.00600
[2023-12-25 02:55:03,903 INFO misc.py line 119 253097] Train: [6/100][11/510] Data 0.004 (0.006) Batch 1.009 (1.139) Remain 15:19:37 loss: 0.3648 Lr: 0.00600
[2023-12-25 02:55:05,063 INFO misc.py line 119 253097] Train: [6/100][12/510] Data 0.004 (0.005) Batch 1.157 (1.141) Remain 15:21:15 loss: 0.7844 Lr: 0.00600
[2023-12-25 02:55:06,171 INFO misc.py line 119 253097] Train: [6/100][13/510] Data 0.007 (0.006) Batch 1.107 (1.138) Remain 15:18:28 loss: 0.8432 Lr: 0.00600
[2023-12-25 02:55:07,099 INFO misc.py line 119 253097] Train: [6/100][14/510] Data 0.007 (0.006) Batch 0.932 (1.119) Remain 15:03:21 loss: 0.4216 Lr: 0.00600
[2023-12-25 02:55:08,393 INFO misc.py line 119 253097] Train: [6/100][15/510] Data 0.003 (0.006) Batch 1.289 (1.133) Remain 15:14:45 loss: 0.5134 Lr: 0.00600
[2023-12-25 02:55:09,557 INFO misc.py line 119 253097] Train: [6/100][16/510] Data 0.013 (0.006) Batch 1.168 (1.136) Remain 15:16:55 loss: 0.5109 Lr: 0.00600
[2023-12-25 02:55:10,685 INFO misc.py line 119 253097] Train: [6/100][17/510] Data 0.004 (0.006) Batch 1.128 (1.135) Remain 15:16:26 loss: 0.5984 Lr: 0.00600
[2023-12-25 02:55:11,951 INFO misc.py line 119 253097] Train: [6/100][18/510] Data 0.005 (0.006) Batch 1.263 (1.144) Remain 15:23:17 loss: 0.7696 Lr: 0.00600
[2023-12-25 02:55:13,014 INFO misc.py line 119 253097] Train: [6/100][19/510] Data 0.008 (0.006) Batch 1.067 (1.139) Remain 15:19:23 loss: 0.4830 Lr: 0.00600
[2023-12-25 02:55:14,380 INFO misc.py line 119 253097] Train: [6/100][20/510] Data 0.003 (0.006) Batch 1.366 (1.152) Remain 15:30:08 loss: 0.7244 Lr: 0.00600
[2023-12-25 02:55:15,427 INFO misc.py line 119 253097] Train: [6/100][21/510] Data 0.005 (0.006) Batch 1.042 (1.146) Remain 15:25:10 loss: 0.3767 Lr: 0.00600
[2023-12-25 02:55:16,595 INFO misc.py line 119 253097] Train: [6/100][22/510] Data 0.009 (0.006) Batch 1.170 (1.147) Remain 15:26:10 loss: 0.5146 Lr: 0.00600
[2023-12-25 02:55:17,868 INFO misc.py line 119 253097] Train: [6/100][23/510] Data 0.007 (0.006) Batch 1.276 (1.154) Remain 15:31:21 loss: 0.5871 Lr: 0.00600
[2023-12-25 02:55:19,071 INFO misc.py line 119 253097] Train: [6/100][24/510] Data 0.003 (0.006) Batch 1.198 (1.156) Remain 15:33:02 loss: 0.6666 Lr: 0.00600
[2023-12-25 02:55:20,159 INFO misc.py line 119 253097] Train: [6/100][25/510] Data 0.007 (0.006) Batch 1.091 (1.153) Remain 15:30:39 loss: 0.5722 Lr: 0.00600
[2023-12-25 02:55:21,324 INFO misc.py line 119 253097] Train: [6/100][26/510] Data 0.005 (0.006) Batch 1.161 (1.153) Remain 15:30:55 loss: 0.2957 Lr: 0.00600
[2023-12-25 02:55:22,398 INFO misc.py line 119 253097] Train: [6/100][27/510] Data 0.008 (0.006) Batch 1.074 (1.150) Remain 15:28:15 loss: 0.5389 Lr: 0.00600
[2023-12-25 02:55:23,443 INFO misc.py line 119 253097] Train: [6/100][28/510] Data 0.007 (0.006) Batch 1.049 (1.146) Remain 15:24:57 loss: 0.8015 Lr: 0.00600
[2023-12-25 02:55:24,635 INFO misc.py line 119 253097] Train: [6/100][29/510] Data 0.003 (0.006) Batch 1.191 (1.148) Remain 15:26:19 loss: 0.4685 Lr: 0.00600
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[2023-12-25 02:55:26,662 INFO misc.py line 119 253097] Train: [6/100][31/510] Data 0.009 (0.006) Batch 1.010 (1.138) Remain 15:18:31 loss: 0.4026 Lr: 0.00600
[2023-12-25 02:55:27,732 INFO misc.py line 119 253097] Train: [6/100][32/510] Data 0.006 (0.006) Batch 1.072 (1.136) Remain 15:16:39 loss: 0.2896 Lr: 0.00600
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[2023-12-25 02:55:34,493 INFO misc.py line 119 253097] Train: [6/100][34/510] Data 0.010 (0.006) Batch 5.522 (1.281) Remain 17:13:28 loss: 0.5525 Lr: 0.00600
[2023-12-25 02:55:35,529 INFO misc.py line 119 253097] Train: [6/100][35/510] Data 0.004 (0.006) Batch 1.037 (1.273) Remain 17:07:17 loss: 0.3313 Lr: 0.00600
[2023-12-25 02:55:36,564 INFO misc.py line 119 253097] Train: [6/100][36/510] Data 0.004 (0.006) Batch 1.035 (1.266) Remain 17:01:26 loss: 0.6447 Lr: 0.00600
[2023-12-25 02:55:37,909 INFO misc.py line 119 253097] Train: [6/100][37/510] Data 0.004 (0.006) Batch 1.342 (1.268) Remain 17:03:13 loss: 0.5178 Lr: 0.00600
[2023-12-25 02:55:39,064 INFO misc.py line 119 253097] Train: [6/100][38/510] Data 0.008 (0.006) Batch 1.156 (1.265) Remain 17:00:36 loss: 0.5233 Lr: 0.00600
[2023-12-25 02:55:40,253 INFO misc.py line 119 253097] Train: [6/100][39/510] Data 0.006 (0.006) Batch 1.191 (1.263) Remain 16:58:56 loss: 0.3879 Lr: 0.00600
[2023-12-25 02:55:42,471 INFO misc.py line 119 253097] Train: [6/100][40/510] Data 0.004 (0.006) Batch 2.218 (1.289) Remain 17:19:45 loss: 0.5303 Lr: 0.00600
[2023-12-25 02:55:43,551 INFO misc.py line 119 253097] Train: [6/100][41/510] Data 0.003 (0.006) Batch 1.080 (1.283) Remain 17:15:17 loss: 0.2830 Lr: 0.00600
[2023-12-25 02:55:44,591 INFO misc.py line 119 253097] Train: [6/100][42/510] Data 0.003 (0.006) Batch 1.038 (1.277) Remain 17:10:11 loss: 0.7384 Lr: 0.00600
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[2023-12-25 02:55:49,963 INFO misc.py line 119 253097] Train: [6/100][47/510] Data 0.003 (0.006) Batch 1.056 (1.254) Remain 16:51:34 loss: 0.4411 Lr: 0.00600
[2023-12-25 02:55:51,020 INFO misc.py line 119 253097] Train: [6/100][48/510] Data 0.004 (0.006) Batch 1.056 (1.250) Remain 16:48:00 loss: 0.3211 Lr: 0.00600
[2023-12-25 02:55:52,154 INFO misc.py line 119 253097] Train: [6/100][49/510] Data 0.004 (0.006) Batch 1.135 (1.247) Remain 16:45:59 loss: 0.5669 Lr: 0.00600
[2023-12-25 02:55:53,285 INFO misc.py line 119 253097] Train: [6/100][50/510] Data 0.003 (0.006) Batch 1.131 (1.245) Remain 16:43:58 loss: 0.3018 Lr: 0.00600
[2023-12-25 02:55:54,718 INFO misc.py line 119 253097] Train: [6/100][51/510] Data 0.003 (0.006) Batch 1.427 (1.248) Remain 16:47:00 loss: 0.4711 Lr: 0.00600
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[2023-12-25 02:55:57,094 INFO misc.py line 119 253097] Train: [6/100][53/510] Data 0.004 (0.006) Batch 1.174 (1.246) Remain 16:45:03 loss: 0.5122 Lr: 0.00600
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[2023-12-25 02:55:59,450 INFO misc.py line 119 253097] Train: [6/100][55/510] Data 0.004 (0.006) Batch 1.169 (1.243) Remain 16:42:53 loss: 0.5095 Lr: 0.00600
[2023-12-25 02:56:00,618 INFO misc.py line 119 253097] Train: [6/100][56/510] Data 0.008 (0.006) Batch 1.169 (1.242) Remain 16:41:44 loss: 0.4620 Lr: 0.00600
[2023-12-25 02:56:01,706 INFO misc.py line 119 253097] Train: [6/100][57/510] Data 0.006 (0.006) Batch 1.087 (1.239) Remain 16:39:25 loss: 0.6544 Lr: 0.00600
[2023-12-25 02:56:02,915 INFO misc.py line 119 253097] Train: [6/100][58/510] Data 0.006 (0.006) Batch 1.210 (1.239) Remain 16:38:58 loss: 0.8648 Lr: 0.00600
[2023-12-25 02:56:04,899 INFO misc.py line 119 253097] Train: [6/100][59/510] Data 0.005 (0.006) Batch 1.984 (1.252) Remain 16:49:41 loss: 0.4155 Lr: 0.00600
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[2023-12-25 02:56:51,985 INFO misc.py line 119 253097] Train: [6/100][83/510] Data 11.288 (0.182) Batch 11.976 (1.465) Remain 19:40:54 loss: 0.4813 Lr: 0.00600
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[2023-12-25 02:57:05,496 INFO misc.py line 119 253097] Train: [6/100][95/510] Data 0.007 (0.159) Batch 1.105 (1.421) Remain 19:04:58 loss: 0.4408 Lr: 0.00600
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[2023-12-25 02:58:37,556 INFO misc.py line 119 253097] Train: [6/100][157/510] Data 7.532 (0.148) Batch 8.303 (1.447) Remain 19:24:17 loss: 0.6325 Lr: 0.00600
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[2023-12-25 03:04:53,439 INFO misc.py line 119 253097] Train: [6/100][410/510] Data 0.003 (0.082) Batch 1.007 (1.471) Remain 19:37:41 loss: 0.4007 Lr: 0.00600
[2023-12-25 03:04:54,675 INFO misc.py line 119 253097] Train: [6/100][411/510] Data 0.003 (0.082) Batch 1.234 (1.470) Remain 19:37:11 loss: 0.5461 Lr: 0.00600
[2023-12-25 03:04:55,873 INFO misc.py line 119 253097] Train: [6/100][412/510] Data 0.005 (0.081) Batch 1.198 (1.470) Remain 19:36:38 loss: 0.8682 Lr: 0.00600
[2023-12-25 03:04:56,986 INFO misc.py line 119 253097] Train: [6/100][413/510] Data 0.005 (0.081) Batch 1.112 (1.469) Remain 19:35:55 loss: 0.9498 Lr: 0.00600
[2023-12-25 03:04:58,143 INFO misc.py line 119 253097] Train: [6/100][414/510] Data 0.006 (0.081) Batch 1.158 (1.468) Remain 19:35:17 loss: 0.4752 Lr: 0.00600
[2023-12-25 03:04:59,338 INFO misc.py line 119 253097] Train: [6/100][415/510] Data 0.006 (0.081) Batch 1.195 (1.467) Remain 19:34:43 loss: 0.4471 Lr: 0.00600
[2023-12-25 03:05:00,380 INFO misc.py line 119 253097] Train: [6/100][416/510] Data 0.004 (0.081) Batch 1.043 (1.466) Remain 19:33:53 loss: 0.7858 Lr: 0.00600
[2023-12-25 03:05:05,199 INFO misc.py line 119 253097] Train: [6/100][417/510] Data 0.004 (0.080) Batch 4.817 (1.474) Remain 19:40:20 loss: 0.4507 Lr: 0.00600
[2023-12-25 03:05:06,421 INFO misc.py line 119 253097] Train: [6/100][418/510] Data 0.006 (0.080) Batch 1.219 (1.474) Remain 19:39:49 loss: 0.6332 Lr: 0.00600
[2023-12-25 03:05:07,689 INFO misc.py line 119 253097] Train: [6/100][419/510] Data 0.010 (0.080) Batch 1.270 (1.473) Remain 19:39:24 loss: 0.3502 Lr: 0.00600
[2023-12-25 03:05:08,857 INFO misc.py line 119 253097] Train: [6/100][420/510] Data 0.007 (0.080) Batch 1.167 (1.473) Remain 19:38:47 loss: 0.3857 Lr: 0.00600
[2023-12-25 03:05:09,829 INFO misc.py line 119 253097] Train: [6/100][421/510] Data 0.009 (0.080) Batch 0.975 (1.471) Remain 19:37:48 loss: 0.4451 Lr: 0.00600
[2023-12-25 03:05:10,784 INFO misc.py line 119 253097] Train: [6/100][422/510] Data 0.004 (0.080) Batch 0.955 (1.470) Remain 19:36:48 loss: 0.5932 Lr: 0.00600
[2023-12-25 03:05:13,271 INFO misc.py line 119 253097] Train: [6/100][423/510] Data 0.005 (0.079) Batch 2.488 (1.473) Remain 19:38:43 loss: 0.9207 Lr: 0.00600
[2023-12-25 03:05:14,427 INFO misc.py line 119 253097] Train: [6/100][424/510] Data 0.005 (0.079) Batch 1.156 (1.472) Remain 19:38:05 loss: 0.6700 Lr: 0.00600
[2023-12-25 03:05:15,509 INFO misc.py line 119 253097] Train: [6/100][425/510] Data 0.003 (0.079) Batch 1.082 (1.471) Remain 19:37:19 loss: 0.2275 Lr: 0.00600
[2023-12-25 03:05:24,375 INFO misc.py line 119 253097] Train: [6/100][426/510] Data 7.532 (0.097) Batch 8.865 (1.488) Remain 19:51:17 loss: 0.6831 Lr: 0.00600
[2023-12-25 03:05:25,397 INFO misc.py line 119 253097] Train: [6/100][427/510] Data 0.005 (0.096) Batch 1.022 (1.487) Remain 19:50:23 loss: 0.5820 Lr: 0.00600
[2023-12-25 03:05:26,570 INFO misc.py line 119 253097] Train: [6/100][428/510] Data 0.005 (0.096) Batch 1.174 (1.487) Remain 19:49:46 loss: 0.3701 Lr: 0.00600
[2023-12-25 03:05:27,735 INFO misc.py line 119 253097] Train: [6/100][429/510] Data 0.004 (0.096) Batch 1.164 (1.486) Remain 19:49:08 loss: 0.4658 Lr: 0.00600
[2023-12-25 03:05:28,846 INFO misc.py line 119 253097] Train: [6/100][430/510] Data 0.004 (0.096) Batch 1.111 (1.485) Remain 19:48:25 loss: 0.5583 Lr: 0.00600
[2023-12-25 03:05:29,922 INFO misc.py line 119 253097] Train: [6/100][431/510] Data 0.005 (0.096) Batch 1.076 (1.484) Remain 19:47:37 loss: 0.4026 Lr: 0.00600
[2023-12-25 03:05:31,126 INFO misc.py line 119 253097] Train: [6/100][432/510] Data 0.004 (0.095) Batch 1.201 (1.483) Remain 19:47:04 loss: 0.6942 Lr: 0.00600
[2023-12-25 03:05:32,189 INFO misc.py line 119 253097] Train: [6/100][433/510] Data 0.007 (0.095) Batch 1.064 (1.482) Remain 19:46:16 loss: 0.4497 Lr: 0.00600
[2023-12-25 03:05:33,439 INFO misc.py line 119 253097] Train: [6/100][434/510] Data 0.007 (0.095) Batch 1.250 (1.482) Remain 19:45:48 loss: 0.2123 Lr: 0.00600
[2023-12-25 03:05:34,627 INFO misc.py line 119 253097] Train: [6/100][435/510] Data 0.008 (0.095) Batch 1.189 (1.481) Remain 19:45:14 loss: 0.4034 Lr: 0.00600
[2023-12-25 03:05:35,901 INFO misc.py line 119 253097] Train: [6/100][436/510] Data 0.006 (0.095) Batch 1.274 (1.481) Remain 19:44:50 loss: 0.5663 Lr: 0.00600
[2023-12-25 03:05:37,173 INFO misc.py line 119 253097] Train: [6/100][437/510] Data 0.005 (0.094) Batch 1.269 (1.480) Remain 19:44:25 loss: 0.5701 Lr: 0.00600
[2023-12-25 03:05:38,401 INFO misc.py line 119 253097] Train: [6/100][438/510] Data 0.008 (0.094) Batch 1.229 (1.480) Remain 19:43:56 loss: 0.5833 Lr: 0.00600
[2023-12-25 03:05:39,611 INFO misc.py line 119 253097] Train: [6/100][439/510] Data 0.006 (0.094) Batch 1.213 (1.479) Remain 19:43:25 loss: 0.4090 Lr: 0.00600
[2023-12-25 03:05:40,709 INFO misc.py line 119 253097] Train: [6/100][440/510] Data 0.004 (0.094) Batch 1.097 (1.478) Remain 19:42:42 loss: 0.3009 Lr: 0.00600
[2023-12-25 03:05:41,865 INFO misc.py line 119 253097] Train: [6/100][441/510] Data 0.004 (0.094) Batch 1.157 (1.477) Remain 19:42:05 loss: 0.8857 Lr: 0.00600
[2023-12-25 03:05:48,491 INFO misc.py line 119 253097] Train: [6/100][442/510] Data 0.004 (0.093) Batch 6.625 (1.489) Remain 19:51:26 loss: 0.4985 Lr: 0.00600
[2023-12-25 03:05:49,627 INFO misc.py line 119 253097] Train: [6/100][443/510] Data 0.007 (0.093) Batch 1.138 (1.488) Remain 19:50:47 loss: 0.4479 Lr: 0.00600
[2023-12-25 03:05:50,755 INFO misc.py line 119 253097] Train: [6/100][444/510] Data 0.004 (0.093) Batch 1.127 (1.487) Remain 19:50:06 loss: 0.6897 Lr: 0.00600
[2023-12-25 03:05:52,001 INFO misc.py line 119 253097] Train: [6/100][445/510] Data 0.005 (0.093) Batch 1.242 (1.487) Remain 19:49:38 loss: 0.5913 Lr: 0.00600
[2023-12-25 03:05:53,092 INFO misc.py line 119 253097] Train: [6/100][446/510] Data 0.009 (0.093) Batch 1.093 (1.486) Remain 19:48:53 loss: 0.5441 Lr: 0.00600
[2023-12-25 03:05:54,191 INFO misc.py line 119 253097] Train: [6/100][447/510] Data 0.008 (0.092) Batch 1.099 (1.485) Remain 19:48:10 loss: 0.4114 Lr: 0.00600
[2023-12-25 03:05:55,251 INFO misc.py line 119 253097] Train: [6/100][448/510] Data 0.006 (0.092) Batch 1.063 (1.484) Remain 19:47:23 loss: 0.5886 Lr: 0.00600
[2023-12-25 03:05:56,282 INFO misc.py line 119 253097] Train: [6/100][449/510] Data 0.004 (0.092) Batch 1.030 (1.483) Remain 19:46:33 loss: 0.4822 Lr: 0.00600
[2023-12-25 03:05:57,399 INFO misc.py line 119 253097] Train: [6/100][450/510] Data 0.004 (0.092) Batch 1.114 (1.482) Remain 19:45:52 loss: 0.3798 Lr: 0.00600
[2023-12-25 03:05:58,557 INFO misc.py line 119 253097] Train: [6/100][451/510] Data 0.007 (0.092) Batch 1.161 (1.482) Remain 19:45:16 loss: 0.4751 Lr: 0.00600
[2023-12-25 03:05:59,677 INFO misc.py line 119 253097] Train: [6/100][452/510] Data 0.004 (0.091) Batch 1.120 (1.481) Remain 19:44:36 loss: 0.2281 Lr: 0.00600
[2023-12-25 03:06:00,992 INFO misc.py line 119 253097] Train: [6/100][453/510] Data 0.004 (0.091) Batch 1.315 (1.480) Remain 19:44:16 loss: 0.1863 Lr: 0.00600
[2023-12-25 03:06:02,296 INFO misc.py line 119 253097] Train: [6/100][454/510] Data 0.005 (0.091) Batch 1.305 (1.480) Remain 19:43:56 loss: 0.5037 Lr: 0.00600
[2023-12-25 03:06:03,446 INFO misc.py line 119 253097] Train: [6/100][455/510] Data 0.005 (0.091) Batch 1.150 (1.479) Remain 19:43:20 loss: 0.2879 Lr: 0.00600
[2023-12-25 03:06:04,631 INFO misc.py line 119 253097] Train: [6/100][456/510] Data 0.004 (0.091) Batch 1.186 (1.479) Remain 19:42:47 loss: 0.4266 Lr: 0.00600
[2023-12-25 03:06:09,052 INFO misc.py line 119 253097] Train: [6/100][457/510] Data 0.004 (0.090) Batch 4.421 (1.485) Remain 19:47:57 loss: 0.6507 Lr: 0.00600
[2023-12-25 03:06:10,179 INFO misc.py line 119 253097] Train: [6/100][458/510] Data 0.004 (0.090) Batch 1.126 (1.484) Remain 19:47:17 loss: 0.3003 Lr: 0.00600
[2023-12-25 03:06:11,408 INFO misc.py line 119 253097] Train: [6/100][459/510] Data 0.004 (0.090) Batch 1.229 (1.484) Remain 19:46:49 loss: 0.3133 Lr: 0.00600
[2023-12-25 03:06:12,657 INFO misc.py line 119 253097] Train: [6/100][460/510] Data 0.004 (0.090) Batch 1.246 (1.483) Remain 19:46:23 loss: 0.7569 Lr: 0.00600
[2023-12-25 03:06:13,850 INFO misc.py line 119 253097] Train: [6/100][461/510] Data 0.007 (0.090) Batch 1.193 (1.483) Remain 19:45:51 loss: 0.5474 Lr: 0.00600
[2023-12-25 03:06:14,938 INFO misc.py line 119 253097] Train: [6/100][462/510] Data 0.006 (0.089) Batch 1.087 (1.482) Remain 19:45:08 loss: 0.7027 Lr: 0.00600
[2023-12-25 03:06:16,205 INFO misc.py line 119 253097] Train: [6/100][463/510] Data 0.008 (0.089) Batch 1.268 (1.481) Remain 19:44:44 loss: 0.3314 Lr: 0.00600
[2023-12-25 03:06:17,419 INFO misc.py line 119 253097] Train: [6/100][464/510] Data 0.006 (0.089) Batch 1.212 (1.481) Remain 19:44:14 loss: 0.5335 Lr: 0.00600
[2023-12-25 03:06:18,484 INFO misc.py line 119 253097] Train: [6/100][465/510] Data 0.009 (0.089) Batch 1.068 (1.480) Remain 19:43:30 loss: 0.3346 Lr: 0.00600
[2023-12-25 03:06:19,547 INFO misc.py line 119 253097] Train: [6/100][466/510] Data 0.006 (0.089) Batch 1.065 (1.479) Remain 19:42:46 loss: 0.5025 Lr: 0.00600
[2023-12-25 03:06:20,842 INFO misc.py line 119 253097] Train: [6/100][467/510] Data 0.004 (0.089) Batch 1.292 (1.479) Remain 19:42:25 loss: 0.3643 Lr: 0.00600
[2023-12-25 03:06:25,397 INFO misc.py line 119 253097] Train: [6/100][468/510] Data 3.628 (0.096) Batch 4.559 (1.485) Remain 19:47:41 loss: 0.5000 Lr: 0.00600
[2023-12-25 03:06:26,560 INFO misc.py line 119 253097] Train: [6/100][469/510] Data 0.003 (0.096) Batch 1.160 (1.484) Remain 19:47:06 loss: 0.5449 Lr: 0.00600
[2023-12-25 03:06:27,853 INFO misc.py line 119 253097] Train: [6/100][470/510] Data 0.007 (0.096) Batch 1.296 (1.484) Remain 19:46:45 loss: 0.3229 Lr: 0.00600
[2023-12-25 03:06:29,073 INFO misc.py line 119 253097] Train: [6/100][471/510] Data 0.003 (0.096) Batch 1.220 (1.484) Remain 19:46:17 loss: 0.6704 Lr: 0.00600
[2023-12-25 03:06:30,148 INFO misc.py line 119 253097] Train: [6/100][472/510] Data 0.003 (0.095) Batch 1.074 (1.483) Remain 19:45:33 loss: 0.4754 Lr: 0.00600
[2023-12-25 03:06:31,141 INFO misc.py line 119 253097] Train: [6/100][473/510] Data 0.005 (0.095) Batch 0.990 (1.482) Remain 19:44:42 loss: 1.0114 Lr: 0.00600
[2023-12-25 03:06:32,472 INFO misc.py line 119 253097] Train: [6/100][474/510] Data 0.007 (0.095) Batch 1.332 (1.481) Remain 19:44:25 loss: 0.3503 Lr: 0.00600
[2023-12-25 03:06:33,747 INFO misc.py line 119 253097] Train: [6/100][475/510] Data 0.006 (0.095) Batch 1.270 (1.481) Remain 19:44:02 loss: 0.5520 Lr: 0.00600
[2023-12-25 03:06:34,861 INFO misc.py line 119 253097] Train: [6/100][476/510] Data 0.010 (0.095) Batch 1.119 (1.480) Remain 19:43:24 loss: 0.7072 Lr: 0.00600
[2023-12-25 03:06:35,884 INFO misc.py line 119 253097] Train: [6/100][477/510] Data 0.006 (0.094) Batch 1.026 (1.479) Remain 19:42:36 loss: 0.6122 Lr: 0.00600
[2023-12-25 03:06:37,071 INFO misc.py line 119 253097] Train: [6/100][478/510] Data 0.004 (0.094) Batch 1.185 (1.478) Remain 19:42:05 loss: 0.4303 Lr: 0.00600
[2023-12-25 03:06:38,129 INFO misc.py line 119 253097] Train: [6/100][479/510] Data 0.007 (0.094) Batch 1.056 (1.478) Remain 19:41:21 loss: 0.3734 Lr: 0.00600
[2023-12-25 03:06:39,405 INFO misc.py line 119 253097] Train: [6/100][480/510] Data 0.008 (0.094) Batch 1.280 (1.477) Remain 19:41:00 loss: 0.5378 Lr: 0.00600
[2023-12-25 03:06:40,590 INFO misc.py line 119 253097] Train: [6/100][481/510] Data 0.004 (0.094) Batch 1.182 (1.477) Remain 19:40:29 loss: 0.3110 Lr: 0.00600
[2023-12-25 03:06:45,453 INFO misc.py line 119 253097] Train: [6/100][482/510] Data 0.007 (0.094) Batch 4.866 (1.484) Remain 19:46:07 loss: 0.4243 Lr: 0.00600
[2023-12-25 03:06:46,573 INFO misc.py line 119 253097] Train: [6/100][483/510] Data 0.004 (0.093) Batch 1.120 (1.483) Remain 19:45:29 loss: 0.3219 Lr: 0.00600
[2023-12-25 03:06:47,732 INFO misc.py line 119 253097] Train: [6/100][484/510] Data 0.004 (0.093) Batch 1.159 (1.482) Remain 19:44:55 loss: 0.6096 Lr: 0.00600
[2023-12-25 03:06:48,800 INFO misc.py line 119 253097] Train: [6/100][485/510] Data 0.003 (0.093) Batch 1.068 (1.481) Remain 19:44:12 loss: 0.3825 Lr: 0.00600
[2023-12-25 03:06:49,818 INFO misc.py line 119 253097] Train: [6/100][486/510] Data 0.003 (0.093) Batch 1.018 (1.480) Remain 19:43:25 loss: 0.2716 Lr: 0.00600
[2023-12-25 03:06:51,091 INFO misc.py line 119 253097] Train: [6/100][487/510] Data 0.004 (0.093) Batch 1.273 (1.480) Remain 19:43:03 loss: 0.6547 Lr: 0.00600
[2023-12-25 03:06:52,195 INFO misc.py line 119 253097] Train: [6/100][488/510] Data 0.005 (0.092) Batch 1.102 (1.479) Remain 19:42:24 loss: 0.6155 Lr: 0.00600
[2023-12-25 03:06:53,539 INFO misc.py line 119 253097] Train: [6/100][489/510] Data 0.006 (0.092) Batch 1.346 (1.479) Remain 19:42:09 loss: 0.7255 Lr: 0.00600
[2023-12-25 03:06:54,720 INFO misc.py line 119 253097] Train: [6/100][490/510] Data 0.004 (0.092) Batch 1.181 (1.478) Remain 19:41:38 loss: 0.4418 Lr: 0.00600
[2023-12-25 03:06:55,989 INFO misc.py line 119 253097] Train: [6/100][491/510] Data 0.004 (0.092) Batch 1.259 (1.478) Remain 19:41:15 loss: 0.4732 Lr: 0.00600
[2023-12-25 03:06:57,268 INFO misc.py line 119 253097] Train: [6/100][492/510] Data 0.015 (0.092) Batch 1.289 (1.477) Remain 19:40:55 loss: 0.4135 Lr: 0.00600
[2023-12-25 03:06:58,287 INFO misc.py line 119 253097] Train: [6/100][493/510] Data 0.004 (0.092) Batch 1.015 (1.477) Remain 19:40:09 loss: 0.5264 Lr: 0.00600
[2023-12-25 03:07:02,985 INFO misc.py line 119 253097] Train: [6/100][494/510] Data 0.008 (0.091) Batch 4.701 (1.483) Remain 19:45:22 loss: 0.6877 Lr: 0.00600
[2023-12-25 03:07:04,127 INFO misc.py line 119 253097] Train: [6/100][495/510] Data 0.005 (0.091) Batch 1.142 (1.482) Remain 19:44:47 loss: 0.5054 Lr: 0.00600
[2023-12-25 03:07:05,177 INFO misc.py line 119 253097] Train: [6/100][496/510] Data 0.005 (0.091) Batch 1.051 (1.482) Remain 19:44:04 loss: 0.5782 Lr: 0.00600
[2023-12-25 03:07:06,238 INFO misc.py line 119 253097] Train: [6/100][497/510] Data 0.004 (0.091) Batch 1.061 (1.481) Remain 19:43:22 loss: 0.4592 Lr: 0.00600
[2023-12-25 03:07:07,436 INFO misc.py line 119 253097] Train: [6/100][498/510] Data 0.004 (0.091) Batch 1.196 (1.480) Remain 19:42:53 loss: 0.4512 Lr: 0.00600
[2023-12-25 03:07:08,655 INFO misc.py line 119 253097] Train: [6/100][499/510] Data 0.007 (0.091) Batch 1.219 (1.480) Remain 19:42:26 loss: 0.4864 Lr: 0.00600
[2023-12-25 03:07:09,670 INFO misc.py line 119 253097] Train: [6/100][500/510] Data 0.006 (0.090) Batch 1.016 (1.479) Remain 19:41:40 loss: 0.5550 Lr: 0.00600
[2023-12-25 03:07:10,549 INFO misc.py line 119 253097] Train: [6/100][501/510] Data 0.004 (0.090) Batch 0.880 (1.477) Remain 19:40:41 loss: 0.4676 Lr: 0.00600
[2023-12-25 03:07:11,482 INFO misc.py line 119 253097] Train: [6/100][502/510] Data 0.004 (0.090) Batch 0.933 (1.476) Remain 19:39:47 loss: 0.3743 Lr: 0.00600
[2023-12-25 03:07:12,724 INFO misc.py line 119 253097] Train: [6/100][503/510] Data 0.004 (0.090) Batch 1.241 (1.476) Remain 19:39:23 loss: 0.6351 Lr: 0.00600
[2023-12-25 03:07:13,795 INFO misc.py line 119 253097] Train: [6/100][504/510] Data 0.006 (0.090) Batch 1.072 (1.475) Remain 19:38:43 loss: 0.6749 Lr: 0.00600
[2023-12-25 03:07:14,827 INFO misc.py line 119 253097] Train: [6/100][505/510] Data 0.007 (0.090) Batch 1.033 (1.474) Remain 19:37:59 loss: 0.3890 Lr: 0.00600
[2023-12-25 03:07:15,853 INFO misc.py line 119 253097] Train: [6/100][506/510] Data 0.003 (0.089) Batch 1.026 (1.473) Remain 19:37:15 loss: 0.3569 Lr: 0.00600
[2023-12-25 03:07:16,943 INFO misc.py line 119 253097] Train: [6/100][507/510] Data 0.003 (0.089) Batch 1.090 (1.473) Remain 19:36:37 loss: 0.4629 Lr: 0.00600
[2023-12-25 03:07:18,130 INFO misc.py line 119 253097] Train: [6/100][508/510] Data 0.004 (0.089) Batch 1.186 (1.472) Remain 19:36:08 loss: 0.3313 Lr: 0.00600
[2023-12-25 03:07:19,345 INFO misc.py line 119 253097] Train: [6/100][509/510] Data 0.004 (0.089) Batch 1.216 (1.471) Remain 19:35:42 loss: 0.6645 Lr: 0.00600
[2023-12-25 03:07:20,501 INFO misc.py line 119 253097] Train: [6/100][510/510] Data 0.005 (0.089) Batch 1.153 (1.471) Remain 19:35:11 loss: 0.9160 Lr: 0.00600
[2023-12-25 03:07:20,501 INFO misc.py line 136 253097] Train result: loss: 0.5148 
[2023-12-25 03:07:20,502 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 03:07:45,297 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5002 
[2023-12-25 03:07:45,662 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.5368 
[2023-12-25 03:07:52,222 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4373 
[2023-12-25 03:07:52,735 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5545 
[2023-12-25 03:07:54,705 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8188 
[2023-12-25 03:07:55,125 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4268 
[2023-12-25 03:07:56,001 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.7988 
[2023-12-25 03:07:56,572 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5515 
[2023-12-25 03:07:58,398 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9448 
[2023-12-25 03:08:00,531 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2978 
[2023-12-25 03:08:01,384 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.4206 
[2023-12-25 03:08:01,805 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8159 
[2023-12-25 03:08:02,704 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5555 
[2023-12-25 03:08:05,645 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.6767 
[2023-12-25 03:08:06,116 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1956 
[2023-12-25 03:08:06,724 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.6007 
[2023-12-25 03:08:07,422 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5236 
[2023-12-25 03:08:08,943 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6390/0.7192/0.8784.
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9001/0.9331
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9775/0.9917
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8171/0.9398
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2708/0.3766
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6059/0.6440
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5790/0.7437
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7907/0.9035
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8720/0.9096
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5816/0.6302
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7082/0.7718
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6695/0.7929
[2023-12-25 03:08:08,944 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5345/0.7134
[2023-12-25 03:08:08,945 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 03:08:08,946 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.6390
[2023-12-25 03:08:08,946 INFO misc.py line 165 253097] Currently Best mIoU: 0.6390
[2023-12-25 03:08:08,946 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 03:08:18,069 INFO misc.py line 119 253097] Train: [7/100][1/510] Data 2.863 (2.863) Batch 3.690 (3.690) Remain 49:07:51 loss: 0.2999 Lr: 0.00600
[2023-12-25 03:08:21,868 INFO misc.py line 119 253097] Train: [7/100][2/510] Data 2.932 (2.932) Batch 3.797 (3.797) Remain 50:33:36 loss: 0.5454 Lr: 0.00600
[2023-12-25 03:08:22,999 INFO misc.py line 119 253097] Train: [7/100][3/510] Data 0.007 (0.007) Batch 1.130 (1.130) Remain 15:02:42 loss: 0.7613 Lr: 0.00600
[2023-12-25 03:08:24,120 INFO misc.py line 119 253097] Train: [7/100][4/510] Data 0.007 (0.007) Batch 1.123 (1.123) Remain 14:57:13 loss: 0.5643 Lr: 0.00600
[2023-12-25 03:08:25,062 INFO misc.py line 119 253097] Train: [7/100][5/510] Data 0.006 (0.006) Batch 0.941 (1.032) Remain 13:44:40 loss: 0.6780 Lr: 0.00600
[2023-12-25 03:08:26,217 INFO misc.py line 119 253097] Train: [7/100][6/510] Data 0.007 (0.006) Batch 1.156 (1.074) Remain 14:17:38 loss: 0.4037 Lr: 0.00600
[2023-12-25 03:08:27,309 INFO misc.py line 119 253097] Train: [7/100][7/510] Data 0.006 (0.006) Batch 1.093 (1.078) Remain 14:21:33 loss: 0.5815 Lr: 0.00600
[2023-12-25 03:08:36,946 INFO misc.py line 119 253097] Train: [7/100][8/510] Data 8.334 (1.672) Batch 9.636 (2.790) Remain 37:08:46 loss: 0.4956 Lr: 0.00600
[2023-12-25 03:08:37,809 INFO misc.py line 119 253097] Train: [7/100][9/510] Data 0.003 (1.394) Batch 0.863 (2.469) Remain 32:52:12 loss: 0.3806 Lr: 0.00600
[2023-12-25 03:08:38,995 INFO misc.py line 119 253097] Train: [7/100][10/510] Data 0.004 (1.195) Batch 1.182 (2.285) Remain 30:25:21 loss: 0.9050 Lr: 0.00600
[2023-12-25 03:08:39,983 INFO misc.py line 119 253097] Train: [7/100][11/510] Data 0.009 (1.047) Batch 0.992 (2.123) Remain 28:16:14 loss: 0.4385 Lr: 0.00600
[2023-12-25 03:08:41,103 INFO misc.py line 119 253097] Train: [7/100][12/510] Data 0.003 (0.931) Batch 1.120 (2.012) Remain 26:47:08 loss: 0.4170 Lr: 0.00600
[2023-12-25 03:08:42,230 INFO misc.py line 119 253097] Train: [7/100][13/510] Data 0.004 (0.838) Batch 1.126 (1.923) Remain 25:36:19 loss: 0.3750 Lr: 0.00600
[2023-12-25 03:08:43,324 INFO misc.py line 119 253097] Train: [7/100][14/510] Data 0.005 (0.762) Batch 1.095 (1.848) Remain 24:36:10 loss: 0.4364 Lr: 0.00600
[2023-12-25 03:08:44,427 INFO misc.py line 119 253097] Train: [7/100][15/510] Data 0.003 (0.699) Batch 1.101 (1.786) Remain 23:46:26 loss: 0.4833 Lr: 0.00600
[2023-12-25 03:08:45,665 INFO misc.py line 119 253097] Train: [7/100][16/510] Data 0.004 (0.646) Batch 1.240 (1.744) Remain 23:12:50 loss: 0.4814 Lr: 0.00600
[2023-12-25 03:08:46,829 INFO misc.py line 119 253097] Train: [7/100][17/510] Data 0.003 (0.600) Batch 1.164 (1.702) Remain 22:39:43 loss: 0.4443 Lr: 0.00600
[2023-12-25 03:08:47,832 INFO misc.py line 119 253097] Train: [7/100][18/510] Data 0.003 (0.560) Batch 1.003 (1.656) Remain 22:02:27 loss: 0.2857 Lr: 0.00600
[2023-12-25 03:08:48,935 INFO misc.py line 119 253097] Train: [7/100][19/510] Data 0.003 (0.525) Batch 1.103 (1.621) Remain 21:34:50 loss: 0.4025 Lr: 0.00600
[2023-12-25 03:08:49,890 INFO misc.py line 119 253097] Train: [7/100][20/510] Data 0.003 (0.495) Batch 0.954 (1.582) Remain 21:03:28 loss: 0.2934 Lr: 0.00600
[2023-12-25 03:08:55,272 INFO misc.py line 119 253097] Train: [7/100][21/510] Data 0.004 (0.467) Batch 5.382 (1.793) Remain 23:52:03 loss: 0.4419 Lr: 0.00600
[2023-12-25 03:08:56,168 INFO misc.py line 119 253097] Train: [7/100][22/510] Data 0.004 (0.443) Batch 0.894 (1.746) Remain 23:14:15 loss: 0.4944 Lr: 0.00600
[2023-12-25 03:08:57,195 INFO misc.py line 119 253097] Train: [7/100][23/510] Data 0.005 (0.421) Batch 1.024 (1.710) Remain 22:45:24 loss: 0.6146 Lr: 0.00600
[2023-12-25 03:08:58,336 INFO misc.py line 119 253097] Train: [7/100][24/510] Data 0.008 (0.401) Batch 1.145 (1.683) Remain 22:23:54 loss: 0.3182 Lr: 0.00600
[2023-12-25 03:08:59,367 INFO misc.py line 119 253097] Train: [7/100][25/510] Data 0.005 (0.383) Batch 1.022 (1.653) Remain 21:59:54 loss: 0.6423 Lr: 0.00600
[2023-12-25 03:09:00,279 INFO misc.py line 119 253097] Train: [7/100][26/510] Data 0.013 (0.367) Batch 0.921 (1.621) Remain 21:34:28 loss: 0.4642 Lr: 0.00600
[2023-12-25 03:09:01,479 INFO misc.py line 119 253097] Train: [7/100][27/510] Data 0.003 (0.352) Batch 1.199 (1.603) Remain 21:20:25 loss: 0.5628 Lr: 0.00600
[2023-12-25 03:09:02,730 INFO misc.py line 119 253097] Train: [7/100][28/510] Data 0.006 (0.338) Batch 1.251 (1.589) Remain 21:09:08 loss: 0.9532 Lr: 0.00600
[2023-12-25 03:09:03,970 INFO misc.py line 119 253097] Train: [7/100][29/510] Data 0.006 (0.325) Batch 1.240 (1.576) Remain 20:58:22 loss: 0.6456 Lr: 0.00600
[2023-12-25 03:09:04,963 INFO misc.py line 119 253097] Train: [7/100][30/510] Data 0.004 (0.314) Batch 0.994 (1.554) Remain 20:41:07 loss: 0.5686 Lr: 0.00600
[2023-12-25 03:09:06,233 INFO misc.py line 119 253097] Train: [7/100][31/510] Data 0.003 (0.302) Batch 1.266 (1.544) Remain 20:32:53 loss: 0.3076 Lr: 0.00600
[2023-12-25 03:09:07,324 INFO misc.py line 119 253097] Train: [7/100][32/510] Data 0.007 (0.292) Batch 1.090 (1.528) Remain 20:20:22 loss: 0.4105 Lr: 0.00600
[2023-12-25 03:09:08,439 INFO misc.py line 119 253097] Train: [7/100][33/510] Data 0.008 (0.283) Batch 1.115 (1.515) Remain 20:09:21 loss: 0.3877 Lr: 0.00600
[2023-12-25 03:09:09,645 INFO misc.py line 119 253097] Train: [7/100][34/510] Data 0.008 (0.274) Batch 1.209 (1.505) Remain 20:01:26 loss: 0.5041 Lr: 0.00600
[2023-12-25 03:09:10,659 INFO misc.py line 119 253097] Train: [7/100][35/510] Data 0.005 (0.266) Batch 1.009 (1.489) Remain 19:49:03 loss: 0.4276 Lr: 0.00600
[2023-12-25 03:09:11,979 INFO misc.py line 119 253097] Train: [7/100][36/510] Data 0.010 (0.258) Batch 1.327 (1.484) Remain 19:45:05 loss: 0.5580 Lr: 0.00600
[2023-12-25 03:09:18,329 INFO misc.py line 119 253097] Train: [7/100][37/510] Data 0.004 (0.250) Batch 6.351 (1.627) Remain 21:39:20 loss: 0.5666 Lr: 0.00600
[2023-12-25 03:09:19,592 INFO misc.py line 119 253097] Train: [7/100][38/510] Data 0.003 (0.243) Batch 1.258 (1.617) Remain 21:30:53 loss: 0.5751 Lr: 0.00600
[2023-12-25 03:09:20,594 INFO misc.py line 119 253097] Train: [7/100][39/510] Data 0.007 (0.237) Batch 1.004 (1.600) Remain 21:17:15 loss: 0.6147 Lr: 0.00600
[2023-12-25 03:09:21,471 INFO misc.py line 119 253097] Train: [7/100][40/510] Data 0.005 (0.230) Batch 0.879 (1.580) Remain 21:01:40 loss: 0.5531 Lr: 0.00600
[2023-12-25 03:09:22,698 INFO misc.py line 119 253097] Train: [7/100][41/510] Data 0.004 (0.224) Batch 1.223 (1.571) Remain 20:54:09 loss: 0.4441 Lr: 0.00600
[2023-12-25 03:09:23,822 INFO misc.py line 119 253097] Train: [7/100][42/510] Data 0.008 (0.219) Batch 1.116 (1.559) Remain 20:44:49 loss: 0.2446 Lr: 0.00600
[2023-12-25 03:09:24,927 INFO misc.py line 119 253097] Train: [7/100][43/510] Data 0.016 (0.214) Batch 1.113 (1.548) Remain 20:35:53 loss: 0.2438 Lr: 0.00600
[2023-12-25 03:09:26,031 INFO misc.py line 119 253097] Train: [7/100][44/510] Data 0.007 (0.209) Batch 1.108 (1.537) Remain 20:27:17 loss: 0.5724 Lr: 0.00600
[2023-12-25 03:09:27,202 INFO misc.py line 119 253097] Train: [7/100][45/510] Data 0.003 (0.204) Batch 1.167 (1.529) Remain 20:20:13 loss: 0.3001 Lr: 0.00600
[2023-12-25 03:09:28,416 INFO misc.py line 119 253097] Train: [7/100][46/510] Data 0.007 (0.199) Batch 1.215 (1.521) Remain 20:14:22 loss: 0.7276 Lr: 0.00600
[2023-12-25 03:09:29,620 INFO misc.py line 119 253097] Train: [7/100][47/510] Data 0.006 (0.195) Batch 1.203 (1.514) Remain 20:08:35 loss: 0.3986 Lr: 0.00600
[2023-12-25 03:09:30,720 INFO misc.py line 119 253097] Train: [7/100][48/510] Data 0.007 (0.191) Batch 1.103 (1.505) Remain 20:01:16 loss: 0.8681 Lr: 0.00600
[2023-12-25 03:09:31,862 INFO misc.py line 119 253097] Train: [7/100][49/510] Data 0.004 (0.187) Batch 1.138 (1.497) Remain 19:54:52 loss: 0.5091 Lr: 0.00600
[2023-12-25 03:09:32,953 INFO misc.py line 119 253097] Train: [7/100][50/510] Data 0.008 (0.183) Batch 1.093 (1.488) Remain 19:47:59 loss: 0.4330 Lr: 0.00600
[2023-12-25 03:09:34,151 INFO misc.py line 119 253097] Train: [7/100][51/510] Data 0.005 (0.179) Batch 1.196 (1.482) Remain 19:43:06 loss: 0.7366 Lr: 0.00600
[2023-12-25 03:09:35,443 INFO misc.py line 119 253097] Train: [7/100][52/510] Data 0.008 (0.176) Batch 1.290 (1.478) Remain 19:39:57 loss: 0.5239 Lr: 0.00600
[2023-12-25 03:09:36,506 INFO misc.py line 119 253097] Train: [7/100][53/510] Data 0.009 (0.172) Batch 1.066 (1.470) Remain 19:33:20 loss: 0.2693 Lr: 0.00600
[2023-12-25 03:09:37,767 INFO misc.py line 119 253097] Train: [7/100][54/510] Data 0.006 (0.169) Batch 1.263 (1.466) Remain 19:30:05 loss: 0.5975 Lr: 0.00600
[2023-12-25 03:09:38,910 INFO misc.py line 119 253097] Train: [7/100][55/510] Data 0.004 (0.166) Batch 1.143 (1.460) Remain 19:25:06 loss: 0.3524 Lr: 0.00600
[2023-12-25 03:09:40,016 INFO misc.py line 119 253097] Train: [7/100][56/510] Data 0.004 (0.163) Batch 1.103 (1.453) Remain 19:19:41 loss: 0.4821 Lr: 0.00600
[2023-12-25 03:09:41,162 INFO misc.py line 119 253097] Train: [7/100][57/510] Data 0.008 (0.160) Batch 1.151 (1.448) Remain 19:15:12 loss: 0.5818 Lr: 0.00600
[2023-12-25 03:09:42,224 INFO misc.py line 119 253097] Train: [7/100][58/510] Data 0.003 (0.157) Batch 1.061 (1.441) Remain 19:09:34 loss: 0.2874 Lr: 0.00600
[2023-12-25 03:09:43,322 INFO misc.py line 119 253097] Train: [7/100][59/510] Data 0.005 (0.154) Batch 1.097 (1.434) Remain 19:04:39 loss: 0.4586 Lr: 0.00600
[2023-12-25 03:09:44,540 INFO misc.py line 119 253097] Train: [7/100][60/510] Data 0.005 (0.152) Batch 1.219 (1.431) Remain 19:01:37 loss: 0.7696 Lr: 0.00600
[2023-12-25 03:09:45,723 INFO misc.py line 119 253097] Train: [7/100][61/510] Data 0.002 (0.149) Batch 1.179 (1.426) Remain 18:58:08 loss: 0.5684 Lr: 0.00600
[2023-12-25 03:09:46,861 INFO misc.py line 119 253097] Train: [7/100][62/510] Data 0.006 (0.147) Batch 1.135 (1.421) Remain 18:54:11 loss: 0.2902 Lr: 0.00600
[2023-12-25 03:09:47,915 INFO misc.py line 119 253097] Train: [7/100][63/510] Data 0.009 (0.144) Batch 1.058 (1.415) Remain 18:49:19 loss: 0.9052 Lr: 0.00600
[2023-12-25 03:09:48,944 INFO misc.py line 119 253097] Train: [7/100][64/510] Data 0.004 (0.142) Batch 1.028 (1.409) Remain 18:44:14 loss: 0.7254 Lr: 0.00600
[2023-12-25 03:09:49,946 INFO misc.py line 119 253097] Train: [7/100][65/510] Data 0.005 (0.140) Batch 0.999 (1.402) Remain 18:38:57 loss: 0.4488 Lr: 0.00600
[2023-12-25 03:09:50,888 INFO misc.py line 119 253097] Train: [7/100][66/510] Data 0.009 (0.138) Batch 0.946 (1.395) Remain 18:33:08 loss: 0.2663 Lr: 0.00600
[2023-12-25 03:09:52,040 INFO misc.py line 119 253097] Train: [7/100][67/510] Data 0.004 (0.136) Batch 1.152 (1.391) Remain 18:30:05 loss: 0.2826 Lr: 0.00600
[2023-12-25 03:09:53,213 INFO misc.py line 119 253097] Train: [7/100][68/510] Data 0.004 (0.134) Batch 1.174 (1.388) Remain 18:27:24 loss: 0.4835 Lr: 0.00600
[2023-12-25 03:09:54,380 INFO misc.py line 119 253097] Train: [7/100][69/510] Data 0.003 (0.132) Batch 1.167 (1.385) Remain 18:24:42 loss: 0.6216 Lr: 0.00600
[2023-12-25 03:09:55,537 INFO misc.py line 119 253097] Train: [7/100][70/510] Data 0.003 (0.130) Batch 1.156 (1.381) Remain 18:21:58 loss: 0.3212 Lr: 0.00600
[2023-12-25 03:09:56,575 INFO misc.py line 119 253097] Train: [7/100][71/510] Data 0.004 (0.128) Batch 1.039 (1.376) Remain 18:17:55 loss: 0.4231 Lr: 0.00600
[2023-12-25 03:09:58,439 INFO misc.py line 119 253097] Train: [7/100][72/510] Data 0.583 (0.135) Batch 1.864 (1.383) Remain 18:23:32 loss: 0.7562 Lr: 0.00600
[2023-12-25 03:09:59,543 INFO misc.py line 119 253097] Train: [7/100][73/510] Data 0.004 (0.133) Batch 1.101 (1.379) Remain 18:20:17 loss: 0.4573 Lr: 0.00600
[2023-12-25 03:10:00,589 INFO misc.py line 119 253097] Train: [7/100][74/510] Data 0.007 (0.131) Batch 1.049 (1.375) Remain 18:16:33 loss: 0.3383 Lr: 0.00600
[2023-12-25 03:10:01,720 INFO misc.py line 119 253097] Train: [7/100][75/510] Data 0.004 (0.129) Batch 1.127 (1.371) Remain 18:13:48 loss: 0.6847 Lr: 0.00600
[2023-12-25 03:10:02,891 INFO misc.py line 119 253097] Train: [7/100][76/510] Data 0.008 (0.128) Batch 1.173 (1.368) Remain 18:11:37 loss: 0.4377 Lr: 0.00600
[2023-12-25 03:10:04,015 INFO misc.py line 119 253097] Train: [7/100][77/510] Data 0.007 (0.126) Batch 1.124 (1.365) Remain 18:08:57 loss: 0.9168 Lr: 0.00600
[2023-12-25 03:10:05,066 INFO misc.py line 119 253097] Train: [7/100][78/510] Data 0.005 (0.124) Batch 1.047 (1.361) Remain 18:05:33 loss: 0.3596 Lr: 0.00600
[2023-12-25 03:10:06,146 INFO misc.py line 119 253097] Train: [7/100][79/510] Data 0.010 (0.123) Batch 1.085 (1.357) Remain 18:02:38 loss: 0.8365 Lr: 0.00600
[2023-12-25 03:10:07,309 INFO misc.py line 119 253097] Train: [7/100][80/510] Data 0.005 (0.121) Batch 1.163 (1.355) Remain 18:00:36 loss: 0.3708 Lr: 0.00600
[2023-12-25 03:10:08,300 INFO misc.py line 119 253097] Train: [7/100][81/510] Data 0.005 (0.120) Batch 0.992 (1.350) Remain 17:56:52 loss: 0.4696 Lr: 0.00600
[2023-12-25 03:10:09,196 INFO misc.py line 119 253097] Train: [7/100][82/510] Data 0.004 (0.118) Batch 0.895 (1.344) Remain 17:52:15 loss: 0.2118 Lr: 0.00600
[2023-12-25 03:10:10,314 INFO misc.py line 119 253097] Train: [7/100][83/510] Data 0.005 (0.117) Batch 1.119 (1.341) Remain 17:49:59 loss: 0.7738 Lr: 0.00600
[2023-12-25 03:10:11,519 INFO misc.py line 119 253097] Train: [7/100][84/510] Data 0.004 (0.116) Batch 1.203 (1.340) Remain 17:48:36 loss: 0.8031 Lr: 0.00600
[2023-12-25 03:10:12,791 INFO misc.py line 119 253097] Train: [7/100][85/510] Data 0.005 (0.114) Batch 1.270 (1.339) Remain 17:47:54 loss: 0.4195 Lr: 0.00600
[2023-12-25 03:10:26,046 INFO misc.py line 119 253097] Train: [7/100][86/510] Data 0.007 (0.113) Batch 13.258 (1.483) Remain 19:42:24 loss: 0.9482 Lr: 0.00600
[2023-12-25 03:10:27,295 INFO misc.py line 119 253097] Train: [7/100][87/510] Data 0.004 (0.112) Batch 1.245 (1.480) Remain 19:40:08 loss: 0.3190 Lr: 0.00600
[2023-12-25 03:10:28,681 INFO misc.py line 119 253097] Train: [7/100][88/510] Data 0.008 (0.110) Batch 1.391 (1.479) Remain 19:39:16 loss: 0.3964 Lr: 0.00600
[2023-12-25 03:10:29,919 INFO misc.py line 119 253097] Train: [7/100][89/510] Data 0.004 (0.109) Batch 1.236 (1.476) Remain 19:36:59 loss: 0.5598 Lr: 0.00600
[2023-12-25 03:10:31,043 INFO misc.py line 119 253097] Train: [7/100][90/510] Data 0.008 (0.108) Batch 1.123 (1.472) Remain 19:33:44 loss: 0.2461 Lr: 0.00600
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[2023-12-25 03:11:16,535 INFO misc.py line 119 253097] Train: [7/100][123/510] Data 0.005 (0.146) Batch 1.107 (1.446) Remain 19:12:30 loss: 0.4586 Lr: 0.00600
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[2023-12-25 03:11:23,588 INFO misc.py line 119 253097] Train: [7/100][126/510] Data 0.005 (0.142) Batch 1.308 (1.468) Remain 19:30:01 loss: 0.4145 Lr: 0.00600
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[2023-12-25 03:11:31,066 INFO misc.py line 119 253097] Train: [7/100][133/510] Data 0.003 (0.135) Batch 1.065 (1.447) Remain 19:12:41 loss: 0.3551 Lr: 0.00600
[2023-12-25 03:11:32,230 INFO misc.py line 119 253097] Train: [7/100][134/510] Data 0.007 (0.134) Batch 1.167 (1.445) Remain 19:10:57 loss: 0.2962 Lr: 0.00600
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[2023-12-25 03:11:40,917 INFO misc.py line 119 253097] Train: [7/100][137/510] Data 0.002 (0.131) Batch 1.041 (1.477) Remain 19:36:44 loss: 0.6773 Lr: 0.00600
[2023-12-25 03:11:42,127 INFO misc.py line 119 253097] Train: [7/100][138/510] Data 0.008 (0.130) Batch 1.215 (1.475) Remain 19:35:09 loss: 0.4362 Lr: 0.00600
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[2023-12-25 03:11:47,880 INFO misc.py line 119 253097] Train: [7/100][140/510] Data 0.003 (0.128) Batch 1.028 (1.496) Remain 19:51:25 loss: 0.4307 Lr: 0.00600
[2023-12-25 03:11:48,881 INFO misc.py line 119 253097] Train: [7/100][141/510] Data 0.004 (0.127) Batch 1.000 (1.492) Remain 19:48:32 loss: 0.2086 Lr: 0.00600
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[2023-12-25 03:11:52,485 INFO misc.py line 119 253097] Train: [7/100][144/510] Data 0.005 (0.125) Batch 1.183 (1.486) Remain 19:43:31 loss: 0.4461 Lr: 0.00600
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[2023-12-25 03:14:05,253 INFO misc.py line 119 253097] Train: [7/100][231/510] Data 0.007 (0.079) Batch 1.206 (1.501) Remain 19:53:36 loss: 0.3800 Lr: 0.00600
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[2023-12-25 03:14:25,682 INFO misc.py line 119 253097] Train: [7/100][241/510] Data 8.821 (0.113) Batch 9.969 (1.524) Remain 20:11:28 loss: 0.6476 Lr: 0.00600
[2023-12-25 03:14:26,766 INFO misc.py line 119 253097] Train: [7/100][242/510] Data 0.002 (0.113) Batch 1.084 (1.522) Remain 20:09:58 loss: 0.3964 Lr: 0.00600
[2023-12-25 03:14:27,949 INFO misc.py line 119 253097] Train: [7/100][243/510] Data 0.002 (0.112) Batch 1.183 (1.521) Remain 20:08:49 loss: 0.3823 Lr: 0.00600
[2023-12-25 03:14:29,246 INFO misc.py line 119 253097] Train: [7/100][244/510] Data 0.003 (0.112) Batch 1.294 (1.520) Remain 20:08:03 loss: 0.4803 Lr: 0.00600
[2023-12-25 03:14:30,500 INFO misc.py line 119 253097] Train: [7/100][245/510] Data 0.006 (0.111) Batch 1.253 (1.519) Remain 20:07:09 loss: 0.5416 Lr: 0.00600
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[2023-12-25 03:14:34,061 INFO misc.py line 119 253097] Train: [7/100][248/510] Data 0.006 (0.110) Batch 1.266 (1.515) Remain 20:03:51 loss: 0.5671 Lr: 0.00600
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[2023-12-25 03:14:36,132 INFO misc.py line 119 253097] Train: [7/100][250/510] Data 0.004 (0.109) Batch 1.193 (1.511) Remain 20:00:43 loss: 0.2832 Lr: 0.00600
[2023-12-25 03:14:37,251 INFO misc.py line 119 253097] Train: [7/100][251/510] Data 0.004 (0.109) Batch 1.120 (1.509) Remain 19:59:27 loss: 0.5207 Lr: 0.00600
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[2023-12-25 03:14:43,221 INFO misc.py line 119 253097] Train: [7/100][256/510] Data 0.003 (0.107) Batch 1.278 (1.503) Remain 19:54:22 loss: 0.4203 Lr: 0.00600
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[2023-12-25 03:14:57,561 INFO misc.py line 119 253097] Train: [7/100][263/510] Data 0.004 (0.111) Batch 0.988 (1.518) Remain 20:05:52 loss: 0.4619 Lr: 0.00600
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[2023-12-25 03:15:05,900 INFO misc.py line 119 253097] Train: [7/100][265/510] Data 4.617 (0.128) Batch 5.693 (1.538) Remain 20:21:54 loss: 0.3597 Lr: 0.00600
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[2023-12-25 03:15:17,900 INFO misc.py line 119 253097] Train: [7/100][272/510] Data 3.811 (0.139) Batch 4.992 (1.542) Remain 20:25:22 loss: 0.7819 Lr: 0.00600
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[2023-12-25 03:18:34,708 INFO misc.py line 119 253097] Train: [7/100][404/510] Data 0.005 (0.139) Batch 1.106 (1.525) Remain 20:08:34 loss: 0.4742 Lr: 0.00599
[2023-12-25 03:18:35,972 INFO misc.py line 119 253097] Train: [7/100][405/510] Data 0.003 (0.139) Batch 1.261 (1.525) Remain 20:08:01 loss: 0.3716 Lr: 0.00599
[2023-12-25 03:18:37,297 INFO misc.py line 119 253097] Train: [7/100][406/510] Data 0.006 (0.139) Batch 1.327 (1.524) Remain 20:07:36 loss: 0.3150 Lr: 0.00599
[2023-12-25 03:18:38,418 INFO misc.py line 119 253097] Train: [7/100][407/510] Data 0.004 (0.138) Batch 1.118 (1.523) Remain 20:06:47 loss: 0.2902 Lr: 0.00599
[2023-12-25 03:18:46,788 INFO misc.py line 119 253097] Train: [7/100][408/510] Data 0.007 (0.138) Batch 8.373 (1.540) Remain 20:20:09 loss: 0.4072 Lr: 0.00599
[2023-12-25 03:18:47,888 INFO misc.py line 119 253097] Train: [7/100][409/510] Data 0.004 (0.138) Batch 1.101 (1.539) Remain 20:19:16 loss: 0.3137 Lr: 0.00599
[2023-12-25 03:18:49,031 INFO misc.py line 119 253097] Train: [7/100][410/510] Data 0.003 (0.137) Batch 1.139 (1.538) Remain 20:18:28 loss: 0.3532 Lr: 0.00599
[2023-12-25 03:18:50,303 INFO misc.py line 119 253097] Train: [7/100][411/510] Data 0.008 (0.137) Batch 1.276 (1.538) Remain 20:17:56 loss: 0.5362 Lr: 0.00599
[2023-12-25 03:18:51,212 INFO misc.py line 119 253097] Train: [7/100][412/510] Data 0.004 (0.137) Batch 0.908 (1.536) Remain 20:16:41 loss: 0.7963 Lr: 0.00599
[2023-12-25 03:18:52,305 INFO misc.py line 119 253097] Train: [7/100][413/510] Data 0.004 (0.136) Batch 1.094 (1.535) Remain 20:15:49 loss: 0.3123 Lr: 0.00599
[2023-12-25 03:18:53,264 INFO misc.py line 119 253097] Train: [7/100][414/510] Data 0.003 (0.136) Batch 0.959 (1.533) Remain 20:14:41 loss: 0.4083 Lr: 0.00599
[2023-12-25 03:18:54,427 INFO misc.py line 119 253097] Train: [7/100][415/510] Data 0.003 (0.136) Batch 1.162 (1.533) Remain 20:13:56 loss: 0.3375 Lr: 0.00599
[2023-12-25 03:18:55,705 INFO misc.py line 119 253097] Train: [7/100][416/510] Data 0.003 (0.136) Batch 1.274 (1.532) Remain 20:13:25 loss: 0.5327 Lr: 0.00599
[2023-12-25 03:18:56,709 INFO misc.py line 119 253097] Train: [7/100][417/510] Data 0.008 (0.135) Batch 1.004 (1.531) Remain 20:12:23 loss: 0.5326 Lr: 0.00599
[2023-12-25 03:18:57,901 INFO misc.py line 119 253097] Train: [7/100][418/510] Data 0.008 (0.135) Batch 1.194 (1.530) Remain 20:11:43 loss: 0.5124 Lr: 0.00599
[2023-12-25 03:18:58,827 INFO misc.py line 119 253097] Train: [7/100][419/510] Data 0.007 (0.135) Batch 0.929 (1.528) Remain 20:10:32 loss: 0.4486 Lr: 0.00599
[2023-12-25 03:18:59,853 INFO misc.py line 119 253097] Train: [7/100][420/510] Data 0.003 (0.134) Batch 1.026 (1.527) Remain 20:09:34 loss: 0.3059 Lr: 0.00599
[2023-12-25 03:19:00,867 INFO misc.py line 119 253097] Train: [7/100][421/510] Data 0.003 (0.134) Batch 1.014 (1.526) Remain 20:08:34 loss: 0.5951 Lr: 0.00599
[2023-12-25 03:19:02,136 INFO misc.py line 119 253097] Train: [7/100][422/510] Data 0.003 (0.134) Batch 1.268 (1.525) Remain 20:08:03 loss: 0.4294 Lr: 0.00599
[2023-12-25 03:19:03,295 INFO misc.py line 119 253097] Train: [7/100][423/510] Data 0.004 (0.133) Batch 1.160 (1.525) Remain 20:07:20 loss: 0.7083 Lr: 0.00599
[2023-12-25 03:19:04,455 INFO misc.py line 119 253097] Train: [7/100][424/510] Data 0.003 (0.133) Batch 1.155 (1.524) Remain 20:06:37 loss: 0.4297 Lr: 0.00599
[2023-12-25 03:19:05,783 INFO misc.py line 119 253097] Train: [7/100][425/510] Data 0.008 (0.133) Batch 1.330 (1.523) Remain 20:06:14 loss: 0.3496 Lr: 0.00599
[2023-12-25 03:19:06,925 INFO misc.py line 119 253097] Train: [7/100][426/510] Data 0.007 (0.132) Batch 1.143 (1.522) Remain 20:05:29 loss: 0.6920 Lr: 0.00599
[2023-12-25 03:19:07,992 INFO misc.py line 119 253097] Train: [7/100][427/510] Data 0.006 (0.132) Batch 1.070 (1.521) Remain 20:04:37 loss: 0.4393 Lr: 0.00599
[2023-12-25 03:19:09,110 INFO misc.py line 119 253097] Train: [7/100][428/510] Data 0.003 (0.132) Batch 1.118 (1.520) Remain 20:03:50 loss: 0.3503 Lr: 0.00599
[2023-12-25 03:19:10,140 INFO misc.py line 119 253097] Train: [7/100][429/510] Data 0.004 (0.132) Batch 1.010 (1.519) Remain 20:02:52 loss: 0.5483 Lr: 0.00599
[2023-12-25 03:19:11,387 INFO misc.py line 119 253097] Train: [7/100][430/510] Data 0.023 (0.131) Batch 1.265 (1.518) Remain 20:02:22 loss: 0.5121 Lr: 0.00599
[2023-12-25 03:19:12,510 INFO misc.py line 119 253097] Train: [7/100][431/510] Data 0.004 (0.131) Batch 1.121 (1.518) Remain 20:01:37 loss: 0.5380 Lr: 0.00599
[2023-12-25 03:19:13,670 INFO misc.py line 119 253097] Train: [7/100][432/510] Data 0.007 (0.131) Batch 1.163 (1.517) Remain 20:00:56 loss: 0.3923 Lr: 0.00599
[2023-12-25 03:19:14,799 INFO misc.py line 119 253097] Train: [7/100][433/510] Data 0.004 (0.130) Batch 1.125 (1.516) Remain 20:00:11 loss: 0.3582 Lr: 0.00599
[2023-12-25 03:19:15,906 INFO misc.py line 119 253097] Train: [7/100][434/510] Data 0.008 (0.130) Batch 1.109 (1.515) Remain 19:59:25 loss: 0.5495 Lr: 0.00599
[2023-12-25 03:19:22,290 INFO misc.py line 119 253097] Train: [7/100][435/510] Data 5.255 (0.142) Batch 6.387 (1.526) Remain 20:08:19 loss: 0.6840 Lr: 0.00599
[2023-12-25 03:19:23,438 INFO misc.py line 119 253097] Train: [7/100][436/510] Data 0.003 (0.142) Batch 1.147 (1.525) Remain 20:07:36 loss: 0.4201 Lr: 0.00599
[2023-12-25 03:19:24,702 INFO misc.py line 119 253097] Train: [7/100][437/510] Data 0.004 (0.141) Batch 1.263 (1.525) Remain 20:07:06 loss: 0.3167 Lr: 0.00599
[2023-12-25 03:19:25,916 INFO misc.py line 119 253097] Train: [7/100][438/510] Data 0.005 (0.141) Batch 1.211 (1.524) Remain 20:06:30 loss: 0.3564 Lr: 0.00599
[2023-12-25 03:19:26,873 INFO misc.py line 119 253097] Train: [7/100][439/510] Data 0.008 (0.141) Batch 0.962 (1.523) Remain 20:05:27 loss: 0.4173 Lr: 0.00599
[2023-12-25 03:19:27,945 INFO misc.py line 119 253097] Train: [7/100][440/510] Data 0.003 (0.140) Batch 1.071 (1.522) Remain 20:04:36 loss: 0.8242 Lr: 0.00599
[2023-12-25 03:19:29,305 INFO misc.py line 119 253097] Train: [7/100][441/510] Data 0.005 (0.140) Batch 1.359 (1.521) Remain 20:04:17 loss: 0.6282 Lr: 0.00599
[2023-12-25 03:19:30,349 INFO misc.py line 119 253097] Train: [7/100][442/510] Data 0.005 (0.140) Batch 1.041 (1.520) Remain 20:03:24 loss: 0.3514 Lr: 0.00599
[2023-12-25 03:19:31,622 INFO misc.py line 119 253097] Train: [7/100][443/510] Data 0.007 (0.139) Batch 1.274 (1.520) Remain 20:02:56 loss: 0.4840 Lr: 0.00599
[2023-12-25 03:19:32,559 INFO misc.py line 119 253097] Train: [7/100][444/510] Data 0.007 (0.139) Batch 0.940 (1.518) Remain 20:01:52 loss: 0.3462 Lr: 0.00599
[2023-12-25 03:19:33,760 INFO misc.py line 119 253097] Train: [7/100][445/510] Data 0.003 (0.139) Batch 1.201 (1.518) Remain 20:01:16 loss: 0.4836 Lr: 0.00599
[2023-12-25 03:19:34,659 INFO misc.py line 119 253097] Train: [7/100][446/510] Data 0.004 (0.139) Batch 0.898 (1.516) Remain 20:00:08 loss: 0.3368 Lr: 0.00599
[2023-12-25 03:19:35,716 INFO misc.py line 119 253097] Train: [7/100][447/510] Data 0.004 (0.138) Batch 1.055 (1.515) Remain 19:59:17 loss: 0.6320 Lr: 0.00599
[2023-12-25 03:19:36,948 INFO misc.py line 119 253097] Train: [7/100][448/510] Data 0.006 (0.138) Batch 1.229 (1.514) Remain 19:58:45 loss: 0.5203 Lr: 0.00599
[2023-12-25 03:19:38,071 INFO misc.py line 119 253097] Train: [7/100][449/510] Data 0.008 (0.138) Batch 1.129 (1.514) Remain 19:58:03 loss: 0.2919 Lr: 0.00599
[2023-12-25 03:19:39,134 INFO misc.py line 119 253097] Train: [7/100][450/510] Data 0.004 (0.137) Batch 1.056 (1.513) Remain 19:57:13 loss: 0.2743 Lr: 0.00599
[2023-12-25 03:19:40,071 INFO misc.py line 119 253097] Train: [7/100][451/510] Data 0.010 (0.137) Batch 0.942 (1.511) Remain 19:56:11 loss: 0.2986 Lr: 0.00599
[2023-12-25 03:19:41,269 INFO misc.py line 119 253097] Train: [7/100][452/510] Data 0.005 (0.137) Batch 1.197 (1.511) Remain 19:55:36 loss: 0.2392 Lr: 0.00599
[2023-12-25 03:19:47,891 INFO misc.py line 119 253097] Train: [7/100][453/510] Data 0.006 (0.137) Batch 6.625 (1.522) Remain 20:04:34 loss: 0.5134 Lr: 0.00599
[2023-12-25 03:19:49,079 INFO misc.py line 119 253097] Train: [7/100][454/510] Data 0.004 (0.136) Batch 1.186 (1.521) Remain 20:03:57 loss: 0.7813 Lr: 0.00599
[2023-12-25 03:19:50,088 INFO misc.py line 119 253097] Train: [7/100][455/510] Data 0.005 (0.136) Batch 1.006 (1.520) Remain 20:03:02 loss: 0.5379 Lr: 0.00599
[2023-12-25 03:19:53,345 INFO misc.py line 119 253097] Train: [7/100][456/510] Data 0.008 (0.136) Batch 3.261 (1.524) Remain 20:06:03 loss: 0.5887 Lr: 0.00599
[2023-12-25 03:19:54,518 INFO misc.py line 119 253097] Train: [7/100][457/510] Data 0.004 (0.135) Batch 1.173 (1.523) Remain 20:05:24 loss: 0.3428 Lr: 0.00599
[2023-12-25 03:19:55,731 INFO misc.py line 119 253097] Train: [7/100][458/510] Data 0.003 (0.135) Batch 1.212 (1.522) Remain 20:04:50 loss: 0.5437 Lr: 0.00599
[2023-12-25 03:19:56,934 INFO misc.py line 119 253097] Train: [7/100][459/510] Data 0.004 (0.135) Batch 1.199 (1.522) Remain 20:04:15 loss: 0.3840 Lr: 0.00599
[2023-12-25 03:19:57,996 INFO misc.py line 119 253097] Train: [7/100][460/510] Data 0.007 (0.134) Batch 1.064 (1.521) Remain 20:03:26 loss: 0.3191 Lr: 0.00599
[2023-12-25 03:19:59,203 INFO misc.py line 119 253097] Train: [7/100][461/510] Data 0.006 (0.134) Batch 1.208 (1.520) Remain 20:02:52 loss: 0.6006 Lr: 0.00599
[2023-12-25 03:20:00,235 INFO misc.py line 119 253097] Train: [7/100][462/510] Data 0.005 (0.134) Batch 1.033 (1.519) Remain 20:02:00 loss: 0.4667 Lr: 0.00599
[2023-12-25 03:20:01,419 INFO misc.py line 119 253097] Train: [7/100][463/510] Data 0.004 (0.134) Batch 1.183 (1.518) Remain 20:01:24 loss: 0.5023 Lr: 0.00599
[2023-12-25 03:20:03,959 INFO misc.py line 119 253097] Train: [7/100][464/510] Data 0.005 (0.133) Batch 2.542 (1.521) Remain 20:03:08 loss: 0.5758 Lr: 0.00599
[2023-12-25 03:20:04,964 INFO misc.py line 119 253097] Train: [7/100][465/510] Data 0.002 (0.133) Batch 1.005 (1.519) Remain 20:02:13 loss: 0.3861 Lr: 0.00599
[2023-12-25 03:20:06,103 INFO misc.py line 119 253097] Train: [7/100][466/510] Data 0.003 (0.133) Batch 1.139 (1.519) Remain 20:01:33 loss: 0.5353 Lr: 0.00599
[2023-12-25 03:20:07,334 INFO misc.py line 119 253097] Train: [7/100][467/510] Data 0.003 (0.133) Batch 1.227 (1.518) Remain 20:01:02 loss: 0.5530 Lr: 0.00599
[2023-12-25 03:20:08,383 INFO misc.py line 119 253097] Train: [7/100][468/510] Data 0.009 (0.132) Batch 1.051 (1.517) Remain 20:00:12 loss: 0.4346 Lr: 0.00599
[2023-12-25 03:20:09,545 INFO misc.py line 119 253097] Train: [7/100][469/510] Data 0.005 (0.132) Batch 1.163 (1.516) Remain 19:59:35 loss: 0.5367 Lr: 0.00599
[2023-12-25 03:20:11,798 INFO misc.py line 119 253097] Train: [7/100][470/510] Data 0.004 (0.132) Batch 2.253 (1.518) Remain 20:00:48 loss: 0.5009 Lr: 0.00599
[2023-12-25 03:20:18,039 INFO misc.py line 119 253097] Train: [7/100][471/510] Data 0.005 (0.131) Batch 6.241 (1.528) Remain 20:08:46 loss: 0.4133 Lr: 0.00599
[2023-12-25 03:20:19,271 INFO misc.py line 119 253097] Train: [7/100][472/510] Data 0.005 (0.131) Batch 1.231 (1.527) Remain 20:08:14 loss: 0.4324 Lr: 0.00599
[2023-12-25 03:20:21,789 INFO misc.py line 119 253097] Train: [7/100][473/510] Data 0.006 (0.131) Batch 2.520 (1.529) Remain 20:09:53 loss: 0.5649 Lr: 0.00599
[2023-12-25 03:20:22,783 INFO misc.py line 119 253097] Train: [7/100][474/510] Data 0.003 (0.131) Batch 0.994 (1.528) Remain 20:08:57 loss: 0.5755 Lr: 0.00599
[2023-12-25 03:20:23,828 INFO misc.py line 119 253097] Train: [7/100][475/510] Data 0.005 (0.130) Batch 1.045 (1.527) Remain 20:08:07 loss: 0.5195 Lr: 0.00599
[2023-12-25 03:20:24,866 INFO misc.py line 119 253097] Train: [7/100][476/510] Data 0.005 (0.130) Batch 1.039 (1.526) Remain 20:07:17 loss: 0.5516 Lr: 0.00599
[2023-12-25 03:20:26,472 INFO misc.py line 119 253097] Train: [7/100][477/510] Data 0.003 (0.130) Batch 1.603 (1.526) Remain 20:07:23 loss: 0.5298 Lr: 0.00599
[2023-12-25 03:20:27,766 INFO misc.py line 119 253097] Train: [7/100][478/510] Data 0.005 (0.130) Batch 1.296 (1.526) Remain 20:06:58 loss: 0.7840 Lr: 0.00599
[2023-12-25 03:20:28,792 INFO misc.py line 119 253097] Train: [7/100][479/510] Data 0.003 (0.129) Batch 1.023 (1.525) Remain 20:06:07 loss: 0.2342 Lr: 0.00599
[2023-12-25 03:20:30,062 INFO misc.py line 119 253097] Train: [7/100][480/510] Data 0.007 (0.129) Batch 1.271 (1.524) Remain 20:05:40 loss: 0.5076 Lr: 0.00599
[2023-12-25 03:20:31,292 INFO misc.py line 119 253097] Train: [7/100][481/510] Data 0.007 (0.129) Batch 1.228 (1.524) Remain 20:05:09 loss: 0.5096 Lr: 0.00599
[2023-12-25 03:20:32,432 INFO misc.py line 119 253097] Train: [7/100][482/510] Data 0.007 (0.129) Batch 1.139 (1.523) Remain 20:04:29 loss: 0.4864 Lr: 0.00599
[2023-12-25 03:20:33,684 INFO misc.py line 119 253097] Train: [7/100][483/510] Data 0.008 (0.128) Batch 1.255 (1.522) Remain 20:04:02 loss: 0.3582 Lr: 0.00599
[2023-12-25 03:20:34,951 INFO misc.py line 119 253097] Train: [7/100][484/510] Data 0.005 (0.128) Batch 1.264 (1.522) Remain 20:03:35 loss: 0.6777 Lr: 0.00599
[2023-12-25 03:20:36,058 INFO misc.py line 119 253097] Train: [7/100][485/510] Data 0.007 (0.128) Batch 1.111 (1.521) Remain 20:02:53 loss: 0.6118 Lr: 0.00599
[2023-12-25 03:20:37,287 INFO misc.py line 119 253097] Train: [7/100][486/510] Data 0.004 (0.128) Batch 1.225 (1.520) Remain 20:02:22 loss: 1.0395 Lr: 0.00599
[2023-12-25 03:20:38,546 INFO misc.py line 119 253097] Train: [7/100][487/510] Data 0.009 (0.127) Batch 1.258 (1.520) Remain 20:01:55 loss: 0.3276 Lr: 0.00599
[2023-12-25 03:20:39,634 INFO misc.py line 119 253097] Train: [7/100][488/510] Data 0.008 (0.127) Batch 1.092 (1.519) Remain 20:01:11 loss: 0.2727 Lr: 0.00599
[2023-12-25 03:20:50,955 INFO misc.py line 119 253097] Train: [7/100][489/510] Data 0.005 (0.127) Batch 11.321 (1.539) Remain 20:17:07 loss: 0.2558 Lr: 0.00599
[2023-12-25 03:20:52,178 INFO misc.py line 119 253097] Train: [7/100][490/510] Data 0.007 (0.127) Batch 1.221 (1.538) Remain 20:16:34 loss: 0.4247 Lr: 0.00599
[2023-12-25 03:20:53,376 INFO misc.py line 119 253097] Train: [7/100][491/510] Data 0.007 (0.126) Batch 1.201 (1.538) Remain 20:16:00 loss: 0.7347 Lr: 0.00599
[2023-12-25 03:20:54,560 INFO misc.py line 119 253097] Train: [7/100][492/510] Data 0.005 (0.126) Batch 1.183 (1.537) Remain 20:15:24 loss: 0.3780 Lr: 0.00599
[2023-12-25 03:20:55,772 INFO misc.py line 119 253097] Train: [7/100][493/510] Data 0.005 (0.126) Batch 1.213 (1.536) Remain 20:14:51 loss: 0.7967 Lr: 0.00599
[2023-12-25 03:20:56,948 INFO misc.py line 119 253097] Train: [7/100][494/510] Data 0.005 (0.126) Batch 1.175 (1.536) Remain 20:14:15 loss: 0.6931 Lr: 0.00599
[2023-12-25 03:20:59,084 INFO misc.py line 119 253097] Train: [7/100][495/510] Data 0.006 (0.125) Batch 2.136 (1.537) Remain 20:15:11 loss: 0.7790 Lr: 0.00599
[2023-12-25 03:21:00,173 INFO misc.py line 119 253097] Train: [7/100][496/510] Data 0.008 (0.125) Batch 1.090 (1.536) Remain 20:14:27 loss: 0.2748 Lr: 0.00599
[2023-12-25 03:21:01,511 INFO misc.py line 119 253097] Train: [7/100][497/510] Data 0.004 (0.125) Batch 1.335 (1.535) Remain 20:14:06 loss: 0.5573 Lr: 0.00599
[2023-12-25 03:21:02,580 INFO misc.py line 119 253097] Train: [7/100][498/510] Data 0.007 (0.125) Batch 1.067 (1.535) Remain 20:13:19 loss: 0.3650 Lr: 0.00599
[2023-12-25 03:21:03,834 INFO misc.py line 119 253097] Train: [7/100][499/510] Data 0.010 (0.124) Batch 1.257 (1.534) Remain 20:12:51 loss: 0.2921 Lr: 0.00599
[2023-12-25 03:21:05,000 INFO misc.py line 119 253097] Train: [7/100][500/510] Data 0.006 (0.124) Batch 1.166 (1.533) Remain 20:12:15 loss: 0.3088 Lr: 0.00599
[2023-12-25 03:21:06,139 INFO misc.py line 119 253097] Train: [7/100][501/510] Data 0.006 (0.124) Batch 1.139 (1.532) Remain 20:11:36 loss: 0.3767 Lr: 0.00599
[2023-12-25 03:21:07,395 INFO misc.py line 119 253097] Train: [7/100][502/510] Data 0.005 (0.124) Batch 1.253 (1.532) Remain 20:11:07 loss: 0.4001 Lr: 0.00599
[2023-12-25 03:21:08,489 INFO misc.py line 119 253097] Train: [7/100][503/510] Data 0.007 (0.123) Batch 1.095 (1.531) Remain 20:10:24 loss: 0.3812 Lr: 0.00599
[2023-12-25 03:21:13,336 INFO misc.py line 119 253097] Train: [7/100][504/510] Data 0.006 (0.123) Batch 4.850 (1.538) Remain 20:15:37 loss: 0.4375 Lr: 0.00599
[2023-12-25 03:21:14,426 INFO misc.py line 119 253097] Train: [7/100][505/510] Data 0.004 (0.123) Batch 1.091 (1.537) Remain 20:14:53 loss: 0.2112 Lr: 0.00599
[2023-12-25 03:21:15,582 INFO misc.py line 119 253097] Train: [7/100][506/510] Data 0.003 (0.123) Batch 1.152 (1.536) Remain 20:14:16 loss: 0.6242 Lr: 0.00599
[2023-12-25 03:21:16,827 INFO misc.py line 119 253097] Train: [7/100][507/510] Data 0.007 (0.122) Batch 1.249 (1.535) Remain 20:13:47 loss: 0.4003 Lr: 0.00599
[2023-12-25 03:21:18,080 INFO misc.py line 119 253097] Train: [7/100][508/510] Data 0.003 (0.122) Batch 1.250 (1.535) Remain 20:13:19 loss: 1.0344 Lr: 0.00599
[2023-12-25 03:21:19,198 INFO misc.py line 119 253097] Train: [7/100][509/510] Data 0.007 (0.122) Batch 1.119 (1.534) Remain 20:12:38 loss: 0.4104 Lr: 0.00599
[2023-12-25 03:21:20,135 INFO misc.py line 119 253097] Train: [7/100][510/510] Data 0.007 (0.122) Batch 0.940 (1.533) Remain 20:11:41 loss: 0.5856 Lr: 0.00599
[2023-12-25 03:21:20,136 INFO misc.py line 136 253097] Train result: loss: 0.4851 
[2023-12-25 03:21:20,136 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 03:21:47,259 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5562 
[2023-12-25 03:21:47,610 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.6348 
[2023-12-25 03:21:52,555 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.7207 
[2023-12-25 03:21:53,069 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.6230 
[2023-12-25 03:21:55,051 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8796 
[2023-12-25 03:21:55,474 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6208 
[2023-12-25 03:21:56,353 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.7620 
[2023-12-25 03:21:56,917 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.6144 
[2023-12-25 03:21:58,738 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0301 
[2023-12-25 03:22:00,866 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4241 
[2023-12-25 03:22:01,719 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.6668 
[2023-12-25 03:22:02,141 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9427 
[2023-12-25 03:22:03,039 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5863 
[2023-12-25 03:22:05,978 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8740 
[2023-12-25 03:22:06,451 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.5312 
[2023-12-25 03:22:07,058 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.7020 
[2023-12-25 03:22:07,756 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.6301 
[2023-12-25 03:22:09,027 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6037/0.7060/0.8746.
[2023-12-25 03:22:09,027 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9265/0.9537
[2023-12-25 03:22:09,027 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9759/0.9879
[2023-12-25 03:22:09,027 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8166/0.9538
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0001/0.0010
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2700/0.3300
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5895/0.6244
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.3270/0.4005
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7626/0.9246
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8626/0.9678
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4835/0.7033
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7061/0.8132
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6052/0.8796
[2023-12-25 03:22:09,028 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5228/0.6378
[2023-12-25 03:22:09,028 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 03:22:09,030 INFO misc.py line 165 253097] Currently Best mIoU: 0.6390
[2023-12-25 03:22:09,030 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 03:22:33,473 INFO misc.py line 119 253097] Train: [8/100][1/510] Data 3.391 (3.391) Batch 22.588 (22.588) Remain 297:35:05 loss: 0.5270 Lr: 0.00599
[2023-12-25 03:22:34,668 INFO misc.py line 119 253097] Train: [8/100][2/510] Data 0.009 (0.009) Batch 1.195 (1.195) Remain 15:44:18 loss: 0.4565 Lr: 0.00599
[2023-12-25 03:22:35,776 INFO misc.py line 119 253097] Train: [8/100][3/510] Data 0.011 (0.011) Batch 1.112 (1.112) Remain 14:38:40 loss: 0.3484 Lr: 0.00599
[2023-12-25 03:22:36,872 INFO misc.py line 119 253097] Train: [8/100][4/510] Data 0.003 (0.003) Batch 1.096 (1.096) Remain 14:26:09 loss: 0.4536 Lr: 0.00599
[2023-12-25 03:22:37,908 INFO misc.py line 119 253097] Train: [8/100][5/510] Data 0.004 (0.003) Batch 1.033 (1.065) Remain 14:01:25 loss: 0.3070 Lr: 0.00599
[2023-12-25 03:22:38,952 INFO misc.py line 119 253097] Train: [8/100][6/510] Data 0.007 (0.005) Batch 1.047 (1.059) Remain 13:56:43 loss: 0.3759 Lr: 0.00599
[2023-12-25 03:22:40,011 INFO misc.py line 119 253097] Train: [8/100][7/510] Data 0.004 (0.004) Batch 1.057 (1.058) Remain 13:56:27 loss: 0.3723 Lr: 0.00599
[2023-12-25 03:22:41,026 INFO misc.py line 119 253097] Train: [8/100][8/510] Data 0.009 (0.005) Batch 1.017 (1.050) Remain 13:49:51 loss: 0.3156 Lr: 0.00599
[2023-12-25 03:22:42,218 INFO misc.py line 119 253097] Train: [8/100][9/510] Data 0.009 (0.006) Batch 1.192 (1.074) Remain 14:08:33 loss: 0.4707 Lr: 0.00599
[2023-12-25 03:22:43,488 INFO misc.py line 119 253097] Train: [8/100][10/510] Data 0.004 (0.006) Batch 1.268 (1.101) Remain 14:30:29 loss: 0.3827 Lr: 0.00599
[2023-12-25 03:22:44,677 INFO misc.py line 119 253097] Train: [8/100][11/510] Data 0.005 (0.006) Batch 1.188 (1.112) Remain 14:39:03 loss: 0.2207 Lr: 0.00599
[2023-12-25 03:22:45,765 INFO misc.py line 119 253097] Train: [8/100][12/510] Data 0.006 (0.006) Batch 1.090 (1.110) Remain 14:37:03 loss: 0.6100 Lr: 0.00599
[2023-12-25 03:22:46,787 INFO misc.py line 119 253097] Train: [8/100][13/510] Data 0.004 (0.005) Batch 1.023 (1.101) Remain 14:30:09 loss: 0.3682 Lr: 0.00599
[2023-12-25 03:22:47,814 INFO misc.py line 119 253097] Train: [8/100][14/510] Data 0.005 (0.005) Batch 1.023 (1.094) Remain 14:24:31 loss: 0.3651 Lr: 0.00599
[2023-12-25 03:22:49,125 INFO misc.py line 119 253097] Train: [8/100][15/510] Data 0.008 (0.006) Batch 1.316 (1.112) Remain 14:39:06 loss: 0.4398 Lr: 0.00599
[2023-12-25 03:22:55,903 INFO misc.py line 119 253097] Train: [8/100][16/510] Data 0.003 (0.005) Batch 6.778 (1.548) Remain 20:23:28 loss: 0.3213 Lr: 0.00599
[2023-12-25 03:22:57,171 INFO misc.py line 119 253097] Train: [8/100][17/510] Data 0.003 (0.005) Batch 1.265 (1.528) Remain 20:07:29 loss: 0.4135 Lr: 0.00599
[2023-12-25 03:22:58,198 INFO misc.py line 119 253097] Train: [8/100][18/510] Data 0.006 (0.005) Batch 1.028 (1.495) Remain 19:41:06 loss: 0.4343 Lr: 0.00599
[2023-12-25 03:22:59,029 INFO misc.py line 119 253097] Train: [8/100][19/510] Data 0.005 (0.005) Batch 0.832 (1.453) Remain 19:08:21 loss: 0.2240 Lr: 0.00599
[2023-12-25 03:23:00,169 INFO misc.py line 119 253097] Train: [8/100][20/510] Data 0.003 (0.005) Batch 1.140 (1.435) Remain 18:53:46 loss: 0.6057 Lr: 0.00599
[2023-12-25 03:23:01,172 INFO misc.py line 119 253097] Train: [8/100][21/510] Data 0.004 (0.005) Batch 1.003 (1.411) Remain 18:34:47 loss: 0.4796 Lr: 0.00599
[2023-12-25 03:23:13,289 INFO misc.py line 119 253097] Train: [8/100][22/510] Data 0.003 (0.005) Batch 12.116 (1.974) Remain 25:59:58 loss: 0.3859 Lr: 0.00599
[2023-12-25 03:23:14,362 INFO misc.py line 119 253097] Train: [8/100][23/510] Data 0.004 (0.005) Batch 1.074 (1.929) Remain 25:24:21 loss: 0.3410 Lr: 0.00599
[2023-12-25 03:23:15,645 INFO misc.py line 119 253097] Train: [8/100][24/510] Data 0.004 (0.005) Batch 1.284 (1.899) Remain 25:00:02 loss: 0.6690 Lr: 0.00599
[2023-12-25 03:23:16,730 INFO misc.py line 119 253097] Train: [8/100][25/510] Data 0.003 (0.005) Batch 1.084 (1.862) Remain 24:30:45 loss: 0.2959 Lr: 0.00599
[2023-12-25 03:23:17,774 INFO misc.py line 119 253097] Train: [8/100][26/510] Data 0.003 (0.005) Batch 1.045 (1.826) Remain 24:02:40 loss: 0.3585 Lr: 0.00599
[2023-12-25 03:23:18,845 INFO misc.py line 119 253097] Train: [8/100][27/510] Data 0.004 (0.005) Batch 1.068 (1.794) Remain 23:37:41 loss: 0.3446 Lr: 0.00599
[2023-12-25 03:23:20,605 INFO misc.py line 119 253097] Train: [8/100][28/510] Data 0.006 (0.005) Batch 1.758 (1.793) Remain 23:36:31 loss: 0.5117 Lr: 0.00599
[2023-12-25 03:23:21,850 INFO misc.py line 119 253097] Train: [8/100][29/510] Data 0.007 (0.005) Batch 1.247 (1.772) Remain 23:19:54 loss: 0.3901 Lr: 0.00599
[2023-12-25 03:23:22,875 INFO misc.py line 119 253097] Train: [8/100][30/510] Data 0.006 (0.005) Batch 1.026 (1.744) Remain 22:58:03 loss: 0.5151 Lr: 0.00599
[2023-12-25 03:23:23,859 INFO misc.py line 119 253097] Train: [8/100][31/510] Data 0.004 (0.005) Batch 0.985 (1.717) Remain 22:36:35 loss: 0.5273 Lr: 0.00599
[2023-12-25 03:23:24,947 INFO misc.py line 119 253097] Train: [8/100][32/510] Data 0.003 (0.005) Batch 1.089 (1.696) Remain 22:19:26 loss: 0.7147 Lr: 0.00599
[2023-12-25 03:23:26,046 INFO misc.py line 119 253097] Train: [8/100][33/510] Data 0.003 (0.005) Batch 1.099 (1.676) Remain 22:03:41 loss: 0.2614 Lr: 0.00599
[2023-12-25 03:23:27,082 INFO misc.py line 119 253097] Train: [8/100][34/510] Data 0.003 (0.005) Batch 1.036 (1.655) Remain 21:47:22 loss: 0.3105 Lr: 0.00599
[2023-12-25 03:23:28,202 INFO misc.py line 119 253097] Train: [8/100][35/510] Data 0.003 (0.005) Batch 1.120 (1.638) Remain 21:34:08 loss: 0.5229 Lr: 0.00599
[2023-12-25 03:23:30,306 INFO misc.py line 119 253097] Train: [8/100][36/510] Data 0.003 (0.005) Batch 2.104 (1.652) Remain 21:45:14 loss: 0.8500 Lr: 0.00599
[2023-12-25 03:23:31,199 INFO misc.py line 119 253097] Train: [8/100][37/510] Data 0.003 (0.005) Batch 0.891 (1.630) Remain 21:27:31 loss: 0.5058 Lr: 0.00599
[2023-12-25 03:23:37,939 INFO misc.py line 119 253097] Train: [8/100][38/510] Data 0.006 (0.005) Batch 6.742 (1.776) Remain 23:22:51 loss: 0.5354 Lr: 0.00599
[2023-12-25 03:23:39,020 INFO misc.py line 119 253097] Train: [8/100][39/510] Data 0.003 (0.005) Batch 1.081 (1.757) Remain 23:07:35 loss: 0.4737 Lr: 0.00599
[2023-12-25 03:23:40,221 INFO misc.py line 119 253097] Train: [8/100][40/510] Data 0.003 (0.005) Batch 1.200 (1.742) Remain 22:55:40 loss: 0.4254 Lr: 0.00599
[2023-12-25 03:23:41,317 INFO misc.py line 119 253097] Train: [8/100][41/510] Data 0.003 (0.004) Batch 1.097 (1.725) Remain 22:42:14 loss: 0.3091 Lr: 0.00599
[2023-12-25 03:23:42,401 INFO misc.py line 119 253097] Train: [8/100][42/510] Data 0.003 (0.004) Batch 1.084 (1.708) Remain 22:29:14 loss: 0.4993 Lr: 0.00599
[2023-12-25 03:23:43,392 INFO misc.py line 119 253097] Train: [8/100][43/510] Data 0.004 (0.004) Batch 0.991 (1.690) Remain 22:15:02 loss: 0.4227 Lr: 0.00599
[2023-12-25 03:23:44,232 INFO misc.py line 119 253097] Train: [8/100][44/510] Data 0.003 (0.004) Batch 0.836 (1.670) Remain 21:58:33 loss: 0.6535 Lr: 0.00599
[2023-12-25 03:23:49,529 INFO misc.py line 119 253097] Train: [8/100][45/510] Data 0.007 (0.004) Batch 5.301 (1.756) Remain 23:06:48 loss: 0.3106 Lr: 0.00599
[2023-12-25 03:23:50,841 INFO misc.py line 119 253097] Train: [8/100][46/510] Data 0.004 (0.004) Batch 1.311 (1.746) Remain 22:58:36 loss: 0.3716 Lr: 0.00599
[2023-12-25 03:23:52,033 INFO misc.py line 119 253097] Train: [8/100][47/510] Data 0.006 (0.004) Batch 1.193 (1.733) Remain 22:48:39 loss: 0.4221 Lr: 0.00599
[2023-12-25 03:23:52,975 INFO misc.py line 119 253097] Train: [8/100][48/510] Data 0.004 (0.004) Batch 0.941 (1.716) Remain 22:34:43 loss: 0.2968 Lr: 0.00599
[2023-12-25 03:23:54,035 INFO misc.py line 119 253097] Train: [8/100][49/510] Data 0.005 (0.004) Batch 1.061 (1.701) Remain 22:23:27 loss: 0.5943 Lr: 0.00599
[2023-12-25 03:23:55,194 INFO misc.py line 119 253097] Train: [8/100][50/510] Data 0.004 (0.004) Batch 1.159 (1.690) Remain 22:14:19 loss: 0.8905 Lr: 0.00599
[2023-12-25 03:23:56,498 INFO misc.py line 119 253097] Train: [8/100][51/510] Data 0.004 (0.004) Batch 1.301 (1.682) Remain 22:07:53 loss: 0.3138 Lr: 0.00599
[2023-12-25 03:23:57,565 INFO misc.py line 119 253097] Train: [8/100][52/510] Data 0.007 (0.005) Batch 1.068 (1.669) Remain 21:57:58 loss: 0.3326 Lr: 0.00599
[2023-12-25 03:23:58,679 INFO misc.py line 119 253097] Train: [8/100][53/510] Data 0.006 (0.005) Batch 1.110 (1.658) Remain 21:49:07 loss: 0.3489 Lr: 0.00599
[2023-12-25 03:23:59,756 INFO misc.py line 119 253097] Train: [8/100][54/510] Data 0.011 (0.005) Batch 1.083 (1.647) Remain 21:40:11 loss: 0.2750 Lr: 0.00599
[2023-12-25 03:24:00,877 INFO misc.py line 119 253097] Train: [8/100][55/510] Data 0.005 (0.005) Batch 1.116 (1.636) Remain 21:32:06 loss: 0.2741 Lr: 0.00599
[2023-12-25 03:24:01,867 INFO misc.py line 119 253097] Train: [8/100][56/510] Data 0.009 (0.005) Batch 0.996 (1.624) Remain 21:22:32 loss: 0.4306 Lr: 0.00599
[2023-12-25 03:24:02,992 INFO misc.py line 119 253097] Train: [8/100][57/510] Data 0.004 (0.005) Batch 1.122 (1.615) Remain 21:15:09 loss: 0.6919 Lr: 0.00599
[2023-12-25 03:24:04,243 INFO misc.py line 119 253097] Train: [8/100][58/510] Data 0.006 (0.005) Batch 1.254 (1.608) Remain 21:09:57 loss: 0.9218 Lr: 0.00599
[2023-12-25 03:24:05,415 INFO misc.py line 119 253097] Train: [8/100][59/510] Data 0.004 (0.005) Batch 1.170 (1.601) Remain 21:03:45 loss: 0.4324 Lr: 0.00599
[2023-12-25 03:24:06,616 INFO misc.py line 119 253097] Train: [8/100][60/510] Data 0.005 (0.005) Batch 1.203 (1.594) Remain 20:58:12 loss: 0.4740 Lr: 0.00599
[2023-12-25 03:24:07,751 INFO misc.py line 119 253097] Train: [8/100][61/510] Data 0.004 (0.005) Batch 1.133 (1.586) Remain 20:51:55 loss: 0.2885 Lr: 0.00599
[2023-12-25 03:24:08,900 INFO misc.py line 119 253097] Train: [8/100][62/510] Data 0.005 (0.005) Batch 1.140 (1.578) Remain 20:45:55 loss: 0.4018 Lr: 0.00599
[2023-12-25 03:24:10,001 INFO misc.py line 119 253097] Train: [8/100][63/510] Data 0.014 (0.005) Batch 1.109 (1.570) Remain 20:39:43 loss: 0.4112 Lr: 0.00599
[2023-12-25 03:24:14,545 INFO misc.py line 119 253097] Train: [8/100][64/510] Data 3.269 (0.058) Batch 4.546 (1.619) Remain 21:18:12 loss: 0.2804 Lr: 0.00599
[2023-12-25 03:24:15,779 INFO misc.py line 119 253097] Train: [8/100][65/510] Data 0.005 (0.058) Batch 1.230 (1.613) Remain 21:13:13 loss: 0.4228 Lr: 0.00599
[2023-12-25 03:24:16,916 INFO misc.py line 119 253097] Train: [8/100][66/510] Data 0.009 (0.057) Batch 1.142 (1.605) Remain 21:07:17 loss: 0.6818 Lr: 0.00599
[2023-12-25 03:24:24,863 INFO misc.py line 119 253097] Train: [8/100][67/510] Data 0.004 (0.056) Batch 7.947 (1.704) Remain 22:25:29 loss: 0.3296 Lr: 0.00599
[2023-12-25 03:24:26,176 INFO misc.py line 119 253097] Train: [8/100][68/510] Data 0.004 (0.055) Batch 1.313 (1.698) Remain 22:20:42 loss: 0.5191 Lr: 0.00599
[2023-12-25 03:24:27,384 INFO misc.py line 119 253097] Train: [8/100][69/510] Data 0.003 (0.054) Batch 1.208 (1.691) Remain 22:14:49 loss: 0.5364 Lr: 0.00599
[2023-12-25 03:24:33,306 INFO misc.py line 119 253097] Train: [8/100][70/510] Data 4.559 (0.122) Batch 5.922 (1.754) Remain 23:04:37 loss: 0.3106 Lr: 0.00599
[2023-12-25 03:24:34,613 INFO misc.py line 119 253097] Train: [8/100][71/510] Data 0.004 (0.120) Batch 1.303 (1.748) Remain 22:59:22 loss: 0.4363 Lr: 0.00599
[2023-12-25 03:24:35,859 INFO misc.py line 119 253097] Train: [8/100][72/510] Data 0.007 (0.118) Batch 1.249 (1.740) Remain 22:53:38 loss: 0.4897 Lr: 0.00599
[2023-12-25 03:24:37,089 INFO misc.py line 119 253097] Train: [8/100][73/510] Data 0.004 (0.117) Batch 1.228 (1.733) Remain 22:47:49 loss: 0.5177 Lr: 0.00599
[2023-12-25 03:24:38,310 INFO misc.py line 119 253097] Train: [8/100][74/510] Data 0.006 (0.115) Batch 1.219 (1.726) Remain 22:42:05 loss: 0.3860 Lr: 0.00599
[2023-12-25 03:24:39,471 INFO misc.py line 119 253097] Train: [8/100][75/510] Data 0.008 (0.114) Batch 1.164 (1.718) Remain 22:35:54 loss: 0.7037 Lr: 0.00599
[2023-12-25 03:24:40,523 INFO misc.py line 119 253097] Train: [8/100][76/510] Data 0.005 (0.112) Batch 1.051 (1.709) Remain 22:28:39 loss: 0.2691 Lr: 0.00599
[2023-12-25 03:24:41,719 INFO misc.py line 119 253097] Train: [8/100][77/510] Data 0.005 (0.111) Batch 1.195 (1.702) Remain 22:23:08 loss: 0.4245 Lr: 0.00599
[2023-12-25 03:24:42,758 INFO misc.py line 119 253097] Train: [8/100][78/510] Data 0.007 (0.109) Batch 1.041 (1.693) Remain 22:16:10 loss: 0.4449 Lr: 0.00599
[2023-12-25 03:24:43,816 INFO misc.py line 119 253097] Train: [8/100][79/510] Data 0.005 (0.108) Batch 1.057 (1.685) Remain 22:09:32 loss: 0.6570 Lr: 0.00599
[2023-12-25 03:24:44,954 INFO misc.py line 119 253097] Train: [8/100][80/510] Data 0.007 (0.107) Batch 1.140 (1.678) Remain 22:03:55 loss: 0.3492 Lr: 0.00599
[2023-12-25 03:24:46,221 INFO misc.py line 119 253097] Train: [8/100][81/510] Data 0.003 (0.105) Batch 1.263 (1.672) Remain 21:59:42 loss: 0.5462 Lr: 0.00599
[2023-12-25 03:24:47,252 INFO misc.py line 119 253097] Train: [8/100][82/510] Data 0.008 (0.104) Batch 1.035 (1.664) Remain 21:53:18 loss: 0.2517 Lr: 0.00599
[2023-12-25 03:24:48,440 INFO misc.py line 119 253097] Train: [8/100][83/510] Data 0.004 (0.103) Batch 1.186 (1.658) Remain 21:48:33 loss: 0.4968 Lr: 0.00599
[2023-12-25 03:24:49,692 INFO misc.py line 119 253097] Train: [8/100][84/510] Data 0.006 (0.102) Batch 1.251 (1.653) Remain 21:44:34 loss: 0.3863 Lr: 0.00599
[2023-12-25 03:24:50,803 INFO misc.py line 119 253097] Train: [8/100][85/510] Data 0.007 (0.100) Batch 1.111 (1.647) Remain 21:39:19 loss: 0.4475 Lr: 0.00599
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[2023-12-25 03:26:07,081 INFO misc.py line 119 253097] Train: [8/100][133/510] Data 0.006 (0.133) Batch 1.119 (1.625) Remain 21:21:17 loss: 0.3388 Lr: 0.00599
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[2023-12-25 03:26:18,869 INFO misc.py line 119 253097] Train: [8/100][139/510] Data 0.004 (0.128) Batch 0.996 (1.640) Remain 21:32:55 loss: 0.3565 Lr: 0.00599
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[2023-12-25 03:28:07,910 INFO misc.py line 119 253097] Train: [8/100][219/510] Data 0.005 (0.082) Batch 1.218 (1.538) Remain 20:09:54 loss: 0.5226 Lr: 0.00599
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[2023-12-25 03:28:19,432 INFO misc.py line 119 253097] Train: [8/100][224/510] Data 0.004 (0.081) Batch 1.012 (1.555) Remain 20:23:25 loss: 0.5951 Lr: 0.00599
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[2023-12-25 03:28:27,136 INFO misc.py line 119 253097] Train: [8/100][230/510] Data 0.007 (0.079) Batch 1.092 (1.548) Remain 20:17:38 loss: 0.6439 Lr: 0.00599
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[2023-12-25 03:28:31,317 INFO misc.py line 119 253097] Train: [8/100][234/510] Data 0.004 (0.077) Batch 1.085 (1.539) Remain 20:10:41 loss: 0.2162 Lr: 0.00599
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[2023-12-25 03:28:36,974 INFO misc.py line 119 253097] Train: [8/100][236/510] Data 0.004 (0.077) Batch 4.675 (1.550) Remain 20:19:20 loss: 0.5865 Lr: 0.00599
[2023-12-25 03:28:38,232 INFO misc.py line 119 253097] Train: [8/100][237/510] Data 0.003 (0.076) Batch 1.258 (1.549) Remain 20:18:19 loss: 0.3865 Lr: 0.00599
[2023-12-25 03:28:39,438 INFO misc.py line 119 253097] Train: [8/100][238/510] Data 0.003 (0.076) Batch 1.206 (1.547) Remain 20:17:09 loss: 0.2888 Lr: 0.00599
[2023-12-25 03:28:40,442 INFO misc.py line 119 253097] Train: [8/100][239/510] Data 0.003 (0.076) Batch 1.001 (1.545) Remain 20:15:18 loss: 0.4391 Lr: 0.00599
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[2023-12-25 03:28:45,398 INFO misc.py line 119 253097] Train: [8/100][242/510] Data 0.004 (0.075) Batch 1.242 (1.547) Remain 20:16:16 loss: 0.2539 Lr: 0.00599
[2023-12-25 03:28:46,487 INFO misc.py line 119 253097] Train: [8/100][243/510] Data 0.008 (0.074) Batch 1.090 (1.545) Remain 20:14:45 loss: 0.3902 Lr: 0.00599
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[2023-12-25 03:28:48,904 INFO misc.py line 119 253097] Train: [8/100][245/510] Data 0.004 (0.074) Batch 1.297 (1.542) Remain 20:12:25 loss: 0.2517 Lr: 0.00599
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[2023-12-25 03:29:23,905 INFO misc.py line 119 253097] Train: [8/100][269/510] Data 7.687 (0.097) Batch 8.708 (1.534) Remain 20:05:59 loss: 0.2834 Lr: 0.00599
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[2023-12-25 03:33:27,174 INFO misc.py line 119 253097] Train: [8/100][435/510] Data 0.009 (0.081) Batch 1.226 (1.508) Remain 19:41:01 loss: 0.5590 Lr: 0.00599
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[2023-12-25 03:33:44,925 INFO misc.py line 119 253097] Train: [8/100][443/510] Data 0.004 (0.080) Batch 1.229 (1.521) Remain 19:50:57 loss: 0.5376 Lr: 0.00599
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[2023-12-25 03:33:47,260 INFO misc.py line 119 253097] Train: [8/100][445/510] Data 0.003 (0.080) Batch 1.128 (1.519) Remain 19:49:39 loss: 0.4155 Lr: 0.00599
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[2023-12-25 03:33:57,706 INFO misc.py line 119 253097] Train: [8/100][454/510] Data 0.007 (0.078) Batch 1.110 (1.512) Remain 19:43:49 loss: 0.3939 Lr: 0.00599
[2023-12-25 03:33:58,915 INFO misc.py line 119 253097] Train: [8/100][455/510] Data 0.002 (0.078) Batch 1.205 (1.511) Remain 19:43:16 loss: 0.4521 Lr: 0.00599
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[2023-12-25 03:34:09,656 INFO misc.py line 119 253097] Train: [8/100][460/510] Data 0.004 (0.077) Batch 6.205 (1.518) Remain 19:48:36 loss: 0.4813 Lr: 0.00599
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[2023-12-25 03:34:41,502 INFO misc.py line 119 253097] Train: [8/100][476/510] Data 0.003 (0.080) Batch 7.926 (1.534) Remain 20:00:41 loss: 0.3122 Lr: 0.00599
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[2023-12-25 03:35:21,413 INFO misc.py line 119 253097] Train: [8/100][504/510] Data 0.008 (0.090) Batch 1.017 (1.528) Remain 19:55:12 loss: 0.3135 Lr: 0.00599
[2023-12-25 03:35:22,475 INFO misc.py line 119 253097] Train: [8/100][505/510] Data 0.004 (0.090) Batch 1.063 (1.527) Remain 19:54:27 loss: 0.4042 Lr: 0.00599
[2023-12-25 03:35:23,732 INFO misc.py line 119 253097] Train: [8/100][506/510] Data 0.004 (0.090) Batch 1.253 (1.527) Remain 19:54:00 loss: 0.3464 Lr: 0.00599
[2023-12-25 03:35:24,935 INFO misc.py line 119 253097] Train: [8/100][507/510] Data 0.007 (0.090) Batch 1.202 (1.526) Remain 19:53:29 loss: 0.2360 Lr: 0.00599
[2023-12-25 03:35:25,867 INFO misc.py line 119 253097] Train: [8/100][508/510] Data 0.008 (0.090) Batch 0.938 (1.525) Remain 19:52:32 loss: 0.4037 Lr: 0.00599
[2023-12-25 03:35:26,996 INFO misc.py line 119 253097] Train: [8/100][509/510] Data 0.002 (0.089) Batch 1.129 (1.524) Remain 19:51:54 loss: 0.3648 Lr: 0.00599
[2023-12-25 03:35:31,480 INFO misc.py line 119 253097] Train: [8/100][510/510] Data 3.232 (0.096) Batch 4.476 (1.530) Remain 19:56:26 loss: 0.6812 Lr: 0.00599
[2023-12-25 03:35:31,482 INFO misc.py line 136 253097] Train result: loss: 0.4485 
[2023-12-25 03:35:31,482 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 03:35:58,682 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5189 
[2023-12-25 03:35:59,051 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4157 
[2023-12-25 03:36:03,991 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5215 
[2023-12-25 03:36:04,515 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.6302 
[2023-12-25 03:36:06,500 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8396 
[2023-12-25 03:36:06,929 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4992 
[2023-12-25 03:36:07,809 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9552 
[2023-12-25 03:36:08,364 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.6408 
[2023-12-25 03:36:10,177 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8653 
[2023-12-25 03:36:12,310 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4267 
[2023-12-25 03:36:13,176 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3702 
[2023-12-25 03:36:13,599 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9845 
[2023-12-25 03:36:14,498 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4895 
[2023-12-25 03:36:17,435 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.6785 
[2023-12-25 03:36:17,911 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.5599 
[2023-12-25 03:36:18,522 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5983 
[2023-12-25 03:36:19,230 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5326 
[2023-12-25 03:36:20,589 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6447/0.7242/0.8765.
[2023-12-25 03:36:20,589 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9111/0.9567
[2023-12-25 03:36:20,589 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9769/0.9927
[2023-12-25 03:36:20,589 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7997/0.9346
[2023-12-25 03:36:20,589 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0008/0.0090
[2023-12-25 03:36:20,589 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3533/0.5707
[2023-12-25 03:36:20,590 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6130/0.6927
[2023-12-25 03:36:20,590 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5080/0.5437
[2023-12-25 03:36:20,590 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7779/0.9000
[2023-12-25 03:36:20,590 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8815/0.9120
[2023-12-25 03:36:20,590 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6801/0.7369
[2023-12-25 03:36:20,590 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7171/0.8218
[2023-12-25 03:36:20,590 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6493/0.7280
[2023-12-25 03:36:20,590 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5130/0.6155
[2023-12-25 03:36:20,590 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 03:36:20,591 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.6447
[2023-12-25 03:36:20,591 INFO misc.py line 165 253097] Currently Best mIoU: 0.6447
[2023-12-25 03:36:20,591 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 03:36:33,723 INFO misc.py line 119 253097] Train: [9/100][1/510] Data 6.901 (6.901) Batch 8.046 (8.046) Remain 104:51:59 loss: 0.2845 Lr: 0.00599
[2023-12-25 03:36:34,840 INFO misc.py line 119 253097] Train: [9/100][2/510] Data 0.003 (0.003) Batch 1.117 (1.117) Remain 14:33:15 loss: 0.3470 Lr: 0.00599
[2023-12-25 03:36:44,715 INFO misc.py line 119 253097] Train: [9/100][3/510] Data 0.003 (0.003) Batch 9.874 (9.874) Remain 128:40:49 loss: 0.2882 Lr: 0.00599
[2023-12-25 03:36:45,993 INFO misc.py line 119 253097] Train: [9/100][4/510] Data 0.005 (0.005) Batch 1.276 (1.276) Remain 16:37:25 loss: 0.3412 Lr: 0.00599
[2023-12-25 03:36:47,068 INFO misc.py line 119 253097] Train: [9/100][5/510] Data 0.007 (0.006) Batch 1.075 (1.175) Remain 15:18:50 loss: 0.3757 Lr: 0.00599
[2023-12-25 03:36:48,310 INFO misc.py line 119 253097] Train: [9/100][6/510] Data 0.007 (0.006) Batch 1.245 (1.198) Remain 15:37:01 loss: 0.5143 Lr: 0.00599
[2023-12-25 03:36:49,546 INFO misc.py line 119 253097] Train: [9/100][7/510] Data 0.004 (0.006) Batch 1.234 (1.207) Remain 15:43:57 loss: 0.4965 Lr: 0.00599
[2023-12-25 03:36:50,748 INFO misc.py line 119 253097] Train: [9/100][8/510] Data 0.009 (0.006) Batch 1.204 (1.207) Remain 15:43:28 loss: 0.3625 Lr: 0.00599
[2023-12-25 03:36:51,912 INFO misc.py line 119 253097] Train: [9/100][9/510] Data 0.004 (0.006) Batch 1.160 (1.199) Remain 15:37:22 loss: 0.3804 Lr: 0.00599
[2023-12-25 03:36:53,049 INFO misc.py line 119 253097] Train: [9/100][10/510] Data 0.008 (0.006) Batch 1.142 (1.191) Remain 15:30:57 loss: 0.3666 Lr: 0.00599
[2023-12-25 03:36:54,091 INFO misc.py line 119 253097] Train: [9/100][11/510] Data 0.004 (0.006) Batch 1.038 (1.172) Remain 15:16:00 loss: 0.5428 Lr: 0.00599
[2023-12-25 03:36:55,129 INFO misc.py line 119 253097] Train: [9/100][12/510] Data 0.008 (0.006) Batch 1.042 (1.157) Remain 15:04:43 loss: 0.4735 Lr: 0.00599
[2023-12-25 03:36:56,365 INFO misc.py line 119 253097] Train: [9/100][13/510] Data 0.003 (0.006) Batch 1.234 (1.165) Remain 15:10:42 loss: 0.2976 Lr: 0.00598
[2023-12-25 03:36:57,566 INFO misc.py line 119 253097] Train: [9/100][14/510] Data 0.006 (0.006) Batch 1.200 (1.168) Remain 15:13:10 loss: 0.3628 Lr: 0.00598
[2023-12-25 03:36:58,626 INFO misc.py line 119 253097] Train: [9/100][15/510] Data 0.006 (0.006) Batch 1.063 (1.159) Remain 15:06:18 loss: 0.5931 Lr: 0.00598
[2023-12-25 03:36:59,740 INFO misc.py line 119 253097] Train: [9/100][16/510] Data 0.003 (0.006) Batch 1.114 (1.156) Remain 15:03:33 loss: 0.4270 Lr: 0.00598
[2023-12-25 03:37:00,946 INFO misc.py line 119 253097] Train: [9/100][17/510] Data 0.003 (0.005) Batch 1.198 (1.159) Remain 15:05:53 loss: 0.5323 Lr: 0.00598
[2023-12-25 03:37:01,858 INFO misc.py line 119 253097] Train: [9/100][18/510] Data 0.012 (0.006) Batch 0.920 (1.143) Remain 14:53:25 loss: 0.3809 Lr: 0.00598
[2023-12-25 03:37:03,042 INFO misc.py line 119 253097] Train: [9/100][19/510] Data 0.004 (0.006) Batch 1.183 (1.145) Remain 14:55:22 loss: 0.3240 Lr: 0.00598
[2023-12-25 03:37:03,999 INFO misc.py line 119 253097] Train: [9/100][20/510] Data 0.004 (0.006) Batch 0.954 (1.134) Remain 14:46:32 loss: 0.4840 Lr: 0.00598
[2023-12-25 03:37:05,122 INFO misc.py line 119 253097] Train: [9/100][21/510] Data 0.007 (0.006) Batch 1.127 (1.134) Remain 14:46:11 loss: 0.5436 Lr: 0.00598
[2023-12-25 03:37:06,115 INFO misc.py line 119 253097] Train: [9/100][22/510] Data 0.004 (0.006) Batch 0.994 (1.126) Remain 14:40:24 loss: 0.3953 Lr: 0.00598
[2023-12-25 03:37:07,226 INFO misc.py line 119 253097] Train: [9/100][23/510] Data 0.003 (0.005) Batch 1.110 (1.126) Remain 14:39:46 loss: 0.5443 Lr: 0.00598
[2023-12-25 03:37:08,506 INFO misc.py line 119 253097] Train: [9/100][24/510] Data 0.003 (0.005) Batch 1.280 (1.133) Remain 14:45:30 loss: 0.5737 Lr: 0.00598
[2023-12-25 03:37:09,624 INFO misc.py line 119 253097] Train: [9/100][25/510] Data 0.004 (0.005) Batch 1.113 (1.132) Remain 14:44:46 loss: 0.5825 Lr: 0.00598
[2023-12-25 03:37:10,767 INFO misc.py line 119 253097] Train: [9/100][26/510] Data 0.009 (0.005) Batch 1.148 (1.133) Remain 14:45:17 loss: 0.3423 Lr: 0.00598
[2023-12-25 03:37:21,193 INFO misc.py line 119 253097] Train: [9/100][27/510] Data 9.277 (0.392) Batch 10.425 (1.520) Remain 19:47:51 loss: 0.3476 Lr: 0.00598
[2023-12-25 03:37:22,391 INFO misc.py line 119 253097] Train: [9/100][28/510] Data 0.005 (0.376) Batch 1.198 (1.507) Remain 19:37:46 loss: 0.3486 Lr: 0.00598
[2023-12-25 03:37:23,579 INFO misc.py line 119 253097] Train: [9/100][29/510] Data 0.007 (0.362) Batch 1.190 (1.495) Remain 19:28:14 loss: 0.7934 Lr: 0.00598
[2023-12-25 03:37:24,797 INFO misc.py line 119 253097] Train: [9/100][30/510] Data 0.004 (0.349) Batch 1.217 (1.485) Remain 19:20:10 loss: 0.3163 Lr: 0.00598
[2023-12-25 03:37:26,005 INFO misc.py line 119 253097] Train: [9/100][31/510] Data 0.003 (0.336) Batch 1.204 (1.475) Remain 19:12:19 loss: 0.5539 Lr: 0.00598
[2023-12-25 03:37:27,184 INFO misc.py line 119 253097] Train: [9/100][32/510] Data 0.007 (0.325) Batch 1.180 (1.464) Remain 19:04:22 loss: 0.4401 Lr: 0.00598
[2023-12-25 03:37:28,568 INFO misc.py line 119 253097] Train: [9/100][33/510] Data 0.389 (0.327) Batch 1.386 (1.462) Remain 19:02:17 loss: 0.3591 Lr: 0.00598
[2023-12-25 03:37:29,788 INFO misc.py line 119 253097] Train: [9/100][34/510] Data 0.005 (0.317) Batch 1.220 (1.454) Remain 18:56:11 loss: 0.4253 Lr: 0.00598
[2023-12-25 03:37:30,963 INFO misc.py line 119 253097] Train: [9/100][35/510] Data 0.004 (0.307) Batch 1.176 (1.445) Remain 18:49:22 loss: 0.4463 Lr: 0.00598
[2023-12-25 03:37:32,261 INFO misc.py line 119 253097] Train: [9/100][36/510] Data 0.004 (0.298) Batch 1.293 (1.441) Remain 18:45:44 loss: 0.3781 Lr: 0.00598
[2023-12-25 03:37:33,521 INFO misc.py line 119 253097] Train: [9/100][37/510] Data 0.008 (0.289) Batch 1.262 (1.435) Remain 18:41:36 loss: 0.3287 Lr: 0.00598
[2023-12-25 03:37:34,648 INFO misc.py line 119 253097] Train: [9/100][38/510] Data 0.006 (0.281) Batch 1.129 (1.427) Remain 18:34:45 loss: 0.6140 Lr: 0.00598
[2023-12-25 03:37:35,741 INFO misc.py line 119 253097] Train: [9/100][39/510] Data 0.004 (0.274) Batch 1.091 (1.417) Remain 18:27:26 loss: 0.4728 Lr: 0.00598
[2023-12-25 03:37:36,999 INFO misc.py line 119 253097] Train: [9/100][40/510] Data 0.007 (0.266) Batch 1.255 (1.413) Remain 18:23:58 loss: 0.5352 Lr: 0.00598
[2023-12-25 03:37:38,081 INFO misc.py line 119 253097] Train: [9/100][41/510] Data 0.009 (0.260) Batch 1.086 (1.404) Remain 18:17:14 loss: 0.3062 Lr: 0.00598
[2023-12-25 03:37:39,118 INFO misc.py line 119 253097] Train: [9/100][42/510] Data 0.006 (0.253) Batch 1.033 (1.395) Remain 18:09:46 loss: 0.5360 Lr: 0.00598
[2023-12-25 03:37:40,118 INFO misc.py line 119 253097] Train: [9/100][43/510] Data 0.009 (0.247) Batch 1.005 (1.385) Remain 18:02:08 loss: 0.4247 Lr: 0.00598
[2023-12-25 03:37:41,395 INFO misc.py line 119 253097] Train: [9/100][44/510] Data 0.004 (0.241) Batch 1.278 (1.382) Remain 18:00:05 loss: 0.5820 Lr: 0.00598
[2023-12-25 03:37:42,684 INFO misc.py line 119 253097] Train: [9/100][45/510] Data 0.003 (0.235) Batch 1.284 (1.380) Remain 17:58:13 loss: 0.4517 Lr: 0.00598
[2023-12-25 03:37:43,839 INFO misc.py line 119 253097] Train: [9/100][46/510] Data 0.007 (0.230) Batch 1.157 (1.375) Remain 17:54:09 loss: 0.4095 Lr: 0.00598
[2023-12-25 03:37:44,900 INFO misc.py line 119 253097] Train: [9/100][47/510] Data 0.006 (0.225) Batch 1.063 (1.368) Remain 17:48:35 loss: 0.3379 Lr: 0.00598
[2023-12-25 03:37:46,081 INFO misc.py line 119 253097] Train: [9/100][48/510] Data 0.003 (0.220) Batch 1.181 (1.364) Remain 17:45:19 loss: 0.3661 Lr: 0.00598
[2023-12-25 03:37:48,618 INFO misc.py line 119 253097] Train: [9/100][49/510] Data 0.003 (0.215) Batch 2.536 (1.389) Remain 18:05:12 loss: 0.3902 Lr: 0.00598
[2023-12-25 03:37:49,709 INFO misc.py line 119 253097] Train: [9/100][50/510] Data 0.005 (0.211) Batch 1.087 (1.383) Remain 18:00:10 loss: 0.4276 Lr: 0.00598
[2023-12-25 03:37:50,732 INFO misc.py line 119 253097] Train: [9/100][51/510] Data 0.008 (0.207) Batch 1.029 (1.375) Remain 17:54:22 loss: 0.5373 Lr: 0.00598
[2023-12-25 03:37:51,931 INFO misc.py line 119 253097] Train: [9/100][52/510] Data 0.003 (0.202) Batch 1.198 (1.372) Remain 17:51:32 loss: 0.6688 Lr: 0.00598
[2023-12-25 03:37:53,262 INFO misc.py line 119 253097] Train: [9/100][53/510] Data 0.003 (0.198) Batch 1.331 (1.371) Remain 17:50:52 loss: 0.3364 Lr: 0.00598
[2023-12-25 03:37:54,382 INFO misc.py line 119 253097] Train: [9/100][54/510] Data 0.005 (0.195) Batch 1.117 (1.366) Remain 17:46:57 loss: 0.4640 Lr: 0.00598
[2023-12-25 03:37:55,572 INFO misc.py line 119 253097] Train: [9/100][55/510] Data 0.007 (0.191) Batch 1.194 (1.363) Remain 17:44:20 loss: 0.4260 Lr: 0.00598
[2023-12-25 03:37:56,694 INFO misc.py line 119 253097] Train: [9/100][56/510] Data 0.003 (0.188) Batch 1.118 (1.358) Remain 17:40:43 loss: 0.3881 Lr: 0.00598
[2023-12-25 03:37:57,947 INFO misc.py line 119 253097] Train: [9/100][57/510] Data 0.007 (0.184) Batch 1.255 (1.356) Remain 17:39:12 loss: 0.5371 Lr: 0.00598
[2023-12-25 03:37:59,084 INFO misc.py line 119 253097] Train: [9/100][58/510] Data 0.005 (0.181) Batch 1.134 (1.352) Remain 17:36:02 loss: 0.5119 Lr: 0.00598
[2023-12-25 03:38:00,051 INFO misc.py line 119 253097] Train: [9/100][59/510] Data 0.006 (0.178) Batch 0.971 (1.345) Remain 17:30:42 loss: 0.4906 Lr: 0.00598
[2023-12-25 03:38:01,221 INFO misc.py line 119 253097] Train: [9/100][60/510] Data 0.003 (0.175) Batch 1.169 (1.342) Remain 17:28:16 loss: 0.4763 Lr: 0.00598
[2023-12-25 03:38:02,362 INFO misc.py line 119 253097] Train: [9/100][61/510] Data 0.003 (0.172) Batch 1.141 (1.339) Remain 17:25:32 loss: 0.3721 Lr: 0.00598
[2023-12-25 03:38:03,407 INFO misc.py line 119 253097] Train: [9/100][62/510] Data 0.003 (0.169) Batch 1.046 (1.334) Remain 17:21:38 loss: 0.3594 Lr: 0.00598
[2023-12-25 03:38:04,395 INFO misc.py line 119 253097] Train: [9/100][63/510] Data 0.003 (0.166) Batch 0.988 (1.328) Remain 17:17:07 loss: 0.2680 Lr: 0.00598
[2023-12-25 03:38:05,494 INFO misc.py line 119 253097] Train: [9/100][64/510] Data 0.003 (0.163) Batch 1.099 (1.324) Remain 17:14:09 loss: 0.4749 Lr: 0.00598
[2023-12-25 03:38:06,587 INFO misc.py line 119 253097] Train: [9/100][65/510] Data 0.003 (0.161) Batch 1.093 (1.321) Remain 17:11:13 loss: 0.5633 Lr: 0.00598
[2023-12-25 03:38:07,783 INFO misc.py line 119 253097] Train: [9/100][66/510] Data 0.003 (0.158) Batch 1.192 (1.318) Remain 17:09:36 loss: 0.4731 Lr: 0.00598
[2023-12-25 03:38:08,871 INFO misc.py line 119 253097] Train: [9/100][67/510] Data 0.008 (0.156) Batch 1.092 (1.315) Remain 17:06:49 loss: 0.7217 Lr: 0.00598
[2023-12-25 03:38:10,047 INFO misc.py line 119 253097] Train: [9/100][68/510] Data 0.003 (0.154) Batch 1.173 (1.313) Remain 17:05:06 loss: 0.3759 Lr: 0.00598
[2023-12-25 03:38:11,221 INFO misc.py line 119 253097] Train: [9/100][69/510] Data 0.006 (0.151) Batch 1.176 (1.311) Remain 17:03:27 loss: 0.5404 Lr: 0.00598
[2023-12-25 03:38:12,408 INFO misc.py line 119 253097] Train: [9/100][70/510] Data 0.004 (0.149) Batch 1.187 (1.309) Remain 17:01:59 loss: 0.2827 Lr: 0.00598
[2023-12-25 03:38:13,574 INFO misc.py line 119 253097] Train: [9/100][71/510] Data 0.004 (0.147) Batch 1.167 (1.307) Remain 17:00:20 loss: 0.2817 Lr: 0.00598
[2023-12-25 03:38:14,613 INFO misc.py line 119 253097] Train: [9/100][72/510] Data 0.003 (0.145) Batch 1.038 (1.303) Remain 16:57:16 loss: 0.5494 Lr: 0.00598
[2023-12-25 03:38:15,801 INFO misc.py line 119 253097] Train: [9/100][73/510] Data 0.004 (0.143) Batch 1.188 (1.301) Remain 16:55:58 loss: 0.4903 Lr: 0.00598
[2023-12-25 03:38:17,071 INFO misc.py line 119 253097] Train: [9/100][74/510] Data 0.004 (0.141) Batch 1.270 (1.301) Remain 16:55:37 loss: 0.4196 Lr: 0.00598
[2023-12-25 03:38:19,748 INFO misc.py line 119 253097] Train: [9/100][75/510] Data 0.004 (0.139) Batch 2.677 (1.320) Remain 17:10:31 loss: 0.5192 Lr: 0.00598
[2023-12-25 03:38:20,879 INFO misc.py line 119 253097] Train: [9/100][76/510] Data 0.004 (0.137) Batch 1.131 (1.317) Remain 17:08:28 loss: 0.4499 Lr: 0.00598
[2023-12-25 03:38:23,760 INFO misc.py line 119 253097] Train: [9/100][77/510] Data 0.002 (0.135) Batch 2.881 (1.338) Remain 17:24:57 loss: 0.2509 Lr: 0.00598
[2023-12-25 03:38:24,896 INFO misc.py line 119 253097] Train: [9/100][78/510] Data 0.003 (0.134) Batch 1.135 (1.336) Remain 17:22:48 loss: 0.5646 Lr: 0.00598
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[2023-12-25 03:39:05,763 INFO misc.py line 119 253097] Train: [9/100][99/510] Data 0.007 (0.106) Batch 1.016 (1.469) Remain 19:06:32 loss: 0.2928 Lr: 0.00598
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[2023-12-25 03:39:13,773 INFO misc.py line 119 253097] Train: [9/100][106/510] Data 0.008 (0.099) Batch 1.057 (1.447) Remain 18:49:06 loss: 0.4233 Lr: 0.00598
[2023-12-25 03:39:15,015 INFO misc.py line 119 253097] Train: [9/100][107/510] Data 0.005 (0.098) Batch 1.244 (1.445) Remain 18:47:34 loss: 0.6936 Lr: 0.00598
[2023-12-25 03:39:19,572 INFO misc.py line 119 253097] Train: [9/100][108/510] Data 3.548 (0.131) Batch 4.558 (1.475) Remain 19:10:40 loss: 0.3167 Lr: 0.00598
[2023-12-25 03:39:20,695 INFO misc.py line 119 253097] Train: [9/100][109/510] Data 0.003 (0.130) Batch 1.123 (1.472) Remain 19:08:03 loss: 0.3694 Lr: 0.00598
[2023-12-25 03:39:25,604 INFO misc.py line 119 253097] Train: [9/100][110/510] Data 3.735 (0.163) Batch 4.908 (1.504) Remain 19:33:05 loss: 0.4038 Lr: 0.00598
[2023-12-25 03:39:26,541 INFO misc.py line 119 253097] Train: [9/100][111/510] Data 0.004 (0.162) Batch 0.938 (1.498) Remain 19:28:58 loss: 0.3269 Lr: 0.00598
[2023-12-25 03:39:27,839 INFO misc.py line 119 253097] Train: [9/100][112/510] Data 0.003 (0.160) Batch 1.298 (1.497) Remain 19:27:30 loss: 0.4398 Lr: 0.00598
[2023-12-25 03:39:29,003 INFO misc.py line 119 253097] Train: [9/100][113/510] Data 0.005 (0.159) Batch 1.159 (1.493) Remain 19:25:05 loss: 0.3885 Lr: 0.00598
[2023-12-25 03:39:30,238 INFO misc.py line 119 253097] Train: [9/100][114/510] Data 0.009 (0.158) Batch 1.236 (1.491) Remain 19:23:15 loss: 0.2194 Lr: 0.00598
[2023-12-25 03:39:31,398 INFO misc.py line 119 253097] Train: [9/100][115/510] Data 0.007 (0.156) Batch 1.164 (1.488) Remain 19:20:56 loss: 0.5542 Lr: 0.00598
[2023-12-25 03:39:32,535 INFO misc.py line 119 253097] Train: [9/100][116/510] Data 0.006 (0.155) Batch 1.136 (1.485) Remain 19:18:29 loss: 0.2789 Lr: 0.00598
[2023-12-25 03:39:38,242 INFO misc.py line 119 253097] Train: [9/100][117/510] Data 4.498 (0.193) Batch 5.711 (1.522) Remain 19:47:22 loss: 1.0531 Lr: 0.00598
[2023-12-25 03:39:39,433 INFO misc.py line 119 253097] Train: [9/100][118/510] Data 0.003 (0.191) Batch 1.185 (1.519) Remain 19:45:03 loss: 0.3430 Lr: 0.00598
[2023-12-25 03:39:40,723 INFO misc.py line 119 253097] Train: [9/100][119/510] Data 0.009 (0.190) Batch 1.295 (1.517) Remain 19:43:31 loss: 0.3034 Lr: 0.00598
[2023-12-25 03:39:41,841 INFO misc.py line 119 253097] Train: [9/100][120/510] Data 0.004 (0.188) Batch 1.116 (1.514) Remain 19:40:49 loss: 0.3096 Lr: 0.00598
[2023-12-25 03:39:43,035 INFO misc.py line 119 253097] Train: [9/100][121/510] Data 0.007 (0.187) Batch 1.197 (1.511) Remain 19:38:42 loss: 0.3403 Lr: 0.00598
[2023-12-25 03:39:44,042 INFO misc.py line 119 253097] Train: [9/100][122/510] Data 0.003 (0.185) Batch 1.005 (1.507) Remain 19:35:21 loss: 0.3334 Lr: 0.00598
[2023-12-25 03:39:45,301 INFO misc.py line 119 253097] Train: [9/100][123/510] Data 0.005 (0.184) Batch 1.259 (1.505) Remain 19:33:43 loss: 0.5453 Lr: 0.00598
[2023-12-25 03:39:46,374 INFO misc.py line 119 253097] Train: [9/100][124/510] Data 0.006 (0.182) Batch 1.075 (1.501) Remain 19:30:55 loss: 0.4417 Lr: 0.00598
[2023-12-25 03:39:47,451 INFO misc.py line 119 253097] Train: [9/100][125/510] Data 0.004 (0.181) Batch 1.075 (1.498) Remain 19:28:10 loss: 0.4421 Lr: 0.00598
[2023-12-25 03:39:48,561 INFO misc.py line 119 253097] Train: [9/100][126/510] Data 0.006 (0.179) Batch 1.107 (1.495) Remain 19:25:40 loss: 0.4830 Lr: 0.00598
[2023-12-25 03:39:49,712 INFO misc.py line 119 253097] Train: [9/100][127/510] Data 0.008 (0.178) Batch 1.154 (1.492) Remain 19:23:30 loss: 0.2457 Lr: 0.00598
[2023-12-25 03:39:50,841 INFO misc.py line 119 253097] Train: [9/100][128/510] Data 0.007 (0.176) Batch 1.129 (1.489) Remain 19:21:13 loss: 0.7320 Lr: 0.00598
[2023-12-25 03:39:51,907 INFO misc.py line 119 253097] Train: [9/100][129/510] Data 0.006 (0.175) Batch 1.067 (1.486) Remain 19:18:34 loss: 0.6441 Lr: 0.00598
[2023-12-25 03:39:53,065 INFO misc.py line 119 253097] Train: [9/100][130/510] Data 0.005 (0.174) Batch 1.153 (1.483) Remain 19:16:30 loss: 0.2393 Lr: 0.00598
[2023-12-25 03:39:54,279 INFO misc.py line 119 253097] Train: [9/100][131/510] Data 0.009 (0.173) Batch 1.220 (1.481) Remain 19:14:53 loss: 0.3693 Lr: 0.00598
[2023-12-25 03:39:55,373 INFO misc.py line 119 253097] Train: [9/100][132/510] Data 0.003 (0.171) Batch 1.094 (1.478) Remain 19:12:31 loss: 0.3436 Lr: 0.00598
[2023-12-25 03:40:01,241 INFO misc.py line 119 253097] Train: [9/100][133/510] Data 0.003 (0.170) Batch 5.859 (1.512) Remain 19:38:46 loss: 0.3109 Lr: 0.00598
[2023-12-25 03:40:02,183 INFO misc.py line 119 253097] Train: [9/100][134/510] Data 0.013 (0.169) Batch 0.950 (1.507) Remain 19:35:24 loss: 0.3169 Lr: 0.00598
[2023-12-25 03:40:03,247 INFO misc.py line 119 253097] Train: [9/100][135/510] Data 0.004 (0.167) Batch 1.064 (1.504) Remain 19:32:46 loss: 0.3885 Lr: 0.00598
[2023-12-25 03:40:04,434 INFO misc.py line 119 253097] Train: [9/100][136/510] Data 0.004 (0.166) Batch 1.187 (1.502) Remain 19:30:53 loss: 0.3143 Lr: 0.00598
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[2023-12-25 03:40:06,911 INFO misc.py line 119 253097] Train: [9/100][138/510] Data 0.007 (0.164) Batch 1.193 (1.498) Remain 19:27:46 loss: 0.5614 Lr: 0.00598
[2023-12-25 03:40:07,956 INFO misc.py line 119 253097] Train: [9/100][139/510] Data 0.006 (0.163) Batch 1.049 (1.494) Remain 19:25:10 loss: 0.4072 Lr: 0.00598
[2023-12-25 03:40:09,233 INFO misc.py line 119 253097] Train: [9/100][140/510] Data 0.003 (0.162) Batch 1.274 (1.493) Remain 19:23:54 loss: 0.3164 Lr: 0.00598
[2023-12-25 03:40:10,466 INFO misc.py line 119 253097] Train: [9/100][141/510] Data 0.006 (0.160) Batch 1.233 (1.491) Remain 19:22:24 loss: 0.3741 Lr: 0.00598
[2023-12-25 03:40:11,599 INFO misc.py line 119 253097] Train: [9/100][142/510] Data 0.007 (0.159) Batch 1.133 (1.488) Remain 19:20:22 loss: 0.3006 Lr: 0.00598
[2023-12-25 03:40:12,656 INFO misc.py line 119 253097] Train: [9/100][143/510] Data 0.005 (0.158) Batch 1.055 (1.485) Remain 19:17:56 loss: 0.3333 Lr: 0.00598
[2023-12-25 03:40:13,640 INFO misc.py line 119 253097] Train: [9/100][144/510] Data 0.007 (0.157) Batch 0.988 (1.482) Remain 19:15:10 loss: 0.3910 Lr: 0.00598
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[2023-12-25 03:40:26,202 INFO misc.py line 119 253097] Train: [9/100][152/510] Data 0.015 (0.149) Batch 1.051 (1.486) Remain 19:18:38 loss: 0.3548 Lr: 0.00598
[2023-12-25 03:40:27,348 INFO misc.py line 119 253097] Train: [9/100][153/510] Data 0.009 (0.148) Batch 1.150 (1.484) Remain 19:16:52 loss: 0.3814 Lr: 0.00598
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[2023-12-25 03:40:30,583 INFO misc.py line 119 253097] Train: [9/100][156/510] Data 0.008 (0.145) Batch 1.105 (1.476) Remain 19:10:35 loss: 0.7710 Lr: 0.00598
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[2023-12-25 03:41:06,729 INFO misc.py line 119 253097] Train: [9/100][182/510] Data 0.008 (0.140) Batch 1.187 (1.464) Remain 19:00:13 loss: 0.4057 Lr: 0.00598
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[2023-12-25 03:41:41,569 INFO misc.py line 119 253097] Train: [9/100][203/510] Data 0.003 (0.126) Batch 1.198 (1.484) Remain 19:15:40 loss: 0.6867 Lr: 0.00598
[2023-12-25 03:41:42,769 INFO misc.py line 119 253097] Train: [9/100][204/510] Data 0.003 (0.125) Batch 1.201 (1.483) Remain 19:14:33 loss: 0.6044 Lr: 0.00598
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[2023-12-25 03:41:45,051 INFO misc.py line 119 253097] Train: [9/100][206/510] Data 0.004 (0.124) Batch 1.136 (1.479) Remain 19:11:52 loss: 0.5008 Lr: 0.00598
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[2023-12-25 03:41:54,393 INFO misc.py line 119 253097] Train: [9/100][213/510] Data 0.009 (0.129) Batch 1.283 (1.475) Remain 19:07:55 loss: 0.4362 Lr: 0.00598
[2023-12-25 03:41:55,539 INFO misc.py line 119 253097] Train: [9/100][214/510] Data 0.009 (0.129) Batch 1.152 (1.473) Remain 19:06:42 loss: 0.4034 Lr: 0.00598
[2023-12-25 03:41:56,733 INFO misc.py line 119 253097] Train: [9/100][215/510] Data 0.003 (0.128) Batch 1.191 (1.472) Remain 19:05:39 loss: 0.7682 Lr: 0.00598
[2023-12-25 03:41:57,973 INFO misc.py line 119 253097] Train: [9/100][216/510] Data 0.005 (0.128) Batch 1.241 (1.471) Remain 19:04:47 loss: 0.4738 Lr: 0.00598
[2023-12-25 03:41:59,100 INFO misc.py line 119 253097] Train: [9/100][217/510] Data 0.004 (0.127) Batch 1.125 (1.469) Remain 19:03:30 loss: 0.2433 Lr: 0.00598
[2023-12-25 03:42:00,798 INFO misc.py line 119 253097] Train: [9/100][218/510] Data 0.633 (0.129) Batch 1.702 (1.470) Remain 19:04:19 loss: 0.3280 Lr: 0.00598
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[2023-12-25 03:42:02,967 INFO misc.py line 119 253097] Train: [9/100][220/510] Data 0.006 (0.128) Batch 1.124 (1.467) Remain 19:01:29 loss: 0.8077 Lr: 0.00598
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[2023-12-25 03:42:07,292 INFO misc.py line 119 253097] Train: [9/100][224/510] Data 0.009 (0.126) Batch 0.983 (1.460) Remain 18:55:58 loss: 0.3562 Lr: 0.00598
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[2023-12-25 03:42:09,518 INFO misc.py line 119 253097] Train: [9/100][226/510] Data 0.003 (0.125) Batch 1.157 (1.457) Remain 18:53:30 loss: 0.3186 Lr: 0.00598
[2023-12-25 03:42:10,560 INFO misc.py line 119 253097] Train: [9/100][227/510] Data 0.003 (0.125) Batch 1.042 (1.455) Remain 18:52:02 loss: 0.5705 Lr: 0.00598
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[2023-12-25 03:42:13,715 INFO misc.py line 119 253097] Train: [9/100][230/510] Data 0.003 (0.123) Batch 0.991 (1.449) Remain 18:47:49 loss: 0.4880 Lr: 0.00598
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[2023-12-25 03:42:21,003 INFO misc.py line 119 253097] Train: [9/100][232/510] Data 0.004 (0.122) Batch 6.019 (1.469) Remain 19:02:41 loss: 0.3361 Lr: 0.00598
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[2023-12-25 03:42:25,910 INFO misc.py line 119 253097] Train: [9/100][236/510] Data 0.008 (0.120) Batch 1.180 (1.464) Remain 18:59:21 loss: 0.6366 Lr: 0.00598
[2023-12-25 03:42:26,832 INFO misc.py line 119 253097] Train: [9/100][237/510] Data 0.004 (0.119) Batch 0.924 (1.462) Remain 18:57:32 loss: 0.6270 Lr: 0.00598
[2023-12-25 03:42:27,903 INFO misc.py line 119 253097] Train: [9/100][238/510] Data 0.003 (0.119) Batch 1.071 (1.460) Remain 18:56:13 loss: 0.4191 Lr: 0.00598
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[2023-12-25 03:42:30,422 INFO misc.py line 119 253097] Train: [9/100][240/510] Data 0.008 (0.118) Batch 1.288 (1.459) Remain 18:54:50 loss: 0.3159 Lr: 0.00598
[2023-12-25 03:42:31,536 INFO misc.py line 119 253097] Train: [9/100][241/510] Data 0.005 (0.117) Batch 1.113 (1.457) Remain 18:53:41 loss: 0.3838 Lr: 0.00598
[2023-12-25 03:42:32,570 INFO misc.py line 119 253097] Train: [9/100][242/510] Data 0.007 (0.117) Batch 1.038 (1.455) Remain 18:52:18 loss: 0.4199 Lr: 0.00598
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[2023-12-25 03:42:38,480 INFO misc.py line 119 253097] Train: [9/100][244/510] Data 0.007 (0.116) Batch 4.557 (1.468) Remain 19:01:56 loss: 0.5143 Lr: 0.00598
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[2023-12-25 03:42:41,929 INFO misc.py line 119 253097] Train: [9/100][247/510] Data 0.004 (0.115) Batch 1.010 (1.464) Remain 18:58:47 loss: 0.2242 Lr: 0.00598
[2023-12-25 03:42:43,143 INFO misc.py line 119 253097] Train: [9/100][248/510] Data 0.012 (0.114) Batch 1.223 (1.463) Remain 18:57:59 loss: 0.4569 Lr: 0.00598
[2023-12-25 03:42:44,303 INFO misc.py line 119 253097] Train: [9/100][249/510] Data 0.004 (0.114) Batch 1.157 (1.462) Remain 18:57:00 loss: 0.5294 Lr: 0.00598
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[2023-12-25 03:42:54,225 INFO misc.py line 119 253097] Train: [9/100][251/510] Data 7.426 (0.143) Batch 8.701 (1.490) Remain 19:18:55 loss: 0.4885 Lr: 0.00598
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[2023-12-25 03:42:56,569 INFO misc.py line 119 253097] Train: [9/100][253/510] Data 0.003 (0.142) Batch 1.056 (1.487) Remain 19:16:53 loss: 0.2856 Lr: 0.00598
[2023-12-25 03:42:57,650 INFO misc.py line 119 253097] Train: [9/100][254/510] Data 0.004 (0.141) Batch 1.080 (1.486) Remain 19:15:36 loss: 0.3439 Lr: 0.00598
[2023-12-25 03:42:58,910 INFO misc.py line 119 253097] Train: [9/100][255/510] Data 0.004 (0.141) Batch 1.258 (1.485) Remain 19:14:52 loss: 0.4032 Lr: 0.00598
[2023-12-25 03:42:59,953 INFO misc.py line 119 253097] Train: [9/100][256/510] Data 0.005 (0.140) Batch 1.042 (1.483) Remain 19:13:29 loss: 0.3734 Lr: 0.00598
[2023-12-25 03:43:01,943 INFO misc.py line 119 253097] Train: [9/100][257/510] Data 0.820 (0.143) Batch 1.994 (1.485) Remain 19:15:01 loss: 0.7295 Lr: 0.00598
[2023-12-25 03:43:02,942 INFO misc.py line 119 253097] Train: [9/100][258/510] Data 0.004 (0.142) Batch 0.997 (1.483) Remain 19:13:30 loss: 0.3629 Lr: 0.00598
[2023-12-25 03:43:04,213 INFO misc.py line 119 253097] Train: [9/100][259/510] Data 0.005 (0.142) Batch 1.273 (1.482) Remain 19:12:51 loss: 0.2620 Lr: 0.00598
[2023-12-25 03:43:05,088 INFO misc.py line 119 253097] Train: [9/100][260/510] Data 0.003 (0.141) Batch 0.874 (1.480) Remain 19:10:59 loss: 0.3925 Lr: 0.00598
[2023-12-25 03:43:06,192 INFO misc.py line 119 253097] Train: [9/100][261/510] Data 0.005 (0.141) Batch 1.105 (1.479) Remain 19:09:49 loss: 0.8499 Lr: 0.00598
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[2023-12-25 03:43:44,986 INFO misc.py line 119 253097] Train: [9/100][287/510] Data 0.015 (0.141) Batch 1.118 (1.480) Remain 19:10:08 loss: 0.3931 Lr: 0.00598
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[2023-12-25 03:44:59,682 INFO misc.py line 119 253097] Train: [9/100][317/510] Data 0.003 (0.128) Batch 15.086 (1.576) Remain 20:24:21 loss: 0.4950 Lr: 0.00598
[2023-12-25 03:45:00,797 INFO misc.py line 119 253097] Train: [9/100][318/510] Data 0.006 (0.128) Batch 1.116 (1.575) Remain 20:23:11 loss: 0.2536 Lr: 0.00598
[2023-12-25 03:45:01,890 INFO misc.py line 119 253097] Train: [9/100][319/510] Data 0.004 (0.127) Batch 1.092 (1.573) Remain 20:21:58 loss: 0.3570 Lr: 0.00598
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[2023-12-25 03:45:06,590 INFO misc.py line 119 253097] Train: [9/100][323/510] Data 0.004 (0.126) Batch 1.193 (1.568) Remain 20:18:00 loss: 0.3314 Lr: 0.00598
[2023-12-25 03:45:07,564 INFO misc.py line 119 253097] Train: [9/100][324/510] Data 0.004 (0.125) Batch 0.974 (1.567) Remain 20:16:32 loss: 0.3400 Lr: 0.00598
[2023-12-25 03:45:08,629 INFO misc.py line 119 253097] Train: [9/100][325/510] Data 0.006 (0.125) Batch 1.066 (1.565) Remain 20:15:18 loss: 0.3660 Lr: 0.00598
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[2023-12-25 03:45:11,836 INFO misc.py line 119 253097] Train: [9/100][328/510] Data 0.005 (0.124) Batch 1.071 (1.560) Remain 20:11:40 loss: 0.3497 Lr: 0.00598
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[2023-12-25 03:45:21,187 INFO misc.py line 119 253097] Train: [9/100][336/510] Data 0.005 (0.121) Batch 1.166 (1.551) Remain 20:04:10 loss: 0.5617 Lr: 0.00598
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[2023-12-25 03:45:28,974 INFO misc.py line 119 253097] Train: [9/100][343/510] Data 0.010 (0.119) Batch 1.071 (1.542) Remain 19:56:58 loss: 0.4260 Lr: 0.00598
[2023-12-25 03:45:30,254 INFO misc.py line 119 253097] Train: [9/100][344/510] Data 0.007 (0.118) Batch 1.282 (1.541) Remain 19:56:21 loss: 0.3564 Lr: 0.00598
[2023-12-25 03:45:31,525 INFO misc.py line 119 253097] Train: [9/100][345/510] Data 0.004 (0.118) Batch 1.268 (1.540) Remain 19:55:42 loss: 0.4097 Lr: 0.00598
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[2023-12-25 03:45:36,498 INFO misc.py line 119 253097] Train: [9/100][349/510] Data 0.011 (0.117) Batch 1.259 (1.537) Remain 19:52:57 loss: 0.3023 Lr: 0.00598
[2023-12-25 03:45:43,722 INFO misc.py line 119 253097] Train: [9/100][350/510] Data 0.003 (0.116) Batch 7.223 (1.553) Remain 20:05:38 loss: 0.5789 Lr: 0.00598
[2023-12-25 03:45:44,609 INFO misc.py line 119 253097] Train: [9/100][351/510] Data 0.005 (0.116) Batch 0.884 (1.551) Remain 20:04:07 loss: 0.4554 Lr: 0.00598
[2023-12-25 03:45:45,638 INFO misc.py line 119 253097] Train: [9/100][352/510] Data 0.008 (0.116) Batch 1.029 (1.550) Remain 20:02:56 loss: 0.5031 Lr: 0.00598
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[2023-12-25 03:46:37,035 INFO misc.py line 119 253097] Train: [9/100][392/510] Data 0.004 (0.104) Batch 1.179 (1.523) Remain 19:40:46 loss: 0.3728 Lr: 0.00598
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[2023-12-25 03:47:38,153 INFO misc.py line 119 253097] Train: [9/100][429/510] Data 0.003 (0.096) Batch 1.203 (1.534) Remain 19:48:31 loss: 0.3517 Lr: 0.00598
[2023-12-25 03:47:39,418 INFO misc.py line 119 253097] Train: [9/100][430/510] Data 0.009 (0.096) Batch 1.269 (1.533) Remain 19:48:01 loss: 0.7033 Lr: 0.00598
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[2023-12-25 03:49:27,294 INFO misc.py line 119 253097] Train: [9/100][497/510] Data 0.004 (0.086) Batch 1.189 (1.544) Remain 19:54:22 loss: 0.2517 Lr: 0.00597
[2023-12-25 03:49:28,349 INFO misc.py line 119 253097] Train: [9/100][498/510] Data 0.004 (0.086) Batch 1.052 (1.543) Remain 19:53:34 loss: 0.4541 Lr: 0.00597
[2023-12-25 03:49:29,440 INFO misc.py line 119 253097] Train: [9/100][499/510] Data 0.007 (0.086) Batch 1.088 (1.542) Remain 19:52:50 loss: 0.4030 Lr: 0.00597
[2023-12-25 03:49:30,397 INFO misc.py line 119 253097] Train: [9/100][500/510] Data 0.010 (0.086) Batch 0.963 (1.541) Remain 19:51:54 loss: 0.7038 Lr: 0.00597
[2023-12-25 03:49:33,242 INFO misc.py line 119 253097] Train: [9/100][501/510] Data 1.615 (0.089) Batch 2.845 (1.543) Remain 19:53:54 loss: 0.2564 Lr: 0.00597
[2023-12-25 03:49:34,362 INFO misc.py line 119 253097] Train: [9/100][502/510] Data 0.004 (0.089) Batch 1.120 (1.542) Remain 19:53:14 loss: 0.2841 Lr: 0.00597
[2023-12-25 03:49:35,364 INFO misc.py line 119 253097] Train: [9/100][503/510] Data 0.003 (0.088) Batch 1.003 (1.541) Remain 19:52:22 loss: 0.3608 Lr: 0.00597
[2023-12-25 03:49:36,489 INFO misc.py line 119 253097] Train: [9/100][504/510] Data 0.003 (0.088) Batch 1.125 (1.540) Remain 19:51:42 loss: 0.2155 Lr: 0.00597
[2023-12-25 03:49:37,523 INFO misc.py line 119 253097] Train: [9/100][505/510] Data 0.004 (0.088) Batch 1.034 (1.539) Remain 19:50:53 loss: 0.4858 Lr: 0.00597
[2023-12-25 03:49:38,643 INFO misc.py line 119 253097] Train: [9/100][506/510] Data 0.003 (0.088) Batch 1.119 (1.539) Remain 19:50:13 loss: 0.3124 Lr: 0.00597
[2023-12-25 03:49:39,930 INFO misc.py line 119 253097] Train: [9/100][507/510] Data 0.004 (0.088) Batch 1.284 (1.538) Remain 19:49:48 loss: 0.2737 Lr: 0.00597
[2023-12-25 03:49:41,075 INFO misc.py line 119 253097] Train: [9/100][508/510] Data 0.007 (0.088) Batch 1.145 (1.537) Remain 19:49:10 loss: 0.5165 Lr: 0.00597
[2023-12-25 03:49:42,363 INFO misc.py line 119 253097] Train: [9/100][509/510] Data 0.007 (0.087) Batch 1.288 (1.537) Remain 19:48:46 loss: 0.2072 Lr: 0.00597
[2023-12-25 03:49:43,525 INFO misc.py line 119 253097] Train: [9/100][510/510] Data 0.007 (0.087) Batch 1.163 (1.536) Remain 19:48:10 loss: 0.4498 Lr: 0.00597
[2023-12-25 03:49:43,526 INFO misc.py line 136 253097] Train result: loss: 0.4336 
[2023-12-25 03:49:43,526 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 03:50:11,207 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6586 
[2023-12-25 03:50:11,564 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.6423 
[2023-12-25 03:50:16,510 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6208 
[2023-12-25 03:50:17,026 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5039 
[2023-12-25 03:50:19,011 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8438 
[2023-12-25 03:50:19,436 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.5075 
[2023-12-25 03:50:20,325 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.7401 
[2023-12-25 03:50:20,887 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3441 
[2023-12-25 03:50:22,705 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0274 
[2023-12-25 03:50:24,829 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2633 
[2023-12-25 03:50:25,703 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2917 
[2023-12-25 03:50:26,125 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8816 
[2023-12-25 03:50:27,027 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6442 
[2023-12-25 03:50:29,967 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8010 
[2023-12-25 03:50:30,435 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4821 
[2023-12-25 03:50:31,044 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.8106 
[2023-12-25 03:50:31,743 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5399 
[2023-12-25 03:50:33,111 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6413/0.7180/0.8871.
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9191/0.9374
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9743/0.9918
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8274/0.9527
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2578/0.2752
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5922/0.6723
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5469/0.6645
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7849/0.9203
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8937/0.9430
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5608/0.6175
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7267/0.8257
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6802/0.8169
[2023-12-25 03:50:33,111 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5726/0.7164
[2023-12-25 03:50:33,112 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 03:50:33,113 INFO misc.py line 165 253097] Currently Best mIoU: 0.6447
[2023-12-25 03:50:33,113 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 03:50:56,805 INFO misc.py line 119 253097] Train: [10/100][1/510] Data 20.807 (20.807) Batch 21.863 (21.863) Remain 281:50:30 loss: 0.6571 Lr: 0.00597
[2023-12-25 03:50:57,809 INFO misc.py line 119 253097] Train: [10/100][2/510] Data 0.002 (0.002) Batch 1.004 (1.004) Remain 12:56:34 loss: 0.3121 Lr: 0.00597
[2023-12-25 03:50:59,018 INFO misc.py line 119 253097] Train: [10/100][3/510] Data 0.002 (0.002) Batch 1.209 (1.209) Remain 15:35:03 loss: 0.2737 Lr: 0.00597
[2023-12-25 03:51:00,029 INFO misc.py line 119 253097] Train: [10/100][4/510] Data 0.003 (0.003) Batch 1.010 (1.010) Remain 13:01:28 loss: 0.2991 Lr: 0.00597
[2023-12-25 03:51:01,249 INFO misc.py line 119 253097] Train: [10/100][5/510] Data 0.003 (0.003) Batch 1.220 (1.115) Remain 14:22:23 loss: 0.2232 Lr: 0.00597
[2023-12-25 03:51:02,164 INFO misc.py line 119 253097] Train: [10/100][6/510] Data 0.004 (0.003) Batch 0.915 (1.048) Remain 13:30:45 loss: 0.3357 Lr: 0.00597
[2023-12-25 03:51:03,385 INFO misc.py line 119 253097] Train: [10/100][7/510] Data 0.004 (0.004) Batch 1.221 (1.092) Remain 14:04:11 loss: 0.2616 Lr: 0.00597
[2023-12-25 03:51:04,528 INFO misc.py line 119 253097] Train: [10/100][8/510] Data 0.004 (0.004) Batch 1.143 (1.102) Remain 14:12:09 loss: 0.3331 Lr: 0.00597
[2023-12-25 03:51:05,687 INFO misc.py line 119 253097] Train: [10/100][9/510] Data 0.004 (0.004) Batch 1.158 (1.111) Remain 14:19:19 loss: 0.4011 Lr: 0.00597
[2023-12-25 03:51:06,443 INFO misc.py line 119 253097] Train: [10/100][10/510] Data 0.005 (0.004) Batch 0.759 (1.061) Remain 13:40:21 loss: 0.3843 Lr: 0.00597
[2023-12-25 03:51:07,559 INFO misc.py line 119 253097] Train: [10/100][11/510] Data 0.003 (0.004) Batch 1.112 (1.067) Remain 13:45:18 loss: 0.3395 Lr: 0.00597
[2023-12-25 03:51:08,834 INFO misc.py line 119 253097] Train: [10/100][12/510] Data 0.006 (0.004) Batch 1.279 (1.091) Remain 14:03:27 loss: 0.3975 Lr: 0.00597
[2023-12-25 03:51:10,173 INFO misc.py line 119 253097] Train: [10/100][13/510] Data 0.021 (0.006) Batch 1.337 (1.115) Remain 14:22:30 loss: 0.3075 Lr: 0.00597
[2023-12-25 03:51:11,432 INFO misc.py line 119 253097] Train: [10/100][14/510] Data 0.007 (0.006) Batch 1.260 (1.129) Remain 14:32:38 loss: 0.2600 Lr: 0.00597
[2023-12-25 03:51:18,771 INFO misc.py line 119 253097] Train: [10/100][15/510] Data 0.004 (0.006) Batch 7.339 (1.646) Remain 21:12:49 loss: 0.4029 Lr: 0.00597
[2023-12-25 03:51:19,930 INFO misc.py line 119 253097] Train: [10/100][16/510] Data 0.003 (0.006) Batch 1.160 (1.609) Remain 20:43:51 loss: 0.3874 Lr: 0.00597
[2023-12-25 03:51:21,093 INFO misc.py line 119 253097] Train: [10/100][17/510] Data 0.003 (0.005) Batch 1.164 (1.577) Remain 20:19:15 loss: 0.2862 Lr: 0.00597
[2023-12-25 03:51:22,253 INFO misc.py line 119 253097] Train: [10/100][18/510] Data 0.003 (0.005) Batch 1.158 (1.549) Remain 19:57:38 loss: 0.3057 Lr: 0.00597
[2023-12-25 03:51:23,380 INFO misc.py line 119 253097] Train: [10/100][19/510] Data 0.004 (0.005) Batch 1.128 (1.523) Remain 19:37:17 loss: 0.3185 Lr: 0.00597
[2023-12-25 03:51:24,635 INFO misc.py line 119 253097] Train: [10/100][20/510] Data 0.003 (0.005) Batch 1.255 (1.507) Remain 19:25:05 loss: 0.4598 Lr: 0.00597
[2023-12-25 03:51:25,920 INFO misc.py line 119 253097] Train: [10/100][21/510] Data 0.003 (0.005) Batch 1.282 (1.494) Remain 19:15:23 loss: 0.3053 Lr: 0.00597
[2023-12-25 03:51:26,947 INFO misc.py line 119 253097] Train: [10/100][22/510] Data 0.007 (0.005) Batch 1.030 (1.470) Remain 18:56:28 loss: 0.2436 Lr: 0.00597
[2023-12-25 03:51:28,528 INFO misc.py line 119 253097] Train: [10/100][23/510] Data 0.003 (0.005) Batch 1.577 (1.475) Remain 19:00:35 loss: 0.4039 Lr: 0.00597
[2023-12-25 03:51:29,685 INFO misc.py line 119 253097] Train: [10/100][24/510] Data 0.006 (0.005) Batch 1.152 (1.460) Remain 18:48:39 loss: 0.1749 Lr: 0.00597
[2023-12-25 03:51:30,756 INFO misc.py line 119 253097] Train: [10/100][25/510] Data 0.012 (0.005) Batch 1.076 (1.442) Remain 18:35:09 loss: 0.2314 Lr: 0.00597
[2023-12-25 03:51:31,757 INFO misc.py line 119 253097] Train: [10/100][26/510] Data 0.006 (0.005) Batch 1.000 (1.423) Remain 18:20:16 loss: 0.5014 Lr: 0.00597
[2023-12-25 03:51:32,919 INFO misc.py line 119 253097] Train: [10/100][27/510] Data 0.007 (0.005) Batch 1.163 (1.412) Remain 18:11:52 loss: 0.4993 Lr: 0.00597
[2023-12-25 03:51:34,015 INFO misc.py line 119 253097] Train: [10/100][28/510] Data 0.006 (0.005) Batch 1.099 (1.400) Remain 18:02:10 loss: 0.3573 Lr: 0.00597
[2023-12-25 03:51:35,272 INFO misc.py line 119 253097] Train: [10/100][29/510] Data 0.002 (0.005) Batch 1.253 (1.394) Remain 17:57:47 loss: 0.4231 Lr: 0.00597
[2023-12-25 03:51:36,614 INFO misc.py line 119 253097] Train: [10/100][30/510] Data 0.007 (0.005) Batch 1.343 (1.392) Remain 17:56:18 loss: 0.4842 Lr: 0.00597
[2023-12-25 03:51:37,787 INFO misc.py line 119 253097] Train: [10/100][31/510] Data 0.005 (0.005) Batch 1.172 (1.385) Remain 17:50:11 loss: 0.4246 Lr: 0.00597
[2023-12-25 03:51:38,935 INFO misc.py line 119 253097] Train: [10/100][32/510] Data 0.007 (0.005) Batch 1.148 (1.376) Remain 17:43:52 loss: 0.3501 Lr: 0.00597
[2023-12-25 03:51:39,837 INFO misc.py line 119 253097] Train: [10/100][33/510] Data 0.005 (0.005) Batch 0.905 (1.361) Remain 17:31:42 loss: 0.6098 Lr: 0.00597
[2023-12-25 03:51:40,917 INFO misc.py line 119 253097] Train: [10/100][34/510] Data 0.003 (0.005) Batch 1.080 (1.352) Remain 17:24:41 loss: 0.3935 Lr: 0.00597
[2023-12-25 03:51:42,117 INFO misc.py line 119 253097] Train: [10/100][35/510] Data 0.003 (0.005) Batch 1.199 (1.347) Remain 17:20:59 loss: 0.2718 Lr: 0.00597
[2023-12-25 03:51:47,597 INFO misc.py line 119 253097] Train: [10/100][36/510] Data 4.469 (0.141) Batch 5.479 (1.472) Remain 18:57:45 loss: 0.2720 Lr: 0.00597
[2023-12-25 03:51:48,769 INFO misc.py line 119 253097] Train: [10/100][37/510] Data 0.004 (0.137) Batch 1.173 (1.463) Remain 18:50:56 loss: 0.4737 Lr: 0.00597
[2023-12-25 03:51:50,100 INFO misc.py line 119 253097] Train: [10/100][38/510] Data 0.003 (0.133) Batch 1.327 (1.459) Remain 18:47:54 loss: 0.2290 Lr: 0.00597
[2023-12-25 03:51:51,258 INFO misc.py line 119 253097] Train: [10/100][39/510] Data 0.007 (0.129) Batch 1.161 (1.451) Remain 18:41:28 loss: 0.6029 Lr: 0.00597
[2023-12-25 03:51:52,456 INFO misc.py line 119 253097] Train: [10/100][40/510] Data 0.004 (0.126) Batch 1.196 (1.444) Remain 18:36:08 loss: 0.5087 Lr: 0.00597
[2023-12-25 03:51:53,591 INFO misc.py line 119 253097] Train: [10/100][41/510] Data 0.005 (0.123) Batch 1.134 (1.436) Remain 18:29:47 loss: 0.5966 Lr: 0.00597
[2023-12-25 03:51:54,808 INFO misc.py line 119 253097] Train: [10/100][42/510] Data 0.007 (0.120) Batch 1.220 (1.431) Remain 18:25:30 loss: 0.3819 Lr: 0.00597
[2023-12-25 03:51:56,045 INFO misc.py line 119 253097] Train: [10/100][43/510] Data 0.003 (0.117) Batch 1.234 (1.426) Remain 18:21:41 loss: 0.5273 Lr: 0.00597
[2023-12-25 03:51:57,105 INFO misc.py line 119 253097] Train: [10/100][44/510] Data 0.006 (0.114) Batch 1.058 (1.417) Remain 18:14:43 loss: 0.5752 Lr: 0.00597
[2023-12-25 03:51:58,438 INFO misc.py line 119 253097] Train: [10/100][45/510] Data 0.009 (0.112) Batch 1.336 (1.415) Remain 18:13:13 loss: 0.5507 Lr: 0.00597
[2023-12-25 03:51:59,593 INFO misc.py line 119 253097] Train: [10/100][46/510] Data 0.005 (0.109) Batch 1.152 (1.409) Remain 18:08:29 loss: 0.2790 Lr: 0.00597
[2023-12-25 03:52:00,766 INFO misc.py line 119 253097] Train: [10/100][47/510] Data 0.008 (0.107) Batch 1.177 (1.403) Remain 18:04:23 loss: 0.3139 Lr: 0.00597
[2023-12-25 03:52:01,899 INFO misc.py line 119 253097] Train: [10/100][48/510] Data 0.004 (0.105) Batch 1.131 (1.397) Remain 17:59:41 loss: 0.6569 Lr: 0.00597
[2023-12-25 03:52:15,074 INFO misc.py line 119 253097] Train: [10/100][49/510] Data 0.006 (0.102) Batch 13.178 (1.653) Remain 21:17:33 loss: 0.1959 Lr: 0.00597
[2023-12-25 03:52:15,978 INFO misc.py line 119 253097] Train: [10/100][50/510] Data 0.003 (0.100) Batch 0.904 (1.637) Remain 21:05:12 loss: 0.3597 Lr: 0.00597
[2023-12-25 03:52:16,999 INFO misc.py line 119 253097] Train: [10/100][51/510] Data 0.003 (0.098) Batch 1.017 (1.625) Remain 20:55:11 loss: 0.3683 Lr: 0.00597
[2023-12-25 03:52:18,002 INFO misc.py line 119 253097] Train: [10/100][52/510] Data 0.007 (0.096) Batch 1.002 (1.612) Remain 20:45:21 loss: 0.3154 Lr: 0.00597
[2023-12-25 03:52:19,163 INFO misc.py line 119 253097] Train: [10/100][53/510] Data 0.008 (0.095) Batch 1.165 (1.603) Remain 20:38:24 loss: 0.2741 Lr: 0.00597
[2023-12-25 03:52:20,320 INFO misc.py line 119 253097] Train: [10/100][54/510] Data 0.005 (0.093) Batch 1.157 (1.594) Remain 20:31:38 loss: 0.2977 Lr: 0.00597
[2023-12-25 03:52:21,526 INFO misc.py line 119 253097] Train: [10/100][55/510] Data 0.004 (0.091) Batch 1.207 (1.587) Remain 20:25:51 loss: 0.6098 Lr: 0.00597
[2023-12-25 03:52:22,797 INFO misc.py line 119 253097] Train: [10/100][56/510] Data 0.003 (0.089) Batch 1.267 (1.581) Remain 20:21:10 loss: 0.4614 Lr: 0.00597
[2023-12-25 03:52:23,830 INFO misc.py line 119 253097] Train: [10/100][57/510] Data 0.007 (0.088) Batch 1.032 (1.571) Remain 20:13:17 loss: 0.3763 Lr: 0.00597
[2023-12-25 03:52:25,010 INFO misc.py line 119 253097] Train: [10/100][58/510] Data 0.009 (0.086) Batch 1.184 (1.563) Remain 20:07:50 loss: 0.3898 Lr: 0.00597
[2023-12-25 03:52:26,170 INFO misc.py line 119 253097] Train: [10/100][59/510] Data 0.004 (0.085) Batch 1.158 (1.556) Remain 20:02:13 loss: 0.5530 Lr: 0.00597
[2023-12-25 03:52:27,274 INFO misc.py line 119 253097] Train: [10/100][60/510] Data 0.005 (0.084) Batch 1.103 (1.548) Remain 19:56:03 loss: 0.2773 Lr: 0.00597
[2023-12-25 03:52:28,202 INFO misc.py line 119 253097] Train: [10/100][61/510] Data 0.007 (0.082) Batch 0.931 (1.538) Remain 19:47:48 loss: 0.3199 Lr: 0.00597
[2023-12-25 03:52:29,175 INFO misc.py line 119 253097] Train: [10/100][62/510] Data 0.004 (0.081) Batch 0.973 (1.528) Remain 19:40:23 loss: 0.3435 Lr: 0.00597
[2023-12-25 03:52:30,355 INFO misc.py line 119 253097] Train: [10/100][63/510] Data 0.004 (0.080) Batch 1.181 (1.522) Remain 19:35:53 loss: 0.3269 Lr: 0.00597
[2023-12-25 03:52:31,571 INFO misc.py line 119 253097] Train: [10/100][64/510] Data 0.003 (0.078) Batch 1.212 (1.517) Remain 19:31:56 loss: 0.6322 Lr: 0.00597
[2023-12-25 03:52:32,735 INFO misc.py line 119 253097] Train: [10/100][65/510] Data 0.007 (0.077) Batch 1.165 (1.512) Remain 19:27:31 loss: 0.5482 Lr: 0.00597
[2023-12-25 03:52:33,905 INFO misc.py line 119 253097] Train: [10/100][66/510] Data 0.007 (0.076) Batch 1.173 (1.506) Remain 19:23:20 loss: 0.3063 Lr: 0.00597
[2023-12-25 03:52:35,101 INFO misc.py line 119 253097] Train: [10/100][67/510] Data 0.003 (0.075) Batch 1.192 (1.501) Remain 19:19:31 loss: 0.5382 Lr: 0.00597
[2023-12-25 03:52:36,342 INFO misc.py line 119 253097] Train: [10/100][68/510] Data 0.008 (0.074) Batch 1.241 (1.497) Remain 19:16:24 loss: 0.2721 Lr: 0.00597
[2023-12-25 03:52:37,547 INFO misc.py line 119 253097] Train: [10/100][69/510] Data 0.008 (0.073) Batch 1.205 (1.493) Remain 19:12:58 loss: 0.3114 Lr: 0.00597
[2023-12-25 03:52:38,671 INFO misc.py line 119 253097] Train: [10/100][70/510] Data 0.007 (0.072) Batch 1.124 (1.487) Remain 19:08:41 loss: 0.2385 Lr: 0.00597
[2023-12-25 03:52:39,842 INFO misc.py line 119 253097] Train: [10/100][71/510] Data 0.006 (0.071) Batch 1.171 (1.483) Remain 19:05:05 loss: 0.4602 Lr: 0.00597
[2023-12-25 03:52:40,768 INFO misc.py line 119 253097] Train: [10/100][72/510] Data 0.006 (0.070) Batch 0.927 (1.475) Remain 18:58:50 loss: 0.1910 Lr: 0.00597
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[2023-12-25 04:03:27,814 INFO misc.py line 119 253097] Train: [10/100][491/510] Data 0.007 (0.149) Batch 1.052 (1.534) Remain 19:34:18 loss: 0.2026 Lr: 0.00596
[2023-12-25 04:03:29,083 INFO misc.py line 119 253097] Train: [10/100][492/510] Data 0.011 (0.148) Batch 1.276 (1.534) Remain 19:33:52 loss: 0.2389 Lr: 0.00596
[2023-12-25 04:03:30,219 INFO misc.py line 119 253097] Train: [10/100][493/510] Data 0.004 (0.148) Batch 1.134 (1.533) Remain 19:33:13 loss: 0.2587 Lr: 0.00596
[2023-12-25 04:03:36,114 INFO misc.py line 119 253097] Train: [10/100][494/510] Data 0.010 (0.148) Batch 5.895 (1.542) Remain 19:39:59 loss: 0.2858 Lr: 0.00596
[2023-12-25 04:03:37,458 INFO misc.py line 119 253097] Train: [10/100][495/510] Data 0.009 (0.148) Batch 1.345 (1.542) Remain 19:39:39 loss: 0.2621 Lr: 0.00596
[2023-12-25 04:03:38,593 INFO misc.py line 119 253097] Train: [10/100][496/510] Data 0.006 (0.147) Batch 1.133 (1.541) Remain 19:39:00 loss: 0.3416 Lr: 0.00596
[2023-12-25 04:03:39,760 INFO misc.py line 119 253097] Train: [10/100][497/510] Data 0.008 (0.147) Batch 1.169 (1.540) Remain 19:38:24 loss: 0.2102 Lr: 0.00596
[2023-12-25 04:03:40,709 INFO misc.py line 119 253097] Train: [10/100][498/510] Data 0.007 (0.147) Batch 0.949 (1.539) Remain 19:37:27 loss: 0.3594 Lr: 0.00596
[2023-12-25 04:03:41,790 INFO misc.py line 119 253097] Train: [10/100][499/510] Data 0.006 (0.147) Batch 1.083 (1.538) Remain 19:36:44 loss: 0.2246 Lr: 0.00596
[2023-12-25 04:03:42,999 INFO misc.py line 119 253097] Train: [10/100][500/510] Data 0.004 (0.146) Batch 1.209 (1.537) Remain 19:36:12 loss: 0.3183 Lr: 0.00596
[2023-12-25 04:03:46,389 INFO misc.py line 119 253097] Train: [10/100][501/510] Data 2.219 (0.150) Batch 3.388 (1.541) Remain 19:39:01 loss: 0.4702 Lr: 0.00596
[2023-12-25 04:03:47,312 INFO misc.py line 119 253097] Train: [10/100][502/510] Data 0.007 (0.150) Batch 0.923 (1.540) Remain 19:38:02 loss: 0.3218 Lr: 0.00596
[2023-12-25 04:03:49,821 INFO misc.py line 119 253097] Train: [10/100][503/510] Data 1.302 (0.152) Batch 2.508 (1.542) Remain 19:39:30 loss: 0.7964 Lr: 0.00596
[2023-12-25 04:03:50,983 INFO misc.py line 119 253097] Train: [10/100][504/510] Data 0.007 (0.152) Batch 1.161 (1.541) Remain 19:38:53 loss: 0.4410 Lr: 0.00596
[2023-12-25 04:03:52,092 INFO misc.py line 119 253097] Train: [10/100][505/510] Data 0.007 (0.152) Batch 1.112 (1.540) Remain 19:38:13 loss: 0.2713 Lr: 0.00596
[2023-12-25 04:03:53,251 INFO misc.py line 119 253097] Train: [10/100][506/510] Data 0.003 (0.152) Batch 1.155 (1.539) Remain 19:37:36 loss: 0.3077 Lr: 0.00596
[2023-12-25 04:03:54,533 INFO misc.py line 119 253097] Train: [10/100][507/510] Data 0.008 (0.151) Batch 1.286 (1.539) Remain 19:37:11 loss: 0.3699 Lr: 0.00596
[2023-12-25 04:03:55,568 INFO misc.py line 119 253097] Train: [10/100][508/510] Data 0.004 (0.151) Batch 1.035 (1.538) Remain 19:36:24 loss: 0.4816 Lr: 0.00596
[2023-12-25 04:03:56,639 INFO misc.py line 119 253097] Train: [10/100][509/510] Data 0.006 (0.151) Batch 1.071 (1.537) Remain 19:35:40 loss: 0.5381 Lr: 0.00596
[2023-12-25 04:03:57,679 INFO misc.py line 119 253097] Train: [10/100][510/510] Data 0.004 (0.150) Batch 1.038 (1.536) Remain 19:34:53 loss: 0.3668 Lr: 0.00596
[2023-12-25 04:03:57,680 INFO misc.py line 136 253097] Train result: loss: 0.4080 
[2023-12-25 04:03:57,680 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 04:04:24,817 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6642 
[2023-12-25 04:04:25,166 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.6003 
[2023-12-25 04:04:30,101 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5350 
[2023-12-25 04:04:30,616 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.7489 
[2023-12-25 04:04:32,584 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7883 
[2023-12-25 04:04:33,008 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.7296 
[2023-12-25 04:04:33,886 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2651 
[2023-12-25 04:04:34,447 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4299 
[2023-12-25 04:04:36,253 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9003 
[2023-12-25 04:04:38,375 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2550 
[2023-12-25 04:04:39,232 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.7894 
[2023-12-25 04:04:39,656 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.2065 
[2023-12-25 04:04:40,558 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6345 
[2023-12-25 04:04:43,501 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7970 
[2023-12-25 04:04:43,973 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.6797 
[2023-12-25 04:04:44,588 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.7553 
[2023-12-25 04:04:45,295 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.7140 
[2023-12-25 04:04:46,497 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.5996/0.7000/0.8534.
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.8749/0.9604
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9290/0.9355
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8090/0.9153
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0098/0.1703
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3501/0.5821
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.4411/0.4489
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.3778/0.4427
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.6998/0.9514
[2023-12-25 04:04:46,497 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8969/0.9513
[2023-12-25 04:04:46,498 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5619/0.5955
[2023-12-25 04:04:46,498 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.6777/0.7497
[2023-12-25 04:04:46,498 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6762/0.6892
[2023-12-25 04:04:46,498 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.4908/0.7082
[2023-12-25 04:04:46,499 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 04:04:46,501 INFO misc.py line 165 253097] Currently Best mIoU: 0.6447
[2023-12-25 04:04:46,501 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 04:04:56,153 INFO misc.py line 119 253097] Train: [11/100][1/510] Data 5.685 (5.685) Batch 6.812 (6.812) Remain 86:51:25 loss: 0.3623 Lr: 0.00596
[2023-12-25 04:04:57,139 INFO misc.py line 119 253097] Train: [11/100][2/510] Data 0.005 (0.005) Batch 0.985 (0.985) Remain 12:33:48 loss: 0.5684 Lr: 0.00596
[2023-12-25 04:05:01,474 INFO misc.py line 119 253097] Train: [11/100][3/510] Data 0.006 (0.006) Batch 4.336 (4.336) Remain 55:16:52 loss: 0.3179 Lr: 0.00596
[2023-12-25 04:05:02,656 INFO misc.py line 119 253097] Train: [11/100][4/510] Data 0.007 (0.007) Batch 1.183 (1.183) Remain 15:04:40 loss: 0.7278 Lr: 0.00596
[2023-12-25 04:05:03,797 INFO misc.py line 119 253097] Train: [11/100][5/510] Data 0.005 (0.006) Batch 1.142 (1.162) Remain 14:48:58 loss: 0.3195 Lr: 0.00596
[2023-12-25 04:05:04,737 INFO misc.py line 119 253097] Train: [11/100][6/510] Data 0.004 (0.005) Batch 0.939 (1.088) Remain 13:52:06 loss: 0.2030 Lr: 0.00596
[2023-12-25 04:05:05,886 INFO misc.py line 119 253097] Train: [11/100][7/510] Data 0.004 (0.005) Batch 1.150 (1.103) Remain 14:03:54 loss: 0.2651 Lr: 0.00596
[2023-12-25 04:05:07,034 INFO misc.py line 119 253097] Train: [11/100][8/510] Data 0.005 (0.005) Batch 1.146 (1.112) Remain 14:10:28 loss: 0.4072 Lr: 0.00596
[2023-12-25 04:05:08,070 INFO misc.py line 119 253097] Train: [11/100][9/510] Data 0.006 (0.005) Batch 1.037 (1.099) Remain 14:00:52 loss: 0.3918 Lr: 0.00596
[2023-12-25 04:05:09,157 INFO misc.py line 119 253097] Train: [11/100][10/510] Data 0.005 (0.005) Batch 1.088 (1.098) Remain 13:59:33 loss: 0.5106 Lr: 0.00596
[2023-12-25 04:05:10,273 INFO misc.py line 119 253097] Train: [11/100][11/510] Data 0.005 (0.005) Batch 1.117 (1.100) Remain 14:01:22 loss: 0.3698 Lr: 0.00596
[2023-12-25 04:05:11,566 INFO misc.py line 119 253097] Train: [11/100][12/510] Data 0.003 (0.005) Batch 1.292 (1.121) Remain 14:17:42 loss: 0.2768 Lr: 0.00596
[2023-12-25 04:05:16,651 INFO misc.py line 119 253097] Train: [11/100][13/510] Data 4.094 (0.414) Batch 5.086 (1.518) Remain 19:20:52 loss: 0.4277 Lr: 0.00596
[2023-12-25 04:05:17,761 INFO misc.py line 119 253097] Train: [11/100][14/510] Data 0.004 (0.376) Batch 1.109 (1.481) Remain 18:52:24 loss: 0.4722 Lr: 0.00596
[2023-12-25 04:05:19,035 INFO misc.py line 119 253097] Train: [11/100][15/510] Data 0.004 (0.345) Batch 1.271 (1.463) Remain 18:39:01 loss: 0.4150 Lr: 0.00596
[2023-12-25 04:05:20,367 INFO misc.py line 119 253097] Train: [11/100][16/510] Data 0.007 (0.319) Batch 1.334 (1.453) Remain 18:31:23 loss: 0.3095 Lr: 0.00596
[2023-12-25 04:05:21,466 INFO misc.py line 119 253097] Train: [11/100][17/510] Data 0.005 (0.297) Batch 1.100 (1.428) Remain 18:12:04 loss: 0.4024 Lr: 0.00596
[2023-12-25 04:05:22,804 INFO misc.py line 119 253097] Train: [11/100][18/510] Data 0.005 (0.277) Batch 1.336 (1.422) Remain 18:07:19 loss: 0.3448 Lr: 0.00596
[2023-12-25 04:05:23,995 INFO misc.py line 119 253097] Train: [11/100][19/510] Data 0.006 (0.260) Batch 1.192 (1.408) Remain 17:56:19 loss: 0.8038 Lr: 0.00596
[2023-12-25 04:05:25,264 INFO misc.py line 119 253097] Train: [11/100][20/510] Data 0.006 (0.245) Batch 1.266 (1.399) Remain 17:49:57 loss: 0.3371 Lr: 0.00596
[2023-12-25 04:05:26,396 INFO misc.py line 119 253097] Train: [11/100][21/510] Data 0.007 (0.232) Batch 1.132 (1.384) Remain 17:38:35 loss: 0.5158 Lr: 0.00596
[2023-12-25 04:05:27,682 INFO misc.py line 119 253097] Train: [11/100][22/510] Data 0.007 (0.220) Batch 1.288 (1.379) Remain 17:34:40 loss: 0.6457 Lr: 0.00596
[2023-12-25 04:05:28,510 INFO misc.py line 119 253097] Train: [11/100][23/510] Data 0.006 (0.210) Batch 0.829 (1.352) Remain 17:13:36 loss: 0.5958 Lr: 0.00596
[2023-12-25 04:05:29,732 INFO misc.py line 119 253097] Train: [11/100][24/510] Data 0.006 (0.200) Batch 1.221 (1.346) Remain 17:08:48 loss: 0.5532 Lr: 0.00596
[2023-12-25 04:05:30,802 INFO misc.py line 119 253097] Train: [11/100][25/510] Data 0.007 (0.191) Batch 1.072 (1.333) Remain 16:59:17 loss: 0.3930 Lr: 0.00596
[2023-12-25 04:05:32,057 INFO misc.py line 119 253097] Train: [11/100][26/510] Data 0.004 (0.183) Batch 1.254 (1.330) Remain 16:56:38 loss: 0.5568 Lr: 0.00596
[2023-12-25 04:05:33,019 INFO misc.py line 119 253097] Train: [11/100][27/510] Data 0.005 (0.176) Batch 0.962 (1.314) Remain 16:44:53 loss: 0.1437 Lr: 0.00596
[2023-12-25 04:05:33,991 INFO misc.py line 119 253097] Train: [11/100][28/510] Data 0.006 (0.169) Batch 0.973 (1.301) Remain 16:34:26 loss: 0.4260 Lr: 0.00596
[2023-12-25 04:05:35,064 INFO misc.py line 119 253097] Train: [11/100][29/510] Data 0.004 (0.163) Batch 1.074 (1.292) Remain 16:27:44 loss: 0.2592 Lr: 0.00596
[2023-12-25 04:05:36,439 INFO misc.py line 119 253097] Train: [11/100][30/510] Data 0.003 (0.157) Batch 1.372 (1.295) Remain 16:29:58 loss: 0.1777 Lr: 0.00596
[2023-12-25 04:05:37,636 INFO misc.py line 119 253097] Train: [11/100][31/510] Data 0.006 (0.151) Batch 1.196 (1.291) Remain 16:27:15 loss: 0.4132 Lr: 0.00596
[2023-12-25 04:05:38,840 INFO misc.py line 119 253097] Train: [11/100][32/510] Data 0.007 (0.146) Batch 1.205 (1.288) Remain 16:24:57 loss: 0.3713 Lr: 0.00596
[2023-12-25 04:05:39,994 INFO misc.py line 119 253097] Train: [11/100][33/510] Data 0.006 (0.142) Batch 1.155 (1.284) Remain 16:21:33 loss: 0.6267 Lr: 0.00596
[2023-12-25 04:05:50,866 INFO misc.py line 119 253097] Train: [11/100][34/510] Data 0.006 (0.137) Batch 10.873 (1.593) Remain 20:17:59 loss: 0.2066 Lr: 0.00596
[2023-12-25 04:05:52,096 INFO misc.py line 119 253097] Train: [11/100][35/510] Data 0.004 (0.133) Batch 1.224 (1.582) Remain 20:09:08 loss: 0.6112 Lr: 0.00596
[2023-12-25 04:05:53,248 INFO misc.py line 119 253097] Train: [11/100][36/510] Data 0.010 (0.129) Batch 1.155 (1.569) Remain 19:59:13 loss: 0.3049 Lr: 0.00596
[2023-12-25 04:05:54,467 INFO misc.py line 119 253097] Train: [11/100][37/510] Data 0.006 (0.126) Batch 1.218 (1.559) Remain 19:51:18 loss: 0.2993 Lr: 0.00596
[2023-12-25 04:05:55,460 INFO misc.py line 119 253097] Train: [11/100][38/510] Data 0.009 (0.122) Batch 0.998 (1.543) Remain 19:39:02 loss: 0.4368 Lr: 0.00596
[2023-12-25 04:05:56,436 INFO misc.py line 119 253097] Train: [11/100][39/510] Data 0.004 (0.119) Batch 0.975 (1.527) Remain 19:26:57 loss: 0.6484 Lr: 0.00596
[2023-12-25 04:05:57,656 INFO misc.py line 119 253097] Train: [11/100][40/510] Data 0.004 (0.116) Batch 1.220 (1.518) Remain 19:20:35 loss: 0.3087 Lr: 0.00596
[2023-12-25 04:05:58,619 INFO misc.py line 119 253097] Train: [11/100][41/510] Data 0.005 (0.113) Batch 0.964 (1.504) Remain 19:09:24 loss: 0.5274 Lr: 0.00596
[2023-12-25 04:05:59,827 INFO misc.py line 119 253097] Train: [11/100][42/510] Data 0.004 (0.110) Batch 1.207 (1.496) Remain 19:03:33 loss: 0.3878 Lr: 0.00596
[2023-12-25 04:06:00,708 INFO misc.py line 119 253097] Train: [11/100][43/510] Data 0.006 (0.108) Batch 0.883 (1.481) Remain 18:51:48 loss: 0.6081 Lr: 0.00596
[2023-12-25 04:06:01,917 INFO misc.py line 119 253097] Train: [11/100][44/510] Data 0.006 (0.105) Batch 1.210 (1.474) Remain 18:46:43 loss: 0.2644 Lr: 0.00596
[2023-12-25 04:06:02,989 INFO misc.py line 119 253097] Train: [11/100][45/510] Data 0.003 (0.103) Batch 1.062 (1.464) Remain 18:39:12 loss: 1.0352 Lr: 0.00596
[2023-12-25 04:06:04,258 INFO misc.py line 119 253097] Train: [11/100][46/510] Data 0.017 (0.101) Batch 1.278 (1.460) Remain 18:35:51 loss: 0.5937 Lr: 0.00596
[2023-12-25 04:06:05,483 INFO misc.py line 119 253097] Train: [11/100][47/510] Data 0.006 (0.099) Batch 1.218 (1.455) Remain 18:31:38 loss: 0.2580 Lr: 0.00596
[2023-12-25 04:06:09,958 INFO misc.py line 119 253097] Train: [11/100][48/510] Data 0.012 (0.097) Batch 4.481 (1.522) Remain 19:23:00 loss: 0.4496 Lr: 0.00596
[2023-12-25 04:06:10,999 INFO misc.py line 119 253097] Train: [11/100][49/510] Data 0.007 (0.095) Batch 1.042 (1.511) Remain 19:15:00 loss: 0.3707 Lr: 0.00596
[2023-12-25 04:06:12,184 INFO misc.py line 119 253097] Train: [11/100][50/510] Data 0.005 (0.093) Batch 1.186 (1.504) Remain 19:09:41 loss: 0.2063 Lr: 0.00596
[2023-12-25 04:06:13,480 INFO misc.py line 119 253097] Train: [11/100][51/510] Data 0.004 (0.091) Batch 1.295 (1.500) Remain 19:06:19 loss: 0.2990 Lr: 0.00596
[2023-12-25 04:06:14,519 INFO misc.py line 119 253097] Train: [11/100][52/510] Data 0.005 (0.089) Batch 1.040 (1.491) Remain 18:59:07 loss: 0.2766 Lr: 0.00596
[2023-12-25 04:06:15,582 INFO misc.py line 119 253097] Train: [11/100][53/510] Data 0.003 (0.087) Batch 1.062 (1.482) Remain 18:52:32 loss: 0.3102 Lr: 0.00596
[2023-12-25 04:06:16,552 INFO misc.py line 119 253097] Train: [11/100][54/510] Data 0.004 (0.086) Batch 0.971 (1.472) Remain 18:44:51 loss: 0.6051 Lr: 0.00596
[2023-12-25 04:06:17,549 INFO misc.py line 119 253097] Train: [11/100][55/510] Data 0.004 (0.084) Batch 0.997 (1.463) Remain 18:37:51 loss: 0.3570 Lr: 0.00596
[2023-12-25 04:06:18,800 INFO misc.py line 119 253097] Train: [11/100][56/510] Data 0.004 (0.083) Batch 1.251 (1.459) Remain 18:34:46 loss: 0.3857 Lr: 0.00596
[2023-12-25 04:06:20,090 INFO misc.py line 119 253097] Train: [11/100][57/510] Data 0.005 (0.081) Batch 1.288 (1.456) Remain 18:32:19 loss: 0.4351 Lr: 0.00596
[2023-12-25 04:06:21,238 INFO misc.py line 119 253097] Train: [11/100][58/510] Data 0.013 (0.080) Batch 1.147 (1.450) Remain 18:28:00 loss: 0.3936 Lr: 0.00596
[2023-12-25 04:06:22,263 INFO misc.py line 119 253097] Train: [11/100][59/510] Data 0.008 (0.079) Batch 1.023 (1.443) Remain 18:22:09 loss: 0.5204 Lr: 0.00596
[2023-12-25 04:06:23,459 INFO misc.py line 119 253097] Train: [11/100][60/510] Data 0.010 (0.078) Batch 1.200 (1.438) Remain 18:18:52 loss: 0.5846 Lr: 0.00596
[2023-12-25 04:06:31,268 INFO misc.py line 119 253097] Train: [11/100][61/510] Data 6.794 (0.193) Batch 7.811 (1.548) Remain 19:42:47 loss: 0.4301 Lr: 0.00596
[2023-12-25 04:06:32,360 INFO misc.py line 119 253097] Train: [11/100][62/510] Data 0.004 (0.190) Batch 1.090 (1.540) Remain 19:36:50 loss: 0.3674 Lr: 0.00596
[2023-12-25 04:06:33,516 INFO misc.py line 119 253097] Train: [11/100][63/510] Data 0.008 (0.187) Batch 1.157 (1.534) Remain 19:31:55 loss: 0.3392 Lr: 0.00596
[2023-12-25 04:06:34,762 INFO misc.py line 119 253097] Train: [11/100][64/510] Data 0.005 (0.184) Batch 1.246 (1.529) Remain 19:28:17 loss: 0.5067 Lr: 0.00596
[2023-12-25 04:06:35,769 INFO misc.py line 119 253097] Train: [11/100][65/510] Data 0.005 (0.181) Batch 1.008 (1.521) Remain 19:21:50 loss: 0.3940 Lr: 0.00596
[2023-12-25 04:06:36,824 INFO misc.py line 119 253097] Train: [11/100][66/510] Data 0.004 (0.178) Batch 1.052 (1.513) Remain 19:16:07 loss: 0.2368 Lr: 0.00596
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[2023-12-25 04:07:24,503 INFO misc.py line 119 253097] Train: [11/100][95/510] Data 0.007 (0.235) Batch 1.091 (1.555) Remain 19:46:45 loss: 0.3831 Lr: 0.00596
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[2023-12-25 04:07:34,021 INFO misc.py line 119 253097] Train: [11/100][97/510] Data 0.006 (0.230) Batch 8.569 (1.623) Remain 20:38:51 loss: 0.4418 Lr: 0.00596
[2023-12-25 04:07:35,225 INFO misc.py line 119 253097] Train: [11/100][98/510] Data 0.004 (0.228) Batch 1.203 (1.618) Remain 20:35:27 loss: 0.3467 Lr: 0.00596
[2023-12-25 04:07:44,947 INFO misc.py line 119 253097] Train: [11/100][99/510] Data 0.004 (0.225) Batch 9.722 (1.703) Remain 21:39:51 loss: 0.2951 Lr: 0.00596
[2023-12-25 04:07:46,251 INFO misc.py line 119 253097] Train: [11/100][100/510] Data 0.005 (0.223) Batch 1.305 (1.699) Remain 21:36:42 loss: 0.4131 Lr: 0.00596
[2023-12-25 04:07:47,414 INFO misc.py line 119 253097] Train: [11/100][101/510] Data 0.004 (0.221) Batch 1.161 (1.693) Remain 21:32:28 loss: 0.5201 Lr: 0.00596
[2023-12-25 04:07:48,581 INFO misc.py line 119 253097] Train: [11/100][102/510] Data 0.007 (0.219) Batch 1.166 (1.688) Remain 21:28:23 loss: 0.3848 Lr: 0.00596
[2023-12-25 04:07:55,064 INFO misc.py line 119 253097] Train: [11/100][103/510] Data 0.008 (0.217) Batch 6.485 (1.736) Remain 22:04:58 loss: 0.3324 Lr: 0.00596
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[2023-12-25 04:08:03,201 INFO misc.py line 119 253097] Train: [11/100][110/510] Data 0.006 (0.203) Batch 1.221 (1.698) Remain 21:36:09 loss: 0.2751 Lr: 0.00596
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[2023-12-25 04:08:07,556 INFO misc.py line 119 253097] Train: [11/100][114/510] Data 0.005 (0.196) Batch 1.050 (1.676) Remain 21:19:16 loss: 0.6360 Lr: 0.00596
[2023-12-25 04:08:08,749 INFO misc.py line 119 253097] Train: [11/100][115/510] Data 0.006 (0.194) Batch 1.191 (1.672) Remain 21:15:55 loss: 0.4946 Lr: 0.00596
[2023-12-25 04:08:16,344 INFO misc.py line 119 253097] Train: [11/100][116/510] Data 6.563 (0.250) Batch 7.599 (1.725) Remain 21:55:55 loss: 0.4541 Lr: 0.00596
[2023-12-25 04:08:17,421 INFO misc.py line 119 253097] Train: [11/100][117/510] Data 0.003 (0.248) Batch 1.077 (1.719) Remain 21:51:33 loss: 0.3166 Lr: 0.00596
[2023-12-25 04:08:18,440 INFO misc.py line 119 253097] Train: [11/100][118/510] Data 0.003 (0.246) Batch 1.018 (1.713) Remain 21:46:53 loss: 0.3223 Lr: 0.00596
[2023-12-25 04:08:19,583 INFO misc.py line 119 253097] Train: [11/100][119/510] Data 0.004 (0.244) Batch 1.144 (1.708) Remain 21:43:06 loss: 0.3772 Lr: 0.00596
[2023-12-25 04:08:20,715 INFO misc.py line 119 253097] Train: [11/100][120/510] Data 0.004 (0.242) Batch 1.131 (1.703) Remain 21:39:19 loss: 0.3440 Lr: 0.00596
[2023-12-25 04:08:21,780 INFO misc.py line 119 253097] Train: [11/100][121/510] Data 0.006 (0.240) Batch 1.062 (1.697) Remain 21:35:09 loss: 0.3317 Lr: 0.00596
[2023-12-25 04:08:22,864 INFO misc.py line 119 253097] Train: [11/100][122/510] Data 0.008 (0.238) Batch 1.088 (1.692) Remain 21:31:13 loss: 0.4021 Lr: 0.00596
[2023-12-25 04:08:24,027 INFO misc.py line 119 253097] Train: [11/100][123/510] Data 0.003 (0.236) Batch 1.162 (1.688) Remain 21:27:49 loss: 0.3851 Lr: 0.00596
[2023-12-25 04:08:25,292 INFO misc.py line 119 253097] Train: [11/100][124/510] Data 0.004 (0.234) Batch 1.264 (1.684) Remain 21:25:07 loss: 0.2972 Lr: 0.00596
[2023-12-25 04:08:26,281 INFO misc.py line 119 253097] Train: [11/100][125/510] Data 0.004 (0.232) Batch 0.989 (1.679) Remain 21:20:44 loss: 0.2298 Lr: 0.00596
[2023-12-25 04:08:27,382 INFO misc.py line 119 253097] Train: [11/100][126/510] Data 0.004 (0.230) Batch 1.102 (1.674) Remain 21:17:08 loss: 0.5065 Lr: 0.00595
[2023-12-25 04:08:28,554 INFO misc.py line 119 253097] Train: [11/100][127/510] Data 0.004 (0.228) Batch 1.173 (1.670) Remain 21:14:01 loss: 0.3478 Lr: 0.00595
[2023-12-25 04:08:29,735 INFO misc.py line 119 253097] Train: [11/100][128/510] Data 0.003 (0.227) Batch 1.179 (1.666) Remain 21:11:00 loss: 0.3574 Lr: 0.00595
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[2023-12-25 04:08:34,305 INFO misc.py line 119 253097] Train: [11/100][132/510] Data 0.006 (0.220) Batch 1.179 (1.650) Remain 20:58:24 loss: 0.3898 Lr: 0.00595
[2023-12-25 04:08:35,425 INFO misc.py line 119 253097] Train: [11/100][133/510] Data 0.021 (0.218) Batch 1.139 (1.646) Remain 20:55:22 loss: 0.4130 Lr: 0.00595
[2023-12-25 04:08:36,444 INFO misc.py line 119 253097] Train: [11/100][134/510] Data 0.003 (0.217) Batch 1.016 (1.641) Remain 20:51:41 loss: 0.2495 Lr: 0.00595
[2023-12-25 04:08:45,945 INFO misc.py line 119 253097] Train: [11/100][135/510] Data 0.006 (0.215) Batch 9.502 (1.701) Remain 21:37:05 loss: 0.2543 Lr: 0.00595
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[2023-12-25 04:08:48,049 INFO misc.py line 119 253097] Train: [11/100][137/510] Data 0.008 (0.212) Batch 1.257 (1.691) Remain 21:29:39 loss: 0.3090 Lr: 0.00595
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[2023-12-25 04:10:22,554 INFO misc.py line 119 253097] Train: [11/100][197/510] Data 0.010 (0.208) Batch 1.238 (1.655) Remain 21:00:39 loss: 0.7581 Lr: 0.00595
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[2023-12-25 04:10:27,314 INFO misc.py line 119 253097] Train: [11/100][200/510] Data 0.004 (0.204) Batch 1.223 (1.654) Remain 20:59:48 loss: 0.3838 Lr: 0.00595
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[2023-12-25 04:10:29,839 INFO misc.py line 119 253097] Train: [11/100][202/510] Data 0.006 (0.202) Batch 1.235 (1.650) Remain 20:56:44 loss: 0.2973 Lr: 0.00595
[2023-12-25 04:10:30,974 INFO misc.py line 119 253097] Train: [11/100][203/510] Data 0.009 (0.202) Batch 1.136 (1.647) Remain 20:54:45 loss: 0.3855 Lr: 0.00595
[2023-12-25 04:10:32,144 INFO misc.py line 119 253097] Train: [11/100][204/510] Data 0.008 (0.201) Batch 1.174 (1.645) Remain 20:52:55 loss: 0.3233 Lr: 0.00595
[2023-12-25 04:10:33,392 INFO misc.py line 119 253097] Train: [11/100][205/510] Data 0.008 (0.200) Batch 1.243 (1.643) Remain 20:51:23 loss: 0.4706 Lr: 0.00595
[2023-12-25 04:10:37,029 INFO misc.py line 119 253097] Train: [11/100][206/510] Data 2.418 (0.211) Batch 3.643 (1.653) Remain 20:58:51 loss: 0.1779 Lr: 0.00595
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[2023-12-25 04:10:40,884 INFO misc.py line 119 253097] Train: [11/100][209/510] Data 0.006 (0.208) Batch 1.252 (1.648) Remain 20:54:41 loss: 0.3572 Lr: 0.00595
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[2023-12-25 04:10:43,149 INFO misc.py line 119 253097] Train: [11/100][211/510] Data 0.007 (0.206) Batch 1.082 (1.643) Remain 20:50:51 loss: 0.2248 Lr: 0.00595
[2023-12-25 04:10:44,098 INFO misc.py line 119 253097] Train: [11/100][212/510] Data 0.005 (0.205) Batch 0.949 (1.639) Remain 20:48:18 loss: 0.3653 Lr: 0.00595
[2023-12-25 04:10:45,396 INFO misc.py line 119 253097] Train: [11/100][213/510] Data 0.005 (0.204) Batch 1.295 (1.638) Remain 20:47:02 loss: 0.3727 Lr: 0.00595
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[2023-12-25 04:10:47,654 INFO misc.py line 119 253097] Train: [11/100][215/510] Data 0.008 (0.202) Batch 1.098 (1.633) Remain 20:43:19 loss: 0.3579 Lr: 0.00595
[2023-12-25 04:10:49,003 INFO misc.py line 119 253097] Train: [11/100][216/510] Data 0.007 (0.201) Batch 1.348 (1.632) Remain 20:42:16 loss: 0.5092 Lr: 0.00595
[2023-12-25 04:10:50,168 INFO misc.py line 119 253097] Train: [11/100][217/510] Data 0.009 (0.200) Batch 1.165 (1.629) Remain 20:40:35 loss: 0.3551 Lr: 0.00595
[2023-12-25 04:10:51,275 INFO misc.py line 119 253097] Train: [11/100][218/510] Data 0.009 (0.199) Batch 1.112 (1.627) Remain 20:38:43 loss: 0.4372 Lr: 0.00595
[2023-12-25 04:10:52,254 INFO misc.py line 119 253097] Train: [11/100][219/510] Data 0.005 (0.198) Batch 0.980 (1.624) Remain 20:36:25 loss: 0.5055 Lr: 0.00595
[2023-12-25 04:11:05,203 INFO misc.py line 119 253097] Train: [11/100][220/510] Data 0.004 (0.197) Batch 12.946 (1.676) Remain 21:16:07 loss: 0.5753 Lr: 0.00595
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[2023-12-25 04:11:09,596 INFO misc.py line 119 253097] Train: [11/100][224/510] Data 0.006 (0.194) Batch 0.997 (1.666) Remain 21:08:01 loss: 0.4516 Lr: 0.00595
[2023-12-25 04:11:10,895 INFO misc.py line 119 253097] Train: [11/100][225/510] Data 0.010 (0.193) Batch 1.300 (1.664) Remain 21:06:44 loss: 0.2884 Lr: 0.00595
[2023-12-25 04:11:12,092 INFO misc.py line 119 253097] Train: [11/100][226/510] Data 0.010 (0.192) Batch 1.191 (1.662) Remain 21:05:06 loss: 0.4775 Lr: 0.00595
[2023-12-25 04:11:13,371 INFO misc.py line 119 253097] Train: [11/100][227/510] Data 0.016 (0.191) Batch 1.289 (1.660) Remain 21:03:48 loss: 0.5388 Lr: 0.00595
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[2023-12-25 04:11:15,564 INFO misc.py line 119 253097] Train: [11/100][229/510] Data 0.009 (0.190) Batch 0.915 (1.655) Remain 20:59:57 loss: 0.6073 Lr: 0.00595
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[2023-12-25 04:11:17,706 INFO misc.py line 119 253097] Train: [11/100][231/510] Data 0.003 (0.188) Batch 1.127 (1.650) Remain 20:56:00 loss: 0.4951 Lr: 0.00595
[2023-12-25 04:11:18,863 INFO misc.py line 119 253097] Train: [11/100][232/510] Data 0.004 (0.187) Batch 1.155 (1.648) Remain 20:54:20 loss: 0.4006 Lr: 0.00595
[2023-12-25 04:11:20,005 INFO misc.py line 119 253097] Train: [11/100][233/510] Data 0.010 (0.187) Batch 1.141 (1.646) Remain 20:52:37 loss: 0.4434 Lr: 0.00595
[2023-12-25 04:11:21,022 INFO misc.py line 119 253097] Train: [11/100][234/510] Data 0.008 (0.186) Batch 1.019 (1.643) Remain 20:50:32 loss: 0.4626 Lr: 0.00595
[2023-12-25 04:11:22,063 INFO misc.py line 119 253097] Train: [11/100][235/510] Data 0.004 (0.185) Batch 1.041 (1.640) Remain 20:48:32 loss: 0.3908 Lr: 0.00595
[2023-12-25 04:11:23,113 INFO misc.py line 119 253097] Train: [11/100][236/510] Data 0.005 (0.184) Batch 1.051 (1.638) Remain 20:46:34 loss: 0.2710 Lr: 0.00595
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[2023-12-25 04:11:26,742 INFO misc.py line 119 253097] Train: [11/100][239/510] Data 0.006 (0.182) Batch 1.177 (1.632) Remain 20:42:20 loss: 0.2968 Lr: 0.00595
[2023-12-25 04:11:27,872 INFO misc.py line 119 253097] Train: [11/100][240/510] Data 0.010 (0.181) Batch 1.133 (1.630) Remain 20:40:42 loss: 0.9392 Lr: 0.00595
[2023-12-25 04:11:29,010 INFO misc.py line 119 253097] Train: [11/100][241/510] Data 0.007 (0.181) Batch 1.139 (1.628) Remain 20:39:06 loss: 0.3341 Lr: 0.00595
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[2023-12-25 04:11:31,249 INFO misc.py line 119 253097] Train: [11/100][243/510] Data 0.004 (0.179) Batch 1.053 (1.624) Remain 20:35:48 loss: 0.3120 Lr: 0.00595
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[2023-12-25 04:11:33,669 INFO misc.py line 119 253097] Train: [11/100][245/510] Data 0.006 (0.178) Batch 1.255 (1.621) Remain 20:33:10 loss: 0.6030 Lr: 0.00595
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[2023-12-25 04:11:37,037 INFO misc.py line 119 253097] Train: [11/100][248/510] Data 0.004 (0.176) Batch 1.134 (1.615) Remain 20:28:26 loss: 0.4318 Lr: 0.00595
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[2023-12-25 04:13:07,530 INFO misc.py line 119 253097] Train: [11/100][302/510] Data 0.005 (0.159) Batch 1.089 (1.626) Remain 20:35:24 loss: 0.5522 Lr: 0.00595
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[2023-12-25 04:13:17,437 INFO misc.py line 119 253097] Train: [11/100][309/510] Data 1.452 (0.161) Batch 2.790 (1.621) Remain 20:31:33 loss: 0.3229 Lr: 0.00595
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[2023-12-25 04:13:24,184 INFO misc.py line 119 253097] Train: [11/100][315/510] Data 0.003 (0.158) Batch 0.902 (1.611) Remain 20:24:08 loss: 0.3588 Lr: 0.00595
[2023-12-25 04:13:25,450 INFO misc.py line 119 253097] Train: [11/100][316/510] Data 0.005 (0.157) Batch 1.262 (1.610) Remain 20:23:16 loss: 0.3868 Lr: 0.00595
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[2023-12-25 04:13:29,840 INFO misc.py line 119 253097] Train: [11/100][320/510] Data 0.004 (0.155) Batch 1.013 (1.604) Remain 20:18:15 loss: 0.2244 Lr: 0.00595
[2023-12-25 04:13:33,863 INFO misc.py line 119 253097] Train: [11/100][321/510] Data 0.007 (0.155) Batch 4.027 (1.611) Remain 20:24:00 loss: 0.2499 Lr: 0.00595
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[2023-12-25 04:13:37,825 INFO misc.py line 119 253097] Train: [11/100][323/510] Data 0.003 (0.154) Batch 2.914 (1.614) Remain 20:25:42 loss: 0.4049 Lr: 0.00595
[2023-12-25 04:13:39,003 INFO misc.py line 119 253097] Train: [11/100][324/510] Data 0.003 (0.153) Batch 1.178 (1.612) Remain 20:24:39 loss: 0.4911 Lr: 0.00595
[2023-12-25 04:13:40,105 INFO misc.py line 119 253097] Train: [11/100][325/510] Data 0.003 (0.153) Batch 1.102 (1.611) Remain 20:23:25 loss: 0.2652 Lr: 0.00595
[2023-12-25 04:13:41,427 INFO misc.py line 119 253097] Train: [11/100][326/510] Data 0.003 (0.152) Batch 1.318 (1.610) Remain 20:22:42 loss: 0.4632 Lr: 0.00595
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[2023-12-25 04:13:43,796 INFO misc.py line 119 253097] Train: [11/100][328/510] Data 0.007 (0.151) Batch 1.261 (1.607) Remain 20:20:40 loss: 0.4135 Lr: 0.00595
[2023-12-25 04:13:44,861 INFO misc.py line 119 253097] Train: [11/100][329/510] Data 0.005 (0.151) Batch 1.065 (1.605) Remain 20:19:23 loss: 0.2986 Lr: 0.00595
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[2023-12-25 04:13:47,192 INFO misc.py line 119 253097] Train: [11/100][331/510] Data 0.006 (0.150) Batch 1.207 (1.603) Remain 20:17:17 loss: 0.4569 Lr: 0.00595
[2023-12-25 04:13:48,322 INFO misc.py line 119 253097] Train: [11/100][332/510] Data 0.008 (0.150) Batch 1.128 (1.601) Remain 20:16:09 loss: 0.4142 Lr: 0.00595
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[2023-12-25 04:16:23,147 INFO misc.py line 119 253097] Train: [11/100][436/510] Data 0.003 (0.117) Batch 4.606 (1.574) Remain 19:52:54 loss: 0.5204 Lr: 0.00594
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[2023-12-25 04:17:33,140 INFO misc.py line 119 253097] Train: [11/100][485/510] Data 0.003 (0.106) Batch 1.102 (1.559) Remain 19:40:23 loss: 0.2961 Lr: 0.00594
[2023-12-25 04:17:34,460 INFO misc.py line 119 253097] Train: [11/100][486/510] Data 0.004 (0.106) Batch 1.313 (1.559) Remain 19:39:58 loss: 0.4436 Lr: 0.00594
[2023-12-25 04:17:35,708 INFO misc.py line 119 253097] Train: [11/100][487/510] Data 0.012 (0.105) Batch 1.255 (1.558) Remain 19:39:28 loss: 0.3659 Lr: 0.00594
[2023-12-25 04:17:36,955 INFO misc.py line 119 253097] Train: [11/100][488/510] Data 0.003 (0.105) Batch 1.244 (1.558) Remain 19:38:57 loss: 0.4690 Lr: 0.00594
[2023-12-25 04:17:38,184 INFO misc.py line 119 253097] Train: [11/100][489/510] Data 0.007 (0.105) Batch 1.224 (1.557) Remain 19:38:25 loss: 0.3528 Lr: 0.00594
[2023-12-25 04:17:39,315 INFO misc.py line 119 253097] Train: [11/100][490/510] Data 0.012 (0.105) Batch 1.127 (1.556) Remain 19:37:43 loss: 0.6572 Lr: 0.00594
[2023-12-25 04:17:43,615 INFO misc.py line 119 253097] Train: [11/100][491/510] Data 0.017 (0.105) Batch 4.310 (1.562) Remain 19:41:58 loss: 0.4798 Lr: 0.00594
[2023-12-25 04:17:55,845 INFO misc.py line 119 253097] Train: [11/100][492/510] Data 0.005 (0.104) Batch 12.232 (1.584) Remain 19:58:27 loss: 0.2820 Lr: 0.00594
[2023-12-25 04:17:57,072 INFO misc.py line 119 253097] Train: [11/100][493/510] Data 0.003 (0.104) Batch 1.227 (1.583) Remain 19:57:52 loss: 0.2810 Lr: 0.00594
[2023-12-25 04:17:58,127 INFO misc.py line 119 253097] Train: [11/100][494/510] Data 0.004 (0.104) Batch 1.054 (1.582) Remain 19:57:02 loss: 0.3834 Lr: 0.00594
[2023-12-25 04:17:59,312 INFO misc.py line 119 253097] Train: [11/100][495/510] Data 0.005 (0.104) Batch 1.185 (1.581) Remain 19:56:23 loss: 0.3091 Lr: 0.00594
[2023-12-25 04:18:00,346 INFO misc.py line 119 253097] Train: [11/100][496/510] Data 0.007 (0.104) Batch 1.031 (1.580) Remain 19:55:31 loss: 0.2821 Lr: 0.00594
[2023-12-25 04:18:01,499 INFO misc.py line 119 253097] Train: [11/100][497/510] Data 0.007 (0.103) Batch 1.151 (1.579) Remain 19:54:50 loss: 0.5035 Lr: 0.00594
[2023-12-25 04:18:02,804 INFO misc.py line 119 253097] Train: [11/100][498/510] Data 0.010 (0.103) Batch 1.309 (1.578) Remain 19:54:24 loss: 0.2460 Lr: 0.00594
[2023-12-25 04:18:03,905 INFO misc.py line 119 253097] Train: [11/100][499/510] Data 0.005 (0.103) Batch 1.101 (1.577) Remain 19:53:39 loss: 0.2723 Lr: 0.00594
[2023-12-25 04:18:05,049 INFO misc.py line 119 253097] Train: [11/100][500/510] Data 0.005 (0.103) Batch 1.145 (1.577) Remain 19:52:58 loss: 0.4757 Lr: 0.00594
[2023-12-25 04:18:06,052 INFO misc.py line 119 253097] Train: [11/100][501/510] Data 0.004 (0.103) Batch 1.001 (1.575) Remain 19:52:04 loss: 0.6493 Lr: 0.00594
[2023-12-25 04:18:07,081 INFO misc.py line 119 253097] Train: [11/100][502/510] Data 0.006 (0.102) Batch 1.028 (1.574) Remain 19:51:12 loss: 0.3369 Lr: 0.00594
[2023-12-25 04:18:08,229 INFO misc.py line 119 253097] Train: [11/100][503/510] Data 0.006 (0.102) Batch 1.148 (1.574) Remain 19:50:32 loss: 0.3200 Lr: 0.00594
[2023-12-25 04:18:09,268 INFO misc.py line 119 253097] Train: [11/100][504/510] Data 0.007 (0.102) Batch 1.042 (1.572) Remain 19:49:42 loss: 0.5229 Lr: 0.00594
[2023-12-25 04:18:10,608 INFO misc.py line 119 253097] Train: [11/100][505/510] Data 0.005 (0.102) Batch 1.338 (1.572) Remain 19:49:19 loss: 0.4259 Lr: 0.00594
[2023-12-25 04:18:11,623 INFO misc.py line 119 253097] Train: [11/100][506/510] Data 0.007 (0.102) Batch 1.015 (1.571) Remain 19:48:27 loss: 0.6315 Lr: 0.00594
[2023-12-25 04:18:12,758 INFO misc.py line 119 253097] Train: [11/100][507/510] Data 0.006 (0.101) Batch 1.133 (1.570) Remain 19:47:46 loss: 0.3316 Lr: 0.00594
[2023-12-25 04:18:13,795 INFO misc.py line 119 253097] Train: [11/100][508/510] Data 0.009 (0.101) Batch 1.038 (1.569) Remain 19:46:57 loss: 0.4251 Lr: 0.00594
[2023-12-25 04:18:21,391 INFO misc.py line 119 253097] Train: [11/100][509/510] Data 6.639 (0.114) Batch 7.599 (1.581) Remain 19:55:56 loss: 0.4530 Lr: 0.00594
[2023-12-25 04:18:22,626 INFO misc.py line 119 253097] Train: [11/100][510/510] Data 0.005 (0.114) Batch 1.236 (1.580) Remain 19:55:24 loss: 0.4879 Lr: 0.00594
[2023-12-25 04:18:22,629 INFO misc.py line 136 253097] Train result: loss: 0.3907 
[2023-12-25 04:18:22,629 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 04:18:47,460 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7793 
[2023-12-25 04:18:47,807 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.7437 
[2023-12-25 04:18:54,029 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5952 
[2023-12-25 04:18:54,549 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4637 
[2023-12-25 04:18:56,585 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8546 
[2023-12-25 04:18:57,010 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6835 
[2023-12-25 04:18:57,891 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.6508 
[2023-12-25 04:18:58,443 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5696 
[2023-12-25 04:19:00,250 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.6569 
[2023-12-25 04:19:02,372 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4569 
[2023-12-25 04:19:03,230 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3915 
[2023-12-25 04:19:03,658 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.1973 
[2023-12-25 04:19:04,562 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5023 
[2023-12-25 04:19:07,509 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 1.0806 
[2023-12-25 04:19:07,975 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.7691 
[2023-12-25 04:19:08,585 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 1.0768 
[2023-12-25 04:19:09,287 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.8860 
[2023-12-25 04:19:10,564 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.5742/0.6300/0.8642.
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9158/0.9566
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9778/0.9899
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7831/0.9748
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.1160/0.1227
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.2625/0.2655
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.4309/0.4878
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7910/0.8332
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9029/0.9479
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5308/0.5690
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7053/0.7961
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.5216/0.5403
[2023-12-25 04:19:10,565 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5272/0.7065
[2023-12-25 04:19:10,566 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 04:19:10,567 INFO misc.py line 165 253097] Currently Best mIoU: 0.6447
[2023-12-25 04:19:10,568 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 04:19:18,072 INFO misc.py line 119 253097] Train: [12/100][1/510] Data 3.754 (3.754) Batch 5.804 (5.804) Remain 73:10:50 loss: 0.3058 Lr: 0.00594
[2023-12-25 04:19:19,195 INFO misc.py line 119 253097] Train: [12/100][2/510] Data 0.008 (0.008) Batch 1.123 (1.123) Remain 14:09:52 loss: 0.5483 Lr: 0.00594
[2023-12-25 04:19:25,450 INFO misc.py line 119 253097] Train: [12/100][3/510] Data 5.041 (5.041) Batch 6.255 (6.255) Remain 78:51:49 loss: 0.4434 Lr: 0.00594
[2023-12-25 04:19:26,596 INFO misc.py line 119 253097] Train: [12/100][4/510] Data 0.005 (0.005) Batch 1.147 (1.147) Remain 14:27:33 loss: 0.5216 Lr: 0.00594
[2023-12-25 04:19:27,760 INFO misc.py line 119 253097] Train: [12/100][5/510] Data 0.005 (0.005) Batch 1.164 (1.155) Remain 14:33:57 loss: 0.3696 Lr: 0.00594
[2023-12-25 04:19:28,988 INFO misc.py line 119 253097] Train: [12/100][6/510] Data 0.004 (0.004) Batch 1.226 (1.179) Remain 14:51:43 loss: 0.3431 Lr: 0.00594
[2023-12-25 04:19:34,120 INFO misc.py line 119 253097] Train: [12/100][7/510] Data 0.007 (0.005) Batch 5.135 (2.168) Remain 27:19:46 loss: 0.5316 Lr: 0.00594
[2023-12-25 04:19:35,447 INFO misc.py line 119 253097] Train: [12/100][8/510] Data 0.004 (0.005) Batch 1.327 (2.000) Remain 25:12:28 loss: 0.4427 Lr: 0.00594
[2023-12-25 04:19:36,563 INFO misc.py line 119 253097] Train: [12/100][9/510] Data 0.004 (0.005) Batch 1.112 (1.852) Remain 23:20:34 loss: 0.5466 Lr: 0.00594
[2023-12-25 04:19:37,754 INFO misc.py line 119 253097] Train: [12/100][10/510] Data 0.008 (0.005) Batch 1.190 (1.757) Remain 22:09:04 loss: 0.1470 Lr: 0.00594
[2023-12-25 04:19:39,025 INFO misc.py line 119 253097] Train: [12/100][11/510] Data 0.008 (0.005) Batch 1.271 (1.696) Remain 21:23:05 loss: 0.3771 Lr: 0.00594
[2023-12-25 04:19:40,271 INFO misc.py line 119 253097] Train: [12/100][12/510] Data 0.008 (0.006) Batch 1.249 (1.647) Remain 20:45:27 loss: 0.3716 Lr: 0.00594
[2023-12-25 04:19:41,392 INFO misc.py line 119 253097] Train: [12/100][13/510] Data 0.005 (0.006) Batch 1.120 (1.594) Remain 20:05:34 loss: 0.5300 Lr: 0.00594
[2023-12-25 04:19:42,480 INFO misc.py line 119 253097] Train: [12/100][14/510] Data 0.006 (0.006) Batch 1.085 (1.548) Remain 19:30:34 loss: 0.4506 Lr: 0.00594
[2023-12-25 04:19:43,739 INFO misc.py line 119 253097] Train: [12/100][15/510] Data 0.009 (0.006) Batch 1.254 (1.523) Remain 19:12:03 loss: 0.2600 Lr: 0.00594
[2023-12-25 04:19:44,821 INFO misc.py line 119 253097] Train: [12/100][16/510] Data 0.013 (0.006) Batch 1.079 (1.489) Remain 18:46:09 loss: 0.4511 Lr: 0.00594
[2023-12-25 04:19:45,993 INFO misc.py line 119 253097] Train: [12/100][17/510] Data 0.018 (0.007) Batch 1.183 (1.467) Remain 18:29:36 loss: 0.3288 Lr: 0.00594
[2023-12-25 04:19:47,170 INFO misc.py line 119 253097] Train: [12/100][18/510] Data 0.006 (0.007) Batch 1.178 (1.448) Remain 18:15:00 loss: 0.3603 Lr: 0.00594
[2023-12-25 04:19:48,359 INFO misc.py line 119 253097] Train: [12/100][19/510] Data 0.005 (0.007) Batch 1.182 (1.431) Remain 18:02:23 loss: 0.1488 Lr: 0.00594
[2023-12-25 04:19:49,260 INFO misc.py line 119 253097] Train: [12/100][20/510] Data 0.012 (0.007) Batch 0.907 (1.401) Remain 17:39:02 loss: 0.2126 Lr: 0.00594
[2023-12-25 04:19:51,924 INFO misc.py line 119 253097] Train: [12/100][21/510] Data 0.006 (0.007) Batch 2.665 (1.471) Remain 18:32:07 loss: 0.3982 Lr: 0.00594
[2023-12-25 04:19:53,204 INFO misc.py line 119 253097] Train: [12/100][22/510] Data 0.005 (0.007) Batch 1.277 (1.461) Remain 18:24:23 loss: 0.1737 Lr: 0.00594
[2023-12-25 04:19:54,478 INFO misc.py line 119 253097] Train: [12/100][23/510] Data 0.008 (0.007) Batch 1.273 (1.451) Remain 18:17:16 loss: 0.5566 Lr: 0.00594
[2023-12-25 04:19:55,724 INFO misc.py line 119 253097] Train: [12/100][24/510] Data 0.009 (0.007) Batch 1.251 (1.442) Remain 18:10:02 loss: 0.9393 Lr: 0.00594
[2023-12-25 04:19:56,882 INFO misc.py line 119 253097] Train: [12/100][25/510] Data 0.004 (0.007) Batch 1.157 (1.429) Remain 18:00:14 loss: 0.8852 Lr: 0.00594
[2023-12-25 04:19:57,944 INFO misc.py line 119 253097] Train: [12/100][26/510] Data 0.005 (0.007) Batch 1.058 (1.413) Remain 17:48:02 loss: 0.2279 Lr: 0.00594
[2023-12-25 04:19:58,952 INFO misc.py line 119 253097] Train: [12/100][27/510] Data 0.009 (0.007) Batch 1.009 (1.396) Remain 17:35:17 loss: 0.3216 Lr: 0.00594
[2023-12-25 04:20:06,518 INFO misc.py line 119 253097] Train: [12/100][28/510] Data 6.547 (0.269) Batch 7.571 (1.643) Remain 20:42:00 loss: 0.4561 Lr: 0.00594
[2023-12-25 04:20:07,490 INFO misc.py line 119 253097] Train: [12/100][29/510] Data 0.003 (0.258) Batch 0.967 (1.617) Remain 20:22:20 loss: 0.3055 Lr: 0.00594
[2023-12-25 04:20:08,509 INFO misc.py line 119 253097] Train: [12/100][30/510] Data 0.008 (0.249) Batch 1.022 (1.595) Remain 20:05:40 loss: 0.8025 Lr: 0.00594
[2023-12-25 04:20:09,613 INFO misc.py line 119 253097] Train: [12/100][31/510] Data 0.005 (0.240) Batch 1.104 (1.577) Remain 19:52:23 loss: 0.2775 Lr: 0.00594
[2023-12-25 04:20:10,906 INFO misc.py line 119 253097] Train: [12/100][32/510] Data 0.005 (0.232) Batch 1.287 (1.567) Remain 19:44:48 loss: 0.3100 Lr: 0.00594
[2023-12-25 04:20:11,757 INFO misc.py line 119 253097] Train: [12/100][33/510] Data 0.010 (0.225) Batch 0.857 (1.544) Remain 19:26:52 loss: 0.6007 Lr: 0.00594
[2023-12-25 04:20:12,932 INFO misc.py line 119 253097] Train: [12/100][34/510] Data 0.004 (0.218) Batch 1.173 (1.532) Remain 19:17:48 loss: 0.3207 Lr: 0.00594
[2023-12-25 04:20:14,214 INFO misc.py line 119 253097] Train: [12/100][35/510] Data 0.006 (0.211) Batch 1.280 (1.524) Remain 19:11:51 loss: 0.2729 Lr: 0.00594
[2023-12-25 04:20:15,305 INFO misc.py line 119 253097] Train: [12/100][36/510] Data 0.008 (0.205) Batch 1.095 (1.511) Remain 19:02:00 loss: 0.4195 Lr: 0.00594
[2023-12-25 04:20:16,380 INFO misc.py line 119 253097] Train: [12/100][37/510] Data 0.004 (0.199) Batch 1.075 (1.498) Remain 18:52:16 loss: 0.3412 Lr: 0.00594
[2023-12-25 04:20:17,436 INFO misc.py line 119 253097] Train: [12/100][38/510] Data 0.004 (0.194) Batch 1.056 (1.485) Remain 18:42:41 loss: 0.2090 Lr: 0.00594
[2023-12-25 04:20:18,615 INFO misc.py line 119 253097] Train: [12/100][39/510] Data 0.007 (0.188) Batch 1.173 (1.477) Remain 18:36:06 loss: 0.2206 Lr: 0.00594
[2023-12-25 04:20:19,802 INFO misc.py line 119 253097] Train: [12/100][40/510] Data 0.013 (0.184) Batch 1.190 (1.469) Remain 18:30:13 loss: 0.2810 Lr: 0.00594
[2023-12-25 04:20:20,834 INFO misc.py line 119 253097] Train: [12/100][41/510] Data 0.008 (0.179) Batch 1.036 (1.457) Remain 18:21:36 loss: 0.3342 Lr: 0.00594
[2023-12-25 04:20:22,005 INFO misc.py line 119 253097] Train: [12/100][42/510] Data 0.004 (0.175) Batch 1.171 (1.450) Remain 18:16:01 loss: 0.4195 Lr: 0.00594
[2023-12-25 04:20:23,024 INFO misc.py line 119 253097] Train: [12/100][43/510] Data 0.004 (0.170) Batch 1.011 (1.439) Remain 18:07:42 loss: 0.3713 Lr: 0.00594
[2023-12-25 04:20:24,271 INFO misc.py line 119 253097] Train: [12/100][44/510] Data 0.012 (0.166) Batch 1.252 (1.435) Remain 18:04:13 loss: 0.4437 Lr: 0.00594
[2023-12-25 04:20:25,395 INFO misc.py line 119 253097] Train: [12/100][45/510] Data 0.007 (0.163) Batch 1.124 (1.427) Remain 17:58:36 loss: 0.1601 Lr: 0.00594
[2023-12-25 04:20:26,510 INFO misc.py line 119 253097] Train: [12/100][46/510] Data 0.007 (0.159) Batch 1.119 (1.420) Remain 17:53:09 loss: 0.1912 Lr: 0.00594
[2023-12-25 04:20:27,695 INFO misc.py line 119 253097] Train: [12/100][47/510] Data 0.005 (0.155) Batch 1.183 (1.415) Remain 17:49:04 loss: 0.2723 Lr: 0.00594
[2023-12-25 04:20:28,771 INFO misc.py line 119 253097] Train: [12/100][48/510] Data 0.006 (0.152) Batch 1.077 (1.407) Remain 17:43:22 loss: 0.1631 Lr: 0.00594
[2023-12-25 04:20:29,760 INFO misc.py line 119 253097] Train: [12/100][49/510] Data 0.004 (0.149) Batch 0.988 (1.398) Remain 17:36:28 loss: 0.4713 Lr: 0.00594
[2023-12-25 04:20:30,834 INFO misc.py line 119 253097] Train: [12/100][50/510] Data 0.004 (0.146) Batch 1.076 (1.391) Remain 17:31:16 loss: 0.3734 Lr: 0.00594
[2023-12-25 04:20:31,953 INFO misc.py line 119 253097] Train: [12/100][51/510] Data 0.003 (0.143) Batch 1.118 (1.385) Remain 17:26:57 loss: 0.2561 Lr: 0.00594
[2023-12-25 04:20:33,125 INFO misc.py line 119 253097] Train: [12/100][52/510] Data 0.004 (0.140) Batch 1.172 (1.381) Remain 17:23:37 loss: 0.3177 Lr: 0.00594
[2023-12-25 04:20:34,331 INFO misc.py line 119 253097] Train: [12/100][53/510] Data 0.004 (0.137) Batch 1.206 (1.378) Remain 17:20:57 loss: 0.3441 Lr: 0.00594
[2023-12-25 04:20:35,430 INFO misc.py line 119 253097] Train: [12/100][54/510] Data 0.003 (0.135) Batch 1.099 (1.372) Remain 17:16:49 loss: 0.2856 Lr: 0.00594
[2023-12-25 04:20:36,423 INFO misc.py line 119 253097] Train: [12/100][55/510] Data 0.004 (0.132) Batch 0.993 (1.365) Remain 17:11:17 loss: 0.4772 Lr: 0.00594
[2023-12-25 04:20:37,443 INFO misc.py line 119 253097] Train: [12/100][56/510] Data 0.003 (0.130) Batch 1.019 (1.358) Remain 17:06:19 loss: 0.2575 Lr: 0.00594
[2023-12-25 04:20:38,694 INFO misc.py line 119 253097] Train: [12/100][57/510] Data 0.005 (0.127) Batch 1.250 (1.356) Remain 17:04:47 loss: 0.1936 Lr: 0.00594
[2023-12-25 04:20:39,810 INFO misc.py line 119 253097] Train: [12/100][58/510] Data 0.005 (0.125) Batch 1.118 (1.352) Remain 17:01:29 loss: 0.3163 Lr: 0.00594
[2023-12-25 04:20:41,022 INFO misc.py line 119 253097] Train: [12/100][59/510] Data 0.005 (0.123) Batch 1.203 (1.349) Remain 16:59:27 loss: 0.3749 Lr: 0.00594
[2023-12-25 04:20:42,067 INFO misc.py line 119 253097] Train: [12/100][60/510] Data 0.013 (0.121) Batch 1.048 (1.344) Remain 16:55:26 loss: 0.4025 Lr: 0.00594
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[2023-12-25 04:21:13,444 INFO misc.py line 119 253097] Train: [12/100][85/510] Data 0.005 (0.086) Batch 1.117 (1.317) Remain 16:34:27 loss: 0.2268 Lr: 0.00594
[2023-12-25 04:21:14,748 INFO misc.py line 119 253097] Train: [12/100][86/510] Data 0.003 (0.085) Batch 1.299 (1.317) Remain 16:34:16 loss: 0.4126 Lr: 0.00594
[2023-12-25 04:21:15,979 INFO misc.py line 119 253097] Train: [12/100][87/510] Data 0.009 (0.084) Batch 1.235 (1.316) Remain 16:33:30 loss: 0.3179 Lr: 0.00594
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[2023-12-25 04:21:19,489 INFO misc.py line 119 253097] Train: [12/100][90/510] Data 0.010 (0.082) Batch 1.181 (1.311) Remain 16:29:37 loss: 0.2369 Lr: 0.00594
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[2023-12-25 04:21:21,570 INFO misc.py line 119 253097] Train: [12/100][92/510] Data 0.004 (0.080) Batch 0.989 (1.305) Remain 16:25:01 loss: 0.2406 Lr: 0.00594
[2023-12-25 04:21:22,757 INFO misc.py line 119 253097] Train: [12/100][93/510] Data 0.005 (0.079) Batch 1.182 (1.303) Remain 16:23:58 loss: 0.2553 Lr: 0.00594
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[2023-12-25 04:21:24,922 INFO misc.py line 119 253097] Train: [12/100][95/510] Data 0.005 (0.078) Batch 0.923 (1.299) Remain 16:20:20 loss: 0.3763 Lr: 0.00594
[2023-12-25 04:21:26,053 INFO misc.py line 119 253097] Train: [12/100][96/510] Data 0.005 (0.077) Batch 1.131 (1.297) Remain 16:18:57 loss: 0.5498 Lr: 0.00594
[2023-12-25 04:21:40,366 INFO misc.py line 119 253097] Train: [12/100][97/510] Data 0.005 (0.076) Batch 14.313 (1.435) Remain 18:03:27 loss: 0.4387 Lr: 0.00594
[2023-12-25 04:21:41,567 INFO misc.py line 119 253097] Train: [12/100][98/510] Data 0.005 (0.075) Batch 1.198 (1.433) Remain 18:01:33 loss: 0.2925 Lr: 0.00594
[2023-12-25 04:21:42,728 INFO misc.py line 119 253097] Train: [12/100][99/510] Data 0.008 (0.075) Batch 1.164 (1.430) Remain 17:59:25 loss: 0.5098 Lr: 0.00594
[2023-12-25 04:21:43,904 INFO misc.py line 119 253097] Train: [12/100][100/510] Data 0.004 (0.074) Batch 1.172 (1.427) Remain 17:57:23 loss: 0.1843 Lr: 0.00594
[2023-12-25 04:21:45,019 INFO misc.py line 119 253097] Train: [12/100][101/510] Data 0.009 (0.073) Batch 1.113 (1.424) Remain 17:54:56 loss: 0.6414 Lr: 0.00594
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[2023-12-25 04:21:47,069 INFO misc.py line 119 253097] Train: [12/100][103/510] Data 0.005 (0.072) Batch 1.060 (1.416) Remain 17:48:54 loss: 0.2997 Lr: 0.00594
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[2023-12-25 04:21:51,626 INFO misc.py line 119 253097] Train: [12/100][107/510] Data 0.008 (0.069) Batch 1.253 (1.406) Remain 17:40:46 loss: 0.4216 Lr: 0.00594
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[2023-12-25 04:21:53,861 INFO misc.py line 119 253097] Train: [12/100][109/510] Data 0.004 (0.068) Batch 1.220 (1.400) Remain 17:36:38 loss: 0.5995 Lr: 0.00594
[2023-12-25 04:21:55,001 INFO misc.py line 119 253097] Train: [12/100][110/510] Data 0.004 (0.068) Batch 1.135 (1.398) Remain 17:34:44 loss: 0.3269 Lr: 0.00594
[2023-12-25 04:21:56,092 INFO misc.py line 119 253097] Train: [12/100][111/510] Data 0.009 (0.067) Batch 1.094 (1.395) Remain 17:32:35 loss: 0.2492 Lr: 0.00594
[2023-12-25 04:21:57,317 INFO misc.py line 119 253097] Train: [12/100][112/510] Data 0.007 (0.066) Batch 1.228 (1.393) Remain 17:31:25 loss: 0.3796 Lr: 0.00594
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[2023-12-25 04:21:59,702 INFO misc.py line 119 253097] Train: [12/100][114/510] Data 0.007 (0.065) Batch 1.288 (1.390) Remain 17:28:35 loss: 0.3039 Lr: 0.00594
[2023-12-25 04:22:00,819 INFO misc.py line 119 253097] Train: [12/100][115/510] Data 0.010 (0.065) Batch 1.118 (1.387) Remain 17:26:44 loss: 0.4809 Lr: 0.00594
[2023-12-25 04:22:11,997 INFO misc.py line 119 253097] Train: [12/100][116/510] Data 10.155 (0.154) Batch 11.183 (1.474) Remain 18:32:08 loss: 0.3887 Lr: 0.00594
[2023-12-25 04:22:13,102 INFO misc.py line 119 253097] Train: [12/100][117/510] Data 0.004 (0.153) Batch 1.104 (1.471) Remain 18:29:39 loss: 0.3258 Lr: 0.00594
[2023-12-25 04:22:14,245 INFO misc.py line 119 253097] Train: [12/100][118/510] Data 0.004 (0.152) Batch 1.143 (1.468) Remain 18:27:29 loss: 0.2031 Lr: 0.00594
[2023-12-25 04:22:15,533 INFO misc.py line 119 253097] Train: [12/100][119/510] Data 0.005 (0.150) Batch 1.283 (1.466) Remain 18:26:15 loss: 0.4836 Lr: 0.00594
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[2023-12-25 04:22:17,947 INFO misc.py line 119 253097] Train: [12/100][121/510] Data 0.012 (0.148) Batch 1.186 (1.462) Remain 18:22:55 loss: 0.5492 Lr: 0.00594
[2023-12-25 04:22:19,036 INFO misc.py line 119 253097] Train: [12/100][122/510] Data 0.007 (0.147) Batch 1.086 (1.459) Remain 18:20:30 loss: 0.2604 Lr: 0.00594
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[2023-12-25 04:22:22,427 INFO misc.py line 119 253097] Train: [12/100][125/510] Data 0.005 (0.143) Batch 0.898 (1.451) Remain 18:14:22 loss: 0.5331 Lr: 0.00594
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[2023-12-25 04:22:24,414 INFO misc.py line 119 253097] Train: [12/100][127/510] Data 0.005 (0.141) Batch 0.866 (1.443) Remain 18:08:46 loss: 0.2692 Lr: 0.00594
[2023-12-25 04:22:25,604 INFO misc.py line 119 253097] Train: [12/100][128/510] Data 0.004 (0.140) Batch 1.187 (1.441) Remain 18:07:12 loss: 0.5805 Lr: 0.00594
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[2023-12-25 04:22:32,701 INFO misc.py line 119 253097] Train: [12/100][134/510] Data 0.004 (0.134) Batch 1.205 (1.429) Remain 17:58:07 loss: 0.1839 Lr: 0.00594
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[2023-12-25 04:22:49,495 INFO misc.py line 119 253097] Train: [12/100][139/510] Data 0.006 (0.129) Batch 1.202 (1.500) Remain 18:51:28 loss: 0.2236 Lr: 0.00594
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[2023-12-25 04:24:08,759 INFO misc.py line 119 253097] Train: [12/100][186/510] Data 4.513 (0.172) Batch 5.311 (1.548) Remain 19:26:22 loss: 0.2524 Lr: 0.00593
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[2023-12-25 04:24:27,508 INFO misc.py line 119 253097] Train: [12/100][198/510] Data 0.009 (0.162) Batch 1.127 (1.549) Remain 19:26:41 loss: 0.2629 Lr: 0.00593
[2023-12-25 04:24:28,571 INFO misc.py line 119 253097] Train: [12/100][199/510] Data 0.010 (0.161) Batch 1.063 (1.547) Remain 19:24:48 loss: 0.4625 Lr: 0.00593
[2023-12-25 04:24:29,734 INFO misc.py line 119 253097] Train: [12/100][200/510] Data 0.009 (0.160) Batch 1.166 (1.545) Remain 19:23:19 loss: 0.4858 Lr: 0.00593
[2023-12-25 04:24:30,928 INFO misc.py line 119 253097] Train: [12/100][201/510] Data 0.006 (0.160) Batch 1.195 (1.543) Remain 19:21:58 loss: 0.4299 Lr: 0.00593
[2023-12-25 04:24:32,044 INFO misc.py line 119 253097] Train: [12/100][202/510] Data 0.005 (0.159) Batch 1.109 (1.541) Remain 19:20:18 loss: 0.2566 Lr: 0.00593
[2023-12-25 04:24:33,128 INFO misc.py line 119 253097] Train: [12/100][203/510] Data 0.014 (0.158) Batch 1.091 (1.538) Remain 19:18:35 loss: 0.2533 Lr: 0.00593
[2023-12-25 04:24:42,058 INFO misc.py line 119 253097] Train: [12/100][204/510] Data 0.005 (0.157) Batch 8.930 (1.575) Remain 19:46:15 loss: 0.4035 Lr: 0.00593
[2023-12-25 04:24:43,363 INFO misc.py line 119 253097] Train: [12/100][205/510] Data 0.005 (0.157) Batch 1.306 (1.574) Remain 19:45:13 loss: 0.3593 Lr: 0.00593
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[2023-12-25 04:24:45,766 INFO misc.py line 119 253097] Train: [12/100][207/510] Data 0.007 (0.155) Batch 1.165 (1.570) Remain 19:42:24 loss: 0.4997 Lr: 0.00593
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[2023-12-25 04:24:48,743 INFO misc.py line 119 253097] Train: [12/100][210/510] Data 0.004 (0.153) Batch 0.907 (1.562) Remain 19:36:00 loss: 0.2784 Lr: 0.00593
[2023-12-25 04:24:49,894 INFO misc.py line 119 253097] Train: [12/100][211/510] Data 0.011 (0.152) Batch 1.158 (1.560) Remain 19:34:31 loss: 0.7423 Lr: 0.00593
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[2023-12-25 04:24:53,297 INFO misc.py line 119 253097] Train: [12/100][214/510] Data 0.004 (0.150) Batch 0.913 (1.554) Remain 19:29:53 loss: 0.4304 Lr: 0.00593
[2023-12-25 04:24:54,420 INFO misc.py line 119 253097] Train: [12/100][215/510] Data 0.004 (0.150) Batch 1.123 (1.552) Remain 19:28:20 loss: 0.3611 Lr: 0.00593
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[2023-12-25 04:24:57,858 INFO misc.py line 119 253097] Train: [12/100][218/510] Data 0.007 (0.148) Batch 1.157 (1.546) Remain 19:23:59 loss: 0.2626 Lr: 0.00593
[2023-12-25 04:24:58,890 INFO misc.py line 119 253097] Train: [12/100][219/510] Data 0.004 (0.147) Batch 1.029 (1.544) Remain 19:22:09 loss: 0.3458 Lr: 0.00593
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[2023-12-25 04:25:20,562 INFO misc.py line 119 253097] Train: [12/100][234/510] Data 4.664 (0.158) Batch 5.451 (1.537) Remain 19:16:57 loss: 0.2254 Lr: 0.00593
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[2023-12-25 04:25:22,867 INFO misc.py line 119 253097] Train: [12/100][236/510] Data 0.004 (0.157) Batch 1.139 (1.534) Remain 19:14:25 loss: 0.5404 Lr: 0.00593
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[2023-12-25 04:25:24,876 INFO misc.py line 119 253097] Train: [12/100][238/510] Data 0.009 (0.155) Batch 0.965 (1.529) Remain 19:10:58 loss: 0.7510 Lr: 0.00593
[2023-12-25 04:25:26,144 INFO misc.py line 119 253097] Train: [12/100][239/510] Data 0.003 (0.155) Batch 1.262 (1.528) Remain 19:10:06 loss: 0.3108 Lr: 0.00593
[2023-12-25 04:25:27,319 INFO misc.py line 119 253097] Train: [12/100][240/510] Data 0.008 (0.154) Batch 1.176 (1.527) Remain 19:08:57 loss: 0.2721 Lr: 0.00593
[2023-12-25 04:25:28,537 INFO misc.py line 119 253097] Train: [12/100][241/510] Data 0.008 (0.153) Batch 1.222 (1.526) Remain 19:07:58 loss: 0.2434 Lr: 0.00593
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[2023-12-25 04:25:32,054 INFO misc.py line 119 253097] Train: [12/100][243/510] Data 0.005 (0.152) Batch 2.546 (1.528) Remain 19:09:22 loss: 0.4414 Lr: 0.00593
[2023-12-25 04:25:33,179 INFO misc.py line 119 253097] Train: [12/100][244/510] Data 0.003 (0.152) Batch 1.125 (1.526) Remain 19:08:06 loss: 0.6718 Lr: 0.00593
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[2023-12-25 04:26:56,809 INFO misc.py line 119 253097] Train: [12/100][300/510] Data 0.003 (0.135) Batch 1.026 (1.520) Remain 19:02:03 loss: 0.3267 Lr: 0.00593
[2023-12-25 04:26:59,909 INFO misc.py line 119 253097] Train: [12/100][301/510] Data 2.122 (0.142) Batch 3.107 (1.525) Remain 19:06:02 loss: 0.4236 Lr: 0.00593
[2023-12-25 04:27:02,527 INFO misc.py line 119 253097] Train: [12/100][302/510] Data 1.662 (0.147) Batch 2.617 (1.529) Remain 19:08:45 loss: 0.1600 Lr: 0.00593
[2023-12-25 04:27:03,698 INFO misc.py line 119 253097] Train: [12/100][303/510] Data 0.005 (0.147) Batch 1.169 (1.527) Remain 19:07:49 loss: 0.2925 Lr: 0.00593
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[2023-12-25 04:27:06,184 INFO misc.py line 119 253097] Train: [12/100][305/510] Data 0.008 (0.146) Batch 1.218 (1.526) Remain 19:06:22 loss: 0.1627 Lr: 0.00593
[2023-12-25 04:27:16,880 INFO misc.py line 119 253097] Train: [12/100][306/510] Data 9.489 (0.176) Batch 10.696 (1.556) Remain 19:29:05 loss: 0.6774 Lr: 0.00593
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[2023-12-25 04:27:21,433 INFO misc.py line 119 253097] Train: [12/100][310/510] Data 0.009 (0.174) Batch 1.277 (1.550) Remain 19:24:53 loss: 0.4341 Lr: 0.00593
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[2023-12-25 04:27:23,940 INFO misc.py line 119 253097] Train: [12/100][312/510] Data 0.008 (0.173) Batch 1.223 (1.549) Remain 19:23:23 loss: 0.3320 Lr: 0.00593
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[2023-12-25 04:27:26,197 INFO misc.py line 119 253097] Train: [12/100][314/510] Data 0.009 (0.172) Batch 1.057 (1.546) Remain 19:21:18 loss: 0.3324 Lr: 0.00593
[2023-12-25 04:27:27,374 INFO misc.py line 119 253097] Train: [12/100][315/510] Data 0.009 (0.172) Batch 1.178 (1.545) Remain 19:20:23 loss: 0.2649 Lr: 0.00593
[2023-12-25 04:27:28,619 INFO misc.py line 119 253097] Train: [12/100][316/510] Data 0.008 (0.171) Batch 1.240 (1.544) Remain 19:19:38 loss: 0.5490 Lr: 0.00593
[2023-12-25 04:27:29,763 INFO misc.py line 119 253097] Train: [12/100][317/510] Data 0.013 (0.171) Batch 1.153 (1.542) Remain 19:18:40 loss: 0.1867 Lr: 0.00593
[2023-12-25 04:27:31,102 INFO misc.py line 119 253097] Train: [12/100][318/510] Data 0.004 (0.170) Batch 1.333 (1.542) Remain 19:18:09 loss: 0.5123 Lr: 0.00593
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[2023-12-25 04:27:33,497 INFO misc.py line 119 253097] Train: [12/100][320/510] Data 0.007 (0.169) Batch 1.178 (1.540) Remain 19:16:28 loss: 0.4509 Lr: 0.00593
[2023-12-25 04:27:34,996 INFO misc.py line 119 253097] Train: [12/100][321/510] Data 0.004 (0.168) Batch 1.500 (1.539) Remain 19:16:21 loss: 0.2653 Lr: 0.00593
[2023-12-25 04:27:36,065 INFO misc.py line 119 253097] Train: [12/100][322/510] Data 0.004 (0.168) Batch 1.055 (1.538) Remain 19:15:11 loss: 0.2141 Lr: 0.00593
[2023-12-25 04:27:39,718 INFO misc.py line 119 253097] Train: [12/100][323/510] Data 2.682 (0.176) Batch 3.666 (1.545) Remain 19:20:09 loss: 0.2442 Lr: 0.00593
[2023-12-25 04:27:41,623 INFO misc.py line 119 253097] Train: [12/100][324/510] Data 0.004 (0.175) Batch 1.903 (1.546) Remain 19:20:58 loss: 0.4609 Lr: 0.00593
[2023-12-25 04:27:42,585 INFO misc.py line 119 253097] Train: [12/100][325/510] Data 0.007 (0.175) Batch 0.964 (1.544) Remain 19:19:35 loss: 0.4374 Lr: 0.00593
[2023-12-25 04:27:45,987 INFO misc.py line 119 253097] Train: [12/100][326/510] Data 0.005 (0.174) Batch 3.401 (1.550) Remain 19:23:53 loss: 0.3866 Lr: 0.00593
[2023-12-25 04:27:47,157 INFO misc.py line 119 253097] Train: [12/100][327/510] Data 0.006 (0.174) Batch 1.172 (1.548) Remain 19:22:59 loss: 0.4688 Lr: 0.00593
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[2023-12-25 04:27:51,311 INFO misc.py line 119 253097] Train: [12/100][331/510] Data 0.004 (0.172) Batch 0.937 (1.542) Remain 19:18:12 loss: 0.5014 Lr: 0.00593
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[2023-12-25 04:27:55,763 INFO misc.py line 119 253097] Train: [12/100][335/510] Data 0.015 (0.170) Batch 1.115 (1.537) Remain 19:14:13 loss: 0.2958 Lr: 0.00593
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[2023-12-25 04:29:28,807 INFO misc.py line 119 253097] Train: [12/100][398/510] Data 0.005 (0.152) Batch 1.152 (1.527) Remain 19:05:24 loss: 0.4450 Lr: 0.00592
[2023-12-25 04:29:29,864 INFO misc.py line 119 253097] Train: [12/100][399/510] Data 0.009 (0.151) Batch 1.058 (1.526) Remain 19:04:29 loss: 0.5464 Lr: 0.00592
[2023-12-25 04:29:36,895 INFO misc.py line 119 253097] Train: [12/100][400/510] Data 0.008 (0.151) Batch 7.034 (1.540) Remain 19:14:51 loss: 0.8406 Lr: 0.00592
[2023-12-25 04:29:37,961 INFO misc.py line 119 253097] Train: [12/100][401/510] Data 0.006 (0.151) Batch 1.064 (1.539) Remain 19:13:56 loss: 0.4602 Lr: 0.00592
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[2023-12-25 04:29:46,457 INFO misc.py line 119 253097] Train: [12/100][406/510] Data 0.009 (0.149) Batch 3.610 (1.541) Remain 19:15:18 loss: 0.4180 Lr: 0.00592
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[2023-12-25 04:29:58,036 INFO misc.py line 119 253097] Train: [12/100][410/510] Data 4.385 (0.158) Batch 5.585 (1.554) Remain 19:25:10 loss: 0.2179 Lr: 0.00592
[2023-12-25 04:29:59,178 INFO misc.py line 119 253097] Train: [12/100][411/510] Data 0.004 (0.158) Batch 1.141 (1.553) Remain 19:24:23 loss: 0.2677 Lr: 0.00592
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[2023-12-25 04:30:02,685 INFO misc.py line 119 253097] Train: [12/100][414/510] Data 0.005 (0.157) Batch 1.192 (1.550) Remain 19:22:13 loss: 0.3416 Lr: 0.00592
[2023-12-25 04:30:03,644 INFO misc.py line 119 253097] Train: [12/100][415/510] Data 0.004 (0.156) Batch 0.958 (1.549) Remain 19:21:06 loss: 0.2955 Lr: 0.00592
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[2023-12-25 04:30:09,278 INFO misc.py line 119 253097] Train: [12/100][420/510] Data 0.003 (0.154) Batch 1.152 (1.544) Remain 19:17:11 loss: 0.4946 Lr: 0.00592
[2023-12-25 04:30:10,231 INFO misc.py line 119 253097] Train: [12/100][421/510] Data 0.007 (0.154) Batch 0.956 (1.543) Remain 19:16:06 loss: 0.4488 Lr: 0.00592
[2023-12-25 04:30:11,468 INFO misc.py line 119 253097] Train: [12/100][422/510] Data 0.003 (0.154) Batch 1.236 (1.542) Remain 19:15:32 loss: 0.5094 Lr: 0.00592
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[2023-12-25 04:30:19,520 INFO misc.py line 119 253097] Train: [12/100][424/510] Data 5.541 (0.166) Batch 6.786 (1.554) Remain 19:24:19 loss: 0.4029 Lr: 0.00592
[2023-12-25 04:30:20,399 INFO misc.py line 119 253097] Train: [12/100][425/510] Data 0.003 (0.166) Batch 0.879 (1.552) Remain 19:23:06 loss: 0.4697 Lr: 0.00592
[2023-12-25 04:30:21,599 INFO misc.py line 119 253097] Train: [12/100][426/510] Data 0.003 (0.165) Batch 1.194 (1.551) Remain 19:22:26 loss: 0.2470 Lr: 0.00592
[2023-12-25 04:30:22,745 INFO misc.py line 119 253097] Train: [12/100][427/510] Data 0.009 (0.165) Batch 1.149 (1.550) Remain 19:21:42 loss: 0.3688 Lr: 0.00592
[2023-12-25 04:30:23,891 INFO misc.py line 119 253097] Train: [12/100][428/510] Data 0.006 (0.165) Batch 1.144 (1.549) Remain 19:20:58 loss: 0.2397 Lr: 0.00592
[2023-12-25 04:30:24,980 INFO misc.py line 119 253097] Train: [12/100][429/510] Data 0.008 (0.164) Batch 1.093 (1.548) Remain 19:20:08 loss: 0.3700 Lr: 0.00592
[2023-12-25 04:30:26,012 INFO misc.py line 119 253097] Train: [12/100][430/510] Data 0.003 (0.164) Batch 1.025 (1.547) Remain 19:19:11 loss: 0.2842 Lr: 0.00592
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[2023-12-25 04:30:39,739 INFO misc.py line 119 253097] Train: [12/100][442/510] Data 0.003 (0.160) Batch 1.171 (1.536) Remain 19:10:38 loss: 0.4673 Lr: 0.00592
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[2023-12-25 04:30:45,695 INFO misc.py line 119 253097] Train: [12/100][444/510] Data 0.008 (0.159) Batch 4.750 (1.543) Remain 19:15:29 loss: 0.2703 Lr: 0.00592
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[2023-12-25 04:31:13,258 INFO misc.py line 119 253097] Train: [12/100][459/510] Data 9.852 (0.175) Batch 11.128 (1.552) Remain 19:22:22 loss: 0.6961 Lr: 0.00592
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[2023-12-25 04:31:36,016 INFO misc.py line 119 253097] Train: [12/100][479/510] Data 0.031 (0.168) Batch 0.990 (1.535) Remain 19:08:36 loss: 0.5477 Lr: 0.00592
[2023-12-25 04:31:46,718 INFO misc.py line 119 253097] Train: [12/100][480/510] Data 0.142 (0.168) Batch 10.838 (1.554) Remain 19:23:10 loss: 0.2876 Lr: 0.00592
[2023-12-25 04:31:47,990 INFO misc.py line 119 253097] Train: [12/100][481/510] Data 0.005 (0.168) Batch 1.274 (1.553) Remain 19:22:43 loss: 0.4666 Lr: 0.00592
[2023-12-25 04:31:49,141 INFO misc.py line 119 253097] Train: [12/100][482/510] Data 0.004 (0.168) Batch 1.151 (1.553) Remain 19:22:03 loss: 0.2237 Lr: 0.00592
[2023-12-25 04:31:50,233 INFO misc.py line 119 253097] Train: [12/100][483/510] Data 0.004 (0.167) Batch 1.093 (1.552) Remain 19:21:19 loss: 0.3010 Lr: 0.00592
[2023-12-25 04:31:51,363 INFO misc.py line 119 253097] Train: [12/100][484/510] Data 0.003 (0.167) Batch 1.128 (1.551) Remain 19:20:38 loss: 0.4121 Lr: 0.00592
[2023-12-25 04:31:52,331 INFO misc.py line 119 253097] Train: [12/100][485/510] Data 0.004 (0.167) Batch 0.968 (1.550) Remain 19:19:42 loss: 0.3869 Lr: 0.00592
[2023-12-25 04:31:53,452 INFO misc.py line 119 253097] Train: [12/100][486/510] Data 0.005 (0.166) Batch 1.121 (1.549) Remain 19:19:00 loss: 0.2366 Lr: 0.00592
[2023-12-25 04:31:54,402 INFO misc.py line 119 253097] Train: [12/100][487/510] Data 0.004 (0.166) Batch 0.949 (1.547) Remain 19:18:03 loss: 0.1921 Lr: 0.00592
[2023-12-25 04:31:55,667 INFO misc.py line 119 253097] Train: [12/100][488/510] Data 0.006 (0.166) Batch 1.264 (1.547) Remain 19:17:35 loss: 0.2229 Lr: 0.00592
[2023-12-25 04:31:56,628 INFO misc.py line 119 253097] Train: [12/100][489/510] Data 0.007 (0.165) Batch 0.961 (1.546) Remain 19:16:40 loss: 0.3741 Lr: 0.00592
[2023-12-25 04:31:57,649 INFO misc.py line 119 253097] Train: [12/100][490/510] Data 0.007 (0.165) Batch 1.024 (1.545) Remain 19:15:50 loss: 0.3090 Lr: 0.00592
[2023-12-25 04:31:58,845 INFO misc.py line 119 253097] Train: [12/100][491/510] Data 0.004 (0.165) Batch 1.196 (1.544) Remain 19:15:16 loss: 0.5439 Lr: 0.00592
[2023-12-25 04:32:07,198 INFO misc.py line 119 253097] Train: [12/100][492/510] Data 0.003 (0.164) Batch 8.353 (1.558) Remain 19:25:40 loss: 0.4313 Lr: 0.00592
[2023-12-25 04:32:08,389 INFO misc.py line 119 253097] Train: [12/100][493/510] Data 0.003 (0.164) Batch 1.191 (1.557) Remain 19:25:05 loss: 0.2542 Lr: 0.00592
[2023-12-25 04:32:09,657 INFO misc.py line 119 253097] Train: [12/100][494/510] Data 0.004 (0.164) Batch 1.268 (1.556) Remain 19:24:37 loss: 0.4722 Lr: 0.00592
[2023-12-25 04:32:10,569 INFO misc.py line 119 253097] Train: [12/100][495/510] Data 0.004 (0.163) Batch 0.913 (1.555) Remain 19:23:37 loss: 0.3778 Lr: 0.00592
[2023-12-25 04:32:11,752 INFO misc.py line 119 253097] Train: [12/100][496/510] Data 0.003 (0.163) Batch 1.182 (1.554) Remain 19:23:01 loss: 0.3499 Lr: 0.00592
[2023-12-25 04:32:12,852 INFO misc.py line 119 253097] Train: [12/100][497/510] Data 0.004 (0.163) Batch 1.101 (1.553) Remain 19:22:18 loss: 0.2239 Lr: 0.00592
[2023-12-25 04:32:18,275 INFO misc.py line 119 253097] Train: [12/100][498/510] Data 0.003 (0.162) Batch 5.422 (1.561) Remain 19:28:08 loss: 0.4917 Lr: 0.00592
[2023-12-25 04:32:19,454 INFO misc.py line 119 253097] Train: [12/100][499/510] Data 0.005 (0.162) Batch 1.179 (1.560) Remain 19:27:32 loss: 0.2718 Lr: 0.00592
[2023-12-25 04:32:20,571 INFO misc.py line 119 253097] Train: [12/100][500/510] Data 0.004 (0.162) Batch 1.116 (1.560) Remain 19:26:50 loss: 0.5609 Lr: 0.00592
[2023-12-25 04:32:21,626 INFO misc.py line 119 253097] Train: [12/100][501/510] Data 0.005 (0.161) Batch 1.054 (1.559) Remain 19:26:03 loss: 0.2884 Lr: 0.00592
[2023-12-25 04:32:22,792 INFO misc.py line 119 253097] Train: [12/100][502/510] Data 0.008 (0.161) Batch 1.168 (1.558) Remain 19:25:26 loss: 0.4704 Lr: 0.00592
[2023-12-25 04:32:23,698 INFO misc.py line 119 253097] Train: [12/100][503/510] Data 0.004 (0.161) Batch 0.905 (1.556) Remain 19:24:26 loss: 0.4504 Lr: 0.00592
[2023-12-25 04:32:24,882 INFO misc.py line 119 253097] Train: [12/100][504/510] Data 0.005 (0.160) Batch 1.186 (1.556) Remain 19:23:51 loss: 0.5148 Lr: 0.00592
[2023-12-25 04:32:26,081 INFO misc.py line 119 253097] Train: [12/100][505/510] Data 0.004 (0.160) Batch 1.195 (1.555) Remain 19:23:17 loss: 0.3534 Lr: 0.00592
[2023-12-25 04:32:27,258 INFO misc.py line 119 253097] Train: [12/100][506/510] Data 0.008 (0.160) Batch 1.181 (1.554) Remain 19:22:42 loss: 0.3993 Lr: 0.00592
[2023-12-25 04:32:28,280 INFO misc.py line 119 253097] Train: [12/100][507/510] Data 0.003 (0.160) Batch 1.019 (1.553) Remain 19:21:53 loss: 0.3481 Lr: 0.00592
[2023-12-25 04:32:29,502 INFO misc.py line 119 253097] Train: [12/100][508/510] Data 0.007 (0.159) Batch 1.223 (1.553) Remain 19:21:22 loss: 0.2935 Lr: 0.00592
[2023-12-25 04:32:30,752 INFO misc.py line 119 253097] Train: [12/100][509/510] Data 0.004 (0.159) Batch 1.250 (1.552) Remain 19:20:54 loss: 0.2646 Lr: 0.00592
[2023-12-25 04:32:31,809 INFO misc.py line 119 253097] Train: [12/100][510/510] Data 0.005 (0.159) Batch 1.057 (1.551) Remain 19:20:08 loss: 0.3152 Lr: 0.00592
[2023-12-25 04:32:31,809 INFO misc.py line 136 253097] Train result: loss: 0.3801 
[2023-12-25 04:32:31,810 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 04:32:57,320 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5471 
[2023-12-25 04:32:57,710 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.5672 
[2023-12-25 04:33:04,314 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6994 
[2023-12-25 04:33:04,839 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.6995 
[2023-12-25 04:33:06,823 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.1876 
[2023-12-25 04:33:07,246 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6477 
[2023-12-25 04:33:08,127 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.7661 
[2023-12-25 04:33:08,679 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.6904 
[2023-12-25 04:33:10,501 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.7769 
[2023-12-25 04:33:12,620 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4560 
[2023-12-25 04:33:13,475 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3647 
[2023-12-25 04:33:13,903 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0198 
[2023-12-25 04:33:14,806 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 1.0960 
[2023-12-25 04:33:17,749 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7501 
[2023-12-25 04:33:18,214 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.6500 
[2023-12-25 04:33:18,823 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5699 
[2023-12-25 04:33:19,523 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5713 
[2023-12-25 04:33:20,924 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6313/0.6989/0.8766.
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9120/0.9606
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9772/0.9912
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7910/0.9563
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0017/0.0192
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2550/0.3062
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6011/0.7082
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.3435/0.3824
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7995/0.9044
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9011/0.9539
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6630/0.7311
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7237/0.7940
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6769/0.7147
[2023-12-25 04:33:20,924 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5616/0.6635
[2023-12-25 04:33:20,925 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 04:33:20,926 INFO misc.py line 165 253097] Currently Best mIoU: 0.6447
[2023-12-25 04:33:20,926 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 04:33:30,111 INFO misc.py line 119 253097] Train: [13/100][1/510] Data 3.475 (3.475) Batch 6.475 (6.475) Remain 80:43:19 loss: 0.8680 Lr: 0.00592
[2023-12-25 04:33:43,207 INFO misc.py line 119 253097] Train: [13/100][2/510] Data 0.005 (0.005) Batch 13.097 (13.097) Remain 163:16:13 loss: 0.4650 Lr: 0.00592
[2023-12-25 04:33:44,363 INFO misc.py line 119 253097] Train: [13/100][3/510] Data 0.003 (0.003) Batch 1.155 (1.155) Remain 14:24:12 loss: 0.6126 Lr: 0.00592
[2023-12-25 04:33:45,475 INFO misc.py line 119 253097] Train: [13/100][4/510] Data 0.003 (0.003) Batch 1.112 (1.112) Remain 13:51:27 loss: 0.3827 Lr: 0.00592
[2023-12-25 04:33:46,574 INFO misc.py line 119 253097] Train: [13/100][5/510] Data 0.005 (0.004) Batch 1.099 (1.106) Remain 13:46:51 loss: 0.3838 Lr: 0.00592
[2023-12-25 04:33:47,603 INFO misc.py line 119 253097] Train: [13/100][6/510] Data 0.003 (0.004) Batch 1.030 (1.080) Remain 13:27:54 loss: 0.3980 Lr: 0.00592
[2023-12-25 04:33:48,832 INFO misc.py line 119 253097] Train: [13/100][7/510] Data 0.003 (0.004) Batch 1.228 (1.117) Remain 13:55:25 loss: 0.2263 Lr: 0.00592
[2023-12-25 04:33:50,111 INFO misc.py line 119 253097] Train: [13/100][8/510] Data 0.004 (0.004) Batch 1.274 (1.148) Remain 14:18:50 loss: 0.1938 Lr: 0.00592
[2023-12-25 04:33:51,051 INFO misc.py line 119 253097] Train: [13/100][9/510] Data 0.010 (0.005) Batch 0.946 (1.115) Remain 13:53:33 loss: 0.6519 Lr: 0.00592
[2023-12-25 04:33:52,378 INFO misc.py line 119 253097] Train: [13/100][10/510] Data 0.005 (0.005) Batch 1.326 (1.145) Remain 14:16:10 loss: 0.3341 Lr: 0.00592
[2023-12-25 04:33:53,533 INFO misc.py line 119 253097] Train: [13/100][11/510] Data 0.005 (0.005) Batch 1.155 (1.146) Remain 14:17:03 loss: 0.3048 Lr: 0.00592
[2023-12-25 04:33:54,823 INFO misc.py line 119 253097] Train: [13/100][12/510] Data 0.005 (0.005) Batch 1.286 (1.162) Remain 14:28:40 loss: 0.3001 Lr: 0.00592
[2023-12-25 04:33:55,826 INFO misc.py line 119 253097] Train: [13/100][13/510] Data 0.009 (0.005) Batch 1.004 (1.146) Remain 14:16:50 loss: 0.2577 Lr: 0.00592
[2023-12-25 04:33:57,062 INFO misc.py line 119 253097] Train: [13/100][14/510] Data 0.008 (0.006) Batch 1.237 (1.154) Remain 14:23:01 loss: 0.3650 Lr: 0.00592
[2023-12-25 04:33:57,874 INFO misc.py line 119 253097] Train: [13/100][15/510] Data 0.007 (0.006) Batch 0.816 (1.126) Remain 14:01:56 loss: 0.3987 Lr: 0.00592
[2023-12-25 04:34:06,017 INFO misc.py line 119 253097] Train: [13/100][16/510] Data 0.003 (0.006) Batch 8.143 (1.666) Remain 20:45:29 loss: 0.5692 Lr: 0.00592
[2023-12-25 04:34:07,307 INFO misc.py line 119 253097] Train: [13/100][17/510] Data 0.004 (0.005) Batch 1.290 (1.639) Remain 20:25:24 loss: 0.3288 Lr: 0.00592
[2023-12-25 04:34:08,606 INFO misc.py line 119 253097] Train: [13/100][18/510] Data 0.004 (0.005) Batch 1.289 (1.616) Remain 20:07:56 loss: 0.3405 Lr: 0.00592
[2023-12-25 04:34:09,735 INFO misc.py line 119 253097] Train: [13/100][19/510] Data 0.018 (0.006) Batch 1.136 (1.586) Remain 19:45:30 loss: 0.4256 Lr: 0.00592
[2023-12-25 04:34:11,055 INFO misc.py line 119 253097] Train: [13/100][20/510] Data 0.008 (0.006) Batch 1.321 (1.570) Remain 19:33:51 loss: 0.4347 Lr: 0.00592
[2023-12-25 04:34:12,156 INFO misc.py line 119 253097] Train: [13/100][21/510] Data 0.006 (0.006) Batch 1.101 (1.544) Remain 19:14:20 loss: 0.1535 Lr: 0.00592
[2023-12-25 04:34:13,303 INFO misc.py line 119 253097] Train: [13/100][22/510] Data 0.007 (0.006) Batch 1.144 (1.523) Remain 18:58:33 loss: 0.2999 Lr: 0.00592
[2023-12-25 04:34:14,523 INFO misc.py line 119 253097] Train: [13/100][23/510] Data 0.008 (0.006) Batch 1.225 (1.508) Remain 18:47:24 loss: 0.4834 Lr: 0.00592
[2023-12-25 04:34:15,652 INFO misc.py line 119 253097] Train: [13/100][24/510] Data 0.004 (0.006) Batch 1.128 (1.490) Remain 18:33:52 loss: 0.3299 Lr: 0.00592
[2023-12-25 04:34:16,717 INFO misc.py line 119 253097] Train: [13/100][25/510] Data 0.006 (0.006) Batch 1.065 (1.471) Remain 18:19:24 loss: 0.2021 Lr: 0.00592
[2023-12-25 04:34:17,563 INFO misc.py line 119 253097] Train: [13/100][26/510] Data 0.005 (0.006) Batch 0.845 (1.443) Remain 17:59:03 loss: 0.2418 Lr: 0.00592
[2023-12-25 04:34:18,693 INFO misc.py line 119 253097] Train: [13/100][27/510] Data 0.006 (0.006) Batch 1.131 (1.430) Remain 17:49:17 loss: 0.1899 Lr: 0.00592
[2023-12-25 04:34:19,822 INFO misc.py line 119 253097] Train: [13/100][28/510] Data 0.005 (0.006) Batch 1.128 (1.418) Remain 17:40:13 loss: 0.3729 Lr: 0.00592
[2023-12-25 04:34:23,586 INFO misc.py line 119 253097] Train: [13/100][29/510] Data 0.006 (0.006) Batch 3.765 (1.509) Remain 18:47:40 loss: 0.4264 Lr: 0.00592
[2023-12-25 04:34:24,762 INFO misc.py line 119 253097] Train: [13/100][30/510] Data 0.004 (0.006) Batch 1.172 (1.496) Remain 18:38:19 loss: 0.3982 Lr: 0.00592
[2023-12-25 04:34:26,045 INFO misc.py line 119 253097] Train: [13/100][31/510] Data 0.009 (0.006) Batch 1.286 (1.489) Remain 18:32:42 loss: 0.2467 Lr: 0.00592
[2023-12-25 04:34:27,240 INFO misc.py line 119 253097] Train: [13/100][32/510] Data 0.004 (0.006) Batch 1.196 (1.479) Remain 18:25:08 loss: 0.1868 Lr: 0.00592
[2023-12-25 04:34:28,387 INFO misc.py line 119 253097] Train: [13/100][33/510] Data 0.004 (0.006) Batch 1.144 (1.467) Remain 18:16:46 loss: 0.3544 Lr: 0.00592
[2023-12-25 04:34:29,673 INFO misc.py line 119 253097] Train: [13/100][34/510] Data 0.007 (0.006) Batch 1.288 (1.462) Remain 18:12:25 loss: 0.3399 Lr: 0.00592
[2023-12-25 04:34:30,886 INFO misc.py line 119 253097] Train: [13/100][35/510] Data 0.006 (0.006) Batch 1.210 (1.454) Remain 18:06:30 loss: 0.4352 Lr: 0.00592
[2023-12-25 04:34:32,014 INFO misc.py line 119 253097] Train: [13/100][36/510] Data 0.009 (0.006) Batch 1.128 (1.444) Remain 17:59:07 loss: 0.3410 Lr: 0.00592
[2023-12-25 04:34:33,295 INFO misc.py line 119 253097] Train: [13/100][37/510] Data 0.008 (0.006) Batch 1.282 (1.439) Remain 17:55:32 loss: 0.2400 Lr: 0.00592
[2023-12-25 04:34:34,308 INFO misc.py line 119 253097] Train: [13/100][38/510] Data 0.007 (0.006) Batch 1.013 (1.427) Remain 17:46:25 loss: 0.3652 Lr: 0.00592
[2023-12-25 04:34:35,380 INFO misc.py line 119 253097] Train: [13/100][39/510] Data 0.007 (0.006) Batch 1.075 (1.417) Remain 17:39:05 loss: 0.5150 Lr: 0.00592
[2023-12-25 04:34:36,631 INFO misc.py line 119 253097] Train: [13/100][40/510] Data 0.004 (0.006) Batch 1.247 (1.413) Remain 17:35:37 loss: 0.3434 Lr: 0.00592
[2023-12-25 04:34:37,740 INFO misc.py line 119 253097] Train: [13/100][41/510] Data 0.008 (0.006) Batch 1.110 (1.405) Remain 17:29:38 loss: 0.2634 Lr: 0.00592
[2023-12-25 04:34:38,835 INFO misc.py line 119 253097] Train: [13/100][42/510] Data 0.008 (0.006) Batch 1.091 (1.397) Remain 17:23:37 loss: 0.2430 Lr: 0.00592
[2023-12-25 04:34:39,867 INFO misc.py line 119 253097] Train: [13/100][43/510] Data 0.012 (0.006) Batch 1.035 (1.387) Remain 17:16:50 loss: 0.3084 Lr: 0.00592
[2023-12-25 04:34:40,922 INFO misc.py line 119 253097] Train: [13/100][44/510] Data 0.008 (0.006) Batch 1.058 (1.379) Remain 17:10:48 loss: 0.3046 Lr: 0.00592
[2023-12-25 04:34:42,022 INFO misc.py line 119 253097] Train: [13/100][45/510] Data 0.006 (0.006) Batch 1.102 (1.373) Remain 17:05:50 loss: 0.5195 Lr: 0.00592
[2023-12-25 04:34:43,043 INFO misc.py line 119 253097] Train: [13/100][46/510] Data 0.004 (0.006) Batch 1.016 (1.365) Remain 16:59:37 loss: 0.5173 Lr: 0.00592
[2023-12-25 04:34:44,175 INFO misc.py line 119 253097] Train: [13/100][47/510] Data 0.009 (0.006) Batch 1.138 (1.359) Remain 16:55:44 loss: 0.5132 Lr: 0.00592
[2023-12-25 04:34:45,425 INFO misc.py line 119 253097] Train: [13/100][48/510] Data 0.003 (0.006) Batch 1.243 (1.357) Remain 16:53:47 loss: 0.5528 Lr: 0.00592
[2023-12-25 04:34:46,560 INFO misc.py line 119 253097] Train: [13/100][49/510] Data 0.011 (0.006) Batch 1.137 (1.352) Remain 16:50:11 loss: 0.5879 Lr: 0.00592
[2023-12-25 04:34:47,607 INFO misc.py line 119 253097] Train: [13/100][50/510] Data 0.009 (0.006) Batch 1.044 (1.345) Remain 16:45:15 loss: 0.2593 Lr: 0.00592
[2023-12-25 04:34:48,791 INFO misc.py line 119 253097] Train: [13/100][51/510] Data 0.012 (0.007) Batch 1.189 (1.342) Remain 16:42:48 loss: 0.2282 Lr: 0.00592
[2023-12-25 04:34:55,651 INFO misc.py line 119 253097] Train: [13/100][52/510] Data 0.008 (0.007) Batch 6.862 (1.455) Remain 18:06:56 loss: 0.4648 Lr: 0.00592
[2023-12-25 04:34:56,709 INFO misc.py line 119 253097] Train: [13/100][53/510] Data 0.005 (0.007) Batch 1.059 (1.447) Remain 18:01:00 loss: 0.1793 Lr: 0.00592
[2023-12-25 04:34:57,811 INFO misc.py line 119 253097] Train: [13/100][54/510] Data 0.006 (0.007) Batch 1.102 (1.440) Remain 17:55:55 loss: 0.2478 Lr: 0.00592
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[2023-12-25 04:36:05,969 INFO misc.py line 119 253097] Train: [13/100][95/510] Data 4.950 (0.060) Batch 6.197 (1.539) Remain 19:08:52 loss: 0.3036 Lr: 0.00592
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[2023-12-25 04:36:26,642 INFO misc.py line 119 253097] Train: [13/100][109/510] Data 1.777 (0.070) Batch 2.732 (1.531) Remain 19:02:21 loss: 0.3034 Lr: 0.00592
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[2023-12-25 04:38:21,932 INFO misc.py line 119 253097] Train: [13/100][182/510] Data 0.004 (0.045) Batch 1.214 (1.551) Remain 19:15:10 loss: 0.3907 Lr: 0.00591
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[2023-12-25 04:38:25,569 INFO misc.py line 119 253097] Train: [13/100][185/510] Data 0.004 (0.044) Batch 1.400 (1.545) Remain 19:10:57 loss: 0.3121 Lr: 0.00591
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[2023-12-25 04:38:27,502 INFO misc.py line 119 253097] Train: [13/100][187/510] Data 0.005 (0.044) Batch 1.095 (1.539) Remain 19:06:13 loss: 0.3397 Lr: 0.00591
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[2023-12-25 04:38:29,922 INFO misc.py line 119 253097] Train: [13/100][189/510] Data 0.013 (0.043) Batch 1.178 (1.535) Remain 19:03:31 loss: 0.3516 Lr: 0.00591
[2023-12-25 04:38:31,179 INFO misc.py line 119 253097] Train: [13/100][190/510] Data 0.008 (0.043) Batch 1.257 (1.534) Remain 19:02:23 loss: 0.3164 Lr: 0.00591
[2023-12-25 04:38:37,847 INFO misc.py line 119 253097] Train: [13/100][191/510] Data 0.007 (0.043) Batch 6.670 (1.561) Remain 19:22:42 loss: 0.2168 Lr: 0.00591
[2023-12-25 04:38:39,061 INFO misc.py line 119 253097] Train: [13/100][192/510] Data 0.006 (0.043) Batch 1.215 (1.559) Remain 19:21:19 loss: 0.4289 Lr: 0.00591
[2023-12-25 04:38:40,143 INFO misc.py line 119 253097] Train: [13/100][193/510] Data 0.004 (0.043) Batch 1.082 (1.557) Remain 19:19:25 loss: 0.3235 Lr: 0.00591
[2023-12-25 04:38:41,460 INFO misc.py line 119 253097] Train: [13/100][194/510] Data 0.004 (0.042) Batch 1.311 (1.555) Remain 19:18:26 loss: 0.5126 Lr: 0.00591
[2023-12-25 04:38:42,563 INFO misc.py line 119 253097] Train: [13/100][195/510] Data 0.010 (0.042) Batch 1.101 (1.553) Remain 19:16:39 loss: 0.1331 Lr: 0.00591
[2023-12-25 04:38:43,761 INFO misc.py line 119 253097] Train: [13/100][196/510] Data 0.012 (0.042) Batch 1.199 (1.551) Remain 19:15:16 loss: 0.3084 Lr: 0.00591
[2023-12-25 04:38:44,970 INFO misc.py line 119 253097] Train: [13/100][197/510] Data 0.010 (0.042) Batch 1.215 (1.550) Remain 19:13:57 loss: 0.1731 Lr: 0.00591
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[2023-12-25 04:38:48,573 INFO misc.py line 119 253097] Train: [13/100][199/510] Data 1.365 (0.049) Batch 2.487 (1.552) Remain 19:15:48 loss: 0.4396 Lr: 0.00591
[2023-12-25 04:38:49,599 INFO misc.py line 119 253097] Train: [13/100][200/510] Data 0.005 (0.048) Batch 1.027 (1.549) Remain 19:13:48 loss: 0.3703 Lr: 0.00591
[2023-12-25 04:38:50,747 INFO misc.py line 119 253097] Train: [13/100][201/510] Data 0.004 (0.048) Batch 1.147 (1.547) Remain 19:12:15 loss: 0.2373 Lr: 0.00591
[2023-12-25 04:38:51,639 INFO misc.py line 119 253097] Train: [13/100][202/510] Data 0.004 (0.048) Batch 0.892 (1.544) Remain 19:09:47 loss: 0.3098 Lr: 0.00591
[2023-12-25 04:38:52,972 INFO misc.py line 119 253097] Train: [13/100][203/510] Data 0.004 (0.048) Batch 1.334 (1.543) Remain 19:08:58 loss: 0.3703 Lr: 0.00591
[2023-12-25 04:38:54,068 INFO misc.py line 119 253097] Train: [13/100][204/510] Data 0.003 (0.047) Batch 1.096 (1.541) Remain 19:07:17 loss: 0.4539 Lr: 0.00591
[2023-12-25 04:39:16,166 INFO misc.py line 119 253097] Train: [13/100][205/510] Data 20.814 (0.150) Batch 22.098 (1.643) Remain 20:23:02 loss: 0.2893 Lr: 0.00591
[2023-12-25 04:39:17,453 INFO misc.py line 119 253097] Train: [13/100][206/510] Data 0.004 (0.149) Batch 1.283 (1.641) Remain 20:21:41 loss: 0.6117 Lr: 0.00591
[2023-12-25 04:39:18,499 INFO misc.py line 119 253097] Train: [13/100][207/510] Data 0.008 (0.149) Batch 1.050 (1.638) Remain 20:19:30 loss: 0.2547 Lr: 0.00591
[2023-12-25 04:39:19,669 INFO misc.py line 119 253097] Train: [13/100][208/510] Data 0.003 (0.148) Batch 1.166 (1.636) Remain 20:17:46 loss: 0.4252 Lr: 0.00591
[2023-12-25 04:39:20,885 INFO misc.py line 119 253097] Train: [13/100][209/510] Data 0.008 (0.147) Batch 1.220 (1.634) Remain 20:16:14 loss: 0.2171 Lr: 0.00591
[2023-12-25 04:39:21,854 INFO misc.py line 119 253097] Train: [13/100][210/510] Data 0.004 (0.147) Batch 0.970 (1.630) Remain 20:13:49 loss: 0.6860 Lr: 0.00591
[2023-12-25 04:39:25,813 INFO misc.py line 119 253097] Train: [13/100][211/510] Data 2.761 (0.159) Batch 3.959 (1.642) Remain 20:22:08 loss: 0.3573 Lr: 0.00591
[2023-12-25 04:39:26,924 INFO misc.py line 119 253097] Train: [13/100][212/510] Data 0.004 (0.159) Batch 1.111 (1.639) Remain 20:20:12 loss: 0.2788 Lr: 0.00591
[2023-12-25 04:39:28,191 INFO misc.py line 119 253097] Train: [13/100][213/510] Data 0.004 (0.158) Batch 1.266 (1.637) Remain 20:18:52 loss: 0.4011 Lr: 0.00591
[2023-12-25 04:39:29,356 INFO misc.py line 119 253097] Train: [13/100][214/510] Data 0.004 (0.157) Batch 1.159 (1.635) Remain 20:17:09 loss: 0.4261 Lr: 0.00591
[2023-12-25 04:39:30,452 INFO misc.py line 119 253097] Train: [13/100][215/510] Data 0.011 (0.156) Batch 1.096 (1.632) Remain 20:15:13 loss: 0.2478 Lr: 0.00591
[2023-12-25 04:39:31,667 INFO misc.py line 119 253097] Train: [13/100][216/510] Data 0.010 (0.156) Batch 1.221 (1.631) Remain 20:13:46 loss: 0.6202 Lr: 0.00591
[2023-12-25 04:39:32,647 INFO misc.py line 119 253097] Train: [13/100][217/510] Data 0.004 (0.155) Batch 0.981 (1.627) Remain 20:11:28 loss: 0.2182 Lr: 0.00591
[2023-12-25 04:39:33,786 INFO misc.py line 119 253097] Train: [13/100][218/510] Data 0.003 (0.154) Batch 1.137 (1.625) Remain 20:09:45 loss: 0.3677 Lr: 0.00591
[2023-12-25 04:39:34,779 INFO misc.py line 119 253097] Train: [13/100][219/510] Data 0.005 (0.154) Batch 0.993 (1.622) Remain 20:07:33 loss: 0.5251 Lr: 0.00591
[2023-12-25 04:39:35,767 INFO misc.py line 119 253097] Train: [13/100][220/510] Data 0.005 (0.153) Batch 0.988 (1.619) Remain 20:05:20 loss: 0.3610 Lr: 0.00591
[2023-12-25 04:39:36,879 INFO misc.py line 119 253097] Train: [13/100][221/510] Data 0.005 (0.152) Batch 1.113 (1.617) Remain 20:03:35 loss: 0.1341 Lr: 0.00591
[2023-12-25 04:39:39,103 INFO misc.py line 119 253097] Train: [13/100][222/510] Data 1.092 (0.157) Batch 2.224 (1.620) Remain 20:05:37 loss: 0.5364 Lr: 0.00591
[2023-12-25 04:39:40,208 INFO misc.py line 119 253097] Train: [13/100][223/510] Data 0.004 (0.156) Batch 1.105 (1.617) Remain 20:03:51 loss: 0.1948 Lr: 0.00591
[2023-12-25 04:39:41,291 INFO misc.py line 119 253097] Train: [13/100][224/510] Data 0.005 (0.155) Batch 1.081 (1.615) Remain 20:02:01 loss: 0.3005 Lr: 0.00591
[2023-12-25 04:39:42,431 INFO misc.py line 119 253097] Train: [13/100][225/510] Data 0.006 (0.154) Batch 1.142 (1.613) Remain 20:00:24 loss: 0.3983 Lr: 0.00591
[2023-12-25 04:39:45,736 INFO misc.py line 119 253097] Train: [13/100][226/510] Data 2.477 (0.165) Batch 3.306 (1.621) Remain 20:06:02 loss: 0.1268 Lr: 0.00591
[2023-12-25 04:39:47,025 INFO misc.py line 119 253097] Train: [13/100][227/510] Data 0.004 (0.164) Batch 1.283 (1.619) Remain 20:04:53 loss: 0.3671 Lr: 0.00591
[2023-12-25 04:39:48,271 INFO misc.py line 119 253097] Train: [13/100][228/510] Data 0.009 (0.163) Batch 1.246 (1.617) Remain 20:03:37 loss: 0.4399 Lr: 0.00591
[2023-12-25 04:39:49,454 INFO misc.py line 119 253097] Train: [13/100][229/510] Data 0.009 (0.163) Batch 1.178 (1.615) Remain 20:02:09 loss: 0.3435 Lr: 0.00591
[2023-12-25 04:39:50,731 INFO misc.py line 119 253097] Train: [13/100][230/510] Data 0.015 (0.162) Batch 1.287 (1.614) Remain 20:01:03 loss: 0.2837 Lr: 0.00591
[2023-12-25 04:39:51,918 INFO misc.py line 119 253097] Train: [13/100][231/510] Data 0.004 (0.161) Batch 1.186 (1.612) Remain 19:59:37 loss: 0.3378 Lr: 0.00591
[2023-12-25 04:39:53,031 INFO misc.py line 119 253097] Train: [13/100][232/510] Data 0.005 (0.161) Batch 1.115 (1.610) Remain 19:57:58 loss: 0.3333 Lr: 0.00591
[2023-12-25 04:39:54,329 INFO misc.py line 119 253097] Train: [13/100][233/510] Data 0.004 (0.160) Batch 1.298 (1.609) Remain 19:56:56 loss: 0.6004 Lr: 0.00591
[2023-12-25 04:39:55,546 INFO misc.py line 119 253097] Train: [13/100][234/510] Data 0.004 (0.159) Batch 1.206 (1.607) Remain 19:55:37 loss: 0.3799 Lr: 0.00591
[2023-12-25 04:39:56,751 INFO misc.py line 119 253097] Train: [13/100][235/510] Data 0.015 (0.159) Batch 1.212 (1.605) Remain 19:54:19 loss: 0.3856 Lr: 0.00591
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[2023-12-25 04:40:13,329 INFO misc.py line 119 253097] Train: [13/100][242/510] Data 0.003 (0.154) Batch 5.893 (1.627) Remain 20:10:46 loss: 0.2122 Lr: 0.00591
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[2023-12-25 04:40:35,172 INFO misc.py line 119 253097] Train: [13/100][261/510] Data 0.005 (0.143) Batch 1.245 (1.592) Remain 19:44:06 loss: 0.3528 Lr: 0.00591
[2023-12-25 04:40:35,984 INFO misc.py line 119 253097] Train: [13/100][262/510] Data 0.004 (0.143) Batch 0.812 (1.589) Remain 19:41:49 loss: 0.2824 Lr: 0.00591
[2023-12-25 04:40:37,304 INFO misc.py line 119 253097] Train: [13/100][263/510] Data 0.003 (0.142) Batch 1.320 (1.588) Remain 19:41:02 loss: 0.5028 Lr: 0.00591
[2023-12-25 04:40:43,350 INFO misc.py line 119 253097] Train: [13/100][264/510] Data 0.004 (0.142) Batch 6.047 (1.605) Remain 19:53:42 loss: 0.2972 Lr: 0.00591
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[2023-12-25 04:40:45,715 INFO misc.py line 119 253097] Train: [13/100][266/510] Data 0.007 (0.141) Batch 1.007 (1.602) Remain 19:51:15 loss: 1.0037 Lr: 0.00591
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[2023-12-25 04:40:57,400 INFO misc.py line 119 253097] Train: [13/100][276/510] Data 0.007 (0.136) Batch 1.108 (1.586) Remain 19:39:10 loss: 0.3558 Lr: 0.00591
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[2023-12-25 04:41:03,438 INFO misc.py line 119 253097] Train: [13/100][281/510] Data 0.004 (0.134) Batch 1.138 (1.579) Remain 19:33:59 loss: 0.3908 Lr: 0.00591
[2023-12-25 04:41:04,628 INFO misc.py line 119 253097] Train: [13/100][282/510] Data 0.003 (0.133) Batch 1.186 (1.578) Remain 19:32:55 loss: 0.4312 Lr: 0.00591
[2023-12-25 04:41:09,780 INFO misc.py line 119 253097] Train: [13/100][283/510] Data 0.008 (0.133) Batch 5.155 (1.591) Remain 19:42:23 loss: 0.3187 Lr: 0.00591
[2023-12-25 04:41:10,947 INFO misc.py line 119 253097] Train: [13/100][284/510] Data 0.005 (0.132) Batch 1.166 (1.589) Remain 19:41:14 loss: 0.3924 Lr: 0.00591
[2023-12-25 04:41:12,075 INFO misc.py line 119 253097] Train: [13/100][285/510] Data 0.006 (0.132) Batch 1.129 (1.588) Remain 19:40:00 loss: 0.4469 Lr: 0.00591
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[2023-12-25 04:41:17,912 INFO misc.py line 119 253097] Train: [13/100][290/510] Data 0.004 (0.130) Batch 1.039 (1.580) Remain 19:34:25 loss: 0.4206 Lr: 0.00591
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[2023-12-25 04:41:21,236 INFO misc.py line 119 253097] Train: [13/100][293/510] Data 0.004 (0.128) Batch 0.946 (1.575) Remain 19:30:43 loss: 0.2305 Lr: 0.00591
[2023-12-25 04:41:22,335 INFO misc.py line 119 253097] Train: [13/100][294/510] Data 0.005 (0.128) Batch 1.099 (1.574) Remain 19:29:28 loss: 0.3459 Lr: 0.00591
[2023-12-25 04:41:23,579 INFO misc.py line 119 253097] Train: [13/100][295/510] Data 0.005 (0.127) Batch 1.242 (1.573) Remain 19:28:36 loss: 0.2249 Lr: 0.00591
[2023-12-25 04:41:24,779 INFO misc.py line 119 253097] Train: [13/100][296/510] Data 0.007 (0.127) Batch 1.200 (1.571) Remain 19:27:38 loss: 0.2508 Lr: 0.00591
[2023-12-25 04:41:25,987 INFO misc.py line 119 253097] Train: [13/100][297/510] Data 0.006 (0.127) Batch 1.210 (1.570) Remain 19:26:41 loss: 0.3853 Lr: 0.00591
[2023-12-25 04:41:27,201 INFO misc.py line 119 253097] Train: [13/100][298/510] Data 0.004 (0.126) Batch 1.210 (1.569) Remain 19:25:45 loss: 0.3213 Lr: 0.00591
[2023-12-25 04:41:28,496 INFO misc.py line 119 253097] Train: [13/100][299/510] Data 0.010 (0.126) Batch 1.300 (1.568) Remain 19:25:03 loss: 0.2856 Lr: 0.00591
[2023-12-25 04:41:29,613 INFO misc.py line 119 253097] Train: [13/100][300/510] Data 0.004 (0.125) Batch 1.117 (1.566) Remain 19:23:54 loss: 0.5345 Lr: 0.00591
[2023-12-25 04:41:30,772 INFO misc.py line 119 253097] Train: [13/100][301/510] Data 0.006 (0.125) Batch 1.155 (1.565) Remain 19:22:51 loss: 0.3992 Lr: 0.00591
[2023-12-25 04:41:31,970 INFO misc.py line 119 253097] Train: [13/100][302/510] Data 0.008 (0.125) Batch 1.202 (1.564) Remain 19:21:55 loss: 0.3573 Lr: 0.00591
[2023-12-25 04:41:33,141 INFO misc.py line 119 253097] Train: [13/100][303/510] Data 0.004 (0.124) Batch 1.170 (1.563) Remain 19:20:55 loss: 0.3115 Lr: 0.00591
[2023-12-25 04:41:34,369 INFO misc.py line 119 253097] Train: [13/100][304/510] Data 0.006 (0.124) Batch 1.228 (1.561) Remain 19:20:04 loss: 0.3674 Lr: 0.00591
[2023-12-25 04:41:44,848 INFO misc.py line 119 253097] Train: [13/100][305/510] Data 9.365 (0.154) Batch 10.481 (1.591) Remain 19:41:59 loss: 0.1914 Lr: 0.00591
[2023-12-25 04:41:46,025 INFO misc.py line 119 253097] Train: [13/100][306/510] Data 0.004 (0.154) Batch 1.177 (1.590) Remain 19:40:56 loss: 0.3762 Lr: 0.00591
[2023-12-25 04:41:47,166 INFO misc.py line 119 253097] Train: [13/100][307/510] Data 0.003 (0.153) Batch 1.141 (1.588) Remain 19:39:49 loss: 0.3318 Lr: 0.00591
[2023-12-25 04:41:48,193 INFO misc.py line 119 253097] Train: [13/100][308/510] Data 0.004 (0.153) Batch 1.027 (1.586) Remain 19:38:25 loss: 0.5153 Lr: 0.00591
[2023-12-25 04:41:49,295 INFO misc.py line 119 253097] Train: [13/100][309/510] Data 0.003 (0.152) Batch 1.101 (1.585) Remain 19:37:13 loss: 0.3354 Lr: 0.00591
[2023-12-25 04:41:50,526 INFO misc.py line 119 253097] Train: [13/100][310/510] Data 0.004 (0.152) Batch 1.231 (1.584) Remain 19:36:20 loss: 0.4244 Lr: 0.00591
[2023-12-25 04:41:51,746 INFO misc.py line 119 253097] Train: [13/100][311/510] Data 0.004 (0.152) Batch 1.214 (1.582) Remain 19:35:25 loss: 0.4685 Lr: 0.00591
[2023-12-25 04:41:52,877 INFO misc.py line 119 253097] Train: [13/100][312/510] Data 0.009 (0.151) Batch 1.137 (1.581) Remain 19:34:19 loss: 0.5489 Lr: 0.00591
[2023-12-25 04:41:54,149 INFO misc.py line 119 253097] Train: [13/100][313/510] Data 0.004 (0.151) Batch 1.268 (1.580) Remain 19:33:33 loss: 0.5236 Lr: 0.00591
[2023-12-25 04:41:55,418 INFO misc.py line 119 253097] Train: [13/100][314/510] Data 0.008 (0.150) Batch 1.268 (1.579) Remain 19:32:46 loss: 0.4160 Lr: 0.00591
[2023-12-25 04:41:56,675 INFO misc.py line 119 253097] Train: [13/100][315/510] Data 0.009 (0.150) Batch 1.260 (1.578) Remain 19:31:59 loss: 0.2310 Lr: 0.00591
[2023-12-25 04:41:57,839 INFO misc.py line 119 253097] Train: [13/100][316/510] Data 0.005 (0.149) Batch 1.162 (1.577) Remain 19:30:58 loss: 0.4933 Lr: 0.00591
[2023-12-25 04:41:58,841 INFO misc.py line 119 253097] Train: [13/100][317/510] Data 0.008 (0.149) Batch 1.004 (1.575) Remain 19:29:36 loss: 0.4579 Lr: 0.00591
[2023-12-25 04:41:59,876 INFO misc.py line 119 253097] Train: [13/100][318/510] Data 0.007 (0.148) Batch 1.033 (1.573) Remain 19:28:17 loss: 0.2844 Lr: 0.00591
[2023-12-25 04:42:00,861 INFO misc.py line 119 253097] Train: [13/100][319/510] Data 0.008 (0.148) Batch 0.990 (1.571) Remain 19:26:54 loss: 0.4015 Lr: 0.00591
[2023-12-25 04:42:01,941 INFO misc.py line 119 253097] Train: [13/100][320/510] Data 0.003 (0.147) Batch 1.080 (1.570) Remain 19:25:43 loss: 0.3129 Lr: 0.00591
[2023-12-25 04:42:03,148 INFO misc.py line 119 253097] Train: [13/100][321/510] Data 0.003 (0.147) Batch 1.202 (1.568) Remain 19:24:50 loss: 0.3770 Lr: 0.00591
[2023-12-25 04:42:04,294 INFO misc.py line 119 253097] Train: [13/100][322/510] Data 0.008 (0.147) Batch 1.146 (1.567) Remain 19:23:49 loss: 0.2860 Lr: 0.00591
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[2023-12-25 04:42:06,628 INFO misc.py line 119 253097] Train: [13/100][324/510] Data 0.007 (0.146) Batch 1.130 (1.565) Remain 19:21:55 loss: 0.3227 Lr: 0.00590
[2023-12-25 04:42:07,798 INFO misc.py line 119 253097] Train: [13/100][325/510] Data 0.007 (0.145) Batch 1.172 (1.563) Remain 19:20:59 loss: 0.3516 Lr: 0.00590
[2023-12-25 04:42:08,949 INFO misc.py line 119 253097] Train: [13/100][326/510] Data 0.004 (0.145) Batch 1.152 (1.562) Remain 19:20:01 loss: 0.4028 Lr: 0.00590
[2023-12-25 04:42:10,040 INFO misc.py line 119 253097] Train: [13/100][327/510] Data 0.004 (0.144) Batch 1.091 (1.561) Remain 19:18:55 loss: 0.4273 Lr: 0.00590
[2023-12-25 04:42:11,135 INFO misc.py line 119 253097] Train: [13/100][328/510] Data 0.004 (0.144) Batch 1.094 (1.559) Remain 19:17:49 loss: 0.1857 Lr: 0.00590
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[2023-12-25 04:42:19,089 INFO misc.py line 119 253097] Train: [13/100][331/510] Data 0.004 (0.143) Batch 5.485 (1.569) Remain 19:25:10 loss: 0.1885 Lr: 0.00590
[2023-12-25 04:42:20,254 INFO misc.py line 119 253097] Train: [13/100][332/510] Data 0.003 (0.142) Batch 1.165 (1.568) Remain 19:24:13 loss: 0.3105 Lr: 0.00590
[2023-12-25 04:42:21,451 INFO misc.py line 119 253097] Train: [13/100][333/510] Data 0.004 (0.142) Batch 1.197 (1.567) Remain 19:23:22 loss: 0.3191 Lr: 0.00590
[2023-12-25 04:42:22,357 INFO misc.py line 119 253097] Train: [13/100][334/510] Data 0.004 (0.141) Batch 0.907 (1.565) Remain 19:21:51 loss: 0.9100 Lr: 0.00590
[2023-12-25 04:42:23,472 INFO misc.py line 119 253097] Train: [13/100][335/510] Data 0.004 (0.141) Batch 1.115 (1.564) Remain 19:20:49 loss: 0.3693 Lr: 0.00590
[2023-12-25 04:42:27,224 INFO misc.py line 119 253097] Train: [13/100][336/510] Data 0.004 (0.141) Batch 3.750 (1.570) Remain 19:25:40 loss: 0.2690 Lr: 0.00590
[2023-12-25 04:42:28,401 INFO misc.py line 119 253097] Train: [13/100][337/510] Data 0.005 (0.140) Batch 1.179 (1.569) Remain 19:24:46 loss: 0.2594 Lr: 0.00590
[2023-12-25 04:42:29,492 INFO misc.py line 119 253097] Train: [13/100][338/510] Data 0.004 (0.140) Batch 1.091 (1.568) Remain 19:23:41 loss: 0.7239 Lr: 0.00590
[2023-12-25 04:42:30,650 INFO misc.py line 119 253097] Train: [13/100][339/510] Data 0.004 (0.139) Batch 1.159 (1.566) Remain 19:22:45 loss: 0.2929 Lr: 0.00590
[2023-12-25 04:42:31,757 INFO misc.py line 119 253097] Train: [13/100][340/510] Data 0.004 (0.139) Batch 1.106 (1.565) Remain 19:21:43 loss: 0.3169 Lr: 0.00590
[2023-12-25 04:42:43,104 INFO misc.py line 119 253097] Train: [13/100][341/510] Data 0.003 (0.139) Batch 11.347 (1.594) Remain 19:43:10 loss: 0.3533 Lr: 0.00590
[2023-12-25 04:42:44,333 INFO misc.py line 119 253097] Train: [13/100][342/510] Data 0.004 (0.138) Batch 1.225 (1.593) Remain 19:42:20 loss: 0.4372 Lr: 0.00590
[2023-12-25 04:42:45,443 INFO misc.py line 119 253097] Train: [13/100][343/510] Data 0.008 (0.138) Batch 1.110 (1.591) Remain 19:41:15 loss: 0.3203 Lr: 0.00590
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[2023-12-25 04:42:47,619 INFO misc.py line 119 253097] Train: [13/100][345/510] Data 0.003 (0.137) Batch 1.197 (1.588) Remain 19:39:02 loss: 0.4756 Lr: 0.00590
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[2023-12-25 04:43:19,479 INFO misc.py line 119 253097] Train: [13/100][365/510] Data 0.004 (0.130) Batch 6.065 (1.589) Remain 19:38:41 loss: 0.4353 Lr: 0.00590
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[2023-12-25 04:44:38,316 INFO misc.py line 119 253097] Train: [13/100][424/510] Data 0.010 (0.113) Batch 5.196 (1.553) Remain 19:10:54 loss: 0.4316 Lr: 0.00590
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[2023-12-25 04:45:56,161 INFO misc.py line 119 253097] Train: [13/100][473/510] Data 0.011 (0.131) Batch 1.183 (1.557) Remain 19:12:22 loss: 0.4129 Lr: 0.00590
[2023-12-25 04:45:57,328 INFO misc.py line 119 253097] Train: [13/100][474/510] Data 0.006 (0.130) Batch 1.169 (1.556) Remain 19:11:44 loss: 0.2913 Lr: 0.00590
[2023-12-25 04:45:58,537 INFO misc.py line 119 253097] Train: [13/100][475/510] Data 0.004 (0.130) Batch 1.203 (1.555) Remain 19:11:09 loss: 0.2481 Lr: 0.00590
[2023-12-25 04:45:59,573 INFO misc.py line 119 253097] Train: [13/100][476/510] Data 0.010 (0.130) Batch 1.038 (1.554) Remain 19:10:19 loss: 0.4571 Lr: 0.00590
[2023-12-25 04:46:00,794 INFO misc.py line 119 253097] Train: [13/100][477/510] Data 0.007 (0.130) Batch 1.224 (1.554) Remain 19:09:46 loss: 0.7205 Lr: 0.00590
[2023-12-25 04:46:01,944 INFO misc.py line 119 253097] Train: [13/100][478/510] Data 0.004 (0.129) Batch 1.149 (1.553) Remain 19:09:07 loss: 0.3157 Lr: 0.00590
[2023-12-25 04:46:03,162 INFO misc.py line 119 253097] Train: [13/100][479/510] Data 0.005 (0.129) Batch 1.219 (1.552) Remain 19:08:34 loss: 0.3279 Lr: 0.00590
[2023-12-25 04:46:04,433 INFO misc.py line 119 253097] Train: [13/100][480/510] Data 0.004 (0.129) Batch 1.268 (1.551) Remain 19:08:06 loss: 0.3446 Lr: 0.00590
[2023-12-25 04:46:05,680 INFO misc.py line 119 253097] Train: [13/100][481/510] Data 0.008 (0.129) Batch 1.247 (1.551) Remain 19:07:36 loss: 0.5008 Lr: 0.00590
[2023-12-25 04:46:06,817 INFO misc.py line 119 253097] Train: [13/100][482/510] Data 0.007 (0.128) Batch 1.140 (1.550) Remain 19:06:57 loss: 0.4255 Lr: 0.00590
[2023-12-25 04:46:08,073 INFO misc.py line 119 253097] Train: [13/100][483/510] Data 0.004 (0.128) Batch 1.248 (1.549) Remain 19:06:27 loss: 0.3085 Lr: 0.00590
[2023-12-25 04:46:09,338 INFO misc.py line 119 253097] Train: [13/100][484/510] Data 0.013 (0.128) Batch 1.269 (1.549) Remain 19:06:00 loss: 0.5821 Lr: 0.00590
[2023-12-25 04:46:10,619 INFO misc.py line 119 253097] Train: [13/100][485/510] Data 0.010 (0.128) Batch 1.281 (1.548) Remain 19:05:33 loss: 0.2714 Lr: 0.00590
[2023-12-25 04:46:11,401 INFO misc.py line 119 253097] Train: [13/100][486/510] Data 0.009 (0.127) Batch 0.785 (1.547) Remain 19:04:22 loss: 0.8906 Lr: 0.00590
[2023-12-25 04:46:20,446 INFO misc.py line 119 253097] Train: [13/100][487/510] Data 0.006 (0.127) Batch 9.047 (1.562) Remain 19:15:48 loss: 0.8536 Lr: 0.00590
[2023-12-25 04:46:21,717 INFO misc.py line 119 253097] Train: [13/100][488/510] Data 0.004 (0.127) Batch 1.266 (1.562) Remain 19:15:20 loss: 0.2619 Lr: 0.00590
[2023-12-25 04:46:22,759 INFO misc.py line 119 253097] Train: [13/100][489/510] Data 0.008 (0.127) Batch 1.042 (1.560) Remain 19:14:31 loss: 0.5268 Lr: 0.00590
[2023-12-25 04:46:24,023 INFO misc.py line 119 253097] Train: [13/100][490/510] Data 0.008 (0.126) Batch 1.263 (1.560) Remain 19:14:02 loss: 0.2331 Lr: 0.00590
[2023-12-25 04:46:25,096 INFO misc.py line 119 253097] Train: [13/100][491/510] Data 0.010 (0.126) Batch 1.073 (1.559) Remain 19:13:16 loss: 0.5856 Lr: 0.00590
[2023-12-25 04:46:26,004 INFO misc.py line 119 253097] Train: [13/100][492/510] Data 0.009 (0.126) Batch 0.914 (1.558) Remain 19:12:16 loss: 0.3897 Lr: 0.00590
[2023-12-25 04:46:27,060 INFO misc.py line 119 253097] Train: [13/100][493/510] Data 0.003 (0.126) Batch 1.056 (1.557) Remain 19:11:29 loss: 0.2617 Lr: 0.00590
[2023-12-25 04:46:28,254 INFO misc.py line 119 253097] Train: [13/100][494/510] Data 0.003 (0.125) Batch 1.193 (1.556) Remain 19:10:55 loss: 0.4049 Lr: 0.00590
[2023-12-25 04:46:29,295 INFO misc.py line 119 253097] Train: [13/100][495/510] Data 0.005 (0.125) Batch 1.041 (1.555) Remain 19:10:06 loss: 0.4495 Lr: 0.00590
[2023-12-25 04:46:30,470 INFO misc.py line 119 253097] Train: [13/100][496/510] Data 0.004 (0.125) Batch 1.175 (1.554) Remain 19:09:31 loss: 0.7232 Lr: 0.00590
[2023-12-25 04:46:31,761 INFO misc.py line 119 253097] Train: [13/100][497/510] Data 0.005 (0.125) Batch 1.286 (1.553) Remain 19:09:05 loss: 0.2042 Lr: 0.00590
[2023-12-25 04:46:32,866 INFO misc.py line 119 253097] Train: [13/100][498/510] Data 0.009 (0.124) Batch 1.110 (1.553) Remain 19:08:24 loss: 0.4642 Lr: 0.00590
[2023-12-25 04:46:34,110 INFO misc.py line 119 253097] Train: [13/100][499/510] Data 0.004 (0.124) Batch 1.244 (1.552) Remain 19:07:55 loss: 0.3564 Lr: 0.00590
[2023-12-25 04:46:42,847 INFO misc.py line 119 253097] Train: [13/100][500/510] Data 0.004 (0.124) Batch 8.737 (1.566) Remain 19:18:35 loss: 0.3352 Lr: 0.00590
[2023-12-25 04:46:44,103 INFO misc.py line 119 253097] Train: [13/100][501/510] Data 0.004 (0.124) Batch 1.256 (1.566) Remain 19:18:06 loss: 0.3685 Lr: 0.00590
[2023-12-25 04:46:45,219 INFO misc.py line 119 253097] Train: [13/100][502/510] Data 0.003 (0.123) Batch 1.116 (1.565) Remain 19:17:24 loss: 0.5052 Lr: 0.00590
[2023-12-25 04:46:46,350 INFO misc.py line 119 253097] Train: [13/100][503/510] Data 0.004 (0.123) Batch 1.131 (1.564) Remain 19:16:44 loss: 0.3162 Lr: 0.00590
[2023-12-25 04:46:47,346 INFO misc.py line 119 253097] Train: [13/100][504/510] Data 0.003 (0.123) Batch 0.997 (1.563) Remain 19:15:52 loss: 0.2365 Lr: 0.00590
[2023-12-25 04:46:48,448 INFO misc.py line 119 253097] Train: [13/100][505/510] Data 0.003 (0.123) Batch 1.101 (1.562) Remain 19:15:10 loss: 0.2652 Lr: 0.00590
[2023-12-25 04:46:49,509 INFO misc.py line 119 253097] Train: [13/100][506/510] Data 0.005 (0.122) Batch 1.061 (1.561) Remain 19:14:24 loss: 0.1856 Lr: 0.00590
[2023-12-25 04:46:50,668 INFO misc.py line 119 253097] Train: [13/100][507/510] Data 0.003 (0.122) Batch 1.159 (1.560) Remain 19:13:47 loss: 0.4557 Lr: 0.00590
[2023-12-25 04:46:51,786 INFO misc.py line 119 253097] Train: [13/100][508/510] Data 0.004 (0.122) Batch 1.118 (1.559) Remain 19:13:07 loss: 0.5073 Lr: 0.00590
[2023-12-25 04:46:53,063 INFO misc.py line 119 253097] Train: [13/100][509/510] Data 0.003 (0.122) Batch 1.277 (1.559) Remain 19:12:40 loss: 0.3196 Lr: 0.00590
[2023-12-25 04:46:54,154 INFO misc.py line 119 253097] Train: [13/100][510/510] Data 0.004 (0.122) Batch 1.088 (1.558) Remain 19:11:58 loss: 0.2835 Lr: 0.00590
[2023-12-25 04:46:54,155 INFO misc.py line 136 253097] Train result: loss: 0.3733 
[2023-12-25 04:46:54,155 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 04:47:19,541 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6253 
[2023-12-25 04:47:19,885 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3650 
[2023-12-25 04:47:24,827 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4589 
[2023-12-25 04:47:25,376 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5080 
[2023-12-25 04:47:27,345 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8072 
[2023-12-25 04:47:27,770 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4311 
[2023-12-25 04:47:28,659 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.8075 
[2023-12-25 04:47:29,218 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3612 
[2023-12-25 04:47:31,026 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.4306 
[2023-12-25 04:47:33,149 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3588 
[2023-12-25 04:47:34,016 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3913 
[2023-12-25 04:47:34,447 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6915 
[2023-12-25 04:47:35,353 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.9875 
[2023-12-25 04:47:38,309 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.5509 
[2023-12-25 04:47:38,776 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2870 
[2023-12-25 04:47:39,385 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4667 
[2023-12-25 04:47:40,097 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5531 
[2023-12-25 04:47:41,393 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6335/0.7307/0.8752.
[2023-12-25 04:47:41,393 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.8995/0.9354
[2023-12-25 04:47:41,393 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9795/0.9907
[2023-12-25 04:47:41,393 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8084/0.9193
[2023-12-25 04:47:41,393 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 04:47:41,393 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3328/0.5165
[2023-12-25 04:47:41,393 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6273/0.7134
[2023-12-25 04:47:41,393 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5249/0.8585
[2023-12-25 04:47:41,394 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7733/0.9106
[2023-12-25 04:47:41,394 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8869/0.9313
[2023-12-25 04:47:41,394 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4760/0.4985
[2023-12-25 04:47:41,394 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7021/0.7501
[2023-12-25 04:47:41,394 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6858/0.8067
[2023-12-25 04:47:41,394 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5395/0.6679
[2023-12-25 04:47:41,394 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 04:47:41,395 INFO misc.py line 165 253097] Currently Best mIoU: 0.6447
[2023-12-25 04:47:41,395 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 04:47:48,303 INFO misc.py line 119 253097] Train: [14/100][1/510] Data 3.684 (3.684) Batch 4.888 (4.888) Remain 60:14:29 loss: 0.2073 Lr: 0.00590
[2023-12-25 04:47:49,390 INFO misc.py line 119 253097] Train: [14/100][2/510] Data 0.007 (0.007) Batch 1.089 (1.089) Remain 13:25:12 loss: 0.2770 Lr: 0.00590
[2023-12-25 04:47:53,244 INFO misc.py line 119 253097] Train: [14/100][3/510] Data 1.843 (1.843) Batch 3.853 (3.853) Remain 47:29:27 loss: 0.6713 Lr: 0.00590
[2023-12-25 04:47:54,451 INFO misc.py line 119 253097] Train: [14/100][4/510] Data 0.005 (0.005) Batch 1.207 (1.207) Remain 14:52:34 loss: 0.4181 Lr: 0.00590
[2023-12-25 04:47:55,639 INFO misc.py line 119 253097] Train: [14/100][5/510] Data 0.005 (0.005) Batch 1.185 (1.196) Remain 14:44:16 loss: 0.4530 Lr: 0.00590
[2023-12-25 04:47:56,784 INFO misc.py line 119 253097] Train: [14/100][6/510] Data 0.008 (0.006) Batch 1.146 (1.179) Remain 14:32:00 loss: 0.1555 Lr: 0.00590
[2023-12-25 04:47:57,845 INFO misc.py line 119 253097] Train: [14/100][7/510] Data 0.009 (0.007) Batch 1.059 (1.149) Remain 14:09:48 loss: 0.2552 Lr: 0.00590
[2023-12-25 04:47:58,954 INFO misc.py line 119 253097] Train: [14/100][8/510] Data 0.009 (0.007) Batch 1.114 (1.142) Remain 14:04:33 loss: 0.2145 Lr: 0.00590
[2023-12-25 04:48:00,236 INFO misc.py line 119 253097] Train: [14/100][9/510] Data 0.112 (0.025) Batch 1.280 (1.165) Remain 14:21:28 loss: 0.2195 Lr: 0.00590
[2023-12-25 04:48:01,263 INFO misc.py line 119 253097] Train: [14/100][10/510] Data 0.006 (0.022) Batch 1.020 (1.144) Remain 14:06:08 loss: 0.3795 Lr: 0.00590
[2023-12-25 04:48:09,763 INFO misc.py line 119 253097] Train: [14/100][11/510] Data 0.014 (0.021) Batch 8.509 (2.065) Remain 25:26:43 loss: 0.3041 Lr: 0.00590
[2023-12-25 04:48:10,920 INFO misc.py line 119 253097] Train: [14/100][12/510] Data 0.004 (0.019) Batch 1.156 (1.964) Remain 24:12:00 loss: 0.3916 Lr: 0.00590
[2023-12-25 04:48:12,177 INFO misc.py line 119 253097] Train: [14/100][13/510] Data 0.004 (0.017) Batch 1.255 (1.893) Remain 23:19:32 loss: 0.3363 Lr: 0.00590
[2023-12-25 04:48:13,281 INFO misc.py line 119 253097] Train: [14/100][14/510] Data 0.007 (0.016) Batch 1.104 (1.821) Remain 22:26:30 loss: 0.4917 Lr: 0.00589
[2023-12-25 04:48:14,642 INFO misc.py line 119 253097] Train: [14/100][15/510] Data 0.006 (0.016) Batch 1.320 (1.780) Remain 21:55:36 loss: 0.3562 Lr: 0.00589
[2023-12-25 04:48:15,820 INFO misc.py line 119 253097] Train: [14/100][16/510] Data 0.047 (0.018) Batch 1.209 (1.736) Remain 21:23:09 loss: 0.2060 Lr: 0.00589
[2023-12-25 04:48:32,670 INFO misc.py line 119 253097] Train: [14/100][17/510] Data 0.016 (0.018) Batch 16.862 (2.816) Remain 34:41:48 loss: 0.4832 Lr: 0.00589
[2023-12-25 04:48:33,765 INFO misc.py line 119 253097] Train: [14/100][18/510] Data 0.004 (0.017) Batch 1.094 (2.701) Remain 33:16:53 loss: 0.3137 Lr: 0.00589
[2023-12-25 04:48:35,014 INFO misc.py line 119 253097] Train: [14/100][19/510] Data 0.004 (0.016) Batch 1.247 (2.611) Remain 32:09:40 loss: 0.4197 Lr: 0.00589
[2023-12-25 04:48:36,078 INFO misc.py line 119 253097] Train: [14/100][20/510] Data 0.006 (0.015) Batch 1.062 (2.519) Remain 31:02:19 loss: 0.3973 Lr: 0.00589
[2023-12-25 04:48:37,301 INFO misc.py line 119 253097] Train: [14/100][21/510] Data 0.008 (0.015) Batch 1.225 (2.448) Remain 30:09:08 loss: 0.2241 Lr: 0.00589
[2023-12-25 04:48:38,418 INFO misc.py line 119 253097] Train: [14/100][22/510] Data 0.005 (0.015) Batch 1.114 (2.377) Remain 29:17:13 loss: 0.3874 Lr: 0.00589
[2023-12-25 04:48:39,702 INFO misc.py line 119 253097] Train: [14/100][23/510] Data 0.008 (0.014) Batch 1.283 (2.323) Remain 28:36:43 loss: 0.3211 Lr: 0.00589
[2023-12-25 04:48:40,897 INFO misc.py line 119 253097] Train: [14/100][24/510] Data 0.009 (0.014) Batch 1.200 (2.269) Remain 27:57:09 loss: 0.3591 Lr: 0.00589
[2023-12-25 04:48:42,120 INFO misc.py line 119 253097] Train: [14/100][25/510] Data 0.005 (0.014) Batch 1.218 (2.221) Remain 27:21:47 loss: 0.2600 Lr: 0.00589
[2023-12-25 04:48:43,342 INFO misc.py line 119 253097] Train: [14/100][26/510] Data 0.010 (0.013) Batch 1.225 (2.178) Remain 26:49:44 loss: 0.5650 Lr: 0.00589
[2023-12-25 04:48:44,543 INFO misc.py line 119 253097] Train: [14/100][27/510] Data 0.007 (0.013) Batch 1.202 (2.137) Remain 26:19:39 loss: 0.2821 Lr: 0.00589
[2023-12-25 04:48:45,502 INFO misc.py line 119 253097] Train: [14/100][28/510] Data 0.008 (0.013) Batch 0.960 (2.090) Remain 25:44:48 loss: 0.4028 Lr: 0.00589
[2023-12-25 04:48:46,599 INFO misc.py line 119 253097] Train: [14/100][29/510] Data 0.006 (0.013) Batch 1.097 (2.052) Remain 25:16:33 loss: 0.3804 Lr: 0.00589
[2023-12-25 04:48:47,832 INFO misc.py line 119 253097] Train: [14/100][30/510] Data 0.005 (0.012) Batch 1.232 (2.022) Remain 24:54:05 loss: 0.2298 Lr: 0.00589
[2023-12-25 04:48:48,963 INFO misc.py line 119 253097] Train: [14/100][31/510] Data 0.006 (0.012) Batch 1.129 (1.990) Remain 24:30:29 loss: 0.5776 Lr: 0.00589
[2023-12-25 04:48:49,944 INFO misc.py line 119 253097] Train: [14/100][32/510] Data 0.007 (0.012) Batch 0.983 (1.955) Remain 24:04:48 loss: 0.4556 Lr: 0.00589
[2023-12-25 04:48:50,928 INFO misc.py line 119 253097] Train: [14/100][33/510] Data 0.005 (0.012) Batch 0.985 (1.923) Remain 23:40:52 loss: 0.4554 Lr: 0.00589
[2023-12-25 04:48:52,110 INFO misc.py line 119 253097] Train: [14/100][34/510] Data 0.004 (0.011) Batch 1.181 (1.899) Remain 23:23:10 loss: 0.5722 Lr: 0.00589
[2023-12-25 04:48:53,188 INFO misc.py line 119 253097] Train: [14/100][35/510] Data 0.004 (0.011) Batch 1.079 (1.873) Remain 23:04:11 loss: 0.7320 Lr: 0.00589
[2023-12-25 04:48:57,093 INFO misc.py line 119 253097] Train: [14/100][36/510] Data 0.004 (0.011) Batch 3.905 (1.935) Remain 23:49:40 loss: 0.6025 Lr: 0.00589
[2023-12-25 04:48:58,207 INFO misc.py line 119 253097] Train: [14/100][37/510] Data 0.004 (0.011) Batch 1.114 (1.911) Remain 23:31:47 loss: 0.4221 Lr: 0.00589
[2023-12-25 04:48:59,355 INFO misc.py line 119 253097] Train: [14/100][38/510] Data 0.003 (0.011) Batch 1.148 (1.889) Remain 23:15:39 loss: 0.3150 Lr: 0.00589
[2023-12-25 04:49:03,130 INFO misc.py line 119 253097] Train: [14/100][39/510] Data 2.525 (0.080) Batch 3.775 (1.941) Remain 23:54:19 loss: 0.2091 Lr: 0.00589
[2023-12-25 04:49:04,316 INFO misc.py line 119 253097] Train: [14/100][40/510] Data 0.004 (0.078) Batch 1.186 (1.921) Remain 23:39:13 loss: 0.2892 Lr: 0.00589
[2023-12-25 04:49:05,544 INFO misc.py line 119 253097] Train: [14/100][41/510] Data 0.003 (0.076) Batch 1.225 (1.903) Remain 23:25:39 loss: 0.4000 Lr: 0.00589
[2023-12-25 04:49:06,653 INFO misc.py line 119 253097] Train: [14/100][42/510] Data 0.006 (0.075) Batch 1.111 (1.882) Remain 23:10:38 loss: 0.1801 Lr: 0.00589
[2023-12-25 04:49:07,895 INFO misc.py line 119 253097] Train: [14/100][43/510] Data 0.004 (0.073) Batch 1.238 (1.866) Remain 22:58:42 loss: 0.2090 Lr: 0.00589
[2023-12-25 04:49:09,089 INFO misc.py line 119 253097] Train: [14/100][44/510] Data 0.009 (0.071) Batch 1.196 (1.850) Remain 22:46:35 loss: 0.2923 Lr: 0.00589
[2023-12-25 04:49:10,027 INFO misc.py line 119 253097] Train: [14/100][45/510] Data 0.006 (0.070) Batch 0.940 (1.828) Remain 22:30:33 loss: 0.5491 Lr: 0.00589
[2023-12-25 04:49:13,595 INFO misc.py line 119 253097] Train: [14/100][46/510] Data 2.727 (0.131) Batch 3.567 (1.869) Remain 23:00:24 loss: 0.2455 Lr: 0.00589
[2023-12-25 04:49:14,615 INFO misc.py line 119 253097] Train: [14/100][47/510] Data 0.006 (0.129) Batch 1.018 (1.849) Remain 22:46:05 loss: 0.3221 Lr: 0.00589
[2023-12-25 04:49:15,694 INFO misc.py line 119 253097] Train: [14/100][48/510] Data 0.006 (0.126) Batch 1.082 (1.832) Remain 22:33:28 loss: 0.4192 Lr: 0.00589
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[2023-12-25 04:50:02,691 INFO misc.py line 119 253097] Train: [14/100][81/510] Data 0.010 (0.075) Batch 1.343 (1.660) Remain 20:25:01 loss: 0.2611 Lr: 0.00589
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[2023-12-25 04:50:10,204 INFO misc.py line 119 253097] Train: [14/100][84/510] Data 0.003 (0.073) Batch 1.033 (1.691) Remain 20:47:58 loss: 0.4087 Lr: 0.00589
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[2023-12-25 04:50:12,588 INFO misc.py line 119 253097] Train: [14/100][86/510] Data 0.005 (0.071) Batch 1.182 (1.679) Remain 20:39:06 loss: 0.4508 Lr: 0.00589
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[2023-12-25 04:50:30,147 INFO misc.py line 119 253097] Train: [14/100][101/510] Data 0.009 (0.061) Batch 1.110 (1.601) Remain 19:41:12 loss: 0.3127 Lr: 0.00589
[2023-12-25 04:50:31,409 INFO misc.py line 119 253097] Train: [14/100][102/510] Data 0.015 (0.061) Batch 1.272 (1.598) Remain 19:38:43 loss: 0.2550 Lr: 0.00589
[2023-12-25 04:50:32,610 INFO misc.py line 119 253097] Train: [14/100][103/510] Data 0.004 (0.060) Batch 1.196 (1.594) Remain 19:35:44 loss: 0.4390 Lr: 0.00589
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[2023-12-25 04:50:46,052 INFO misc.py line 119 253097] Train: [14/100][105/510] Data 0.003 (0.059) Batch 1.332 (1.694) Remain 20:49:52 loss: 0.3225 Lr: 0.00589
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[2023-12-25 04:52:30,469 INFO misc.py line 119 253097] Train: [14/100][176/510] Data 0.005 (0.102) Batch 1.122 (1.602) Remain 19:40:18 loss: 0.2942 Lr: 0.00589
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[2023-12-25 04:52:33,799 INFO misc.py line 119 253097] Train: [14/100][179/510] Data 0.007 (0.100) Batch 1.097 (1.594) Remain 19:34:03 loss: 0.2887 Lr: 0.00589
[2023-12-25 04:52:35,047 INFO misc.py line 119 253097] Train: [14/100][180/510] Data 0.004 (0.099) Batch 1.248 (1.592) Remain 19:32:35 loss: 0.2669 Lr: 0.00589
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[2023-12-25 04:52:42,026 INFO misc.py line 119 253097] Train: [14/100][182/510] Data 0.007 (0.098) Batch 5.836 (1.613) Remain 19:48:08 loss: 0.3842 Lr: 0.00589
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[2023-12-25 04:52:45,734 INFO misc.py line 119 253097] Train: [14/100][185/510] Data 0.008 (0.097) Batch 1.294 (1.607) Remain 19:43:29 loss: 0.1924 Lr: 0.00589
[2023-12-25 04:52:46,998 INFO misc.py line 119 253097] Train: [14/100][186/510] Data 0.004 (0.096) Batch 1.264 (1.605) Remain 19:42:04 loss: 0.1897 Lr: 0.00589
[2023-12-25 04:52:48,266 INFO misc.py line 119 253097] Train: [14/100][187/510] Data 0.005 (0.096) Batch 1.269 (1.603) Remain 19:40:42 loss: 0.3941 Lr: 0.00589
[2023-12-25 04:52:49,307 INFO misc.py line 119 253097] Train: [14/100][188/510] Data 0.004 (0.095) Batch 1.038 (1.600) Remain 19:38:25 loss: 0.2736 Lr: 0.00589
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[2023-12-25 04:52:51,772 INFO misc.py line 119 253097] Train: [14/100][190/510] Data 0.008 (0.094) Batch 1.185 (1.596) Remain 19:35:28 loss: 0.2580 Lr: 0.00589
[2023-12-25 04:52:52,597 INFO misc.py line 119 253097] Train: [14/100][191/510] Data 0.005 (0.094) Batch 0.827 (1.592) Remain 19:32:26 loss: 0.3645 Lr: 0.00589
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[2023-12-25 04:52:54,820 INFO misc.py line 119 253097] Train: [14/100][193/510] Data 0.005 (0.093) Batch 1.090 (1.587) Remain 19:28:39 loss: 0.3676 Lr: 0.00589
[2023-12-25 04:52:56,098 INFO misc.py line 119 253097] Train: [14/100][194/510] Data 0.003 (0.093) Batch 1.275 (1.586) Remain 19:27:26 loss: 0.5933 Lr: 0.00589
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[2023-12-25 04:52:59,510 INFO misc.py line 119 253097] Train: [14/100][196/510] Data 0.006 (0.092) Batch 2.335 (1.587) Remain 19:28:17 loss: 0.2696 Lr: 0.00589
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[2023-12-25 04:53:06,588 INFO misc.py line 119 253097] Train: [14/100][200/510] Data 2.528 (0.103) Batch 3.471 (1.591) Remain 19:30:56 loss: 0.3672 Lr: 0.00589
[2023-12-25 04:53:07,704 INFO misc.py line 119 253097] Train: [14/100][201/510] Data 0.005 (0.102) Batch 1.112 (1.588) Remain 19:29:07 loss: 0.2248 Lr: 0.00589
[2023-12-25 04:53:08,730 INFO misc.py line 119 253097] Train: [14/100][202/510] Data 0.008 (0.102) Batch 1.017 (1.585) Remain 19:26:59 loss: 0.3571 Lr: 0.00589
[2023-12-25 04:53:09,617 INFO misc.py line 119 253097] Train: [14/100][203/510] Data 0.017 (0.101) Batch 0.901 (1.582) Remain 19:24:26 loss: 0.2912 Lr: 0.00589
[2023-12-25 04:53:10,922 INFO misc.py line 119 253097] Train: [14/100][204/510] Data 0.003 (0.101) Batch 1.300 (1.580) Remain 19:23:22 loss: 0.3501 Lr: 0.00589
[2023-12-25 04:53:11,994 INFO misc.py line 119 253097] Train: [14/100][205/510] Data 0.009 (0.100) Batch 1.076 (1.578) Remain 19:21:31 loss: 0.4171 Lr: 0.00589
[2023-12-25 04:53:13,206 INFO misc.py line 119 253097] Train: [14/100][206/510] Data 0.004 (0.100) Batch 1.208 (1.576) Remain 19:20:08 loss: 0.2256 Lr: 0.00588
[2023-12-25 04:53:14,310 INFO misc.py line 119 253097] Train: [14/100][207/510] Data 0.008 (0.099) Batch 1.103 (1.574) Remain 19:18:25 loss: 0.3450 Lr: 0.00588
[2023-12-25 04:53:15,266 INFO misc.py line 119 253097] Train: [14/100][208/510] Data 0.008 (0.099) Batch 0.962 (1.571) Remain 19:16:11 loss: 0.2216 Lr: 0.00588
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[2023-12-25 04:53:17,456 INFO misc.py line 119 253097] Train: [14/100][210/510] Data 0.008 (0.098) Batch 0.952 (1.566) Remain 19:12:45 loss: 0.5114 Lr: 0.00588
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[2023-12-25 04:53:19,451 INFO misc.py line 119 253097] Train: [14/100][212/510] Data 0.003 (0.097) Batch 1.048 (1.561) Remain 19:08:41 loss: 0.2003 Lr: 0.00588
[2023-12-25 04:53:20,622 INFO misc.py line 119 253097] Train: [14/100][213/510] Data 0.005 (0.097) Batch 1.172 (1.559) Remain 19:07:18 loss: 0.3939 Lr: 0.00588
[2023-12-25 04:53:21,845 INFO misc.py line 119 253097] Train: [14/100][214/510] Data 0.003 (0.096) Batch 1.222 (1.557) Remain 19:06:06 loss: 0.2624 Lr: 0.00588
[2023-12-25 04:53:22,958 INFO misc.py line 119 253097] Train: [14/100][215/510] Data 0.004 (0.096) Batch 1.108 (1.555) Remain 19:04:31 loss: 0.8952 Lr: 0.00588
[2023-12-25 04:53:24,074 INFO misc.py line 119 253097] Train: [14/100][216/510] Data 0.009 (0.096) Batch 1.115 (1.553) Remain 19:02:58 loss: 0.1541 Lr: 0.00588
[2023-12-25 04:53:25,129 INFO misc.py line 119 253097] Train: [14/100][217/510] Data 0.011 (0.095) Batch 1.056 (1.551) Remain 19:01:14 loss: 0.3556 Lr: 0.00588
[2023-12-25 04:53:29,934 INFO misc.py line 119 253097] Train: [14/100][218/510] Data 3.775 (0.112) Batch 4.811 (1.566) Remain 19:12:22 loss: 0.6829 Lr: 0.00588
[2023-12-25 04:53:30,892 INFO misc.py line 119 253097] Train: [14/100][219/510] Data 0.004 (0.112) Batch 0.958 (1.563) Remain 19:10:16 loss: 0.3004 Lr: 0.00588
[2023-12-25 04:53:32,102 INFO misc.py line 119 253097] Train: [14/100][220/510] Data 0.004 (0.111) Batch 1.209 (1.562) Remain 19:09:02 loss: 0.2771 Lr: 0.00588
[2023-12-25 04:53:33,181 INFO misc.py line 119 253097] Train: [14/100][221/510] Data 0.004 (0.111) Batch 1.079 (1.559) Remain 19:07:23 loss: 0.1604 Lr: 0.00588
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[2023-12-25 04:53:36,518 INFO misc.py line 119 253097] Train: [14/100][224/510] Data 0.004 (0.109) Batch 1.139 (1.553) Remain 19:02:50 loss: 0.3241 Lr: 0.00588
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[2023-12-25 04:53:44,615 INFO misc.py line 119 253097] Train: [14/100][231/510] Data 0.004 (0.106) Batch 1.208 (1.541) Remain 18:53:42 loss: 0.1626 Lr: 0.00588
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[2023-12-25 04:55:19,655 INFO misc.py line 119 253097] Train: [14/100][289/510] Data 0.025 (0.086) Batch 0.939 (1.561) Remain 19:06:45 loss: 0.3076 Lr: 0.00588
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[2023-12-25 04:55:38,974 INFO misc.py line 119 253097] Train: [14/100][298/510] Data 0.006 (0.084) Batch 1.040 (1.579) Remain 19:19:38 loss: 0.3977 Lr: 0.00588
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[2023-12-25 04:55:42,143 INFO misc.py line 119 253097] Train: [14/100][301/510] Data 0.006 (0.083) Batch 1.113 (1.573) Remain 19:15:42 loss: 0.3369 Lr: 0.00588
[2023-12-25 04:55:43,234 INFO misc.py line 119 253097] Train: [14/100][302/510] Data 0.004 (0.083) Batch 1.091 (1.572) Remain 19:14:29 loss: 0.3471 Lr: 0.00588
[2023-12-25 04:55:44,475 INFO misc.py line 119 253097] Train: [14/100][303/510] Data 0.004 (0.082) Batch 1.240 (1.571) Remain 19:13:39 loss: 0.1923 Lr: 0.00588
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[2023-12-25 04:55:52,350 INFO misc.py line 119 253097] Train: [14/100][310/510] Data 0.007 (0.081) Batch 1.064 (1.561) Remain 19:06:00 loss: 0.4820 Lr: 0.00588
[2023-12-25 04:55:53,502 INFO misc.py line 119 253097] Train: [14/100][311/510] Data 0.005 (0.080) Batch 1.152 (1.559) Remain 19:05:00 loss: 0.0829 Lr: 0.00588
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[2023-12-25 04:56:08,676 INFO misc.py line 119 253097] Train: [14/100][324/510] Data 0.029 (0.077) Batch 1.177 (1.543) Remain 18:53:00 loss: 0.2026 Lr: 0.00588
[2023-12-25 04:56:09,718 INFO misc.py line 119 253097] Train: [14/100][325/510] Data 0.005 (0.077) Batch 1.035 (1.542) Remain 18:51:49 loss: 0.2635 Lr: 0.00588
[2023-12-25 04:56:18,893 INFO misc.py line 119 253097] Train: [14/100][326/510] Data 8.190 (0.102) Batch 9.182 (1.565) Remain 19:09:09 loss: 0.1582 Lr: 0.00588
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[2023-12-25 04:56:22,196 INFO misc.py line 119 253097] Train: [14/100][329/510] Data 0.006 (0.101) Batch 1.094 (1.561) Remain 19:05:56 loss: 0.7800 Lr: 0.00588
[2023-12-25 04:56:23,396 INFO misc.py line 119 253097] Train: [14/100][330/510] Data 0.006 (0.101) Batch 1.201 (1.560) Remain 19:05:06 loss: 0.3341 Lr: 0.00588
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[2023-12-25 04:56:38,870 INFO misc.py line 119 253097] Train: [14/100][332/510] Data 13.067 (0.140) Batch 14.212 (1.598) Remain 19:32:37 loss: 0.3398 Lr: 0.00588
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[2023-12-25 04:57:53,905 INFO misc.py line 119 253097] Train: [14/100][383/510] Data 0.006 (0.126) Batch 1.174 (1.581) Remain 19:18:49 loss: 0.6709 Lr: 0.00588
[2023-12-25 04:57:55,143 INFO misc.py line 119 253097] Train: [14/100][384/510] Data 0.005 (0.126) Batch 1.235 (1.580) Remain 19:18:08 loss: 0.3197 Lr: 0.00588
[2023-12-25 04:57:59,244 INFO misc.py line 119 253097] Train: [14/100][385/510] Data 2.882 (0.133) Batch 4.103 (1.586) Remain 19:22:57 loss: 0.1551 Lr: 0.00588
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[2023-12-25 04:58:02,475 INFO misc.py line 119 253097] Train: [14/100][388/510] Data 0.003 (0.132) Batch 1.105 (1.582) Remain 19:19:57 loss: 0.1959 Lr: 0.00588
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[2023-12-25 04:58:07,752 INFO misc.py line 119 253097] Train: [14/100][393/510] Data 0.004 (0.131) Batch 1.157 (1.576) Remain 19:14:52 loss: 0.8026 Lr: 0.00587
[2023-12-25 04:58:08,931 INFO misc.py line 119 253097] Train: [14/100][394/510] Data 0.004 (0.130) Batch 1.177 (1.575) Remain 19:14:06 loss: 0.2664 Lr: 0.00587
[2023-12-25 04:58:10,108 INFO misc.py line 119 253097] Train: [14/100][395/510] Data 0.005 (0.130) Batch 1.178 (1.574) Remain 19:13:20 loss: 0.2394 Lr: 0.00587
[2023-12-25 04:58:11,093 INFO misc.py line 119 253097] Train: [14/100][396/510] Data 0.005 (0.130) Batch 0.985 (1.572) Remain 19:12:13 loss: 0.3576 Lr: 0.00587
[2023-12-25 04:58:12,230 INFO misc.py line 119 253097] Train: [14/100][397/510] Data 0.005 (0.129) Batch 1.137 (1.571) Remain 19:11:22 loss: 0.2192 Lr: 0.00587
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[2023-12-25 04:58:14,547 INFO misc.py line 119 253097] Train: [14/100][399/510] Data 0.007 (0.129) Batch 0.965 (1.569) Remain 19:09:48 loss: 0.4226 Lr: 0.00587
[2023-12-25 04:58:15,458 INFO misc.py line 119 253097] Train: [14/100][400/510] Data 0.005 (0.128) Batch 0.910 (1.567) Remain 19:08:33 loss: 0.4247 Lr: 0.00587
[2023-12-25 04:58:16,581 INFO misc.py line 119 253097] Train: [14/100][401/510] Data 0.005 (0.128) Batch 1.124 (1.566) Remain 19:07:43 loss: 0.3346 Lr: 0.00587
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[2023-12-25 04:58:19,012 INFO misc.py line 119 253097] Train: [14/100][403/510] Data 0.010 (0.128) Batch 1.294 (1.564) Remain 19:06:22 loss: 0.6280 Lr: 0.00587
[2023-12-25 04:58:20,107 INFO misc.py line 119 253097] Train: [14/100][404/510] Data 0.008 (0.127) Batch 1.095 (1.563) Remain 19:05:29 loss: 0.6215 Lr: 0.00587
[2023-12-25 04:58:21,251 INFO misc.py line 119 253097] Train: [14/100][405/510] Data 0.008 (0.127) Batch 1.147 (1.562) Remain 19:04:42 loss: 0.4508 Lr: 0.00587
[2023-12-25 04:58:22,569 INFO misc.py line 119 253097] Train: [14/100][406/510] Data 0.005 (0.127) Batch 1.318 (1.562) Remain 19:04:14 loss: 0.3527 Lr: 0.00587
[2023-12-25 04:58:35,832 INFO misc.py line 119 253097] Train: [14/100][407/510] Data 0.005 (0.126) Batch 13.264 (1.591) Remain 19:25:25 loss: 0.3177 Lr: 0.00587
[2023-12-25 04:58:37,061 INFO misc.py line 119 253097] Train: [14/100][408/510] Data 0.004 (0.126) Batch 1.230 (1.590) Remain 19:24:45 loss: 0.4707 Lr: 0.00587
[2023-12-25 04:58:38,088 INFO misc.py line 119 253097] Train: [14/100][409/510] Data 0.003 (0.126) Batch 1.026 (1.588) Remain 19:23:42 loss: 0.1769 Lr: 0.00587
[2023-12-25 04:58:39,213 INFO misc.py line 119 253097] Train: [14/100][410/510] Data 0.004 (0.125) Batch 1.125 (1.587) Remain 19:22:50 loss: 0.5967 Lr: 0.00587
[2023-12-25 04:58:40,276 INFO misc.py line 119 253097] Train: [14/100][411/510] Data 0.004 (0.125) Batch 1.063 (1.586) Remain 19:21:52 loss: 0.2864 Lr: 0.00587
[2023-12-25 04:58:41,405 INFO misc.py line 119 253097] Train: [14/100][412/510] Data 0.005 (0.125) Batch 1.127 (1.585) Remain 19:21:02 loss: 0.2269 Lr: 0.00587
[2023-12-25 04:58:42,492 INFO misc.py line 119 253097] Train: [14/100][413/510] Data 0.006 (0.125) Batch 1.089 (1.584) Remain 19:20:07 loss: 0.2153 Lr: 0.00587
[2023-12-25 04:58:43,616 INFO misc.py line 119 253097] Train: [14/100][414/510] Data 0.004 (0.124) Batch 1.124 (1.582) Remain 19:19:16 loss: 0.6977 Lr: 0.00587
[2023-12-25 04:58:44,742 INFO misc.py line 119 253097] Train: [14/100][415/510] Data 0.006 (0.124) Batch 1.125 (1.581) Remain 19:18:26 loss: 0.1500 Lr: 0.00587
[2023-12-25 04:58:46,038 INFO misc.py line 119 253097] Train: [14/100][416/510] Data 0.007 (0.124) Batch 1.298 (1.581) Remain 19:17:54 loss: 0.5557 Lr: 0.00587
[2023-12-25 04:58:47,356 INFO misc.py line 119 253097] Train: [14/100][417/510] Data 0.004 (0.123) Batch 1.317 (1.580) Remain 19:17:24 loss: 0.3012 Lr: 0.00587
[2023-12-25 04:58:48,389 INFO misc.py line 119 253097] Train: [14/100][418/510] Data 0.004 (0.123) Batch 1.034 (1.579) Remain 19:16:25 loss: 0.4271 Lr: 0.00587
[2023-12-25 04:58:53,441 INFO misc.py line 119 253097] Train: [14/100][419/510] Data 0.004 (0.123) Batch 5.050 (1.587) Remain 19:22:30 loss: 0.2853 Lr: 0.00587
[2023-12-25 04:58:54,659 INFO misc.py line 119 253097] Train: [14/100][420/510] Data 0.006 (0.123) Batch 1.218 (1.586) Remain 19:21:50 loss: 0.2372 Lr: 0.00587
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[2023-12-25 04:59:03,559 INFO misc.py line 119 253097] Train: [14/100][428/510] Data 0.004 (0.120) Batch 1.152 (1.577) Remain 19:15:05 loss: 0.3202 Lr: 0.00587
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[2023-12-25 05:00:07,160 INFO misc.py line 119 253097] Train: [14/100][467/510] Data 0.005 (0.115) Batch 2.579 (1.582) Remain 19:17:21 loss: 0.2622 Lr: 0.00587
[2023-12-25 05:00:13,366 INFO misc.py line 119 253097] Train: [14/100][468/510] Data 0.005 (0.114) Batch 6.207 (1.592) Remain 19:24:37 loss: 0.3718 Lr: 0.00587
[2023-12-25 05:00:14,463 INFO misc.py line 119 253097] Train: [14/100][469/510] Data 0.005 (0.114) Batch 1.099 (1.591) Remain 19:23:48 loss: 0.2846 Lr: 0.00587
[2023-12-25 05:00:15,491 INFO misc.py line 119 253097] Train: [14/100][470/510] Data 0.003 (0.114) Batch 1.026 (1.589) Remain 19:22:54 loss: 0.3590 Lr: 0.00587
[2023-12-25 05:00:16,454 INFO misc.py line 119 253097] Train: [14/100][471/510] Data 0.005 (0.114) Batch 0.964 (1.588) Remain 19:21:54 loss: 0.1208 Lr: 0.00587
[2023-12-25 05:00:17,598 INFO misc.py line 119 253097] Train: [14/100][472/510] Data 0.003 (0.113) Batch 1.140 (1.587) Remain 19:21:10 loss: 0.3574 Lr: 0.00587
[2023-12-25 05:00:18,786 INFO misc.py line 119 253097] Train: [14/100][473/510] Data 0.008 (0.113) Batch 1.191 (1.586) Remain 19:20:31 loss: 0.2447 Lr: 0.00587
[2023-12-25 05:00:19,849 INFO misc.py line 119 253097] Train: [14/100][474/510] Data 0.005 (0.113) Batch 1.063 (1.585) Remain 19:19:41 loss: 0.5035 Lr: 0.00587
[2023-12-25 05:00:20,923 INFO misc.py line 119 253097] Train: [14/100][475/510] Data 0.005 (0.113) Batch 1.075 (1.584) Remain 19:18:52 loss: 0.3822 Lr: 0.00587
[2023-12-25 05:00:22,112 INFO misc.py line 119 253097] Train: [14/100][476/510] Data 0.004 (0.113) Batch 1.188 (1.583) Remain 19:18:14 loss: 0.4211 Lr: 0.00587
[2023-12-25 05:00:23,425 INFO misc.py line 119 253097] Train: [14/100][477/510] Data 0.004 (0.112) Batch 1.310 (1.583) Remain 19:17:47 loss: 0.2031 Lr: 0.00587
[2023-12-25 05:00:26,727 INFO misc.py line 119 253097] Train: [14/100][478/510] Data 0.008 (0.112) Batch 3.306 (1.586) Remain 19:20:25 loss: 0.4395 Lr: 0.00587
[2023-12-25 05:00:27,879 INFO misc.py line 119 253097] Train: [14/100][479/510] Data 0.005 (0.112) Batch 1.152 (1.585) Remain 19:19:43 loss: 0.3083 Lr: 0.00587
[2023-12-25 05:00:28,973 INFO misc.py line 119 253097] Train: [14/100][480/510] Data 0.004 (0.112) Batch 1.093 (1.584) Remain 19:18:56 loss: 0.1774 Lr: 0.00587
[2023-12-25 05:00:30,201 INFO misc.py line 119 253097] Train: [14/100][481/510] Data 0.005 (0.111) Batch 1.223 (1.584) Remain 19:18:21 loss: 0.3263 Lr: 0.00587
[2023-12-25 05:00:31,226 INFO misc.py line 119 253097] Train: [14/100][482/510] Data 0.010 (0.111) Batch 1.030 (1.582) Remain 19:17:29 loss: 0.4099 Lr: 0.00587
[2023-12-25 05:00:32,480 INFO misc.py line 119 253097] Train: [14/100][483/510] Data 0.004 (0.111) Batch 1.254 (1.582) Remain 19:16:57 loss: 0.2357 Lr: 0.00587
[2023-12-25 05:00:33,647 INFO misc.py line 119 253097] Train: [14/100][484/510] Data 0.005 (0.111) Batch 1.163 (1.581) Remain 19:16:18 loss: 0.1959 Lr: 0.00587
[2023-12-25 05:00:34,707 INFO misc.py line 119 253097] Train: [14/100][485/510] Data 0.008 (0.111) Batch 1.035 (1.580) Remain 19:15:26 loss: 0.2272 Lr: 0.00587
[2023-12-25 05:00:35,962 INFO misc.py line 119 253097] Train: [14/100][486/510] Data 0.033 (0.110) Batch 1.282 (1.579) Remain 19:14:58 loss: 0.3250 Lr: 0.00587
[2023-12-25 05:00:36,930 INFO misc.py line 119 253097] Train: [14/100][487/510] Data 0.007 (0.110) Batch 0.970 (1.578) Remain 19:14:01 loss: 0.4409 Lr: 0.00587
[2023-12-25 05:00:38,111 INFO misc.py line 119 253097] Train: [14/100][488/510] Data 0.003 (0.110) Batch 1.180 (1.577) Remain 19:13:23 loss: 0.3244 Lr: 0.00587
[2023-12-25 05:00:39,237 INFO misc.py line 119 253097] Train: [14/100][489/510] Data 0.004 (0.110) Batch 1.126 (1.576) Remain 19:12:41 loss: 0.2356 Lr: 0.00587
[2023-12-25 05:00:40,436 INFO misc.py line 119 253097] Train: [14/100][490/510] Data 0.005 (0.110) Batch 1.200 (1.575) Remain 19:12:06 loss: 0.2896 Lr: 0.00587
[2023-12-25 05:00:42,081 INFO misc.py line 119 253097] Train: [14/100][491/510] Data 0.632 (0.111) Batch 1.645 (1.575) Remain 19:12:10 loss: 0.6069 Lr: 0.00587
[2023-12-25 05:00:43,226 INFO misc.py line 119 253097] Train: [14/100][492/510] Data 0.003 (0.110) Batch 1.138 (1.575) Remain 19:11:29 loss: 0.3579 Lr: 0.00587
[2023-12-25 05:00:44,430 INFO misc.py line 119 253097] Train: [14/100][493/510] Data 0.010 (0.110) Batch 1.211 (1.574) Remain 19:10:55 loss: 0.2058 Lr: 0.00587
[2023-12-25 05:00:45,553 INFO misc.py line 119 253097] Train: [14/100][494/510] Data 0.003 (0.110) Batch 1.115 (1.573) Remain 19:10:13 loss: 0.5646 Lr: 0.00587
[2023-12-25 05:00:46,816 INFO misc.py line 119 253097] Train: [14/100][495/510] Data 0.012 (0.110) Batch 1.267 (1.572) Remain 19:09:44 loss: 0.3227 Lr: 0.00587
[2023-12-25 05:00:47,904 INFO misc.py line 119 253097] Train: [14/100][496/510] Data 0.008 (0.110) Batch 1.092 (1.571) Remain 19:08:59 loss: 0.5253 Lr: 0.00587
[2023-12-25 05:00:48,950 INFO misc.py line 119 253097] Train: [14/100][497/510] Data 0.005 (0.109) Batch 1.041 (1.570) Remain 19:08:11 loss: 0.3466 Lr: 0.00587
[2023-12-25 05:00:50,053 INFO misc.py line 119 253097] Train: [14/100][498/510] Data 0.010 (0.109) Batch 1.104 (1.569) Remain 19:07:28 loss: 0.2571 Lr: 0.00587
[2023-12-25 05:00:53,196 INFO misc.py line 119 253097] Train: [14/100][499/510] Data 0.009 (0.109) Batch 3.147 (1.572) Remain 19:09:46 loss: 0.2035 Lr: 0.00587
[2023-12-25 05:00:54,490 INFO misc.py line 119 253097] Train: [14/100][500/510] Data 0.004 (0.109) Batch 1.292 (1.572) Remain 19:09:20 loss: 0.2996 Lr: 0.00587
[2023-12-25 05:00:55,689 INFO misc.py line 119 253097] Train: [14/100][501/510] Data 0.007 (0.109) Batch 1.201 (1.571) Remain 19:08:45 loss: 0.2179 Lr: 0.00587
[2023-12-25 05:00:56,903 INFO misc.py line 119 253097] Train: [14/100][502/510] Data 0.004 (0.108) Batch 1.212 (1.570) Remain 19:08:12 loss: 0.3226 Lr: 0.00587
[2023-12-25 05:00:58,037 INFO misc.py line 119 253097] Train: [14/100][503/510] Data 0.005 (0.108) Batch 1.135 (1.570) Remain 19:07:32 loss: 0.5137 Lr: 0.00587
[2023-12-25 05:00:59,342 INFO misc.py line 119 253097] Train: [14/100][504/510] Data 0.004 (0.108) Batch 1.306 (1.569) Remain 19:07:08 loss: 0.3026 Lr: 0.00587
[2023-12-25 05:01:00,502 INFO misc.py line 119 253097] Train: [14/100][505/510] Data 0.004 (0.108) Batch 1.160 (1.568) Remain 19:06:31 loss: 0.2609 Lr: 0.00587
[2023-12-25 05:01:01,683 INFO misc.py line 119 253097] Train: [14/100][506/510] Data 0.003 (0.107) Batch 1.175 (1.567) Remain 19:05:55 loss: 0.4073 Lr: 0.00587
[2023-12-25 05:01:02,979 INFO misc.py line 119 253097] Train: [14/100][507/510] Data 0.010 (0.107) Batch 1.297 (1.567) Remain 19:05:30 loss: 0.6139 Lr: 0.00587
[2023-12-25 05:01:04,019 INFO misc.py line 119 253097] Train: [14/100][508/510] Data 0.008 (0.107) Batch 1.044 (1.566) Remain 19:04:43 loss: 0.3208 Lr: 0.00587
[2023-12-25 05:01:05,147 INFO misc.py line 119 253097] Train: [14/100][509/510] Data 0.003 (0.107) Batch 1.128 (1.565) Remain 19:04:03 loss: 0.2542 Lr: 0.00587
[2023-12-25 05:01:06,305 INFO misc.py line 119 253097] Train: [14/100][510/510] Data 0.003 (0.107) Batch 1.157 (1.564) Remain 19:03:26 loss: 0.3631 Lr: 0.00587
[2023-12-25 05:01:06,305 INFO misc.py line 136 253097] Train result: loss: 0.3553 
[2023-12-25 05:01:06,305 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 05:01:32,034 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.3603 
[2023-12-25 05:01:32,391 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4465 
[2023-12-25 05:01:38,856 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4526 
[2023-12-25 05:01:39,374 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4656 
[2023-12-25 05:01:41,349 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7227 
[2023-12-25 05:01:41,794 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.5096 
[2023-12-25 05:01:42,675 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0037 
[2023-12-25 05:01:43,228 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4020 
[2023-12-25 05:01:45,045 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1868 
[2023-12-25 05:01:47,168 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2642 
[2023-12-25 05:01:48,025 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3266 
[2023-12-25 05:01:48,451 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6619 
[2023-12-25 05:01:49,352 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4666 
[2023-12-25 05:01:52,297 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7311 
[2023-12-25 05:01:52,769 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3061 
[2023-12-25 05:01:53,379 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.6984 
[2023-12-25 05:01:54,080 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.6713 
[2023-12-25 05:01:55,423 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6329/0.7154/0.8873.
[2023-12-25 05:01:55,423 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9121/0.9364
[2023-12-25 05:01:55,423 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9795/0.9903
[2023-12-25 05:01:55,423 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8194/0.9618
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3514/0.4057
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.4898/0.4990
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6483/0.7867
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8166/0.9205
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9083/0.9450
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4163/0.4315
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7331/0.8180
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.5823/0.9080
[2023-12-25 05:01:55,424 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5710/0.6967
[2023-12-25 05:01:55,424 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 05:01:55,425 INFO misc.py line 165 253097] Currently Best mIoU: 0.6447
[2023-12-25 05:01:55,425 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 05:02:03,404 INFO misc.py line 119 253097] Train: [15/100][1/510] Data 2.515 (2.515) Batch 5.582 (5.582) Remain 68:00:29 loss: 0.2301 Lr: 0.00587
[2023-12-25 05:02:04,498 INFO misc.py line 119 253097] Train: [15/100][2/510] Data 0.004 (0.004) Batch 1.095 (1.095) Remain 13:20:07 loss: 0.2552 Lr: 0.00587
[2023-12-25 05:02:05,549 INFO misc.py line 119 253097] Train: [15/100][3/510] Data 0.003 (0.003) Batch 1.051 (1.051) Remain 12:48:31 loss: 0.2120 Lr: 0.00587
[2023-12-25 05:02:06,749 INFO misc.py line 119 253097] Train: [15/100][4/510] Data 0.003 (0.003) Batch 1.199 (1.199) Remain 14:36:21 loss: 0.1539 Lr: 0.00587
[2023-12-25 05:02:07,837 INFO misc.py line 119 253097] Train: [15/100][5/510] Data 0.004 (0.003) Batch 1.089 (1.144) Remain 13:56:02 loss: 0.2856 Lr: 0.00587
[2023-12-25 05:02:08,925 INFO misc.py line 119 253097] Train: [15/100][6/510] Data 0.003 (0.003) Batch 1.087 (1.125) Remain 13:42:12 loss: 0.2201 Lr: 0.00587
[2023-12-25 05:02:09,988 INFO misc.py line 119 253097] Train: [15/100][7/510] Data 0.004 (0.003) Batch 1.065 (1.110) Remain 13:31:08 loss: 0.1423 Lr: 0.00587
[2023-12-25 05:02:11,092 INFO misc.py line 119 253097] Train: [15/100][8/510] Data 0.003 (0.003) Batch 1.103 (1.108) Remain 13:30:06 loss: 0.2318 Lr: 0.00587
[2023-12-25 05:02:12,269 INFO misc.py line 119 253097] Train: [15/100][9/510] Data 0.004 (0.003) Batch 1.176 (1.120) Remain 13:38:16 loss: 0.2584 Lr: 0.00587
[2023-12-25 05:02:13,431 INFO misc.py line 119 253097] Train: [15/100][10/510] Data 0.006 (0.004) Batch 1.163 (1.126) Remain 13:42:45 loss: 0.2936 Lr: 0.00587
[2023-12-25 05:02:14,514 INFO misc.py line 119 253097] Train: [15/100][11/510] Data 0.005 (0.004) Batch 1.084 (1.121) Remain 13:38:53 loss: 0.4219 Lr: 0.00587
[2023-12-25 05:02:20,501 INFO misc.py line 119 253097] Train: [15/100][12/510] Data 4.672 (0.523) Batch 5.987 (1.661) Remain 20:14:00 loss: 0.1666 Lr: 0.00587
[2023-12-25 05:02:21,704 INFO misc.py line 119 253097] Train: [15/100][13/510] Data 0.004 (0.471) Batch 1.200 (1.615) Remain 19:40:17 loss: 0.1935 Lr: 0.00587
[2023-12-25 05:02:22,848 INFO misc.py line 119 253097] Train: [15/100][14/510] Data 0.007 (0.429) Batch 1.148 (1.573) Remain 19:09:13 loss: 0.2699 Lr: 0.00587
[2023-12-25 05:02:24,035 INFO misc.py line 119 253097] Train: [15/100][15/510] Data 0.003 (0.393) Batch 1.187 (1.540) Remain 18:45:41 loss: 0.6828 Lr: 0.00587
[2023-12-25 05:02:25,366 INFO misc.py line 119 253097] Train: [15/100][16/510] Data 0.004 (0.363) Batch 1.328 (1.524) Remain 18:33:43 loss: 0.2091 Lr: 0.00587
[2023-12-25 05:02:26,554 INFO misc.py line 119 253097] Train: [15/100][17/510] Data 0.007 (0.338) Batch 1.188 (1.500) Remain 18:16:08 loss: 0.3232 Lr: 0.00587
[2023-12-25 05:02:27,820 INFO misc.py line 119 253097] Train: [15/100][18/510] Data 0.007 (0.316) Batch 1.264 (1.484) Remain 18:04:37 loss: 0.2038 Lr: 0.00587
[2023-12-25 05:02:28,768 INFO misc.py line 119 253097] Train: [15/100][19/510] Data 0.009 (0.297) Batch 0.952 (1.451) Remain 17:40:18 loss: 0.3862 Lr: 0.00587
[2023-12-25 05:02:29,773 INFO misc.py line 119 253097] Train: [15/100][20/510] Data 0.004 (0.279) Batch 1.004 (1.425) Remain 17:21:04 loss: 0.2533 Lr: 0.00587
[2023-12-25 05:02:30,944 INFO misc.py line 119 253097] Train: [15/100][21/510] Data 0.005 (0.264) Batch 1.173 (1.411) Remain 17:10:49 loss: 0.4456 Lr: 0.00587
[2023-12-25 05:02:32,041 INFO misc.py line 119 253097] Train: [15/100][22/510] Data 0.003 (0.250) Batch 1.096 (1.394) Remain 16:58:41 loss: 0.2813 Lr: 0.00587
[2023-12-25 05:02:33,109 INFO misc.py line 119 253097] Train: [15/100][23/510] Data 0.004 (0.238) Batch 1.068 (1.378) Remain 16:46:45 loss: 0.3670 Lr: 0.00587
[2023-12-25 05:02:34,291 INFO misc.py line 119 253097] Train: [15/100][24/510] Data 0.004 (0.227) Batch 1.181 (1.369) Remain 16:39:52 loss: 0.4340 Lr: 0.00587
[2023-12-25 05:02:39,974 INFO misc.py line 119 253097] Train: [15/100][25/510] Data 4.838 (0.437) Batch 5.683 (1.565) Remain 19:03:08 loss: 0.4573 Lr: 0.00587
[2023-12-25 05:02:41,122 INFO misc.py line 119 253097] Train: [15/100][26/510] Data 0.004 (0.418) Batch 1.149 (1.547) Remain 18:49:54 loss: 0.4183 Lr: 0.00587
[2023-12-25 05:02:43,608 INFO misc.py line 119 253097] Train: [15/100][27/510] Data 1.553 (0.465) Batch 2.486 (1.586) Remain 19:18:28 loss: 0.0846 Lr: 0.00587
[2023-12-25 05:02:44,751 INFO misc.py line 119 253097] Train: [15/100][28/510] Data 0.004 (0.447) Batch 1.143 (1.568) Remain 19:05:30 loss: 0.2997 Lr: 0.00587
[2023-12-25 05:02:45,922 INFO misc.py line 119 253097] Train: [15/100][29/510] Data 0.003 (0.430) Batch 1.171 (1.553) Remain 18:54:19 loss: 0.2656 Lr: 0.00587
[2023-12-25 05:02:46,960 INFO misc.py line 119 253097] Train: [15/100][30/510] Data 0.004 (0.414) Batch 1.038 (1.534) Remain 18:40:21 loss: 0.3652 Lr: 0.00587
[2023-12-25 05:02:48,206 INFO misc.py line 119 253097] Train: [15/100][31/510] Data 0.005 (0.399) Batch 1.247 (1.523) Remain 18:32:51 loss: 0.1845 Lr: 0.00587
[2023-12-25 05:02:49,344 INFO misc.py line 119 253097] Train: [15/100][32/510] Data 0.004 (0.386) Batch 1.137 (1.510) Remain 18:23:06 loss: 0.2594 Lr: 0.00587
[2023-12-25 05:02:53,427 INFO misc.py line 119 253097] Train: [15/100][33/510] Data 2.938 (0.471) Batch 4.083 (1.596) Remain 19:25:43 loss: 0.3855 Lr: 0.00587
[2023-12-25 05:02:54,283 INFO misc.py line 119 253097] Train: [15/100][34/510] Data 0.004 (0.456) Batch 0.856 (1.572) Remain 19:08:15 loss: 0.3294 Lr: 0.00587
[2023-12-25 05:02:55,365 INFO misc.py line 119 253097] Train: [15/100][35/510] Data 0.005 (0.442) Batch 1.080 (1.557) Remain 18:56:59 loss: 0.3785 Lr: 0.00587
[2023-12-25 05:02:56,352 INFO misc.py line 119 253097] Train: [15/100][36/510] Data 0.006 (0.428) Batch 0.990 (1.539) Remain 18:44:25 loss: 0.2686 Lr: 0.00587
[2023-12-25 05:02:57,473 INFO misc.py line 119 253097] Train: [15/100][37/510] Data 0.004 (0.416) Batch 1.120 (1.527) Remain 18:35:23 loss: 0.7362 Lr: 0.00587
[2023-12-25 05:02:58,299 INFO misc.py line 119 253097] Train: [15/100][38/510] Data 0.004 (0.404) Batch 0.826 (1.507) Remain 18:20:44 loss: 0.3985 Lr: 0.00587
[2023-12-25 05:02:59,485 INFO misc.py line 119 253097] Train: [15/100][39/510] Data 0.004 (0.393) Batch 1.175 (1.498) Remain 18:13:59 loss: 0.3048 Lr: 0.00587
[2023-12-25 05:03:03,638 INFO misc.py line 119 253097] Train: [15/100][40/510] Data 0.015 (0.383) Batch 4.162 (1.570) Remain 19:06:33 loss: 0.2344 Lr: 0.00587
[2023-12-25 05:03:04,723 INFO misc.py line 119 253097] Train: [15/100][41/510] Data 0.006 (0.373) Batch 1.087 (1.557) Remain 18:57:14 loss: 0.2404 Lr: 0.00587
[2023-12-25 05:03:05,870 INFO misc.py line 119 253097] Train: [15/100][42/510] Data 0.003 (0.363) Batch 1.147 (1.547) Remain 18:49:31 loss: 0.3023 Lr: 0.00587
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[2023-12-25 05:06:29,646 INFO misc.py line 119 253097] Train: [15/100][181/510] Data 2.247 (0.271) Batch 3.537 (1.484) Remain 18:00:06 loss: 0.3148 Lr: 0.00586
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[2023-12-25 05:06:36,033 INFO misc.py line 119 253097] Train: [15/100][187/510] Data 0.005 (0.262) Batch 1.087 (1.470) Remain 17:49:59 loss: 0.1523 Lr: 0.00586
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[2023-12-25 05:06:46,549 INFO misc.py line 119 253097] Train: [15/100][196/510] Data 0.006 (0.250) Batch 1.237 (1.456) Remain 17:39:32 loss: 0.3425 Lr: 0.00586
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[2023-12-25 05:06:48,893 INFO misc.py line 119 253097] Train: [15/100][198/510] Data 0.004 (0.248) Batch 1.116 (1.453) Remain 17:37:22 loss: 0.3178 Lr: 0.00586
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[2023-12-25 05:06:55,067 INFO misc.py line 119 253097] Train: [15/100][200/510] Data 0.004 (0.245) Batch 4.950 (1.470) Remain 17:49:23 loss: 0.4319 Lr: 0.00586
[2023-12-25 05:06:56,186 INFO misc.py line 119 253097] Train: [15/100][201/510] Data 0.004 (0.244) Batch 1.119 (1.468) Remain 17:48:05 loss: 0.2475 Lr: 0.00586
[2023-12-25 05:06:57,134 INFO misc.py line 119 253097] Train: [15/100][202/510] Data 0.004 (0.243) Batch 0.948 (1.465) Remain 17:46:09 loss: 0.2744 Lr: 0.00586
[2023-12-25 05:06:58,408 INFO misc.py line 119 253097] Train: [15/100][203/510] Data 0.005 (0.242) Batch 1.274 (1.464) Remain 17:45:26 loss: 0.3815 Lr: 0.00586
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[2023-12-25 05:07:11,127 INFO misc.py line 119 253097] Train: [15/100][214/510] Data 0.011 (0.229) Batch 1.020 (1.448) Remain 17:33:29 loss: 0.4719 Lr: 0.00586
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[2023-12-25 05:07:19,871 INFO misc.py line 119 253097] Train: [15/100][216/510] Data 6.296 (0.257) Batch 7.436 (1.476) Remain 17:53:24 loss: 0.1808 Lr: 0.00586
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[2023-12-25 05:07:29,915 INFO misc.py line 119 253097] Train: [15/100][225/510] Data 0.011 (0.247) Batch 1.247 (1.461) Remain 17:42:34 loss: 0.2966 Lr: 0.00586
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[2023-12-25 05:09:06,718 INFO misc.py line 119 253097] Train: [15/100][283/510] Data 0.006 (0.259) Batch 1.230 (1.504) Remain 18:12:27 loss: 0.3323 Lr: 0.00585
[2023-12-25 05:09:07,867 INFO misc.py line 119 253097] Train: [15/100][284/510] Data 0.004 (0.258) Batch 1.149 (1.503) Remain 18:11:30 loss: 0.3250 Lr: 0.00585
[2023-12-25 05:09:09,060 INFO misc.py line 119 253097] Train: [15/100][285/510] Data 0.003 (0.258) Batch 1.191 (1.502) Remain 18:10:40 loss: 0.5486 Lr: 0.00585
[2023-12-25 05:09:19,718 INFO misc.py line 119 253097] Train: [15/100][286/510] Data 9.389 (0.290) Batch 10.659 (1.534) Remain 18:34:09 loss: 0.1945 Lr: 0.00585
[2023-12-25 05:09:20,859 INFO misc.py line 119 253097] Train: [15/100][287/510] Data 0.006 (0.289) Batch 1.141 (1.533) Remain 18:33:07 loss: 0.2876 Lr: 0.00585
[2023-12-25 05:09:21,944 INFO misc.py line 119 253097] Train: [15/100][288/510] Data 0.005 (0.288) Batch 1.086 (1.531) Remain 18:31:57 loss: 0.2431 Lr: 0.00585
[2023-12-25 05:09:23,049 INFO misc.py line 119 253097] Train: [15/100][289/510] Data 0.005 (0.287) Batch 1.103 (1.530) Remain 18:30:50 loss: 0.3441 Lr: 0.00585
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[2023-12-25 05:09:32,627 INFO misc.py line 119 253097] Train: [15/100][292/510] Data 0.005 (0.284) Batch 7.361 (1.547) Remain 18:43:18 loss: 0.3843 Lr: 0.00585
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[2023-12-25 05:09:35,871 INFO misc.py line 119 253097] Train: [15/100][295/510] Data 0.004 (0.281) Batch 0.924 (1.542) Remain 18:39:45 loss: 0.4360 Lr: 0.00585
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[2023-12-25 05:09:41,853 INFO misc.py line 119 253097] Train: [15/100][300/510] Data 0.006 (0.276) Batch 1.206 (1.536) Remain 18:35:23 loss: 0.2190 Lr: 0.00585
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[2023-12-25 05:09:44,191 INFO misc.py line 119 253097] Train: [15/100][302/510] Data 0.010 (0.275) Batch 1.246 (1.534) Remain 18:33:32 loss: 0.2286 Lr: 0.00585
[2023-12-25 05:09:45,286 INFO misc.py line 119 253097] Train: [15/100][303/510] Data 0.015 (0.274) Batch 1.106 (1.532) Remain 18:32:28 loss: 0.3145 Lr: 0.00585
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[2023-12-25 05:09:48,762 INFO misc.py line 119 253097] Train: [15/100][306/510] Data 0.010 (0.271) Batch 1.263 (1.529) Remain 18:29:41 loss: 0.3459 Lr: 0.00585
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[2023-12-25 05:09:51,071 INFO misc.py line 119 253097] Train: [15/100][308/510] Data 0.010 (0.269) Batch 1.156 (1.526) Remain 18:27:52 loss: 0.4577 Lr: 0.00585
[2023-12-25 05:09:51,891 INFO misc.py line 119 253097] Train: [15/100][309/510] Data 0.007 (0.269) Batch 0.823 (1.524) Remain 18:26:11 loss: 0.3254 Lr: 0.00585
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[2023-12-25 05:09:55,180 INFO misc.py line 119 253097] Train: [15/100][312/510] Data 0.005 (0.266) Batch 1.102 (1.520) Remain 18:23:05 loss: 0.3918 Lr: 0.00585
[2023-12-25 05:09:56,180 INFO misc.py line 119 253097] Train: [15/100][313/510] Data 0.004 (0.265) Batch 1.000 (1.518) Remain 18:21:51 loss: 0.4158 Lr: 0.00585
[2023-12-25 05:09:57,426 INFO misc.py line 119 253097] Train: [15/100][314/510] Data 0.005 (0.264) Batch 1.247 (1.517) Remain 18:21:11 loss: 0.2623 Lr: 0.00585
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[2023-12-25 05:13:43,900 INFO misc.py line 119 253097] Train: [15/100][461/510] Data 0.006 (0.191) Batch 1.071 (1.525) Remain 18:22:53 loss: 0.1573 Lr: 0.00584
[2023-12-25 05:13:53,016 INFO misc.py line 119 253097] Train: [15/100][462/510] Data 0.005 (0.191) Batch 9.115 (1.541) Remain 18:34:50 loss: 0.3731 Lr: 0.00584
[2023-12-25 05:13:54,269 INFO misc.py line 119 253097] Train: [15/100][463/510] Data 0.005 (0.190) Batch 1.249 (1.541) Remain 18:34:20 loss: 0.3507 Lr: 0.00584
[2023-12-25 05:13:55,380 INFO misc.py line 119 253097] Train: [15/100][464/510] Data 0.010 (0.190) Batch 1.112 (1.540) Remain 18:33:38 loss: 0.4741 Lr: 0.00584
[2023-12-25 05:13:56,431 INFO misc.py line 119 253097] Train: [15/100][465/510] Data 0.008 (0.189) Batch 1.051 (1.539) Remain 18:32:51 loss: 0.3595 Lr: 0.00584
[2023-12-25 05:13:57,398 INFO misc.py line 119 253097] Train: [15/100][466/510] Data 0.008 (0.189) Batch 0.970 (1.537) Remain 18:31:56 loss: 0.3466 Lr: 0.00584
[2023-12-25 05:13:58,620 INFO misc.py line 119 253097] Train: [15/100][467/510] Data 0.005 (0.189) Batch 1.218 (1.537) Remain 18:31:25 loss: 0.2067 Lr: 0.00584
[2023-12-25 05:13:59,883 INFO misc.py line 119 253097] Train: [15/100][468/510] Data 0.009 (0.188) Batch 1.268 (1.536) Remain 18:30:58 loss: 0.3258 Lr: 0.00584
[2023-12-25 05:14:00,960 INFO misc.py line 119 253097] Train: [15/100][469/510] Data 0.004 (0.188) Batch 1.074 (1.535) Remain 18:30:14 loss: 0.3491 Lr: 0.00584
[2023-12-25 05:14:02,139 INFO misc.py line 119 253097] Train: [15/100][470/510] Data 0.007 (0.187) Batch 1.179 (1.534) Remain 18:29:39 loss: 0.3419 Lr: 0.00584
[2023-12-25 05:14:03,281 INFO misc.py line 119 253097] Train: [15/100][471/510] Data 0.007 (0.187) Batch 1.139 (1.534) Remain 18:29:01 loss: 0.1820 Lr: 0.00584
[2023-12-25 05:14:04,254 INFO misc.py line 119 253097] Train: [15/100][472/510] Data 0.009 (0.187) Batch 0.978 (1.532) Remain 18:28:08 loss: 0.4684 Lr: 0.00584
[2023-12-25 05:14:05,184 INFO misc.py line 119 253097] Train: [15/100][473/510] Data 0.004 (0.186) Batch 0.928 (1.531) Remain 18:27:11 loss: 0.5743 Lr: 0.00584
[2023-12-25 05:14:07,750 INFO misc.py line 119 253097] Train: [15/100][474/510] Data 1.622 (0.189) Batch 2.567 (1.533) Remain 18:28:45 loss: 0.2083 Lr: 0.00584
[2023-12-25 05:14:08,987 INFO misc.py line 119 253097] Train: [15/100][475/510] Data 0.005 (0.189) Batch 1.237 (1.533) Remain 18:28:16 loss: 1.1026 Lr: 0.00584
[2023-12-25 05:14:10,171 INFO misc.py line 119 253097] Train: [15/100][476/510] Data 0.005 (0.188) Batch 1.183 (1.532) Remain 18:27:42 loss: 0.3549 Lr: 0.00584
[2023-12-25 05:14:11,425 INFO misc.py line 119 253097] Train: [15/100][477/510] Data 0.006 (0.188) Batch 1.253 (1.531) Remain 18:27:15 loss: 0.4077 Lr: 0.00584
[2023-12-25 05:14:12,575 INFO misc.py line 119 253097] Train: [15/100][478/510] Data 0.007 (0.188) Batch 1.149 (1.531) Remain 18:26:39 loss: 0.4131 Lr: 0.00584
[2023-12-25 05:14:13,646 INFO misc.py line 119 253097] Train: [15/100][479/510] Data 0.008 (0.187) Batch 1.069 (1.530) Remain 18:25:55 loss: 0.3345 Lr: 0.00584
[2023-12-25 05:14:14,701 INFO misc.py line 119 253097] Train: [15/100][480/510] Data 0.010 (0.187) Batch 1.058 (1.529) Remain 18:25:11 loss: 0.4360 Lr: 0.00584
[2023-12-25 05:14:15,969 INFO misc.py line 119 253097] Train: [15/100][481/510] Data 0.007 (0.187) Batch 1.270 (1.528) Remain 18:24:46 loss: 0.8182 Lr: 0.00584
[2023-12-25 05:14:17,123 INFO misc.py line 119 253097] Train: [15/100][482/510] Data 0.005 (0.186) Batch 1.151 (1.527) Remain 18:24:10 loss: 0.2913 Lr: 0.00584
[2023-12-25 05:14:18,071 INFO misc.py line 119 253097] Train: [15/100][483/510] Data 0.009 (0.186) Batch 0.952 (1.526) Remain 18:23:16 loss: 0.4170 Lr: 0.00584
[2023-12-25 05:14:19,216 INFO misc.py line 119 253097] Train: [15/100][484/510] Data 0.006 (0.185) Batch 1.145 (1.525) Remain 18:22:40 loss: 0.4661 Lr: 0.00584
[2023-12-25 05:14:22,480 INFO misc.py line 119 253097] Train: [15/100][485/510] Data 0.005 (0.185) Batch 3.265 (1.529) Remain 18:25:15 loss: 0.3341 Lr: 0.00584
[2023-12-25 05:14:23,630 INFO misc.py line 119 253097] Train: [15/100][486/510] Data 0.004 (0.185) Batch 1.150 (1.528) Remain 18:24:40 loss: 0.2966 Lr: 0.00584
[2023-12-25 05:14:24,706 INFO misc.py line 119 253097] Train: [15/100][487/510] Data 0.005 (0.184) Batch 1.076 (1.527) Remain 18:23:58 loss: 0.1919 Lr: 0.00584
[2023-12-25 05:14:27,788 INFO misc.py line 119 253097] Train: [15/100][488/510] Data 0.005 (0.184) Batch 3.082 (1.530) Remain 18:26:15 loss: 0.4443 Lr: 0.00584
[2023-12-25 05:14:29,001 INFO misc.py line 119 253097] Train: [15/100][489/510] Data 0.004 (0.184) Batch 1.215 (1.530) Remain 18:25:46 loss: 0.5757 Lr: 0.00584
[2023-12-25 05:14:34,416 INFO misc.py line 119 253097] Train: [15/100][490/510] Data 4.319 (0.192) Batch 5.414 (1.538) Remain 18:31:30 loss: 0.1350 Lr: 0.00584
[2023-12-25 05:14:35,621 INFO misc.py line 119 253097] Train: [15/100][491/510] Data 0.004 (0.192) Batch 1.205 (1.537) Remain 18:30:59 loss: 0.4900 Lr: 0.00584
[2023-12-25 05:14:36,889 INFO misc.py line 119 253097] Train: [15/100][492/510] Data 0.003 (0.191) Batch 1.264 (1.536) Remain 18:30:33 loss: 0.5173 Lr: 0.00584
[2023-12-25 05:14:38,294 INFO misc.py line 119 253097] Train: [15/100][493/510] Data 0.252 (0.191) Batch 1.409 (1.536) Remain 18:30:20 loss: 0.2651 Lr: 0.00584
[2023-12-25 05:14:39,413 INFO misc.py line 119 253097] Train: [15/100][494/510] Data 0.004 (0.191) Batch 1.116 (1.535) Remain 18:29:42 loss: 0.3281 Lr: 0.00584
[2023-12-25 05:14:40,505 INFO misc.py line 119 253097] Train: [15/100][495/510] Data 0.007 (0.191) Batch 1.096 (1.534) Remain 18:29:01 loss: 0.4994 Lr: 0.00584
[2023-12-25 05:14:44,702 INFO misc.py line 119 253097] Train: [15/100][496/510] Data 0.004 (0.190) Batch 4.197 (1.540) Remain 18:32:54 loss: 0.6293 Lr: 0.00584
[2023-12-25 05:14:45,970 INFO misc.py line 119 253097] Train: [15/100][497/510] Data 0.004 (0.190) Batch 1.269 (1.539) Remain 18:32:29 loss: 0.4388 Lr: 0.00584
[2023-12-25 05:14:47,235 INFO misc.py line 119 253097] Train: [15/100][498/510] Data 0.003 (0.190) Batch 1.264 (1.539) Remain 18:32:03 loss: 0.1987 Lr: 0.00584
[2023-12-25 05:14:48,528 INFO misc.py line 119 253097] Train: [15/100][499/510] Data 0.004 (0.189) Batch 1.293 (1.538) Remain 18:31:40 loss: 0.2100 Lr: 0.00584
[2023-12-25 05:14:49,631 INFO misc.py line 119 253097] Train: [15/100][500/510] Data 0.004 (0.189) Batch 1.099 (1.537) Remain 18:31:00 loss: 0.5344 Lr: 0.00584
[2023-12-25 05:14:50,853 INFO misc.py line 119 253097] Train: [15/100][501/510] Data 0.008 (0.188) Batch 1.226 (1.537) Remain 18:30:32 loss: 0.6266 Lr: 0.00584
[2023-12-25 05:14:52,037 INFO misc.py line 119 253097] Train: [15/100][502/510] Data 0.003 (0.188) Batch 1.178 (1.536) Remain 18:29:59 loss: 0.3907 Lr: 0.00584
[2023-12-25 05:14:53,251 INFO misc.py line 119 253097] Train: [15/100][503/510] Data 0.009 (0.188) Batch 1.212 (1.535) Remain 18:29:29 loss: 0.1647 Lr: 0.00584
[2023-12-25 05:14:54,428 INFO misc.py line 119 253097] Train: [15/100][504/510] Data 0.011 (0.187) Batch 1.181 (1.535) Remain 18:28:57 loss: 0.2648 Lr: 0.00584
[2023-12-25 05:14:55,537 INFO misc.py line 119 253097] Train: [15/100][505/510] Data 0.008 (0.187) Batch 1.108 (1.534) Remain 18:28:19 loss: 0.2687 Lr: 0.00584
[2023-12-25 05:14:56,789 INFO misc.py line 119 253097] Train: [15/100][506/510] Data 0.009 (0.187) Batch 1.258 (1.533) Remain 18:27:53 loss: 0.3238 Lr: 0.00584
[2023-12-25 05:14:57,846 INFO misc.py line 119 253097] Train: [15/100][507/510] Data 0.004 (0.186) Batch 1.055 (1.532) Remain 18:27:11 loss: 0.4414 Lr: 0.00584
[2023-12-25 05:14:58,939 INFO misc.py line 119 253097] Train: [15/100][508/510] Data 0.006 (0.186) Batch 1.093 (1.531) Remain 18:26:31 loss: 0.5449 Lr: 0.00584
[2023-12-25 05:14:59,735 INFO misc.py line 119 253097] Train: [15/100][509/510] Data 0.006 (0.186) Batch 0.797 (1.530) Remain 18:25:27 loss: 0.2909 Lr: 0.00584
[2023-12-25 05:15:00,780 INFO misc.py line 119 253097] Train: [15/100][510/510] Data 0.005 (0.185) Batch 1.045 (1.529) Remain 18:24:44 loss: 0.2791 Lr: 0.00584
[2023-12-25 05:15:00,781 INFO misc.py line 136 253097] Train result: loss: 0.3464 
[2023-12-25 05:15:00,782 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 05:15:27,680 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.8169 
[2023-12-25 05:15:28,023 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4286 
[2023-12-25 05:15:32,956 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5289 
[2023-12-25 05:15:33,481 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4986 
[2023-12-25 05:15:35,449 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0002 
[2023-12-25 05:15:35,885 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4574 
[2023-12-25 05:15:36,764 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.7526 
[2023-12-25 05:15:37,316 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4474 
[2023-12-25 05:15:39,122 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.3235 
[2023-12-25 05:15:41,246 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2313 
[2023-12-25 05:15:42,105 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3338 
[2023-12-25 05:15:42,587 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7077 
[2023-12-25 05:15:43,488 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.8418 
[2023-12-25 05:15:46,441 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9072 
[2023-12-25 05:15:46,914 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2050 
[2023-12-25 05:15:47,523 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.6394 
[2023-12-25 05:15:48,234 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4952 
[2023-12-25 05:15:49,405 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6291/0.7142/0.8880.
[2023-12-25 05:15:49,405 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9068/0.9635
[2023-12-25 05:15:49,405 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9762/0.9940
[2023-12-25 05:15:49,405 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8361/0.9547
[2023-12-25 05:15:49,405 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 05:15:49,405 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2243/0.2675
[2023-12-25 05:15:49,406 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5993/0.6316
[2023-12-25 05:15:49,406 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6352/0.8085
[2023-12-25 05:15:49,406 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7928/0.9202
[2023-12-25 05:15:49,406 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8924/0.9544
[2023-12-25 05:15:49,406 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4107/0.4779
[2023-12-25 05:15:49,406 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7364/0.8352
[2023-12-25 05:15:49,406 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6362/0.8661
[2023-12-25 05:15:49,406 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5322/0.6114
[2023-12-25 05:15:49,406 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 05:15:49,408 INFO misc.py line 165 253097] Currently Best mIoU: 0.6447
[2023-12-25 05:15:49,408 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 05:15:58,375 INFO misc.py line 119 253097] Train: [16/100][1/510] Data 3.096 (3.096) Batch 7.056 (7.056) Remain 84:58:07 loss: 0.4213 Lr: 0.00584
[2023-12-25 05:15:59,449 INFO misc.py line 119 253097] Train: [16/100][2/510] Data 0.007 (0.007) Batch 1.069 (1.069) Remain 12:52:03 loss: 0.2354 Lr: 0.00584
[2023-12-25 05:16:00,642 INFO misc.py line 119 253097] Train: [16/100][3/510] Data 0.010 (0.010) Batch 1.197 (1.197) Remain 14:24:35 loss: 0.3546 Lr: 0.00584
[2023-12-25 05:16:01,919 INFO misc.py line 119 253097] Train: [16/100][4/510] Data 0.006 (0.006) Batch 1.276 (1.276) Remain 15:22:03 loss: 0.3426 Lr: 0.00584
[2023-12-25 05:16:02,887 INFO misc.py line 119 253097] Train: [16/100][5/510] Data 0.007 (0.006) Batch 0.971 (1.124) Remain 13:31:54 loss: 0.3642 Lr: 0.00584
[2023-12-25 05:16:03,980 INFO misc.py line 119 253097] Train: [16/100][6/510] Data 0.003 (0.005) Batch 1.093 (1.113) Remain 13:24:22 loss: 0.4223 Lr: 0.00584
[2023-12-25 05:16:05,042 INFO misc.py line 119 253097] Train: [16/100][7/510] Data 0.003 (0.005) Batch 1.062 (1.101) Remain 13:15:05 loss: 0.4137 Lr: 0.00584
[2023-12-25 05:16:10,443 INFO misc.py line 119 253097] Train: [16/100][8/510] Data 0.003 (0.004) Batch 5.400 (1.961) Remain 23:36:16 loss: 0.1552 Lr: 0.00584
[2023-12-25 05:16:11,482 INFO misc.py line 119 253097] Train: [16/100][9/510] Data 0.004 (0.004) Batch 1.039 (1.807) Remain 21:45:19 loss: 0.1459 Lr: 0.00584
[2023-12-25 05:16:12,554 INFO misc.py line 119 253097] Train: [16/100][10/510] Data 0.003 (0.004) Batch 1.073 (1.702) Remain 20:29:30 loss: 0.3522 Lr: 0.00584
[2023-12-25 05:16:13,766 INFO misc.py line 119 253097] Train: [16/100][11/510] Data 0.003 (0.004) Batch 1.208 (1.640) Remain 19:44:50 loss: 0.3149 Lr: 0.00584
[2023-12-25 05:16:14,962 INFO misc.py line 119 253097] Train: [16/100][12/510] Data 0.007 (0.004) Batch 1.195 (1.591) Remain 19:09:05 loss: 0.2971 Lr: 0.00584
[2023-12-25 05:16:16,811 INFO misc.py line 119 253097] Train: [16/100][13/510] Data 0.557 (0.060) Batch 1.853 (1.617) Remain 19:28:00 loss: 0.2639 Lr: 0.00584
[2023-12-25 05:16:17,894 INFO misc.py line 119 253097] Train: [16/100][14/510] Data 0.005 (0.055) Batch 1.083 (1.569) Remain 18:52:54 loss: 0.2848 Lr: 0.00584
[2023-12-25 05:16:19,081 INFO misc.py line 119 253097] Train: [16/100][15/510] Data 0.004 (0.050) Batch 1.186 (1.537) Remain 18:29:51 loss: 0.3919 Lr: 0.00584
[2023-12-25 05:16:22,809 INFO misc.py line 119 253097] Train: [16/100][16/510] Data 0.004 (0.047) Batch 3.729 (1.705) Remain 20:31:38 loss: 0.2496 Lr: 0.00584
[2023-12-25 05:16:23,975 INFO misc.py line 119 253097] Train: [16/100][17/510] Data 0.003 (0.044) Batch 1.165 (1.667) Remain 20:03:45 loss: 0.3381 Lr: 0.00584
[2023-12-25 05:16:24,891 INFO misc.py line 119 253097] Train: [16/100][18/510] Data 0.004 (0.041) Batch 0.917 (1.617) Remain 19:27:36 loss: 0.3159 Lr: 0.00584
[2023-12-25 05:16:26,133 INFO misc.py line 119 253097] Train: [16/100][19/510] Data 0.004 (0.039) Batch 1.241 (1.593) Remain 19:10:37 loss: 0.4118 Lr: 0.00584
[2023-12-25 05:16:27,503 INFO misc.py line 119 253097] Train: [16/100][20/510] Data 0.006 (0.037) Batch 1.318 (1.577) Remain 18:58:54 loss: 0.3483 Lr: 0.00584
[2023-12-25 05:16:28,712 INFO misc.py line 119 253097] Train: [16/100][21/510] Data 0.057 (0.038) Batch 1.252 (1.559) Remain 18:45:49 loss: 0.2717 Lr: 0.00584
[2023-12-25 05:16:29,916 INFO misc.py line 119 253097] Train: [16/100][22/510] Data 0.015 (0.037) Batch 1.200 (1.540) Remain 18:32:08 loss: 0.2214 Lr: 0.00584
[2023-12-25 05:16:31,258 INFO misc.py line 119 253097] Train: [16/100][23/510] Data 0.019 (0.036) Batch 1.351 (1.531) Remain 18:25:16 loss: 0.2918 Lr: 0.00584
[2023-12-25 05:16:32,421 INFO misc.py line 119 253097] Train: [16/100][24/510] Data 0.010 (0.035) Batch 1.165 (1.513) Remain 18:12:39 loss: 0.1529 Lr: 0.00584
[2023-12-25 05:16:33,566 INFO misc.py line 119 253097] Train: [16/100][25/510] Data 0.008 (0.033) Batch 1.147 (1.497) Remain 18:00:37 loss: 0.4113 Lr: 0.00584
[2023-12-25 05:16:34,685 INFO misc.py line 119 253097] Train: [16/100][26/510] Data 0.006 (0.032) Batch 1.121 (1.480) Remain 17:48:47 loss: 0.3207 Lr: 0.00584
[2023-12-25 05:16:35,855 INFO misc.py line 119 253097] Train: [16/100][27/510] Data 0.004 (0.031) Batch 1.168 (1.467) Remain 17:39:23 loss: 0.2841 Lr: 0.00584
[2023-12-25 05:16:37,008 INFO misc.py line 119 253097] Train: [16/100][28/510] Data 0.007 (0.030) Batch 1.156 (1.455) Remain 17:30:22 loss: 0.4747 Lr: 0.00584
[2023-12-25 05:16:38,299 INFO misc.py line 119 253097] Train: [16/100][29/510] Data 0.004 (0.029) Batch 1.290 (1.448) Remain 17:25:46 loss: 0.2059 Lr: 0.00584
[2023-12-25 05:16:47,713 INFO misc.py line 119 253097] Train: [16/100][30/510] Data 0.004 (0.028) Batch 9.414 (1.743) Remain 20:58:45 loss: 0.2148 Lr: 0.00584
[2023-12-25 05:16:48,898 INFO misc.py line 119 253097] Train: [16/100][31/510] Data 0.005 (0.027) Batch 1.184 (1.723) Remain 20:44:17 loss: 0.3214 Lr: 0.00584
[2023-12-25 05:16:50,041 INFO misc.py line 119 253097] Train: [16/100][32/510] Data 0.006 (0.026) Batch 1.145 (1.704) Remain 20:29:52 loss: 0.2784 Lr: 0.00584
[2023-12-25 05:16:51,189 INFO misc.py line 119 253097] Train: [16/100][33/510] Data 0.004 (0.026) Batch 1.148 (1.685) Remain 20:16:27 loss: 0.2147 Lr: 0.00584
[2023-12-25 05:16:52,234 INFO misc.py line 119 253097] Train: [16/100][34/510] Data 0.004 (0.025) Batch 1.045 (1.664) Remain 20:01:32 loss: 0.2710 Lr: 0.00584
[2023-12-25 05:16:53,429 INFO misc.py line 119 253097] Train: [16/100][35/510] Data 0.004 (0.024) Batch 1.195 (1.650) Remain 19:50:56 loss: 0.2269 Lr: 0.00584
[2023-12-25 05:16:54,776 INFO misc.py line 119 253097] Train: [16/100][36/510] Data 0.003 (0.024) Batch 1.346 (1.640) Remain 19:44:16 loss: 0.3949 Lr: 0.00584
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[2023-12-25 05:22:49,820 INFO misc.py line 119 253097] Train: [16/100][269/510] Data 0.003 (0.095) Batch 1.235 (1.538) Remain 18:24:24 loss: 0.2047 Lr: 0.00582
[2023-12-25 05:22:50,813 INFO misc.py line 119 253097] Train: [16/100][270/510] Data 0.039 (0.095) Batch 1.028 (1.536) Remain 18:23:00 loss: 0.1596 Lr: 0.00582
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[2023-12-25 05:22:58,286 INFO misc.py line 119 253097] Train: [16/100][276/510] Data 0.011 (0.093) Batch 1.181 (1.530) Remain 18:18:15 loss: 0.2292 Lr: 0.00582
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[2023-12-25 05:23:03,821 INFO misc.py line 119 253097] Train: [16/100][278/510] Data 0.006 (0.092) Batch 4.483 (1.539) Remain 18:24:40 loss: 0.3999 Lr: 0.00582
[2023-12-25 05:23:05,140 INFO misc.py line 119 253097] Train: [16/100][279/510] Data 0.004 (0.092) Batch 1.320 (1.538) Remain 18:24:04 loss: 0.3239 Lr: 0.00582
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[2023-12-25 05:23:07,171 INFO misc.py line 119 253097] Train: [16/100][281/510] Data 0.004 (0.091) Batch 0.993 (1.534) Remain 18:21:20 loss: 0.3846 Lr: 0.00582
[2023-12-25 05:23:08,251 INFO misc.py line 119 253097] Train: [16/100][282/510] Data 0.003 (0.091) Batch 1.080 (1.533) Remain 18:20:08 loss: 0.3802 Lr: 0.00582
[2023-12-25 05:23:09,410 INFO misc.py line 119 253097] Train: [16/100][283/510] Data 0.003 (0.090) Batch 1.158 (1.531) Remain 18:19:09 loss: 0.4093 Lr: 0.00582
[2023-12-25 05:23:23,109 INFO misc.py line 119 253097] Train: [16/100][284/510] Data 0.004 (0.090) Batch 13.699 (1.575) Remain 18:50:12 loss: 0.4629 Lr: 0.00582
[2023-12-25 05:23:24,182 INFO misc.py line 119 253097] Train: [16/100][285/510] Data 0.006 (0.090) Batch 1.072 (1.573) Remain 18:48:54 loss: 0.1764 Lr: 0.00582
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[2023-12-25 05:23:26,586 INFO misc.py line 119 253097] Train: [16/100][287/510] Data 0.004 (0.089) Batch 1.164 (1.570) Remain 18:46:58 loss: 0.1853 Lr: 0.00582
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[2023-12-25 05:23:28,925 INFO misc.py line 119 253097] Train: [16/100][289/510] Data 0.012 (0.089) Batch 1.189 (1.567) Remain 18:44:54 loss: 0.4833 Lr: 0.00582
[2023-12-25 05:23:30,786 INFO misc.py line 119 253097] Train: [16/100][290/510] Data 0.008 (0.088) Batch 1.861 (1.568) Remain 18:45:36 loss: 0.2290 Lr: 0.00582
[2023-12-25 05:23:32,024 INFO misc.py line 119 253097] Train: [16/100][291/510] Data 0.009 (0.088) Batch 1.239 (1.567) Remain 18:44:45 loss: 0.2031 Lr: 0.00582
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[2023-12-25 05:23:34,294 INFO misc.py line 119 253097] Train: [16/100][293/510] Data 0.006 (0.088) Batch 1.100 (1.564) Remain 18:42:34 loss: 0.2730 Lr: 0.00582
[2023-12-25 05:23:35,505 INFO misc.py line 119 253097] Train: [16/100][294/510] Data 0.010 (0.087) Batch 1.215 (1.563) Remain 18:41:40 loss: 0.3732 Lr: 0.00582
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[2023-12-25 05:23:39,261 INFO misc.py line 119 253097] Train: [16/100][297/510] Data 0.006 (0.086) Batch 1.307 (1.560) Remain 18:39:19 loss: 0.3454 Lr: 0.00582
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[2023-12-25 05:23:41,455 INFO misc.py line 119 253097] Train: [16/100][299/510] Data 0.008 (0.086) Batch 1.089 (1.557) Remain 18:37:01 loss: 0.3904 Lr: 0.00582
[2023-12-25 05:23:42,737 INFO misc.py line 119 253097] Train: [16/100][300/510] Data 0.005 (0.086) Batch 1.282 (1.556) Remain 18:36:20 loss: 0.5000 Lr: 0.00582
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[2023-12-25 05:23:45,221 INFO misc.py line 119 253097] Train: [16/100][302/510] Data 0.011 (0.085) Batch 1.210 (1.554) Remain 18:34:47 loss: 0.5475 Lr: 0.00582
[2023-12-25 05:23:46,269 INFO misc.py line 119 253097] Train: [16/100][303/510] Data 0.004 (0.085) Batch 1.041 (1.552) Remain 18:33:32 loss: 0.3068 Lr: 0.00582
[2023-12-25 05:23:47,359 INFO misc.py line 119 253097] Train: [16/100][304/510] Data 0.010 (0.085) Batch 1.092 (1.551) Remain 18:32:24 loss: 0.3064 Lr: 0.00582
[2023-12-25 05:23:48,577 INFO misc.py line 119 253097] Train: [16/100][305/510] Data 0.009 (0.084) Batch 1.216 (1.549) Remain 18:31:35 loss: 0.4055 Lr: 0.00582
[2023-12-25 05:23:50,675 INFO misc.py line 119 253097] Train: [16/100][306/510] Data 1.025 (0.087) Batch 2.105 (1.551) Remain 18:32:52 loss: 0.1742 Lr: 0.00582
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[2023-12-25 05:23:56,801 INFO misc.py line 119 253097] Train: [16/100][309/510] Data 0.004 (0.087) Batch 3.647 (1.556) Remain 18:36:15 loss: 0.3012 Lr: 0.00582
[2023-12-25 05:23:57,867 INFO misc.py line 119 253097] Train: [16/100][310/510] Data 0.006 (0.086) Batch 1.066 (1.554) Remain 18:35:04 loss: 0.3153 Lr: 0.00582
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[2023-12-25 05:26:15,665 INFO misc.py line 119 253097] Train: [16/100][404/510] Data 0.003 (0.070) Batch 0.939 (1.534) Remain 18:17:47 loss: 0.1944 Lr: 0.00581
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[2023-12-25 05:26:22,004 INFO misc.py line 119 253097] Train: [16/100][409/510] Data 0.011 (0.069) Batch 1.231 (1.530) Remain 18:15:18 loss: 0.3821 Lr: 0.00581
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[2023-12-25 05:27:25,693 INFO misc.py line 119 253097] Train: [16/100][455/510] Data 0.012 (0.066) Batch 1.248 (1.516) Remain 18:03:29 loss: 0.3762 Lr: 0.00581
[2023-12-25 05:27:26,655 INFO misc.py line 119 253097] Train: [16/100][456/510] Data 0.031 (0.066) Batch 0.986 (1.514) Remain 18:02:37 loss: 0.3192 Lr: 0.00581
[2023-12-25 05:27:27,808 INFO misc.py line 119 253097] Train: [16/100][457/510] Data 0.013 (0.066) Batch 1.151 (1.514) Remain 18:02:01 loss: 0.3087 Lr: 0.00581
[2023-12-25 05:27:29,166 INFO misc.py line 119 253097] Train: [16/100][458/510] Data 0.008 (0.066) Batch 1.362 (1.513) Remain 18:01:45 loss: 0.1795 Lr: 0.00581
[2023-12-25 05:27:37,580 INFO misc.py line 119 253097] Train: [16/100][459/510] Data 0.004 (0.066) Batch 8.414 (1.528) Remain 18:12:33 loss: 0.3185 Lr: 0.00581
[2023-12-25 05:27:38,668 INFO misc.py line 119 253097] Train: [16/100][460/510] Data 0.004 (0.065) Batch 1.088 (1.527) Remain 18:11:50 loss: 0.4147 Lr: 0.00581
[2023-12-25 05:27:39,848 INFO misc.py line 119 253097] Train: [16/100][461/510] Data 0.004 (0.065) Batch 1.180 (1.527) Remain 18:11:16 loss: 0.4487 Lr: 0.00581
[2023-12-25 05:27:41,077 INFO misc.py line 119 253097] Train: [16/100][462/510] Data 0.005 (0.065) Batch 1.229 (1.526) Remain 18:10:47 loss: 0.1637 Lr: 0.00581
[2023-12-25 05:27:42,287 INFO misc.py line 119 253097] Train: [16/100][463/510] Data 0.004 (0.065) Batch 1.209 (1.525) Remain 18:10:16 loss: 0.3020 Lr: 0.00581
[2023-12-25 05:27:43,391 INFO misc.py line 119 253097] Train: [16/100][464/510] Data 0.004 (0.065) Batch 1.103 (1.524) Remain 18:09:35 loss: 0.2249 Lr: 0.00581
[2023-12-25 05:27:44,336 INFO misc.py line 119 253097] Train: [16/100][465/510] Data 0.005 (0.065) Batch 0.947 (1.523) Remain 18:08:40 loss: 0.4425 Lr: 0.00581
[2023-12-25 05:27:45,654 INFO misc.py line 119 253097] Train: [16/100][466/510] Data 0.004 (0.065) Batch 1.318 (1.523) Remain 18:08:19 loss: 0.5532 Lr: 0.00581
[2023-12-25 05:27:46,464 INFO misc.py line 119 253097] Train: [16/100][467/510] Data 0.003 (0.064) Batch 0.810 (1.521) Remain 18:07:12 loss: 0.3001 Lr: 0.00581
[2023-12-25 05:27:47,487 INFO misc.py line 119 253097] Train: [16/100][468/510] Data 0.003 (0.064) Batch 1.023 (1.520) Remain 18:06:24 loss: 0.5348 Lr: 0.00581
[2023-12-25 05:27:48,475 INFO misc.py line 119 253097] Train: [16/100][469/510] Data 0.003 (0.064) Batch 0.988 (1.519) Remain 18:05:34 loss: 0.3647 Lr: 0.00581
[2023-12-25 05:27:49,667 INFO misc.py line 119 253097] Train: [16/100][470/510] Data 0.004 (0.064) Batch 1.191 (1.518) Remain 18:05:02 loss: 0.1528 Lr: 0.00581
[2023-12-25 05:27:52,338 INFO misc.py line 119 253097] Train: [16/100][471/510] Data 0.004 (0.064) Batch 2.672 (1.521) Remain 18:06:47 loss: 0.1780 Lr: 0.00581
[2023-12-25 05:27:53,301 INFO misc.py line 119 253097] Train: [16/100][472/510] Data 0.003 (0.064) Batch 0.963 (1.520) Remain 18:05:54 loss: 0.1846 Lr: 0.00581
[2023-12-25 05:27:54,603 INFO misc.py line 119 253097] Train: [16/100][473/510] Data 0.003 (0.064) Batch 1.296 (1.519) Remain 18:05:32 loss: 0.3672 Lr: 0.00581
[2023-12-25 05:27:55,814 INFO misc.py line 119 253097] Train: [16/100][474/510] Data 0.010 (0.064) Batch 1.209 (1.518) Remain 18:05:02 loss: 0.3517 Lr: 0.00581
[2023-12-25 05:27:56,823 INFO misc.py line 119 253097] Train: [16/100][475/510] Data 0.012 (0.063) Batch 1.008 (1.517) Remain 18:04:14 loss: 0.1940 Lr: 0.00581
[2023-12-25 05:27:57,788 INFO misc.py line 119 253097] Train: [16/100][476/510] Data 0.014 (0.063) Batch 0.974 (1.516) Remain 18:03:24 loss: 0.3656 Lr: 0.00581
[2023-12-25 05:27:58,794 INFO misc.py line 119 253097] Train: [16/100][477/510] Data 0.005 (0.063) Batch 1.007 (1.515) Remain 18:02:36 loss: 0.3328 Lr: 0.00581
[2023-12-25 05:27:59,964 INFO misc.py line 119 253097] Train: [16/100][478/510] Data 0.005 (0.063) Batch 1.169 (1.514) Remain 18:02:03 loss: 0.4275 Lr: 0.00581
[2023-12-25 05:28:01,256 INFO misc.py line 119 253097] Train: [16/100][479/510] Data 0.005 (0.063) Batch 1.293 (1.514) Remain 18:01:42 loss: 0.2829 Lr: 0.00581
[2023-12-25 05:28:02,446 INFO misc.py line 119 253097] Train: [16/100][480/510] Data 0.004 (0.063) Batch 1.173 (1.513) Remain 18:01:10 loss: 0.3000 Lr: 0.00581
[2023-12-25 05:28:03,617 INFO misc.py line 119 253097] Train: [16/100][481/510] Data 0.022 (0.063) Batch 1.182 (1.512) Remain 18:00:38 loss: 0.3409 Lr: 0.00581
[2023-12-25 05:28:04,573 INFO misc.py line 119 253097] Train: [16/100][482/510] Data 0.011 (0.063) Batch 0.962 (1.511) Remain 17:59:48 loss: 0.3371 Lr: 0.00581
[2023-12-25 05:28:13,076 INFO misc.py line 119 253097] Train: [16/100][483/510] Data 0.003 (0.063) Batch 8.504 (1.526) Remain 18:10:11 loss: 0.2896 Lr: 0.00581
[2023-12-25 05:28:14,186 INFO misc.py line 119 253097] Train: [16/100][484/510] Data 0.003 (0.062) Batch 1.109 (1.525) Remain 18:09:32 loss: 0.1890 Lr: 0.00581
[2023-12-25 05:28:15,368 INFO misc.py line 119 253097] Train: [16/100][485/510] Data 0.004 (0.062) Batch 1.181 (1.524) Remain 18:09:00 loss: 0.1703 Lr: 0.00581
[2023-12-25 05:28:16,479 INFO misc.py line 119 253097] Train: [16/100][486/510] Data 0.004 (0.062) Batch 1.112 (1.523) Remain 18:08:22 loss: 0.2685 Lr: 0.00581
[2023-12-25 05:28:17,541 INFO misc.py line 119 253097] Train: [16/100][487/510] Data 0.003 (0.062) Batch 1.061 (1.523) Remain 18:07:39 loss: 0.2545 Lr: 0.00581
[2023-12-25 05:28:18,666 INFO misc.py line 119 253097] Train: [16/100][488/510] Data 0.004 (0.062) Batch 1.126 (1.522) Remain 18:07:03 loss: 0.4303 Lr: 0.00581
[2023-12-25 05:28:25,225 INFO misc.py line 119 253097] Train: [16/100][489/510] Data 0.003 (0.062) Batch 6.559 (1.532) Remain 18:14:25 loss: 0.3978 Lr: 0.00581
[2023-12-25 05:28:26,422 INFO misc.py line 119 253097] Train: [16/100][490/510] Data 0.004 (0.062) Batch 1.195 (1.531) Remain 18:13:54 loss: 0.6163 Lr: 0.00581
[2023-12-25 05:28:27,455 INFO misc.py line 119 253097] Train: [16/100][491/510] Data 0.006 (0.062) Batch 1.033 (1.530) Remain 18:13:09 loss: 0.3221 Lr: 0.00581
[2023-12-25 05:28:28,639 INFO misc.py line 119 253097] Train: [16/100][492/510] Data 0.006 (0.061) Batch 1.186 (1.530) Remain 18:12:37 loss: 0.3220 Lr: 0.00581
[2023-12-25 05:28:29,762 INFO misc.py line 119 253097] Train: [16/100][493/510] Data 0.004 (0.061) Batch 1.122 (1.529) Remain 18:12:00 loss: 0.3585 Lr: 0.00581
[2023-12-25 05:28:30,859 INFO misc.py line 119 253097] Train: [16/100][494/510] Data 0.006 (0.061) Batch 1.098 (1.528) Remain 18:11:21 loss: 0.3211 Lr: 0.00580
[2023-12-25 05:28:32,046 INFO misc.py line 119 253097] Train: [16/100][495/510] Data 0.004 (0.061) Batch 1.188 (1.527) Remain 18:10:50 loss: 0.6643 Lr: 0.00580
[2023-12-25 05:28:33,247 INFO misc.py line 119 253097] Train: [16/100][496/510] Data 0.004 (0.061) Batch 1.200 (1.527) Remain 18:10:20 loss: 0.2934 Lr: 0.00580
[2023-12-25 05:28:34,224 INFO misc.py line 119 253097] Train: [16/100][497/510] Data 0.005 (0.061) Batch 0.977 (1.525) Remain 18:09:30 loss: 0.2066 Lr: 0.00580
[2023-12-25 05:28:35,463 INFO misc.py line 119 253097] Train: [16/100][498/510] Data 0.005 (0.061) Batch 1.239 (1.525) Remain 18:09:04 loss: 0.3913 Lr: 0.00580
[2023-12-25 05:28:36,656 INFO misc.py line 119 253097] Train: [16/100][499/510] Data 0.005 (0.061) Batch 1.193 (1.524) Remain 18:08:34 loss: 0.4777 Lr: 0.00580
[2023-12-25 05:28:37,878 INFO misc.py line 119 253097] Train: [16/100][500/510] Data 0.004 (0.061) Batch 1.222 (1.524) Remain 18:08:06 loss: 0.1979 Lr: 0.00580
[2023-12-25 05:28:38,928 INFO misc.py line 119 253097] Train: [16/100][501/510] Data 0.004 (0.060) Batch 1.050 (1.523) Remain 18:07:24 loss: 0.4264 Lr: 0.00580
[2023-12-25 05:28:40,066 INFO misc.py line 119 253097] Train: [16/100][502/510] Data 0.005 (0.060) Batch 1.138 (1.522) Remain 18:06:50 loss: 0.3662 Lr: 0.00580
[2023-12-25 05:28:41,221 INFO misc.py line 119 253097] Train: [16/100][503/510] Data 0.004 (0.060) Batch 1.154 (1.521) Remain 18:06:17 loss: 0.3080 Lr: 0.00580
[2023-12-25 05:28:42,429 INFO misc.py line 119 253097] Train: [16/100][504/510] Data 0.005 (0.060) Batch 1.208 (1.521) Remain 18:05:48 loss: 0.4437 Lr: 0.00580
[2023-12-25 05:28:43,564 INFO misc.py line 119 253097] Train: [16/100][505/510] Data 0.004 (0.060) Batch 1.134 (1.520) Remain 18:05:14 loss: 0.2923 Lr: 0.00580
[2023-12-25 05:28:44,834 INFO misc.py line 119 253097] Train: [16/100][506/510] Data 0.005 (0.060) Batch 1.269 (1.519) Remain 18:04:51 loss: 0.4176 Lr: 0.00580
[2023-12-25 05:28:46,069 INFO misc.py line 119 253097] Train: [16/100][507/510] Data 0.008 (0.060) Batch 1.234 (1.519) Remain 18:04:25 loss: 0.2713 Lr: 0.00580
[2023-12-25 05:28:47,237 INFO misc.py line 119 253097] Train: [16/100][508/510] Data 0.008 (0.060) Batch 1.172 (1.518) Remain 18:03:54 loss: 0.2904 Lr: 0.00580
[2023-12-25 05:28:48,044 INFO misc.py line 119 253097] Train: [16/100][509/510] Data 0.004 (0.060) Batch 0.807 (1.517) Remain 18:02:52 loss: 0.4658 Lr: 0.00580
[2023-12-25 05:28:49,234 INFO misc.py line 119 253097] Train: [16/100][510/510] Data 0.004 (0.059) Batch 1.190 (1.516) Remain 18:02:23 loss: 0.2891 Lr: 0.00580
[2023-12-25 05:28:49,235 INFO misc.py line 136 253097] Train result: loss: 0.3308 
[2023-12-25 05:28:49,235 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 05:29:16,921 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5050 
[2023-12-25 05:29:17,277 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4415 
[2023-12-25 05:29:22,217 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4976 
[2023-12-25 05:29:22,741 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5469 
[2023-12-25 05:29:24,716 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8369 
[2023-12-25 05:29:25,152 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3704 
[2023-12-25 05:29:26,040 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.7745 
[2023-12-25 05:29:26,594 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2859 
[2023-12-25 05:29:28,400 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9978 
[2023-12-25 05:29:30,528 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2243 
[2023-12-25 05:29:31,386 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2400 
[2023-12-25 05:29:31,813 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.5958 
[2023-12-25 05:29:32,719 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.9062 
[2023-12-25 05:29:35,659 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8241 
[2023-12-25 05:29:36,128 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2008 
[2023-12-25 05:29:36,746 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4733 
[2023-12-25 05:29:37,447 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3643 
[2023-12-25 05:29:38,906 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6476/0.7311/0.8881.
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9149/0.9341
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9802/0.9878
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8323/0.9536
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2408/0.2586
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5769/0.6505
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5392/0.9482
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8260/0.8992
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9004/0.9422
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5455/0.5949
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7438/0.8155
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7468/0.8477
[2023-12-25 05:29:38,907 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5723/0.6723
[2023-12-25 05:29:38,908 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 05:29:38,909 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.6476
[2023-12-25 05:29:38,909 INFO misc.py line 165 253097] Currently Best mIoU: 0.6476
[2023-12-25 05:29:38,909 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 05:29:48,956 INFO misc.py line 119 253097] Train: [17/100][1/510] Data 2.426 (2.426) Batch 3.214 (3.214) Remain 38:14:43 loss: 0.2411 Lr: 0.00580
[2023-12-25 05:29:50,254 INFO misc.py line 119 253097] Train: [17/100][2/510] Data 0.325 (0.325) Batch 1.299 (1.299) Remain 15:27:14 loss: 0.3644 Lr: 0.00580
[2023-12-25 05:29:51,601 INFO misc.py line 119 253097] Train: [17/100][3/510] Data 0.174 (0.174) Batch 1.347 (1.347) Remain 16:01:38 loss: 0.2951 Lr: 0.00580
[2023-12-25 05:29:52,716 INFO misc.py line 119 253097] Train: [17/100][4/510] Data 0.003 (0.003) Batch 1.106 (1.106) Remain 13:09:34 loss: 0.2042 Lr: 0.00580
[2023-12-25 05:29:53,842 INFO misc.py line 119 253097] Train: [17/100][5/510] Data 0.013 (0.008) Batch 1.127 (1.116) Remain 13:16:54 loss: 0.3906 Lr: 0.00580
[2023-12-25 05:29:55,083 INFO misc.py line 119 253097] Train: [17/100][6/510] Data 0.013 (0.010) Batch 1.246 (1.160) Remain 13:47:49 loss: 0.2284 Lr: 0.00580
[2023-12-25 05:29:56,351 INFO misc.py line 119 253097] Train: [17/100][7/510] Data 0.007 (0.009) Batch 1.268 (1.187) Remain 14:07:08 loss: 0.5405 Lr: 0.00580
[2023-12-25 05:29:57,433 INFO misc.py line 119 253097] Train: [17/100][8/510] Data 0.008 (0.009) Batch 1.080 (1.165) Remain 13:51:51 loss: 0.3149 Lr: 0.00580
[2023-12-25 05:29:58,567 INFO misc.py line 119 253097] Train: [17/100][9/510] Data 0.008 (0.009) Batch 1.139 (1.161) Remain 13:48:44 loss: 0.4135 Lr: 0.00580
[2023-12-25 05:29:59,806 INFO misc.py line 119 253097] Train: [17/100][10/510] Data 0.005 (0.008) Batch 1.238 (1.172) Remain 13:56:34 loss: 0.3425 Lr: 0.00580
[2023-12-25 05:30:00,754 INFO misc.py line 119 253097] Train: [17/100][11/510] Data 0.005 (0.008) Batch 0.948 (1.144) Remain 13:36:35 loss: 0.4523 Lr: 0.00580
[2023-12-25 05:30:02,055 INFO misc.py line 119 253097] Train: [17/100][12/510] Data 0.004 (0.007) Batch 1.300 (1.161) Remain 13:48:58 loss: 0.3341 Lr: 0.00580
[2023-12-25 05:30:03,262 INFO misc.py line 119 253097] Train: [17/100][13/510] Data 0.006 (0.007) Batch 1.205 (1.166) Remain 13:52:04 loss: 0.5345 Lr: 0.00580
[2023-12-25 05:30:04,377 INFO misc.py line 119 253097] Train: [17/100][14/510] Data 0.007 (0.007) Batch 1.117 (1.161) Remain 13:48:53 loss: 0.4316 Lr: 0.00580
[2023-12-25 05:30:05,574 INFO misc.py line 119 253097] Train: [17/100][15/510] Data 0.005 (0.007) Batch 1.198 (1.164) Remain 13:51:04 loss: 0.3271 Lr: 0.00580
[2023-12-25 05:30:06,696 INFO misc.py line 119 253097] Train: [17/100][16/510] Data 0.004 (0.007) Batch 1.121 (1.161) Remain 13:48:41 loss: 0.3386 Lr: 0.00580
[2023-12-25 05:30:07,787 INFO misc.py line 119 253097] Train: [17/100][17/510] Data 0.004 (0.007) Batch 1.091 (1.156) Remain 13:45:05 loss: 0.5171 Lr: 0.00580
[2023-12-25 05:30:08,809 INFO misc.py line 119 253097] Train: [17/100][18/510] Data 0.006 (0.007) Batch 1.017 (1.147) Remain 13:38:28 loss: 0.2667 Lr: 0.00580
[2023-12-25 05:30:10,064 INFO misc.py line 119 253097] Train: [17/100][19/510] Data 0.010 (0.007) Batch 1.254 (1.154) Remain 13:43:15 loss: 0.1958 Lr: 0.00580
[2023-12-25 05:30:11,261 INFO misc.py line 119 253097] Train: [17/100][20/510] Data 0.010 (0.007) Batch 1.201 (1.156) Remain 13:45:13 loss: 0.4831 Lr: 0.00580
[2023-12-25 05:30:12,338 INFO misc.py line 119 253097] Train: [17/100][21/510] Data 0.006 (0.007) Batch 1.079 (1.152) Remain 13:42:08 loss: 0.2761 Lr: 0.00580
[2023-12-25 05:30:13,447 INFO misc.py line 119 253097] Train: [17/100][22/510] Data 0.004 (0.007) Batch 1.101 (1.149) Remain 13:40:13 loss: 0.3517 Lr: 0.00580
[2023-12-25 05:30:14,713 INFO misc.py line 119 253097] Train: [17/100][23/510] Data 0.011 (0.007) Batch 1.268 (1.155) Remain 13:44:26 loss: 0.2202 Lr: 0.00580
[2023-12-25 05:30:15,822 INFO misc.py line 119 253097] Train: [17/100][24/510] Data 0.008 (0.007) Batch 1.110 (1.153) Remain 13:42:53 loss: 0.2893 Lr: 0.00580
[2023-12-25 05:30:17,084 INFO misc.py line 119 253097] Train: [17/100][25/510] Data 0.008 (0.007) Batch 1.266 (1.158) Remain 13:46:32 loss: 0.1315 Lr: 0.00580
[2023-12-25 05:30:18,052 INFO misc.py line 119 253097] Train: [17/100][26/510] Data 0.004 (0.007) Batch 0.967 (1.150) Remain 13:40:35 loss: 0.4861 Lr: 0.00580
[2023-12-25 05:30:19,439 INFO misc.py line 119 253097] Train: [17/100][27/510] Data 0.196 (0.015) Batch 1.387 (1.160) Remain 13:47:38 loss: 0.2447 Lr: 0.00580
[2023-12-25 05:30:27,833 INFO misc.py line 119 253097] Train: [17/100][28/510] Data 0.004 (0.014) Batch 8.394 (1.449) Remain 17:14:06 loss: 0.1654 Lr: 0.00580
[2023-12-25 05:30:28,735 INFO misc.py line 119 253097] Train: [17/100][29/510] Data 0.003 (0.014) Batch 0.898 (1.428) Remain 16:58:57 loss: 0.5211 Lr: 0.00580
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[2023-12-25 05:34:13,294 INFO misc.py line 119 253097] Train: [17/100][169/510] Data 0.005 (0.019) Batch 1.046 (1.576) Remain 18:41:09 loss: 0.3839 Lr: 0.00579
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[2023-12-25 05:34:15,599 INFO misc.py line 119 253097] Train: [17/100][171/510] Data 0.007 (0.019) Batch 1.172 (1.571) Remain 18:37:29 loss: 0.2164 Lr: 0.00579
[2023-12-25 05:34:20,602 INFO misc.py line 119 253097] Train: [17/100][172/510] Data 2.198 (0.032) Batch 5.006 (1.592) Remain 18:51:55 loss: 0.2424 Lr: 0.00579
[2023-12-25 05:34:21,697 INFO misc.py line 119 253097] Train: [17/100][173/510] Data 0.004 (0.032) Batch 1.094 (1.589) Remain 18:49:48 loss: 0.3530 Lr: 0.00579
[2023-12-25 05:34:23,029 INFO misc.py line 119 253097] Train: [17/100][174/510] Data 0.005 (0.031) Batch 1.329 (1.587) Remain 18:48:42 loss: 0.3739 Lr: 0.00579
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[2023-12-25 05:34:27,389 INFO misc.py line 119 253097] Train: [17/100][177/510] Data 0.004 (0.031) Batch 2.181 (1.585) Remain 18:46:59 loss: 0.4492 Lr: 0.00579
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[2023-12-25 05:34:29,752 INFO misc.py line 119 253097] Train: [17/100][179/510] Data 0.006 (0.031) Batch 1.134 (1.580) Remain 18:43:41 loss: 0.3753 Lr: 0.00579
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[2023-12-25 05:34:36,333 INFO misc.py line 119 253097] Train: [17/100][182/510] Data 0.007 (0.030) Batch 4.057 (1.591) Remain 18:50:55 loss: 0.5537 Lr: 0.00579
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[2023-12-25 05:34:38,761 INFO misc.py line 119 253097] Train: [17/100][184/510] Data 0.003 (0.030) Batch 1.223 (1.587) Remain 18:47:54 loss: 0.3162 Lr: 0.00579
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[2023-12-25 05:34:41,077 INFO misc.py line 119 253097] Train: [17/100][186/510] Data 0.007 (0.030) Batch 1.254 (1.582) Remain 18:44:30 loss: 0.3418 Lr: 0.00579
[2023-12-25 05:34:42,329 INFO misc.py line 119 253097] Train: [17/100][187/510] Data 0.007 (0.030) Batch 1.254 (1.580) Remain 18:43:13 loss: 0.2020 Lr: 0.00579
[2023-12-25 05:34:44,392 INFO misc.py line 119 253097] Train: [17/100][188/510] Data 0.004 (0.029) Batch 2.064 (1.583) Remain 18:45:03 loss: 0.4534 Lr: 0.00579
[2023-12-25 05:34:45,541 INFO misc.py line 119 253097] Train: [17/100][189/510] Data 0.003 (0.029) Batch 1.149 (1.580) Remain 18:43:22 loss: 0.3060 Lr: 0.00579
[2023-12-25 05:34:46,572 INFO misc.py line 119 253097] Train: [17/100][190/510] Data 0.005 (0.029) Batch 1.032 (1.577) Remain 18:41:15 loss: 0.4436 Lr: 0.00579
[2023-12-25 05:34:47,805 INFO misc.py line 119 253097] Train: [17/100][191/510] Data 0.004 (0.029) Batch 1.233 (1.576) Remain 18:39:55 loss: 0.3357 Lr: 0.00579
[2023-12-25 05:34:48,959 INFO misc.py line 119 253097] Train: [17/100][192/510] Data 0.003 (0.029) Batch 1.154 (1.573) Remain 18:38:18 loss: 0.3840 Lr: 0.00579
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[2023-12-25 05:34:52,499 INFO misc.py line 119 253097] Train: [17/100][195/510] Data 0.010 (0.029) Batch 1.287 (1.567) Remain 18:33:51 loss: 0.4718 Lr: 0.00579
[2023-12-25 05:34:53,736 INFO misc.py line 119 253097] Train: [17/100][196/510] Data 0.008 (0.028) Batch 1.237 (1.565) Remain 18:32:36 loss: 0.3202 Lr: 0.00579
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[2023-12-25 05:34:56,124 INFO misc.py line 119 253097] Train: [17/100][198/510] Data 0.009 (0.028) Batch 1.248 (1.562) Remain 18:29:51 loss: 0.5255 Lr: 0.00579
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[2023-12-25 05:34:58,437 INFO misc.py line 119 253097] Train: [17/100][200/510] Data 0.005 (0.028) Batch 1.138 (1.558) Remain 18:26:52 loss: 0.4331 Lr: 0.00579
[2023-12-25 05:35:01,279 INFO misc.py line 119 253097] Train: [17/100][201/510] Data 1.698 (0.036) Batch 2.847 (1.564) Remain 18:31:28 loss: 0.2036 Lr: 0.00579
[2023-12-25 05:35:02,490 INFO misc.py line 119 253097] Train: [17/100][202/510] Data 0.007 (0.036) Batch 1.212 (1.562) Remain 18:30:11 loss: 0.2155 Lr: 0.00579
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[2023-12-25 05:36:35,536 INFO misc.py line 119 253097] Train: [17/100][272/510] Data 4.737 (0.046) Batch 6.054 (1.502) Remain 17:45:19 loss: 0.7262 Lr: 0.00578
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[2023-12-25 05:36:49,206 INFO misc.py line 119 253097] Train: [17/100][284/510] Data 0.008 (0.044) Batch 1.179 (1.486) Remain 17:34:03 loss: 0.4420 Lr: 0.00578
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[2023-12-25 05:37:02,157 INFO misc.py line 119 253097] Train: [17/100][287/510] Data 0.004 (0.044) Batch 10.546 (1.516) Remain 17:55:11 loss: 0.3440 Lr: 0.00578
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[2023-12-25 05:37:24,147 INFO misc.py line 119 253097] Train: [17/100][302/510] Data 0.004 (0.042) Batch 2.143 (1.514) Remain 17:53:02 loss: 0.2620 Lr: 0.00578
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[2023-12-25 05:37:39,010 INFO misc.py line 119 253097] Train: [17/100][309/510] Data 5.671 (0.060) Batch 6.812 (1.527) Remain 18:02:45 loss: 0.3693 Lr: 0.00578
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[2023-12-25 05:37:45,836 INFO misc.py line 119 253097] Train: [17/100][315/510] Data 0.019 (0.059) Batch 1.055 (1.520) Remain 17:57:16 loss: 0.3786 Lr: 0.00578
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[2023-12-25 05:39:23,331 INFO misc.py line 119 253097] Train: [17/100][385/510] Data 0.004 (0.083) Batch 1.136 (1.497) Remain 17:39:01 loss: 0.2806 Lr: 0.00578
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[2023-12-25 05:39:39,887 INFO misc.py line 119 253097] Train: [17/100][392/510] Data 0.006 (0.082) Batch 9.761 (1.512) Remain 17:49:54 loss: 0.5075 Lr: 0.00578
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[2023-12-25 05:39:48,356 INFO misc.py line 119 253097] Train: [17/100][399/510] Data 0.006 (0.080) Batch 1.267 (1.507) Remain 17:45:56 loss: 0.3744 Lr: 0.00578
[2023-12-25 05:39:49,599 INFO misc.py line 119 253097] Train: [17/100][400/510] Data 0.006 (0.080) Batch 1.245 (1.506) Remain 17:45:26 loss: 0.3526 Lr: 0.00578
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[2023-12-25 05:41:02,959 INFO misc.py line 119 253097] Train: [17/100][448/510] Data 0.005 (0.085) Batch 1.158 (1.509) Remain 17:45:55 loss: 0.2681 Lr: 0.00577
[2023-12-25 05:41:03,819 INFO misc.py line 119 253097] Train: [17/100][449/510] Data 0.008 (0.085) Batch 0.864 (1.507) Remain 17:44:52 loss: 0.1310 Lr: 0.00577
[2023-12-25 05:41:05,022 INFO misc.py line 119 253097] Train: [17/100][450/510] Data 0.003 (0.085) Batch 1.202 (1.507) Remain 17:44:21 loss: 0.3493 Lr: 0.00577
[2023-12-25 05:41:06,097 INFO misc.py line 119 253097] Train: [17/100][451/510] Data 0.005 (0.085) Batch 1.075 (1.506) Remain 17:43:39 loss: 0.2474 Lr: 0.00577
[2023-12-25 05:41:07,305 INFO misc.py line 119 253097] Train: [17/100][452/510] Data 0.005 (0.085) Batch 1.208 (1.505) Remain 17:43:09 loss: 0.2308 Lr: 0.00577
[2023-12-25 05:41:15,567 INFO misc.py line 119 253097] Train: [17/100][453/510] Data 0.005 (0.085) Batch 8.260 (1.520) Remain 17:53:44 loss: 0.4039 Lr: 0.00577
[2023-12-25 05:41:16,820 INFO misc.py line 119 253097] Train: [17/100][454/510] Data 0.008 (0.084) Batch 1.256 (1.519) Remain 17:53:18 loss: 0.2080 Lr: 0.00577
[2023-12-25 05:41:18,011 INFO misc.py line 119 253097] Train: [17/100][455/510] Data 0.003 (0.084) Batch 1.190 (1.519) Remain 17:52:45 loss: 0.3804 Lr: 0.00577
[2023-12-25 05:41:19,086 INFO misc.py line 119 253097] Train: [17/100][456/510] Data 0.004 (0.084) Batch 1.075 (1.518) Remain 17:52:02 loss: 0.5396 Lr: 0.00577
[2023-12-25 05:41:20,180 INFO misc.py line 119 253097] Train: [17/100][457/510] Data 0.004 (0.084) Batch 1.093 (1.517) Remain 17:51:21 loss: 0.3570 Lr: 0.00577
[2023-12-25 05:41:21,255 INFO misc.py line 119 253097] Train: [17/100][458/510] Data 0.005 (0.084) Batch 1.076 (1.516) Remain 17:50:39 loss: 0.2055 Lr: 0.00577
[2023-12-25 05:41:22,478 INFO misc.py line 119 253097] Train: [17/100][459/510] Data 0.004 (0.084) Batch 1.223 (1.515) Remain 17:50:10 loss: 0.2472 Lr: 0.00577
[2023-12-25 05:41:23,546 INFO misc.py line 119 253097] Train: [17/100][460/510] Data 0.005 (0.083) Batch 1.069 (1.514) Remain 17:49:27 loss: 0.3950 Lr: 0.00577
[2023-12-25 05:41:24,802 INFO misc.py line 119 253097] Train: [17/100][461/510] Data 0.003 (0.083) Batch 1.253 (1.514) Remain 17:49:01 loss: 0.2858 Lr: 0.00577
[2023-12-25 05:41:26,125 INFO misc.py line 119 253097] Train: [17/100][462/510] Data 0.007 (0.083) Batch 1.322 (1.513) Remain 17:48:42 loss: 0.1922 Lr: 0.00577
[2023-12-25 05:41:27,132 INFO misc.py line 119 253097] Train: [17/100][463/510] Data 0.007 (0.083) Batch 1.004 (1.512) Remain 17:47:54 loss: 0.3958 Lr: 0.00577
[2023-12-25 05:41:28,345 INFO misc.py line 119 253097] Train: [17/100][464/510] Data 0.010 (0.083) Batch 1.215 (1.511) Remain 17:47:25 loss: 0.6875 Lr: 0.00577
[2023-12-25 05:41:29,457 INFO misc.py line 119 253097] Train: [17/100][465/510] Data 0.008 (0.083) Batch 1.112 (1.510) Remain 17:46:47 loss: 0.2745 Lr: 0.00577
[2023-12-25 05:41:30,623 INFO misc.py line 119 253097] Train: [17/100][466/510] Data 0.009 (0.082) Batch 1.169 (1.510) Remain 17:46:14 loss: 0.4329 Lr: 0.00577
[2023-12-25 05:41:31,680 INFO misc.py line 119 253097] Train: [17/100][467/510] Data 0.007 (0.082) Batch 1.053 (1.509) Remain 17:45:31 loss: 0.3618 Lr: 0.00577
[2023-12-25 05:41:32,886 INFO misc.py line 119 253097] Train: [17/100][468/510] Data 0.011 (0.082) Batch 1.208 (1.508) Remain 17:45:02 loss: 0.3061 Lr: 0.00577
[2023-12-25 05:41:34,073 INFO misc.py line 119 253097] Train: [17/100][469/510] Data 0.009 (0.082) Batch 1.191 (1.507) Remain 17:44:32 loss: 0.1482 Lr: 0.00577
[2023-12-25 05:41:35,239 INFO misc.py line 119 253097] Train: [17/100][470/510] Data 0.005 (0.082) Batch 1.166 (1.507) Remain 17:43:59 loss: 0.5658 Lr: 0.00577
[2023-12-25 05:41:36,306 INFO misc.py line 119 253097] Train: [17/100][471/510] Data 0.004 (0.082) Batch 1.062 (1.506) Remain 17:43:17 loss: 0.1687 Lr: 0.00577
[2023-12-25 05:41:37,588 INFO misc.py line 119 253097] Train: [17/100][472/510] Data 0.009 (0.081) Batch 1.281 (1.505) Remain 17:42:55 loss: 0.5242 Lr: 0.00577
[2023-12-25 05:41:38,719 INFO misc.py line 119 253097] Train: [17/100][473/510] Data 0.011 (0.081) Batch 1.136 (1.505) Remain 17:42:21 loss: 0.4097 Lr: 0.00577
[2023-12-25 05:41:39,727 INFO misc.py line 119 253097] Train: [17/100][474/510] Data 0.006 (0.081) Batch 1.004 (1.503) Remain 17:41:34 loss: 0.5277 Lr: 0.00577
[2023-12-25 05:41:48,651 INFO misc.py line 119 253097] Train: [17/100][475/510] Data 0.010 (0.081) Batch 8.928 (1.519) Remain 17:52:39 loss: 0.3702 Lr: 0.00577
[2023-12-25 05:41:49,550 INFO misc.py line 119 253097] Train: [17/100][476/510] Data 0.005 (0.081) Batch 0.900 (1.518) Remain 17:51:42 loss: 0.3663 Lr: 0.00577
[2023-12-25 05:41:50,706 INFO misc.py line 119 253097] Train: [17/100][477/510] Data 0.003 (0.081) Batch 1.155 (1.517) Remain 17:51:08 loss: 0.3528 Lr: 0.00577
[2023-12-25 05:41:51,799 INFO misc.py line 119 253097] Train: [17/100][478/510] Data 0.003 (0.080) Batch 1.092 (1.516) Remain 17:50:29 loss: 0.1721 Lr: 0.00577
[2023-12-25 05:41:52,810 INFO misc.py line 119 253097] Train: [17/100][479/510] Data 0.004 (0.080) Batch 1.011 (1.515) Remain 17:49:42 loss: 0.5387 Lr: 0.00577
[2023-12-25 05:41:53,896 INFO misc.py line 119 253097] Train: [17/100][480/510] Data 0.005 (0.080) Batch 1.083 (1.514) Remain 17:49:03 loss: 0.1392 Lr: 0.00577
[2023-12-25 05:41:55,018 INFO misc.py line 119 253097] Train: [17/100][481/510] Data 0.007 (0.080) Batch 1.119 (1.513) Remain 17:48:26 loss: 0.3328 Lr: 0.00577
[2023-12-25 05:41:56,281 INFO misc.py line 119 253097] Train: [17/100][482/510] Data 0.010 (0.080) Batch 1.268 (1.513) Remain 17:48:03 loss: 0.5221 Lr: 0.00577
[2023-12-25 05:41:57,472 INFO misc.py line 119 253097] Train: [17/100][483/510] Data 0.005 (0.080) Batch 1.191 (1.512) Remain 17:47:33 loss: 0.2891 Lr: 0.00577
[2023-12-25 05:41:58,794 INFO misc.py line 119 253097] Train: [17/100][484/510] Data 0.004 (0.080) Batch 1.321 (1.512) Remain 17:47:15 loss: 0.4255 Lr: 0.00577
[2023-12-25 05:41:59,891 INFO misc.py line 119 253097] Train: [17/100][485/510] Data 0.005 (0.079) Batch 1.088 (1.511) Remain 17:46:36 loss: 0.2807 Lr: 0.00577
[2023-12-25 05:42:01,029 INFO misc.py line 119 253097] Train: [17/100][486/510] Data 0.015 (0.079) Batch 1.148 (1.510) Remain 17:46:02 loss: 0.5413 Lr: 0.00577
[2023-12-25 05:42:02,141 INFO misc.py line 119 253097] Train: [17/100][487/510] Data 0.005 (0.079) Batch 1.110 (1.509) Remain 17:45:26 loss: 0.3030 Lr: 0.00577
[2023-12-25 05:42:03,382 INFO misc.py line 119 253097] Train: [17/100][488/510] Data 0.006 (0.079) Batch 1.243 (1.509) Remain 17:45:01 loss: 0.1829 Lr: 0.00577
[2023-12-25 05:42:04,667 INFO misc.py line 119 253097] Train: [17/100][489/510] Data 0.004 (0.079) Batch 1.284 (1.508) Remain 17:44:40 loss: 0.5063 Lr: 0.00577
[2023-12-25 05:42:10,192 INFO misc.py line 119 253097] Train: [17/100][490/510] Data 0.005 (0.079) Batch 5.525 (1.517) Remain 17:50:28 loss: 0.2039 Lr: 0.00577
[2023-12-25 05:42:11,340 INFO misc.py line 119 253097] Train: [17/100][491/510] Data 0.005 (0.078) Batch 1.149 (1.516) Remain 17:49:54 loss: 0.1765 Lr: 0.00577
[2023-12-25 05:42:12,350 INFO misc.py line 119 253097] Train: [17/100][492/510] Data 0.004 (0.078) Batch 1.008 (1.515) Remain 17:49:09 loss: 0.2682 Lr: 0.00577
[2023-12-25 05:42:13,378 INFO misc.py line 119 253097] Train: [17/100][493/510] Data 0.005 (0.078) Batch 1.028 (1.514) Remain 17:48:25 loss: 0.2714 Lr: 0.00577
[2023-12-25 05:42:14,531 INFO misc.py line 119 253097] Train: [17/100][494/510] Data 0.005 (0.078) Batch 1.154 (1.513) Remain 17:47:53 loss: 0.2765 Lr: 0.00577
[2023-12-25 05:42:15,766 INFO misc.py line 119 253097] Train: [17/100][495/510] Data 0.003 (0.078) Batch 1.233 (1.513) Remain 17:47:27 loss: 0.4060 Lr: 0.00577
[2023-12-25 05:42:16,977 INFO misc.py line 119 253097] Train: [17/100][496/510] Data 0.006 (0.078) Batch 1.211 (1.512) Remain 17:47:00 loss: 0.4356 Lr: 0.00577
[2023-12-25 05:42:18,062 INFO misc.py line 119 253097] Train: [17/100][497/510] Data 0.006 (0.078) Batch 1.087 (1.511) Remain 17:46:22 loss: 0.1584 Lr: 0.00577
[2023-12-25 05:42:19,354 INFO misc.py line 119 253097] Train: [17/100][498/510] Data 0.003 (0.077) Batch 1.285 (1.511) Remain 17:46:01 loss: 0.4130 Lr: 0.00577
[2023-12-25 05:42:20,438 INFO misc.py line 119 253097] Train: [17/100][499/510] Data 0.011 (0.077) Batch 1.089 (1.510) Remain 17:45:24 loss: 0.4091 Lr: 0.00577
[2023-12-25 05:42:21,719 INFO misc.py line 119 253097] Train: [17/100][500/510] Data 0.006 (0.077) Batch 1.281 (1.509) Remain 17:45:03 loss: 0.4376 Lr: 0.00577
[2023-12-25 05:42:22,723 INFO misc.py line 119 253097] Train: [17/100][501/510] Data 0.006 (0.077) Batch 1.006 (1.508) Remain 17:44:18 loss: 0.2827 Lr: 0.00577
[2023-12-25 05:42:24,724 INFO misc.py line 119 253097] Train: [17/100][502/510] Data 0.004 (0.077) Batch 2.003 (1.509) Remain 17:44:59 loss: 0.2145 Lr: 0.00577
[2023-12-25 05:42:30,654 INFO misc.py line 119 253097] Train: [17/100][503/510] Data 0.003 (0.077) Batch 5.929 (1.518) Remain 17:51:11 loss: 0.2337 Lr: 0.00577
[2023-12-25 05:42:31,874 INFO misc.py line 119 253097] Train: [17/100][504/510] Data 0.004 (0.077) Batch 1.220 (1.518) Remain 17:50:45 loss: 0.3529 Lr: 0.00577
[2023-12-25 05:42:33,026 INFO misc.py line 119 253097] Train: [17/100][505/510] Data 0.004 (0.076) Batch 1.152 (1.517) Remain 17:50:12 loss: 0.1962 Lr: 0.00577
[2023-12-25 05:42:34,233 INFO misc.py line 119 253097] Train: [17/100][506/510] Data 0.004 (0.076) Batch 1.199 (1.516) Remain 17:49:44 loss: 0.4672 Lr: 0.00577
[2023-12-25 05:42:35,495 INFO misc.py line 119 253097] Train: [17/100][507/510] Data 0.012 (0.076) Batch 1.269 (1.516) Remain 17:49:22 loss: 0.3084 Lr: 0.00577
[2023-12-25 05:42:36,419 INFO misc.py line 119 253097] Train: [17/100][508/510] Data 0.005 (0.076) Batch 0.924 (1.514) Remain 17:48:31 loss: 0.1150 Lr: 0.00577
[2023-12-25 05:42:37,708 INFO misc.py line 119 253097] Train: [17/100][509/510] Data 0.005 (0.076) Batch 1.289 (1.514) Remain 17:48:10 loss: 0.1863 Lr: 0.00577
[2023-12-25 05:42:38,844 INFO misc.py line 119 253097] Train: [17/100][510/510] Data 0.005 (0.076) Batch 1.121 (1.513) Remain 17:47:36 loss: 0.4142 Lr: 0.00577
[2023-12-25 05:42:38,845 INFO misc.py line 136 253097] Train result: loss: 0.3314 
[2023-12-25 05:42:38,846 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 05:43:05,313 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.3565 
[2023-12-25 05:43:05,660 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4324 
[2023-12-25 05:43:11,674 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4577 
[2023-12-25 05:43:12,191 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4677 
[2023-12-25 05:43:14,168 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8613 
[2023-12-25 05:43:14,591 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3604 
[2023-12-25 05:43:15,469 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2353 
[2023-12-25 05:43:16,025 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5355 
[2023-12-25 05:43:17,835 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.3258 
[2023-12-25 05:43:19,955 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2523 
[2023-12-25 05:43:20,816 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3182 
[2023-12-25 05:43:21,244 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6606 
[2023-12-25 05:43:22,144 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4790 
[2023-12-25 05:43:25,092 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7981 
[2023-12-25 05:43:25,562 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.5857 
[2023-12-25 05:43:26,176 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5678 
[2023-12-25 05:43:26,878 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3827 
[2023-12-25 05:43:28,092 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6222/0.6860/0.8838.
[2023-12-25 05:43:28,092 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9086/0.9537
[2023-12-25 05:43:28,092 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9756/0.9881
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8216/0.9682
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2245/0.2498
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5681/0.5845
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6116/0.6876
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8167/0.9092
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8969/0.9550
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4164/0.4619
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7127/0.8077
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.5900/0.6745
[2023-12-25 05:43:28,093 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5463/0.6774
[2023-12-25 05:43:28,093 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 05:43:28,094 INFO misc.py line 165 253097] Currently Best mIoU: 0.6476
[2023-12-25 05:43:28,094 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 05:43:35,307 INFO misc.py line 119 253097] Train: [18/100][1/510] Data 1.526 (1.526) Batch 4.897 (4.897) Remain 57:35:01 loss: 0.4478 Lr: 0.00577
[2023-12-25 05:43:55,270 INFO misc.py line 119 253097] Train: [18/100][2/510] Data 18.796 (18.796) Batch 19.963 (19.963) Remain 234:43:28 loss: 0.3915 Lr: 0.00577
[2023-12-25 05:43:56,505 INFO misc.py line 119 253097] Train: [18/100][3/510] Data 0.004 (0.004) Batch 1.231 (1.231) Remain 14:28:33 loss: 0.5344 Lr: 0.00577
[2023-12-25 05:43:57,689 INFO misc.py line 119 253097] Train: [18/100][4/510] Data 0.007 (0.007) Batch 1.185 (1.185) Remain 13:55:39 loss: 0.4376 Lr: 0.00577
[2023-12-25 05:43:58,870 INFO misc.py line 119 253097] Train: [18/100][5/510] Data 0.007 (0.007) Batch 1.181 (1.183) Remain 13:54:14 loss: 0.3589 Lr: 0.00577
[2023-12-25 05:44:00,060 INFO misc.py line 119 253097] Train: [18/100][6/510] Data 0.007 (0.007) Batch 1.193 (1.186) Remain 13:56:39 loss: 0.3912 Lr: 0.00577
[2023-12-25 05:44:01,314 INFO misc.py line 119 253097] Train: [18/100][7/510] Data 0.005 (0.006) Batch 1.254 (1.203) Remain 14:08:38 loss: 0.2586 Lr: 0.00577
[2023-12-25 05:44:07,122 INFO misc.py line 119 253097] Train: [18/100][8/510] Data 4.504 (0.906) Batch 5.808 (2.124) Remain 24:58:18 loss: 0.3042 Lr: 0.00577
[2023-12-25 05:44:08,282 INFO misc.py line 119 253097] Train: [18/100][9/510] Data 0.004 (0.756) Batch 1.160 (1.963) Remain 23:04:56 loss: 0.5040 Lr: 0.00577
[2023-12-25 05:44:09,542 INFO misc.py line 119 253097] Train: [18/100][10/510] Data 0.004 (0.648) Batch 1.260 (1.863) Remain 21:53:58 loss: 0.3757 Lr: 0.00577
[2023-12-25 05:44:10,465 INFO misc.py line 119 253097] Train: [18/100][11/510] Data 0.004 (0.568) Batch 0.924 (1.746) Remain 20:31:09 loss: 0.1379 Lr: 0.00577
[2023-12-25 05:44:11,618 INFO misc.py line 119 253097] Train: [18/100][12/510] Data 0.004 (0.505) Batch 1.152 (1.680) Remain 19:44:35 loss: 0.3602 Lr: 0.00577
[2023-12-25 05:44:12,647 INFO misc.py line 119 253097] Train: [18/100][13/510] Data 0.005 (0.455) Batch 1.026 (1.614) Remain 18:58:26 loss: 0.3844 Lr: 0.00577
[2023-12-25 05:44:15,684 INFO misc.py line 119 253097] Train: [18/100][14/510] Data 1.853 (0.582) Batch 3.040 (1.744) Remain 20:29:51 loss: 0.3031 Lr: 0.00577
[2023-12-25 05:44:16,675 INFO misc.py line 119 253097] Train: [18/100][15/510] Data 0.005 (0.534) Batch 0.992 (1.681) Remain 19:45:36 loss: 0.1673 Lr: 0.00577
[2023-12-25 05:44:17,922 INFO misc.py line 119 253097] Train: [18/100][16/510] Data 0.006 (0.493) Batch 1.246 (1.648) Remain 19:21:57 loss: 0.7293 Lr: 0.00577
[2023-12-25 05:44:19,001 INFO misc.py line 119 253097] Train: [18/100][17/510] Data 0.007 (0.459) Batch 1.080 (1.607) Remain 18:53:21 loss: 0.1933 Lr: 0.00577
[2023-12-25 05:44:19,853 INFO misc.py line 119 253097] Train: [18/100][18/510] Data 0.005 (0.428) Batch 0.852 (1.557) Remain 18:17:50 loss: 0.2463 Lr: 0.00577
[2023-12-25 05:44:20,959 INFO misc.py line 119 253097] Train: [18/100][19/510] Data 0.004 (0.402) Batch 1.105 (1.529) Remain 17:57:55 loss: 0.2180 Lr: 0.00577
[2023-12-25 05:44:22,057 INFO misc.py line 119 253097] Train: [18/100][20/510] Data 0.005 (0.379) Batch 1.091 (1.503) Remain 17:39:44 loss: 0.2065 Lr: 0.00577
[2023-12-25 05:44:23,297 INFO misc.py line 119 253097] Train: [18/100][21/510] Data 0.012 (0.358) Batch 1.241 (1.488) Remain 17:29:27 loss: 0.2535 Lr: 0.00577
[2023-12-25 05:44:24,517 INFO misc.py line 119 253097] Train: [18/100][22/510] Data 0.012 (0.340) Batch 1.226 (1.474) Remain 17:19:40 loss: 0.7106 Lr: 0.00577
[2023-12-25 05:44:25,437 INFO misc.py line 119 253097] Train: [18/100][23/510] Data 0.005 (0.323) Batch 0.922 (1.447) Remain 17:00:10 loss: 0.2910 Lr: 0.00577
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[2023-12-25 05:44:27,923 INFO misc.py line 119 253097] Train: [18/100][25/510] Data 0.005 (0.294) Batch 1.202 (1.428) Remain 16:47:02 loss: 0.2748 Lr: 0.00577
[2023-12-25 05:44:28,938 INFO misc.py line 119 253097] Train: [18/100][26/510] Data 0.005 (0.282) Batch 1.011 (1.410) Remain 16:34:14 loss: 0.2557 Lr: 0.00577
[2023-12-25 05:44:29,867 INFO misc.py line 119 253097] Train: [18/100][27/510] Data 0.008 (0.270) Batch 0.932 (1.390) Remain 16:20:11 loss: 0.5011 Lr: 0.00577
[2023-12-25 05:44:30,970 INFO misc.py line 119 253097] Train: [18/100][28/510] Data 0.004 (0.260) Batch 1.104 (1.379) Remain 16:12:05 loss: 0.3157 Lr: 0.00576
[2023-12-25 05:44:32,031 INFO misc.py line 119 253097] Train: [18/100][29/510] Data 0.003 (0.250) Batch 1.058 (1.366) Remain 16:03:22 loss: 0.3220 Lr: 0.00576
[2023-12-25 05:44:33,112 INFO misc.py line 119 253097] Train: [18/100][30/510] Data 0.006 (0.241) Batch 1.084 (1.356) Remain 15:55:58 loss: 0.2307 Lr: 0.00576
[2023-12-25 05:44:34,346 INFO misc.py line 119 253097] Train: [18/100][31/510] Data 0.003 (0.232) Batch 1.229 (1.351) Remain 15:52:44 loss: 0.3228 Lr: 0.00576
[2023-12-25 05:44:35,533 INFO misc.py line 119 253097] Train: [18/100][32/510] Data 0.009 (0.224) Batch 1.192 (1.346) Remain 15:48:50 loss: 0.2604 Lr: 0.00576
[2023-12-25 05:44:36,747 INFO misc.py line 119 253097] Train: [18/100][33/510] Data 0.003 (0.217) Batch 1.209 (1.341) Remain 15:45:35 loss: 0.3875 Lr: 0.00576
[2023-12-25 05:44:37,900 INFO misc.py line 119 253097] Train: [18/100][34/510] Data 0.009 (0.210) Batch 1.157 (1.335) Remain 15:41:23 loss: 0.2901 Lr: 0.00576
[2023-12-25 05:44:39,056 INFO misc.py line 119 253097] Train: [18/100][35/510] Data 0.004 (0.204) Batch 1.154 (1.330) Remain 15:37:23 loss: 0.5138 Lr: 0.00576
[2023-12-25 05:44:40,322 INFO misc.py line 119 253097] Train: [18/100][36/510] Data 0.006 (0.198) Batch 1.263 (1.328) Remain 15:35:56 loss: 0.3739 Lr: 0.00576
[2023-12-25 05:44:41,410 INFO misc.py line 119 253097] Train: [18/100][37/510] Data 0.009 (0.192) Batch 1.089 (1.321) Remain 15:30:58 loss: 0.4025 Lr: 0.00576
[2023-12-25 05:44:42,606 INFO misc.py line 119 253097] Train: [18/100][38/510] Data 0.007 (0.187) Batch 1.196 (1.317) Remain 15:28:25 loss: 0.3526 Lr: 0.00576
[2023-12-25 05:44:43,882 INFO misc.py line 119 253097] Train: [18/100][39/510] Data 0.008 (0.182) Batch 1.275 (1.316) Remain 15:27:34 loss: 0.6288 Lr: 0.00576
[2023-12-25 05:44:44,754 INFO misc.py line 119 253097] Train: [18/100][40/510] Data 0.009 (0.177) Batch 0.876 (1.304) Remain 15:19:10 loss: 0.2332 Lr: 0.00576
[2023-12-25 05:44:53,733 INFO misc.py line 119 253097] Train: [18/100][41/510] Data 0.004 (0.173) Batch 8.980 (1.506) Remain 17:41:31 loss: 0.3154 Lr: 0.00576
[2023-12-25 05:44:54,632 INFO misc.py line 119 253097] Train: [18/100][42/510] Data 0.003 (0.169) Batch 0.898 (1.491) Remain 17:30:30 loss: 0.2913 Lr: 0.00576
[2023-12-25 05:44:55,564 INFO misc.py line 119 253097] Train: [18/100][43/510] Data 0.005 (0.164) Batch 0.933 (1.477) Remain 17:20:39 loss: 0.2210 Lr: 0.00576
[2023-12-25 05:44:56,825 INFO misc.py line 119 253097] Train: [18/100][44/510] Data 0.003 (0.161) Batch 1.261 (1.471) Remain 17:16:55 loss: 0.3599 Lr: 0.00576
[2023-12-25 05:44:58,079 INFO misc.py line 119 253097] Train: [18/100][45/510] Data 0.004 (0.157) Batch 1.253 (1.466) Remain 17:13:14 loss: 0.4051 Lr: 0.00576
[2023-12-25 05:44:59,257 INFO misc.py line 119 253097] Train: [18/100][46/510] Data 0.004 (0.153) Batch 1.177 (1.459) Remain 17:08:29 loss: 0.2705 Lr: 0.00576
[2023-12-25 05:45:02,057 INFO misc.py line 119 253097] Train: [18/100][47/510] Data 0.005 (0.150) Batch 2.801 (1.490) Remain 17:29:57 loss: 0.2347 Lr: 0.00576
[2023-12-25 05:45:03,090 INFO misc.py line 119 253097] Train: [18/100][48/510] Data 0.004 (0.147) Batch 1.033 (1.480) Remain 17:22:46 loss: 0.2555 Lr: 0.00576
[2023-12-25 05:45:04,187 INFO misc.py line 119 253097] Train: [18/100][49/510] Data 0.004 (0.144) Batch 1.097 (1.471) Remain 17:16:53 loss: 0.4194 Lr: 0.00576
[2023-12-25 05:45:05,158 INFO misc.py line 119 253097] Train: [18/100][50/510] Data 0.004 (0.141) Batch 0.971 (1.461) Remain 17:09:22 loss: 0.0875 Lr: 0.00576
[2023-12-25 05:45:06,166 INFO misc.py line 119 253097] Train: [18/100][51/510] Data 0.004 (0.138) Batch 1.008 (1.451) Remain 17:02:42 loss: 0.2317 Lr: 0.00576
[2023-12-25 05:45:07,414 INFO misc.py line 119 253097] Train: [18/100][52/510] Data 0.003 (0.135) Batch 1.247 (1.447) Remain 16:59:44 loss: 0.2065 Lr: 0.00576
[2023-12-25 05:45:08,478 INFO misc.py line 119 253097] Train: [18/100][53/510] Data 0.004 (0.132) Batch 1.064 (1.440) Remain 16:54:19 loss: 0.2647 Lr: 0.00576
[2023-12-25 05:45:09,656 INFO misc.py line 119 253097] Train: [18/100][54/510] Data 0.004 (0.130) Batch 1.178 (1.434) Remain 16:50:41 loss: 0.3565 Lr: 0.00576
[2023-12-25 05:45:10,712 INFO misc.py line 119 253097] Train: [18/100][55/510] Data 0.004 (0.127) Batch 1.056 (1.427) Remain 16:45:31 loss: 0.2007 Lr: 0.00576
[2023-12-25 05:45:11,949 INFO misc.py line 119 253097] Train: [18/100][56/510] Data 0.003 (0.125) Batch 1.232 (1.423) Remain 16:42:55 loss: 0.5725 Lr: 0.00576
[2023-12-25 05:45:17,484 INFO misc.py line 119 253097] Train: [18/100][57/510] Data 0.010 (0.123) Batch 5.539 (1.500) Remain 17:36:35 loss: 0.2195 Lr: 0.00576
[2023-12-25 05:45:18,691 INFO misc.py line 119 253097] Train: [18/100][58/510] Data 0.005 (0.121) Batch 1.209 (1.494) Remain 17:32:50 loss: 0.1779 Lr: 0.00576
[2023-12-25 05:45:19,777 INFO misc.py line 119 253097] Train: [18/100][59/510] Data 0.003 (0.119) Batch 1.084 (1.487) Remain 17:27:38 loss: 0.4456 Lr: 0.00576
[2023-12-25 05:45:20,911 INFO misc.py line 119 253097] Train: [18/100][60/510] Data 0.005 (0.117) Batch 1.135 (1.481) Remain 17:23:16 loss: 0.3141 Lr: 0.00576
[2023-12-25 05:45:24,925 INFO misc.py line 119 253097] Train: [18/100][61/510] Data 0.004 (0.115) Batch 4.014 (1.525) Remain 17:54:00 loss: 0.3438 Lr: 0.00576
[2023-12-25 05:45:26,207 INFO misc.py line 119 253097] Train: [18/100][62/510] Data 0.005 (0.113) Batch 1.281 (1.520) Remain 17:51:04 loss: 1.0380 Lr: 0.00576
[2023-12-25 05:45:27,419 INFO misc.py line 119 253097] Train: [18/100][63/510] Data 0.007 (0.111) Batch 1.214 (1.515) Remain 17:47:26 loss: 0.2785 Lr: 0.00576
[2023-12-25 05:45:28,713 INFO misc.py line 119 253097] Train: [18/100][64/510] Data 0.004 (0.109) Batch 1.290 (1.512) Remain 17:44:49 loss: 0.5732 Lr: 0.00576
[2023-12-25 05:45:29,714 INFO misc.py line 119 253097] Train: [18/100][65/510] Data 0.007 (0.108) Batch 0.998 (1.503) Remain 17:38:57 loss: 0.5414 Lr: 0.00576
[2023-12-25 05:45:30,897 INFO misc.py line 119 253097] Train: [18/100][66/510] Data 0.011 (0.106) Batch 1.185 (1.498) Remain 17:35:22 loss: 0.4834 Lr: 0.00576
[2023-12-25 05:45:32,082 INFO misc.py line 119 253097] Train: [18/100][67/510] Data 0.009 (0.105) Batch 1.188 (1.493) Remain 17:31:56 loss: 0.4544 Lr: 0.00576
[2023-12-25 05:45:33,218 INFO misc.py line 119 253097] Train: [18/100][68/510] Data 0.006 (0.103) Batch 1.132 (1.488) Remain 17:27:59 loss: 0.4521 Lr: 0.00576
[2023-12-25 05:45:34,461 INFO misc.py line 119 253097] Train: [18/100][69/510] Data 0.010 (0.102) Batch 1.249 (1.484) Remain 17:25:25 loss: 0.2663 Lr: 0.00576
[2023-12-25 05:45:35,629 INFO misc.py line 119 253097] Train: [18/100][70/510] Data 0.005 (0.100) Batch 1.165 (1.479) Remain 17:22:02 loss: 0.1754 Lr: 0.00576
[2023-12-25 05:45:36,743 INFO misc.py line 119 253097] Train: [18/100][71/510] Data 0.007 (0.099) Batch 1.115 (1.474) Remain 17:18:14 loss: 0.3035 Lr: 0.00576
[2023-12-25 05:45:37,942 INFO misc.py line 119 253097] Train: [18/100][72/510] Data 0.006 (0.098) Batch 1.201 (1.470) Remain 17:15:25 loss: 0.2289 Lr: 0.00576
[2023-12-25 05:45:38,826 INFO misc.py line 119 253097] Train: [18/100][73/510] Data 0.005 (0.096) Batch 0.883 (1.462) Remain 17:09:29 loss: 0.3416 Lr: 0.00576
[2023-12-25 05:45:39,933 INFO misc.py line 119 253097] Train: [18/100][74/510] Data 0.006 (0.095) Batch 1.108 (1.457) Remain 17:05:57 loss: 0.1760 Lr: 0.00576
[2023-12-25 05:45:41,126 INFO misc.py line 119 253097] Train: [18/100][75/510] Data 0.003 (0.094) Batch 1.192 (1.453) Remain 17:03:21 loss: 0.3871 Lr: 0.00576
[2023-12-25 05:45:50,797 INFO misc.py line 119 253097] Train: [18/100][76/510] Data 8.435 (0.208) Batch 9.672 (1.566) Remain 18:22:37 loss: 0.1450 Lr: 0.00576
[2023-12-25 05:45:52,013 INFO misc.py line 119 253097] Train: [18/100][77/510] Data 0.004 (0.205) Batch 1.210 (1.561) Remain 18:19:12 loss: 0.4247 Lr: 0.00576
[2023-12-25 05:45:53,283 INFO misc.py line 119 253097] Train: [18/100][78/510] Data 0.009 (0.203) Batch 1.274 (1.557) Remain 18:16:29 loss: 0.2984 Lr: 0.00576
[2023-12-25 05:45:54,437 INFO misc.py line 119 253097] Train: [18/100][79/510] Data 0.005 (0.200) Batch 1.154 (1.552) Remain 18:12:43 loss: 0.1228 Lr: 0.00576
[2023-12-25 05:45:55,595 INFO misc.py line 119 253097] Train: [18/100][80/510] Data 0.005 (0.198) Batch 1.156 (1.547) Remain 18:09:05 loss: 0.2042 Lr: 0.00576
[2023-12-25 05:45:57,343 INFO misc.py line 119 253097] Train: [18/100][81/510] Data 0.007 (0.195) Batch 1.749 (1.549) Remain 18:10:53 loss: 0.2165 Lr: 0.00576
[2023-12-25 05:45:58,393 INFO misc.py line 119 253097] Train: [18/100][82/510] Data 0.007 (0.193) Batch 1.051 (1.543) Remain 18:06:25 loss: 0.6565 Lr: 0.00576
[2023-12-25 05:45:59,482 INFO misc.py line 119 253097] Train: [18/100][83/510] Data 0.006 (0.190) Batch 1.088 (1.537) Remain 18:02:23 loss: 0.3501 Lr: 0.00576
[2023-12-25 05:46:00,502 INFO misc.py line 119 253097] Train: [18/100][84/510] Data 0.006 (0.188) Batch 1.017 (1.531) Remain 17:57:50 loss: 0.2605 Lr: 0.00576
[2023-12-25 05:46:01,652 INFO misc.py line 119 253097] Train: [18/100][85/510] Data 0.008 (0.186) Batch 1.152 (1.526) Remain 17:54:34 loss: 0.3250 Lr: 0.00576
[2023-12-25 05:46:02,918 INFO misc.py line 119 253097] Train: [18/100][86/510] Data 0.007 (0.184) Batch 1.269 (1.523) Remain 17:52:21 loss: 0.4573 Lr: 0.00576
[2023-12-25 05:46:03,805 INFO misc.py line 119 253097] Train: [18/100][87/510] Data 0.004 (0.182) Batch 0.886 (1.516) Remain 17:46:59 loss: 0.4197 Lr: 0.00576
[2023-12-25 05:46:05,013 INFO misc.py line 119 253097] Train: [18/100][88/510] Data 0.004 (0.180) Batch 1.204 (1.512) Remain 17:44:23 loss: 0.2518 Lr: 0.00576
[2023-12-25 05:46:06,102 INFO misc.py line 119 253097] Train: [18/100][89/510] Data 0.010 (0.178) Batch 1.094 (1.507) Remain 17:40:56 loss: 0.2450 Lr: 0.00576
[2023-12-25 05:46:09,531 INFO misc.py line 119 253097] Train: [18/100][90/510] Data 0.004 (0.176) Batch 3.428 (1.529) Remain 17:56:27 loss: 0.1592 Lr: 0.00576
[2023-12-25 05:46:14,545 INFO misc.py line 119 253097] Train: [18/100][91/510] Data 0.006 (0.174) Batch 5.016 (1.569) Remain 18:24:19 loss: 0.2585 Lr: 0.00576
[2023-12-25 05:46:15,719 INFO misc.py line 119 253097] Train: [18/100][92/510] Data 0.004 (0.172) Batch 1.173 (1.564) Remain 18:21:10 loss: 0.2493 Lr: 0.00576
[2023-12-25 05:46:16,811 INFO misc.py line 119 253097] Train: [18/100][93/510] Data 0.004 (0.170) Batch 1.092 (1.559) Remain 18:17:27 loss: 0.2543 Lr: 0.00576
[2023-12-25 05:46:17,877 INFO misc.py line 119 253097] Train: [18/100][94/510] Data 0.005 (0.168) Batch 1.065 (1.554) Remain 18:13:36 loss: 0.2094 Lr: 0.00576
[2023-12-25 05:46:18,954 INFO misc.py line 119 253097] Train: [18/100][95/510] Data 0.005 (0.166) Batch 1.077 (1.548) Remain 18:09:56 loss: 0.2474 Lr: 0.00576
[2023-12-25 05:46:20,154 INFO misc.py line 119 253097] Train: [18/100][96/510] Data 0.005 (0.165) Batch 1.201 (1.545) Remain 18:07:16 loss: 0.1677 Lr: 0.00576
[2023-12-25 05:46:21,215 INFO misc.py line 119 253097] Train: [18/100][97/510] Data 0.004 (0.163) Batch 1.061 (1.540) Remain 18:03:38 loss: 0.5210 Lr: 0.00576
[2023-12-25 05:46:22,176 INFO misc.py line 119 253097] Train: [18/100][98/510] Data 0.003 (0.161) Batch 0.961 (1.533) Remain 17:59:19 loss: 0.5298 Lr: 0.00576
[2023-12-25 05:46:23,234 INFO misc.py line 119 253097] Train: [18/100][99/510] Data 0.004 (0.159) Batch 1.057 (1.528) Remain 17:55:48 loss: 0.2129 Lr: 0.00576
[2023-12-25 05:46:30,682 INFO misc.py line 119 253097] Train: [18/100][100/510] Data 6.429 (0.224) Batch 7.448 (1.589) Remain 18:38:44 loss: 0.1520 Lr: 0.00576
[2023-12-25 05:46:31,767 INFO misc.py line 119 253097] Train: [18/100][101/510] Data 0.005 (0.222) Batch 1.084 (1.584) Remain 18:35:04 loss: 0.3359 Lr: 0.00576
[2023-12-25 05:46:32,996 INFO misc.py line 119 253097] Train: [18/100][102/510] Data 0.004 (0.220) Batch 1.230 (1.581) Remain 18:32:32 loss: 0.3400 Lr: 0.00576
[2023-12-25 05:46:34,013 INFO misc.py line 119 253097] Train: [18/100][103/510] Data 0.003 (0.218) Batch 1.017 (1.575) Remain 18:28:32 loss: 0.2702 Lr: 0.00576
[2023-12-25 05:46:35,283 INFO misc.py line 119 253097] Train: [18/100][104/510] Data 0.004 (0.215) Batch 1.269 (1.572) Remain 18:26:22 loss: 0.3863 Lr: 0.00576
[2023-12-25 05:46:36,433 INFO misc.py line 119 253097] Train: [18/100][105/510] Data 0.005 (0.213) Batch 1.147 (1.568) Remain 18:23:25 loss: 0.3366 Lr: 0.00576
[2023-12-25 05:46:38,664 INFO misc.py line 119 253097] Train: [18/100][106/510] Data 1.180 (0.223) Batch 2.236 (1.574) Remain 18:27:57 loss: 0.3375 Lr: 0.00576
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[2023-12-25 05:46:43,350 INFO misc.py line 119 253097] Train: [18/100][110/510] Data 0.008 (0.215) Batch 0.952 (1.559) Remain 18:17:15 loss: 0.6304 Lr: 0.00576
[2023-12-25 05:46:44,504 INFO misc.py line 119 253097] Train: [18/100][111/510] Data 0.003 (0.213) Batch 1.154 (1.556) Remain 18:14:35 loss: 0.2065 Lr: 0.00576
[2023-12-25 05:46:45,544 INFO misc.py line 119 253097] Train: [18/100][112/510] Data 0.004 (0.211) Batch 1.040 (1.551) Remain 18:11:13 loss: 0.4530 Lr: 0.00576
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[2023-12-25 05:46:52,394 INFO misc.py line 119 253097] Train: [18/100][118/510] Data 0.004 (0.200) Batch 1.070 (1.529) Remain 17:56:01 loss: 0.4107 Lr: 0.00576
[2023-12-25 05:46:53,634 INFO misc.py line 119 253097] Train: [18/100][119/510] Data 0.008 (0.198) Batch 1.239 (1.527) Remain 17:54:14 loss: 0.2850 Lr: 0.00576
[2023-12-25 05:46:54,734 INFO misc.py line 119 253097] Train: [18/100][120/510] Data 0.008 (0.197) Batch 1.099 (1.523) Remain 17:51:38 loss: 0.3721 Lr: 0.00576
[2023-12-25 05:46:55,877 INFO misc.py line 119 253097] Train: [18/100][121/510] Data 0.009 (0.195) Batch 1.145 (1.520) Remain 17:49:21 loss: 0.2236 Lr: 0.00576
[2023-12-25 05:46:56,975 INFO misc.py line 119 253097] Train: [18/100][122/510] Data 0.008 (0.194) Batch 1.098 (1.517) Remain 17:46:50 loss: 0.3349 Lr: 0.00576
[2023-12-25 05:46:58,187 INFO misc.py line 119 253097] Train: [18/100][123/510] Data 0.008 (0.192) Batch 1.216 (1.514) Remain 17:45:03 loss: 0.2161 Lr: 0.00576
[2023-12-25 05:46:59,356 INFO misc.py line 119 253097] Train: [18/100][124/510] Data 0.004 (0.191) Batch 1.169 (1.511) Remain 17:43:01 loss: 0.2297 Lr: 0.00576
[2023-12-25 05:47:00,557 INFO misc.py line 119 253097] Train: [18/100][125/510] Data 0.004 (0.189) Batch 1.197 (1.509) Remain 17:41:11 loss: 0.4852 Lr: 0.00576
[2023-12-25 05:47:01,782 INFO misc.py line 119 253097] Train: [18/100][126/510] Data 0.008 (0.188) Batch 1.227 (1.506) Remain 17:39:33 loss: 0.2101 Lr: 0.00576
[2023-12-25 05:47:03,012 INFO misc.py line 119 253097] Train: [18/100][127/510] Data 0.010 (0.186) Batch 1.232 (1.504) Remain 17:37:58 loss: 0.4848 Lr: 0.00576
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[2023-12-25 05:47:09,678 INFO misc.py line 119 253097] Train: [18/100][132/510] Data 0.376 (0.182) Batch 1.664 (1.497) Remain 17:33:11 loss: 0.4808 Lr: 0.00576
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[2023-12-25 05:47:28,505 INFO misc.py line 119 253097] Train: [18/100][144/510] Data 0.008 (0.167) Batch 1.043 (1.504) Remain 17:37:08 loss: 0.2637 Lr: 0.00576
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[2023-12-25 05:47:31,798 INFO misc.py line 119 253097] Train: [18/100][147/510] Data 0.006 (0.164) Batch 1.019 (1.495) Remain 17:31:07 loss: 0.3556 Lr: 0.00576
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[2023-12-25 05:47:34,193 INFO misc.py line 119 253097] Train: [18/100][149/510] Data 0.005 (0.162) Batch 1.172 (1.491) Remain 17:28:12 loss: 0.2648 Lr: 0.00576
[2023-12-25 05:47:35,269 INFO misc.py line 119 253097] Train: [18/100][150/510] Data 0.009 (0.160) Batch 1.081 (1.488) Remain 17:26:12 loss: 0.2561 Lr: 0.00576
[2023-12-25 05:47:36,534 INFO misc.py line 119 253097] Train: [18/100][151/510] Data 0.004 (0.159) Batch 1.264 (1.487) Remain 17:25:07 loss: 0.3061 Lr: 0.00576
[2023-12-25 05:47:37,632 INFO misc.py line 119 253097] Train: [18/100][152/510] Data 0.004 (0.158) Batch 1.096 (1.484) Remain 17:23:15 loss: 0.2370 Lr: 0.00576
[2023-12-25 05:47:38,900 INFO misc.py line 119 253097] Train: [18/100][153/510] Data 0.007 (0.157) Batch 1.271 (1.483) Remain 17:22:13 loss: 0.4746 Lr: 0.00576
[2023-12-25 05:47:46,997 INFO misc.py line 119 253097] Train: [18/100][154/510] Data 0.005 (0.156) Batch 8.097 (1.526) Remain 17:52:59 loss: 0.3723 Lr: 0.00576
[2023-12-25 05:47:48,230 INFO misc.py line 119 253097] Train: [18/100][155/510] Data 0.004 (0.155) Batch 1.232 (1.525) Remain 17:51:36 loss: 0.4052 Lr: 0.00576
[2023-12-25 05:47:49,313 INFO misc.py line 119 253097] Train: [18/100][156/510] Data 0.005 (0.154) Batch 1.084 (1.522) Remain 17:49:33 loss: 0.4003 Lr: 0.00576
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[2023-12-25 05:47:55,179 INFO misc.py line 119 253097] Train: [18/100][161/510] Data 0.006 (0.150) Batch 1.062 (1.511) Remain 17:41:41 loss: 0.7860 Lr: 0.00575
[2023-12-25 05:47:56,438 INFO misc.py line 119 253097] Train: [18/100][162/510] Data 0.004 (0.149) Batch 1.254 (1.509) Remain 17:40:31 loss: 0.1817 Lr: 0.00575
[2023-12-25 05:47:57,416 INFO misc.py line 119 253097] Train: [18/100][163/510] Data 0.009 (0.148) Batch 0.981 (1.506) Remain 17:38:11 loss: 0.4703 Lr: 0.00575
[2023-12-25 05:47:58,601 INFO misc.py line 119 253097] Train: [18/100][164/510] Data 0.006 (0.147) Batch 1.187 (1.504) Remain 17:36:46 loss: 0.3662 Lr: 0.00575
[2023-12-25 05:47:59,879 INFO misc.py line 119 253097] Train: [18/100][165/510] Data 0.003 (0.146) Batch 1.277 (1.502) Remain 17:35:45 loss: 0.3143 Lr: 0.00575
[2023-12-25 05:48:06,214 INFO misc.py line 119 253097] Train: [18/100][166/510] Data 0.005 (0.145) Batch 6.336 (1.532) Remain 17:56:34 loss: 0.4802 Lr: 0.00575
[2023-12-25 05:48:07,304 INFO misc.py line 119 253097] Train: [18/100][167/510] Data 0.003 (0.144) Batch 1.089 (1.529) Remain 17:54:39 loss: 0.1795 Lr: 0.00575
[2023-12-25 05:48:08,340 INFO misc.py line 119 253097] Train: [18/100][168/510] Data 0.005 (0.143) Batch 1.036 (1.526) Remain 17:52:31 loss: 0.2460 Lr: 0.00575
[2023-12-25 05:48:09,349 INFO misc.py line 119 253097] Train: [18/100][169/510] Data 0.004 (0.143) Batch 1.009 (1.523) Remain 17:50:18 loss: 0.1492 Lr: 0.00575
[2023-12-25 05:48:10,562 INFO misc.py line 119 253097] Train: [18/100][170/510] Data 0.004 (0.142) Batch 1.212 (1.521) Remain 17:48:58 loss: 0.6035 Lr: 0.00575
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[2023-12-25 05:48:13,073 INFO misc.py line 119 253097] Train: [18/100][172/510] Data 0.006 (0.140) Batch 1.266 (1.518) Remain 17:46:42 loss: 0.4038 Lr: 0.00575
[2023-12-25 05:48:14,326 INFO misc.py line 119 253097] Train: [18/100][173/510] Data 0.006 (0.139) Batch 1.253 (1.517) Remain 17:45:35 loss: 0.2661 Lr: 0.00575
[2023-12-25 05:48:16,172 INFO misc.py line 119 253097] Train: [18/100][174/510] Data 0.006 (0.139) Batch 1.847 (1.519) Remain 17:46:55 loss: 0.3651 Lr: 0.00575
[2023-12-25 05:48:17,307 INFO misc.py line 119 253097] Train: [18/100][175/510] Data 0.004 (0.138) Batch 1.135 (1.516) Remain 17:45:19 loss: 0.3087 Lr: 0.00575
[2023-12-25 05:48:18,242 INFO misc.py line 119 253097] Train: [18/100][176/510] Data 0.005 (0.137) Batch 0.935 (1.513) Remain 17:42:56 loss: 0.4436 Lr: 0.00575
[2023-12-25 05:48:19,500 INFO misc.py line 119 253097] Train: [18/100][177/510] Data 0.005 (0.136) Batch 1.259 (1.511) Remain 17:41:53 loss: 0.2445 Lr: 0.00575
[2023-12-25 05:48:20,741 INFO misc.py line 119 253097] Train: [18/100][178/510] Data 0.004 (0.136) Batch 1.231 (1.510) Remain 17:40:44 loss: 0.3027 Lr: 0.00575
[2023-12-25 05:48:21,920 INFO misc.py line 119 253097] Train: [18/100][179/510] Data 0.015 (0.135) Batch 1.186 (1.508) Remain 17:39:25 loss: 0.4372 Lr: 0.00575
[2023-12-25 05:48:23,168 INFO misc.py line 119 253097] Train: [18/100][180/510] Data 0.007 (0.134) Batch 1.249 (1.507) Remain 17:38:22 loss: 0.4945 Lr: 0.00575
[2023-12-25 05:48:24,369 INFO misc.py line 119 253097] Train: [18/100][181/510] Data 0.006 (0.133) Batch 1.190 (1.505) Remain 17:37:05 loss: 0.2369 Lr: 0.00575
[2023-12-25 05:48:25,340 INFO misc.py line 119 253097] Train: [18/100][182/510] Data 0.018 (0.133) Batch 0.984 (1.502) Remain 17:35:01 loss: 0.2729 Lr: 0.00575
[2023-12-25 05:48:26,530 INFO misc.py line 119 253097] Train: [18/100][183/510] Data 0.005 (0.132) Batch 1.189 (1.500) Remain 17:33:46 loss: 0.1622 Lr: 0.00575
[2023-12-25 05:48:31,762 INFO misc.py line 119 253097] Train: [18/100][184/510] Data 0.006 (0.131) Batch 5.234 (1.521) Remain 17:48:14 loss: 0.4090 Lr: 0.00575
[2023-12-25 05:48:33,031 INFO misc.py line 119 253097] Train: [18/100][185/510] Data 0.004 (0.131) Batch 1.267 (1.519) Remain 17:47:14 loss: 0.3743 Lr: 0.00575
[2023-12-25 05:48:34,360 INFO misc.py line 119 253097] Train: [18/100][186/510] Data 0.006 (0.130) Batch 1.331 (1.518) Remain 17:46:29 loss: 0.3192 Lr: 0.00575
[2023-12-25 05:48:35,369 INFO misc.py line 119 253097] Train: [18/100][187/510] Data 0.004 (0.129) Batch 1.006 (1.516) Remain 17:44:30 loss: 0.1314 Lr: 0.00575
[2023-12-25 05:48:36,430 INFO misc.py line 119 253097] Train: [18/100][188/510] Data 0.007 (0.129) Batch 1.056 (1.513) Remain 17:42:44 loss: 0.2515 Lr: 0.00575
[2023-12-25 05:48:37,545 INFO misc.py line 119 253097] Train: [18/100][189/510] Data 0.011 (0.128) Batch 1.120 (1.511) Remain 17:41:13 loss: 0.1956 Lr: 0.00575
[2023-12-25 05:48:38,853 INFO misc.py line 119 253097] Train: [18/100][190/510] Data 0.006 (0.127) Batch 1.306 (1.510) Remain 17:40:26 loss: 0.3642 Lr: 0.00575
[2023-12-25 05:48:41,489 INFO misc.py line 119 253097] Train: [18/100][191/510] Data 0.008 (0.127) Batch 2.639 (1.516) Remain 17:44:37 loss: 0.2699 Lr: 0.00575
[2023-12-25 05:48:42,685 INFO misc.py line 119 253097] Train: [18/100][192/510] Data 0.005 (0.126) Batch 1.195 (1.514) Remain 17:43:24 loss: 0.6268 Lr: 0.00575
[2023-12-25 05:48:43,911 INFO misc.py line 119 253097] Train: [18/100][193/510] Data 0.005 (0.125) Batch 1.227 (1.513) Remain 17:42:19 loss: 0.3247 Lr: 0.00575
[2023-12-25 05:48:45,051 INFO misc.py line 119 253097] Train: [18/100][194/510] Data 0.005 (0.125) Batch 1.141 (1.511) Remain 17:40:56 loss: 0.4189 Lr: 0.00575
[2023-12-25 05:48:46,012 INFO misc.py line 119 253097] Train: [18/100][195/510] Data 0.004 (0.124) Batch 0.961 (1.508) Remain 17:38:53 loss: 0.2446 Lr: 0.00575
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[2023-12-25 05:48:48,184 INFO misc.py line 119 253097] Train: [18/100][197/510] Data 0.006 (0.123) Batch 1.101 (1.504) Remain 17:35:47 loss: 0.2884 Lr: 0.00575
[2023-12-25 05:48:49,308 INFO misc.py line 119 253097] Train: [18/100][198/510] Data 0.006 (0.122) Batch 1.125 (1.502) Remain 17:34:24 loss: 0.6432 Lr: 0.00575
[2023-12-25 05:48:50,521 INFO misc.py line 119 253097] Train: [18/100][199/510] Data 0.004 (0.122) Batch 1.213 (1.500) Remain 17:33:20 loss: 0.3058 Lr: 0.00575
[2023-12-25 05:48:51,618 INFO misc.py line 119 253097] Train: [18/100][200/510] Data 0.004 (0.121) Batch 1.097 (1.498) Remain 17:31:53 loss: 0.5158 Lr: 0.00575
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[2023-12-25 05:49:02,921 INFO misc.py line 119 253097] Train: [18/100][202/510] Data 0.003 (0.120) Batch 10.073 (1.540) Remain 18:01:08 loss: 0.2643 Lr: 0.00575
[2023-12-25 05:49:04,103 INFO misc.py line 119 253097] Train: [18/100][203/510] Data 0.003 (0.119) Batch 1.181 (1.538) Remain 17:59:51 loss: 0.1875 Lr: 0.00575
[2023-12-25 05:49:05,103 INFO misc.py line 119 253097] Train: [18/100][204/510] Data 0.004 (0.119) Batch 1.001 (1.535) Remain 17:57:57 loss: 0.2175 Lr: 0.00575
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[2023-12-25 05:49:07,411 INFO misc.py line 119 253097] Train: [18/100][206/510] Data 0.003 (0.118) Batch 1.243 (1.532) Remain 17:55:16 loss: 0.5318 Lr: 0.00575
[2023-12-25 05:49:08,631 INFO misc.py line 119 253097] Train: [18/100][207/510] Data 0.003 (0.117) Batch 1.220 (1.530) Remain 17:54:10 loss: 0.3179 Lr: 0.00575
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[2023-12-25 05:49:11,047 INFO misc.py line 119 253097] Train: [18/100][209/510] Data 0.004 (0.116) Batch 1.256 (1.527) Remain 17:51:54 loss: 0.3686 Lr: 0.00575
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[2023-12-25 05:49:16,975 INFO misc.py line 119 253097] Train: [18/100][214/510] Data 0.003 (0.113) Batch 1.123 (1.519) Remain 17:46:06 loss: 0.4810 Lr: 0.00575
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[2023-12-25 05:49:35,153 INFO misc.py line 119 253097] Train: [18/100][226/510] Data 0.005 (0.108) Batch 1.240 (1.519) Remain 17:45:39 loss: 0.4143 Lr: 0.00575
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[2023-12-25 05:49:39,669 INFO misc.py line 119 253097] Train: [18/100][230/510] Data 0.003 (0.106) Batch 1.333 (1.512) Remain 17:40:44 loss: 0.3754 Lr: 0.00575
[2023-12-25 05:49:41,021 INFO misc.py line 119 253097] Train: [18/100][231/510] Data 0.004 (0.105) Batch 1.348 (1.511) Remain 17:40:12 loss: 0.4129 Lr: 0.00575
[2023-12-25 05:49:42,210 INFO misc.py line 119 253097] Train: [18/100][232/510] Data 0.008 (0.105) Batch 1.190 (1.510) Remain 17:39:12 loss: 0.3850 Lr: 0.00575
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[2023-12-25 05:50:42,617 INFO misc.py line 119 253097] Train: [18/100][268/510] Data 0.004 (0.144) Batch 0.999 (1.533) Remain 17:54:20 loss: 0.3372 Lr: 0.00575
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[2023-12-25 05:53:49,156 INFO misc.py line 119 253097] Train: [18/100][387/510] Data 0.006 (0.126) Batch 12.725 (1.543) Remain 17:58:53 loss: 0.3588 Lr: 0.00574
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[2023-12-25 05:53:58,936 INFO misc.py line 119 253097] Train: [18/100][393/510] Data 0.009 (0.125) Batch 3.629 (1.545) Remain 17:59:39 loss: 0.1876 Lr: 0.00574
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[2023-12-25 05:54:08,078 INFO misc.py line 119 253097] Train: [18/100][399/510] Data 0.004 (0.123) Batch 3.378 (1.544) Remain 17:59:17 loss: 0.3240 Lr: 0.00574
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[2023-12-25 05:55:04,347 INFO misc.py line 119 253097] Train: [18/100][442/510] Data 0.010 (0.127) Batch 0.999 (1.521) Remain 17:42:03 loss: 0.3820 Lr: 0.00573
[2023-12-25 05:55:09,203 INFO misc.py line 119 253097] Train: [18/100][443/510] Data 0.006 (0.127) Batch 4.858 (1.529) Remain 17:47:19 loss: 0.3093 Lr: 0.00573
[2023-12-25 05:55:10,505 INFO misc.py line 119 253097] Train: [18/100][444/510] Data 0.005 (0.126) Batch 1.303 (1.528) Remain 17:46:56 loss: 0.2972 Lr: 0.00573
[2023-12-25 05:55:11,620 INFO misc.py line 119 253097] Train: [18/100][445/510] Data 0.003 (0.126) Batch 1.115 (1.527) Remain 17:46:15 loss: 0.3271 Lr: 0.00573
[2023-12-25 05:55:12,735 INFO misc.py line 119 253097] Train: [18/100][446/510] Data 0.004 (0.126) Batch 1.114 (1.526) Remain 17:45:35 loss: 0.5025 Lr: 0.00573
[2023-12-25 05:55:14,058 INFO misc.py line 119 253097] Train: [18/100][447/510] Data 0.004 (0.126) Batch 1.321 (1.526) Remain 17:45:14 loss: 0.4990 Lr: 0.00573
[2023-12-25 05:55:15,094 INFO misc.py line 119 253097] Train: [18/100][448/510] Data 0.007 (0.125) Batch 1.039 (1.525) Remain 17:44:26 loss: 0.4279 Lr: 0.00573
[2023-12-25 05:55:16,035 INFO misc.py line 119 253097] Train: [18/100][449/510] Data 0.004 (0.125) Batch 0.941 (1.524) Remain 17:43:30 loss: 0.1852 Lr: 0.00573
[2023-12-25 05:55:17,128 INFO misc.py line 119 253097] Train: [18/100][450/510] Data 0.004 (0.125) Batch 1.092 (1.523) Remain 17:42:48 loss: 0.1857 Lr: 0.00573
[2023-12-25 05:55:18,413 INFO misc.py line 119 253097] Train: [18/100][451/510] Data 0.008 (0.124) Batch 1.285 (1.522) Remain 17:42:24 loss: 0.2179 Lr: 0.00573
[2023-12-25 05:55:19,885 INFO misc.py line 119 253097] Train: [18/100][452/510] Data 0.005 (0.124) Batch 1.474 (1.522) Remain 17:42:18 loss: 0.5430 Lr: 0.00573
[2023-12-25 05:55:20,994 INFO misc.py line 119 253097] Train: [18/100][453/510] Data 0.004 (0.124) Batch 1.106 (1.521) Remain 17:41:38 loss: 0.4287 Lr: 0.00573
[2023-12-25 05:55:22,295 INFO misc.py line 119 253097] Train: [18/100][454/510] Data 0.007 (0.124) Batch 1.300 (1.521) Remain 17:41:16 loss: 0.2527 Lr: 0.00573
[2023-12-25 05:55:33,136 INFO misc.py line 119 253097] Train: [18/100][455/510] Data 0.007 (0.123) Batch 10.839 (1.541) Remain 17:55:38 loss: 0.2571 Lr: 0.00573
[2023-12-25 05:55:34,149 INFO misc.py line 119 253097] Train: [18/100][456/510] Data 0.010 (0.123) Batch 1.018 (1.540) Remain 17:54:48 loss: 0.3361 Lr: 0.00573
[2023-12-25 05:55:35,377 INFO misc.py line 119 253097] Train: [18/100][457/510] Data 0.005 (0.123) Batch 1.225 (1.539) Remain 17:54:17 loss: 0.3434 Lr: 0.00573
[2023-12-25 05:55:36,432 INFO misc.py line 119 253097] Train: [18/100][458/510] Data 0.007 (0.123) Batch 1.058 (1.538) Remain 17:53:31 loss: 0.2457 Lr: 0.00573
[2023-12-25 05:55:37,555 INFO misc.py line 119 253097] Train: [18/100][459/510] Data 0.004 (0.122) Batch 1.123 (1.537) Remain 17:52:52 loss: 0.1538 Lr: 0.00573
[2023-12-25 05:55:38,528 INFO misc.py line 119 253097] Train: [18/100][460/510] Data 0.004 (0.122) Batch 0.973 (1.536) Remain 17:51:59 loss: 0.1678 Lr: 0.00573
[2023-12-25 05:55:39,664 INFO misc.py line 119 253097] Train: [18/100][461/510] Data 0.005 (0.122) Batch 1.136 (1.535) Remain 17:51:20 loss: 0.3298 Lr: 0.00573
[2023-12-25 05:55:40,840 INFO misc.py line 119 253097] Train: [18/100][462/510] Data 0.005 (0.122) Batch 1.176 (1.535) Remain 17:50:46 loss: 0.2614 Lr: 0.00573
[2023-12-25 05:55:42,053 INFO misc.py line 119 253097] Train: [18/100][463/510] Data 0.004 (0.121) Batch 1.213 (1.534) Remain 17:50:15 loss: 0.1540 Lr: 0.00573
[2023-12-25 05:55:43,199 INFO misc.py line 119 253097] Train: [18/100][464/510] Data 0.004 (0.121) Batch 1.146 (1.533) Remain 17:49:39 loss: 0.3213 Lr: 0.00573
[2023-12-25 05:55:44,056 INFO misc.py line 119 253097] Train: [18/100][465/510] Data 0.007 (0.121) Batch 0.857 (1.532) Remain 17:48:36 loss: 0.2282 Lr: 0.00573
[2023-12-25 05:55:45,310 INFO misc.py line 119 253097] Train: [18/100][466/510] Data 0.004 (0.121) Batch 1.251 (1.531) Remain 17:48:09 loss: 0.2173 Lr: 0.00573
[2023-12-25 05:55:52,596 INFO misc.py line 119 253097] Train: [18/100][467/510] Data 0.007 (0.120) Batch 7.289 (1.543) Remain 17:56:47 loss: 0.3308 Lr: 0.00573
[2023-12-25 05:55:53,857 INFO misc.py line 119 253097] Train: [18/100][468/510] Data 0.004 (0.120) Batch 1.260 (1.543) Remain 17:56:20 loss: 0.2726 Lr: 0.00573
[2023-12-25 05:55:54,882 INFO misc.py line 119 253097] Train: [18/100][469/510] Data 0.005 (0.120) Batch 1.026 (1.542) Remain 17:55:32 loss: 0.2730 Lr: 0.00573
[2023-12-25 05:55:56,118 INFO misc.py line 119 253097] Train: [18/100][470/510] Data 0.003 (0.120) Batch 1.234 (1.541) Remain 17:55:03 loss: 0.3360 Lr: 0.00573
[2023-12-25 05:55:57,086 INFO misc.py line 119 253097] Train: [18/100][471/510] Data 0.005 (0.119) Batch 0.969 (1.540) Remain 17:54:10 loss: 0.2637 Lr: 0.00573
[2023-12-25 05:55:58,703 INFO misc.py line 119 253097] Train: [18/100][472/510] Data 0.005 (0.119) Batch 1.614 (1.540) Remain 17:54:15 loss: 0.4754 Lr: 0.00573
[2023-12-25 05:56:00,006 INFO misc.py line 119 253097] Train: [18/100][473/510] Data 0.007 (0.119) Batch 1.306 (1.539) Remain 17:53:53 loss: 0.4015 Lr: 0.00573
[2023-12-25 05:56:01,013 INFO misc.py line 119 253097] Train: [18/100][474/510] Data 0.005 (0.119) Batch 1.003 (1.538) Remain 17:53:04 loss: 0.1732 Lr: 0.00573
[2023-12-25 05:56:02,123 INFO misc.py line 119 253097] Train: [18/100][475/510] Data 0.009 (0.118) Batch 1.113 (1.537) Remain 17:52:24 loss: 0.1985 Lr: 0.00573
[2023-12-25 05:56:03,347 INFO misc.py line 119 253097] Train: [18/100][476/510] Data 0.005 (0.118) Batch 1.226 (1.537) Remain 17:51:55 loss: 0.2294 Lr: 0.00573
[2023-12-25 05:56:04,624 INFO misc.py line 119 253097] Train: [18/100][477/510] Data 0.004 (0.118) Batch 1.277 (1.536) Remain 17:51:31 loss: 0.4664 Lr: 0.00573
[2023-12-25 05:56:05,759 INFO misc.py line 119 253097] Train: [18/100][478/510] Data 0.005 (0.118) Batch 1.134 (1.535) Remain 17:50:54 loss: 0.4472 Lr: 0.00573
[2023-12-25 05:56:16,440 INFO misc.py line 119 253097] Train: [18/100][479/510] Data 9.374 (0.137) Batch 10.682 (1.554) Remain 18:04:17 loss: 0.0953 Lr: 0.00573
[2023-12-25 05:56:17,587 INFO misc.py line 119 253097] Train: [18/100][480/510] Data 0.004 (0.137) Batch 1.146 (1.554) Remain 18:03:39 loss: 0.2341 Lr: 0.00573
[2023-12-25 05:56:18,734 INFO misc.py line 119 253097] Train: [18/100][481/510] Data 0.005 (0.137) Batch 1.148 (1.553) Remain 18:03:02 loss: 0.5653 Lr: 0.00573
[2023-12-25 05:56:20,007 INFO misc.py line 119 253097] Train: [18/100][482/510] Data 0.004 (0.136) Batch 1.273 (1.552) Remain 18:02:36 loss: 0.2589 Lr: 0.00573
[2023-12-25 05:56:21,266 INFO misc.py line 119 253097] Train: [18/100][483/510] Data 0.004 (0.136) Batch 1.254 (1.552) Remain 18:02:09 loss: 0.5361 Lr: 0.00573
[2023-12-25 05:56:22,422 INFO misc.py line 119 253097] Train: [18/100][484/510] Data 0.009 (0.136) Batch 1.160 (1.551) Remain 18:01:33 loss: 0.3988 Lr: 0.00573
[2023-12-25 05:56:23,579 INFO misc.py line 119 253097] Train: [18/100][485/510] Data 0.004 (0.136) Batch 1.154 (1.550) Remain 18:00:57 loss: 0.2035 Lr: 0.00573
[2023-12-25 05:56:24,676 INFO misc.py line 119 253097] Train: [18/100][486/510] Data 0.007 (0.135) Batch 1.100 (1.549) Remain 18:00:16 loss: 0.5162 Lr: 0.00573
[2023-12-25 05:56:25,613 INFO misc.py line 119 253097] Train: [18/100][487/510] Data 0.003 (0.135) Batch 0.936 (1.548) Remain 17:59:22 loss: 0.2640 Lr: 0.00573
[2023-12-25 05:56:26,774 INFO misc.py line 119 253097] Train: [18/100][488/510] Data 0.004 (0.135) Batch 1.161 (1.547) Remain 17:58:47 loss: 0.6994 Lr: 0.00573
[2023-12-25 05:56:28,077 INFO misc.py line 119 253097] Train: [18/100][489/510] Data 0.005 (0.134) Batch 1.301 (1.546) Remain 17:58:24 loss: 0.3148 Lr: 0.00573
[2023-12-25 05:56:29,200 INFO misc.py line 119 253097] Train: [18/100][490/510] Data 0.006 (0.134) Batch 1.123 (1.546) Remain 17:57:46 loss: 0.3089 Lr: 0.00573
[2023-12-25 05:56:30,505 INFO misc.py line 119 253097] Train: [18/100][491/510] Data 0.006 (0.134) Batch 1.304 (1.545) Remain 17:57:24 loss: 0.7520 Lr: 0.00573
[2023-12-25 05:56:31,407 INFO misc.py line 119 253097] Train: [18/100][492/510] Data 0.008 (0.134) Batch 0.905 (1.544) Remain 17:56:28 loss: 0.2229 Lr: 0.00573
[2023-12-25 05:56:32,590 INFO misc.py line 119 253097] Train: [18/100][493/510] Data 0.004 (0.133) Batch 1.183 (1.543) Remain 17:55:56 loss: 0.4684 Lr: 0.00573
[2023-12-25 05:56:33,803 INFO misc.py line 119 253097] Train: [18/100][494/510] Data 0.003 (0.133) Batch 1.207 (1.542) Remain 17:55:25 loss: 0.5031 Lr: 0.00573
[2023-12-25 05:56:34,965 INFO misc.py line 119 253097] Train: [18/100][495/510] Data 0.009 (0.133) Batch 1.167 (1.542) Remain 17:54:52 loss: 0.3563 Lr: 0.00573
[2023-12-25 05:56:36,033 INFO misc.py line 119 253097] Train: [18/100][496/510] Data 0.005 (0.133) Batch 1.068 (1.541) Remain 17:54:10 loss: 0.4587 Lr: 0.00573
[2023-12-25 05:56:37,774 INFO misc.py line 119 253097] Train: [18/100][497/510] Data 0.446 (0.133) Batch 1.742 (1.541) Remain 17:54:26 loss: 0.3937 Lr: 0.00573
[2023-12-25 05:56:38,767 INFO misc.py line 119 253097] Train: [18/100][498/510] Data 0.003 (0.133) Batch 0.992 (1.540) Remain 17:53:38 loss: 0.4233 Lr: 0.00573
[2023-12-25 05:56:39,844 INFO misc.py line 119 253097] Train: [18/100][499/510] Data 0.003 (0.133) Batch 1.072 (1.539) Remain 17:52:57 loss: 0.4541 Lr: 0.00573
[2023-12-25 05:56:45,462 INFO misc.py line 119 253097] Train: [18/100][500/510] Data 4.413 (0.141) Batch 5.624 (1.547) Remain 17:58:39 loss: 0.2505 Lr: 0.00573
[2023-12-25 05:56:46,779 INFO misc.py line 119 253097] Train: [18/100][501/510] Data 0.003 (0.141) Batch 1.315 (1.547) Remain 17:58:18 loss: 0.5135 Lr: 0.00573
[2023-12-25 05:56:47,994 INFO misc.py line 119 253097] Train: [18/100][502/510] Data 0.004 (0.141) Batch 1.216 (1.546) Remain 17:57:49 loss: 0.2264 Lr: 0.00573
[2023-12-25 05:56:49,290 INFO misc.py line 119 253097] Train: [18/100][503/510] Data 0.003 (0.141) Batch 1.296 (1.546) Remain 17:57:26 loss: 0.3890 Lr: 0.00573
[2023-12-25 05:56:50,279 INFO misc.py line 119 253097] Train: [18/100][504/510] Data 0.003 (0.140) Batch 0.989 (1.544) Remain 17:56:38 loss: 0.2535 Lr: 0.00573
[2023-12-25 05:56:51,327 INFO misc.py line 119 253097] Train: [18/100][505/510] Data 0.003 (0.140) Batch 1.047 (1.543) Remain 17:55:55 loss: 0.2036 Lr: 0.00573
[2023-12-25 05:56:52,318 INFO misc.py line 119 253097] Train: [18/100][506/510] Data 0.004 (0.140) Batch 0.992 (1.542) Remain 17:55:08 loss: 0.2515 Lr: 0.00573
[2023-12-25 05:56:53,465 INFO misc.py line 119 253097] Train: [18/100][507/510] Data 0.003 (0.139) Batch 1.146 (1.542) Remain 17:54:34 loss: 0.3249 Lr: 0.00573
[2023-12-25 05:56:54,433 INFO misc.py line 119 253097] Train: [18/100][508/510] Data 0.004 (0.139) Batch 0.967 (1.540) Remain 17:53:44 loss: 0.1189 Lr: 0.00573
[2023-12-25 05:56:55,709 INFO misc.py line 119 253097] Train: [18/100][509/510] Data 0.004 (0.139) Batch 1.272 (1.540) Remain 17:53:21 loss: 0.3646 Lr: 0.00573
[2023-12-25 05:56:56,821 INFO misc.py line 119 253097] Train: [18/100][510/510] Data 0.009 (0.139) Batch 1.109 (1.539) Remain 17:52:44 loss: 0.1767 Lr: 0.00573
[2023-12-25 05:56:56,822 INFO misc.py line 136 253097] Train result: loss: 0.3353 
[2023-12-25 05:56:56,823 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 05:57:25,720 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6171 
[2023-12-25 05:57:26,076 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.5419 
[2023-12-25 05:57:31,010 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5549 
[2023-12-25 05:57:31,527 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4257 
[2023-12-25 05:57:33,499 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8153 
[2023-12-25 05:57:33,924 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.5260 
[2023-12-25 05:57:34,803 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.4367 
[2023-12-25 05:57:35,359 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4628 
[2023-12-25 05:57:37,170 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.2986 
[2023-12-25 05:57:39,294 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4097 
[2023-12-25 05:57:40,150 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.4489 
[2023-12-25 05:57:40,574 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7853 
[2023-12-25 05:57:41,476 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5082 
[2023-12-25 05:57:44,423 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7347 
[2023-12-25 05:57:44,892 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3875 
[2023-12-25 05:57:45,503 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5267 
[2023-12-25 05:57:46,203 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5247 
[2023-12-25 05:57:48,194 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6213/0.6904/0.8799.
[2023-12-25 05:57:48,194 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9193/0.9497
[2023-12-25 05:57:48,194 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9783/0.9914
[2023-12-25 05:57:48,194 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.7962/0.9802
[2023-12-25 05:57:48,194 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 05:57:48,194 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2367/0.2620
[2023-12-25 05:57:48,194 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.4384/0.4463
[2023-12-25 05:57:48,194 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5631/0.6153
[2023-12-25 05:57:48,194 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8226/0.8733
[2023-12-25 05:57:48,195 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8933/0.9624
[2023-12-25 05:57:48,195 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4454/0.6374
[2023-12-25 05:57:48,195 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7285/0.8064
[2023-12-25 05:57:48,195 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7058/0.7820
[2023-12-25 05:57:48,195 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5487/0.6693
[2023-12-25 05:57:48,195 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 05:57:48,197 INFO misc.py line 165 253097] Currently Best mIoU: 0.6476
[2023-12-25 05:57:48,197 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 05:57:57,665 INFO misc.py line 119 253097] Train: [19/100][1/510] Data 6.393 (6.393) Batch 7.495 (7.495) Remain 87:04:08 loss: 0.2952 Lr: 0.00573
[2023-12-25 05:57:58,864 INFO misc.py line 119 253097] Train: [19/100][2/510] Data 0.005 (0.005) Batch 1.200 (1.200) Remain 13:56:36 loss: 0.3489 Lr: 0.00573
[2023-12-25 05:57:59,926 INFO misc.py line 119 253097] Train: [19/100][3/510] Data 0.003 (0.003) Batch 1.060 (1.060) Remain 12:19:04 loss: 0.3914 Lr: 0.00573
[2023-12-25 05:58:01,064 INFO misc.py line 119 253097] Train: [19/100][4/510] Data 0.004 (0.004) Batch 1.138 (1.138) Remain 13:13:13 loss: 0.3086 Lr: 0.00573
[2023-12-25 05:58:02,166 INFO misc.py line 119 253097] Train: [19/100][5/510] Data 0.004 (0.004) Batch 1.102 (1.120) Remain 13:00:31 loss: 0.2186 Lr: 0.00573
[2023-12-25 05:58:03,239 INFO misc.py line 119 253097] Train: [19/100][6/510] Data 0.004 (0.004) Batch 1.073 (1.104) Remain 12:49:42 loss: 0.2716 Lr: 0.00573
[2023-12-25 05:58:05,233 INFO misc.py line 119 253097] Train: [19/100][7/510] Data 0.003 (0.004) Batch 1.990 (1.326) Remain 15:23:54 loss: 0.6712 Lr: 0.00573
[2023-12-25 05:58:06,544 INFO misc.py line 119 253097] Train: [19/100][8/510] Data 0.008 (0.005) Batch 1.313 (1.323) Remain 15:22:06 loss: 0.4513 Lr: 0.00573
[2023-12-25 05:58:07,781 INFO misc.py line 119 253097] Train: [19/100][9/510] Data 0.006 (0.005) Batch 1.235 (1.309) Remain 15:11:52 loss: 0.3653 Lr: 0.00573
[2023-12-25 05:58:18,463 INFO misc.py line 119 253097] Train: [19/100][10/510] Data 9.517 (1.364) Batch 10.687 (2.648) Remain 30:45:25 loss: 0.2554 Lr: 0.00573
[2023-12-25 05:58:19,675 INFO misc.py line 119 253097] Train: [19/100][11/510] Data 0.004 (1.194) Batch 1.212 (2.469) Remain 28:40:15 loss: 0.2518 Lr: 0.00573
[2023-12-25 05:58:20,836 INFO misc.py line 119 253097] Train: [19/100][12/510] Data 0.007 (1.062) Batch 1.161 (2.323) Remain 26:58:55 loss: 0.4274 Lr: 0.00573
[2023-12-25 05:58:22,340 INFO misc.py line 119 253097] Train: [19/100][13/510] Data 0.295 (0.985) Batch 1.505 (2.242) Remain 26:01:50 loss: 0.2501 Lr: 0.00573
[2023-12-25 05:58:23,439 INFO misc.py line 119 253097] Train: [19/100][14/510] Data 0.005 (0.896) Batch 1.094 (2.137) Remain 24:49:07 loss: 0.3646 Lr: 0.00573
[2023-12-25 05:58:24,543 INFO misc.py line 119 253097] Train: [19/100][15/510] Data 0.008 (0.822) Batch 1.104 (2.051) Remain 23:49:05 loss: 0.2096 Lr: 0.00573
[2023-12-25 05:58:25,724 INFO misc.py line 119 253097] Train: [19/100][16/510] Data 0.009 (0.760) Batch 1.186 (1.985) Remain 23:02:40 loss: 0.2432 Lr: 0.00573
[2023-12-25 05:58:26,850 INFO misc.py line 119 253097] Train: [19/100][17/510] Data 0.005 (0.706) Batch 1.126 (1.923) Remain 22:19:55 loss: 0.2933 Lr: 0.00573
[2023-12-25 05:58:28,100 INFO misc.py line 119 253097] Train: [19/100][18/510] Data 0.004 (0.659) Batch 1.246 (1.878) Remain 21:48:25 loss: 0.2634 Lr: 0.00573
[2023-12-25 05:58:29,211 INFO misc.py line 119 253097] Train: [19/100][19/510] Data 0.008 (0.618) Batch 1.114 (1.830) Remain 21:15:07 loss: 0.1639 Lr: 0.00573
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[2023-12-25 05:58:31,514 INFO misc.py line 119 253097] Train: [19/100][21/510] Data 0.003 (0.550) Batch 1.106 (1.755) Remain 20:22:31 loss: 0.2215 Lr: 0.00573
[2023-12-25 05:58:32,449 INFO misc.py line 119 253097] Train: [19/100][22/510] Data 0.005 (0.521) Batch 0.936 (1.712) Remain 19:52:28 loss: 0.2583 Lr: 0.00573
[2023-12-25 05:58:33,652 INFO misc.py line 119 253097] Train: [19/100][23/510] Data 0.004 (0.495) Batch 1.203 (1.686) Remain 19:34:42 loss: 0.5806 Lr: 0.00573
[2023-12-25 05:58:34,650 INFO misc.py line 119 253097] Train: [19/100][24/510] Data 0.005 (0.472) Batch 0.998 (1.654) Remain 19:11:51 loss: 0.3274 Lr: 0.00573
[2023-12-25 05:58:35,671 INFO misc.py line 119 253097] Train: [19/100][25/510] Data 0.005 (0.451) Batch 1.020 (1.625) Remain 18:51:47 loss: 0.2438 Lr: 0.00573
[2023-12-25 05:58:36,792 INFO misc.py line 119 253097] Train: [19/100][26/510] Data 0.005 (0.431) Batch 1.121 (1.603) Remain 18:36:30 loss: 0.3072 Lr: 0.00573
[2023-12-25 05:58:37,929 INFO misc.py line 119 253097] Train: [19/100][27/510] Data 0.005 (0.414) Batch 1.138 (1.583) Remain 18:22:58 loss: 0.1427 Lr: 0.00573
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[2023-12-25 05:58:46,834 INFO misc.py line 119 253097] Train: [19/100][29/510] Data 0.004 (0.382) Batch 7.621 (1.804) Remain 20:56:38 loss: 0.4342 Lr: 0.00572
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[2023-12-25 05:58:48,641 INFO misc.py line 119 253097] Train: [19/100][31/510] Data 0.008 (0.355) Batch 1.053 (1.740) Remain 20:11:43 loss: 0.3163 Lr: 0.00572
[2023-12-25 05:58:49,906 INFO misc.py line 119 253097] Train: [19/100][32/510] Data 0.005 (0.343) Batch 1.267 (1.723) Remain 20:00:20 loss: 0.4292 Lr: 0.00572
[2023-12-25 05:58:51,051 INFO misc.py line 119 253097] Train: [19/100][33/510] Data 0.004 (0.332) Batch 1.136 (1.704) Remain 19:46:40 loss: 0.3185 Lr: 0.00572
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[2023-12-25 05:58:53,364 INFO misc.py line 119 253097] Train: [19/100][35/510] Data 0.009 (0.312) Batch 1.072 (1.670) Remain 19:22:55 loss: 0.1638 Lr: 0.00572
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[2023-12-25 05:58:55,833 INFO misc.py line 119 253097] Train: [19/100][37/510] Data 0.006 (0.294) Batch 1.327 (1.644) Remain 19:04:59 loss: 0.4518 Lr: 0.00572
[2023-12-25 05:58:56,848 INFO misc.py line 119 253097] Train: [19/100][38/510] Data 0.009 (0.286) Batch 1.018 (1.626) Remain 18:52:30 loss: 0.4292 Lr: 0.00572
[2023-12-25 05:58:57,925 INFO misc.py line 119 253097] Train: [19/100][39/510] Data 0.006 (0.278) Batch 1.080 (1.611) Remain 18:41:54 loss: 0.2237 Lr: 0.00572
[2023-12-25 05:58:59,082 INFO misc.py line 119 253097] Train: [19/100][40/510] Data 0.003 (0.271) Batch 1.152 (1.599) Remain 18:33:14 loss: 0.2268 Lr: 0.00572
[2023-12-25 05:59:00,262 INFO misc.py line 119 253097] Train: [19/100][41/510] Data 0.007 (0.264) Batch 1.182 (1.588) Remain 18:25:35 loss: 0.8306 Lr: 0.00572
[2023-12-25 05:59:01,543 INFO misc.py line 119 253097] Train: [19/100][42/510] Data 0.006 (0.257) Batch 1.282 (1.580) Remain 18:20:05 loss: 0.1809 Lr: 0.00572
[2023-12-25 05:59:02,763 INFO misc.py line 119 253097] Train: [19/100][43/510] Data 0.005 (0.251) Batch 1.216 (1.571) Remain 18:13:44 loss: 0.4208 Lr: 0.00572
[2023-12-25 05:59:03,892 INFO misc.py line 119 253097] Train: [19/100][44/510] Data 0.009 (0.245) Batch 1.129 (1.560) Remain 18:06:12 loss: 0.2819 Lr: 0.00572
[2023-12-25 05:59:04,915 INFO misc.py line 119 253097] Train: [19/100][45/510] Data 0.009 (0.239) Batch 1.026 (1.547) Remain 17:57:20 loss: 0.3479 Lr: 0.00572
[2023-12-25 05:59:06,013 INFO misc.py line 119 253097] Train: [19/100][46/510] Data 0.006 (0.234) Batch 1.092 (1.537) Remain 17:49:56 loss: 0.2245 Lr: 0.00572
[2023-12-25 05:59:07,022 INFO misc.py line 119 253097] Train: [19/100][47/510] Data 0.011 (0.229) Batch 1.010 (1.525) Remain 17:41:35 loss: 0.2082 Lr: 0.00572
[2023-12-25 05:59:08,280 INFO misc.py line 119 253097] Train: [19/100][48/510] Data 0.009 (0.224) Batch 1.261 (1.519) Remain 17:37:28 loss: 0.4061 Lr: 0.00572
[2023-12-25 05:59:09,446 INFO misc.py line 119 253097] Train: [19/100][49/510] Data 0.007 (0.219) Batch 1.166 (1.511) Remain 17:32:06 loss: 0.2920 Lr: 0.00572
[2023-12-25 05:59:10,564 INFO misc.py line 119 253097] Train: [19/100][50/510] Data 0.007 (0.215) Batch 1.121 (1.503) Remain 17:26:18 loss: 0.2477 Lr: 0.00572
[2023-12-25 05:59:20,148 INFO misc.py line 119 253097] Train: [19/100][51/510] Data 0.004 (0.210) Batch 9.584 (1.671) Remain 19:23:28 loss: 0.1508 Lr: 0.00572
[2023-12-25 05:59:21,139 INFO misc.py line 119 253097] Train: [19/100][52/510] Data 0.004 (0.206) Batch 0.991 (1.657) Remain 19:13:47 loss: 0.2770 Lr: 0.00572
[2023-12-25 05:59:22,445 INFO misc.py line 119 253097] Train: [19/100][53/510] Data 0.003 (0.202) Batch 1.305 (1.650) Remain 19:08:51 loss: 0.2779 Lr: 0.00572
[2023-12-25 05:59:23,505 INFO misc.py line 119 253097] Train: [19/100][54/510] Data 0.006 (0.198) Batch 1.061 (1.639) Remain 19:00:46 loss: 0.2071 Lr: 0.00572
[2023-12-25 05:59:24,812 INFO misc.py line 119 253097] Train: [19/100][55/510] Data 0.005 (0.194) Batch 1.303 (1.632) Remain 18:56:15 loss: 0.2334 Lr: 0.00572
[2023-12-25 05:59:26,139 INFO misc.py line 119 253097] Train: [19/100][56/510] Data 0.008 (0.191) Batch 1.326 (1.627) Remain 18:52:12 loss: 0.3635 Lr: 0.00572
[2023-12-25 05:59:27,223 INFO misc.py line 119 253097] Train: [19/100][57/510] Data 0.008 (0.188) Batch 1.085 (1.617) Remain 18:45:12 loss: 0.4239 Lr: 0.00572
[2023-12-25 05:59:28,144 INFO misc.py line 119 253097] Train: [19/100][58/510] Data 0.007 (0.184) Batch 0.924 (1.604) Remain 18:36:24 loss: 0.3375 Lr: 0.00572
[2023-12-25 05:59:29,299 INFO misc.py line 119 253097] Train: [19/100][59/510] Data 0.005 (0.181) Batch 1.157 (1.596) Remain 18:30:49 loss: 0.2461 Lr: 0.00572
[2023-12-25 05:59:30,579 INFO misc.py line 119 253097] Train: [19/100][60/510] Data 0.003 (0.178) Batch 1.277 (1.590) Remain 18:26:54 loss: 0.5142 Lr: 0.00572
[2023-12-25 05:59:31,681 INFO misc.py line 119 253097] Train: [19/100][61/510] Data 0.007 (0.175) Batch 1.102 (1.582) Remain 18:21:01 loss: 0.4802 Lr: 0.00572
[2023-12-25 05:59:32,707 INFO misc.py line 119 253097] Train: [19/100][62/510] Data 0.006 (0.172) Batch 1.023 (1.573) Remain 18:14:24 loss: 0.2997 Lr: 0.00572
[2023-12-25 05:59:33,864 INFO misc.py line 119 253097] Train: [19/100][63/510] Data 0.008 (0.169) Batch 1.157 (1.566) Remain 18:09:33 loss: 0.3872 Lr: 0.00572
[2023-12-25 05:59:35,094 INFO misc.py line 119 253097] Train: [19/100][64/510] Data 0.008 (0.167) Batch 1.235 (1.560) Remain 18:05:45 loss: 0.4831 Lr: 0.00572
[2023-12-25 05:59:36,171 INFO misc.py line 119 253097] Train: [19/100][65/510] Data 0.003 (0.164) Batch 1.076 (1.552) Remain 18:00:18 loss: 0.5606 Lr: 0.00572
[2023-12-25 05:59:37,417 INFO misc.py line 119 253097] Train: [19/100][66/510] Data 0.004 (0.162) Batch 1.242 (1.547) Remain 17:56:50 loss: 0.2816 Lr: 0.00572
[2023-12-25 05:59:38,736 INFO misc.py line 119 253097] Train: [19/100][67/510] Data 0.009 (0.159) Batch 1.324 (1.544) Remain 17:54:23 loss: 0.1436 Lr: 0.00572
[2023-12-25 05:59:39,912 INFO misc.py line 119 253097] Train: [19/100][68/510] Data 0.004 (0.157) Batch 1.169 (1.538) Remain 17:50:20 loss: 0.3131 Lr: 0.00572
[2023-12-25 05:59:41,019 INFO misc.py line 119 253097] Train: [19/100][69/510] Data 0.010 (0.155) Batch 1.110 (1.532) Remain 17:45:48 loss: 0.5027 Lr: 0.00572
[2023-12-25 05:59:42,141 INFO misc.py line 119 253097] Train: [19/100][70/510] Data 0.009 (0.152) Batch 1.126 (1.526) Remain 17:41:34 loss: 0.2271 Lr: 0.00572
[2023-12-25 05:59:43,272 INFO misc.py line 119 253097] Train: [19/100][71/510] Data 0.004 (0.150) Batch 1.123 (1.520) Remain 17:37:25 loss: 0.2690 Lr: 0.00572
[2023-12-25 05:59:44,427 INFO misc.py line 119 253097] Train: [19/100][72/510] Data 0.011 (0.148) Batch 1.158 (1.514) Remain 17:33:45 loss: 0.2901 Lr: 0.00572
[2023-12-25 05:59:45,403 INFO misc.py line 119 253097] Train: [19/100][73/510] Data 0.009 (0.146) Batch 0.981 (1.507) Remain 17:28:25 loss: 0.6786 Lr: 0.00572
[2023-12-25 05:59:46,754 INFO misc.py line 119 253097] Train: [19/100][74/510] Data 0.003 (0.144) Batch 1.343 (1.505) Remain 17:26:48 loss: 0.2234 Lr: 0.00572
[2023-12-25 05:59:47,867 INFO misc.py line 119 253097] Train: [19/100][75/510] Data 0.011 (0.142) Batch 1.120 (1.499) Remain 17:23:03 loss: 0.5979 Lr: 0.00572
[2023-12-25 05:59:48,917 INFO misc.py line 119 253097] Train: [19/100][76/510] Data 0.003 (0.140) Batch 1.049 (1.493) Remain 17:18:45 loss: 0.2808 Lr: 0.00572
[2023-12-25 06:00:02,537 INFO misc.py line 119 253097] Train: [19/100][77/510] Data 0.004 (0.139) Batch 13.618 (1.657) Remain 19:12:43 loss: 0.4682 Lr: 0.00572
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[2023-12-25 06:00:07,372 INFO misc.py line 119 253097] Train: [19/100][81/510] Data 0.009 (0.132) Batch 1.313 (1.634) Remain 18:56:37 loss: 0.3924 Lr: 0.00572
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[2023-12-25 06:00:09,788 INFO misc.py line 119 253097] Train: [19/100][83/510] Data 0.012 (0.129) Batch 1.031 (1.623) Remain 18:48:47 loss: 0.3455 Lr: 0.00572
[2023-12-25 06:00:10,927 INFO misc.py line 119 253097] Train: [19/100][84/510] Data 0.048 (0.128) Batch 1.183 (1.617) Remain 18:44:59 loss: 0.5337 Lr: 0.00572
[2023-12-25 06:00:12,092 INFO misc.py line 119 253097] Train: [19/100][85/510] Data 0.004 (0.126) Batch 1.159 (1.612) Remain 18:41:04 loss: 0.3109 Lr: 0.00572
[2023-12-25 06:00:13,018 INFO misc.py line 119 253097] Train: [19/100][86/510] Data 0.010 (0.125) Batch 0.932 (1.604) Remain 18:35:21 loss: 0.3926 Lr: 0.00572
[2023-12-25 06:00:14,173 INFO misc.py line 119 253097] Train: [19/100][87/510] Data 0.004 (0.123) Batch 1.155 (1.598) Remain 18:31:36 loss: 0.4007 Lr: 0.00572
[2023-12-25 06:00:15,518 INFO misc.py line 119 253097] Train: [19/100][88/510] Data 0.005 (0.122) Batch 1.343 (1.595) Remain 18:29:29 loss: 0.1857 Lr: 0.00572
[2023-12-25 06:00:16,742 INFO misc.py line 119 253097] Train: [19/100][89/510] Data 0.007 (0.121) Batch 1.224 (1.591) Remain 18:26:28 loss: 0.1407 Lr: 0.00572
[2023-12-25 06:00:19,256 INFO misc.py line 119 253097] Train: [19/100][90/510] Data 0.006 (0.119) Batch 2.516 (1.601) Remain 18:33:50 loss: 0.5117 Lr: 0.00572
[2023-12-25 06:00:20,460 INFO misc.py line 119 253097] Train: [19/100][91/510] Data 0.004 (0.118) Batch 1.201 (1.597) Remain 18:30:38 loss: 0.2482 Lr: 0.00572
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[2023-12-25 06:00:27,625 INFO misc.py line 119 253097] Train: [19/100][93/510] Data 0.004 (0.116) Batch 6.087 (1.641) Remain 19:01:18 loss: 0.1945 Lr: 0.00572
[2023-12-25 06:00:28,894 INFO misc.py line 119 253097] Train: [19/100][94/510] Data 0.004 (0.114) Batch 1.268 (1.637) Remain 18:58:25 loss: 0.1840 Lr: 0.00572
[2023-12-25 06:00:30,038 INFO misc.py line 119 253097] Train: [19/100][95/510] Data 0.004 (0.113) Batch 1.143 (1.632) Remain 18:54:39 loss: 0.2822 Lr: 0.00572
[2023-12-25 06:00:31,241 INFO misc.py line 119 253097] Train: [19/100][96/510] Data 0.006 (0.112) Batch 1.202 (1.627) Remain 18:51:25 loss: 0.2743 Lr: 0.00572
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[2023-12-25 06:00:33,339 INFO misc.py line 119 253097] Train: [19/100][98/510] Data 0.004 (0.110) Batch 1.078 (1.615) Remain 18:42:55 loss: 0.1253 Lr: 0.00572
[2023-12-25 06:00:34,668 INFO misc.py line 119 253097] Train: [19/100][99/510] Data 0.006 (0.109) Batch 1.327 (1.612) Remain 18:40:48 loss: 0.2953 Lr: 0.00572
[2023-12-25 06:00:35,679 INFO misc.py line 119 253097] Train: [19/100][100/510] Data 0.008 (0.108) Batch 1.014 (1.606) Remain 18:36:29 loss: 0.2324 Lr: 0.00572
[2023-12-25 06:00:36,867 INFO misc.py line 119 253097] Train: [19/100][101/510] Data 0.004 (0.107) Batch 1.185 (1.601) Remain 18:33:29 loss: 0.3484 Lr: 0.00572
[2023-12-25 06:00:41,682 INFO misc.py line 119 253097] Train: [19/100][102/510] Data 0.007 (0.106) Batch 4.818 (1.634) Remain 18:56:03 loss: 0.3940 Lr: 0.00572
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[2023-12-25 06:01:03,965 INFO misc.py line 119 253097] Train: [19/100][118/510] Data 0.003 (0.092) Batch 5.297 (1.600) Remain 18:32:17 loss: 0.3235 Lr: 0.00572
[2023-12-25 06:01:05,050 INFO misc.py line 119 253097] Train: [19/100][119/510] Data 0.005 (0.091) Batch 1.086 (1.596) Remain 18:29:10 loss: 0.3339 Lr: 0.00572
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[2023-12-25 06:01:07,318 INFO misc.py line 119 253097] Train: [19/100][121/510] Data 0.003 (0.089) Batch 1.098 (1.588) Remain 18:23:41 loss: 0.3964 Lr: 0.00572
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[2023-12-25 06:01:10,665 INFO misc.py line 119 253097] Train: [19/100][124/510] Data 0.003 (0.090) Batch 1.068 (1.576) Remain 18:15:27 loss: 0.3425 Lr: 0.00572
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[2023-12-25 06:01:40,983 INFO misc.py line 119 253097] Train: [19/100][137/510] Data 0.003 (0.082) Batch 0.992 (1.650) Remain 19:06:03 loss: 0.1679 Lr: 0.00572
[2023-12-25 06:01:42,177 INFO misc.py line 119 253097] Train: [19/100][138/510] Data 0.004 (0.081) Batch 1.193 (1.646) Remain 19:03:41 loss: 0.4132 Lr: 0.00572
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[2023-12-25 06:01:44,511 INFO misc.py line 119 253097] Train: [19/100][140/510] Data 0.003 (0.080) Batch 1.119 (1.639) Remain 18:58:46 loss: 0.2381 Lr: 0.00572
[2023-12-25 06:01:45,509 INFO misc.py line 119 253097] Train: [19/100][141/510] Data 0.005 (0.080) Batch 0.999 (1.635) Remain 18:55:31 loss: 0.2527 Lr: 0.00572
[2023-12-25 06:01:46,724 INFO misc.py line 119 253097] Train: [19/100][142/510] Data 0.003 (0.079) Batch 1.212 (1.632) Remain 18:53:23 loss: 0.4291 Lr: 0.00572
[2023-12-25 06:01:47,950 INFO misc.py line 119 253097] Train: [19/100][143/510] Data 0.007 (0.079) Batch 1.226 (1.629) Remain 18:51:20 loss: 0.4602 Lr: 0.00572
[2023-12-25 06:01:49,057 INFO misc.py line 119 253097] Train: [19/100][144/510] Data 0.006 (0.078) Batch 1.109 (1.625) Remain 18:48:45 loss: 0.3914 Lr: 0.00572
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[2023-12-25 06:01:51,644 INFO misc.py line 119 253097] Train: [19/100][146/510] Data 0.006 (0.077) Batch 1.496 (1.620) Remain 18:45:26 loss: 0.2323 Lr: 0.00572
[2023-12-25 06:01:52,649 INFO misc.py line 119 253097] Train: [19/100][147/510] Data 0.011 (0.077) Batch 1.008 (1.616) Remain 18:42:28 loss: 0.2134 Lr: 0.00572
[2023-12-25 06:01:56,260 INFO misc.py line 119 253097] Train: [19/100][148/510] Data 2.319 (0.092) Batch 3.615 (1.630) Remain 18:52:00 loss: 0.1617 Lr: 0.00572
[2023-12-25 06:01:57,291 INFO misc.py line 119 253097] Train: [19/100][149/510] Data 0.004 (0.091) Batch 1.030 (1.626) Remain 18:49:08 loss: 0.2623 Lr: 0.00572
[2023-12-25 06:01:58,530 INFO misc.py line 119 253097] Train: [19/100][150/510] Data 0.005 (0.091) Batch 1.239 (1.623) Remain 18:47:16 loss: 0.5807 Lr: 0.00571
[2023-12-25 06:01:59,779 INFO misc.py line 119 253097] Train: [19/100][151/510] Data 0.005 (0.090) Batch 1.244 (1.621) Remain 18:45:28 loss: 0.3916 Lr: 0.00571
[2023-12-25 06:02:00,879 INFO misc.py line 119 253097] Train: [19/100][152/510] Data 0.009 (0.090) Batch 1.102 (1.617) Remain 18:43:01 loss: 0.2697 Lr: 0.00571
[2023-12-25 06:02:02,138 INFO misc.py line 119 253097] Train: [19/100][153/510] Data 0.008 (0.089) Batch 1.258 (1.615) Remain 18:41:20 loss: 0.2562 Lr: 0.00571
[2023-12-25 06:02:03,314 INFO misc.py line 119 253097] Train: [19/100][154/510] Data 0.008 (0.089) Batch 1.173 (1.612) Remain 18:39:17 loss: 0.2916 Lr: 0.00571
[2023-12-25 06:02:04,368 INFO misc.py line 119 253097] Train: [19/100][155/510] Data 0.012 (0.088) Batch 1.053 (1.608) Remain 18:36:42 loss: 0.2455 Lr: 0.00571
[2023-12-25 06:02:05,415 INFO misc.py line 119 253097] Train: [19/100][156/510] Data 0.013 (0.088) Batch 1.055 (1.605) Remain 18:34:10 loss: 0.3616 Lr: 0.00571
[2023-12-25 06:02:06,589 INFO misc.py line 119 253097] Train: [19/100][157/510] Data 0.004 (0.087) Batch 1.170 (1.602) Remain 18:32:10 loss: 0.2683 Lr: 0.00571
[2023-12-25 06:02:07,866 INFO misc.py line 119 253097] Train: [19/100][158/510] Data 0.009 (0.087) Batch 1.278 (1.600) Remain 18:30:42 loss: 0.3356 Lr: 0.00571
[2023-12-25 06:02:09,125 INFO misc.py line 119 253097] Train: [19/100][159/510] Data 0.008 (0.086) Batch 1.244 (1.597) Remain 18:29:05 loss: 0.4525 Lr: 0.00571
[2023-12-25 06:02:10,443 INFO misc.py line 119 253097] Train: [19/100][160/510] Data 0.023 (0.086) Batch 1.329 (1.596) Remain 18:27:52 loss: 0.3908 Lr: 0.00571
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[2023-12-25 06:02:12,745 INFO misc.py line 119 253097] Train: [19/100][162/510] Data 0.005 (0.085) Batch 1.316 (1.590) Remain 18:23:58 loss: 0.4286 Lr: 0.00571
[2023-12-25 06:02:14,052 INFO misc.py line 119 253097] Train: [19/100][163/510] Data 0.005 (0.084) Batch 1.303 (1.588) Remain 18:22:42 loss: 0.5455 Lr: 0.00571
[2023-12-25 06:02:15,192 INFO misc.py line 119 253097] Train: [19/100][164/510] Data 0.008 (0.084) Batch 1.144 (1.586) Remain 18:20:45 loss: 0.3105 Lr: 0.00571
[2023-12-25 06:02:16,462 INFO misc.py line 119 253097] Train: [19/100][165/510] Data 0.005 (0.083) Batch 1.268 (1.584) Remain 18:19:22 loss: 0.1564 Lr: 0.00571
[2023-12-25 06:02:17,515 INFO misc.py line 119 253097] Train: [19/100][166/510] Data 0.006 (0.083) Batch 1.055 (1.580) Remain 18:17:05 loss: 0.3727 Lr: 0.00571
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[2023-12-25 06:02:19,834 INFO misc.py line 119 253097] Train: [19/100][168/510] Data 0.008 (0.082) Batch 1.222 (1.575) Remain 18:13:30 loss: 0.4046 Lr: 0.00571
[2023-12-25 06:02:20,983 INFO misc.py line 119 253097] Train: [19/100][169/510] Data 0.004 (0.081) Batch 1.147 (1.573) Remain 18:11:41 loss: 0.5283 Lr: 0.00571
[2023-12-25 06:02:26,315 INFO misc.py line 119 253097] Train: [19/100][170/510] Data 0.007 (0.081) Batch 5.335 (1.595) Remain 18:27:17 loss: 0.4002 Lr: 0.00571
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[2023-12-25 06:02:28,824 INFO misc.py line 119 253097] Train: [19/100][172/510] Data 0.006 (0.080) Batch 1.288 (1.591) Remain 18:24:26 loss: 0.2252 Lr: 0.00571
[2023-12-25 06:02:29,924 INFO misc.py line 119 253097] Train: [19/100][173/510] Data 0.004 (0.080) Batch 1.099 (1.588) Remain 18:22:24 loss: 0.6408 Lr: 0.00571
[2023-12-25 06:02:31,110 INFO misc.py line 119 253097] Train: [19/100][174/510] Data 0.005 (0.079) Batch 1.182 (1.586) Remain 18:20:44 loss: 0.1874 Lr: 0.00571
[2023-12-25 06:02:32,310 INFO misc.py line 119 253097] Train: [19/100][175/510] Data 0.012 (0.079) Batch 1.198 (1.584) Remain 18:19:08 loss: 0.4392 Lr: 0.00571
[2023-12-25 06:02:34,094 INFO misc.py line 119 253097] Train: [19/100][176/510] Data 0.010 (0.078) Batch 1.783 (1.585) Remain 18:19:55 loss: 0.7326 Lr: 0.00571
[2023-12-25 06:02:35,184 INFO misc.py line 119 253097] Train: [19/100][177/510] Data 0.010 (0.078) Batch 1.095 (1.582) Remain 18:17:56 loss: 0.3337 Lr: 0.00571
[2023-12-25 06:02:36,417 INFO misc.py line 119 253097] Train: [19/100][178/510] Data 0.006 (0.078) Batch 1.233 (1.580) Remain 18:16:31 loss: 0.3226 Lr: 0.00571
[2023-12-25 06:02:37,418 INFO misc.py line 119 253097] Train: [19/100][179/510] Data 0.006 (0.077) Batch 1.001 (1.577) Remain 18:14:13 loss: 0.1890 Lr: 0.00571
[2023-12-25 06:02:38,752 INFO misc.py line 119 253097] Train: [19/100][180/510] Data 0.006 (0.077) Batch 1.335 (1.575) Remain 18:13:15 loss: 0.5219 Lr: 0.00571
[2023-12-25 06:02:40,141 INFO misc.py line 119 253097] Train: [19/100][181/510] Data 0.294 (0.078) Batch 1.389 (1.574) Remain 18:12:29 loss: 0.0911 Lr: 0.00571
[2023-12-25 06:02:41,336 INFO misc.py line 119 253097] Train: [19/100][182/510] Data 0.005 (0.078) Batch 1.195 (1.572) Remain 18:11:00 loss: 0.2983 Lr: 0.00571
[2023-12-25 06:02:42,473 INFO misc.py line 119 253097] Train: [19/100][183/510] Data 0.004 (0.077) Batch 1.117 (1.570) Remain 18:09:13 loss: 0.3010 Lr: 0.00571
[2023-12-25 06:02:43,561 INFO misc.py line 119 253097] Train: [19/100][184/510] Data 0.027 (0.077) Batch 1.102 (1.567) Remain 18:07:24 loss: 0.1748 Lr: 0.00571
[2023-12-25 06:02:48,430 INFO misc.py line 119 253097] Train: [19/100][185/510] Data 3.845 (0.098) Batch 4.874 (1.585) Remain 18:19:59 loss: 0.2161 Lr: 0.00571
[2023-12-25 06:02:49,486 INFO misc.py line 119 253097] Train: [19/100][186/510] Data 0.005 (0.097) Batch 1.057 (1.582) Remain 18:17:57 loss: 0.2760 Lr: 0.00571
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[2023-12-25 06:02:51,724 INFO misc.py line 119 253097] Train: [19/100][188/510] Data 0.003 (0.096) Batch 1.232 (1.577) Remain 18:14:25 loss: 0.3124 Lr: 0.00571
[2023-12-25 06:02:56,742 INFO misc.py line 119 253097] Train: [19/100][189/510] Data 3.984 (0.117) Batch 5.019 (1.596) Remain 18:27:14 loss: 0.1686 Lr: 0.00571
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[2023-12-25 06:03:09,776 INFO misc.py line 119 253097] Train: [19/100][200/510] Data 0.009 (0.111) Batch 1.251 (1.573) Remain 18:11:01 loss: 0.4623 Lr: 0.00571
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[2023-12-25 06:04:00,761 INFO misc.py line 119 253097] Train: [19/100][226/510] Data 0.004 (0.098) Batch 1.170 (1.618) Remain 18:41:42 loss: 0.5227 Lr: 0.00571
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[2023-12-25 06:04:23,023 INFO misc.py line 119 253097] Train: [19/100][239/510] Data 3.397 (0.127) Batch 4.564 (1.623) Remain 18:44:58 loss: 0.2495 Lr: 0.00571
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[2023-12-25 06:04:28,904 INFO misc.py line 119 253097] Train: [19/100][244/510] Data 0.008 (0.125) Batch 1.131 (1.614) Remain 18:38:24 loss: 0.4246 Lr: 0.00571
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[2023-12-25 06:04:45,321 INFO misc.py line 119 253097] Train: [19/100][256/510] Data 0.006 (0.119) Batch 1.057 (1.602) Remain 18:29:59 loss: 0.3008 Lr: 0.00571
[2023-12-25 06:04:46,485 INFO misc.py line 119 253097] Train: [19/100][257/510] Data 0.008 (0.118) Batch 1.162 (1.601) Remain 18:28:45 loss: 0.2676 Lr: 0.00571
[2023-12-25 06:04:47,723 INFO misc.py line 119 253097] Train: [19/100][258/510] Data 0.010 (0.118) Batch 1.243 (1.599) Remain 18:27:46 loss: 0.1831 Lr: 0.00571
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[2023-12-25 06:04:51,398 INFO misc.py line 119 253097] Train: [19/100][261/510] Data 0.008 (0.117) Batch 1.277 (1.595) Remain 18:24:40 loss: 0.4466 Lr: 0.00571
[2023-12-25 06:04:52,436 INFO misc.py line 119 253097] Train: [19/100][262/510] Data 0.005 (0.116) Batch 1.034 (1.593) Remain 18:23:08 loss: 0.2930 Lr: 0.00571
[2023-12-25 06:04:53,501 INFO misc.py line 119 253097] Train: [19/100][263/510] Data 0.009 (0.116) Batch 1.066 (1.591) Remain 18:21:43 loss: 0.2777 Lr: 0.00571
[2023-12-25 06:04:54,755 INFO misc.py line 119 253097] Train: [19/100][264/510] Data 0.007 (0.115) Batch 1.258 (1.589) Remain 18:20:48 loss: 0.1243 Lr: 0.00571
[2023-12-25 06:04:55,760 INFO misc.py line 119 253097] Train: [19/100][265/510] Data 0.004 (0.115) Batch 1.004 (1.587) Remain 18:19:14 loss: 0.2129 Lr: 0.00571
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[2023-12-25 06:05:03,040 INFO misc.py line 119 253097] Train: [19/100][268/510] Data 0.008 (0.114) Batch 1.045 (1.597) Remain 18:25:43 loss: 0.2132 Lr: 0.00571
[2023-12-25 06:05:04,253 INFO misc.py line 119 253097] Train: [19/100][269/510] Data 0.011 (0.113) Batch 1.218 (1.595) Remain 18:24:42 loss: 0.4856 Lr: 0.00570
[2023-12-25 06:05:05,386 INFO misc.py line 119 253097] Train: [19/100][270/510] Data 0.006 (0.113) Batch 1.134 (1.593) Remain 18:23:29 loss: 0.3474 Lr: 0.00570
[2023-12-25 06:05:06,438 INFO misc.py line 119 253097] Train: [19/100][271/510] Data 0.006 (0.113) Batch 1.054 (1.591) Remain 18:22:03 loss: 0.3091 Lr: 0.00570
[2023-12-25 06:05:09,251 INFO misc.py line 119 253097] Train: [19/100][272/510] Data 0.004 (0.112) Batch 2.813 (1.596) Remain 18:25:10 loss: 0.1421 Lr: 0.00570
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[2023-12-25 06:05:11,741 INFO misc.py line 119 253097] Train: [19/100][274/510] Data 0.004 (0.111) Batch 1.331 (1.593) Remain 18:23:19 loss: 0.3016 Lr: 0.00570
[2023-12-25 06:05:12,875 INFO misc.py line 119 253097] Train: [19/100][275/510] Data 0.008 (0.111) Batch 1.137 (1.592) Remain 18:22:08 loss: 0.3564 Lr: 0.00570
[2023-12-25 06:05:14,065 INFO misc.py line 119 253097] Train: [19/100][276/510] Data 0.005 (0.111) Batch 1.191 (1.590) Remain 18:21:05 loss: 0.2328 Lr: 0.00570
[2023-12-25 06:05:15,241 INFO misc.py line 119 253097] Train: [19/100][277/510] Data 0.005 (0.110) Batch 1.172 (1.589) Remain 18:20:00 loss: 0.2167 Lr: 0.00570
[2023-12-25 06:05:16,507 INFO misc.py line 119 253097] Train: [19/100][278/510] Data 0.008 (0.110) Batch 1.267 (1.588) Remain 18:19:10 loss: 0.2987 Lr: 0.00570
[2023-12-25 06:05:17,740 INFO misc.py line 119 253097] Train: [19/100][279/510] Data 0.006 (0.110) Batch 1.235 (1.586) Remain 18:18:15 loss: 0.6871 Lr: 0.00570
[2023-12-25 06:05:18,858 INFO misc.py line 119 253097] Train: [19/100][280/510] Data 0.005 (0.109) Batch 1.115 (1.585) Remain 18:17:03 loss: 0.2327 Lr: 0.00570
[2023-12-25 06:05:20,171 INFO misc.py line 119 253097] Train: [19/100][281/510] Data 0.008 (0.109) Batch 1.312 (1.584) Remain 18:16:21 loss: 0.3097 Lr: 0.00570
[2023-12-25 06:05:21,383 INFO misc.py line 119 253097] Train: [19/100][282/510] Data 0.010 (0.108) Batch 1.216 (1.582) Remain 18:15:24 loss: 0.1751 Lr: 0.00570
[2023-12-25 06:05:22,733 INFO misc.py line 119 253097] Train: [19/100][283/510] Data 0.007 (0.108) Batch 1.350 (1.581) Remain 18:14:48 loss: 0.3673 Lr: 0.00570
[2023-12-25 06:05:23,663 INFO misc.py line 119 253097] Train: [19/100][284/510] Data 0.005 (0.108) Batch 0.929 (1.579) Remain 18:13:10 loss: 0.3235 Lr: 0.00570
[2023-12-25 06:05:24,958 INFO misc.py line 119 253097] Train: [19/100][285/510] Data 0.007 (0.107) Batch 1.297 (1.578) Remain 18:12:27 loss: 0.4654 Lr: 0.00570
[2023-12-25 06:05:26,030 INFO misc.py line 119 253097] Train: [19/100][286/510] Data 0.004 (0.107) Batch 1.073 (1.576) Remain 18:11:11 loss: 0.3056 Lr: 0.00570
[2023-12-25 06:05:27,243 INFO misc.py line 119 253097] Train: [19/100][287/510] Data 0.004 (0.107) Batch 1.209 (1.575) Remain 18:10:16 loss: 0.2596 Lr: 0.00570
[2023-12-25 06:05:28,394 INFO misc.py line 119 253097] Train: [19/100][288/510] Data 0.008 (0.106) Batch 1.153 (1.574) Remain 18:09:13 loss: 0.2767 Lr: 0.00570
[2023-12-25 06:05:29,569 INFO misc.py line 119 253097] Train: [19/100][289/510] Data 0.005 (0.106) Batch 1.177 (1.572) Remain 18:08:14 loss: 0.3127 Lr: 0.00570
[2023-12-25 06:05:30,563 INFO misc.py line 119 253097] Train: [19/100][290/510] Data 0.004 (0.106) Batch 0.994 (1.570) Remain 18:06:48 loss: 0.4882 Lr: 0.00570
[2023-12-25 06:05:31,682 INFO misc.py line 119 253097] Train: [19/100][291/510] Data 0.004 (0.105) Batch 1.116 (1.569) Remain 18:05:41 loss: 0.3594 Lr: 0.00570
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[2023-12-25 06:05:34,010 INFO misc.py line 119 253097] Train: [19/100][293/510] Data 0.004 (0.105) Batch 1.189 (1.566) Remain 18:03:43 loss: 0.1495 Lr: 0.00570
[2023-12-25 06:05:39,366 INFO misc.py line 119 253097] Train: [19/100][294/510] Data 0.004 (0.104) Batch 5.355 (1.579) Remain 18:12:42 loss: 0.4274 Lr: 0.00570
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[2023-12-25 06:05:41,571 INFO misc.py line 119 253097] Train: [19/100][296/510] Data 0.005 (0.103) Batch 1.114 (1.576) Remain 18:10:24 loss: 0.1785 Lr: 0.00570
[2023-12-25 06:05:42,734 INFO misc.py line 119 253097] Train: [19/100][297/510] Data 0.006 (0.103) Batch 1.165 (1.574) Remain 18:09:24 loss: 0.2966 Lr: 0.00570
[2023-12-25 06:05:44,082 INFO misc.py line 119 253097] Train: [19/100][298/510] Data 0.004 (0.103) Batch 1.346 (1.573) Remain 18:08:50 loss: 0.2701 Lr: 0.00570
[2023-12-25 06:05:45,341 INFO misc.py line 119 253097] Train: [19/100][299/510] Data 0.006 (0.102) Batch 1.255 (1.572) Remain 18:08:04 loss: 0.3221 Lr: 0.00570
[2023-12-25 06:05:46,441 INFO misc.py line 119 253097] Train: [19/100][300/510] Data 0.010 (0.102) Batch 1.105 (1.571) Remain 18:06:57 loss: 0.2737 Lr: 0.00570
[2023-12-25 06:05:47,393 INFO misc.py line 119 253097] Train: [19/100][301/510] Data 0.005 (0.102) Batch 0.953 (1.569) Remain 18:05:30 loss: 0.3658 Lr: 0.00570
[2023-12-25 06:05:51,043 INFO misc.py line 119 253097] Train: [19/100][302/510] Data 0.004 (0.102) Batch 3.650 (1.576) Remain 18:10:17 loss: 0.2571 Lr: 0.00570
[2023-12-25 06:05:52,285 INFO misc.py line 119 253097] Train: [19/100][303/510] Data 0.004 (0.101) Batch 1.237 (1.575) Remain 18:09:29 loss: 0.1779 Lr: 0.00570
[2023-12-25 06:05:53,452 INFO misc.py line 119 253097] Train: [19/100][304/510] Data 0.011 (0.101) Batch 1.172 (1.573) Remain 18:08:32 loss: 0.2914 Lr: 0.00570
[2023-12-25 06:05:54,533 INFO misc.py line 119 253097] Train: [19/100][305/510] Data 0.004 (0.101) Batch 1.080 (1.572) Remain 18:07:22 loss: 0.2059 Lr: 0.00570
[2023-12-25 06:05:55,650 INFO misc.py line 119 253097] Train: [19/100][306/510] Data 0.004 (0.100) Batch 1.117 (1.570) Remain 18:06:18 loss: 0.1734 Lr: 0.00570
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[2023-12-25 06:06:30,857 INFO misc.py line 119 253097] Train: [19/100][331/510] Data 0.007 (0.103) Batch 1.118 (1.558) Remain 17:57:06 loss: 0.1577 Lr: 0.00570
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[2023-12-25 06:07:05,558 INFO misc.py line 119 253097] Train: [19/100][351/510] Data 0.004 (0.121) Batch 0.898 (1.568) Remain 18:03:39 loss: 0.1671 Lr: 0.00570
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[2023-12-25 06:07:23,003 INFO misc.py line 119 253097] Train: [19/100][363/510] Data 0.011 (0.117) Batch 3.910 (1.564) Remain 18:00:42 loss: 0.2265 Lr: 0.00570
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[2023-12-25 06:07:38,057 INFO misc.py line 119 253097] Train: [19/100][368/510] Data 0.005 (0.116) Batch 5.184 (1.584) Remain 18:14:16 loss: 0.2548 Lr: 0.00570
[2023-12-25 06:07:39,191 INFO misc.py line 119 253097] Train: [19/100][369/510] Data 0.005 (0.115) Batch 1.135 (1.583) Remain 18:13:24 loss: 0.3595 Lr: 0.00570
[2023-12-25 06:07:40,293 INFO misc.py line 119 253097] Train: [19/100][370/510] Data 0.004 (0.115) Batch 1.103 (1.581) Remain 18:12:28 loss: 0.1725 Lr: 0.00570
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[2023-12-25 06:07:42,538 INFO misc.py line 119 253097] Train: [19/100][372/510] Data 0.008 (0.115) Batch 1.082 (1.579) Remain 18:10:41 loss: 0.2245 Lr: 0.00570
[2023-12-25 06:07:43,731 INFO misc.py line 119 253097] Train: [19/100][373/510] Data 0.004 (0.114) Batch 1.194 (1.578) Remain 18:09:57 loss: 0.3023 Lr: 0.00570
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[2023-12-25 06:07:45,737 INFO misc.py line 119 253097] Train: [19/100][375/510] Data 0.005 (0.114) Batch 0.984 (1.575) Remain 18:07:45 loss: 0.1682 Lr: 0.00570
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[2023-12-25 06:07:51,173 INFO misc.py line 119 253097] Train: [19/100][380/510] Data 0.003 (0.112) Batch 0.936 (1.568) Remain 18:03:10 loss: 0.2829 Lr: 0.00570
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[2023-12-25 06:07:53,183 INFO misc.py line 119 253097] Train: [19/100][382/510] Data 0.011 (0.112) Batch 0.995 (1.565) Remain 18:01:03 loss: 0.4654 Lr: 0.00570
[2023-12-25 06:07:54,413 INFO misc.py line 119 253097] Train: [19/100][383/510] Data 0.004 (0.111) Batch 1.225 (1.564) Remain 18:00:25 loss: 0.2588 Lr: 0.00570
[2023-12-25 06:08:07,121 INFO misc.py line 119 253097] Train: [19/100][384/510] Data 0.009 (0.111) Batch 12.712 (1.594) Remain 18:20:35 loss: 0.2568 Lr: 0.00570
[2023-12-25 06:08:08,222 INFO misc.py line 119 253097] Train: [19/100][385/510] Data 0.004 (0.111) Batch 1.102 (1.592) Remain 18:19:41 loss: 0.2492 Lr: 0.00570
[2023-12-25 06:08:09,251 INFO misc.py line 119 253097] Train: [19/100][386/510] Data 0.004 (0.110) Batch 1.028 (1.591) Remain 18:18:38 loss: 0.1754 Lr: 0.00570
[2023-12-25 06:08:10,442 INFO misc.py line 119 253097] Train: [19/100][387/510] Data 0.004 (0.110) Batch 1.192 (1.590) Remain 18:17:53 loss: 0.2848 Lr: 0.00569
[2023-12-25 06:08:11,461 INFO misc.py line 119 253097] Train: [19/100][388/510] Data 0.004 (0.110) Batch 1.017 (1.588) Remain 18:16:50 loss: 0.2168 Lr: 0.00569
[2023-12-25 06:08:12,368 INFO misc.py line 119 253097] Train: [19/100][389/510] Data 0.005 (0.110) Batch 0.909 (1.587) Remain 18:15:35 loss: 0.2073 Lr: 0.00569
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[2023-12-25 06:08:31,110 INFO misc.py line 119 253097] Train: [19/100][397/510] Data 0.005 (0.108) Batch 11.068 (1.602) Remain 18:25:59 loss: 0.1461 Lr: 0.00569
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[2023-12-25 06:08:33,272 INFO misc.py line 119 253097] Train: [19/100][399/510] Data 0.004 (0.107) Batch 1.049 (1.599) Remain 18:24:07 loss: 0.2914 Lr: 0.00569
[2023-12-25 06:08:34,273 INFO misc.py line 119 253097] Train: [19/100][400/510] Data 0.003 (0.107) Batch 1.000 (1.598) Remain 18:23:02 loss: 0.3960 Lr: 0.00569
[2023-12-25 06:08:35,510 INFO misc.py line 119 253097] Train: [19/100][401/510] Data 0.004 (0.107) Batch 1.233 (1.597) Remain 18:22:23 loss: 0.3401 Lr: 0.00569
[2023-12-25 06:08:36,795 INFO misc.py line 119 253097] Train: [19/100][402/510] Data 0.008 (0.106) Batch 1.284 (1.596) Remain 18:21:49 loss: 0.2706 Lr: 0.00569
[2023-12-25 06:08:37,935 INFO misc.py line 119 253097] Train: [19/100][403/510] Data 0.009 (0.106) Batch 1.146 (1.595) Remain 18:21:01 loss: 0.2244 Lr: 0.00569
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[2023-12-25 06:08:41,334 INFO misc.py line 119 253097] Train: [19/100][406/510] Data 0.006 (0.105) Batch 1.173 (1.592) Remain 18:18:33 loss: 0.1960 Lr: 0.00569
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[2023-12-25 06:08:43,578 INFO misc.py line 119 253097] Train: [19/100][408/510] Data 0.006 (0.105) Batch 1.069 (1.589) Remain 18:16:54 loss: 0.3296 Lr: 0.00569
[2023-12-25 06:08:44,863 INFO misc.py line 119 253097] Train: [19/100][409/510] Data 0.009 (0.105) Batch 1.281 (1.588) Remain 18:16:21 loss: 0.2558 Lr: 0.00569
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[2023-12-25 06:09:26,229 INFO misc.py line 119 253097] Train: [19/100][436/510] Data 0.005 (0.098) Batch 1.182 (1.585) Remain 18:13:13 loss: 0.3249 Lr: 0.00569
[2023-12-25 06:09:27,212 INFO misc.py line 119 253097] Train: [19/100][437/510] Data 0.005 (0.098) Batch 0.981 (1.584) Remain 18:12:14 loss: 0.2747 Lr: 0.00569
[2023-12-25 06:09:28,255 INFO misc.py line 119 253097] Train: [19/100][438/510] Data 0.006 (0.098) Batch 1.044 (1.582) Remain 18:11:21 loss: 0.6081 Lr: 0.00569
[2023-12-25 06:09:29,529 INFO misc.py line 119 253097] Train: [19/100][439/510] Data 0.005 (0.098) Batch 1.274 (1.582) Remain 18:10:50 loss: 0.2637 Lr: 0.00569
[2023-12-25 06:09:30,536 INFO misc.py line 119 253097] Train: [19/100][440/510] Data 0.005 (0.098) Batch 1.004 (1.580) Remain 18:09:54 loss: 0.3703 Lr: 0.00569
[2023-12-25 06:09:31,634 INFO misc.py line 119 253097] Train: [19/100][441/510] Data 0.008 (0.097) Batch 1.099 (1.579) Remain 18:09:07 loss: 0.5057 Lr: 0.00569
[2023-12-25 06:09:32,599 INFO misc.py line 119 253097] Train: [19/100][442/510] Data 0.007 (0.097) Batch 0.968 (1.578) Remain 18:08:07 loss: 0.3463 Lr: 0.00569
[2023-12-25 06:09:33,629 INFO misc.py line 119 253097] Train: [19/100][443/510] Data 0.005 (0.097) Batch 1.030 (1.577) Remain 18:07:14 loss: 0.2229 Lr: 0.00569
[2023-12-25 06:09:34,600 INFO misc.py line 119 253097] Train: [19/100][444/510] Data 0.004 (0.097) Batch 0.968 (1.575) Remain 18:06:16 loss: 0.3112 Lr: 0.00569
[2023-12-25 06:09:35,695 INFO misc.py line 119 253097] Train: [19/100][445/510] Data 0.008 (0.097) Batch 1.098 (1.574) Remain 18:05:29 loss: 0.2352 Lr: 0.00569
[2023-12-25 06:09:43,380 INFO misc.py line 119 253097] Train: [19/100][446/510] Data 0.004 (0.096) Batch 7.684 (1.588) Remain 18:14:58 loss: 0.4804 Lr: 0.00569
[2023-12-25 06:09:44,594 INFO misc.py line 119 253097] Train: [19/100][447/510] Data 0.005 (0.096) Batch 1.216 (1.587) Remain 18:14:22 loss: 0.2498 Lr: 0.00569
[2023-12-25 06:09:45,647 INFO misc.py line 119 253097] Train: [19/100][448/510] Data 0.004 (0.096) Batch 1.052 (1.586) Remain 18:13:31 loss: 0.6199 Lr: 0.00569
[2023-12-25 06:09:46,758 INFO misc.py line 119 253097] Train: [19/100][449/510] Data 0.005 (0.096) Batch 1.108 (1.585) Remain 18:12:45 loss: 0.2911 Lr: 0.00569
[2023-12-25 06:09:48,027 INFO misc.py line 119 253097] Train: [19/100][450/510] Data 0.007 (0.096) Batch 1.272 (1.584) Remain 18:12:14 loss: 0.1601 Lr: 0.00569
[2023-12-25 06:09:49,237 INFO misc.py line 119 253097] Train: [19/100][451/510] Data 0.004 (0.095) Batch 1.206 (1.583) Remain 18:11:38 loss: 0.1048 Lr: 0.00569
[2023-12-25 06:09:50,300 INFO misc.py line 119 253097] Train: [19/100][452/510] Data 0.009 (0.095) Batch 1.067 (1.582) Remain 18:10:49 loss: 0.3086 Lr: 0.00569
[2023-12-25 06:09:51,392 INFO misc.py line 119 253097] Train: [19/100][453/510] Data 0.005 (0.095) Batch 1.083 (1.581) Remain 18:10:01 loss: 0.3498 Lr: 0.00569
[2023-12-25 06:09:52,677 INFO misc.py line 119 253097] Train: [19/100][454/510] Data 0.013 (0.095) Batch 1.290 (1.580) Remain 18:09:33 loss: 0.2221 Lr: 0.00569
[2023-12-25 06:09:53,831 INFO misc.py line 119 253097] Train: [19/100][455/510] Data 0.009 (0.095) Batch 1.158 (1.579) Remain 18:08:53 loss: 0.2517 Lr: 0.00569
[2023-12-25 06:09:55,050 INFO misc.py line 119 253097] Train: [19/100][456/510] Data 0.005 (0.094) Batch 1.221 (1.579) Remain 18:08:18 loss: 0.4791 Lr: 0.00569
[2023-12-25 06:09:56,173 INFO misc.py line 119 253097] Train: [19/100][457/510] Data 0.003 (0.094) Batch 1.118 (1.578) Remain 18:07:35 loss: 0.2507 Lr: 0.00569
[2023-12-25 06:09:57,301 INFO misc.py line 119 253097] Train: [19/100][458/510] Data 0.008 (0.094) Batch 1.131 (1.577) Remain 18:06:53 loss: 0.1763 Lr: 0.00569
[2023-12-25 06:09:58,443 INFO misc.py line 119 253097] Train: [19/100][459/510] Data 0.007 (0.094) Batch 1.139 (1.576) Remain 18:06:11 loss: 0.1866 Lr: 0.00569
[2023-12-25 06:09:59,613 INFO misc.py line 119 253097] Train: [19/100][460/510] Data 0.008 (0.094) Batch 1.170 (1.575) Remain 18:05:33 loss: 0.2448 Lr: 0.00569
[2023-12-25 06:10:00,885 INFO misc.py line 119 253097] Train: [19/100][461/510] Data 0.008 (0.093) Batch 1.271 (1.574) Remain 18:05:04 loss: 0.3009 Lr: 0.00569
[2023-12-25 06:10:02,046 INFO misc.py line 119 253097] Train: [19/100][462/510] Data 0.008 (0.093) Batch 1.166 (1.573) Remain 18:04:26 loss: 0.3878 Lr: 0.00569
[2023-12-25 06:10:03,220 INFO misc.py line 119 253097] Train: [19/100][463/510] Data 0.004 (0.093) Batch 1.173 (1.572) Remain 18:03:48 loss: 0.4015 Lr: 0.00569
[2023-12-25 06:10:07,907 INFO misc.py line 119 253097] Train: [19/100][464/510] Data 0.005 (0.093) Batch 4.687 (1.579) Remain 18:08:26 loss: 0.3565 Lr: 0.00569
[2023-12-25 06:10:08,922 INFO misc.py line 119 253097] Train: [19/100][465/510] Data 0.005 (0.093) Batch 1.015 (1.578) Remain 18:07:34 loss: 0.1782 Lr: 0.00569
[2023-12-25 06:10:10,185 INFO misc.py line 119 253097] Train: [19/100][466/510] Data 0.004 (0.092) Batch 1.258 (1.577) Remain 18:07:04 loss: 0.2408 Lr: 0.00569
[2023-12-25 06:10:11,196 INFO misc.py line 119 253097] Train: [19/100][467/510] Data 0.009 (0.092) Batch 1.013 (1.576) Remain 18:06:12 loss: 0.2221 Lr: 0.00569
[2023-12-25 06:10:12,131 INFO misc.py line 119 253097] Train: [19/100][468/510] Data 0.008 (0.092) Batch 0.939 (1.575) Remain 18:05:14 loss: 0.3432 Lr: 0.00569
[2023-12-25 06:10:13,199 INFO misc.py line 119 253097] Train: [19/100][469/510] Data 0.004 (0.092) Batch 1.068 (1.574) Remain 18:04:27 loss: 0.3158 Lr: 0.00569
[2023-12-25 06:10:14,386 INFO misc.py line 119 253097] Train: [19/100][470/510] Data 0.004 (0.092) Batch 1.186 (1.573) Remain 18:03:51 loss: 0.2087 Lr: 0.00569
[2023-12-25 06:10:17,133 INFO misc.py line 119 253097] Train: [19/100][471/510] Data 0.004 (0.092) Batch 2.747 (1.575) Remain 18:05:34 loss: 0.2605 Lr: 0.00569
[2023-12-25 06:10:18,214 INFO misc.py line 119 253097] Train: [19/100][472/510] Data 0.005 (0.091) Batch 1.079 (1.574) Remain 18:04:48 loss: 0.4535 Lr: 0.00569
[2023-12-25 06:10:19,395 INFO misc.py line 119 253097] Train: [19/100][473/510] Data 0.006 (0.091) Batch 1.180 (1.573) Remain 18:04:12 loss: 0.3321 Lr: 0.00569
[2023-12-25 06:10:20,673 INFO misc.py line 119 253097] Train: [19/100][474/510] Data 0.006 (0.091) Batch 1.280 (1.573) Remain 18:03:45 loss: 0.4371 Lr: 0.00569
[2023-12-25 06:10:21,935 INFO misc.py line 119 253097] Train: [19/100][475/510] Data 0.004 (0.091) Batch 1.260 (1.572) Remain 18:03:16 loss: 0.2177 Lr: 0.00569
[2023-12-25 06:10:23,071 INFO misc.py line 119 253097] Train: [19/100][476/510] Data 0.006 (0.091) Batch 1.137 (1.571) Remain 18:02:36 loss: 0.2056 Lr: 0.00569
[2023-12-25 06:10:24,108 INFO misc.py line 119 253097] Train: [19/100][477/510] Data 0.005 (0.090) Batch 1.030 (1.570) Remain 18:01:48 loss: 0.2898 Lr: 0.00569
[2023-12-25 06:10:25,161 INFO misc.py line 119 253097] Train: [19/100][478/510] Data 0.011 (0.090) Batch 1.059 (1.569) Remain 18:01:01 loss: 0.4552 Lr: 0.00569
[2023-12-25 06:10:26,246 INFO misc.py line 119 253097] Train: [19/100][479/510] Data 0.006 (0.090) Batch 1.087 (1.568) Remain 18:00:18 loss: 0.3047 Lr: 0.00569
[2023-12-25 06:10:27,224 INFO misc.py line 119 253097] Train: [19/100][480/510] Data 0.005 (0.090) Batch 0.978 (1.567) Remain 17:59:25 loss: 0.2261 Lr: 0.00569
[2023-12-25 06:10:28,318 INFO misc.py line 119 253097] Train: [19/100][481/510] Data 0.005 (0.090) Batch 1.094 (1.566) Remain 17:58:43 loss: 0.3604 Lr: 0.00569
[2023-12-25 06:10:29,328 INFO misc.py line 119 253097] Train: [19/100][482/510] Data 0.004 (0.090) Batch 1.010 (1.565) Remain 17:57:53 loss: 0.6270 Lr: 0.00569
[2023-12-25 06:10:30,534 INFO misc.py line 119 253097] Train: [19/100][483/510] Data 0.005 (0.089) Batch 1.207 (1.564) Remain 17:57:21 loss: 0.3012 Lr: 0.00569
[2023-12-25 06:10:31,851 INFO misc.py line 119 253097] Train: [19/100][484/510] Data 0.004 (0.089) Batch 1.313 (1.563) Remain 17:56:58 loss: 0.2917 Lr: 0.00569
[2023-12-25 06:10:32,886 INFO misc.py line 119 253097] Train: [19/100][485/510] Data 0.007 (0.089) Batch 1.037 (1.562) Remain 17:56:11 loss: 0.1268 Lr: 0.00569
[2023-12-25 06:10:33,886 INFO misc.py line 119 253097] Train: [19/100][486/510] Data 0.006 (0.089) Batch 0.999 (1.561) Remain 17:55:21 loss: 0.2175 Lr: 0.00569
[2023-12-25 06:10:36,011 INFO misc.py line 119 253097] Train: [19/100][487/510] Data 1.299 (0.091) Batch 2.128 (1.562) Remain 17:56:08 loss: 0.2022 Lr: 0.00569
[2023-12-25 06:10:39,805 INFO misc.py line 119 253097] Train: [19/100][488/510] Data 0.004 (0.091) Batch 3.790 (1.567) Remain 17:59:17 loss: 0.2462 Lr: 0.00569
[2023-12-25 06:10:41,004 INFO misc.py line 119 253097] Train: [19/100][489/510] Data 0.007 (0.091) Batch 1.197 (1.566) Remain 17:58:44 loss: 0.3175 Lr: 0.00569
[2023-12-25 06:10:41,988 INFO misc.py line 119 253097] Train: [19/100][490/510] Data 0.009 (0.091) Batch 0.989 (1.565) Remain 17:57:53 loss: 0.2967 Lr: 0.00569
[2023-12-25 06:10:43,141 INFO misc.py line 119 253097] Train: [19/100][491/510] Data 0.003 (0.091) Batch 1.152 (1.564) Remain 17:57:17 loss: 0.4493 Lr: 0.00569
[2023-12-25 06:10:44,341 INFO misc.py line 119 253097] Train: [19/100][492/510] Data 0.005 (0.090) Batch 1.200 (1.563) Remain 17:56:44 loss: 0.2301 Lr: 0.00569
[2023-12-25 06:10:45,531 INFO misc.py line 119 253097] Train: [19/100][493/510] Data 0.005 (0.090) Batch 1.190 (1.562) Remain 17:56:11 loss: 0.3240 Lr: 0.00569
[2023-12-25 06:10:49,717 INFO misc.py line 119 253097] Train: [19/100][494/510] Data 0.004 (0.090) Batch 4.187 (1.568) Remain 17:59:50 loss: 0.3246 Lr: 0.00569
[2023-12-25 06:10:50,897 INFO misc.py line 119 253097] Train: [19/100][495/510] Data 0.004 (0.090) Batch 1.177 (1.567) Remain 17:59:16 loss: 0.3896 Lr: 0.00569
[2023-12-25 06:10:52,116 INFO misc.py line 119 253097] Train: [19/100][496/510] Data 0.007 (0.090) Batch 1.222 (1.566) Remain 17:58:46 loss: 0.3847 Lr: 0.00569
[2023-12-25 06:10:53,278 INFO misc.py line 119 253097] Train: [19/100][497/510] Data 0.004 (0.090) Batch 1.161 (1.565) Remain 17:58:10 loss: 0.2768 Lr: 0.00569
[2023-12-25 06:10:54,421 INFO misc.py line 119 253097] Train: [19/100][498/510] Data 0.006 (0.089) Batch 1.142 (1.565) Remain 17:57:33 loss: 0.2616 Lr: 0.00569
[2023-12-25 06:10:55,464 INFO misc.py line 119 253097] Train: [19/100][499/510] Data 0.007 (0.089) Batch 1.043 (1.564) Remain 17:56:48 loss: 0.6686 Lr: 0.00569
[2023-12-25 06:10:56,578 INFO misc.py line 119 253097] Train: [19/100][500/510] Data 0.006 (0.089) Batch 1.114 (1.563) Remain 17:56:09 loss: 0.1933 Lr: 0.00569
[2023-12-25 06:10:57,673 INFO misc.py line 119 253097] Train: [19/100][501/510] Data 0.006 (0.089) Batch 1.096 (1.562) Remain 17:55:29 loss: 0.1723 Lr: 0.00569
[2023-12-25 06:10:58,742 INFO misc.py line 119 253097] Train: [19/100][502/510] Data 0.005 (0.089) Batch 1.065 (1.561) Remain 17:54:46 loss: 0.2986 Lr: 0.00569
[2023-12-25 06:10:59,880 INFO misc.py line 119 253097] Train: [19/100][503/510] Data 0.009 (0.089) Batch 1.144 (1.560) Remain 17:54:10 loss: 0.2647 Lr: 0.00569
[2023-12-25 06:11:00,772 INFO misc.py line 119 253097] Train: [19/100][504/510] Data 0.004 (0.088) Batch 0.893 (1.559) Remain 17:53:14 loss: 0.2749 Lr: 0.00568
[2023-12-25 06:11:01,886 INFO misc.py line 119 253097] Train: [19/100][505/510] Data 0.003 (0.088) Batch 1.112 (1.558) Remain 17:52:35 loss: 0.3842 Lr: 0.00568
[2023-12-25 06:11:03,064 INFO misc.py line 119 253097] Train: [19/100][506/510] Data 0.005 (0.088) Batch 1.181 (1.557) Remain 17:52:03 loss: 0.4075 Lr: 0.00568
[2023-12-25 06:11:04,168 INFO misc.py line 119 253097] Train: [19/100][507/510] Data 0.003 (0.088) Batch 1.103 (1.556) Remain 17:51:24 loss: 0.3881 Lr: 0.00568
[2023-12-25 06:11:05,074 INFO misc.py line 119 253097] Train: [19/100][508/510] Data 0.003 (0.088) Batch 0.906 (1.555) Remain 17:50:29 loss: 0.2444 Lr: 0.00568
[2023-12-25 06:11:06,234 INFO misc.py line 119 253097] Train: [19/100][509/510] Data 0.005 (0.088) Batch 1.160 (1.554) Remain 17:49:55 loss: 0.3287 Lr: 0.00568
[2023-12-25 06:11:07,463 INFO misc.py line 119 253097] Train: [19/100][510/510] Data 0.004 (0.087) Batch 1.228 (1.553) Remain 17:49:27 loss: 0.3388 Lr: 0.00568
[2023-12-25 06:11:07,465 INFO misc.py line 136 253097] Train result: loss: 0.3105 
[2023-12-25 06:11:07,466 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 06:11:34,719 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6449 
[2023-12-25 06:11:35,073 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.9393 
[2023-12-25 06:11:40,009 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.7560 
[2023-12-25 06:11:40,533 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4726 
[2023-12-25 06:11:42,510 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6548 
[2023-12-25 06:11:42,937 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6754 
[2023-12-25 06:11:43,823 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.6370 
[2023-12-25 06:11:44,379 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5629 
[2023-12-25 06:11:46,188 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.7637 
[2023-12-25 06:11:48,312 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.5940 
[2023-12-25 06:11:49,168 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.4229 
[2023-12-25 06:11:49,592 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7567 
[2023-12-25 06:11:50,494 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5108 
[2023-12-25 06:11:53,439 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9706 
[2023-12-25 06:11:53,905 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.5609 
[2023-12-25 06:11:54,519 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 1.1055 
[2023-12-25 06:11:55,218 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5490 
[2023-12-25 06:11:56,468 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.5806/0.6765/0.8673.
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9120/0.9419
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9778/0.9875
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8064/0.9453
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2600/0.2985
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.3553/0.3581
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.4995/0.5793
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7425/0.8721
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8655/0.8964
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4901/0.5111
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7143/0.8731
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.4019/0.8983
[2023-12-25 06:11:56,469 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5219/0.6323
[2023-12-25 06:11:56,470 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 06:11:56,471 INFO misc.py line 165 253097] Currently Best mIoU: 0.6476
[2023-12-25 06:11:56,471 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 06:12:02,551 INFO misc.py line 119 253097] Train: [20/100][1/510] Data 2.546 (2.546) Batch 4.189 (4.189) Remain 48:04:17 loss: 0.4829 Lr: 0.00568
[2023-12-25 06:12:03,553 INFO misc.py line 119 253097] Train: [20/100][2/510] Data 0.007 (0.007) Batch 1.005 (1.005) Remain 11:31:38 loss: 0.2152 Lr: 0.00568
[2023-12-25 06:12:04,626 INFO misc.py line 119 253097] Train: [20/100][3/510] Data 0.004 (0.004) Batch 1.073 (1.073) Remain 12:18:31 loss: 0.2572 Lr: 0.00568
[2023-12-25 06:12:15,878 INFO misc.py line 119 253097] Train: [20/100][4/510] Data 0.004 (0.004) Batch 11.252 (11.252) Remain 129:06:09 loss: 0.2170 Lr: 0.00568
[2023-12-25 06:12:16,958 INFO misc.py line 119 253097] Train: [20/100][5/510] Data 0.004 (0.004) Batch 1.079 (6.165) Remain 70:44:22 loss: 0.2635 Lr: 0.00568
[2023-12-25 06:12:17,877 INFO misc.py line 119 253097] Train: [20/100][6/510] Data 0.005 (0.004) Batch 0.920 (4.417) Remain 50:40:40 loss: 0.2997 Lr: 0.00568
[2023-12-25 06:12:18,787 INFO misc.py line 119 253097] Train: [20/100][7/510] Data 0.003 (0.004) Batch 0.910 (3.540) Remain 40:37:05 loss: 0.5328 Lr: 0.00568
[2023-12-25 06:12:19,745 INFO misc.py line 119 253097] Train: [20/100][8/510] Data 0.003 (0.004) Batch 0.955 (3.023) Remain 34:41:05 loss: 0.3408 Lr: 0.00568
[2023-12-25 06:12:20,741 INFO misc.py line 119 253097] Train: [20/100][9/510] Data 0.006 (0.004) Batch 0.998 (2.686) Remain 30:48:44 loss: 0.2867 Lr: 0.00568
[2023-12-25 06:12:21,866 INFO misc.py line 119 253097] Train: [20/100][10/510] Data 0.004 (0.004) Batch 1.126 (2.463) Remain 28:15:17 loss: 0.3038 Lr: 0.00568
[2023-12-25 06:12:22,797 INFO misc.py line 119 253097] Train: [20/100][11/510] Data 0.003 (0.004) Batch 0.931 (2.271) Remain 26:03:25 loss: 0.3256 Lr: 0.00568
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[2023-12-25 06:15:48,297 INFO misc.py line 119 253097] Train: [20/100][139/510] Data 0.004 (0.175) Batch 1.175 (1.645) Remain 18:48:30 loss: 0.3523 Lr: 0.00567
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[2023-12-25 06:15:58,065 INFO misc.py line 119 253097] Train: [20/100][147/510] Data 0.004 (0.166) Batch 1.083 (1.621) Remain 18:32:08 loss: 0.2431 Lr: 0.00567
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[2023-12-25 06:16:03,891 INFO misc.py line 119 253097] Train: [20/100][152/510] Data 0.005 (0.161) Batch 1.254 (1.606) Remain 18:21:31 loss: 0.1943 Lr: 0.00567
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[2023-12-25 06:16:07,465 INFO misc.py line 119 253097] Train: [20/100][155/510] Data 0.012 (0.158) Batch 1.368 (1.598) Remain 18:15:49 loss: 0.4697 Lr: 0.00567
[2023-12-25 06:16:08,680 INFO misc.py line 119 253097] Train: [20/100][156/510] Data 0.005 (0.157) Batch 1.210 (1.595) Remain 18:14:04 loss: 0.2397 Lr: 0.00567
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[2023-12-25 06:16:18,582 INFO misc.py line 119 253097] Train: [20/100][158/510] Data 7.466 (0.203) Batch 8.636 (1.638) Remain 18:43:44 loss: 0.3405 Lr: 0.00567
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[2023-12-25 06:16:21,912 INFO misc.py line 119 253097] Train: [20/100][161/510] Data 0.005 (0.199) Batch 1.236 (1.628) Remain 18:36:46 loss: 0.2236 Lr: 0.00567
[2023-12-25 06:16:23,053 INFO misc.py line 119 253097] Train: [20/100][162/510] Data 0.003 (0.198) Batch 1.141 (1.625) Remain 18:34:38 loss: 0.2399 Lr: 0.00567
[2023-12-25 06:16:24,157 INFO misc.py line 119 253097] Train: [20/100][163/510] Data 0.004 (0.197) Batch 1.104 (1.622) Remain 18:32:23 loss: 0.1814 Lr: 0.00567
[2023-12-25 06:16:28,600 INFO misc.py line 119 253097] Train: [20/100][164/510] Data 3.273 (0.216) Batch 4.443 (1.640) Remain 18:44:22 loss: 0.5325 Lr: 0.00567
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[2023-12-25 06:16:35,276 INFO misc.py line 119 253097] Train: [20/100][170/510] Data 0.004 (0.208) Batch 1.056 (1.621) Remain 18:31:12 loss: 0.1904 Lr: 0.00567
[2023-12-25 06:16:36,412 INFO misc.py line 119 253097] Train: [20/100][171/510] Data 0.009 (0.207) Batch 1.136 (1.618) Remain 18:29:12 loss: 0.2584 Lr: 0.00567
[2023-12-25 06:16:37,632 INFO misc.py line 119 253097] Train: [20/100][172/510] Data 0.007 (0.206) Batch 1.222 (1.615) Remain 18:27:34 loss: 0.2531 Lr: 0.00567
[2023-12-25 06:16:38,625 INFO misc.py line 119 253097] Train: [20/100][173/510] Data 0.005 (0.205) Batch 0.994 (1.612) Remain 18:25:02 loss: 0.2941 Lr: 0.00567
[2023-12-25 06:16:39,454 INFO misc.py line 119 253097] Train: [20/100][174/510] Data 0.005 (0.203) Batch 0.829 (1.607) Remain 18:21:52 loss: 0.2941 Lr: 0.00567
[2023-12-25 06:16:40,535 INFO misc.py line 119 253097] Train: [20/100][175/510] Data 0.004 (0.202) Batch 1.081 (1.604) Remain 18:19:45 loss: 0.2569 Lr: 0.00567
[2023-12-25 06:16:41,683 INFO misc.py line 119 253097] Train: [20/100][176/510] Data 0.004 (0.201) Batch 1.146 (1.601) Remain 18:17:54 loss: 0.3880 Lr: 0.00567
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[2023-12-25 06:16:48,799 INFO misc.py line 119 253097] Train: [20/100][182/510] Data 0.006 (0.195) Batch 1.083 (1.588) Remain 18:08:12 loss: 0.3880 Lr: 0.00567
[2023-12-25 06:16:50,143 INFO misc.py line 119 253097] Train: [20/100][183/510] Data 0.007 (0.193) Batch 1.341 (1.586) Remain 18:07:14 loss: 0.3926 Lr: 0.00567
[2023-12-25 06:16:51,357 INFO misc.py line 119 253097] Train: [20/100][184/510] Data 0.008 (0.192) Batch 1.218 (1.584) Remain 18:05:49 loss: 0.2401 Lr: 0.00567
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[2023-12-25 06:17:03,567 INFO misc.py line 119 253097] Train: [20/100][188/510] Data 0.004 (0.188) Batch 1.101 (1.616) Remain 18:27:28 loss: 0.4154 Lr: 0.00567
[2023-12-25 06:17:04,831 INFO misc.py line 119 253097] Train: [20/100][189/510] Data 0.006 (0.187) Batch 1.265 (1.614) Remain 18:26:09 loss: 0.1314 Lr: 0.00567
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[2023-12-25 06:17:09,246 INFO misc.py line 119 253097] Train: [20/100][193/510] Data 0.003 (0.184) Batch 1.070 (1.603) Remain 18:18:41 loss: 0.3871 Lr: 0.00567
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[2023-12-25 06:17:50,517 INFO misc.py line 119 253097] Train: [20/100][218/510] Data 9.171 (0.206) Batch 10.347 (1.609) Remain 18:21:48 loss: 0.3209 Lr: 0.00567
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[2023-12-25 06:18:29,721 INFO misc.py line 119 253097] Train: [20/100][248/510] Data 0.007 (0.182) Batch 1.072 (1.572) Remain 17:55:41 loss: 0.6178 Lr: 0.00566
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[2023-12-25 06:18:36,385 INFO misc.py line 119 253097] Train: [20/100][253/510] Data 0.011 (0.179) Batch 1.320 (1.567) Remain 17:52:17 loss: 0.2064 Lr: 0.00566
[2023-12-25 06:18:37,603 INFO misc.py line 119 253097] Train: [20/100][254/510] Data 0.008 (0.178) Batch 1.219 (1.566) Remain 17:51:18 loss: 0.1696 Lr: 0.00566
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[2023-12-25 06:18:40,166 INFO misc.py line 119 253097] Train: [20/100][256/510] Data 0.008 (0.177) Batch 1.271 (1.563) Remain 17:49:43 loss: 0.2931 Lr: 0.00566
[2023-12-25 06:18:41,111 INFO misc.py line 119 253097] Train: [20/100][257/510] Data 0.008 (0.176) Batch 0.948 (1.561) Remain 17:48:02 loss: 0.2464 Lr: 0.00566
[2023-12-25 06:18:42,321 INFO misc.py line 119 253097] Train: [20/100][258/510] Data 0.006 (0.176) Batch 1.210 (1.560) Remain 17:47:04 loss: 0.2979 Lr: 0.00566
[2023-12-25 06:18:43,524 INFO misc.py line 119 253097] Train: [20/100][259/510] Data 0.005 (0.175) Batch 1.199 (1.558) Remain 17:46:04 loss: 0.5392 Lr: 0.00566
[2023-12-25 06:18:44,574 INFO misc.py line 119 253097] Train: [20/100][260/510] Data 0.008 (0.174) Batch 1.050 (1.556) Remain 17:44:41 loss: 0.1429 Lr: 0.00566
[2023-12-25 06:18:45,821 INFO misc.py line 119 253097] Train: [20/100][261/510] Data 0.009 (0.174) Batch 1.245 (1.555) Remain 17:43:50 loss: 0.4612 Lr: 0.00566
[2023-12-25 06:18:46,903 INFO misc.py line 119 253097] Train: [20/100][262/510] Data 0.011 (0.173) Batch 1.088 (1.553) Remain 17:42:35 loss: 0.3418 Lr: 0.00566
[2023-12-25 06:18:47,903 INFO misc.py line 119 253097] Train: [20/100][263/510] Data 0.005 (0.172) Batch 0.994 (1.551) Remain 17:41:05 loss: 0.2858 Lr: 0.00566
[2023-12-25 06:18:49,162 INFO misc.py line 119 253097] Train: [20/100][264/510] Data 0.011 (0.172) Batch 1.259 (1.550) Remain 17:40:17 loss: 0.2302 Lr: 0.00566
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[2023-12-25 06:18:52,219 INFO misc.py line 119 253097] Train: [20/100][266/510] Data 0.009 (0.171) Batch 1.906 (1.550) Remain 17:40:09 loss: 0.1894 Lr: 0.00566
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[2023-12-25 06:18:54,632 INFO misc.py line 119 253097] Train: [20/100][268/510] Data 0.009 (0.169) Batch 1.240 (1.547) Remain 17:38:19 loss: 0.2942 Lr: 0.00566
[2023-12-25 06:18:55,823 INFO misc.py line 119 253097] Train: [20/100][269/510] Data 0.009 (0.169) Batch 1.195 (1.546) Remain 17:37:23 loss: 0.2702 Lr: 0.00566
[2023-12-25 06:18:56,829 INFO misc.py line 119 253097] Train: [20/100][270/510] Data 0.004 (0.168) Batch 1.000 (1.544) Remain 17:35:57 loss: 0.2822 Lr: 0.00566
[2023-12-25 06:19:02,824 INFO misc.py line 119 253097] Train: [20/100][271/510] Data 4.906 (0.186) Batch 6.001 (1.560) Remain 17:47:18 loss: 0.1603 Lr: 0.00566
[2023-12-25 06:19:03,870 INFO misc.py line 119 253097] Train: [20/100][272/510] Data 0.003 (0.185) Batch 1.044 (1.559) Remain 17:45:58 loss: 0.2116 Lr: 0.00566
[2023-12-25 06:19:12,546 INFO misc.py line 119 253097] Train: [20/100][273/510] Data 0.005 (0.184) Batch 8.677 (1.585) Remain 18:03:58 loss: 0.2231 Lr: 0.00566
[2023-12-25 06:19:13,617 INFO misc.py line 119 253097] Train: [20/100][274/510] Data 0.004 (0.184) Batch 1.072 (1.583) Remain 18:02:39 loss: 0.3415 Lr: 0.00566
[2023-12-25 06:19:14,828 INFO misc.py line 119 253097] Train: [20/100][275/510] Data 0.003 (0.183) Batch 1.210 (1.582) Remain 18:01:41 loss: 0.2088 Lr: 0.00566
[2023-12-25 06:19:15,934 INFO misc.py line 119 253097] Train: [20/100][276/510] Data 0.004 (0.182) Batch 1.102 (1.580) Remain 18:00:28 loss: 0.2084 Lr: 0.00566
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[2023-12-25 06:19:18,359 INFO misc.py line 119 253097] Train: [20/100][278/510] Data 0.004 (0.181) Batch 1.154 (1.577) Remain 17:58:34 loss: 0.3742 Lr: 0.00566
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[2023-12-25 06:19:20,648 INFO misc.py line 119 253097] Train: [20/100][280/510] Data 0.006 (0.180) Batch 1.288 (1.574) Remain 17:56:24 loss: 0.2591 Lr: 0.00566
[2023-12-25 06:19:21,773 INFO misc.py line 119 253097] Train: [20/100][281/510] Data 0.004 (0.179) Batch 1.124 (1.572) Remain 17:55:16 loss: 0.2537 Lr: 0.00566
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[2023-12-25 06:19:27,635 INFO misc.py line 119 253097] Train: [20/100][286/510] Data 0.008 (0.176) Batch 0.983 (1.565) Remain 17:50:18 loss: 0.2800 Lr: 0.00566
[2023-12-25 06:19:37,385 INFO misc.py line 119 253097] Train: [20/100][287/510] Data 0.005 (0.176) Batch 9.752 (1.594) Remain 18:09:59 loss: 0.2905 Lr: 0.00566
[2023-12-25 06:19:38,557 INFO misc.py line 119 253097] Train: [20/100][288/510] Data 0.004 (0.175) Batch 1.172 (1.593) Remain 18:08:57 loss: 0.1735 Lr: 0.00566
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[2023-12-25 06:19:52,508 INFO misc.py line 119 253097] Train: [20/100][300/510] Data 0.003 (0.168) Batch 1.180 (1.575) Remain 17:56:45 loss: 0.2562 Lr: 0.00566
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[2023-12-25 06:20:32,103 INFO misc.py line 119 253097] Train: [20/100][320/510] Data 10.461 (0.191) Batch 11.701 (1.601) Remain 18:13:39 loss: 0.1899 Lr: 0.00566
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[2023-12-25 06:21:32,200 INFO misc.py line 119 253097] Train: [20/100][365/510] Data 0.193 (0.169) Batch 1.425 (1.568) Remain 17:49:56 loss: 0.2629 Lr: 0.00565
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[2023-12-25 06:21:43,457 INFO misc.py line 119 253097] Train: [20/100][369/510] Data 6.676 (0.185) Batch 7.695 (1.582) Remain 17:59:08 loss: 0.2218 Lr: 0.00565
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[2023-12-25 06:21:49,169 INFO misc.py line 119 253097] Train: [20/100][373/510] Data 0.004 (0.187) Batch 1.141 (1.580) Remain 17:57:54 loss: 0.1624 Lr: 0.00565
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[2023-12-25 06:21:52,687 INFO misc.py line 119 253097] Train: [20/100][375/510] Data 1.380 (0.190) Batch 2.393 (1.581) Remain 17:58:30 loss: 0.4791 Lr: 0.00565
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[2023-12-25 06:21:57,106 INFO misc.py line 119 253097] Train: [20/100][379/510] Data 0.004 (0.188) Batch 1.056 (1.576) Remain 17:54:56 loss: 0.2674 Lr: 0.00565
[2023-12-25 06:21:58,393 INFO misc.py line 119 253097] Train: [20/100][380/510] Data 0.004 (0.187) Batch 1.283 (1.575) Remain 17:54:23 loss: 0.1600 Lr: 0.00565
[2023-12-25 06:21:59,980 INFO misc.py line 119 253097] Train: [20/100][381/510] Data 0.383 (0.188) Batch 1.589 (1.575) Remain 17:54:23 loss: 0.2660 Lr: 0.00565
[2023-12-25 06:22:01,178 INFO misc.py line 119 253097] Train: [20/100][382/510] Data 0.007 (0.187) Batch 1.197 (1.574) Remain 17:53:40 loss: 0.2209 Lr: 0.00565
[2023-12-25 06:22:02,278 INFO misc.py line 119 253097] Train: [20/100][383/510] Data 0.008 (0.187) Batch 1.103 (1.573) Remain 17:52:48 loss: 0.2762 Lr: 0.00565
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[2023-12-25 06:22:32,664 INFO misc.py line 119 253097] Train: [20/100][399/510] Data 0.004 (0.186) Batch 1.200 (1.586) Remain 18:01:22 loss: 0.4487 Lr: 0.00565
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[2023-12-25 06:23:11,532 INFO misc.py line 119 253097] Train: [20/100][430/510] Data 0.006 (0.182) Batch 1.117 (1.562) Remain 17:44:07 loss: 0.4180 Lr: 0.00565
[2023-12-25 06:23:12,669 INFO misc.py line 119 253097] Train: [20/100][431/510] Data 0.009 (0.181) Batch 1.142 (1.561) Remain 17:43:25 loss: 0.4303 Lr: 0.00565
[2023-12-25 06:23:13,963 INFO misc.py line 119 253097] Train: [20/100][432/510] Data 0.004 (0.181) Batch 1.292 (1.560) Remain 17:42:58 loss: 0.1269 Lr: 0.00565
[2023-12-25 06:23:15,131 INFO misc.py line 119 253097] Train: [20/100][433/510] Data 0.006 (0.181) Batch 1.170 (1.559) Remain 17:42:19 loss: 0.2169 Lr: 0.00565
[2023-12-25 06:23:16,145 INFO misc.py line 119 253097] Train: [20/100][434/510] Data 0.004 (0.180) Batch 1.014 (1.558) Remain 17:41:26 loss: 0.1577 Lr: 0.00565
[2023-12-25 06:23:17,276 INFO misc.py line 119 253097] Train: [20/100][435/510] Data 0.005 (0.180) Batch 1.131 (1.557) Remain 17:40:44 loss: 0.2921 Lr: 0.00565
[2023-12-25 06:23:21,907 INFO misc.py line 119 253097] Train: [20/100][436/510] Data 0.004 (0.179) Batch 4.630 (1.564) Remain 17:45:33 loss: 0.3512 Lr: 0.00565
[2023-12-25 06:23:22,961 INFO misc.py line 119 253097] Train: [20/100][437/510] Data 0.005 (0.179) Batch 1.055 (1.563) Remain 17:44:43 loss: 0.3387 Lr: 0.00565
[2023-12-25 06:23:24,129 INFO misc.py line 119 253097] Train: [20/100][438/510] Data 0.004 (0.179) Batch 1.168 (1.562) Remain 17:44:05 loss: 0.2668 Lr: 0.00565
[2023-12-25 06:23:25,242 INFO misc.py line 119 253097] Train: [20/100][439/510] Data 0.004 (0.178) Batch 1.113 (1.561) Remain 17:43:21 loss: 0.1958 Lr: 0.00565
[2023-12-25 06:23:32,262 INFO misc.py line 119 253097] Train: [20/100][440/510] Data 5.717 (0.191) Batch 7.021 (1.574) Remain 17:51:50 loss: 0.2288 Lr: 0.00565
[2023-12-25 06:23:33,472 INFO misc.py line 119 253097] Train: [20/100][441/510] Data 0.003 (0.190) Batch 1.207 (1.573) Remain 17:51:14 loss: 0.2375 Lr: 0.00565
[2023-12-25 06:23:34,599 INFO misc.py line 119 253097] Train: [20/100][442/510] Data 0.007 (0.190) Batch 1.129 (1.572) Remain 17:50:31 loss: 0.2891 Lr: 0.00564
[2023-12-25 06:23:35,615 INFO misc.py line 119 253097] Train: [20/100][443/510] Data 0.003 (0.190) Batch 1.016 (1.570) Remain 17:49:38 loss: 0.2100 Lr: 0.00564
[2023-12-25 06:23:36,810 INFO misc.py line 119 253097] Train: [20/100][444/510] Data 0.004 (0.189) Batch 1.188 (1.570) Remain 17:49:01 loss: 0.1686 Lr: 0.00564
[2023-12-25 06:23:37,770 INFO misc.py line 119 253097] Train: [20/100][445/510] Data 0.010 (0.189) Batch 0.967 (1.568) Remain 17:48:04 loss: 0.3140 Lr: 0.00564
[2023-12-25 06:23:39,033 INFO misc.py line 119 253097] Train: [20/100][446/510] Data 0.004 (0.188) Batch 1.258 (1.567) Remain 17:47:34 loss: 0.3013 Lr: 0.00564
[2023-12-25 06:23:40,219 INFO misc.py line 119 253097] Train: [20/100][447/510] Data 0.008 (0.188) Batch 1.187 (1.567) Remain 17:46:57 loss: 0.3114 Lr: 0.00564
[2023-12-25 06:23:41,274 INFO misc.py line 119 253097] Train: [20/100][448/510] Data 0.007 (0.187) Batch 1.056 (1.565) Remain 17:46:09 loss: 0.2017 Lr: 0.00564
[2023-12-25 06:23:42,321 INFO misc.py line 119 253097] Train: [20/100][449/510] Data 0.006 (0.187) Batch 1.046 (1.564) Remain 17:45:20 loss: 0.2288 Lr: 0.00564
[2023-12-25 06:23:43,401 INFO misc.py line 119 253097] Train: [20/100][450/510] Data 0.007 (0.187) Batch 1.083 (1.563) Remain 17:44:34 loss: 0.5446 Lr: 0.00564
[2023-12-25 06:23:44,580 INFO misc.py line 119 253097] Train: [20/100][451/510] Data 0.005 (0.186) Batch 1.175 (1.562) Remain 17:43:57 loss: 0.4152 Lr: 0.00564
[2023-12-25 06:23:45,815 INFO misc.py line 119 253097] Train: [20/100][452/510] Data 0.007 (0.186) Batch 1.233 (1.562) Remain 17:43:25 loss: 0.4482 Lr: 0.00564
[2023-12-25 06:23:48,567 INFO misc.py line 119 253097] Train: [20/100][453/510] Data 1.555 (0.189) Batch 2.758 (1.564) Remain 17:45:13 loss: 0.1386 Lr: 0.00564
[2023-12-25 06:23:49,313 INFO misc.py line 119 253097] Train: [20/100][454/510] Data 0.004 (0.188) Batch 0.747 (1.562) Remain 17:43:57 loss: 0.4392 Lr: 0.00564
[2023-12-25 06:23:50,340 INFO misc.py line 119 253097] Train: [20/100][455/510] Data 0.003 (0.188) Batch 1.026 (1.561) Remain 17:43:07 loss: 0.3246 Lr: 0.00564
[2023-12-25 06:23:51,191 INFO misc.py line 119 253097] Train: [20/100][456/510] Data 0.005 (0.188) Batch 0.851 (1.560) Remain 17:42:01 loss: 0.2335 Lr: 0.00564
[2023-12-25 06:23:52,424 INFO misc.py line 119 253097] Train: [20/100][457/510] Data 0.004 (0.187) Batch 1.230 (1.559) Remain 17:41:30 loss: 0.5529 Lr: 0.00564
[2023-12-25 06:23:53,679 INFO misc.py line 119 253097] Train: [20/100][458/510] Data 0.006 (0.187) Batch 1.254 (1.558) Remain 17:41:01 loss: 0.2228 Lr: 0.00564
[2023-12-25 06:23:54,910 INFO misc.py line 119 253097] Train: [20/100][459/510] Data 0.007 (0.186) Batch 1.194 (1.558) Remain 17:40:27 loss: 0.2843 Lr: 0.00564
[2023-12-25 06:23:56,021 INFO misc.py line 119 253097] Train: [20/100][460/510] Data 0.044 (0.186) Batch 1.146 (1.557) Remain 17:39:49 loss: 0.1316 Lr: 0.00564
[2023-12-25 06:23:57,011 INFO misc.py line 119 253097] Train: [20/100][461/510] Data 0.010 (0.186) Batch 0.995 (1.555) Remain 17:38:57 loss: 0.1349 Lr: 0.00564
[2023-12-25 06:23:58,159 INFO misc.py line 119 253097] Train: [20/100][462/510] Data 0.005 (0.185) Batch 1.149 (1.555) Remain 17:38:19 loss: 0.1538 Lr: 0.00564
[2023-12-25 06:23:59,427 INFO misc.py line 119 253097] Train: [20/100][463/510] Data 0.003 (0.185) Batch 1.262 (1.554) Remain 17:37:52 loss: 0.1970 Lr: 0.00564
[2023-12-25 06:24:00,548 INFO misc.py line 119 253097] Train: [20/100][464/510] Data 0.011 (0.185) Batch 1.126 (1.553) Remain 17:37:12 loss: 0.4700 Lr: 0.00564
[2023-12-25 06:24:01,739 INFO misc.py line 119 253097] Train: [20/100][465/510] Data 0.006 (0.184) Batch 1.191 (1.552) Remain 17:36:39 loss: 0.5303 Lr: 0.00564
[2023-12-25 06:24:02,961 INFO misc.py line 119 253097] Train: [20/100][466/510] Data 0.005 (0.184) Batch 1.218 (1.551) Remain 17:36:08 loss: 0.2088 Lr: 0.00564
[2023-12-25 06:24:04,181 INFO misc.py line 119 253097] Train: [20/100][467/510] Data 0.009 (0.183) Batch 1.223 (1.551) Remain 17:35:37 loss: 0.2097 Lr: 0.00564
[2023-12-25 06:24:05,313 INFO misc.py line 119 253097] Train: [20/100][468/510] Data 0.006 (0.183) Batch 1.129 (1.550) Remain 17:34:59 loss: 0.1592 Lr: 0.00564
[2023-12-25 06:24:06,389 INFO misc.py line 119 253097] Train: [20/100][469/510] Data 0.009 (0.183) Batch 1.077 (1.549) Remain 17:34:16 loss: 0.1917 Lr: 0.00564
[2023-12-25 06:24:11,216 INFO misc.py line 119 253097] Train: [20/100][470/510] Data 0.008 (0.182) Batch 4.829 (1.556) Remain 17:39:01 loss: 0.3089 Lr: 0.00564
[2023-12-25 06:24:12,151 INFO misc.py line 119 253097] Train: [20/100][471/510] Data 0.006 (0.182) Batch 0.936 (1.555) Remain 17:38:05 loss: 0.4010 Lr: 0.00564
[2023-12-25 06:24:13,389 INFO misc.py line 119 253097] Train: [20/100][472/510] Data 0.005 (0.182) Batch 1.238 (1.554) Remain 17:37:36 loss: 0.3792 Lr: 0.00564
[2023-12-25 06:24:14,674 INFO misc.py line 119 253097] Train: [20/100][473/510] Data 0.004 (0.181) Batch 1.280 (1.553) Remain 17:37:11 loss: 0.1843 Lr: 0.00564
[2023-12-25 06:24:15,531 INFO misc.py line 119 253097] Train: [20/100][474/510] Data 0.009 (0.181) Batch 0.861 (1.552) Remain 17:36:09 loss: 0.2765 Lr: 0.00564
[2023-12-25 06:24:16,728 INFO misc.py line 119 253097] Train: [20/100][475/510] Data 0.005 (0.180) Batch 1.198 (1.551) Remain 17:35:37 loss: 0.2664 Lr: 0.00564
[2023-12-25 06:24:17,716 INFO misc.py line 119 253097] Train: [20/100][476/510] Data 0.003 (0.180) Batch 0.987 (1.550) Remain 17:34:47 loss: 0.2114 Lr: 0.00564
[2023-12-25 06:24:18,770 INFO misc.py line 119 253097] Train: [20/100][477/510] Data 0.005 (0.180) Batch 1.055 (1.549) Remain 17:34:03 loss: 0.3361 Lr: 0.00564
[2023-12-25 06:24:20,031 INFO misc.py line 119 253097] Train: [20/100][478/510] Data 0.004 (0.179) Batch 1.254 (1.548) Remain 17:33:36 loss: 0.6932 Lr: 0.00564
[2023-12-25 06:24:21,310 INFO misc.py line 119 253097] Train: [20/100][479/510] Data 0.012 (0.179) Batch 1.284 (1.548) Remain 17:33:12 loss: 0.2628 Lr: 0.00564
[2023-12-25 06:24:22,419 INFO misc.py line 119 253097] Train: [20/100][480/510] Data 0.007 (0.179) Batch 1.107 (1.547) Remain 17:32:32 loss: 0.1065 Lr: 0.00564
[2023-12-25 06:24:24,107 INFO misc.py line 119 253097] Train: [20/100][481/510] Data 0.007 (0.178) Batch 1.688 (1.547) Remain 17:32:43 loss: 0.2305 Lr: 0.00564
[2023-12-25 06:24:25,298 INFO misc.py line 119 253097] Train: [20/100][482/510] Data 0.007 (0.178) Batch 1.194 (1.546) Remain 17:32:11 loss: 0.2872 Lr: 0.00564
[2023-12-25 06:24:26,329 INFO misc.py line 119 253097] Train: [20/100][483/510] Data 0.004 (0.178) Batch 1.028 (1.545) Remain 17:31:26 loss: 0.4204 Lr: 0.00564
[2023-12-25 06:24:27,574 INFO misc.py line 119 253097] Train: [20/100][484/510] Data 0.008 (0.177) Batch 1.248 (1.545) Remain 17:30:59 loss: 0.2809 Lr: 0.00564
[2023-12-25 06:24:31,804 INFO misc.py line 119 253097] Train: [20/100][485/510] Data 3.233 (0.184) Batch 4.231 (1.550) Remain 17:34:45 loss: 0.1435 Lr: 0.00564
[2023-12-25 06:24:32,679 INFO misc.py line 119 253097] Train: [20/100][486/510] Data 0.003 (0.183) Batch 0.870 (1.549) Remain 17:33:46 loss: 0.3935 Lr: 0.00564
[2023-12-25 06:24:33,832 INFO misc.py line 119 253097] Train: [20/100][487/510] Data 0.009 (0.183) Batch 1.154 (1.548) Remain 17:33:11 loss: 0.2682 Lr: 0.00564
[2023-12-25 06:24:35,037 INFO misc.py line 119 253097] Train: [20/100][488/510] Data 0.007 (0.182) Batch 1.207 (1.547) Remain 17:32:41 loss: 0.2585 Lr: 0.00564
[2023-12-25 06:24:39,296 INFO misc.py line 119 253097] Train: [20/100][489/510] Data 0.005 (0.182) Batch 4.261 (1.553) Remain 17:36:27 loss: 0.3525 Lr: 0.00564
[2023-12-25 06:24:40,573 INFO misc.py line 119 253097] Train: [20/100][490/510] Data 0.003 (0.182) Batch 1.272 (1.552) Remain 17:36:02 loss: 0.2466 Lr: 0.00564
[2023-12-25 06:24:41,786 INFO misc.py line 119 253097] Train: [20/100][491/510] Data 0.008 (0.181) Batch 1.216 (1.552) Remain 17:35:32 loss: 0.2168 Lr: 0.00564
[2023-12-25 06:24:42,842 INFO misc.py line 119 253097] Train: [20/100][492/510] Data 0.006 (0.181) Batch 1.053 (1.551) Remain 17:34:49 loss: 0.3560 Lr: 0.00564
[2023-12-25 06:24:44,133 INFO misc.py line 119 253097] Train: [20/100][493/510] Data 0.009 (0.181) Batch 1.292 (1.550) Remain 17:34:26 loss: 0.6529 Lr: 0.00564
[2023-12-25 06:24:45,178 INFO misc.py line 119 253097] Train: [20/100][494/510] Data 0.007 (0.180) Batch 1.045 (1.549) Remain 17:33:42 loss: 0.2851 Lr: 0.00564
[2023-12-25 06:24:46,379 INFO misc.py line 119 253097] Train: [20/100][495/510] Data 0.008 (0.180) Batch 1.204 (1.548) Remain 17:33:12 loss: 0.2476 Lr: 0.00564
[2023-12-25 06:24:47,334 INFO misc.py line 119 253097] Train: [20/100][496/510] Data 0.004 (0.180) Batch 0.954 (1.547) Remain 17:32:22 loss: 0.3267 Lr: 0.00564
[2023-12-25 06:24:48,479 INFO misc.py line 119 253097] Train: [20/100][497/510] Data 0.005 (0.179) Batch 1.145 (1.546) Remain 17:31:47 loss: 0.2313 Lr: 0.00564
[2023-12-25 06:24:49,526 INFO misc.py line 119 253097] Train: [20/100][498/510] Data 0.005 (0.179) Batch 1.048 (1.545) Remain 17:31:04 loss: 0.2139 Lr: 0.00564
[2023-12-25 06:24:50,575 INFO misc.py line 119 253097] Train: [20/100][499/510] Data 0.003 (0.179) Batch 1.049 (1.544) Remain 17:30:22 loss: 0.2411 Lr: 0.00564
[2023-12-25 06:24:51,681 INFO misc.py line 119 253097] Train: [20/100][500/510] Data 0.004 (0.178) Batch 1.104 (1.543) Remain 17:29:44 loss: 0.2757 Lr: 0.00564
[2023-12-25 06:24:52,930 INFO misc.py line 119 253097] Train: [20/100][501/510] Data 0.006 (0.178) Batch 1.250 (1.543) Remain 17:29:19 loss: 0.2765 Lr: 0.00564
[2023-12-25 06:24:54,089 INFO misc.py line 119 253097] Train: [20/100][502/510] Data 0.006 (0.177) Batch 1.158 (1.542) Remain 17:28:45 loss: 0.4652 Lr: 0.00564
[2023-12-25 06:24:55,154 INFO misc.py line 119 253097] Train: [20/100][503/510] Data 0.007 (0.177) Batch 1.068 (1.541) Remain 17:28:05 loss: 0.2022 Lr: 0.00564
[2023-12-25 06:24:56,397 INFO misc.py line 119 253097] Train: [20/100][504/510] Data 0.004 (0.177) Batch 1.240 (1.540) Remain 17:27:39 loss: 0.2369 Lr: 0.00564
[2023-12-25 06:24:57,333 INFO misc.py line 119 253097] Train: [20/100][505/510] Data 0.008 (0.176) Batch 0.940 (1.539) Remain 17:26:49 loss: 0.5603 Lr: 0.00564
[2023-12-25 06:24:58,521 INFO misc.py line 119 253097] Train: [20/100][506/510] Data 0.003 (0.176) Batch 1.188 (1.539) Remain 17:26:19 loss: 0.4069 Lr: 0.00564
[2023-12-25 06:24:59,560 INFO misc.py line 119 253097] Train: [20/100][507/510] Data 0.004 (0.176) Batch 1.038 (1.538) Remain 17:25:37 loss: 0.2623 Lr: 0.00564
[2023-12-25 06:25:00,601 INFO misc.py line 119 253097] Train: [20/100][508/510] Data 0.004 (0.175) Batch 1.042 (1.537) Remain 17:24:55 loss: 0.4942 Lr: 0.00564
[2023-12-25 06:25:01,683 INFO misc.py line 119 253097] Train: [20/100][509/510] Data 0.004 (0.175) Batch 1.082 (1.536) Remain 17:24:17 loss: 0.3133 Lr: 0.00564
[2023-12-25 06:25:02,684 INFO misc.py line 119 253097] Train: [20/100][510/510] Data 0.004 (0.175) Batch 1.001 (1.535) Remain 17:23:32 loss: 0.4508 Lr: 0.00564
[2023-12-25 06:25:02,685 INFO misc.py line 136 253097] Train result: loss: 0.2902 
[2023-12-25 06:25:02,685 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 06:25:30,385 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7265 
[2023-12-25 06:25:30,745 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.7216 
[2023-12-25 06:25:36,208 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6588 
[2023-12-25 06:25:36,732 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4112 
[2023-12-25 06:25:38,698 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8608 
[2023-12-25 06:25:39,125 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.7018 
[2023-12-25 06:25:40,003 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.5322 
[2023-12-25 06:25:40,557 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.6311 
[2023-12-25 06:25:42,365 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6318 
[2023-12-25 06:25:44,486 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.5411 
[2023-12-25 06:25:45,341 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3186 
[2023-12-25 06:25:45,778 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8276 
[2023-12-25 06:25:46,677 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6638 
[2023-12-25 06:25:49,621 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 1.0159 
[2023-12-25 06:25:50,088 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.7867 
[2023-12-25 06:25:50,696 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 1.0546 
[2023-12-25 06:25:51,395 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.7754 
[2023-12-25 06:25:53,251 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.5982/0.6614/0.8724.
[2023-12-25 06:25:53,252 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9045/0.9553
[2023-12-25 06:25:53,252 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9792/0.9925
[2023-12-25 06:25:53,252 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8049/0.9715
[2023-12-25 06:25:53,252 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0008/0.0237
[2023-12-25 06:25:53,252 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.1424/0.1554
[2023-12-25 06:25:53,252 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.3487/0.3517
[2023-12-25 06:25:53,252 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5571/0.6290
[2023-12-25 06:25:53,252 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7871/0.9282
[2023-12-25 06:25:53,253 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8904/0.9484
[2023-12-25 06:25:53,253 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4912/0.5180
[2023-12-25 06:25:53,253 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7324/0.8123
[2023-12-25 06:25:53,253 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6102/0.6679
[2023-12-25 06:25:53,253 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5275/0.6444
[2023-12-25 06:25:53,253 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 06:25:53,256 INFO misc.py line 165 253097] Currently Best mIoU: 0.6476
[2023-12-25 06:25:53,256 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 06:26:05,764 INFO misc.py line 119 253097] Train: [21/100][1/510] Data 9.673 (9.673) Batch 10.598 (10.598) Remain 120:06:44 loss: 0.2871 Lr: 0.00564
[2023-12-25 06:26:06,872 INFO misc.py line 119 253097] Train: [21/100][2/510] Data 0.003 (0.003) Batch 1.108 (1.108) Remain 12:33:28 loss: 0.2357 Lr: 0.00564
[2023-12-25 06:26:07,969 INFO misc.py line 119 253097] Train: [21/100][3/510] Data 0.004 (0.004) Batch 1.097 (1.097) Remain 12:26:14 loss: 0.4684 Lr: 0.00564
[2023-12-25 06:26:08,900 INFO misc.py line 119 253097] Train: [21/100][4/510] Data 0.004 (0.004) Batch 0.931 (0.931) Remain 10:32:41 loss: 0.4769 Lr: 0.00564
[2023-12-25 06:26:10,111 INFO misc.py line 119 253097] Train: [21/100][5/510] Data 0.004 (0.004) Batch 1.205 (1.068) Remain 12:05:53 loss: 0.5231 Lr: 0.00564
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[2023-12-25 06:26:25,791 INFO misc.py line 119 253097] Train: [21/100][7/510] Data 0.005 (0.006) Batch 14.410 (4.455) Remain 50:28:55 loss: 0.3864 Lr: 0.00564
[2023-12-25 06:26:26,850 INFO misc.py line 119 253097] Train: [21/100][8/510] Data 0.006 (0.006) Batch 1.060 (3.776) Remain 42:47:12 loss: 0.1086 Lr: 0.00564
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[2023-12-25 06:26:34,381 INFO misc.py line 119 253097] Train: [21/100][14/510] Data 0.011 (0.006) Batch 1.292 (2.400) Remain 27:11:34 loss: 0.3909 Lr: 0.00564
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[2023-12-25 06:26:36,566 INFO misc.py line 119 253097] Train: [21/100][16/510] Data 0.009 (0.007) Batch 1.093 (2.200) Remain 24:55:15 loss: 0.3674 Lr: 0.00564
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[2023-12-25 06:26:39,784 INFO misc.py line 119 253097] Train: [21/100][19/510] Data 0.004 (0.007) Batch 1.296 (1.988) Remain 22:31:18 loss: 0.2357 Lr: 0.00564
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[2023-12-25 06:26:42,125 INFO misc.py line 119 253097] Train: [21/100][21/510] Data 0.004 (0.006) Batch 1.237 (1.897) Remain 21:29:37 loss: 0.2314 Lr: 0.00564
[2023-12-25 06:26:52,282 INFO misc.py line 119 253097] Train: [21/100][22/510] Data 8.965 (0.478) Batch 10.158 (2.332) Remain 26:25:05 loss: 0.2305 Lr: 0.00564
[2023-12-25 06:26:53,477 INFO misc.py line 119 253097] Train: [21/100][23/510] Data 0.004 (0.454) Batch 1.189 (2.275) Remain 25:46:11 loss: 0.4832 Lr: 0.00564
[2023-12-25 06:26:54,582 INFO misc.py line 119 253097] Train: [21/100][24/510] Data 0.011 (0.433) Batch 1.110 (2.220) Remain 25:08:27 loss: 0.3136 Lr: 0.00564
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[2023-12-25 06:26:56,862 INFO misc.py line 119 253097] Train: [21/100][26/510] Data 0.008 (0.396) Batch 1.254 (2.126) Remain 24:04:25 loss: 0.2322 Lr: 0.00564
[2023-12-25 06:26:57,950 INFO misc.py line 119 253097] Train: [21/100][27/510] Data 0.010 (0.380) Batch 1.091 (2.082) Remain 23:35:06 loss: 0.2923 Lr: 0.00564
[2023-12-25 06:26:59,164 INFO misc.py line 119 253097] Train: [21/100][28/510] Data 0.006 (0.365) Batch 1.215 (2.048) Remain 23:11:30 loss: 0.2968 Lr: 0.00564
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[2023-12-25 06:27:03,979 INFO misc.py line 119 253097] Train: [21/100][31/510] Data 0.005 (0.364) Batch 1.193 (2.000) Remain 22:39:05 loss: 0.2510 Lr: 0.00564
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[2023-12-25 06:27:07,432 INFO misc.py line 119 253097] Train: [21/100][33/510] Data 0.003 (0.340) Batch 1.177 (1.982) Remain 22:26:43 loss: 0.2983 Lr: 0.00564
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[2023-12-25 06:27:09,772 INFO misc.py line 119 253097] Train: [21/100][35/510] Data 0.009 (0.319) Batch 1.095 (1.931) Remain 21:52:08 loss: 0.2517 Lr: 0.00564
[2023-12-25 06:27:10,883 INFO misc.py line 119 253097] Train: [21/100][36/510] Data 0.006 (0.310) Batch 1.110 (1.906) Remain 21:35:11 loss: 0.1913 Lr: 0.00564
[2023-12-25 06:27:11,836 INFO misc.py line 119 253097] Train: [21/100][37/510] Data 0.008 (0.301) Batch 0.957 (1.878) Remain 21:16:11 loss: 0.1827 Lr: 0.00564
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[2023-12-25 06:27:17,264 INFO misc.py line 119 253097] Train: [21/100][39/510] Data 0.003 (0.284) Batch 1.201 (1.925) Remain 21:47:38 loss: 0.3891 Lr: 0.00564
[2023-12-25 06:27:18,364 INFO misc.py line 119 253097] Train: [21/100][40/510] Data 0.005 (0.277) Batch 1.101 (1.903) Remain 21:32:28 loss: 0.3426 Lr: 0.00563
[2023-12-25 06:27:19,446 INFO misc.py line 119 253097] Train: [21/100][41/510] Data 0.004 (0.269) Batch 1.079 (1.881) Remain 21:17:42 loss: 0.3934 Lr: 0.00563
[2023-12-25 06:27:20,667 INFO misc.py line 119 253097] Train: [21/100][42/510] Data 0.007 (0.263) Batch 1.209 (1.864) Remain 21:05:58 loss: 0.3532 Lr: 0.00563
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[2023-12-25 06:27:23,036 INFO misc.py line 119 253097] Train: [21/100][44/510] Data 0.008 (0.251) Batch 1.139 (1.831) Remain 20:43:33 loss: 0.3897 Lr: 0.00563
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[2023-12-25 06:27:25,115 INFO misc.py line 119 253097] Train: [21/100][46/510] Data 0.005 (0.239) Batch 0.924 (1.794) Remain 20:18:36 loss: 0.3517 Lr: 0.00563
[2023-12-25 06:27:26,368 INFO misc.py line 119 253097] Train: [21/100][47/510] Data 0.003 (0.234) Batch 1.247 (1.782) Remain 20:10:08 loss: 0.3097 Lr: 0.00563
[2023-12-25 06:27:27,326 INFO misc.py line 119 253097] Train: [21/100][48/510] Data 0.009 (0.229) Batch 0.959 (1.763) Remain 19:57:41 loss: 0.2070 Lr: 0.00563
[2023-12-25 06:27:28,533 INFO misc.py line 119 253097] Train: [21/100][49/510] Data 0.009 (0.224) Batch 1.207 (1.751) Remain 19:49:27 loss: 0.2514 Lr: 0.00563
[2023-12-25 06:27:29,848 INFO misc.py line 119 253097] Train: [21/100][50/510] Data 0.008 (0.219) Batch 1.312 (1.742) Remain 19:43:04 loss: 0.2192 Lr: 0.00563
[2023-12-25 06:27:30,947 INFO misc.py line 119 253097] Train: [21/100][51/510] Data 0.011 (0.215) Batch 1.104 (1.729) Remain 19:34:01 loss: 0.4210 Lr: 0.00563
[2023-12-25 06:27:32,147 INFO misc.py line 119 253097] Train: [21/100][52/510] Data 0.006 (0.211) Batch 1.195 (1.718) Remain 19:26:35 loss: 0.2910 Lr: 0.00563
[2023-12-25 06:27:39,566 INFO misc.py line 119 253097] Train: [21/100][53/510] Data 0.011 (0.207) Batch 7.425 (1.832) Remain 20:44:04 loss: 0.2811 Lr: 0.00563
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[2023-12-25 06:27:42,043 INFO misc.py line 119 253097] Train: [21/100][55/510] Data 0.009 (0.199) Batch 1.202 (1.809) Remain 20:28:28 loss: 0.2172 Lr: 0.00563
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[2023-12-25 06:27:47,061 INFO misc.py line 119 253097] Train: [21/100][57/510] Data 0.003 (0.192) Batch 3.799 (1.835) Remain 20:46:03 loss: 0.4787 Lr: 0.00563
[2023-12-25 06:27:48,363 INFO misc.py line 119 253097] Train: [21/100][58/510] Data 0.005 (0.189) Batch 1.300 (1.825) Remain 20:39:25 loss: 0.2014 Lr: 0.00563
[2023-12-25 06:27:49,483 INFO misc.py line 119 253097] Train: [21/100][59/510] Data 0.007 (0.185) Batch 1.117 (1.813) Remain 20:30:48 loss: 0.2589 Lr: 0.00563
[2023-12-25 06:27:50,757 INFO misc.py line 119 253097] Train: [21/100][60/510] Data 0.010 (0.182) Batch 1.275 (1.803) Remain 20:24:22 loss: 0.3027 Lr: 0.00563
[2023-12-25 06:27:51,998 INFO misc.py line 119 253097] Train: [21/100][61/510] Data 0.009 (0.179) Batch 1.240 (1.794) Remain 20:17:45 loss: 0.4141 Lr: 0.00563
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[2023-12-25 06:27:57,190 INFO misc.py line 119 253097] Train: [21/100][63/510] Data 0.004 (0.174) Batch 4.089 (1.820) Remain 20:35:55 loss: 0.1956 Lr: 0.00563
[2023-12-25 06:27:58,481 INFO misc.py line 119 253097] Train: [21/100][64/510] Data 0.004 (0.171) Batch 1.286 (1.812) Remain 20:29:56 loss: 0.4476 Lr: 0.00563
[2023-12-25 06:27:59,629 INFO misc.py line 119 253097] Train: [21/100][65/510] Data 0.009 (0.168) Batch 1.148 (1.801) Remain 20:22:38 loss: 0.3244 Lr: 0.00563
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[2023-12-25 06:28:09,965 INFO misc.py line 119 253097] Train: [21/100][74/510] Data 0.006 (0.148) Batch 1.231 (1.718) Remain 19:26:17 loss: 0.1342 Lr: 0.00563
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[2023-12-25 06:29:04,237 INFO misc.py line 119 253097] Train: [21/100][109/510] Data 0.003 (0.101) Batch 1.278 (1.663) Remain 18:47:43 loss: 0.1990 Lr: 0.00563
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[2023-12-25 06:29:13,386 INFO misc.py line 119 253097] Train: [21/100][112/510] Data 0.004 (0.098) Batch 6.581 (1.701) Remain 19:13:33 loss: 0.2178 Lr: 0.00563
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[2023-12-25 06:29:27,696 INFO misc.py line 119 253097] Train: [21/100][123/510] Data 0.009 (0.090) Batch 1.219 (1.664) Remain 18:48:20 loss: 0.2817 Lr: 0.00563
[2023-12-25 06:29:34,200 INFO misc.py line 119 253097] Train: [21/100][124/510] Data 0.010 (0.089) Batch 6.504 (1.704) Remain 19:15:25 loss: 0.4435 Lr: 0.00563
[2023-12-25 06:29:35,384 INFO misc.py line 119 253097] Train: [21/100][125/510] Data 0.010 (0.088) Batch 1.188 (1.700) Remain 19:12:31 loss: 0.2172 Lr: 0.00563
[2023-12-25 06:29:41,055 INFO misc.py line 119 253097] Train: [21/100][126/510] Data 0.006 (0.088) Batch 5.671 (1.732) Remain 19:34:23 loss: 0.2458 Lr: 0.00563
[2023-12-25 06:29:42,198 INFO misc.py line 119 253097] Train: [21/100][127/510] Data 0.007 (0.087) Batch 1.145 (1.728) Remain 19:31:08 loss: 0.3470 Lr: 0.00563
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[2023-12-25 06:29:44,509 INFO misc.py line 119 253097] Train: [21/100][129/510] Data 0.004 (0.086) Batch 1.116 (1.719) Remain 19:24:55 loss: 0.1857 Lr: 0.00563
[2023-12-25 06:29:45,716 INFO misc.py line 119 253097] Train: [21/100][130/510] Data 0.003 (0.085) Batch 1.208 (1.715) Remain 19:22:10 loss: 0.1527 Lr: 0.00563
[2023-12-25 06:29:46,850 INFO misc.py line 119 253097] Train: [21/100][131/510] Data 0.003 (0.084) Batch 1.133 (1.710) Remain 19:19:04 loss: 0.2752 Lr: 0.00563
[2023-12-25 06:29:48,082 INFO misc.py line 119 253097] Train: [21/100][132/510] Data 0.004 (0.084) Batch 1.232 (1.706) Remain 19:16:31 loss: 0.1641 Lr: 0.00563
[2023-12-25 06:29:49,251 INFO misc.py line 119 253097] Train: [21/100][133/510] Data 0.005 (0.083) Batch 1.164 (1.702) Remain 19:13:40 loss: 0.6154 Lr: 0.00563
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[2023-12-25 06:29:53,008 INFO misc.py line 119 253097] Train: [21/100][136/510] Data 0.008 (0.081) Batch 1.295 (1.692) Remain 19:06:43 loss: 0.3236 Lr: 0.00563
[2023-12-25 06:29:54,085 INFO misc.py line 119 253097] Train: [21/100][137/510] Data 0.006 (0.081) Batch 1.078 (1.687) Remain 19:03:35 loss: 0.2609 Lr: 0.00563
[2023-12-25 06:29:55,237 INFO misc.py line 119 253097] Train: [21/100][138/510] Data 0.005 (0.080) Batch 1.150 (1.683) Remain 19:00:52 loss: 0.2185 Lr: 0.00563
[2023-12-25 06:29:56,379 INFO misc.py line 119 253097] Train: [21/100][139/510] Data 0.007 (0.080) Batch 1.143 (1.679) Remain 18:58:08 loss: 0.2103 Lr: 0.00563
[2023-12-25 06:29:59,443 INFO misc.py line 119 253097] Train: [21/100][140/510] Data 2.406 (0.097) Batch 3.065 (1.690) Remain 19:04:58 loss: 0.1688 Lr: 0.00563
[2023-12-25 06:30:00,599 INFO misc.py line 119 253097] Train: [21/100][141/510] Data 0.004 (0.096) Batch 1.155 (1.686) Remain 19:02:19 loss: 0.3696 Lr: 0.00563
[2023-12-25 06:30:01,793 INFO misc.py line 119 253097] Train: [21/100][142/510] Data 0.005 (0.095) Batch 1.189 (1.682) Remain 18:59:52 loss: 0.3884 Lr: 0.00563
[2023-12-25 06:30:02,977 INFO misc.py line 119 253097] Train: [21/100][143/510] Data 0.010 (0.095) Batch 1.186 (1.679) Remain 18:57:26 loss: 0.1800 Lr: 0.00563
[2023-12-25 06:30:04,346 INFO misc.py line 119 253097] Train: [21/100][144/510] Data 0.009 (0.094) Batch 1.369 (1.676) Remain 18:55:55 loss: 0.2812 Lr: 0.00563
[2023-12-25 06:30:05,544 INFO misc.py line 119 253097] Train: [21/100][145/510] Data 0.008 (0.094) Batch 1.202 (1.673) Remain 18:53:38 loss: 0.2719 Lr: 0.00563
[2023-12-25 06:30:06,624 INFO misc.py line 119 253097] Train: [21/100][146/510] Data 0.004 (0.093) Batch 1.076 (1.669) Remain 18:50:46 loss: 0.4651 Lr: 0.00563
[2023-12-25 06:30:07,865 INFO misc.py line 119 253097] Train: [21/100][147/510] Data 0.008 (0.092) Batch 1.241 (1.666) Remain 18:48:44 loss: 0.2142 Lr: 0.00562
[2023-12-25 06:30:08,838 INFO misc.py line 119 253097] Train: [21/100][148/510] Data 0.008 (0.092) Batch 0.977 (1.661) Remain 18:45:29 loss: 0.3543 Lr: 0.00562
[2023-12-25 06:30:10,119 INFO misc.py line 119 253097] Train: [21/100][149/510] Data 0.003 (0.091) Batch 1.278 (1.659) Remain 18:43:41 loss: 0.5000 Lr: 0.00562
[2023-12-25 06:30:11,390 INFO misc.py line 119 253097] Train: [21/100][150/510] Data 0.006 (0.091) Batch 1.272 (1.656) Remain 18:41:52 loss: 0.1766 Lr: 0.00562
[2023-12-25 06:30:15,596 INFO misc.py line 119 253097] Train: [21/100][151/510] Data 0.005 (0.090) Batch 4.206 (1.673) Remain 18:53:31 loss: 0.2765 Lr: 0.00562
[2023-12-25 06:30:16,868 INFO misc.py line 119 253097] Train: [21/100][152/510] Data 0.005 (0.090) Batch 1.272 (1.670) Remain 18:51:40 loss: 0.1841 Lr: 0.00562
[2023-12-25 06:30:18,729 INFO misc.py line 119 253097] Train: [21/100][153/510] Data 0.849 (0.095) Batch 1.862 (1.672) Remain 18:52:30 loss: 0.5329 Lr: 0.00562
[2023-12-25 06:30:19,568 INFO misc.py line 119 253097] Train: [21/100][154/510] Data 0.004 (0.094) Batch 0.839 (1.666) Remain 18:48:45 loss: 0.3690 Lr: 0.00562
[2023-12-25 06:30:20,767 INFO misc.py line 119 253097] Train: [21/100][155/510] Data 0.005 (0.093) Batch 1.198 (1.663) Remain 18:46:38 loss: 0.5667 Lr: 0.00562
[2023-12-25 06:30:21,965 INFO misc.py line 119 253097] Train: [21/100][156/510] Data 0.004 (0.093) Batch 1.194 (1.660) Remain 18:44:31 loss: 0.2859 Lr: 0.00562
[2023-12-25 06:30:22,926 INFO misc.py line 119 253097] Train: [21/100][157/510] Data 0.008 (0.092) Batch 0.966 (1.656) Remain 18:41:27 loss: 0.2332 Lr: 0.00562
[2023-12-25 06:30:23,979 INFO misc.py line 119 253097] Train: [21/100][158/510] Data 0.004 (0.092) Batch 1.053 (1.652) Remain 18:38:47 loss: 0.1704 Lr: 0.00562
[2023-12-25 06:30:29,803 INFO misc.py line 119 253097] Train: [21/100][159/510] Data 4.460 (0.120) Batch 5.823 (1.678) Remain 18:56:52 loss: 0.1820 Lr: 0.00562
[2023-12-25 06:30:31,104 INFO misc.py line 119 253097] Train: [21/100][160/510] Data 0.005 (0.119) Batch 1.297 (1.676) Remain 18:55:12 loss: 0.3422 Lr: 0.00562
[2023-12-25 06:30:32,166 INFO misc.py line 119 253097] Train: [21/100][161/510] Data 0.008 (0.118) Batch 1.067 (1.672) Remain 18:52:33 loss: 0.1710 Lr: 0.00562
[2023-12-25 06:30:33,230 INFO misc.py line 119 253097] Train: [21/100][162/510] Data 0.004 (0.118) Batch 1.062 (1.668) Remain 18:49:56 loss: 0.2504 Lr: 0.00562
[2023-12-25 06:30:34,534 INFO misc.py line 119 253097] Train: [21/100][163/510] Data 0.006 (0.117) Batch 1.302 (1.666) Remain 18:48:21 loss: 0.3732 Lr: 0.00562
[2023-12-25 06:30:35,867 INFO misc.py line 119 253097] Train: [21/100][164/510] Data 0.007 (0.116) Batch 1.338 (1.664) Remain 18:46:56 loss: 0.2332 Lr: 0.00562
[2023-12-25 06:30:37,048 INFO misc.py line 119 253097] Train: [21/100][165/510] Data 0.003 (0.115) Batch 1.181 (1.661) Remain 18:44:53 loss: 0.4371 Lr: 0.00562
[2023-12-25 06:30:38,251 INFO misc.py line 119 253097] Train: [21/100][166/510] Data 0.004 (0.115) Batch 1.202 (1.658) Remain 18:42:57 loss: 0.3023 Lr: 0.00562
[2023-12-25 06:30:39,463 INFO misc.py line 119 253097] Train: [21/100][167/510] Data 0.004 (0.114) Batch 1.212 (1.655) Remain 18:41:05 loss: 0.3541 Lr: 0.00562
[2023-12-25 06:30:40,616 INFO misc.py line 119 253097] Train: [21/100][168/510] Data 0.004 (0.113) Batch 1.150 (1.652) Remain 18:38:59 loss: 0.2322 Lr: 0.00562
[2023-12-25 06:30:41,746 INFO misc.py line 119 253097] Train: [21/100][169/510] Data 0.006 (0.113) Batch 1.131 (1.649) Remain 18:36:50 loss: 0.2636 Lr: 0.00562
[2023-12-25 06:30:42,691 INFO misc.py line 119 253097] Train: [21/100][170/510] Data 0.006 (0.112) Batch 0.947 (1.645) Remain 18:33:57 loss: 0.4152 Lr: 0.00562
[2023-12-25 06:30:43,604 INFO misc.py line 119 253097] Train: [21/100][171/510] Data 0.005 (0.112) Batch 0.913 (1.641) Remain 18:30:59 loss: 0.2632 Lr: 0.00562
[2023-12-25 06:30:44,676 INFO misc.py line 119 253097] Train: [21/100][172/510] Data 0.005 (0.111) Batch 1.066 (1.637) Remain 18:28:39 loss: 0.2606 Lr: 0.00562
[2023-12-25 06:30:45,792 INFO misc.py line 119 253097] Train: [21/100][173/510] Data 0.011 (0.110) Batch 1.116 (1.634) Remain 18:26:33 loss: 0.1573 Lr: 0.00562
[2023-12-25 06:30:47,024 INFO misc.py line 119 253097] Train: [21/100][174/510] Data 0.010 (0.110) Batch 1.237 (1.632) Remain 18:24:57 loss: 0.3205 Lr: 0.00562
[2023-12-25 06:30:47,988 INFO misc.py line 119 253097] Train: [21/100][175/510] Data 0.004 (0.109) Batch 0.963 (1.628) Remain 18:22:17 loss: 0.2886 Lr: 0.00562
[2023-12-25 06:30:49,085 INFO misc.py line 119 253097] Train: [21/100][176/510] Data 0.005 (0.109) Batch 1.099 (1.625) Remain 18:20:12 loss: 0.3808 Lr: 0.00562
[2023-12-25 06:30:59,006 INFO misc.py line 119 253097] Train: [21/100][177/510] Data 0.003 (0.108) Batch 9.920 (1.673) Remain 18:52:26 loss: 0.2299 Lr: 0.00562
[2023-12-25 06:31:00,169 INFO misc.py line 119 253097] Train: [21/100][178/510] Data 0.004 (0.107) Batch 1.162 (1.670) Remain 18:50:26 loss: 0.3809 Lr: 0.00562
[2023-12-25 06:31:01,380 INFO misc.py line 119 253097] Train: [21/100][179/510] Data 0.004 (0.107) Batch 1.208 (1.667) Remain 18:48:38 loss: 0.2634 Lr: 0.00562
[2023-12-25 06:31:02,627 INFO misc.py line 119 253097] Train: [21/100][180/510] Data 0.008 (0.106) Batch 1.246 (1.665) Remain 18:47:00 loss: 0.3805 Lr: 0.00562
[2023-12-25 06:31:03,654 INFO misc.py line 119 253097] Train: [21/100][181/510] Data 0.009 (0.106) Batch 1.021 (1.661) Remain 18:44:31 loss: 0.1503 Lr: 0.00562
[2023-12-25 06:31:04,697 INFO misc.py line 119 253097] Train: [21/100][182/510] Data 0.015 (0.105) Batch 1.052 (1.658) Remain 18:42:11 loss: 0.2719 Lr: 0.00562
[2023-12-25 06:31:05,918 INFO misc.py line 119 253097] Train: [21/100][183/510] Data 0.005 (0.105) Batch 1.223 (1.655) Remain 18:40:32 loss: 0.2004 Lr: 0.00562
[2023-12-25 06:31:07,163 INFO misc.py line 119 253097] Train: [21/100][184/510] Data 0.003 (0.104) Batch 1.244 (1.653) Remain 18:38:58 loss: 0.2221 Lr: 0.00562
[2023-12-25 06:31:08,231 INFO misc.py line 119 253097] Train: [21/100][185/510] Data 0.005 (0.103) Batch 1.063 (1.650) Remain 18:36:44 loss: 0.2213 Lr: 0.00562
[2023-12-25 06:31:09,276 INFO misc.py line 119 253097] Train: [21/100][186/510] Data 0.011 (0.103) Batch 1.052 (1.646) Remain 18:34:30 loss: 0.2845 Lr: 0.00562
[2023-12-25 06:31:10,405 INFO misc.py line 119 253097] Train: [21/100][187/510] Data 0.003 (0.102) Batch 1.129 (1.644) Remain 18:32:34 loss: 0.2858 Lr: 0.00562
[2023-12-25 06:31:11,726 INFO misc.py line 119 253097] Train: [21/100][188/510] Data 0.003 (0.102) Batch 1.321 (1.642) Remain 18:31:22 loss: 0.1967 Lr: 0.00562
[2023-12-25 06:31:12,759 INFO misc.py line 119 253097] Train: [21/100][189/510] Data 0.003 (0.101) Batch 1.032 (1.639) Remain 18:29:07 loss: 0.2924 Lr: 0.00562
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[2023-12-25 06:31:27,084 INFO misc.py line 119 253097] Train: [21/100][196/510] Data 0.009 (0.098) Batch 7.151 (1.653) Remain 18:38:56 loss: 0.3643 Lr: 0.00562
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[2023-12-25 06:31:29,445 INFO misc.py line 119 253097] Train: [21/100][198/510] Data 0.005 (0.097) Batch 1.263 (1.649) Remain 18:35:36 loss: 0.3525 Lr: 0.00562
[2023-12-25 06:31:30,620 INFO misc.py line 119 253097] Train: [21/100][199/510] Data 0.003 (0.096) Batch 1.171 (1.646) Remain 18:33:55 loss: 0.3691 Lr: 0.00562
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[2023-12-25 06:31:37,579 INFO misc.py line 119 253097] Train: [21/100][205/510] Data 0.004 (0.094) Batch 1.254 (1.632) Remain 18:23:59 loss: 0.3923 Lr: 0.00562
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[2023-12-25 06:31:42,183 INFO misc.py line 119 253097] Train: [21/100][209/510] Data 0.017 (0.092) Batch 1.082 (1.622) Remain 18:17:34 loss: 0.3735 Lr: 0.00562
[2023-12-25 06:31:43,366 INFO misc.py line 119 253097] Train: [21/100][210/510] Data 0.004 (0.092) Batch 1.183 (1.620) Remain 18:16:06 loss: 0.2460 Lr: 0.00562
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[2023-12-25 06:31:46,627 INFO misc.py line 119 253097] Train: [21/100][213/510] Data 0.005 (0.090) Batch 1.029 (1.613) Remain 18:10:51 loss: 0.2197 Lr: 0.00562
[2023-12-25 06:31:52,158 INFO misc.py line 119 253097] Train: [21/100][214/510] Data 0.011 (0.090) Batch 5.538 (1.631) Remain 18:23:24 loss: 0.2114 Lr: 0.00562
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[2023-12-25 06:31:54,384 INFO misc.py line 119 253097] Train: [21/100][216/510] Data 0.006 (0.089) Batch 1.148 (1.626) Remain 18:20:04 loss: 0.3429 Lr: 0.00562
[2023-12-25 06:31:55,475 INFO misc.py line 119 253097] Train: [21/100][217/510] Data 0.004 (0.089) Batch 1.088 (1.624) Remain 18:18:20 loss: 0.1949 Lr: 0.00562
[2023-12-25 06:32:00,359 INFO misc.py line 119 253097] Train: [21/100][218/510] Data 3.952 (0.107) Batch 4.888 (1.639) Remain 18:28:34 loss: 0.2223 Lr: 0.00562
[2023-12-25 06:32:01,459 INFO misc.py line 119 253097] Train: [21/100][219/510] Data 0.004 (0.106) Batch 1.095 (1.637) Remain 18:26:50 loss: 0.3680 Lr: 0.00562
[2023-12-25 06:32:02,729 INFO misc.py line 119 253097] Train: [21/100][220/510] Data 0.008 (0.106) Batch 1.270 (1.635) Remain 18:25:40 loss: 0.1801 Lr: 0.00562
[2023-12-25 06:32:03,783 INFO misc.py line 119 253097] Train: [21/100][221/510] Data 0.008 (0.105) Batch 1.054 (1.632) Remain 18:23:51 loss: 0.1320 Lr: 0.00562
[2023-12-25 06:32:05,031 INFO misc.py line 119 253097] Train: [21/100][222/510] Data 0.008 (0.105) Batch 1.247 (1.630) Remain 18:22:38 loss: 0.4121 Lr: 0.00562
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[2023-12-25 06:32:08,645 INFO misc.py line 119 253097] Train: [21/100][224/510] Data 1.509 (0.111) Batch 2.307 (1.632) Remain 18:23:40 loss: 0.4027 Lr: 0.00562
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[2023-12-25 06:32:10,848 INFO misc.py line 119 253097] Train: [21/100][226/510] Data 0.027 (0.110) Batch 0.936 (1.627) Remain 18:20:24 loss: 0.1332 Lr: 0.00562
[2023-12-25 06:32:11,892 INFO misc.py line 119 253097] Train: [21/100][227/510] Data 0.005 (0.110) Batch 1.044 (1.625) Remain 18:18:37 loss: 0.2777 Lr: 0.00562
[2023-12-25 06:32:13,005 INFO misc.py line 119 253097] Train: [21/100][228/510] Data 0.003 (0.109) Batch 1.112 (1.622) Remain 18:17:03 loss: 0.2069 Lr: 0.00562
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[2023-12-25 06:32:14,788 INFO misc.py line 119 253097] Train: [21/100][230/510] Data 0.004 (0.108) Batch 0.962 (1.616) Remain 18:12:38 loss: 0.2168 Lr: 0.00562
[2023-12-25 06:32:16,008 INFO misc.py line 119 253097] Train: [21/100][231/510] Data 0.004 (0.108) Batch 1.188 (1.614) Remain 18:11:20 loss: 0.2099 Lr: 0.00562
[2023-12-25 06:32:22,643 INFO misc.py line 119 253097] Train: [21/100][232/510] Data 0.036 (0.107) Batch 6.667 (1.636) Remain 18:26:14 loss: 0.4146 Lr: 0.00562
[2023-12-25 06:32:23,885 INFO misc.py line 119 253097] Train: [21/100][233/510] Data 0.004 (0.107) Batch 1.241 (1.634) Remain 18:25:03 loss: 0.1874 Lr: 0.00562
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[2023-12-25 06:32:26,121 INFO misc.py line 119 253097] Train: [21/100][235/510] Data 0.003 (0.106) Batch 1.122 (1.630) Remain 18:21:58 loss: 0.4569 Lr: 0.00562
[2023-12-25 06:32:27,035 INFO misc.py line 119 253097] Train: [21/100][236/510] Data 0.007 (0.106) Batch 0.917 (1.627) Remain 18:19:53 loss: 0.2629 Lr: 0.00562
[2023-12-25 06:32:28,216 INFO misc.py line 119 253097] Train: [21/100][237/510] Data 0.003 (0.105) Batch 1.181 (1.625) Remain 18:18:34 loss: 0.2638 Lr: 0.00562
[2023-12-25 06:32:29,259 INFO misc.py line 119 253097] Train: [21/100][238/510] Data 0.004 (0.105) Batch 1.043 (1.623) Remain 18:16:52 loss: 0.2603 Lr: 0.00562
[2023-12-25 06:32:30,342 INFO misc.py line 119 253097] Train: [21/100][239/510] Data 0.004 (0.104) Batch 1.083 (1.620) Remain 18:15:17 loss: 0.2881 Lr: 0.00562
[2023-12-25 06:32:31,419 INFO misc.py line 119 253097] Train: [21/100][240/510] Data 0.005 (0.104) Batch 1.077 (1.618) Remain 18:13:43 loss: 0.2880 Lr: 0.00562
[2023-12-25 06:32:32,504 INFO misc.py line 119 253097] Train: [21/100][241/510] Data 0.005 (0.104) Batch 1.086 (1.616) Remain 18:12:10 loss: 0.3870 Lr: 0.00562
[2023-12-25 06:32:33,700 INFO misc.py line 119 253097] Train: [21/100][242/510] Data 0.003 (0.103) Batch 1.196 (1.614) Remain 18:10:58 loss: 0.3437 Lr: 0.00562
[2023-12-25 06:32:34,989 INFO misc.py line 119 253097] Train: [21/100][243/510] Data 0.004 (0.103) Batch 1.285 (1.613) Remain 18:10:00 loss: 0.4054 Lr: 0.00562
[2023-12-25 06:32:37,756 INFO misc.py line 119 253097] Train: [21/100][244/510] Data 0.008 (0.102) Batch 2.772 (1.617) Remain 18:13:14 loss: 0.3054 Lr: 0.00562
[2023-12-25 06:32:38,872 INFO misc.py line 119 253097] Train: [21/100][245/510] Data 0.004 (0.102) Batch 1.115 (1.615) Remain 18:11:48 loss: 0.1919 Lr: 0.00562
[2023-12-25 06:32:40,149 INFO misc.py line 119 253097] Train: [21/100][246/510] Data 0.004 (0.102) Batch 1.277 (1.614) Remain 18:10:50 loss: 0.1784 Lr: 0.00562
[2023-12-25 06:32:41,336 INFO misc.py line 119 253097] Train: [21/100][247/510] Data 0.004 (0.101) Batch 1.187 (1.612) Remain 18:09:37 loss: 0.3102 Lr: 0.00562
[2023-12-25 06:32:52,493 INFO misc.py line 119 253097] Train: [21/100][248/510] Data 0.004 (0.101) Batch 11.157 (1.651) Remain 18:35:56 loss: 0.3547 Lr: 0.00562
[2023-12-25 06:32:53,810 INFO misc.py line 119 253097] Train: [21/100][249/510] Data 0.004 (0.100) Batch 1.314 (1.650) Remain 18:34:58 loss: 0.3404 Lr: 0.00562
[2023-12-25 06:32:54,947 INFO misc.py line 119 253097] Train: [21/100][250/510] Data 0.006 (0.100) Batch 1.098 (1.648) Remain 18:33:26 loss: 0.2646 Lr: 0.00562
[2023-12-25 06:32:56,071 INFO misc.py line 119 253097] Train: [21/100][251/510] Data 0.049 (0.100) Batch 1.165 (1.646) Remain 18:32:06 loss: 0.3069 Lr: 0.00562
[2023-12-25 06:32:57,246 INFO misc.py line 119 253097] Train: [21/100][252/510] Data 0.004 (0.099) Batch 1.173 (1.644) Remain 18:30:47 loss: 0.1993 Lr: 0.00561
[2023-12-25 06:32:58,501 INFO misc.py line 119 253097] Train: [21/100][253/510] Data 0.006 (0.099) Batch 1.254 (1.642) Remain 18:29:42 loss: 0.2619 Lr: 0.00561
[2023-12-25 06:32:59,582 INFO misc.py line 119 253097] Train: [21/100][254/510] Data 0.008 (0.099) Batch 1.084 (1.640) Remain 18:28:10 loss: 0.2656 Lr: 0.00561
[2023-12-25 06:33:00,843 INFO misc.py line 119 253097] Train: [21/100][255/510] Data 0.005 (0.098) Batch 1.262 (1.638) Remain 18:27:08 loss: 0.4965 Lr: 0.00561
[2023-12-25 06:33:02,083 INFO misc.py line 119 253097] Train: [21/100][256/510] Data 0.005 (0.098) Batch 1.241 (1.637) Remain 18:26:02 loss: 0.3542 Lr: 0.00561
[2023-12-25 06:33:03,294 INFO misc.py line 119 253097] Train: [21/100][257/510] Data 0.004 (0.098) Batch 1.207 (1.635) Remain 18:24:52 loss: 0.4719 Lr: 0.00561
[2023-12-25 06:33:04,468 INFO misc.py line 119 253097] Train: [21/100][258/510] Data 0.008 (0.097) Batch 1.175 (1.633) Remain 18:23:37 loss: 0.3024 Lr: 0.00561
[2023-12-25 06:33:05,595 INFO misc.py line 119 253097] Train: [21/100][259/510] Data 0.006 (0.097) Batch 1.130 (1.631) Remain 18:22:16 loss: 0.3386 Lr: 0.00561
[2023-12-25 06:33:06,740 INFO misc.py line 119 253097] Train: [21/100][260/510] Data 0.004 (0.096) Batch 1.138 (1.629) Remain 18:20:56 loss: 0.1960 Lr: 0.00561
[2023-12-25 06:33:07,845 INFO misc.py line 119 253097] Train: [21/100][261/510] Data 0.012 (0.096) Batch 1.107 (1.627) Remain 18:19:33 loss: 0.2428 Lr: 0.00561
[2023-12-25 06:33:08,975 INFO misc.py line 119 253097] Train: [21/100][262/510] Data 0.010 (0.096) Batch 1.133 (1.625) Remain 18:18:14 loss: 0.1789 Lr: 0.00561
[2023-12-25 06:33:10,085 INFO misc.py line 119 253097] Train: [21/100][263/510] Data 0.007 (0.095) Batch 1.108 (1.624) Remain 18:16:51 loss: 0.2390 Lr: 0.00561
[2023-12-25 06:33:11,259 INFO misc.py line 119 253097] Train: [21/100][264/510] Data 0.009 (0.095) Batch 1.177 (1.622) Remain 18:15:40 loss: 0.2816 Lr: 0.00561
[2023-12-25 06:33:12,400 INFO misc.py line 119 253097] Train: [21/100][265/510] Data 0.006 (0.095) Batch 1.142 (1.620) Remain 18:14:25 loss: 0.3152 Lr: 0.00561
[2023-12-25 06:33:13,596 INFO misc.py line 119 253097] Train: [21/100][266/510] Data 0.006 (0.094) Batch 1.193 (1.618) Remain 18:13:17 loss: 0.1513 Lr: 0.00561
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[2023-12-25 06:33:15,678 INFO misc.py line 119 253097] Train: [21/100][268/510] Data 0.004 (0.094) Batch 0.932 (1.614) Remain 18:10:18 loss: 0.2641 Lr: 0.00561
[2023-12-25 06:33:16,728 INFO misc.py line 119 253097] Train: [21/100][269/510] Data 0.005 (0.093) Batch 1.051 (1.612) Remain 18:08:50 loss: 0.2139 Lr: 0.00561
[2023-12-25 06:33:18,031 INFO misc.py line 119 253097] Train: [21/100][270/510] Data 0.003 (0.093) Batch 1.301 (1.611) Remain 18:08:02 loss: 0.3176 Lr: 0.00561
[2023-12-25 06:33:19,122 INFO misc.py line 119 253097] Train: [21/100][271/510] Data 0.005 (0.093) Batch 1.090 (1.609) Remain 18:06:41 loss: 0.2678 Lr: 0.00561
[2023-12-25 06:33:27,017 INFO misc.py line 119 253097] Train: [21/100][272/510] Data 0.006 (0.092) Batch 7.896 (1.632) Remain 18:22:27 loss: 0.2961 Lr: 0.00561
[2023-12-25 06:33:28,191 INFO misc.py line 119 253097] Train: [21/100][273/510] Data 0.005 (0.092) Batch 1.175 (1.630) Remain 18:21:17 loss: 0.1458 Lr: 0.00561
[2023-12-25 06:33:29,277 INFO misc.py line 119 253097] Train: [21/100][274/510] Data 0.004 (0.092) Batch 1.085 (1.628) Remain 18:19:53 loss: 0.3753 Lr: 0.00561
[2023-12-25 06:33:30,453 INFO misc.py line 119 253097] Train: [21/100][275/510] Data 0.004 (0.091) Batch 1.176 (1.627) Remain 18:18:44 loss: 0.5193 Lr: 0.00561
[2023-12-25 06:33:31,636 INFO misc.py line 119 253097] Train: [21/100][276/510] Data 0.005 (0.091) Batch 1.183 (1.625) Remain 18:17:37 loss: 0.2677 Lr: 0.00561
[2023-12-25 06:33:32,731 INFO misc.py line 119 253097] Train: [21/100][277/510] Data 0.005 (0.091) Batch 1.096 (1.623) Remain 18:16:17 loss: 0.2209 Lr: 0.00561
[2023-12-25 06:33:33,936 INFO misc.py line 119 253097] Train: [21/100][278/510] Data 0.004 (0.091) Batch 1.205 (1.622) Remain 18:15:14 loss: 0.3216 Lr: 0.00561
[2023-12-25 06:33:35,120 INFO misc.py line 119 253097] Train: [21/100][279/510] Data 0.004 (0.090) Batch 1.179 (1.620) Remain 18:14:07 loss: 0.0985 Lr: 0.00561
[2023-12-25 06:33:36,399 INFO misc.py line 119 253097] Train: [21/100][280/510] Data 0.008 (0.090) Batch 1.282 (1.619) Remain 18:13:16 loss: 0.2191 Lr: 0.00561
[2023-12-25 06:33:37,490 INFO misc.py line 119 253097] Train: [21/100][281/510] Data 0.006 (0.090) Batch 1.088 (1.617) Remain 18:11:57 loss: 0.2222 Lr: 0.00561
[2023-12-25 06:33:38,701 INFO misc.py line 119 253097] Train: [21/100][282/510] Data 0.009 (0.089) Batch 1.212 (1.616) Remain 18:10:57 loss: 0.5882 Lr: 0.00561
[2023-12-25 06:33:39,880 INFO misc.py line 119 253097] Train: [21/100][283/510] Data 0.008 (0.089) Batch 1.183 (1.614) Remain 18:09:53 loss: 0.1904 Lr: 0.00561
[2023-12-25 06:33:41,048 INFO misc.py line 119 253097] Train: [21/100][284/510] Data 0.004 (0.089) Batch 1.163 (1.612) Remain 18:08:46 loss: 0.4873 Lr: 0.00561
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[2023-12-25 06:33:43,529 INFO misc.py line 119 253097] Train: [21/100][286/510] Data 0.005 (0.088) Batch 1.266 (1.610) Remain 18:06:57 loss: 0.3136 Lr: 0.00561
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[2023-12-25 06:33:46,771 INFO misc.py line 119 253097] Train: [21/100][289/510] Data 0.005 (0.087) Batch 1.091 (1.604) Remain 18:03:07 loss: 0.4145 Lr: 0.00561
[2023-12-25 06:33:47,982 INFO misc.py line 119 253097] Train: [21/100][290/510] Data 0.009 (0.087) Batch 1.211 (1.603) Remain 18:02:09 loss: 0.2137 Lr: 0.00561
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[2023-12-25 06:33:50,280 INFO misc.py line 119 253097] Train: [21/100][292/510] Data 0.004 (0.086) Batch 1.194 (1.600) Remain 17:59:59 loss: 0.5473 Lr: 0.00561
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[2023-12-25 06:34:28,595 INFO misc.py line 119 253097] Train: [21/100][316/510] Data 0.004 (0.114) Batch 1.048 (1.599) Remain 17:59:11 loss: 0.1728 Lr: 0.00561
[2023-12-25 06:34:29,630 INFO misc.py line 119 253097] Train: [21/100][317/510] Data 0.005 (0.113) Batch 1.035 (1.598) Remain 17:57:57 loss: 0.2126 Lr: 0.00561
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[2023-12-25 06:36:04,590 INFO misc.py line 119 253097] Train: [21/100][375/510] Data 0.005 (0.134) Batch 1.101 (1.604) Remain 18:00:34 loss: 0.2303 Lr: 0.00560
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[2023-12-25 06:37:15,067 INFO misc.py line 119 253097] Train: [21/100][424/510] Data 0.009 (0.119) Batch 1.258 (1.585) Remain 17:46:17 loss: 0.2064 Lr: 0.00560
[2023-12-25 06:37:16,165 INFO misc.py line 119 253097] Train: [21/100][425/510] Data 0.004 (0.119) Batch 1.095 (1.583) Remain 17:45:29 loss: 0.1844 Lr: 0.00560
[2023-12-25 06:37:17,164 INFO misc.py line 119 253097] Train: [21/100][426/510] Data 0.005 (0.118) Batch 0.998 (1.582) Remain 17:44:32 loss: 0.2554 Lr: 0.00560
[2023-12-25 06:37:18,401 INFO misc.py line 119 253097] Train: [21/100][427/510] Data 0.008 (0.118) Batch 1.237 (1.581) Remain 17:43:57 loss: 0.2441 Lr: 0.00560
[2023-12-25 06:37:19,317 INFO misc.py line 119 253097] Train: [21/100][428/510] Data 0.008 (0.118) Batch 0.920 (1.580) Remain 17:42:53 loss: 0.1936 Lr: 0.00560
[2023-12-25 06:37:20,475 INFO misc.py line 119 253097] Train: [21/100][429/510] Data 0.005 (0.117) Batch 1.158 (1.579) Remain 17:42:11 loss: 0.1264 Lr: 0.00560
[2023-12-25 06:37:21,517 INFO misc.py line 119 253097] Train: [21/100][430/510] Data 0.004 (0.117) Batch 1.042 (1.577) Remain 17:41:19 loss: 0.3023 Lr: 0.00560
[2023-12-25 06:37:22,611 INFO misc.py line 119 253097] Train: [21/100][431/510] Data 0.006 (0.117) Batch 1.090 (1.576) Remain 17:40:31 loss: 0.2190 Lr: 0.00560
[2023-12-25 06:37:23,812 INFO misc.py line 119 253097] Train: [21/100][432/510] Data 0.008 (0.117) Batch 1.201 (1.575) Remain 17:39:54 loss: 0.3303 Lr: 0.00560
[2023-12-25 06:37:35,263 INFO misc.py line 119 253097] Train: [21/100][433/510] Data 0.008 (0.116) Batch 11.455 (1.598) Remain 17:55:20 loss: 0.4641 Lr: 0.00560
[2023-12-25 06:37:36,476 INFO misc.py line 119 253097] Train: [21/100][434/510] Data 0.003 (0.116) Batch 1.213 (1.597) Remain 17:54:43 loss: 0.4351 Lr: 0.00560
[2023-12-25 06:37:37,608 INFO misc.py line 119 253097] Train: [21/100][435/510] Data 0.003 (0.116) Batch 1.128 (1.596) Remain 17:53:57 loss: 0.4092 Lr: 0.00560
[2023-12-25 06:37:38,943 INFO misc.py line 119 253097] Train: [21/100][436/510] Data 0.007 (0.116) Batch 1.340 (1.596) Remain 17:53:32 loss: 0.3482 Lr: 0.00560
[2023-12-25 06:37:40,235 INFO misc.py line 119 253097] Train: [21/100][437/510] Data 0.003 (0.115) Batch 1.290 (1.595) Remain 17:53:02 loss: 0.1867 Lr: 0.00560
[2023-12-25 06:37:41,505 INFO misc.py line 119 253097] Train: [21/100][438/510] Data 0.005 (0.115) Batch 1.267 (1.594) Remain 17:52:30 loss: 0.3845 Lr: 0.00560
[2023-12-25 06:37:42,512 INFO misc.py line 119 253097] Train: [21/100][439/510] Data 0.009 (0.115) Batch 1.009 (1.593) Remain 17:51:34 loss: 0.2712 Lr: 0.00560
[2023-12-25 06:37:43,537 INFO misc.py line 119 253097] Train: [21/100][440/510] Data 0.006 (0.115) Batch 1.025 (1.592) Remain 17:50:40 loss: 0.2634 Lr: 0.00560
[2023-12-25 06:37:44,809 INFO misc.py line 119 253097] Train: [21/100][441/510] Data 0.007 (0.114) Batch 1.274 (1.591) Remain 17:50:09 loss: 0.3798 Lr: 0.00560
[2023-12-25 06:37:45,883 INFO misc.py line 119 253097] Train: [21/100][442/510] Data 0.004 (0.114) Batch 1.074 (1.590) Remain 17:49:20 loss: 0.4820 Lr: 0.00560
[2023-12-25 06:37:46,911 INFO misc.py line 119 253097] Train: [21/100][443/510] Data 0.005 (0.114) Batch 1.025 (1.588) Remain 17:48:26 loss: 0.3500 Lr: 0.00560
[2023-12-25 06:37:47,758 INFO misc.py line 119 253097] Train: [21/100][444/510] Data 0.007 (0.114) Batch 0.848 (1.587) Remain 17:47:17 loss: 0.3350 Lr: 0.00560
[2023-12-25 06:37:48,947 INFO misc.py line 119 253097] Train: [21/100][445/510] Data 0.006 (0.113) Batch 1.191 (1.586) Remain 17:46:39 loss: 0.1780 Lr: 0.00560
[2023-12-25 06:37:50,288 INFO misc.py line 119 253097] Train: [21/100][446/510] Data 0.003 (0.113) Batch 1.340 (1.585) Remain 17:46:15 loss: 0.2483 Lr: 0.00560
[2023-12-25 06:37:53,879 INFO misc.py line 119 253097] Train: [21/100][447/510] Data 0.005 (0.113) Batch 3.590 (1.590) Remain 17:49:16 loss: 0.3868 Lr: 0.00560
[2023-12-25 06:37:59,302 INFO misc.py line 119 253097] Train: [21/100][448/510] Data 0.005 (0.113) Batch 5.423 (1.598) Remain 17:55:02 loss: 0.3868 Lr: 0.00560
[2023-12-25 06:38:00,563 INFO misc.py line 119 253097] Train: [21/100][449/510] Data 0.004 (0.112) Batch 1.260 (1.598) Remain 17:54:30 loss: 0.6092 Lr: 0.00560
[2023-12-25 06:38:01,879 INFO misc.py line 119 253097] Train: [21/100][450/510] Data 0.006 (0.112) Batch 1.317 (1.597) Remain 17:54:03 loss: 0.3372 Lr: 0.00560
[2023-12-25 06:38:03,034 INFO misc.py line 119 253097] Train: [21/100][451/510] Data 0.004 (0.112) Batch 1.154 (1.596) Remain 17:53:21 loss: 0.3769 Lr: 0.00560
[2023-12-25 06:38:04,231 INFO misc.py line 119 253097] Train: [21/100][452/510] Data 0.005 (0.112) Batch 1.199 (1.595) Remain 17:52:44 loss: 0.1805 Lr: 0.00560
[2023-12-25 06:38:05,509 INFO misc.py line 119 253097] Train: [21/100][453/510] Data 0.004 (0.111) Batch 1.277 (1.595) Remain 17:52:14 loss: 0.4069 Lr: 0.00560
[2023-12-25 06:38:15,660 INFO misc.py line 119 253097] Train: [21/100][454/510] Data 0.005 (0.111) Batch 10.152 (1.614) Remain 18:04:58 loss: 0.5087 Lr: 0.00560
[2023-12-25 06:38:16,705 INFO misc.py line 119 253097] Train: [21/100][455/510] Data 0.003 (0.111) Batch 1.044 (1.612) Remain 18:04:05 loss: 0.3267 Lr: 0.00560
[2023-12-25 06:38:17,656 INFO misc.py line 119 253097] Train: [21/100][456/510] Data 0.005 (0.111) Batch 0.949 (1.611) Remain 18:03:05 loss: 0.2806 Lr: 0.00560
[2023-12-25 06:38:18,873 INFO misc.py line 119 253097] Train: [21/100][457/510] Data 0.007 (0.111) Batch 1.220 (1.610) Remain 18:02:28 loss: 0.3530 Lr: 0.00560
[2023-12-25 06:38:20,144 INFO misc.py line 119 253097] Train: [21/100][458/510] Data 0.003 (0.110) Batch 1.266 (1.609) Remain 18:01:56 loss: 0.1803 Lr: 0.00560
[2023-12-25 06:38:21,419 INFO misc.py line 119 253097] Train: [21/100][459/510] Data 0.008 (0.110) Batch 1.274 (1.608) Remain 18:01:25 loss: 0.2684 Lr: 0.00559
[2023-12-25 06:38:22,691 INFO misc.py line 119 253097] Train: [21/100][460/510] Data 0.010 (0.110) Batch 1.270 (1.608) Remain 18:00:54 loss: 0.2525 Lr: 0.00559
[2023-12-25 06:38:23,900 INFO misc.py line 119 253097] Train: [21/100][461/510] Data 0.012 (0.110) Batch 1.215 (1.607) Remain 18:00:18 loss: 0.1911 Lr: 0.00559
[2023-12-25 06:38:25,149 INFO misc.py line 119 253097] Train: [21/100][462/510] Data 0.004 (0.109) Batch 1.250 (1.606) Remain 17:59:45 loss: 0.1968 Lr: 0.00559
[2023-12-25 06:38:26,114 INFO misc.py line 119 253097] Train: [21/100][463/510] Data 0.003 (0.109) Batch 0.965 (1.605) Remain 17:58:47 loss: 0.1982 Lr: 0.00559
[2023-12-25 06:38:27,301 INFO misc.py line 119 253097] Train: [21/100][464/510] Data 0.005 (0.109) Batch 1.187 (1.604) Remain 17:58:09 loss: 0.2429 Lr: 0.00559
[2023-12-25 06:38:28,385 INFO misc.py line 119 253097] Train: [21/100][465/510] Data 0.003 (0.109) Batch 1.083 (1.603) Remain 17:57:22 loss: 0.1039 Lr: 0.00559
[2023-12-25 06:38:29,458 INFO misc.py line 119 253097] Train: [21/100][466/510] Data 0.004 (0.109) Batch 1.073 (1.601) Remain 17:56:34 loss: 0.2762 Lr: 0.00559
[2023-12-25 06:38:30,603 INFO misc.py line 119 253097] Train: [21/100][467/510] Data 0.003 (0.108) Batch 1.145 (1.601) Remain 17:55:53 loss: 0.2763 Lr: 0.00559
[2023-12-25 06:38:31,668 INFO misc.py line 119 253097] Train: [21/100][468/510] Data 0.004 (0.108) Batch 1.065 (1.599) Remain 17:55:05 loss: 0.4757 Lr: 0.00559
[2023-12-25 06:38:32,694 INFO misc.py line 119 253097] Train: [21/100][469/510] Data 0.004 (0.108) Batch 1.025 (1.598) Remain 17:54:13 loss: 0.1512 Lr: 0.00559
[2023-12-25 06:38:33,917 INFO misc.py line 119 253097] Train: [21/100][470/510] Data 0.005 (0.108) Batch 1.224 (1.597) Remain 17:53:39 loss: 0.3624 Lr: 0.00559
[2023-12-25 06:38:34,902 INFO misc.py line 119 253097] Train: [21/100][471/510] Data 0.005 (0.107) Batch 0.983 (1.596) Remain 17:52:45 loss: 0.2703 Lr: 0.00559
[2023-12-25 06:38:36,054 INFO misc.py line 119 253097] Train: [21/100][472/510] Data 0.006 (0.107) Batch 1.154 (1.595) Remain 17:52:05 loss: 0.2057 Lr: 0.00559
[2023-12-25 06:38:38,432 INFO misc.py line 119 253097] Train: [21/100][473/510] Data 1.047 (0.109) Batch 2.374 (1.597) Remain 17:53:10 loss: 0.3480 Lr: 0.00559
[2023-12-25 06:38:39,478 INFO misc.py line 119 253097] Train: [21/100][474/510] Data 0.009 (0.109) Batch 1.049 (1.596) Remain 17:52:22 loss: 0.2533 Lr: 0.00559
[2023-12-25 06:38:43,482 INFO misc.py line 119 253097] Train: [21/100][475/510] Data 0.006 (0.109) Batch 4.003 (1.601) Remain 17:55:46 loss: 0.3669 Lr: 0.00559
[2023-12-25 06:38:44,484 INFO misc.py line 119 253097] Train: [21/100][476/510] Data 0.005 (0.109) Batch 1.003 (1.599) Remain 17:54:53 loss: 0.2286 Lr: 0.00559
[2023-12-25 06:38:45,646 INFO misc.py line 119 253097] Train: [21/100][477/510] Data 0.005 (0.108) Batch 1.164 (1.598) Remain 17:54:15 loss: 0.5924 Lr: 0.00559
[2023-12-25 06:38:46,671 INFO misc.py line 119 253097] Train: [21/100][478/510] Data 0.003 (0.108) Batch 1.024 (1.597) Remain 17:53:24 loss: 0.3659 Lr: 0.00559
[2023-12-25 06:38:47,795 INFO misc.py line 119 253097] Train: [21/100][479/510] Data 0.006 (0.108) Batch 1.125 (1.596) Remain 17:52:43 loss: 0.5074 Lr: 0.00559
[2023-12-25 06:38:48,843 INFO misc.py line 119 253097] Train: [21/100][480/510] Data 0.003 (0.108) Batch 1.047 (1.595) Remain 17:51:55 loss: 0.3522 Lr: 0.00559
[2023-12-25 06:38:58,882 INFO misc.py line 119 253097] Train: [21/100][481/510] Data 0.005 (0.107) Batch 10.038 (1.613) Remain 18:03:45 loss: 0.2660 Lr: 0.00559
[2023-12-25 06:38:59,946 INFO misc.py line 119 253097] Train: [21/100][482/510] Data 0.005 (0.107) Batch 1.065 (1.612) Remain 18:02:58 loss: 0.2170 Lr: 0.00559
[2023-12-25 06:39:00,912 INFO misc.py line 119 253097] Train: [21/100][483/510] Data 0.005 (0.107) Batch 0.967 (1.610) Remain 18:02:02 loss: 0.1334 Lr: 0.00559
[2023-12-25 06:39:02,135 INFO misc.py line 119 253097] Train: [21/100][484/510] Data 0.003 (0.107) Batch 1.222 (1.609) Remain 18:01:28 loss: 0.1520 Lr: 0.00559
[2023-12-25 06:39:03,396 INFO misc.py line 119 253097] Train: [21/100][485/510] Data 0.004 (0.107) Batch 1.259 (1.609) Remain 18:00:57 loss: 0.2970 Lr: 0.00559
[2023-12-25 06:39:04,663 INFO misc.py line 119 253097] Train: [21/100][486/510] Data 0.007 (0.106) Batch 1.269 (1.608) Remain 18:00:27 loss: 0.2473 Lr: 0.00559
[2023-12-25 06:39:09,359 INFO misc.py line 119 253097] Train: [21/100][487/510] Data 0.004 (0.106) Batch 4.696 (1.614) Remain 18:04:42 loss: 0.3130 Lr: 0.00559
[2023-12-25 06:39:10,379 INFO misc.py line 119 253097] Train: [21/100][488/510] Data 0.005 (0.106) Batch 1.020 (1.613) Remain 18:03:51 loss: 0.4244 Lr: 0.00559
[2023-12-25 06:39:11,614 INFO misc.py line 119 253097] Train: [21/100][489/510] Data 0.004 (0.106) Batch 1.234 (1.612) Remain 18:03:18 loss: 0.2865 Lr: 0.00559
[2023-12-25 06:39:12,682 INFO misc.py line 119 253097] Train: [21/100][490/510] Data 0.006 (0.106) Batch 1.070 (1.611) Remain 18:02:32 loss: 0.3720 Lr: 0.00559
[2023-12-25 06:39:13,868 INFO misc.py line 119 253097] Train: [21/100][491/510] Data 0.004 (0.105) Batch 1.185 (1.610) Remain 18:01:55 loss: 0.2027 Lr: 0.00559
[2023-12-25 06:39:14,897 INFO misc.py line 119 253097] Train: [21/100][492/510] Data 0.005 (0.105) Batch 1.026 (1.609) Remain 18:01:05 loss: 0.2636 Lr: 0.00559
[2023-12-25 06:39:16,151 INFO misc.py line 119 253097] Train: [21/100][493/510] Data 0.007 (0.105) Batch 1.257 (1.609) Remain 18:00:35 loss: 0.2535 Lr: 0.00559
[2023-12-25 06:39:17,415 INFO misc.py line 119 253097] Train: [21/100][494/510] Data 0.004 (0.105) Batch 1.264 (1.608) Remain 18:00:05 loss: 0.2894 Lr: 0.00559
[2023-12-25 06:39:18,433 INFO misc.py line 119 253097] Train: [21/100][495/510] Data 0.004 (0.105) Batch 1.017 (1.607) Remain 17:59:15 loss: 0.2357 Lr: 0.00559
[2023-12-25 06:39:19,574 INFO misc.py line 119 253097] Train: [21/100][496/510] Data 0.006 (0.104) Batch 1.139 (1.606) Remain 17:58:35 loss: 0.2302 Lr: 0.00559
[2023-12-25 06:39:20,802 INFO misc.py line 119 253097] Train: [21/100][497/510] Data 0.007 (0.104) Batch 1.230 (1.605) Remain 17:58:03 loss: 0.3041 Lr: 0.00559
[2023-12-25 06:39:21,935 INFO misc.py line 119 253097] Train: [21/100][498/510] Data 0.005 (0.104) Batch 1.135 (1.604) Remain 17:57:23 loss: 0.3147 Lr: 0.00559
[2023-12-25 06:39:23,007 INFO misc.py line 119 253097] Train: [21/100][499/510] Data 0.004 (0.104) Batch 1.067 (1.603) Remain 17:56:37 loss: 0.1986 Lr: 0.00559
[2023-12-25 06:39:24,114 INFO misc.py line 119 253097] Train: [21/100][500/510] Data 0.009 (0.104) Batch 1.109 (1.602) Remain 17:55:56 loss: 0.3150 Lr: 0.00559
[2023-12-25 06:39:25,407 INFO misc.py line 119 253097] Train: [21/100][501/510] Data 0.007 (0.103) Batch 1.294 (1.601) Remain 17:55:29 loss: 0.4697 Lr: 0.00559
[2023-12-25 06:39:26,595 INFO misc.py line 119 253097] Train: [21/100][502/510] Data 0.005 (0.103) Batch 1.186 (1.600) Remain 17:54:54 loss: 0.2026 Lr: 0.00559
[2023-12-25 06:39:27,822 INFO misc.py line 119 253097] Train: [21/100][503/510] Data 0.009 (0.103) Batch 1.231 (1.600) Remain 17:54:23 loss: 0.2505 Lr: 0.00559
[2023-12-25 06:39:29,043 INFO misc.py line 119 253097] Train: [21/100][504/510] Data 0.004 (0.103) Batch 1.219 (1.599) Remain 17:53:51 loss: 0.4019 Lr: 0.00559
[2023-12-25 06:39:30,169 INFO misc.py line 119 253097] Train: [21/100][505/510] Data 0.006 (0.103) Batch 1.128 (1.598) Remain 17:53:11 loss: 0.2440 Lr: 0.00559
[2023-12-25 06:39:31,492 INFO misc.py line 119 253097] Train: [21/100][506/510] Data 0.004 (0.102) Batch 1.318 (1.597) Remain 17:52:47 loss: 0.2066 Lr: 0.00559
[2023-12-25 06:39:32,598 INFO misc.py line 119 253097] Train: [21/100][507/510] Data 0.008 (0.102) Batch 1.106 (1.596) Remain 17:52:06 loss: 0.2864 Lr: 0.00559
[2023-12-25 06:39:33,892 INFO misc.py line 119 253097] Train: [21/100][508/510] Data 0.009 (0.102) Batch 1.296 (1.596) Remain 17:51:41 loss: 0.1708 Lr: 0.00559
[2023-12-25 06:39:35,130 INFO misc.py line 119 253097] Train: [21/100][509/510] Data 0.008 (0.102) Batch 1.237 (1.595) Remain 17:51:10 loss: 0.3411 Lr: 0.00559
[2023-12-25 06:39:36,396 INFO misc.py line 119 253097] Train: [21/100][510/510] Data 0.008 (0.102) Batch 1.270 (1.595) Remain 17:50:43 loss: 0.1711 Lr: 0.00559
[2023-12-25 06:39:36,397 INFO misc.py line 136 253097] Train result: loss: 0.2988 
[2023-12-25 06:39:36,397 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 06:40:02,100 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5886 
[2023-12-25 06:40:02,465 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4115 
[2023-12-25 06:40:08,111 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4422 
[2023-12-25 06:40:08,629 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4255 
[2023-12-25 06:40:10,608 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7277 
[2023-12-25 06:40:11,032 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4200 
[2023-12-25 06:40:11,911 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3490 
[2023-12-25 06:40:12,462 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3939 
[2023-12-25 06:40:14,282 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1133 
[2023-12-25 06:40:16,401 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1711 
[2023-12-25 06:40:17,262 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2150 
[2023-12-25 06:40:17,685 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9622 
[2023-12-25 06:40:18,588 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6869 
[2023-12-25 06:40:21,533 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8633 
[2023-12-25 06:40:22,000 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3733 
[2023-12-25 06:40:22,608 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3521 
[2023-12-25 06:40:23,308 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5223 
[2023-12-25 06:40:24,477 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6481/0.7140/0.8897.
[2023-12-25 06:40:24,477 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9106/0.9482
[2023-12-25 06:40:24,477 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9712/0.9778
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8301/0.9418
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0003/0.0056
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2475/0.2920
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5678/0.5931
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6794/0.8148
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8076/0.9208
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8834/0.9323
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4214/0.4435
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7683/0.8576
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7742/0.8264
[2023-12-25 06:40:24,478 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5629/0.7286
[2023-12-25 06:40:24,478 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 06:40:24,480 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.6481
[2023-12-25 06:40:24,480 INFO misc.py line 165 253097] Currently Best mIoU: 0.6481
[2023-12-25 06:40:24,480 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 06:40:37,739 INFO misc.py line 119 253097] Train: [22/100][1/510] Data 6.578 (6.578) Batch 7.769 (7.769) Remain 86:56:38 loss: 0.3407 Lr: 0.00559
[2023-12-25 06:40:39,028 INFO misc.py line 119 253097] Train: [22/100][2/510] Data 0.004 (0.004) Batch 1.288 (1.288) Remain 14:24:55 loss: 0.1711 Lr: 0.00559
[2023-12-25 06:40:41,791 INFO misc.py line 119 253097] Train: [22/100][3/510] Data 0.006 (0.006) Batch 2.765 (2.765) Remain 30:56:40 loss: 0.2298 Lr: 0.00559
[2023-12-25 06:40:42,729 INFO misc.py line 119 253097] Train: [22/100][4/510] Data 0.003 (0.003) Batch 0.936 (0.936) Remain 10:28:45 loss: 0.1823 Lr: 0.00559
[2023-12-25 06:40:43,797 INFO misc.py line 119 253097] Train: [22/100][5/510] Data 0.005 (0.004) Batch 1.065 (1.001) Remain 11:11:45 loss: 0.2683 Lr: 0.00559
[2023-12-25 06:40:44,912 INFO misc.py line 119 253097] Train: [22/100][6/510] Data 0.009 (0.006) Batch 1.082 (1.028) Remain 11:29:58 loss: 0.3612 Lr: 0.00559
[2023-12-25 06:40:46,044 INFO misc.py line 119 253097] Train: [22/100][7/510] Data 0.056 (0.018) Batch 1.165 (1.062) Remain 11:52:57 loss: 0.1340 Lr: 0.00559
[2023-12-25 06:40:51,532 INFO misc.py line 119 253097] Train: [22/100][8/510] Data 0.008 (0.016) Batch 5.493 (1.948) Remain 21:47:56 loss: 0.3861 Lr: 0.00559
[2023-12-25 06:40:52,843 INFO misc.py line 119 253097] Train: [22/100][9/510] Data 0.004 (0.014) Batch 1.307 (1.841) Remain 20:36:11 loss: 0.3380 Lr: 0.00559
[2023-12-25 06:40:54,070 INFO misc.py line 119 253097] Train: [22/100][10/510] Data 0.007 (0.013) Batch 1.228 (1.754) Remain 19:37:19 loss: 0.2836 Lr: 0.00559
[2023-12-25 06:40:55,156 INFO misc.py line 119 253097] Train: [22/100][11/510] Data 0.006 (0.012) Batch 1.085 (1.670) Remain 18:41:10 loss: 0.1759 Lr: 0.00559
[2023-12-25 06:40:56,372 INFO misc.py line 119 253097] Train: [22/100][12/510] Data 0.008 (0.012) Batch 1.217 (1.620) Remain 18:07:22 loss: 0.3077 Lr: 0.00559
[2023-12-25 06:40:57,615 INFO misc.py line 119 253097] Train: [22/100][13/510] Data 0.006 (0.011) Batch 1.242 (1.582) Remain 17:41:59 loss: 0.3749 Lr: 0.00559
[2023-12-25 06:40:58,894 INFO misc.py line 119 253097] Train: [22/100][14/510] Data 0.007 (0.011) Batch 1.279 (1.555) Remain 17:23:29 loss: 0.2550 Lr: 0.00559
[2023-12-25 06:40:59,959 INFO misc.py line 119 253097] Train: [22/100][15/510] Data 0.007 (0.011) Batch 1.063 (1.514) Remain 16:55:58 loss: 0.3773 Lr: 0.00559
[2023-12-25 06:41:01,097 INFO misc.py line 119 253097] Train: [22/100][16/510] Data 0.009 (0.011) Batch 1.139 (1.485) Remain 16:36:37 loss: 0.4334 Lr: 0.00559
[2023-12-25 06:41:02,293 INFO misc.py line 119 253097] Train: [22/100][17/510] Data 0.008 (0.010) Batch 1.196 (1.464) Remain 16:22:46 loss: 0.2218 Lr: 0.00559
[2023-12-25 06:41:03,386 INFO misc.py line 119 253097] Train: [22/100][18/510] Data 0.008 (0.010) Batch 1.094 (1.439) Remain 16:06:09 loss: 0.2562 Lr: 0.00559
[2023-12-25 06:41:04,676 INFO misc.py line 119 253097] Train: [22/100][19/510] Data 0.007 (0.010) Batch 1.290 (1.430) Remain 15:59:51 loss: 0.2291 Lr: 0.00559
[2023-12-25 06:41:05,749 INFO misc.py line 119 253097] Train: [22/100][20/510] Data 0.007 (0.010) Batch 1.075 (1.409) Remain 15:45:49 loss: 0.4241 Lr: 0.00559
[2023-12-25 06:41:11,200 INFO misc.py line 119 253097] Train: [22/100][21/510] Data 0.006 (0.010) Batch 5.452 (1.634) Remain 18:16:33 loss: 0.2469 Lr: 0.00559
[2023-12-25 06:41:15,359 INFO misc.py line 119 253097] Train: [22/100][22/510] Data 0.003 (0.009) Batch 4.159 (1.767) Remain 19:45:43 loss: 0.1517 Lr: 0.00559
[2023-12-25 06:41:16,431 INFO misc.py line 119 253097] Train: [22/100][23/510] Data 0.004 (0.009) Batch 1.071 (1.732) Remain 19:22:20 loss: 0.4101 Lr: 0.00559
[2023-12-25 06:41:17,539 INFO misc.py line 119 253097] Train: [22/100][24/510] Data 0.004 (0.009) Batch 1.107 (1.702) Remain 19:02:21 loss: 0.1307 Lr: 0.00559
[2023-12-25 06:41:18,669 INFO misc.py line 119 253097] Train: [22/100][25/510] Data 0.006 (0.009) Batch 1.130 (1.676) Remain 18:44:52 loss: 0.4656 Lr: 0.00559
[2023-12-25 06:41:19,719 INFO misc.py line 119 253097] Train: [22/100][26/510] Data 0.005 (0.008) Batch 1.051 (1.649) Remain 18:26:36 loss: 0.3095 Lr: 0.00559
[2023-12-25 06:41:20,876 INFO misc.py line 119 253097] Train: [22/100][27/510] Data 0.004 (0.008) Batch 1.157 (1.629) Remain 18:12:48 loss: 0.3265 Lr: 0.00559
[2023-12-25 06:41:21,909 INFO misc.py line 119 253097] Train: [22/100][28/510] Data 0.004 (0.008) Batch 1.033 (1.605) Remain 17:56:48 loss: 0.2277 Lr: 0.00559
[2023-12-25 06:41:23,193 INFO misc.py line 119 253097] Train: [22/100][29/510] Data 0.004 (0.008) Batch 1.284 (1.592) Remain 17:48:29 loss: 0.2705 Lr: 0.00559
[2023-12-25 06:41:24,361 INFO misc.py line 119 253097] Train: [22/100][30/510] Data 0.005 (0.008) Batch 1.163 (1.576) Remain 17:37:47 loss: 0.3269 Lr: 0.00559
[2023-12-25 06:41:25,281 INFO misc.py line 119 253097] Train: [22/100][31/510] Data 0.009 (0.008) Batch 0.925 (1.553) Remain 17:22:10 loss: 0.2573 Lr: 0.00559
[2023-12-25 06:41:26,578 INFO misc.py line 119 253097] Train: [22/100][32/510] Data 0.004 (0.008) Batch 1.295 (1.544) Remain 17:16:10 loss: 0.7195 Lr: 0.00559
[2023-12-25 06:41:27,792 INFO misc.py line 119 253097] Train: [22/100][33/510] Data 0.007 (0.008) Batch 1.212 (1.533) Remain 17:08:43 loss: 0.4080 Lr: 0.00559
[2023-12-25 06:41:29,007 INFO misc.py line 119 253097] Train: [22/100][34/510] Data 0.007 (0.008) Batch 1.212 (1.523) Remain 17:01:44 loss: 0.3901 Lr: 0.00559
[2023-12-25 06:41:30,068 INFO misc.py line 119 253097] Train: [22/100][35/510] Data 0.011 (0.008) Batch 1.068 (1.509) Remain 16:52:09 loss: 0.2240 Lr: 0.00559
[2023-12-25 06:41:31,280 INFO misc.py line 119 253097] Train: [22/100][36/510] Data 0.004 (0.008) Batch 1.212 (1.500) Remain 16:46:06 loss: 0.2184 Lr: 0.00559
[2023-12-25 06:41:36,810 INFO misc.py line 119 253097] Train: [22/100][37/510] Data 0.005 (0.008) Batch 5.531 (1.618) Remain 18:05:37 loss: 0.2728 Lr: 0.00559
[2023-12-25 06:41:38,055 INFO misc.py line 119 253097] Train: [22/100][38/510] Data 0.257 (0.015) Batch 1.244 (1.608) Remain 17:58:25 loss: 0.2163 Lr: 0.00559
[2023-12-25 06:41:39,229 INFO misc.py line 119 253097] Train: [22/100][39/510] Data 0.005 (0.014) Batch 1.174 (1.595) Remain 17:50:19 loss: 0.3852 Lr: 0.00559
[2023-12-25 06:41:40,432 INFO misc.py line 119 253097] Train: [22/100][40/510] Data 0.005 (0.014) Batch 1.198 (1.585) Remain 17:43:04 loss: 0.2574 Lr: 0.00559
[2023-12-25 06:41:41,413 INFO misc.py line 119 253097] Train: [22/100][41/510] Data 0.010 (0.014) Batch 0.986 (1.569) Remain 17:32:29 loss: 0.1486 Lr: 0.00559
[2023-12-25 06:41:42,441 INFO misc.py line 119 253097] Train: [22/100][42/510] Data 0.004 (0.014) Batch 1.029 (1.555) Remain 17:23:10 loss: 0.3685 Lr: 0.00559
[2023-12-25 06:41:43,482 INFO misc.py line 119 253097] Train: [22/100][43/510] Data 0.004 (0.014) Batch 1.037 (1.542) Remain 17:14:27 loss: 0.1778 Lr: 0.00559
[2023-12-25 06:41:47,212 INFO misc.py line 119 253097] Train: [22/100][44/510] Data 2.423 (0.072) Batch 3.734 (1.596) Remain 17:50:17 loss: 0.3261 Lr: 0.00559
[2023-12-25 06:41:48,223 INFO misc.py line 119 253097] Train: [22/100][45/510] Data 0.004 (0.071) Batch 1.011 (1.582) Remain 17:40:55 loss: 0.3825 Lr: 0.00559
[2023-12-25 06:41:49,345 INFO misc.py line 119 253097] Train: [22/100][46/510] Data 0.004 (0.069) Batch 1.120 (1.571) Remain 17:33:42 loss: 0.2977 Lr: 0.00559
[2023-12-25 06:41:50,241 INFO misc.py line 119 253097] Train: [22/100][47/510] Data 0.006 (0.068) Batch 0.897 (1.556) Remain 17:23:24 loss: 0.2292 Lr: 0.00559
[2023-12-25 06:41:51,218 INFO misc.py line 119 253097] Train: [22/100][48/510] Data 0.005 (0.066) Batch 0.976 (1.543) Remain 17:14:44 loss: 0.2547 Lr: 0.00559
[2023-12-25 06:41:52,218 INFO misc.py line 119 253097] Train: [22/100][49/510] Data 0.005 (0.065) Batch 0.998 (1.531) Remain 17:06:46 loss: 0.2388 Lr: 0.00559
[2023-12-25 06:41:53,263 INFO misc.py line 119 253097] Train: [22/100][50/510] Data 0.007 (0.064) Batch 1.049 (1.521) Remain 16:59:52 loss: 0.1937 Lr: 0.00559
[2023-12-25 06:41:54,538 INFO misc.py line 119 253097] Train: [22/100][51/510] Data 0.003 (0.063) Batch 1.250 (1.515) Remain 16:56:03 loss: 0.3190 Lr: 0.00558
[2023-12-25 06:41:55,724 INFO misc.py line 119 253097] Train: [22/100][52/510] Data 0.030 (0.062) Batch 1.207 (1.509) Remain 16:51:49 loss: 0.5650 Lr: 0.00558
[2023-12-25 06:41:56,895 INFO misc.py line 119 253097] Train: [22/100][53/510] Data 0.007 (0.061) Batch 1.169 (1.502) Remain 16:47:14 loss: 0.2226 Lr: 0.00558
[2023-12-25 06:41:57,889 INFO misc.py line 119 253097] Train: [22/100][54/510] Data 0.009 (0.060) Batch 0.999 (1.492) Remain 16:40:36 loss: 0.2530 Lr: 0.00558
[2023-12-25 06:41:58,704 INFO misc.py line 119 253097] Train: [22/100][55/510] Data 0.004 (0.059) Batch 0.815 (1.479) Remain 16:31:50 loss: 0.2163 Lr: 0.00558
[2023-12-25 06:41:59,917 INFO misc.py line 119 253097] Train: [22/100][56/510] Data 0.004 (0.058) Batch 1.214 (1.474) Remain 16:28:28 loss: 0.2029 Lr: 0.00558
[2023-12-25 06:42:01,007 INFO misc.py line 119 253097] Train: [22/100][57/510] Data 0.003 (0.057) Batch 1.088 (1.467) Remain 16:23:38 loss: 0.3329 Lr: 0.00558
[2023-12-25 06:42:02,179 INFO misc.py line 119 253097] Train: [22/100][58/510] Data 0.006 (0.056) Batch 1.172 (1.462) Remain 16:20:01 loss: 0.1947 Lr: 0.00558
[2023-12-25 06:42:03,353 INFO misc.py line 119 253097] Train: [22/100][59/510] Data 0.006 (0.055) Batch 1.176 (1.456) Remain 16:16:34 loss: 0.2333 Lr: 0.00558
[2023-12-25 06:42:04,645 INFO misc.py line 119 253097] Train: [22/100][60/510] Data 0.003 (0.054) Batch 1.291 (1.454) Remain 16:14:36 loss: 0.4514 Lr: 0.00558
[2023-12-25 06:42:05,583 INFO misc.py line 119 253097] Train: [22/100][61/510] Data 0.005 (0.053) Batch 0.939 (1.445) Remain 16:08:38 loss: 0.1676 Lr: 0.00558
[2023-12-25 06:42:06,770 INFO misc.py line 119 253097] Train: [22/100][62/510] Data 0.003 (0.052) Batch 1.186 (1.440) Remain 16:05:40 loss: 0.1114 Lr: 0.00558
[2023-12-25 06:42:07,910 INFO misc.py line 119 253097] Train: [22/100][63/510] Data 0.004 (0.051) Batch 1.139 (1.435) Remain 16:02:17 loss: 0.2144 Lr: 0.00558
[2023-12-25 06:42:17,157 INFO misc.py line 119 253097] Train: [22/100][64/510] Data 8.273 (0.186) Batch 9.249 (1.563) Remain 17:28:08 loss: 0.2149 Lr: 0.00558
[2023-12-25 06:42:18,385 INFO misc.py line 119 253097] Train: [22/100][65/510] Data 0.003 (0.183) Batch 1.227 (1.558) Remain 17:24:28 loss: 0.2531 Lr: 0.00558
[2023-12-25 06:42:19,459 INFO misc.py line 119 253097] Train: [22/100][66/510] Data 0.004 (0.180) Batch 1.075 (1.550) Remain 17:19:18 loss: 0.3438 Lr: 0.00558
[2023-12-25 06:42:20,719 INFO misc.py line 119 253097] Train: [22/100][67/510] Data 0.004 (0.178) Batch 1.254 (1.546) Remain 17:16:10 loss: 0.4550 Lr: 0.00558
[2023-12-25 06:42:21,757 INFO misc.py line 119 253097] Train: [22/100][68/510] Data 0.010 (0.175) Batch 1.039 (1.538) Remain 17:10:55 loss: 0.2915 Lr: 0.00558
[2023-12-25 06:42:22,863 INFO misc.py line 119 253097] Train: [22/100][69/510] Data 0.011 (0.173) Batch 1.111 (1.531) Remain 17:06:33 loss: 0.2814 Lr: 0.00558
[2023-12-25 06:42:26,577 INFO misc.py line 119 253097] Train: [22/100][70/510] Data 2.393 (0.206) Batch 3.715 (1.564) Remain 17:28:22 loss: 0.1867 Lr: 0.00558
[2023-12-25 06:42:27,694 INFO misc.py line 119 253097] Train: [22/100][71/510] Data 0.004 (0.203) Batch 1.117 (1.557) Remain 17:23:56 loss: 0.3242 Lr: 0.00558
[2023-12-25 06:42:28,667 INFO misc.py line 119 253097] Train: [22/100][72/510] Data 0.004 (0.200) Batch 0.973 (1.549) Remain 17:18:14 loss: 0.2455 Lr: 0.00558
[2023-12-25 06:42:29,889 INFO misc.py line 119 253097] Train: [22/100][73/510] Data 0.004 (0.197) Batch 1.221 (1.544) Remain 17:15:04 loss: 0.1568 Lr: 0.00558
[2023-12-25 06:42:31,077 INFO misc.py line 119 253097] Train: [22/100][74/510] Data 0.004 (0.194) Batch 1.186 (1.539) Remain 17:11:40 loss: 0.2216 Lr: 0.00558
[2023-12-25 06:42:32,312 INFO misc.py line 119 253097] Train: [22/100][75/510] Data 0.006 (0.192) Batch 1.237 (1.535) Remain 17:08:50 loss: 0.2177 Lr: 0.00558
[2023-12-25 06:42:33,358 INFO misc.py line 119 253097] Train: [22/100][76/510] Data 0.005 (0.189) Batch 1.044 (1.528) Remain 17:04:18 loss: 0.2062 Lr: 0.00558
[2023-12-25 06:42:39,341 INFO misc.py line 119 253097] Train: [22/100][77/510] Data 0.007 (0.187) Batch 5.985 (1.589) Remain 17:44:38 loss: 0.2109 Lr: 0.00558
[2023-12-25 06:42:40,374 INFO misc.py line 119 253097] Train: [22/100][78/510] Data 0.005 (0.184) Batch 1.032 (1.581) Remain 17:39:38 loss: 0.3221 Lr: 0.00558
[2023-12-25 06:42:41,572 INFO misc.py line 119 253097] Train: [22/100][79/510] Data 0.005 (0.182) Batch 1.199 (1.576) Remain 17:36:14 loss: 0.1134 Lr: 0.00558
[2023-12-25 06:42:42,802 INFO misc.py line 119 253097] Train: [22/100][80/510] Data 0.005 (0.180) Batch 1.226 (1.572) Remain 17:33:10 loss: 0.2433 Lr: 0.00558
[2023-12-25 06:42:44,052 INFO misc.py line 119 253097] Train: [22/100][81/510] Data 0.009 (0.177) Batch 1.251 (1.567) Remain 17:30:23 loss: 0.1747 Lr: 0.00558
[2023-12-25 06:42:45,318 INFO misc.py line 119 253097] Train: [22/100][82/510] Data 0.007 (0.175) Batch 1.270 (1.564) Remain 17:27:50 loss: 0.2131 Lr: 0.00558
[2023-12-25 06:42:46,420 INFO misc.py line 119 253097] Train: [22/100][83/510] Data 0.003 (0.173) Batch 1.102 (1.558) Remain 17:23:56 loss: 0.1871 Lr: 0.00558
[2023-12-25 06:42:47,356 INFO misc.py line 119 253097] Train: [22/100][84/510] Data 0.004 (0.171) Batch 0.936 (1.550) Remain 17:18:46 loss: 0.2046 Lr: 0.00558
[2023-12-25 06:42:48,578 INFO misc.py line 119 253097] Train: [22/100][85/510] Data 0.004 (0.169) Batch 1.223 (1.546) Remain 17:16:04 loss: 0.2923 Lr: 0.00558
[2023-12-25 06:42:49,584 INFO misc.py line 119 253097] Train: [22/100][86/510] Data 0.004 (0.167) Batch 1.006 (1.540) Remain 17:11:41 loss: 0.2632 Lr: 0.00558
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[2023-12-25 06:43:03,219 INFO misc.py line 119 253097] Train: [22/100][98/510] Data 0.011 (0.147) Batch 1.175 (1.489) Remain 16:37:11 loss: 0.4348 Lr: 0.00558
[2023-12-25 06:43:04,431 INFO misc.py line 119 253097] Train: [22/100][99/510] Data 0.009 (0.145) Batch 1.217 (1.486) Remain 16:35:16 loss: 0.3570 Lr: 0.00558
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[2023-12-25 06:43:12,385 INFO misc.py line 119 253097] Train: [22/100][102/510] Data 0.004 (0.189) Batch 0.991 (1.521) Remain 16:58:51 loss: 0.4316 Lr: 0.00558
[2023-12-25 06:43:13,728 INFO misc.py line 119 253097] Train: [22/100][103/510] Data 0.004 (0.187) Batch 1.343 (1.519) Remain 16:57:38 loss: 0.1878 Lr: 0.00558
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[2023-12-25 06:43:38,026 INFO misc.py line 119 253097] Train: [22/100][124/510] Data 0.004 (0.156) Batch 1.040 (1.456) Remain 16:15:01 loss: 0.2746 Lr: 0.00558
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[2023-12-25 06:43:50,998 INFO misc.py line 119 253097] Train: [22/100][128/510] Data 0.004 (0.151) Batch 1.025 (1.514) Remain 16:53:11 loss: 0.2457 Lr: 0.00558
[2023-12-25 06:43:52,097 INFO misc.py line 119 253097] Train: [22/100][129/510] Data 0.004 (0.150) Batch 1.098 (1.510) Remain 16:50:57 loss: 0.3672 Lr: 0.00558
[2023-12-25 06:43:53,359 INFO misc.py line 119 253097] Train: [22/100][130/510] Data 0.005 (0.149) Batch 1.259 (1.508) Remain 16:49:36 loss: 0.3137 Lr: 0.00558
[2023-12-25 06:43:54,369 INFO misc.py line 119 253097] Train: [22/100][131/510] Data 0.008 (0.148) Batch 1.009 (1.504) Remain 16:46:58 loss: 0.3750 Lr: 0.00558
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[2023-12-25 06:43:57,807 INFO misc.py line 119 253097] Train: [22/100][134/510] Data 0.009 (0.145) Batch 1.186 (1.496) Remain 16:41:24 loss: 0.1922 Lr: 0.00558
[2023-12-25 06:43:58,989 INFO misc.py line 119 253097] Train: [22/100][135/510] Data 0.007 (0.144) Batch 1.179 (1.494) Remain 16:39:46 loss: 0.1581 Lr: 0.00558
[2023-12-25 06:44:00,192 INFO misc.py line 119 253097] Train: [22/100][136/510] Data 0.010 (0.143) Batch 1.208 (1.492) Remain 16:38:18 loss: 0.2190 Lr: 0.00558
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[2023-12-25 06:44:02,555 INFO misc.py line 119 253097] Train: [22/100][138/510] Data 0.005 (0.141) Batch 1.091 (1.487) Remain 16:35:07 loss: 0.2474 Lr: 0.00558
[2023-12-25 06:44:03,724 INFO misc.py line 119 253097] Train: [22/100][139/510] Data 0.017 (0.140) Batch 1.182 (1.485) Remain 16:33:36 loss: 0.2268 Lr: 0.00558
[2023-12-25 06:44:04,843 INFO misc.py line 119 253097] Train: [22/100][140/510] Data 0.003 (0.139) Batch 1.112 (1.482) Remain 16:31:45 loss: 0.3748 Lr: 0.00558
[2023-12-25 06:44:06,006 INFO misc.py line 119 253097] Train: [22/100][141/510] Data 0.011 (0.138) Batch 1.166 (1.480) Remain 16:30:11 loss: 0.2796 Lr: 0.00558
[2023-12-25 06:44:07,128 INFO misc.py line 119 253097] Train: [22/100][142/510] Data 0.008 (0.137) Batch 1.125 (1.477) Remain 16:28:27 loss: 0.3701 Lr: 0.00558
[2023-12-25 06:44:10,741 INFO misc.py line 119 253097] Train: [22/100][143/510] Data 0.006 (0.136) Batch 3.614 (1.492) Remain 16:38:39 loss: 0.2054 Lr: 0.00558
[2023-12-25 06:44:12,632 INFO misc.py line 119 253097] Train: [22/100][144/510] Data 0.777 (0.140) Batch 1.891 (1.495) Remain 16:40:31 loss: 0.4382 Lr: 0.00558
[2023-12-25 06:44:13,896 INFO misc.py line 119 253097] Train: [22/100][145/510] Data 0.002 (0.140) Batch 1.260 (1.494) Remain 16:39:23 loss: 0.2010 Lr: 0.00558
[2023-12-25 06:44:15,089 INFO misc.py line 119 253097] Train: [22/100][146/510] Data 0.007 (0.139) Batch 1.194 (1.492) Remain 16:37:57 loss: 0.1299 Lr: 0.00558
[2023-12-25 06:44:16,161 INFO misc.py line 119 253097] Train: [22/100][147/510] Data 0.007 (0.138) Batch 1.071 (1.489) Remain 16:35:58 loss: 0.1735 Lr: 0.00558
[2023-12-25 06:44:17,260 INFO misc.py line 119 253097] Train: [22/100][148/510] Data 0.008 (0.137) Batch 1.103 (1.486) Remain 16:34:10 loss: 0.2520 Lr: 0.00558
[2023-12-25 06:44:18,198 INFO misc.py line 119 253097] Train: [22/100][149/510] Data 0.004 (0.136) Batch 0.938 (1.482) Remain 16:31:38 loss: 0.2710 Lr: 0.00558
[2023-12-25 06:44:29,355 INFO misc.py line 119 253097] Train: [22/100][150/510] Data 0.004 (0.135) Batch 11.157 (1.548) Remain 17:15:38 loss: 0.2484 Lr: 0.00558
[2023-12-25 06:44:30,438 INFO misc.py line 119 253097] Train: [22/100][151/510] Data 0.004 (0.134) Batch 1.084 (1.545) Remain 17:13:31 loss: 0.3233 Lr: 0.00558
[2023-12-25 06:44:31,610 INFO misc.py line 119 253097] Train: [22/100][152/510] Data 0.003 (0.133) Batch 1.171 (1.542) Remain 17:11:48 loss: 0.2437 Lr: 0.00557
[2023-12-25 06:44:32,754 INFO misc.py line 119 253097] Train: [22/100][153/510] Data 0.004 (0.132) Batch 1.144 (1.540) Remain 17:10:00 loss: 0.2476 Lr: 0.00557
[2023-12-25 06:44:33,762 INFO misc.py line 119 253097] Train: [22/100][154/510] Data 0.004 (0.132) Batch 1.008 (1.536) Remain 17:07:37 loss: 0.2925 Lr: 0.00557
[2023-12-25 06:44:34,806 INFO misc.py line 119 253097] Train: [22/100][155/510] Data 0.005 (0.131) Batch 1.044 (1.533) Remain 17:05:26 loss: 0.8410 Lr: 0.00557
[2023-12-25 06:44:36,112 INFO misc.py line 119 253097] Train: [22/100][156/510] Data 0.004 (0.130) Batch 1.299 (1.531) Remain 17:04:23 loss: 0.1570 Lr: 0.00557
[2023-12-25 06:44:37,231 INFO misc.py line 119 253097] Train: [22/100][157/510] Data 0.012 (0.129) Batch 1.123 (1.529) Remain 17:02:35 loss: 0.3347 Lr: 0.00557
[2023-12-25 06:44:38,252 INFO misc.py line 119 253097] Train: [22/100][158/510] Data 0.007 (0.128) Batch 1.019 (1.526) Remain 17:00:22 loss: 0.1827 Lr: 0.00557
[2023-12-25 06:44:39,525 INFO misc.py line 119 253097] Train: [22/100][159/510] Data 0.009 (0.128) Batch 1.274 (1.524) Remain 16:59:15 loss: 0.2284 Lr: 0.00557
[2023-12-25 06:44:40,939 INFO misc.py line 119 253097] Train: [22/100][160/510] Data 0.008 (0.127) Batch 1.414 (1.523) Remain 16:58:46 loss: 0.1959 Lr: 0.00557
[2023-12-25 06:44:42,105 INFO misc.py line 119 253097] Train: [22/100][161/510] Data 0.009 (0.126) Batch 1.168 (1.521) Remain 16:57:14 loss: 0.4023 Lr: 0.00557
[2023-12-25 06:44:43,327 INFO misc.py line 119 253097] Train: [22/100][162/510] Data 0.006 (0.125) Batch 1.224 (1.519) Remain 16:55:58 loss: 0.3002 Lr: 0.00557
[2023-12-25 06:44:44,462 INFO misc.py line 119 253097] Train: [22/100][163/510] Data 0.004 (0.125) Batch 1.132 (1.517) Remain 16:54:19 loss: 0.3699 Lr: 0.00557
[2023-12-25 06:44:45,628 INFO misc.py line 119 253097] Train: [22/100][164/510] Data 0.008 (0.124) Batch 1.169 (1.515) Remain 16:52:51 loss: 0.1487 Lr: 0.00557
[2023-12-25 06:44:53,982 INFO misc.py line 119 253097] Train: [22/100][165/510] Data 0.004 (0.123) Batch 8.354 (1.557) Remain 17:21:03 loss: 0.4155 Lr: 0.00557
[2023-12-25 06:44:55,275 INFO misc.py line 119 253097] Train: [22/100][166/510] Data 0.004 (0.122) Batch 1.293 (1.555) Remain 17:19:57 loss: 0.2203 Lr: 0.00557
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[2023-12-25 06:44:57,364 INFO misc.py line 119 253097] Train: [22/100][168/510] Data 0.009 (0.121) Batch 0.959 (1.549) Remain 17:15:45 loss: 0.1335 Lr: 0.00557
[2023-12-25 06:44:58,514 INFO misc.py line 119 253097] Train: [22/100][169/510] Data 0.005 (0.120) Batch 1.150 (1.547) Remain 17:14:07 loss: 0.3351 Lr: 0.00557
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[2023-12-25 06:45:01,774 INFO misc.py line 119 253097] Train: [22/100][172/510] Data 0.004 (0.118) Batch 1.083 (1.538) Remain 17:08:35 loss: 0.2891 Lr: 0.00557
[2023-12-25 06:45:02,908 INFO misc.py line 119 253097] Train: [22/100][173/510] Data 0.004 (0.117) Batch 1.134 (1.536) Remain 17:06:58 loss: 0.4412 Lr: 0.00557
[2023-12-25 06:45:03,909 INFO misc.py line 119 253097] Train: [22/100][174/510] Data 0.005 (0.117) Batch 1.001 (1.533) Remain 17:04:51 loss: 0.4208 Lr: 0.00557
[2023-12-25 06:45:05,040 INFO misc.py line 119 253097] Train: [22/100][175/510] Data 0.005 (0.116) Batch 1.132 (1.531) Remain 17:03:16 loss: 0.1397 Lr: 0.00557
[2023-12-25 06:45:06,033 INFO misc.py line 119 253097] Train: [22/100][176/510] Data 0.005 (0.116) Batch 0.994 (1.527) Remain 17:01:10 loss: 0.1877 Lr: 0.00557
[2023-12-25 06:45:07,143 INFO misc.py line 119 253097] Train: [22/100][177/510] Data 0.004 (0.115) Batch 1.109 (1.525) Remain 16:59:32 loss: 0.0864 Lr: 0.00557
[2023-12-25 06:45:08,231 INFO misc.py line 119 253097] Train: [22/100][178/510] Data 0.005 (0.114) Batch 1.088 (1.523) Remain 16:57:50 loss: 0.3214 Lr: 0.00557
[2023-12-25 06:45:09,446 INFO misc.py line 119 253097] Train: [22/100][179/510] Data 0.004 (0.114) Batch 1.216 (1.521) Remain 16:56:39 loss: 0.3097 Lr: 0.00557
[2023-12-25 06:45:16,607 INFO misc.py line 119 253097] Train: [22/100][180/510] Data 0.003 (0.113) Batch 7.160 (1.553) Remain 17:17:55 loss: 0.3430 Lr: 0.00557
[2023-12-25 06:45:17,622 INFO misc.py line 119 253097] Train: [22/100][181/510] Data 0.004 (0.112) Batch 1.015 (1.550) Remain 17:15:53 loss: 0.2982 Lr: 0.00557
[2023-12-25 06:45:18,732 INFO misc.py line 119 253097] Train: [22/100][182/510] Data 0.004 (0.112) Batch 1.111 (1.547) Remain 17:14:13 loss: 0.1625 Lr: 0.00557
[2023-12-25 06:45:19,843 INFO misc.py line 119 253097] Train: [22/100][183/510] Data 0.003 (0.111) Batch 1.111 (1.545) Remain 17:12:34 loss: 0.1863 Lr: 0.00557
[2023-12-25 06:45:21,053 INFO misc.py line 119 253097] Train: [22/100][184/510] Data 0.003 (0.111) Batch 1.204 (1.543) Remain 17:11:17 loss: 0.2233 Lr: 0.00557
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[2023-12-25 06:45:52,410 INFO misc.py line 119 253097] Train: [22/100][208/510] Data 0.003 (0.098) Batch 1.234 (1.515) Remain 16:52:12 loss: 0.2024 Lr: 0.00557
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[2023-12-25 06:45:55,659 INFO misc.py line 119 253097] Train: [22/100][211/510] Data 0.003 (0.097) Batch 1.262 (1.509) Remain 16:47:57 loss: 0.2540 Lr: 0.00557
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[2023-12-25 06:46:03,820 INFO misc.py line 119 253097] Train: [22/100][213/510] Data 6.016 (0.125) Batch 6.852 (1.533) Remain 17:04:16 loss: 0.4286 Lr: 0.00557
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[2023-12-25 06:46:08,047 INFO misc.py line 119 253097] Train: [22/100][217/510] Data 0.003 (0.122) Batch 0.988 (1.525) Remain 16:58:13 loss: 0.2707 Lr: 0.00557
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[2023-12-25 06:46:10,366 INFO misc.py line 119 253097] Train: [22/100][219/510] Data 0.005 (0.121) Batch 1.069 (1.521) Remain 16:55:55 loss: 0.3206 Lr: 0.00557
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[2023-12-25 06:46:12,896 INFO misc.py line 119 253097] Train: [22/100][221/510] Data 0.006 (0.120) Batch 1.246 (1.519) Remain 16:54:17 loss: 0.2057 Lr: 0.00557
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[2023-12-25 06:46:15,365 INFO misc.py line 119 253097] Train: [22/100][223/510] Data 0.005 (0.119) Batch 1.211 (1.516) Remain 16:52:30 loss: 0.3242 Lr: 0.00557
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[2023-12-25 06:46:18,802 INFO misc.py line 119 253097] Train: [22/100][226/510] Data 0.003 (0.118) Batch 1.175 (1.511) Remain 16:49:05 loss: 0.2690 Lr: 0.00557
[2023-12-25 06:46:19,987 INFO misc.py line 119 253097] Train: [22/100][227/510] Data 0.012 (0.117) Batch 1.193 (1.510) Remain 16:48:07 loss: 0.4636 Lr: 0.00557
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[2023-12-25 06:46:22,198 INFO misc.py line 119 253097] Train: [22/100][229/510] Data 0.007 (0.116) Batch 1.015 (1.506) Remain 16:45:40 loss: 0.4454 Lr: 0.00557
[2023-12-25 06:46:23,480 INFO misc.py line 119 253097] Train: [22/100][230/510] Data 0.004 (0.116) Batch 1.281 (1.505) Remain 16:44:59 loss: 0.3089 Lr: 0.00557
[2023-12-25 06:46:24,599 INFO misc.py line 119 253097] Train: [22/100][231/510] Data 0.006 (0.115) Batch 1.121 (1.504) Remain 16:43:50 loss: 0.2254 Lr: 0.00557
[2023-12-25 06:46:25,873 INFO misc.py line 119 253097] Train: [22/100][232/510] Data 0.004 (0.115) Batch 1.271 (1.503) Remain 16:43:08 loss: 0.3743 Lr: 0.00557
[2023-12-25 06:46:27,103 INFO misc.py line 119 253097] Train: [22/100][233/510] Data 0.007 (0.114) Batch 1.234 (1.501) Remain 16:42:19 loss: 0.2117 Lr: 0.00557
[2023-12-25 06:46:29,206 INFO misc.py line 119 253097] Train: [22/100][234/510] Data 0.004 (0.114) Batch 2.103 (1.504) Remain 16:44:02 loss: 0.4885 Lr: 0.00557
[2023-12-25 06:46:30,348 INFO misc.py line 119 253097] Train: [22/100][235/510] Data 0.003 (0.113) Batch 1.142 (1.502) Remain 16:42:58 loss: 0.3450 Lr: 0.00557
[2023-12-25 06:46:31,397 INFO misc.py line 119 253097] Train: [22/100][236/510] Data 0.004 (0.113) Batch 1.049 (1.500) Remain 16:41:39 loss: 0.1996 Lr: 0.00557
[2023-12-25 06:46:32,579 INFO misc.py line 119 253097] Train: [22/100][237/510] Data 0.003 (0.112) Batch 1.179 (1.499) Remain 16:40:42 loss: 0.2324 Lr: 0.00557
[2023-12-25 06:46:33,841 INFO misc.py line 119 253097] Train: [22/100][238/510] Data 0.007 (0.112) Batch 1.264 (1.498) Remain 16:40:01 loss: 0.2909 Lr: 0.00557
[2023-12-25 06:46:35,032 INFO misc.py line 119 253097] Train: [22/100][239/510] Data 0.004 (0.112) Batch 1.185 (1.497) Remain 16:39:06 loss: 0.3304 Lr: 0.00557
[2023-12-25 06:46:36,393 INFO misc.py line 119 253097] Train: [22/100][240/510] Data 0.125 (0.112) Batch 1.367 (1.496) Remain 16:38:43 loss: 0.2758 Lr: 0.00557
[2023-12-25 06:46:37,482 INFO misc.py line 119 253097] Train: [22/100][241/510] Data 0.004 (0.111) Batch 1.085 (1.494) Remain 16:37:32 loss: 0.2206 Lr: 0.00557
[2023-12-25 06:46:38,594 INFO misc.py line 119 253097] Train: [22/100][242/510] Data 0.008 (0.111) Batch 1.111 (1.493) Remain 16:36:26 loss: 0.3163 Lr: 0.00557
[2023-12-25 06:46:39,778 INFO misc.py line 119 253097] Train: [22/100][243/510] Data 0.008 (0.110) Batch 1.183 (1.492) Remain 16:35:33 loss: 0.1476 Lr: 0.00557
[2023-12-25 06:46:40,872 INFO misc.py line 119 253097] Train: [22/100][244/510] Data 0.008 (0.110) Batch 1.100 (1.490) Remain 16:34:26 loss: 0.2675 Lr: 0.00557
[2023-12-25 06:46:41,943 INFO misc.py line 119 253097] Train: [22/100][245/510] Data 0.004 (0.109) Batch 1.065 (1.488) Remain 16:33:15 loss: 0.3621 Lr: 0.00557
[2023-12-25 06:46:43,449 INFO misc.py line 119 253097] Train: [22/100][246/510] Data 0.417 (0.111) Batch 1.513 (1.488) Remain 16:33:17 loss: 0.2721 Lr: 0.00557
[2023-12-25 06:46:44,658 INFO misc.py line 119 253097] Train: [22/100][247/510] Data 0.003 (0.110) Batch 1.203 (1.487) Remain 16:32:29 loss: 0.2728 Lr: 0.00557
[2023-12-25 06:46:45,853 INFO misc.py line 119 253097] Train: [22/100][248/510] Data 0.009 (0.110) Batch 1.193 (1.486) Remain 16:31:39 loss: 0.1688 Lr: 0.00557
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[2023-12-25 06:46:48,234 INFO misc.py line 119 253097] Train: [22/100][250/510] Data 0.010 (0.109) Batch 1.248 (1.484) Remain 16:30:02 loss: 0.3254 Lr: 0.00557
[2023-12-25 06:46:49,475 INFO misc.py line 119 253097] Train: [22/100][251/510] Data 0.004 (0.109) Batch 1.235 (1.483) Remain 16:29:20 loss: 0.2223 Lr: 0.00557
[2023-12-25 06:46:50,702 INFO misc.py line 119 253097] Train: [22/100][252/510] Data 0.011 (0.108) Batch 1.227 (1.482) Remain 16:28:37 loss: 0.2592 Lr: 0.00556
[2023-12-25 06:46:51,790 INFO misc.py line 119 253097] Train: [22/100][253/510] Data 0.011 (0.108) Batch 1.091 (1.480) Remain 16:27:33 loss: 0.2831 Lr: 0.00556
[2023-12-25 06:47:02,707 INFO misc.py line 119 253097] Train: [22/100][254/510] Data 0.008 (0.107) Batch 10.920 (1.518) Remain 16:52:38 loss: 0.4770 Lr: 0.00556
[2023-12-25 06:47:03,634 INFO misc.py line 119 253097] Train: [22/100][255/510] Data 0.004 (0.107) Batch 0.927 (1.515) Remain 16:51:02 loss: 0.4892 Lr: 0.00556
[2023-12-25 06:47:04,814 INFO misc.py line 119 253097] Train: [22/100][256/510] Data 0.004 (0.107) Batch 1.181 (1.514) Remain 16:50:08 loss: 0.1559 Lr: 0.00556
[2023-12-25 06:47:05,929 INFO misc.py line 119 253097] Train: [22/100][257/510] Data 0.003 (0.106) Batch 1.115 (1.512) Remain 16:49:04 loss: 0.3509 Lr: 0.00556
[2023-12-25 06:47:07,162 INFO misc.py line 119 253097] Train: [22/100][258/510] Data 0.003 (0.106) Batch 1.228 (1.511) Remain 16:48:17 loss: 0.2537 Lr: 0.00556
[2023-12-25 06:47:08,441 INFO misc.py line 119 253097] Train: [22/100][259/510] Data 0.009 (0.105) Batch 1.284 (1.510) Remain 16:47:40 loss: 0.5755 Lr: 0.00556
[2023-12-25 06:47:17,086 INFO misc.py line 119 253097] Train: [22/100][260/510] Data 0.004 (0.105) Batch 8.644 (1.538) Remain 17:06:10 loss: 0.1795 Lr: 0.00556
[2023-12-25 06:47:18,321 INFO misc.py line 119 253097] Train: [22/100][261/510] Data 0.004 (0.105) Batch 1.236 (1.537) Remain 17:05:21 loss: 0.2470 Lr: 0.00556
[2023-12-25 06:47:19,360 INFO misc.py line 119 253097] Train: [22/100][262/510] Data 0.003 (0.104) Batch 1.032 (1.535) Remain 17:04:02 loss: 0.2689 Lr: 0.00556
[2023-12-25 06:47:20,535 INFO misc.py line 119 253097] Train: [22/100][263/510] Data 0.012 (0.104) Batch 1.182 (1.534) Remain 17:03:06 loss: 0.2679 Lr: 0.00556
[2023-12-25 06:47:21,531 INFO misc.py line 119 253097] Train: [22/100][264/510] Data 0.003 (0.103) Batch 0.995 (1.532) Remain 17:01:42 loss: 0.2760 Lr: 0.00556
[2023-12-25 06:47:22,697 INFO misc.py line 119 253097] Train: [22/100][265/510] Data 0.004 (0.103) Batch 1.166 (1.530) Remain 17:00:45 loss: 0.3252 Lr: 0.00556
[2023-12-25 06:47:24,009 INFO misc.py line 119 253097] Train: [22/100][266/510] Data 0.005 (0.103) Batch 1.312 (1.529) Remain 17:00:10 loss: 0.1688 Lr: 0.00556
[2023-12-25 06:47:25,295 INFO misc.py line 119 253097] Train: [22/100][267/510] Data 0.004 (0.102) Batch 1.284 (1.528) Remain 16:59:31 loss: 0.1706 Lr: 0.00556
[2023-12-25 06:47:26,329 INFO misc.py line 119 253097] Train: [22/100][268/510] Data 0.006 (0.102) Batch 1.031 (1.527) Remain 16:58:15 loss: 0.5793 Lr: 0.00556
[2023-12-25 06:47:27,483 INFO misc.py line 119 253097] Train: [22/100][269/510] Data 0.009 (0.102) Batch 1.160 (1.525) Remain 16:57:18 loss: 0.2171 Lr: 0.00556
[2023-12-25 06:47:28,696 INFO misc.py line 119 253097] Train: [22/100][270/510] Data 0.004 (0.101) Batch 1.207 (1.524) Remain 16:56:29 loss: 0.1705 Lr: 0.00556
[2023-12-25 06:47:29,973 INFO misc.py line 119 253097] Train: [22/100][271/510] Data 0.009 (0.101) Batch 1.279 (1.523) Remain 16:55:51 loss: 0.2809 Lr: 0.00556
[2023-12-25 06:47:31,056 INFO misc.py line 119 253097] Train: [22/100][272/510] Data 0.006 (0.101) Batch 1.082 (1.521) Remain 16:54:43 loss: 0.2363 Lr: 0.00556
[2023-12-25 06:47:38,839 INFO misc.py line 119 253097] Train: [22/100][273/510] Data 6.640 (0.125) Batch 7.785 (1.545) Remain 17:10:10 loss: 0.0809 Lr: 0.00556
[2023-12-25 06:47:39,751 INFO misc.py line 119 253097] Train: [22/100][274/510] Data 0.005 (0.124) Batch 0.914 (1.542) Remain 17:08:36 loss: 0.2037 Lr: 0.00556
[2023-12-25 06:47:40,993 INFO misc.py line 119 253097] Train: [22/100][275/510] Data 0.004 (0.124) Batch 1.241 (1.541) Remain 17:07:50 loss: 0.1944 Lr: 0.00556
[2023-12-25 06:47:42,291 INFO misc.py line 119 253097] Train: [22/100][276/510] Data 0.005 (0.123) Batch 1.299 (1.540) Remain 17:07:13 loss: 0.1487 Lr: 0.00556
[2023-12-25 06:47:43,443 INFO misc.py line 119 253097] Train: [22/100][277/510] Data 0.004 (0.123) Batch 1.149 (1.539) Remain 17:06:14 loss: 0.3499 Lr: 0.00556
[2023-12-25 06:47:44,554 INFO misc.py line 119 253097] Train: [22/100][278/510] Data 0.006 (0.123) Batch 1.114 (1.537) Remain 17:05:11 loss: 0.4040 Lr: 0.00556
[2023-12-25 06:47:45,755 INFO misc.py line 119 253097] Train: [22/100][279/510] Data 0.004 (0.122) Batch 1.197 (1.536) Remain 17:04:20 loss: 0.2835 Lr: 0.00556
[2023-12-25 06:47:46,845 INFO misc.py line 119 253097] Train: [22/100][280/510] Data 0.008 (0.122) Batch 1.093 (1.534) Remain 17:03:14 loss: 0.3502 Lr: 0.00556
[2023-12-25 06:47:47,867 INFO misc.py line 119 253097] Train: [22/100][281/510] Data 0.004 (0.121) Batch 1.018 (1.533) Remain 17:01:58 loss: 0.3854 Lr: 0.00556
[2023-12-25 06:47:49,129 INFO misc.py line 119 253097] Train: [22/100][282/510] Data 0.008 (0.121) Batch 1.263 (1.532) Remain 17:01:18 loss: 0.2937 Lr: 0.00556
[2023-12-25 06:47:50,184 INFO misc.py line 119 253097] Train: [22/100][283/510] Data 0.007 (0.121) Batch 1.053 (1.530) Remain 17:00:08 loss: 0.3331 Lr: 0.00556
[2023-12-25 06:47:51,394 INFO misc.py line 119 253097] Train: [22/100][284/510] Data 0.008 (0.120) Batch 1.210 (1.529) Remain 16:59:21 loss: 0.1056 Lr: 0.00556
[2023-12-25 06:47:52,772 INFO misc.py line 119 253097] Train: [22/100][285/510] Data 0.096 (0.120) Batch 1.383 (1.528) Remain 16:58:59 loss: 0.3049 Lr: 0.00556
[2023-12-25 06:47:53,848 INFO misc.py line 119 253097] Train: [22/100][286/510] Data 0.004 (0.120) Batch 1.073 (1.527) Remain 16:57:53 loss: 0.1513 Lr: 0.00556
[2023-12-25 06:47:55,137 INFO misc.py line 119 253097] Train: [22/100][287/510] Data 0.008 (0.119) Batch 1.292 (1.526) Remain 16:57:18 loss: 0.2536 Lr: 0.00556
[2023-12-25 06:47:56,228 INFO misc.py line 119 253097] Train: [22/100][288/510] Data 0.005 (0.119) Batch 1.088 (1.524) Remain 16:56:15 loss: 0.2133 Lr: 0.00556
[2023-12-25 06:48:01,025 INFO misc.py line 119 253097] Train: [22/100][289/510] Data 0.008 (0.118) Batch 4.802 (1.536) Remain 17:03:52 loss: 0.3118 Lr: 0.00556
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[2023-12-25 06:48:10,535 INFO misc.py line 119 253097] Train: [22/100][292/510] Data 0.008 (0.117) Batch 6.961 (1.553) Remain 17:15:06 loss: 0.1557 Lr: 0.00556
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[2023-12-25 06:48:31,960 INFO misc.py line 119 253097] Train: [22/100][311/510] Data 0.005 (0.110) Batch 1.047 (1.527) Remain 16:57:08 loss: 0.5823 Lr: 0.00556
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[2023-12-25 06:51:22,551 INFO misc.py line 119 253097] Train: [22/100][418/510] Data 0.006 (0.101) Batch 1.236 (1.544) Remain 17:06:02 loss: 0.1885 Lr: 0.00555
[2023-12-25 06:51:23,640 INFO misc.py line 119 253097] Train: [22/100][419/510] Data 0.005 (0.101) Batch 1.083 (1.543) Remain 17:05:16 loss: 0.2526 Lr: 0.00555
[2023-12-25 06:51:24,819 INFO misc.py line 119 253097] Train: [22/100][420/510] Data 0.010 (0.101) Batch 1.183 (1.542) Remain 17:04:40 loss: 0.1932 Lr: 0.00555
[2023-12-25 06:51:26,019 INFO misc.py line 119 253097] Train: [22/100][421/510] Data 0.006 (0.100) Batch 1.199 (1.541) Remain 17:04:06 loss: 0.2303 Lr: 0.00555
[2023-12-25 06:51:27,037 INFO misc.py line 119 253097] Train: [22/100][422/510] Data 0.008 (0.100) Batch 1.021 (1.540) Remain 17:03:15 loss: 0.2664 Lr: 0.00555
[2023-12-25 06:51:28,176 INFO misc.py line 119 253097] Train: [22/100][423/510] Data 0.005 (0.100) Batch 1.139 (1.539) Remain 17:02:35 loss: 0.1627 Lr: 0.00555
[2023-12-25 06:51:32,578 INFO misc.py line 119 253097] Train: [22/100][424/510] Data 3.194 (0.107) Batch 4.403 (1.546) Remain 17:07:05 loss: 0.1906 Lr: 0.00555
[2023-12-25 06:51:33,476 INFO misc.py line 119 253097] Train: [22/100][425/510] Data 0.004 (0.107) Batch 0.898 (1.544) Remain 17:06:02 loss: 0.2008 Lr: 0.00555
[2023-12-25 06:51:34,748 INFO misc.py line 119 253097] Train: [22/100][426/510] Data 0.004 (0.107) Batch 1.272 (1.544) Remain 17:05:35 loss: 0.2287 Lr: 0.00555
[2023-12-25 06:51:36,047 INFO misc.py line 119 253097] Train: [22/100][427/510] Data 0.004 (0.107) Batch 1.299 (1.543) Remain 17:05:10 loss: 0.2535 Lr: 0.00555
[2023-12-25 06:51:37,248 INFO misc.py line 119 253097] Train: [22/100][428/510] Data 0.003 (0.106) Batch 1.201 (1.542) Remain 17:04:37 loss: 0.2652 Lr: 0.00555
[2023-12-25 06:51:38,446 INFO misc.py line 119 253097] Train: [22/100][429/510] Data 0.004 (0.106) Batch 1.198 (1.541) Remain 17:04:03 loss: 0.2484 Lr: 0.00555
[2023-12-25 06:51:39,682 INFO misc.py line 119 253097] Train: [22/100][430/510] Data 0.003 (0.106) Batch 1.236 (1.541) Remain 17:03:33 loss: 0.2015 Lr: 0.00555
[2023-12-25 06:51:40,803 INFO misc.py line 119 253097] Train: [22/100][431/510] Data 0.004 (0.106) Batch 1.122 (1.540) Remain 17:02:52 loss: 0.1246 Lr: 0.00555
[2023-12-25 06:51:41,709 INFO misc.py line 119 253097] Train: [22/100][432/510] Data 0.004 (0.105) Batch 0.906 (1.538) Remain 17:01:52 loss: 0.2157 Lr: 0.00555
[2023-12-25 06:51:42,923 INFO misc.py line 119 253097] Train: [22/100][433/510] Data 0.004 (0.105) Batch 1.214 (1.538) Remain 17:01:20 loss: 0.1770 Lr: 0.00555
[2023-12-25 06:51:44,206 INFO misc.py line 119 253097] Train: [22/100][434/510] Data 0.004 (0.105) Batch 1.282 (1.537) Remain 17:00:55 loss: 0.1944 Lr: 0.00555
[2023-12-25 06:51:45,362 INFO misc.py line 119 253097] Train: [22/100][435/510] Data 0.004 (0.105) Batch 1.152 (1.536) Remain 17:00:18 loss: 0.5694 Lr: 0.00555
[2023-12-25 06:51:46,333 INFO misc.py line 119 253097] Train: [22/100][436/510] Data 0.009 (0.104) Batch 0.975 (1.535) Remain 16:59:25 loss: 0.2211 Lr: 0.00555
[2023-12-25 06:51:47,572 INFO misc.py line 119 253097] Train: [22/100][437/510] Data 0.004 (0.104) Batch 1.235 (1.534) Remain 16:58:56 loss: 0.5076 Lr: 0.00555
[2023-12-25 06:51:48,654 INFO misc.py line 119 253097] Train: [22/100][438/510] Data 0.008 (0.104) Batch 1.084 (1.533) Remain 16:58:13 loss: 0.1469 Lr: 0.00555
[2023-12-25 06:51:49,742 INFO misc.py line 119 253097] Train: [22/100][439/510] Data 0.008 (0.104) Batch 1.090 (1.532) Remain 16:57:31 loss: 0.1234 Lr: 0.00555
[2023-12-25 06:51:50,988 INFO misc.py line 119 253097] Train: [22/100][440/510] Data 0.006 (0.104) Batch 1.242 (1.531) Remain 16:57:03 loss: 0.1714 Lr: 0.00555
[2023-12-25 06:51:59,829 INFO misc.py line 119 253097] Train: [22/100][441/510] Data 0.009 (0.103) Batch 8.845 (1.548) Remain 17:08:07 loss: 0.3160 Lr: 0.00555
[2023-12-25 06:52:01,073 INFO misc.py line 119 253097] Train: [22/100][442/510] Data 0.003 (0.103) Batch 1.244 (1.547) Remain 17:07:38 loss: 0.2531 Lr: 0.00555
[2023-12-25 06:52:02,133 INFO misc.py line 119 253097] Train: [22/100][443/510] Data 0.004 (0.103) Batch 1.060 (1.546) Remain 17:06:52 loss: 0.2322 Lr: 0.00555
[2023-12-25 06:52:03,298 INFO misc.py line 119 253097] Train: [22/100][444/510] Data 0.005 (0.103) Batch 1.165 (1.545) Remain 17:06:16 loss: 0.3634 Lr: 0.00555
[2023-12-25 06:52:04,624 INFO misc.py line 119 253097] Train: [22/100][445/510] Data 0.004 (0.102) Batch 1.324 (1.545) Remain 17:05:55 loss: 0.3851 Lr: 0.00555
[2023-12-25 06:52:05,822 INFO misc.py line 119 253097] Train: [22/100][446/510] Data 0.006 (0.102) Batch 1.200 (1.544) Remain 17:05:22 loss: 0.2209 Lr: 0.00555
[2023-12-25 06:52:07,078 INFO misc.py line 119 253097] Train: [22/100][447/510] Data 0.004 (0.102) Batch 1.253 (1.543) Remain 17:04:54 loss: 0.1760 Lr: 0.00555
[2023-12-25 06:52:08,281 INFO misc.py line 119 253097] Train: [22/100][448/510] Data 0.007 (0.102) Batch 1.204 (1.543) Remain 17:04:22 loss: 0.1913 Lr: 0.00554
[2023-12-25 06:52:09,174 INFO misc.py line 119 253097] Train: [22/100][449/510] Data 0.007 (0.102) Batch 0.895 (1.541) Remain 17:03:23 loss: 0.3411 Lr: 0.00554
[2023-12-25 06:52:10,387 INFO misc.py line 119 253097] Train: [22/100][450/510] Data 0.004 (0.101) Batch 1.213 (1.540) Remain 17:02:52 loss: 0.2604 Lr: 0.00554
[2023-12-25 06:52:11,517 INFO misc.py line 119 253097] Train: [22/100][451/510] Data 0.005 (0.101) Batch 1.132 (1.540) Remain 17:02:14 loss: 0.3929 Lr: 0.00554
[2023-12-25 06:52:12,712 INFO misc.py line 119 253097] Train: [22/100][452/510] Data 0.003 (0.101) Batch 1.194 (1.539) Remain 17:01:42 loss: 0.3404 Lr: 0.00554
[2023-12-25 06:52:13,931 INFO misc.py line 119 253097] Train: [22/100][453/510] Data 0.004 (0.101) Batch 1.215 (1.538) Remain 17:01:12 loss: 0.2299 Lr: 0.00554
[2023-12-25 06:52:15,038 INFO misc.py line 119 253097] Train: [22/100][454/510] Data 0.007 (0.101) Batch 1.102 (1.537) Remain 17:00:32 loss: 0.1708 Lr: 0.00554
[2023-12-25 06:52:16,258 INFO misc.py line 119 253097] Train: [22/100][455/510] Data 0.014 (0.100) Batch 1.226 (1.536) Remain 17:00:03 loss: 0.3670 Lr: 0.00554
[2023-12-25 06:52:17,393 INFO misc.py line 119 253097] Train: [22/100][456/510] Data 0.007 (0.100) Batch 1.134 (1.536) Remain 16:59:26 loss: 0.2152 Lr: 0.00554
[2023-12-25 06:52:18,533 INFO misc.py line 119 253097] Train: [22/100][457/510] Data 0.008 (0.100) Batch 1.140 (1.535) Remain 16:58:50 loss: 0.4704 Lr: 0.00554
[2023-12-25 06:52:19,675 INFO misc.py line 119 253097] Train: [22/100][458/510] Data 0.008 (0.100) Batch 1.147 (1.534) Remain 16:58:14 loss: 0.2363 Lr: 0.00554
[2023-12-25 06:52:20,659 INFO misc.py line 119 253097] Train: [22/100][459/510] Data 0.003 (0.099) Batch 0.983 (1.533) Remain 16:57:25 loss: 0.3217 Lr: 0.00554
[2023-12-25 06:52:21,915 INFO misc.py line 119 253097] Train: [22/100][460/510] Data 0.004 (0.099) Batch 1.255 (1.532) Remain 16:56:59 loss: 0.2491 Lr: 0.00554
[2023-12-25 06:52:23,132 INFO misc.py line 119 253097] Train: [22/100][461/510] Data 0.006 (0.099) Batch 1.218 (1.531) Remain 16:56:30 loss: 0.2367 Lr: 0.00554
[2023-12-25 06:52:24,170 INFO misc.py line 119 253097] Train: [22/100][462/510] Data 0.004 (0.099) Batch 1.034 (1.530) Remain 16:55:45 loss: 0.2389 Lr: 0.00554
[2023-12-25 06:52:25,262 INFO misc.py line 119 253097] Train: [22/100][463/510] Data 0.008 (0.099) Batch 1.091 (1.529) Remain 16:55:06 loss: 0.5223 Lr: 0.00554
[2023-12-25 06:52:26,562 INFO misc.py line 119 253097] Train: [22/100][464/510] Data 0.009 (0.098) Batch 1.305 (1.529) Remain 16:54:45 loss: 0.2492 Lr: 0.00554
[2023-12-25 06:52:27,763 INFO misc.py line 119 253097] Train: [22/100][465/510] Data 0.004 (0.098) Batch 1.197 (1.528) Remain 16:54:15 loss: 0.3367 Lr: 0.00554
[2023-12-25 06:52:29,017 INFO misc.py line 119 253097] Train: [22/100][466/510] Data 0.008 (0.098) Batch 1.257 (1.527) Remain 16:53:50 loss: 0.2993 Lr: 0.00554
[2023-12-25 06:52:29,952 INFO misc.py line 119 253097] Train: [22/100][467/510] Data 0.005 (0.098) Batch 0.937 (1.526) Remain 16:52:58 loss: 0.4220 Lr: 0.00554
[2023-12-25 06:52:31,275 INFO misc.py line 119 253097] Train: [22/100][468/510] Data 0.003 (0.098) Batch 1.322 (1.526) Remain 16:52:39 loss: 0.2418 Lr: 0.00554
[2023-12-25 06:52:32,560 INFO misc.py line 119 253097] Train: [22/100][469/510] Data 0.003 (0.097) Batch 1.284 (1.525) Remain 16:52:17 loss: 0.2067 Lr: 0.00554
[2023-12-25 06:52:33,621 INFO misc.py line 119 253097] Train: [22/100][470/510] Data 0.004 (0.097) Batch 1.060 (1.524) Remain 16:51:35 loss: 0.3614 Lr: 0.00554
[2023-12-25 06:52:34,695 INFO misc.py line 119 253097] Train: [22/100][471/510] Data 0.006 (0.097) Batch 1.076 (1.523) Remain 16:50:56 loss: 0.6856 Lr: 0.00554
[2023-12-25 06:52:35,824 INFO misc.py line 119 253097] Train: [22/100][472/510] Data 0.004 (0.097) Batch 1.126 (1.522) Remain 16:50:20 loss: 0.2782 Lr: 0.00554
[2023-12-25 06:52:36,992 INFO misc.py line 119 253097] Train: [22/100][473/510] Data 0.008 (0.097) Batch 1.171 (1.522) Remain 16:49:49 loss: 0.2527 Lr: 0.00554
[2023-12-25 06:52:38,298 INFO misc.py line 119 253097] Train: [22/100][474/510] Data 0.004 (0.096) Batch 1.301 (1.521) Remain 16:49:29 loss: 0.4844 Lr: 0.00554
[2023-12-25 06:52:39,329 INFO misc.py line 119 253097] Train: [22/100][475/510] Data 0.009 (0.096) Batch 1.037 (1.520) Remain 16:48:47 loss: 0.1503 Lr: 0.00554
[2023-12-25 06:52:40,169 INFO misc.py line 119 253097] Train: [22/100][476/510] Data 0.003 (0.096) Batch 0.839 (1.519) Remain 16:47:48 loss: 0.1759 Lr: 0.00554
[2023-12-25 06:52:41,420 INFO misc.py line 119 253097] Train: [22/100][477/510] Data 0.005 (0.096) Batch 1.250 (1.518) Remain 16:47:24 loss: 0.2438 Lr: 0.00554
[2023-12-25 06:52:42,652 INFO misc.py line 119 253097] Train: [22/100][478/510] Data 0.005 (0.096) Batch 1.226 (1.518) Remain 16:46:58 loss: 0.2166 Lr: 0.00554
[2023-12-25 06:52:43,652 INFO misc.py line 119 253097] Train: [22/100][479/510] Data 0.012 (0.096) Batch 1.003 (1.517) Remain 16:46:13 loss: 0.3360 Lr: 0.00554
[2023-12-25 06:52:44,594 INFO misc.py line 119 253097] Train: [22/100][480/510] Data 0.008 (0.095) Batch 0.946 (1.515) Remain 16:45:24 loss: 0.2763 Lr: 0.00554
[2023-12-25 06:52:45,889 INFO misc.py line 119 253097] Train: [22/100][481/510] Data 0.004 (0.095) Batch 1.294 (1.515) Remain 16:45:04 loss: 0.2312 Lr: 0.00554
[2023-12-25 06:52:46,948 INFO misc.py line 119 253097] Train: [22/100][482/510] Data 0.008 (0.095) Batch 1.060 (1.514) Remain 16:44:25 loss: 0.1642 Lr: 0.00554
[2023-12-25 06:52:48,046 INFO misc.py line 119 253097] Train: [22/100][483/510] Data 0.005 (0.095) Batch 1.100 (1.513) Remain 16:43:49 loss: 0.5029 Lr: 0.00554
[2023-12-25 06:52:49,227 INFO misc.py line 119 253097] Train: [22/100][484/510] Data 0.003 (0.095) Batch 1.178 (1.512) Remain 16:43:19 loss: 0.1577 Lr: 0.00554
[2023-12-25 06:52:50,333 INFO misc.py line 119 253097] Train: [22/100][485/510] Data 0.006 (0.094) Batch 1.107 (1.511) Remain 16:42:44 loss: 0.1548 Lr: 0.00554
[2023-12-25 06:52:51,499 INFO misc.py line 119 253097] Train: [22/100][486/510] Data 0.005 (0.094) Batch 1.162 (1.511) Remain 16:42:14 loss: 0.2312 Lr: 0.00554
[2023-12-25 06:52:52,737 INFO misc.py line 119 253097] Train: [22/100][487/510] Data 0.010 (0.094) Batch 1.240 (1.510) Remain 16:41:50 loss: 0.1332 Lr: 0.00554
[2023-12-25 06:52:53,877 INFO misc.py line 119 253097] Train: [22/100][488/510] Data 0.006 (0.094) Batch 1.140 (1.509) Remain 16:41:19 loss: 0.1927 Lr: 0.00554
[2023-12-25 06:52:55,055 INFO misc.py line 119 253097] Train: [22/100][489/510] Data 0.006 (0.094) Batch 1.177 (1.509) Remain 16:40:50 loss: 0.4357 Lr: 0.00554
[2023-12-25 06:52:56,235 INFO misc.py line 119 253097] Train: [22/100][490/510] Data 0.006 (0.094) Batch 1.183 (1.508) Remain 16:40:22 loss: 0.3216 Lr: 0.00554
[2023-12-25 06:52:57,509 INFO misc.py line 119 253097] Train: [22/100][491/510] Data 0.004 (0.093) Batch 1.274 (1.508) Remain 16:40:01 loss: 0.2008 Lr: 0.00554
[2023-12-25 06:52:58,588 INFO misc.py line 119 253097] Train: [22/100][492/510] Data 0.004 (0.093) Batch 1.079 (1.507) Remain 16:39:25 loss: 0.4127 Lr: 0.00554
[2023-12-25 06:52:59,653 INFO misc.py line 119 253097] Train: [22/100][493/510] Data 0.005 (0.093) Batch 1.065 (1.506) Remain 16:38:47 loss: 0.2477 Lr: 0.00554
[2023-12-25 06:53:00,917 INFO misc.py line 119 253097] Train: [22/100][494/510] Data 0.004 (0.093) Batch 1.264 (1.505) Remain 16:38:26 loss: 0.3558 Lr: 0.00554
[2023-12-25 06:53:02,139 INFO misc.py line 119 253097] Train: [22/100][495/510] Data 0.005 (0.093) Batch 1.222 (1.505) Remain 16:38:02 loss: 0.4531 Lr: 0.00554
[2023-12-25 06:53:03,352 INFO misc.py line 119 253097] Train: [22/100][496/510] Data 0.004 (0.092) Batch 1.212 (1.504) Remain 16:37:37 loss: 0.3155 Lr: 0.00554
[2023-12-25 06:53:04,306 INFO misc.py line 119 253097] Train: [22/100][497/510] Data 0.005 (0.092) Batch 0.955 (1.503) Remain 16:36:51 loss: 0.1982 Lr: 0.00554
[2023-12-25 06:53:05,349 INFO misc.py line 119 253097] Train: [22/100][498/510] Data 0.004 (0.092) Batch 1.044 (1.502) Remain 16:36:13 loss: 0.2139 Lr: 0.00554
[2023-12-25 06:53:06,627 INFO misc.py line 119 253097] Train: [22/100][499/510] Data 0.004 (0.092) Batch 1.272 (1.502) Remain 16:35:53 loss: 0.3939 Lr: 0.00554
[2023-12-25 06:53:08,971 INFO misc.py line 119 253097] Train: [22/100][500/510] Data 0.010 (0.092) Batch 2.349 (1.503) Remain 16:36:59 loss: 0.1309 Lr: 0.00554
[2023-12-25 06:53:13,623 INFO misc.py line 119 253097] Train: [22/100][501/510] Data 0.004 (0.092) Batch 4.652 (1.510) Remain 16:41:09 loss: 0.2513 Lr: 0.00554
[2023-12-25 06:53:14,667 INFO misc.py line 119 253097] Train: [22/100][502/510] Data 0.005 (0.091) Batch 1.044 (1.509) Remain 16:40:30 loss: 0.2814 Lr: 0.00554
[2023-12-25 06:53:15,720 INFO misc.py line 119 253097] Train: [22/100][503/510] Data 0.005 (0.091) Batch 1.055 (1.508) Remain 16:39:53 loss: 0.2241 Lr: 0.00554
[2023-12-25 06:53:16,867 INFO misc.py line 119 253097] Train: [22/100][504/510] Data 0.003 (0.091) Batch 1.145 (1.507) Remain 16:39:22 loss: 0.3788 Lr: 0.00554
[2023-12-25 06:53:17,818 INFO misc.py line 119 253097] Train: [22/100][505/510] Data 0.005 (0.091) Batch 0.953 (1.506) Remain 16:38:37 loss: 0.4781 Lr: 0.00554
[2023-12-25 06:53:18,870 INFO misc.py line 119 253097] Train: [22/100][506/510] Data 0.003 (0.091) Batch 1.053 (1.505) Remain 16:37:59 loss: 0.2495 Lr: 0.00554
[2023-12-25 06:53:19,813 INFO misc.py line 119 253097] Train: [22/100][507/510] Data 0.003 (0.091) Batch 0.942 (1.504) Remain 16:37:14 loss: 0.1378 Lr: 0.00554
[2023-12-25 06:53:20,756 INFO misc.py line 119 253097] Train: [22/100][508/510] Data 0.003 (0.090) Batch 0.943 (1.503) Remain 16:36:28 loss: 0.2525 Lr: 0.00554
[2023-12-25 06:53:27,894 INFO misc.py line 119 253097] Train: [22/100][509/510] Data 0.004 (0.090) Batch 7.135 (1.514) Remain 16:43:49 loss: 0.2713 Lr: 0.00554
[2023-12-25 06:53:28,982 INFO misc.py line 119 253097] Train: [22/100][510/510] Data 0.006 (0.090) Batch 1.090 (1.513) Remain 16:43:14 loss: 0.1576 Lr: 0.00554
[2023-12-25 06:53:28,982 INFO misc.py line 136 253097] Train result: loss: 0.2790 
[2023-12-25 06:53:28,983 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 06:53:54,770 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6195 
[2023-12-25 06:53:55,126 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3886 
[2023-12-25 06:54:00,746 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6242 
[2023-12-25 06:54:01,275 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4236 
[2023-12-25 06:54:03,265 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8800 
[2023-12-25 06:54:03,688 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3726 
[2023-12-25 06:54:04,567 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1501 
[2023-12-25 06:54:05,129 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5925 
[2023-12-25 06:54:06,934 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.5072 
[2023-12-25 06:54:09,053 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2149 
[2023-12-25 06:54:09,915 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.4664 
[2023-12-25 06:54:10,338 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7483 
[2023-12-25 06:54:11,237 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4450 
[2023-12-25 06:54:14,191 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8049 
[2023-12-25 06:54:14,657 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2735 
[2023-12-25 06:54:15,267 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5019 
[2023-12-25 06:54:15,964 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4329 
[2023-12-25 06:54:17,374 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6390/0.7152/0.8869.
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9044/0.9618
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9791/0.9914
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8317/0.9704
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2600/0.2745
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5204/0.5290
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5659/0.6829
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8274/0.9008
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9013/0.9331
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6486/0.7144
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7413/0.8518
[2023-12-25 06:54:17,375 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.5990/0.8636
[2023-12-25 06:54:17,376 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5278/0.6244
[2023-12-25 06:54:17,376 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 06:54:17,377 INFO misc.py line 165 253097] Currently Best mIoU: 0.6481
[2023-12-25 06:54:17,378 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 06:54:23,531 INFO misc.py line 119 253097] Train: [23/100][1/510] Data 2.986 (2.986) Batch 3.695 (3.695) Remain 40:49:24 loss: 0.1259 Lr: 0.00554
[2023-12-25 06:54:28,198 INFO misc.py line 119 253097] Train: [23/100][2/510] Data 0.770 (0.770) Batch 4.667 (4.667) Remain 51:33:50 loss: 0.1586 Lr: 0.00554
[2023-12-25 06:54:41,722 INFO misc.py line 119 253097] Train: [23/100][3/510] Data 0.003 (0.003) Batch 13.525 (13.525) Remain 149:26:09 loss: 0.2286 Lr: 0.00554
[2023-12-25 06:54:42,681 INFO misc.py line 119 253097] Train: [23/100][4/510] Data 0.003 (0.003) Batch 0.959 (0.959) Remain 10:35:49 loss: 0.1559 Lr: 0.00554
[2023-12-25 06:54:43,642 INFO misc.py line 119 253097] Train: [23/100][5/510] Data 0.003 (0.003) Batch 0.961 (0.960) Remain 10:36:22 loss: 0.1790 Lr: 0.00554
[2023-12-25 06:54:44,616 INFO misc.py line 119 253097] Train: [23/100][6/510] Data 0.003 (0.003) Batch 0.971 (0.964) Remain 10:38:47 loss: 0.3860 Lr: 0.00554
[2023-12-25 06:54:45,821 INFO misc.py line 119 253097] Train: [23/100][7/510] Data 0.007 (0.004) Batch 1.207 (1.025) Remain 11:19:10 loss: 0.3737 Lr: 0.00554
[2023-12-25 06:54:47,129 INFO misc.py line 119 253097] Train: [23/100][8/510] Data 0.005 (0.004) Batch 1.304 (1.080) Remain 11:56:12 loss: 0.5398 Lr: 0.00554
[2023-12-25 06:54:48,145 INFO misc.py line 119 253097] Train: [23/100][9/510] Data 0.008 (0.005) Batch 1.016 (1.070) Remain 11:49:06 loss: 0.2883 Lr: 0.00554
[2023-12-25 06:54:49,393 INFO misc.py line 119 253097] Train: [23/100][10/510] Data 0.007 (0.005) Batch 1.246 (1.095) Remain 12:05:43 loss: 0.2876 Lr: 0.00554
[2023-12-25 06:54:50,461 INFO misc.py line 119 253097] Train: [23/100][11/510] Data 0.011 (0.006) Batch 1.075 (1.092) Remain 12:04:02 loss: 0.1750 Lr: 0.00554
[2023-12-25 06:54:51,677 INFO misc.py line 119 253097] Train: [23/100][12/510] Data 0.003 (0.006) Batch 1.210 (1.105) Remain 12:12:40 loss: 0.1258 Lr: 0.00554
[2023-12-25 06:54:52,968 INFO misc.py line 119 253097] Train: [23/100][13/510] Data 0.009 (0.006) Batch 1.290 (1.124) Remain 12:24:53 loss: 0.2670 Lr: 0.00554
[2023-12-25 06:54:53,971 INFO misc.py line 119 253097] Train: [23/100][14/510] Data 0.013 (0.007) Batch 1.006 (1.113) Remain 12:17:46 loss: 0.2701 Lr: 0.00554
[2023-12-25 06:54:59,867 INFO misc.py line 119 253097] Train: [23/100][15/510] Data 0.007 (0.007) Batch 5.898 (1.512) Remain 16:42:01 loss: 0.1983 Lr: 0.00554
[2023-12-25 06:55:00,926 INFO misc.py line 119 253097] Train: [23/100][16/510] Data 0.005 (0.007) Batch 1.061 (1.477) Remain 16:18:59 loss: 0.2395 Lr: 0.00554
[2023-12-25 06:55:02,011 INFO misc.py line 119 253097] Train: [23/100][17/510] Data 0.003 (0.006) Batch 1.085 (1.449) Remain 16:00:23 loss: 0.1931 Lr: 0.00554
[2023-12-25 06:55:03,230 INFO misc.py line 119 253097] Train: [23/100][18/510] Data 0.004 (0.006) Batch 1.219 (1.434) Remain 15:50:10 loss: 0.2811 Lr: 0.00554
[2023-12-25 06:55:04,445 INFO misc.py line 119 253097] Train: [23/100][19/510] Data 0.004 (0.006) Batch 1.212 (1.420) Remain 15:40:57 loss: 0.4480 Lr: 0.00554
[2023-12-25 06:55:05,525 INFO misc.py line 119 253097] Train: [23/100][20/510] Data 0.008 (0.006) Batch 1.084 (1.400) Remain 15:27:49 loss: 0.3922 Lr: 0.00554
[2023-12-25 06:55:06,611 INFO misc.py line 119 253097] Train: [23/100][21/510] Data 0.004 (0.006) Batch 1.080 (1.382) Remain 15:16:01 loss: 0.1544 Lr: 0.00554
[2023-12-25 06:55:07,714 INFO misc.py line 119 253097] Train: [23/100][22/510] Data 0.009 (0.006) Batch 1.105 (1.368) Remain 15:06:20 loss: 0.4512 Lr: 0.00554
[2023-12-25 06:55:08,952 INFO misc.py line 119 253097] Train: [23/100][23/510] Data 0.007 (0.006) Batch 1.238 (1.361) Remain 15:02:01 loss: 0.2831 Lr: 0.00554
[2023-12-25 06:55:09,956 INFO misc.py line 119 253097] Train: [23/100][24/510] Data 0.007 (0.006) Batch 1.001 (1.344) Remain 14:50:38 loss: 0.1812 Lr: 0.00554
[2023-12-25 06:55:11,064 INFO misc.py line 119 253097] Train: [23/100][25/510] Data 0.010 (0.006) Batch 1.108 (1.333) Remain 14:43:31 loss: 0.2186 Lr: 0.00554
[2023-12-25 06:55:12,325 INFO misc.py line 119 253097] Train: [23/100][26/510] Data 0.010 (0.007) Batch 1.260 (1.330) Remain 14:41:23 loss: 0.1962 Lr: 0.00554
[2023-12-25 06:55:13,453 INFO misc.py line 119 253097] Train: [23/100][27/510] Data 0.012 (0.007) Batch 1.135 (1.322) Remain 14:35:57 loss: 0.3677 Lr: 0.00554
[2023-12-25 06:55:14,778 INFO misc.py line 119 253097] Train: [23/100][28/510] Data 0.004 (0.007) Batch 1.325 (1.322) Remain 14:36:01 loss: 0.2406 Lr: 0.00554
[2023-12-25 06:55:15,876 INFO misc.py line 119 253097] Train: [23/100][29/510] Data 0.003 (0.007) Batch 1.092 (1.313) Remain 14:30:07 loss: 0.3309 Lr: 0.00554
[2023-12-25 06:55:17,149 INFO misc.py line 119 253097] Train: [23/100][30/510] Data 0.064 (0.009) Batch 1.280 (1.312) Remain 14:29:16 loss: 0.2258 Lr: 0.00554
[2023-12-25 06:55:18,409 INFO misc.py line 119 253097] Train: [23/100][31/510] Data 0.003 (0.009) Batch 1.260 (1.310) Remain 14:28:00 loss: 0.2392 Lr: 0.00554
[2023-12-25 06:55:19,633 INFO misc.py line 119 253097] Train: [23/100][32/510] Data 0.003 (0.008) Batch 1.219 (1.307) Remain 14:25:53 loss: 0.2452 Lr: 0.00554
[2023-12-25 06:55:20,884 INFO misc.py line 119 253097] Train: [23/100][33/510] Data 0.009 (0.008) Batch 1.253 (1.305) Remain 14:24:40 loss: 0.2191 Lr: 0.00554
[2023-12-25 06:55:21,838 INFO misc.py line 119 253097] Train: [23/100][34/510] Data 0.007 (0.008) Batch 0.957 (1.294) Remain 14:17:12 loss: 0.3751 Lr: 0.00554
[2023-12-25 06:55:22,837 INFO misc.py line 119 253097] Train: [23/100][35/510] Data 0.004 (0.008) Batch 0.999 (1.285) Remain 14:11:05 loss: 0.1231 Lr: 0.00553
[2023-12-25 06:55:34,223 INFO misc.py line 119 253097] Train: [23/100][36/510] Data 10.240 (0.318) Batch 11.386 (1.591) Remain 17:33:49 loss: 0.1331 Lr: 0.00553
[2023-12-25 06:55:35,500 INFO misc.py line 119 253097] Train: [23/100][37/510] Data 0.004 (0.309) Batch 1.271 (1.582) Remain 17:27:33 loss: 0.1388 Lr: 0.00553
[2023-12-25 06:55:36,451 INFO misc.py line 119 253097] Train: [23/100][38/510] Data 0.010 (0.300) Batch 0.957 (1.564) Remain 17:15:43 loss: 0.2667 Lr: 0.00553
[2023-12-25 06:55:37,712 INFO misc.py line 119 253097] Train: [23/100][39/510] Data 0.004 (0.292) Batch 1.261 (1.555) Remain 17:10:08 loss: 0.2055 Lr: 0.00553
[2023-12-25 06:55:39,019 INFO misc.py line 119 253097] Train: [23/100][40/510] Data 0.003 (0.284) Batch 1.300 (1.548) Remain 17:05:32 loss: 0.1794 Lr: 0.00553
[2023-12-25 06:55:40,267 INFO misc.py line 119 253097] Train: [23/100][41/510] Data 0.011 (0.277) Batch 1.253 (1.541) Remain 17:00:22 loss: 0.2286 Lr: 0.00553
[2023-12-25 06:55:41,411 INFO misc.py line 119 253097] Train: [23/100][42/510] Data 0.006 (0.270) Batch 1.145 (1.530) Remain 16:53:37 loss: 0.3403 Lr: 0.00553
[2023-12-25 06:55:42,624 INFO misc.py line 119 253097] Train: [23/100][43/510] Data 0.006 (0.264) Batch 1.209 (1.522) Remain 16:48:16 loss: 0.4702 Lr: 0.00553
[2023-12-25 06:55:43,715 INFO misc.py line 119 253097] Train: [23/100][44/510] Data 0.008 (0.257) Batch 1.093 (1.512) Remain 16:41:18 loss: 0.2359 Lr: 0.00553
[2023-12-25 06:55:44,936 INFO misc.py line 119 253097] Train: [23/100][45/510] Data 0.007 (0.251) Batch 1.190 (1.504) Remain 16:36:13 loss: 0.2630 Lr: 0.00553
[2023-12-25 06:55:46,059 INFO misc.py line 119 253097] Train: [23/100][46/510] Data 0.037 (0.246) Batch 1.155 (1.496) Remain 16:30:49 loss: 0.3209 Lr: 0.00553
[2023-12-25 06:55:47,233 INFO misc.py line 119 253097] Train: [23/100][47/510] Data 0.004 (0.241) Batch 1.174 (1.489) Remain 16:25:56 loss: 0.2880 Lr: 0.00553
[2023-12-25 06:55:48,462 INFO misc.py line 119 253097] Train: [23/100][48/510] Data 0.005 (0.236) Batch 1.229 (1.483) Remain 16:22:05 loss: 0.1926 Lr: 0.00553
[2023-12-25 06:55:49,747 INFO misc.py line 119 253097] Train: [23/100][49/510] Data 0.005 (0.231) Batch 1.283 (1.479) Remain 16:19:11 loss: 0.4785 Lr: 0.00553
[2023-12-25 06:55:51,098 INFO misc.py line 119 253097] Train: [23/100][50/510] Data 0.006 (0.226) Batch 1.350 (1.476) Remain 16:17:21 loss: 0.3501 Lr: 0.00553
[2023-12-25 06:55:52,180 INFO misc.py line 119 253097] Train: [23/100][51/510] Data 0.008 (0.221) Batch 1.084 (1.468) Remain 16:11:55 loss: 0.2606 Lr: 0.00553
[2023-12-25 06:55:53,547 INFO misc.py line 119 253097] Train: [23/100][52/510] Data 0.006 (0.217) Batch 1.367 (1.466) Remain 16:10:31 loss: 0.2341 Lr: 0.00553
[2023-12-25 06:55:54,644 INFO misc.py line 119 253097] Train: [23/100][53/510] Data 0.008 (0.213) Batch 1.096 (1.458) Remain 16:05:36 loss: 0.2257 Lr: 0.00553
[2023-12-25 06:55:55,747 INFO misc.py line 119 253097] Train: [23/100][54/510] Data 0.007 (0.209) Batch 1.104 (1.451) Remain 16:00:58 loss: 0.2338 Lr: 0.00553
[2023-12-25 06:55:56,861 INFO misc.py line 119 253097] Train: [23/100][55/510] Data 0.006 (0.205) Batch 1.113 (1.445) Remain 15:56:38 loss: 0.1883 Lr: 0.00553
[2023-12-25 06:55:58,026 INFO misc.py line 119 253097] Train: [23/100][56/510] Data 0.007 (0.201) Batch 1.167 (1.440) Remain 15:53:09 loss: 0.2345 Lr: 0.00553
[2023-12-25 06:55:59,167 INFO misc.py line 119 253097] Train: [23/100][57/510] Data 0.005 (0.197) Batch 1.142 (1.434) Remain 15:49:28 loss: 0.1458 Lr: 0.00553
[2023-12-25 06:56:00,362 INFO misc.py line 119 253097] Train: [23/100][58/510] Data 0.004 (0.194) Batch 1.193 (1.430) Remain 15:46:33 loss: 0.4187 Lr: 0.00553
[2023-12-25 06:56:01,356 INFO misc.py line 119 253097] Train: [23/100][59/510] Data 0.007 (0.191) Batch 0.994 (1.422) Remain 15:41:22 loss: 0.2732 Lr: 0.00553
[2023-12-25 06:56:02,506 INFO misc.py line 119 253097] Train: [23/100][60/510] Data 0.007 (0.187) Batch 1.152 (1.417) Remain 15:38:13 loss: 0.2193 Lr: 0.00553
[2023-12-25 06:56:03,725 INFO misc.py line 119 253097] Train: [23/100][61/510] Data 0.004 (0.184) Batch 1.214 (1.414) Remain 15:35:52 loss: 0.1734 Lr: 0.00553
[2023-12-25 06:56:05,049 INFO misc.py line 119 253097] Train: [23/100][62/510] Data 0.008 (0.181) Batch 1.329 (1.412) Remain 15:34:54 loss: 0.5704 Lr: 0.00553
[2023-12-25 06:56:06,378 INFO misc.py line 119 253097] Train: [23/100][63/510] Data 0.004 (0.178) Batch 1.325 (1.411) Remain 15:33:54 loss: 0.3332 Lr: 0.00553
[2023-12-25 06:56:07,634 INFO misc.py line 119 253097] Train: [23/100][64/510] Data 0.009 (0.176) Batch 1.260 (1.408) Remain 15:32:15 loss: 0.2539 Lr: 0.00553
[2023-12-25 06:56:08,841 INFO misc.py line 119 253097] Train: [23/100][65/510] Data 0.004 (0.173) Batch 1.203 (1.405) Remain 15:30:02 loss: 0.2199 Lr: 0.00553
[2023-12-25 06:56:10,109 INFO misc.py line 119 253097] Train: [23/100][66/510] Data 0.009 (0.170) Batch 1.273 (1.403) Remain 15:28:37 loss: 0.1867 Lr: 0.00553
[2023-12-25 06:56:11,336 INFO misc.py line 119 253097] Train: [23/100][67/510] Data 0.004 (0.168) Batch 1.226 (1.400) Remain 15:26:46 loss: 0.1957 Lr: 0.00553
[2023-12-25 06:56:12,468 INFO misc.py line 119 253097] Train: [23/100][68/510] Data 0.004 (0.165) Batch 1.131 (1.396) Remain 15:24:00 loss: 0.3293 Lr: 0.00553
[2023-12-25 06:56:13,635 INFO misc.py line 119 253097] Train: [23/100][69/510] Data 0.005 (0.163) Batch 1.168 (1.393) Remain 15:21:42 loss: 0.3985 Lr: 0.00553
[2023-12-25 06:56:14,711 INFO misc.py line 119 253097] Train: [23/100][70/510] Data 0.004 (0.160) Batch 1.072 (1.388) Remain 15:18:30 loss: 0.2533 Lr: 0.00553
[2023-12-25 06:56:15,788 INFO misc.py line 119 253097] Train: [23/100][71/510] Data 0.008 (0.158) Batch 1.074 (1.383) Remain 15:15:25 loss: 0.3555 Lr: 0.00553
[2023-12-25 06:56:16,954 INFO misc.py line 119 253097] Train: [23/100][72/510] Data 0.012 (0.156) Batch 1.169 (1.380) Remain 15:13:21 loss: 0.2478 Lr: 0.00553
[2023-12-25 06:56:23,882 INFO misc.py line 119 253097] Train: [23/100][73/510] Data 0.008 (0.154) Batch 6.931 (1.459) Remain 16:05:48 loss: 0.3062 Lr: 0.00553
[2023-12-25 06:56:24,866 INFO misc.py line 119 253097] Train: [23/100][74/510] Data 0.005 (0.152) Batch 0.984 (1.453) Remain 16:01:21 loss: 0.2005 Lr: 0.00553
[2023-12-25 06:56:26,112 INFO misc.py line 119 253097] Train: [23/100][75/510] Data 0.005 (0.150) Batch 1.247 (1.450) Remain 15:59:26 loss: 0.1966 Lr: 0.00553
[2023-12-25 06:56:27,413 INFO misc.py line 119 253097] Train: [23/100][76/510] Data 0.005 (0.148) Batch 1.292 (1.448) Remain 15:57:59 loss: 0.1960 Lr: 0.00553
[2023-12-25 06:56:28,757 INFO misc.py line 119 253097] Train: [23/100][77/510] Data 0.013 (0.146) Batch 1.347 (1.446) Remain 15:57:03 loss: 0.3694 Lr: 0.00553
[2023-12-25 06:56:30,035 INFO misc.py line 119 253097] Train: [23/100][78/510] Data 0.010 (0.144) Batch 1.279 (1.444) Remain 15:55:33 loss: 0.1615 Lr: 0.00553
[2023-12-25 06:56:31,064 INFO misc.py line 119 253097] Train: [23/100][79/510] Data 0.009 (0.142) Batch 1.032 (1.439) Remain 15:51:56 loss: 0.2710 Lr: 0.00553
[2023-12-25 06:56:40,316 INFO misc.py line 119 253097] Train: [23/100][80/510] Data 0.006 (0.140) Batch 9.255 (1.540) Remain 16:59:05 loss: 0.2372 Lr: 0.00553
[2023-12-25 06:56:41,570 INFO misc.py line 119 253097] Train: [23/100][81/510] Data 0.003 (0.139) Batch 1.253 (1.537) Remain 16:56:37 loss: 0.2389 Lr: 0.00553
[2023-12-25 06:56:42,738 INFO misc.py line 119 253097] Train: [23/100][82/510] Data 0.004 (0.137) Batch 1.168 (1.532) Remain 16:53:30 loss: 0.4877 Lr: 0.00553
[2023-12-25 06:56:44,060 INFO misc.py line 119 253097] Train: [23/100][83/510] Data 0.003 (0.135) Batch 1.317 (1.529) Remain 16:51:43 loss: 0.5241 Lr: 0.00553
[2023-12-25 06:56:45,196 INFO misc.py line 119 253097] Train: [23/100][84/510] Data 0.008 (0.134) Batch 1.139 (1.524) Remain 16:48:30 loss: 0.3075 Lr: 0.00553
[2023-12-25 06:56:46,354 INFO misc.py line 119 253097] Train: [23/100][85/510] Data 0.005 (0.132) Batch 1.159 (1.520) Remain 16:45:32 loss: 0.4272 Lr: 0.00553
[2023-12-25 06:56:47,655 INFO misc.py line 119 253097] Train: [23/100][86/510] Data 0.003 (0.131) Batch 1.300 (1.517) Remain 16:43:45 loss: 0.2045 Lr: 0.00553
[2023-12-25 06:56:48,842 INFO misc.py line 119 253097] Train: [23/100][87/510] Data 0.005 (0.129) Batch 1.186 (1.513) Remain 16:41:07 loss: 0.2342 Lr: 0.00553
[2023-12-25 06:56:50,058 INFO misc.py line 119 253097] Train: [23/100][88/510] Data 0.006 (0.128) Batch 1.217 (1.510) Remain 16:38:47 loss: 0.2999 Lr: 0.00553
[2023-12-25 06:56:51,262 INFO misc.py line 119 253097] Train: [23/100][89/510] Data 0.006 (0.126) Batch 1.205 (1.506) Remain 16:36:25 loss: 0.3247 Lr: 0.00553
[2023-12-25 06:56:52,514 INFO misc.py line 119 253097] Train: [23/100][90/510] Data 0.005 (0.125) Batch 1.248 (1.503) Remain 16:34:25 loss: 0.3858 Lr: 0.00553
[2023-12-25 06:56:53,708 INFO misc.py line 119 253097] Train: [23/100][91/510] Data 0.009 (0.124) Batch 1.192 (1.500) Remain 16:32:04 loss: 0.3492 Lr: 0.00553
[2023-12-25 06:56:54,957 INFO misc.py line 119 253097] Train: [23/100][92/510] Data 0.011 (0.122) Batch 1.253 (1.497) Remain 16:30:12 loss: 0.2504 Lr: 0.00553
[2023-12-25 06:56:56,026 INFO misc.py line 119 253097] Train: [23/100][93/510] Data 0.006 (0.121) Batch 1.071 (1.492) Remain 16:27:03 loss: 0.2366 Lr: 0.00553
[2023-12-25 06:56:57,099 INFO misc.py line 119 253097] Train: [23/100][94/510] Data 0.005 (0.120) Batch 1.074 (1.488) Remain 16:23:59 loss: 0.2334 Lr: 0.00553
[2023-12-25 06:56:58,254 INFO misc.py line 119 253097] Train: [23/100][95/510] Data 0.004 (0.118) Batch 1.152 (1.484) Remain 16:21:32 loss: 0.2674 Lr: 0.00553
[2023-12-25 06:56:59,406 INFO misc.py line 119 253097] Train: [23/100][96/510] Data 0.006 (0.117) Batch 1.148 (1.480) Remain 16:19:08 loss: 0.2889 Lr: 0.00553
[2023-12-25 06:57:00,543 INFO misc.py line 119 253097] Train: [23/100][97/510] Data 0.010 (0.116) Batch 1.142 (1.477) Remain 16:16:43 loss: 0.4222 Lr: 0.00553
[2023-12-25 06:57:01,738 INFO misc.py line 119 253097] Train: [23/100][98/510] Data 0.005 (0.115) Batch 1.195 (1.474) Remain 16:14:44 loss: 0.3465 Lr: 0.00553
[2023-12-25 06:57:02,981 INFO misc.py line 119 253097] Train: [23/100][99/510] Data 0.006 (0.114) Batch 1.238 (1.471) Remain 16:13:05 loss: 0.1586 Lr: 0.00553
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[2023-12-25 07:05:17,614 INFO misc.py line 119 253097] Train: [23/100][413/510] Data 0.008 (0.055) Batch 1.227 (1.551) Remain 16:57:36 loss: 0.1950 Lr: 0.00549
[2023-12-25 07:05:18,464 INFO misc.py line 119 253097] Train: [23/100][414/510] Data 0.004 (0.055) Batch 0.849 (1.549) Remain 16:56:27 loss: 0.3472 Lr: 0.00549
[2023-12-25 07:05:19,612 INFO misc.py line 119 253097] Train: [23/100][415/510] Data 0.005 (0.055) Batch 1.149 (1.548) Remain 16:55:47 loss: 0.2396 Lr: 0.00549
[2023-12-25 07:05:20,833 INFO misc.py line 119 253097] Train: [23/100][416/510] Data 0.004 (0.055) Batch 1.220 (1.547) Remain 16:55:14 loss: 0.1993 Lr: 0.00549
[2023-12-25 07:05:22,018 INFO misc.py line 119 253097] Train: [23/100][417/510] Data 0.009 (0.054) Batch 1.185 (1.547) Remain 16:54:38 loss: 0.3329 Lr: 0.00549
[2023-12-25 07:05:22,986 INFO misc.py line 119 253097] Train: [23/100][418/510] Data 0.005 (0.054) Batch 0.968 (1.545) Remain 16:53:42 loss: 0.2702 Lr: 0.00549
[2023-12-25 07:05:24,089 INFO misc.py line 119 253097] Train: [23/100][419/510] Data 0.006 (0.054) Batch 1.104 (1.544) Remain 16:52:59 loss: 0.3806 Lr: 0.00549
[2023-12-25 07:05:25,036 INFO misc.py line 119 253097] Train: [23/100][420/510] Data 0.005 (0.054) Batch 0.946 (1.543) Remain 16:52:01 loss: 0.4159 Lr: 0.00549
[2023-12-25 07:05:26,038 INFO misc.py line 119 253097] Train: [23/100][421/510] Data 0.005 (0.054) Batch 1.001 (1.541) Remain 16:51:08 loss: 0.1968 Lr: 0.00549
[2023-12-25 07:05:27,214 INFO misc.py line 119 253097] Train: [23/100][422/510] Data 0.006 (0.054) Batch 1.172 (1.541) Remain 16:50:32 loss: 0.3029 Lr: 0.00549
[2023-12-25 07:05:28,463 INFO misc.py line 119 253097] Train: [23/100][423/510] Data 0.010 (0.054) Batch 1.253 (1.540) Remain 16:50:03 loss: 0.2020 Lr: 0.00549
[2023-12-25 07:05:29,521 INFO misc.py line 119 253097] Train: [23/100][424/510] Data 0.006 (0.054) Batch 1.058 (1.539) Remain 16:49:17 loss: 0.2984 Lr: 0.00549
[2023-12-25 07:05:30,752 INFO misc.py line 119 253097] Train: [23/100][425/510] Data 0.006 (0.054) Batch 1.229 (1.538) Remain 16:48:46 loss: 0.4126 Lr: 0.00549
[2023-12-25 07:05:42,636 INFO misc.py line 119 253097] Train: [23/100][426/510] Data 0.010 (0.053) Batch 11.887 (1.562) Remain 17:04:48 loss: 0.2674 Lr: 0.00549
[2023-12-25 07:05:43,745 INFO misc.py line 119 253097] Train: [23/100][427/510] Data 0.004 (0.053) Batch 1.110 (1.561) Remain 17:04:04 loss: 0.2490 Lr: 0.00549
[2023-12-25 07:05:44,903 INFO misc.py line 119 253097] Train: [23/100][428/510] Data 0.004 (0.053) Batch 1.157 (1.560) Remain 17:03:25 loss: 0.2759 Lr: 0.00549
[2023-12-25 07:05:46,099 INFO misc.py line 119 253097] Train: [23/100][429/510] Data 0.004 (0.053) Batch 1.197 (1.560) Remain 17:02:50 loss: 0.1911 Lr: 0.00549
[2023-12-25 07:05:47,250 INFO misc.py line 119 253097] Train: [23/100][430/510] Data 0.004 (0.053) Batch 1.150 (1.559) Remain 17:02:11 loss: 0.2459 Lr: 0.00549
[2023-12-25 07:05:48,343 INFO misc.py line 119 253097] Train: [23/100][431/510] Data 0.005 (0.053) Batch 1.094 (1.558) Remain 17:01:26 loss: 0.4266 Lr: 0.00549
[2023-12-25 07:05:49,534 INFO misc.py line 119 253097] Train: [23/100][432/510] Data 0.004 (0.053) Batch 1.191 (1.557) Remain 17:00:51 loss: 0.5118 Lr: 0.00549
[2023-12-25 07:05:50,728 INFO misc.py line 119 253097] Train: [23/100][433/510] Data 0.006 (0.053) Batch 1.194 (1.556) Remain 17:00:16 loss: 0.4730 Lr: 0.00549
[2023-12-25 07:05:51,914 INFO misc.py line 119 253097] Train: [23/100][434/510] Data 0.005 (0.053) Batch 1.187 (1.555) Remain 16:59:41 loss: 0.2447 Lr: 0.00549
[2023-12-25 07:05:52,831 INFO misc.py line 119 253097] Train: [23/100][435/510] Data 0.003 (0.052) Batch 0.916 (1.553) Remain 16:58:41 loss: 0.1098 Lr: 0.00549
[2023-12-25 07:05:54,080 INFO misc.py line 119 253097] Train: [23/100][436/510] Data 0.005 (0.052) Batch 1.250 (1.553) Remain 16:58:12 loss: 0.2985 Lr: 0.00549
[2023-12-25 07:05:55,390 INFO misc.py line 119 253097] Train: [23/100][437/510] Data 0.003 (0.052) Batch 1.306 (1.552) Remain 16:57:49 loss: 0.3294 Lr: 0.00549
[2023-12-25 07:05:56,518 INFO misc.py line 119 253097] Train: [23/100][438/510] Data 0.007 (0.052) Batch 1.125 (1.551) Remain 16:57:08 loss: 0.3362 Lr: 0.00549
[2023-12-25 07:05:57,659 INFO misc.py line 119 253097] Train: [23/100][439/510] Data 0.010 (0.052) Batch 1.141 (1.550) Remain 16:56:30 loss: 0.2761 Lr: 0.00549
[2023-12-25 07:05:58,816 INFO misc.py line 119 253097] Train: [23/100][440/510] Data 0.010 (0.052) Batch 1.161 (1.549) Remain 16:55:53 loss: 0.2629 Lr: 0.00549
[2023-12-25 07:06:00,000 INFO misc.py line 119 253097] Train: [23/100][441/510] Data 0.006 (0.052) Batch 1.186 (1.549) Remain 16:55:19 loss: 0.2531 Lr: 0.00549
[2023-12-25 07:06:01,177 INFO misc.py line 119 253097] Train: [23/100][442/510] Data 0.003 (0.052) Batch 1.174 (1.548) Remain 16:54:44 loss: 0.2761 Lr: 0.00549
[2023-12-25 07:06:02,431 INFO misc.py line 119 253097] Train: [23/100][443/510] Data 0.008 (0.052) Batch 1.256 (1.547) Remain 16:54:16 loss: 0.3204 Lr: 0.00549
[2023-12-25 07:06:03,578 INFO misc.py line 119 253097] Train: [23/100][444/510] Data 0.004 (0.052) Batch 1.147 (1.546) Remain 16:53:39 loss: 0.2098 Lr: 0.00549
[2023-12-25 07:06:04,794 INFO misc.py line 119 253097] Train: [23/100][445/510] Data 0.004 (0.051) Batch 1.211 (1.545) Remain 16:53:08 loss: 0.2667 Lr: 0.00549
[2023-12-25 07:06:11,381 INFO misc.py line 119 253097] Train: [23/100][446/510] Data 0.009 (0.051) Batch 6.592 (1.557) Remain 17:00:34 loss: 0.3294 Lr: 0.00549
[2023-12-25 07:06:12,679 INFO misc.py line 119 253097] Train: [23/100][447/510] Data 0.004 (0.051) Batch 1.292 (1.556) Remain 17:00:09 loss: 0.3485 Lr: 0.00549
[2023-12-25 07:06:13,574 INFO misc.py line 119 253097] Train: [23/100][448/510] Data 0.009 (0.051) Batch 0.900 (1.555) Remain 16:59:10 loss: 0.2739 Lr: 0.00549
[2023-12-25 07:06:14,730 INFO misc.py line 119 253097] Train: [23/100][449/510] Data 0.004 (0.051) Batch 1.156 (1.554) Remain 16:58:33 loss: 0.2573 Lr: 0.00549
[2023-12-25 07:06:15,814 INFO misc.py line 119 253097] Train: [23/100][450/510] Data 0.004 (0.051) Batch 1.083 (1.553) Remain 16:57:50 loss: 0.1008 Lr: 0.00549
[2023-12-25 07:06:16,965 INFO misc.py line 119 253097] Train: [23/100][451/510] Data 0.005 (0.051) Batch 1.152 (1.552) Remain 16:57:13 loss: 0.5769 Lr: 0.00549
[2023-12-25 07:06:18,439 INFO misc.py line 119 253097] Train: [23/100][452/510] Data 0.003 (0.051) Batch 1.468 (1.552) Remain 16:57:04 loss: 0.3992 Lr: 0.00549
[2023-12-25 07:06:19,588 INFO misc.py line 119 253097] Train: [23/100][453/510] Data 0.009 (0.051) Batch 1.149 (1.551) Remain 16:56:28 loss: 0.1702 Lr: 0.00549
[2023-12-25 07:06:20,729 INFO misc.py line 119 253097] Train: [23/100][454/510] Data 0.010 (0.050) Batch 1.147 (1.550) Remain 16:55:51 loss: 0.2462 Lr: 0.00549
[2023-12-25 07:06:21,945 INFO misc.py line 119 253097] Train: [23/100][455/510] Data 0.004 (0.050) Batch 1.211 (1.549) Remain 16:55:20 loss: 0.2010 Lr: 0.00549
[2023-12-25 07:06:23,275 INFO misc.py line 119 253097] Train: [23/100][456/510] Data 0.010 (0.050) Batch 1.336 (1.549) Remain 16:55:00 loss: 0.2153 Lr: 0.00549
[2023-12-25 07:06:24,522 INFO misc.py line 119 253097] Train: [23/100][457/510] Data 0.005 (0.050) Batch 1.244 (1.548) Remain 16:54:32 loss: 0.1141 Lr: 0.00549
[2023-12-25 07:06:25,707 INFO misc.py line 119 253097] Train: [23/100][458/510] Data 0.007 (0.050) Batch 1.185 (1.547) Remain 16:53:59 loss: 0.2544 Lr: 0.00549
[2023-12-25 07:06:26,726 INFO misc.py line 119 253097] Train: [23/100][459/510] Data 0.007 (0.050) Batch 1.021 (1.546) Remain 16:53:12 loss: 0.2057 Lr: 0.00549
[2023-12-25 07:06:28,030 INFO misc.py line 119 253097] Train: [23/100][460/510] Data 0.005 (0.050) Batch 1.303 (1.546) Remain 16:52:49 loss: 0.0927 Lr: 0.00549
[2023-12-25 07:06:29,292 INFO misc.py line 119 253097] Train: [23/100][461/510] Data 0.007 (0.050) Batch 1.255 (1.545) Remain 16:52:23 loss: 0.3658 Lr: 0.00549
[2023-12-25 07:06:30,339 INFO misc.py line 119 253097] Train: [23/100][462/510] Data 0.014 (0.050) Batch 1.057 (1.544) Remain 16:51:40 loss: 0.1822 Lr: 0.00549
[2023-12-25 07:06:31,433 INFO misc.py line 119 253097] Train: [23/100][463/510] Data 0.003 (0.050) Batch 1.087 (1.543) Remain 16:50:59 loss: 0.4032 Lr: 0.00549
[2023-12-25 07:06:32,746 INFO misc.py line 119 253097] Train: [23/100][464/510] Data 0.010 (0.050) Batch 1.319 (1.542) Remain 16:50:39 loss: 0.2123 Lr: 0.00549
[2023-12-25 07:06:35,086 INFO misc.py line 119 253097] Train: [23/100][465/510] Data 1.382 (0.052) Batch 2.339 (1.544) Remain 16:51:45 loss: 1.0237 Lr: 0.00549
[2023-12-25 07:06:44,302 INFO misc.py line 119 253097] Train: [23/100][466/510] Data 7.998 (0.070) Batch 9.216 (1.561) Remain 17:02:35 loss: 0.1921 Lr: 0.00549
[2023-12-25 07:06:45,424 INFO misc.py line 119 253097] Train: [23/100][467/510] Data 0.005 (0.069) Batch 1.123 (1.560) Remain 17:01:56 loss: 0.2528 Lr: 0.00549
[2023-12-25 07:06:46,558 INFO misc.py line 119 253097] Train: [23/100][468/510] Data 0.004 (0.069) Batch 1.133 (1.559) Remain 17:01:18 loss: 0.1735 Lr: 0.00549
[2023-12-25 07:06:47,719 INFO misc.py line 119 253097] Train: [23/100][469/510] Data 0.004 (0.069) Batch 1.159 (1.558) Remain 17:00:43 loss: 0.2466 Lr: 0.00549
[2023-12-25 07:06:48,908 INFO misc.py line 119 253097] Train: [23/100][470/510] Data 0.007 (0.069) Batch 1.190 (1.557) Remain 17:00:11 loss: 0.1561 Lr: 0.00549
[2023-12-25 07:06:49,969 INFO misc.py line 119 253097] Train: [23/100][471/510] Data 0.006 (0.069) Batch 1.062 (1.556) Remain 16:59:27 loss: 0.2010 Lr: 0.00549
[2023-12-25 07:06:51,061 INFO misc.py line 119 253097] Train: [23/100][472/510] Data 0.005 (0.069) Batch 1.091 (1.555) Remain 16:58:47 loss: 0.4894 Lr: 0.00549
[2023-12-25 07:06:52,318 INFO misc.py line 119 253097] Train: [23/100][473/510] Data 0.006 (0.069) Batch 1.259 (1.554) Remain 16:58:21 loss: 0.2350 Lr: 0.00549
[2023-12-25 07:06:53,613 INFO misc.py line 119 253097] Train: [23/100][474/510] Data 0.004 (0.069) Batch 1.290 (1.554) Remain 16:57:57 loss: 0.2376 Lr: 0.00549
[2023-12-25 07:06:54,614 INFO misc.py line 119 253097] Train: [23/100][475/510] Data 0.009 (0.068) Batch 1.003 (1.553) Remain 16:57:09 loss: 0.3886 Lr: 0.00549
[2023-12-25 07:06:55,687 INFO misc.py line 119 253097] Train: [23/100][476/510] Data 0.007 (0.068) Batch 1.071 (1.552) Remain 16:56:28 loss: 0.2262 Lr: 0.00549
[2023-12-25 07:06:56,735 INFO misc.py line 119 253097] Train: [23/100][477/510] Data 0.008 (0.068) Batch 1.048 (1.551) Remain 16:55:45 loss: 0.1360 Lr: 0.00549
[2023-12-25 07:06:58,037 INFO misc.py line 119 253097] Train: [23/100][478/510] Data 0.009 (0.068) Batch 1.303 (1.550) Remain 16:55:23 loss: 0.4502 Lr: 0.00549
[2023-12-25 07:06:58,985 INFO misc.py line 119 253097] Train: [23/100][479/510] Data 0.007 (0.068) Batch 0.951 (1.549) Remain 16:54:32 loss: 0.3681 Lr: 0.00549
[2023-12-25 07:07:00,038 INFO misc.py line 119 253097] Train: [23/100][480/510] Data 0.004 (0.068) Batch 1.054 (1.548) Remain 16:53:49 loss: 0.1626 Lr: 0.00549
[2023-12-25 07:07:01,109 INFO misc.py line 119 253097] Train: [23/100][481/510] Data 0.004 (0.068) Batch 1.070 (1.547) Remain 16:53:08 loss: 0.2675 Lr: 0.00549
[2023-12-25 07:07:02,222 INFO misc.py line 119 253097] Train: [23/100][482/510] Data 0.005 (0.067) Batch 1.114 (1.546) Remain 16:52:31 loss: 0.2243 Lr: 0.00549
[2023-12-25 07:07:08,913 INFO misc.py line 119 253097] Train: [23/100][483/510] Data 0.003 (0.067) Batch 6.690 (1.557) Remain 16:59:31 loss: 0.2082 Lr: 0.00549
[2023-12-25 07:07:09,827 INFO misc.py line 119 253097] Train: [23/100][484/510] Data 0.005 (0.067) Batch 0.915 (1.555) Remain 16:58:37 loss: 0.7351 Lr: 0.00549
[2023-12-25 07:07:13,356 INFO misc.py line 119 253097] Train: [23/100][485/510] Data 0.003 (0.067) Batch 3.527 (1.559) Remain 17:01:16 loss: 0.2226 Lr: 0.00549
[2023-12-25 07:07:14,380 INFO misc.py line 119 253097] Train: [23/100][486/510] Data 0.005 (0.067) Batch 1.025 (1.558) Remain 17:00:31 loss: 0.3767 Lr: 0.00549
[2023-12-25 07:07:15,654 INFO misc.py line 119 253097] Train: [23/100][487/510] Data 0.005 (0.067) Batch 1.274 (1.558) Remain 17:00:06 loss: 0.3651 Lr: 0.00549
[2023-12-25 07:07:16,883 INFO misc.py line 119 253097] Train: [23/100][488/510] Data 0.004 (0.067) Batch 1.229 (1.557) Remain 16:59:38 loss: 0.5223 Lr: 0.00549
[2023-12-25 07:07:18,078 INFO misc.py line 119 253097] Train: [23/100][489/510] Data 0.004 (0.067) Batch 1.195 (1.556) Remain 16:59:07 loss: 0.4023 Lr: 0.00549
[2023-12-25 07:07:19,040 INFO misc.py line 119 253097] Train: [23/100][490/510] Data 0.005 (0.066) Batch 0.962 (1.555) Remain 16:58:18 loss: 0.2070 Lr: 0.00549
[2023-12-25 07:07:20,183 INFO misc.py line 119 253097] Train: [23/100][491/510] Data 0.004 (0.066) Batch 1.143 (1.554) Remain 16:57:43 loss: 0.2097 Lr: 0.00549
[2023-12-25 07:07:21,422 INFO misc.py line 119 253097] Train: [23/100][492/510] Data 0.003 (0.066) Batch 1.239 (1.554) Remain 16:57:16 loss: 0.3540 Lr: 0.00549
[2023-12-25 07:07:22,605 INFO misc.py line 119 253097] Train: [23/100][493/510] Data 0.003 (0.066) Batch 1.178 (1.553) Remain 16:56:45 loss: 0.2765 Lr: 0.00549
[2023-12-25 07:07:23,640 INFO misc.py line 119 253097] Train: [23/100][494/510] Data 0.008 (0.066) Batch 1.035 (1.552) Remain 16:56:02 loss: 0.2871 Lr: 0.00549
[2023-12-25 07:07:24,687 INFO misc.py line 119 253097] Train: [23/100][495/510] Data 0.009 (0.066) Batch 1.048 (1.551) Remain 16:55:20 loss: 0.2488 Lr: 0.00549
[2023-12-25 07:07:25,721 INFO misc.py line 119 253097] Train: [23/100][496/510] Data 0.008 (0.066) Batch 1.037 (1.550) Remain 16:54:37 loss: 0.3071 Lr: 0.00549
[2023-12-25 07:07:26,768 INFO misc.py line 119 253097] Train: [23/100][497/510] Data 0.007 (0.066) Batch 1.045 (1.549) Remain 16:53:56 loss: 0.2049 Lr: 0.00549
[2023-12-25 07:07:27,811 INFO misc.py line 119 253097] Train: [23/100][498/510] Data 0.008 (0.065) Batch 1.046 (1.548) Remain 16:53:14 loss: 0.1535 Lr: 0.00549
[2023-12-25 07:07:28,979 INFO misc.py line 119 253097] Train: [23/100][499/510] Data 0.004 (0.065) Batch 1.168 (1.547) Remain 16:52:43 loss: 0.2079 Lr: 0.00549
[2023-12-25 07:07:30,084 INFO misc.py line 119 253097] Train: [23/100][500/510] Data 0.005 (0.065) Batch 1.103 (1.546) Remain 16:52:06 loss: 0.1937 Lr: 0.00549
[2023-12-25 07:07:31,326 INFO misc.py line 119 253097] Train: [23/100][501/510] Data 0.006 (0.065) Batch 1.241 (1.545) Remain 16:51:40 loss: 0.2480 Lr: 0.00549
[2023-12-25 07:07:32,413 INFO misc.py line 119 253097] Train: [23/100][502/510] Data 0.007 (0.065) Batch 1.087 (1.544) Remain 16:51:03 loss: 0.2160 Lr: 0.00549
[2023-12-25 07:07:33,505 INFO misc.py line 119 253097] Train: [23/100][503/510] Data 0.007 (0.065) Batch 1.096 (1.544) Remain 16:50:26 loss: 0.2195 Lr: 0.00549
[2023-12-25 07:07:34,513 INFO misc.py line 119 253097] Train: [23/100][504/510] Data 0.003 (0.065) Batch 1.002 (1.542) Remain 16:49:42 loss: 0.1594 Lr: 0.00548
[2023-12-25 07:07:35,395 INFO misc.py line 119 253097] Train: [23/100][505/510] Data 0.010 (0.065) Batch 0.887 (1.541) Remain 16:48:49 loss: 0.6500 Lr: 0.00548
[2023-12-25 07:07:36,510 INFO misc.py line 119 253097] Train: [23/100][506/510] Data 0.006 (0.065) Batch 1.115 (1.540) Remain 16:48:14 loss: 0.2646 Lr: 0.00548
[2023-12-25 07:07:37,592 INFO misc.py line 119 253097] Train: [23/100][507/510] Data 0.004 (0.064) Batch 1.083 (1.539) Remain 16:47:37 loss: 0.1947 Lr: 0.00548
[2023-12-25 07:07:38,971 INFO misc.py line 119 253097] Train: [23/100][508/510] Data 0.003 (0.064) Batch 1.379 (1.539) Remain 16:47:23 loss: 0.3790 Lr: 0.00548
[2023-12-25 07:07:40,230 INFO misc.py line 119 253097] Train: [23/100][509/510] Data 0.004 (0.064) Batch 1.254 (1.539) Remain 16:47:00 loss: 0.1338 Lr: 0.00548
[2023-12-25 07:07:47,611 INFO misc.py line 119 253097] Train: [23/100][510/510] Data 0.009 (0.064) Batch 7.385 (1.550) Remain 16:54:31 loss: 0.3076 Lr: 0.00548
[2023-12-25 07:07:47,612 INFO misc.py line 136 253097] Train result: loss: 0.2789 
[2023-12-25 07:07:47,612 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 07:08:13,767 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7191 
[2023-12-25 07:08:14,115 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2984 
[2023-12-25 07:08:20,392 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3360 
[2023-12-25 07:08:20,916 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2581 
[2023-12-25 07:08:22,887 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7916 
[2023-12-25 07:08:23,316 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2928 
[2023-12-25 07:08:24,193 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0253 
[2023-12-25 07:08:24,744 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2669 
[2023-12-25 07:08:26,549 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6803 
[2023-12-25 07:08:28,671 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2019 
[2023-12-25 07:08:29,532 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2854 
[2023-12-25 07:08:29,971 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6165 
[2023-12-25 07:08:30,874 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3740 
[2023-12-25 07:08:33,821 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.6624 
[2023-12-25 07:08:34,288 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2306 
[2023-12-25 07:08:34,897 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4271 
[2023-12-25 07:08:35,602 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2708 
[2023-12-25 07:08:36,997 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6552/0.7280/0.8964.
[2023-12-25 07:08:36,997 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9109/0.9441
[2023-12-25 07:08:36,997 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9794/0.9913
[2023-12-25 07:08:36,997 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8414/0.9556
[2023-12-25 07:08:36,997 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 07:08:36,997 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3238/0.3759
[2023-12-25 07:08:36,997 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6306/0.6571
[2023-12-25 07:08:36,997 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7106/0.8228
[2023-12-25 07:08:36,997 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7828/0.9375
[2023-12-25 07:08:36,998 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8913/0.9495
[2023-12-25 07:08:36,998 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.3987/0.4181
[2023-12-25 07:08:36,998 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7527/0.8135
[2023-12-25 07:08:36,998 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7203/0.8642
[2023-12-25 07:08:36,998 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5744/0.7341
[2023-12-25 07:08:36,998 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 07:08:36,999 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.6552
[2023-12-25 07:08:36,999 INFO misc.py line 165 253097] Currently Best mIoU: 0.6552
[2023-12-25 07:08:36,999 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 07:08:46,319 INFO misc.py line 119 253097] Train: [24/100][1/510] Data 3.305 (3.305) Batch 4.223 (4.223) Remain 46:03:58 loss: 0.3292 Lr: 0.00548
[2023-12-25 07:08:47,566 INFO misc.py line 119 253097] Train: [24/100][2/510] Data 0.005 (0.005) Batch 1.246 (1.246) Remain 13:35:43 loss: 0.1874 Lr: 0.00548
[2023-12-25 07:08:48,617 INFO misc.py line 119 253097] Train: [24/100][3/510] Data 0.006 (0.006) Batch 1.047 (1.047) Remain 11:25:30 loss: 0.1184 Lr: 0.00548
[2023-12-25 07:08:49,741 INFO misc.py line 119 253097] Train: [24/100][4/510] Data 0.009 (0.009) Batch 1.130 (1.130) Remain 12:19:19 loss: 0.2540 Lr: 0.00548
[2023-12-25 07:08:50,921 INFO misc.py line 119 253097] Train: [24/100][5/510] Data 0.004 (0.007) Batch 1.178 (1.154) Remain 12:35:03 loss: 0.3481 Lr: 0.00548
[2023-12-25 07:08:52,020 INFO misc.py line 119 253097] Train: [24/100][6/510] Data 0.006 (0.006) Batch 1.101 (1.136) Remain 12:23:29 loss: 0.2567 Lr: 0.00548
[2023-12-25 07:08:53,102 INFO misc.py line 119 253097] Train: [24/100][7/510] Data 0.003 (0.006) Batch 1.082 (1.123) Remain 12:14:39 loss: 0.4389 Lr: 0.00548
[2023-12-25 07:08:56,612 INFO misc.py line 119 253097] Train: [24/100][8/510] Data 0.003 (0.005) Batch 3.508 (1.600) Remain 17:26:50 loss: 0.4381 Lr: 0.00548
[2023-12-25 07:08:57,989 INFO misc.py line 119 253097] Train: [24/100][9/510] Data 0.005 (0.005) Batch 1.373 (1.562) Remain 17:02:02 loss: 0.1713 Lr: 0.00548
[2023-12-25 07:08:59,088 INFO misc.py line 119 253097] Train: [24/100][10/510] Data 0.010 (0.006) Batch 1.105 (1.497) Remain 16:19:17 loss: 0.2483 Lr: 0.00548
[2023-12-25 07:09:00,203 INFO misc.py line 119 253097] Train: [24/100][11/510] Data 0.004 (0.005) Batch 1.114 (1.449) Remain 15:47:56 loss: 0.4031 Lr: 0.00548
[2023-12-25 07:09:01,282 INFO misc.py line 119 253097] Train: [24/100][12/510] Data 0.005 (0.005) Batch 1.076 (1.407) Remain 15:20:50 loss: 0.2569 Lr: 0.00548
[2023-12-25 07:09:02,417 INFO misc.py line 119 253097] Train: [24/100][13/510] Data 0.008 (0.006) Batch 1.140 (1.381) Remain 15:03:18 loss: 0.1940 Lr: 0.00548
[2023-12-25 07:09:03,654 INFO misc.py line 119 253097] Train: [24/100][14/510] Data 0.004 (0.005) Batch 1.232 (1.367) Remain 14:54:28 loss: 0.3972 Lr: 0.00548
[2023-12-25 07:09:04,757 INFO misc.py line 119 253097] Train: [24/100][15/510] Data 0.007 (0.006) Batch 1.104 (1.345) Remain 14:40:05 loss: 0.4534 Lr: 0.00548
[2023-12-25 07:09:06,003 INFO misc.py line 119 253097] Train: [24/100][16/510] Data 0.007 (0.006) Batch 1.246 (1.338) Remain 14:35:05 loss: 0.2743 Lr: 0.00548
[2023-12-25 07:09:06,977 INFO misc.py line 119 253097] Train: [24/100][17/510] Data 0.007 (0.006) Batch 0.977 (1.312) Remain 14:18:13 loss: 0.4109 Lr: 0.00548
[2023-12-25 07:09:08,155 INFO misc.py line 119 253097] Train: [24/100][18/510] Data 0.004 (0.006) Batch 1.177 (1.303) Remain 14:12:18 loss: 0.2422 Lr: 0.00548
[2023-12-25 07:09:09,436 INFO misc.py line 119 253097] Train: [24/100][19/510] Data 0.005 (0.006) Batch 1.277 (1.301) Remain 14:11:12 loss: 0.2113 Lr: 0.00548
[2023-12-25 07:09:14,192 INFO misc.py line 119 253097] Train: [24/100][20/510] Data 0.010 (0.006) Batch 4.761 (1.505) Remain 16:24:19 loss: 0.4264 Lr: 0.00548
[2023-12-25 07:09:15,233 INFO misc.py line 119 253097] Train: [24/100][21/510] Data 0.004 (0.006) Batch 1.042 (1.479) Remain 16:07:28 loss: 0.2019 Lr: 0.00548
[2023-12-25 07:09:16,451 INFO misc.py line 119 253097] Train: [24/100][22/510] Data 0.004 (0.006) Batch 1.218 (1.465) Remain 15:58:27 loss: 0.2492 Lr: 0.00548
[2023-12-25 07:09:17,625 INFO misc.py line 119 253097] Train: [24/100][23/510] Data 0.003 (0.006) Batch 1.171 (1.451) Remain 15:48:47 loss: 0.2928 Lr: 0.00548
[2023-12-25 07:09:18,481 INFO misc.py line 119 253097] Train: [24/100][24/510] Data 0.008 (0.006) Batch 0.859 (1.422) Remain 15:30:21 loss: 0.2618 Lr: 0.00548
[2023-12-25 07:09:19,622 INFO misc.py line 119 253097] Train: [24/100][25/510] Data 0.003 (0.006) Batch 1.141 (1.410) Remain 15:21:57 loss: 0.2794 Lr: 0.00548
[2023-12-25 07:09:21,813 INFO misc.py line 119 253097] Train: [24/100][26/510] Data 0.004 (0.005) Batch 2.191 (1.444) Remain 15:44:09 loss: 0.2318 Lr: 0.00548
[2023-12-25 07:09:22,795 INFO misc.py line 119 253097] Train: [24/100][27/510] Data 0.005 (0.005) Batch 0.980 (1.424) Remain 15:31:30 loss: 0.3306 Lr: 0.00548
[2023-12-25 07:09:23,932 INFO misc.py line 119 253097] Train: [24/100][28/510] Data 0.007 (0.006) Batch 1.139 (1.413) Remain 15:24:02 loss: 0.1485 Lr: 0.00548
[2023-12-25 07:09:25,161 INFO misc.py line 119 253097] Train: [24/100][29/510] Data 0.004 (0.005) Batch 1.229 (1.406) Remain 15:19:22 loss: 0.3189 Lr: 0.00548
[2023-12-25 07:09:26,373 INFO misc.py line 119 253097] Train: [24/100][30/510] Data 0.003 (0.005) Batch 1.212 (1.399) Remain 15:14:39 loss: 0.4254 Lr: 0.00548
[2023-12-25 07:09:27,551 INFO misc.py line 119 253097] Train: [24/100][31/510] Data 0.005 (0.005) Batch 1.173 (1.391) Remain 15:09:22 loss: 0.1881 Lr: 0.00548
[2023-12-25 07:09:28,602 INFO misc.py line 119 253097] Train: [24/100][32/510] Data 0.010 (0.005) Batch 1.057 (1.379) Remain 15:01:49 loss: 0.3311 Lr: 0.00548
[2023-12-25 07:09:29,664 INFO misc.py line 119 253097] Train: [24/100][33/510] Data 0.003 (0.005) Batch 1.056 (1.368) Remain 14:54:46 loss: 0.1252 Lr: 0.00548
[2023-12-25 07:09:30,657 INFO misc.py line 119 253097] Train: [24/100][34/510] Data 0.008 (0.005) Batch 0.998 (1.356) Remain 14:46:56 loss: 0.1088 Lr: 0.00548
[2023-12-25 07:09:31,953 INFO misc.py line 119 253097] Train: [24/100][35/510] Data 0.004 (0.005) Batch 1.290 (1.354) Remain 14:45:33 loss: 0.3248 Lr: 0.00548
[2023-12-25 07:09:32,882 INFO misc.py line 119 253097] Train: [24/100][36/510] Data 0.010 (0.006) Batch 0.934 (1.342) Remain 14:37:12 loss: 0.1356 Lr: 0.00548
[2023-12-25 07:09:34,101 INFO misc.py line 119 253097] Train: [24/100][37/510] Data 0.004 (0.006) Batch 1.219 (1.338) Remain 14:34:50 loss: 0.2884 Lr: 0.00548
[2023-12-25 07:09:36,107 INFO misc.py line 119 253097] Train: [24/100][38/510] Data 0.003 (0.005) Batch 2.002 (1.357) Remain 14:47:13 loss: 0.3158 Lr: 0.00548
[2023-12-25 07:09:37,218 INFO misc.py line 119 253097] Train: [24/100][39/510] Data 0.007 (0.005) Batch 1.111 (1.350) Remain 14:42:44 loss: 0.2087 Lr: 0.00548
[2023-12-25 07:09:38,508 INFO misc.py line 119 253097] Train: [24/100][40/510] Data 0.007 (0.006) Batch 1.289 (1.348) Remain 14:41:38 loss: 0.3492 Lr: 0.00548
[2023-12-25 07:09:39,659 INFO misc.py line 119 253097] Train: [24/100][41/510] Data 0.008 (0.006) Batch 1.152 (1.343) Remain 14:38:13 loss: 0.2424 Lr: 0.00548
[2023-12-25 07:09:40,713 INFO misc.py line 119 253097] Train: [24/100][42/510] Data 0.008 (0.006) Batch 1.057 (1.336) Remain 14:33:25 loss: 0.2147 Lr: 0.00548
[2023-12-25 07:09:41,780 INFO misc.py line 119 253097] Train: [24/100][43/510] Data 0.005 (0.006) Batch 1.063 (1.329) Remain 14:28:55 loss: 0.3644 Lr: 0.00548
[2023-12-25 07:09:45,406 INFO misc.py line 119 253097] Train: [24/100][44/510] Data 0.008 (0.006) Batch 3.630 (1.385) Remain 15:05:36 loss: 0.3520 Lr: 0.00548
[2023-12-25 07:09:46,564 INFO misc.py line 119 253097] Train: [24/100][45/510] Data 0.005 (0.006) Batch 1.153 (1.380) Remain 15:01:58 loss: 0.1788 Lr: 0.00548
[2023-12-25 07:09:47,681 INFO misc.py line 119 253097] Train: [24/100][46/510] Data 0.010 (0.006) Batch 1.120 (1.374) Remain 14:57:59 loss: 0.2498 Lr: 0.00548
[2023-12-25 07:09:48,836 INFO misc.py line 119 253097] Train: [24/100][47/510] Data 0.008 (0.006) Batch 1.149 (1.369) Remain 14:54:38 loss: 0.3332 Lr: 0.00548
[2023-12-25 07:09:49,912 INFO misc.py line 119 253097] Train: [24/100][48/510] Data 0.013 (0.006) Batch 1.080 (1.362) Remain 14:50:25 loss: 0.2317 Lr: 0.00548
[2023-12-25 07:09:53,593 INFO misc.py line 119 253097] Train: [24/100][49/510] Data 0.009 (0.006) Batch 3.686 (1.413) Remain 15:23:25 loss: 0.4870 Lr: 0.00548
[2023-12-25 07:09:59,351 INFO misc.py line 119 253097] Train: [24/100][50/510] Data 0.004 (0.006) Batch 5.757 (1.505) Remain 16:23:49 loss: 0.2570 Lr: 0.00548
[2023-12-25 07:10:00,475 INFO misc.py line 119 253097] Train: [24/100][51/510] Data 0.007 (0.006) Batch 1.124 (1.497) Remain 16:18:36 loss: 0.2245 Lr: 0.00548
[2023-12-25 07:10:01,446 INFO misc.py line 119 253097] Train: [24/100][52/510] Data 0.004 (0.006) Batch 0.971 (1.486) Remain 16:11:34 loss: 0.2737 Lr: 0.00548
[2023-12-25 07:10:02,507 INFO misc.py line 119 253097] Train: [24/100][53/510] Data 0.004 (0.006) Batch 1.061 (1.478) Remain 16:05:59 loss: 0.2475 Lr: 0.00548
[2023-12-25 07:10:03,437 INFO misc.py line 119 253097] Train: [24/100][54/510] Data 0.004 (0.006) Batch 0.929 (1.467) Remain 15:58:55 loss: 0.2548 Lr: 0.00548
[2023-12-25 07:10:04,582 INFO misc.py line 119 253097] Train: [24/100][55/510] Data 0.005 (0.006) Batch 1.146 (1.461) Remain 15:54:51 loss: 0.4814 Lr: 0.00548
[2023-12-25 07:10:08,432 INFO misc.py line 119 253097] Train: [24/100][56/510] Data 0.005 (0.006) Batch 3.850 (1.506) Remain 16:24:17 loss: 0.2770 Lr: 0.00548
[2023-12-25 07:10:09,667 INFO misc.py line 119 253097] Train: [24/100][57/510] Data 0.004 (0.006) Batch 1.236 (1.501) Remain 16:20:59 loss: 0.1342 Lr: 0.00548
[2023-12-25 07:10:10,878 INFO misc.py line 119 253097] Train: [24/100][58/510] Data 0.004 (0.006) Batch 1.210 (1.496) Remain 16:17:30 loss: 0.2537 Lr: 0.00548
[2023-12-25 07:10:12,203 INFO misc.py line 119 253097] Train: [24/100][59/510] Data 0.005 (0.006) Batch 1.325 (1.493) Remain 16:15:29 loss: 0.4414 Lr: 0.00548
[2023-12-25 07:10:13,363 INFO misc.py line 119 253097] Train: [24/100][60/510] Data 0.006 (0.006) Batch 1.161 (1.487) Remain 16:11:40 loss: 0.1020 Lr: 0.00548
[2023-12-25 07:10:14,675 INFO misc.py line 119 253097] Train: [24/100][61/510] Data 0.004 (0.006) Batch 1.311 (1.484) Remain 16:09:40 loss: 0.1403 Lr: 0.00548
[2023-12-25 07:10:15,943 INFO misc.py line 119 253097] Train: [24/100][62/510] Data 0.006 (0.006) Batch 1.264 (1.480) Remain 16:07:12 loss: 0.2238 Lr: 0.00548
[2023-12-25 07:10:17,198 INFO misc.py line 119 253097] Train: [24/100][63/510] Data 0.008 (0.006) Batch 1.256 (1.476) Remain 16:04:44 loss: 0.1555 Lr: 0.00548
[2023-12-25 07:10:18,354 INFO misc.py line 119 253097] Train: [24/100][64/510] Data 0.012 (0.006) Batch 1.158 (1.471) Remain 16:01:18 loss: 0.3359 Lr: 0.00548
[2023-12-25 07:10:19,383 INFO misc.py line 119 253097] Train: [24/100][65/510] Data 0.005 (0.006) Batch 1.030 (1.464) Remain 15:56:38 loss: 0.2595 Lr: 0.00548
[2023-12-25 07:10:20,408 INFO misc.py line 119 253097] Train: [24/100][66/510] Data 0.004 (0.006) Batch 1.023 (1.457) Remain 15:52:02 loss: 0.3348 Lr: 0.00548
[2023-12-25 07:10:21,414 INFO misc.py line 119 253097] Train: [24/100][67/510] Data 0.006 (0.006) Batch 1.007 (1.450) Remain 15:47:25 loss: 0.1461 Lr: 0.00548
[2023-12-25 07:10:22,536 INFO misc.py line 119 253097] Train: [24/100][68/510] Data 0.005 (0.006) Batch 1.121 (1.445) Remain 15:44:05 loss: 0.2906 Lr: 0.00548
[2023-12-25 07:10:23,744 INFO misc.py line 119 253097] Train: [24/100][69/510] Data 0.006 (0.006) Batch 1.203 (1.441) Remain 15:41:40 loss: 0.1863 Lr: 0.00548
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[2023-12-25 07:10:26,192 INFO misc.py line 119 253097] Train: [24/100][71/510] Data 0.009 (0.006) Batch 1.136 (1.435) Remain 15:37:29 loss: 0.2066 Lr: 0.00548
[2023-12-25 07:10:28,238 INFO misc.py line 119 253097] Train: [24/100][72/510] Data 0.006 (0.006) Batch 2.044 (1.444) Remain 15:43:14 loss: 0.2494 Lr: 0.00548
[2023-12-25 07:10:29,470 INFO misc.py line 119 253097] Train: [24/100][73/510] Data 0.008 (0.006) Batch 1.236 (1.441) Remain 15:41:16 loss: 0.2217 Lr: 0.00548
[2023-12-25 07:10:30,731 INFO misc.py line 119 253097] Train: [24/100][74/510] Data 0.005 (0.006) Batch 1.258 (1.438) Remain 15:39:33 loss: 0.2382 Lr: 0.00548
[2023-12-25 07:10:32,023 INFO misc.py line 119 253097] Train: [24/100][75/510] Data 0.007 (0.006) Batch 1.288 (1.436) Remain 15:38:10 loss: 0.1348 Lr: 0.00548
[2023-12-25 07:10:33,135 INFO misc.py line 119 253097] Train: [24/100][76/510] Data 0.012 (0.006) Batch 1.118 (1.432) Remain 15:35:17 loss: 0.2063 Lr: 0.00548
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[2023-12-25 07:10:35,395 INFO misc.py line 119 253097] Train: [24/100][78/510] Data 0.011 (0.006) Batch 1.072 (1.424) Remain 15:29:57 loss: 0.2314 Lr: 0.00548
[2023-12-25 07:10:36,494 INFO misc.py line 119 253097] Train: [24/100][79/510] Data 0.009 (0.006) Batch 1.104 (1.419) Remain 15:27:11 loss: 0.2455 Lr: 0.00548
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[2023-12-25 07:10:38,592 INFO misc.py line 119 253097] Train: [24/100][81/510] Data 0.004 (0.006) Batch 0.938 (1.410) Remain 15:20:56 loss: 0.2224 Lr: 0.00548
[2023-12-25 07:10:39,861 INFO misc.py line 119 253097] Train: [24/100][82/510] Data 0.005 (0.006) Batch 1.265 (1.408) Remain 15:19:42 loss: 0.1089 Lr: 0.00548
[2023-12-25 07:10:40,932 INFO misc.py line 119 253097] Train: [24/100][83/510] Data 0.008 (0.006) Batch 1.073 (1.404) Remain 15:16:57 loss: 0.3457 Lr: 0.00548
[2023-12-25 07:10:41,952 INFO misc.py line 119 253097] Train: [24/100][84/510] Data 0.006 (0.006) Batch 1.012 (1.399) Remain 15:13:46 loss: 0.3562 Lr: 0.00548
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[2023-12-25 07:10:53,286 INFO misc.py line 119 253097] Train: [24/100][89/510] Data 0.004 (0.006) Batch 1.210 (1.450) Remain 15:46:41 loss: 0.1365 Lr: 0.00547
[2023-12-25 07:10:54,503 INFO misc.py line 119 253097] Train: [24/100][90/510] Data 0.003 (0.006) Batch 1.216 (1.447) Remain 15:44:54 loss: 0.1808 Lr: 0.00547
[2023-12-25 07:10:55,624 INFO misc.py line 119 253097] Train: [24/100][91/510] Data 0.005 (0.006) Batch 1.122 (1.443) Remain 15:42:28 loss: 0.2101 Lr: 0.00547
[2023-12-25 07:10:56,618 INFO misc.py line 119 253097] Train: [24/100][92/510] Data 0.003 (0.006) Batch 0.993 (1.438) Remain 15:39:08 loss: 0.1719 Lr: 0.00547
[2023-12-25 07:10:57,646 INFO misc.py line 119 253097] Train: [24/100][93/510] Data 0.005 (0.006) Batch 1.024 (1.434) Remain 15:36:06 loss: 0.2214 Lr: 0.00547
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[2023-12-25 07:11:42,921 INFO misc.py line 119 253097] Train: [24/100][118/510] Data 0.007 (0.044) Batch 2.266 (1.516) Remain 16:29:03 loss: 0.5379 Lr: 0.00547
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[2023-12-25 07:12:14,708 INFO misc.py line 119 253097] Train: [24/100][138/510] Data 5.517 (0.079) Batch 6.224 (1.527) Remain 16:35:40 loss: 0.2066 Lr: 0.00547
[2023-12-25 07:12:17,613 INFO misc.py line 119 253097] Train: [24/100][139/510] Data 0.004 (0.078) Batch 2.906 (1.537) Remain 16:42:15 loss: 0.2480 Lr: 0.00547
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[2023-12-25 07:12:19,837 INFO misc.py line 119 253097] Train: [24/100][141/510] Data 0.008 (0.077) Batch 1.236 (1.531) Remain 16:38:11 loss: 0.4084 Lr: 0.00547
[2023-12-25 07:12:21,125 INFO misc.py line 119 253097] Train: [24/100][142/510] Data 0.004 (0.077) Batch 1.287 (1.529) Remain 16:37:01 loss: 0.1700 Lr: 0.00547
[2023-12-25 07:12:22,181 INFO misc.py line 119 253097] Train: [24/100][143/510] Data 0.005 (0.076) Batch 1.057 (1.525) Remain 16:34:47 loss: 0.2366 Lr: 0.00547
[2023-12-25 07:12:23,357 INFO misc.py line 119 253097] Train: [24/100][144/510] Data 0.004 (0.076) Batch 1.175 (1.523) Remain 16:33:09 loss: 0.2318 Lr: 0.00547
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[2023-12-25 07:12:26,651 INFO misc.py line 119 253097] Train: [24/100][147/510] Data 0.005 (0.074) Batch 1.104 (1.514) Remain 16:27:18 loss: 0.2303 Lr: 0.00547
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[2023-12-25 07:12:28,832 INFO misc.py line 119 253097] Train: [24/100][149/510] Data 0.003 (0.073) Batch 1.095 (1.508) Remain 16:23:28 loss: 0.1870 Lr: 0.00547
[2023-12-25 07:12:30,168 INFO misc.py line 119 253097] Train: [24/100][150/510] Data 0.005 (0.073) Batch 1.331 (1.507) Remain 16:22:39 loss: 0.2673 Lr: 0.00547
[2023-12-25 07:12:31,277 INFO misc.py line 119 253097] Train: [24/100][151/510] Data 0.009 (0.072) Batch 1.113 (1.504) Remain 16:20:54 loss: 0.2642 Lr: 0.00547
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[2023-12-25 07:12:35,576 INFO misc.py line 119 253097] Train: [24/100][153/510] Data 0.006 (0.085) Batch 1.103 (1.513) Remain 16:26:27 loss: 0.2767 Lr: 0.00547
[2023-12-25 07:12:36,745 INFO misc.py line 119 253097] Train: [24/100][154/510] Data 0.004 (0.085) Batch 1.168 (1.511) Remain 16:24:56 loss: 0.3953 Lr: 0.00547
[2023-12-25 07:12:37,941 INFO misc.py line 119 253097] Train: [24/100][155/510] Data 0.005 (0.084) Batch 1.196 (1.509) Remain 16:23:34 loss: 0.3468 Lr: 0.00547
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[2023-12-25 07:12:47,238 INFO misc.py line 119 253097] Train: [24/100][157/510] Data 7.072 (0.129) Batch 8.056 (1.550) Remain 16:50:06 loss: 0.2903 Lr: 0.00547
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[2023-12-25 07:14:32,175 INFO misc.py line 119 253097] Train: [24/100][230/510] Data 0.005 (0.108) Batch 5.999 (1.513) Remain 16:24:46 loss: 0.2286 Lr: 0.00546
[2023-12-25 07:14:33,241 INFO misc.py line 119 253097] Train: [24/100][231/510] Data 0.005 (0.107) Batch 1.067 (1.512) Remain 16:23:28 loss: 0.2101 Lr: 0.00546
[2023-12-25 07:14:34,474 INFO misc.py line 119 253097] Train: [24/100][232/510] Data 0.004 (0.107) Batch 1.231 (1.510) Remain 16:22:39 loss: 0.1876 Lr: 0.00546
[2023-12-25 07:14:35,366 INFO misc.py line 119 253097] Train: [24/100][233/510] Data 0.005 (0.106) Batch 0.894 (1.508) Remain 16:20:53 loss: 0.2178 Lr: 0.00546
[2023-12-25 07:14:36,432 INFO misc.py line 119 253097] Train: [24/100][234/510] Data 0.003 (0.106) Batch 1.065 (1.506) Remain 16:19:37 loss: 0.1957 Lr: 0.00546
[2023-12-25 07:14:37,489 INFO misc.py line 119 253097] Train: [24/100][235/510] Data 0.004 (0.105) Batch 1.055 (1.504) Remain 16:18:19 loss: 0.9724 Lr: 0.00546
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[2023-12-25 07:14:40,705 INFO misc.py line 119 253097] Train: [24/100][238/510] Data 0.004 (0.104) Batch 1.092 (1.498) Remain 16:14:40 loss: 0.4143 Lr: 0.00546
[2023-12-25 07:14:41,810 INFO misc.py line 119 253097] Train: [24/100][239/510] Data 0.005 (0.104) Batch 1.104 (1.497) Remain 16:13:33 loss: 0.2873 Lr: 0.00546
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[2023-12-25 07:14:44,075 INFO misc.py line 119 253097] Train: [24/100][241/510] Data 0.008 (0.103) Batch 1.194 (1.494) Remain 16:11:31 loss: 0.2159 Lr: 0.00546
[2023-12-25 07:14:45,245 INFO misc.py line 119 253097] Train: [24/100][242/510] Data 0.005 (0.102) Batch 1.172 (1.492) Remain 16:10:37 loss: 0.3532 Lr: 0.00546
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[2023-12-25 07:14:47,623 INFO misc.py line 119 253097] Train: [24/100][244/510] Data 0.004 (0.102) Batch 1.220 (1.490) Remain 16:08:55 loss: 0.3613 Lr: 0.00546
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[2023-12-25 07:14:55,255 INFO misc.py line 119 253097] Train: [24/100][246/510] Data 0.005 (0.101) Batch 1.252 (1.509) Remain 16:21:19 loss: 0.3158 Lr: 0.00546
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[2023-12-25 07:14:58,654 INFO misc.py line 119 253097] Train: [24/100][249/510] Data 0.007 (0.100) Batch 1.030 (1.504) Remain 16:18:15 loss: 0.3954 Lr: 0.00546
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[2023-12-25 07:15:01,973 INFO misc.py line 119 253097] Train: [24/100][252/510] Data 0.004 (0.099) Batch 0.995 (1.499) Remain 16:15:05 loss: 0.5012 Lr: 0.00546
[2023-12-25 07:15:03,121 INFO misc.py line 119 253097] Train: [24/100][253/510] Data 0.004 (0.098) Batch 1.149 (1.498) Remain 16:14:08 loss: 0.2231 Lr: 0.00546
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[2023-12-25 07:15:06,957 INFO misc.py line 119 253097] Train: [24/100][255/510] Data 0.003 (0.097) Batch 1.018 (1.501) Remain 16:16:15 loss: 0.4876 Lr: 0.00546
[2023-12-25 07:15:08,000 INFO misc.py line 119 253097] Train: [24/100][256/510] Data 0.007 (0.097) Batch 1.046 (1.500) Remain 16:15:03 loss: 0.3352 Lr: 0.00546
[2023-12-25 07:15:09,230 INFO misc.py line 119 253097] Train: [24/100][257/510] Data 0.005 (0.097) Batch 1.226 (1.498) Remain 16:14:20 loss: 0.3347 Lr: 0.00546
[2023-12-25 07:15:10,292 INFO misc.py line 119 253097] Train: [24/100][258/510] Data 0.009 (0.096) Batch 1.063 (1.497) Remain 16:13:11 loss: 0.3005 Lr: 0.00546
[2023-12-25 07:15:11,381 INFO misc.py line 119 253097] Train: [24/100][259/510] Data 0.008 (0.096) Batch 1.088 (1.495) Remain 16:12:08 loss: 0.5713 Lr: 0.00546
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[2023-12-25 07:15:13,646 INFO misc.py line 119 253097] Train: [24/100][261/510] Data 0.011 (0.095) Batch 1.183 (1.492) Remain 16:10:15 loss: 0.1462 Lr: 0.00546
[2023-12-25 07:15:14,680 INFO misc.py line 119 253097] Train: [24/100][262/510] Data 0.009 (0.095) Batch 1.035 (1.491) Remain 16:09:05 loss: 0.1844 Lr: 0.00546
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[2023-12-25 07:15:25,927 INFO misc.py line 119 253097] Train: [24/100][267/510] Data 0.006 (0.093) Batch 6.319 (1.505) Remain 16:18:18 loss: 0.1796 Lr: 0.00545
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[2023-12-25 07:15:31,840 INFO misc.py line 119 253097] Train: [24/100][270/510] Data 0.005 (0.092) Batch 1.250 (1.510) Remain 16:21:37 loss: 0.3087 Lr: 0.00545
[2023-12-25 07:15:33,014 INFO misc.py line 119 253097] Train: [24/100][271/510] Data 0.009 (0.092) Batch 1.175 (1.509) Remain 16:20:47 loss: 0.3877 Lr: 0.00545
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[2023-12-25 07:15:35,113 INFO misc.py line 119 253097] Train: [24/100][273/510] Data 0.004 (0.091) Batch 1.185 (1.506) Remain 16:18:32 loss: 0.2181 Lr: 0.00545
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[2023-12-25 07:17:53,961 INFO misc.py line 119 253097] Train: [24/100][345/510] Data 4.777 (0.108) Batch 5.865 (1.595) Remain 17:14:29 loss: 0.2695 Lr: 0.00545
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[2023-12-25 07:17:59,676 INFO misc.py line 119 253097] Train: [24/100][350/510] Data 0.004 (0.107) Batch 1.084 (1.588) Remain 17:10:07 loss: 0.2662 Lr: 0.00545
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[2023-12-25 07:18:16,302 INFO misc.py line 119 253097] Train: [24/100][361/510] Data 0.008 (0.104) Batch 4.974 (1.586) Remain 17:08:19 loss: 0.2905 Lr: 0.00544
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[2023-12-25 07:19:21,472 INFO misc.py line 119 253097] Train: [24/100][408/510] Data 0.005 (0.118) Batch 1.203 (1.563) Remain 16:52:06 loss: 0.2171 Lr: 0.00544
[2023-12-25 07:19:22,696 INFO misc.py line 119 253097] Train: [24/100][409/510] Data 0.005 (0.118) Batch 1.224 (1.562) Remain 16:51:32 loss: 0.1343 Lr: 0.00544
[2023-12-25 07:19:23,781 INFO misc.py line 119 253097] Train: [24/100][410/510] Data 0.005 (0.117) Batch 1.084 (1.561) Remain 16:50:45 loss: 0.3410 Lr: 0.00544
[2023-12-25 07:19:25,057 INFO misc.py line 119 253097] Train: [24/100][411/510] Data 0.006 (0.117) Batch 1.269 (1.560) Remain 16:50:15 loss: 0.2229 Lr: 0.00544
[2023-12-25 07:19:26,652 INFO misc.py line 119 253097] Train: [24/100][412/510] Data 0.013 (0.117) Batch 1.596 (1.560) Remain 16:50:17 loss: 0.2189 Lr: 0.00544
[2023-12-25 07:19:27,920 INFO misc.py line 119 253097] Train: [24/100][413/510] Data 0.012 (0.117) Batch 1.274 (1.559) Remain 16:49:48 loss: 0.3680 Lr: 0.00544
[2023-12-25 07:19:29,000 INFO misc.py line 119 253097] Train: [24/100][414/510] Data 0.007 (0.116) Batch 1.082 (1.558) Remain 16:49:02 loss: 0.1607 Lr: 0.00544
[2023-12-25 07:19:30,302 INFO misc.py line 119 253097] Train: [24/100][415/510] Data 0.005 (0.116) Batch 1.297 (1.557) Remain 16:48:36 loss: 0.3491 Lr: 0.00544
[2023-12-25 07:19:31,592 INFO misc.py line 119 253097] Train: [24/100][416/510] Data 0.011 (0.116) Batch 1.295 (1.557) Remain 16:48:09 loss: 0.2616 Lr: 0.00544
[2023-12-25 07:19:36,459 INFO misc.py line 119 253097] Train: [24/100][417/510] Data 0.005 (0.116) Batch 4.869 (1.565) Remain 16:53:19 loss: 0.2936 Lr: 0.00544
[2023-12-25 07:19:37,425 INFO misc.py line 119 253097] Train: [24/100][418/510] Data 0.004 (0.115) Batch 0.966 (1.563) Remain 16:52:21 loss: 0.2205 Lr: 0.00544
[2023-12-25 07:19:38,548 INFO misc.py line 119 253097] Train: [24/100][419/510] Data 0.003 (0.115) Batch 1.122 (1.562) Remain 16:51:38 loss: 0.2451 Lr: 0.00544
[2023-12-25 07:19:39,495 INFO misc.py line 119 253097] Train: [24/100][420/510] Data 0.004 (0.115) Batch 0.947 (1.561) Remain 16:50:39 loss: 0.2301 Lr: 0.00544
[2023-12-25 07:19:40,621 INFO misc.py line 119 253097] Train: [24/100][421/510] Data 0.005 (0.114) Batch 1.125 (1.560) Remain 16:49:57 loss: 0.4108 Lr: 0.00544
[2023-12-25 07:19:41,737 INFO misc.py line 119 253097] Train: [24/100][422/510] Data 0.006 (0.114) Batch 1.117 (1.559) Remain 16:49:15 loss: 0.3170 Lr: 0.00544
[2023-12-25 07:19:45,324 INFO misc.py line 119 253097] Train: [24/100][423/510] Data 0.005 (0.114) Batch 3.586 (1.564) Remain 16:52:20 loss: 0.3004 Lr: 0.00544
[2023-12-25 07:19:46,489 INFO misc.py line 119 253097] Train: [24/100][424/510] Data 0.005 (0.114) Batch 1.167 (1.563) Remain 16:51:42 loss: 0.2236 Lr: 0.00544
[2023-12-25 07:19:47,541 INFO misc.py line 119 253097] Train: [24/100][425/510] Data 0.004 (0.113) Batch 1.052 (1.561) Remain 16:50:54 loss: 0.2457 Lr: 0.00544
[2023-12-25 07:19:48,876 INFO misc.py line 119 253097] Train: [24/100][426/510] Data 0.004 (0.113) Batch 1.335 (1.561) Remain 16:50:31 loss: 0.2564 Lr: 0.00544
[2023-12-25 07:19:50,069 INFO misc.py line 119 253097] Train: [24/100][427/510] Data 0.003 (0.113) Batch 1.190 (1.560) Remain 16:49:56 loss: 0.1874 Lr: 0.00544
[2023-12-25 07:19:51,058 INFO misc.py line 119 253097] Train: [24/100][428/510] Data 0.007 (0.113) Batch 0.992 (1.559) Remain 16:49:02 loss: 0.3359 Lr: 0.00544
[2023-12-25 07:19:52,313 INFO misc.py line 119 253097] Train: [24/100][429/510] Data 0.003 (0.112) Batch 1.250 (1.558) Remain 16:48:33 loss: 0.1720 Lr: 0.00544
[2023-12-25 07:20:05,954 INFO misc.py line 119 253097] Train: [24/100][430/510] Data 0.008 (0.112) Batch 13.645 (1.586) Remain 17:06:51 loss: 0.1808 Lr: 0.00544
[2023-12-25 07:20:07,127 INFO misc.py line 119 253097] Train: [24/100][431/510] Data 0.005 (0.112) Batch 1.174 (1.585) Remain 17:06:12 loss: 0.2149 Lr: 0.00544
[2023-12-25 07:20:08,276 INFO misc.py line 119 253097] Train: [24/100][432/510] Data 0.003 (0.112) Batch 1.149 (1.584) Remain 17:05:30 loss: 0.3085 Lr: 0.00544
[2023-12-25 07:20:09,491 INFO misc.py line 119 253097] Train: [24/100][433/510] Data 0.003 (0.111) Batch 1.214 (1.583) Remain 17:04:55 loss: 0.1463 Lr: 0.00544
[2023-12-25 07:20:10,516 INFO misc.py line 119 253097] Train: [24/100][434/510] Data 0.004 (0.111) Batch 1.026 (1.582) Remain 17:04:04 loss: 0.1432 Lr: 0.00544
[2023-12-25 07:20:11,580 INFO misc.py line 119 253097] Train: [24/100][435/510] Data 0.003 (0.111) Batch 1.063 (1.581) Remain 17:03:15 loss: 0.3218 Lr: 0.00544
[2023-12-25 07:20:12,783 INFO misc.py line 119 253097] Train: [24/100][436/510] Data 0.004 (0.111) Batch 1.203 (1.580) Remain 17:02:40 loss: 0.1391 Lr: 0.00544
[2023-12-25 07:20:13,843 INFO misc.py line 119 253097] Train: [24/100][437/510] Data 0.003 (0.110) Batch 1.055 (1.579) Remain 17:01:51 loss: 0.2766 Lr: 0.00544
[2023-12-25 07:20:15,065 INFO misc.py line 119 253097] Train: [24/100][438/510] Data 0.009 (0.110) Batch 1.222 (1.578) Remain 17:01:18 loss: 0.2367 Lr: 0.00544
[2023-12-25 07:20:16,310 INFO misc.py line 119 253097] Train: [24/100][439/510] Data 0.008 (0.110) Batch 1.246 (1.577) Remain 17:00:47 loss: 0.2171 Lr: 0.00544
[2023-12-25 07:20:17,394 INFO misc.py line 119 253097] Train: [24/100][440/510] Data 0.007 (0.110) Batch 1.084 (1.576) Remain 17:00:01 loss: 0.3470 Lr: 0.00544
[2023-12-25 07:20:18,511 INFO misc.py line 119 253097] Train: [24/100][441/510] Data 0.007 (0.109) Batch 1.121 (1.575) Remain 16:59:20 loss: 0.2655 Lr: 0.00544
[2023-12-25 07:20:19,384 INFO misc.py line 119 253097] Train: [24/100][442/510] Data 0.003 (0.109) Batch 0.873 (1.574) Remain 16:58:16 loss: 0.1221 Lr: 0.00544
[2023-12-25 07:20:20,416 INFO misc.py line 119 253097] Train: [24/100][443/510] Data 0.003 (0.109) Batch 1.033 (1.572) Remain 16:57:27 loss: 0.2275 Lr: 0.00544
[2023-12-25 07:20:21,431 INFO misc.py line 119 253097] Train: [24/100][444/510] Data 0.003 (0.109) Batch 1.013 (1.571) Remain 16:56:36 loss: 0.2162 Lr: 0.00543
[2023-12-25 07:20:22,492 INFO misc.py line 119 253097] Train: [24/100][445/510] Data 0.006 (0.109) Batch 1.063 (1.570) Remain 16:55:50 loss: 0.2614 Lr: 0.00543
[2023-12-25 07:20:23,750 INFO misc.py line 119 253097] Train: [24/100][446/510] Data 0.003 (0.108) Batch 1.253 (1.569) Remain 16:55:20 loss: 0.2310 Lr: 0.00543
[2023-12-25 07:20:24,695 INFO misc.py line 119 253097] Train: [24/100][447/510] Data 0.007 (0.108) Batch 0.947 (1.568) Remain 16:54:24 loss: 0.3181 Lr: 0.00543
[2023-12-25 07:20:26,022 INFO misc.py line 119 253097] Train: [24/100][448/510] Data 0.005 (0.108) Batch 1.329 (1.567) Remain 16:54:02 loss: 0.3245 Lr: 0.00543
[2023-12-25 07:20:33,335 INFO misc.py line 119 253097] Train: [24/100][449/510] Data 0.005 (0.108) Batch 7.313 (1.580) Remain 17:02:20 loss: 0.4707 Lr: 0.00543
[2023-12-25 07:20:34,530 INFO misc.py line 119 253097] Train: [24/100][450/510] Data 0.004 (0.107) Batch 1.194 (1.579) Remain 17:01:45 loss: 0.2385 Lr: 0.00543
[2023-12-25 07:20:35,727 INFO misc.py line 119 253097] Train: [24/100][451/510] Data 0.005 (0.107) Batch 1.199 (1.578) Remain 17:01:11 loss: 0.1879 Lr: 0.00543
[2023-12-25 07:20:36,957 INFO misc.py line 119 253097] Train: [24/100][452/510] Data 0.003 (0.107) Batch 1.229 (1.578) Remain 17:00:39 loss: 0.2845 Lr: 0.00543
[2023-12-25 07:20:37,865 INFO misc.py line 119 253097] Train: [24/100][453/510] Data 0.004 (0.107) Batch 0.909 (1.576) Remain 16:59:40 loss: 0.2918 Lr: 0.00543
[2023-12-25 07:20:38,965 INFO misc.py line 119 253097] Train: [24/100][454/510] Data 0.004 (0.106) Batch 1.100 (1.575) Remain 16:58:57 loss: 0.2369 Lr: 0.00543
[2023-12-25 07:20:40,223 INFO misc.py line 119 253097] Train: [24/100][455/510] Data 0.003 (0.106) Batch 1.249 (1.574) Remain 16:58:28 loss: 0.2087 Lr: 0.00543
[2023-12-25 07:20:41,500 INFO misc.py line 119 253097] Train: [24/100][456/510] Data 0.011 (0.106) Batch 1.282 (1.574) Remain 16:58:01 loss: 0.2953 Lr: 0.00543
[2023-12-25 07:20:42,695 INFO misc.py line 119 253097] Train: [24/100][457/510] Data 0.007 (0.106) Batch 1.198 (1.573) Remain 16:57:27 loss: 0.2990 Lr: 0.00543
[2023-12-25 07:20:45,033 INFO misc.py line 119 253097] Train: [24/100][458/510] Data 0.003 (0.106) Batch 2.337 (1.575) Remain 16:58:31 loss: 0.3346 Lr: 0.00543
[2023-12-25 07:20:46,353 INFO misc.py line 119 253097] Train: [24/100][459/510] Data 0.004 (0.105) Batch 1.320 (1.574) Remain 16:58:08 loss: 0.3252 Lr: 0.00543
[2023-12-25 07:20:47,444 INFO misc.py line 119 253097] Train: [24/100][460/510] Data 0.003 (0.105) Batch 1.091 (1.573) Remain 16:57:25 loss: 0.2817 Lr: 0.00543
[2023-12-25 07:20:48,692 INFO misc.py line 119 253097] Train: [24/100][461/510] Data 0.003 (0.105) Batch 1.245 (1.572) Remain 16:56:56 loss: 0.2378 Lr: 0.00543
[2023-12-25 07:20:49,726 INFO misc.py line 119 253097] Train: [24/100][462/510] Data 0.007 (0.105) Batch 1.037 (1.571) Remain 16:56:09 loss: 0.1682 Lr: 0.00543
[2023-12-25 07:20:50,780 INFO misc.py line 119 253097] Train: [24/100][463/510] Data 0.005 (0.104) Batch 1.049 (1.570) Remain 16:55:23 loss: 0.2590 Lr: 0.00543
[2023-12-25 07:20:51,794 INFO misc.py line 119 253097] Train: [24/100][464/510] Data 0.009 (0.104) Batch 1.016 (1.569) Remain 16:54:35 loss: 0.2426 Lr: 0.00543
[2023-12-25 07:20:53,008 INFO misc.py line 119 253097] Train: [24/100][465/510] Data 0.007 (0.104) Batch 1.214 (1.568) Remain 16:54:04 loss: 0.2106 Lr: 0.00543
[2023-12-25 07:20:54,119 INFO misc.py line 119 253097] Train: [24/100][466/510] Data 0.007 (0.104) Batch 1.114 (1.567) Remain 16:53:24 loss: 0.3277 Lr: 0.00543
[2023-12-25 07:20:55,352 INFO misc.py line 119 253097] Train: [24/100][467/510] Data 0.004 (0.104) Batch 1.230 (1.566) Remain 16:52:54 loss: 0.2257 Lr: 0.00543
[2023-12-25 07:20:56,400 INFO misc.py line 119 253097] Train: [24/100][468/510] Data 0.008 (0.103) Batch 1.048 (1.565) Remain 16:52:10 loss: 0.3135 Lr: 0.00543
[2023-12-25 07:20:57,606 INFO misc.py line 119 253097] Train: [24/100][469/510] Data 0.007 (0.103) Batch 1.210 (1.564) Remain 16:51:38 loss: 0.1587 Lr: 0.00543
[2023-12-25 07:20:58,883 INFO misc.py line 119 253097] Train: [24/100][470/510] Data 0.003 (0.103) Batch 1.275 (1.564) Remain 16:51:13 loss: 0.0898 Lr: 0.00543
[2023-12-25 07:20:59,874 INFO misc.py line 119 253097] Train: [24/100][471/510] Data 0.005 (0.103) Batch 0.994 (1.563) Remain 16:50:24 loss: 0.2225 Lr: 0.00543
[2023-12-25 07:21:01,017 INFO misc.py line 119 253097] Train: [24/100][472/510] Data 0.003 (0.103) Batch 1.143 (1.562) Remain 16:49:48 loss: 0.2095 Lr: 0.00543
[2023-12-25 07:21:02,194 INFO misc.py line 119 253097] Train: [24/100][473/510] Data 0.003 (0.102) Batch 1.176 (1.561) Remain 16:49:14 loss: 0.3246 Lr: 0.00543
[2023-12-25 07:21:03,423 INFO misc.py line 119 253097] Train: [24/100][474/510] Data 0.003 (0.102) Batch 1.225 (1.560) Remain 16:48:45 loss: 0.2073 Lr: 0.00543
[2023-12-25 07:21:05,630 INFO misc.py line 119 253097] Train: [24/100][475/510] Data 0.007 (0.102) Batch 2.206 (1.561) Remain 16:49:37 loss: 0.1971 Lr: 0.00543
[2023-12-25 07:21:06,776 INFO misc.py line 119 253097] Train: [24/100][476/510] Data 0.008 (0.102) Batch 1.150 (1.561) Remain 16:49:01 loss: 0.1200 Lr: 0.00543
[2023-12-25 07:21:07,964 INFO misc.py line 119 253097] Train: [24/100][477/510] Data 0.004 (0.102) Batch 1.186 (1.560) Remain 16:48:29 loss: 0.3234 Lr: 0.00543
[2023-12-25 07:21:09,156 INFO misc.py line 119 253097] Train: [24/100][478/510] Data 0.006 (0.101) Batch 1.193 (1.559) Remain 16:47:58 loss: 0.3052 Lr: 0.00543
[2023-12-25 07:21:10,323 INFO misc.py line 119 253097] Train: [24/100][479/510] Data 0.005 (0.101) Batch 1.166 (1.558) Remain 16:47:24 loss: 0.2579 Lr: 0.00543
[2023-12-25 07:21:11,482 INFO misc.py line 119 253097] Train: [24/100][480/510] Data 0.005 (0.101) Batch 1.148 (1.557) Remain 16:46:49 loss: 0.2232 Lr: 0.00543
[2023-12-25 07:21:12,615 INFO misc.py line 119 253097] Train: [24/100][481/510] Data 0.021 (0.101) Batch 1.143 (1.556) Remain 16:46:14 loss: 0.2667 Lr: 0.00543
[2023-12-25 07:21:16,821 INFO misc.py line 119 253097] Train: [24/100][482/510] Data 0.006 (0.101) Batch 4.206 (1.562) Remain 16:49:47 loss: 0.2322 Lr: 0.00543
[2023-12-25 07:21:18,086 INFO misc.py line 119 253097] Train: [24/100][483/510] Data 0.005 (0.100) Batch 1.266 (1.561) Remain 16:49:22 loss: 0.1644 Lr: 0.00543
[2023-12-25 07:21:26,718 INFO misc.py line 119 253097] Train: [24/100][484/510] Data 7.436 (0.116) Batch 8.632 (1.576) Remain 16:58:50 loss: 0.2507 Lr: 0.00543
[2023-12-25 07:21:27,920 INFO misc.py line 119 253097] Train: [24/100][485/510] Data 0.004 (0.115) Batch 1.198 (1.575) Remain 16:58:18 loss: 0.5178 Lr: 0.00543
[2023-12-25 07:21:29,035 INFO misc.py line 119 253097] Train: [24/100][486/510] Data 0.008 (0.115) Batch 1.119 (1.574) Remain 16:57:40 loss: 0.2613 Lr: 0.00543
[2023-12-25 07:21:30,096 INFO misc.py line 119 253097] Train: [24/100][487/510] Data 0.003 (0.115) Batch 1.061 (1.573) Remain 16:56:57 loss: 0.1954 Lr: 0.00543
[2023-12-25 07:21:31,235 INFO misc.py line 119 253097] Train: [24/100][488/510] Data 0.004 (0.115) Batch 1.135 (1.572) Remain 16:56:21 loss: 0.1429 Lr: 0.00543
[2023-12-25 07:21:32,465 INFO misc.py line 119 253097] Train: [24/100][489/510] Data 0.007 (0.114) Batch 1.233 (1.572) Remain 16:55:52 loss: 0.3688 Lr: 0.00543
[2023-12-25 07:21:33,562 INFO misc.py line 119 253097] Train: [24/100][490/510] Data 0.005 (0.114) Batch 1.098 (1.571) Remain 16:55:13 loss: 0.5075 Lr: 0.00543
[2023-12-25 07:21:34,853 INFO misc.py line 119 253097] Train: [24/100][491/510] Data 0.004 (0.114) Batch 1.284 (1.570) Remain 16:54:48 loss: 0.2300 Lr: 0.00543
[2023-12-25 07:21:36,020 INFO misc.py line 119 253097] Train: [24/100][492/510] Data 0.011 (0.114) Batch 1.170 (1.569) Remain 16:54:15 loss: 0.2134 Lr: 0.00543
[2023-12-25 07:21:37,183 INFO misc.py line 119 253097] Train: [24/100][493/510] Data 0.007 (0.114) Batch 1.163 (1.569) Remain 16:53:41 loss: 0.3363 Lr: 0.00543
[2023-12-25 07:21:38,100 INFO misc.py line 119 253097] Train: [24/100][494/510] Data 0.007 (0.113) Batch 0.920 (1.567) Remain 16:52:49 loss: 0.2951 Lr: 0.00543
[2023-12-25 07:21:39,193 INFO misc.py line 119 253097] Train: [24/100][495/510] Data 0.005 (0.113) Batch 1.092 (1.566) Remain 16:52:10 loss: 0.3362 Lr: 0.00543
[2023-12-25 07:21:40,068 INFO misc.py line 119 253097] Train: [24/100][496/510] Data 0.005 (0.113) Batch 0.876 (1.565) Remain 16:51:14 loss: 0.2917 Lr: 0.00543
[2023-12-25 07:21:41,152 INFO misc.py line 119 253097] Train: [24/100][497/510] Data 0.004 (0.113) Batch 1.084 (1.564) Remain 16:50:34 loss: 0.2076 Lr: 0.00543
[2023-12-25 07:21:42,233 INFO misc.py line 119 253097] Train: [24/100][498/510] Data 0.005 (0.113) Batch 1.082 (1.563) Remain 16:49:55 loss: 0.4019 Lr: 0.00543
[2023-12-25 07:21:43,465 INFO misc.py line 119 253097] Train: [24/100][499/510] Data 0.005 (0.112) Batch 1.227 (1.562) Remain 16:49:27 loss: 0.3385 Lr: 0.00543
[2023-12-25 07:21:44,647 INFO misc.py line 119 253097] Train: [24/100][500/510] Data 0.009 (0.112) Batch 1.183 (1.561) Remain 16:48:56 loss: 0.1466 Lr: 0.00543
[2023-12-25 07:21:55,250 INFO misc.py line 119 253097] Train: [24/100][501/510] Data 0.008 (0.112) Batch 10.607 (1.580) Remain 17:00:39 loss: 0.1552 Lr: 0.00543
[2023-12-25 07:21:56,514 INFO misc.py line 119 253097] Train: [24/100][502/510] Data 0.005 (0.112) Batch 1.263 (1.579) Remain 17:00:13 loss: 0.1527 Lr: 0.00543
[2023-12-25 07:21:57,733 INFO misc.py line 119 253097] Train: [24/100][503/510] Data 0.004 (0.111) Batch 1.219 (1.578) Remain 16:59:43 loss: 0.2642 Lr: 0.00543
[2023-12-25 07:21:58,759 INFO misc.py line 119 253097] Train: [24/100][504/510] Data 0.006 (0.111) Batch 1.022 (1.577) Remain 16:58:58 loss: 0.1849 Lr: 0.00543
[2023-12-25 07:21:59,970 INFO misc.py line 119 253097] Train: [24/100][505/510] Data 0.009 (0.111) Batch 1.216 (1.576) Remain 16:58:29 loss: 0.1297 Lr: 0.00543
[2023-12-25 07:22:00,878 INFO misc.py line 119 253097] Train: [24/100][506/510] Data 0.005 (0.111) Batch 0.908 (1.575) Remain 16:57:36 loss: 0.1661 Lr: 0.00543
[2023-12-25 07:22:02,078 INFO misc.py line 119 253097] Train: [24/100][507/510] Data 0.005 (0.111) Batch 1.200 (1.574) Remain 16:57:05 loss: 0.2401 Lr: 0.00543
[2023-12-25 07:22:03,253 INFO misc.py line 119 253097] Train: [24/100][508/510] Data 0.004 (0.110) Batch 1.175 (1.574) Remain 16:56:33 loss: 0.6400 Lr: 0.00543
[2023-12-25 07:22:04,454 INFO misc.py line 119 253097] Train: [24/100][509/510] Data 0.006 (0.110) Batch 1.199 (1.573) Remain 16:56:03 loss: 0.3939 Lr: 0.00543
[2023-12-25 07:22:05,711 INFO misc.py line 119 253097] Train: [24/100][510/510] Data 0.005 (0.110) Batch 1.259 (1.572) Remain 16:55:37 loss: 0.2700 Lr: 0.00543
[2023-12-25 07:22:05,712 INFO misc.py line 136 253097] Train result: loss: 0.2764 
[2023-12-25 07:22:05,713 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 07:22:32,624 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5997 
[2023-12-25 07:22:32,974 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4801 
[2023-12-25 07:22:38,900 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6486 
[2023-12-25 07:22:39,431 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3529 
[2023-12-25 07:22:41,400 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8691 
[2023-12-25 07:22:41,823 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6219 
[2023-12-25 07:22:42,702 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9493 
[2023-12-25 07:22:43,259 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4585 
[2023-12-25 07:22:45,071 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.2613 
[2023-12-25 07:22:47,209 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2587 
[2023-12-25 07:22:48,075 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.4979 
[2023-12-25 07:22:48,498 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0182 
[2023-12-25 07:22:49,407 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4342 
[2023-12-25 07:22:52,357 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7484 
[2023-12-25 07:22:52,829 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4624 
[2023-12-25 07:22:53,444 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5298 
[2023-12-25 07:22:54,154 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3804 
[2023-12-25 07:22:55,641 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6232/0.7293/0.8827.
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9188/0.9353
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9764/0.9846
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8313/0.9256
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3652/0.5600
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5940/0.6137
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5262/0.6654
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7873/0.9085
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8862/0.9631
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4371/0.4655
[2023-12-25 07:22:55,641 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7363/0.8018
[2023-12-25 07:22:55,642 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.4675/0.8963
[2023-12-25 07:22:55,642 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5759/0.7615
[2023-12-25 07:22:55,642 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 07:22:55,643 INFO misc.py line 165 253097] Currently Best mIoU: 0.6552
[2023-12-25 07:22:55,643 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 07:23:08,374 INFO misc.py line 119 253097] Train: [25/100][1/510] Data 2.813 (2.813) Batch 10.060 (10.060) Remain 108:18:45 loss: 0.2344 Lr: 0.00543
[2023-12-25 07:23:09,309 INFO misc.py line 119 253097] Train: [25/100][2/510] Data 0.010 (0.010) Batch 0.940 (0.940) Remain 10:07:14 loss: 0.3394 Lr: 0.00543
[2023-12-25 07:23:10,404 INFO misc.py line 119 253097] Train: [25/100][3/510] Data 0.005 (0.005) Batch 1.095 (1.095) Remain 11:47:27 loss: 0.2313 Lr: 0.00543
[2023-12-25 07:23:11,506 INFO misc.py line 119 253097] Train: [25/100][4/510] Data 0.005 (0.005) Batch 1.102 (1.102) Remain 11:52:03 loss: 0.3228 Lr: 0.00543
[2023-12-25 07:23:12,506 INFO misc.py line 119 253097] Train: [25/100][5/510] Data 0.004 (0.005) Batch 1.000 (1.051) Remain 11:18:50 loss: 0.1997 Lr: 0.00543
[2023-12-25 07:23:13,736 INFO misc.py line 119 253097] Train: [25/100][6/510] Data 0.005 (0.005) Batch 1.230 (1.111) Remain 11:57:24 loss: 0.2697 Lr: 0.00543
[2023-12-25 07:23:14,727 INFO misc.py line 119 253097] Train: [25/100][7/510] Data 0.005 (0.005) Batch 0.991 (1.081) Remain 11:37:58 loss: 0.4079 Lr: 0.00543
[2023-12-25 07:23:15,826 INFO misc.py line 119 253097] Train: [25/100][8/510] Data 0.005 (0.005) Batch 1.099 (1.084) Remain 11:40:23 loss: 0.4089 Lr: 0.00543
[2023-12-25 07:23:17,092 INFO misc.py line 119 253097] Train: [25/100][9/510] Data 0.005 (0.005) Batch 1.263 (1.114) Remain 11:59:35 loss: 0.4181 Lr: 0.00543
[2023-12-25 07:23:18,299 INFO misc.py line 119 253097] Train: [25/100][10/510] Data 0.007 (0.005) Batch 1.210 (1.128) Remain 12:08:25 loss: 0.5706 Lr: 0.00543
[2023-12-25 07:23:19,493 INFO misc.py line 119 253097] Train: [25/100][11/510] Data 0.005 (0.005) Batch 1.194 (1.136) Remain 12:13:42 loss: 0.3899 Lr: 0.00543
[2023-12-25 07:23:20,561 INFO misc.py line 119 253097] Train: [25/100][12/510] Data 0.005 (0.005) Batch 1.068 (1.129) Remain 12:08:47 loss: 0.2295 Lr: 0.00543
[2023-12-25 07:23:21,767 INFO misc.py line 119 253097] Train: [25/100][13/510] Data 0.005 (0.005) Batch 1.207 (1.136) Remain 12:13:49 loss: 0.3282 Lr: 0.00543
[2023-12-25 07:23:22,959 INFO misc.py line 119 253097] Train: [25/100][14/510] Data 0.005 (0.005) Batch 1.192 (1.141) Remain 12:17:03 loss: 0.5150 Lr: 0.00543
[2023-12-25 07:23:32,460 INFO misc.py line 119 253097] Train: [25/100][15/510] Data 6.993 (0.587) Batch 9.502 (1.838) Remain 19:46:56 loss: 0.8340 Lr: 0.00543
[2023-12-25 07:23:33,494 INFO misc.py line 119 253097] Train: [25/100][16/510] Data 0.004 (0.542) Batch 1.034 (1.776) Remain 19:06:59 loss: 0.2325 Lr: 0.00543
[2023-12-25 07:23:34,743 INFO misc.py line 119 253097] Train: [25/100][17/510] Data 0.003 (0.504) Batch 1.247 (1.738) Remain 18:42:33 loss: 0.2898 Lr: 0.00543
[2023-12-25 07:23:36,000 INFO misc.py line 119 253097] Train: [25/100][18/510] Data 0.005 (0.471) Batch 1.255 (1.706) Remain 18:21:43 loss: 0.2672 Lr: 0.00543
[2023-12-25 07:23:36,780 INFO misc.py line 119 253097] Train: [25/100][19/510] Data 0.007 (0.442) Batch 0.783 (1.649) Remain 17:44:27 loss: 0.3311 Lr: 0.00543
[2023-12-25 07:23:37,916 INFO misc.py line 119 253097] Train: [25/100][20/510] Data 0.004 (0.416) Batch 1.135 (1.618) Remain 17:24:55 loss: 0.1990 Lr: 0.00543
[2023-12-25 07:23:45,540 INFO misc.py line 119 253097] Train: [25/100][21/510] Data 6.620 (0.761) Batch 7.625 (1.952) Remain 21:00:20 loss: 0.5402 Lr: 0.00543
[2023-12-25 07:23:46,753 INFO misc.py line 119 253097] Train: [25/100][22/510] Data 0.004 (0.721) Batch 1.210 (1.913) Remain 20:35:06 loss: 0.2244 Lr: 0.00542
[2023-12-25 07:23:47,833 INFO misc.py line 119 253097] Train: [25/100][23/510] Data 0.007 (0.685) Batch 1.080 (1.871) Remain 20:08:10 loss: 0.1765 Lr: 0.00542
[2023-12-25 07:23:48,969 INFO misc.py line 119 253097] Train: [25/100][24/510] Data 0.007 (0.653) Batch 1.137 (1.836) Remain 19:45:35 loss: 0.3125 Lr: 0.00542
[2023-12-25 07:23:50,156 INFO misc.py line 119 253097] Train: [25/100][25/510] Data 0.004 (0.623) Batch 1.188 (1.807) Remain 19:26:31 loss: 0.3076 Lr: 0.00542
[2023-12-25 07:23:51,231 INFO misc.py line 119 253097] Train: [25/100][26/510] Data 0.004 (0.596) Batch 1.075 (1.775) Remain 19:05:57 loss: 0.2669 Lr: 0.00542
[2023-12-25 07:23:52,173 INFO misc.py line 119 253097] Train: [25/100][27/510] Data 0.004 (0.572) Batch 0.941 (1.740) Remain 18:43:29 loss: 0.2016 Lr: 0.00542
[2023-12-25 07:23:53,354 INFO misc.py line 119 253097] Train: [25/100][28/510] Data 0.005 (0.549) Batch 1.182 (1.718) Remain 18:29:02 loss: 0.3396 Lr: 0.00542
[2023-12-25 07:23:54,551 INFO misc.py line 119 253097] Train: [25/100][29/510] Data 0.004 (0.528) Batch 1.196 (1.698) Remain 18:16:03 loss: 0.2716 Lr: 0.00542
[2023-12-25 07:23:55,795 INFO misc.py line 119 253097] Train: [25/100][30/510] Data 0.004 (0.509) Batch 1.238 (1.681) Remain 18:05:02 loss: 0.1551 Lr: 0.00542
[2023-12-25 07:23:58,062 INFO misc.py line 119 253097] Train: [25/100][31/510] Data 0.010 (0.491) Batch 2.272 (1.702) Remain 18:18:38 loss: 0.3074 Lr: 0.00542
[2023-12-25 07:23:58,922 INFO misc.py line 119 253097] Train: [25/100][32/510] Data 0.005 (0.474) Batch 0.861 (1.673) Remain 17:59:54 loss: 0.2130 Lr: 0.00542
[2023-12-25 07:24:00,117 INFO misc.py line 119 253097] Train: [25/100][33/510] Data 0.003 (0.458) Batch 1.191 (1.657) Remain 17:49:30 loss: 0.2514 Lr: 0.00542
[2023-12-25 07:24:01,467 INFO misc.py line 119 253097] Train: [25/100][34/510] Data 0.008 (0.444) Batch 1.353 (1.647) Remain 17:43:08 loss: 0.2860 Lr: 0.00542
[2023-12-25 07:24:02,655 INFO misc.py line 119 253097] Train: [25/100][35/510] Data 0.004 (0.430) Batch 1.185 (1.633) Remain 17:33:47 loss: 0.4806 Lr: 0.00542
[2023-12-25 07:24:03,940 INFO misc.py line 119 253097] Train: [25/100][36/510] Data 0.008 (0.417) Batch 1.280 (1.622) Remain 17:26:52 loss: 0.4702 Lr: 0.00542
[2023-12-25 07:24:05,268 INFO misc.py line 119 253097] Train: [25/100][37/510] Data 0.013 (0.405) Batch 1.336 (1.614) Remain 17:21:24 loss: 0.1562 Lr: 0.00542
[2023-12-25 07:24:06,370 INFO misc.py line 119 253097] Train: [25/100][38/510] Data 0.005 (0.394) Batch 1.102 (1.599) Remain 17:11:56 loss: 0.3558 Lr: 0.00542
[2023-12-25 07:24:14,168 INFO misc.py line 119 253097] Train: [25/100][39/510] Data 0.006 (0.383) Batch 7.796 (1.771) Remain 19:03:01 loss: 0.2871 Lr: 0.00542
[2023-12-25 07:24:15,194 INFO misc.py line 119 253097] Train: [25/100][40/510] Data 0.006 (0.373) Batch 1.028 (1.751) Remain 18:50:01 loss: 0.3213 Lr: 0.00542
[2023-12-25 07:24:16,525 INFO misc.py line 119 253097] Train: [25/100][41/510] Data 0.005 (0.363) Batch 1.331 (1.740) Remain 18:42:51 loss: 0.3283 Lr: 0.00542
[2023-12-25 07:24:17,742 INFO misc.py line 119 253097] Train: [25/100][42/510] Data 0.005 (0.354) Batch 1.215 (1.727) Remain 18:34:08 loss: 0.2583 Lr: 0.00542
[2023-12-25 07:24:19,005 INFO misc.py line 119 253097] Train: [25/100][43/510] Data 0.007 (0.345) Batch 1.263 (1.715) Remain 18:26:38 loss: 0.3306 Lr: 0.00542
[2023-12-25 07:24:20,027 INFO misc.py line 119 253097] Train: [25/100][44/510] Data 0.007 (0.337) Batch 1.019 (1.698) Remain 18:15:39 loss: 0.1409 Lr: 0.00542
[2023-12-25 07:24:21,257 INFO misc.py line 119 253097] Train: [25/100][45/510] Data 0.010 (0.329) Batch 1.233 (1.687) Remain 18:08:28 loss: 0.1662 Lr: 0.00542
[2023-12-25 07:24:22,464 INFO misc.py line 119 253097] Train: [25/100][46/510] Data 0.008 (0.322) Batch 1.206 (1.676) Remain 18:01:13 loss: 0.4982 Lr: 0.00542
[2023-12-25 07:24:23,640 INFO misc.py line 119 253097] Train: [25/100][47/510] Data 0.009 (0.315) Batch 1.178 (1.664) Remain 17:53:54 loss: 0.2305 Lr: 0.00542
[2023-12-25 07:24:24,698 INFO misc.py line 119 253097] Train: [25/100][48/510] Data 0.006 (0.308) Batch 1.052 (1.651) Remain 17:45:06 loss: 0.1318 Lr: 0.00542
[2023-12-25 07:24:25,918 INFO misc.py line 119 253097] Train: [25/100][49/510] Data 0.011 (0.301) Batch 1.229 (1.642) Remain 17:39:09 loss: 0.3317 Lr: 0.00542
[2023-12-25 07:24:26,895 INFO misc.py line 119 253097] Train: [25/100][50/510] Data 0.004 (0.295) Batch 0.975 (1.627) Remain 17:29:58 loss: 0.2869 Lr: 0.00542
[2023-12-25 07:24:31,029 INFO misc.py line 119 253097] Train: [25/100][51/510] Data 0.005 (0.289) Batch 4.136 (1.680) Remain 18:03:40 loss: 0.1998 Lr: 0.00542
[2023-12-25 07:24:32,312 INFO misc.py line 119 253097] Train: [25/100][52/510] Data 0.003 (0.283) Batch 1.278 (1.672) Remain 17:58:21 loss: 0.2668 Lr: 0.00542
[2023-12-25 07:24:33,515 INFO misc.py line 119 253097] Train: [25/100][53/510] Data 0.008 (0.278) Batch 1.207 (1.662) Remain 17:52:20 loss: 0.2180 Lr: 0.00542
[2023-12-25 07:24:36,833 INFO misc.py line 119 253097] Train: [25/100][54/510] Data 2.074 (0.313) Batch 3.312 (1.695) Remain 18:13:10 loss: 0.1611 Lr: 0.00542
[2023-12-25 07:24:39,366 INFO misc.py line 119 253097] Train: [25/100][55/510] Data 0.009 (0.307) Batch 2.538 (1.711) Remain 18:23:37 loss: 0.2146 Lr: 0.00542
[2023-12-25 07:24:40,614 INFO misc.py line 119 253097] Train: [25/100][56/510] Data 0.003 (0.301) Batch 1.247 (1.702) Remain 18:17:56 loss: 0.2443 Lr: 0.00542
[2023-12-25 07:24:41,763 INFO misc.py line 119 253097] Train: [25/100][57/510] Data 0.004 (0.296) Batch 1.149 (1.692) Remain 18:11:18 loss: 0.2806 Lr: 0.00542
[2023-12-25 07:24:42,859 INFO misc.py line 119 253097] Train: [25/100][58/510] Data 0.005 (0.291) Batch 1.091 (1.681) Remain 18:04:13 loss: 0.3141 Lr: 0.00542
[2023-12-25 07:24:44,045 INFO misc.py line 119 253097] Train: [25/100][59/510] Data 0.011 (0.286) Batch 1.188 (1.672) Remain 17:58:31 loss: 0.1469 Lr: 0.00542
[2023-12-25 07:24:45,018 INFO misc.py line 119 253097] Train: [25/100][60/510] Data 0.008 (0.281) Batch 0.977 (1.660) Remain 17:50:37 loss: 0.1729 Lr: 0.00542
[2023-12-25 07:25:02,234 INFO misc.py line 119 253097] Train: [25/100][61/510] Data 0.005 (0.276) Batch 17.215 (1.928) Remain 20:43:34 loss: 0.1746 Lr: 0.00542
[2023-12-25 07:25:03,224 INFO misc.py line 119 253097] Train: [25/100][62/510] Data 0.005 (0.271) Batch 0.992 (1.912) Remain 20:33:19 loss: 0.3731 Lr: 0.00542
[2023-12-25 07:25:04,410 INFO misc.py line 119 253097] Train: [25/100][63/510] Data 0.003 (0.267) Batch 1.185 (1.900) Remain 20:25:28 loss: 0.4027 Lr: 0.00542
[2023-12-25 07:25:05,349 INFO misc.py line 119 253097] Train: [25/100][64/510] Data 0.004 (0.263) Batch 0.939 (1.884) Remain 20:15:16 loss: 0.3104 Lr: 0.00542
[2023-12-25 07:25:06,356 INFO misc.py line 119 253097] Train: [25/100][65/510] Data 0.004 (0.258) Batch 1.007 (1.870) Remain 20:06:06 loss: 0.2950 Lr: 0.00542
[2023-12-25 07:25:07,464 INFO misc.py line 119 253097] Train: [25/100][66/510] Data 0.005 (0.254) Batch 1.108 (1.858) Remain 19:58:17 loss: 0.2873 Lr: 0.00542
[2023-12-25 07:25:08,699 INFO misc.py line 119 253097] Train: [25/100][67/510] Data 0.004 (0.250) Batch 1.234 (1.848) Remain 19:51:57 loss: 0.2245 Lr: 0.00542
[2023-12-25 07:25:09,767 INFO misc.py line 119 253097] Train: [25/100][68/510] Data 0.007 (0.247) Batch 1.069 (1.836) Remain 19:44:12 loss: 0.3899 Lr: 0.00542
[2023-12-25 07:25:10,862 INFO misc.py line 119 253097] Train: [25/100][69/510] Data 0.005 (0.243) Batch 1.095 (1.825) Remain 19:36:56 loss: 0.3132 Lr: 0.00542
[2023-12-25 07:25:11,842 INFO misc.py line 119 253097] Train: [25/100][70/510] Data 0.003 (0.239) Batch 0.977 (1.812) Remain 19:28:44 loss: 0.1089 Lr: 0.00542
[2023-12-25 07:25:12,851 INFO misc.py line 119 253097] Train: [25/100][71/510] Data 0.008 (0.236) Batch 1.011 (1.801) Remain 19:21:06 loss: 0.1808 Lr: 0.00542
[2023-12-25 07:25:14,083 INFO misc.py line 119 253097] Train: [25/100][72/510] Data 0.005 (0.233) Batch 1.233 (1.792) Remain 19:15:46 loss: 0.2269 Lr: 0.00542
[2023-12-25 07:25:15,183 INFO misc.py line 119 253097] Train: [25/100][73/510] Data 0.004 (0.229) Batch 1.099 (1.783) Remain 19:09:21 loss: 0.3325 Lr: 0.00542
[2023-12-25 07:25:16,364 INFO misc.py line 119 253097] Train: [25/100][74/510] Data 0.005 (0.226) Batch 1.182 (1.774) Remain 19:03:52 loss: 0.1900 Lr: 0.00542
[2023-12-25 07:25:17,576 INFO misc.py line 119 253097] Train: [25/100][75/510] Data 0.004 (0.223) Batch 1.212 (1.766) Remain 18:58:48 loss: 0.1211 Lr: 0.00542
[2023-12-25 07:25:18,601 INFO misc.py line 119 253097] Train: [25/100][76/510] Data 0.004 (0.220) Batch 1.025 (1.756) Remain 18:52:14 loss: 0.4199 Lr: 0.00542
[2023-12-25 07:25:19,775 INFO misc.py line 119 253097] Train: [25/100][77/510] Data 0.004 (0.217) Batch 1.174 (1.748) Remain 18:47:07 loss: 0.3454 Lr: 0.00542
[2023-12-25 07:25:20,994 INFO misc.py line 119 253097] Train: [25/100][78/510] Data 0.004 (0.214) Batch 1.218 (1.741) Remain 18:42:32 loss: 0.2063 Lr: 0.00542
[2023-12-25 07:25:22,167 INFO misc.py line 119 253097] Train: [25/100][79/510] Data 0.007 (0.212) Batch 1.174 (1.734) Remain 18:37:42 loss: 0.3028 Lr: 0.00542
[2023-12-25 07:25:23,350 INFO misc.py line 119 253097] Train: [25/100][80/510] Data 0.003 (0.209) Batch 1.183 (1.727) Remain 18:33:04 loss: 0.2932 Lr: 0.00542
[2023-12-25 07:25:24,252 INFO misc.py line 119 253097] Train: [25/100][81/510] Data 0.003 (0.206) Batch 0.900 (1.716) Remain 18:26:12 loss: 0.3596 Lr: 0.00542
[2023-12-25 07:25:25,475 INFO misc.py line 119 253097] Train: [25/100][82/510] Data 0.005 (0.204) Batch 1.224 (1.710) Remain 18:22:10 loss: 0.7047 Lr: 0.00542
[2023-12-25 07:25:26,488 INFO misc.py line 119 253097] Train: [25/100][83/510] Data 0.005 (0.201) Batch 1.012 (1.701) Remain 18:16:31 loss: 0.3364 Lr: 0.00542
[2023-12-25 07:25:27,668 INFO misc.py line 119 253097] Train: [25/100][84/510] Data 0.005 (0.199) Batch 1.181 (1.695) Remain 18:12:20 loss: 0.1062 Lr: 0.00542
[2023-12-25 07:25:28,702 INFO misc.py line 119 253097] Train: [25/100][85/510] Data 0.005 (0.197) Batch 1.034 (1.687) Remain 18:07:07 loss: 0.2078 Lr: 0.00542
[2023-12-25 07:25:29,797 INFO misc.py line 119 253097] Train: [25/100][86/510] Data 0.005 (0.194) Batch 1.088 (1.679) Remain 18:02:26 loss: 0.2452 Lr: 0.00542
[2023-12-25 07:25:30,956 INFO misc.py line 119 253097] Train: [25/100][87/510] Data 0.012 (0.192) Batch 1.166 (1.673) Remain 17:58:28 loss: 0.2156 Lr: 0.00542
[2023-12-25 07:25:32,054 INFO misc.py line 119 253097] Train: [25/100][88/510] Data 0.005 (0.190) Batch 1.099 (1.666) Remain 17:54:05 loss: 0.2786 Lr: 0.00542
[2023-12-25 07:25:33,173 INFO misc.py line 119 253097] Train: [25/100][89/510] Data 0.003 (0.188) Batch 1.119 (1.660) Remain 17:49:57 loss: 0.1154 Lr: 0.00542
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[2023-12-25 07:26:26,587 INFO misc.py line 119 253097] Train: [25/100][119/510] Data 0.004 (0.141) Batch 1.179 (1.691) Remain 18:09:10 loss: 0.4336 Lr: 0.00541
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[2023-12-25 07:26:30,022 INFO misc.py line 119 253097] Train: [25/100][122/510] Data 0.004 (0.137) Batch 1.145 (1.677) Remain 18:00:13 loss: 0.2936 Lr: 0.00541
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[2023-12-25 07:26:37,433 INFO misc.py line 119 253097] Train: [25/100][125/510] Data 0.003 (0.134) Batch 5.090 (1.697) Remain 18:12:41 loss: 0.3183 Lr: 0.00541
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[2023-12-25 07:26:46,683 INFO misc.py line 119 253097] Train: [25/100][129/510] Data 0.011 (0.130) Batch 0.973 (1.716) Remain 18:25:09 loss: 0.1701 Lr: 0.00541
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[2023-12-25 07:26:53,376 INFO misc.py line 119 253097] Train: [25/100][134/510] Data 0.010 (0.125) Batch 1.182 (1.702) Remain 18:15:42 loss: 0.4183 Lr: 0.00541
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[2023-12-25 07:26:55,634 INFO misc.py line 119 253097] Train: [25/100][136/510] Data 0.006 (0.123) Batch 1.094 (1.693) Remain 18:10:08 loss: 0.1189 Lr: 0.00541
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[2023-12-25 07:26:57,757 INFO misc.py line 119 253097] Train: [25/100][138/510] Data 0.013 (0.122) Batch 1.027 (1.684) Remain 18:04:01 loss: 0.2120 Lr: 0.00541
[2023-12-25 07:26:58,917 INFO misc.py line 119 253097] Train: [25/100][139/510] Data 0.011 (0.121) Batch 1.160 (1.680) Remain 18:01:30 loss: 0.2847 Lr: 0.00541
[2023-12-25 07:26:59,866 INFO misc.py line 119 253097] Train: [25/100][140/510] Data 0.010 (0.120) Batch 0.955 (1.675) Remain 17:58:04 loss: 0.3576 Lr: 0.00541
[2023-12-25 07:27:01,229 INFO misc.py line 119 253097] Train: [25/100][141/510] Data 0.005 (0.119) Batch 1.359 (1.673) Remain 17:56:34 loss: 0.1711 Lr: 0.00541
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[2023-12-25 07:28:16,022 INFO misc.py line 119 253097] Train: [25/100][193/510] Data 0.004 (0.120) Batch 1.272 (1.609) Remain 17:13:55 loss: 0.6369 Lr: 0.00541
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[2023-12-25 07:28:31,624 INFO misc.py line 119 253097] Train: [25/100][207/510] Data 0.010 (0.113) Batch 1.033 (1.574) Remain 16:51:41 loss: 0.3264 Lr: 0.00540
[2023-12-25 07:28:34,886 INFO misc.py line 119 253097] Train: [25/100][208/510] Data 2.262 (0.123) Batch 3.282 (1.583) Remain 16:57:01 loss: 0.3095 Lr: 0.00540
[2023-12-25 07:28:35,985 INFO misc.py line 119 253097] Train: [25/100][209/510] Data 0.006 (0.123) Batch 1.102 (1.580) Remain 16:55:29 loss: 0.1871 Lr: 0.00540
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[2023-12-25 07:28:45,272 INFO misc.py line 119 253097] Train: [25/100][211/510] Data 6.777 (0.154) Batch 8.008 (1.610) Remain 17:14:21 loss: 0.2041 Lr: 0.00540
[2023-12-25 07:28:46,432 INFO misc.py line 119 253097] Train: [25/100][212/510] Data 0.004 (0.153) Batch 1.161 (1.608) Remain 17:12:57 loss: 0.3692 Lr: 0.00540
[2023-12-25 07:28:47,761 INFO misc.py line 119 253097] Train: [25/100][213/510] Data 0.004 (0.153) Batch 1.329 (1.606) Remain 17:12:04 loss: 0.1995 Lr: 0.00540
[2023-12-25 07:28:48,776 INFO misc.py line 119 253097] Train: [25/100][214/510] Data 0.004 (0.152) Batch 1.014 (1.604) Remain 17:10:14 loss: 0.2861 Lr: 0.00540
[2023-12-25 07:28:50,065 INFO misc.py line 119 253097] Train: [25/100][215/510] Data 0.005 (0.151) Batch 1.283 (1.602) Remain 17:09:14 loss: 0.1597 Lr: 0.00540
[2023-12-25 07:28:51,317 INFO misc.py line 119 253097] Train: [25/100][216/510] Data 0.021 (0.151) Batch 1.252 (1.600) Remain 17:08:09 loss: 0.2705 Lr: 0.00540
[2023-12-25 07:28:55,628 INFO misc.py line 119 253097] Train: [25/100][217/510] Data 3.093 (0.164) Batch 4.319 (1.613) Remain 17:16:17 loss: 0.1890 Lr: 0.00540
[2023-12-25 07:28:56,822 INFO misc.py line 119 253097] Train: [25/100][218/510] Data 0.003 (0.164) Batch 1.186 (1.611) Remain 17:14:59 loss: 0.2917 Lr: 0.00540
[2023-12-25 07:28:58,029 INFO misc.py line 119 253097] Train: [25/100][219/510] Data 0.012 (0.163) Batch 1.215 (1.609) Remain 17:13:46 loss: 0.1529 Lr: 0.00540
[2023-12-25 07:28:59,101 INFO misc.py line 119 253097] Train: [25/100][220/510] Data 0.004 (0.162) Batch 1.070 (1.607) Remain 17:12:09 loss: 0.3353 Lr: 0.00540
[2023-12-25 07:29:00,370 INFO misc.py line 119 253097] Train: [25/100][221/510] Data 0.006 (0.161) Batch 1.266 (1.605) Remain 17:11:07 loss: 0.4651 Lr: 0.00540
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[2023-12-25 07:29:02,802 INFO misc.py line 119 253097] Train: [25/100][223/510] Data 0.013 (0.160) Batch 1.244 (1.602) Remain 17:08:48 loss: 0.1807 Lr: 0.00540
[2023-12-25 07:29:04,022 INFO misc.py line 119 253097] Train: [25/100][224/510] Data 0.007 (0.159) Batch 1.223 (1.600) Remain 17:07:40 loss: 0.1939 Lr: 0.00540
[2023-12-25 07:29:05,184 INFO misc.py line 119 253097] Train: [25/100][225/510] Data 0.004 (0.159) Batch 1.159 (1.598) Remain 17:06:22 loss: 0.4060 Lr: 0.00540
[2023-12-25 07:29:06,481 INFO misc.py line 119 253097] Train: [25/100][226/510] Data 0.008 (0.158) Batch 1.299 (1.597) Remain 17:05:29 loss: 0.1354 Lr: 0.00540
[2023-12-25 07:29:07,743 INFO misc.py line 119 253097] Train: [25/100][227/510] Data 0.004 (0.157) Batch 1.262 (1.595) Remain 17:04:30 loss: 0.1930 Lr: 0.00540
[2023-12-25 07:29:08,873 INFO misc.py line 119 253097] Train: [25/100][228/510] Data 0.005 (0.157) Batch 1.124 (1.593) Remain 17:03:07 loss: 0.3975 Lr: 0.00540
[2023-12-25 07:29:09,876 INFO misc.py line 119 253097] Train: [25/100][229/510] Data 0.011 (0.156) Batch 1.008 (1.591) Remain 17:01:26 loss: 0.4280 Lr: 0.00540
[2023-12-25 07:29:10,984 INFO misc.py line 119 253097] Train: [25/100][230/510] Data 0.006 (0.155) Batch 1.110 (1.588) Remain 17:00:03 loss: 0.1929 Lr: 0.00540
[2023-12-25 07:29:12,054 INFO misc.py line 119 253097] Train: [25/100][231/510] Data 0.005 (0.155) Batch 1.060 (1.586) Remain 16:58:32 loss: 0.3192 Lr: 0.00540
[2023-12-25 07:29:13,668 INFO misc.py line 119 253097] Train: [25/100][232/510] Data 0.014 (0.154) Batch 1.619 (1.586) Remain 16:58:36 loss: 0.1429 Lr: 0.00540
[2023-12-25 07:29:14,849 INFO misc.py line 119 253097] Train: [25/100][233/510] Data 0.008 (0.153) Batch 1.179 (1.585) Remain 16:57:26 loss: 0.1884 Lr: 0.00540
[2023-12-25 07:29:15,707 INFO misc.py line 119 253097] Train: [25/100][234/510] Data 0.010 (0.153) Batch 0.864 (1.581) Remain 16:55:24 loss: 0.3614 Lr: 0.00540
[2023-12-25 07:29:16,830 INFO misc.py line 119 253097] Train: [25/100][235/510] Data 0.003 (0.152) Batch 1.122 (1.579) Remain 16:54:07 loss: 0.2592 Lr: 0.00540
[2023-12-25 07:29:20,167 INFO misc.py line 119 253097] Train: [25/100][236/510] Data 2.446 (0.162) Batch 3.337 (1.587) Remain 16:58:56 loss: 0.2159 Lr: 0.00540
[2023-12-25 07:29:21,333 INFO misc.py line 119 253097] Train: [25/100][237/510] Data 0.005 (0.161) Batch 1.165 (1.585) Remain 16:57:45 loss: 0.3169 Lr: 0.00540
[2023-12-25 07:29:22,287 INFO misc.py line 119 253097] Train: [25/100][238/510] Data 0.005 (0.161) Batch 0.955 (1.582) Remain 16:56:00 loss: 0.1975 Lr: 0.00540
[2023-12-25 07:29:23,469 INFO misc.py line 119 253097] Train: [25/100][239/510] Data 0.004 (0.160) Batch 1.182 (1.581) Remain 16:54:53 loss: 0.2225 Lr: 0.00540
[2023-12-25 07:29:24,667 INFO misc.py line 119 253097] Train: [25/100][240/510] Data 0.004 (0.159) Batch 1.198 (1.579) Remain 16:53:49 loss: 0.2787 Lr: 0.00540
[2023-12-25 07:29:25,835 INFO misc.py line 119 253097] Train: [25/100][241/510] Data 0.005 (0.159) Batch 1.169 (1.577) Remain 16:52:41 loss: 0.2905 Lr: 0.00540
[2023-12-25 07:29:27,183 INFO misc.py line 119 253097] Train: [25/100][242/510] Data 0.485 (0.160) Batch 1.347 (1.576) Remain 16:52:02 loss: 0.2084 Lr: 0.00540
[2023-12-25 07:29:28,415 INFO misc.py line 119 253097] Train: [25/100][243/510] Data 0.004 (0.159) Batch 1.226 (1.575) Remain 16:51:05 loss: 0.1507 Lr: 0.00540
[2023-12-25 07:29:29,667 INFO misc.py line 119 253097] Train: [25/100][244/510] Data 0.011 (0.159) Batch 1.247 (1.574) Remain 16:50:11 loss: 0.2590 Lr: 0.00540
[2023-12-25 07:29:30,784 INFO misc.py line 119 253097] Train: [25/100][245/510] Data 0.015 (0.158) Batch 1.128 (1.572) Remain 16:48:58 loss: 0.5723 Lr: 0.00540
[2023-12-25 07:29:32,002 INFO misc.py line 119 253097] Train: [25/100][246/510] Data 0.004 (0.158) Batch 1.217 (1.570) Remain 16:48:00 loss: 0.1882 Lr: 0.00540
[2023-12-25 07:29:33,078 INFO misc.py line 119 253097] Train: [25/100][247/510] Data 0.005 (0.157) Batch 1.074 (1.568) Remain 16:46:40 loss: 0.3343 Lr: 0.00540
[2023-12-25 07:29:34,191 INFO misc.py line 119 253097] Train: [25/100][248/510] Data 0.006 (0.156) Batch 1.115 (1.566) Remain 16:45:28 loss: 0.2263 Lr: 0.00540
[2023-12-25 07:29:35,490 INFO misc.py line 119 253097] Train: [25/100][249/510] Data 0.004 (0.156) Batch 1.293 (1.565) Remain 16:44:43 loss: 0.4179 Lr: 0.00540
[2023-12-25 07:29:36,680 INFO misc.py line 119 253097] Train: [25/100][250/510] Data 0.010 (0.155) Batch 1.192 (1.564) Remain 16:43:44 loss: 0.2893 Lr: 0.00540
[2023-12-25 07:29:37,715 INFO misc.py line 119 253097] Train: [25/100][251/510] Data 0.008 (0.154) Batch 1.038 (1.562) Remain 16:42:20 loss: 0.2310 Lr: 0.00540
[2023-12-25 07:29:42,892 INFO misc.py line 119 253097] Train: [25/100][252/510] Data 3.895 (0.169) Batch 5.178 (1.576) Remain 16:51:38 loss: 0.3861 Lr: 0.00540
[2023-12-25 07:29:44,062 INFO misc.py line 119 253097] Train: [25/100][253/510] Data 0.004 (0.169) Batch 1.170 (1.575) Remain 16:50:34 loss: 0.1660 Lr: 0.00540
[2023-12-25 07:29:45,223 INFO misc.py line 119 253097] Train: [25/100][254/510] Data 0.004 (0.168) Batch 1.162 (1.573) Remain 16:49:29 loss: 0.2514 Lr: 0.00540
[2023-12-25 07:29:46,445 INFO misc.py line 119 253097] Train: [25/100][255/510] Data 0.003 (0.168) Batch 1.220 (1.572) Remain 16:48:33 loss: 0.1479 Lr: 0.00540
[2023-12-25 07:29:47,611 INFO misc.py line 119 253097] Train: [25/100][256/510] Data 0.006 (0.167) Batch 1.165 (1.570) Remain 16:47:30 loss: 0.3685 Lr: 0.00540
[2023-12-25 07:29:49,658 INFO misc.py line 119 253097] Train: [25/100][257/510] Data 1.157 (0.171) Batch 2.049 (1.572) Remain 16:48:41 loss: 0.7321 Lr: 0.00540
[2023-12-25 07:29:50,922 INFO misc.py line 119 253097] Train: [25/100][258/510] Data 0.006 (0.170) Batch 1.259 (1.571) Remain 16:47:52 loss: 0.2990 Lr: 0.00540
[2023-12-25 07:29:52,128 INFO misc.py line 119 253097] Train: [25/100][259/510] Data 0.009 (0.169) Batch 1.211 (1.569) Remain 16:46:57 loss: 0.3295 Lr: 0.00540
[2023-12-25 07:29:53,396 INFO misc.py line 119 253097] Train: [25/100][260/510] Data 0.004 (0.169) Batch 1.267 (1.568) Remain 16:46:10 loss: 0.2411 Lr: 0.00540
[2023-12-25 07:29:54,542 INFO misc.py line 119 253097] Train: [25/100][261/510] Data 0.004 (0.168) Batch 1.145 (1.566) Remain 16:45:05 loss: 0.5041 Lr: 0.00540
[2023-12-25 07:29:56,815 INFO misc.py line 119 253097] Train: [25/100][262/510] Data 1.238 (0.172) Batch 2.275 (1.569) Remain 16:46:49 loss: 0.4276 Lr: 0.00540
[2023-12-25 07:29:58,046 INFO misc.py line 119 253097] Train: [25/100][263/510] Data 0.003 (0.172) Batch 1.231 (1.568) Remain 16:45:57 loss: 0.2597 Lr: 0.00540
[2023-12-25 07:29:59,117 INFO misc.py line 119 253097] Train: [25/100][264/510] Data 0.003 (0.171) Batch 1.070 (1.566) Remain 16:44:42 loss: 0.3348 Lr: 0.00540
[2023-12-25 07:30:00,328 INFO misc.py line 119 253097] Train: [25/100][265/510] Data 0.004 (0.170) Batch 1.209 (1.565) Remain 16:43:48 loss: 0.3174 Lr: 0.00540
[2023-12-25 07:30:01,539 INFO misc.py line 119 253097] Train: [25/100][266/510] Data 0.007 (0.170) Batch 1.212 (1.563) Remain 16:42:55 loss: 0.4993 Lr: 0.00540
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[2023-12-25 07:30:03,885 INFO misc.py line 119 253097] Train: [25/100][268/510] Data 0.005 (0.169) Batch 1.134 (1.560) Remain 16:40:59 loss: 0.2492 Lr: 0.00540
[2023-12-25 07:30:04,859 INFO misc.py line 119 253097] Train: [25/100][269/510] Data 0.005 (0.168) Batch 0.972 (1.558) Remain 16:39:32 loss: 0.3882 Lr: 0.00540
[2023-12-25 07:30:05,934 INFO misc.py line 119 253097] Train: [25/100][270/510] Data 0.007 (0.167) Batch 1.077 (1.556) Remain 16:38:21 loss: 0.6254 Lr: 0.00540
[2023-12-25 07:30:06,968 INFO misc.py line 119 253097] Train: [25/100][271/510] Data 0.004 (0.167) Batch 1.035 (1.554) Remain 16:37:05 loss: 0.4081 Lr: 0.00540
[2023-12-25 07:30:08,313 INFO misc.py line 119 253097] Train: [25/100][272/510] Data 0.004 (0.166) Batch 1.340 (1.554) Remain 16:36:32 loss: 0.2076 Lr: 0.00540
[2023-12-25 07:30:09,545 INFO misc.py line 119 253097] Train: [25/100][273/510] Data 0.008 (0.166) Batch 1.237 (1.552) Remain 16:35:46 loss: 0.3430 Lr: 0.00540
[2023-12-25 07:30:14,095 INFO misc.py line 119 253097] Train: [25/100][274/510] Data 3.317 (0.177) Batch 4.550 (1.563) Remain 16:42:50 loss: 0.3241 Lr: 0.00540
[2023-12-25 07:30:15,121 INFO misc.py line 119 253097] Train: [25/100][275/510] Data 0.003 (0.177) Batch 1.026 (1.561) Remain 16:41:32 loss: 0.4877 Lr: 0.00540
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[2023-12-25 07:30:21,667 INFO misc.py line 119 253097] Train: [25/100][279/510] Data 0.004 (0.174) Batch 3.307 (1.563) Remain 16:42:08 loss: 0.3226 Lr: 0.00540
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[2023-12-25 07:30:43,442 INFO misc.py line 119 253097] Train: [25/100][298/510] Data 0.004 (0.163) Batch 1.298 (1.536) Remain 16:24:26 loss: 0.3910 Lr: 0.00539
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[2023-12-25 07:30:46,688 INFO misc.py line 119 253097] Train: [25/100][301/510] Data 0.020 (0.162) Batch 1.097 (1.531) Remain 16:21:26 loss: 0.2566 Lr: 0.00539
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[2023-12-25 07:31:00,057 INFO misc.py line 119 253097] Train: [25/100][306/510] Data 7.253 (0.183) Batch 8.484 (1.550) Remain 16:33:23 loss: 0.3253 Lr: 0.00539
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[2023-12-25 07:31:05,474 INFO misc.py line 119 253097] Train: [25/100][311/510] Data 0.005 (0.180) Batch 1.006 (1.542) Remain 16:28:24 loss: 0.3507 Lr: 0.00539
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[2023-12-25 07:31:14,949 INFO misc.py line 119 253097] Train: [25/100][318/510] Data 1.342 (0.181) Batch 2.566 (1.538) Remain 16:25:32 loss: 0.4307 Lr: 0.00539
[2023-12-25 07:31:16,029 INFO misc.py line 119 253097] Train: [25/100][319/510] Data 0.004 (0.180) Batch 1.079 (1.537) Remain 16:24:35 loss: 0.2411 Lr: 0.00539
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[2023-12-25 07:31:18,407 INFO misc.py line 119 253097] Train: [25/100][321/510] Data 0.005 (0.179) Batch 1.149 (1.535) Remain 16:23:08 loss: 0.3708 Lr: 0.00539
[2023-12-25 07:31:19,667 INFO misc.py line 119 253097] Train: [25/100][322/510] Data 0.004 (0.178) Batch 1.260 (1.534) Remain 16:22:33 loss: 0.2261 Lr: 0.00539
[2023-12-25 07:31:20,720 INFO misc.py line 119 253097] Train: [25/100][323/510] Data 0.003 (0.178) Batch 1.051 (1.532) Remain 16:21:34 loss: 0.2621 Lr: 0.00539
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[2023-12-25 07:31:22,847 INFO misc.py line 119 253097] Train: [25/100][325/510] Data 0.006 (0.177) Batch 0.981 (1.529) Remain 16:19:39 loss: 0.1539 Lr: 0.00539
[2023-12-25 07:31:24,142 INFO misc.py line 119 253097] Train: [25/100][326/510] Data 0.004 (0.176) Batch 1.290 (1.529) Remain 16:19:09 loss: 0.3712 Lr: 0.00539
[2023-12-25 07:31:25,366 INFO misc.py line 119 253097] Train: [25/100][327/510] Data 0.009 (0.176) Batch 1.227 (1.528) Remain 16:18:32 loss: 0.4551 Lr: 0.00539
[2023-12-25 07:31:26,489 INFO misc.py line 119 253097] Train: [25/100][328/510] Data 0.007 (0.175) Batch 1.123 (1.526) Remain 16:17:42 loss: 0.2294 Lr: 0.00539
[2023-12-25 07:31:27,809 INFO misc.py line 119 253097] Train: [25/100][329/510] Data 0.006 (0.175) Batch 1.319 (1.526) Remain 16:17:16 loss: 0.5150 Lr: 0.00539
[2023-12-25 07:31:28,940 INFO misc.py line 119 253097] Train: [25/100][330/510] Data 0.007 (0.174) Batch 1.134 (1.525) Remain 16:16:29 loss: 0.2912 Lr: 0.00539
[2023-12-25 07:31:35,988 INFO misc.py line 119 253097] Train: [25/100][331/510] Data 0.004 (0.174) Batch 7.049 (1.541) Remain 16:27:15 loss: 0.2919 Lr: 0.00539
[2023-12-25 07:31:37,202 INFO misc.py line 119 253097] Train: [25/100][332/510] Data 0.003 (0.173) Batch 1.213 (1.540) Remain 16:26:35 loss: 0.1883 Lr: 0.00539
[2023-12-25 07:31:38,488 INFO misc.py line 119 253097] Train: [25/100][333/510] Data 0.005 (0.173) Batch 1.282 (1.540) Remain 16:26:03 loss: 0.1843 Lr: 0.00539
[2023-12-25 07:31:39,758 INFO misc.py line 119 253097] Train: [25/100][334/510] Data 0.009 (0.172) Batch 1.275 (1.539) Remain 16:25:31 loss: 0.4755 Lr: 0.00539
[2023-12-25 07:31:40,793 INFO misc.py line 119 253097] Train: [25/100][335/510] Data 0.004 (0.172) Batch 1.031 (1.537) Remain 16:24:30 loss: 0.3583 Lr: 0.00539
[2023-12-25 07:31:42,069 INFO misc.py line 119 253097] Train: [25/100][336/510] Data 0.008 (0.171) Batch 1.277 (1.537) Remain 16:23:59 loss: 0.3299 Lr: 0.00539
[2023-12-25 07:31:43,337 INFO misc.py line 119 253097] Train: [25/100][337/510] Data 0.007 (0.171) Batch 1.267 (1.536) Remain 16:23:26 loss: 0.1539 Lr: 0.00539
[2023-12-25 07:31:44,591 INFO misc.py line 119 253097] Train: [25/100][338/510] Data 0.008 (0.170) Batch 1.254 (1.535) Remain 16:22:52 loss: 0.2550 Lr: 0.00539
[2023-12-25 07:31:45,745 INFO misc.py line 119 253097] Train: [25/100][339/510] Data 0.008 (0.170) Batch 1.155 (1.534) Remain 16:22:07 loss: 0.1340 Lr: 0.00539
[2023-12-25 07:31:46,774 INFO misc.py line 119 253097] Train: [25/100][340/510] Data 0.007 (0.169) Batch 1.027 (1.532) Remain 16:21:08 loss: 0.3423 Lr: 0.00539
[2023-12-25 07:31:47,927 INFO misc.py line 119 253097] Train: [25/100][341/510] Data 0.008 (0.169) Batch 1.157 (1.531) Remain 16:20:24 loss: 0.2773 Lr: 0.00539
[2023-12-25 07:31:49,107 INFO misc.py line 119 253097] Train: [25/100][342/510] Data 0.005 (0.168) Batch 1.180 (1.530) Remain 16:19:43 loss: 0.1776 Lr: 0.00539
[2023-12-25 07:31:50,160 INFO misc.py line 119 253097] Train: [25/100][343/510] Data 0.006 (0.168) Batch 1.054 (1.529) Remain 16:18:47 loss: 0.2524 Lr: 0.00539
[2023-12-25 07:31:51,329 INFO misc.py line 119 253097] Train: [25/100][344/510] Data 0.003 (0.167) Batch 1.164 (1.528) Remain 16:18:05 loss: 0.3694 Lr: 0.00539
[2023-12-25 07:31:52,575 INFO misc.py line 119 253097] Train: [25/100][345/510] Data 0.008 (0.167) Batch 1.250 (1.527) Remain 16:17:32 loss: 0.3462 Lr: 0.00539
[2023-12-25 07:31:53,786 INFO misc.py line 119 253097] Train: [25/100][346/510] Data 0.004 (0.166) Batch 1.211 (1.526) Remain 16:16:55 loss: 0.3110 Lr: 0.00539
[2023-12-25 07:31:57,325 INFO misc.py line 119 253097] Train: [25/100][347/510] Data 2.416 (0.173) Batch 3.539 (1.532) Remain 16:20:38 loss: 0.1290 Lr: 0.00539
[2023-12-25 07:31:58,242 INFO misc.py line 119 253097] Train: [25/100][348/510] Data 0.003 (0.172) Batch 0.918 (1.530) Remain 16:19:29 loss: 0.1956 Lr: 0.00539
[2023-12-25 07:31:59,229 INFO misc.py line 119 253097] Train: [25/100][349/510] Data 0.003 (0.172) Batch 0.985 (1.528) Remain 16:18:27 loss: 0.3369 Lr: 0.00539
[2023-12-25 07:32:00,471 INFO misc.py line 119 253097] Train: [25/100][350/510] Data 0.004 (0.171) Batch 1.239 (1.528) Remain 16:17:53 loss: 0.2777 Lr: 0.00539
[2023-12-25 07:32:01,723 INFO misc.py line 119 253097] Train: [25/100][351/510] Data 0.007 (0.171) Batch 1.250 (1.527) Remain 16:17:21 loss: 0.2966 Lr: 0.00539
[2023-12-25 07:32:02,857 INFO misc.py line 119 253097] Train: [25/100][352/510] Data 0.010 (0.170) Batch 1.133 (1.526) Remain 16:16:36 loss: 0.5128 Lr: 0.00539
[2023-12-25 07:32:03,980 INFO misc.py line 119 253097] Train: [25/100][353/510] Data 0.011 (0.170) Batch 1.130 (1.525) Remain 16:15:51 loss: 0.1989 Lr: 0.00539
[2023-12-25 07:32:05,180 INFO misc.py line 119 253097] Train: [25/100][354/510] Data 0.004 (0.169) Batch 1.197 (1.524) Remain 16:15:14 loss: 0.3304 Lr: 0.00539
[2023-12-25 07:32:06,279 INFO misc.py line 119 253097] Train: [25/100][355/510] Data 0.006 (0.169) Batch 1.101 (1.522) Remain 16:14:26 loss: 0.1757 Lr: 0.00539
[2023-12-25 07:32:07,152 INFO misc.py line 119 253097] Train: [25/100][356/510] Data 0.004 (0.169) Batch 0.874 (1.521) Remain 16:13:14 loss: 0.2159 Lr: 0.00539
[2023-12-25 07:32:08,291 INFO misc.py line 119 253097] Train: [25/100][357/510] Data 0.003 (0.168) Batch 1.138 (1.519) Remain 16:12:31 loss: 0.3983 Lr: 0.00539
[2023-12-25 07:32:09,331 INFO misc.py line 119 253097] Train: [25/100][358/510] Data 0.004 (0.168) Batch 1.040 (1.518) Remain 16:11:38 loss: 0.2299 Lr: 0.00539
[2023-12-25 07:32:10,383 INFO misc.py line 119 253097] Train: [25/100][359/510] Data 0.003 (0.167) Batch 1.052 (1.517) Remain 16:10:46 loss: 0.2424 Lr: 0.00539
[2023-12-25 07:32:11,482 INFO misc.py line 119 253097] Train: [25/100][360/510] Data 0.004 (0.167) Batch 1.099 (1.516) Remain 16:09:59 loss: 0.3369 Lr: 0.00539
[2023-12-25 07:32:12,659 INFO misc.py line 119 253097] Train: [25/100][361/510] Data 0.004 (0.166) Batch 1.177 (1.515) Remain 16:09:22 loss: 0.3246 Lr: 0.00539
[2023-12-25 07:32:13,784 INFO misc.py line 119 253097] Train: [25/100][362/510] Data 0.004 (0.166) Batch 1.125 (1.514) Remain 16:08:38 loss: 0.3869 Lr: 0.00539
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[2023-12-25 07:32:15,979 INFO misc.py line 119 253097] Train: [25/100][364/510] Data 0.005 (0.165) Batch 1.058 (1.511) Remain 16:07:07 loss: 0.2041 Lr: 0.00539
[2023-12-25 07:32:17,010 INFO misc.py line 119 253097] Train: [25/100][365/510] Data 0.004 (0.164) Batch 1.032 (1.510) Remain 16:06:14 loss: 0.2892 Lr: 0.00539
[2023-12-25 07:32:17,985 INFO misc.py line 119 253097] Train: [25/100][366/510] Data 0.003 (0.164) Batch 0.973 (1.508) Remain 16:05:16 loss: 0.2202 Lr: 0.00538
[2023-12-25 07:32:24,663 INFO misc.py line 119 253097] Train: [25/100][367/510] Data 5.727 (0.179) Batch 6.678 (1.523) Remain 16:14:20 loss: 0.3777 Lr: 0.00538
[2023-12-25 07:32:25,891 INFO misc.py line 119 253097] Train: [25/100][368/510] Data 0.006 (0.179) Batch 1.229 (1.522) Remain 16:13:47 loss: 0.1476 Lr: 0.00538
[2023-12-25 07:32:27,117 INFO misc.py line 119 253097] Train: [25/100][369/510] Data 0.004 (0.178) Batch 1.226 (1.521) Remain 16:13:15 loss: 0.2062 Lr: 0.00538
[2023-12-25 07:32:28,295 INFO misc.py line 119 253097] Train: [25/100][370/510] Data 0.005 (0.178) Batch 1.178 (1.520) Remain 16:12:38 loss: 0.2475 Lr: 0.00538
[2023-12-25 07:32:29,513 INFO misc.py line 119 253097] Train: [25/100][371/510] Data 0.004 (0.177) Batch 1.218 (1.519) Remain 16:12:05 loss: 0.3409 Lr: 0.00538
[2023-12-25 07:32:30,724 INFO misc.py line 119 253097] Train: [25/100][372/510] Data 0.004 (0.177) Batch 1.209 (1.518) Remain 16:11:31 loss: 0.2931 Lr: 0.00538
[2023-12-25 07:32:31,921 INFO misc.py line 119 253097] Train: [25/100][373/510] Data 0.006 (0.176) Batch 1.199 (1.518) Remain 16:10:56 loss: 0.1499 Lr: 0.00538
[2023-12-25 07:32:33,113 INFO misc.py line 119 253097] Train: [25/100][374/510] Data 0.005 (0.176) Batch 1.189 (1.517) Remain 16:10:20 loss: 0.1313 Lr: 0.00538
[2023-12-25 07:32:34,430 INFO misc.py line 119 253097] Train: [25/100][375/510] Data 0.008 (0.176) Batch 1.317 (1.516) Remain 16:09:58 loss: 0.3333 Lr: 0.00538
[2023-12-25 07:32:35,729 INFO misc.py line 119 253097] Train: [25/100][376/510] Data 0.008 (0.175) Batch 1.300 (1.516) Remain 16:09:35 loss: 0.3949 Lr: 0.00538
[2023-12-25 07:32:36,941 INFO misc.py line 119 253097] Train: [25/100][377/510] Data 0.006 (0.175) Batch 1.214 (1.515) Remain 16:09:02 loss: 0.2198 Lr: 0.00538
[2023-12-25 07:32:37,967 INFO misc.py line 119 253097] Train: [25/100][378/510] Data 0.004 (0.174) Batch 1.022 (1.513) Remain 16:08:10 loss: 0.3747 Lr: 0.00538
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[2023-12-25 07:32:48,733 INFO misc.py line 119 253097] Train: [25/100][381/510] Data 0.004 (0.173) Batch 1.184 (1.530) Remain 16:18:38 loss: 0.1728 Lr: 0.00538
[2023-12-25 07:32:49,904 INFO misc.py line 119 253097] Train: [25/100][382/510] Data 0.008 (0.172) Batch 1.174 (1.529) Remain 16:18:00 loss: 0.1888 Lr: 0.00538
[2023-12-25 07:32:51,160 INFO misc.py line 119 253097] Train: [25/100][383/510] Data 0.005 (0.172) Batch 1.247 (1.528) Remain 16:17:30 loss: 0.3887 Lr: 0.00538
[2023-12-25 07:32:52,449 INFO misc.py line 119 253097] Train: [25/100][384/510] Data 0.015 (0.172) Batch 1.300 (1.528) Remain 16:17:06 loss: 0.1892 Lr: 0.00538
[2023-12-25 07:32:53,491 INFO misc.py line 119 253097] Train: [25/100][385/510] Data 0.003 (0.171) Batch 1.038 (1.526) Remain 16:16:15 loss: 0.1205 Lr: 0.00538
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[2023-12-25 07:33:03,826 INFO misc.py line 119 253097] Train: [25/100][394/510] Data 0.018 (0.167) Batch 1.011 (1.518) Remain 16:10:27 loss: 0.1416 Lr: 0.00538
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[2023-12-25 07:33:06,168 INFO misc.py line 119 253097] Train: [25/100][396/510] Data 0.018 (0.167) Batch 1.234 (1.516) Remain 16:09:17 loss: 0.2355 Lr: 0.00538
[2023-12-25 07:33:07,393 INFO misc.py line 119 253097] Train: [25/100][397/510] Data 0.007 (0.166) Batch 1.221 (1.515) Remain 16:08:46 loss: 0.1415 Lr: 0.00538
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[2023-12-25 07:33:09,642 INFO misc.py line 119 253097] Train: [25/100][399/510] Data 0.003 (0.165) Batch 0.957 (1.513) Remain 16:07:28 loss: 0.3842 Lr: 0.00538
[2023-12-25 07:33:10,664 INFO misc.py line 119 253097] Train: [25/100][400/510] Data 0.005 (0.165) Batch 1.022 (1.512) Remain 16:06:39 loss: 0.2420 Lr: 0.00538
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[2023-12-25 07:33:13,934 INFO misc.py line 119 253097] Train: [25/100][403/510] Data 0.006 (0.164) Batch 1.040 (1.509) Remain 16:04:33 loss: 0.4788 Lr: 0.00538
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[2023-12-25 07:33:27,125 INFO misc.py line 119 253097] Train: [25/100][415/510] Data 0.004 (0.159) Batch 1.161 (1.497) Remain 15:56:38 loss: 0.3223 Lr: 0.00538
[2023-12-25 07:33:34,470 INFO misc.py line 119 253097] Train: [25/100][416/510] Data 0.003 (0.159) Batch 7.343 (1.511) Remain 16:05:39 loss: 0.7749 Lr: 0.00538
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[2023-12-25 07:33:43,320 INFO misc.py line 119 253097] Train: [25/100][424/510] Data 0.006 (0.156) Batch 0.926 (1.503) Remain 16:00:32 loss: 0.2756 Lr: 0.00538
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[2023-12-25 07:33:47,209 INFO misc.py line 119 253097] Train: [25/100][427/510] Data 0.004 (0.155) Batch 1.434 (1.502) Remain 15:59:32 loss: 0.2265 Lr: 0.00538
[2023-12-25 07:33:48,337 INFO misc.py line 119 253097] Train: [25/100][428/510] Data 0.006 (0.154) Batch 1.126 (1.501) Remain 15:58:56 loss: 0.3260 Lr: 0.00538
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[2023-12-25 07:33:50,833 INFO misc.py line 119 253097] Train: [25/100][430/510] Data 0.005 (0.154) Batch 1.169 (1.500) Remain 15:58:08 loss: 0.1405 Lr: 0.00538
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[2023-12-25 07:33:53,369 INFO misc.py line 119 253097] Train: [25/100][432/510] Data 0.012 (0.153) Batch 1.294 (1.499) Remain 15:57:24 loss: 0.1891 Lr: 0.00538
[2023-12-25 07:33:54,667 INFO misc.py line 119 253097] Train: [25/100][433/510] Data 0.005 (0.153) Batch 1.295 (1.498) Remain 15:57:04 loss: 0.3724 Lr: 0.00538
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[2023-12-25 07:33:57,825 INFO misc.py line 119 253097] Train: [25/100][436/510] Data 0.003 (0.152) Batch 1.139 (1.495) Remain 15:55:01 loss: 0.2695 Lr: 0.00538
[2023-12-25 07:33:58,784 INFO misc.py line 119 253097] Train: [25/100][437/510] Data 0.003 (0.151) Batch 0.959 (1.494) Remain 15:54:13 loss: 0.2083 Lr: 0.00538
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[2023-12-25 07:34:01,109 INFO misc.py line 119 253097] Train: [25/100][439/510] Data 0.005 (0.151) Batch 1.086 (1.492) Remain 15:53:11 loss: 0.1207 Lr: 0.00538
[2023-12-25 07:34:02,342 INFO misc.py line 119 253097] Train: [25/100][440/510] Data 0.004 (0.150) Batch 1.226 (1.492) Remain 15:52:47 loss: 0.2644 Lr: 0.00538
[2023-12-25 07:34:03,609 INFO misc.py line 119 253097] Train: [25/100][441/510] Data 0.011 (0.150) Batch 1.274 (1.491) Remain 15:52:26 loss: 0.1649 Lr: 0.00538
[2023-12-25 07:34:04,851 INFO misc.py line 119 253097] Train: [25/100][442/510] Data 0.003 (0.150) Batch 1.238 (1.491) Remain 15:52:02 loss: 0.1653 Lr: 0.00538
[2023-12-25 07:34:07,270 INFO misc.py line 119 253097] Train: [25/100][443/510] Data 1.376 (0.152) Batch 2.422 (1.493) Remain 15:53:22 loss: 0.2774 Lr: 0.00538
[2023-12-25 07:34:08,310 INFO misc.py line 119 253097] Train: [25/100][444/510] Data 0.004 (0.152) Batch 1.040 (1.492) Remain 15:52:41 loss: 0.3649 Lr: 0.00538
[2023-12-25 07:34:17,308 INFO misc.py line 119 253097] Train: [25/100][445/510] Data 7.732 (0.169) Batch 8.997 (1.509) Remain 16:03:30 loss: 0.1592 Lr: 0.00538
[2023-12-25 07:34:18,571 INFO misc.py line 119 253097] Train: [25/100][446/510] Data 0.004 (0.169) Batch 1.261 (1.508) Remain 16:03:07 loss: 0.2769 Lr: 0.00538
[2023-12-25 07:34:19,677 INFO misc.py line 119 253097] Train: [25/100][447/510] Data 0.006 (0.169) Batch 1.104 (1.507) Remain 16:02:31 loss: 0.1562 Lr: 0.00538
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[2023-12-25 07:34:22,711 INFO misc.py line 119 253097] Train: [25/100][450/510] Data 0.003 (0.167) Batch 1.201 (1.504) Remain 16:00:19 loss: 0.1825 Lr: 0.00537
[2023-12-25 07:34:23,788 INFO misc.py line 119 253097] Train: [25/100][451/510] Data 0.004 (0.167) Batch 1.075 (1.503) Remain 15:59:41 loss: 0.3244 Lr: 0.00537
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[2023-12-25 07:34:27,128 INFO misc.py line 119 253097] Train: [25/100][454/510] Data 0.007 (0.166) Batch 1.209 (1.500) Remain 15:57:57 loss: 0.1922 Lr: 0.00537
[2023-12-25 07:34:28,147 INFO misc.py line 119 253097] Train: [25/100][455/510] Data 0.011 (0.166) Batch 1.026 (1.499) Remain 15:57:15 loss: 0.1059 Lr: 0.00537
[2023-12-25 07:34:29,162 INFO misc.py line 119 253097] Train: [25/100][456/510] Data 0.006 (0.165) Batch 1.011 (1.498) Remain 15:56:32 loss: 0.1839 Lr: 0.00537
[2023-12-25 07:34:30,282 INFO misc.py line 119 253097] Train: [25/100][457/510] Data 0.009 (0.165) Batch 1.121 (1.498) Remain 15:55:59 loss: 0.1856 Lr: 0.00537
[2023-12-25 07:34:31,538 INFO misc.py line 119 253097] Train: [25/100][458/510] Data 0.007 (0.165) Batch 1.252 (1.497) Remain 15:55:37 loss: 0.2832 Lr: 0.00537
[2023-12-25 07:34:32,507 INFO misc.py line 119 253097] Train: [25/100][459/510] Data 0.011 (0.164) Batch 0.974 (1.496) Remain 15:54:51 loss: 0.1115 Lr: 0.00537
[2023-12-25 07:34:33,799 INFO misc.py line 119 253097] Train: [25/100][460/510] Data 0.008 (0.164) Batch 1.288 (1.495) Remain 15:54:33 loss: 0.2930 Lr: 0.00537
[2023-12-25 07:34:35,069 INFO misc.py line 119 253097] Train: [25/100][461/510] Data 0.010 (0.164) Batch 1.272 (1.495) Remain 15:54:12 loss: 0.1684 Lr: 0.00537
[2023-12-25 07:34:36,303 INFO misc.py line 119 253097] Train: [25/100][462/510] Data 0.008 (0.163) Batch 1.237 (1.494) Remain 15:53:49 loss: 0.1669 Lr: 0.00537
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[2023-12-25 07:34:42,539 INFO misc.py line 119 253097] Train: [25/100][465/510] Data 2.362 (0.167) Batch 3.637 (1.498) Remain 15:56:10 loss: 0.1803 Lr: 0.00537
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[2023-12-25 07:34:47,309 INFO misc.py line 119 253097] Train: [25/100][469/510] Data 0.005 (0.166) Batch 1.167 (1.496) Remain 15:54:24 loss: 0.2639 Lr: 0.00537
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[2023-12-25 07:34:49,393 INFO misc.py line 119 253097] Train: [25/100][471/510] Data 0.004 (0.165) Batch 1.085 (1.494) Remain 15:53:07 loss: 0.4008 Lr: 0.00537
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[2023-12-25 07:36:00,917 INFO misc.py line 119 253097] Train: [25/100][508/510] Data 0.004 (0.156) Batch 1.294 (1.526) Remain 16:12:43 loss: 0.2985 Lr: 0.00537
[2023-12-25 07:36:01,960 INFO misc.py line 119 253097] Train: [25/100][509/510] Data 0.010 (0.155) Batch 1.048 (1.525) Remain 16:12:05 loss: 0.2377 Lr: 0.00537
[2023-12-25 07:36:03,196 INFO misc.py line 119 253097] Train: [25/100][510/510] Data 0.004 (0.155) Batch 1.232 (1.524) Remain 16:11:41 loss: 0.1388 Lr: 0.00537
[2023-12-25 07:36:03,196 INFO misc.py line 136 253097] Train result: loss: 0.2798 
[2023-12-25 07:36:03,197 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 07:36:30,635 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5441 
[2023-12-25 07:36:30,984 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.6125 
[2023-12-25 07:36:35,912 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.7362 
[2023-12-25 07:36:36,433 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5406 
[2023-12-25 07:36:38,400 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8052 
[2023-12-25 07:36:38,823 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6064 
[2023-12-25 07:36:39,703 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9885 
[2023-12-25 07:36:40,264 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4769 
[2023-12-25 07:36:42,075 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9641 
[2023-12-25 07:36:44,197 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1768 
[2023-12-25 07:36:45,050 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3266 
[2023-12-25 07:36:45,472 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9390 
[2023-12-25 07:36:46,373 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5211 
[2023-12-25 07:36:49,318 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7557 
[2023-12-25 07:36:49,785 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3300 
[2023-12-25 07:36:50,395 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.6469 
[2023-12-25 07:36:51,100 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4065 
[2023-12-25 07:36:52,892 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6009/0.6853/0.8847.
[2023-12-25 07:36:52,892 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9196/0.9525
[2023-12-25 07:36:52,892 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9799/0.9915
[2023-12-25 07:36:52,892 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8260/0.9509
[2023-12-25 07:36:52,892 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 07:36:52,892 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3299/0.4554
[2023-12-25 07:36:52,892 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6176/0.6460
[2023-12-25 07:36:52,892 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.4157/0.4924
[2023-12-25 07:36:52,892 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8124/0.8958
[2023-12-25 07:36:52,893 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8850/0.9692
[2023-12-25 07:36:52,893 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.1557/0.1559
[2023-12-25 07:36:52,893 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7334/0.7915
[2023-12-25 07:36:52,893 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.5521/0.8599
[2023-12-25 07:36:52,893 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5838/0.7483
[2023-12-25 07:36:52,893 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 07:36:52,894 INFO misc.py line 165 253097] Currently Best mIoU: 0.6552
[2023-12-25 07:36:52,894 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 07:37:07,294 INFO misc.py line 119 253097] Train: [26/100][1/510] Data 10.985 (10.985) Batch 12.215 (12.215) Remain 129:46:47 loss: 0.4182 Lr: 0.00537
[2023-12-25 07:37:08,248 INFO misc.py line 119 253097] Train: [26/100][2/510] Data 0.005 (0.005) Batch 0.955 (0.955) Remain 10:08:47 loss: 0.2928 Lr: 0.00537
[2023-12-25 07:37:09,296 INFO misc.py line 119 253097] Train: [26/100][3/510] Data 0.003 (0.003) Batch 1.047 (1.047) Remain 11:07:12 loss: 0.1104 Lr: 0.00537
[2023-12-25 07:37:10,237 INFO misc.py line 119 253097] Train: [26/100][4/510] Data 0.005 (0.005) Batch 0.941 (0.941) Remain 09:59:37 loss: 0.3238 Lr: 0.00537
[2023-12-25 07:37:11,397 INFO misc.py line 119 253097] Train: [26/100][5/510] Data 0.007 (0.006) Batch 1.160 (1.050) Remain 11:09:31 loss: 0.2391 Lr: 0.00537
[2023-12-25 07:37:14,042 INFO misc.py line 119 253097] Train: [26/100][6/510] Data 0.005 (0.006) Batch 2.646 (1.582) Remain 16:48:29 loss: 0.2131 Lr: 0.00537
[2023-12-25 07:37:16,125 INFO misc.py line 119 253097] Train: [26/100][7/510] Data 1.053 (0.268) Batch 2.083 (1.707) Remain 18:08:14 loss: 0.2566 Lr: 0.00537
[2023-12-25 07:37:17,436 INFO misc.py line 119 253097] Train: [26/100][8/510] Data 0.004 (0.215) Batch 1.308 (1.628) Remain 17:17:20 loss: 0.3049 Lr: 0.00537
[2023-12-25 07:37:18,527 INFO misc.py line 119 253097] Train: [26/100][9/510] Data 0.007 (0.180) Batch 1.092 (1.538) Remain 16:20:25 loss: 0.4076 Lr: 0.00537
[2023-12-25 07:37:19,673 INFO misc.py line 119 253097] Train: [26/100][10/510] Data 0.006 (0.155) Batch 1.148 (1.483) Remain 15:44:53 loss: 0.1903 Lr: 0.00537
[2023-12-25 07:37:20,708 INFO misc.py line 119 253097] Train: [26/100][11/510] Data 0.003 (0.136) Batch 1.029 (1.426) Remain 15:08:43 loss: 0.2347 Lr: 0.00537
[2023-12-25 07:37:21,822 INFO misc.py line 119 253097] Train: [26/100][12/510] Data 0.010 (0.122) Batch 1.079 (1.387) Remain 14:44:10 loss: 0.2737 Lr: 0.00537
[2023-12-25 07:37:22,934 INFO misc.py line 119 253097] Train: [26/100][13/510] Data 0.044 (0.114) Batch 1.147 (1.363) Remain 14:28:48 loss: 0.2875 Lr: 0.00537
[2023-12-25 07:37:24,093 INFO misc.py line 119 253097] Train: [26/100][14/510] Data 0.010 (0.105) Batch 1.163 (1.345) Remain 14:17:10 loss: 0.6639 Lr: 0.00537
[2023-12-25 07:37:25,347 INFO misc.py line 119 253097] Train: [26/100][15/510] Data 0.007 (0.097) Batch 1.255 (1.338) Remain 14:12:21 loss: 0.3052 Lr: 0.00537
[2023-12-25 07:37:26,535 INFO misc.py line 119 253097] Train: [26/100][16/510] Data 0.005 (0.090) Batch 1.189 (1.326) Remain 14:05:03 loss: 0.2916 Lr: 0.00537
[2023-12-25 07:37:27,659 INFO misc.py line 119 253097] Train: [26/100][17/510] Data 0.003 (0.084) Batch 1.123 (1.312) Remain 13:55:48 loss: 0.1940 Lr: 0.00537
[2023-12-25 07:37:28,867 INFO misc.py line 119 253097] Train: [26/100][18/510] Data 0.004 (0.078) Batch 1.207 (1.305) Remain 13:51:20 loss: 0.3760 Lr: 0.00537
[2023-12-25 07:37:30,130 INFO misc.py line 119 253097] Train: [26/100][19/510] Data 0.006 (0.074) Batch 1.264 (1.302) Remain 13:49:41 loss: 0.2328 Lr: 0.00537
[2023-12-25 07:37:31,333 INFO misc.py line 119 253097] Train: [26/100][20/510] Data 0.004 (0.070) Batch 1.202 (1.296) Remain 13:45:55 loss: 0.0914 Lr: 0.00537
[2023-12-25 07:37:32,316 INFO misc.py line 119 253097] Train: [26/100][21/510] Data 0.006 (0.066) Batch 0.981 (1.279) Remain 13:34:45 loss: 0.6669 Lr: 0.00537
[2023-12-25 07:37:39,082 INFO misc.py line 119 253097] Train: [26/100][22/510] Data 0.007 (0.063) Batch 6.769 (1.568) Remain 16:38:50 loss: 0.1786 Lr: 0.00537
[2023-12-25 07:37:40,139 INFO misc.py line 119 253097] Train: [26/100][23/510] Data 0.005 (0.060) Batch 1.057 (1.542) Remain 16:22:33 loss: 0.1555 Lr: 0.00537
[2023-12-25 07:37:41,457 INFO misc.py line 119 253097] Train: [26/100][24/510] Data 0.004 (0.057) Batch 1.312 (1.531) Remain 16:15:32 loss: 0.4223 Lr: 0.00536
[2023-12-25 07:37:42,540 INFO misc.py line 119 253097] Train: [26/100][25/510] Data 0.010 (0.055) Batch 1.087 (1.511) Remain 16:02:37 loss: 0.3400 Lr: 0.00536
[2023-12-25 07:37:43,469 INFO misc.py line 119 253097] Train: [26/100][26/510] Data 0.006 (0.053) Batch 0.931 (1.486) Remain 15:46:31 loss: 0.2306 Lr: 0.00536
[2023-12-25 07:37:44,544 INFO misc.py line 119 253097] Train: [26/100][27/510] Data 0.005 (0.051) Batch 1.076 (1.469) Remain 15:35:37 loss: 0.3483 Lr: 0.00536
[2023-12-25 07:37:45,556 INFO misc.py line 119 253097] Train: [26/100][28/510] Data 0.004 (0.049) Batch 1.012 (1.450) Remain 15:23:58 loss: 0.2181 Lr: 0.00536
[2023-12-25 07:37:46,823 INFO misc.py line 119 253097] Train: [26/100][29/510] Data 0.003 (0.047) Batch 1.237 (1.442) Remain 15:18:42 loss: 0.1651 Lr: 0.00536
[2023-12-25 07:37:47,918 INFO misc.py line 119 253097] Train: [26/100][30/510] Data 0.034 (0.047) Batch 1.123 (1.430) Remain 15:11:08 loss: 0.2115 Lr: 0.00536
[2023-12-25 07:37:49,146 INFO misc.py line 119 253097] Train: [26/100][31/510] Data 0.007 (0.046) Batch 1.229 (1.423) Remain 15:06:31 loss: 0.3628 Lr: 0.00536
[2023-12-25 07:37:50,403 INFO misc.py line 119 253097] Train: [26/100][32/510] Data 0.007 (0.044) Batch 1.256 (1.417) Remain 15:02:49 loss: 0.7396 Lr: 0.00536
[2023-12-25 07:37:51,536 INFO misc.py line 119 253097] Train: [26/100][33/510] Data 0.007 (0.043) Batch 1.131 (1.408) Remain 14:56:43 loss: 0.2106 Lr: 0.00536
[2023-12-25 07:37:52,819 INFO misc.py line 119 253097] Train: [26/100][34/510] Data 0.009 (0.042) Batch 1.280 (1.404) Remain 14:54:04 loss: 0.3047 Lr: 0.00536
[2023-12-25 07:37:53,958 INFO misc.py line 119 253097] Train: [26/100][35/510] Data 0.012 (0.041) Batch 1.139 (1.395) Remain 14:48:46 loss: 0.1682 Lr: 0.00536
[2023-12-25 07:37:54,966 INFO misc.py line 119 253097] Train: [26/100][36/510] Data 0.012 (0.040) Batch 1.013 (1.384) Remain 14:41:22 loss: 0.6552 Lr: 0.00536
[2023-12-25 07:37:56,163 INFO misc.py line 119 253097] Train: [26/100][37/510] Data 0.008 (0.039) Batch 1.194 (1.378) Remain 14:37:47 loss: 0.2211 Lr: 0.00536
[2023-12-25 07:37:57,126 INFO misc.py line 119 253097] Train: [26/100][38/510] Data 0.010 (0.038) Batch 0.970 (1.367) Remain 14:30:20 loss: 0.4137 Lr: 0.00536
[2023-12-25 07:38:05,555 INFO misc.py line 119 253097] Train: [26/100][39/510] Data 0.003 (0.037) Batch 8.428 (1.563) Remain 16:35:14 loss: 0.2705 Lr: 0.00536
[2023-12-25 07:38:06,728 INFO misc.py line 119 253097] Train: [26/100][40/510] Data 0.006 (0.036) Batch 1.173 (1.552) Remain 16:28:29 loss: 0.2900 Lr: 0.00536
[2023-12-25 07:38:08,009 INFO misc.py line 119 253097] Train: [26/100][41/510] Data 0.005 (0.036) Batch 1.280 (1.545) Remain 16:23:54 loss: 0.1971 Lr: 0.00536
[2023-12-25 07:38:09,124 INFO misc.py line 119 253097] Train: [26/100][42/510] Data 0.006 (0.035) Batch 1.114 (1.534) Remain 16:16:50 loss: 0.2226 Lr: 0.00536
[2023-12-25 07:38:10,186 INFO misc.py line 119 253097] Train: [26/100][43/510] Data 0.007 (0.034) Batch 1.065 (1.522) Remain 16:09:20 loss: 0.2604 Lr: 0.00536
[2023-12-25 07:38:11,485 INFO misc.py line 119 253097] Train: [26/100][44/510] Data 0.004 (0.033) Batch 1.300 (1.517) Remain 16:05:51 loss: 0.2289 Lr: 0.00536
[2023-12-25 07:38:13,490 INFO misc.py line 119 253097] Train: [26/100][45/510] Data 0.004 (0.033) Batch 2.005 (1.528) Remain 16:13:13 loss: 0.4883 Lr: 0.00536
[2023-12-25 07:38:14,751 INFO misc.py line 119 253097] Train: [26/100][46/510] Data 0.006 (0.032) Batch 1.254 (1.522) Remain 16:09:08 loss: 0.2522 Lr: 0.00536
[2023-12-25 07:38:15,881 INFO misc.py line 119 253097] Train: [26/100][47/510] Data 0.012 (0.032) Batch 1.135 (1.513) Remain 16:03:30 loss: 0.1314 Lr: 0.00536
[2023-12-25 07:38:17,097 INFO misc.py line 119 253097] Train: [26/100][48/510] Data 0.007 (0.031) Batch 1.218 (1.507) Remain 15:59:18 loss: 0.3200 Lr: 0.00536
[2023-12-25 07:38:18,027 INFO misc.py line 119 253097] Train: [26/100][49/510] Data 0.005 (0.031) Batch 0.932 (1.494) Remain 15:51:19 loss: 0.3478 Lr: 0.00536
[2023-12-25 07:38:19,129 INFO misc.py line 119 253097] Train: [26/100][50/510] Data 0.004 (0.030) Batch 1.101 (1.486) Remain 15:45:58 loss: 0.3122 Lr: 0.00536
[2023-12-25 07:38:20,342 INFO misc.py line 119 253097] Train: [26/100][51/510] Data 0.004 (0.029) Batch 1.211 (1.480) Remain 15:42:18 loss: 0.3358 Lr: 0.00536
[2023-12-25 07:38:21,435 INFO misc.py line 119 253097] Train: [26/100][52/510] Data 0.007 (0.029) Batch 1.095 (1.472) Remain 15:37:16 loss: 0.2803 Lr: 0.00536
[2023-12-25 07:38:22,604 INFO misc.py line 119 253097] Train: [26/100][53/510] Data 0.005 (0.028) Batch 1.166 (1.466) Remain 15:33:21 loss: 0.1973 Lr: 0.00536
[2023-12-25 07:38:23,890 INFO misc.py line 119 253097] Train: [26/100][54/510] Data 0.007 (0.028) Batch 1.283 (1.463) Remain 15:31:02 loss: 0.2155 Lr: 0.00536
[2023-12-25 07:38:30,754 INFO misc.py line 119 253097] Train: [26/100][55/510] Data 5.657 (0.136) Batch 6.870 (1.567) Remain 16:37:12 loss: 0.3975 Lr: 0.00536
[2023-12-25 07:38:31,804 INFO misc.py line 119 253097] Train: [26/100][56/510] Data 0.005 (0.134) Batch 1.050 (1.557) Remain 16:30:58 loss: 0.2603 Lr: 0.00536
[2023-12-25 07:38:32,866 INFO misc.py line 119 253097] Train: [26/100][57/510] Data 0.004 (0.131) Batch 1.060 (1.548) Remain 16:25:05 loss: 0.1514 Lr: 0.00536
[2023-12-25 07:38:33,896 INFO misc.py line 119 253097] Train: [26/100][58/510] Data 0.006 (0.129) Batch 1.032 (1.538) Remain 16:19:06 loss: 0.4049 Lr: 0.00536
[2023-12-25 07:38:34,787 INFO misc.py line 119 253097] Train: [26/100][59/510] Data 0.005 (0.127) Batch 0.891 (1.527) Remain 16:11:43 loss: 0.2972 Lr: 0.00536
[2023-12-25 07:38:35,737 INFO misc.py line 119 253097] Train: [26/100][60/510] Data 0.005 (0.125) Batch 0.950 (1.517) Remain 16:05:15 loss: 0.1296 Lr: 0.00536
[2023-12-25 07:38:36,952 INFO misc.py line 119 253097] Train: [26/100][61/510] Data 0.005 (0.123) Batch 1.213 (1.511) Remain 16:01:54 loss: 0.3696 Lr: 0.00536
[2023-12-25 07:38:38,187 INFO misc.py line 119 253097] Train: [26/100][62/510] Data 0.006 (0.121) Batch 1.237 (1.507) Remain 15:58:55 loss: 0.1306 Lr: 0.00536
[2023-12-25 07:38:39,181 INFO misc.py line 119 253097] Train: [26/100][63/510] Data 0.004 (0.119) Batch 0.995 (1.498) Remain 15:53:28 loss: 0.3333 Lr: 0.00536
[2023-12-25 07:38:40,158 INFO misc.py line 119 253097] Train: [26/100][64/510] Data 0.003 (0.117) Batch 0.976 (1.490) Remain 15:48:00 loss: 0.4282 Lr: 0.00536
[2023-12-25 07:38:41,381 INFO misc.py line 119 253097] Train: [26/100][65/510] Data 0.004 (0.115) Batch 1.218 (1.485) Remain 15:45:11 loss: 0.4481 Lr: 0.00536
[2023-12-25 07:38:42,596 INFO misc.py line 119 253097] Train: [26/100][66/510] Data 0.008 (0.113) Batch 1.220 (1.481) Remain 15:42:29 loss: 0.2653 Lr: 0.00536
[2023-12-25 07:38:43,886 INFO misc.py line 119 253097] Train: [26/100][67/510] Data 0.003 (0.112) Batch 1.286 (1.478) Remain 15:40:31 loss: 0.1830 Lr: 0.00536
[2023-12-25 07:38:44,945 INFO misc.py line 119 253097] Train: [26/100][68/510] Data 0.008 (0.110) Batch 1.058 (1.471) Remain 15:36:23 loss: 0.1491 Lr: 0.00536
[2023-12-25 07:38:46,215 INFO misc.py line 119 253097] Train: [26/100][69/510] Data 0.009 (0.109) Batch 1.274 (1.468) Remain 15:34:27 loss: 0.2472 Lr: 0.00536
[2023-12-25 07:38:47,515 INFO misc.py line 119 253097] Train: [26/100][70/510] Data 0.005 (0.107) Batch 1.299 (1.466) Remain 15:32:49 loss: 0.3090 Lr: 0.00536
[2023-12-25 07:38:54,349 INFO misc.py line 119 253097] Train: [26/100][71/510] Data 0.005 (0.105) Batch 6.836 (1.545) Remain 16:23:03 loss: 0.2627 Lr: 0.00536
[2023-12-25 07:38:55,419 INFO misc.py line 119 253097] Train: [26/100][72/510] Data 0.004 (0.104) Batch 1.068 (1.538) Remain 16:18:38 loss: 0.1192 Lr: 0.00536
[2023-12-25 07:38:56,571 INFO misc.py line 119 253097] Train: [26/100][73/510] Data 0.005 (0.103) Batch 1.153 (1.533) Remain 16:15:06 loss: 0.1757 Lr: 0.00536
[2023-12-25 07:38:57,719 INFO misc.py line 119 253097] Train: [26/100][74/510] Data 0.005 (0.101) Batch 1.148 (1.527) Remain 16:11:38 loss: 0.2227 Lr: 0.00536
[2023-12-25 07:38:58,932 INFO misc.py line 119 253097] Train: [26/100][75/510] Data 0.004 (0.100) Batch 1.212 (1.523) Remain 16:08:49 loss: 0.3233 Lr: 0.00536
[2023-12-25 07:38:59,952 INFO misc.py line 119 253097] Train: [26/100][76/510] Data 0.004 (0.099) Batch 1.021 (1.516) Remain 16:04:25 loss: 0.3103 Lr: 0.00536
[2023-12-25 07:39:01,129 INFO misc.py line 119 253097] Train: [26/100][77/510] Data 0.004 (0.097) Batch 1.177 (1.511) Remain 16:01:29 loss: 0.2085 Lr: 0.00536
[2023-12-25 07:39:02,311 INFO misc.py line 119 253097] Train: [26/100][78/510] Data 0.004 (0.096) Batch 1.182 (1.507) Remain 15:58:40 loss: 0.2885 Lr: 0.00536
[2023-12-25 07:39:03,429 INFO misc.py line 119 253097] Train: [26/100][79/510] Data 0.004 (0.095) Batch 1.119 (1.502) Remain 15:55:23 loss: 0.2889 Lr: 0.00536
[2023-12-25 07:39:04,402 INFO misc.py line 119 253097] Train: [26/100][80/510] Data 0.003 (0.094) Batch 0.973 (1.495) Remain 15:51:00 loss: 0.3007 Lr: 0.00536
[2023-12-25 07:39:05,578 INFO misc.py line 119 253097] Train: [26/100][81/510] Data 0.003 (0.092) Batch 1.175 (1.491) Remain 15:48:22 loss: 0.2419 Lr: 0.00536
[2023-12-25 07:39:06,871 INFO misc.py line 119 253097] Train: [26/100][82/510] Data 0.004 (0.091) Batch 1.289 (1.488) Remain 15:46:43 loss: 0.1249 Lr: 0.00536
[2023-12-25 07:39:07,907 INFO misc.py line 119 253097] Train: [26/100][83/510] Data 0.008 (0.090) Batch 1.037 (1.483) Remain 15:43:06 loss: 0.2245 Lr: 0.00536
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[2023-12-25 07:39:29,088 INFO misc.py line 119 253097] Train: [26/100][101/510] Data 0.012 (0.077) Batch 1.200 (1.426) Remain 15:06:57 loss: 0.3098 Lr: 0.00536
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[2023-12-25 07:39:31,194 INFO misc.py line 119 253097] Train: [26/100][103/510] Data 0.006 (0.076) Batch 1.136 (1.419) Remain 15:02:10 loss: 0.2952 Lr: 0.00536
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[2023-12-25 07:39:41,159 INFO misc.py line 119 253097] Train: [26/100][109/510] Data 0.009 (0.072) Batch 1.182 (1.433) Remain 15:10:41 loss: 0.3497 Lr: 0.00535
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[2023-12-25 07:39:45,851 INFO misc.py line 119 253097] Train: [26/100][113/510] Data 0.011 (0.069) Batch 1.230 (1.423) Remain 15:04:35 loss: 0.2567 Lr: 0.00535
[2023-12-25 07:39:46,989 INFO misc.py line 119 253097] Train: [26/100][114/510] Data 0.010 (0.069) Batch 1.140 (1.421) Remain 15:02:56 loss: 0.3258 Lr: 0.00535
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[2023-12-25 07:39:59,279 INFO misc.py line 119 253097] Train: [26/100][119/510] Data 0.005 (0.066) Batch 7.787 (1.465) Remain 15:31:16 loss: 0.3960 Lr: 0.00535
[2023-12-25 07:40:00,366 INFO misc.py line 119 253097] Train: [26/100][120/510] Data 0.004 (0.065) Batch 1.087 (1.462) Remain 15:29:11 loss: 0.2660 Lr: 0.00535
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[2023-12-25 07:40:03,959 INFO misc.py line 119 253097] Train: [26/100][123/510] Data 0.007 (0.064) Batch 0.973 (1.456) Remain 15:24:54 loss: 0.1797 Lr: 0.00535
[2023-12-25 07:40:04,960 INFO misc.py line 119 253097] Train: [26/100][124/510] Data 0.003 (0.064) Batch 1.001 (1.452) Remain 15:22:30 loss: 0.3031 Lr: 0.00535
[2023-12-25 07:40:06,234 INFO misc.py line 119 253097] Train: [26/100][125/510] Data 0.004 (0.063) Batch 1.274 (1.450) Remain 15:21:33 loss: 0.1821 Lr: 0.00535
[2023-12-25 07:40:07,082 INFO misc.py line 119 253097] Train: [26/100][126/510] Data 0.004 (0.063) Batch 0.847 (1.445) Remain 15:18:24 loss: 0.1192 Lr: 0.00535
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[2023-12-25 07:40:09,278 INFO misc.py line 119 253097] Train: [26/100][128/510] Data 0.004 (0.062) Batch 1.093 (1.440) Remain 15:14:50 loss: 0.2973 Lr: 0.00535
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[2023-12-25 07:40:12,707 INFO misc.py line 119 253097] Train: [26/100][131/510] Data 0.008 (0.060) Batch 0.974 (1.433) Remain 15:10:20 loss: 0.1843 Lr: 0.00535
[2023-12-25 07:40:13,737 INFO misc.py line 119 253097] Train: [26/100][132/510] Data 0.005 (0.060) Batch 1.031 (1.430) Remain 15:08:20 loss: 0.3749 Lr: 0.00535
[2023-12-25 07:40:14,899 INFO misc.py line 119 253097] Train: [26/100][133/510] Data 0.003 (0.059) Batch 1.162 (1.428) Remain 15:07:00 loss: 0.3463 Lr: 0.00535
[2023-12-25 07:40:16,114 INFO misc.py line 119 253097] Train: [26/100][134/510] Data 0.004 (0.059) Batch 1.212 (1.426) Remain 15:05:56 loss: 0.4961 Lr: 0.00535
[2023-12-25 07:40:17,858 INFO misc.py line 119 253097] Train: [26/100][135/510] Data 0.007 (0.059) Batch 1.747 (1.429) Remain 15:07:27 loss: 0.1506 Lr: 0.00535
[2023-12-25 07:40:19,113 INFO misc.py line 119 253097] Train: [26/100][136/510] Data 0.003 (0.058) Batch 1.253 (1.427) Remain 15:06:35 loss: 0.2734 Lr: 0.00535
[2023-12-25 07:40:20,028 INFO misc.py line 119 253097] Train: [26/100][137/510] Data 0.006 (0.058) Batch 0.916 (1.423) Remain 15:04:08 loss: 0.2526 Lr: 0.00535
[2023-12-25 07:40:21,078 INFO misc.py line 119 253097] Train: [26/100][138/510] Data 0.005 (0.057) Batch 1.052 (1.421) Remain 15:02:22 loss: 0.1708 Lr: 0.00535
[2023-12-25 07:40:22,315 INFO misc.py line 119 253097] Train: [26/100][139/510] Data 0.003 (0.057) Batch 1.236 (1.419) Remain 15:01:29 loss: 0.2087 Lr: 0.00535
[2023-12-25 07:40:23,106 INFO misc.py line 119 253097] Train: [26/100][140/510] Data 0.005 (0.057) Batch 0.791 (1.415) Remain 14:58:33 loss: 0.1611 Lr: 0.00535
[2023-12-25 07:40:29,608 INFO misc.py line 119 253097] Train: [26/100][141/510] Data 5.284 (0.094) Batch 6.501 (1.452) Remain 15:21:56 loss: 0.3188 Lr: 0.00535
[2023-12-25 07:40:30,740 INFO misc.py line 119 253097] Train: [26/100][142/510] Data 0.007 (0.094) Batch 1.134 (1.449) Remain 15:20:27 loss: 0.1877 Lr: 0.00535
[2023-12-25 07:40:35,078 INFO misc.py line 119 253097] Train: [26/100][143/510] Data 0.003 (0.093) Batch 4.337 (1.470) Remain 15:33:32 loss: 0.2075 Lr: 0.00535
[2023-12-25 07:40:36,217 INFO misc.py line 119 253097] Train: [26/100][144/510] Data 0.005 (0.093) Batch 1.140 (1.468) Remain 15:32:01 loss: 0.2112 Lr: 0.00535
[2023-12-25 07:40:37,385 INFO misc.py line 119 253097] Train: [26/100][145/510] Data 0.005 (0.092) Batch 1.164 (1.465) Remain 15:30:38 loss: 0.3969 Lr: 0.00535
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[2023-12-25 07:40:39,625 INFO misc.py line 119 253097] Train: [26/100][147/510] Data 0.006 (0.091) Batch 1.170 (1.461) Remain 15:27:34 loss: 0.2841 Lr: 0.00535
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[2023-12-25 07:40:43,511 INFO misc.py line 119 253097] Train: [26/100][149/510] Data 0.007 (0.090) Batch 2.736 (1.467) Remain 15:31:42 loss: 0.1810 Lr: 0.00535
[2023-12-25 07:40:44,656 INFO misc.py line 119 253097] Train: [26/100][150/510] Data 0.006 (0.089) Batch 1.134 (1.465) Remain 15:30:14 loss: 0.3068 Lr: 0.00535
[2023-12-25 07:40:45,757 INFO misc.py line 119 253097] Train: [26/100][151/510] Data 0.015 (0.089) Batch 1.108 (1.463) Remain 15:28:41 loss: 0.2064 Lr: 0.00535
[2023-12-25 07:40:46,719 INFO misc.py line 119 253097] Train: [26/100][152/510] Data 0.008 (0.088) Batch 0.966 (1.459) Remain 15:26:33 loss: 0.2972 Lr: 0.00535
[2023-12-25 07:40:47,745 INFO misc.py line 119 253097] Train: [26/100][153/510] Data 0.004 (0.087) Batch 1.026 (1.456) Remain 15:24:41 loss: 0.1272 Lr: 0.00535
[2023-12-25 07:40:49,006 INFO misc.py line 119 253097] Train: [26/100][154/510] Data 0.005 (0.087) Batch 1.252 (1.455) Remain 15:23:48 loss: 0.2197 Lr: 0.00535
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[2023-12-25 07:40:51,581 INFO misc.py line 119 253097] Train: [26/100][156/510] Data 0.005 (0.086) Batch 1.114 (1.453) Remain 15:22:23 loss: 0.1716 Lr: 0.00535
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[2023-12-25 07:40:54,989 INFO misc.py line 119 253097] Train: [26/100][159/510] Data 0.004 (0.084) Batch 1.114 (1.447) Remain 15:18:27 loss: 0.3457 Lr: 0.00535
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[2023-12-25 07:40:56,985 INFO misc.py line 119 253097] Train: [26/100][161/510] Data 0.007 (0.083) Batch 0.962 (1.441) Remain 15:14:48 loss: 0.1408 Lr: 0.00535
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[2023-12-25 07:41:29,976 INFO misc.py line 119 253097] Train: [26/100][182/510] Data 0.003 (0.074) Batch 1.018 (1.456) Remain 15:23:58 loss: 0.2878 Lr: 0.00535
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[2023-12-25 07:42:04,794 INFO misc.py line 119 253097] Train: [26/100][207/510] Data 0.006 (0.066) Batch 0.987 (1.449) Remain 15:18:26 loss: 0.1093 Lr: 0.00534
[2023-12-25 07:42:05,856 INFO misc.py line 119 253097] Train: [26/100][208/510] Data 0.004 (0.066) Batch 1.062 (1.447) Remain 15:17:13 loss: 0.2960 Lr: 0.00534
[2023-12-25 07:42:06,705 INFO misc.py line 119 253097] Train: [26/100][209/510] Data 0.004 (0.065) Batch 0.849 (1.444) Remain 15:15:21 loss: 0.2791 Lr: 0.00534
[2023-12-25 07:42:07,773 INFO misc.py line 119 253097] Train: [26/100][210/510] Data 0.003 (0.065) Batch 1.063 (1.442) Remain 15:14:09 loss: 0.2479 Lr: 0.00534
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[2023-12-25 07:42:09,851 INFO misc.py line 119 253097] Train: [26/100][212/510] Data 0.005 (0.064) Batch 0.999 (1.438) Remain 15:11:41 loss: 0.3958 Lr: 0.00534
[2023-12-25 07:42:11,023 INFO misc.py line 119 253097] Train: [26/100][213/510] Data 0.003 (0.064) Batch 1.172 (1.437) Remain 15:10:51 loss: 0.1559 Lr: 0.00534
[2023-12-25 07:42:21,625 INFO misc.py line 119 253097] Train: [26/100][214/510] Data 0.003 (0.064) Batch 10.600 (1.480) Remain 15:38:21 loss: 0.2098 Lr: 0.00534
[2023-12-25 07:42:22,709 INFO misc.py line 119 253097] Train: [26/100][215/510] Data 0.007 (0.063) Batch 1.080 (1.478) Remain 15:37:08 loss: 0.2281 Lr: 0.00534
[2023-12-25 07:42:23,895 INFO misc.py line 119 253097] Train: [26/100][216/510] Data 0.010 (0.063) Batch 1.185 (1.477) Remain 15:36:14 loss: 0.3098 Lr: 0.00534
[2023-12-25 07:42:25,070 INFO misc.py line 119 253097] Train: [26/100][217/510] Data 0.011 (0.063) Batch 1.177 (1.476) Remain 15:35:19 loss: 0.1771 Lr: 0.00534
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[2023-12-25 07:42:27,330 INFO misc.py line 119 253097] Train: [26/100][219/510] Data 0.011 (0.063) Batch 1.154 (1.472) Remain 15:33:14 loss: 0.4211 Lr: 0.00534
[2023-12-25 07:42:28,624 INFO misc.py line 119 253097] Train: [26/100][220/510] Data 0.011 (0.062) Batch 1.300 (1.472) Remain 15:32:43 loss: 0.1631 Lr: 0.00534
[2023-12-25 07:42:29,805 INFO misc.py line 119 253097] Train: [26/100][221/510] Data 0.005 (0.062) Batch 1.179 (1.470) Remain 15:31:50 loss: 0.1566 Lr: 0.00534
[2023-12-25 07:42:31,030 INFO misc.py line 119 253097] Train: [26/100][222/510] Data 0.007 (0.062) Batch 1.227 (1.469) Remain 15:31:07 loss: 0.1982 Lr: 0.00534
[2023-12-25 07:42:34,140 INFO misc.py line 119 253097] Train: [26/100][223/510] Data 1.904 (0.070) Batch 3.111 (1.477) Remain 15:35:49 loss: 0.2772 Lr: 0.00534
[2023-12-25 07:42:35,417 INFO misc.py line 119 253097] Train: [26/100][224/510] Data 0.003 (0.070) Batch 1.277 (1.476) Remain 15:35:13 loss: 0.2361 Lr: 0.00534
[2023-12-25 07:42:36,508 INFO misc.py line 119 253097] Train: [26/100][225/510] Data 0.003 (0.070) Batch 1.088 (1.474) Remain 15:34:05 loss: 0.2225 Lr: 0.00534
[2023-12-25 07:42:37,641 INFO misc.py line 119 253097] Train: [26/100][226/510] Data 0.007 (0.069) Batch 1.136 (1.472) Remain 15:33:06 loss: 0.3484 Lr: 0.00534
[2023-12-25 07:42:38,963 INFO misc.py line 119 253097] Train: [26/100][227/510] Data 0.004 (0.069) Batch 1.316 (1.472) Remain 15:32:38 loss: 0.2756 Lr: 0.00534
[2023-12-25 07:42:40,064 INFO misc.py line 119 253097] Train: [26/100][228/510] Data 0.009 (0.069) Batch 1.101 (1.470) Remain 15:31:34 loss: 0.1244 Lr: 0.00534
[2023-12-25 07:42:41,323 INFO misc.py line 119 253097] Train: [26/100][229/510] Data 0.009 (0.068) Batch 1.264 (1.469) Remain 15:30:58 loss: 0.2514 Lr: 0.00534
[2023-12-25 07:42:42,353 INFO misc.py line 119 253097] Train: [26/100][230/510] Data 0.004 (0.068) Batch 1.027 (1.467) Remain 15:29:42 loss: 0.3207 Lr: 0.00534
[2023-12-25 07:42:43,527 INFO misc.py line 119 253097] Train: [26/100][231/510] Data 0.008 (0.068) Batch 1.177 (1.466) Remain 15:28:52 loss: 0.1420 Lr: 0.00534
[2023-12-25 07:42:44,670 INFO misc.py line 119 253097] Train: [26/100][232/510] Data 0.004 (0.068) Batch 1.140 (1.465) Remain 15:27:57 loss: 0.2420 Lr: 0.00534
[2023-12-25 07:42:48,165 INFO misc.py line 119 253097] Train: [26/100][233/510] Data 0.008 (0.067) Batch 3.496 (1.473) Remain 15:33:31 loss: 0.3409 Lr: 0.00534
[2023-12-25 07:42:49,371 INFO misc.py line 119 253097] Train: [26/100][234/510] Data 0.006 (0.067) Batch 1.208 (1.472) Remain 15:32:46 loss: 0.1435 Lr: 0.00534
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[2023-12-25 07:42:51,560 INFO misc.py line 119 253097] Train: [26/100][236/510] Data 0.004 (0.067) Batch 1.195 (1.469) Remain 15:30:40 loss: 0.2710 Lr: 0.00534
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[2023-12-25 07:42:53,773 INFO misc.py line 119 253097] Train: [26/100][238/510] Data 0.009 (0.066) Batch 1.099 (1.466) Remain 15:28:40 loss: 0.2084 Lr: 0.00534
[2023-12-25 07:42:55,101 INFO misc.py line 119 253097] Train: [26/100][239/510] Data 0.003 (0.066) Batch 1.328 (1.465) Remain 15:28:16 loss: 0.2621 Lr: 0.00534
[2023-12-25 07:42:56,269 INFO misc.py line 119 253097] Train: [26/100][240/510] Data 0.004 (0.065) Batch 1.166 (1.464) Remain 15:27:27 loss: 0.2771 Lr: 0.00534
[2023-12-25 07:42:57,329 INFO misc.py line 119 253097] Train: [26/100][241/510] Data 0.006 (0.065) Batch 1.059 (1.462) Remain 15:26:20 loss: 0.2846 Lr: 0.00534
[2023-12-25 07:42:58,337 INFO misc.py line 119 253097] Train: [26/100][242/510] Data 0.008 (0.065) Batch 1.009 (1.460) Remain 15:25:07 loss: 0.1154 Lr: 0.00534
[2023-12-25 07:42:59,493 INFO misc.py line 119 253097] Train: [26/100][243/510] Data 0.007 (0.065) Batch 1.155 (1.459) Remain 15:24:17 loss: 0.5680 Lr: 0.00534
[2023-12-25 07:43:00,747 INFO misc.py line 119 253097] Train: [26/100][244/510] Data 0.008 (0.065) Batch 1.255 (1.458) Remain 15:23:43 loss: 0.2899 Lr: 0.00534
[2023-12-25 07:43:01,793 INFO misc.py line 119 253097] Train: [26/100][245/510] Data 0.007 (0.064) Batch 1.048 (1.457) Remain 15:22:37 loss: 0.3133 Lr: 0.00534
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[2023-12-25 07:43:04,043 INFO misc.py line 119 253097] Train: [26/100][247/510] Data 0.016 (0.064) Batch 1.083 (1.454) Remain 15:20:51 loss: 0.1953 Lr: 0.00534
[2023-12-25 07:43:05,089 INFO misc.py line 119 253097] Train: [26/100][248/510] Data 0.007 (0.064) Batch 1.049 (1.452) Remain 15:19:47 loss: 0.4356 Lr: 0.00534
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[2023-12-25 07:43:07,460 INFO misc.py line 119 253097] Train: [26/100][250/510] Data 0.004 (0.063) Batch 1.144 (1.450) Remain 15:18:22 loss: 0.3726 Lr: 0.00534
[2023-12-25 07:43:08,551 INFO misc.py line 119 253097] Train: [26/100][251/510] Data 0.004 (0.063) Batch 1.091 (1.449) Remain 15:17:25 loss: 0.1997 Lr: 0.00534
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[2023-12-25 07:43:13,542 INFO misc.py line 119 253097] Train: [26/100][255/510] Data 0.007 (0.062) Batch 1.347 (1.445) Remain 15:15:18 loss: 0.1883 Lr: 0.00534
[2023-12-25 07:43:14,833 INFO misc.py line 119 253097] Train: [26/100][256/510] Data 0.003 (0.062) Batch 1.288 (1.445) Remain 15:14:53 loss: 0.2145 Lr: 0.00534
[2023-12-25 07:43:15,901 INFO misc.py line 119 253097] Train: [26/100][257/510] Data 0.006 (0.062) Batch 1.066 (1.443) Remain 15:13:55 loss: 0.5912 Lr: 0.00534
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[2023-12-25 07:43:17,985 INFO misc.py line 119 253097] Train: [26/100][259/510] Data 0.008 (0.061) Batch 1.061 (1.440) Remain 15:11:53 loss: 0.2795 Lr: 0.00534
[2023-12-25 07:43:20,340 INFO misc.py line 119 253097] Train: [26/100][260/510] Data 1.161 (0.065) Batch 2.359 (1.444) Remain 15:14:08 loss: 0.1882 Lr: 0.00534
[2023-12-25 07:43:21,417 INFO misc.py line 119 253097] Train: [26/100][261/510] Data 0.004 (0.065) Batch 1.076 (1.442) Remain 15:13:12 loss: 0.2114 Lr: 0.00534
[2023-12-25 07:43:22,631 INFO misc.py line 119 253097] Train: [26/100][262/510] Data 0.003 (0.065) Batch 1.213 (1.441) Remain 15:12:37 loss: 0.0998 Lr: 0.00534
[2023-12-25 07:43:23,734 INFO misc.py line 119 253097] Train: [26/100][263/510] Data 0.004 (0.065) Batch 1.103 (1.440) Remain 15:11:46 loss: 0.1728 Lr: 0.00534
[2023-12-25 07:43:24,969 INFO misc.py line 119 253097] Train: [26/100][264/510] Data 0.005 (0.064) Batch 1.234 (1.439) Remain 15:11:15 loss: 0.1029 Lr: 0.00534
[2023-12-25 07:43:26,184 INFO misc.py line 119 253097] Train: [26/100][265/510] Data 0.006 (0.064) Batch 1.216 (1.439) Remain 15:10:41 loss: 0.2766 Lr: 0.00534
[2023-12-25 07:43:27,173 INFO misc.py line 119 253097] Train: [26/100][266/510] Data 0.005 (0.064) Batch 0.988 (1.437) Remain 15:09:35 loss: 0.2121 Lr: 0.00534
[2023-12-25 07:43:28,209 INFO misc.py line 119 253097] Train: [26/100][267/510] Data 0.006 (0.064) Batch 1.038 (1.435) Remain 15:08:36 loss: 0.3137 Lr: 0.00534
[2023-12-25 07:43:29,498 INFO misc.py line 119 253097] Train: [26/100][268/510] Data 0.004 (0.064) Batch 1.289 (1.435) Remain 15:08:13 loss: 0.3385 Lr: 0.00534
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[2023-12-25 07:43:31,789 INFO misc.py line 119 253097] Train: [26/100][270/510] Data 0.008 (0.063) Batch 1.061 (1.433) Remain 15:06:47 loss: 0.3241 Lr: 0.00534
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[2023-12-25 07:47:44,427 INFO misc.py line 119 253097] Train: [26/100][433/510] Data 0.007 (0.048) Batch 1.095 (1.477) Remain 15:30:57 loss: 0.2660 Lr: 0.00532
[2023-12-25 07:47:45,624 INFO misc.py line 119 253097] Train: [26/100][434/510] Data 0.004 (0.047) Batch 1.190 (1.476) Remain 15:30:30 loss: 0.2540 Lr: 0.00532
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[2023-12-25 07:47:48,233 INFO misc.py line 119 253097] Train: [26/100][436/510] Data 0.006 (0.047) Batch 1.109 (1.476) Remain 15:29:58 loss: 0.2012 Lr: 0.00531
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[2023-12-25 07:48:09,828 INFO misc.py line 119 253097] Train: [26/100][448/510] Data 6.433 (0.061) Batch 7.487 (1.484) Remain 15:35:11 loss: 0.6930 Lr: 0.00531
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[2023-12-25 07:48:14,799 INFO misc.py line 119 253097] Train: [26/100][452/510] Data 0.009 (0.060) Batch 1.203 (1.482) Remain 15:33:43 loss: 0.2640 Lr: 0.00531
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[2023-12-25 07:48:18,278 INFO misc.py line 119 253097] Train: [26/100][455/510] Data 0.005 (0.060) Batch 1.308 (1.480) Remain 15:32:18 loss: 0.3934 Lr: 0.00531
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[2023-12-25 07:48:34,900 INFO misc.py line 119 253097] Train: [26/100][460/510] Data 10.505 (0.082) Batch 11.712 (1.500) Remain 15:44:53 loss: 0.7697 Lr: 0.00531
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[2023-12-25 07:48:40,103 INFO misc.py line 119 253097] Train: [26/100][465/510] Data 0.003 (0.081) Batch 0.962 (1.495) Remain 15:41:38 loss: 0.3167 Lr: 0.00531
[2023-12-25 07:48:42,743 INFO misc.py line 119 253097] Train: [26/100][466/510] Data 0.003 (0.081) Batch 2.640 (1.498) Remain 15:43:10 loss: 0.2485 Lr: 0.00531
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[2023-12-25 07:48:49,572 INFO misc.py line 119 253097] Train: [26/100][472/510] Data 0.005 (0.080) Batch 1.255 (1.493) Remain 15:40:07 loss: 0.2088 Lr: 0.00531
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[2023-12-25 07:49:30,385 INFO misc.py line 119 253097] Train: [26/100][502/510] Data 0.007 (0.088) Batch 1.151 (1.485) Remain 15:34:21 loss: 0.2933 Lr: 0.00531
[2023-12-25 07:49:36,312 INFO misc.py line 119 253097] Train: [26/100][503/510] Data 0.006 (0.088) Batch 5.928 (1.494) Remain 15:39:55 loss: 0.1863 Lr: 0.00531
[2023-12-25 07:49:37,593 INFO misc.py line 119 253097] Train: [26/100][504/510] Data 0.005 (0.088) Batch 1.280 (1.494) Remain 15:39:37 loss: 0.4375 Lr: 0.00531
[2023-12-25 07:49:38,768 INFO misc.py line 119 253097] Train: [26/100][505/510] Data 0.007 (0.087) Batch 1.177 (1.493) Remain 15:39:12 loss: 0.4547 Lr: 0.00531
[2023-12-25 07:49:39,970 INFO misc.py line 119 253097] Train: [26/100][506/510] Data 0.004 (0.087) Batch 1.194 (1.492) Remain 15:38:48 loss: 0.2954 Lr: 0.00531
[2023-12-25 07:49:41,083 INFO misc.py line 119 253097] Train: [26/100][507/510] Data 0.013 (0.087) Batch 1.121 (1.492) Remain 15:38:18 loss: 0.2509 Lr: 0.00531
[2023-12-25 07:49:42,378 INFO misc.py line 119 253097] Train: [26/100][508/510] Data 0.004 (0.087) Batch 1.294 (1.491) Remain 15:38:02 loss: 0.4041 Lr: 0.00531
[2023-12-25 07:49:43,575 INFO misc.py line 119 253097] Train: [26/100][509/510] Data 0.005 (0.087) Batch 1.199 (1.491) Remain 15:37:39 loss: 0.1808 Lr: 0.00531
[2023-12-25 07:49:44,802 INFO misc.py line 119 253097] Train: [26/100][510/510] Data 0.004 (0.087) Batch 1.222 (1.490) Remain 15:37:17 loss: 0.2682 Lr: 0.00531
[2023-12-25 07:49:44,803 INFO misc.py line 136 253097] Train result: loss: 0.2682 
[2023-12-25 07:49:44,804 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 07:50:12,923 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.8365 
[2023-12-25 07:50:13,270 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4128 
[2023-12-25 07:50:18,205 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5898 
[2023-12-25 07:50:18,728 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5065 
[2023-12-25 07:50:20,701 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8429 
[2023-12-25 07:50:21,134 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6418 
[2023-12-25 07:50:22,012 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1487 
[2023-12-25 07:50:22,569 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3269 
[2023-12-25 07:50:24,377 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8898 
[2023-12-25 07:50:26,497 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2603 
[2023-12-25 07:50:27,351 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2615 
[2023-12-25 07:50:27,776 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0648 
[2023-12-25 07:50:28,678 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4291 
[2023-12-25 07:50:31,631 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8706 
[2023-12-25 07:50:32,099 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4530 
[2023-12-25 07:50:32,708 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5179 
[2023-12-25 07:50:33,407 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5327 
[2023-12-25 07:50:34,696 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6460/0.7038/0.8838.
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9161/0.9341
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9697/0.9749
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8098/0.9650
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.1777/0.1842
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5845/0.6037
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5710/0.6094
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7843/0.8370
[2023-12-25 07:50:34,696 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8852/0.9228
[2023-12-25 07:50:34,697 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6934/0.7651
[2023-12-25 07:50:34,697 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7397/0.8124
[2023-12-25 07:50:34,697 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6979/0.7407
[2023-12-25 07:50:34,697 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5687/0.8000
[2023-12-25 07:50:34,697 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 07:50:34,699 INFO misc.py line 165 253097] Currently Best mIoU: 0.6552
[2023-12-25 07:50:34,699 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 07:50:59,224 INFO misc.py line 119 253097] Train: [27/100][1/510] Data 21.092 (21.092) Batch 22.025 (22.025) Remain 230:53:23 loss: 0.1742 Lr: 0.00531
[2023-12-25 07:51:00,292 INFO misc.py line 119 253097] Train: [27/100][2/510] Data 0.004 (0.004) Batch 1.067 (1.067) Remain 11:11:19 loss: 0.3065 Lr: 0.00531
[2023-12-25 07:51:01,343 INFO misc.py line 119 253097] Train: [27/100][3/510] Data 0.004 (0.004) Batch 1.050 (1.050) Remain 11:00:24 loss: 0.3719 Lr: 0.00531
[2023-12-25 07:51:02,574 INFO misc.py line 119 253097] Train: [27/100][4/510] Data 0.006 (0.006) Batch 1.228 (1.228) Remain 12:52:10 loss: 0.2323 Lr: 0.00531
[2023-12-25 07:51:03,609 INFO misc.py line 119 253097] Train: [27/100][5/510] Data 0.009 (0.007) Batch 1.041 (1.134) Remain 11:53:18 loss: 0.2192 Lr: 0.00531
[2023-12-25 07:51:04,684 INFO misc.py line 119 253097] Train: [27/100][6/510] Data 0.003 (0.006) Batch 1.067 (1.112) Remain 11:39:15 loss: 0.2957 Lr: 0.00531
[2023-12-25 07:51:08,438 INFO misc.py line 119 253097] Train: [27/100][7/510] Data 2.499 (0.629) Batch 3.761 (1.774) Remain 18:35:45 loss: 0.1915 Lr: 0.00530
[2023-12-25 07:51:09,534 INFO misc.py line 119 253097] Train: [27/100][8/510] Data 0.003 (0.504) Batch 1.096 (1.638) Remain 17:10:22 loss: 0.1746 Lr: 0.00530
[2023-12-25 07:51:10,636 INFO misc.py line 119 253097] Train: [27/100][9/510] Data 0.005 (0.421) Batch 1.102 (1.549) Remain 16:14:08 loss: 0.1992 Lr: 0.00530
[2023-12-25 07:51:11,786 INFO misc.py line 119 253097] Train: [27/100][10/510] Data 0.003 (0.361) Batch 1.150 (1.492) Remain 15:38:17 loss: 0.2447 Lr: 0.00530
[2023-12-25 07:51:13,029 INFO misc.py line 119 253097] Train: [27/100][11/510] Data 0.004 (0.316) Batch 1.239 (1.461) Remain 15:18:23 loss: 0.2510 Lr: 0.00530
[2023-12-25 07:51:14,129 INFO misc.py line 119 253097] Train: [27/100][12/510] Data 0.007 (0.282) Batch 1.098 (1.420) Remain 14:53:04 loss: 0.2298 Lr: 0.00530
[2023-12-25 07:51:15,078 INFO misc.py line 119 253097] Train: [27/100][13/510] Data 0.008 (0.255) Batch 0.954 (1.374) Remain 14:23:44 loss: 0.2539 Lr: 0.00530
[2023-12-25 07:51:16,082 INFO misc.py line 119 253097] Train: [27/100][14/510] Data 0.002 (0.232) Batch 1.003 (1.340) Remain 14:02:30 loss: 0.2027 Lr: 0.00530
[2023-12-25 07:51:17,178 INFO misc.py line 119 253097] Train: [27/100][15/510] Data 0.004 (0.213) Batch 1.097 (1.320) Remain 13:49:44 loss: 0.4902 Lr: 0.00530
[2023-12-25 07:51:18,447 INFO misc.py line 119 253097] Train: [27/100][16/510] Data 0.004 (0.197) Batch 1.270 (1.316) Remain 13:47:18 loss: 0.4558 Lr: 0.00530
[2023-12-25 07:51:19,586 INFO misc.py line 119 253097] Train: [27/100][17/510] Data 0.004 (0.183) Batch 1.138 (1.303) Remain 13:39:17 loss: 0.4117 Lr: 0.00530
[2023-12-25 07:51:24,992 INFO misc.py line 119 253097] Train: [27/100][18/510] Data 0.005 (0.171) Batch 5.407 (1.577) Remain 16:31:16 loss: 0.1485 Lr: 0.00530
[2023-12-25 07:51:26,144 INFO misc.py line 119 253097] Train: [27/100][19/510] Data 0.003 (0.161) Batch 1.152 (1.550) Remain 16:14:33 loss: 0.2674 Lr: 0.00530
[2023-12-25 07:51:27,389 INFO misc.py line 119 253097] Train: [27/100][20/510] Data 0.004 (0.151) Batch 1.244 (1.532) Remain 16:03:13 loss: 0.3879 Lr: 0.00530
[2023-12-25 07:51:28,372 INFO misc.py line 119 253097] Train: [27/100][21/510] Data 0.005 (0.143) Batch 0.984 (1.502) Remain 15:44:03 loss: 0.3972 Lr: 0.00530
[2023-12-25 07:51:29,705 INFO misc.py line 119 253097] Train: [27/100][22/510] Data 0.004 (0.136) Batch 1.331 (1.493) Remain 15:38:22 loss: 0.1977 Lr: 0.00530
[2023-12-25 07:51:30,874 INFO misc.py line 119 253097] Train: [27/100][23/510] Data 0.006 (0.129) Batch 1.170 (1.477) Remain 15:28:12 loss: 0.1859 Lr: 0.00530
[2023-12-25 07:51:31,883 INFO misc.py line 119 253097] Train: [27/100][24/510] Data 0.005 (0.123) Batch 1.006 (1.454) Remain 15:14:05 loss: 0.2583 Lr: 0.00530
[2023-12-25 07:51:32,940 INFO misc.py line 119 253097] Train: [27/100][25/510] Data 0.008 (0.118) Batch 1.061 (1.436) Remain 15:02:50 loss: 0.4100 Lr: 0.00530
[2023-12-25 07:51:34,039 INFO misc.py line 119 253097] Train: [27/100][26/510] Data 0.003 (0.113) Batch 1.092 (1.421) Remain 14:53:24 loss: 0.3199 Lr: 0.00530
[2023-12-25 07:51:35,152 INFO misc.py line 119 253097] Train: [27/100][27/510] Data 0.010 (0.109) Batch 1.116 (1.409) Remain 14:45:24 loss: 0.2033 Lr: 0.00530
[2023-12-25 07:51:36,242 INFO misc.py line 119 253097] Train: [27/100][28/510] Data 0.007 (0.105) Batch 1.086 (1.396) Remain 14:37:16 loss: 0.7140 Lr: 0.00530
[2023-12-25 07:51:37,405 INFO misc.py line 119 253097] Train: [27/100][29/510] Data 0.011 (0.101) Batch 1.169 (1.387) Remain 14:31:45 loss: 0.2118 Lr: 0.00530
[2023-12-25 07:51:38,468 INFO misc.py line 119 253097] Train: [27/100][30/510] Data 0.006 (0.098) Batch 1.064 (1.375) Remain 14:24:13 loss: 0.1276 Lr: 0.00530
[2023-12-25 07:51:39,652 INFO misc.py line 119 253097] Train: [27/100][31/510] Data 0.004 (0.094) Batch 1.180 (1.368) Remain 14:19:49 loss: 0.1363 Lr: 0.00530
[2023-12-25 07:51:42,657 INFO misc.py line 119 253097] Train: [27/100][32/510] Data 0.008 (0.091) Batch 3.008 (1.425) Remain 14:55:21 loss: 0.2556 Lr: 0.00530
[2023-12-25 07:51:43,953 INFO misc.py line 119 253097] Train: [27/100][33/510] Data 0.004 (0.088) Batch 1.293 (1.420) Remain 14:52:35 loss: 0.2393 Lr: 0.00530
[2023-12-25 07:51:45,075 INFO misc.py line 119 253097] Train: [27/100][34/510] Data 0.006 (0.086) Batch 1.121 (1.411) Remain 14:46:30 loss: 0.1534 Lr: 0.00530
[2023-12-25 07:51:46,241 INFO misc.py line 119 253097] Train: [27/100][35/510] Data 0.006 (0.083) Batch 1.170 (1.403) Remain 14:41:44 loss: 0.2112 Lr: 0.00530
[2023-12-25 07:51:47,306 INFO misc.py line 119 253097] Train: [27/100][36/510] Data 0.003 (0.081) Batch 1.064 (1.393) Remain 14:35:15 loss: 0.2733 Lr: 0.00530
[2023-12-25 07:51:48,378 INFO misc.py line 119 253097] Train: [27/100][37/510] Data 0.005 (0.079) Batch 1.071 (1.383) Remain 14:29:17 loss: 0.1793 Lr: 0.00530
[2023-12-25 07:51:49,605 INFO misc.py line 119 253097] Train: [27/100][38/510] Data 0.006 (0.077) Batch 1.228 (1.379) Remain 14:26:28 loss: 0.2132 Lr: 0.00530
[2023-12-25 07:51:50,736 INFO misc.py line 119 253097] Train: [27/100][39/510] Data 0.004 (0.075) Batch 1.131 (1.372) Remain 14:22:07 loss: 0.1852 Lr: 0.00530
[2023-12-25 07:51:51,713 INFO misc.py line 119 253097] Train: [27/100][40/510] Data 0.004 (0.073) Batch 0.979 (1.361) Remain 14:15:25 loss: 0.3557 Lr: 0.00530
[2023-12-25 07:51:52,910 INFO misc.py line 119 253097] Train: [27/100][41/510] Data 0.003 (0.071) Batch 1.197 (1.357) Remain 14:12:40 loss: 0.4246 Lr: 0.00530
[2023-12-25 07:51:54,084 INFO misc.py line 119 253097] Train: [27/100][42/510] Data 0.004 (0.069) Batch 1.173 (1.352) Remain 14:09:41 loss: 0.3020 Lr: 0.00530
[2023-12-25 07:51:55,180 INFO misc.py line 119 253097] Train: [27/100][43/510] Data 0.004 (0.067) Batch 1.096 (1.346) Remain 14:05:38 loss: 0.2777 Lr: 0.00530
[2023-12-25 07:51:56,357 INFO misc.py line 119 253097] Train: [27/100][44/510] Data 0.004 (0.066) Batch 1.176 (1.342) Remain 14:03:00 loss: 0.5353 Lr: 0.00530
[2023-12-25 07:51:57,465 INFO misc.py line 119 253097] Train: [27/100][45/510] Data 0.005 (0.064) Batch 1.108 (1.336) Remain 13:59:29 loss: 0.2497 Lr: 0.00530
[2023-12-25 07:51:58,591 INFO misc.py line 119 253097] Train: [27/100][46/510] Data 0.005 (0.063) Batch 1.127 (1.331) Remain 13:56:25 loss: 0.2467 Lr: 0.00530
[2023-12-25 07:51:59,678 INFO misc.py line 119 253097] Train: [27/100][47/510] Data 0.003 (0.062) Batch 1.087 (1.326) Remain 13:52:54 loss: 0.3330 Lr: 0.00530
[2023-12-25 07:52:00,916 INFO misc.py line 119 253097] Train: [27/100][48/510] Data 0.004 (0.060) Batch 1.234 (1.324) Remain 13:51:35 loss: 0.2743 Lr: 0.00530
[2023-12-25 07:52:02,062 INFO misc.py line 119 253097] Train: [27/100][49/510] Data 0.008 (0.059) Batch 1.149 (1.320) Remain 13:49:11 loss: 0.4013 Lr: 0.00530
[2023-12-25 07:52:03,147 INFO misc.py line 119 253097] Train: [27/100][50/510] Data 0.005 (0.058) Batch 1.069 (1.315) Remain 13:45:48 loss: 0.0900 Lr: 0.00530
[2023-12-25 07:52:04,323 INFO misc.py line 119 253097] Train: [27/100][51/510] Data 0.022 (0.057) Batch 1.193 (1.312) Remain 13:44:11 loss: 0.2347 Lr: 0.00530
[2023-12-25 07:52:24,048 INFO misc.py line 119 253097] Train: [27/100][52/510] Data 0.005 (0.056) Batch 19.726 (1.688) Remain 17:40:12 loss: 0.1602 Lr: 0.00530
[2023-12-25 07:52:25,333 INFO misc.py line 119 253097] Train: [27/100][53/510] Data 0.005 (0.055) Batch 1.283 (1.680) Remain 17:35:05 loss: 0.2346 Lr: 0.00530
[2023-12-25 07:52:26,589 INFO misc.py line 119 253097] Train: [27/100][54/510] Data 0.006 (0.054) Batch 1.259 (1.672) Remain 17:29:53 loss: 0.1588 Lr: 0.00530
[2023-12-25 07:52:27,773 INFO misc.py line 119 253097] Train: [27/100][55/510] Data 0.004 (0.053) Batch 1.178 (1.662) Remain 17:23:53 loss: 0.1569 Lr: 0.00530
[2023-12-25 07:52:28,832 INFO misc.py line 119 253097] Train: [27/100][56/510] Data 0.010 (0.053) Batch 1.058 (1.651) Remain 17:16:42 loss: 0.2113 Lr: 0.00530
[2023-12-25 07:52:30,061 INFO misc.py line 119 253097] Train: [27/100][57/510] Data 0.011 (0.052) Batch 1.236 (1.643) Remain 17:11:51 loss: 0.1541 Lr: 0.00530
[2023-12-25 07:52:31,248 INFO misc.py line 119 253097] Train: [27/100][58/510] Data 0.002 (0.051) Batch 1.184 (1.635) Remain 17:06:35 loss: 0.2475 Lr: 0.00530
[2023-12-25 07:52:32,399 INFO misc.py line 119 253097] Train: [27/100][59/510] Data 0.006 (0.050) Batch 1.153 (1.626) Remain 17:01:09 loss: 0.3271 Lr: 0.00530
[2023-12-25 07:52:33,419 INFO misc.py line 119 253097] Train: [27/100][60/510] Data 0.005 (0.049) Batch 1.019 (1.615) Remain 16:54:27 loss: 0.2981 Lr: 0.00530
[2023-12-25 07:52:34,421 INFO misc.py line 119 253097] Train: [27/100][61/510] Data 0.005 (0.049) Batch 0.999 (1.605) Remain 16:47:45 loss: 0.0900 Lr: 0.00530
[2023-12-25 07:52:35,629 INFO misc.py line 119 253097] Train: [27/100][62/510] Data 0.008 (0.048) Batch 1.205 (1.598) Remain 16:43:28 loss: 0.2380 Lr: 0.00530
[2023-12-25 07:52:36,803 INFO misc.py line 119 253097] Train: [27/100][63/510] Data 0.012 (0.047) Batch 1.173 (1.591) Remain 16:39:00 loss: 0.1459 Lr: 0.00530
[2023-12-25 07:52:37,858 INFO misc.py line 119 253097] Train: [27/100][64/510] Data 0.012 (0.047) Batch 1.062 (1.582) Remain 16:33:32 loss: 0.2024 Lr: 0.00530
[2023-12-25 07:52:41,607 INFO misc.py line 119 253097] Train: [27/100][65/510] Data 0.004 (0.046) Batch 3.748 (1.617) Remain 16:55:26 loss: 0.0853 Lr: 0.00530
[2023-12-25 07:52:42,796 INFO misc.py line 119 253097] Train: [27/100][66/510] Data 0.005 (0.045) Batch 1.190 (1.610) Remain 16:51:09 loss: 0.5904 Lr: 0.00530
[2023-12-25 07:52:43,925 INFO misc.py line 119 253097] Train: [27/100][67/510] Data 0.004 (0.045) Batch 1.128 (1.603) Remain 16:46:23 loss: 0.2370 Lr: 0.00530
[2023-12-25 07:52:44,984 INFO misc.py line 119 253097] Train: [27/100][68/510] Data 0.006 (0.044) Batch 1.060 (1.594) Remain 16:41:07 loss: 0.1132 Lr: 0.00530
[2023-12-25 07:52:46,136 INFO misc.py line 119 253097] Train: [27/100][69/510] Data 0.005 (0.043) Batch 1.152 (1.588) Remain 16:36:53 loss: 0.4363 Lr: 0.00530
[2023-12-25 07:52:47,332 INFO misc.py line 119 253097] Train: [27/100][70/510] Data 0.003 (0.043) Batch 1.196 (1.582) Remain 16:33:11 loss: 0.2585 Lr: 0.00530
[2023-12-25 07:52:56,264 INFO misc.py line 119 253097] Train: [27/100][71/510] Data 0.004 (0.042) Batch 8.932 (1.690) Remain 17:41:02 loss: 0.1515 Lr: 0.00530
[2023-12-25 07:52:57,352 INFO misc.py line 119 253097] Train: [27/100][72/510] Data 0.003 (0.042) Batch 1.086 (1.681) Remain 17:35:30 loss: 0.1953 Lr: 0.00530
[2023-12-25 07:52:58,385 INFO misc.py line 119 253097] Train: [27/100][73/510] Data 0.005 (0.041) Batch 1.034 (1.672) Remain 17:29:40 loss: 0.3560 Lr: 0.00530
[2023-12-25 07:52:59,531 INFO misc.py line 119 253097] Train: [27/100][74/510] Data 0.004 (0.041) Batch 1.147 (1.665) Remain 17:25:00 loss: 0.2809 Lr: 0.00530
[2023-12-25 07:53:00,706 INFO misc.py line 119 253097] Train: [27/100][75/510] Data 0.003 (0.040) Batch 1.175 (1.658) Remain 17:20:42 loss: 0.4850 Lr: 0.00530
[2023-12-25 07:53:01,901 INFO misc.py line 119 253097] Train: [27/100][76/510] Data 0.004 (0.040) Batch 1.192 (1.651) Remain 17:16:40 loss: 0.3415 Lr: 0.00530
[2023-12-25 07:53:03,046 INFO misc.py line 119 253097] Train: [27/100][77/510] Data 0.007 (0.039) Batch 1.148 (1.645) Remain 17:12:22 loss: 0.1857 Lr: 0.00530
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[2023-12-25 07:54:35,290 INFO misc.py line 119 253097] Train: [27/100][139/510] Data 0.004 (0.053) Batch 1.225 (1.573) Remain 16:25:51 loss: 0.2728 Lr: 0.00529
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[2023-12-25 07:54:38,579 INFO misc.py line 119 253097] Train: [27/100][142/510] Data 0.004 (0.052) Batch 1.260 (1.563) Remain 16:19:19 loss: 0.2526 Lr: 0.00529
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[2023-12-25 07:56:32,470 INFO misc.py line 119 253097] Train: [27/100][215/510] Data 0.006 (0.041) Batch 1.014 (1.562) Remain 16:16:51 loss: 0.1982 Lr: 0.00528
[2023-12-25 07:56:33,673 INFO misc.py line 119 253097] Train: [27/100][216/510] Data 0.003 (0.040) Batch 1.200 (1.560) Remain 16:15:46 loss: 0.5147 Lr: 0.00528
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[2023-12-25 07:56:37,096 INFO misc.py line 119 253097] Train: [27/100][219/510] Data 0.008 (0.040) Batch 1.125 (1.554) Remain 16:12:03 loss: 0.2092 Lr: 0.00528
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[2023-12-25 07:56:42,572 INFO misc.py line 119 253097] Train: [27/100][224/510] Data 0.003 (0.039) Batch 1.217 (1.544) Remain 16:05:25 loss: 0.2432 Lr: 0.00528
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[2023-12-25 07:56:44,760 INFO misc.py line 119 253097] Train: [27/100][226/510] Data 0.004 (0.039) Batch 1.083 (1.540) Remain 16:02:51 loss: 0.2672 Lr: 0.00528
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[2023-12-25 07:56:51,442 INFO misc.py line 119 253097] Train: [27/100][232/510] Data 0.003 (0.038) Batch 1.025 (1.529) Remain 15:55:42 loss: 0.5362 Lr: 0.00528
[2023-12-25 07:56:52,625 INFO misc.py line 119 253097] Train: [27/100][233/510] Data 0.007 (0.038) Batch 1.187 (1.527) Remain 15:54:45 loss: 0.3115 Lr: 0.00528
[2023-12-25 07:56:53,677 INFO misc.py line 119 253097] Train: [27/100][234/510] Data 0.004 (0.038) Batch 1.048 (1.525) Remain 15:53:25 loss: 0.1192 Lr: 0.00528
[2023-12-25 07:56:54,743 INFO misc.py line 119 253097] Train: [27/100][235/510] Data 0.007 (0.037) Batch 1.066 (1.523) Remain 15:52:10 loss: 0.2075 Lr: 0.00528
[2023-12-25 07:56:55,956 INFO misc.py line 119 253097] Train: [27/100][236/510] Data 0.008 (0.037) Batch 1.210 (1.522) Remain 15:51:18 loss: 0.2236 Lr: 0.00528
[2023-12-25 07:57:02,536 INFO misc.py line 119 253097] Train: [27/100][237/510] Data 5.770 (0.062) Batch 6.586 (1.544) Remain 16:04:48 loss: 0.2643 Lr: 0.00528
[2023-12-25 07:57:03,703 INFO misc.py line 119 253097] Train: [27/100][238/510] Data 0.003 (0.062) Batch 1.163 (1.542) Remain 16:03:46 loss: 0.2805 Lr: 0.00528
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[2023-12-25 07:57:23,367 INFO misc.py line 119 253097] Train: [27/100][253/510] Data 0.004 (0.058) Batch 1.040 (1.528) Remain 15:54:43 loss: 0.2893 Lr: 0.00527
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[2023-12-25 07:57:26,824 INFO misc.py line 119 253097] Train: [27/100][256/510] Data 0.003 (0.058) Batch 1.298 (1.524) Remain 15:51:52 loss: 0.1946 Lr: 0.00527
[2023-12-25 07:57:28,042 INFO misc.py line 119 253097] Train: [27/100][257/510] Data 0.005 (0.057) Batch 1.219 (1.522) Remain 15:51:05 loss: 0.2778 Lr: 0.00527
[2023-12-25 07:57:33,413 INFO misc.py line 119 253097] Train: [27/100][258/510] Data 0.004 (0.057) Batch 5.370 (1.538) Remain 16:00:29 loss: 0.3441 Lr: 0.00527
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[2023-12-25 07:58:41,094 INFO misc.py line 119 253097] Train: [27/100][308/510] Data 0.004 (0.063) Batch 1.012 (1.507) Remain 15:40:24 loss: 0.3122 Lr: 0.00527
[2023-12-25 07:58:42,181 INFO misc.py line 119 253097] Train: [27/100][309/510] Data 0.006 (0.063) Batch 1.080 (1.506) Remain 15:39:30 loss: 0.1408 Lr: 0.00527
[2023-12-25 07:58:43,365 INFO misc.py line 119 253097] Train: [27/100][310/510] Data 0.012 (0.063) Batch 1.192 (1.505) Remain 15:38:50 loss: 0.1253 Lr: 0.00527
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[2023-12-25 07:58:45,798 INFO misc.py line 119 253097] Train: [27/100][312/510] Data 0.009 (0.063) Batch 1.112 (1.503) Remain 15:37:37 loss: 0.1875 Lr: 0.00527
[2023-12-25 07:58:46,845 INFO misc.py line 119 253097] Train: [27/100][313/510] Data 0.004 (0.062) Batch 1.044 (1.502) Remain 15:36:40 loss: 0.2027 Lr: 0.00527
[2023-12-25 07:58:48,073 INFO misc.py line 119 253097] Train: [27/100][314/510] Data 0.007 (0.062) Batch 1.227 (1.501) Remain 15:36:06 loss: 0.2286 Lr: 0.00527
[2023-12-25 07:58:49,334 INFO misc.py line 119 253097] Train: [27/100][315/510] Data 0.009 (0.062) Batch 1.265 (1.500) Remain 15:35:36 loss: 0.2247 Lr: 0.00527
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[2023-12-25 07:58:57,531 INFO misc.py line 119 253097] Train: [27/100][320/510] Data 0.004 (0.061) Batch 1.162 (1.502) Remain 15:36:51 loss: 0.2505 Lr: 0.00527
[2023-12-25 07:58:58,528 INFO misc.py line 119 253097] Train: [27/100][321/510] Data 0.003 (0.061) Batch 0.997 (1.501) Remain 15:35:50 loss: 0.2677 Lr: 0.00527
[2023-12-25 07:58:59,758 INFO misc.py line 119 253097] Train: [27/100][322/510] Data 0.003 (0.061) Batch 1.230 (1.500) Remain 15:35:17 loss: 0.1805 Lr: 0.00527
[2023-12-25 07:59:09,914 INFO misc.py line 119 253097] Train: [27/100][323/510] Data 0.003 (0.061) Batch 10.155 (1.527) Remain 15:52:07 loss: 0.1532 Lr: 0.00527
[2023-12-25 07:59:11,046 INFO misc.py line 119 253097] Train: [27/100][324/510] Data 0.005 (0.060) Batch 1.133 (1.526) Remain 15:51:20 loss: 0.2120 Lr: 0.00526
[2023-12-25 07:59:12,266 INFO misc.py line 119 253097] Train: [27/100][325/510] Data 0.003 (0.060) Batch 1.219 (1.525) Remain 15:50:43 loss: 0.1672 Lr: 0.00526
[2023-12-25 07:59:13,337 INFO misc.py line 119 253097] Train: [27/100][326/510] Data 0.003 (0.060) Batch 1.070 (1.523) Remain 15:49:49 loss: 0.1564 Lr: 0.00526
[2023-12-25 07:59:14,569 INFO misc.py line 119 253097] Train: [27/100][327/510] Data 0.005 (0.060) Batch 1.232 (1.522) Remain 15:49:13 loss: 0.1818 Lr: 0.00526
[2023-12-25 07:59:15,757 INFO misc.py line 119 253097] Train: [27/100][328/510] Data 0.007 (0.060) Batch 1.189 (1.521) Remain 15:48:33 loss: 0.1740 Lr: 0.00526
[2023-12-25 07:59:16,697 INFO misc.py line 119 253097] Train: [27/100][329/510] Data 0.004 (0.060) Batch 0.940 (1.519) Remain 15:47:25 loss: 0.1800 Lr: 0.00526
[2023-12-25 07:59:17,957 INFO misc.py line 119 253097] Train: [27/100][330/510] Data 0.004 (0.059) Batch 1.260 (1.519) Remain 15:46:54 loss: 0.1307 Lr: 0.00526
[2023-12-25 07:59:19,121 INFO misc.py line 119 253097] Train: [27/100][331/510] Data 0.004 (0.059) Batch 1.163 (1.518) Remain 15:46:12 loss: 0.1759 Lr: 0.00526
[2023-12-25 07:59:20,078 INFO misc.py line 119 253097] Train: [27/100][332/510] Data 0.006 (0.059) Batch 0.959 (1.516) Remain 15:45:07 loss: 0.1767 Lr: 0.00526
[2023-12-25 07:59:21,317 INFO misc.py line 119 253097] Train: [27/100][333/510] Data 0.003 (0.059) Batch 1.238 (1.515) Remain 15:44:34 loss: 0.1489 Lr: 0.00526
[2023-12-25 07:59:22,288 INFO misc.py line 119 253097] Train: [27/100][334/510] Data 0.004 (0.059) Batch 0.969 (1.513) Remain 15:43:31 loss: 0.2042 Lr: 0.00526
[2023-12-25 07:59:23,384 INFO misc.py line 119 253097] Train: [27/100][335/510] Data 0.006 (0.059) Batch 1.096 (1.512) Remain 15:42:42 loss: 0.2762 Lr: 0.00526
[2023-12-25 07:59:24,578 INFO misc.py line 119 253097] Train: [27/100][336/510] Data 0.007 (0.058) Batch 1.194 (1.511) Remain 15:42:05 loss: 0.2494 Lr: 0.00526
[2023-12-25 07:59:25,636 INFO misc.py line 119 253097] Train: [27/100][337/510] Data 0.007 (0.058) Batch 1.059 (1.510) Remain 15:41:13 loss: 0.1868 Lr: 0.00526
[2023-12-25 07:59:33,324 INFO misc.py line 119 253097] Train: [27/100][338/510] Data 6.358 (0.077) Batch 7.688 (1.528) Remain 15:52:41 loss: 0.3762 Lr: 0.00526
[2023-12-25 07:59:34,494 INFO misc.py line 119 253097] Train: [27/100][339/510] Data 0.004 (0.077) Batch 1.171 (1.527) Remain 15:52:00 loss: 0.2610 Lr: 0.00526
[2023-12-25 07:59:35,515 INFO misc.py line 119 253097] Train: [27/100][340/510] Data 0.004 (0.077) Batch 1.022 (1.526) Remain 15:51:02 loss: 0.2511 Lr: 0.00526
[2023-12-25 07:59:36,698 INFO misc.py line 119 253097] Train: [27/100][341/510] Data 0.003 (0.076) Batch 1.181 (1.525) Remain 15:50:22 loss: 0.1458 Lr: 0.00526
[2023-12-25 07:59:38,073 INFO misc.py line 119 253097] Train: [27/100][342/510] Data 0.006 (0.076) Batch 1.374 (1.524) Remain 15:50:04 loss: 0.2026 Lr: 0.00526
[2023-12-25 07:59:39,188 INFO misc.py line 119 253097] Train: [27/100][343/510] Data 0.005 (0.076) Batch 1.112 (1.523) Remain 15:49:17 loss: 0.4256 Lr: 0.00526
[2023-12-25 07:59:40,322 INFO misc.py line 119 253097] Train: [27/100][344/510] Data 0.010 (0.076) Batch 1.139 (1.522) Remain 15:48:34 loss: 0.2218 Lr: 0.00526
[2023-12-25 07:59:41,481 INFO misc.py line 119 253097] Train: [27/100][345/510] Data 0.004 (0.076) Batch 1.157 (1.521) Remain 15:47:52 loss: 0.1197 Lr: 0.00526
[2023-12-25 07:59:42,666 INFO misc.py line 119 253097] Train: [27/100][346/510] Data 0.007 (0.075) Batch 1.182 (1.520) Remain 15:47:14 loss: 0.3119 Lr: 0.00526
[2023-12-25 07:59:43,858 INFO misc.py line 119 253097] Train: [27/100][347/510] Data 0.010 (0.075) Batch 1.195 (1.519) Remain 15:46:37 loss: 0.1762 Lr: 0.00526
[2023-12-25 07:59:44,883 INFO misc.py line 119 253097] Train: [27/100][348/510] Data 0.007 (0.075) Batch 1.024 (1.518) Remain 15:45:42 loss: 0.2477 Lr: 0.00526
[2023-12-25 07:59:45,911 INFO misc.py line 119 253097] Train: [27/100][349/510] Data 0.007 (0.075) Batch 1.024 (1.516) Remain 15:44:47 loss: 0.2434 Lr: 0.00526
[2023-12-25 07:59:47,231 INFO misc.py line 119 253097] Train: [27/100][350/510] Data 0.011 (0.075) Batch 1.327 (1.516) Remain 15:44:25 loss: 0.1182 Lr: 0.00526
[2023-12-25 07:59:48,504 INFO misc.py line 119 253097] Train: [27/100][351/510] Data 0.005 (0.074) Batch 1.271 (1.515) Remain 15:43:57 loss: 0.3117 Lr: 0.00526
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[2023-12-25 07:59:50,810 INFO misc.py line 119 253097] Train: [27/100][353/510] Data 0.013 (0.074) Batch 1.108 (1.513) Remain 15:42:37 loss: 0.2280 Lr: 0.00526
[2023-12-25 07:59:51,924 INFO misc.py line 119 253097] Train: [27/100][354/510] Data 0.009 (0.074) Batch 1.116 (1.512) Remain 15:41:53 loss: 0.1279 Lr: 0.00526
[2023-12-25 07:59:53,238 INFO misc.py line 119 253097] Train: [27/100][355/510] Data 0.006 (0.074) Batch 1.313 (1.511) Remain 15:41:30 loss: 0.3882 Lr: 0.00526
[2023-12-25 07:59:54,399 INFO misc.py line 119 253097] Train: [27/100][356/510] Data 0.007 (0.074) Batch 1.161 (1.510) Remain 15:40:52 loss: 0.1561 Lr: 0.00526
[2023-12-25 08:00:08,581 INFO misc.py line 119 253097] Train: [27/100][357/510] Data 13.020 (0.110) Batch 14.186 (1.546) Remain 16:03:09 loss: 0.1476 Lr: 0.00526
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[2023-12-25 08:00:10,537 INFO misc.py line 119 253097] Train: [27/100][359/510] Data 0.003 (0.109) Batch 1.048 (1.543) Remain 16:01:07 loss: 0.2488 Lr: 0.00526
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[2023-12-25 08:00:18,284 INFO misc.py line 119 253097] Train: [27/100][366/510] Data 0.004 (0.107) Batch 1.128 (1.534) Remain 15:55:42 loss: 0.3487 Lr: 0.00526
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[2023-12-25 08:03:38,211 INFO misc.py line 119 253097] Train: [27/100][496/510] Data 0.004 (0.081) Batch 1.069 (1.535) Remain 15:52:57 loss: 0.2339 Lr: 0.00524
[2023-12-25 08:03:39,433 INFO misc.py line 119 253097] Train: [27/100][497/510] Data 0.008 (0.081) Batch 1.225 (1.535) Remain 15:52:32 loss: 0.3109 Lr: 0.00524
[2023-12-25 08:03:40,609 INFO misc.py line 119 253097] Train: [27/100][498/510] Data 0.005 (0.080) Batch 1.176 (1.534) Remain 15:52:04 loss: 0.1793 Lr: 0.00524
[2023-12-25 08:03:41,492 INFO misc.py line 119 253097] Train: [27/100][499/510] Data 0.004 (0.080) Batch 0.884 (1.533) Remain 15:51:14 loss: 0.3412 Lr: 0.00524
[2023-12-25 08:03:42,529 INFO misc.py line 119 253097] Train: [27/100][500/510] Data 0.003 (0.080) Batch 1.036 (1.532) Remain 15:50:35 loss: 0.4572 Lr: 0.00524
[2023-12-25 08:03:43,622 INFO misc.py line 119 253097] Train: [27/100][501/510] Data 0.004 (0.080) Batch 1.093 (1.531) Remain 15:50:00 loss: 0.1524 Lr: 0.00524
[2023-12-25 08:03:44,805 INFO misc.py line 119 253097] Train: [27/100][502/510] Data 0.004 (0.080) Batch 1.183 (1.530) Remain 15:49:33 loss: 0.2479 Lr: 0.00524
[2023-12-25 08:03:45,987 INFO misc.py line 119 253097] Train: [27/100][503/510] Data 0.005 (0.080) Batch 1.178 (1.529) Remain 15:49:05 loss: 0.1965 Lr: 0.00524
[2023-12-25 08:03:46,936 INFO misc.py line 119 253097] Train: [27/100][504/510] Data 0.010 (0.080) Batch 0.954 (1.528) Remain 15:48:21 loss: 0.1039 Lr: 0.00524
[2023-12-25 08:03:48,125 INFO misc.py line 119 253097] Train: [27/100][505/510] Data 0.004 (0.079) Batch 1.190 (1.527) Remain 15:47:54 loss: 0.2773 Lr: 0.00524
[2023-12-25 08:03:51,470 INFO misc.py line 119 253097] Train: [27/100][506/510] Data 2.440 (0.084) Batch 3.344 (1.531) Remain 15:50:07 loss: 0.1559 Lr: 0.00524
[2023-12-25 08:03:52,663 INFO misc.py line 119 253097] Train: [27/100][507/510] Data 0.004 (0.084) Batch 1.193 (1.530) Remain 15:49:41 loss: 0.1827 Lr: 0.00524
[2023-12-25 08:03:53,958 INFO misc.py line 119 253097] Train: [27/100][508/510] Data 0.003 (0.084) Batch 1.290 (1.530) Remain 15:49:22 loss: 0.4259 Lr: 0.00524
[2023-12-25 08:03:55,204 INFO misc.py line 119 253097] Train: [27/100][509/510] Data 0.008 (0.084) Batch 1.246 (1.529) Remain 15:48:59 loss: 0.2752 Lr: 0.00524
[2023-12-25 08:04:00,681 INFO misc.py line 119 253097] Train: [27/100][510/510] Data 0.008 (0.083) Batch 5.481 (1.537) Remain 15:53:48 loss: 0.1155 Lr: 0.00524
[2023-12-25 08:04:00,682 INFO misc.py line 136 253097] Train result: loss: 0.2504 
[2023-12-25 08:04:00,682 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 08:04:28,337 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6123 
[2023-12-25 08:04:28,683 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4844 
[2023-12-25 08:04:33,712 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5945 
[2023-12-25 08:04:34,238 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5178 
[2023-12-25 08:04:36,209 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9127 
[2023-12-25 08:04:36,641 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6503 
[2023-12-25 08:04:37,520 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1184 
[2023-12-25 08:04:38,078 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2237 
[2023-12-25 08:04:39,884 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1440 
[2023-12-25 08:04:42,007 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1745 
[2023-12-25 08:04:42,863 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3986 
[2023-12-25 08:04:43,291 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0723 
[2023-12-25 08:04:44,194 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.7177 
[2023-12-25 08:04:47,136 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9854 
[2023-12-25 08:04:47,603 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1621 
[2023-12-25 08:04:48,213 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.6222 
[2023-12-25 08:04:48,916 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4080 
[2023-12-25 08:04:50,476 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6414/0.7375/0.8849.
[2023-12-25 08:04:50,476 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9140/0.9453
[2023-12-25 08:04:50,476 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9748/0.9890
[2023-12-25 08:04:50,476 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8213/0.9684
[2023-12-25 08:04:50,476 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0476/0.3443
[2023-12-25 08:04:50,476 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2194/0.2405
[2023-12-25 08:04:50,476 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.4753/0.4820
[2023-12-25 08:04:50,477 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5928/0.7039
[2023-12-25 08:04:50,477 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7961/0.8747
[2023-12-25 08:04:50,477 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9085/0.9576
[2023-12-25 08:04:50,477 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6751/0.7163
[2023-12-25 08:04:50,477 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7264/0.8000
[2023-12-25 08:04:50,477 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6074/0.8286
[2023-12-25 08:04:50,477 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5796/0.7363
[2023-12-25 08:04:50,477 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 08:04:50,479 INFO misc.py line 165 253097] Currently Best mIoU: 0.6552
[2023-12-25 08:04:50,479 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 08:05:04,462 INFO misc.py line 119 253097] Train: [28/100][1/510] Data 10.163 (10.163) Batch 11.030 (11.030) Remain 114:04:06 loss: 0.1576 Lr: 0.00524
[2023-12-25 08:05:05,524 INFO misc.py line 119 253097] Train: [28/100][2/510] Data 0.003 (0.003) Batch 1.057 (1.057) Remain 10:55:51 loss: 0.2495 Lr: 0.00524
[2023-12-25 08:05:06,733 INFO misc.py line 119 253097] Train: [28/100][3/510] Data 0.009 (0.009) Batch 1.212 (1.212) Remain 12:32:17 loss: 0.2072 Lr: 0.00524
[2023-12-25 08:05:07,913 INFO misc.py line 119 253097] Train: [28/100][4/510] Data 0.004 (0.004) Batch 1.178 (1.178) Remain 12:10:46 loss: 0.3619 Lr: 0.00524
[2023-12-25 08:05:09,237 INFO misc.py line 119 253097] Train: [28/100][5/510] Data 0.007 (0.006) Batch 1.321 (1.250) Remain 12:55:17 loss: 0.1781 Lr: 0.00524
[2023-12-25 08:05:10,437 INFO misc.py line 119 253097] Train: [28/100][6/510] Data 0.010 (0.007) Batch 1.205 (1.235) Remain 12:46:02 loss: 0.3380 Lr: 0.00524
[2023-12-25 08:05:11,446 INFO misc.py line 119 253097] Train: [28/100][7/510] Data 0.004 (0.006) Batch 1.001 (1.176) Remain 12:09:45 loss: 0.1221 Lr: 0.00524
[2023-12-25 08:05:12,389 INFO misc.py line 119 253097] Train: [28/100][8/510] Data 0.012 (0.008) Batch 0.952 (1.131) Remain 11:41:53 loss: 0.2078 Lr: 0.00524
[2023-12-25 08:05:13,335 INFO misc.py line 119 253097] Train: [28/100][9/510] Data 0.003 (0.007) Batch 0.946 (1.101) Remain 11:22:44 loss: 0.5022 Lr: 0.00524
[2023-12-25 08:05:16,634 INFO misc.py line 119 253097] Train: [28/100][10/510] Data 0.003 (0.006) Batch 3.299 (1.415) Remain 14:37:30 loss: 0.2950 Lr: 0.00524
[2023-12-25 08:05:17,783 INFO misc.py line 119 253097] Train: [28/100][11/510] Data 0.003 (0.006) Batch 1.149 (1.381) Remain 14:16:55 loss: 0.3175 Lr: 0.00524
[2023-12-25 08:05:19,076 INFO misc.py line 119 253097] Train: [28/100][12/510] Data 0.003 (0.006) Batch 1.292 (1.371) Remain 14:10:44 loss: 0.2585 Lr: 0.00524
[2023-12-25 08:05:20,162 INFO misc.py line 119 253097] Train: [28/100][13/510] Data 0.004 (0.005) Batch 1.086 (1.343) Remain 13:53:01 loss: 0.3338 Lr: 0.00524
[2023-12-25 08:05:21,366 INFO misc.py line 119 253097] Train: [28/100][14/510] Data 0.004 (0.005) Batch 1.201 (1.330) Remain 13:45:01 loss: 0.1981 Lr: 0.00524
[2023-12-25 08:05:22,646 INFO misc.py line 119 253097] Train: [28/100][15/510] Data 0.007 (0.005) Batch 1.279 (1.326) Remain 13:42:22 loss: 0.6036 Lr: 0.00524
[2023-12-25 08:05:25,015 INFO misc.py line 119 253097] Train: [28/100][16/510] Data 0.007 (0.006) Batch 2.372 (1.406) Remain 14:32:16 loss: 0.2121 Lr: 0.00524
[2023-12-25 08:05:28,567 INFO misc.py line 119 253097] Train: [28/100][17/510] Data 0.004 (0.005) Batch 3.553 (1.560) Remain 16:07:21 loss: 0.2268 Lr: 0.00524
[2023-12-25 08:05:29,695 INFO misc.py line 119 253097] Train: [28/100][18/510] Data 0.003 (0.005) Batch 1.127 (1.531) Remain 15:49:26 loss: 0.3021 Lr: 0.00524
[2023-12-25 08:05:30,764 INFO misc.py line 119 253097] Train: [28/100][19/510] Data 0.003 (0.005) Batch 1.068 (1.502) Remain 15:31:27 loss: 0.2854 Lr: 0.00524
[2023-12-25 08:05:31,965 INFO misc.py line 119 253097] Train: [28/100][20/510] Data 0.006 (0.005) Batch 1.196 (1.484) Remain 15:20:17 loss: 0.2772 Lr: 0.00524
[2023-12-25 08:05:33,094 INFO misc.py line 119 253097] Train: [28/100][21/510] Data 0.010 (0.005) Batch 1.131 (1.464) Remain 15:08:05 loss: 0.1856 Lr: 0.00524
[2023-12-25 08:05:34,326 INFO misc.py line 119 253097] Train: [28/100][22/510] Data 0.009 (0.006) Batch 1.237 (1.452) Remain 15:00:38 loss: 0.1921 Lr: 0.00524
[2023-12-25 08:05:36,447 INFO misc.py line 119 253097] Train: [28/100][23/510] Data 0.004 (0.006) Batch 2.121 (1.486) Remain 15:21:21 loss: 0.3240 Lr: 0.00524
[2023-12-25 08:05:37,517 INFO misc.py line 119 253097] Train: [28/100][24/510] Data 0.003 (0.005) Batch 1.068 (1.466) Remain 15:08:59 loss: 0.1319 Lr: 0.00524
[2023-12-25 08:05:38,547 INFO misc.py line 119 253097] Train: [28/100][25/510] Data 0.005 (0.005) Batch 1.031 (1.446) Remain 14:56:43 loss: 0.4552 Lr: 0.00524
[2023-12-25 08:05:39,734 INFO misc.py line 119 253097] Train: [28/100][26/510] Data 0.004 (0.005) Batch 1.186 (1.435) Remain 14:49:41 loss: 0.1919 Lr: 0.00524
[2023-12-25 08:05:40,767 INFO misc.py line 119 253097] Train: [28/100][27/510] Data 0.005 (0.005) Batch 1.034 (1.418) Remain 14:39:19 loss: 0.3039 Lr: 0.00524
[2023-12-25 08:05:41,825 INFO misc.py line 119 253097] Train: [28/100][28/510] Data 0.003 (0.005) Batch 1.058 (1.404) Remain 14:30:21 loss: 0.2483 Lr: 0.00524
[2023-12-25 08:05:42,893 INFO misc.py line 119 253097] Train: [28/100][29/510] Data 0.004 (0.005) Batch 1.067 (1.391) Remain 14:22:17 loss: 0.1961 Lr: 0.00524
[2023-12-25 08:05:44,126 INFO misc.py line 119 253097] Train: [28/100][30/510] Data 0.005 (0.005) Batch 1.230 (1.385) Remain 14:18:35 loss: 0.4662 Lr: 0.00524
[2023-12-25 08:05:46,450 INFO misc.py line 119 253097] Train: [28/100][31/510] Data 0.008 (0.005) Batch 2.328 (1.419) Remain 14:39:27 loss: 0.2636 Lr: 0.00524
[2023-12-25 08:05:47,606 INFO misc.py line 119 253097] Train: [28/100][32/510] Data 0.004 (0.005) Batch 1.156 (1.409) Remain 14:33:49 loss: 0.2712 Lr: 0.00524
[2023-12-25 08:05:48,596 INFO misc.py line 119 253097] Train: [28/100][33/510] Data 0.004 (0.005) Batch 0.989 (1.395) Remain 14:25:06 loss: 0.2479 Lr: 0.00524
[2023-12-25 08:05:49,604 INFO misc.py line 119 253097] Train: [28/100][34/510] Data 0.004 (0.005) Batch 1.007 (1.383) Remain 14:17:19 loss: 0.4998 Lr: 0.00524
[2023-12-25 08:05:50,655 INFO misc.py line 119 253097] Train: [28/100][35/510] Data 0.004 (0.005) Batch 1.052 (1.373) Remain 14:10:53 loss: 0.3734 Lr: 0.00524
[2023-12-25 08:05:51,827 INFO misc.py line 119 253097] Train: [28/100][36/510] Data 0.004 (0.005) Batch 1.172 (1.367) Remain 14:07:06 loss: 0.2319 Lr: 0.00524
[2023-12-25 08:05:52,956 INFO misc.py line 119 253097] Train: [28/100][37/510] Data 0.004 (0.005) Batch 1.129 (1.360) Remain 14:02:44 loss: 0.2317 Lr: 0.00524
[2023-12-25 08:05:54,256 INFO misc.py line 119 253097] Train: [28/100][38/510] Data 0.003 (0.005) Batch 1.299 (1.358) Remain 14:01:39 loss: 0.0783 Lr: 0.00524
[2023-12-25 08:05:55,466 INFO misc.py line 119 253097] Train: [28/100][39/510] Data 0.005 (0.005) Batch 1.209 (1.354) Remain 13:59:04 loss: 0.3651 Lr: 0.00524
[2023-12-25 08:05:56,470 INFO misc.py line 119 253097] Train: [28/100][40/510] Data 0.006 (0.005) Batch 1.005 (1.344) Remain 13:53:12 loss: 0.2117 Lr: 0.00524
[2023-12-25 08:05:57,798 INFO misc.py line 119 253097] Train: [28/100][41/510] Data 0.005 (0.005) Batch 1.328 (1.344) Remain 13:52:55 loss: 0.2187 Lr: 0.00524
[2023-12-25 08:06:03,118 INFO misc.py line 119 253097] Train: [28/100][42/510] Data 0.004 (0.005) Batch 5.319 (1.446) Remain 14:56:05 loss: 0.2628 Lr: 0.00524
[2023-12-25 08:06:04,396 INFO misc.py line 119 253097] Train: [28/100][43/510] Data 0.005 (0.005) Batch 1.279 (1.442) Remain 14:53:29 loss: 0.2688 Lr: 0.00524
[2023-12-25 08:06:05,725 INFO misc.py line 119 253097] Train: [28/100][44/510] Data 0.004 (0.005) Batch 1.327 (1.439) Remain 14:51:43 loss: 0.3591 Lr: 0.00524
[2023-12-25 08:06:06,987 INFO misc.py line 119 253097] Train: [28/100][45/510] Data 0.006 (0.005) Batch 1.262 (1.435) Remain 14:49:05 loss: 0.2715 Lr: 0.00524
[2023-12-25 08:06:07,903 INFO misc.py line 119 253097] Train: [28/100][46/510] Data 0.006 (0.005) Batch 0.918 (1.423) Remain 14:41:37 loss: 0.6767 Lr: 0.00524
[2023-12-25 08:06:09,067 INFO misc.py line 119 253097] Train: [28/100][47/510] Data 0.004 (0.005) Batch 1.164 (1.417) Remain 14:37:57 loss: 0.2027 Lr: 0.00523
[2023-12-25 08:06:10,307 INFO misc.py line 119 253097] Train: [28/100][48/510] Data 0.003 (0.005) Batch 1.239 (1.413) Remain 14:35:28 loss: 0.1945 Lr: 0.00523
[2023-12-25 08:06:11,567 INFO misc.py line 119 253097] Train: [28/100][49/510] Data 0.005 (0.005) Batch 1.255 (1.409) Remain 14:33:19 loss: 0.4711 Lr: 0.00523
[2023-12-25 08:06:12,762 INFO misc.py line 119 253097] Train: [28/100][50/510] Data 0.011 (0.005) Batch 1.199 (1.405) Remain 14:30:31 loss: 0.1538 Lr: 0.00523
[2023-12-25 08:06:13,995 INFO misc.py line 119 253097] Train: [28/100][51/510] Data 0.007 (0.005) Batch 1.236 (1.401) Remain 14:28:19 loss: 0.2379 Lr: 0.00523
[2023-12-25 08:06:14,927 INFO misc.py line 119 253097] Train: [28/100][52/510] Data 0.004 (0.005) Batch 0.932 (1.392) Remain 14:22:22 loss: 0.1633 Lr: 0.00523
[2023-12-25 08:06:16,107 INFO misc.py line 119 253097] Train: [28/100][53/510] Data 0.003 (0.005) Batch 1.179 (1.387) Remain 14:19:42 loss: 0.3068 Lr: 0.00523
[2023-12-25 08:06:17,254 INFO misc.py line 119 253097] Train: [28/100][54/510] Data 0.005 (0.005) Batch 1.148 (1.383) Remain 14:16:46 loss: 0.3096 Lr: 0.00523
[2023-12-25 08:06:18,409 INFO misc.py line 119 253097] Train: [28/100][55/510] Data 0.004 (0.005) Batch 1.155 (1.378) Remain 14:14:02 loss: 0.7635 Lr: 0.00523
[2023-12-25 08:06:19,612 INFO misc.py line 119 253097] Train: [28/100][56/510] Data 0.004 (0.005) Batch 1.200 (1.375) Remain 14:11:55 loss: 0.1902 Lr: 0.00523
[2023-12-25 08:06:24,885 INFO misc.py line 119 253097] Train: [28/100][57/510] Data 4.365 (0.086) Batch 5.276 (1.447) Remain 14:56:39 loss: 0.3099 Lr: 0.00523
[2023-12-25 08:06:27,753 INFO misc.py line 119 253097] Train: [28/100][58/510] Data 0.003 (0.084) Batch 2.867 (1.473) Remain 15:12:37 loss: 0.1855 Lr: 0.00523
[2023-12-25 08:06:28,714 INFO misc.py line 119 253097] Train: [28/100][59/510] Data 0.005 (0.083) Batch 0.960 (1.464) Remain 15:06:55 loss: 0.2346 Lr: 0.00523
[2023-12-25 08:06:29,789 INFO misc.py line 119 253097] Train: [28/100][60/510] Data 0.006 (0.082) Batch 1.076 (1.457) Remain 15:02:41 loss: 0.1629 Lr: 0.00523
[2023-12-25 08:06:31,009 INFO misc.py line 119 253097] Train: [28/100][61/510] Data 0.004 (0.080) Batch 1.221 (1.453) Remain 15:00:08 loss: 0.3743 Lr: 0.00523
[2023-12-25 08:06:32,296 INFO misc.py line 119 253097] Train: [28/100][62/510] Data 0.003 (0.079) Batch 1.286 (1.450) Remain 14:58:21 loss: 0.3459 Lr: 0.00523
[2023-12-25 08:06:33,355 INFO misc.py line 119 253097] Train: [28/100][63/510] Data 0.004 (0.078) Batch 1.060 (1.444) Remain 14:54:18 loss: 0.2955 Lr: 0.00523
[2023-12-25 08:06:34,600 INFO misc.py line 119 253097] Train: [28/100][64/510] Data 0.003 (0.076) Batch 1.241 (1.440) Remain 14:52:13 loss: 0.2427 Lr: 0.00523
[2023-12-25 08:06:35,644 INFO misc.py line 119 253097] Train: [28/100][65/510] Data 0.007 (0.075) Batch 1.048 (1.434) Remain 14:48:16 loss: 0.2413 Lr: 0.00523
[2023-12-25 08:06:37,134 INFO misc.py line 119 253097] Train: [28/100][66/510] Data 0.004 (0.074) Batch 1.487 (1.435) Remain 14:48:46 loss: 0.2023 Lr: 0.00523
[2023-12-25 08:06:38,184 INFO misc.py line 119 253097] Train: [28/100][67/510] Data 0.008 (0.073) Batch 1.047 (1.429) Remain 14:44:59 loss: 0.1583 Lr: 0.00523
[2023-12-25 08:06:39,327 INFO misc.py line 119 253097] Train: [28/100][68/510] Data 0.011 (0.072) Batch 1.146 (1.424) Remain 14:42:16 loss: 0.2978 Lr: 0.00523
[2023-12-25 08:06:40,485 INFO misc.py line 119 253097] Train: [28/100][69/510] Data 0.007 (0.071) Batch 1.160 (1.420) Remain 14:39:46 loss: 0.3836 Lr: 0.00523
[2023-12-25 08:06:41,718 INFO misc.py line 119 253097] Train: [28/100][70/510] Data 0.005 (0.070) Batch 1.232 (1.418) Remain 14:38:00 loss: 0.2482 Lr: 0.00523
[2023-12-25 08:06:42,961 INFO misc.py line 119 253097] Train: [28/100][71/510] Data 0.007 (0.069) Batch 1.245 (1.415) Remain 14:36:24 loss: 0.4770 Lr: 0.00523
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[2023-12-25 08:18:07,896 INFO misc.py line 119 253097] Train: [28/100][490/510] Data 0.003 (0.152) Batch 0.983 (1.604) Remain 16:22:12 loss: 0.3014 Lr: 0.00518
[2023-12-25 08:18:09,010 INFO misc.py line 119 253097] Train: [28/100][491/510] Data 0.003 (0.152) Batch 1.114 (1.603) Remain 16:21:33 loss: 0.1435 Lr: 0.00518
[2023-12-25 08:18:10,245 INFO misc.py line 119 253097] Train: [28/100][492/510] Data 0.004 (0.151) Batch 1.228 (1.602) Remain 16:21:03 loss: 0.2898 Lr: 0.00518
[2023-12-25 08:18:11,442 INFO misc.py line 119 253097] Train: [28/100][493/510] Data 0.011 (0.151) Batch 1.204 (1.601) Remain 16:20:32 loss: 0.1491 Lr: 0.00518
[2023-12-25 08:18:12,434 INFO misc.py line 119 253097] Train: [28/100][494/510] Data 0.004 (0.151) Batch 0.991 (1.600) Remain 16:19:45 loss: 0.1637 Lr: 0.00518
[2023-12-25 08:18:13,506 INFO misc.py line 119 253097] Train: [28/100][495/510] Data 0.005 (0.151) Batch 1.074 (1.599) Remain 16:19:04 loss: 0.1610 Lr: 0.00518
[2023-12-25 08:18:15,273 INFO misc.py line 119 253097] Train: [28/100][496/510] Data 0.003 (0.150) Batch 1.762 (1.599) Remain 16:19:14 loss: 0.1590 Lr: 0.00518
[2023-12-25 08:18:16,482 INFO misc.py line 119 253097] Train: [28/100][497/510] Data 0.008 (0.150) Batch 1.213 (1.599) Remain 16:18:44 loss: 0.2581 Lr: 0.00518
[2023-12-25 08:18:17,619 INFO misc.py line 119 253097] Train: [28/100][498/510] Data 0.004 (0.150) Batch 1.134 (1.598) Remain 16:18:08 loss: 0.2752 Lr: 0.00518
[2023-12-25 08:18:18,915 INFO misc.py line 119 253097] Train: [28/100][499/510] Data 0.008 (0.149) Batch 1.293 (1.597) Remain 16:17:44 loss: 0.2479 Lr: 0.00518
[2023-12-25 08:18:29,014 INFO misc.py line 119 253097] Train: [28/100][500/510] Data 9.005 (0.167) Batch 10.106 (1.614) Remain 16:28:11 loss: 0.0877 Lr: 0.00518
[2023-12-25 08:18:30,164 INFO misc.py line 119 253097] Train: [28/100][501/510] Data 0.004 (0.167) Batch 1.149 (1.613) Remain 16:27:35 loss: 0.1928 Lr: 0.00518
[2023-12-25 08:18:31,233 INFO misc.py line 119 253097] Train: [28/100][502/510] Data 0.004 (0.167) Batch 1.069 (1.612) Remain 16:26:53 loss: 0.3204 Lr: 0.00517
[2023-12-25 08:18:32,415 INFO misc.py line 119 253097] Train: [28/100][503/510] Data 0.004 (0.166) Batch 1.181 (1.611) Remain 16:26:20 loss: 0.3646 Lr: 0.00517
[2023-12-25 08:18:33,446 INFO misc.py line 119 253097] Train: [28/100][504/510] Data 0.005 (0.166) Batch 1.032 (1.610) Remain 16:25:36 loss: 0.0817 Lr: 0.00517
[2023-12-25 08:18:34,788 INFO misc.py line 119 253097] Train: [28/100][505/510] Data 0.004 (0.166) Batch 1.336 (1.610) Remain 16:25:14 loss: 0.3674 Lr: 0.00517
[2023-12-25 08:18:37,965 INFO misc.py line 119 253097] Train: [28/100][506/510] Data 2.026 (0.169) Batch 3.182 (1.613) Remain 16:27:07 loss: 0.0770 Lr: 0.00517
[2023-12-25 08:18:39,193 INFO misc.py line 119 253097] Train: [28/100][507/510] Data 0.005 (0.169) Batch 1.224 (1.612) Remain 16:26:38 loss: 0.2400 Lr: 0.00517
[2023-12-25 08:18:40,147 INFO misc.py line 119 253097] Train: [28/100][508/510] Data 0.009 (0.169) Batch 0.959 (1.611) Remain 16:25:48 loss: 0.3085 Lr: 0.00517
[2023-12-25 08:18:41,307 INFO misc.py line 119 253097] Train: [28/100][509/510] Data 0.004 (0.168) Batch 1.160 (1.610) Remain 16:25:14 loss: 0.1806 Lr: 0.00517
[2023-12-25 08:18:42,556 INFO misc.py line 119 253097] Train: [28/100][510/510] Data 0.004 (0.168) Batch 1.244 (1.609) Remain 16:24:46 loss: 0.2594 Lr: 0.00517
[2023-12-25 08:18:42,556 INFO misc.py line 136 253097] Train result: loss: 0.2633 
[2023-12-25 08:18:42,557 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 08:19:11,492 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6402 
[2023-12-25 08:19:11,842 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.5008 
[2023-12-25 08:19:16,778 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4355 
[2023-12-25 08:19:17,294 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4420 
[2023-12-25 08:19:19,274 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9695 
[2023-12-25 08:19:19,704 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.5126 
[2023-12-25 08:19:20,590 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.4837 
[2023-12-25 08:19:21,143 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3254 
[2023-12-25 08:19:22,947 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1687 
[2023-12-25 08:19:25,076 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3230 
[2023-12-25 08:19:25,929 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3171 
[2023-12-25 08:19:26,354 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6375 
[2023-12-25 08:19:27,254 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4161 
[2023-12-25 08:19:30,196 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9803 
[2023-12-25 08:19:30,667 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1770 
[2023-12-25 08:19:31,276 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4396 
[2023-12-25 08:19:31,991 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5226 
[2023-12-25 08:19:33,811 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6425/0.7143/0.8894.
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9215/0.9524
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9787/0.9936
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8470/0.9543
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.1402/0.1456
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.4933/0.5057
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6316/0.8712
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8015/0.8892
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9101/0.9503
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5778/0.6198
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7276/0.7930
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7573/0.8444
[2023-12-25 08:19:33,811 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5664/0.7659
[2023-12-25 08:19:33,812 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 08:19:33,814 INFO misc.py line 165 253097] Currently Best mIoU: 0.6552
[2023-12-25 08:19:33,814 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 08:19:42,938 INFO misc.py line 119 253097] Train: [29/100][1/510] Data 5.159 (5.159) Batch 6.151 (6.151) Remain 62:44:36 loss: 0.2621 Lr: 0.00517
[2023-12-25 08:19:44,101 INFO misc.py line 119 253097] Train: [29/100][2/510] Data 0.004 (0.004) Batch 1.163 (1.163) Remain 11:51:28 loss: 0.2112 Lr: 0.00517
[2023-12-25 08:19:45,324 INFO misc.py line 119 253097] Train: [29/100][3/510] Data 0.004 (0.004) Batch 1.223 (1.223) Remain 12:28:07 loss: 0.2818 Lr: 0.00517
[2023-12-25 08:19:46,580 INFO misc.py line 119 253097] Train: [29/100][4/510] Data 0.005 (0.005) Batch 1.255 (1.255) Remain 12:47:53 loss: 0.1907 Lr: 0.00517
[2023-12-25 08:19:47,804 INFO misc.py line 119 253097] Train: [29/100][5/510] Data 0.007 (0.006) Batch 1.225 (1.240) Remain 12:38:43 loss: 0.2199 Lr: 0.00517
[2023-12-25 08:19:48,922 INFO misc.py line 119 253097] Train: [29/100][6/510] Data 0.006 (0.006) Batch 1.118 (1.199) Remain 12:13:50 loss: 0.5098 Lr: 0.00517
[2023-12-25 08:19:50,092 INFO misc.py line 119 253097] Train: [29/100][7/510] Data 0.005 (0.006) Batch 1.170 (1.192) Remain 12:09:19 loss: 0.2817 Lr: 0.00517
[2023-12-25 08:19:51,156 INFO misc.py line 119 253097] Train: [29/100][8/510] Data 0.007 (0.006) Batch 1.059 (1.165) Remain 11:53:01 loss: 0.4623 Lr: 0.00517
[2023-12-25 08:19:52,049 INFO misc.py line 119 253097] Train: [29/100][9/510] Data 0.010 (0.007) Batch 0.900 (1.121) Remain 11:25:54 loss: 0.3993 Lr: 0.00517
[2023-12-25 08:19:53,007 INFO misc.py line 119 253097] Train: [29/100][10/510] Data 0.004 (0.006) Batch 0.956 (1.097) Remain 11:11:26 loss: 0.1961 Lr: 0.00517
[2023-12-25 08:19:54,071 INFO misc.py line 119 253097] Train: [29/100][11/510] Data 0.006 (0.006) Batch 1.062 (1.093) Remain 11:08:43 loss: 0.1559 Lr: 0.00517
[2023-12-25 08:19:55,285 INFO misc.py line 119 253097] Train: [29/100][12/510] Data 0.008 (0.006) Batch 1.217 (1.107) Remain 11:17:07 loss: 0.3422 Lr: 0.00517
[2023-12-25 08:19:57,988 INFO misc.py line 119 253097] Train: [29/100][13/510] Data 1.838 (0.190) Batch 2.703 (1.266) Remain 12:54:45 loss: 0.3357 Lr: 0.00517
[2023-12-25 08:19:59,200 INFO misc.py line 119 253097] Train: [29/100][14/510] Data 0.005 (0.173) Batch 1.213 (1.262) Remain 12:51:46 loss: 0.4194 Lr: 0.00517
[2023-12-25 08:20:00,450 INFO misc.py line 119 253097] Train: [29/100][15/510] Data 0.004 (0.159) Batch 1.250 (1.261) Remain 12:51:09 loss: 0.3495 Lr: 0.00517
[2023-12-25 08:20:01,568 INFO misc.py line 119 253097] Train: [29/100][16/510] Data 0.004 (0.147) Batch 1.114 (1.249) Remain 12:44:12 loss: 0.3519 Lr: 0.00517
[2023-12-25 08:20:02,744 INFO misc.py line 119 253097] Train: [29/100][17/510] Data 0.008 (0.137) Batch 1.177 (1.244) Remain 12:41:00 loss: 0.1573 Lr: 0.00517
[2023-12-25 08:20:03,852 INFO misc.py line 119 253097] Train: [29/100][18/510] Data 0.007 (0.128) Batch 1.107 (1.235) Remain 12:35:23 loss: 0.2726 Lr: 0.00517
[2023-12-25 08:20:04,904 INFO misc.py line 119 253097] Train: [29/100][19/510] Data 0.009 (0.121) Batch 1.052 (1.223) Remain 12:28:22 loss: 0.3280 Lr: 0.00517
[2023-12-25 08:20:06,220 INFO misc.py line 119 253097] Train: [29/100][20/510] Data 0.009 (0.114) Batch 1.321 (1.229) Remain 12:31:51 loss: 0.1977 Lr: 0.00517
[2023-12-25 08:20:07,459 INFO misc.py line 119 253097] Train: [29/100][21/510] Data 0.005 (0.108) Batch 1.236 (1.230) Remain 12:32:03 loss: 0.1467 Lr: 0.00517
[2023-12-25 08:20:18,246 INFO misc.py line 119 253097] Train: [29/100][22/510] Data 9.525 (0.604) Batch 10.788 (1.733) Remain 17:39:43 loss: 0.2083 Lr: 0.00517
[2023-12-25 08:20:19,514 INFO misc.py line 119 253097] Train: [29/100][23/510] Data 0.008 (0.574) Batch 1.268 (1.709) Remain 17:25:28 loss: 0.1760 Lr: 0.00517
[2023-12-25 08:20:20,639 INFO misc.py line 119 253097] Train: [29/100][24/510] Data 0.007 (0.547) Batch 1.129 (1.682) Remain 17:08:33 loss: 0.3209 Lr: 0.00517
[2023-12-25 08:20:21,834 INFO misc.py line 119 253097] Train: [29/100][25/510] Data 0.003 (0.522) Batch 1.194 (1.660) Remain 16:54:58 loss: 0.2371 Lr: 0.00517
[2023-12-25 08:20:23,044 INFO misc.py line 119 253097] Train: [29/100][26/510] Data 0.004 (0.500) Batch 1.204 (1.640) Remain 16:42:49 loss: 0.5609 Lr: 0.00517
[2023-12-25 08:20:24,242 INFO misc.py line 119 253097] Train: [29/100][27/510] Data 0.010 (0.479) Batch 1.202 (1.622) Remain 16:31:38 loss: 0.2565 Lr: 0.00517
[2023-12-25 08:20:25,417 INFO misc.py line 119 253097] Train: [29/100][28/510] Data 0.006 (0.460) Batch 1.178 (1.604) Remain 16:20:45 loss: 0.3196 Lr: 0.00517
[2023-12-25 08:20:26,439 INFO misc.py line 119 253097] Train: [29/100][29/510] Data 0.005 (0.443) Batch 1.021 (1.581) Remain 16:07:02 loss: 0.3452 Lr: 0.00517
[2023-12-25 08:20:27,346 INFO misc.py line 119 253097] Train: [29/100][30/510] Data 0.004 (0.427) Batch 0.907 (1.556) Remain 15:51:43 loss: 0.1515 Lr: 0.00517
[2023-12-25 08:20:28,347 INFO misc.py line 119 253097] Train: [29/100][31/510] Data 0.004 (0.412) Batch 1.000 (1.537) Remain 15:39:33 loss: 0.2403 Lr: 0.00517
[2023-12-25 08:20:39,446 INFO misc.py line 119 253097] Train: [29/100][32/510] Data 10.100 (0.746) Batch 11.100 (1.866) Remain 19:01:10 loss: 0.2884 Lr: 0.00517
[2023-12-25 08:20:40,441 INFO misc.py line 119 253097] Train: [29/100][33/510] Data 0.004 (0.721) Batch 0.993 (1.837) Remain 18:43:21 loss: 0.1501 Lr: 0.00517
[2023-12-25 08:20:41,668 INFO misc.py line 119 253097] Train: [29/100][34/510] Data 0.006 (0.698) Batch 1.222 (1.817) Remain 18:31:11 loss: 0.1645 Lr: 0.00517
[2023-12-25 08:20:42,718 INFO misc.py line 119 253097] Train: [29/100][35/510] Data 0.010 (0.676) Batch 1.055 (1.794) Remain 18:16:36 loss: 0.3541 Lr: 0.00517
[2023-12-25 08:20:43,756 INFO misc.py line 119 253097] Train: [29/100][36/510] Data 0.006 (0.656) Batch 1.036 (1.771) Remain 18:02:32 loss: 0.2598 Lr: 0.00517
[2023-12-25 08:20:44,760 INFO misc.py line 119 253097] Train: [29/100][37/510] Data 0.007 (0.637) Batch 1.001 (1.748) Remain 17:48:39 loss: 0.2574 Lr: 0.00517
[2023-12-25 08:20:46,002 INFO misc.py line 119 253097] Train: [29/100][38/510] Data 0.011 (0.619) Batch 1.243 (1.734) Remain 17:39:48 loss: 0.1654 Lr: 0.00517
[2023-12-25 08:20:47,080 INFO misc.py line 119 253097] Train: [29/100][39/510] Data 0.009 (0.602) Batch 1.079 (1.715) Remain 17:28:40 loss: 0.2170 Lr: 0.00517
[2023-12-25 08:20:48,191 INFO misc.py line 119 253097] Train: [29/100][40/510] Data 0.009 (0.586) Batch 1.115 (1.699) Remain 17:18:43 loss: 0.2223 Lr: 0.00517
[2023-12-25 08:20:49,163 INFO misc.py line 119 253097] Train: [29/100][41/510] Data 0.004 (0.571) Batch 0.972 (1.680) Remain 17:07:00 loss: 0.1717 Lr: 0.00517
[2023-12-25 08:20:50,247 INFO misc.py line 119 253097] Train: [29/100][42/510] Data 0.005 (0.556) Batch 1.084 (1.665) Remain 16:57:37 loss: 0.1082 Lr: 0.00517
[2023-12-25 08:20:51,465 INFO misc.py line 119 253097] Train: [29/100][43/510] Data 0.004 (0.542) Batch 1.219 (1.654) Remain 16:50:47 loss: 0.1953 Lr: 0.00517
[2023-12-25 08:20:52,496 INFO misc.py line 119 253097] Train: [29/100][44/510] Data 0.003 (0.529) Batch 1.030 (1.638) Remain 16:41:28 loss: 0.1895 Lr: 0.00517
[2023-12-25 08:20:53,394 INFO misc.py line 119 253097] Train: [29/100][45/510] Data 0.004 (0.517) Batch 0.898 (1.621) Remain 16:30:39 loss: 0.1960 Lr: 0.00517
[2023-12-25 08:20:54,552 INFO misc.py line 119 253097] Train: [29/100][46/510] Data 0.005 (0.505) Batch 1.159 (1.610) Remain 16:24:04 loss: 0.1928 Lr: 0.00517
[2023-12-25 08:20:55,774 INFO misc.py line 119 253097] Train: [29/100][47/510] Data 0.004 (0.494) Batch 1.221 (1.601) Remain 16:18:38 loss: 0.1433 Lr: 0.00517
[2023-12-25 08:20:57,058 INFO misc.py line 119 253097] Train: [29/100][48/510] Data 0.006 (0.483) Batch 1.284 (1.594) Remain 16:14:18 loss: 0.3244 Lr: 0.00517
[2023-12-25 08:20:58,293 INFO misc.py line 119 253097] Train: [29/100][49/510] Data 0.004 (0.472) Batch 1.233 (1.586) Remain 16:09:29 loss: 0.3518 Lr: 0.00517
[2023-12-25 08:20:59,508 INFO misc.py line 119 253097] Train: [29/100][50/510] Data 0.006 (0.462) Batch 1.217 (1.578) Remain 16:04:39 loss: 0.1527 Lr: 0.00517
[2023-12-25 08:21:00,526 INFO misc.py line 119 253097] Train: [29/100][51/510] Data 0.004 (0.453) Batch 1.015 (1.567) Remain 15:57:27 loss: 0.2108 Lr: 0.00517
[2023-12-25 08:21:01,610 INFO misc.py line 119 253097] Train: [29/100][52/510] Data 0.008 (0.444) Batch 1.086 (1.557) Remain 15:51:26 loss: 0.1608 Lr: 0.00517
[2023-12-25 08:21:02,812 INFO misc.py line 119 253097] Train: [29/100][53/510] Data 0.005 (0.435) Batch 1.201 (1.550) Remain 15:47:04 loss: 0.2868 Lr: 0.00517
[2023-12-25 08:21:04,106 INFO misc.py line 119 253097] Train: [29/100][54/510] Data 0.006 (0.427) Batch 1.296 (1.545) Remain 15:44:00 loss: 0.2266 Lr: 0.00517
[2023-12-25 08:21:05,308 INFO misc.py line 119 253097] Train: [29/100][55/510] Data 0.003 (0.418) Batch 1.201 (1.538) Remain 15:39:56 loss: 0.2828 Lr: 0.00517
[2023-12-25 08:21:06,366 INFO misc.py line 119 253097] Train: [29/100][56/510] Data 0.004 (0.411) Batch 1.053 (1.529) Remain 15:34:19 loss: 0.1730 Lr: 0.00517
[2023-12-25 08:21:07,603 INFO misc.py line 119 253097] Train: [29/100][57/510] Data 0.009 (0.403) Batch 1.233 (1.524) Remain 15:30:57 loss: 0.1829 Lr: 0.00517
[2023-12-25 08:21:08,615 INFO misc.py line 119 253097] Train: [29/100][58/510] Data 0.014 (0.396) Batch 1.017 (1.514) Remain 15:25:18 loss: 0.1413 Lr: 0.00517
[2023-12-25 08:21:09,755 INFO misc.py line 119 253097] Train: [29/100][59/510] Data 0.006 (0.389) Batch 1.141 (1.508) Remain 15:21:12 loss: 0.2207 Lr: 0.00517
[2023-12-25 08:21:10,955 INFO misc.py line 119 253097] Train: [29/100][60/510] Data 0.007 (0.382) Batch 1.199 (1.502) Remain 15:17:52 loss: 0.2962 Lr: 0.00517
[2023-12-25 08:21:12,117 INFO misc.py line 119 253097] Train: [29/100][61/510] Data 0.007 (0.376) Batch 1.165 (1.496) Remain 15:14:17 loss: 0.3319 Lr: 0.00517
[2023-12-25 08:21:13,025 INFO misc.py line 119 253097] Train: [29/100][62/510] Data 0.004 (0.370) Batch 0.902 (1.486) Remain 15:08:06 loss: 0.1603 Lr: 0.00517
[2023-12-25 08:21:13,928 INFO misc.py line 119 253097] Train: [29/100][63/510] Data 0.011 (0.364) Batch 0.911 (1.477) Remain 15:02:13 loss: 0.2584 Lr: 0.00517
[2023-12-25 08:21:22,856 INFO misc.py line 119 253097] Train: [29/100][64/510] Data 0.003 (0.358) Batch 8.927 (1.599) Remain 16:16:48 loss: 0.2802 Lr: 0.00517
[2023-12-25 08:21:24,109 INFO misc.py line 119 253097] Train: [29/100][65/510] Data 0.005 (0.352) Batch 1.249 (1.593) Remain 16:13:20 loss: 0.3363 Lr: 0.00517
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[2023-12-25 08:21:56,541 INFO misc.py line 119 253097] Train: [29/100][87/510] Data 0.004 (0.262) Batch 0.990 (1.562) Remain 15:53:44 loss: 0.1941 Lr: 0.00516
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[2023-12-25 08:22:09,391 INFO misc.py line 119 253097] Train: [29/100][95/510] Data 0.003 (0.280) Batch 1.255 (1.566) Remain 15:55:51 loss: 0.1497 Lr: 0.00516
[2023-12-25 08:22:11,186 INFO misc.py line 119 253097] Train: [29/100][96/510] Data 0.007 (0.277) Batch 1.795 (1.568) Remain 15:57:20 loss: 0.1728 Lr: 0.00516
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[2023-12-25 08:22:14,533 INFO misc.py line 119 253097] Train: [29/100][99/510] Data 0.004 (0.268) Batch 1.057 (1.554) Remain 15:48:36 loss: 0.1746 Lr: 0.00516
[2023-12-25 08:22:15,794 INFO misc.py line 119 253097] Train: [29/100][100/510] Data 0.008 (0.265) Batch 1.266 (1.551) Remain 15:46:46 loss: 0.3433 Lr: 0.00516
[2023-12-25 08:22:17,074 INFO misc.py line 119 253097] Train: [29/100][101/510] Data 0.004 (0.263) Batch 1.280 (1.548) Remain 15:45:03 loss: 0.2018 Lr: 0.00516
[2023-12-25 08:22:18,208 INFO misc.py line 119 253097] Train: [29/100][102/510] Data 0.004 (0.260) Batch 1.128 (1.544) Remain 15:42:26 loss: 0.1409 Lr: 0.00516
[2023-12-25 08:22:19,260 INFO misc.py line 119 253097] Train: [29/100][103/510] Data 0.010 (0.258) Batch 1.057 (1.539) Remain 15:39:26 loss: 0.3705 Lr: 0.00516
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[2023-12-25 08:22:24,491 INFO misc.py line 119 253097] Train: [29/100][106/510] Data 0.005 (0.250) Batch 1.225 (1.545) Remain 15:43:00 loss: 0.2484 Lr: 0.00516
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[2023-12-25 08:22:28,118 INFO misc.py line 119 253097] Train: [29/100][109/510] Data 0.012 (0.243) Batch 1.244 (1.536) Remain 15:37:05 loss: 0.2970 Lr: 0.00516
[2023-12-25 08:22:29,320 INFO misc.py line 119 253097] Train: [29/100][110/510] Data 0.010 (0.241) Batch 1.200 (1.533) Remain 15:35:09 loss: 0.1923 Lr: 0.00516
[2023-12-25 08:22:30,347 INFO misc.py line 119 253097] Train: [29/100][111/510] Data 0.011 (0.239) Batch 1.023 (1.528) Remain 15:32:14 loss: 0.4752 Lr: 0.00516
[2023-12-25 08:22:31,587 INFO misc.py line 119 253097] Train: [29/100][112/510] Data 0.014 (0.237) Batch 1.244 (1.525) Remain 15:30:37 loss: 0.2327 Lr: 0.00516
[2023-12-25 08:22:32,617 INFO misc.py line 119 253097] Train: [29/100][113/510] Data 0.011 (0.235) Batch 1.016 (1.521) Remain 15:27:47 loss: 0.1815 Lr: 0.00516
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[2023-12-25 08:22:35,105 INFO misc.py line 119 253097] Train: [29/100][115/510] Data 0.016 (0.231) Batch 1.413 (1.516) Remain 15:24:47 loss: 0.1556 Lr: 0.00516
[2023-12-25 08:22:36,076 INFO misc.py line 119 253097] Train: [29/100][116/510] Data 0.011 (0.229) Batch 0.977 (1.511) Remain 15:21:51 loss: 0.3376 Lr: 0.00516
[2023-12-25 08:22:37,298 INFO misc.py line 119 253097] Train: [29/100][117/510] Data 0.005 (0.227) Batch 1.223 (1.509) Remain 15:20:17 loss: 0.3014 Lr: 0.00516
[2023-12-25 08:22:38,448 INFO misc.py line 119 253097] Train: [29/100][118/510] Data 0.003 (0.225) Batch 1.150 (1.505) Remain 15:18:21 loss: 0.1278 Lr: 0.00516
[2023-12-25 08:22:39,680 INFO misc.py line 119 253097] Train: [29/100][119/510] Data 0.004 (0.223) Batch 1.230 (1.503) Remain 15:16:53 loss: 0.2119 Lr: 0.00516
[2023-12-25 08:22:40,827 INFO misc.py line 119 253097] Train: [29/100][120/510] Data 0.006 (0.222) Batch 1.146 (1.500) Remain 15:15:00 loss: 0.2166 Lr: 0.00516
[2023-12-25 08:22:46,057 INFO misc.py line 119 253097] Train: [29/100][121/510] Data 0.007 (0.220) Batch 5.232 (1.532) Remain 15:34:16 loss: 0.2224 Lr: 0.00516
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[2023-12-25 08:22:48,116 INFO misc.py line 119 253097] Train: [29/100][123/510] Data 0.004 (0.216) Batch 0.899 (1.523) Remain 15:29:07 loss: 0.3477 Lr: 0.00516
[2023-12-25 08:22:51,606 INFO misc.py line 119 253097] Train: [29/100][124/510] Data 0.004 (0.214) Batch 3.490 (1.540) Remain 15:39:00 loss: 0.1093 Lr: 0.00516
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[2023-12-25 08:22:54,909 INFO misc.py line 119 253097] Train: [29/100][127/510] Data 0.004 (0.209) Batch 1.078 (1.529) Remain 15:32:27 loss: 0.1843 Lr: 0.00516
[2023-12-25 08:22:55,958 INFO misc.py line 119 253097] Train: [29/100][128/510] Data 0.007 (0.208) Batch 1.048 (1.525) Remain 15:30:05 loss: 0.3757 Lr: 0.00516
[2023-12-25 08:22:57,065 INFO misc.py line 119 253097] Train: [29/100][129/510] Data 0.006 (0.206) Batch 1.109 (1.522) Remain 15:28:02 loss: 0.2559 Lr: 0.00516
[2023-12-25 08:23:00,342 INFO misc.py line 119 253097] Train: [29/100][130/510] Data 0.004 (0.204) Batch 3.276 (1.536) Remain 15:36:26 loss: 0.2537 Lr: 0.00516
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[2023-12-25 08:23:02,515 INFO misc.py line 119 253097] Train: [29/100][132/510] Data 0.005 (0.201) Batch 1.132 (1.529) Remain 15:32:08 loss: 0.1498 Lr: 0.00516
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[2023-12-25 08:23:04,725 INFO misc.py line 119 253097] Train: [29/100][134/510] Data 0.005 (0.198) Batch 1.138 (1.522) Remain 15:28:08 loss: 0.3596 Lr: 0.00516
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[2023-12-25 08:23:11,459 INFO misc.py line 119 253097] Train: [29/100][136/510] Data 4.442 (0.229) Batch 5.701 (1.550) Remain 15:45:01 loss: 0.1711 Lr: 0.00516
[2023-12-25 08:23:12,496 INFO misc.py line 119 253097] Train: [29/100][137/510] Data 0.004 (0.227) Batch 1.037 (1.546) Remain 15:42:39 loss: 0.1923 Lr: 0.00516
[2023-12-25 08:23:13,445 INFO misc.py line 119 253097] Train: [29/100][138/510] Data 0.004 (0.225) Batch 0.949 (1.542) Remain 15:39:56 loss: 0.1275 Lr: 0.00516
[2023-12-25 08:23:14,706 INFO misc.py line 119 253097] Train: [29/100][139/510] Data 0.004 (0.224) Batch 1.260 (1.540) Remain 15:38:39 loss: 0.1872 Lr: 0.00516
[2023-12-25 08:23:15,761 INFO misc.py line 119 253097] Train: [29/100][140/510] Data 0.005 (0.222) Batch 1.050 (1.536) Remain 15:36:26 loss: 0.3483 Lr: 0.00516
[2023-12-25 08:23:16,778 INFO misc.py line 119 253097] Train: [29/100][141/510] Data 0.010 (0.221) Batch 1.019 (1.532) Remain 15:34:08 loss: 0.5657 Lr: 0.00515
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[2023-12-25 08:23:37,084 INFO misc.py line 119 253097] Train: [29/100][149/510] Data 11.535 (0.288) Batch 12.705 (1.587) Remain 16:07:32 loss: 0.1976 Lr: 0.00515
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[2023-12-25 08:24:33,566 INFO misc.py line 119 253097] Train: [29/100][182/510] Data 0.011 (0.236) Batch 1.336 (1.610) Remain 16:20:36 loss: 0.1637 Lr: 0.00515
[2023-12-25 08:24:42,013 INFO misc.py line 119 253097] Train: [29/100][183/510] Data 0.006 (0.235) Batch 8.448 (1.648) Remain 16:43:42 loss: 0.2532 Lr: 0.00515
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[2023-12-25 08:24:45,208 INFO misc.py line 119 253097] Train: [29/100][186/510] Data 0.009 (0.231) Batch 0.922 (1.639) Remain 16:37:48 loss: 0.2228 Lr: 0.00515
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[2023-12-25 08:24:48,754 INFO misc.py line 119 253097] Train: [29/100][189/510] Data 0.004 (0.228) Batch 1.303 (1.631) Remain 16:33:14 loss: 0.3300 Lr: 0.00515
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[2023-12-25 08:24:54,459 INFO misc.py line 119 253097] Train: [29/100][194/510] Data 0.008 (0.222) Batch 1.270 (1.618) Remain 16:25:15 loss: 0.3133 Lr: 0.00515
[2023-12-25 08:24:55,691 INFO misc.py line 119 253097] Train: [29/100][195/510] Data 0.013 (0.221) Batch 1.242 (1.616) Remain 16:24:02 loss: 0.2994 Lr: 0.00515
[2023-12-25 08:24:56,810 INFO misc.py line 119 253097] Train: [29/100][196/510] Data 0.004 (0.220) Batch 1.117 (1.614) Remain 16:22:26 loss: 0.2518 Lr: 0.00515
[2023-12-25 08:24:57,955 INFO misc.py line 119 253097] Train: [29/100][197/510] Data 0.006 (0.219) Batch 1.140 (1.611) Remain 16:20:55 loss: 0.2706 Lr: 0.00515
[2023-12-25 08:25:00,199 INFO misc.py line 119 253097] Train: [29/100][198/510] Data 0.937 (0.222) Batch 2.250 (1.615) Remain 16:22:53 loss: 0.2856 Lr: 0.00515
[2023-12-25 08:25:06,497 INFO misc.py line 119 253097] Train: [29/100][199/510] Data 5.168 (0.248) Batch 6.298 (1.639) Remain 16:37:24 loss: 0.2791 Lr: 0.00515
[2023-12-25 08:25:07,710 INFO misc.py line 119 253097] Train: [29/100][200/510] Data 0.004 (0.246) Batch 1.214 (1.636) Remain 16:36:04 loss: 0.4876 Lr: 0.00515
[2023-12-25 08:25:08,840 INFO misc.py line 119 253097] Train: [29/100][201/510] Data 0.004 (0.245) Batch 1.128 (1.634) Remain 16:34:28 loss: 0.2229 Lr: 0.00515
[2023-12-25 08:25:10,002 INFO misc.py line 119 253097] Train: [29/100][202/510] Data 0.006 (0.244) Batch 1.158 (1.632) Remain 16:32:59 loss: 0.2795 Lr: 0.00515
[2023-12-25 08:25:11,098 INFO misc.py line 119 253097] Train: [29/100][203/510] Data 0.011 (0.243) Batch 1.093 (1.629) Remain 16:31:19 loss: 0.3649 Lr: 0.00515
[2023-12-25 08:25:15,614 INFO misc.py line 119 253097] Train: [29/100][204/510] Data 0.013 (0.242) Batch 4.525 (1.643) Remain 16:40:04 loss: 0.2755 Lr: 0.00515
[2023-12-25 08:25:16,576 INFO misc.py line 119 253097] Train: [29/100][205/510] Data 0.004 (0.240) Batch 0.961 (1.640) Remain 16:37:59 loss: 0.5077 Lr: 0.00515
[2023-12-25 08:25:17,734 INFO misc.py line 119 253097] Train: [29/100][206/510] Data 0.006 (0.239) Batch 1.159 (1.637) Remain 16:36:31 loss: 0.3089 Lr: 0.00515
[2023-12-25 08:25:19,060 INFO misc.py line 119 253097] Train: [29/100][207/510] Data 0.004 (0.238) Batch 1.326 (1.636) Remain 16:35:33 loss: 0.2463 Lr: 0.00515
[2023-12-25 08:25:19,985 INFO misc.py line 119 253097] Train: [29/100][208/510] Data 0.005 (0.237) Batch 0.926 (1.632) Remain 16:33:25 loss: 0.1597 Lr: 0.00515
[2023-12-25 08:25:21,077 INFO misc.py line 119 253097] Train: [29/100][209/510] Data 0.003 (0.236) Batch 1.091 (1.630) Remain 16:31:48 loss: 0.2335 Lr: 0.00515
[2023-12-25 08:25:22,055 INFO misc.py line 119 253097] Train: [29/100][210/510] Data 0.004 (0.235) Batch 0.978 (1.627) Remain 16:29:51 loss: 0.1887 Lr: 0.00515
[2023-12-25 08:25:28,930 INFO misc.py line 119 253097] Train: [29/100][211/510] Data 0.006 (0.234) Batch 6.876 (1.652) Remain 16:45:11 loss: 0.2920 Lr: 0.00515
[2023-12-25 08:25:30,016 INFO misc.py line 119 253097] Train: [29/100][212/510] Data 0.004 (0.232) Batch 1.087 (1.649) Remain 16:43:30 loss: 0.1257 Lr: 0.00515
[2023-12-25 08:25:30,960 INFO misc.py line 119 253097] Train: [29/100][213/510] Data 0.003 (0.231) Batch 0.942 (1.646) Remain 16:41:26 loss: 0.4055 Lr: 0.00515
[2023-12-25 08:25:32,118 INFO misc.py line 119 253097] Train: [29/100][214/510] Data 0.005 (0.230) Batch 1.158 (1.644) Remain 16:40:00 loss: 0.2307 Lr: 0.00515
[2023-12-25 08:25:33,222 INFO misc.py line 119 253097] Train: [29/100][215/510] Data 0.005 (0.229) Batch 1.104 (1.641) Remain 16:38:25 loss: 0.1221 Lr: 0.00514
[2023-12-25 08:25:34,380 INFO misc.py line 119 253097] Train: [29/100][216/510] Data 0.006 (0.228) Batch 1.159 (1.639) Remain 16:37:01 loss: 0.2305 Lr: 0.00514
[2023-12-25 08:25:35,515 INFO misc.py line 119 253097] Train: [29/100][217/510] Data 0.004 (0.227) Batch 1.135 (1.636) Remain 16:35:33 loss: 0.3594 Lr: 0.00514
[2023-12-25 08:25:36,663 INFO misc.py line 119 253097] Train: [29/100][218/510] Data 0.004 (0.226) Batch 1.146 (1.634) Remain 16:34:08 loss: 0.1722 Lr: 0.00514
[2023-12-25 08:25:37,913 INFO misc.py line 119 253097] Train: [29/100][219/510] Data 0.007 (0.225) Batch 1.250 (1.632) Remain 16:33:02 loss: 0.2982 Lr: 0.00514
[2023-12-25 08:25:38,914 INFO misc.py line 119 253097] Train: [29/100][220/510] Data 0.006 (0.224) Batch 1.003 (1.629) Remain 16:31:14 loss: 0.2871 Lr: 0.00514
[2023-12-25 08:25:40,157 INFO misc.py line 119 253097] Train: [29/100][221/510] Data 0.004 (0.223) Batch 1.240 (1.628) Remain 16:30:08 loss: 0.1542 Lr: 0.00514
[2023-12-25 08:25:41,037 INFO misc.py line 119 253097] Train: [29/100][222/510] Data 0.007 (0.222) Batch 0.883 (1.624) Remain 16:28:02 loss: 0.1189 Lr: 0.00514
[2023-12-25 08:25:42,354 INFO misc.py line 119 253097] Train: [29/100][223/510] Data 0.004 (0.221) Batch 1.317 (1.623) Remain 16:27:09 loss: 0.1844 Lr: 0.00514
[2023-12-25 08:25:43,553 INFO misc.py line 119 253097] Train: [29/100][224/510] Data 0.005 (0.220) Batch 1.195 (1.621) Remain 16:25:57 loss: 0.3199 Lr: 0.00514
[2023-12-25 08:25:45,557 INFO misc.py line 119 253097] Train: [29/100][225/510] Data 0.008 (0.219) Batch 2.009 (1.623) Remain 16:26:59 loss: 0.2208 Lr: 0.00514
[2023-12-25 08:25:46,491 INFO misc.py line 119 253097] Train: [29/100][226/510] Data 0.003 (0.218) Batch 0.934 (1.620) Remain 16:25:05 loss: 0.2028 Lr: 0.00514
[2023-12-25 08:25:47,656 INFO misc.py line 119 253097] Train: [29/100][227/510] Data 0.003 (0.217) Batch 1.164 (1.618) Remain 16:23:49 loss: 0.2652 Lr: 0.00514
[2023-12-25 08:25:48,630 INFO misc.py line 119 253097] Train: [29/100][228/510] Data 0.004 (0.216) Batch 0.974 (1.615) Remain 16:22:03 loss: 0.3218 Lr: 0.00514
[2023-12-25 08:25:49,728 INFO misc.py line 119 253097] Train: [29/100][229/510] Data 0.003 (0.215) Batch 1.098 (1.612) Remain 16:20:38 loss: 0.2247 Lr: 0.00514
[2023-12-25 08:25:50,787 INFO misc.py line 119 253097] Train: [29/100][230/510] Data 0.004 (0.214) Batch 1.059 (1.610) Remain 16:19:07 loss: 0.2482 Lr: 0.00514
[2023-12-25 08:25:56,328 INFO misc.py line 119 253097] Train: [29/100][231/510] Data 0.003 (0.214) Batch 5.540 (1.627) Remain 16:29:35 loss: 0.2890 Lr: 0.00514
[2023-12-25 08:25:57,556 INFO misc.py line 119 253097] Train: [29/100][232/510] Data 0.005 (0.213) Batch 1.228 (1.625) Remain 16:28:29 loss: 0.2349 Lr: 0.00514
[2023-12-25 08:25:58,612 INFO misc.py line 119 253097] Train: [29/100][233/510] Data 0.005 (0.212) Batch 1.053 (1.623) Remain 16:26:57 loss: 0.2051 Lr: 0.00514
[2023-12-25 08:25:59,946 INFO misc.py line 119 253097] Train: [29/100][234/510] Data 0.280 (0.212) Batch 1.338 (1.622) Remain 16:26:10 loss: 0.1746 Lr: 0.00514
[2023-12-25 08:26:01,088 INFO misc.py line 119 253097] Train: [29/100][235/510] Data 0.004 (0.211) Batch 1.139 (1.620) Remain 16:24:53 loss: 0.4056 Lr: 0.00514
[2023-12-25 08:26:02,069 INFO misc.py line 119 253097] Train: [29/100][236/510] Data 0.008 (0.210) Batch 0.985 (1.617) Remain 16:23:12 loss: 0.3064 Lr: 0.00514
[2023-12-25 08:26:03,173 INFO misc.py line 119 253097] Train: [29/100][237/510] Data 0.003 (0.209) Batch 1.103 (1.615) Remain 16:21:50 loss: 0.1514 Lr: 0.00514
[2023-12-25 08:26:04,500 INFO misc.py line 119 253097] Train: [29/100][238/510] Data 0.004 (0.208) Batch 1.326 (1.614) Remain 16:21:04 loss: 0.1314 Lr: 0.00514
[2023-12-25 08:26:05,721 INFO misc.py line 119 253097] Train: [29/100][239/510] Data 0.007 (0.208) Batch 1.221 (1.612) Remain 16:20:01 loss: 0.1848 Lr: 0.00514
[2023-12-25 08:26:06,849 INFO misc.py line 119 253097] Train: [29/100][240/510] Data 0.006 (0.207) Batch 1.124 (1.610) Remain 16:18:45 loss: 0.3207 Lr: 0.00514
[2023-12-25 08:26:08,032 INFO misc.py line 119 253097] Train: [29/100][241/510] Data 0.009 (0.206) Batch 1.181 (1.608) Remain 16:17:37 loss: 0.1499 Lr: 0.00514
[2023-12-25 08:26:09,215 INFO misc.py line 119 253097] Train: [29/100][242/510] Data 0.012 (0.205) Batch 1.191 (1.606) Remain 16:16:32 loss: 0.2050 Lr: 0.00514
[2023-12-25 08:26:10,420 INFO misc.py line 119 253097] Train: [29/100][243/510] Data 0.004 (0.204) Batch 1.201 (1.605) Remain 16:15:29 loss: 0.2304 Lr: 0.00514
[2023-12-25 08:26:11,644 INFO misc.py line 119 253097] Train: [29/100][244/510] Data 0.009 (0.203) Batch 1.226 (1.603) Remain 16:14:30 loss: 0.2460 Lr: 0.00514
[2023-12-25 08:26:12,975 INFO misc.py line 119 253097] Train: [29/100][245/510] Data 0.006 (0.203) Batch 1.330 (1.602) Remain 16:13:47 loss: 0.1855 Lr: 0.00514
[2023-12-25 08:26:14,048 INFO misc.py line 119 253097] Train: [29/100][246/510] Data 0.007 (0.202) Batch 1.074 (1.600) Remain 16:12:26 loss: 0.1235 Lr: 0.00514
[2023-12-25 08:26:20,348 INFO misc.py line 119 253097] Train: [29/100][247/510] Data 0.006 (0.201) Batch 6.302 (1.619) Remain 16:24:08 loss: 0.3228 Lr: 0.00514
[2023-12-25 08:26:21,504 INFO misc.py line 119 253097] Train: [29/100][248/510] Data 0.005 (0.200) Batch 1.154 (1.617) Remain 16:22:57 loss: 0.2429 Lr: 0.00514
[2023-12-25 08:26:25,459 INFO misc.py line 119 253097] Train: [29/100][249/510] Data 2.847 (0.211) Batch 3.957 (1.627) Remain 16:28:42 loss: 0.2200 Lr: 0.00514
[2023-12-25 08:26:26,568 INFO misc.py line 119 253097] Train: [29/100][250/510] Data 0.004 (0.210) Batch 1.108 (1.624) Remain 16:27:24 loss: 0.3267 Lr: 0.00514
[2023-12-25 08:26:27,630 INFO misc.py line 119 253097] Train: [29/100][251/510] Data 0.005 (0.209) Batch 1.063 (1.622) Remain 16:26:00 loss: 0.3385 Lr: 0.00514
[2023-12-25 08:26:28,835 INFO misc.py line 119 253097] Train: [29/100][252/510] Data 0.004 (0.209) Batch 1.201 (1.621) Remain 16:24:56 loss: 0.2036 Lr: 0.00514
[2023-12-25 08:26:29,910 INFO misc.py line 119 253097] Train: [29/100][253/510] Data 0.007 (0.208) Batch 1.073 (1.618) Remain 16:23:35 loss: 0.2167 Lr: 0.00514
[2023-12-25 08:26:30,960 INFO misc.py line 119 253097] Train: [29/100][254/510] Data 0.009 (0.207) Batch 1.051 (1.616) Remain 16:22:11 loss: 0.1746 Lr: 0.00514
[2023-12-25 08:26:33,187 INFO misc.py line 119 253097] Train: [29/100][255/510] Data 1.396 (0.212) Batch 2.229 (1.619) Remain 16:23:38 loss: 0.4971 Lr: 0.00514
[2023-12-25 08:26:34,358 INFO misc.py line 119 253097] Train: [29/100][256/510] Data 0.005 (0.211) Batch 1.172 (1.617) Remain 16:22:32 loss: 0.3045 Lr: 0.00514
[2023-12-25 08:26:35,505 INFO misc.py line 119 253097] Train: [29/100][257/510] Data 0.004 (0.210) Batch 1.147 (1.615) Remain 16:21:23 loss: 0.2294 Lr: 0.00514
[2023-12-25 08:26:36,659 INFO misc.py line 119 253097] Train: [29/100][258/510] Data 0.004 (0.209) Batch 1.155 (1.613) Remain 16:20:16 loss: 0.2108 Lr: 0.00514
[2023-12-25 08:26:37,865 INFO misc.py line 119 253097] Train: [29/100][259/510] Data 0.003 (0.208) Batch 1.205 (1.611) Remain 16:19:16 loss: 0.1913 Lr: 0.00514
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[2023-12-25 08:26:41,222 INFO misc.py line 119 253097] Train: [29/100][262/510] Data 0.004 (0.206) Batch 1.183 (1.606) Remain 16:15:42 loss: 0.3631 Lr: 0.00514
[2023-12-25 08:26:42,375 INFO misc.py line 119 253097] Train: [29/100][263/510] Data 0.009 (0.205) Batch 1.155 (1.604) Remain 16:14:38 loss: 0.2694 Lr: 0.00514
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[2023-12-25 08:26:44,572 INFO misc.py line 119 253097] Train: [29/100][265/510] Data 0.007 (0.204) Batch 1.067 (1.600) Remain 16:12:14 loss: 0.3968 Lr: 0.00514
[2023-12-25 08:26:45,857 INFO misc.py line 119 253097] Train: [29/100][266/510] Data 0.009 (0.203) Batch 1.290 (1.599) Remain 16:11:29 loss: 0.3559 Lr: 0.00514
[2023-12-25 08:26:46,946 INFO misc.py line 119 253097] Train: [29/100][267/510] Data 0.004 (0.202) Batch 1.086 (1.597) Remain 16:10:16 loss: 0.1848 Lr: 0.00514
[2023-12-25 08:26:48,107 INFO misc.py line 119 253097] Train: [29/100][268/510] Data 0.008 (0.202) Batch 1.165 (1.595) Remain 16:09:15 loss: 0.1794 Lr: 0.00514
[2023-12-25 08:26:49,187 INFO misc.py line 119 253097] Train: [29/100][269/510] Data 0.003 (0.201) Batch 1.079 (1.593) Remain 16:08:03 loss: 0.2726 Lr: 0.00514
[2023-12-25 08:26:50,328 INFO misc.py line 119 253097] Train: [29/100][270/510] Data 0.005 (0.200) Batch 1.139 (1.592) Remain 16:06:59 loss: 0.1305 Lr: 0.00514
[2023-12-25 08:26:57,806 INFO misc.py line 119 253097] Train: [29/100][271/510] Data 6.422 (0.223) Batch 7.480 (1.614) Remain 16:20:19 loss: 0.1293 Lr: 0.00514
[2023-12-25 08:26:58,968 INFO misc.py line 119 253097] Train: [29/100][272/510] Data 0.004 (0.222) Batch 1.161 (1.612) Remain 16:19:16 loss: 0.2489 Lr: 0.00514
[2023-12-25 08:27:00,066 INFO misc.py line 119 253097] Train: [29/100][273/510] Data 0.005 (0.222) Batch 1.100 (1.610) Remain 16:18:05 loss: 0.2675 Lr: 0.00514
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[2023-12-25 08:28:04,860 INFO misc.py line 119 253097] Train: [29/100][313/510] Data 9.941 (0.226) Batch 11.027 (1.611) Remain 16:17:46 loss: 0.4184 Lr: 0.00513
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[2023-12-25 08:32:23,929 INFO misc.py line 119 253097] Train: [29/100][484/510] Data 0.005 (0.183) Batch 1.151 (1.577) Remain 15:52:28 loss: 0.4335 Lr: 0.00511
[2023-12-25 08:32:25,097 INFO misc.py line 119 253097] Train: [29/100][485/510] Data 0.009 (0.182) Batch 1.169 (1.576) Remain 15:51:56 loss: 0.1790 Lr: 0.00511
[2023-12-25 08:32:26,232 INFO misc.py line 119 253097] Train: [29/100][486/510] Data 0.007 (0.182) Batch 1.138 (1.575) Remain 15:51:22 loss: 0.2243 Lr: 0.00511
[2023-12-25 08:32:32,946 INFO misc.py line 119 253097] Train: [29/100][487/510] Data 5.576 (0.193) Batch 6.715 (1.586) Remain 15:57:45 loss: 0.1657 Lr: 0.00511
[2023-12-25 08:32:34,230 INFO misc.py line 119 253097] Train: [29/100][488/510] Data 0.003 (0.193) Batch 1.281 (1.585) Remain 15:57:21 loss: 0.3520 Lr: 0.00511
[2023-12-25 08:32:35,463 INFO misc.py line 119 253097] Train: [29/100][489/510] Data 0.007 (0.192) Batch 1.235 (1.585) Remain 15:56:53 loss: 0.2103 Lr: 0.00511
[2023-12-25 08:32:36,531 INFO misc.py line 119 253097] Train: [29/100][490/510] Data 0.004 (0.192) Batch 1.068 (1.584) Remain 15:56:13 loss: 0.2918 Lr: 0.00511
[2023-12-25 08:32:37,698 INFO misc.py line 119 253097] Train: [29/100][491/510] Data 0.004 (0.192) Batch 1.166 (1.583) Remain 15:55:40 loss: 0.2629 Lr: 0.00511
[2023-12-25 08:32:38,807 INFO misc.py line 119 253097] Train: [29/100][492/510] Data 0.005 (0.191) Batch 1.106 (1.582) Remain 15:55:03 loss: 0.3459 Lr: 0.00511
[2023-12-25 08:32:39,793 INFO misc.py line 119 253097] Train: [29/100][493/510] Data 0.008 (0.191) Batch 0.990 (1.581) Remain 15:54:18 loss: 0.2838 Lr: 0.00511
[2023-12-25 08:32:41,001 INFO misc.py line 119 253097] Train: [29/100][494/510] Data 0.004 (0.190) Batch 1.207 (1.580) Remain 15:53:49 loss: 0.2693 Lr: 0.00511
[2023-12-25 08:32:42,105 INFO misc.py line 119 253097] Train: [29/100][495/510] Data 0.005 (0.190) Batch 1.101 (1.579) Remain 15:53:12 loss: 0.2755 Lr: 0.00511
[2023-12-25 08:32:43,267 INFO misc.py line 119 253097] Train: [29/100][496/510] Data 0.008 (0.190) Batch 1.166 (1.578) Remain 15:52:40 loss: 0.1632 Lr: 0.00511
[2023-12-25 08:32:44,474 INFO misc.py line 119 253097] Train: [29/100][497/510] Data 0.005 (0.189) Batch 1.204 (1.577) Remain 15:52:11 loss: 0.2018 Lr: 0.00511
[2023-12-25 08:32:45,451 INFO misc.py line 119 253097] Train: [29/100][498/510] Data 0.009 (0.189) Batch 0.980 (1.576) Remain 15:51:26 loss: 0.2625 Lr: 0.00511
[2023-12-25 08:32:46,645 INFO misc.py line 119 253097] Train: [29/100][499/510] Data 0.005 (0.189) Batch 1.194 (1.575) Remain 15:50:56 loss: 0.2142 Lr: 0.00511
[2023-12-25 08:32:47,632 INFO misc.py line 119 253097] Train: [29/100][500/510] Data 0.004 (0.188) Batch 0.987 (1.574) Remain 15:50:12 loss: 0.1182 Lr: 0.00511
[2023-12-25 08:32:48,789 INFO misc.py line 119 253097] Train: [29/100][501/510] Data 0.004 (0.188) Batch 1.159 (1.573) Remain 15:49:40 loss: 0.2707 Lr: 0.00511
[2023-12-25 08:32:49,898 INFO misc.py line 119 253097] Train: [29/100][502/510] Data 0.003 (0.187) Batch 1.108 (1.572) Remain 15:49:05 loss: 0.1245 Lr: 0.00511
[2023-12-25 08:32:51,050 INFO misc.py line 119 253097] Train: [29/100][503/510] Data 0.004 (0.187) Batch 1.153 (1.571) Remain 15:48:33 loss: 0.2879 Lr: 0.00511
[2023-12-25 08:32:52,155 INFO misc.py line 119 253097] Train: [29/100][504/510] Data 0.003 (0.187) Batch 1.104 (1.571) Remain 15:47:57 loss: 0.1563 Lr: 0.00511
[2023-12-25 08:32:53,144 INFO misc.py line 119 253097] Train: [29/100][505/510] Data 0.005 (0.186) Batch 0.990 (1.569) Remain 15:47:14 loss: 0.2833 Lr: 0.00511
[2023-12-25 08:32:54,226 INFO misc.py line 119 253097] Train: [29/100][506/510] Data 0.003 (0.186) Batch 1.078 (1.568) Remain 15:46:37 loss: 0.2086 Lr: 0.00510
[2023-12-25 08:32:55,483 INFO misc.py line 119 253097] Train: [29/100][507/510] Data 0.007 (0.186) Batch 1.262 (1.568) Remain 15:46:13 loss: 0.2296 Lr: 0.00510
[2023-12-25 08:32:57,352 INFO misc.py line 119 253097] Train: [29/100][508/510] Data 0.003 (0.185) Batch 1.869 (1.568) Remain 15:46:33 loss: 0.1936 Lr: 0.00510
[2023-12-25 08:32:58,635 INFO misc.py line 119 253097] Train: [29/100][509/510] Data 0.003 (0.185) Batch 1.283 (1.568) Remain 15:46:11 loss: 0.3058 Lr: 0.00510
[2023-12-25 08:32:59,786 INFO misc.py line 119 253097] Train: [29/100][510/510] Data 0.003 (0.185) Batch 1.146 (1.567) Remain 15:45:40 loss: 0.2000 Lr: 0.00510
[2023-12-25 08:32:59,787 INFO misc.py line 136 253097] Train result: loss: 0.2559 
[2023-12-25 08:32:59,787 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 08:33:26,408 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6479 
[2023-12-25 08:33:26,752 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3969 
[2023-12-25 08:33:31,690 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4577 
[2023-12-25 08:33:32,221 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2433 
[2023-12-25 08:33:34,198 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6155 
[2023-12-25 08:33:34,624 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2869 
[2023-12-25 08:33:35,501 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0117 
[2023-12-25 08:33:36,065 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5044 
[2023-12-25 08:33:37,871 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0956 
[2023-12-25 08:33:39,993 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3016 
[2023-12-25 08:33:40,847 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3111 
[2023-12-25 08:33:41,301 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7586 
[2023-12-25 08:33:42,202 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.8674 
[2023-12-25 08:33:45,153 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8787 
[2023-12-25 08:33:45,621 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3903 
[2023-12-25 08:33:46,232 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5373 
[2023-12-25 08:33:46,933 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3684 
[2023-12-25 08:33:48,278 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6440/0.7062/0.8914.
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9079/0.9484
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9800/0.9906
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8308/0.9680
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3508/0.4325
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5747/0.5839
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5792/0.6986
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8042/0.9195
[2023-12-25 08:33:48,278 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8947/0.9217
[2023-12-25 08:33:48,279 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4668/0.4749
[2023-12-25 08:33:48,279 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7595/0.8489
[2023-12-25 08:33:48,279 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6566/0.7132
[2023-12-25 08:33:48,279 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5669/0.6805
[2023-12-25 08:33:48,279 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 08:33:48,280 INFO misc.py line 165 253097] Currently Best mIoU: 0.6552
[2023-12-25 08:33:48,281 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 08:33:57,944 INFO misc.py line 119 253097] Train: [30/100][1/510] Data 6.329 (6.329) Batch 7.588 (7.588) Remain 76:19:00 loss: 0.1931 Lr: 0.00510
[2023-12-25 08:33:59,141 INFO misc.py line 119 253097] Train: [30/100][2/510] Data 0.003 (0.003) Batch 1.196 (1.196) Remain 12:01:44 loss: 0.2391 Lr: 0.00510
[2023-12-25 08:34:00,990 INFO misc.py line 119 253097] Train: [30/100][3/510] Data 0.005 (0.005) Batch 1.848 (1.848) Remain 18:35:18 loss: 0.2442 Lr: 0.00510
[2023-12-25 08:34:02,109 INFO misc.py line 119 253097] Train: [30/100][4/510] Data 0.006 (0.006) Batch 1.119 (1.119) Remain 11:15:14 loss: 0.2609 Lr: 0.00510
[2023-12-25 08:34:03,353 INFO misc.py line 119 253097] Train: [30/100][5/510] Data 0.006 (0.006) Batch 1.246 (1.182) Remain 11:53:24 loss: 0.2421 Lr: 0.00510
[2023-12-25 08:34:04,443 INFO misc.py line 119 253097] Train: [30/100][6/510] Data 0.004 (0.005) Batch 1.088 (1.151) Remain 11:34:22 loss: 0.1747 Lr: 0.00510
[2023-12-25 08:34:05,557 INFO misc.py line 119 253097] Train: [30/100][7/510] Data 0.006 (0.006) Batch 1.111 (1.141) Remain 11:28:19 loss: 0.0935 Lr: 0.00510
[2023-12-25 08:34:09,483 INFO misc.py line 119 253097] Train: [30/100][8/510] Data 0.010 (0.006) Batch 3.930 (1.699) Remain 17:04:53 loss: 0.2299 Lr: 0.00510
[2023-12-25 08:34:10,622 INFO misc.py line 119 253097] Train: [30/100][9/510] Data 0.006 (0.006) Batch 1.140 (1.606) Remain 16:08:42 loss: 0.2545 Lr: 0.00510
[2023-12-25 08:34:11,786 INFO misc.py line 119 253097] Train: [30/100][10/510] Data 0.004 (0.006) Batch 1.164 (1.543) Remain 15:30:38 loss: 0.3153 Lr: 0.00510
[2023-12-25 08:34:12,945 INFO misc.py line 119 253097] Train: [30/100][11/510] Data 0.003 (0.006) Batch 1.159 (1.495) Remain 15:01:40 loss: 0.2317 Lr: 0.00510
[2023-12-25 08:34:14,244 INFO misc.py line 119 253097] Train: [30/100][12/510] Data 0.005 (0.006) Batch 1.297 (1.473) Remain 14:48:25 loss: 0.1945 Lr: 0.00510
[2023-12-25 08:34:15,408 INFO misc.py line 119 253097] Train: [30/100][13/510] Data 0.006 (0.006) Batch 1.160 (1.441) Remain 14:29:33 loss: 0.2498 Lr: 0.00510
[2023-12-25 08:34:16,724 INFO misc.py line 119 253097] Train: [30/100][14/510] Data 0.009 (0.006) Batch 1.319 (1.430) Remain 14:22:50 loss: 0.1558 Lr: 0.00510
[2023-12-25 08:34:17,819 INFO misc.py line 119 253097] Train: [30/100][15/510] Data 0.006 (0.006) Batch 1.097 (1.403) Remain 14:06:04 loss: 0.1863 Lr: 0.00510
[2023-12-25 08:34:19,055 INFO misc.py line 119 253097] Train: [30/100][16/510] Data 0.005 (0.006) Batch 1.218 (1.388) Remain 13:57:27 loss: 0.2974 Lr: 0.00510
[2023-12-25 08:34:20,338 INFO misc.py line 119 253097] Train: [30/100][17/510] Data 0.022 (0.007) Batch 1.297 (1.382) Remain 13:53:29 loss: 0.1536 Lr: 0.00510
[2023-12-25 08:34:21,465 INFO misc.py line 119 253097] Train: [30/100][18/510] Data 0.009 (0.007) Batch 1.132 (1.365) Remain 13:43:25 loss: 0.2003 Lr: 0.00510
[2023-12-25 08:34:22,566 INFO misc.py line 119 253097] Train: [30/100][19/510] Data 0.004 (0.007) Batch 1.099 (1.349) Remain 13:33:23 loss: 0.6592 Lr: 0.00510
[2023-12-25 08:34:23,668 INFO misc.py line 119 253097] Train: [30/100][20/510] Data 0.006 (0.007) Batch 1.104 (1.334) Remain 13:24:41 loss: 0.2516 Lr: 0.00510
[2023-12-25 08:34:24,976 INFO misc.py line 119 253097] Train: [30/100][21/510] Data 0.003 (0.007) Batch 1.305 (1.332) Remain 13:23:41 loss: 0.2378 Lr: 0.00510
[2023-12-25 08:34:25,832 INFO misc.py line 119 253097] Train: [30/100][22/510] Data 0.007 (0.007) Batch 0.859 (1.308) Remain 13:08:38 loss: 0.1274 Lr: 0.00510
[2023-12-25 08:34:26,900 INFO misc.py line 119 253097] Train: [30/100][23/510] Data 0.005 (0.007) Batch 1.066 (1.296) Remain 13:01:20 loss: 0.1225 Lr: 0.00510
[2023-12-25 08:34:28,153 INFO misc.py line 119 253097] Train: [30/100][24/510] Data 0.013 (0.007) Batch 1.251 (1.293) Remain 13:00:01 loss: 0.2109 Lr: 0.00510
[2023-12-25 08:34:29,132 INFO misc.py line 119 253097] Train: [30/100][25/510] Data 0.008 (0.007) Batch 0.982 (1.279) Remain 12:51:27 loss: 0.2208 Lr: 0.00510
[2023-12-25 08:34:30,373 INFO misc.py line 119 253097] Train: [30/100][26/510] Data 0.005 (0.007) Batch 1.236 (1.277) Remain 12:50:18 loss: 0.4111 Lr: 0.00510
[2023-12-25 08:34:31,483 INFO misc.py line 119 253097] Train: [30/100][27/510] Data 0.010 (0.007) Batch 1.116 (1.271) Remain 12:46:13 loss: 0.2362 Lr: 0.00510
[2023-12-25 08:34:32,658 INFO misc.py line 119 253097] Train: [30/100][28/510] Data 0.005 (0.007) Batch 1.175 (1.267) Remain 12:43:54 loss: 0.3806 Lr: 0.00510
[2023-12-25 08:34:33,607 INFO misc.py line 119 253097] Train: [30/100][29/510] Data 0.004 (0.007) Batch 0.949 (1.255) Remain 12:36:30 loss: 0.2429 Lr: 0.00510
[2023-12-25 08:34:34,748 INFO misc.py line 119 253097] Train: [30/100][30/510] Data 0.004 (0.007) Batch 1.141 (1.250) Remain 12:33:58 loss: 0.2905 Lr: 0.00510
[2023-12-25 08:34:41,504 INFO misc.py line 119 253097] Train: [30/100][31/510] Data 0.004 (0.007) Batch 6.755 (1.447) Remain 14:32:29 loss: 0.1884 Lr: 0.00510
[2023-12-25 08:34:42,791 INFO misc.py line 119 253097] Train: [30/100][32/510] Data 0.005 (0.007) Batch 1.285 (1.441) Remain 14:29:05 loss: 0.2674 Lr: 0.00510
[2023-12-25 08:34:43,859 INFO misc.py line 119 253097] Train: [30/100][33/510] Data 0.007 (0.007) Batch 1.060 (1.429) Remain 14:21:24 loss: 0.1518 Lr: 0.00510
[2023-12-25 08:34:44,870 INFO misc.py line 119 253097] Train: [30/100][34/510] Data 0.015 (0.007) Batch 1.013 (1.415) Remain 14:13:18 loss: 0.2379 Lr: 0.00510
[2023-12-25 08:34:46,021 INFO misc.py line 119 253097] Train: [30/100][35/510] Data 0.013 (0.007) Batch 1.158 (1.407) Remain 14:08:26 loss: 0.0995 Lr: 0.00510
[2023-12-25 08:34:47,106 INFO misc.py line 119 253097] Train: [30/100][36/510] Data 0.006 (0.007) Batch 1.084 (1.397) Remain 14:02:30 loss: 0.1501 Lr: 0.00510
[2023-12-25 08:34:48,115 INFO misc.py line 119 253097] Train: [30/100][37/510] Data 0.006 (0.007) Batch 1.005 (1.386) Remain 13:55:31 loss: 0.2220 Lr: 0.00510
[2023-12-25 08:34:49,267 INFO misc.py line 119 253097] Train: [30/100][38/510] Data 0.010 (0.007) Batch 1.156 (1.379) Remain 13:51:32 loss: 0.1768 Lr: 0.00510
[2023-12-25 08:34:50,372 INFO misc.py line 119 253097] Train: [30/100][39/510] Data 0.006 (0.007) Batch 1.105 (1.372) Remain 13:46:55 loss: 0.2787 Lr: 0.00510
[2023-12-25 08:34:51,663 INFO misc.py line 119 253097] Train: [30/100][40/510] Data 0.007 (0.007) Batch 1.289 (1.369) Remain 13:45:33 loss: 0.2723 Lr: 0.00510
[2023-12-25 08:34:52,924 INFO misc.py line 119 253097] Train: [30/100][41/510] Data 0.008 (0.007) Batch 1.261 (1.367) Remain 13:43:49 loss: 0.4686 Lr: 0.00510
[2023-12-25 08:34:54,015 INFO misc.py line 119 253097] Train: [30/100][42/510] Data 0.008 (0.007) Batch 1.089 (1.360) Remain 13:39:30 loss: 0.2208 Lr: 0.00510
[2023-12-25 08:34:55,114 INFO misc.py line 119 253097] Train: [30/100][43/510] Data 0.010 (0.007) Batch 1.106 (1.353) Remain 13:35:39 loss: 0.6592 Lr: 0.00510
[2023-12-25 08:34:56,211 INFO misc.py line 119 253097] Train: [30/100][44/510] Data 0.003 (0.007) Batch 1.092 (1.347) Remain 13:31:47 loss: 0.3930 Lr: 0.00510
[2023-12-25 08:35:00,793 INFO misc.py line 119 253097] Train: [30/100][45/510] Data 0.008 (0.007) Batch 4.587 (1.424) Remain 14:18:16 loss: 0.4367 Lr: 0.00510
[2023-12-25 08:35:11,521 INFO misc.py line 119 253097] Train: [30/100][46/510] Data 0.004 (0.007) Batch 10.727 (1.640) Remain 16:28:38 loss: 0.1633 Lr: 0.00510
[2023-12-25 08:35:12,690 INFO misc.py line 119 253097] Train: [30/100][47/510] Data 0.004 (0.007) Batch 1.168 (1.630) Remain 16:22:09 loss: 0.3613 Lr: 0.00510
[2023-12-25 08:35:13,700 INFO misc.py line 119 253097] Train: [30/100][48/510] Data 0.005 (0.007) Batch 1.012 (1.616) Remain 16:13:51 loss: 0.2977 Lr: 0.00510
[2023-12-25 08:35:14,991 INFO misc.py line 119 253097] Train: [30/100][49/510] Data 0.003 (0.007) Batch 1.290 (1.609) Remain 16:09:33 loss: 0.3461 Lr: 0.00510
[2023-12-25 08:35:16,233 INFO misc.py line 119 253097] Train: [30/100][50/510] Data 0.004 (0.007) Batch 1.242 (1.601) Remain 16:04:50 loss: 0.4659 Lr: 0.00510
[2023-12-25 08:35:17,492 INFO misc.py line 119 253097] Train: [30/100][51/510] Data 0.004 (0.007) Batch 1.258 (1.594) Remain 16:00:30 loss: 0.2960 Lr: 0.00510
[2023-12-25 08:35:18,731 INFO misc.py line 119 253097] Train: [30/100][52/510] Data 0.005 (0.007) Batch 1.239 (1.587) Remain 15:56:07 loss: 0.3448 Lr: 0.00510
[2023-12-25 08:35:19,846 INFO misc.py line 119 253097] Train: [30/100][53/510] Data 0.005 (0.007) Batch 1.116 (1.577) Remain 15:50:25 loss: 0.2596 Lr: 0.00510
[2023-12-25 08:35:21,079 INFO misc.py line 119 253097] Train: [30/100][54/510] Data 0.004 (0.007) Batch 1.229 (1.570) Remain 15:46:16 loss: 0.1720 Lr: 0.00510
[2023-12-25 08:35:22,263 INFO misc.py line 119 253097] Train: [30/100][55/510] Data 0.007 (0.007) Batch 1.186 (1.563) Remain 15:41:47 loss: 0.1758 Lr: 0.00510
[2023-12-25 08:35:23,605 INFO misc.py line 119 253097] Train: [30/100][56/510] Data 0.005 (0.007) Batch 1.342 (1.559) Remain 15:39:15 loss: 0.0931 Lr: 0.00510
[2023-12-25 08:35:24,780 INFO misc.py line 119 253097] Train: [30/100][57/510] Data 0.007 (0.007) Batch 1.175 (1.552) Remain 15:34:56 loss: 0.2083 Lr: 0.00510
[2023-12-25 08:35:25,823 INFO misc.py line 119 253097] Train: [30/100][58/510] Data 0.006 (0.007) Batch 1.046 (1.542) Remain 15:29:22 loss: 0.3706 Lr: 0.00510
[2023-12-25 08:35:27,017 INFO misc.py line 119 253097] Train: [30/100][59/510] Data 0.004 (0.006) Batch 1.193 (1.536) Remain 15:25:35 loss: 0.3121 Lr: 0.00510
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[2023-12-25 08:39:19,799 INFO misc.py line 119 253097] Train: [30/100][217/510] Data 0.003 (0.133) Batch 1.052 (1.490) Remain 14:53:40 loss: 0.3779 Lr: 0.00507
[2023-12-25 08:39:21,051 INFO misc.py line 119 253097] Train: [30/100][218/510] Data 0.009 (0.133) Batch 1.256 (1.489) Remain 14:52:59 loss: 0.1985 Lr: 0.00507
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[2023-12-25 08:39:41,946 INFO misc.py line 119 253097] Train: [30/100][232/510] Data 5.111 (0.147) Batch 6.300 (1.489) Remain 14:52:47 loss: 0.1542 Lr: 0.00507
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[2023-12-25 08:42:04,790 INFO misc.py line 119 253097] Train: [30/100][310/510] Data 14.409 (0.200) Batch 15.532 (1.576) Remain 15:42:54 loss: 0.2229 Lr: 0.00506
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[2023-12-25 08:46:35,843 INFO misc.py line 119 253097] Train: [30/100][478/510] Data 0.004 (0.137) Batch 1.315 (1.589) Remain 15:46:23 loss: 0.3380 Lr: 0.00504
[2023-12-25 08:46:37,047 INFO misc.py line 119 253097] Train: [30/100][479/510] Data 0.005 (0.137) Batch 1.204 (1.588) Remain 15:45:53 loss: 0.3560 Lr: 0.00504
[2023-12-25 08:46:38,252 INFO misc.py line 119 253097] Train: [30/100][480/510] Data 0.004 (0.136) Batch 1.198 (1.588) Remain 15:45:22 loss: 0.2455 Lr: 0.00504
[2023-12-25 08:46:39,504 INFO misc.py line 119 253097] Train: [30/100][481/510] Data 0.011 (0.136) Batch 1.252 (1.587) Remain 15:44:56 loss: 0.2380 Lr: 0.00504
[2023-12-25 08:46:47,157 INFO misc.py line 119 253097] Train: [30/100][482/510] Data 0.010 (0.136) Batch 7.660 (1.600) Remain 15:52:27 loss: 0.1675 Lr: 0.00504
[2023-12-25 08:46:48,198 INFO misc.py line 119 253097] Train: [30/100][483/510] Data 0.003 (0.136) Batch 1.041 (1.598) Remain 15:51:44 loss: 0.1962 Lr: 0.00504
[2023-12-25 08:46:49,594 INFO misc.py line 119 253097] Train: [30/100][484/510] Data 0.004 (0.135) Batch 1.390 (1.598) Remain 15:51:27 loss: 0.4567 Lr: 0.00504
[2023-12-25 08:46:50,615 INFO misc.py line 119 253097] Train: [30/100][485/510] Data 0.010 (0.135) Batch 1.023 (1.597) Remain 15:50:43 loss: 0.2670 Lr: 0.00504
[2023-12-25 08:46:51,800 INFO misc.py line 119 253097] Train: [30/100][486/510] Data 0.007 (0.135) Batch 1.186 (1.596) Remain 15:50:11 loss: 0.2173 Lr: 0.00504
[2023-12-25 08:46:53,135 INFO misc.py line 119 253097] Train: [30/100][487/510] Data 0.006 (0.134) Batch 1.335 (1.595) Remain 15:49:50 loss: 0.3225 Lr: 0.00504
[2023-12-25 08:46:54,200 INFO misc.py line 119 253097] Train: [30/100][488/510] Data 0.007 (0.134) Batch 1.065 (1.594) Remain 15:49:09 loss: 0.2271 Lr: 0.00504
[2023-12-25 08:46:55,359 INFO misc.py line 119 253097] Train: [30/100][489/510] Data 0.007 (0.134) Batch 1.158 (1.593) Remain 15:48:35 loss: 0.2548 Lr: 0.00504
[2023-12-25 08:46:56,481 INFO misc.py line 119 253097] Train: [30/100][490/510] Data 0.008 (0.134) Batch 1.125 (1.592) Remain 15:47:59 loss: 0.2641 Lr: 0.00504
[2023-12-25 08:46:57,516 INFO misc.py line 119 253097] Train: [30/100][491/510] Data 0.005 (0.133) Batch 1.035 (1.591) Remain 15:47:17 loss: 0.1133 Lr: 0.00504
[2023-12-25 08:46:58,787 INFO misc.py line 119 253097] Train: [30/100][492/510] Data 0.005 (0.133) Batch 1.271 (1.591) Remain 15:46:52 loss: 0.3427 Lr: 0.00504
[2023-12-25 08:47:06,691 INFO misc.py line 119 253097] Train: [30/100][493/510] Data 0.005 (0.133) Batch 7.903 (1.603) Remain 15:54:31 loss: 0.1279 Lr: 0.00503
[2023-12-25 08:47:07,911 INFO misc.py line 119 253097] Train: [30/100][494/510] Data 0.007 (0.133) Batch 1.222 (1.603) Remain 15:54:01 loss: 0.4264 Lr: 0.00503
[2023-12-25 08:47:08,968 INFO misc.py line 119 253097] Train: [30/100][495/510] Data 0.004 (0.132) Batch 1.057 (1.602) Remain 15:53:20 loss: 0.1740 Lr: 0.00503
[2023-12-25 08:47:10,160 INFO misc.py line 119 253097] Train: [30/100][496/510] Data 0.004 (0.132) Batch 1.192 (1.601) Remain 15:52:49 loss: 0.1716 Lr: 0.00503
[2023-12-25 08:47:11,309 INFO misc.py line 119 253097] Train: [30/100][497/510] Data 0.005 (0.132) Batch 1.148 (1.600) Remain 15:52:14 loss: 0.3238 Lr: 0.00503
[2023-12-25 08:47:12,498 INFO misc.py line 119 253097] Train: [30/100][498/510] Data 0.006 (0.132) Batch 1.190 (1.599) Remain 15:51:43 loss: 0.2159 Lr: 0.00503
[2023-12-25 08:47:13,683 INFO misc.py line 119 253097] Train: [30/100][499/510] Data 0.004 (0.131) Batch 1.184 (1.598) Remain 15:51:12 loss: 0.2628 Lr: 0.00503
[2023-12-25 08:47:14,976 INFO misc.py line 119 253097] Train: [30/100][500/510] Data 0.006 (0.131) Batch 1.290 (1.598) Remain 15:50:48 loss: 0.1185 Lr: 0.00503
[2023-12-25 08:47:16,049 INFO misc.py line 119 253097] Train: [30/100][501/510] Data 0.008 (0.131) Batch 1.072 (1.596) Remain 15:50:09 loss: 0.1622 Lr: 0.00503
[2023-12-25 08:47:17,251 INFO misc.py line 119 253097] Train: [30/100][502/510] Data 0.007 (0.131) Batch 1.206 (1.596) Remain 15:49:39 loss: 0.1983 Lr: 0.00503
[2023-12-25 08:47:18,231 INFO misc.py line 119 253097] Train: [30/100][503/510] Data 0.004 (0.130) Batch 0.980 (1.594) Remain 15:48:54 loss: 0.0986 Lr: 0.00503
[2023-12-25 08:47:19,532 INFO misc.py line 119 253097] Train: [30/100][504/510] Data 0.003 (0.130) Batch 1.299 (1.594) Remain 15:48:31 loss: 0.1548 Lr: 0.00503
[2023-12-25 08:47:20,293 INFO misc.py line 119 253097] Train: [30/100][505/510] Data 0.009 (0.130) Batch 0.760 (1.592) Remain 15:47:30 loss: 0.3081 Lr: 0.00503
[2023-12-25 08:47:21,416 INFO misc.py line 119 253097] Train: [30/100][506/510] Data 0.006 (0.130) Batch 1.126 (1.591) Remain 15:46:55 loss: 0.1418 Lr: 0.00503
[2023-12-25 08:47:33,938 INFO misc.py line 119 253097] Train: [30/100][507/510] Data 0.003 (0.129) Batch 12.520 (1.613) Remain 15:59:48 loss: 0.4927 Lr: 0.00503
[2023-12-25 08:47:34,927 INFO misc.py line 119 253097] Train: [30/100][508/510] Data 0.006 (0.129) Batch 0.991 (1.612) Remain 15:59:02 loss: 0.1492 Lr: 0.00503
[2023-12-25 08:47:36,080 INFO misc.py line 119 253097] Train: [30/100][509/510] Data 0.003 (0.129) Batch 1.153 (1.611) Remain 15:58:29 loss: 0.2359 Lr: 0.00503
[2023-12-25 08:47:37,175 INFO misc.py line 119 253097] Train: [30/100][510/510] Data 0.003 (0.129) Batch 1.091 (1.610) Remain 15:57:50 loss: 0.2085 Lr: 0.00503
[2023-12-25 08:47:37,176 INFO misc.py line 136 253097] Train result: loss: 0.2546 
[2023-12-25 08:47:37,183 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 08:48:06,526 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5842 
[2023-12-25 08:48:06,882 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3034 
[2023-12-25 08:48:11,830 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3622 
[2023-12-25 08:48:12,357 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3600 
[2023-12-25 08:48:14,343 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9482 
[2023-12-25 08:48:14,775 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2497 
[2023-12-25 08:48:15,659 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2054 
[2023-12-25 08:48:16,213 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5195 
[2023-12-25 08:48:18,025 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0733 
[2023-12-25 08:48:20,144 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2376 
[2023-12-25 08:48:21,001 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2822 
[2023-12-25 08:48:21,427 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7376 
[2023-12-25 08:48:22,335 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.7396 
[2023-12-25 08:48:25,285 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9214 
[2023-12-25 08:48:25,751 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.8764 
[2023-12-25 08:48:26,368 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5332 
[2023-12-25 08:48:27,072 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4014 
[2023-12-25 08:48:28,647 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6581/0.7244/0.8962.
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9173/0.9454
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9777/0.9901
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8381/0.9683
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2956/0.3217
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6390/0.6784
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6113/0.6936
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8158/0.8843
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8851/0.9653
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5072/0.5648
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7372/0.8757
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7526/0.8490
[2023-12-25 08:48:28,648 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5783/0.6809
[2023-12-25 08:48:28,649 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 08:48:28,652 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.6581
[2023-12-25 08:48:28,652 INFO misc.py line 165 253097] Currently Best mIoU: 0.6581
[2023-12-25 08:48:28,652 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 08:48:45,326 INFO misc.py line 119 253097] Train: [31/100][1/510] Data 9.288 (9.288) Batch 10.349 (10.349) Remain 102:37:29 loss: 0.1676 Lr: 0.00503
[2023-12-25 08:48:46,482 INFO misc.py line 119 253097] Train: [31/100][2/510] Data 0.005 (0.005) Batch 1.156 (1.156) Remain 11:27:29 loss: 0.3236 Lr: 0.00503
[2023-12-25 08:48:47,568 INFO misc.py line 119 253097] Train: [31/100][3/510] Data 0.005 (0.005) Batch 1.084 (1.084) Remain 10:45:10 loss: 0.1417 Lr: 0.00503
[2023-12-25 08:48:48,834 INFO misc.py line 119 253097] Train: [31/100][4/510] Data 0.008 (0.008) Batch 1.268 (1.268) Remain 12:34:15 loss: 0.2055 Lr: 0.00503
[2023-12-25 08:48:50,043 INFO misc.py line 119 253097] Train: [31/100][5/510] Data 0.005 (0.007) Batch 1.187 (1.227) Remain 12:10:07 loss: 0.2278 Lr: 0.00503
[2023-12-25 08:48:51,258 INFO misc.py line 119 253097] Train: [31/100][6/510] Data 0.031 (0.015) Batch 1.236 (1.230) Remain 12:11:53 loss: 0.2357 Lr: 0.00503
[2023-12-25 08:48:52,321 INFO misc.py line 119 253097] Train: [31/100][7/510] Data 0.005 (0.012) Batch 1.059 (1.187) Remain 11:46:23 loss: 0.3861 Lr: 0.00503
[2023-12-25 08:48:53,409 INFO misc.py line 119 253097] Train: [31/100][8/510] Data 0.012 (0.012) Batch 1.088 (1.167) Remain 11:34:29 loss: 0.1461 Lr: 0.00503
[2023-12-25 08:48:58,776 INFO misc.py line 119 253097] Train: [31/100][9/510] Data 4.195 (0.709) Batch 5.373 (1.868) Remain 18:31:26 loss: 0.1425 Lr: 0.00503
[2023-12-25 08:48:59,954 INFO misc.py line 119 253097] Train: [31/100][10/510] Data 0.004 (0.609) Batch 1.177 (1.770) Remain 17:32:39 loss: 0.2427 Lr: 0.00503
[2023-12-25 08:49:01,015 INFO misc.py line 119 253097] Train: [31/100][11/510] Data 0.005 (0.533) Batch 1.060 (1.681) Remain 16:39:53 loss: 0.2119 Lr: 0.00503
[2023-12-25 08:49:02,212 INFO misc.py line 119 253097] Train: [31/100][12/510] Data 0.006 (0.474) Batch 1.196 (1.627) Remain 16:07:50 loss: 0.2595 Lr: 0.00503
[2023-12-25 08:49:03,551 INFO misc.py line 119 253097] Train: [31/100][13/510] Data 0.006 (0.428) Batch 1.336 (1.598) Remain 15:50:30 loss: 0.2893 Lr: 0.00503
[2023-12-25 08:49:04,682 INFO misc.py line 119 253097] Train: [31/100][14/510] Data 0.009 (0.390) Batch 1.135 (1.556) Remain 15:25:27 loss: 0.4305 Lr: 0.00503
[2023-12-25 08:49:06,246 INFO misc.py line 119 253097] Train: [31/100][15/510] Data 0.634 (0.410) Batch 1.558 (1.556) Remain 15:25:30 loss: 0.3564 Lr: 0.00503
[2023-12-25 08:49:07,358 INFO misc.py line 119 253097] Train: [31/100][16/510] Data 0.011 (0.379) Batch 1.115 (1.522) Remain 15:05:17 loss: 0.2915 Lr: 0.00503
[2023-12-25 08:49:08,334 INFO misc.py line 119 253097] Train: [31/100][17/510] Data 0.008 (0.353) Batch 0.977 (1.483) Remain 14:42:06 loss: 0.2284 Lr: 0.00503
[2023-12-25 08:49:09,359 INFO misc.py line 119 253097] Train: [31/100][18/510] Data 0.007 (0.330) Batch 1.027 (1.453) Remain 14:24:00 loss: 0.2459 Lr: 0.00503
[2023-12-25 08:49:10,495 INFO misc.py line 119 253097] Train: [31/100][19/510] Data 0.005 (0.309) Batch 1.136 (1.433) Remain 14:12:13 loss: 0.1818 Lr: 0.00503
[2023-12-25 08:49:11,658 INFO misc.py line 119 253097] Train: [31/100][20/510] Data 0.005 (0.291) Batch 1.162 (1.417) Remain 14:02:42 loss: 0.1816 Lr: 0.00503
[2023-12-25 08:49:12,789 INFO misc.py line 119 253097] Train: [31/100][21/510] Data 0.006 (0.276) Batch 1.131 (1.401) Remain 13:53:14 loss: 0.3522 Lr: 0.00503
[2023-12-25 08:49:14,721 INFO misc.py line 119 253097] Train: [31/100][22/510] Data 0.801 (0.303) Batch 1.927 (1.429) Remain 14:09:41 loss: 0.3119 Lr: 0.00503
[2023-12-25 08:49:15,861 INFO misc.py line 119 253097] Train: [31/100][23/510] Data 0.010 (0.289) Batch 1.142 (1.415) Remain 14:01:07 loss: 0.2692 Lr: 0.00503
[2023-12-25 08:49:17,151 INFO misc.py line 119 253097] Train: [31/100][24/510] Data 0.008 (0.275) Batch 1.289 (1.409) Remain 13:57:32 loss: 0.1760 Lr: 0.00503
[2023-12-25 08:49:18,361 INFO misc.py line 119 253097] Train: [31/100][25/510] Data 0.009 (0.263) Batch 1.214 (1.400) Remain 13:52:15 loss: 0.3697 Lr: 0.00503
[2023-12-25 08:49:19,550 INFO misc.py line 119 253097] Train: [31/100][26/510] Data 0.005 (0.252) Batch 1.186 (1.390) Remain 13:46:42 loss: 0.1728 Lr: 0.00503
[2023-12-25 08:49:22,810 INFO misc.py line 119 253097] Train: [31/100][27/510] Data 0.007 (0.242) Batch 3.263 (1.468) Remain 14:33:04 loss: 0.2174 Lr: 0.00503
[2023-12-25 08:49:23,865 INFO misc.py line 119 253097] Train: [31/100][28/510] Data 0.005 (0.232) Batch 1.056 (1.452) Remain 14:23:14 loss: 0.1408 Lr: 0.00503
[2023-12-25 08:49:25,127 INFO misc.py line 119 253097] Train: [31/100][29/510] Data 0.004 (0.223) Batch 1.257 (1.444) Remain 14:18:45 loss: 0.2135 Lr: 0.00503
[2023-12-25 08:49:26,329 INFO misc.py line 119 253097] Train: [31/100][30/510] Data 0.009 (0.216) Batch 1.206 (1.436) Remain 14:13:28 loss: 0.2152 Lr: 0.00503
[2023-12-25 08:49:27,453 INFO misc.py line 119 253097] Train: [31/100][31/510] Data 0.005 (0.208) Batch 1.121 (1.424) Remain 14:06:46 loss: 0.1986 Lr: 0.00503
[2023-12-25 08:49:28,480 INFO misc.py line 119 253097] Train: [31/100][32/510] Data 0.008 (0.201) Batch 1.030 (1.411) Remain 13:58:40 loss: 0.2620 Lr: 0.00503
[2023-12-25 08:49:29,586 INFO misc.py line 119 253097] Train: [31/100][33/510] Data 0.004 (0.195) Batch 1.101 (1.400) Remain 13:52:31 loss: 0.4372 Lr: 0.00503
[2023-12-25 08:49:30,898 INFO misc.py line 119 253097] Train: [31/100][34/510] Data 0.011 (0.189) Batch 1.313 (1.398) Remain 13:50:49 loss: 0.1494 Lr: 0.00503
[2023-12-25 08:49:31,888 INFO misc.py line 119 253097] Train: [31/100][35/510] Data 0.009 (0.183) Batch 0.995 (1.385) Remain 13:43:18 loss: 0.2241 Lr: 0.00503
[2023-12-25 08:49:33,127 INFO misc.py line 119 253097] Train: [31/100][36/510] Data 0.003 (0.178) Batch 1.238 (1.381) Remain 13:40:38 loss: 0.1710 Lr: 0.00503
[2023-12-25 08:49:34,435 INFO misc.py line 119 253097] Train: [31/100][37/510] Data 0.005 (0.172) Batch 1.303 (1.378) Remain 13:39:14 loss: 0.1459 Lr: 0.00503
[2023-12-25 08:49:35,636 INFO misc.py line 119 253097] Train: [31/100][38/510] Data 0.010 (0.168) Batch 1.202 (1.373) Remain 13:36:13 loss: 0.1593 Lr: 0.00503
[2023-12-25 08:49:36,710 INFO misc.py line 119 253097] Train: [31/100][39/510] Data 0.010 (0.163) Batch 1.080 (1.365) Remain 13:31:22 loss: 0.4944 Lr: 0.00503
[2023-12-25 08:49:37,675 INFO misc.py line 119 253097] Train: [31/100][40/510] Data 0.003 (0.159) Batch 0.964 (1.354) Remain 13:24:54 loss: 0.0542 Lr: 0.00503
[2023-12-25 08:49:38,799 INFO misc.py line 119 253097] Train: [31/100][41/510] Data 0.004 (0.155) Batch 1.123 (1.348) Remain 13:21:15 loss: 0.2111 Lr: 0.00503
[2023-12-25 08:49:40,077 INFO misc.py line 119 253097] Train: [31/100][42/510] Data 0.007 (0.151) Batch 1.280 (1.346) Remain 13:20:12 loss: 0.2549 Lr: 0.00503
[2023-12-25 08:49:41,198 INFO misc.py line 119 253097] Train: [31/100][43/510] Data 0.004 (0.148) Batch 1.117 (1.341) Remain 13:16:46 loss: 0.2772 Lr: 0.00503
[2023-12-25 08:49:42,078 INFO misc.py line 119 253097] Train: [31/100][44/510] Data 0.007 (0.144) Batch 0.883 (1.330) Remain 13:10:06 loss: 0.3084 Lr: 0.00503
[2023-12-25 08:49:43,133 INFO misc.py line 119 253097] Train: [31/100][45/510] Data 0.006 (0.141) Batch 1.053 (1.323) Remain 13:06:10 loss: 0.0843 Lr: 0.00503
[2023-12-25 08:49:44,405 INFO misc.py line 119 253097] Train: [31/100][46/510] Data 0.007 (0.138) Batch 1.266 (1.322) Remain 13:05:21 loss: 0.4017 Lr: 0.00503
[2023-12-25 08:49:45,545 INFO misc.py line 119 253097] Train: [31/100][47/510] Data 0.012 (0.135) Batch 1.146 (1.318) Remain 13:02:57 loss: 0.0962 Lr: 0.00503
[2023-12-25 08:49:46,695 INFO misc.py line 119 253097] Train: [31/100][48/510] Data 0.009 (0.132) Batch 1.149 (1.314) Remain 13:00:43 loss: 0.4693 Lr: 0.00503
[2023-12-25 08:49:54,253 INFO misc.py line 119 253097] Train: [31/100][49/510] Data 0.007 (0.129) Batch 7.562 (1.450) Remain 14:21:24 loss: 0.1927 Lr: 0.00503
[2023-12-25 08:49:55,503 INFO misc.py line 119 253097] Train: [31/100][50/510] Data 0.004 (0.127) Batch 1.249 (1.445) Remain 14:18:50 loss: 0.2490 Lr: 0.00503
[2023-12-25 08:49:56,736 INFO misc.py line 119 253097] Train: [31/100][51/510] Data 0.005 (0.124) Batch 1.229 (1.441) Remain 14:16:08 loss: 0.2831 Lr: 0.00503
[2023-12-25 08:49:58,004 INFO misc.py line 119 253097] Train: [31/100][52/510] Data 0.009 (0.122) Batch 1.271 (1.437) Remain 14:14:03 loss: 0.1393 Lr: 0.00503
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[2023-12-25 09:01:16,674 INFO misc.py line 119 253097] Train: [31/100][471/510] Data 7.603 (0.083) Batch 8.622 (1.601) Remain 15:39:49 loss: 0.2688 Lr: 0.00496
[2023-12-25 09:01:17,903 INFO misc.py line 119 253097] Train: [31/100][472/510] Data 0.004 (0.082) Batch 1.228 (1.600) Remain 15:39:19 loss: 0.2055 Lr: 0.00496
[2023-12-25 09:01:18,927 INFO misc.py line 119 253097] Train: [31/100][473/510] Data 0.005 (0.082) Batch 1.023 (1.599) Remain 15:38:35 loss: 0.1268 Lr: 0.00496
[2023-12-25 09:01:20,076 INFO misc.py line 119 253097] Train: [31/100][474/510] Data 0.007 (0.082) Batch 1.150 (1.598) Remain 15:37:59 loss: 0.2402 Lr: 0.00496
[2023-12-25 09:01:20,930 INFO misc.py line 119 253097] Train: [31/100][475/510] Data 0.005 (0.082) Batch 0.856 (1.596) Remain 15:37:02 loss: 0.2331 Lr: 0.00496
[2023-12-25 09:01:22,087 INFO misc.py line 119 253097] Train: [31/100][476/510] Data 0.003 (0.082) Batch 1.156 (1.595) Remain 15:36:28 loss: 0.2193 Lr: 0.00496
[2023-12-25 09:01:23,235 INFO misc.py line 119 253097] Train: [31/100][477/510] Data 0.004 (0.082) Batch 1.145 (1.594) Remain 15:35:53 loss: 0.2167 Lr: 0.00496
[2023-12-25 09:01:24,339 INFO misc.py line 119 253097] Train: [31/100][478/510] Data 0.007 (0.081) Batch 1.103 (1.593) Remain 15:35:15 loss: 0.1857 Lr: 0.00496
[2023-12-25 09:01:25,624 INFO misc.py line 119 253097] Train: [31/100][479/510] Data 0.008 (0.081) Batch 1.290 (1.593) Remain 15:34:51 loss: 0.1462 Lr: 0.00496
[2023-12-25 09:01:26,830 INFO misc.py line 119 253097] Train: [31/100][480/510] Data 0.003 (0.081) Batch 1.201 (1.592) Remain 15:34:20 loss: 0.1362 Lr: 0.00496
[2023-12-25 09:01:28,094 INFO misc.py line 119 253097] Train: [31/100][481/510] Data 0.008 (0.081) Batch 1.264 (1.591) Remain 15:33:55 loss: 0.0958 Lr: 0.00496
[2023-12-25 09:01:29,295 INFO misc.py line 119 253097] Train: [31/100][482/510] Data 0.007 (0.081) Batch 1.202 (1.590) Remain 15:33:25 loss: 0.2107 Lr: 0.00496
[2023-12-25 09:01:30,341 INFO misc.py line 119 253097] Train: [31/100][483/510] Data 0.008 (0.081) Batch 1.047 (1.589) Remain 15:32:43 loss: 0.3707 Lr: 0.00496
[2023-12-25 09:01:31,464 INFO misc.py line 119 253097] Train: [31/100][484/510] Data 0.006 (0.081) Batch 1.124 (1.588) Remain 15:32:07 loss: 0.1980 Lr: 0.00496
[2023-12-25 09:01:32,683 INFO misc.py line 119 253097] Train: [31/100][485/510] Data 0.006 (0.080) Batch 1.218 (1.587) Remain 15:31:39 loss: 0.2748 Lr: 0.00496
[2023-12-25 09:01:33,743 INFO misc.py line 119 253097] Train: [31/100][486/510] Data 0.008 (0.080) Batch 1.063 (1.586) Remain 15:30:59 loss: 0.2341 Lr: 0.00496
[2023-12-25 09:01:34,879 INFO misc.py line 119 253097] Train: [31/100][487/510] Data 0.004 (0.080) Batch 1.137 (1.585) Remain 15:30:25 loss: 0.3118 Lr: 0.00496
[2023-12-25 09:01:43,412 INFO misc.py line 119 253097] Train: [31/100][488/510] Data 0.003 (0.080) Batch 8.532 (1.600) Remain 15:38:47 loss: 0.3795 Lr: 0.00496
[2023-12-25 09:01:44,635 INFO misc.py line 119 253097] Train: [31/100][489/510] Data 0.005 (0.080) Batch 1.223 (1.599) Remain 15:38:19 loss: 0.1496 Lr: 0.00496
[2023-12-25 09:01:45,797 INFO misc.py line 119 253097] Train: [31/100][490/510] Data 0.003 (0.080) Batch 1.158 (1.598) Remain 15:37:45 loss: 0.2577 Lr: 0.00496
[2023-12-25 09:01:47,043 INFO misc.py line 119 253097] Train: [31/100][491/510] Data 0.007 (0.079) Batch 1.244 (1.597) Remain 15:37:18 loss: 0.4152 Lr: 0.00496
[2023-12-25 09:01:48,221 INFO misc.py line 119 253097] Train: [31/100][492/510] Data 0.009 (0.079) Batch 1.183 (1.596) Remain 15:36:47 loss: 0.2279 Lr: 0.00496
[2023-12-25 09:01:49,309 INFO misc.py line 119 253097] Train: [31/100][493/510] Data 0.005 (0.079) Batch 1.087 (1.595) Remain 15:36:08 loss: 0.2902 Lr: 0.00496
[2023-12-25 09:01:50,484 INFO misc.py line 119 253097] Train: [31/100][494/510] Data 0.005 (0.079) Batch 1.170 (1.595) Remain 15:35:36 loss: 0.2921 Lr: 0.00496
[2023-12-25 09:01:51,636 INFO misc.py line 119 253097] Train: [31/100][495/510] Data 0.010 (0.079) Batch 1.157 (1.594) Remain 15:35:03 loss: 0.1684 Lr: 0.00496
[2023-12-25 09:01:52,901 INFO misc.py line 119 253097] Train: [31/100][496/510] Data 0.006 (0.079) Batch 1.267 (1.593) Remain 15:34:38 loss: 0.1117 Lr: 0.00496
[2023-12-25 09:01:54,141 INFO misc.py line 119 253097] Train: [31/100][497/510] Data 0.003 (0.079) Batch 1.218 (1.592) Remain 15:34:10 loss: 0.1681 Lr: 0.00496
[2023-12-25 09:01:55,277 INFO misc.py line 119 253097] Train: [31/100][498/510] Data 0.025 (0.078) Batch 1.156 (1.591) Remain 15:33:38 loss: 0.3531 Lr: 0.00496
[2023-12-25 09:01:56,308 INFO misc.py line 119 253097] Train: [31/100][499/510] Data 0.005 (0.078) Batch 1.029 (1.590) Remain 15:32:56 loss: 0.3143 Lr: 0.00496
[2023-12-25 09:01:57,414 INFO misc.py line 119 253097] Train: [31/100][500/510] Data 0.007 (0.078) Batch 1.108 (1.589) Remain 15:32:20 loss: 0.4452 Lr: 0.00496
[2023-12-25 09:01:58,685 INFO misc.py line 119 253097] Train: [31/100][501/510] Data 0.005 (0.078) Batch 1.272 (1.589) Remain 15:31:56 loss: 0.1557 Lr: 0.00496
[2023-12-25 09:01:59,566 INFO misc.py line 119 253097] Train: [31/100][502/510] Data 0.004 (0.078) Batch 0.881 (1.587) Remain 15:31:05 loss: 0.2395 Lr: 0.00496
[2023-12-25 09:02:00,772 INFO misc.py line 119 253097] Train: [31/100][503/510] Data 0.003 (0.078) Batch 1.202 (1.586) Remain 15:30:36 loss: 0.3175 Lr: 0.00496
[2023-12-25 09:02:01,715 INFO misc.py line 119 253097] Train: [31/100][504/510] Data 0.008 (0.078) Batch 0.948 (1.585) Remain 15:29:50 loss: 0.3200 Lr: 0.00496
[2023-12-25 09:02:02,772 INFO misc.py line 119 253097] Train: [31/100][505/510] Data 0.003 (0.077) Batch 1.056 (1.584) Remain 15:29:11 loss: 0.1675 Lr: 0.00496
[2023-12-25 09:02:03,900 INFO misc.py line 119 253097] Train: [31/100][506/510] Data 0.005 (0.077) Batch 1.129 (1.583) Remain 15:28:38 loss: 0.3859 Lr: 0.00496
[2023-12-25 09:02:04,940 INFO misc.py line 119 253097] Train: [31/100][507/510] Data 0.003 (0.077) Batch 1.040 (1.582) Remain 15:27:58 loss: 0.1436 Lr: 0.00496
[2023-12-25 09:02:06,119 INFO misc.py line 119 253097] Train: [31/100][508/510] Data 0.003 (0.077) Batch 1.177 (1.581) Remain 15:27:28 loss: 0.2857 Lr: 0.00496
[2023-12-25 09:02:07,289 INFO misc.py line 119 253097] Train: [31/100][509/510] Data 0.005 (0.077) Batch 1.168 (1.580) Remain 15:26:58 loss: 0.3075 Lr: 0.00496
[2023-12-25 09:02:08,474 INFO misc.py line 119 253097] Train: [31/100][510/510] Data 0.008 (0.077) Batch 1.188 (1.580) Remain 15:26:29 loss: 0.3550 Lr: 0.00496
[2023-12-25 09:02:08,474 INFO misc.py line 136 253097] Train result: loss: 0.2394 
[2023-12-25 09:02:08,475 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 09:02:37,626 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.8359 
[2023-12-25 09:02:37,971 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.5298 
[2023-12-25 09:02:42,910 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5231 
[2023-12-25 09:02:43,426 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5959 
[2023-12-25 09:02:45,401 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6956 
[2023-12-25 09:02:45,824 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.5535 
[2023-12-25 09:02:46,704 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2415 
[2023-12-25 09:02:47,268 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.7117 
[2023-12-25 09:02:49,080 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.4022 
[2023-12-25 09:02:51,209 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.5367 
[2023-12-25 09:02:52,074 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2898 
[2023-12-25 09:02:52,499 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.1116 
[2023-12-25 09:02:53,409 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5606 
[2023-12-25 09:02:56,356 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 1.1391 
[2023-12-25 09:02:56,822 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.7079 
[2023-12-25 09:02:57,437 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5803 
[2023-12-25 09:02:58,153 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4658 
[2023-12-25 09:02:59,536 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6149/0.6815/0.8753.
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.8994/0.9466
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9623/0.9740
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8086/0.9719
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0047/0.0662
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.1276/0.1299
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5709/0.5840
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.4297/0.4516
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7975/0.8894
[2023-12-25 09:02:59,536 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9030/0.9410
[2023-12-25 09:02:59,537 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5361/0.6570
[2023-12-25 09:02:59,537 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7108/0.8482
[2023-12-25 09:02:59,537 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7028/0.7340
[2023-12-25 09:02:59,537 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5404/0.6659
[2023-12-25 09:02:59,537 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 09:02:59,539 INFO misc.py line 165 253097] Currently Best mIoU: 0.6581
[2023-12-25 09:02:59,539 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 09:03:11,282 INFO misc.py line 119 253097] Train: [32/100][1/510] Data 4.173 (4.173) Batch 8.938 (8.938) Remain 87:21:46 loss: 0.3907 Lr: 0.00496
[2023-12-25 09:03:13,376 INFO misc.py line 119 253097] Train: [32/100][2/510] Data 0.976 (0.976) Batch 2.095 (2.095) Remain 20:28:54 loss: 0.1705 Lr: 0.00496
[2023-12-25 09:03:14,362 INFO misc.py line 119 253097] Train: [32/100][3/510] Data 0.004 (0.004) Batch 0.986 (0.986) Remain 09:38:07 loss: 0.2696 Lr: 0.00496
[2023-12-25 09:03:15,590 INFO misc.py line 119 253097] Train: [32/100][4/510] Data 0.004 (0.004) Batch 1.228 (1.228) Remain 12:00:11 loss: 0.2128 Lr: 0.00496
[2023-12-25 09:03:22,809 INFO misc.py line 119 253097] Train: [32/100][5/510] Data 4.995 (2.499) Batch 7.220 (4.224) Remain 41:16:57 loss: 0.2188 Lr: 0.00496
[2023-12-25 09:03:23,831 INFO misc.py line 119 253097] Train: [32/100][6/510] Data 0.004 (1.667) Batch 1.022 (3.156) Remain 30:50:56 loss: 0.1345 Lr: 0.00496
[2023-12-25 09:03:25,074 INFO misc.py line 119 253097] Train: [32/100][7/510] Data 0.004 (1.252) Batch 1.228 (2.674) Remain 26:08:09 loss: 0.2676 Lr: 0.00496
[2023-12-25 09:03:26,339 INFO misc.py line 119 253097] Train: [32/100][8/510] Data 0.019 (1.005) Batch 1.273 (2.394) Remain 23:23:44 loss: 0.3529 Lr: 0.00496
[2023-12-25 09:03:27,443 INFO misc.py line 119 253097] Train: [32/100][9/510] Data 0.012 (0.839) Batch 1.111 (2.180) Remain 21:18:21 loss: 0.1614 Lr: 0.00496
[2023-12-25 09:03:28,629 INFO misc.py line 119 253097] Train: [32/100][10/510] Data 0.003 (0.720) Batch 1.186 (2.038) Remain 19:55:00 loss: 0.2563 Lr: 0.00496
[2023-12-25 09:03:41,503 INFO misc.py line 119 253097] Train: [32/100][11/510] Data 11.862 (2.113) Batch 12.873 (3.393) Remain 33:09:05 loss: 0.1418 Lr: 0.00496
[2023-12-25 09:03:42,815 INFO misc.py line 119 253097] Train: [32/100][12/510] Data 0.004 (1.879) Batch 1.313 (3.161) Remain 30:53:34 loss: 0.4828 Lr: 0.00496
[2023-12-25 09:03:44,086 INFO misc.py line 119 253097] Train: [32/100][13/510] Data 0.004 (1.691) Batch 1.262 (2.972) Remain 29:02:10 loss: 0.1883 Lr: 0.00496
[2023-12-25 09:03:45,145 INFO misc.py line 119 253097] Train: [32/100][14/510] Data 0.012 (1.538) Batch 1.063 (2.798) Remain 27:20:25 loss: 0.1457 Lr: 0.00496
[2023-12-25 09:03:46,264 INFO misc.py line 119 253097] Train: [32/100][15/510] Data 0.008 (1.411) Batch 1.121 (2.658) Remain 25:58:25 loss: 0.1440 Lr: 0.00496
[2023-12-25 09:03:47,446 INFO misc.py line 119 253097] Train: [32/100][16/510] Data 0.007 (1.303) Batch 1.182 (2.545) Remain 24:51:46 loss: 0.1488 Lr: 0.00496
[2023-12-25 09:03:48,630 INFO misc.py line 119 253097] Train: [32/100][17/510] Data 0.008 (1.210) Batch 1.175 (2.447) Remain 23:54:24 loss: 0.4702 Lr: 0.00496
[2023-12-25 09:03:49,865 INFO misc.py line 119 253097] Train: [32/100][18/510] Data 0.016 (1.131) Batch 1.243 (2.367) Remain 23:07:18 loss: 0.4598 Lr: 0.00496
[2023-12-25 09:03:51,072 INFO misc.py line 119 253097] Train: [32/100][19/510] Data 0.007 (1.061) Batch 1.211 (2.294) Remain 22:24:55 loss: 0.2000 Lr: 0.00496
[2023-12-25 09:03:52,161 INFO misc.py line 119 253097] Train: [32/100][20/510] Data 0.004 (0.998) Batch 1.086 (2.223) Remain 21:43:13 loss: 0.2342 Lr: 0.00496
[2023-12-25 09:03:53,123 INFO misc.py line 119 253097] Train: [32/100][21/510] Data 0.007 (0.943) Batch 0.965 (2.153) Remain 21:02:12 loss: 0.2290 Lr: 0.00496
[2023-12-25 09:03:54,280 INFO misc.py line 119 253097] Train: [32/100][22/510] Data 0.005 (0.894) Batch 1.157 (2.101) Remain 20:31:25 loss: 0.2150 Lr: 0.00496
[2023-12-25 09:03:55,481 INFO misc.py line 119 253097] Train: [32/100][23/510] Data 0.004 (0.849) Batch 1.200 (2.056) Remain 20:05:00 loss: 0.3283 Lr: 0.00496
[2023-12-25 09:03:56,556 INFO misc.py line 119 253097] Train: [32/100][24/510] Data 0.005 (0.809) Batch 1.075 (2.009) Remain 19:37:35 loss: 0.4869 Lr: 0.00495
[2023-12-25 09:03:57,807 INFO misc.py line 119 253097] Train: [32/100][25/510] Data 0.005 (0.773) Batch 1.246 (1.975) Remain 19:17:13 loss: 0.1567 Lr: 0.00495
[2023-12-25 09:03:58,860 INFO misc.py line 119 253097] Train: [32/100][26/510] Data 0.010 (0.739) Batch 1.054 (1.935) Remain 18:53:45 loss: 0.1250 Lr: 0.00495
[2023-12-25 09:04:00,137 INFO misc.py line 119 253097] Train: [32/100][27/510] Data 0.008 (0.709) Batch 1.275 (1.907) Remain 18:37:37 loss: 0.1782 Lr: 0.00495
[2023-12-25 09:04:21,590 INFO misc.py line 119 253097] Train: [32/100][28/510] Data 20.391 (1.496) Batch 21.459 (2.689) Remain 26:15:54 loss: 1.1007 Lr: 0.00495
[2023-12-25 09:04:22,702 INFO misc.py line 119 253097] Train: [32/100][29/510] Data 0.004 (1.439) Batch 1.110 (2.628) Remain 25:40:15 loss: 0.1836 Lr: 0.00495
[2023-12-25 09:04:23,948 INFO misc.py line 119 253097] Train: [32/100][30/510] Data 0.006 (1.386) Batch 1.248 (2.577) Remain 25:10:15 loss: 0.4173 Lr: 0.00495
[2023-12-25 09:04:25,145 INFO misc.py line 119 253097] Train: [32/100][31/510] Data 0.005 (1.336) Batch 1.191 (2.528) Remain 24:41:12 loss: 0.2233 Lr: 0.00495
[2023-12-25 09:04:26,349 INFO misc.py line 119 253097] Train: [32/100][32/510] Data 0.011 (1.291) Batch 1.209 (2.482) Remain 24:14:31 loss: 0.1616 Lr: 0.00495
[2023-12-25 09:04:27,400 INFO misc.py line 119 253097] Train: [32/100][33/510] Data 0.005 (1.248) Batch 1.053 (2.435) Remain 23:46:33 loss: 0.1194 Lr: 0.00495
[2023-12-25 09:04:28,691 INFO misc.py line 119 253097] Train: [32/100][34/510] Data 0.003 (1.208) Batch 1.288 (2.398) Remain 23:24:50 loss: 0.6470 Lr: 0.00495
[2023-12-25 09:04:29,915 INFO misc.py line 119 253097] Train: [32/100][35/510] Data 0.007 (1.170) Batch 1.223 (2.361) Remain 23:03:18 loss: 0.3544 Lr: 0.00495
[2023-12-25 09:04:31,215 INFO misc.py line 119 253097] Train: [32/100][36/510] Data 0.008 (1.135) Batch 1.302 (2.329) Remain 22:44:27 loss: 0.4157 Lr: 0.00495
[2023-12-25 09:04:32,363 INFO misc.py line 119 253097] Train: [32/100][37/510] Data 0.006 (1.102) Batch 1.147 (2.294) Remain 22:24:02 loss: 0.2364 Lr: 0.00495
[2023-12-25 09:04:33,341 INFO misc.py line 119 253097] Train: [32/100][38/510] Data 0.007 (1.071) Batch 0.982 (2.257) Remain 22:02:02 loss: 0.3271 Lr: 0.00495
[2023-12-25 09:04:34,469 INFO misc.py line 119 253097] Train: [32/100][39/510] Data 0.004 (1.041) Batch 1.127 (2.225) Remain 21:43:37 loss: 0.1561 Lr: 0.00495
[2023-12-25 09:04:35,676 INFO misc.py line 119 253097] Train: [32/100][40/510] Data 0.004 (1.013) Batch 1.207 (2.198) Remain 21:27:28 loss: 0.2900 Lr: 0.00495
[2023-12-25 09:04:36,881 INFO misc.py line 119 253097] Train: [32/100][41/510] Data 0.003 (0.986) Batch 1.199 (2.171) Remain 21:12:02 loss: 0.2994 Lr: 0.00495
[2023-12-25 09:04:37,892 INFO misc.py line 119 253097] Train: [32/100][42/510] Data 0.009 (0.961) Batch 1.012 (2.142) Remain 20:54:35 loss: 0.1736 Lr: 0.00495
[2023-12-25 09:04:39,113 INFO misc.py line 119 253097] Train: [32/100][43/510] Data 0.010 (0.937) Batch 1.225 (2.119) Remain 20:41:07 loss: 0.3041 Lr: 0.00495
[2023-12-25 09:04:40,200 INFO misc.py line 119 253097] Train: [32/100][44/510] Data 0.005 (0.915) Batch 1.088 (2.094) Remain 20:26:22 loss: 0.3182 Lr: 0.00495
[2023-12-25 09:04:41,307 INFO misc.py line 119 253097] Train: [32/100][45/510] Data 0.004 (0.893) Batch 1.106 (2.070) Remain 20:12:33 loss: 0.1901 Lr: 0.00495
[2023-12-25 09:04:42,560 INFO misc.py line 119 253097] Train: [32/100][46/510] Data 0.004 (0.872) Batch 1.244 (2.051) Remain 20:01:16 loss: 0.1420 Lr: 0.00495
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[2023-12-25 09:04:53,116 INFO misc.py line 119 253097] Train: [32/100][48/510] Data 0.003 (0.834) Batch 1.196 (2.195) Remain 21:25:20 loss: 0.1976 Lr: 0.00495
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[2023-12-25 09:05:02,838 INFO misc.py line 119 253097] Train: [32/100][56/510] Data 0.003 (0.709) Batch 1.251 (2.047) Remain 19:58:26 loss: 0.2821 Lr: 0.00495
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[2023-12-25 09:05:15,173 INFO misc.py line 119 253097] Train: [32/100][67/510] Data 0.006 (0.588) Batch 1.284 (1.888) Remain 18:24:59 loss: 0.3162 Lr: 0.00495
[2023-12-25 09:05:16,351 INFO misc.py line 119 253097] Train: [32/100][68/510] Data 0.006 (0.579) Batch 1.179 (1.877) Remain 18:18:35 loss: 0.2216 Lr: 0.00495
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[2023-12-25 09:05:33,722 INFO misc.py line 119 253097] Train: [32/100][75/510] Data 0.003 (0.524) Batch 1.003 (1.936) Remain 18:52:46 loss: 0.1623 Lr: 0.00495
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[2023-12-25 09:05:36,060 INFO misc.py line 119 253097] Train: [32/100][77/510] Data 0.007 (0.510) Batch 1.179 (1.915) Remain 18:40:33 loss: 0.3842 Lr: 0.00495
[2023-12-25 09:05:37,345 INFO misc.py line 119 253097] Train: [32/100][78/510] Data 0.008 (0.503) Batch 1.289 (1.906) Remain 18:35:39 loss: 0.5444 Lr: 0.00495
[2023-12-25 09:05:38,551 INFO misc.py line 119 253097] Train: [32/100][79/510] Data 0.004 (0.496) Batch 1.202 (1.897) Remain 18:30:11 loss: 0.1860 Lr: 0.00495
[2023-12-25 09:05:39,702 INFO misc.py line 119 253097] Train: [32/100][80/510] Data 0.008 (0.490) Batch 1.153 (1.888) Remain 18:24:30 loss: 0.4066 Lr: 0.00495
[2023-12-25 09:05:40,838 INFO misc.py line 119 253097] Train: [32/100][81/510] Data 0.006 (0.484) Batch 1.136 (1.878) Remain 18:18:49 loss: 0.3393 Lr: 0.00495
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[2023-12-25 09:05:43,148 INFO misc.py line 119 253097] Train: [32/100][83/510] Data 0.009 (0.472) Batch 1.226 (1.860) Remain 18:08:11 loss: 0.4140 Lr: 0.00495
[2023-12-25 09:05:44,134 INFO misc.py line 119 253097] Train: [32/100][84/510] Data 0.008 (0.466) Batch 0.990 (1.849) Remain 18:01:52 loss: 0.2076 Lr: 0.00495
[2023-12-25 09:05:45,373 INFO misc.py line 119 253097] Train: [32/100][85/510] Data 0.003 (0.460) Batch 1.235 (1.842) Remain 17:57:27 loss: 0.2170 Lr: 0.00495
[2023-12-25 09:05:46,590 INFO misc.py line 119 253097] Train: [32/100][86/510] Data 0.008 (0.455) Batch 1.220 (1.834) Remain 17:53:02 loss: 0.3094 Lr: 0.00495
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[2023-12-25 09:05:49,995 INFO misc.py line 119 253097] Train: [32/100][89/510] Data 0.004 (0.439) Batch 1.253 (1.810) Remain 17:38:41 loss: 0.1507 Lr: 0.00495
[2023-12-25 09:05:51,234 INFO misc.py line 119 253097] Train: [32/100][90/510] Data 0.004 (0.434) Batch 1.238 (1.803) Remain 17:34:49 loss: 0.2452 Lr: 0.00495
[2023-12-25 09:05:52,265 INFO misc.py line 119 253097] Train: [32/100][91/510] Data 0.005 (0.429) Batch 1.032 (1.794) Remain 17:29:39 loss: 0.1480 Lr: 0.00495
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[2023-12-25 09:05:55,515 INFO misc.py line 119 253097] Train: [32/100][94/510] Data 0.008 (0.415) Batch 1.134 (1.771) Remain 17:15:51 loss: 0.3054 Lr: 0.00494
[2023-12-25 09:05:56,406 INFO misc.py line 119 253097] Train: [32/100][95/510] Data 0.004 (0.411) Batch 0.891 (1.761) Remain 17:10:14 loss: 0.2853 Lr: 0.00494
[2023-12-25 09:05:57,508 INFO misc.py line 119 253097] Train: [32/100][96/510] Data 0.004 (0.407) Batch 1.100 (1.754) Remain 17:06:03 loss: 0.2887 Lr: 0.00494
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[2023-12-25 09:05:59,584 INFO misc.py line 119 253097] Train: [32/100][98/510] Data 0.003 (0.398) Batch 1.076 (1.739) Remain 16:57:11 loss: 0.2436 Lr: 0.00494
[2023-12-25 09:06:00,841 INFO misc.py line 119 253097] Train: [32/100][99/510] Data 0.005 (0.394) Batch 1.253 (1.734) Remain 16:54:11 loss: 0.1773 Lr: 0.00494
[2023-12-25 09:06:01,966 INFO misc.py line 119 253097] Train: [32/100][100/510] Data 0.009 (0.390) Batch 1.128 (1.728) Remain 16:50:30 loss: 0.2427 Lr: 0.00494
[2023-12-25 09:06:03,107 INFO misc.py line 119 253097] Train: [32/100][101/510] Data 0.005 (0.386) Batch 1.140 (1.722) Remain 16:46:58 loss: 0.3164 Lr: 0.00494
[2023-12-25 09:06:04,105 INFO misc.py line 119 253097] Train: [32/100][102/510] Data 0.007 (0.382) Batch 0.995 (1.715) Remain 16:42:39 loss: 0.1622 Lr: 0.00494
[2023-12-25 09:06:05,195 INFO misc.py line 119 253097] Train: [32/100][103/510] Data 0.009 (0.379) Batch 1.089 (1.708) Remain 16:38:57 loss: 0.1571 Lr: 0.00494
[2023-12-25 09:06:06,415 INFO misc.py line 119 253097] Train: [32/100][104/510] Data 0.010 (0.375) Batch 1.223 (1.703) Remain 16:36:07 loss: 0.2769 Lr: 0.00494
[2023-12-25 09:06:07,255 INFO misc.py line 119 253097] Train: [32/100][105/510] Data 0.007 (0.371) Batch 0.840 (1.695) Remain 16:31:09 loss: 0.3321 Lr: 0.00494
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[2023-12-25 09:06:11,836 INFO misc.py line 119 253097] Train: [32/100][109/510] Data 0.005 (0.357) Batch 1.175 (1.674) Remain 16:18:55 loss: 0.3571 Lr: 0.00494
[2023-12-25 09:06:12,936 INFO misc.py line 119 253097] Train: [32/100][110/510] Data 0.003 (0.354) Batch 1.099 (1.669) Remain 16:15:45 loss: 0.1907 Lr: 0.00494
[2023-12-25 09:06:13,907 INFO misc.py line 119 253097] Train: [32/100][111/510] Data 0.004 (0.351) Batch 0.970 (1.662) Remain 16:11:56 loss: 0.2149 Lr: 0.00494
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[2023-12-25 09:06:16,320 INFO misc.py line 119 253097] Train: [32/100][113/510] Data 0.004 (0.345) Batch 1.248 (1.654) Remain 16:07:03 loss: 0.1010 Lr: 0.00494
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[2023-12-25 09:06:23,438 INFO misc.py line 119 253097] Train: [32/100][119/510] Data 0.007 (0.327) Batch 1.246 (1.630) Remain 15:52:43 loss: 0.2350 Lr: 0.00494
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[2023-12-25 09:06:33,684 INFO misc.py line 119 253097] Train: [32/100][123/510] Data 0.004 (0.316) Batch 6.535 (1.661) Remain 16:10:46 loss: 0.2350 Lr: 0.00494
[2023-12-25 09:06:35,070 INFO misc.py line 119 253097] Train: [32/100][124/510] Data 0.004 (0.314) Batch 1.344 (1.658) Remain 16:09:13 loss: 0.2485 Lr: 0.00494
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[2023-12-25 09:06:39,635 INFO misc.py line 119 253097] Train: [32/100][128/510] Data 0.006 (0.304) Batch 1.154 (1.642) Remain 15:59:37 loss: 0.3799 Lr: 0.00494
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[2023-12-25 09:07:07,972 INFO misc.py line 119 253097] Train: [32/100][150/510] Data 0.006 (0.260) Batch 0.982 (1.589) Remain 15:28:04 loss: 0.2096 Lr: 0.00494
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[2023-12-25 09:07:12,142 INFO misc.py line 119 253097] Train: [32/100][154/510] Data 0.004 (0.253) Batch 1.068 (1.575) Remain 15:19:31 loss: 0.3859 Lr: 0.00494
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[2023-12-25 09:07:14,149 INFO misc.py line 119 253097] Train: [32/100][156/510] Data 0.004 (0.250) Batch 0.947 (1.567) Remain 15:15:06 loss: 0.3491 Lr: 0.00494
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[2023-12-25 09:07:24,904 INFO misc.py line 119 253097] Train: [32/100][163/510] Data 0.003 (0.239) Batch 1.148 (1.566) Remain 15:14:08 loss: 0.2131 Lr: 0.00493
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[2023-12-25 09:07:37,471 INFO misc.py line 119 253097] Train: [32/100][166/510] Data 9.147 (0.291) Batch 10.181 (1.614) Remain 15:42:14 loss: 0.3934 Lr: 0.00493
[2023-12-25 09:07:38,574 INFO misc.py line 119 253097] Train: [32/100][167/510] Data 0.003 (0.289) Batch 1.103 (1.611) Remain 15:40:23 loss: 0.1986 Lr: 0.00493
[2023-12-25 09:07:39,666 INFO misc.py line 119 253097] Train: [32/100][168/510] Data 0.003 (0.287) Batch 1.092 (1.608) Remain 15:38:31 loss: 0.4808 Lr: 0.00493
[2023-12-25 09:07:40,929 INFO misc.py line 119 253097] Train: [32/100][169/510] Data 0.004 (0.285) Batch 1.263 (1.606) Remain 15:37:17 loss: 0.1861 Lr: 0.00493
[2023-12-25 09:07:42,231 INFO misc.py line 119 253097] Train: [32/100][170/510] Data 0.003 (0.284) Batch 1.302 (1.604) Remain 15:36:12 loss: 0.2356 Lr: 0.00493
[2023-12-25 09:07:43,375 INFO misc.py line 119 253097] Train: [32/100][171/510] Data 0.004 (0.282) Batch 1.144 (1.601) Remain 15:34:34 loss: 0.3145 Lr: 0.00493
[2023-12-25 09:07:44,609 INFO misc.py line 119 253097] Train: [32/100][172/510] Data 0.005 (0.280) Batch 1.232 (1.599) Remain 15:33:16 loss: 0.2887 Lr: 0.00493
[2023-12-25 09:07:45,711 INFO misc.py line 119 253097] Train: [32/100][173/510] Data 0.006 (0.279) Batch 1.100 (1.596) Remain 15:31:32 loss: 0.2293 Lr: 0.00493
[2023-12-25 09:07:46,899 INFO misc.py line 119 253097] Train: [32/100][174/510] Data 0.008 (0.277) Batch 1.194 (1.594) Remain 15:30:07 loss: 0.5961 Lr: 0.00493
[2023-12-25 09:07:48,078 INFO misc.py line 119 253097] Train: [32/100][175/510] Data 0.003 (0.276) Batch 1.175 (1.591) Remain 15:28:41 loss: 0.2290 Lr: 0.00493
[2023-12-25 09:07:49,150 INFO misc.py line 119 253097] Train: [32/100][176/510] Data 0.007 (0.274) Batch 1.072 (1.588) Remain 15:26:54 loss: 0.1647 Lr: 0.00493
[2023-12-25 09:07:50,075 INFO misc.py line 119 253097] Train: [32/100][177/510] Data 0.008 (0.273) Batch 0.926 (1.585) Remain 15:24:39 loss: 0.2575 Lr: 0.00493
[2023-12-25 09:07:51,119 INFO misc.py line 119 253097] Train: [32/100][178/510] Data 0.006 (0.271) Batch 1.044 (1.581) Remain 15:22:49 loss: 0.3384 Lr: 0.00493
[2023-12-25 09:07:52,253 INFO misc.py line 119 253097] Train: [32/100][179/510] Data 0.006 (0.270) Batch 1.135 (1.579) Remain 15:21:19 loss: 0.2889 Lr: 0.00493
[2023-12-25 09:07:53,300 INFO misc.py line 119 253097] Train: [32/100][180/510] Data 0.005 (0.268) Batch 1.047 (1.576) Remain 15:19:32 loss: 0.1819 Lr: 0.00493
[2023-12-25 09:07:54,430 INFO misc.py line 119 253097] Train: [32/100][181/510] Data 0.004 (0.267) Batch 1.131 (1.573) Remain 15:18:03 loss: 0.1981 Lr: 0.00493
[2023-12-25 09:07:55,699 INFO misc.py line 119 253097] Train: [32/100][182/510] Data 0.004 (0.265) Batch 1.264 (1.572) Remain 15:17:01 loss: 0.1188 Lr: 0.00493
[2023-12-25 09:07:56,965 INFO misc.py line 119 253097] Train: [32/100][183/510] Data 0.009 (0.264) Batch 1.270 (1.570) Remain 15:16:01 loss: 0.2120 Lr: 0.00493
[2023-12-25 09:07:58,163 INFO misc.py line 119 253097] Train: [32/100][184/510] Data 0.005 (0.262) Batch 1.195 (1.568) Remain 15:14:47 loss: 0.1543 Lr: 0.00493
[2023-12-25 09:07:59,183 INFO misc.py line 119 253097] Train: [32/100][185/510] Data 0.007 (0.261) Batch 1.023 (1.565) Remain 15:13:00 loss: 0.3247 Lr: 0.00493
[2023-12-25 09:08:00,343 INFO misc.py line 119 253097] Train: [32/100][186/510] Data 0.005 (0.259) Batch 1.155 (1.563) Remain 15:11:40 loss: 0.2518 Lr: 0.00493
[2023-12-25 09:08:01,449 INFO misc.py line 119 253097] Train: [32/100][187/510] Data 0.011 (0.258) Batch 1.106 (1.560) Remain 15:10:12 loss: 0.2866 Lr: 0.00493
[2023-12-25 09:08:02,651 INFO misc.py line 119 253097] Train: [32/100][188/510] Data 0.009 (0.257) Batch 1.206 (1.558) Remain 15:09:03 loss: 0.2699 Lr: 0.00493
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[2023-12-25 09:08:06,208 INFO misc.py line 119 253097] Train: [32/100][191/510] Data 0.003 (0.253) Batch 1.259 (1.552) Remain 15:05:31 loss: 0.2108 Lr: 0.00493
[2023-12-25 09:08:09,829 INFO misc.py line 119 253097] Train: [32/100][192/510] Data 0.004 (0.251) Batch 3.622 (1.563) Remain 15:11:53 loss: 0.3444 Lr: 0.00493
[2023-12-25 09:08:10,761 INFO misc.py line 119 253097] Train: [32/100][193/510] Data 0.004 (0.250) Batch 0.932 (1.560) Remain 15:09:55 loss: 0.4045 Lr: 0.00493
[2023-12-25 09:08:12,070 INFO misc.py line 119 253097] Train: [32/100][194/510] Data 0.002 (0.249) Batch 1.293 (1.559) Remain 15:09:04 loss: 0.2624 Lr: 0.00493
[2023-12-25 09:08:13,282 INFO misc.py line 119 253097] Train: [32/100][195/510] Data 0.020 (0.248) Batch 1.225 (1.557) Remain 15:08:02 loss: 0.1188 Lr: 0.00493
[2023-12-25 09:08:14,578 INFO misc.py line 119 253097] Train: [32/100][196/510] Data 0.006 (0.246) Batch 1.288 (1.555) Remain 15:07:12 loss: 0.1759 Lr: 0.00493
[2023-12-25 09:08:25,396 INFO misc.py line 119 253097] Train: [32/100][197/510] Data 0.015 (0.245) Batch 10.828 (1.603) Remain 15:35:02 loss: 0.1427 Lr: 0.00493
[2023-12-25 09:08:26,510 INFO misc.py line 119 253097] Train: [32/100][198/510] Data 0.005 (0.244) Batch 1.114 (1.601) Remain 15:33:33 loss: 0.2079 Lr: 0.00493
[2023-12-25 09:08:27,656 INFO misc.py line 119 253097] Train: [32/100][199/510] Data 0.005 (0.243) Batch 1.147 (1.598) Remain 15:32:10 loss: 0.2158 Lr: 0.00493
[2023-12-25 09:08:28,796 INFO misc.py line 119 253097] Train: [32/100][200/510] Data 0.005 (0.242) Batch 1.141 (1.596) Remain 15:30:47 loss: 0.1411 Lr: 0.00493
[2023-12-25 09:08:30,081 INFO misc.py line 119 253097] Train: [32/100][201/510] Data 0.004 (0.240) Batch 1.283 (1.595) Remain 15:29:51 loss: 0.2674 Lr: 0.00493
[2023-12-25 09:08:31,248 INFO misc.py line 119 253097] Train: [32/100][202/510] Data 0.005 (0.239) Batch 1.164 (1.592) Remain 15:28:33 loss: 0.1588 Lr: 0.00493
[2023-12-25 09:08:32,412 INFO misc.py line 119 253097] Train: [32/100][203/510] Data 0.009 (0.238) Batch 1.164 (1.590) Remain 15:27:17 loss: 0.2861 Lr: 0.00493
[2023-12-25 09:08:33,646 INFO misc.py line 119 253097] Train: [32/100][204/510] Data 0.010 (0.237) Batch 1.236 (1.588) Remain 15:26:13 loss: 0.2419 Lr: 0.00493
[2023-12-25 09:08:34,956 INFO misc.py line 119 253097] Train: [32/100][205/510] Data 0.007 (0.236) Batch 1.310 (1.587) Remain 15:25:24 loss: 0.2414 Lr: 0.00493
[2023-12-25 09:08:36,109 INFO misc.py line 119 253097] Train: [32/100][206/510] Data 0.007 (0.235) Batch 1.152 (1.585) Remain 15:24:07 loss: 0.1361 Lr: 0.00493
[2023-12-25 09:08:37,222 INFO misc.py line 119 253097] Train: [32/100][207/510] Data 0.008 (0.234) Batch 1.112 (1.583) Remain 15:22:44 loss: 0.1532 Lr: 0.00493
[2023-12-25 09:08:38,255 INFO misc.py line 119 253097] Train: [32/100][208/510] Data 0.009 (0.232) Batch 1.039 (1.580) Remain 15:21:10 loss: 0.2206 Lr: 0.00493
[2023-12-25 09:08:39,518 INFO misc.py line 119 253097] Train: [32/100][209/510] Data 0.004 (0.231) Batch 1.259 (1.578) Remain 15:20:14 loss: 0.1464 Lr: 0.00493
[2023-12-25 09:08:40,539 INFO misc.py line 119 253097] Train: [32/100][210/510] Data 0.007 (0.230) Batch 1.017 (1.576) Remain 15:18:38 loss: 0.2497 Lr: 0.00493
[2023-12-25 09:08:41,344 INFO misc.py line 119 253097] Train: [32/100][211/510] Data 0.010 (0.229) Batch 0.811 (1.572) Remain 15:16:27 loss: 0.1176 Lr: 0.00493
[2023-12-25 09:08:42,464 INFO misc.py line 119 253097] Train: [32/100][212/510] Data 0.004 (0.228) Batch 1.120 (1.570) Remain 15:15:10 loss: 0.3270 Lr: 0.00493
[2023-12-25 09:08:43,565 INFO misc.py line 119 253097] Train: [32/100][213/510] Data 0.004 (0.227) Batch 1.100 (1.568) Remain 15:13:50 loss: 0.4481 Lr: 0.00493
[2023-12-25 09:08:44,693 INFO misc.py line 119 253097] Train: [32/100][214/510] Data 0.005 (0.226) Batch 1.128 (1.566) Remain 15:12:36 loss: 0.1679 Lr: 0.00493
[2023-12-25 09:08:45,865 INFO misc.py line 119 253097] Train: [32/100][215/510] Data 0.005 (0.225) Batch 1.173 (1.564) Remain 15:11:30 loss: 0.2067 Lr: 0.00493
[2023-12-25 09:08:47,075 INFO misc.py line 119 253097] Train: [32/100][216/510] Data 0.004 (0.224) Batch 1.209 (1.562) Remain 15:10:30 loss: 0.2269 Lr: 0.00493
[2023-12-25 09:08:48,282 INFO misc.py line 119 253097] Train: [32/100][217/510] Data 0.004 (0.223) Batch 1.207 (1.560) Remain 15:09:30 loss: 0.2560 Lr: 0.00493
[2023-12-25 09:08:51,688 INFO misc.py line 119 253097] Train: [32/100][218/510] Data 0.004 (0.222) Batch 3.406 (1.569) Remain 15:14:29 loss: 0.3256 Lr: 0.00493
[2023-12-25 09:08:52,908 INFO misc.py line 119 253097] Train: [32/100][219/510] Data 0.003 (0.221) Batch 1.219 (1.567) Remain 15:13:31 loss: 0.2819 Lr: 0.00493
[2023-12-25 09:08:54,087 INFO misc.py line 119 253097] Train: [32/100][220/510] Data 0.005 (0.220) Batch 1.181 (1.566) Remain 15:12:27 loss: 0.3934 Lr: 0.00493
[2023-12-25 09:08:55,164 INFO misc.py line 119 253097] Train: [32/100][221/510] Data 0.003 (0.219) Batch 1.076 (1.563) Remain 15:11:07 loss: 0.2788 Lr: 0.00493
[2023-12-25 09:08:57,954 INFO misc.py line 119 253097] Train: [32/100][222/510] Data 0.006 (0.218) Batch 2.791 (1.569) Remain 15:14:21 loss: 0.1713 Lr: 0.00493
[2023-12-25 09:09:00,882 INFO misc.py line 119 253097] Train: [32/100][223/510] Data 1.827 (0.225) Batch 2.929 (1.575) Remain 15:17:56 loss: 0.3240 Lr: 0.00493
[2023-12-25 09:09:02,046 INFO misc.py line 119 253097] Train: [32/100][224/510] Data 0.003 (0.224) Batch 1.164 (1.573) Remain 15:16:49 loss: 0.4077 Lr: 0.00493
[2023-12-25 09:09:03,352 INFO misc.py line 119 253097] Train: [32/100][225/510] Data 0.004 (0.223) Batch 1.305 (1.572) Remain 15:16:05 loss: 0.2532 Lr: 0.00493
[2023-12-25 09:09:04,425 INFO misc.py line 119 253097] Train: [32/100][226/510] Data 0.005 (0.222) Batch 1.064 (1.570) Remain 15:14:44 loss: 0.3014 Lr: 0.00492
[2023-12-25 09:09:05,690 INFO misc.py line 119 253097] Train: [32/100][227/510] Data 0.013 (0.221) Batch 1.271 (1.568) Remain 15:13:56 loss: 0.3502 Lr: 0.00492
[2023-12-25 09:09:06,972 INFO misc.py line 119 253097] Train: [32/100][228/510] Data 0.007 (0.220) Batch 1.271 (1.567) Remain 15:13:08 loss: 0.4228 Lr: 0.00492
[2023-12-25 09:09:08,190 INFO misc.py line 119 253097] Train: [32/100][229/510] Data 0.017 (0.219) Batch 1.210 (1.566) Remain 15:12:11 loss: 0.1438 Lr: 0.00492
[2023-12-25 09:09:09,359 INFO misc.py line 119 253097] Train: [32/100][230/510] Data 0.025 (0.219) Batch 1.190 (1.564) Remain 15:11:12 loss: 0.2586 Lr: 0.00492
[2023-12-25 09:09:10,461 INFO misc.py line 119 253097] Train: [32/100][231/510] Data 0.004 (0.218) Batch 1.100 (1.562) Remain 15:09:59 loss: 0.0901 Lr: 0.00492
[2023-12-25 09:09:11,629 INFO misc.py line 119 253097] Train: [32/100][232/510] Data 0.008 (0.217) Batch 1.172 (1.560) Remain 15:08:58 loss: 0.2189 Lr: 0.00492
[2023-12-25 09:09:23,387 INFO misc.py line 119 253097] Train: [32/100][233/510] Data 0.004 (0.216) Batch 11.757 (1.604) Remain 15:34:46 loss: 0.1837 Lr: 0.00492
[2023-12-25 09:09:24,324 INFO misc.py line 119 253097] Train: [32/100][234/510] Data 0.005 (0.215) Batch 0.938 (1.602) Remain 15:33:04 loss: 0.2605 Lr: 0.00492
[2023-12-25 09:09:25,362 INFO misc.py line 119 253097] Train: [32/100][235/510] Data 0.004 (0.214) Batch 1.036 (1.599) Remain 15:31:37 loss: 0.1634 Lr: 0.00492
[2023-12-25 09:09:26,529 INFO misc.py line 119 253097] Train: [32/100][236/510] Data 0.007 (0.213) Batch 1.169 (1.597) Remain 15:30:31 loss: 0.2062 Lr: 0.00492
[2023-12-25 09:09:27,617 INFO misc.py line 119 253097] Train: [32/100][237/510] Data 0.005 (0.212) Batch 1.088 (1.595) Remain 15:29:13 loss: 0.2578 Lr: 0.00492
[2023-12-25 09:09:28,511 INFO misc.py line 119 253097] Train: [32/100][238/510] Data 0.003 (0.211) Batch 0.893 (1.592) Remain 15:27:27 loss: 0.2040 Lr: 0.00492
[2023-12-25 09:09:31,142 INFO misc.py line 119 253097] Train: [32/100][239/510] Data 0.005 (0.210) Batch 2.631 (1.597) Remain 15:29:59 loss: 0.2035 Lr: 0.00492
[2023-12-25 09:09:32,335 INFO misc.py line 119 253097] Train: [32/100][240/510] Data 0.005 (0.210) Batch 1.190 (1.595) Remain 15:28:58 loss: 0.2265 Lr: 0.00492
[2023-12-25 09:09:33,553 INFO misc.py line 119 253097] Train: [32/100][241/510] Data 0.008 (0.209) Batch 1.216 (1.593) Remain 15:28:01 loss: 0.2560 Lr: 0.00492
[2023-12-25 09:09:34,818 INFO misc.py line 119 253097] Train: [32/100][242/510] Data 0.009 (0.208) Batch 1.267 (1.592) Remain 15:27:11 loss: 0.1194 Lr: 0.00492
[2023-12-25 09:09:36,066 INFO misc.py line 119 253097] Train: [32/100][243/510] Data 0.008 (0.207) Batch 1.240 (1.590) Remain 15:26:19 loss: 0.2266 Lr: 0.00492
[2023-12-25 09:09:37,223 INFO misc.py line 119 253097] Train: [32/100][244/510] Data 0.017 (0.206) Batch 1.169 (1.589) Remain 15:25:16 loss: 0.2010 Lr: 0.00492
[2023-12-25 09:09:38,335 INFO misc.py line 119 253097] Train: [32/100][245/510] Data 0.004 (0.205) Batch 1.108 (1.587) Remain 15:24:05 loss: 0.2229 Lr: 0.00492
[2023-12-25 09:09:39,538 INFO misc.py line 119 253097] Train: [32/100][246/510] Data 0.008 (0.205) Batch 1.200 (1.585) Remain 15:23:08 loss: 0.2457 Lr: 0.00492
[2023-12-25 09:09:40,784 INFO misc.py line 119 253097] Train: [32/100][247/510] Data 0.011 (0.204) Batch 1.253 (1.584) Remain 15:22:19 loss: 0.4255 Lr: 0.00492
[2023-12-25 09:09:41,805 INFO misc.py line 119 253097] Train: [32/100][248/510] Data 0.004 (0.203) Batch 1.018 (1.581) Remain 15:20:56 loss: 0.4816 Lr: 0.00492
[2023-12-25 09:09:42,929 INFO misc.py line 119 253097] Train: [32/100][249/510] Data 0.006 (0.202) Batch 1.127 (1.580) Remain 15:19:50 loss: 0.3159 Lr: 0.00492
[2023-12-25 09:09:44,064 INFO misc.py line 119 253097] Train: [32/100][250/510] Data 0.004 (0.201) Batch 1.133 (1.578) Remain 15:18:45 loss: 0.1788 Lr: 0.00492
[2023-12-25 09:09:45,256 INFO misc.py line 119 253097] Train: [32/100][251/510] Data 0.006 (0.201) Batch 1.190 (1.576) Remain 15:17:49 loss: 0.2416 Lr: 0.00492
[2023-12-25 09:09:46,348 INFO misc.py line 119 253097] Train: [32/100][252/510] Data 0.007 (0.200) Batch 1.092 (1.574) Remain 15:16:40 loss: 0.3985 Lr: 0.00492
[2023-12-25 09:09:47,425 INFO misc.py line 119 253097] Train: [32/100][253/510] Data 0.008 (0.199) Batch 1.073 (1.572) Remain 15:15:28 loss: 0.2930 Lr: 0.00492
[2023-12-25 09:09:48,528 INFO misc.py line 119 253097] Train: [32/100][254/510] Data 0.011 (0.198) Batch 1.105 (1.570) Remain 15:14:22 loss: 0.2444 Lr: 0.00492
[2023-12-25 09:09:49,735 INFO misc.py line 119 253097] Train: [32/100][255/510] Data 0.009 (0.198) Batch 1.202 (1.569) Remain 15:13:29 loss: 0.3001 Lr: 0.00492
[2023-12-25 09:09:50,951 INFO misc.py line 119 253097] Train: [32/100][256/510] Data 0.015 (0.197) Batch 1.221 (1.568) Remain 15:12:39 loss: 0.2133 Lr: 0.00492
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[2023-12-25 09:10:08,581 INFO misc.py line 119 253097] Train: [32/100][261/510] Data 0.004 (0.193) Batch 1.298 (1.605) Remain 15:34:37 loss: 0.1687 Lr: 0.00492
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[2023-12-25 09:10:31,950 INFO misc.py line 119 253097] Train: [32/100][272/510] Data 0.005 (0.186) Batch 1.242 (1.627) Remain 15:46:41 loss: 0.1888 Lr: 0.00492
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[2023-12-25 09:10:35,372 INFO misc.py line 119 253097] Train: [32/100][275/510] Data 0.007 (0.184) Batch 1.231 (1.621) Remain 15:43:29 loss: 0.4018 Lr: 0.00492
[2023-12-25 09:10:36,625 INFO misc.py line 119 253097] Train: [32/100][276/510] Data 0.003 (0.183) Batch 1.247 (1.620) Remain 15:42:40 loss: 0.1986 Lr: 0.00492
[2023-12-25 09:10:37,775 INFO misc.py line 119 253097] Train: [32/100][277/510] Data 0.009 (0.182) Batch 1.152 (1.618) Remain 15:41:39 loss: 0.2768 Lr: 0.00492
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[2023-12-25 09:10:41,187 INFO misc.py line 119 253097] Train: [32/100][280/510] Data 0.011 (0.180) Batch 0.996 (1.613) Remain 15:38:32 loss: 0.1083 Lr: 0.00492
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[2023-12-25 09:10:43,486 INFO misc.py line 119 253097] Train: [32/100][282/510] Data 0.006 (0.179) Batch 1.065 (1.610) Remain 15:36:33 loss: 0.2383 Lr: 0.00492
[2023-12-25 09:10:44,590 INFO misc.py line 119 253097] Train: [32/100][283/510] Data 0.004 (0.179) Batch 1.099 (1.608) Remain 15:35:28 loss: 0.1738 Lr: 0.00492
[2023-12-25 09:10:45,785 INFO misc.py line 119 253097] Train: [32/100][284/510] Data 0.009 (0.178) Batch 1.196 (1.606) Remain 15:34:35 loss: 0.2065 Lr: 0.00492
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[2023-12-25 09:10:50,262 INFO misc.py line 119 253097] Train: [32/100][288/510] Data 0.004 (0.175) Batch 1.106 (1.600) Remain 15:30:30 loss: 0.1549 Lr: 0.00492
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[2023-12-25 09:10:55,114 INFO misc.py line 119 253097] Train: [32/100][292/510] Data 0.006 (0.173) Batch 1.265 (1.594) Remain 15:27:16 loss: 0.3167 Lr: 0.00492
[2023-12-25 09:10:56,326 INFO misc.py line 119 253097] Train: [32/100][293/510] Data 0.011 (0.173) Batch 1.218 (1.593) Remain 15:26:30 loss: 0.2551 Lr: 0.00491
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[2023-12-25 09:10:58,724 INFO misc.py line 119 253097] Train: [32/100][295/510] Data 0.008 (0.171) Batch 1.128 (1.590) Remain 15:24:52 loss: 0.2500 Lr: 0.00491
[2023-12-25 09:10:59,803 INFO misc.py line 119 253097] Train: [32/100][296/510] Data 0.005 (0.171) Batch 1.073 (1.589) Remain 15:23:49 loss: 0.3199 Lr: 0.00491
[2023-12-25 09:11:00,900 INFO misc.py line 119 253097] Train: [32/100][297/510] Data 0.011 (0.170) Batch 1.098 (1.587) Remain 15:22:49 loss: 0.3718 Lr: 0.00491
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[2023-12-25 09:11:19,430 INFO misc.py line 119 253097] Train: [32/100][303/510] Data 0.008 (0.167) Batch 12.437 (1.617) Remain 15:40:08 loss: 0.4133 Lr: 0.00491
[2023-12-25 09:11:20,537 INFO misc.py line 119 253097] Train: [32/100][304/510] Data 0.005 (0.167) Batch 1.107 (1.615) Remain 15:39:07 loss: 0.1590 Lr: 0.00491
[2023-12-25 09:11:21,726 INFO misc.py line 119 253097] Train: [32/100][305/510] Data 0.004 (0.166) Batch 1.189 (1.614) Remain 15:38:16 loss: 0.2370 Lr: 0.00491
[2023-12-25 09:11:22,794 INFO misc.py line 119 253097] Train: [32/100][306/510] Data 0.003 (0.165) Batch 1.067 (1.612) Remain 15:37:12 loss: 0.4091 Lr: 0.00491
[2023-12-25 09:11:23,834 INFO misc.py line 119 253097] Train: [32/100][307/510] Data 0.005 (0.165) Batch 1.042 (1.610) Remain 15:36:05 loss: 0.2005 Lr: 0.00491
[2023-12-25 09:11:24,923 INFO misc.py line 119 253097] Train: [32/100][308/510] Data 0.003 (0.164) Batch 1.087 (1.608) Remain 15:35:03 loss: 0.2532 Lr: 0.00491
[2023-12-25 09:11:26,256 INFO misc.py line 119 253097] Train: [32/100][309/510] Data 0.006 (0.164) Batch 1.333 (1.607) Remain 15:34:30 loss: 0.2408 Lr: 0.00491
[2023-12-25 09:11:27,458 INFO misc.py line 119 253097] Train: [32/100][310/510] Data 0.006 (0.163) Batch 1.201 (1.606) Remain 15:33:43 loss: 0.2486 Lr: 0.00491
[2023-12-25 09:11:28,574 INFO misc.py line 119 253097] Train: [32/100][311/510] Data 0.007 (0.163) Batch 1.117 (1.605) Remain 15:32:45 loss: 0.1615 Lr: 0.00491
[2023-12-25 09:11:29,831 INFO misc.py line 119 253097] Train: [32/100][312/510] Data 0.006 (0.162) Batch 1.254 (1.603) Remain 15:32:04 loss: 0.2250 Lr: 0.00491
[2023-12-25 09:11:30,982 INFO misc.py line 119 253097] Train: [32/100][313/510] Data 0.008 (0.162) Batch 1.152 (1.602) Remain 15:31:12 loss: 0.3225 Lr: 0.00491
[2023-12-25 09:11:32,248 INFO misc.py line 119 253097] Train: [32/100][314/510] Data 0.008 (0.161) Batch 1.266 (1.601) Remain 15:30:33 loss: 0.1964 Lr: 0.00491
[2023-12-25 09:11:33,224 INFO misc.py line 119 253097] Train: [32/100][315/510] Data 0.008 (0.161) Batch 0.980 (1.599) Remain 15:29:22 loss: 0.3187 Lr: 0.00491
[2023-12-25 09:11:34,426 INFO misc.py line 119 253097] Train: [32/100][316/510] Data 0.003 (0.160) Batch 1.194 (1.598) Remain 15:28:35 loss: 0.1803 Lr: 0.00491
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[2023-12-25 09:11:36,602 INFO misc.py line 119 253097] Train: [32/100][318/510] Data 0.005 (0.159) Batch 1.135 (1.594) Remain 15:26:39 loss: 0.1446 Lr: 0.00491
[2023-12-25 09:11:43,735 INFO misc.py line 119 253097] Train: [32/100][319/510] Data 0.009 (0.159) Batch 7.139 (1.612) Remain 15:36:49 loss: 0.3331 Lr: 0.00491
[2023-12-25 09:11:44,807 INFO misc.py line 119 253097] Train: [32/100][320/510] Data 0.004 (0.158) Batch 1.067 (1.610) Remain 15:35:48 loss: 0.2875 Lr: 0.00491
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[2023-12-25 09:11:48,428 INFO misc.py line 119 253097] Train: [32/100][323/510] Data 0.007 (0.157) Batch 1.200 (1.606) Remain 15:33:31 loss: 0.2682 Lr: 0.00491
[2023-12-25 09:11:49,693 INFO misc.py line 119 253097] Train: [32/100][324/510] Data 0.008 (0.157) Batch 1.269 (1.605) Remain 15:32:53 loss: 0.3194 Lr: 0.00491
[2023-12-25 09:11:50,835 INFO misc.py line 119 253097] Train: [32/100][325/510] Data 0.004 (0.156) Batch 1.142 (1.604) Remain 15:32:01 loss: 0.3826 Lr: 0.00491
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[2023-12-25 09:13:05,294 INFO misc.py line 119 253097] Train: [32/100][381/510] Data 0.007 (0.134) Batch 1.283 (1.563) Remain 15:06:57 loss: 0.2152 Lr: 0.00490
[2023-12-25 09:13:06,283 INFO misc.py line 119 253097] Train: [32/100][382/510] Data 0.004 (0.134) Batch 0.989 (1.562) Remain 15:06:02 loss: 0.1649 Lr: 0.00490
[2023-12-25 09:13:07,279 INFO misc.py line 119 253097] Train: [32/100][383/510] Data 0.004 (0.133) Batch 0.995 (1.560) Remain 15:05:09 loss: 0.3966 Lr: 0.00490
[2023-12-25 09:13:08,169 INFO misc.py line 119 253097] Train: [32/100][384/510] Data 0.005 (0.133) Batch 0.890 (1.559) Remain 15:04:06 loss: 0.3379 Lr: 0.00490
[2023-12-25 09:13:22,283 INFO misc.py line 119 253097] Train: [32/100][385/510] Data 0.005 (0.133) Batch 14.115 (1.591) Remain 15:23:09 loss: 0.2947 Lr: 0.00490
[2023-12-25 09:13:23,293 INFO misc.py line 119 253097] Train: [32/100][386/510] Data 0.004 (0.132) Batch 1.010 (1.590) Remain 15:22:14 loss: 0.4766 Lr: 0.00490
[2023-12-25 09:13:24,450 INFO misc.py line 119 253097] Train: [32/100][387/510] Data 0.004 (0.132) Batch 1.156 (1.589) Remain 15:21:33 loss: 0.1892 Lr: 0.00490
[2023-12-25 09:13:25,706 INFO misc.py line 119 253097] Train: [32/100][388/510] Data 0.005 (0.132) Batch 1.257 (1.588) Remain 15:21:02 loss: 0.1798 Lr: 0.00490
[2023-12-25 09:13:26,914 INFO misc.py line 119 253097] Train: [32/100][389/510] Data 0.004 (0.131) Batch 1.209 (1.587) Remain 15:20:26 loss: 0.2779 Lr: 0.00490
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[2023-12-25 09:13:31,408 INFO misc.py line 119 253097] Train: [32/100][391/510] Data 0.003 (0.131) Batch 3.262 (1.590) Remain 15:22:21 loss: 0.1773 Lr: 0.00490
[2023-12-25 09:13:32,579 INFO misc.py line 119 253097] Train: [32/100][392/510] Data 0.004 (0.130) Batch 1.171 (1.589) Remain 15:21:42 loss: 0.1796 Lr: 0.00490
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[2023-12-25 09:13:34,932 INFO misc.py line 119 253097] Train: [32/100][394/510] Data 0.004 (0.130) Batch 1.296 (1.587) Remain 15:20:25 loss: 0.1530 Lr: 0.00490
[2023-12-25 09:13:36,097 INFO misc.py line 119 253097] Train: [32/100][395/510] Data 0.005 (0.129) Batch 1.163 (1.586) Remain 15:19:46 loss: 0.2306 Lr: 0.00490
[2023-12-25 09:13:37,579 INFO misc.py line 119 253097] Train: [32/100][396/510] Data 0.008 (0.129) Batch 1.485 (1.586) Remain 15:19:35 loss: 0.1790 Lr: 0.00490
[2023-12-25 09:13:39,127 INFO misc.py line 119 253097] Train: [32/100][397/510] Data 0.005 (0.129) Batch 1.548 (1.586) Remain 15:19:31 loss: 0.2537 Lr: 0.00490
[2023-12-25 09:13:40,064 INFO misc.py line 119 253097] Train: [32/100][398/510] Data 0.004 (0.128) Batch 0.938 (1.584) Remain 15:18:32 loss: 0.2012 Lr: 0.00490
[2023-12-25 09:13:41,422 INFO misc.py line 119 253097] Train: [32/100][399/510] Data 0.003 (0.128) Batch 1.351 (1.583) Remain 15:18:10 loss: 0.1247 Lr: 0.00490
[2023-12-25 09:13:42,617 INFO misc.py line 119 253097] Train: [32/100][400/510] Data 0.011 (0.128) Batch 1.190 (1.582) Remain 15:17:34 loss: 0.4111 Lr: 0.00490
[2023-12-25 09:13:43,745 INFO misc.py line 119 253097] Train: [32/100][401/510] Data 0.017 (0.127) Batch 1.131 (1.581) Remain 15:16:53 loss: 0.2713 Lr: 0.00490
[2023-12-25 09:13:46,999 INFO misc.py line 119 253097] Train: [32/100][402/510] Data 0.013 (0.127) Batch 3.263 (1.586) Remain 15:19:18 loss: 0.1868 Lr: 0.00490
[2023-12-25 09:13:48,145 INFO misc.py line 119 253097] Train: [32/100][403/510] Data 0.004 (0.127) Batch 1.135 (1.584) Remain 15:18:37 loss: 0.2652 Lr: 0.00490
[2023-12-25 09:13:49,235 INFO misc.py line 119 253097] Train: [32/100][404/510] Data 0.015 (0.127) Batch 1.095 (1.583) Remain 15:17:53 loss: 0.2226 Lr: 0.00490
[2023-12-25 09:13:50,334 INFO misc.py line 119 253097] Train: [32/100][405/510] Data 0.011 (0.126) Batch 1.101 (1.582) Remain 15:17:10 loss: 0.2381 Lr: 0.00490
[2023-12-25 09:13:52,330 INFO misc.py line 119 253097] Train: [32/100][406/510] Data 0.829 (0.128) Batch 2.001 (1.583) Remain 15:17:44 loss: 0.1787 Lr: 0.00490
[2023-12-25 09:13:53,396 INFO misc.py line 119 253097] Train: [32/100][407/510] Data 0.003 (0.128) Batch 1.065 (1.582) Remain 15:16:58 loss: 0.1337 Lr: 0.00490
[2023-12-25 09:13:57,881 INFO misc.py line 119 253097] Train: [32/100][408/510] Data 0.004 (0.127) Batch 4.486 (1.589) Remain 15:21:06 loss: 0.4370 Lr: 0.00490
[2023-12-25 09:13:58,952 INFO misc.py line 119 253097] Train: [32/100][409/510] Data 0.003 (0.127) Batch 1.069 (1.588) Remain 15:20:20 loss: 0.2406 Lr: 0.00490
[2023-12-25 09:14:00,200 INFO misc.py line 119 253097] Train: [32/100][410/510] Data 0.004 (0.127) Batch 1.246 (1.587) Remain 15:19:49 loss: 0.2791 Lr: 0.00490
[2023-12-25 09:14:01,385 INFO misc.py line 119 253097] Train: [32/100][411/510] Data 0.007 (0.127) Batch 1.179 (1.586) Remain 15:19:13 loss: 0.3268 Lr: 0.00490
[2023-12-25 09:14:02,397 INFO misc.py line 119 253097] Train: [32/100][412/510] Data 0.012 (0.126) Batch 1.014 (1.584) Remain 15:18:22 loss: 0.1346 Lr: 0.00490
[2023-12-25 09:14:03,363 INFO misc.py line 119 253097] Train: [32/100][413/510] Data 0.010 (0.126) Batch 0.973 (1.583) Remain 15:17:29 loss: 0.1742 Lr: 0.00490
[2023-12-25 09:14:04,618 INFO misc.py line 119 253097] Train: [32/100][414/510] Data 0.003 (0.126) Batch 1.247 (1.582) Remain 15:16:59 loss: 0.2169 Lr: 0.00490
[2023-12-25 09:14:05,685 INFO misc.py line 119 253097] Train: [32/100][415/510] Data 0.012 (0.125) Batch 1.073 (1.581) Remain 15:16:14 loss: 0.2230 Lr: 0.00490
[2023-12-25 09:14:06,827 INFO misc.py line 119 253097] Train: [32/100][416/510] Data 0.006 (0.125) Batch 1.144 (1.580) Remain 15:15:36 loss: 0.3099 Lr: 0.00490
[2023-12-25 09:14:07,866 INFO misc.py line 119 253097] Train: [32/100][417/510] Data 0.003 (0.125) Batch 1.034 (1.578) Remain 15:14:49 loss: 0.2637 Lr: 0.00490
[2023-12-25 09:14:08,793 INFO misc.py line 119 253097] Train: [32/100][418/510] Data 0.009 (0.124) Batch 0.931 (1.577) Remain 15:13:53 loss: 0.1796 Lr: 0.00490
[2023-12-25 09:14:09,854 INFO misc.py line 119 253097] Train: [32/100][419/510] Data 0.004 (0.124) Batch 1.061 (1.576) Remain 15:13:08 loss: 0.1838 Lr: 0.00490
[2023-12-25 09:14:11,061 INFO misc.py line 119 253097] Train: [32/100][420/510] Data 0.004 (0.124) Batch 1.207 (1.575) Remain 15:12:36 loss: 0.2175 Lr: 0.00490
[2023-12-25 09:14:12,170 INFO misc.py line 119 253097] Train: [32/100][421/510] Data 0.004 (0.124) Batch 1.109 (1.574) Remain 15:11:56 loss: 0.1974 Lr: 0.00490
[2023-12-25 09:14:13,204 INFO misc.py line 119 253097] Train: [32/100][422/510] Data 0.004 (0.123) Batch 1.033 (1.572) Remain 15:11:09 loss: 0.2612 Lr: 0.00490
[2023-12-25 09:14:14,334 INFO misc.py line 119 253097] Train: [32/100][423/510] Data 0.005 (0.123) Batch 1.131 (1.571) Remain 15:10:31 loss: 0.4254 Lr: 0.00490
[2023-12-25 09:14:20,610 INFO misc.py line 119 253097] Train: [32/100][424/510] Data 4.959 (0.135) Batch 6.276 (1.583) Remain 15:16:58 loss: 0.2011 Lr: 0.00490
[2023-12-25 09:14:21,671 INFO misc.py line 119 253097] Train: [32/100][425/510] Data 0.004 (0.134) Batch 1.060 (1.581) Remain 15:16:13 loss: 0.3251 Lr: 0.00490
[2023-12-25 09:14:22,908 INFO misc.py line 119 253097] Train: [32/100][426/510] Data 0.004 (0.134) Batch 1.238 (1.580) Remain 15:15:43 loss: 0.2148 Lr: 0.00489
[2023-12-25 09:14:24,056 INFO misc.py line 119 253097] Train: [32/100][427/510] Data 0.003 (0.134) Batch 1.147 (1.579) Remain 15:15:06 loss: 0.4099 Lr: 0.00489
[2023-12-25 09:14:25,095 INFO misc.py line 119 253097] Train: [32/100][428/510] Data 0.006 (0.133) Batch 1.037 (1.578) Remain 15:14:20 loss: 0.1562 Lr: 0.00489
[2023-12-25 09:14:26,123 INFO misc.py line 119 253097] Train: [32/100][429/510] Data 0.006 (0.133) Batch 1.025 (1.577) Remain 15:13:34 loss: 0.2939 Lr: 0.00489
[2023-12-25 09:14:30,918 INFO misc.py line 119 253097] Train: [32/100][430/510] Data 0.009 (0.133) Batch 4.799 (1.584) Remain 15:17:54 loss: 0.1976 Lr: 0.00489
[2023-12-25 09:14:31,816 INFO misc.py line 119 253097] Train: [32/100][431/510] Data 0.006 (0.132) Batch 0.899 (1.583) Remain 15:16:57 loss: 0.3554 Lr: 0.00489
[2023-12-25 09:14:32,826 INFO misc.py line 119 253097] Train: [32/100][432/510] Data 0.004 (0.132) Batch 1.008 (1.581) Remain 15:16:09 loss: 0.1529 Lr: 0.00489
[2023-12-25 09:14:34,095 INFO misc.py line 119 253097] Train: [32/100][433/510] Data 0.007 (0.132) Batch 1.268 (1.581) Remain 15:15:42 loss: 0.2895 Lr: 0.00489
[2023-12-25 09:14:35,382 INFO misc.py line 119 253097] Train: [32/100][434/510] Data 0.008 (0.132) Batch 1.290 (1.580) Remain 15:15:17 loss: 0.2751 Lr: 0.00489
[2023-12-25 09:14:36,356 INFO misc.py line 119 253097] Train: [32/100][435/510] Data 0.004 (0.131) Batch 0.973 (1.579) Remain 15:14:27 loss: 0.3846 Lr: 0.00489
[2023-12-25 09:14:46,183 INFO misc.py line 119 253097] Train: [32/100][436/510] Data 0.006 (0.131) Batch 9.828 (1.598) Remain 15:25:27 loss: 0.2443 Lr: 0.00489
[2023-12-25 09:14:47,207 INFO misc.py line 119 253097] Train: [32/100][437/510] Data 0.003 (0.131) Batch 1.025 (1.596) Remain 15:24:40 loss: 0.3000 Lr: 0.00489
[2023-12-25 09:14:48,441 INFO misc.py line 119 253097] Train: [32/100][438/510] Data 0.004 (0.130) Batch 1.233 (1.596) Remain 15:24:09 loss: 0.3000 Lr: 0.00489
[2023-12-25 09:14:49,643 INFO misc.py line 119 253097] Train: [32/100][439/510] Data 0.004 (0.130) Batch 1.202 (1.595) Remain 15:23:36 loss: 0.1894 Lr: 0.00489
[2023-12-25 09:14:50,895 INFO misc.py line 119 253097] Train: [32/100][440/510] Data 0.005 (0.130) Batch 1.248 (1.594) Remain 15:23:07 loss: 0.1998 Lr: 0.00489
[2023-12-25 09:14:51,954 INFO misc.py line 119 253097] Train: [32/100][441/510] Data 0.008 (0.130) Batch 1.060 (1.593) Remain 15:22:23 loss: 0.2787 Lr: 0.00489
[2023-12-25 09:14:53,166 INFO misc.py line 119 253097] Train: [32/100][442/510] Data 0.007 (0.129) Batch 1.212 (1.592) Remain 15:21:51 loss: 0.3360 Lr: 0.00489
[2023-12-25 09:14:54,332 INFO misc.py line 119 253097] Train: [32/100][443/510] Data 0.008 (0.129) Batch 1.164 (1.591) Remain 15:21:16 loss: 0.3695 Lr: 0.00489
[2023-12-25 09:14:55,368 INFO misc.py line 119 253097] Train: [32/100][444/510] Data 0.009 (0.129) Batch 1.039 (1.590) Remain 15:20:31 loss: 0.3777 Lr: 0.00489
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[2023-12-25 09:15:32,008 INFO misc.py line 119 253097] Train: [32/100][464/510] Data 0.004 (0.123) Batch 8.829 (1.600) Remain 15:26:04 loss: 0.2343 Lr: 0.00489
[2023-12-25 09:15:33,140 INFO misc.py line 119 253097] Train: [32/100][465/510] Data 0.005 (0.123) Batch 1.133 (1.599) Remain 15:25:28 loss: 0.2864 Lr: 0.00489
[2023-12-25 09:15:34,381 INFO misc.py line 119 253097] Train: [32/100][466/510] Data 0.003 (0.123) Batch 1.236 (1.598) Remain 15:24:59 loss: 0.2316 Lr: 0.00489
[2023-12-25 09:15:35,596 INFO misc.py line 119 253097] Train: [32/100][467/510] Data 0.008 (0.123) Batch 1.215 (1.597) Remain 15:24:29 loss: 0.2513 Lr: 0.00489
[2023-12-25 09:15:36,705 INFO misc.py line 119 253097] Train: [32/100][468/510] Data 0.008 (0.122) Batch 1.114 (1.596) Remain 15:23:51 loss: 0.3345 Lr: 0.00489
[2023-12-25 09:15:38,018 INFO misc.py line 119 253097] Train: [32/100][469/510] Data 0.004 (0.122) Batch 1.312 (1.596) Remain 15:23:28 loss: 0.2156 Lr: 0.00489
[2023-12-25 09:15:39,240 INFO misc.py line 119 253097] Train: [32/100][470/510] Data 0.004 (0.122) Batch 1.221 (1.595) Remain 15:22:59 loss: 0.2418 Lr: 0.00489
[2023-12-25 09:15:40,374 INFO misc.py line 119 253097] Train: [32/100][471/510] Data 0.005 (0.122) Batch 1.134 (1.594) Remain 15:22:23 loss: 0.2114 Lr: 0.00489
[2023-12-25 09:15:41,395 INFO misc.py line 119 253097] Train: [32/100][472/510] Data 0.005 (0.121) Batch 1.018 (1.593) Remain 15:21:39 loss: 0.7454 Lr: 0.00489
[2023-12-25 09:15:42,633 INFO misc.py line 119 253097] Train: [32/100][473/510] Data 0.007 (0.121) Batch 1.239 (1.592) Remain 15:21:11 loss: 0.1514 Lr: 0.00489
[2023-12-25 09:15:43,772 INFO misc.py line 119 253097] Train: [32/100][474/510] Data 0.006 (0.121) Batch 1.141 (1.591) Remain 15:20:36 loss: 0.1746 Lr: 0.00489
[2023-12-25 09:15:45,053 INFO misc.py line 119 253097] Train: [32/100][475/510] Data 0.004 (0.121) Batch 1.259 (1.590) Remain 15:20:10 loss: 0.1474 Lr: 0.00489
[2023-12-25 09:15:46,292 INFO misc.py line 119 253097] Train: [32/100][476/510] Data 0.026 (0.120) Batch 1.260 (1.590) Remain 15:19:44 loss: 0.1859 Lr: 0.00489
[2023-12-25 09:15:47,630 INFO misc.py line 119 253097] Train: [32/100][477/510] Data 0.570 (0.121) Batch 1.339 (1.589) Remain 15:19:24 loss: 0.1421 Lr: 0.00489
[2023-12-25 09:15:48,754 INFO misc.py line 119 253097] Train: [32/100][478/510] Data 0.005 (0.121) Batch 1.121 (1.588) Remain 15:18:49 loss: 0.3679 Lr: 0.00489
[2023-12-25 09:15:50,950 INFO misc.py line 119 253097] Train: [32/100][479/510] Data 0.885 (0.123) Batch 2.196 (1.589) Remain 15:19:31 loss: 0.4189 Lr: 0.00489
[2023-12-25 09:15:52,130 INFO misc.py line 119 253097] Train: [32/100][480/510] Data 0.007 (0.122) Batch 1.180 (1.589) Remain 15:19:00 loss: 0.3647 Lr: 0.00489
[2023-12-25 09:15:53,214 INFO misc.py line 119 253097] Train: [32/100][481/510] Data 0.007 (0.122) Batch 1.085 (1.588) Remain 15:18:22 loss: 0.2441 Lr: 0.00489
[2023-12-25 09:15:54,450 INFO misc.py line 119 253097] Train: [32/100][482/510] Data 0.007 (0.122) Batch 1.237 (1.587) Remain 15:17:55 loss: 0.1356 Lr: 0.00489
[2023-12-25 09:15:55,545 INFO misc.py line 119 253097] Train: [32/100][483/510] Data 0.006 (0.122) Batch 1.093 (1.586) Remain 15:17:17 loss: 0.2305 Lr: 0.00489
[2023-12-25 09:15:56,713 INFO misc.py line 119 253097] Train: [32/100][484/510] Data 0.008 (0.122) Batch 1.172 (1.585) Remain 15:16:46 loss: 0.2466 Lr: 0.00489
[2023-12-25 09:15:57,816 INFO misc.py line 119 253097] Train: [32/100][485/510] Data 0.004 (0.121) Batch 1.098 (1.584) Remain 15:16:09 loss: 0.1821 Lr: 0.00489
[2023-12-25 09:15:59,034 INFO misc.py line 119 253097] Train: [32/100][486/510] Data 0.009 (0.121) Batch 1.206 (1.583) Remain 15:15:41 loss: 0.3227 Lr: 0.00489
[2023-12-25 09:16:00,103 INFO misc.py line 119 253097] Train: [32/100][487/510] Data 0.021 (0.121) Batch 1.086 (1.582) Remain 15:15:03 loss: 0.2920 Lr: 0.00489
[2023-12-25 09:16:01,361 INFO misc.py line 119 253097] Train: [32/100][488/510] Data 0.004 (0.121) Batch 1.254 (1.581) Remain 15:14:38 loss: 0.1991 Lr: 0.00489
[2023-12-25 09:16:02,552 INFO misc.py line 119 253097] Train: [32/100][489/510] Data 0.008 (0.120) Batch 1.189 (1.581) Remain 15:14:09 loss: 0.2345 Lr: 0.00489
[2023-12-25 09:16:03,510 INFO misc.py line 119 253097] Train: [32/100][490/510] Data 0.009 (0.120) Batch 0.963 (1.579) Remain 15:13:23 loss: 0.2498 Lr: 0.00489
[2023-12-25 09:16:04,548 INFO misc.py line 119 253097] Train: [32/100][491/510] Data 0.004 (0.120) Batch 1.039 (1.578) Remain 15:12:43 loss: 0.2332 Lr: 0.00489
[2023-12-25 09:16:05,671 INFO misc.py line 119 253097] Train: [32/100][492/510] Data 0.003 (0.120) Batch 1.121 (1.577) Remain 15:12:09 loss: 0.1918 Lr: 0.00488
[2023-12-25 09:16:06,702 INFO misc.py line 119 253097] Train: [32/100][493/510] Data 0.006 (0.119) Batch 1.033 (1.576) Remain 15:11:29 loss: 0.2627 Lr: 0.00488
[2023-12-25 09:16:07,774 INFO misc.py line 119 253097] Train: [32/100][494/510] Data 0.005 (0.119) Batch 1.073 (1.575) Remain 15:10:52 loss: 0.2230 Lr: 0.00488
[2023-12-25 09:16:09,052 INFO misc.py line 119 253097] Train: [32/100][495/510] Data 0.004 (0.119) Batch 1.276 (1.575) Remain 15:10:29 loss: 0.4996 Lr: 0.00488
[2023-12-25 09:16:10,083 INFO misc.py line 119 253097] Train: [32/100][496/510] Data 0.005 (0.119) Batch 1.030 (1.573) Remain 15:09:49 loss: 0.2691 Lr: 0.00488
[2023-12-25 09:16:11,211 INFO misc.py line 119 253097] Train: [32/100][497/510] Data 0.005 (0.119) Batch 1.123 (1.573) Remain 15:09:16 loss: 0.2811 Lr: 0.00488
[2023-12-25 09:16:21,403 INFO misc.py line 119 253097] Train: [32/100][498/510] Data 0.011 (0.118) Batch 10.198 (1.590) Remain 15:19:19 loss: 0.2294 Lr: 0.00488
[2023-12-25 09:16:22,740 INFO misc.py line 119 253097] Train: [32/100][499/510] Data 0.004 (0.118) Batch 1.333 (1.589) Remain 15:18:59 loss: 0.1735 Lr: 0.00488
[2023-12-25 09:16:23,907 INFO misc.py line 119 253097] Train: [32/100][500/510] Data 0.008 (0.118) Batch 1.163 (1.589) Remain 15:18:28 loss: 0.3657 Lr: 0.00488
[2023-12-25 09:16:25,185 INFO misc.py line 119 253097] Train: [32/100][501/510] Data 0.013 (0.118) Batch 1.287 (1.588) Remain 15:18:05 loss: 0.1732 Lr: 0.00488
[2023-12-25 09:16:26,121 INFO misc.py line 119 253097] Train: [32/100][502/510] Data 0.004 (0.117) Batch 0.934 (1.587) Remain 15:17:18 loss: 0.2140 Lr: 0.00488
[2023-12-25 09:16:27,365 INFO misc.py line 119 253097] Train: [32/100][503/510] Data 0.005 (0.117) Batch 1.246 (1.586) Remain 15:16:53 loss: 0.1328 Lr: 0.00488
[2023-12-25 09:16:28,547 INFO misc.py line 119 253097] Train: [32/100][504/510] Data 0.004 (0.117) Batch 1.181 (1.585) Remain 15:16:24 loss: 0.1966 Lr: 0.00488
[2023-12-25 09:16:29,706 INFO misc.py line 119 253097] Train: [32/100][505/510] Data 0.005 (0.117) Batch 1.158 (1.584) Remain 15:15:52 loss: 0.3395 Lr: 0.00488
[2023-12-25 09:16:30,939 INFO misc.py line 119 253097] Train: [32/100][506/510] Data 0.006 (0.116) Batch 1.235 (1.584) Remain 15:15:27 loss: 0.2782 Lr: 0.00488
[2023-12-25 09:16:32,191 INFO misc.py line 119 253097] Train: [32/100][507/510] Data 0.004 (0.116) Batch 1.247 (1.583) Remain 15:15:02 loss: 0.2406 Lr: 0.00488
[2023-12-25 09:16:33,483 INFO misc.py line 119 253097] Train: [32/100][508/510] Data 0.009 (0.116) Batch 1.297 (1.582) Remain 15:14:41 loss: 0.1937 Lr: 0.00488
[2023-12-25 09:16:34,748 INFO misc.py line 119 253097] Train: [32/100][509/510] Data 0.004 (0.116) Batch 1.260 (1.582) Remain 15:14:17 loss: 0.2829 Lr: 0.00488
[2023-12-25 09:16:35,957 INFO misc.py line 119 253097] Train: [32/100][510/510] Data 0.008 (0.116) Batch 1.208 (1.581) Remain 15:13:50 loss: 0.1033 Lr: 0.00488
[2023-12-25 09:16:35,959 INFO misc.py line 136 253097] Train result: loss: 0.2544 
[2023-12-25 09:16:35,959 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 09:17:02,919 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4338 
[2023-12-25 09:17:03,281 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3612 
[2023-12-25 09:17:08,223 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4323 
[2023-12-25 09:17:08,738 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2927 
[2023-12-25 09:17:10,723 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6494 
[2023-12-25 09:17:11,145 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3250 
[2023-12-25 09:17:12,024 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1010 
[2023-12-25 09:17:12,576 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4756 
[2023-12-25 09:17:14,394 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0224 
[2023-12-25 09:17:16,535 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2509 
[2023-12-25 09:17:17,401 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2981 
[2023-12-25 09:17:17,827 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6827 
[2023-12-25 09:17:18,739 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6673 
[2023-12-25 09:17:21,682 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9590 
[2023-12-25 09:17:22,155 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3587 
[2023-12-25 09:17:22,766 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3468 
[2023-12-25 09:17:23,474 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3397 
[2023-12-25 09:17:25,142 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6477/0.7148/0.8928.
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9136/0.9493
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9818/0.9897
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8268/0.9679
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2867/0.3222
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5948/0.6141
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5935/0.6664
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8152/0.9269
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8957/0.9555
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5133/0.5703
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7630/0.8560
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6611/0.7878
[2023-12-25 09:17:25,142 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5744/0.6858
[2023-12-25 09:17:25,143 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 09:17:25,144 INFO misc.py line 165 253097] Currently Best mIoU: 0.6581
[2023-12-25 09:17:25,144 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 09:17:31,467 INFO misc.py line 119 253097] Train: [33/100][1/510] Data 3.187 (3.187) Batch 3.967 (3.967) Remain 38:12:53 loss: 0.1684 Lr: 0.00488
[2023-12-25 09:17:33,862 INFO misc.py line 119 253097] Train: [33/100][2/510] Data 0.005 (0.005) Batch 2.396 (2.396) Remain 23:04:40 loss: 0.2595 Lr: 0.00488
[2023-12-25 09:17:35,110 INFO misc.py line 119 253097] Train: [33/100][3/510] Data 0.005 (0.005) Batch 1.249 (1.249) Remain 12:01:37 loss: 0.2356 Lr: 0.00488
[2023-12-25 09:17:36,277 INFO misc.py line 119 253097] Train: [33/100][4/510] Data 0.004 (0.004) Batch 1.160 (1.160) Remain 11:10:16 loss: 0.1445 Lr: 0.00488
[2023-12-25 09:17:37,409 INFO misc.py line 119 253097] Train: [33/100][5/510] Data 0.012 (0.008) Batch 1.136 (1.148) Remain 11:03:29 loss: 0.1236 Lr: 0.00488
[2023-12-25 09:17:44,593 INFO misc.py line 119 253097] Train: [33/100][6/510] Data 6.143 (2.053) Batch 7.185 (3.160) Remain 30:26:19 loss: 0.1777 Lr: 0.00488
[2023-12-25 09:17:45,697 INFO misc.py line 119 253097] Train: [33/100][7/510] Data 0.005 (1.541) Batch 1.106 (2.647) Remain 25:29:29 loss: 0.1442 Lr: 0.00488
[2023-12-25 09:17:46,689 INFO misc.py line 119 253097] Train: [33/100][8/510] Data 0.004 (1.234) Batch 0.993 (2.316) Remain 22:18:17 loss: 0.3306 Lr: 0.00488
[2023-12-25 09:17:47,983 INFO misc.py line 119 253097] Train: [33/100][9/510] Data 0.004 (1.029) Batch 1.294 (2.146) Remain 20:39:47 loss: 0.0876 Lr: 0.00488
[2023-12-25 09:17:49,140 INFO misc.py line 119 253097] Train: [33/100][10/510] Data 0.003 (0.882) Batch 1.155 (2.004) Remain 19:17:57 loss: 0.1740 Lr: 0.00488
[2023-12-25 09:17:50,243 INFO misc.py line 119 253097] Train: [33/100][11/510] Data 0.006 (0.773) Batch 1.105 (1.892) Remain 18:12:58 loss: 0.2106 Lr: 0.00488
[2023-12-25 09:17:51,512 INFO misc.py line 119 253097] Train: [33/100][12/510] Data 0.004 (0.687) Batch 1.267 (1.822) Remain 17:32:48 loss: 0.2170 Lr: 0.00488
[2023-12-25 09:17:52,715 INFO misc.py line 119 253097] Train: [33/100][13/510] Data 0.006 (0.619) Batch 1.201 (1.760) Remain 16:56:55 loss: 0.1407 Lr: 0.00488
[2023-12-25 09:17:53,648 INFO misc.py line 119 253097] Train: [33/100][14/510] Data 0.008 (0.564) Batch 0.938 (1.685) Remain 16:13:43 loss: 0.2168 Lr: 0.00488
[2023-12-25 09:17:54,717 INFO misc.py line 119 253097] Train: [33/100][15/510] Data 0.003 (0.517) Batch 1.068 (1.634) Remain 15:43:57 loss: 0.3036 Lr: 0.00488
[2023-12-25 09:17:55,837 INFO misc.py line 119 253097] Train: [33/100][16/510] Data 0.005 (0.478) Batch 1.120 (1.594) Remain 15:21:06 loss: 0.2951 Lr: 0.00488
[2023-12-25 09:17:57,111 INFO misc.py line 119 253097] Train: [33/100][17/510] Data 0.004 (0.444) Batch 1.236 (1.569) Remain 15:06:18 loss: 0.1073 Lr: 0.00488
[2023-12-25 09:18:04,956 INFO misc.py line 119 253097] Train: [33/100][18/510] Data 6.576 (0.853) Batch 7.882 (1.990) Remain 19:09:26 loss: 0.1711 Lr: 0.00488
[2023-12-25 09:18:06,019 INFO misc.py line 119 253097] Train: [33/100][19/510] Data 0.004 (0.799) Batch 1.064 (1.932) Remain 18:35:59 loss: 0.1469 Lr: 0.00488
[2023-12-25 09:18:07,111 INFO misc.py line 119 253097] Train: [33/100][20/510] Data 0.003 (0.753) Batch 1.092 (1.882) Remain 18:07:24 loss: 0.2088 Lr: 0.00488
[2023-12-25 09:18:08,010 INFO misc.py line 119 253097] Train: [33/100][21/510] Data 0.004 (0.711) Batch 0.898 (1.828) Remain 17:35:46 loss: 0.2747 Lr: 0.00488
[2023-12-25 09:18:09,205 INFO misc.py line 119 253097] Train: [33/100][22/510] Data 0.005 (0.674) Batch 1.195 (1.794) Remain 17:16:30 loss: 0.1937 Lr: 0.00488
[2023-12-25 09:18:10,132 INFO misc.py line 119 253097] Train: [33/100][23/510] Data 0.006 (0.640) Batch 0.929 (1.751) Remain 16:51:28 loss: 0.2022 Lr: 0.00488
[2023-12-25 09:18:15,740 INFO misc.py line 119 253097] Train: [33/100][24/510] Data 4.323 (0.816) Batch 5.607 (1.935) Remain 18:37:30 loss: 0.3119 Lr: 0.00488
[2023-12-25 09:18:16,695 INFO misc.py line 119 253097] Train: [33/100][25/510] Data 0.004 (0.779) Batch 0.954 (1.890) Remain 18:11:44 loss: 0.2175 Lr: 0.00488
[2023-12-25 09:18:17,951 INFO misc.py line 119 253097] Train: [33/100][26/510] Data 0.008 (0.745) Batch 1.255 (1.863) Remain 17:55:45 loss: 0.1757 Lr: 0.00488
[2023-12-25 09:18:19,098 INFO misc.py line 119 253097] Train: [33/100][27/510] Data 0.005 (0.715) Batch 1.148 (1.833) Remain 17:38:31 loss: 0.1465 Lr: 0.00488
[2023-12-25 09:18:20,457 INFO misc.py line 119 253097] Train: [33/100][28/510] Data 0.006 (0.686) Batch 1.358 (1.814) Remain 17:27:31 loss: 0.1180 Lr: 0.00488
[2023-12-25 09:18:21,802 INFO misc.py line 119 253097] Train: [33/100][29/510] Data 0.006 (0.660) Batch 1.345 (1.796) Remain 17:17:04 loss: 0.3008 Lr: 0.00488
[2023-12-25 09:18:22,887 INFO misc.py line 119 253097] Train: [33/100][30/510] Data 0.006 (0.636) Batch 1.087 (1.770) Remain 17:01:53 loss: 0.2767 Lr: 0.00488
[2023-12-25 09:18:23,801 INFO misc.py line 119 253097] Train: [33/100][31/510] Data 0.004 (0.613) Batch 0.912 (1.739) Remain 16:44:11 loss: 0.5644 Lr: 0.00488
[2023-12-25 09:18:25,054 INFO misc.py line 119 253097] Train: [33/100][32/510] Data 0.005 (0.592) Batch 1.235 (1.722) Remain 16:34:07 loss: 0.5153 Lr: 0.00488
[2023-12-25 09:18:26,129 INFO misc.py line 119 253097] Train: [33/100][33/510] Data 0.023 (0.573) Batch 1.094 (1.701) Remain 16:22:00 loss: 0.1814 Lr: 0.00488
[2023-12-25 09:18:27,123 INFO misc.py line 119 253097] Train: [33/100][34/510] Data 0.005 (0.555) Batch 0.991 (1.678) Remain 16:08:45 loss: 0.1941 Lr: 0.00488
[2023-12-25 09:18:28,334 INFO misc.py line 119 253097] Train: [33/100][35/510] Data 0.007 (0.538) Batch 1.213 (1.663) Remain 16:00:20 loss: 0.3612 Lr: 0.00488
[2023-12-25 09:18:29,594 INFO misc.py line 119 253097] Train: [33/100][36/510] Data 0.006 (0.522) Batch 1.256 (1.651) Remain 15:53:11 loss: 0.1148 Lr: 0.00488
[2023-12-25 09:18:30,569 INFO misc.py line 119 253097] Train: [33/100][37/510] Data 0.010 (0.507) Batch 0.980 (1.631) Remain 15:41:46 loss: 0.2695 Lr: 0.00488
[2023-12-25 09:18:31,583 INFO misc.py line 119 253097] Train: [33/100][38/510] Data 0.005 (0.492) Batch 1.015 (1.613) Remain 15:31:34 loss: 0.2807 Lr: 0.00488
[2023-12-25 09:18:40,705 INFO misc.py line 119 253097] Train: [33/100][39/510] Data 8.017 (0.701) Batch 9.123 (1.822) Remain 17:31:58 loss: 0.2652 Lr: 0.00488
[2023-12-25 09:18:41,782 INFO misc.py line 119 253097] Train: [33/100][40/510] Data 0.004 (0.683) Batch 1.077 (1.802) Remain 17:20:19 loss: 0.1924 Lr: 0.00488
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[2023-12-25 09:18:45,229 INFO misc.py line 119 253097] Train: [33/100][43/510] Data 0.006 (0.632) Batch 0.985 (1.753) Remain 16:51:57 loss: 0.1638 Lr: 0.00488
[2023-12-25 09:18:46,476 INFO misc.py line 119 253097] Train: [33/100][44/510] Data 0.003 (0.616) Batch 1.242 (1.741) Remain 16:44:44 loss: 0.2080 Lr: 0.00488
[2023-12-25 09:18:47,613 INFO misc.py line 119 253097] Train: [33/100][45/510] Data 0.009 (0.602) Batch 1.142 (1.726) Remain 16:36:29 loss: 0.1966 Lr: 0.00488
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[2023-12-25 09:18:59,148 INFO misc.py line 119 253097] Train: [33/100][55/510] Data 0.005 (0.487) Batch 1.257 (1.616) Remain 15:32:35 loss: 0.3154 Lr: 0.00487
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[2023-12-25 09:19:02,471 INFO misc.py line 119 253097] Train: [33/100][58/510] Data 0.005 (0.461) Batch 1.150 (1.588) Remain 15:16:30 loss: 0.4946 Lr: 0.00487
[2023-12-25 09:19:03,662 INFO misc.py line 119 253097] Train: [33/100][59/510] Data 0.007 (0.453) Batch 1.194 (1.581) Remain 15:12:25 loss: 0.3101 Lr: 0.00487
[2023-12-25 09:19:04,936 INFO misc.py line 119 253097] Train: [33/100][60/510] Data 0.004 (0.445) Batch 1.274 (1.576) Remain 15:09:17 loss: 0.1215 Lr: 0.00487
[2023-12-25 09:19:06,133 INFO misc.py line 119 253097] Train: [33/100][61/510] Data 0.003 (0.438) Batch 1.197 (1.569) Remain 15:05:29 loss: 0.2785 Lr: 0.00487
[2023-12-25 09:19:07,381 INFO misc.py line 119 253097] Train: [33/100][62/510] Data 0.004 (0.430) Batch 1.249 (1.564) Remain 15:02:19 loss: 0.3292 Lr: 0.00487
[2023-12-25 09:19:08,425 INFO misc.py line 119 253097] Train: [33/100][63/510] Data 0.003 (0.423) Batch 1.043 (1.555) Remain 14:57:17 loss: 0.1390 Lr: 0.00487
[2023-12-25 09:19:18,772 INFO misc.py line 119 253097] Train: [33/100][64/510] Data 0.004 (0.416) Batch 10.347 (1.699) Remain 16:20:25 loss: 0.3838 Lr: 0.00487
[2023-12-25 09:19:20,070 INFO misc.py line 119 253097] Train: [33/100][65/510] Data 0.004 (0.410) Batch 1.298 (1.693) Remain 16:16:39 loss: 0.5254 Lr: 0.00487
[2023-12-25 09:19:21,243 INFO misc.py line 119 253097] Train: [33/100][66/510] Data 0.005 (0.403) Batch 1.173 (1.685) Remain 16:11:51 loss: 0.3968 Lr: 0.00487
[2023-12-25 09:19:22,524 INFO misc.py line 119 253097] Train: [33/100][67/510] Data 0.004 (0.397) Batch 1.281 (1.678) Remain 16:08:12 loss: 0.2470 Lr: 0.00487
[2023-12-25 09:19:23,426 INFO misc.py line 119 253097] Train: [33/100][68/510] Data 0.004 (0.391) Batch 0.903 (1.666) Remain 16:01:17 loss: 0.2648 Lr: 0.00487
[2023-12-25 09:19:24,610 INFO misc.py line 119 253097] Train: [33/100][69/510] Data 0.004 (0.385) Batch 1.184 (1.659) Remain 15:57:02 loss: 0.1630 Lr: 0.00487
[2023-12-25 09:19:25,942 INFO misc.py line 119 253097] Train: [33/100][70/510] Data 0.005 (0.379) Batch 1.331 (1.654) Remain 15:54:11 loss: 0.1447 Lr: 0.00487
[2023-12-25 09:19:27,026 INFO misc.py line 119 253097] Train: [33/100][71/510] Data 0.006 (0.374) Batch 1.080 (1.646) Remain 15:49:17 loss: 0.1370 Lr: 0.00487
[2023-12-25 09:19:27,861 INFO misc.py line 119 253097] Train: [33/100][72/510] Data 0.009 (0.369) Batch 0.841 (1.634) Remain 15:42:32 loss: 0.3539 Lr: 0.00487
[2023-12-25 09:19:28,931 INFO misc.py line 119 253097] Train: [33/100][73/510] Data 0.003 (0.363) Batch 1.069 (1.626) Remain 15:37:50 loss: 0.1532 Lr: 0.00487
[2023-12-25 09:19:30,158 INFO misc.py line 119 253097] Train: [33/100][74/510] Data 0.005 (0.358) Batch 1.226 (1.620) Remain 15:34:34 loss: 0.1472 Lr: 0.00487
[2023-12-25 09:19:31,352 INFO misc.py line 119 253097] Train: [33/100][75/510] Data 0.007 (0.353) Batch 1.196 (1.614) Remain 15:31:08 loss: 0.1921 Lr: 0.00487
[2023-12-25 09:19:32,560 INFO misc.py line 119 253097] Train: [33/100][76/510] Data 0.004 (0.349) Batch 1.207 (1.609) Remain 15:27:53 loss: 0.1729 Lr: 0.00487
[2023-12-25 09:19:33,769 INFO misc.py line 119 253097] Train: [33/100][77/510] Data 0.006 (0.344) Batch 1.209 (1.603) Remain 15:24:45 loss: 0.2731 Lr: 0.00487
[2023-12-25 09:19:35,518 INFO misc.py line 119 253097] Train: [33/100][78/510] Data 0.005 (0.339) Batch 1.748 (1.605) Remain 15:25:50 loss: 0.4672 Lr: 0.00487
[2023-12-25 09:19:36,345 INFO misc.py line 119 253097] Train: [33/100][79/510] Data 0.006 (0.335) Batch 0.826 (1.595) Remain 15:19:54 loss: 0.2410 Lr: 0.00487
[2023-12-25 09:19:37,416 INFO misc.py line 119 253097] Train: [33/100][80/510] Data 0.007 (0.331) Batch 1.073 (1.588) Remain 15:15:58 loss: 0.1868 Lr: 0.00487
[2023-12-25 09:19:38,483 INFO misc.py line 119 253097] Train: [33/100][81/510] Data 0.005 (0.327) Batch 1.067 (1.582) Remain 15:12:05 loss: 0.4107 Lr: 0.00487
[2023-12-25 09:19:39,739 INFO misc.py line 119 253097] Train: [33/100][82/510] Data 0.004 (0.323) Batch 1.256 (1.578) Remain 15:09:41 loss: 0.2156 Lr: 0.00487
[2023-12-25 09:19:40,928 INFO misc.py line 119 253097] Train: [33/100][83/510] Data 0.004 (0.319) Batch 1.180 (1.573) Remain 15:06:47 loss: 0.2307 Lr: 0.00487
[2023-12-25 09:19:41,942 INFO misc.py line 119 253097] Train: [33/100][84/510] Data 0.012 (0.315) Batch 1.018 (1.566) Remain 15:02:49 loss: 0.1173 Lr: 0.00487
[2023-12-25 09:19:50,846 INFO misc.py line 119 253097] Train: [33/100][85/510] Data 0.008 (0.311) Batch 8.908 (1.655) Remain 15:54:25 loss: 0.3128 Lr: 0.00487
[2023-12-25 09:19:52,006 INFO misc.py line 119 253097] Train: [33/100][86/510] Data 0.005 (0.307) Batch 1.161 (1.649) Remain 15:50:57 loss: 0.5351 Lr: 0.00487
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[2023-12-25 09:19:54,133 INFO misc.py line 119 253097] Train: [33/100][88/510] Data 0.009 (0.300) Batch 1.100 (1.636) Remain 15:42:57 loss: 0.1850 Lr: 0.00487
[2023-12-25 09:19:55,165 INFO misc.py line 119 253097] Train: [33/100][89/510] Data 0.004 (0.297) Batch 1.022 (1.628) Remain 15:38:48 loss: 0.1773 Lr: 0.00487
[2023-12-25 09:19:56,250 INFO misc.py line 119 253097] Train: [33/100][90/510] Data 0.014 (0.294) Batch 1.090 (1.622) Remain 15:35:13 loss: 0.1353 Lr: 0.00487
[2023-12-25 09:19:57,238 INFO misc.py line 119 253097] Train: [33/100][91/510] Data 0.009 (0.290) Batch 0.992 (1.615) Remain 15:31:03 loss: 0.1935 Lr: 0.00487
[2023-12-25 09:19:58,447 INFO misc.py line 119 253097] Train: [33/100][92/510] Data 0.005 (0.287) Batch 1.211 (1.611) Remain 15:28:25 loss: 0.1748 Lr: 0.00487
[2023-12-25 09:19:59,655 INFO misc.py line 119 253097] Train: [33/100][93/510] Data 0.005 (0.284) Batch 1.207 (1.606) Remain 15:25:48 loss: 0.1966 Lr: 0.00487
[2023-12-25 09:20:00,764 INFO misc.py line 119 253097] Train: [33/100][94/510] Data 0.004 (0.281) Batch 1.107 (1.601) Remain 15:22:37 loss: 0.2157 Lr: 0.00487
[2023-12-25 09:20:02,099 INFO misc.py line 119 253097] Train: [33/100][95/510] Data 0.006 (0.278) Batch 1.337 (1.598) Remain 15:20:56 loss: 0.1849 Lr: 0.00487
[2023-12-25 09:20:03,340 INFO misc.py line 119 253097] Train: [33/100][96/510] Data 0.004 (0.275) Batch 1.240 (1.594) Remain 15:18:41 loss: 0.3501 Lr: 0.00487
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[2023-12-25 09:20:06,560 INFO misc.py line 119 253097] Train: [33/100][99/510] Data 0.003 (0.267) Batch 1.190 (1.578) Remain 15:09:14 loss: 0.0877 Lr: 0.00487
[2023-12-25 09:20:07,632 INFO misc.py line 119 253097] Train: [33/100][100/510] Data 0.006 (0.264) Batch 1.073 (1.572) Remain 15:06:12 loss: 0.1808 Lr: 0.00487
[2023-12-25 09:20:08,717 INFO misc.py line 119 253097] Train: [33/100][101/510] Data 0.004 (0.261) Batch 1.083 (1.567) Remain 15:03:18 loss: 0.1546 Lr: 0.00487
[2023-12-25 09:20:09,842 INFO misc.py line 119 253097] Train: [33/100][102/510] Data 0.007 (0.259) Batch 1.128 (1.563) Remain 15:00:43 loss: 0.1282 Lr: 0.00487
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[2023-12-25 09:20:22,882 INFO misc.py line 119 253097] Train: [33/100][104/510] Data 0.004 (0.254) Batch 11.897 (1.661) Remain 15:57:14 loss: 0.1733 Lr: 0.00487
[2023-12-25 09:20:24,096 INFO misc.py line 119 253097] Train: [33/100][105/510] Data 0.003 (0.251) Batch 1.211 (1.657) Remain 15:54:40 loss: 0.2128 Lr: 0.00487
[2023-12-25 09:20:25,240 INFO misc.py line 119 253097] Train: [33/100][106/510] Data 0.007 (0.249) Batch 1.146 (1.652) Remain 15:51:47 loss: 0.1856 Lr: 0.00487
[2023-12-25 09:20:26,540 INFO misc.py line 119 253097] Train: [33/100][107/510] Data 0.006 (0.246) Batch 1.296 (1.648) Remain 15:49:47 loss: 0.1886 Lr: 0.00487
[2023-12-25 09:20:27,703 INFO misc.py line 119 253097] Train: [33/100][108/510] Data 0.009 (0.244) Batch 1.167 (1.644) Remain 15:47:07 loss: 0.1770 Lr: 0.00487
[2023-12-25 09:20:28,862 INFO misc.py line 119 253097] Train: [33/100][109/510] Data 0.006 (0.242) Batch 1.160 (1.639) Remain 15:44:27 loss: 0.1671 Lr: 0.00487
[2023-12-25 09:20:29,892 INFO misc.py line 119 253097] Train: [33/100][110/510] Data 0.003 (0.240) Batch 1.026 (1.633) Remain 15:41:08 loss: 0.2068 Lr: 0.00487
[2023-12-25 09:20:30,848 INFO misc.py line 119 253097] Train: [33/100][111/510] Data 0.007 (0.238) Batch 0.959 (1.627) Remain 15:37:30 loss: 0.1090 Lr: 0.00487
[2023-12-25 09:20:31,975 INFO misc.py line 119 253097] Train: [33/100][112/510] Data 0.004 (0.235) Batch 1.127 (1.623) Remain 15:34:50 loss: 0.4214 Lr: 0.00487
[2023-12-25 09:20:33,198 INFO misc.py line 119 253097] Train: [33/100][113/510] Data 0.005 (0.233) Batch 1.221 (1.619) Remain 15:32:42 loss: 0.2503 Lr: 0.00487
[2023-12-25 09:20:34,373 INFO misc.py line 119 253097] Train: [33/100][114/510] Data 0.007 (0.231) Batch 1.178 (1.615) Remain 15:30:23 loss: 0.2802 Lr: 0.00486
[2023-12-25 09:20:35,452 INFO misc.py line 119 253097] Train: [33/100][115/510] Data 0.003 (0.229) Batch 1.078 (1.610) Remain 15:27:36 loss: 0.2070 Lr: 0.00486
[2023-12-25 09:20:36,674 INFO misc.py line 119 253097] Train: [33/100][116/510] Data 0.004 (0.227) Batch 1.222 (1.607) Remain 15:25:35 loss: 0.1814 Lr: 0.00486
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[2023-12-25 09:20:38,829 INFO misc.py line 119 253097] Train: [33/100][118/510] Data 0.010 (0.223) Batch 1.093 (1.598) Remain 15:20:14 loss: 0.1259 Lr: 0.00486
[2023-12-25 09:20:40,141 INFO misc.py line 119 253097] Train: [33/100][119/510] Data 0.005 (0.222) Batch 1.286 (1.595) Remain 15:18:39 loss: 0.2161 Lr: 0.00486
[2023-12-25 09:20:41,112 INFO misc.py line 119 253097] Train: [33/100][120/510] Data 0.032 (0.220) Batch 0.997 (1.590) Remain 15:15:41 loss: 0.2418 Lr: 0.00486
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[2023-12-25 09:21:19,298 INFO misc.py line 119 253097] Train: [33/100][144/510] Data 0.007 (0.183) Batch 1.090 (1.590) Remain 15:15:09 loss: 0.3028 Lr: 0.00486
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[2023-12-25 09:21:34,885 INFO misc.py line 119 253097] Train: [33/100][151/510] Data 0.008 (0.175) Batch 1.209 (1.620) Remain 15:32:20 loss: 0.3391 Lr: 0.00486
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[2023-12-25 09:21:37,152 INFO misc.py line 119 253097] Train: [33/100][153/510] Data 0.008 (0.173) Batch 1.065 (1.614) Remain 15:28:32 loss: 0.3380 Lr: 0.00486
[2023-12-25 09:21:38,232 INFO misc.py line 119 253097] Train: [33/100][154/510] Data 0.007 (0.172) Batch 1.081 (1.610) Remain 15:26:28 loss: 0.1657 Lr: 0.00486
[2023-12-25 09:21:39,278 INFO misc.py line 119 253097] Train: [33/100][155/510] Data 0.006 (0.171) Batch 1.045 (1.606) Remain 15:24:18 loss: 0.1421 Lr: 0.00486
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[2023-12-25 09:21:44,262 INFO misc.py line 119 253097] Train: [33/100][159/510] Data 0.008 (0.166) Batch 1.210 (1.597) Remain 15:18:53 loss: 0.1559 Lr: 0.00486
[2023-12-25 09:21:45,378 INFO misc.py line 119 253097] Train: [33/100][160/510] Data 0.008 (0.165) Batch 1.120 (1.594) Remain 15:17:07 loss: 0.4155 Lr: 0.00486
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[2023-12-25 09:21:47,558 INFO misc.py line 119 253097] Train: [33/100][162/510] Data 0.009 (0.163) Batch 1.023 (1.588) Remain 15:13:24 loss: 0.2930 Lr: 0.00486
[2023-12-25 09:21:56,078 INFO misc.py line 119 253097] Train: [33/100][163/510] Data 0.007 (0.162) Batch 8.525 (1.631) Remain 15:38:19 loss: 0.6727 Lr: 0.00486
[2023-12-25 09:21:57,423 INFO misc.py line 119 253097] Train: [33/100][164/510] Data 0.003 (0.161) Batch 1.344 (1.629) Remain 15:37:15 loss: 0.1199 Lr: 0.00486
[2023-12-25 09:21:58,689 INFO misc.py line 119 253097] Train: [33/100][165/510] Data 0.004 (0.160) Batch 1.265 (1.627) Remain 15:35:56 loss: 0.2878 Lr: 0.00486
[2023-12-25 09:21:59,894 INFO misc.py line 119 253097] Train: [33/100][166/510] Data 0.004 (0.159) Batch 1.205 (1.624) Remain 15:34:25 loss: 0.1579 Lr: 0.00486
[2023-12-25 09:22:01,006 INFO misc.py line 119 253097] Train: [33/100][167/510] Data 0.004 (0.159) Batch 1.109 (1.621) Remain 15:32:35 loss: 0.2152 Lr: 0.00486
[2023-12-25 09:22:02,295 INFO misc.py line 119 253097] Train: [33/100][168/510] Data 0.008 (0.158) Batch 1.292 (1.619) Remain 15:31:25 loss: 0.1568 Lr: 0.00486
[2023-12-25 09:22:03,329 INFO misc.py line 119 253097] Train: [33/100][169/510] Data 0.004 (0.157) Batch 1.034 (1.616) Remain 15:29:21 loss: 0.2061 Lr: 0.00486
[2023-12-25 09:22:04,606 INFO misc.py line 119 253097] Train: [33/100][170/510] Data 0.005 (0.156) Batch 1.278 (1.614) Remain 15:28:10 loss: 0.1477 Lr: 0.00486
[2023-12-25 09:22:05,841 INFO misc.py line 119 253097] Train: [33/100][171/510] Data 0.004 (0.155) Batch 1.231 (1.611) Remain 15:26:49 loss: 0.1586 Lr: 0.00486
[2023-12-25 09:22:07,014 INFO misc.py line 119 253097] Train: [33/100][172/510] Data 0.008 (0.154) Batch 1.175 (1.609) Remain 15:25:19 loss: 0.2593 Lr: 0.00486
[2023-12-25 09:22:08,123 INFO misc.py line 119 253097] Train: [33/100][173/510] Data 0.006 (0.153) Batch 1.104 (1.606) Remain 15:23:35 loss: 0.2161 Lr: 0.00486
[2023-12-25 09:22:11,510 INFO misc.py line 119 253097] Train: [33/100][174/510] Data 0.011 (0.152) Batch 3.394 (1.616) Remain 15:29:34 loss: 0.2946 Lr: 0.00486
[2023-12-25 09:22:12,761 INFO misc.py line 119 253097] Train: [33/100][175/510] Data 0.005 (0.151) Batch 1.250 (1.614) Remain 15:28:19 loss: 0.2488 Lr: 0.00486
[2023-12-25 09:22:14,056 INFO misc.py line 119 253097] Train: [33/100][176/510] Data 0.007 (0.151) Batch 1.290 (1.612) Remain 15:27:13 loss: 0.1841 Lr: 0.00486
[2023-12-25 09:22:15,114 INFO misc.py line 119 253097] Train: [33/100][177/510] Data 0.011 (0.150) Batch 1.061 (1.609) Remain 15:25:22 loss: 0.1937 Lr: 0.00486
[2023-12-25 09:22:16,301 INFO misc.py line 119 253097] Train: [33/100][178/510] Data 0.007 (0.149) Batch 1.182 (1.607) Remain 15:23:56 loss: 0.2639 Lr: 0.00486
[2023-12-25 09:22:17,279 INFO misc.py line 119 253097] Train: [33/100][179/510] Data 0.015 (0.148) Batch 0.985 (1.603) Remain 15:21:52 loss: 0.2583 Lr: 0.00486
[2023-12-25 09:22:18,311 INFO misc.py line 119 253097] Train: [33/100][180/510] Data 0.005 (0.147) Batch 1.033 (1.600) Remain 15:20:00 loss: 0.4663 Lr: 0.00485
[2023-12-25 09:22:26,936 INFO misc.py line 119 253097] Train: [33/100][181/510] Data 0.004 (0.147) Batch 8.625 (1.639) Remain 15:42:40 loss: 0.3130 Lr: 0.00485
[2023-12-25 09:22:28,125 INFO misc.py line 119 253097] Train: [33/100][182/510] Data 0.004 (0.146) Batch 1.189 (1.637) Remain 15:41:11 loss: 0.5517 Lr: 0.00485
[2023-12-25 09:22:29,269 INFO misc.py line 119 253097] Train: [33/100][183/510] Data 0.004 (0.145) Batch 1.145 (1.634) Remain 15:39:35 loss: 0.3258 Lr: 0.00485
[2023-12-25 09:22:30,513 INFO misc.py line 119 253097] Train: [33/100][184/510] Data 0.003 (0.144) Batch 1.242 (1.632) Remain 15:38:19 loss: 0.1748 Lr: 0.00485
[2023-12-25 09:22:31,835 INFO misc.py line 119 253097] Train: [33/100][185/510] Data 0.005 (0.143) Batch 1.323 (1.630) Remain 15:37:18 loss: 0.1807 Lr: 0.00485
[2023-12-25 09:22:33,106 INFO misc.py line 119 253097] Train: [33/100][186/510] Data 0.005 (0.143) Batch 1.268 (1.628) Remain 15:36:09 loss: 0.1424 Lr: 0.00485
[2023-12-25 09:22:34,113 INFO misc.py line 119 253097] Train: [33/100][187/510] Data 0.008 (0.142) Batch 1.010 (1.625) Remain 15:34:11 loss: 0.3132 Lr: 0.00485
[2023-12-25 09:22:35,318 INFO misc.py line 119 253097] Train: [33/100][188/510] Data 0.005 (0.141) Batch 1.204 (1.623) Remain 15:32:51 loss: 0.2942 Lr: 0.00485
[2023-12-25 09:22:36,483 INFO misc.py line 119 253097] Train: [33/100][189/510] Data 0.005 (0.141) Batch 1.152 (1.620) Remain 15:31:22 loss: 0.0961 Lr: 0.00485
[2023-12-25 09:22:37,589 INFO misc.py line 119 253097] Train: [33/100][190/510] Data 0.021 (0.140) Batch 1.120 (1.618) Remain 15:29:48 loss: 0.1348 Lr: 0.00485
[2023-12-25 09:22:38,822 INFO misc.py line 119 253097] Train: [33/100][191/510] Data 0.005 (0.139) Batch 1.228 (1.615) Remain 15:28:35 loss: 0.2403 Lr: 0.00485
[2023-12-25 09:22:39,830 INFO misc.py line 119 253097] Train: [33/100][192/510] Data 0.010 (0.138) Batch 1.011 (1.612) Remain 15:26:43 loss: 0.2590 Lr: 0.00485
[2023-12-25 09:22:47,923 INFO misc.py line 119 253097] Train: [33/100][193/510] Data 0.006 (0.138) Batch 8.095 (1.646) Remain 15:46:18 loss: 0.1318 Lr: 0.00485
[2023-12-25 09:22:49,104 INFO misc.py line 119 253097] Train: [33/100][194/510] Data 0.005 (0.137) Batch 1.181 (1.644) Remain 15:44:53 loss: 0.3577 Lr: 0.00485
[2023-12-25 09:22:50,176 INFO misc.py line 119 253097] Train: [33/100][195/510] Data 0.004 (0.136) Batch 1.072 (1.641) Remain 15:43:08 loss: 0.2313 Lr: 0.00485
[2023-12-25 09:22:51,295 INFO misc.py line 119 253097] Train: [33/100][196/510] Data 0.004 (0.136) Batch 1.120 (1.638) Remain 15:41:33 loss: 0.2468 Lr: 0.00485
[2023-12-25 09:22:52,441 INFO misc.py line 119 253097] Train: [33/100][197/510] Data 0.003 (0.135) Batch 1.144 (1.636) Remain 15:40:04 loss: 0.2558 Lr: 0.00485
[2023-12-25 09:22:53,522 INFO misc.py line 119 253097] Train: [33/100][198/510] Data 0.006 (0.134) Batch 1.083 (1.633) Remain 15:38:25 loss: 0.1631 Lr: 0.00485
[2023-12-25 09:22:54,672 INFO misc.py line 119 253097] Train: [33/100][199/510] Data 0.004 (0.134) Batch 1.150 (1.630) Remain 15:36:58 loss: 0.2010 Lr: 0.00485
[2023-12-25 09:22:55,981 INFO misc.py line 119 253097] Train: [33/100][200/510] Data 0.004 (0.133) Batch 1.306 (1.629) Remain 15:35:59 loss: 0.1689 Lr: 0.00485
[2023-12-25 09:22:57,202 INFO misc.py line 119 253097] Train: [33/100][201/510] Data 0.007 (0.132) Batch 1.223 (1.627) Remain 15:34:47 loss: 0.3034 Lr: 0.00485
[2023-12-25 09:22:58,326 INFO misc.py line 119 253097] Train: [33/100][202/510] Data 0.006 (0.132) Batch 1.120 (1.624) Remain 15:33:18 loss: 0.1810 Lr: 0.00485
[2023-12-25 09:22:59,535 INFO misc.py line 119 253097] Train: [33/100][203/510] Data 0.010 (0.131) Batch 1.213 (1.622) Remain 15:32:05 loss: 0.2153 Lr: 0.00485
[2023-12-25 09:23:00,718 INFO misc.py line 119 253097] Train: [33/100][204/510] Data 0.005 (0.131) Batch 1.179 (1.620) Remain 15:30:48 loss: 0.2742 Lr: 0.00485
[2023-12-25 09:23:01,802 INFO misc.py line 119 253097] Train: [33/100][205/510] Data 0.009 (0.130) Batch 1.085 (1.617) Remain 15:29:15 loss: 0.2456 Lr: 0.00485
[2023-12-25 09:23:03,102 INFO misc.py line 119 253097] Train: [33/100][206/510] Data 0.008 (0.129) Batch 1.302 (1.616) Remain 15:28:20 loss: 0.3074 Lr: 0.00485
[2023-12-25 09:23:09,458 INFO misc.py line 119 253097] Train: [33/100][207/510] Data 5.564 (0.156) Batch 6.359 (1.639) Remain 15:41:39 loss: 0.1581 Lr: 0.00485
[2023-12-25 09:23:10,677 INFO misc.py line 119 253097] Train: [33/100][208/510] Data 0.004 (0.155) Batch 1.215 (1.637) Remain 15:40:26 loss: 0.1128 Lr: 0.00485
[2023-12-25 09:23:11,783 INFO misc.py line 119 253097] Train: [33/100][209/510] Data 0.007 (0.155) Batch 1.105 (1.634) Remain 15:38:56 loss: 0.3941 Lr: 0.00485
[2023-12-25 09:23:13,113 INFO misc.py line 119 253097] Train: [33/100][210/510] Data 0.008 (0.154) Batch 1.334 (1.633) Remain 15:38:04 loss: 0.2017 Lr: 0.00485
[2023-12-25 09:23:14,110 INFO misc.py line 119 253097] Train: [33/100][211/510] Data 0.004 (0.153) Batch 0.997 (1.630) Remain 15:36:17 loss: 0.3778 Lr: 0.00485
[2023-12-25 09:23:17,156 INFO misc.py line 119 253097] Train: [33/100][212/510] Data 1.812 (0.161) Batch 3.046 (1.637) Remain 15:40:09 loss: 0.2344 Lr: 0.00485
[2023-12-25 09:23:18,332 INFO misc.py line 119 253097] Train: [33/100][213/510] Data 0.004 (0.160) Batch 1.176 (1.634) Remain 15:38:52 loss: 0.2324 Lr: 0.00485
[2023-12-25 09:23:19,307 INFO misc.py line 119 253097] Train: [33/100][214/510] Data 0.004 (0.160) Batch 0.976 (1.631) Remain 15:37:03 loss: 0.1484 Lr: 0.00485
[2023-12-25 09:23:20,166 INFO misc.py line 119 253097] Train: [33/100][215/510] Data 0.003 (0.159) Batch 0.857 (1.628) Remain 15:34:55 loss: 0.1941 Lr: 0.00485
[2023-12-25 09:23:21,352 INFO misc.py line 119 253097] Train: [33/100][216/510] Data 0.004 (0.158) Batch 1.185 (1.626) Remain 15:33:42 loss: 0.2310 Lr: 0.00485
[2023-12-25 09:23:22,446 INFO misc.py line 119 253097] Train: [33/100][217/510] Data 0.005 (0.157) Batch 1.086 (1.623) Remain 15:32:14 loss: 0.1690 Lr: 0.00485
[2023-12-25 09:23:23,626 INFO misc.py line 119 253097] Train: [33/100][218/510] Data 0.014 (0.157) Batch 1.188 (1.621) Remain 15:31:02 loss: 0.1699 Lr: 0.00485
[2023-12-25 09:23:24,747 INFO misc.py line 119 253097] Train: [33/100][219/510] Data 0.006 (0.156) Batch 1.122 (1.619) Remain 15:29:41 loss: 0.1863 Lr: 0.00485
[2023-12-25 09:23:25,907 INFO misc.py line 119 253097] Train: [33/100][220/510] Data 0.004 (0.155) Batch 1.151 (1.617) Remain 15:28:25 loss: 0.2388 Lr: 0.00485
[2023-12-25 09:23:29,622 INFO misc.py line 119 253097] Train: [33/100][221/510] Data 0.014 (0.155) Batch 3.725 (1.626) Remain 15:33:57 loss: 0.2238 Lr: 0.00485
[2023-12-25 09:23:30,796 INFO misc.py line 119 253097] Train: [33/100][222/510] Data 0.004 (0.154) Batch 1.174 (1.624) Remain 15:32:44 loss: 0.2408 Lr: 0.00485
[2023-12-25 09:23:31,866 INFO misc.py line 119 253097] Train: [33/100][223/510] Data 0.003 (0.153) Batch 1.070 (1.622) Remain 15:31:16 loss: 0.2789 Lr: 0.00485
[2023-12-25 09:23:33,134 INFO misc.py line 119 253097] Train: [33/100][224/510] Data 0.004 (0.153) Batch 1.266 (1.620) Remain 15:30:19 loss: 0.2321 Lr: 0.00485
[2023-12-25 09:23:34,316 INFO misc.py line 119 253097] Train: [33/100][225/510] Data 0.005 (0.152) Batch 1.183 (1.618) Remain 15:29:09 loss: 0.1763 Lr: 0.00485
[2023-12-25 09:23:35,624 INFO misc.py line 119 253097] Train: [33/100][226/510] Data 0.004 (0.151) Batch 1.308 (1.617) Remain 15:28:20 loss: 0.1894 Lr: 0.00485
[2023-12-25 09:23:40,724 INFO misc.py line 119 253097] Train: [33/100][227/510] Data 0.005 (0.151) Batch 5.100 (1.632) Remain 15:37:14 loss: 0.2075 Lr: 0.00485
[2023-12-25 09:23:42,008 INFO misc.py line 119 253097] Train: [33/100][228/510] Data 0.005 (0.150) Batch 1.284 (1.631) Remain 15:36:19 loss: 0.1229 Lr: 0.00485
[2023-12-25 09:23:43,181 INFO misc.py line 119 253097] Train: [33/100][229/510] Data 0.004 (0.149) Batch 1.171 (1.629) Remain 15:35:07 loss: 0.1939 Lr: 0.00485
[2023-12-25 09:23:44,204 INFO misc.py line 119 253097] Train: [33/100][230/510] Data 0.007 (0.149) Batch 1.023 (1.626) Remain 15:33:33 loss: 0.2649 Lr: 0.00485
[2023-12-25 09:23:45,160 INFO misc.py line 119 253097] Train: [33/100][231/510] Data 0.007 (0.148) Batch 0.959 (1.623) Remain 15:31:51 loss: 0.1938 Lr: 0.00485
[2023-12-25 09:23:46,201 INFO misc.py line 119 253097] Train: [33/100][232/510] Data 0.004 (0.147) Batch 1.041 (1.620) Remain 15:30:22 loss: 0.1609 Lr: 0.00485
[2023-12-25 09:23:47,261 INFO misc.py line 119 253097] Train: [33/100][233/510] Data 0.003 (0.147) Batch 1.059 (1.618) Remain 15:28:56 loss: 0.2782 Lr: 0.00485
[2023-12-25 09:23:48,240 INFO misc.py line 119 253097] Train: [33/100][234/510] Data 0.004 (0.146) Batch 0.980 (1.615) Remain 15:27:19 loss: 0.2367 Lr: 0.00485
[2023-12-25 09:23:49,286 INFO misc.py line 119 253097] Train: [33/100][235/510] Data 0.003 (0.146) Batch 1.046 (1.613) Remain 15:25:53 loss: 0.2111 Lr: 0.00485
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[2023-12-25 09:23:51,656 INFO misc.py line 119 253097] Train: [33/100][237/510] Data 0.003 (0.144) Batch 1.300 (1.609) Remain 15:23:43 loss: 0.2260 Lr: 0.00485
[2023-12-25 09:23:52,761 INFO misc.py line 119 253097] Train: [33/100][238/510] Data 0.011 (0.144) Batch 1.108 (1.607) Remain 15:22:28 loss: 0.3381 Lr: 0.00485
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[2023-12-25 09:23:55,125 INFO misc.py line 119 253097] Train: [33/100][240/510] Data 0.007 (0.143) Batch 1.143 (1.603) Remain 15:20:21 loss: 0.2782 Lr: 0.00485
[2023-12-25 09:23:56,343 INFO misc.py line 119 253097] Train: [33/100][241/510] Data 0.007 (0.142) Batch 1.217 (1.602) Remain 15:19:24 loss: 0.2935 Lr: 0.00485
[2023-12-25 09:23:57,392 INFO misc.py line 119 253097] Train: [33/100][242/510] Data 0.007 (0.142) Batch 1.050 (1.599) Remain 15:18:03 loss: 0.2132 Lr: 0.00485
[2023-12-25 09:23:58,498 INFO misc.py line 119 253097] Train: [33/100][243/510] Data 0.007 (0.141) Batch 1.108 (1.597) Remain 15:16:51 loss: 0.1557 Lr: 0.00485
[2023-12-25 09:23:59,734 INFO misc.py line 119 253097] Train: [33/100][244/510] Data 0.006 (0.140) Batch 1.235 (1.596) Remain 15:15:57 loss: 0.1627 Lr: 0.00485
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[2023-12-25 09:24:01,979 INFO misc.py line 119 253097] Train: [33/100][246/510] Data 0.006 (0.139) Batch 1.122 (1.592) Remain 15:13:40 loss: 0.2869 Lr: 0.00484
[2023-12-25 09:24:03,206 INFO misc.py line 119 253097] Train: [33/100][247/510] Data 0.005 (0.139) Batch 1.224 (1.591) Remain 15:12:47 loss: 0.2967 Lr: 0.00484
[2023-12-25 09:24:04,230 INFO misc.py line 119 253097] Train: [33/100][248/510] Data 0.008 (0.138) Batch 1.028 (1.588) Remain 15:11:26 loss: 0.1981 Lr: 0.00484
[2023-12-25 09:24:05,361 INFO misc.py line 119 253097] Train: [33/100][249/510] Data 0.003 (0.138) Batch 1.128 (1.586) Remain 15:10:20 loss: 0.1563 Lr: 0.00484
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[2023-12-25 09:24:19,859 INFO misc.py line 119 253097] Train: [33/100][262/510] Data 0.007 (0.131) Batch 1.204 (1.563) Remain 14:56:25 loss: 0.2076 Lr: 0.00484
[2023-12-25 09:24:21,004 INFO misc.py line 119 253097] Train: [33/100][263/510] Data 0.007 (0.131) Batch 1.146 (1.561) Remain 14:55:29 loss: 0.1774 Lr: 0.00484
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[2023-12-25 09:24:32,075 INFO misc.py line 119 253097] Train: [33/100][265/510] Data 0.006 (0.130) Batch 10.033 (1.591) Remain 15:12:50 loss: 0.1987 Lr: 0.00484
[2023-12-25 09:24:33,217 INFO misc.py line 119 253097] Train: [33/100][266/510] Data 0.004 (0.129) Batch 1.141 (1.590) Remain 15:11:49 loss: 0.2627 Lr: 0.00484
[2023-12-25 09:24:34,504 INFO misc.py line 119 253097] Train: [33/100][267/510] Data 0.005 (0.129) Batch 1.280 (1.589) Remain 15:11:07 loss: 0.2584 Lr: 0.00484
[2023-12-25 09:24:35,703 INFO misc.py line 119 253097] Train: [33/100][268/510] Data 0.012 (0.128) Batch 1.201 (1.587) Remain 15:10:15 loss: 0.2251 Lr: 0.00484
[2023-12-25 09:24:36,981 INFO misc.py line 119 253097] Train: [33/100][269/510] Data 0.010 (0.128) Batch 1.280 (1.586) Remain 15:09:34 loss: 0.4030 Lr: 0.00484
[2023-12-25 09:24:37,985 INFO misc.py line 119 253097] Train: [33/100][270/510] Data 0.007 (0.127) Batch 1.008 (1.584) Remain 15:08:18 loss: 0.1866 Lr: 0.00484
[2023-12-25 09:24:38,941 INFO misc.py line 119 253097] Train: [33/100][271/510] Data 0.003 (0.127) Batch 0.956 (1.581) Remain 15:06:56 loss: 0.1792 Lr: 0.00484
[2023-12-25 09:24:40,244 INFO misc.py line 119 253097] Train: [33/100][272/510] Data 0.004 (0.126) Batch 1.300 (1.580) Remain 15:06:18 loss: 0.2059 Lr: 0.00484
[2023-12-25 09:24:41,274 INFO misc.py line 119 253097] Train: [33/100][273/510] Data 0.006 (0.126) Batch 1.029 (1.578) Remain 15:05:06 loss: 0.1913 Lr: 0.00484
[2023-12-25 09:24:42,170 INFO misc.py line 119 253097] Train: [33/100][274/510] Data 0.008 (0.126) Batch 0.899 (1.576) Remain 15:03:39 loss: 0.2684 Lr: 0.00484
[2023-12-25 09:24:43,191 INFO misc.py line 119 253097] Train: [33/100][275/510] Data 0.005 (0.125) Batch 1.022 (1.574) Remain 15:02:27 loss: 0.3155 Lr: 0.00484
[2023-12-25 09:24:44,322 INFO misc.py line 119 253097] Train: [33/100][276/510] Data 0.004 (0.125) Batch 1.130 (1.572) Remain 15:01:29 loss: 0.2557 Lr: 0.00484
[2023-12-25 09:24:45,319 INFO misc.py line 119 253097] Train: [33/100][277/510] Data 0.005 (0.124) Batch 0.999 (1.570) Remain 15:00:16 loss: 0.2165 Lr: 0.00484
[2023-12-25 09:24:46,491 INFO misc.py line 119 253097] Train: [33/100][278/510] Data 0.003 (0.124) Batch 1.172 (1.569) Remain 14:59:24 loss: 0.2537 Lr: 0.00484
[2023-12-25 09:24:53,985 INFO misc.py line 119 253097] Train: [33/100][279/510] Data 0.003 (0.123) Batch 7.494 (1.590) Remain 15:11:41 loss: 0.1270 Lr: 0.00484
[2023-12-25 09:24:55,092 INFO misc.py line 119 253097] Train: [33/100][280/510] Data 0.003 (0.123) Batch 1.107 (1.588) Remain 15:10:40 loss: 0.1801 Lr: 0.00484
[2023-12-25 09:24:56,180 INFO misc.py line 119 253097] Train: [33/100][281/510] Data 0.003 (0.122) Batch 1.088 (1.587) Remain 15:09:36 loss: 0.1595 Lr: 0.00484
[2023-12-25 09:24:57,437 INFO misc.py line 119 253097] Train: [33/100][282/510] Data 0.004 (0.122) Batch 1.252 (1.585) Remain 15:08:53 loss: 0.3266 Lr: 0.00484
[2023-12-25 09:24:58,423 INFO misc.py line 119 253097] Train: [33/100][283/510] Data 0.008 (0.122) Batch 0.992 (1.583) Remain 15:07:39 loss: 0.3210 Lr: 0.00484
[2023-12-25 09:24:59,520 INFO misc.py line 119 253097] Train: [33/100][284/510] Data 0.003 (0.121) Batch 1.097 (1.582) Remain 15:06:38 loss: 0.3528 Lr: 0.00484
[2023-12-25 09:25:16,013 INFO misc.py line 119 253097] Train: [33/100][285/510] Data 0.003 (0.121) Batch 16.491 (1.634) Remain 15:36:55 loss: 0.2461 Lr: 0.00484
[2023-12-25 09:25:17,089 INFO misc.py line 119 253097] Train: [33/100][286/510] Data 0.005 (0.120) Batch 1.077 (1.632) Remain 15:35:45 loss: 0.2706 Lr: 0.00484
[2023-12-25 09:25:18,278 INFO misc.py line 119 253097] Train: [33/100][287/510] Data 0.003 (0.120) Batch 1.188 (1.631) Remain 15:34:50 loss: 0.1821 Lr: 0.00484
[2023-12-25 09:25:19,437 INFO misc.py line 119 253097] Train: [33/100][288/510] Data 0.004 (0.120) Batch 1.160 (1.629) Remain 15:33:51 loss: 0.2549 Lr: 0.00484
[2023-12-25 09:25:20,658 INFO misc.py line 119 253097] Train: [33/100][289/510] Data 0.004 (0.119) Batch 1.220 (1.628) Remain 15:33:01 loss: 0.1985 Lr: 0.00484
[2023-12-25 09:25:21,808 INFO misc.py line 119 253097] Train: [33/100][290/510] Data 0.006 (0.119) Batch 1.148 (1.626) Remain 15:32:02 loss: 0.3105 Lr: 0.00484
[2023-12-25 09:25:23,304 INFO misc.py line 119 253097] Train: [33/100][291/510] Data 0.008 (0.118) Batch 1.495 (1.626) Remain 15:31:44 loss: 0.1333 Lr: 0.00484
[2023-12-25 09:25:24,433 INFO misc.py line 119 253097] Train: [33/100][292/510] Data 0.008 (0.118) Batch 1.133 (1.624) Remain 15:30:44 loss: 0.3811 Lr: 0.00484
[2023-12-25 09:25:25,735 INFO misc.py line 119 253097] Train: [33/100][293/510] Data 0.004 (0.118) Batch 1.299 (1.623) Remain 15:30:04 loss: 0.2395 Lr: 0.00484
[2023-12-25 09:25:26,904 INFO misc.py line 119 253097] Train: [33/100][294/510] Data 0.007 (0.117) Batch 1.163 (1.621) Remain 15:29:08 loss: 0.1771 Lr: 0.00484
[2023-12-25 09:25:27,997 INFO misc.py line 119 253097] Train: [33/100][295/510] Data 0.013 (0.117) Batch 1.102 (1.619) Remain 15:28:05 loss: 0.1585 Lr: 0.00484
[2023-12-25 09:25:29,169 INFO misc.py line 119 253097] Train: [33/100][296/510] Data 0.004 (0.116) Batch 1.171 (1.618) Remain 15:27:11 loss: 0.2574 Lr: 0.00484
[2023-12-25 09:25:30,595 INFO misc.py line 119 253097] Train: [33/100][297/510] Data 0.005 (0.116) Batch 1.426 (1.617) Remain 15:26:47 loss: 0.2010 Lr: 0.00484
[2023-12-25 09:25:31,841 INFO misc.py line 119 253097] Train: [33/100][298/510] Data 0.006 (0.116) Batch 1.247 (1.616) Remain 15:26:02 loss: 0.3530 Lr: 0.00484
[2023-12-25 09:25:33,004 INFO misc.py line 119 253097] Train: [33/100][299/510] Data 0.004 (0.115) Batch 1.163 (1.615) Remain 15:25:08 loss: 0.2848 Lr: 0.00484
[2023-12-25 09:25:33,906 INFO misc.py line 119 253097] Train: [33/100][300/510] Data 0.004 (0.115) Batch 0.903 (1.612) Remain 15:23:44 loss: 0.1895 Lr: 0.00484
[2023-12-25 09:25:34,911 INFO misc.py line 119 253097] Train: [33/100][301/510] Data 0.005 (0.115) Batch 1.005 (1.610) Remain 15:22:32 loss: 0.2384 Lr: 0.00484
[2023-12-25 09:25:36,024 INFO misc.py line 119 253097] Train: [33/100][302/510] Data 0.004 (0.114) Batch 1.113 (1.608) Remain 15:21:33 loss: 0.1141 Lr: 0.00484
[2023-12-25 09:25:37,240 INFO misc.py line 119 253097] Train: [33/100][303/510] Data 0.003 (0.114) Batch 1.210 (1.607) Remain 15:20:46 loss: 0.2366 Lr: 0.00484
[2023-12-25 09:25:38,317 INFO misc.py line 119 253097] Train: [33/100][304/510] Data 0.010 (0.114) Batch 1.083 (1.605) Remain 15:19:45 loss: 0.4200 Lr: 0.00484
[2023-12-25 09:25:48,316 INFO misc.py line 119 253097] Train: [33/100][305/510] Data 0.004 (0.113) Batch 9.998 (1.633) Remain 15:35:38 loss: 0.1354 Lr: 0.00484
[2023-12-25 09:25:49,514 INFO misc.py line 119 253097] Train: [33/100][306/510] Data 0.005 (0.113) Batch 1.199 (1.632) Remain 15:34:47 loss: 0.1573 Lr: 0.00484
[2023-12-25 09:25:50,697 INFO misc.py line 119 253097] Train: [33/100][307/510] Data 0.004 (0.112) Batch 1.183 (1.630) Remain 15:33:55 loss: 0.1496 Lr: 0.00484
[2023-12-25 09:25:51,763 INFO misc.py line 119 253097] Train: [33/100][308/510] Data 0.004 (0.112) Batch 1.065 (1.628) Remain 15:32:50 loss: 0.2840 Lr: 0.00484
[2023-12-25 09:25:52,829 INFO misc.py line 119 253097] Train: [33/100][309/510] Data 0.005 (0.112) Batch 1.066 (1.627) Remain 15:31:45 loss: 0.1070 Lr: 0.00484
[2023-12-25 09:25:53,962 INFO misc.py line 119 253097] Train: [33/100][310/510] Data 0.005 (0.111) Batch 1.134 (1.625) Remain 15:30:48 loss: 0.1410 Lr: 0.00483
[2023-12-25 09:25:55,125 INFO misc.py line 119 253097] Train: [33/100][311/510] Data 0.004 (0.111) Batch 1.164 (1.623) Remain 15:29:55 loss: 0.2201 Lr: 0.00483
[2023-12-25 09:25:56,143 INFO misc.py line 119 253097] Train: [33/100][312/510] Data 0.004 (0.111) Batch 1.017 (1.621) Remain 15:28:46 loss: 0.1829 Lr: 0.00483
[2023-12-25 09:25:57,307 INFO misc.py line 119 253097] Train: [33/100][313/510] Data 0.004 (0.110) Batch 1.164 (1.620) Remain 15:27:54 loss: 0.1606 Lr: 0.00483
[2023-12-25 09:25:58,336 INFO misc.py line 119 253097] Train: [33/100][314/510] Data 0.005 (0.110) Batch 1.030 (1.618) Remain 15:26:47 loss: 0.1856 Lr: 0.00483
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[2023-12-25 09:26:05,950 INFO misc.py line 119 253097] Train: [33/100][318/510] Data 0.005 (0.109) Batch 3.882 (1.622) Remain 15:28:45 loss: 0.1354 Lr: 0.00483
[2023-12-25 09:26:11,899 INFO misc.py line 119 253097] Train: [33/100][319/510] Data 0.003 (0.109) Batch 5.949 (1.635) Remain 15:36:34 loss: 0.1790 Lr: 0.00483
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[2023-12-25 09:26:16,948 INFO misc.py line 119 253097] Train: [33/100][321/510] Data 0.003 (0.108) Batch 3.996 (1.641) Remain 15:39:42 loss: 0.2653 Lr: 0.00483
[2023-12-25 09:26:18,082 INFO misc.py line 119 253097] Train: [33/100][322/510] Data 0.006 (0.108) Batch 1.136 (1.639) Remain 15:38:46 loss: 0.0395 Lr: 0.00483
[2023-12-25 09:26:19,229 INFO misc.py line 119 253097] Train: [33/100][323/510] Data 0.004 (0.108) Batch 1.147 (1.638) Remain 15:37:52 loss: 0.3336 Lr: 0.00483
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[2023-12-25 09:27:55,221 INFO misc.py line 119 253097] Train: [33/100][389/510] Data 0.008 (0.090) Batch 1.264 (1.607) Remain 15:18:08 loss: 0.2248 Lr: 0.00482
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[2023-12-25 09:28:22,981 INFO misc.py line 119 253097] Train: [33/100][400/510] Data 0.003 (0.088) Batch 1.157 (1.632) Remain 15:32:21 loss: 0.3429 Lr: 0.00482
[2023-12-25 09:28:24,158 INFO misc.py line 119 253097] Train: [33/100][401/510] Data 0.004 (0.088) Batch 1.177 (1.631) Remain 15:31:41 loss: 0.1571 Lr: 0.00482
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[2023-12-25 09:28:26,444 INFO misc.py line 119 253097] Train: [33/100][403/510] Data 0.012 (0.087) Batch 1.002 (1.628) Remain 15:30:14 loss: 0.1732 Lr: 0.00482
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[2023-12-25 09:28:30,055 INFO misc.py line 119 253097] Train: [33/100][406/510] Data 0.004 (0.087) Batch 1.072 (1.625) Remain 15:28:18 loss: 0.1162 Lr: 0.00482
[2023-12-25 09:28:31,057 INFO misc.py line 119 253097] Train: [33/100][407/510] Data 0.038 (0.086) Batch 1.033 (1.624) Remain 15:27:26 loss: 0.0989 Lr: 0.00482
[2023-12-25 09:28:32,289 INFO misc.py line 119 253097] Train: [33/100][408/510] Data 0.007 (0.086) Batch 1.235 (1.623) Remain 15:26:51 loss: 0.2814 Lr: 0.00482
[2023-12-25 09:28:33,405 INFO misc.py line 119 253097] Train: [33/100][409/510] Data 0.006 (0.086) Batch 1.115 (1.621) Remain 15:26:07 loss: 0.1929 Lr: 0.00482
[2023-12-25 09:28:34,546 INFO misc.py line 119 253097] Train: [33/100][410/510] Data 0.004 (0.086) Batch 1.140 (1.620) Remain 15:25:25 loss: 0.1585 Lr: 0.00482
[2023-12-25 09:28:44,809 INFO misc.py line 119 253097] Train: [33/100][411/510] Data 9.041 (0.108) Batch 10.266 (1.641) Remain 15:37:29 loss: 0.0660 Lr: 0.00482
[2023-12-25 09:28:45,930 INFO misc.py line 119 253097] Train: [33/100][412/510] Data 0.004 (0.108) Batch 1.120 (1.640) Remain 15:36:44 loss: 0.2087 Lr: 0.00482
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[2023-12-25 09:28:49,348 INFO misc.py line 119 253097] Train: [33/100][415/510] Data 0.004 (0.107) Batch 1.125 (1.636) Remain 15:34:34 loss: 0.1681 Lr: 0.00482
[2023-12-25 09:28:50,333 INFO misc.py line 119 253097] Train: [33/100][416/510] Data 0.008 (0.107) Batch 0.988 (1.635) Remain 15:33:38 loss: 0.1918 Lr: 0.00482
[2023-12-25 09:28:51,404 INFO misc.py line 119 253097] Train: [33/100][417/510] Data 0.004 (0.106) Batch 1.070 (1.634) Remain 15:32:50 loss: 0.1265 Lr: 0.00482
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[2023-12-25 09:29:49,261 INFO misc.py line 119 253097] Train: [33/100][459/510] Data 0.006 (0.097) Batch 1.215 (1.610) Remain 15:18:14 loss: 0.2247 Lr: 0.00481
[2023-12-25 09:29:50,475 INFO misc.py line 119 253097] Train: [33/100][460/510] Data 0.006 (0.097) Batch 1.206 (1.609) Remain 15:17:43 loss: 0.4412 Lr: 0.00481
[2023-12-25 09:29:51,620 INFO misc.py line 119 253097] Train: [33/100][461/510] Data 0.013 (0.097) Batch 1.153 (1.608) Remain 15:17:07 loss: 0.1752 Lr: 0.00481
[2023-12-25 09:29:52,781 INFO misc.py line 119 253097] Train: [33/100][462/510] Data 0.006 (0.097) Batch 1.160 (1.607) Remain 15:16:32 loss: 0.1368 Lr: 0.00481
[2023-12-25 09:29:53,939 INFO misc.py line 119 253097] Train: [33/100][463/510] Data 0.006 (0.096) Batch 1.153 (1.606) Remain 15:15:57 loss: 0.2369 Lr: 0.00481
[2023-12-25 09:29:55,125 INFO misc.py line 119 253097] Train: [33/100][464/510] Data 0.011 (0.096) Batch 1.191 (1.605) Remain 15:15:24 loss: 0.3664 Lr: 0.00481
[2023-12-25 09:30:00,146 INFO misc.py line 119 253097] Train: [33/100][465/510] Data 0.006 (0.096) Batch 5.024 (1.613) Remain 15:19:36 loss: 0.1530 Lr: 0.00481
[2023-12-25 09:30:01,396 INFO misc.py line 119 253097] Train: [33/100][466/510] Data 0.004 (0.096) Batch 1.250 (1.612) Remain 15:19:07 loss: 0.1226 Lr: 0.00481
[2023-12-25 09:30:02,498 INFO misc.py line 119 253097] Train: [33/100][467/510] Data 0.004 (0.096) Batch 1.097 (1.611) Remain 15:18:28 loss: 0.2830 Lr: 0.00481
[2023-12-25 09:30:03,333 INFO misc.py line 119 253097] Train: [33/100][468/510] Data 0.008 (0.095) Batch 0.839 (1.609) Remain 15:17:29 loss: 0.4863 Lr: 0.00481
[2023-12-25 09:30:04,574 INFO misc.py line 119 253097] Train: [33/100][469/510] Data 0.005 (0.095) Batch 1.240 (1.608) Remain 15:17:01 loss: 0.2199 Lr: 0.00481
[2023-12-25 09:30:05,590 INFO misc.py line 119 253097] Train: [33/100][470/510] Data 0.006 (0.095) Batch 1.018 (1.607) Remain 15:16:16 loss: 0.4873 Lr: 0.00481
[2023-12-25 09:30:06,774 INFO misc.py line 119 253097] Train: [33/100][471/510] Data 0.004 (0.095) Batch 1.183 (1.606) Remain 15:15:43 loss: 0.2202 Lr: 0.00481
[2023-12-25 09:30:08,092 INFO misc.py line 119 253097] Train: [33/100][472/510] Data 0.005 (0.095) Batch 1.313 (1.605) Remain 15:15:20 loss: 0.1817 Lr: 0.00481
[2023-12-25 09:30:09,276 INFO misc.py line 119 253097] Train: [33/100][473/510] Data 0.011 (0.094) Batch 1.183 (1.605) Remain 15:14:48 loss: 0.3119 Lr: 0.00481
[2023-12-25 09:30:10,530 INFO misc.py line 119 253097] Train: [33/100][474/510] Data 0.011 (0.094) Batch 1.261 (1.604) Remain 15:14:21 loss: 0.1242 Lr: 0.00481
[2023-12-25 09:30:11,532 INFO misc.py line 119 253097] Train: [33/100][475/510] Data 0.004 (0.094) Batch 1.002 (1.603) Remain 15:13:36 loss: 0.1955 Lr: 0.00481
[2023-12-25 09:30:12,750 INFO misc.py line 119 253097] Train: [33/100][476/510] Data 0.003 (0.094) Batch 1.218 (1.602) Remain 15:13:07 loss: 0.3305 Lr: 0.00481
[2023-12-25 09:30:13,883 INFO misc.py line 119 253097] Train: [33/100][477/510] Data 0.004 (0.094) Batch 1.133 (1.601) Remain 15:12:31 loss: 0.2356 Lr: 0.00481
[2023-12-25 09:30:15,055 INFO misc.py line 119 253097] Train: [33/100][478/510] Data 0.005 (0.093) Batch 1.137 (1.600) Remain 15:11:56 loss: 0.2263 Lr: 0.00481
[2023-12-25 09:30:16,055 INFO misc.py line 119 253097] Train: [33/100][479/510] Data 0.040 (0.093) Batch 1.035 (1.599) Remain 15:11:14 loss: 0.2838 Lr: 0.00481
[2023-12-25 09:30:17,298 INFO misc.py line 119 253097] Train: [33/100][480/510] Data 0.005 (0.093) Batch 1.239 (1.598) Remain 15:10:47 loss: 0.2650 Lr: 0.00481
[2023-12-25 09:30:18,597 INFO misc.py line 119 253097] Train: [33/100][481/510] Data 0.009 (0.093) Batch 1.297 (1.597) Remain 15:10:23 loss: 0.1392 Lr: 0.00481
[2023-12-25 09:30:19,823 INFO misc.py line 119 253097] Train: [33/100][482/510] Data 0.012 (0.093) Batch 1.229 (1.596) Remain 15:09:55 loss: 0.3541 Lr: 0.00481
[2023-12-25 09:30:21,034 INFO misc.py line 119 253097] Train: [33/100][483/510] Data 0.009 (0.093) Batch 1.211 (1.596) Remain 15:09:26 loss: 0.2553 Lr: 0.00481
[2023-12-25 09:30:22,296 INFO misc.py line 119 253097] Train: [33/100][484/510] Data 0.008 (0.092) Batch 1.262 (1.595) Remain 15:09:01 loss: 0.2710 Lr: 0.00481
[2023-12-25 09:30:30,052 INFO misc.py line 119 253097] Train: [33/100][485/510] Data 0.009 (0.092) Batch 7.760 (1.608) Remain 15:16:17 loss: 0.1989 Lr: 0.00481
[2023-12-25 09:30:31,351 INFO misc.py line 119 253097] Train: [33/100][486/510] Data 0.004 (0.092) Batch 1.293 (1.607) Remain 15:15:53 loss: 0.1663 Lr: 0.00481
[2023-12-25 09:30:32,610 INFO misc.py line 119 253097] Train: [33/100][487/510] Data 0.010 (0.092) Batch 1.263 (1.606) Remain 15:15:27 loss: 0.1605 Lr: 0.00481
[2023-12-25 09:30:33,762 INFO misc.py line 119 253097] Train: [33/100][488/510] Data 0.007 (0.092) Batch 1.150 (1.605) Remain 15:14:53 loss: 0.3620 Lr: 0.00481
[2023-12-25 09:30:35,000 INFO misc.py line 119 253097] Train: [33/100][489/510] Data 0.008 (0.092) Batch 1.239 (1.605) Remain 15:14:26 loss: 0.4250 Lr: 0.00481
[2023-12-25 09:30:36,182 INFO misc.py line 119 253097] Train: [33/100][490/510] Data 0.007 (0.091) Batch 1.183 (1.604) Remain 15:13:55 loss: 0.3446 Lr: 0.00481
[2023-12-25 09:30:46,093 INFO misc.py line 119 253097] Train: [33/100][491/510] Data 0.007 (0.091) Batch 9.913 (1.621) Remain 15:23:35 loss: 0.2248 Lr: 0.00481
[2023-12-25 09:30:47,190 INFO misc.py line 119 253097] Train: [33/100][492/510] Data 0.005 (0.091) Batch 1.097 (1.620) Remain 15:22:57 loss: 0.1779 Lr: 0.00481
[2023-12-25 09:30:48,497 INFO misc.py line 119 253097] Train: [33/100][493/510] Data 0.004 (0.091) Batch 1.307 (1.619) Remain 15:22:34 loss: 0.0916 Lr: 0.00481
[2023-12-25 09:30:49,751 INFO misc.py line 119 253097] Train: [33/100][494/510] Data 0.004 (0.091) Batch 1.256 (1.618) Remain 15:22:07 loss: 0.1357 Lr: 0.00481
[2023-12-25 09:30:50,900 INFO misc.py line 119 253097] Train: [33/100][495/510] Data 0.002 (0.091) Batch 1.146 (1.617) Remain 15:21:32 loss: 0.4298 Lr: 0.00481
[2023-12-25 09:30:52,020 INFO misc.py line 119 253097] Train: [33/100][496/510] Data 0.006 (0.090) Batch 1.121 (1.616) Remain 15:20:56 loss: 0.2207 Lr: 0.00481
[2023-12-25 09:30:54,504 INFO misc.py line 119 253097] Train: [33/100][497/510] Data 0.004 (0.090) Batch 2.485 (1.618) Remain 15:21:55 loss: 0.1823 Lr: 0.00481
[2023-12-25 09:30:55,667 INFO misc.py line 119 253097] Train: [33/100][498/510] Data 0.003 (0.090) Batch 1.162 (1.617) Remain 15:21:22 loss: 0.2371 Lr: 0.00481
[2023-12-25 09:30:56,981 INFO misc.py line 119 253097] Train: [33/100][499/510] Data 0.003 (0.090) Batch 1.314 (1.617) Remain 15:20:59 loss: 0.0722 Lr: 0.00481
[2023-12-25 09:30:58,157 INFO misc.py line 119 253097] Train: [33/100][500/510] Data 0.004 (0.090) Batch 1.171 (1.616) Remain 15:20:27 loss: 0.2023 Lr: 0.00481
[2023-12-25 09:30:59,428 INFO misc.py line 119 253097] Train: [33/100][501/510] Data 0.009 (0.090) Batch 1.270 (1.615) Remain 15:20:01 loss: 0.2523 Lr: 0.00481
[2023-12-25 09:31:00,456 INFO misc.py line 119 253097] Train: [33/100][502/510] Data 0.010 (0.089) Batch 1.027 (1.614) Remain 15:19:20 loss: 0.1898 Lr: 0.00481
[2023-12-25 09:31:03,179 INFO misc.py line 119 253097] Train: [33/100][503/510] Data 0.011 (0.089) Batch 2.729 (1.616) Remain 15:20:34 loss: 0.3272 Lr: 0.00481
[2023-12-25 09:31:04,149 INFO misc.py line 119 253097] Train: [33/100][504/510] Data 0.005 (0.089) Batch 0.971 (1.615) Remain 15:19:48 loss: 0.1498 Lr: 0.00480
[2023-12-25 09:31:05,327 INFO misc.py line 119 253097] Train: [33/100][505/510] Data 0.003 (0.089) Batch 1.178 (1.614) Remain 15:19:17 loss: 0.1473 Lr: 0.00480
[2023-12-25 09:31:06,359 INFO misc.py line 119 253097] Train: [33/100][506/510] Data 0.004 (0.089) Batch 1.032 (1.613) Remain 15:18:36 loss: 0.2206 Lr: 0.00480
[2023-12-25 09:31:07,315 INFO misc.py line 119 253097] Train: [33/100][507/510] Data 0.003 (0.089) Batch 0.955 (1.612) Remain 15:17:50 loss: 0.1077 Lr: 0.00480
[2023-12-25 09:31:08,360 INFO misc.py line 119 253097] Train: [33/100][508/510] Data 0.004 (0.088) Batch 1.045 (1.610) Remain 15:17:10 loss: 0.1605 Lr: 0.00480
[2023-12-25 09:31:09,609 INFO misc.py line 119 253097] Train: [33/100][509/510] Data 0.004 (0.088) Batch 1.250 (1.610) Remain 15:16:44 loss: 0.1329 Lr: 0.00480
[2023-12-25 09:31:10,857 INFO misc.py line 119 253097] Train: [33/100][510/510] Data 0.004 (0.088) Batch 1.235 (1.609) Remain 15:16:17 loss: 0.6685 Lr: 0.00480
[2023-12-25 09:31:10,858 INFO misc.py line 136 253097] Train result: loss: 0.2411 
[2023-12-25 09:31:10,858 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 09:31:38,389 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7633 
[2023-12-25 09:31:38,737 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4554 
[2023-12-25 09:31:44,029 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6614 
[2023-12-25 09:31:44,543 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3983 
[2023-12-25 09:31:46,510 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8033 
[2023-12-25 09:31:46,933 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.5379 
[2023-12-25 09:31:47,815 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2302 
[2023-12-25 09:31:48,367 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2752 
[2023-12-25 09:31:50,174 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8731 
[2023-12-25 09:31:52,296 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2386 
[2023-12-25 09:31:53,153 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3644 
[2023-12-25 09:31:53,576 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6103 
[2023-12-25 09:31:54,476 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5848 
[2023-12-25 09:31:57,418 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 1.0489 
[2023-12-25 09:31:57,887 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2540 
[2023-12-25 09:31:58,498 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3930 
[2023-12-25 09:31:59,202 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5213 
[2023-12-25 09:32:00,457 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6440/0.7098/0.8927.
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9057/0.9471
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9763/0.9873
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8379/0.9626
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2140/0.2267
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5940/0.6217
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5889/0.7868
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7904/0.9195
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9051/0.9480
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4120/0.4412
[2023-12-25 09:32:00,457 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7690/0.8447
[2023-12-25 09:32:00,458 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8074/0.8414
[2023-12-25 09:32:00,458 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5708/0.7010
[2023-12-25 09:32:00,458 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 09:32:00,459 INFO misc.py line 165 253097] Currently Best mIoU: 0.6581
[2023-12-25 09:32:00,459 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 09:32:21,875 INFO misc.py line 119 253097] Train: [34/100][1/510] Data 3.342 (3.342) Batch 18.215 (18.215) Remain 172:52:58 loss: 0.1310 Lr: 0.00480
[2023-12-25 09:32:23,029 INFO misc.py line 119 253097] Train: [34/100][2/510] Data 0.005 (0.005) Batch 1.155 (1.155) Remain 10:57:32 loss: 0.3767 Lr: 0.00480
[2023-12-25 09:32:24,180 INFO misc.py line 119 253097] Train: [34/100][3/510] Data 0.004 (0.004) Batch 1.150 (1.150) Remain 10:54:47 loss: 0.1461 Lr: 0.00480
[2023-12-25 09:32:25,173 INFO misc.py line 119 253097] Train: [34/100][4/510] Data 0.005 (0.005) Batch 0.995 (0.995) Remain 09:26:41 loss: 0.1914 Lr: 0.00480
[2023-12-25 09:32:26,450 INFO misc.py line 119 253097] Train: [34/100][5/510] Data 0.003 (0.004) Batch 1.272 (1.133) Remain 10:45:24 loss: 0.2660 Lr: 0.00480
[2023-12-25 09:32:27,627 INFO misc.py line 119 253097] Train: [34/100][6/510] Data 0.008 (0.006) Batch 1.181 (1.149) Remain 10:54:24 loss: 0.1229 Lr: 0.00480
[2023-12-25 09:32:28,705 INFO misc.py line 119 253097] Train: [34/100][7/510] Data 0.004 (0.005) Batch 1.072 (1.130) Remain 10:43:25 loss: 0.1320 Lr: 0.00480
[2023-12-25 09:32:29,750 INFO misc.py line 119 253097] Train: [34/100][8/510] Data 0.010 (0.006) Batch 1.051 (1.114) Remain 10:34:22 loss: 0.2908 Lr: 0.00480
[2023-12-25 09:32:30,825 INFO misc.py line 119 253097] Train: [34/100][9/510] Data 0.006 (0.006) Batch 1.075 (1.108) Remain 10:30:38 loss: 0.1635 Lr: 0.00480
[2023-12-25 09:32:31,877 INFO misc.py line 119 253097] Train: [34/100][10/510] Data 0.005 (0.006) Batch 1.053 (1.100) Remain 10:26:09 loss: 0.2268 Lr: 0.00480
[2023-12-25 09:32:33,111 INFO misc.py line 119 253097] Train: [34/100][11/510] Data 0.003 (0.006) Batch 1.228 (1.116) Remain 10:35:14 loss: 0.1329 Lr: 0.00480
[2023-12-25 09:32:34,336 INFO misc.py line 119 253097] Train: [34/100][12/510] Data 0.010 (0.006) Batch 1.226 (1.128) Remain 10:42:10 loss: 0.1797 Lr: 0.00480
[2023-12-25 09:32:35,458 INFO misc.py line 119 253097] Train: [34/100][13/510] Data 0.010 (0.007) Batch 1.122 (1.127) Remain 10:41:49 loss: 0.3372 Lr: 0.00480
[2023-12-25 09:32:36,582 INFO misc.py line 119 253097] Train: [34/100][14/510] Data 0.010 (0.007) Batch 1.125 (1.127) Remain 10:41:40 loss: 0.1306 Lr: 0.00480
[2023-12-25 09:32:37,801 INFO misc.py line 119 253097] Train: [34/100][15/510] Data 0.008 (0.007) Batch 1.220 (1.135) Remain 10:46:04 loss: 0.1302 Lr: 0.00480
[2023-12-25 09:32:39,064 INFO misc.py line 119 253097] Train: [34/100][16/510] Data 0.007 (0.007) Batch 1.262 (1.145) Remain 10:51:38 loss: 0.2398 Lr: 0.00480
[2023-12-25 09:32:40,384 INFO misc.py line 119 253097] Train: [34/100][17/510] Data 0.007 (0.007) Batch 1.324 (1.158) Remain 10:58:53 loss: 0.1080 Lr: 0.00480
[2023-12-25 09:32:41,536 INFO misc.py line 119 253097] Train: [34/100][18/510] Data 0.004 (0.007) Batch 1.152 (1.157) Remain 10:58:39 loss: 0.3428 Lr: 0.00480
[2023-12-25 09:32:42,784 INFO misc.py line 119 253097] Train: [34/100][19/510] Data 0.005 (0.007) Batch 1.248 (1.163) Remain 11:01:53 loss: 0.1508 Lr: 0.00480
[2023-12-25 09:32:44,073 INFO misc.py line 119 253097] Train: [34/100][20/510] Data 0.002 (0.006) Batch 1.285 (1.170) Remain 11:05:57 loss: 0.3354 Lr: 0.00480
[2023-12-25 09:32:45,249 INFO misc.py line 119 253097] Train: [34/100][21/510] Data 0.008 (0.006) Batch 1.178 (1.170) Remain 11:06:11 loss: 0.3442 Lr: 0.00480
[2023-12-25 09:32:46,375 INFO misc.py line 119 253097] Train: [34/100][22/510] Data 0.006 (0.006) Batch 1.128 (1.168) Remain 11:04:52 loss: 0.2682 Lr: 0.00480
[2023-12-25 09:32:47,553 INFO misc.py line 119 253097] Train: [34/100][23/510] Data 0.004 (0.006) Batch 1.177 (1.169) Remain 11:05:06 loss: 0.2727 Lr: 0.00480
[2023-12-25 09:32:48,818 INFO misc.py line 119 253097] Train: [34/100][24/510] Data 0.005 (0.006) Batch 1.264 (1.173) Remain 11:07:39 loss: 0.1913 Lr: 0.00480
[2023-12-25 09:32:50,129 INFO misc.py line 119 253097] Train: [34/100][25/510] Data 0.006 (0.006) Batch 1.306 (1.179) Remain 11:11:05 loss: 0.1898 Lr: 0.00480
[2023-12-25 09:32:54,164 INFO misc.py line 119 253097] Train: [34/100][26/510] Data 0.011 (0.006) Batch 4.041 (1.304) Remain 12:21:53 loss: 0.2292 Lr: 0.00480
[2023-12-25 09:32:59,136 INFO misc.py line 119 253097] Train: [34/100][27/510] Data 1.744 (0.079) Batch 4.972 (1.457) Remain 13:48:50 loss: 0.1530 Lr: 0.00480
[2023-12-25 09:33:00,139 INFO misc.py line 119 253097] Train: [34/100][28/510] Data 0.005 (0.076) Batch 1.002 (1.438) Remain 13:38:27 loss: 0.2216 Lr: 0.00480
[2023-12-25 09:33:01,135 INFO misc.py line 119 253097] Train: [34/100][29/510] Data 0.006 (0.073) Batch 0.992 (1.421) Remain 13:28:40 loss: 0.1490 Lr: 0.00480
[2023-12-25 09:33:02,267 INFO misc.py line 119 253097] Train: [34/100][30/510] Data 0.010 (0.071) Batch 1.135 (1.411) Remain 13:22:36 loss: 0.2546 Lr: 0.00480
[2023-12-25 09:33:03,201 INFO misc.py line 119 253097] Train: [34/100][31/510] Data 0.007 (0.069) Batch 0.936 (1.394) Remain 13:12:57 loss: 0.2014 Lr: 0.00480
[2023-12-25 09:33:04,269 INFO misc.py line 119 253097] Train: [34/100][32/510] Data 0.005 (0.066) Batch 1.069 (1.382) Remain 13:06:33 loss: 0.3446 Lr: 0.00480
[2023-12-25 09:33:05,505 INFO misc.py line 119 253097] Train: [34/100][33/510] Data 0.003 (0.064) Batch 1.236 (1.378) Remain 13:03:45 loss: 0.2622 Lr: 0.00480
[2023-12-25 09:33:06,598 INFO misc.py line 119 253097] Train: [34/100][34/510] Data 0.004 (0.062) Batch 1.085 (1.368) Remain 12:58:21 loss: 0.1834 Lr: 0.00480
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[2023-12-25 09:33:33,875 INFO misc.py line 119 253097] Train: [34/100][57/510] Data 0.004 (0.038) Batch 0.976 (1.291) Remain 12:13:48 loss: 0.1619 Lr: 0.00480
[2023-12-25 09:33:34,960 INFO misc.py line 119 253097] Train: [34/100][58/510] Data 0.005 (0.038) Batch 1.078 (1.287) Remain 12:11:35 loss: 0.1671 Lr: 0.00479
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[2023-12-25 09:33:40,485 INFO misc.py line 119 253097] Train: [34/100][63/510] Data 0.005 (0.035) Batch 1.124 (1.272) Remain 12:02:54 loss: 0.1622 Lr: 0.00479
[2023-12-25 09:33:41,263 INFO misc.py line 119 253097] Train: [34/100][64/510] Data 0.007 (0.035) Batch 0.781 (1.264) Remain 11:58:18 loss: 0.3587 Lr: 0.00479
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[2023-12-25 09:33:43,466 INFO misc.py line 119 253097] Train: [34/100][66/510] Data 0.005 (0.034) Batch 1.224 (1.259) Remain 11:55:20 loss: 0.2150 Lr: 0.00479
[2023-12-25 09:33:44,666 INFO misc.py line 119 253097] Train: [34/100][67/510] Data 0.004 (0.033) Batch 1.201 (1.258) Remain 11:54:48 loss: 0.2469 Lr: 0.00479
[2023-12-25 09:33:49,357 INFO misc.py line 119 253097] Train: [34/100][68/510] Data 3.615 (0.088) Batch 4.691 (1.310) Remain 12:24:48 loss: 0.3749 Lr: 0.00479
[2023-12-25 09:33:50,384 INFO misc.py line 119 253097] Train: [34/100][69/510] Data 0.004 (0.087) Batch 1.027 (1.306) Remain 12:22:20 loss: 0.3086 Lr: 0.00479
[2023-12-25 09:33:51,449 INFO misc.py line 119 253097] Train: [34/100][70/510] Data 0.004 (0.086) Batch 1.066 (1.303) Remain 12:20:17 loss: 0.1281 Lr: 0.00479
[2023-12-25 09:33:52,477 INFO misc.py line 119 253097] Train: [34/100][71/510] Data 0.003 (0.085) Batch 1.027 (1.299) Remain 12:17:57 loss: 0.3259 Lr: 0.00479
[2023-12-25 09:33:59,011 INFO misc.py line 119 253097] Train: [34/100][72/510] Data 5.416 (0.162) Batch 6.534 (1.374) Remain 13:01:03 loss: 0.1592 Lr: 0.00479
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[2023-12-25 09:34:02,352 INFO misc.py line 119 253097] Train: [34/100][75/510] Data 0.003 (0.155) Batch 1.207 (1.364) Remain 12:54:48 loss: 0.1581 Lr: 0.00479
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[2023-12-25 09:34:07,099 INFO misc.py line 119 253097] Train: [34/100][78/510] Data 1.345 (0.167) Batch 2.408 (1.372) Remain 12:59:43 loss: 0.1317 Lr: 0.00479
[2023-12-25 09:34:08,303 INFO misc.py line 119 253097] Train: [34/100][79/510] Data 0.004 (0.165) Batch 1.204 (1.370) Remain 12:58:26 loss: 0.4084 Lr: 0.00479
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[2023-12-25 09:34:10,888 INFO misc.py line 119 253097] Train: [34/100][81/510] Data 0.005 (0.161) Batch 1.277 (1.368) Remain 12:57:06 loss: 0.1511 Lr: 0.00479
[2023-12-25 09:34:12,020 INFO misc.py line 119 253097] Train: [34/100][82/510] Data 0.027 (0.159) Batch 1.151 (1.365) Remain 12:55:31 loss: 0.2980 Lr: 0.00479
[2023-12-25 09:34:13,299 INFO misc.py line 119 253097] Train: [34/100][83/510] Data 0.006 (0.157) Batch 1.278 (1.364) Remain 12:54:53 loss: 0.2181 Lr: 0.00479
[2023-12-25 09:34:14,318 INFO misc.py line 119 253097] Train: [34/100][84/510] Data 0.007 (0.155) Batch 1.007 (1.360) Remain 12:52:21 loss: 0.2261 Lr: 0.00479
[2023-12-25 09:34:15,432 INFO misc.py line 119 253097] Train: [34/100][85/510] Data 0.020 (0.154) Batch 1.111 (1.357) Remain 12:50:36 loss: 0.1675 Lr: 0.00479
[2023-12-25 09:34:16,683 INFO misc.py line 119 253097] Train: [34/100][86/510] Data 0.023 (0.152) Batch 1.266 (1.355) Remain 12:49:58 loss: 0.2417 Lr: 0.00479
[2023-12-25 09:34:17,920 INFO misc.py line 119 253097] Train: [34/100][87/510] Data 0.009 (0.151) Batch 1.226 (1.354) Remain 12:49:04 loss: 0.2713 Lr: 0.00479
[2023-12-25 09:34:19,152 INFO misc.py line 119 253097] Train: [34/100][88/510] Data 0.019 (0.149) Batch 1.240 (1.353) Remain 12:48:17 loss: 0.2356 Lr: 0.00479
[2023-12-25 09:34:20,312 INFO misc.py line 119 253097] Train: [34/100][89/510] Data 0.012 (0.147) Batch 1.165 (1.350) Remain 12:47:01 loss: 0.1055 Lr: 0.00479
[2023-12-25 09:34:21,367 INFO misc.py line 119 253097] Train: [34/100][90/510] Data 0.006 (0.146) Batch 1.053 (1.347) Remain 12:45:03 loss: 0.2399 Lr: 0.00479
[2023-12-25 09:34:28,444 INFO misc.py line 119 253097] Train: [34/100][91/510] Data 0.008 (0.144) Batch 7.081 (1.412) Remain 13:22:03 loss: 0.2544 Lr: 0.00479
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[2023-12-25 09:34:31,540 INFO misc.py line 119 253097] Train: [34/100][94/510] Data 0.004 (0.140) Batch 1.090 (1.400) Remain 13:14:50 loss: 0.2296 Lr: 0.00479
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[2023-12-25 09:34:37,301 INFO misc.py line 119 253097] Train: [34/100][99/510] Data 0.007 (0.133) Batch 1.017 (1.387) Remain 13:07:25 loss: 0.3308 Lr: 0.00479
[2023-12-25 09:34:38,451 INFO misc.py line 119 253097] Train: [34/100][100/510] Data 0.005 (0.132) Batch 1.151 (1.384) Remain 13:06:01 loss: 0.1385 Lr: 0.00479
[2023-12-25 09:34:39,874 INFO misc.py line 119 253097] Train: [34/100][101/510] Data 0.003 (0.130) Batch 1.419 (1.385) Remain 13:06:12 loss: 0.2229 Lr: 0.00479
[2023-12-25 09:34:41,007 INFO misc.py line 119 253097] Train: [34/100][102/510] Data 0.007 (0.129) Batch 1.137 (1.382) Remain 13:04:45 loss: 0.2518 Lr: 0.00479
[2023-12-25 09:34:42,190 INFO misc.py line 119 253097] Train: [34/100][103/510] Data 0.004 (0.128) Batch 1.178 (1.380) Remain 13:03:34 loss: 0.0732 Lr: 0.00479
[2023-12-25 09:34:43,265 INFO misc.py line 119 253097] Train: [34/100][104/510] Data 0.009 (0.127) Batch 1.079 (1.377) Remain 13:01:51 loss: 0.3169 Lr: 0.00479
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[2023-12-25 09:34:46,406 INFO misc.py line 119 253097] Train: [34/100][107/510] Data 0.009 (0.123) Batch 1.002 (1.368) Remain 12:56:23 loss: 0.2318 Lr: 0.00479
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[2023-12-25 09:34:49,775 INFO misc.py line 119 253097] Train: [34/100][110/510] Data 0.006 (0.120) Batch 1.184 (1.361) Remain 12:52:25 loss: 0.2148 Lr: 0.00479
[2023-12-25 09:34:50,753 INFO misc.py line 119 253097] Train: [34/100][111/510] Data 0.005 (0.119) Batch 0.980 (1.357) Remain 12:50:23 loss: 0.2404 Lr: 0.00479
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[2023-12-25 09:35:31,710 INFO misc.py line 119 253097] Train: [34/100][138/510] Data 0.004 (0.096) Batch 1.139 (1.389) Remain 13:07:54 loss: 0.2514 Lr: 0.00478
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[2023-12-25 09:36:24,077 INFO misc.py line 119 253097] Train: [34/100][172/510] Data 0.006 (0.081) Batch 1.123 (1.420) Remain 13:24:20 loss: 0.1859 Lr: 0.00478
[2023-12-25 09:36:25,268 INFO misc.py line 119 253097] Train: [34/100][173/510] Data 0.007 (0.080) Batch 1.193 (1.418) Remain 13:23:33 loss: 0.1471 Lr: 0.00478
[2023-12-25 09:36:26,379 INFO misc.py line 119 253097] Train: [34/100][174/510] Data 0.005 (0.080) Batch 1.111 (1.416) Remain 13:22:30 loss: 0.1644 Lr: 0.00478
[2023-12-25 09:36:31,770 INFO misc.py line 119 253097] Train: [34/100][175/510] Data 0.006 (0.079) Batch 5.393 (1.439) Remain 13:35:35 loss: 0.1123 Lr: 0.00478
[2023-12-25 09:36:32,908 INFO misc.py line 119 253097] Train: [34/100][176/510] Data 0.003 (0.079) Batch 1.136 (1.438) Remain 13:34:34 loss: 0.1658 Lr: 0.00478
[2023-12-25 09:36:34,026 INFO misc.py line 119 253097] Train: [34/100][177/510] Data 0.005 (0.078) Batch 1.119 (1.436) Remain 13:33:30 loss: 0.1042 Lr: 0.00478
[2023-12-25 09:36:35,266 INFO misc.py line 119 253097] Train: [34/100][178/510] Data 0.004 (0.078) Batch 1.229 (1.435) Remain 13:32:49 loss: 0.2271 Lr: 0.00478
[2023-12-25 09:36:36,227 INFO misc.py line 119 253097] Train: [34/100][179/510] Data 0.015 (0.078) Batch 0.972 (1.432) Remain 13:31:18 loss: 0.1932 Lr: 0.00478
[2023-12-25 09:36:37,187 INFO misc.py line 119 253097] Train: [34/100][180/510] Data 0.004 (0.077) Batch 0.959 (1.429) Remain 13:29:46 loss: 0.2454 Lr: 0.00478
[2023-12-25 09:36:38,217 INFO misc.py line 119 253097] Train: [34/100][181/510] Data 0.004 (0.077) Batch 1.030 (1.427) Remain 13:28:28 loss: 0.2700 Lr: 0.00478
[2023-12-25 09:36:39,471 INFO misc.py line 119 253097] Train: [34/100][182/510] Data 0.004 (0.076) Batch 1.247 (1.426) Remain 13:27:52 loss: 0.5713 Lr: 0.00478
[2023-12-25 09:36:40,656 INFO misc.py line 119 253097] Train: [34/100][183/510] Data 0.011 (0.076) Batch 1.192 (1.425) Remain 13:27:07 loss: 0.2501 Lr: 0.00478
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[2023-12-25 09:36:43,144 INFO misc.py line 119 253097] Train: [34/100][185/510] Data 0.011 (0.075) Batch 1.270 (1.423) Remain 13:25:56 loss: 0.2125 Lr: 0.00478
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[2023-12-25 09:36:54,904 INFO misc.py line 119 253097] Train: [34/100][188/510] Data 0.009 (0.074) Batch 9.403 (1.463) Remain 13:48:48 loss: 0.1518 Lr: 0.00477
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[2023-12-25 09:36:57,143 INFO misc.py line 119 253097] Train: [34/100][190/510] Data 0.005 (0.073) Batch 1.101 (1.460) Remain 13:46:40 loss: 0.1959 Lr: 0.00477
[2023-12-25 09:36:58,141 INFO misc.py line 119 253097] Train: [34/100][191/510] Data 0.003 (0.073) Batch 0.997 (1.457) Remain 13:45:15 loss: 0.3309 Lr: 0.00477
[2023-12-25 09:36:59,483 INFO misc.py line 119 253097] Train: [34/100][192/510] Data 0.004 (0.073) Batch 1.342 (1.457) Remain 13:44:53 loss: 0.1666 Lr: 0.00477
[2023-12-25 09:37:00,704 INFO misc.py line 119 253097] Train: [34/100][193/510] Data 0.004 (0.072) Batch 1.216 (1.455) Remain 13:44:08 loss: 0.2688 Lr: 0.00477
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[2023-12-25 09:37:03,893 INFO misc.py line 119 253097] Train: [34/100][196/510] Data 0.004 (0.071) Batch 1.052 (1.449) Remain 13:40:38 loss: 0.1591 Lr: 0.00477
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[2023-12-25 09:37:14,310 INFO misc.py line 119 253097] Train: [34/100][205/510] Data 0.009 (0.068) Batch 0.978 (1.436) Remain 13:33:03 loss: 0.1876 Lr: 0.00477
[2023-12-25 09:37:15,471 INFO misc.py line 119 253097] Train: [34/100][206/510] Data 0.004 (0.068) Batch 1.159 (1.435) Remain 13:32:15 loss: 0.2746 Lr: 0.00477
[2023-12-25 09:37:16,583 INFO misc.py line 119 253097] Train: [34/100][207/510] Data 0.006 (0.068) Batch 1.114 (1.433) Remain 13:31:20 loss: 0.2180 Lr: 0.00477
[2023-12-25 09:37:19,228 INFO misc.py line 119 253097] Train: [34/100][208/510] Data 0.005 (0.068) Batch 2.645 (1.439) Remain 13:34:40 loss: 0.1600 Lr: 0.00477
[2023-12-25 09:37:20,131 INFO misc.py line 119 253097] Train: [34/100][209/510] Data 0.005 (0.067) Batch 0.899 (1.437) Remain 13:33:09 loss: 0.3213 Lr: 0.00477
[2023-12-25 09:37:21,090 INFO misc.py line 119 253097] Train: [34/100][210/510] Data 0.008 (0.067) Batch 0.963 (1.434) Remain 13:31:50 loss: 0.1360 Lr: 0.00477
[2023-12-25 09:37:22,200 INFO misc.py line 119 253097] Train: [34/100][211/510] Data 0.005 (0.067) Batch 1.110 (1.433) Remain 13:30:56 loss: 0.3090 Lr: 0.00477
[2023-12-25 09:37:23,384 INFO misc.py line 119 253097] Train: [34/100][212/510] Data 0.004 (0.066) Batch 1.184 (1.432) Remain 13:30:14 loss: 0.3143 Lr: 0.00477
[2023-12-25 09:37:24,572 INFO misc.py line 119 253097] Train: [34/100][213/510] Data 0.004 (0.066) Batch 1.188 (1.430) Remain 13:29:33 loss: 0.1102 Lr: 0.00477
[2023-12-25 09:37:25,722 INFO misc.py line 119 253097] Train: [34/100][214/510] Data 0.003 (0.066) Batch 1.151 (1.429) Remain 13:28:47 loss: 0.6497 Lr: 0.00477
[2023-12-25 09:37:26,867 INFO misc.py line 119 253097] Train: [34/100][215/510] Data 0.003 (0.065) Batch 1.144 (1.428) Remain 13:28:00 loss: 0.2445 Lr: 0.00477
[2023-12-25 09:37:28,062 INFO misc.py line 119 253097] Train: [34/100][216/510] Data 0.004 (0.065) Batch 1.195 (1.427) Remain 13:27:21 loss: 0.2967 Lr: 0.00477
[2023-12-25 09:37:30,412 INFO misc.py line 119 253097] Train: [34/100][217/510] Data 0.004 (0.065) Batch 2.350 (1.431) Remain 13:29:46 loss: 0.2512 Lr: 0.00477
[2023-12-25 09:37:31,308 INFO misc.py line 119 253097] Train: [34/100][218/510] Data 0.003 (0.065) Batch 0.895 (1.429) Remain 13:28:20 loss: 0.1406 Lr: 0.00477
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[2023-12-25 09:37:33,784 INFO misc.py line 119 253097] Train: [34/100][220/510] Data 0.007 (0.064) Batch 1.256 (1.427) Remain 13:27:17 loss: 0.2153 Lr: 0.00477
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[2023-12-25 09:37:36,129 INFO misc.py line 119 253097] Train: [34/100][222/510] Data 0.005 (0.064) Batch 1.211 (1.424) Remain 13:25:56 loss: 0.2855 Lr: 0.00477
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[2023-12-25 09:37:38,356 INFO misc.py line 119 253097] Train: [34/100][224/510] Data 0.006 (0.063) Batch 1.069 (1.422) Remain 13:24:17 loss: 0.2288 Lr: 0.00477
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[2023-12-25 09:38:46,064 INFO misc.py line 119 253097] Train: [34/100][272/510] Data 0.003 (0.068) Batch 9.100 (1.420) Remain 13:22:02 loss: 0.1207 Lr: 0.00476
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[2023-12-25 09:38:49,427 INFO misc.py line 119 253097] Train: [34/100][275/510] Data 0.006 (0.068) Batch 1.038 (1.416) Remain 13:20:06 loss: 0.2161 Lr: 0.00476
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[2023-12-25 09:38:51,803 INFO misc.py line 119 253097] Train: [34/100][277/510] Data 0.004 (0.067) Batch 1.145 (1.415) Remain 13:19:07 loss: 0.1304 Lr: 0.00476
[2023-12-25 09:38:53,053 INFO misc.py line 119 253097] Train: [34/100][278/510] Data 0.004 (0.067) Batch 1.251 (1.414) Remain 13:18:46 loss: 0.3453 Lr: 0.00476
[2023-12-25 09:38:53,945 INFO misc.py line 119 253097] Train: [34/100][279/510] Data 0.003 (0.067) Batch 0.890 (1.412) Remain 13:17:40 loss: 0.1897 Lr: 0.00476
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[2023-12-25 09:38:56,431 INFO misc.py line 119 253097] Train: [34/100][281/510] Data 0.008 (0.066) Batch 1.187 (1.411) Remain 13:16:53 loss: 0.3108 Lr: 0.00476
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[2023-12-25 09:39:01,568 INFO misc.py line 119 253097] Train: [34/100][284/510] Data 0.008 (0.066) Batch 2.755 (1.414) Remain 13:18:41 loss: 0.1264 Lr: 0.00476
[2023-12-25 09:39:07,391 INFO misc.py line 119 253097] Train: [34/100][285/510] Data 4.598 (0.082) Batch 5.823 (1.430) Remain 13:27:29 loss: 0.2059 Lr: 0.00476
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[2023-12-25 09:39:09,856 INFO misc.py line 119 253097] Train: [34/100][287/510] Data 0.004 (0.081) Batch 1.237 (1.428) Remain 13:26:38 loss: 0.1570 Lr: 0.00476
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[2023-12-25 09:39:13,306 INFO misc.py line 119 253097] Train: [34/100][290/510] Data 0.009 (0.081) Batch 1.195 (1.426) Remain 13:24:56 loss: 0.3609 Lr: 0.00476
[2023-12-25 09:39:14,516 INFO misc.py line 119 253097] Train: [34/100][291/510] Data 0.005 (0.080) Batch 1.208 (1.425) Remain 13:24:29 loss: 0.1825 Lr: 0.00476
[2023-12-25 09:39:15,656 INFO misc.py line 119 253097] Train: [34/100][292/510] Data 0.008 (0.080) Batch 1.144 (1.424) Remain 13:23:55 loss: 0.2432 Lr: 0.00476
[2023-12-25 09:39:16,669 INFO misc.py line 119 253097] Train: [34/100][293/510] Data 0.004 (0.080) Batch 1.012 (1.422) Remain 13:23:05 loss: 0.1987 Lr: 0.00476
[2023-12-25 09:39:17,884 INFO misc.py line 119 253097] Train: [34/100][294/510] Data 0.004 (0.080) Batch 1.214 (1.422) Remain 13:22:40 loss: 0.1898 Lr: 0.00476
[2023-12-25 09:39:19,224 INFO misc.py line 119 253097] Train: [34/100][295/510] Data 0.006 (0.079) Batch 1.339 (1.421) Remain 13:22:29 loss: 0.1698 Lr: 0.00476
[2023-12-25 09:39:20,361 INFO misc.py line 119 253097] Train: [34/100][296/510] Data 0.007 (0.079) Batch 1.139 (1.420) Remain 13:21:55 loss: 0.5014 Lr: 0.00476
[2023-12-25 09:39:21,630 INFO misc.py line 119 253097] Train: [34/100][297/510] Data 0.005 (0.079) Batch 1.266 (1.420) Remain 13:21:35 loss: 0.3038 Lr: 0.00476
[2023-12-25 09:39:28,045 INFO misc.py line 119 253097] Train: [34/100][298/510] Data 0.008 (0.079) Batch 6.419 (1.437) Remain 13:31:08 loss: 0.1866 Lr: 0.00476
[2023-12-25 09:39:29,232 INFO misc.py line 119 253097] Train: [34/100][299/510] Data 0.003 (0.078) Batch 1.186 (1.436) Remain 13:30:38 loss: 0.2684 Lr: 0.00476
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[2023-12-25 09:39:31,510 INFO misc.py line 119 253097] Train: [34/100][301/510] Data 0.004 (0.078) Batch 1.123 (1.434) Remain 13:29:26 loss: 0.1325 Lr: 0.00476
[2023-12-25 09:39:32,784 INFO misc.py line 119 253097] Train: [34/100][302/510] Data 0.021 (0.078) Batch 1.287 (1.433) Remain 13:29:08 loss: 0.1266 Lr: 0.00476
[2023-12-25 09:39:34,023 INFO misc.py line 119 253097] Train: [34/100][303/510] Data 0.006 (0.077) Batch 1.242 (1.433) Remain 13:28:45 loss: 0.1721 Lr: 0.00476
[2023-12-25 09:39:35,112 INFO misc.py line 119 253097] Train: [34/100][304/510] Data 0.003 (0.077) Batch 1.088 (1.432) Remain 13:28:04 loss: 0.1423 Lr: 0.00476
[2023-12-25 09:39:36,241 INFO misc.py line 119 253097] Train: [34/100][305/510] Data 0.005 (0.077) Batch 1.120 (1.431) Remain 13:27:28 loss: 0.3098 Lr: 0.00476
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[2023-12-25 09:39:44,905 INFO misc.py line 119 253097] Train: [34/100][312/510] Data 0.003 (0.075) Batch 1.227 (1.426) Remain 13:24:51 loss: 0.1125 Lr: 0.00476
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[2023-12-25 09:39:48,540 INFO misc.py line 119 253097] Train: [34/100][315/510] Data 0.010 (0.075) Batch 1.156 (1.424) Remain 13:23:37 loss: 0.1946 Lr: 0.00475
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[2023-12-25 09:39:59,525 INFO misc.py line 119 253097] Train: [34/100][317/510] Data 0.004 (0.074) Batch 9.846 (1.450) Remain 13:38:11 loss: 0.1305 Lr: 0.00475
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[2023-12-25 09:41:35,892 INFO misc.py line 119 253097] Train: [34/100][382/510] Data 0.007 (0.094) Batch 1.172 (1.456) Remain 13:39:44 loss: 0.2702 Lr: 0.00474
[2023-12-25 09:41:37,061 INFO misc.py line 119 253097] Train: [34/100][383/510] Data 0.009 (0.094) Batch 1.170 (1.455) Remain 13:39:18 loss: 0.1834 Lr: 0.00474
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[2023-12-25 09:41:40,656 INFO misc.py line 119 253097] Train: [34/100][386/510] Data 0.004 (0.093) Batch 1.131 (1.453) Remain 13:38:05 loss: 0.1212 Lr: 0.00474
[2023-12-25 09:41:41,826 INFO misc.py line 119 253097] Train: [34/100][387/510] Data 0.007 (0.093) Batch 1.172 (1.452) Remain 13:37:39 loss: 0.2150 Lr: 0.00474
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[2023-12-25 09:41:53,275 INFO misc.py line 119 253097] Train: [34/100][392/510] Data 0.007 (0.092) Batch 6.848 (1.463) Remain 13:43:36 loss: 0.1901 Lr: 0.00474
[2023-12-25 09:41:54,318 INFO misc.py line 119 253097] Train: [34/100][393/510] Data 0.007 (0.092) Batch 1.044 (1.462) Remain 13:42:58 loss: 0.1421 Lr: 0.00474
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[2023-12-25 09:41:57,666 INFO misc.py line 119 253097] Train: [34/100][396/510] Data 0.005 (0.091) Batch 1.149 (1.459) Remain 13:41:24 loss: 0.1597 Lr: 0.00474
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[2023-12-25 09:42:01,833 INFO misc.py line 119 253097] Train: [34/100][399/510] Data 0.004 (0.093) Batch 1.187 (1.459) Remain 13:41:02 loss: 0.1267 Lr: 0.00474
[2023-12-25 09:42:03,020 INFO misc.py line 119 253097] Train: [34/100][400/510] Data 0.006 (0.092) Batch 1.186 (1.458) Remain 13:40:37 loss: 0.3322 Lr: 0.00474
[2023-12-25 09:42:04,351 INFO misc.py line 119 253097] Train: [34/100][401/510] Data 0.006 (0.092) Batch 1.327 (1.458) Remain 13:40:25 loss: 0.2172 Lr: 0.00474
[2023-12-25 09:42:05,594 INFO misc.py line 119 253097] Train: [34/100][402/510] Data 0.010 (0.092) Batch 1.244 (1.457) Remain 13:40:05 loss: 0.3296 Lr: 0.00474
[2023-12-25 09:42:06,607 INFO misc.py line 119 253097] Train: [34/100][403/510] Data 0.010 (0.092) Batch 1.010 (1.456) Remain 13:39:26 loss: 0.1845 Lr: 0.00474
[2023-12-25 09:42:09,963 INFO misc.py line 119 253097] Train: [34/100][404/510] Data 0.013 (0.092) Batch 3.365 (1.461) Remain 13:42:05 loss: 0.1471 Lr: 0.00474
[2023-12-25 09:42:11,228 INFO misc.py line 119 253097] Train: [34/100][405/510] Data 0.004 (0.091) Batch 1.264 (1.460) Remain 13:41:47 loss: 0.2042 Lr: 0.00474
[2023-12-25 09:42:12,352 INFO misc.py line 119 253097] Train: [34/100][406/510] Data 0.005 (0.091) Batch 1.119 (1.459) Remain 13:41:17 loss: 0.1703 Lr: 0.00474
[2023-12-25 09:42:13,432 INFO misc.py line 119 253097] Train: [34/100][407/510] Data 0.009 (0.091) Batch 1.087 (1.459) Remain 13:40:44 loss: 0.3685 Lr: 0.00474
[2023-12-25 09:42:14,581 INFO misc.py line 119 253097] Train: [34/100][408/510] Data 0.003 (0.091) Batch 1.137 (1.458) Remain 13:40:16 loss: 0.2384 Lr: 0.00474
[2023-12-25 09:42:15,675 INFO misc.py line 119 253097] Train: [34/100][409/510] Data 0.015 (0.091) Batch 1.102 (1.457) Remain 13:39:45 loss: 0.2347 Lr: 0.00474
[2023-12-25 09:42:16,866 INFO misc.py line 119 253097] Train: [34/100][410/510] Data 0.007 (0.090) Batch 1.191 (1.456) Remain 13:39:22 loss: 0.2216 Lr: 0.00474
[2023-12-25 09:42:18,161 INFO misc.py line 119 253097] Train: [34/100][411/510] Data 0.008 (0.090) Batch 1.299 (1.456) Remain 13:39:07 loss: 0.1595 Lr: 0.00474
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[2023-12-25 09:42:23,519 INFO misc.py line 119 253097] Train: [34/100][416/510] Data 0.004 (0.089) Batch 1.048 (1.451) Remain 13:36:23 loss: 0.3041 Lr: 0.00474
[2023-12-25 09:42:24,556 INFO misc.py line 119 253097] Train: [34/100][417/510] Data 0.005 (0.089) Batch 1.037 (1.450) Remain 13:35:48 loss: 0.2317 Lr: 0.00474
[2023-12-25 09:42:29,988 INFO misc.py line 119 253097] Train: [34/100][418/510] Data 0.005 (0.089) Batch 5.432 (1.460) Remain 13:41:10 loss: 0.3685 Lr: 0.00474
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[2023-12-25 09:42:32,716 INFO misc.py line 119 253097] Train: [34/100][420/510] Data 0.011 (0.088) Batch 0.898 (1.459) Remain 13:40:52 loss: 0.3751 Lr: 0.00474
[2023-12-25 09:42:33,740 INFO misc.py line 119 253097] Train: [34/100][421/510] Data 0.005 (0.088) Batch 1.025 (1.458) Remain 13:40:15 loss: 0.1431 Lr: 0.00474
[2023-12-25 09:42:34,863 INFO misc.py line 119 253097] Train: [34/100][422/510] Data 0.003 (0.088) Batch 1.123 (1.457) Remain 13:39:47 loss: 0.2112 Lr: 0.00474
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[2023-12-25 09:42:53,851 INFO misc.py line 119 253097] Train: [34/100][425/510] Data 0.005 (0.087) Batch 16.611 (1.492) Remain 13:59:11 loss: 0.1602 Lr: 0.00474
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[2023-12-25 09:43:30,646 INFO misc.py line 119 253097] Train: [34/100][448/510] Data 8.461 (0.102) Batch 9.637 (1.498) Remain 14:01:44 loss: 0.4539 Lr: 0.00473
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[2023-12-25 09:43:36,438 INFO misc.py line 119 253097] Train: [34/100][453/510] Data 0.003 (0.101) Batch 1.099 (1.494) Remain 13:59:29 loss: 0.3304 Lr: 0.00473
[2023-12-25 09:43:37,486 INFO misc.py line 119 253097] Train: [34/100][454/510] Data 0.007 (0.101) Batch 1.047 (1.493) Remain 13:58:54 loss: 0.1197 Lr: 0.00473
[2023-12-25 09:43:38,728 INFO misc.py line 119 253097] Train: [34/100][455/510] Data 0.008 (0.101) Batch 1.244 (1.492) Remain 13:58:34 loss: 0.2527 Lr: 0.00473
[2023-12-25 09:43:39,933 INFO misc.py line 119 253097] Train: [34/100][456/510] Data 0.007 (0.100) Batch 1.205 (1.492) Remain 13:58:11 loss: 0.1855 Lr: 0.00473
[2023-12-25 09:43:40,891 INFO misc.py line 119 253097] Train: [34/100][457/510] Data 0.010 (0.100) Batch 0.960 (1.491) Remain 13:57:31 loss: 0.1715 Lr: 0.00473
[2023-12-25 09:43:41,883 INFO misc.py line 119 253097] Train: [34/100][458/510] Data 0.004 (0.100) Batch 0.993 (1.489) Remain 13:56:52 loss: 0.1334 Lr: 0.00473
[2023-12-25 09:43:43,156 INFO misc.py line 119 253097] Train: [34/100][459/510] Data 0.004 (0.100) Batch 1.267 (1.489) Remain 13:56:34 loss: 0.1497 Lr: 0.00473
[2023-12-25 09:43:44,416 INFO misc.py line 119 253097] Train: [34/100][460/510] Data 0.010 (0.100) Batch 1.260 (1.488) Remain 13:56:16 loss: 0.2381 Lr: 0.00473
[2023-12-25 09:43:45,775 INFO misc.py line 119 253097] Train: [34/100][461/510] Data 0.010 (0.099) Batch 1.365 (1.488) Remain 13:56:05 loss: 0.3256 Lr: 0.00473
[2023-12-25 09:43:46,680 INFO misc.py line 119 253097] Train: [34/100][462/510] Data 0.004 (0.099) Batch 0.904 (1.487) Remain 13:55:21 loss: 0.2408 Lr: 0.00473
[2023-12-25 09:43:47,745 INFO misc.py line 119 253097] Train: [34/100][463/510] Data 0.005 (0.099) Batch 1.066 (1.486) Remain 13:54:48 loss: 0.1120 Lr: 0.00473
[2023-12-25 09:43:48,976 INFO misc.py line 119 253097] Train: [34/100][464/510] Data 0.004 (0.099) Batch 1.228 (1.485) Remain 13:54:28 loss: 0.1055 Lr: 0.00473
[2023-12-25 09:43:50,183 INFO misc.py line 119 253097] Train: [34/100][465/510] Data 0.007 (0.099) Batch 1.208 (1.485) Remain 13:54:06 loss: 0.3293 Lr: 0.00473
[2023-12-25 09:43:51,234 INFO misc.py line 119 253097] Train: [34/100][466/510] Data 0.006 (0.098) Batch 1.054 (1.484) Remain 13:53:34 loss: 0.6233 Lr: 0.00473
[2023-12-25 09:43:52,420 INFO misc.py line 119 253097] Train: [34/100][467/510] Data 0.004 (0.098) Batch 1.185 (1.483) Remain 13:53:10 loss: 0.1700 Lr: 0.00473
[2023-12-25 09:43:53,493 INFO misc.py line 119 253097] Train: [34/100][468/510] Data 0.004 (0.098) Batch 1.073 (1.482) Remain 13:52:39 loss: 0.1882 Lr: 0.00473
[2023-12-25 09:43:54,533 INFO misc.py line 119 253097] Train: [34/100][469/510] Data 0.006 (0.098) Batch 1.040 (1.481) Remain 13:52:06 loss: 0.3159 Lr: 0.00473
[2023-12-25 09:43:55,766 INFO misc.py line 119 253097] Train: [34/100][470/510] Data 0.004 (0.098) Batch 1.230 (1.481) Remain 13:51:46 loss: 0.3660 Lr: 0.00473
[2023-12-25 09:44:06,702 INFO misc.py line 119 253097] Train: [34/100][471/510] Data 9.812 (0.118) Batch 10.939 (1.501) Remain 14:03:06 loss: 0.1155 Lr: 0.00473
[2023-12-25 09:44:08,265 INFO misc.py line 119 253097] Train: [34/100][472/510] Data 0.524 (0.119) Batch 1.563 (1.501) Remain 14:03:09 loss: 0.2792 Lr: 0.00473
[2023-12-25 09:44:09,391 INFO misc.py line 119 253097] Train: [34/100][473/510] Data 0.003 (0.119) Batch 1.120 (1.500) Remain 14:02:40 loss: 0.2190 Lr: 0.00473
[2023-12-25 09:44:10,351 INFO misc.py line 119 253097] Train: [34/100][474/510] Data 0.010 (0.119) Batch 0.965 (1.499) Remain 14:02:00 loss: 0.1598 Lr: 0.00473
[2023-12-25 09:44:11,503 INFO misc.py line 119 253097] Train: [34/100][475/510] Data 0.005 (0.119) Batch 1.152 (1.499) Remain 14:01:34 loss: 0.2734 Lr: 0.00473
[2023-12-25 09:44:12,742 INFO misc.py line 119 253097] Train: [34/100][476/510] Data 0.006 (0.118) Batch 1.239 (1.498) Remain 14:01:14 loss: 0.1391 Lr: 0.00473
[2023-12-25 09:44:13,910 INFO misc.py line 119 253097] Train: [34/100][477/510] Data 0.004 (0.118) Batch 1.165 (1.497) Remain 14:00:49 loss: 0.2212 Lr: 0.00473
[2023-12-25 09:44:14,980 INFO misc.py line 119 253097] Train: [34/100][478/510] Data 0.008 (0.118) Batch 1.069 (1.496) Remain 14:00:17 loss: 0.1386 Lr: 0.00473
[2023-12-25 09:44:16,256 INFO misc.py line 119 253097] Train: [34/100][479/510] Data 0.008 (0.118) Batch 1.276 (1.496) Remain 14:00:00 loss: 0.1736 Lr: 0.00473
[2023-12-25 09:44:18,178 INFO misc.py line 119 253097] Train: [34/100][480/510] Data 0.009 (0.117) Batch 1.926 (1.497) Remain 14:00:28 loss: 0.2512 Lr: 0.00473
[2023-12-25 09:44:19,366 INFO misc.py line 119 253097] Train: [34/100][481/510] Data 0.005 (0.117) Batch 1.186 (1.496) Remain 14:00:05 loss: 0.2585 Lr: 0.00473
[2023-12-25 09:44:20,374 INFO misc.py line 119 253097] Train: [34/100][482/510] Data 0.006 (0.117) Batch 1.011 (1.495) Remain 13:59:29 loss: 0.1994 Lr: 0.00473
[2023-12-25 09:44:21,638 INFO misc.py line 119 253097] Train: [34/100][483/510] Data 0.004 (0.117) Batch 1.259 (1.495) Remain 13:59:11 loss: 0.1814 Lr: 0.00473
[2023-12-25 09:44:35,176 INFO misc.py line 119 253097] Train: [34/100][484/510] Data 12.453 (0.142) Batch 13.543 (1.520) Remain 14:13:14 loss: 0.1489 Lr: 0.00473
[2023-12-25 09:44:36,313 INFO misc.py line 119 253097] Train: [34/100][485/510] Data 0.004 (0.142) Batch 1.136 (1.519) Remain 14:12:45 loss: 0.1485 Lr: 0.00473
[2023-12-25 09:44:37,293 INFO misc.py line 119 253097] Train: [34/100][486/510] Data 0.005 (0.142) Batch 0.981 (1.518) Remain 14:12:06 loss: 0.2440 Lr: 0.00473
[2023-12-25 09:44:38,499 INFO misc.py line 119 253097] Train: [34/100][487/510] Data 0.004 (0.141) Batch 1.205 (1.517) Remain 14:11:43 loss: 0.1967 Lr: 0.00473
[2023-12-25 09:44:39,457 INFO misc.py line 119 253097] Train: [34/100][488/510] Data 0.004 (0.141) Batch 0.959 (1.516) Remain 14:11:03 loss: 0.3564 Lr: 0.00473
[2023-12-25 09:44:40,529 INFO misc.py line 119 253097] Train: [34/100][489/510] Data 0.004 (0.141) Batch 1.072 (1.515) Remain 14:10:30 loss: 0.1524 Lr: 0.00473
[2023-12-25 09:44:43,677 INFO misc.py line 119 253097] Train: [34/100][490/510] Data 1.854 (0.144) Batch 3.148 (1.518) Remain 14:12:22 loss: 0.2365 Lr: 0.00473
[2023-12-25 09:44:44,731 INFO misc.py line 119 253097] Train: [34/100][491/510] Data 0.004 (0.144) Batch 1.054 (1.518) Remain 14:11:48 loss: 0.3476 Lr: 0.00473
[2023-12-25 09:44:45,808 INFO misc.py line 119 253097] Train: [34/100][492/510] Data 0.004 (0.144) Batch 1.076 (1.517) Remain 14:11:16 loss: 0.1577 Lr: 0.00473
[2023-12-25 09:44:46,880 INFO misc.py line 119 253097] Train: [34/100][493/510] Data 0.005 (0.144) Batch 1.070 (1.516) Remain 14:10:44 loss: 0.2585 Lr: 0.00473
[2023-12-25 09:44:48,058 INFO misc.py line 119 253097] Train: [34/100][494/510] Data 0.006 (0.143) Batch 1.180 (1.515) Remain 14:10:20 loss: 0.1827 Lr: 0.00473
[2023-12-25 09:44:49,382 INFO misc.py line 119 253097] Train: [34/100][495/510] Data 0.004 (0.143) Batch 1.316 (1.515) Remain 14:10:04 loss: 0.1381 Lr: 0.00473
[2023-12-25 09:44:50,561 INFO misc.py line 119 253097] Train: [34/100][496/510] Data 0.013 (0.143) Batch 1.183 (1.514) Remain 14:09:40 loss: 0.2500 Lr: 0.00473
[2023-12-25 09:44:51,807 INFO misc.py line 119 253097] Train: [34/100][497/510] Data 0.008 (0.142) Batch 1.250 (1.513) Remain 14:09:21 loss: 0.1250 Lr: 0.00473
[2023-12-25 09:44:53,076 INFO misc.py line 119 253097] Train: [34/100][498/510] Data 0.004 (0.142) Batch 1.269 (1.513) Remain 14:09:03 loss: 0.3952 Lr: 0.00473
[2023-12-25 09:44:58,902 INFO misc.py line 119 253097] Train: [34/100][499/510] Data 0.004 (0.142) Batch 5.826 (1.522) Remain 14:13:54 loss: 0.2566 Lr: 0.00473
[2023-12-25 09:44:59,945 INFO misc.py line 119 253097] Train: [34/100][500/510] Data 0.003 (0.142) Batch 1.042 (1.521) Remain 14:13:20 loss: 0.2306 Lr: 0.00473
[2023-12-25 09:45:01,192 INFO misc.py line 119 253097] Train: [34/100][501/510] Data 0.003 (0.141) Batch 1.246 (1.520) Remain 14:13:00 loss: 0.3007 Lr: 0.00473
[2023-12-25 09:45:02,310 INFO misc.py line 119 253097] Train: [34/100][502/510] Data 0.005 (0.141) Batch 1.115 (1.519) Remain 14:12:31 loss: 0.1925 Lr: 0.00473
[2023-12-25 09:45:03,494 INFO misc.py line 119 253097] Train: [34/100][503/510] Data 0.007 (0.141) Batch 1.187 (1.519) Remain 14:12:07 loss: 0.3195 Lr: 0.00473
[2023-12-25 09:45:04,685 INFO misc.py line 119 253097] Train: [34/100][504/510] Data 0.005 (0.140) Batch 1.188 (1.518) Remain 14:11:43 loss: 0.1671 Lr: 0.00472
[2023-12-25 09:45:05,764 INFO misc.py line 119 253097] Train: [34/100][505/510] Data 0.009 (0.140) Batch 1.083 (1.517) Remain 14:11:13 loss: 0.2391 Lr: 0.00472
[2023-12-25 09:45:06,792 INFO misc.py line 119 253097] Train: [34/100][506/510] Data 0.005 (0.140) Batch 1.028 (1.516) Remain 14:10:38 loss: 0.1049 Lr: 0.00472
[2023-12-25 09:45:07,855 INFO misc.py line 119 253097] Train: [34/100][507/510] Data 0.004 (0.140) Batch 1.060 (1.515) Remain 14:10:06 loss: 0.1936 Lr: 0.00472
[2023-12-25 09:45:08,877 INFO misc.py line 119 253097] Train: [34/100][508/510] Data 0.008 (0.139) Batch 1.026 (1.514) Remain 14:09:32 loss: 0.1618 Lr: 0.00472
[2023-12-25 09:45:10,134 INFO misc.py line 119 253097] Train: [34/100][509/510] Data 0.003 (0.139) Batch 1.257 (1.514) Remain 14:09:14 loss: 0.2251 Lr: 0.00472
[2023-12-25 09:45:12,952 INFO misc.py line 119 253097] Train: [34/100][510/510] Data 0.003 (0.139) Batch 2.817 (1.516) Remain 14:10:39 loss: 0.3669 Lr: 0.00472
[2023-12-25 09:45:12,953 INFO misc.py line 136 253097] Train result: loss: 0.2271 
[2023-12-25 09:45:12,954 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 09:45:39,212 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4833 
[2023-12-25 09:45:39,568 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4798 
[2023-12-25 09:45:44,512 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6060 
[2023-12-25 09:45:45,026 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.6021 
[2023-12-25 09:45:47,003 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7526 
[2023-12-25 09:45:47,425 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.5582 
[2023-12-25 09:45:48,303 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3202 
[2023-12-25 09:45:48,863 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2607 
[2023-12-25 09:45:50,673 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0881 
[2023-12-25 09:45:52,795 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2974 
[2023-12-25 09:45:53,651 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2518 
[2023-12-25 09:45:54,077 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9431 
[2023-12-25 09:45:54,976 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5411 
[2023-12-25 09:45:57,920 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7361 
[2023-12-25 09:45:58,394 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3737 
[2023-12-25 09:45:59,003 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4642 
[2023-12-25 09:45:59,704 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4260 
[2023-12-25 09:46:01,454 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6444/0.7083/0.8926.
[2023-12-25 09:46:01,454 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9176/0.9534
[2023-12-25 09:46:01,454 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9812/0.9910
[2023-12-25 09:46:01,454 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8294/0.9619
[2023-12-25 09:46:01,454 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3338/0.3788
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5600/0.5738
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5575/0.6624
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8178/0.8977
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9145/0.9601
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4250/0.4378
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7505/0.8289
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7030/0.8221
[2023-12-25 09:46:01,455 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5865/0.7397
[2023-12-25 09:46:01,456 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 09:46:01,458 INFO misc.py line 165 253097] Currently Best mIoU: 0.6581
[2023-12-25 09:46:01,458 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 09:46:22,821 INFO misc.py line 119 253097] Train: [35/100][1/510] Data 18.769 (18.769) Batch 19.474 (19.474) Remain 182:04:45 loss: 0.1656 Lr: 0.00472
[2023-12-25 09:46:24,041 INFO misc.py line 119 253097] Train: [35/100][2/510] Data 0.005 (0.005) Batch 1.221 (1.221) Remain 11:24:48 loss: 0.2894 Lr: 0.00472
[2023-12-25 09:46:25,119 INFO misc.py line 119 253097] Train: [35/100][3/510] Data 0.004 (0.004) Batch 1.077 (1.077) Remain 10:04:15 loss: 0.1577 Lr: 0.00472
[2023-12-25 09:46:26,260 INFO misc.py line 119 253097] Train: [35/100][4/510] Data 0.004 (0.004) Batch 1.139 (1.139) Remain 10:39:09 loss: 0.1785 Lr: 0.00472
[2023-12-25 09:46:27,452 INFO misc.py line 119 253097] Train: [35/100][5/510] Data 0.006 (0.005) Batch 1.190 (1.165) Remain 10:53:20 loss: 0.1266 Lr: 0.00472
[2023-12-25 09:46:28,693 INFO misc.py line 119 253097] Train: [35/100][6/510] Data 0.008 (0.006) Batch 1.242 (1.190) Remain 11:07:40 loss: 0.3740 Lr: 0.00472
[2023-12-25 09:46:30,044 INFO misc.py line 119 253097] Train: [35/100][7/510] Data 0.008 (0.007) Batch 1.326 (1.224) Remain 11:26:39 loss: 0.2056 Lr: 0.00472
[2023-12-25 09:46:30,965 INFO misc.py line 119 253097] Train: [35/100][8/510] Data 0.032 (0.012) Batch 0.950 (1.169) Remain 10:55:49 loss: 0.1919 Lr: 0.00472
[2023-12-25 09:46:32,057 INFO misc.py line 119 253097] Train: [35/100][9/510] Data 0.004 (0.010) Batch 1.092 (1.156) Remain 10:48:36 loss: 0.1507 Lr: 0.00472
[2023-12-25 09:46:33,288 INFO misc.py line 119 253097] Train: [35/100][10/510] Data 0.004 (0.009) Batch 1.227 (1.167) Remain 10:54:13 loss: 0.2598 Lr: 0.00472
[2023-12-25 09:46:34,468 INFO misc.py line 119 253097] Train: [35/100][11/510] Data 0.008 (0.009) Batch 1.185 (1.169) Remain 10:55:31 loss: 0.2618 Lr: 0.00472
[2023-12-25 09:46:35,778 INFO misc.py line 119 253097] Train: [35/100][12/510] Data 0.003 (0.009) Batch 1.304 (1.184) Remain 11:03:57 loss: 0.1726 Lr: 0.00472
[2023-12-25 09:46:36,935 INFO misc.py line 119 253097] Train: [35/100][13/510] Data 0.008 (0.009) Batch 1.162 (1.182) Remain 11:02:42 loss: 0.2655 Lr: 0.00472
[2023-12-25 09:46:42,590 INFO misc.py line 119 253097] Train: [35/100][14/510] Data 0.003 (0.008) Batch 5.655 (1.588) Remain 14:50:42 loss: 0.1325 Lr: 0.00472
[2023-12-25 09:46:43,874 INFO misc.py line 119 253097] Train: [35/100][15/510] Data 0.003 (0.008) Batch 1.279 (1.563) Remain 14:36:13 loss: 0.3804 Lr: 0.00472
[2023-12-25 09:46:44,925 INFO misc.py line 119 253097] Train: [35/100][16/510] Data 0.008 (0.008) Batch 1.052 (1.523) Remain 14:14:10 loss: 0.1133 Lr: 0.00472
[2023-12-25 09:46:46,144 INFO misc.py line 119 253097] Train: [35/100][17/510] Data 0.007 (0.008) Batch 1.219 (1.502) Remain 14:01:57 loss: 0.1391 Lr: 0.00472
[2023-12-25 09:46:47,248 INFO misc.py line 119 253097] Train: [35/100][18/510] Data 0.008 (0.008) Batch 1.108 (1.475) Remain 13:47:13 loss: 0.1568 Lr: 0.00472
[2023-12-25 09:46:48,175 INFO misc.py line 119 253097] Train: [35/100][19/510] Data 0.004 (0.007) Batch 0.928 (1.441) Remain 13:27:59 loss: 0.2759 Lr: 0.00472
[2023-12-25 09:46:49,474 INFO misc.py line 119 253097] Train: [35/100][20/510] Data 0.003 (0.007) Batch 1.299 (1.433) Remain 13:23:16 loss: 0.1622 Lr: 0.00472
[2023-12-25 09:46:51,318 INFO misc.py line 119 253097] Train: [35/100][21/510] Data 0.004 (0.007) Batch 1.840 (1.455) Remain 13:35:56 loss: 0.2037 Lr: 0.00472
[2023-12-25 09:46:52,290 INFO misc.py line 119 253097] Train: [35/100][22/510] Data 0.008 (0.007) Batch 0.976 (1.430) Remain 13:21:45 loss: 0.2285 Lr: 0.00472
[2023-12-25 09:46:57,179 INFO misc.py line 119 253097] Train: [35/100][23/510] Data 3.781 (0.196) Batch 4.890 (1.603) Remain 14:58:42 loss: 0.1398 Lr: 0.00472
[2023-12-25 09:46:58,251 INFO misc.py line 119 253097] Train: [35/100][24/510] Data 0.003 (0.187) Batch 1.072 (1.578) Remain 14:44:30 loss: 0.2554 Lr: 0.00472
[2023-12-25 09:46:59,427 INFO misc.py line 119 253097] Train: [35/100][25/510] Data 0.004 (0.178) Batch 1.175 (1.559) Remain 14:34:13 loss: 0.1781 Lr: 0.00472
[2023-12-25 09:47:00,683 INFO misc.py line 119 253097] Train: [35/100][26/510] Data 0.004 (0.171) Batch 1.252 (1.546) Remain 14:26:41 loss: 0.1527 Lr: 0.00472
[2023-12-25 09:47:01,889 INFO misc.py line 119 253097] Train: [35/100][27/510] Data 0.008 (0.164) Batch 1.207 (1.532) Remain 14:18:45 loss: 0.2498 Lr: 0.00472
[2023-12-25 09:47:02,979 INFO misc.py line 119 253097] Train: [35/100][28/510] Data 0.007 (0.158) Batch 1.093 (1.514) Remain 14:08:53 loss: 0.1270 Lr: 0.00472
[2023-12-25 09:47:04,244 INFO misc.py line 119 253097] Train: [35/100][29/510] Data 0.004 (0.152) Batch 1.229 (1.503) Remain 14:02:42 loss: 0.1513 Lr: 0.00472
[2023-12-25 09:47:05,251 INFO misc.py line 119 253097] Train: [35/100][30/510] Data 0.041 (0.148) Batch 1.037 (1.486) Remain 13:53:00 loss: 0.2695 Lr: 0.00472
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[2023-12-25 09:47:28,422 INFO misc.py line 119 253097] Train: [35/100][33/510] Data 0.003 (0.134) Batch 20.864 (2.110) Remain 19:42:37 loss: 0.4020 Lr: 0.00472
[2023-12-25 09:47:29,757 INFO misc.py line 119 253097] Train: [35/100][34/510] Data 0.004 (0.129) Batch 1.332 (2.085) Remain 19:28:30 loss: 0.2442 Lr: 0.00472
[2023-12-25 09:47:30,751 INFO misc.py line 119 253097] Train: [35/100][35/510] Data 0.007 (0.126) Batch 0.997 (2.051) Remain 19:09:25 loss: 0.1392 Lr: 0.00472
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[2023-12-25 09:47:36,774 INFO misc.py line 119 253097] Train: [35/100][40/510] Data 0.007 (0.110) Batch 1.075 (1.937) Remain 18:05:09 loss: 0.2763 Lr: 0.00472
[2023-12-25 09:47:37,851 INFO misc.py line 119 253097] Train: [35/100][41/510] Data 0.004 (0.107) Batch 1.077 (1.914) Remain 17:52:27 loss: 0.2415 Lr: 0.00472
[2023-12-25 09:47:38,992 INFO misc.py line 119 253097] Train: [35/100][42/510] Data 0.005 (0.104) Batch 1.134 (1.894) Remain 17:41:12 loss: 0.2642 Lr: 0.00472
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[2023-12-25 09:47:42,441 INFO misc.py line 119 253097] Train: [35/100][45/510] Data 0.005 (0.097) Batch 1.040 (1.841) Remain 17:11:25 loss: 0.1512 Lr: 0.00472
[2023-12-25 09:47:43,702 INFO misc.py line 119 253097] Train: [35/100][46/510] Data 0.004 (0.095) Batch 1.257 (1.827) Remain 17:03:47 loss: 0.3933 Lr: 0.00472
[2023-12-25 09:47:44,769 INFO misc.py line 119 253097] Train: [35/100][47/510] Data 0.008 (0.093) Batch 1.071 (1.810) Remain 16:54:07 loss: 0.1198 Lr: 0.00472
[2023-12-25 09:47:45,902 INFO misc.py line 119 253097] Train: [35/100][48/510] Data 0.005 (0.091) Batch 1.134 (1.795) Remain 16:45:40 loss: 0.2458 Lr: 0.00472
[2023-12-25 09:47:47,193 INFO misc.py line 119 253097] Train: [35/100][49/510] Data 0.004 (0.089) Batch 1.286 (1.784) Remain 16:39:26 loss: 0.3643 Lr: 0.00472
[2023-12-25 09:47:48,329 INFO misc.py line 119 253097] Train: [35/100][50/510] Data 0.008 (0.088) Batch 1.137 (1.770) Remain 16:31:42 loss: 0.1835 Lr: 0.00472
[2023-12-25 09:47:49,552 INFO misc.py line 119 253097] Train: [35/100][51/510] Data 0.008 (0.086) Batch 1.223 (1.759) Remain 16:25:17 loss: 0.1297 Lr: 0.00472
[2023-12-25 09:47:50,543 INFO misc.py line 119 253097] Train: [35/100][52/510] Data 0.007 (0.084) Batch 0.995 (1.743) Remain 16:16:31 loss: 0.1238 Lr: 0.00472
[2023-12-25 09:47:51,726 INFO misc.py line 119 253097] Train: [35/100][53/510] Data 0.003 (0.083) Batch 1.182 (1.732) Remain 16:10:12 loss: 0.1183 Lr: 0.00472
[2023-12-25 09:47:52,605 INFO misc.py line 119 253097] Train: [35/100][54/510] Data 0.005 (0.081) Batch 0.880 (1.715) Remain 16:00:48 loss: 0.4892 Lr: 0.00472
[2023-12-25 09:47:53,708 INFO misc.py line 119 253097] Train: [35/100][55/510] Data 0.004 (0.080) Batch 1.102 (1.704) Remain 15:54:10 loss: 0.3982 Lr: 0.00472
[2023-12-25 09:47:54,740 INFO misc.py line 119 253097] Train: [35/100][56/510] Data 0.005 (0.078) Batch 1.033 (1.691) Remain 15:47:03 loss: 0.4058 Lr: 0.00471
[2023-12-25 09:47:55,874 INFO misc.py line 119 253097] Train: [35/100][57/510] Data 0.005 (0.077) Batch 1.134 (1.681) Remain 15:41:15 loss: 0.1181 Lr: 0.00471
[2023-12-25 09:47:57,071 INFO misc.py line 119 253097] Train: [35/100][58/510] Data 0.004 (0.076) Batch 1.197 (1.672) Remain 15:36:18 loss: 0.2065 Lr: 0.00471
[2023-12-25 09:48:05,940 INFO misc.py line 119 253097] Train: [35/100][59/510] Data 0.005 (0.074) Batch 8.869 (1.800) Remain 16:48:14 loss: 0.2164 Lr: 0.00471
[2023-12-25 09:48:11,097 INFO misc.py line 119 253097] Train: [35/100][60/510] Data 0.004 (0.073) Batch 5.157 (1.859) Remain 17:21:11 loss: 0.1401 Lr: 0.00471
[2023-12-25 09:48:12,232 INFO misc.py line 119 253097] Train: [35/100][61/510] Data 0.003 (0.072) Batch 1.133 (1.847) Remain 17:14:09 loss: 0.3796 Lr: 0.00471
[2023-12-25 09:48:13,203 INFO misc.py line 119 253097] Train: [35/100][62/510] Data 0.005 (0.071) Batch 0.972 (1.832) Remain 17:05:49 loss: 0.1936 Lr: 0.00471
[2023-12-25 09:48:14,411 INFO misc.py line 119 253097] Train: [35/100][63/510] Data 0.004 (0.070) Batch 1.209 (1.822) Remain 16:59:59 loss: 0.2641 Lr: 0.00471
[2023-12-25 09:48:15,643 INFO misc.py line 119 253097] Train: [35/100][64/510] Data 0.004 (0.069) Batch 1.225 (1.812) Remain 16:54:28 loss: 0.1441 Lr: 0.00471
[2023-12-25 09:48:16,785 INFO misc.py line 119 253097] Train: [35/100][65/510] Data 0.010 (0.068) Batch 1.145 (1.801) Remain 16:48:25 loss: 0.2293 Lr: 0.00471
[2023-12-25 09:48:17,973 INFO misc.py line 119 253097] Train: [35/100][66/510] Data 0.007 (0.067) Batch 1.189 (1.791) Remain 16:42:57 loss: 0.3323 Lr: 0.00471
[2023-12-25 09:48:19,070 INFO misc.py line 119 253097] Train: [35/100][67/510] Data 0.007 (0.066) Batch 1.093 (1.780) Remain 16:36:48 loss: 0.1336 Lr: 0.00471
[2023-12-25 09:48:20,182 INFO misc.py line 119 253097] Train: [35/100][68/510] Data 0.011 (0.065) Batch 1.113 (1.770) Remain 16:31:02 loss: 0.2172 Lr: 0.00471
[2023-12-25 09:48:21,275 INFO misc.py line 119 253097] Train: [35/100][69/510] Data 0.010 (0.064) Batch 1.098 (1.760) Remain 16:25:18 loss: 0.2595 Lr: 0.00471
[2023-12-25 09:48:22,336 INFO misc.py line 119 253097] Train: [35/100][70/510] Data 0.005 (0.063) Batch 1.061 (1.750) Remain 16:19:25 loss: 0.1694 Lr: 0.00471
[2023-12-25 09:48:23,428 INFO misc.py line 119 253097] Train: [35/100][71/510] Data 0.004 (0.062) Batch 1.086 (1.740) Remain 16:13:56 loss: 0.2510 Lr: 0.00471
[2023-12-25 09:48:29,853 INFO misc.py line 119 253097] Train: [35/100][72/510] Data 0.010 (0.062) Batch 6.431 (1.808) Remain 16:51:58 loss: 0.2223 Lr: 0.00471
[2023-12-25 09:48:30,893 INFO misc.py line 119 253097] Train: [35/100][73/510] Data 0.005 (0.061) Batch 1.036 (1.797) Remain 16:45:46 loss: 0.9578 Lr: 0.00471
[2023-12-25 09:48:32,038 INFO misc.py line 119 253097] Train: [35/100][74/510] Data 0.009 (0.060) Batch 1.149 (1.788) Remain 16:40:38 loss: 0.2288 Lr: 0.00471
[2023-12-25 09:48:33,093 INFO misc.py line 119 253097] Train: [35/100][75/510] Data 0.004 (0.059) Batch 1.052 (1.777) Remain 16:34:53 loss: 0.3110 Lr: 0.00471
[2023-12-25 09:48:34,206 INFO misc.py line 119 253097] Train: [35/100][76/510] Data 0.008 (0.058) Batch 1.113 (1.768) Remain 16:29:45 loss: 0.0954 Lr: 0.00471
[2023-12-25 09:48:35,191 INFO misc.py line 119 253097] Train: [35/100][77/510] Data 0.008 (0.058) Batch 0.989 (1.758) Remain 16:23:50 loss: 0.2022 Lr: 0.00471
[2023-12-25 09:48:36,317 INFO misc.py line 119 253097] Train: [35/100][78/510] Data 0.003 (0.057) Batch 1.125 (1.749) Remain 16:19:05 loss: 0.1422 Lr: 0.00471
[2023-12-25 09:48:37,610 INFO misc.py line 119 253097] Train: [35/100][79/510] Data 0.005 (0.056) Batch 1.288 (1.743) Remain 16:15:39 loss: 0.2723 Lr: 0.00471
[2023-12-25 09:48:38,765 INFO misc.py line 119 253097] Train: [35/100][80/510] Data 0.010 (0.056) Batch 1.157 (1.736) Remain 16:11:22 loss: 0.1324 Lr: 0.00471
[2023-12-25 09:48:39,917 INFO misc.py line 119 253097] Train: [35/100][81/510] Data 0.008 (0.055) Batch 1.152 (1.728) Remain 16:07:09 loss: 0.2526 Lr: 0.00471
[2023-12-25 09:48:41,026 INFO misc.py line 119 253097] Train: [35/100][82/510] Data 0.008 (0.055) Batch 1.113 (1.720) Remain 16:02:46 loss: 0.1438 Lr: 0.00471
[2023-12-25 09:48:42,226 INFO misc.py line 119 253097] Train: [35/100][83/510] Data 0.004 (0.054) Batch 1.197 (1.714) Remain 15:59:04 loss: 0.2190 Lr: 0.00471
[2023-12-25 09:48:43,442 INFO misc.py line 119 253097] Train: [35/100][84/510] Data 0.008 (0.053) Batch 1.214 (1.708) Remain 15:55:35 loss: 0.1574 Lr: 0.00471
[2023-12-25 09:48:44,600 INFO misc.py line 119 253097] Train: [35/100][85/510] Data 0.009 (0.053) Batch 1.162 (1.701) Remain 15:51:50 loss: 0.1626 Lr: 0.00471
[2023-12-25 09:48:45,532 INFO misc.py line 119 253097] Train: [35/100][86/510] Data 0.004 (0.052) Batch 0.932 (1.692) Remain 15:46:38 loss: 0.2988 Lr: 0.00471
[2023-12-25 09:49:03,221 INFO misc.py line 119 253097] Train: [35/100][87/510] Data 0.004 (0.052) Batch 17.686 (1.882) Remain 17:33:09 loss: 0.2485 Lr: 0.00471
[2023-12-25 09:49:04,505 INFO misc.py line 119 253097] Train: [35/100][88/510] Data 0.006 (0.051) Batch 1.287 (1.875) Remain 17:29:12 loss: 0.4586 Lr: 0.00471
[2023-12-25 09:49:05,729 INFO misc.py line 119 253097] Train: [35/100][89/510] Data 0.004 (0.051) Batch 1.224 (1.868) Remain 17:24:56 loss: 0.1476 Lr: 0.00471
[2023-12-25 09:49:06,859 INFO misc.py line 119 253097] Train: [35/100][90/510] Data 0.004 (0.050) Batch 1.130 (1.859) Remain 17:20:09 loss: 0.1659 Lr: 0.00471
[2023-12-25 09:49:08,167 INFO misc.py line 119 253097] Train: [35/100][91/510] Data 0.004 (0.050) Batch 1.303 (1.853) Remain 17:16:35 loss: 0.3200 Lr: 0.00471
[2023-12-25 09:49:09,338 INFO misc.py line 119 253097] Train: [35/100][92/510] Data 0.009 (0.049) Batch 1.173 (1.845) Remain 17:12:17 loss: 0.1794 Lr: 0.00471
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[2023-12-25 09:49:11,819 INFO misc.py line 119 253097] Train: [35/100][94/510] Data 0.004 (0.048) Batch 1.258 (1.832) Remain 17:04:48 loss: 0.2025 Lr: 0.00471
[2023-12-25 09:49:12,997 INFO misc.py line 119 253097] Train: [35/100][95/510] Data 0.005 (0.048) Batch 1.175 (1.825) Remain 17:00:47 loss: 0.1966 Lr: 0.00471
[2023-12-25 09:49:14,087 INFO misc.py line 119 253097] Train: [35/100][96/510] Data 0.007 (0.047) Batch 1.092 (1.817) Remain 16:56:20 loss: 0.1746 Lr: 0.00471
[2023-12-25 09:49:15,180 INFO misc.py line 119 253097] Train: [35/100][97/510] Data 0.005 (0.047) Batch 1.088 (1.809) Remain 16:51:58 loss: 0.2134 Lr: 0.00471
[2023-12-25 09:49:16,318 INFO misc.py line 119 253097] Train: [35/100][98/510] Data 0.010 (0.046) Batch 1.142 (1.802) Remain 16:48:01 loss: 0.2198 Lr: 0.00471
[2023-12-25 09:49:17,612 INFO misc.py line 119 253097] Train: [35/100][99/510] Data 0.007 (0.046) Batch 1.290 (1.797) Remain 16:45:00 loss: 0.1444 Lr: 0.00471
[2023-12-25 09:49:18,636 INFO misc.py line 119 253097] Train: [35/100][100/510] Data 0.011 (0.046) Batch 1.029 (1.789) Remain 16:40:33 loss: 0.2094 Lr: 0.00471
[2023-12-25 09:49:19,921 INFO misc.py line 119 253097] Train: [35/100][101/510] Data 0.006 (0.045) Batch 1.284 (1.784) Remain 16:37:38 loss: 0.2233 Lr: 0.00471
[2023-12-25 09:49:21,001 INFO misc.py line 119 253097] Train: [35/100][102/510] Data 0.007 (0.045) Batch 1.080 (1.777) Remain 16:33:38 loss: 0.2001 Lr: 0.00471
[2023-12-25 09:49:26,508 INFO misc.py line 119 253097] Train: [35/100][103/510] Data 0.006 (0.044) Batch 5.509 (1.814) Remain 16:54:29 loss: 0.0900 Lr: 0.00471
[2023-12-25 09:49:27,647 INFO misc.py line 119 253097] Train: [35/100][104/510] Data 0.004 (0.044) Batch 1.139 (1.807) Remain 16:50:42 loss: 0.2089 Lr: 0.00471
[2023-12-25 09:49:28,919 INFO misc.py line 119 253097] Train: [35/100][105/510] Data 0.003 (0.044) Batch 1.266 (1.802) Remain 16:47:43 loss: 0.2806 Lr: 0.00471
[2023-12-25 09:49:30,095 INFO misc.py line 119 253097] Train: [35/100][106/510] Data 0.010 (0.043) Batch 1.181 (1.796) Remain 16:44:18 loss: 0.1329 Lr: 0.00471
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[2023-12-25 09:49:35,089 INFO misc.py line 119 253097] Train: [35/100][110/510] Data 0.006 (0.042) Batch 1.447 (1.775) Remain 16:32:44 loss: 0.1521 Lr: 0.00471
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[2023-12-25 09:49:37,410 INFO misc.py line 119 253097] Train: [35/100][112/510] Data 0.006 (0.041) Batch 1.119 (1.764) Remain 16:26:22 loss: 0.2281 Lr: 0.00471
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[2023-12-25 09:51:02,943 INFO misc.py line 119 253097] Train: [35/100][168/510] Data 0.004 (0.041) Batch 5.141 (1.684) Remain 15:39:53 loss: 0.2036 Lr: 0.00470
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[2023-12-25 09:51:20,023 INFO misc.py line 119 253097] Train: [35/100][180/510] Data 0.005 (0.039) Batch 1.059 (1.666) Remain 15:29:41 loss: 0.2759 Lr: 0.00470
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[2023-12-25 09:51:22,253 INFO misc.py line 119 253097] Train: [35/100][182/510] Data 0.005 (0.038) Batch 1.080 (1.660) Remain 15:26:12 loss: 0.2061 Lr: 0.00469
[2023-12-25 09:51:23,498 INFO misc.py line 119 253097] Train: [35/100][183/510] Data 0.004 (0.038) Batch 1.242 (1.658) Remain 15:24:52 loss: 0.1973 Lr: 0.00469
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[2023-12-25 09:51:26,812 INFO misc.py line 119 253097] Train: [35/100][186/510] Data 0.004 (0.038) Batch 1.077 (1.649) Remain 15:19:43 loss: 0.1159 Lr: 0.00469
[2023-12-25 09:51:29,456 INFO misc.py line 119 253097] Train: [35/100][187/510] Data 1.458 (0.045) Batch 2.651 (1.654) Remain 15:22:44 loss: 0.2123 Lr: 0.00469
[2023-12-25 09:51:30,749 INFO misc.py line 119 253097] Train: [35/100][188/510] Data 0.005 (0.045) Batch 1.289 (1.652) Remain 15:21:36 loss: 0.2053 Lr: 0.00469
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[2023-12-25 09:51:40,050 INFO misc.py line 119 253097] Train: [35/100][195/510] Data 0.006 (0.044) Batch 3.010 (1.640) Remain 15:14:51 loss: 0.3746 Lr: 0.00469
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[2023-12-25 09:51:52,796 INFO misc.py line 119 253097] Train: [35/100][200/510] Data 7.329 (0.080) Batch 8.337 (1.663) Remain 15:27:35 loss: 0.0602 Lr: 0.00469
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[2023-12-25 09:53:36,868 INFO misc.py line 119 253097] Train: [35/100][270/510] Data 0.005 (0.151) Batch 1.122 (1.617) Remain 14:59:52 loss: 0.1476 Lr: 0.00468
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[2023-12-25 09:54:02,508 INFO misc.py line 119 253097] Train: [35/100][289/510] Data 0.016 (0.142) Batch 1.103 (1.599) Remain 14:49:27 loss: 0.1622 Lr: 0.00468
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[2023-12-25 09:54:13,725 INFO misc.py line 119 253097] Train: [35/100][293/510] Data 4.236 (0.155) Batch 5.508 (1.616) Remain 14:58:37 loss: 0.2808 Lr: 0.00468
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[2023-12-25 09:54:28,452 INFO misc.py line 119 253097] Train: [35/100][305/510] Data 0.003 (0.149) Batch 1.117 (1.600) Remain 14:49:42 loss: 0.4777 Lr: 0.00468
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[2023-12-25 09:54:40,554 INFO misc.py line 119 253097] Train: [35/100][308/510] Data 8.490 (0.176) Batch 9.777 (1.624) Remain 15:02:56 loss: 0.1914 Lr: 0.00467
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[2023-12-25 09:56:19,231 INFO misc.py line 119 253097] Train: [35/100][376/510] Data 0.006 (0.169) Batch 1.251 (1.593) Remain 14:43:34 loss: 0.2362 Lr: 0.00466
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[2023-12-25 09:56:27,284 INFO misc.py line 119 253097] Train: [35/100][383/510] Data 0.004 (0.166) Batch 1.101 (1.585) Remain 14:38:52 loss: 0.2347 Lr: 0.00466
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[2023-12-25 09:56:32,708 INFO misc.py line 119 253097] Train: [35/100][388/510] Data 0.005 (0.164) Batch 0.943 (1.578) Remain 14:35:08 loss: 0.3414 Lr: 0.00466
[2023-12-25 09:56:34,020 INFO misc.py line 119 253097] Train: [35/100][389/510] Data 0.004 (0.164) Batch 1.312 (1.577) Remain 14:34:43 loss: 0.2757 Lr: 0.00466
[2023-12-25 09:56:35,291 INFO misc.py line 119 253097] Train: [35/100][390/510] Data 0.004 (0.163) Batch 1.271 (1.577) Remain 14:34:15 loss: 0.2999 Lr: 0.00466
[2023-12-25 09:56:36,425 INFO misc.py line 119 253097] Train: [35/100][391/510] Data 0.005 (0.163) Batch 1.134 (1.576) Remain 14:33:36 loss: 0.2059 Lr: 0.00466
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[2023-12-25 09:56:38,369 INFO misc.py line 119 253097] Train: [35/100][393/510] Data 0.004 (0.162) Batch 1.089 (1.572) Remain 14:31:50 loss: 0.2574 Lr: 0.00466
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[2023-12-25 09:56:40,684 INFO misc.py line 119 253097] Train: [35/100][395/510] Data 0.004 (0.161) Batch 1.099 (1.570) Remain 14:30:36 loss: 0.2787 Lr: 0.00466
[2023-12-25 09:56:41,784 INFO misc.py line 119 253097] Train: [35/100][396/510] Data 0.006 (0.161) Batch 1.098 (1.569) Remain 14:29:55 loss: 0.3087 Lr: 0.00466
[2023-12-25 09:56:42,660 INFO misc.py line 119 253097] Train: [35/100][397/510] Data 0.006 (0.161) Batch 0.878 (1.567) Remain 14:28:55 loss: 0.2677 Lr: 0.00466
[2023-12-25 09:56:43,695 INFO misc.py line 119 253097] Train: [35/100][398/510] Data 0.005 (0.160) Batch 1.035 (1.566) Remain 14:28:08 loss: 0.0919 Lr: 0.00466
[2023-12-25 09:56:44,839 INFO misc.py line 119 253097] Train: [35/100][399/510] Data 0.006 (0.160) Batch 1.145 (1.565) Remain 14:27:31 loss: 0.2047 Lr: 0.00466
[2023-12-25 09:56:48,779 INFO misc.py line 119 253097] Train: [35/100][400/510] Data 2.126 (0.165) Batch 3.940 (1.571) Remain 14:30:49 loss: 0.2644 Lr: 0.00466
[2023-12-25 09:56:49,889 INFO misc.py line 119 253097] Train: [35/100][401/510] Data 0.004 (0.164) Batch 1.110 (1.570) Remain 14:30:09 loss: 0.1878 Lr: 0.00466
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[2023-12-25 09:57:02,211 INFO misc.py line 119 253097] Train: [35/100][409/510] Data 0.009 (0.161) Batch 1.026 (1.569) Remain 14:29:36 loss: 0.2139 Lr: 0.00466
[2023-12-25 09:57:03,347 INFO misc.py line 119 253097] Train: [35/100][410/510] Data 0.009 (0.161) Batch 1.138 (1.568) Remain 14:29:00 loss: 0.2353 Lr: 0.00466
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[2023-12-25 09:57:15,055 INFO misc.py line 119 253097] Train: [35/100][412/510] Data 0.008 (0.160) Batch 10.581 (1.589) Remain 14:40:33 loss: 0.2544 Lr: 0.00466
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[2023-12-25 09:57:17,523 INFO misc.py line 119 253097] Train: [35/100][414/510] Data 0.004 (0.159) Batch 1.140 (1.587) Remain 14:39:33 loss: 0.3481 Lr: 0.00466
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[2023-12-25 09:58:01,114 INFO misc.py line 119 253097] Train: [35/100][447/510] Data 0.003 (0.148) Batch 1.140 (1.568) Remain 14:27:43 loss: 0.2594 Lr: 0.00465
[2023-12-25 09:58:02,351 INFO misc.py line 119 253097] Train: [35/100][448/510] Data 0.004 (0.148) Batch 1.235 (1.567) Remain 14:27:16 loss: 0.1943 Lr: 0.00465
[2023-12-25 09:58:03,620 INFO misc.py line 119 253097] Train: [35/100][449/510] Data 0.005 (0.147) Batch 1.266 (1.566) Remain 14:26:52 loss: 0.2808 Lr: 0.00465
[2023-12-25 09:58:04,773 INFO misc.py line 119 253097] Train: [35/100][450/510] Data 0.008 (0.147) Batch 1.153 (1.565) Remain 14:26:20 loss: 0.1757 Lr: 0.00465
[2023-12-25 09:58:05,826 INFO misc.py line 119 253097] Train: [35/100][451/510] Data 0.007 (0.147) Batch 1.050 (1.564) Remain 14:25:40 loss: 0.1904 Lr: 0.00465
[2023-12-25 09:58:06,951 INFO misc.py line 119 253097] Train: [35/100][452/510] Data 0.011 (0.146) Batch 1.132 (1.563) Remain 14:25:07 loss: 0.1037 Lr: 0.00465
[2023-12-25 09:58:08,181 INFO misc.py line 119 253097] Train: [35/100][453/510] Data 0.004 (0.146) Batch 1.227 (1.562) Remain 14:24:41 loss: 0.2499 Lr: 0.00465
[2023-12-25 09:58:09,304 INFO misc.py line 119 253097] Train: [35/100][454/510] Data 0.006 (0.146) Batch 1.122 (1.561) Remain 14:24:07 loss: 0.4517 Lr: 0.00465
[2023-12-25 09:58:10,256 INFO misc.py line 119 253097] Train: [35/100][455/510] Data 0.007 (0.145) Batch 0.954 (1.560) Remain 14:23:20 loss: 0.2073 Lr: 0.00465
[2023-12-25 09:58:11,296 INFO misc.py line 119 253097] Train: [35/100][456/510] Data 0.005 (0.145) Batch 1.041 (1.559) Remain 14:22:41 loss: 0.1567 Lr: 0.00465
[2023-12-25 09:58:12,578 INFO misc.py line 119 253097] Train: [35/100][457/510] Data 0.004 (0.145) Batch 1.282 (1.558) Remain 14:22:19 loss: 0.1776 Lr: 0.00465
[2023-12-25 09:58:13,771 INFO misc.py line 119 253097] Train: [35/100][458/510] Data 0.003 (0.145) Batch 1.188 (1.557) Remain 14:21:50 loss: 0.1073 Lr: 0.00465
[2023-12-25 09:58:14,924 INFO misc.py line 119 253097] Train: [35/100][459/510] Data 0.010 (0.144) Batch 1.156 (1.557) Remain 14:21:20 loss: 0.2211 Lr: 0.00465
[2023-12-25 09:58:15,877 INFO misc.py line 119 253097] Train: [35/100][460/510] Data 0.006 (0.144) Batch 0.955 (1.555) Remain 14:20:34 loss: 0.4499 Lr: 0.00465
[2023-12-25 09:58:16,959 INFO misc.py line 119 253097] Train: [35/100][461/510] Data 0.003 (0.144) Batch 1.081 (1.554) Remain 14:19:59 loss: 0.2450 Lr: 0.00465
[2023-12-25 09:58:18,045 INFO misc.py line 119 253097] Train: [35/100][462/510] Data 0.004 (0.143) Batch 1.086 (1.553) Remain 14:19:23 loss: 0.2541 Lr: 0.00465
[2023-12-25 09:58:25,654 INFO misc.py line 119 253097] Train: [35/100][463/510] Data 6.498 (0.157) Batch 7.610 (1.566) Remain 14:26:39 loss: 0.1735 Lr: 0.00465
[2023-12-25 09:58:26,677 INFO misc.py line 119 253097] Train: [35/100][464/510] Data 0.003 (0.157) Batch 1.024 (1.565) Remain 14:25:58 loss: 0.2498 Lr: 0.00465
[2023-12-25 09:58:27,762 INFO misc.py line 119 253097] Train: [35/100][465/510] Data 0.003 (0.156) Batch 1.084 (1.564) Remain 14:25:22 loss: 0.1666 Lr: 0.00465
[2023-12-25 09:58:28,732 INFO misc.py line 119 253097] Train: [35/100][466/510] Data 0.004 (0.156) Batch 0.970 (1.563) Remain 14:24:38 loss: 0.1746 Lr: 0.00465
[2023-12-25 09:58:29,988 INFO misc.py line 119 253097] Train: [35/100][467/510] Data 0.003 (0.156) Batch 1.257 (1.562) Remain 14:24:14 loss: 0.2364 Lr: 0.00465
[2023-12-25 09:58:31,288 INFO misc.py line 119 253097] Train: [35/100][468/510] Data 0.003 (0.155) Batch 1.296 (1.562) Remain 14:23:54 loss: 0.1578 Lr: 0.00465
[2023-12-25 09:58:32,523 INFO misc.py line 119 253097] Train: [35/100][469/510] Data 0.006 (0.155) Batch 1.237 (1.561) Remain 14:23:29 loss: 0.3073 Lr: 0.00465
[2023-12-25 09:58:33,772 INFO misc.py line 119 253097] Train: [35/100][470/510] Data 0.005 (0.155) Batch 1.250 (1.560) Remain 14:23:05 loss: 0.2450 Lr: 0.00465
[2023-12-25 09:58:34,832 INFO misc.py line 119 253097] Train: [35/100][471/510] Data 0.003 (0.154) Batch 1.058 (1.559) Remain 14:22:28 loss: 0.1545 Lr: 0.00465
[2023-12-25 09:58:35,901 INFO misc.py line 119 253097] Train: [35/100][472/510] Data 0.005 (0.154) Batch 1.065 (1.558) Remain 14:21:52 loss: 0.2794 Lr: 0.00465
[2023-12-25 09:58:37,152 INFO misc.py line 119 253097] Train: [35/100][473/510] Data 0.008 (0.154) Batch 1.251 (1.558) Remain 14:21:29 loss: 0.1340 Lr: 0.00465
[2023-12-25 09:58:37,976 INFO misc.py line 119 253097] Train: [35/100][474/510] Data 0.008 (0.154) Batch 0.828 (1.556) Remain 14:20:36 loss: 0.2160 Lr: 0.00465
[2023-12-25 09:58:39,207 INFO misc.py line 119 253097] Train: [35/100][475/510] Data 0.004 (0.153) Batch 1.232 (1.555) Remain 14:20:11 loss: 0.1284 Lr: 0.00465
[2023-12-25 09:58:40,371 INFO misc.py line 119 253097] Train: [35/100][476/510] Data 0.003 (0.153) Batch 1.160 (1.554) Remain 14:19:42 loss: 0.4196 Lr: 0.00465
[2023-12-25 09:58:41,502 INFO misc.py line 119 253097] Train: [35/100][477/510] Data 0.008 (0.153) Batch 1.130 (1.554) Remain 14:19:11 loss: 0.2901 Lr: 0.00465
[2023-12-25 09:58:42,491 INFO misc.py line 119 253097] Train: [35/100][478/510] Data 0.009 (0.152) Batch 0.995 (1.552) Remain 14:18:30 loss: 0.2079 Lr: 0.00465
[2023-12-25 09:58:43,629 INFO misc.py line 119 253097] Train: [35/100][479/510] Data 0.003 (0.152) Batch 1.135 (1.551) Remain 14:17:59 loss: 0.3177 Lr: 0.00465
[2023-12-25 09:58:44,803 INFO misc.py line 119 253097] Train: [35/100][480/510] Data 0.005 (0.152) Batch 1.174 (1.551) Remain 14:17:32 loss: 0.3381 Lr: 0.00465
[2023-12-25 09:58:45,753 INFO misc.py line 119 253097] Train: [35/100][481/510] Data 0.006 (0.151) Batch 0.952 (1.549) Remain 14:16:48 loss: 0.2098 Lr: 0.00465
[2023-12-25 09:58:46,893 INFO misc.py line 119 253097] Train: [35/100][482/510] Data 0.003 (0.151) Batch 1.140 (1.549) Remain 14:16:19 loss: 0.1449 Lr: 0.00465
[2023-12-25 09:58:48,130 INFO misc.py line 119 253097] Train: [35/100][483/510] Data 0.003 (0.151) Batch 1.233 (1.548) Remain 14:15:55 loss: 0.1849 Lr: 0.00465
[2023-12-25 09:58:49,280 INFO misc.py line 119 253097] Train: [35/100][484/510] Data 0.008 (0.150) Batch 1.149 (1.547) Remain 14:15:26 loss: 0.3868 Lr: 0.00465
[2023-12-25 09:58:50,355 INFO misc.py line 119 253097] Train: [35/100][485/510] Data 0.009 (0.150) Batch 1.074 (1.546) Remain 14:14:52 loss: 0.2404 Lr: 0.00465
[2023-12-25 09:58:51,571 INFO misc.py line 119 253097] Train: [35/100][486/510] Data 0.011 (0.150) Batch 1.222 (1.545) Remain 14:14:28 loss: 0.2036 Lr: 0.00465
[2023-12-25 09:58:57,949 INFO misc.py line 119 253097] Train: [35/100][487/510] Data 5.490 (0.161) Batch 6.378 (1.555) Remain 14:19:58 loss: 0.1178 Lr: 0.00465
[2023-12-25 09:58:59,273 INFO misc.py line 119 253097] Train: [35/100][488/510] Data 0.005 (0.161) Batch 1.324 (1.555) Remain 14:19:41 loss: 0.2434 Lr: 0.00465
[2023-12-25 09:59:00,517 INFO misc.py line 119 253097] Train: [35/100][489/510] Data 0.005 (0.160) Batch 1.245 (1.554) Remain 14:19:18 loss: 0.1602 Lr: 0.00465
[2023-12-25 09:59:01,754 INFO misc.py line 119 253097] Train: [35/100][490/510] Data 0.003 (0.160) Batch 1.236 (1.554) Remain 14:18:55 loss: 0.2132 Lr: 0.00465
[2023-12-25 09:59:03,030 INFO misc.py line 119 253097] Train: [35/100][491/510] Data 0.004 (0.160) Batch 1.272 (1.553) Remain 14:18:34 loss: 0.2776 Lr: 0.00464
[2023-12-25 09:59:03,950 INFO misc.py line 119 253097] Train: [35/100][492/510] Data 0.008 (0.159) Batch 0.924 (1.552) Remain 14:17:50 loss: 0.1323 Lr: 0.00464
[2023-12-25 09:59:05,082 INFO misc.py line 119 253097] Train: [35/100][493/510] Data 0.004 (0.159) Batch 1.132 (1.551) Remain 14:17:20 loss: 0.2246 Lr: 0.00464
[2023-12-25 09:59:06,338 INFO misc.py line 119 253097] Train: [35/100][494/510] Data 0.005 (0.159) Batch 1.250 (1.550) Remain 14:16:58 loss: 0.1779 Lr: 0.00464
[2023-12-25 09:59:07,692 INFO misc.py line 119 253097] Train: [35/100][495/510] Data 0.010 (0.158) Batch 1.350 (1.550) Remain 14:16:43 loss: 0.1000 Lr: 0.00464
[2023-12-25 09:59:08,882 INFO misc.py line 119 253097] Train: [35/100][496/510] Data 0.015 (0.158) Batch 1.196 (1.549) Remain 14:16:17 loss: 0.1850 Lr: 0.00464
[2023-12-25 09:59:10,009 INFO misc.py line 119 253097] Train: [35/100][497/510] Data 0.009 (0.158) Batch 1.131 (1.548) Remain 14:15:48 loss: 0.2684 Lr: 0.00464
[2023-12-25 09:59:11,334 INFO misc.py line 119 253097] Train: [35/100][498/510] Data 0.005 (0.157) Batch 1.323 (1.548) Remain 14:15:31 loss: 0.1476 Lr: 0.00464
[2023-12-25 09:59:12,398 INFO misc.py line 119 253097] Train: [35/100][499/510] Data 0.007 (0.157) Batch 1.067 (1.547) Remain 14:14:57 loss: 0.2672 Lr: 0.00464
[2023-12-25 09:59:13,583 INFO misc.py line 119 253097] Train: [35/100][500/510] Data 0.003 (0.157) Batch 1.179 (1.546) Remain 14:14:31 loss: 0.1929 Lr: 0.00464
[2023-12-25 09:59:14,714 INFO misc.py line 119 253097] Train: [35/100][501/510] Data 0.010 (0.157) Batch 1.133 (1.545) Remain 14:14:02 loss: 0.1955 Lr: 0.00464
[2023-12-25 09:59:15,626 INFO misc.py line 119 253097] Train: [35/100][502/510] Data 0.009 (0.156) Batch 0.916 (1.544) Remain 14:13:19 loss: 0.3346 Lr: 0.00464
[2023-12-25 09:59:16,512 INFO misc.py line 119 253097] Train: [35/100][503/510] Data 0.004 (0.156) Batch 0.886 (1.543) Remain 14:12:34 loss: 0.2332 Lr: 0.00464
[2023-12-25 09:59:17,699 INFO misc.py line 119 253097] Train: [35/100][504/510] Data 0.004 (0.156) Batch 1.187 (1.542) Remain 14:12:09 loss: 0.1811 Lr: 0.00464
[2023-12-25 09:59:18,810 INFO misc.py line 119 253097] Train: [35/100][505/510] Data 0.003 (0.155) Batch 1.112 (1.541) Remain 14:11:39 loss: 0.2150 Lr: 0.00464
[2023-12-25 09:59:19,896 INFO misc.py line 119 253097] Train: [35/100][506/510] Data 0.003 (0.155) Batch 1.086 (1.540) Remain 14:11:07 loss: 0.2343 Lr: 0.00464
[2023-12-25 09:59:20,886 INFO misc.py line 119 253097] Train: [35/100][507/510] Data 0.003 (0.155) Batch 0.988 (1.539) Remain 14:10:29 loss: 0.2078 Lr: 0.00464
[2023-12-25 09:59:21,945 INFO misc.py line 119 253097] Train: [35/100][508/510] Data 0.005 (0.154) Batch 1.060 (1.538) Remain 14:09:56 loss: 0.2145 Lr: 0.00464
[2023-12-25 09:59:23,125 INFO misc.py line 119 253097] Train: [35/100][509/510] Data 0.004 (0.154) Batch 1.180 (1.538) Remain 14:09:31 loss: 0.3364 Lr: 0.00464
[2023-12-25 09:59:24,336 INFO misc.py line 119 253097] Train: [35/100][510/510] Data 0.004 (0.154) Batch 1.211 (1.537) Remain 14:09:08 loss: 0.0981 Lr: 0.00464
[2023-12-25 09:59:24,337 INFO misc.py line 136 253097] Train result: loss: 0.2347 
[2023-12-25 09:59:24,338 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 09:59:52,469 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4681 
[2023-12-25 09:59:52,830 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3758 
[2023-12-25 09:59:58,339 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6013 
[2023-12-25 09:59:58,863 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.6734 
[2023-12-25 10:00:00,847 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9233 
[2023-12-25 10:00:01,275 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4802 
[2023-12-25 10:00:02,152 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1841 
[2023-12-25 10:00:02,717 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.7128 
[2023-12-25 10:00:04,537 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8191 
[2023-12-25 10:00:06,660 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4064 
[2023-12-25 10:00:07,522 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.6117 
[2023-12-25 10:00:07,944 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0769 
[2023-12-25 10:00:08,846 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.7731 
[2023-12-25 10:00:11,785 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8328 
[2023-12-25 10:00:12,252 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4740 
[2023-12-25 10:00:12,865 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.8054 
[2023-12-25 10:00:13,565 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3349 
[2023-12-25 10:00:14,855 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6413/0.7004/0.8845.
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9047/0.9487
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9785/0.9875
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8051/0.9740
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3698/0.4436
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5944/0.6207
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.3720/0.3877
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7929/0.9164
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8895/0.9260
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5506/0.5653
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7513/0.8429
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7597/0.8261
[2023-12-25 10:00:14,855 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5681/0.6666
[2023-12-25 10:00:14,856 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 10:00:14,857 INFO misc.py line 165 253097] Currently Best mIoU: 0.6581
[2023-12-25 10:00:14,857 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 10:00:26,119 INFO misc.py line 119 253097] Train: [36/100][1/510] Data 5.434 (5.434) Batch 6.597 (6.597) Remain 60:44:44 loss: 0.1301 Lr: 0.00464
[2023-12-25 10:00:27,330 INFO misc.py line 119 253097] Train: [36/100][2/510] Data 0.006 (0.006) Batch 1.212 (1.212) Remain 11:09:28 loss: 0.1652 Lr: 0.00464
[2023-12-25 10:00:28,475 INFO misc.py line 119 253097] Train: [36/100][3/510] Data 0.005 (0.005) Batch 1.145 (1.145) Remain 10:32:37 loss: 0.1668 Lr: 0.00464
[2023-12-25 10:00:29,770 INFO misc.py line 119 253097] Train: [36/100][4/510] Data 0.298 (0.298) Batch 1.289 (1.289) Remain 11:52:11 loss: 0.1489 Lr: 0.00464
[2023-12-25 10:00:31,038 INFO misc.py line 119 253097] Train: [36/100][5/510] Data 0.011 (0.154) Batch 1.273 (1.281) Remain 11:47:38 loss: 0.2786 Lr: 0.00464
[2023-12-25 10:00:33,276 INFO misc.py line 119 253097] Train: [36/100][6/510] Data 0.005 (0.105) Batch 2.240 (1.601) Remain 14:44:14 loss: 0.1537 Lr: 0.00464
[2023-12-25 10:00:34,599 INFO misc.py line 119 253097] Train: [36/100][7/510] Data 0.003 (0.079) Batch 1.318 (1.530) Remain 14:05:11 loss: 0.2289 Lr: 0.00464
[2023-12-25 10:00:35,619 INFO misc.py line 119 253097] Train: [36/100][8/510] Data 0.009 (0.065) Batch 1.021 (1.428) Remain 13:08:53 loss: 0.1977 Lr: 0.00464
[2023-12-25 10:00:36,730 INFO misc.py line 119 253097] Train: [36/100][9/510] Data 0.008 (0.056) Batch 1.112 (1.375) Remain 12:39:43 loss: 0.2538 Lr: 0.00464
[2023-12-25 10:00:37,706 INFO misc.py line 119 253097] Train: [36/100][10/510] Data 0.007 (0.049) Batch 0.979 (1.319) Remain 12:08:24 loss: 0.1370 Lr: 0.00464
[2023-12-25 10:00:44,241 INFO misc.py line 119 253097] Train: [36/100][11/510] Data 0.004 (0.043) Batch 6.534 (1.971) Remain 18:08:25 loss: 0.3105 Lr: 0.00464
[2023-12-25 10:00:45,418 INFO misc.py line 119 253097] Train: [36/100][12/510] Data 0.006 (0.039) Batch 1.179 (1.883) Remain 17:19:48 loss: 0.1708 Lr: 0.00464
[2023-12-25 10:00:46,475 INFO misc.py line 119 253097] Train: [36/100][13/510] Data 0.004 (0.035) Batch 1.053 (1.800) Remain 16:33:58 loss: 0.0925 Lr: 0.00464
[2023-12-25 10:00:47,694 INFO misc.py line 119 253097] Train: [36/100][14/510] Data 0.007 (0.033) Batch 1.220 (1.747) Remain 16:04:50 loss: 0.2147 Lr: 0.00464
[2023-12-25 10:00:48,533 INFO misc.py line 119 253097] Train: [36/100][15/510] Data 0.006 (0.031) Batch 0.841 (1.672) Remain 15:23:07 loss: 0.2717 Lr: 0.00464
[2023-12-25 10:00:49,633 INFO misc.py line 119 253097] Train: [36/100][16/510] Data 0.004 (0.029) Batch 1.099 (1.628) Remain 14:58:45 loss: 0.4156 Lr: 0.00464
[2023-12-25 10:00:50,680 INFO misc.py line 119 253097] Train: [36/100][17/510] Data 0.005 (0.027) Batch 1.046 (1.586) Remain 14:35:48 loss: 0.1629 Lr: 0.00464
[2023-12-25 10:00:51,834 INFO misc.py line 119 253097] Train: [36/100][18/510] Data 0.009 (0.026) Batch 1.156 (1.557) Remain 14:19:58 loss: 0.2399 Lr: 0.00464
[2023-12-25 10:00:52,992 INFO misc.py line 119 253097] Train: [36/100][19/510] Data 0.003 (0.024) Batch 1.157 (1.532) Remain 14:06:07 loss: 0.1919 Lr: 0.00464
[2023-12-25 10:00:54,092 INFO misc.py line 119 253097] Train: [36/100][20/510] Data 0.005 (0.023) Batch 1.100 (1.507) Remain 13:52:02 loss: 0.2889 Lr: 0.00464
[2023-12-25 10:00:55,147 INFO misc.py line 119 253097] Train: [36/100][21/510] Data 0.005 (0.022) Batch 1.056 (1.482) Remain 13:38:11 loss: 0.1961 Lr: 0.00464
[2023-12-25 10:00:56,098 INFO misc.py line 119 253097] Train: [36/100][22/510] Data 0.004 (0.021) Batch 0.951 (1.454) Remain 13:22:45 loss: 0.3889 Lr: 0.00464
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[2023-12-25 10:08:04,843 INFO misc.py line 119 253097] Train: [36/100][286/510] Data 5.377 (0.132) Batch 6.486 (1.613) Remain 14:43:16 loss: 0.1991 Lr: 0.00460
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[2023-12-25 10:12:09,175 INFO misc.py line 119 253097] Train: [36/100][441/510] Data 0.007 (0.108) Batch 1.066 (1.600) Remain 14:32:06 loss: 0.2436 Lr: 0.00457
[2023-12-25 10:12:10,390 INFO misc.py line 119 253097] Train: [36/100][442/510] Data 0.005 (0.108) Batch 1.212 (1.599) Remain 14:31:36 loss: 0.1252 Lr: 0.00457
[2023-12-25 10:12:11,720 INFO misc.py line 119 253097] Train: [36/100][443/510] Data 0.008 (0.108) Batch 1.329 (1.598) Remain 14:31:14 loss: 0.2604 Lr: 0.00457
[2023-12-25 10:12:12,852 INFO misc.py line 119 253097] Train: [36/100][444/510] Data 0.010 (0.108) Batch 1.134 (1.597) Remain 14:30:38 loss: 0.1066 Lr: 0.00457
[2023-12-25 10:12:13,907 INFO misc.py line 119 253097] Train: [36/100][445/510] Data 0.007 (0.107) Batch 1.054 (1.596) Remain 14:29:56 loss: 0.1106 Lr: 0.00457
[2023-12-25 10:12:15,192 INFO misc.py line 119 253097] Train: [36/100][446/510] Data 0.009 (0.107) Batch 1.291 (1.595) Remain 14:29:32 loss: 0.2421 Lr: 0.00457
[2023-12-25 10:12:16,288 INFO misc.py line 119 253097] Train: [36/100][447/510] Data 0.003 (0.107) Batch 1.095 (1.594) Remain 14:28:54 loss: 0.1248 Lr: 0.00457
[2023-12-25 10:12:17,416 INFO misc.py line 119 253097] Train: [36/100][448/510] Data 0.005 (0.107) Batch 1.122 (1.593) Remain 14:28:17 loss: 0.1304 Lr: 0.00457
[2023-12-25 10:12:18,451 INFO misc.py line 119 253097] Train: [36/100][449/510] Data 0.010 (0.106) Batch 1.041 (1.592) Remain 14:27:35 loss: 0.1845 Lr: 0.00457
[2023-12-25 10:12:19,633 INFO misc.py line 119 253097] Train: [36/100][450/510] Data 0.004 (0.106) Batch 1.177 (1.591) Remain 14:27:03 loss: 0.1939 Lr: 0.00457
[2023-12-25 10:12:20,863 INFO misc.py line 119 253097] Train: [36/100][451/510] Data 0.008 (0.106) Batch 1.231 (1.590) Remain 14:26:36 loss: 0.1160 Lr: 0.00457
[2023-12-25 10:12:21,893 INFO misc.py line 119 253097] Train: [36/100][452/510] Data 0.008 (0.106) Batch 1.032 (1.589) Remain 14:25:53 loss: 0.0544 Lr: 0.00457
[2023-12-25 10:12:23,029 INFO misc.py line 119 253097] Train: [36/100][453/510] Data 0.007 (0.106) Batch 1.136 (1.588) Remain 14:25:19 loss: 0.1424 Lr: 0.00457
[2023-12-25 10:12:24,233 INFO misc.py line 119 253097] Train: [36/100][454/510] Data 0.006 (0.105) Batch 1.202 (1.587) Remain 14:24:49 loss: 0.2377 Lr: 0.00457
[2023-12-25 10:12:42,452 INFO misc.py line 119 253097] Train: [36/100][455/510] Data 0.008 (0.105) Batch 18.223 (1.624) Remain 14:44:51 loss: 0.2413 Lr: 0.00457
[2023-12-25 10:12:43,611 INFO misc.py line 119 253097] Train: [36/100][456/510] Data 0.004 (0.105) Batch 1.159 (1.623) Remain 14:44:16 loss: 0.3588 Lr: 0.00457
[2023-12-25 10:12:44,688 INFO misc.py line 119 253097] Train: [36/100][457/510] Data 0.004 (0.105) Batch 1.076 (1.622) Remain 14:43:35 loss: 0.1380 Lr: 0.00457
[2023-12-25 10:12:45,830 INFO misc.py line 119 253097] Train: [36/100][458/510] Data 0.004 (0.104) Batch 1.143 (1.621) Remain 14:42:59 loss: 0.2324 Lr: 0.00457
[2023-12-25 10:12:47,020 INFO misc.py line 119 253097] Train: [36/100][459/510] Data 0.003 (0.104) Batch 1.188 (1.620) Remain 14:42:26 loss: 0.2510 Lr: 0.00457
[2023-12-25 10:12:47,930 INFO misc.py line 119 253097] Train: [36/100][460/510] Data 0.006 (0.104) Batch 0.910 (1.618) Remain 14:41:34 loss: 0.1245 Lr: 0.00457
[2023-12-25 10:12:49,268 INFO misc.py line 119 253097] Train: [36/100][461/510] Data 0.005 (0.104) Batch 1.332 (1.617) Remain 14:41:12 loss: 0.1970 Lr: 0.00457
[2023-12-25 10:12:50,495 INFO misc.py line 119 253097] Train: [36/100][462/510] Data 0.012 (0.104) Batch 1.232 (1.617) Remain 14:40:43 loss: 0.1385 Lr: 0.00457
[2023-12-25 10:12:51,460 INFO misc.py line 119 253097] Train: [36/100][463/510] Data 0.007 (0.103) Batch 0.968 (1.615) Remain 14:39:55 loss: 0.1734 Lr: 0.00457
[2023-12-25 10:12:52,720 INFO misc.py line 119 253097] Train: [36/100][464/510] Data 0.004 (0.103) Batch 1.259 (1.614) Remain 14:39:28 loss: 0.1869 Lr: 0.00457
[2023-12-25 10:12:53,701 INFO misc.py line 119 253097] Train: [36/100][465/510] Data 0.004 (0.103) Batch 0.982 (1.613) Remain 14:38:42 loss: 0.2340 Lr: 0.00457
[2023-12-25 10:12:54,736 INFO misc.py line 119 253097] Train: [36/100][466/510] Data 0.003 (0.103) Batch 1.035 (1.612) Remain 14:37:59 loss: 0.1706 Lr: 0.00457
[2023-12-25 10:12:55,896 INFO misc.py line 119 253097] Train: [36/100][467/510] Data 0.003 (0.103) Batch 1.160 (1.611) Remain 14:37:26 loss: 0.2819 Lr: 0.00457
[2023-12-25 10:12:56,941 INFO misc.py line 119 253097] Train: [36/100][468/510] Data 0.003 (0.102) Batch 1.044 (1.610) Remain 14:36:45 loss: 0.2908 Lr: 0.00457
[2023-12-25 10:12:57,891 INFO misc.py line 119 253097] Train: [36/100][469/510] Data 0.005 (0.102) Batch 0.952 (1.608) Remain 14:35:57 loss: 0.1974 Lr: 0.00456
[2023-12-25 10:12:59,067 INFO misc.py line 119 253097] Train: [36/100][470/510] Data 0.003 (0.102) Batch 1.172 (1.607) Remain 14:35:25 loss: 0.1771 Lr: 0.00456
[2023-12-25 10:13:00,245 INFO misc.py line 119 253097] Train: [36/100][471/510] Data 0.007 (0.102) Batch 1.178 (1.606) Remain 14:34:53 loss: 0.2141 Lr: 0.00456
[2023-12-25 10:13:01,693 INFO misc.py line 119 253097] Train: [36/100][472/510] Data 0.327 (0.102) Batch 1.452 (1.606) Remain 14:34:41 loss: 0.2102 Lr: 0.00456
[2023-12-25 10:13:02,805 INFO misc.py line 119 253097] Train: [36/100][473/510] Data 0.003 (0.102) Batch 1.108 (1.605) Remain 14:34:05 loss: 0.2013 Lr: 0.00456
[2023-12-25 10:13:03,947 INFO misc.py line 119 253097] Train: [36/100][474/510] Data 0.007 (0.102) Batch 1.142 (1.604) Remain 14:33:31 loss: 0.2277 Lr: 0.00456
[2023-12-25 10:13:05,131 INFO misc.py line 119 253097] Train: [36/100][475/510] Data 0.005 (0.102) Batch 1.175 (1.603) Remain 14:32:59 loss: 0.1149 Lr: 0.00456
[2023-12-25 10:13:06,377 INFO misc.py line 119 253097] Train: [36/100][476/510] Data 0.016 (0.101) Batch 1.254 (1.602) Remain 14:32:34 loss: 0.4203 Lr: 0.00456
[2023-12-25 10:13:07,492 INFO misc.py line 119 253097] Train: [36/100][477/510] Data 0.007 (0.101) Batch 1.119 (1.601) Remain 14:31:59 loss: 0.1634 Lr: 0.00456
[2023-12-25 10:13:08,752 INFO misc.py line 119 253097] Train: [36/100][478/510] Data 0.004 (0.101) Batch 1.255 (1.601) Remain 14:31:33 loss: 0.1077 Lr: 0.00456
[2023-12-25 10:13:09,790 INFO misc.py line 119 253097] Train: [36/100][479/510] Data 0.009 (0.101) Batch 1.039 (1.599) Remain 14:30:53 loss: 0.1768 Lr: 0.00456
[2023-12-25 10:13:10,783 INFO misc.py line 119 253097] Train: [36/100][480/510] Data 0.007 (0.101) Batch 0.998 (1.598) Remain 14:30:10 loss: 0.4192 Lr: 0.00456
[2023-12-25 10:13:12,036 INFO misc.py line 119 253097] Train: [36/100][481/510] Data 0.003 (0.100) Batch 1.249 (1.597) Remain 14:29:45 loss: 0.1293 Lr: 0.00456
[2023-12-25 10:13:13,279 INFO misc.py line 119 253097] Train: [36/100][482/510] Data 0.008 (0.100) Batch 1.244 (1.597) Remain 14:29:19 loss: 0.1509 Lr: 0.00456
[2023-12-25 10:13:16,626 INFO misc.py line 119 253097] Train: [36/100][483/510] Data 0.006 (0.100) Batch 3.351 (1.600) Remain 14:31:17 loss: 0.1457 Lr: 0.00456
[2023-12-25 10:13:17,883 INFO misc.py line 119 253097] Train: [36/100][484/510] Data 0.003 (0.100) Batch 1.257 (1.600) Remain 14:30:52 loss: 0.1302 Lr: 0.00456
[2023-12-25 10:13:18,825 INFO misc.py line 119 253097] Train: [36/100][485/510] Data 0.003 (0.100) Batch 0.943 (1.598) Remain 14:30:06 loss: 0.1743 Lr: 0.00456
[2023-12-25 10:13:20,131 INFO misc.py line 119 253097] Train: [36/100][486/510] Data 0.004 (0.099) Batch 1.305 (1.598) Remain 14:29:44 loss: 0.1132 Lr: 0.00456
[2023-12-25 10:13:21,235 INFO misc.py line 119 253097] Train: [36/100][487/510] Data 0.004 (0.099) Batch 1.106 (1.597) Remain 14:29:10 loss: 0.1359 Lr: 0.00456
[2023-12-25 10:13:22,355 INFO misc.py line 119 253097] Train: [36/100][488/510] Data 0.003 (0.099) Batch 1.120 (1.596) Remain 14:28:36 loss: 0.2408 Lr: 0.00456
[2023-12-25 10:13:23,445 INFO misc.py line 119 253097] Train: [36/100][489/510] Data 0.004 (0.099) Batch 1.089 (1.595) Remain 14:28:00 loss: 0.1539 Lr: 0.00456
[2023-12-25 10:13:24,480 INFO misc.py line 119 253097] Train: [36/100][490/510] Data 0.004 (0.099) Batch 1.034 (1.593) Remain 14:27:21 loss: 0.2498 Lr: 0.00456
[2023-12-25 10:13:25,669 INFO misc.py line 119 253097] Train: [36/100][491/510] Data 0.006 (0.098) Batch 1.189 (1.593) Remain 14:26:53 loss: 0.1617 Lr: 0.00456
[2023-12-25 10:13:26,630 INFO misc.py line 119 253097] Train: [36/100][492/510] Data 0.004 (0.098) Batch 0.962 (1.591) Remain 14:26:09 loss: 0.0722 Lr: 0.00456
[2023-12-25 10:13:27,775 INFO misc.py line 119 253097] Train: [36/100][493/510] Data 0.003 (0.098) Batch 1.145 (1.590) Remain 14:25:37 loss: 0.3317 Lr: 0.00456
[2023-12-25 10:13:28,622 INFO misc.py line 119 253097] Train: [36/100][494/510] Data 0.004 (0.098) Batch 0.844 (1.589) Remain 14:24:46 loss: 0.0766 Lr: 0.00456
[2023-12-25 10:13:29,845 INFO misc.py line 119 253097] Train: [36/100][495/510] Data 0.006 (0.098) Batch 1.220 (1.588) Remain 14:24:20 loss: 0.2882 Lr: 0.00456
[2023-12-25 10:13:31,197 INFO misc.py line 119 253097] Train: [36/100][496/510] Data 0.009 (0.097) Batch 1.357 (1.588) Remain 14:24:03 loss: 0.2005 Lr: 0.00456
[2023-12-25 10:13:32,433 INFO misc.py line 119 253097] Train: [36/100][497/510] Data 0.005 (0.097) Batch 1.231 (1.587) Remain 14:23:38 loss: 0.1602 Lr: 0.00456
[2023-12-25 10:13:33,481 INFO misc.py line 119 253097] Train: [36/100][498/510] Data 0.010 (0.097) Batch 1.050 (1.586) Remain 14:23:01 loss: 0.2025 Lr: 0.00456
[2023-12-25 10:13:34,732 INFO misc.py line 119 253097] Train: [36/100][499/510] Data 0.007 (0.097) Batch 1.254 (1.585) Remain 14:22:38 loss: 0.1926 Lr: 0.00456
[2023-12-25 10:13:40,400 INFO misc.py line 119 253097] Train: [36/100][500/510] Data 0.004 (0.097) Batch 5.667 (1.593) Remain 14:27:04 loss: 0.1325 Lr: 0.00456
[2023-12-25 10:13:52,671 INFO misc.py line 119 253097] Train: [36/100][501/510] Data 0.005 (0.097) Batch 12.270 (1.615) Remain 14:38:43 loss: 0.1626 Lr: 0.00456
[2023-12-25 10:13:53,948 INFO misc.py line 119 253097] Train: [36/100][502/510] Data 0.006 (0.096) Batch 1.279 (1.614) Remain 14:38:19 loss: 0.2055 Lr: 0.00456
[2023-12-25 10:13:55,234 INFO misc.py line 119 253097] Train: [36/100][503/510] Data 0.005 (0.096) Batch 1.281 (1.614) Remain 14:37:56 loss: 0.2558 Lr: 0.00456
[2023-12-25 10:13:56,497 INFO misc.py line 119 253097] Train: [36/100][504/510] Data 0.009 (0.096) Batch 1.265 (1.613) Remain 14:37:31 loss: 0.1203 Lr: 0.00456
[2023-12-25 10:13:57,645 INFO misc.py line 119 253097] Train: [36/100][505/510] Data 0.008 (0.096) Batch 1.147 (1.612) Remain 14:36:59 loss: 0.1107 Lr: 0.00456
[2023-12-25 10:13:58,691 INFO misc.py line 119 253097] Train: [36/100][506/510] Data 0.009 (0.096) Batch 1.050 (1.611) Remain 14:36:21 loss: 0.1413 Lr: 0.00456
[2023-12-25 10:13:59,759 INFO misc.py line 119 253097] Train: [36/100][507/510] Data 0.004 (0.095) Batch 1.064 (1.610) Remain 14:35:44 loss: 0.1405 Lr: 0.00456
[2023-12-25 10:14:00,870 INFO misc.py line 119 253097] Train: [36/100][508/510] Data 0.009 (0.095) Batch 1.116 (1.609) Remain 14:35:11 loss: 0.1466 Lr: 0.00456
[2023-12-25 10:14:01,893 INFO misc.py line 119 253097] Train: [36/100][509/510] Data 0.003 (0.095) Batch 1.023 (1.608) Remain 14:34:31 loss: 0.1510 Lr: 0.00456
[2023-12-25 10:14:02,938 INFO misc.py line 119 253097] Train: [36/100][510/510] Data 0.004 (0.095) Batch 1.042 (1.606) Remain 14:33:53 loss: 0.1789 Lr: 0.00456
[2023-12-25 10:14:02,939 INFO misc.py line 136 253097] Train result: loss: 0.2220 
[2023-12-25 10:14:02,940 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 10:14:31,311 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4161 
[2023-12-25 10:14:31,656 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2997 
[2023-12-25 10:14:36,600 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4262 
[2023-12-25 10:14:37,132 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3395 
[2023-12-25 10:14:39,107 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.4358 
[2023-12-25 10:14:39,534 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3626 
[2023-12-25 10:14:40,412 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0372 
[2023-12-25 10:14:40,968 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5276 
[2023-12-25 10:14:42,780 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8984 
[2023-12-25 10:14:44,901 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1812 
[2023-12-25 10:14:45,762 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.1753 
[2023-12-25 10:14:46,190 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8830 
[2023-12-25 10:14:47,098 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5159 
[2023-12-25 10:14:50,051 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.5759 
[2023-12-25 10:14:50,518 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2631 
[2023-12-25 10:14:51,130 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4372 
[2023-12-25 10:14:51,849 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3334 
[2023-12-25 10:14:53,435 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6817/0.7570/0.8998.
[2023-12-25 10:14:53,435 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9212/0.9400
[2023-12-25 10:14:53,435 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9815/0.9871
[2023-12-25 10:14:53,435 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8472/0.9497
[2023-12-25 10:14:53,435 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 10:14:53,435 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.4759/0.6898
[2023-12-25 10:14:53,436 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6082/0.6331
[2023-12-25 10:14:53,436 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5906/0.6677
[2023-12-25 10:14:53,436 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8192/0.8933
[2023-12-25 10:14:53,436 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9091/0.9629
[2023-12-25 10:14:53,436 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6013/0.6591
[2023-12-25 10:14:53,436 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7444/0.8892
[2023-12-25 10:14:53,436 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7588/0.8362
[2023-12-25 10:14:53,436 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6043/0.7335
[2023-12-25 10:14:53,436 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 10:14:53,438 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.6817
[2023-12-25 10:14:53,438 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 10:14:53,438 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 10:15:06,278 INFO misc.py line 119 253097] Train: [37/100][1/510] Data 4.836 (4.836) Batch 6.038 (6.038) Remain 54:44:21 loss: 0.2421 Lr: 0.00456
[2023-12-25 10:15:07,397 INFO misc.py line 119 253097] Train: [37/100][2/510] Data 0.009 (0.009) Batch 1.125 (1.125) Remain 10:11:45 loss: 0.1536 Lr: 0.00456
[2023-12-25 10:15:08,394 INFO misc.py line 119 253097] Train: [37/100][3/510] Data 0.003 (0.003) Batch 0.998 (0.998) Remain 09:02:39 loss: 0.3300 Lr: 0.00456
[2023-12-25 10:15:09,346 INFO misc.py line 119 253097] Train: [37/100][4/510] Data 0.003 (0.003) Batch 0.950 (0.950) Remain 08:36:58 loss: 0.2856 Lr: 0.00456
[2023-12-25 10:15:10,409 INFO misc.py line 119 253097] Train: [37/100][5/510] Data 0.004 (0.004) Batch 1.063 (1.007) Remain 09:07:28 loss: 0.1689 Lr: 0.00456
[2023-12-25 10:15:11,627 INFO misc.py line 119 253097] Train: [37/100][6/510] Data 0.005 (0.004) Batch 1.219 (1.077) Remain 09:45:54 loss: 0.1335 Lr: 0.00456
[2023-12-25 10:15:12,739 INFO misc.py line 119 253097] Train: [37/100][7/510] Data 0.005 (0.004) Batch 1.109 (1.085) Remain 09:50:16 loss: 0.1851 Lr: 0.00456
[2023-12-25 10:15:18,479 INFO misc.py line 119 253097] Train: [37/100][8/510] Data 4.488 (0.901) Batch 5.742 (2.017) Remain 18:16:46 loss: 0.1397 Lr: 0.00456
[2023-12-25 10:15:19,663 INFO misc.py line 119 253097] Train: [37/100][9/510] Data 0.005 (0.752) Batch 1.186 (1.878) Remain 17:01:26 loss: 0.1751 Lr: 0.00456
[2023-12-25 10:15:20,920 INFO misc.py line 119 253097] Train: [37/100][10/510] Data 0.003 (0.645) Batch 1.253 (1.789) Remain 16:12:50 loss: 0.1792 Lr: 0.00456
[2023-12-25 10:15:22,157 INFO misc.py line 119 253097] Train: [37/100][11/510] Data 0.007 (0.565) Batch 1.235 (1.720) Remain 15:35:08 loss: 0.1807 Lr: 0.00456
[2023-12-25 10:15:23,368 INFO misc.py line 119 253097] Train: [37/100][12/510] Data 0.011 (0.504) Batch 1.211 (1.663) Remain 15:04:21 loss: 0.0988 Lr: 0.00456
[2023-12-25 10:15:24,613 INFO misc.py line 119 253097] Train: [37/100][13/510] Data 0.010 (0.454) Batch 1.244 (1.621) Remain 14:41:32 loss: 0.1133 Lr: 0.00456
[2023-12-25 10:15:25,863 INFO misc.py line 119 253097] Train: [37/100][14/510] Data 0.011 (0.414) Batch 1.256 (1.588) Remain 14:23:27 loss: 0.1668 Lr: 0.00456
[2023-12-25 10:15:27,009 INFO misc.py line 119 253097] Train: [37/100][15/510] Data 0.005 (0.380) Batch 1.142 (1.551) Remain 14:03:13 loss: 0.2981 Lr: 0.00456
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[2023-12-25 10:16:53,150 INFO misc.py line 119 253097] Train: [37/100][69/510] Data 4.256 (0.317) Batch 5.279 (1.587) Remain 14:21:36 loss: 0.3145 Lr: 0.00455
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[2023-12-25 10:18:40,938 INFO misc.py line 119 253097] Train: [37/100][140/510] Data 0.004 (0.213) Batch 2.084 (1.551) Remain 14:00:20 loss: 0.2011 Lr: 0.00453
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[2023-12-25 10:18:52,066 INFO misc.py line 119 253097] Train: [37/100][147/510] Data 2.957 (0.223) Batch 4.136 (1.553) Remain 14:01:10 loss: 0.2234 Lr: 0.00453
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[2023-12-25 10:18:55,581 INFO misc.py line 119 253097] Train: [37/100][150/510] Data 0.005 (0.219) Batch 1.128 (1.545) Remain 13:56:52 loss: 0.1423 Lr: 0.00453
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[2023-12-25 10:18:57,906 INFO misc.py line 119 253097] Train: [37/100][152/510] Data 0.004 (0.216) Batch 1.090 (1.540) Remain 13:54:01 loss: 0.1369 Lr: 0.00453
[2023-12-25 10:18:59,059 INFO misc.py line 119 253097] Train: [37/100][153/510] Data 0.007 (0.215) Batch 1.154 (1.538) Remain 13:52:36 loss: 0.4557 Lr: 0.00453
[2023-12-25 10:19:00,152 INFO misc.py line 119 253097] Train: [37/100][154/510] Data 0.007 (0.213) Batch 1.093 (1.535) Remain 13:50:59 loss: 0.2385 Lr: 0.00453
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[2023-12-25 10:19:04,962 INFO misc.py line 119 253097] Train: [37/100][158/510] Data 0.016 (0.208) Batch 1.400 (1.526) Remain 13:46:15 loss: 0.4205 Lr: 0.00453
[2023-12-25 10:19:11,092 INFO misc.py line 119 253097] Train: [37/100][159/510] Data 5.042 (0.239) Batch 6.130 (1.556) Remain 14:02:12 loss: 0.1463 Lr: 0.00453
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[2023-12-25 10:19:25,785 INFO misc.py line 119 253097] Train: [37/100][171/510] Data 0.004 (0.222) Batch 1.182 (1.532) Remain 13:49:05 loss: 0.1714 Lr: 0.00453
[2023-12-25 10:19:26,834 INFO misc.py line 119 253097] Train: [37/100][172/510] Data 0.003 (0.221) Batch 1.049 (1.529) Remain 13:47:30 loss: 0.1586 Lr: 0.00453
[2023-12-25 10:19:28,039 INFO misc.py line 119 253097] Train: [37/100][173/510] Data 0.003 (0.220) Batch 1.200 (1.527) Remain 13:46:26 loss: 0.1661 Lr: 0.00453
[2023-12-25 10:19:30,990 INFO misc.py line 119 253097] Train: [37/100][174/510] Data 0.009 (0.218) Batch 2.955 (1.536) Remain 13:50:56 loss: 0.3111 Lr: 0.00453
[2023-12-25 10:19:31,897 INFO misc.py line 119 253097] Train: [37/100][175/510] Data 0.005 (0.217) Batch 0.883 (1.532) Remain 13:48:51 loss: 0.1371 Lr: 0.00453
[2023-12-25 10:19:32,987 INFO misc.py line 119 253097] Train: [37/100][176/510] Data 0.035 (0.216) Batch 1.113 (1.529) Remain 13:47:31 loss: 0.1817 Lr: 0.00453
[2023-12-25 10:19:34,284 INFO misc.py line 119 253097] Train: [37/100][177/510] Data 0.006 (0.215) Batch 1.292 (1.528) Remain 13:46:45 loss: 0.2459 Lr: 0.00453
[2023-12-25 10:19:43,083 INFO misc.py line 119 253097] Train: [37/100][178/510] Data 7.543 (0.257) Batch 8.806 (1.570) Remain 14:09:13 loss: 0.1625 Lr: 0.00453
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[2023-12-25 10:19:45,677 INFO misc.py line 119 253097] Train: [37/100][180/510] Data 0.004 (0.254) Batch 1.283 (1.567) Remain 14:07:30 loss: 0.1357 Lr: 0.00453
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[2023-12-25 10:19:49,017 INFO misc.py line 119 253097] Train: [37/100][183/510] Data 0.006 (0.250) Batch 1.104 (1.559) Remain 14:03:20 loss: 0.1779 Lr: 0.00453
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[2023-12-25 10:19:51,260 INFO misc.py line 119 253097] Train: [37/100][185/510] Data 0.004 (0.247) Batch 0.989 (1.554) Remain 14:00:40 loss: 0.1289 Lr: 0.00453
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[2023-12-25 10:20:17,924 INFO misc.py line 119 253097] Train: [37/100][204/510] Data 0.006 (0.224) Batch 1.150 (1.540) Remain 13:52:29 loss: 0.2069 Lr: 0.00452
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[2023-12-25 10:21:52,107 INFO misc.py line 119 253097] Train: [37/100][261/510] Data 0.005 (0.227) Batch 0.889 (1.565) Remain 14:04:25 loss: 0.2677 Lr: 0.00451
[2023-12-25 10:21:53,304 INFO misc.py line 119 253097] Train: [37/100][262/510] Data 0.005 (0.226) Batch 1.197 (1.563) Remain 14:03:38 loss: 0.3839 Lr: 0.00451
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[2023-12-25 10:21:55,535 INFO misc.py line 119 253097] Train: [37/100][264/510] Data 0.005 (0.224) Batch 1.075 (1.560) Remain 14:01:43 loss: 0.2248 Lr: 0.00451
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[2023-12-25 10:22:21,462 INFO misc.py line 119 253097] Train: [37/100][286/510] Data 0.011 (0.208) Batch 1.341 (1.530) Remain 13:45:10 loss: 0.2859 Lr: 0.00451
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[2023-12-25 10:22:27,885 INFO misc.py line 119 253097] Train: [37/100][288/510] Data 4.086 (0.220) Batch 5.403 (1.542) Remain 13:51:28 loss: 0.1374 Lr: 0.00451
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[2023-12-25 10:22:42,349 INFO misc.py line 119 253097] Train: [37/100][300/510] Data 0.013 (0.212) Batch 1.283 (1.528) Remain 13:43:50 loss: 0.2584 Lr: 0.00451
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[2023-12-25 10:26:19,412 INFO misc.py line 119 253097] Train: [37/100][434/510] Data 0.008 (0.234) Batch 1.110 (1.557) Remain 13:55:40 loss: 0.2711 Lr: 0.00449
[2023-12-25 10:26:25,781 INFO misc.py line 119 253097] Train: [37/100][435/510] Data 5.450 (0.246) Batch 6.373 (1.568) Remain 14:01:38 loss: 0.2304 Lr: 0.00449
[2023-12-25 10:26:26,881 INFO misc.py line 119 253097] Train: [37/100][436/510] Data 0.005 (0.245) Batch 1.101 (1.567) Remain 14:01:01 loss: 0.1415 Lr: 0.00448
[2023-12-25 10:26:28,068 INFO misc.py line 119 253097] Train: [37/100][437/510] Data 0.004 (0.245) Batch 1.182 (1.566) Remain 14:00:31 loss: 0.1682 Lr: 0.00448
[2023-12-25 10:26:29,221 INFO misc.py line 119 253097] Train: [37/100][438/510] Data 0.009 (0.244) Batch 1.156 (1.565) Remain 13:59:59 loss: 0.1582 Lr: 0.00448
[2023-12-25 10:26:30,302 INFO misc.py line 119 253097] Train: [37/100][439/510] Data 0.007 (0.244) Batch 1.082 (1.564) Remain 13:59:22 loss: 0.2030 Lr: 0.00448
[2023-12-25 10:26:31,424 INFO misc.py line 119 253097] Train: [37/100][440/510] Data 0.005 (0.243) Batch 1.095 (1.563) Remain 13:58:46 loss: 0.2248 Lr: 0.00448
[2023-12-25 10:26:32,624 INFO misc.py line 119 253097] Train: [37/100][441/510] Data 0.032 (0.243) Batch 1.194 (1.562) Remain 13:58:17 loss: 0.3173 Lr: 0.00448
[2023-12-25 10:26:33,760 INFO misc.py line 119 253097] Train: [37/100][442/510] Data 0.038 (0.242) Batch 1.166 (1.561) Remain 13:57:47 loss: 0.2517 Lr: 0.00448
[2023-12-25 10:26:34,814 INFO misc.py line 119 253097] Train: [37/100][443/510] Data 0.007 (0.242) Batch 1.051 (1.560) Remain 13:57:08 loss: 0.3262 Lr: 0.00448
[2023-12-25 10:26:36,043 INFO misc.py line 119 253097] Train: [37/100][444/510] Data 0.011 (0.241) Batch 1.229 (1.559) Remain 13:56:42 loss: 0.0839 Lr: 0.00448
[2023-12-25 10:26:37,154 INFO misc.py line 119 253097] Train: [37/100][445/510] Data 0.012 (0.241) Batch 1.113 (1.558) Remain 13:56:08 loss: 0.1783 Lr: 0.00448
[2023-12-25 10:26:38,435 INFO misc.py line 119 253097] Train: [37/100][446/510] Data 0.010 (0.240) Batch 1.282 (1.558) Remain 13:55:46 loss: 0.1539 Lr: 0.00448
[2023-12-25 10:26:39,682 INFO misc.py line 119 253097] Train: [37/100][447/510] Data 0.009 (0.240) Batch 1.250 (1.557) Remain 13:55:22 loss: 0.2807 Lr: 0.00448
[2023-12-25 10:26:40,625 INFO misc.py line 119 253097] Train: [37/100][448/510] Data 0.005 (0.239) Batch 0.943 (1.556) Remain 13:54:36 loss: 0.1260 Lr: 0.00448
[2023-12-25 10:26:41,853 INFO misc.py line 119 253097] Train: [37/100][449/510] Data 0.005 (0.239) Batch 1.228 (1.555) Remain 13:54:11 loss: 0.3487 Lr: 0.00448
[2023-12-25 10:26:42,953 INFO misc.py line 119 253097] Train: [37/100][450/510] Data 0.005 (0.238) Batch 1.100 (1.554) Remain 13:53:37 loss: 0.2571 Lr: 0.00448
[2023-12-25 10:26:43,939 INFO misc.py line 119 253097] Train: [37/100][451/510] Data 0.005 (0.238) Batch 0.985 (1.553) Remain 13:52:55 loss: 0.2160 Lr: 0.00448
[2023-12-25 10:26:45,143 INFO misc.py line 119 253097] Train: [37/100][452/510] Data 0.005 (0.237) Batch 1.204 (1.552) Remain 13:52:28 loss: 0.1150 Lr: 0.00448
[2023-12-25 10:26:46,315 INFO misc.py line 119 253097] Train: [37/100][453/510] Data 0.005 (0.237) Batch 1.171 (1.551) Remain 13:51:59 loss: 0.1988 Lr: 0.00448
[2023-12-25 10:26:47,381 INFO misc.py line 119 253097] Train: [37/100][454/510] Data 0.006 (0.236) Batch 1.067 (1.550) Remain 13:51:23 loss: 0.3984 Lr: 0.00448
[2023-12-25 10:26:55,920 INFO misc.py line 119 253097] Train: [37/100][455/510] Data 7.431 (0.252) Batch 8.540 (1.565) Remain 13:59:39 loss: 0.2187 Lr: 0.00448
[2023-12-25 10:26:57,008 INFO misc.py line 119 253097] Train: [37/100][456/510] Data 0.005 (0.251) Batch 1.088 (1.564) Remain 13:59:04 loss: 0.1384 Lr: 0.00448
[2023-12-25 10:26:58,280 INFO misc.py line 119 253097] Train: [37/100][457/510] Data 0.004 (0.251) Batch 1.272 (1.564) Remain 13:58:42 loss: 0.1655 Lr: 0.00448
[2023-12-25 10:26:59,482 INFO misc.py line 119 253097] Train: [37/100][458/510] Data 0.004 (0.250) Batch 1.196 (1.563) Remain 13:58:14 loss: 0.2176 Lr: 0.00448
[2023-12-25 10:27:00,684 INFO misc.py line 119 253097] Train: [37/100][459/510] Data 0.011 (0.250) Batch 1.208 (1.562) Remain 13:57:47 loss: 0.2907 Lr: 0.00448
[2023-12-25 10:27:01,946 INFO misc.py line 119 253097] Train: [37/100][460/510] Data 0.005 (0.249) Batch 1.263 (1.561) Remain 13:57:25 loss: 0.2667 Lr: 0.00448
[2023-12-25 10:27:03,036 INFO misc.py line 119 253097] Train: [37/100][461/510] Data 0.004 (0.249) Batch 1.086 (1.560) Remain 13:56:50 loss: 0.1378 Lr: 0.00448
[2023-12-25 10:27:04,356 INFO misc.py line 119 253097] Train: [37/100][462/510] Data 0.009 (0.248) Batch 1.324 (1.560) Remain 13:56:32 loss: 0.2483 Lr: 0.00448
[2023-12-25 10:27:05,586 INFO misc.py line 119 253097] Train: [37/100][463/510] Data 0.005 (0.248) Batch 1.225 (1.559) Remain 13:56:07 loss: 0.2045 Lr: 0.00448
[2023-12-25 10:27:06,724 INFO misc.py line 119 253097] Train: [37/100][464/510] Data 0.012 (0.247) Batch 1.138 (1.558) Remain 13:55:36 loss: 0.3333 Lr: 0.00448
[2023-12-25 10:27:08,018 INFO misc.py line 119 253097] Train: [37/100][465/510] Data 0.010 (0.247) Batch 1.299 (1.558) Remain 13:55:16 loss: 0.1359 Lr: 0.00448
[2023-12-25 10:27:09,237 INFO misc.py line 119 253097] Train: [37/100][466/510] Data 0.005 (0.246) Batch 1.216 (1.557) Remain 13:54:51 loss: 0.1633 Lr: 0.00448
[2023-12-25 10:27:10,279 INFO misc.py line 119 253097] Train: [37/100][467/510] Data 0.008 (0.246) Batch 1.046 (1.556) Remain 13:54:14 loss: 0.2276 Lr: 0.00448
[2023-12-25 10:27:11,180 INFO misc.py line 119 253097] Train: [37/100][468/510] Data 0.004 (0.245) Batch 0.901 (1.554) Remain 13:53:27 loss: 0.3930 Lr: 0.00448
[2023-12-25 10:27:12,726 INFO misc.py line 119 253097] Train: [37/100][469/510] Data 0.003 (0.245) Batch 1.537 (1.554) Remain 13:53:24 loss: 0.3079 Lr: 0.00448
[2023-12-25 10:27:14,029 INFO misc.py line 119 253097] Train: [37/100][470/510] Data 0.013 (0.244) Batch 1.304 (1.554) Remain 13:53:05 loss: 0.2106 Lr: 0.00448
[2023-12-25 10:27:22,327 INFO misc.py line 119 253097] Train: [37/100][471/510] Data 0.012 (0.244) Batch 8.306 (1.568) Remain 14:00:48 loss: 0.1664 Lr: 0.00448
[2023-12-25 10:27:23,533 INFO misc.py line 119 253097] Train: [37/100][472/510] Data 0.004 (0.243) Batch 1.206 (1.567) Remain 14:00:21 loss: 0.2019 Lr: 0.00448
[2023-12-25 10:27:24,783 INFO misc.py line 119 253097] Train: [37/100][473/510] Data 0.004 (0.243) Batch 1.246 (1.567) Remain 13:59:58 loss: 0.1713 Lr: 0.00448
[2023-12-25 10:27:30,838 INFO misc.py line 119 253097] Train: [37/100][474/510] Data 0.008 (0.242) Batch 6.054 (1.576) Remain 14:05:03 loss: 0.1967 Lr: 0.00448
[2023-12-25 10:27:32,013 INFO misc.py line 119 253097] Train: [37/100][475/510] Data 0.009 (0.242) Batch 1.164 (1.575) Remain 14:04:33 loss: 0.2157 Lr: 0.00448
[2023-12-25 10:27:33,145 INFO misc.py line 119 253097] Train: [37/100][476/510] Data 0.020 (0.241) Batch 1.146 (1.575) Remain 14:04:02 loss: 0.1625 Lr: 0.00448
[2023-12-25 10:27:34,103 INFO misc.py line 119 253097] Train: [37/100][477/510] Data 0.006 (0.241) Batch 0.957 (1.573) Remain 14:03:19 loss: 0.2965 Lr: 0.00448
[2023-12-25 10:27:35,136 INFO misc.py line 119 253097] Train: [37/100][478/510] Data 0.006 (0.240) Batch 1.033 (1.572) Remain 14:02:41 loss: 0.1871 Lr: 0.00448
[2023-12-25 10:27:36,153 INFO misc.py line 119 253097] Train: [37/100][479/510] Data 0.006 (0.240) Batch 1.019 (1.571) Remain 14:02:02 loss: 0.2236 Lr: 0.00448
[2023-12-25 10:27:37,267 INFO misc.py line 119 253097] Train: [37/100][480/510] Data 0.004 (0.239) Batch 1.113 (1.570) Remain 14:01:29 loss: 0.2718 Lr: 0.00448
[2023-12-25 10:27:38,411 INFO misc.py line 119 253097] Train: [37/100][481/510] Data 0.005 (0.239) Batch 1.145 (1.569) Remain 14:00:59 loss: 0.3128 Lr: 0.00448
[2023-12-25 10:27:39,615 INFO misc.py line 119 253097] Train: [37/100][482/510] Data 0.003 (0.238) Batch 1.203 (1.568) Remain 14:00:33 loss: 0.2738 Lr: 0.00448
[2023-12-25 10:27:40,681 INFO misc.py line 119 253097] Train: [37/100][483/510] Data 0.005 (0.238) Batch 1.065 (1.567) Remain 13:59:58 loss: 0.1731 Lr: 0.00448
[2023-12-25 10:27:41,755 INFO misc.py line 119 253097] Train: [37/100][484/510] Data 0.005 (0.237) Batch 1.075 (1.566) Remain 13:59:23 loss: 0.3573 Lr: 0.00448
[2023-12-25 10:27:43,068 INFO misc.py line 119 253097] Train: [37/100][485/510] Data 0.005 (0.237) Batch 1.312 (1.566) Remain 13:59:05 loss: 0.2021 Lr: 0.00448
[2023-12-25 10:27:44,192 INFO misc.py line 119 253097] Train: [37/100][486/510] Data 0.005 (0.236) Batch 1.124 (1.565) Remain 13:58:34 loss: 0.1961 Lr: 0.00448
[2023-12-25 10:27:45,341 INFO misc.py line 119 253097] Train: [37/100][487/510] Data 0.005 (0.236) Batch 1.140 (1.564) Remain 13:58:04 loss: 0.3601 Lr: 0.00448
[2023-12-25 10:27:46,376 INFO misc.py line 119 253097] Train: [37/100][488/510] Data 0.015 (0.235) Batch 1.035 (1.563) Remain 13:57:27 loss: 0.1175 Lr: 0.00448
[2023-12-25 10:27:47,670 INFO misc.py line 119 253097] Train: [37/100][489/510] Data 0.014 (0.235) Batch 1.303 (1.562) Remain 13:57:09 loss: 0.3027 Lr: 0.00448
[2023-12-25 10:27:48,867 INFO misc.py line 119 253097] Train: [37/100][490/510] Data 0.005 (0.234) Batch 1.198 (1.562) Remain 13:56:43 loss: 0.1962 Lr: 0.00448
[2023-12-25 10:27:50,020 INFO misc.py line 119 253097] Train: [37/100][491/510] Data 0.004 (0.234) Batch 1.139 (1.561) Remain 13:56:14 loss: 0.2635 Lr: 0.00448
[2023-12-25 10:27:51,240 INFO misc.py line 119 253097] Train: [37/100][492/510] Data 0.018 (0.233) Batch 1.221 (1.560) Remain 13:55:50 loss: 0.2285 Lr: 0.00448
[2023-12-25 10:27:52,275 INFO misc.py line 119 253097] Train: [37/100][493/510] Data 0.019 (0.233) Batch 1.045 (1.559) Remain 13:55:14 loss: 0.1350 Lr: 0.00448
[2023-12-25 10:27:53,695 INFO misc.py line 119 253097] Train: [37/100][494/510] Data 0.007 (0.233) Batch 1.420 (1.559) Remain 13:55:04 loss: 0.1657 Lr: 0.00448
[2023-12-25 10:27:54,909 INFO misc.py line 119 253097] Train: [37/100][495/510] Data 0.008 (0.232) Batch 1.218 (1.558) Remain 13:54:40 loss: 0.2579 Lr: 0.00447
[2023-12-25 10:28:00,423 INFO misc.py line 119 253097] Train: [37/100][496/510] Data 0.970 (0.234) Batch 5.513 (1.566) Remain 13:58:56 loss: 0.3081 Lr: 0.00447
[2023-12-25 10:28:01,683 INFO misc.py line 119 253097] Train: [37/100][497/510] Data 0.005 (0.233) Batch 1.261 (1.565) Remain 13:58:35 loss: 0.4893 Lr: 0.00447
[2023-12-25 10:28:02,616 INFO misc.py line 119 253097] Train: [37/100][498/510] Data 0.003 (0.233) Batch 0.933 (1.564) Remain 13:57:52 loss: 0.2054 Lr: 0.00447
[2023-12-25 10:28:03,631 INFO misc.py line 119 253097] Train: [37/100][499/510] Data 0.004 (0.232) Batch 1.014 (1.563) Remain 13:57:15 loss: 0.3207 Lr: 0.00447
[2023-12-25 10:28:04,769 INFO misc.py line 119 253097] Train: [37/100][500/510] Data 0.004 (0.232) Batch 1.138 (1.562) Remain 13:56:46 loss: 0.2635 Lr: 0.00447
[2023-12-25 10:28:05,811 INFO misc.py line 119 253097] Train: [37/100][501/510] Data 0.004 (0.231) Batch 1.043 (1.561) Remain 13:56:11 loss: 0.2783 Lr: 0.00447
[2023-12-25 10:28:08,518 INFO misc.py line 119 253097] Train: [37/100][502/510] Data 0.003 (0.231) Batch 2.706 (1.563) Remain 13:57:23 loss: 0.1195 Lr: 0.00447
[2023-12-25 10:28:09,524 INFO misc.py line 119 253097] Train: [37/100][503/510] Data 0.005 (0.230) Batch 1.003 (1.562) Remain 13:56:46 loss: 0.1830 Lr: 0.00447
[2023-12-25 10:28:10,657 INFO misc.py line 119 253097] Train: [37/100][504/510] Data 0.007 (0.230) Batch 1.133 (1.561) Remain 13:56:16 loss: 0.4795 Lr: 0.00447
[2023-12-25 10:28:11,904 INFO misc.py line 119 253097] Train: [37/100][505/510] Data 0.007 (0.229) Batch 1.241 (1.561) Remain 13:55:54 loss: 0.2105 Lr: 0.00447
[2023-12-25 10:28:13,061 INFO misc.py line 119 253097] Train: [37/100][506/510] Data 0.013 (0.229) Batch 1.164 (1.560) Remain 13:55:27 loss: 0.1606 Lr: 0.00447
[2023-12-25 10:28:14,416 INFO misc.py line 119 253097] Train: [37/100][507/510] Data 0.006 (0.229) Batch 1.355 (1.560) Remain 13:55:13 loss: 0.1736 Lr: 0.00447
[2023-12-25 10:28:15,579 INFO misc.py line 119 253097] Train: [37/100][508/510] Data 0.007 (0.228) Batch 1.161 (1.559) Remain 13:54:46 loss: 0.5064 Lr: 0.00447
[2023-12-25 10:28:16,917 INFO misc.py line 119 253097] Train: [37/100][509/510] Data 0.008 (0.228) Batch 1.337 (1.558) Remain 13:54:30 loss: 0.1444 Lr: 0.00447
[2023-12-25 10:28:18,163 INFO misc.py line 119 253097] Train: [37/100][510/510] Data 0.009 (0.227) Batch 1.244 (1.558) Remain 13:54:09 loss: 0.1817 Lr: 0.00447
[2023-12-25 10:28:18,164 INFO misc.py line 136 253097] Train result: loss: 0.2255 
[2023-12-25 10:28:18,164 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 10:28:46,300 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.3884 
[2023-12-25 10:28:46,656 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.5162 
[2023-12-25 10:28:52,072 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5170 
[2023-12-25 10:28:52,586 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3990 
[2023-12-25 10:28:54,561 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6922 
[2023-12-25 10:28:54,984 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4080 
[2023-12-25 10:28:55,863 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.8522 
[2023-12-25 10:28:56,423 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5375 
[2023-12-25 10:28:58,229 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8022 
[2023-12-25 10:29:00,348 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3633 
[2023-12-25 10:29:01,206 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.4556 
[2023-12-25 10:29:01,632 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8153 
[2023-12-25 10:29:02,540 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5927 
[2023-12-25 10:29:05,486 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7043 
[2023-12-25 10:29:05,955 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3898 
[2023-12-25 10:29:06,567 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.6097 
[2023-12-25 10:29:07,271 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3521 
[2023-12-25 10:29:09,117 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6647/0.7432/0.8938.
[2023-12-25 10:29:09,117 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9235/0.9571
[2023-12-25 10:29:09,117 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9806/0.9874
[2023-12-25 10:29:09,117 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8417/0.9588
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3645/0.4665
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5983/0.6341
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5674/0.6679
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7952/0.9181
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8996/0.9437
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6996/0.7420
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7324/0.8026
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6546/0.8512
[2023-12-25 10:29:09,118 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5834/0.7316
[2023-12-25 10:29:09,118 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 10:29:09,120 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 10:29:09,120 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 10:29:17,026 INFO misc.py line 119 253097] Train: [38/100][1/510] Data 4.651 (4.651) Batch 5.762 (5.762) Remain 51:25:22 loss: 0.1319 Lr: 0.00447
[2023-12-25 10:29:17,934 INFO misc.py line 119 253097] Train: [38/100][2/510] Data 0.005 (0.005) Batch 0.909 (0.909) Remain 08:06:44 loss: 0.1878 Lr: 0.00447
[2023-12-25 10:29:21,211 INFO misc.py line 119 253097] Train: [38/100][3/510] Data 0.003 (0.003) Batch 3.275 (3.275) Remain 29:13:27 loss: 0.1996 Lr: 0.00447
[2023-12-25 10:29:22,392 INFO misc.py line 119 253097] Train: [38/100][4/510] Data 0.007 (0.007) Batch 1.183 (1.183) Remain 10:33:19 loss: 0.2806 Lr: 0.00447
[2023-12-25 10:29:23,624 INFO misc.py line 119 253097] Train: [38/100][5/510] Data 0.004 (0.005) Batch 1.229 (1.206) Remain 10:45:47 loss: 0.2194 Lr: 0.00447
[2023-12-25 10:29:24,766 INFO misc.py line 119 253097] Train: [38/100][6/510] Data 0.007 (0.006) Batch 1.143 (1.185) Remain 10:34:28 loss: 0.2777 Lr: 0.00447
[2023-12-25 10:29:25,925 INFO misc.py line 119 253097] Train: [38/100][7/510] Data 0.006 (0.006) Batch 1.155 (1.178) Remain 10:30:25 loss: 0.5384 Lr: 0.00447
[2023-12-25 10:29:26,805 INFO misc.py line 119 253097] Train: [38/100][8/510] Data 0.010 (0.007) Batch 0.886 (1.119) Remain 09:59:11 loss: 0.1401 Lr: 0.00447
[2023-12-25 10:29:28,040 INFO misc.py line 119 253097] Train: [38/100][9/510] Data 0.004 (0.006) Batch 1.235 (1.139) Remain 10:09:30 loss: 0.2591 Lr: 0.00447
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[2023-12-25 10:30:24,793 INFO misc.py line 119 253097] Train: [38/100][37/510] Data 8.147 (0.534) Batch 9.422 (1.870) Remain 16:40:18 loss: 0.2811 Lr: 0.00447
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[2023-12-25 10:30:29,348 INFO misc.py line 119 253097] Train: [38/100][41/510] Data 0.004 (0.478) Batch 1.235 (1.793) Remain 15:58:58 loss: 0.1922 Lr: 0.00447
[2023-12-25 10:30:30,465 INFO misc.py line 119 253097] Train: [38/100][42/510] Data 0.006 (0.466) Batch 1.113 (1.776) Remain 15:49:37 loss: 0.1654 Lr: 0.00447
[2023-12-25 10:30:31,716 INFO misc.py line 119 253097] Train: [38/100][43/510] Data 0.009 (0.455) Batch 1.248 (1.762) Remain 15:42:32 loss: 0.2841 Lr: 0.00447
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[2023-12-25 10:30:33,875 INFO misc.py line 119 253097] Train: [38/100][45/510] Data 0.007 (0.434) Batch 1.059 (1.730) Remain 15:25:09 loss: 0.1710 Lr: 0.00446
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[2023-12-25 10:30:38,585 INFO misc.py line 119 253097] Train: [38/100][49/510] Data 0.008 (0.397) Batch 1.256 (1.682) Remain 14:59:13 loss: 0.2480 Lr: 0.00446
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[2023-12-25 10:31:15,007 INFO misc.py line 119 253097] Train: [38/100][70/510] Data 0.004 (0.274) Batch 1.260 (1.698) Remain 15:07:30 loss: 0.2253 Lr: 0.00446
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[2023-12-25 10:32:35,239 INFO misc.py line 119 253097] Train: [38/100][133/510] Data 0.486 (0.148) Batch 1.725 (1.493) Remain 13:15:56 loss: 0.2410 Lr: 0.00445
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[2023-12-25 10:33:02,653 INFO misc.py line 119 253097] Train: [38/100][148/510] Data 0.005 (0.138) Batch 9.147 (1.527) Remain 13:34:02 loss: 0.2310 Lr: 0.00445
[2023-12-25 10:33:03,902 INFO misc.py line 119 253097] Train: [38/100][149/510] Data 0.003 (0.137) Batch 1.249 (1.525) Remain 13:33:00 loss: 0.1432 Lr: 0.00445
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[2023-12-25 10:33:19,264 INFO misc.py line 119 253097] Train: [38/100][154/510] Data 0.007 (0.133) Batch 10.798 (1.577) Remain 14:00:10 loss: 0.2359 Lr: 0.00445
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[2023-12-25 10:33:40,769 INFO misc.py line 119 253097] Train: [38/100][172/510] Data 0.023 (0.119) Batch 1.406 (1.536) Remain 13:38:02 loss: 0.3679 Lr: 0.00444
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[2023-12-25 10:35:29,090 INFO misc.py line 119 253097] Train: [38/100][241/510] Data 0.004 (0.087) Batch 1.088 (1.546) Remain 13:41:31 loss: 0.2007 Lr: 0.00443
[2023-12-25 10:35:30,088 INFO misc.py line 119 253097] Train: [38/100][242/510] Data 0.003 (0.086) Batch 0.996 (1.543) Remain 13:40:16 loss: 0.4042 Lr: 0.00443
[2023-12-25 10:35:31,143 INFO misc.py line 119 253097] Train: [38/100][243/510] Data 0.005 (0.086) Batch 1.056 (1.541) Remain 13:39:10 loss: 0.1456 Lr: 0.00443
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[2023-12-25 10:35:34,620 INFO misc.py line 119 253097] Train: [38/100][246/510] Data 0.003 (0.085) Batch 1.222 (1.537) Remain 13:36:35 loss: 0.1908 Lr: 0.00443
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[2023-12-25 10:35:36,800 INFO misc.py line 119 253097] Train: [38/100][248/510] Data 0.007 (0.084) Batch 0.952 (1.533) Remain 13:34:35 loss: 0.1863 Lr: 0.00443
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[2023-12-25 10:35:40,122 INFO misc.py line 119 253097] Train: [38/100][251/510] Data 0.005 (0.083) Batch 1.005 (1.528) Remain 13:31:45 loss: 0.1317 Lr: 0.00443
[2023-12-25 10:35:47,711 INFO misc.py line 119 253097] Train: [38/100][252/510] Data 0.014 (0.083) Batch 7.598 (1.552) Remain 13:44:41 loss: 0.2630 Lr: 0.00443
[2023-12-25 10:35:49,001 INFO misc.py line 119 253097] Train: [38/100][253/510] Data 0.005 (0.083) Batch 1.287 (1.551) Remain 13:44:06 loss: 0.1842 Lr: 0.00443
[2023-12-25 10:35:50,191 INFO misc.py line 119 253097] Train: [38/100][254/510] Data 0.009 (0.082) Batch 1.189 (1.550) Remain 13:43:18 loss: 0.1692 Lr: 0.00443
[2023-12-25 10:35:51,307 INFO misc.py line 119 253097] Train: [38/100][255/510] Data 0.008 (0.082) Batch 1.119 (1.548) Remain 13:42:22 loss: 0.2783 Lr: 0.00443
[2023-12-25 10:35:52,504 INFO misc.py line 119 253097] Train: [38/100][256/510] Data 0.006 (0.082) Batch 1.199 (1.547) Remain 13:41:36 loss: 0.1923 Lr: 0.00443
[2023-12-25 10:35:53,712 INFO misc.py line 119 253097] Train: [38/100][257/510] Data 0.004 (0.082) Batch 1.203 (1.545) Remain 13:40:52 loss: 0.1816 Lr: 0.00443
[2023-12-25 10:35:54,624 INFO misc.py line 119 253097] Train: [38/100][258/510] Data 0.009 (0.081) Batch 0.916 (1.543) Remain 13:39:32 loss: 0.3211 Lr: 0.00443
[2023-12-25 10:35:55,850 INFO misc.py line 119 253097] Train: [38/100][259/510] Data 0.005 (0.081) Batch 1.221 (1.542) Remain 13:38:50 loss: 0.4028 Lr: 0.00443
[2023-12-25 10:35:56,777 INFO misc.py line 119 253097] Train: [38/100][260/510] Data 0.010 (0.081) Batch 0.933 (1.539) Remain 13:37:33 loss: 0.2455 Lr: 0.00443
[2023-12-25 10:35:57,963 INFO misc.py line 119 253097] Train: [38/100][261/510] Data 0.004 (0.080) Batch 1.186 (1.538) Remain 13:36:48 loss: 0.3060 Lr: 0.00443
[2023-12-25 10:35:59,321 INFO misc.py line 119 253097] Train: [38/100][262/510] Data 0.003 (0.080) Batch 1.356 (1.537) Remain 13:36:24 loss: 0.5197 Lr: 0.00443
[2023-12-25 10:36:00,293 INFO misc.py line 119 253097] Train: [38/100][263/510] Data 0.006 (0.080) Batch 0.972 (1.535) Remain 13:35:13 loss: 0.3078 Lr: 0.00443
[2023-12-25 10:36:01,615 INFO misc.py line 119 253097] Train: [38/100][264/510] Data 0.006 (0.080) Batch 1.324 (1.534) Remain 13:34:46 loss: 0.1947 Lr: 0.00443
[2023-12-25 10:36:02,715 INFO misc.py line 119 253097] Train: [38/100][265/510] Data 0.003 (0.079) Batch 1.097 (1.532) Remain 13:33:51 loss: 0.2007 Lr: 0.00443
[2023-12-25 10:36:03,953 INFO misc.py line 119 253097] Train: [38/100][266/510] Data 0.007 (0.079) Batch 1.238 (1.531) Remain 13:33:14 loss: 0.4022 Lr: 0.00443
[2023-12-25 10:36:05,041 INFO misc.py line 119 253097] Train: [38/100][267/510] Data 0.007 (0.079) Batch 1.084 (1.530) Remain 13:32:18 loss: 0.1899 Lr: 0.00443
[2023-12-25 10:36:06,147 INFO misc.py line 119 253097] Train: [38/100][268/510] Data 0.011 (0.078) Batch 1.111 (1.528) Remain 13:31:27 loss: 0.2038 Lr: 0.00443
[2023-12-25 10:36:14,004 INFO misc.py line 119 253097] Train: [38/100][269/510] Data 0.006 (0.078) Batch 7.857 (1.552) Remain 13:44:03 loss: 0.2422 Lr: 0.00443
[2023-12-25 10:36:15,116 INFO misc.py line 119 253097] Train: [38/100][270/510] Data 0.006 (0.078) Batch 1.111 (1.550) Remain 13:43:09 loss: 0.1910 Lr: 0.00443
[2023-12-25 10:36:16,032 INFO misc.py line 119 253097] Train: [38/100][271/510] Data 0.006 (0.078) Batch 0.918 (1.548) Remain 13:41:52 loss: 0.2178 Lr: 0.00443
[2023-12-25 10:36:18,539 INFO misc.py line 119 253097] Train: [38/100][272/510] Data 0.003 (0.077) Batch 2.506 (1.551) Remain 13:43:44 loss: 0.2267 Lr: 0.00443
[2023-12-25 10:36:19,683 INFO misc.py line 119 253097] Train: [38/100][273/510] Data 0.005 (0.077) Batch 1.145 (1.550) Remain 13:42:55 loss: 0.2522 Lr: 0.00443
[2023-12-25 10:36:20,835 INFO misc.py line 119 253097] Train: [38/100][274/510] Data 0.004 (0.077) Batch 1.152 (1.548) Remain 13:42:06 loss: 0.1790 Lr: 0.00443
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[2023-12-25 10:36:23,023 INFO misc.py line 119 253097] Train: [38/100][276/510] Data 0.004 (0.076) Batch 0.977 (1.545) Remain 13:40:17 loss: 0.2176 Lr: 0.00443
[2023-12-25 10:36:24,184 INFO misc.py line 119 253097] Train: [38/100][277/510] Data 0.004 (0.076) Batch 1.160 (1.544) Remain 13:39:31 loss: 0.1212 Lr: 0.00443
[2023-12-25 10:36:26,863 INFO misc.py line 119 253097] Train: [38/100][278/510] Data 0.004 (0.076) Batch 2.680 (1.548) Remain 13:41:41 loss: 0.2788 Lr: 0.00443
[2023-12-25 10:36:28,149 INFO misc.py line 119 253097] Train: [38/100][279/510] Data 0.004 (0.076) Batch 1.287 (1.547) Remain 13:41:09 loss: 0.1780 Lr: 0.00442
[2023-12-25 10:36:29,158 INFO misc.py line 119 253097] Train: [38/100][280/510] Data 0.003 (0.075) Batch 1.007 (1.545) Remain 13:40:06 loss: 0.2500 Lr: 0.00442
[2023-12-25 10:36:30,209 INFO misc.py line 119 253097] Train: [38/100][281/510] Data 0.006 (0.075) Batch 1.052 (1.543) Remain 13:39:08 loss: 0.2452 Lr: 0.00442
[2023-12-25 10:36:31,358 INFO misc.py line 119 253097] Train: [38/100][282/510] Data 0.005 (0.075) Batch 1.148 (1.542) Remain 13:38:21 loss: 0.2343 Lr: 0.00442
[2023-12-25 10:36:32,710 INFO misc.py line 119 253097] Train: [38/100][283/510] Data 0.005 (0.075) Batch 1.351 (1.541) Remain 13:37:58 loss: 0.4670 Lr: 0.00442
[2023-12-25 10:36:33,739 INFO misc.py line 119 253097] Train: [38/100][284/510] Data 0.007 (0.074) Batch 1.030 (1.539) Remain 13:36:58 loss: 0.2305 Lr: 0.00442
[2023-12-25 10:36:35,008 INFO misc.py line 119 253097] Train: [38/100][285/510] Data 0.007 (0.074) Batch 1.270 (1.538) Remain 13:36:26 loss: 0.1963 Lr: 0.00442
[2023-12-25 10:36:36,354 INFO misc.py line 119 253097] Train: [38/100][286/510] Data 0.004 (0.074) Batch 1.347 (1.538) Remain 13:36:03 loss: 0.1082 Lr: 0.00442
[2023-12-25 10:36:37,574 INFO misc.py line 119 253097] Train: [38/100][287/510] Data 0.004 (0.074) Batch 1.220 (1.536) Remain 13:35:26 loss: 0.1650 Lr: 0.00442
[2023-12-25 10:36:38,735 INFO misc.py line 119 253097] Train: [38/100][288/510] Data 0.004 (0.073) Batch 1.154 (1.535) Remain 13:34:42 loss: 0.1806 Lr: 0.00442
[2023-12-25 10:36:39,798 INFO misc.py line 119 253097] Train: [38/100][289/510] Data 0.011 (0.073) Batch 1.069 (1.534) Remain 13:33:49 loss: 0.1949 Lr: 0.00442
[2023-12-25 10:36:40,880 INFO misc.py line 119 253097] Train: [38/100][290/510] Data 0.004 (0.073) Batch 1.083 (1.532) Remain 13:32:57 loss: 0.2052 Lr: 0.00442
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[2023-12-25 10:36:44,454 INFO misc.py line 119 253097] Train: [38/100][293/510] Data 0.009 (0.072) Batch 1.032 (1.528) Remain 13:31:00 loss: 0.1435 Lr: 0.00442
[2023-12-25 10:36:45,578 INFO misc.py line 119 253097] Train: [38/100][294/510] Data 0.005 (0.072) Batch 1.121 (1.527) Remain 13:30:14 loss: 0.1715 Lr: 0.00442
[2023-12-25 10:36:46,580 INFO misc.py line 119 253097] Train: [38/100][295/510] Data 0.008 (0.072) Batch 1.003 (1.525) Remain 13:29:15 loss: 0.2516 Lr: 0.00442
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[2023-12-25 10:36:49,008 INFO misc.py line 119 253097] Train: [38/100][297/510] Data 0.017 (0.071) Batch 1.246 (1.523) Remain 13:28:05 loss: 0.2754 Lr: 0.00442
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[2023-12-25 10:36:51,416 INFO misc.py line 119 253097] Train: [38/100][299/510] Data 0.023 (0.071) Batch 1.253 (1.521) Remain 13:26:53 loss: 0.2135 Lr: 0.00442
[2023-12-25 10:36:52,385 INFO misc.py line 119 253097] Train: [38/100][300/510] Data 0.012 (0.071) Batch 0.976 (1.519) Remain 13:25:53 loss: 0.1745 Lr: 0.00442
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[2023-12-25 10:37:29,044 INFO misc.py line 119 253097] Train: [38/100][323/510] Data 0.003 (0.076) Batch 1.065 (1.524) Remain 13:28:09 loss: 0.2343 Lr: 0.00442
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[2023-12-25 10:38:27,156 INFO misc.py line 119 253097] Train: [38/100][360/510] Data 0.005 (0.083) Batch 4.978 (1.529) Remain 13:29:44 loss: 0.1888 Lr: 0.00441
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[2023-12-25 10:38:31,760 INFO misc.py line 119 253097] Train: [38/100][364/510] Data 0.003 (0.082) Batch 1.202 (1.525) Remain 13:27:25 loss: 0.4022 Lr: 0.00441
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[2023-12-25 10:38:35,084 INFO misc.py line 119 253097] Train: [38/100][367/510] Data 0.004 (0.081) Batch 1.084 (1.522) Remain 13:25:31 loss: 0.2387 Lr: 0.00441
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[2023-12-25 10:38:58,015 INFO misc.py line 119 253097] Train: [38/100][379/510] Data 0.011 (0.079) Batch 0.931 (1.534) Remain 13:31:47 loss: 0.1366 Lr: 0.00441
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[2023-12-25 10:39:03,703 INFO misc.py line 119 253097] Train: [38/100][384/510] Data 0.005 (0.078) Batch 1.159 (1.529) Remain 13:28:54 loss: 0.1792 Lr: 0.00441
[2023-12-25 10:39:04,843 INFO misc.py line 119 253097] Train: [38/100][385/510] Data 0.007 (0.078) Batch 1.143 (1.528) Remain 13:28:21 loss: 0.1848 Lr: 0.00441
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[2023-12-25 10:39:18,345 INFO misc.py line 119 253097] Train: [38/100][392/510] Data 0.009 (0.077) Batch 1.050 (1.535) Remain 13:31:59 loss: 0.3392 Lr: 0.00441
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[2023-12-25 10:39:28,209 INFO misc.py line 119 253097] Train: [38/100][400/510] Data 0.005 (0.075) Batch 1.220 (1.529) Remain 13:28:33 loss: 0.1681 Lr: 0.00440
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[2023-12-25 10:40:19,771 INFO misc.py line 119 253097] Train: [38/100][428/510] Data 0.004 (0.071) Batch 1.080 (1.550) Remain 13:38:43 loss: 0.2015 Lr: 0.00440
[2023-12-25 10:40:20,717 INFO misc.py line 119 253097] Train: [38/100][429/510] Data 0.005 (0.070) Batch 0.947 (1.548) Remain 13:37:57 loss: 0.1364 Lr: 0.00440
[2023-12-25 10:40:21,913 INFO misc.py line 119 253097] Train: [38/100][430/510] Data 0.003 (0.070) Batch 1.195 (1.547) Remain 13:37:29 loss: 0.1290 Lr: 0.00440
[2023-12-25 10:40:23,059 INFO misc.py line 119 253097] Train: [38/100][431/510] Data 0.005 (0.070) Batch 1.147 (1.546) Remain 13:36:58 loss: 0.2234 Lr: 0.00440
[2023-12-25 10:40:24,326 INFO misc.py line 119 253097] Train: [38/100][432/510] Data 0.004 (0.070) Batch 1.264 (1.546) Remain 13:36:36 loss: 0.1135 Lr: 0.00440
[2023-12-25 10:40:25,592 INFO misc.py line 119 253097] Train: [38/100][433/510] Data 0.007 (0.070) Batch 1.269 (1.545) Remain 13:36:14 loss: 0.1673 Lr: 0.00440
[2023-12-25 10:40:26,744 INFO misc.py line 119 253097] Train: [38/100][434/510] Data 0.004 (0.070) Batch 1.152 (1.544) Remain 13:35:43 loss: 0.1585 Lr: 0.00440
[2023-12-25 10:40:27,891 INFO misc.py line 119 253097] Train: [38/100][435/510] Data 0.004 (0.069) Batch 1.147 (1.543) Remain 13:35:13 loss: 0.2550 Lr: 0.00440
[2023-12-25 10:40:29,005 INFO misc.py line 119 253097] Train: [38/100][436/510] Data 0.004 (0.069) Batch 1.112 (1.542) Remain 13:34:39 loss: 0.2717 Lr: 0.00440
[2023-12-25 10:40:30,319 INFO misc.py line 119 253097] Train: [38/100][437/510] Data 0.007 (0.069) Batch 1.303 (1.542) Remain 13:34:20 loss: 0.3129 Lr: 0.00440
[2023-12-25 10:40:31,445 INFO misc.py line 119 253097] Train: [38/100][438/510] Data 0.017 (0.069) Batch 1.135 (1.541) Remain 13:33:49 loss: 0.5406 Lr: 0.00440
[2023-12-25 10:40:32,476 INFO misc.py line 119 253097] Train: [38/100][439/510] Data 0.008 (0.069) Batch 1.034 (1.540) Remain 13:33:11 loss: 0.2267 Lr: 0.00440
[2023-12-25 10:40:33,612 INFO misc.py line 119 253097] Train: [38/100][440/510] Data 0.004 (0.069) Batch 1.122 (1.539) Remain 13:32:39 loss: 0.1662 Lr: 0.00440
[2023-12-25 10:40:34,620 INFO misc.py line 119 253097] Train: [38/100][441/510] Data 0.019 (0.069) Batch 1.011 (1.537) Remain 13:31:59 loss: 0.1351 Lr: 0.00440
[2023-12-25 10:40:35,664 INFO misc.py line 119 253097] Train: [38/100][442/510] Data 0.017 (0.069) Batch 1.051 (1.536) Remain 13:31:23 loss: 0.2606 Lr: 0.00440
[2023-12-25 10:40:36,856 INFO misc.py line 119 253097] Train: [38/100][443/510] Data 0.010 (0.068) Batch 1.185 (1.536) Remain 13:30:56 loss: 0.2289 Lr: 0.00440
[2023-12-25 10:40:48,154 INFO misc.py line 119 253097] Train: [38/100][444/510] Data 10.036 (0.091) Batch 11.311 (1.558) Remain 13:42:37 loss: 0.2333 Lr: 0.00440
[2023-12-25 10:40:49,273 INFO misc.py line 119 253097] Train: [38/100][445/510] Data 0.003 (0.091) Batch 1.118 (1.557) Remain 13:42:04 loss: 0.2556 Lr: 0.00440
[2023-12-25 10:40:50,242 INFO misc.py line 119 253097] Train: [38/100][446/510] Data 0.004 (0.091) Batch 0.968 (1.555) Remain 13:41:20 loss: 0.1706 Lr: 0.00440
[2023-12-25 10:40:51,386 INFO misc.py line 119 253097] Train: [38/100][447/510] Data 0.005 (0.090) Batch 1.146 (1.554) Remain 13:40:49 loss: 0.3012 Lr: 0.00440
[2023-12-25 10:40:52,663 INFO misc.py line 119 253097] Train: [38/100][448/510] Data 0.003 (0.090) Batch 1.272 (1.554) Remain 13:40:28 loss: 0.1793 Lr: 0.00440
[2023-12-25 10:40:53,747 INFO misc.py line 119 253097] Train: [38/100][449/510] Data 0.009 (0.090) Batch 1.088 (1.553) Remain 13:39:53 loss: 0.4993 Lr: 0.00440
[2023-12-25 10:40:54,877 INFO misc.py line 119 253097] Train: [38/100][450/510] Data 0.005 (0.090) Batch 1.129 (1.552) Remain 13:39:21 loss: 0.2199 Lr: 0.00440
[2023-12-25 10:40:56,022 INFO misc.py line 119 253097] Train: [38/100][451/510] Data 0.005 (0.090) Batch 1.142 (1.551) Remain 13:38:51 loss: 0.1370 Lr: 0.00440
[2023-12-25 10:40:57,017 INFO misc.py line 119 253097] Train: [38/100][452/510] Data 0.009 (0.089) Batch 0.999 (1.550) Remain 13:38:10 loss: 0.1585 Lr: 0.00440
[2023-12-25 10:40:58,187 INFO misc.py line 119 253097] Train: [38/100][453/510] Data 0.004 (0.089) Batch 1.171 (1.549) Remain 13:37:42 loss: 0.3781 Lr: 0.00440
[2023-12-25 10:40:59,501 INFO misc.py line 119 253097] Train: [38/100][454/510] Data 0.003 (0.089) Batch 1.310 (1.548) Remain 13:37:24 loss: 0.1878 Lr: 0.00439
[2023-12-25 10:41:00,705 INFO misc.py line 119 253097] Train: [38/100][455/510] Data 0.008 (0.089) Batch 1.208 (1.548) Remain 13:36:58 loss: 0.3310 Lr: 0.00439
[2023-12-25 10:41:01,723 INFO misc.py line 119 253097] Train: [38/100][456/510] Data 0.004 (0.089) Batch 1.017 (1.546) Remain 13:36:20 loss: 0.2639 Lr: 0.00439
[2023-12-25 10:41:02,927 INFO misc.py line 119 253097] Train: [38/100][457/510] Data 0.005 (0.089) Batch 1.206 (1.546) Remain 13:35:54 loss: 0.2846 Lr: 0.00439
[2023-12-25 10:41:04,087 INFO misc.py line 119 253097] Train: [38/100][458/510] Data 0.003 (0.088) Batch 1.145 (1.545) Remain 13:35:25 loss: 0.2572 Lr: 0.00439
[2023-12-25 10:41:05,064 INFO misc.py line 119 253097] Train: [38/100][459/510] Data 0.018 (0.088) Batch 0.991 (1.544) Remain 13:34:45 loss: 0.1130 Lr: 0.00439
[2023-12-25 10:41:06,071 INFO misc.py line 119 253097] Train: [38/100][460/510] Data 0.004 (0.088) Batch 1.007 (1.542) Remain 13:34:06 loss: 0.1812 Lr: 0.00439
[2023-12-25 10:41:07,187 INFO misc.py line 119 253097] Train: [38/100][461/510] Data 0.004 (0.088) Batch 1.115 (1.541) Remain 13:33:35 loss: 0.1992 Lr: 0.00439
[2023-12-25 10:41:08,481 INFO misc.py line 119 253097] Train: [38/100][462/510] Data 0.005 (0.088) Batch 1.291 (1.541) Remain 13:33:16 loss: 0.1966 Lr: 0.00439
[2023-12-25 10:41:09,572 INFO misc.py line 119 253097] Train: [38/100][463/510] Data 0.007 (0.087) Batch 1.094 (1.540) Remain 13:32:44 loss: 0.2098 Lr: 0.00439
[2023-12-25 10:41:10,635 INFO misc.py line 119 253097] Train: [38/100][464/510] Data 0.005 (0.087) Batch 1.065 (1.539) Remain 13:32:10 loss: 0.1537 Lr: 0.00439
[2023-12-25 10:41:11,621 INFO misc.py line 119 253097] Train: [38/100][465/510] Data 0.003 (0.087) Batch 0.985 (1.538) Remain 13:31:30 loss: 0.2810 Lr: 0.00439
[2023-12-25 10:41:12,779 INFO misc.py line 119 253097] Train: [38/100][466/510] Data 0.004 (0.087) Batch 1.159 (1.537) Remain 13:31:03 loss: 0.1978 Lr: 0.00439
[2023-12-25 10:41:13,856 INFO misc.py line 119 253097] Train: [38/100][467/510] Data 0.003 (0.087) Batch 1.077 (1.536) Remain 13:30:30 loss: 0.0875 Lr: 0.00439
[2023-12-25 10:41:15,167 INFO misc.py line 119 253097] Train: [38/100][468/510] Data 0.004 (0.087) Batch 1.305 (1.535) Remain 13:30:13 loss: 0.2206 Lr: 0.00439
[2023-12-25 10:41:16,168 INFO misc.py line 119 253097] Train: [38/100][469/510] Data 0.010 (0.086) Batch 1.005 (1.534) Remain 13:29:35 loss: 0.2132 Lr: 0.00439
[2023-12-25 10:41:17,264 INFO misc.py line 119 253097] Train: [38/100][470/510] Data 0.006 (0.086) Batch 1.092 (1.533) Remain 13:29:04 loss: 0.1882 Lr: 0.00439
[2023-12-25 10:41:18,311 INFO misc.py line 119 253097] Train: [38/100][471/510] Data 0.009 (0.086) Batch 1.042 (1.532) Remain 13:28:29 loss: 0.2828 Lr: 0.00439
[2023-12-25 10:41:29,789 INFO misc.py line 119 253097] Train: [38/100][472/510] Data 10.196 (0.108) Batch 11.488 (1.553) Remain 13:39:39 loss: 0.2421 Lr: 0.00439
[2023-12-25 10:41:30,920 INFO misc.py line 119 253097] Train: [38/100][473/510] Data 0.004 (0.107) Batch 1.130 (1.553) Remain 13:39:09 loss: 0.1493 Lr: 0.00439
[2023-12-25 10:41:32,079 INFO misc.py line 119 253097] Train: [38/100][474/510] Data 0.005 (0.107) Batch 1.159 (1.552) Remain 13:38:41 loss: 0.1733 Lr: 0.00439
[2023-12-25 10:41:33,140 INFO misc.py line 119 253097] Train: [38/100][475/510] Data 0.004 (0.107) Batch 1.058 (1.551) Remain 13:38:07 loss: 0.1264 Lr: 0.00439
[2023-12-25 10:41:34,414 INFO misc.py line 119 253097] Train: [38/100][476/510] Data 0.008 (0.107) Batch 1.276 (1.550) Remain 13:37:47 loss: 0.0988 Lr: 0.00439
[2023-12-25 10:41:35,662 INFO misc.py line 119 253097] Train: [38/100][477/510] Data 0.007 (0.107) Batch 1.246 (1.549) Remain 13:37:25 loss: 0.2082 Lr: 0.00439
[2023-12-25 10:41:39,021 INFO misc.py line 119 253097] Train: [38/100][478/510] Data 2.217 (0.111) Batch 3.363 (1.553) Remain 13:39:24 loss: 0.2651 Lr: 0.00439
[2023-12-25 10:41:40,269 INFO misc.py line 119 253097] Train: [38/100][479/510] Data 0.003 (0.111) Batch 1.247 (1.553) Remain 13:39:02 loss: 0.3173 Lr: 0.00439
[2023-12-25 10:41:41,291 INFO misc.py line 119 253097] Train: [38/100][480/510] Data 0.004 (0.111) Batch 1.021 (1.552) Remain 13:38:25 loss: 0.2112 Lr: 0.00439
[2023-12-25 10:41:42,489 INFO misc.py line 119 253097] Train: [38/100][481/510] Data 0.006 (0.110) Batch 1.199 (1.551) Remain 13:38:01 loss: 0.1890 Lr: 0.00439
[2023-12-25 10:41:43,814 INFO misc.py line 119 253097] Train: [38/100][482/510] Data 0.006 (0.110) Batch 1.323 (1.550) Remain 13:37:44 loss: 0.1167 Lr: 0.00439
[2023-12-25 10:41:45,031 INFO misc.py line 119 253097] Train: [38/100][483/510] Data 0.007 (0.110) Batch 1.216 (1.550) Remain 13:37:20 loss: 0.2365 Lr: 0.00439
[2023-12-25 10:41:46,351 INFO misc.py line 119 253097] Train: [38/100][484/510] Data 0.008 (0.110) Batch 1.320 (1.549) Remain 13:37:04 loss: 0.1525 Lr: 0.00439
[2023-12-25 10:41:50,288 INFO misc.py line 119 253097] Train: [38/100][485/510] Data 0.008 (0.109) Batch 3.940 (1.554) Remain 13:39:39 loss: 0.1429 Lr: 0.00439
[2023-12-25 10:41:51,398 INFO misc.py line 119 253097] Train: [38/100][486/510] Data 0.004 (0.109) Batch 1.108 (1.553) Remain 13:39:08 loss: 0.1660 Lr: 0.00439
[2023-12-25 10:41:52,540 INFO misc.py line 119 253097] Train: [38/100][487/510] Data 0.006 (0.109) Batch 1.143 (1.552) Remain 13:38:40 loss: 0.1061 Lr: 0.00439
[2023-12-25 10:41:53,682 INFO misc.py line 119 253097] Train: [38/100][488/510] Data 0.004 (0.109) Batch 1.142 (1.551) Remain 13:38:12 loss: 0.1715 Lr: 0.00439
[2023-12-25 10:41:54,620 INFO misc.py line 119 253097] Train: [38/100][489/510] Data 0.004 (0.109) Batch 0.939 (1.550) Remain 13:37:30 loss: 0.1286 Lr: 0.00439
[2023-12-25 10:41:55,819 INFO misc.py line 119 253097] Train: [38/100][490/510] Data 0.003 (0.108) Batch 1.195 (1.549) Remain 13:37:06 loss: 0.2307 Lr: 0.00439
[2023-12-25 10:41:57,077 INFO misc.py line 119 253097] Train: [38/100][491/510] Data 0.007 (0.108) Batch 1.257 (1.549) Remain 13:36:45 loss: 0.3342 Lr: 0.00439
[2023-12-25 10:41:58,215 INFO misc.py line 119 253097] Train: [38/100][492/510] Data 0.008 (0.108) Batch 1.138 (1.548) Remain 13:36:17 loss: 0.1799 Lr: 0.00439
[2023-12-25 10:41:59,385 INFO misc.py line 119 253097] Train: [38/100][493/510] Data 0.008 (0.108) Batch 1.171 (1.547) Remain 13:35:51 loss: 0.2800 Lr: 0.00439
[2023-12-25 10:42:00,566 INFO misc.py line 119 253097] Train: [38/100][494/510] Data 0.007 (0.108) Batch 1.180 (1.547) Remain 13:35:26 loss: 0.2724 Lr: 0.00439
[2023-12-25 10:42:01,640 INFO misc.py line 119 253097] Train: [38/100][495/510] Data 0.008 (0.107) Batch 1.077 (1.546) Remain 13:34:54 loss: 0.2501 Lr: 0.00439
[2023-12-25 10:42:02,890 INFO misc.py line 119 253097] Train: [38/100][496/510] Data 0.006 (0.107) Batch 1.250 (1.545) Remain 13:34:34 loss: 0.2192 Lr: 0.00439
[2023-12-25 10:42:04,168 INFO misc.py line 119 253097] Train: [38/100][497/510] Data 0.005 (0.107) Batch 1.277 (1.544) Remain 13:34:15 loss: 0.2772 Lr: 0.00439
[2023-12-25 10:42:13,292 INFO misc.py line 119 253097] Train: [38/100][498/510] Data 0.006 (0.107) Batch 9.127 (1.560) Remain 13:42:18 loss: 0.2707 Lr: 0.00439
[2023-12-25 10:42:14,476 INFO misc.py line 119 253097] Train: [38/100][499/510] Data 0.004 (0.107) Batch 1.182 (1.559) Remain 13:41:52 loss: 0.2311 Lr: 0.00439
[2023-12-25 10:42:15,597 INFO misc.py line 119 253097] Train: [38/100][500/510] Data 0.005 (0.106) Batch 1.117 (1.558) Remain 13:41:23 loss: 0.3213 Lr: 0.00439
[2023-12-25 10:42:16,644 INFO misc.py line 119 253097] Train: [38/100][501/510] Data 0.009 (0.106) Batch 1.048 (1.557) Remain 13:40:49 loss: 0.2902 Lr: 0.00439
[2023-12-25 10:42:17,884 INFO misc.py line 119 253097] Train: [38/100][502/510] Data 0.008 (0.106) Batch 1.238 (1.556) Remain 13:40:27 loss: 0.2299 Lr: 0.00439
[2023-12-25 10:42:18,915 INFO misc.py line 119 253097] Train: [38/100][503/510] Data 0.010 (0.106) Batch 1.032 (1.555) Remain 13:39:52 loss: 0.2327 Lr: 0.00439
[2023-12-25 10:42:22,861 INFO misc.py line 119 253097] Train: [38/100][504/510] Data 0.009 (0.106) Batch 3.952 (1.560) Remain 13:42:22 loss: 0.1673 Lr: 0.00439
[2023-12-25 10:42:23,822 INFO misc.py line 119 253097] Train: [38/100][505/510] Data 0.003 (0.105) Batch 0.961 (1.559) Remain 13:41:43 loss: 0.1025 Lr: 0.00439
[2023-12-25 10:42:24,987 INFO misc.py line 119 253097] Train: [38/100][506/510] Data 0.003 (0.105) Batch 1.165 (1.558) Remain 13:41:16 loss: 0.1890 Lr: 0.00439
[2023-12-25 10:42:26,218 INFO misc.py line 119 253097] Train: [38/100][507/510] Data 0.003 (0.105) Batch 1.225 (1.558) Remain 13:40:54 loss: 0.1032 Lr: 0.00439
[2023-12-25 10:42:27,223 INFO misc.py line 119 253097] Train: [38/100][508/510] Data 0.009 (0.105) Batch 1.011 (1.556) Remain 13:40:18 loss: 0.1435 Lr: 0.00439
[2023-12-25 10:42:28,183 INFO misc.py line 119 253097] Train: [38/100][509/510] Data 0.003 (0.105) Batch 0.960 (1.555) Remain 13:39:39 loss: 0.3016 Lr: 0.00439
[2023-12-25 10:42:29,193 INFO misc.py line 119 253097] Train: [38/100][510/510] Data 0.004 (0.104) Batch 1.011 (1.554) Remain 13:39:04 loss: 0.2690 Lr: 0.00439
[2023-12-25 10:42:29,194 INFO misc.py line 136 253097] Train result: loss: 0.2221 
[2023-12-25 10:42:29,194 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 10:42:57,274 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5619 
[2023-12-25 10:42:57,623 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3921 
[2023-12-25 10:43:02,563 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5027 
[2023-12-25 10:43:03,079 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3457 
[2023-12-25 10:43:05,053 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7835 
[2023-12-25 10:43:05,474 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3897 
[2023-12-25 10:43:06,350 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2105 
[2023-12-25 10:43:06,904 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4169 
[2023-12-25 10:43:08,715 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0058 
[2023-12-25 10:43:10,834 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3357 
[2023-12-25 10:43:11,687 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2369 
[2023-12-25 10:43:12,113 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7076 
[2023-12-25 10:43:13,037 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4076 
[2023-12-25 10:43:15,982 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8509 
[2023-12-25 10:43:16,456 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.5336 
[2023-12-25 10:43:17,076 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4660 
[2023-12-25 10:43:17,790 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4498 
[2023-12-25 10:43:19,657 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6483/0.7104/0.8937.
[2023-12-25 10:43:19,657 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9145/0.9468
[2023-12-25 10:43:19,657 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9783/0.9892
[2023-12-25 10:43:19,657 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8363/0.9625
[2023-12-25 10:43:19,657 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2294/0.2402
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6065/0.6354
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5960/0.6556
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8107/0.9178
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9012/0.9638
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5170/0.5435
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7706/0.8540
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6966/0.7904
[2023-12-25 10:43:19,658 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5714/0.7354
[2023-12-25 10:43:19,659 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 10:43:19,660 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 10:43:19,660 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 10:43:29,913 INFO misc.py line 119 253097] Train: [39/100][1/510] Data 3.090 (3.090) Batch 7.824 (7.824) Remain 68:43:06 loss: 0.1926 Lr: 0.00439
[2023-12-25 10:43:35,527 INFO misc.py line 119 253097] Train: [39/100][2/510] Data 0.004 (0.004) Batch 5.614 (5.614) Remain 49:18:28 loss: 0.1598 Lr: 0.00438
[2023-12-25 10:43:36,360 INFO misc.py line 119 253097] Train: [39/100][3/510] Data 0.003 (0.003) Batch 0.828 (0.828) Remain 07:16:13 loss: 0.1921 Lr: 0.00438
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[2023-12-25 10:46:57,684 INFO misc.py line 119 253097] Train: [39/100][134/510] Data 0.009 (0.006) Batch 1.163 (1.537) Remain 13:26:28 loss: 0.1385 Lr: 0.00436
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[2023-12-25 10:47:08,235 INFO misc.py line 119 253097] Train: [39/100][140/510] Data 0.007 (0.006) Batch 4.897 (1.547) Remain 13:31:25 loss: 0.4425 Lr: 0.00436
[2023-12-25 10:47:09,380 INFO misc.py line 119 253097] Train: [39/100][141/510] Data 0.004 (0.006) Batch 1.145 (1.544) Remain 13:29:52 loss: 0.2321 Lr: 0.00436
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[2023-12-25 10:47:18,311 INFO misc.py line 119 253097] Train: [39/100][144/510] Data 5.362 (0.044) Batch 6.575 (1.574) Remain 13:45:48 loss: 0.3984 Lr: 0.00436
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[2023-12-25 10:47:22,021 INFO misc.py line 119 253097] Train: [39/100][147/510] Data 0.004 (0.043) Batch 1.341 (1.567) Remain 13:42:01 loss: 0.2517 Lr: 0.00436
[2023-12-25 10:47:23,240 INFO misc.py line 119 253097] Train: [39/100][148/510] Data 0.004 (0.043) Batch 1.216 (1.565) Remain 13:40:44 loss: 0.1371 Lr: 0.00436
[2023-12-25 10:47:24,563 INFO misc.py line 119 253097] Train: [39/100][149/510] Data 0.007 (0.043) Batch 1.321 (1.563) Remain 13:39:50 loss: 0.1400 Lr: 0.00436
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[2023-12-25 10:47:28,889 INFO misc.py line 119 253097] Train: [39/100][153/510] Data 0.010 (0.042) Batch 1.001 (1.550) Remain 13:33:00 loss: 0.4051 Lr: 0.00436
[2023-12-25 10:47:29,963 INFO misc.py line 119 253097] Train: [39/100][154/510] Data 0.004 (0.042) Batch 1.069 (1.547) Remain 13:31:18 loss: 0.1137 Lr: 0.00436
[2023-12-25 10:47:31,260 INFO misc.py line 119 253097] Train: [39/100][155/510] Data 0.010 (0.041) Batch 1.297 (1.545) Remain 13:30:25 loss: 0.1598 Lr: 0.00436
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[2023-12-25 10:47:37,351 INFO misc.py line 119 253097] Train: [39/100][160/510] Data 0.020 (0.040) Batch 1.177 (1.535) Remain 13:24:50 loss: 0.2811 Lr: 0.00436
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[2023-12-25 10:47:39,861 INFO misc.py line 119 253097] Train: [39/100][162/510] Data 0.013 (0.040) Batch 1.207 (1.531) Remain 13:22:57 loss: 0.1531 Lr: 0.00436
[2023-12-25 10:47:41,150 INFO misc.py line 119 253097] Train: [39/100][163/510] Data 0.005 (0.040) Batch 1.279 (1.530) Remain 13:22:06 loss: 0.3784 Lr: 0.00436
[2023-12-25 10:47:42,041 INFO misc.py line 119 253097] Train: [39/100][164/510] Data 0.014 (0.040) Batch 0.902 (1.526) Remain 13:20:01 loss: 0.1013 Lr: 0.00436
[2023-12-25 10:47:43,202 INFO misc.py line 119 253097] Train: [39/100][165/510] Data 0.003 (0.039) Batch 1.160 (1.524) Remain 13:18:49 loss: 0.3325 Lr: 0.00436
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[2023-12-25 10:47:48,913 INFO misc.py line 119 253097] Train: [39/100][170/510] Data 0.006 (0.038) Batch 1.087 (1.512) Remain 13:12:42 loss: 0.1506 Lr: 0.00436
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[2023-12-25 10:47:51,307 INFO misc.py line 119 253097] Train: [39/100][172/510] Data 0.004 (0.038) Batch 1.242 (1.509) Remain 13:10:41 loss: 0.1876 Lr: 0.00436
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[2023-12-25 10:48:01,594 INFO misc.py line 119 253097] Train: [39/100][175/510] Data 6.673 (0.076) Batch 7.797 (1.542) Remain 13:28:10 loss: 0.1321 Lr: 0.00435
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[2023-12-25 10:48:24,130 INFO misc.py line 119 253097] Train: [39/100][181/510] Data 12.182 (0.142) Batch 13.366 (1.617) Remain 14:07:07 loss: 0.1018 Lr: 0.00435
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[2023-12-25 10:49:47,080 INFO misc.py line 119 253097] Train: [39/100][233/510] Data 10.996 (0.159) Batch 12.333 (1.612) Remain 14:03:10 loss: 0.2176 Lr: 0.00434
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[2023-12-25 10:49:58,907 INFO misc.py line 119 253097] Train: [39/100][242/510] Data 0.013 (0.159) Batch 1.233 (1.601) Remain 13:57:04 loss: 0.2445 Lr: 0.00434
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[2023-12-25 10:50:03,592 INFO misc.py line 119 253097] Train: [39/100][246/510] Data 0.005 (0.156) Batch 1.153 (1.594) Remain 13:53:16 loss: 0.2701 Lr: 0.00434
[2023-12-25 10:50:04,640 INFO misc.py line 119 253097] Train: [39/100][247/510] Data 0.004 (0.155) Batch 1.042 (1.591) Remain 13:52:03 loss: 0.2294 Lr: 0.00434
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[2023-12-25 10:50:06,947 INFO misc.py line 119 253097] Train: [39/100][249/510] Data 0.007 (0.154) Batch 1.065 (1.588) Remain 13:50:09 loss: 0.1831 Lr: 0.00434
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[2023-12-25 10:50:11,261 INFO misc.py line 119 253097] Train: [39/100][253/510] Data 0.007 (0.152) Batch 0.993 (1.580) Remain 13:45:48 loss: 0.1392 Lr: 0.00434
[2023-12-25 10:50:16,862 INFO misc.py line 119 253097] Train: [39/100][254/510] Data 0.003 (0.151) Batch 5.600 (1.596) Remain 13:54:08 loss: 0.2109 Lr: 0.00434
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[2023-12-25 10:50:19,243 INFO misc.py line 119 253097] Train: [39/100][256/510] Data 0.012 (0.150) Batch 1.271 (1.592) Remain 13:52:25 loss: 0.1867 Lr: 0.00434
[2023-12-25 10:50:20,323 INFO misc.py line 119 253097] Train: [39/100][257/510] Data 0.006 (0.150) Batch 1.077 (1.590) Remain 13:51:19 loss: 0.1335 Lr: 0.00434
[2023-12-25 10:50:21,655 INFO misc.py line 119 253097] Train: [39/100][258/510] Data 0.007 (0.149) Batch 1.333 (1.589) Remain 13:50:46 loss: 0.1846 Lr: 0.00434
[2023-12-25 10:50:22,847 INFO misc.py line 119 253097] Train: [39/100][259/510] Data 0.007 (0.149) Batch 1.180 (1.588) Remain 13:49:54 loss: 0.2230 Lr: 0.00434
[2023-12-25 10:50:23,956 INFO misc.py line 119 253097] Train: [39/100][260/510] Data 0.019 (0.148) Batch 1.118 (1.586) Remain 13:48:56 loss: 0.2739 Lr: 0.00434
[2023-12-25 10:50:24,943 INFO misc.py line 119 253097] Train: [39/100][261/510] Data 0.009 (0.147) Batch 0.990 (1.584) Remain 13:47:42 loss: 0.1928 Lr: 0.00434
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[2023-12-25 10:50:27,506 INFO misc.py line 119 253097] Train: [39/100][263/510] Data 0.004 (0.146) Batch 1.581 (1.581) Remain 13:46:25 loss: 0.1834 Lr: 0.00434
[2023-12-25 10:50:28,623 INFO misc.py line 119 253097] Train: [39/100][264/510] Data 0.007 (0.146) Batch 1.120 (1.580) Remain 13:45:28 loss: 0.3789 Lr: 0.00434
[2023-12-25 10:50:29,552 INFO misc.py line 119 253097] Train: [39/100][265/510] Data 0.005 (0.145) Batch 0.929 (1.577) Remain 13:44:09 loss: 0.2792 Lr: 0.00434
[2023-12-25 10:50:30,734 INFO misc.py line 119 253097] Train: [39/100][266/510] Data 0.005 (0.145) Batch 1.182 (1.576) Remain 13:43:20 loss: 0.1550 Lr: 0.00434
[2023-12-25 10:50:31,880 INFO misc.py line 119 253097] Train: [39/100][267/510] Data 0.004 (0.144) Batch 1.145 (1.574) Remain 13:42:28 loss: 0.1384 Lr: 0.00434
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[2023-12-25 10:50:34,273 INFO misc.py line 119 253097] Train: [39/100][269/510] Data 0.004 (0.143) Batch 1.228 (1.571) Remain 13:40:55 loss: 0.2265 Lr: 0.00434
[2023-12-25 10:50:35,451 INFO misc.py line 119 253097] Train: [39/100][270/510] Data 0.009 (0.143) Batch 1.180 (1.570) Remain 13:40:07 loss: 0.2079 Lr: 0.00434
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[2023-12-25 10:50:43,372 INFO misc.py line 119 253097] Train: [39/100][277/510] Data 0.014 (0.139) Batch 1.087 (1.558) Remain 13:34:06 loss: 0.1733 Lr: 0.00434
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[2023-12-25 10:50:57,605 INFO misc.py line 119 253097] Train: [39/100][280/510] Data 0.004 (0.138) Batch 12.004 (1.593) Remain 13:52:03 loss: 0.2302 Lr: 0.00434
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[2023-12-25 10:50:59,974 INFO misc.py line 119 253097] Train: [39/100][282/510] Data 0.004 (0.137) Batch 1.124 (1.590) Remain 13:50:27 loss: 0.1824 Lr: 0.00434
[2023-12-25 10:51:01,231 INFO misc.py line 119 253097] Train: [39/100][283/510] Data 0.010 (0.136) Batch 1.258 (1.589) Remain 13:49:48 loss: 0.1542 Lr: 0.00434
[2023-12-25 10:51:02,368 INFO misc.py line 119 253097] Train: [39/100][284/510] Data 0.009 (0.136) Batch 1.141 (1.587) Remain 13:48:57 loss: 0.1931 Lr: 0.00434
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[2023-12-25 10:53:31,169 INFO misc.py line 119 253097] Train: [39/100][380/510] Data 11.484 (0.133) Batch 12.837 (1.578) Remain 13:41:29 loss: 0.3898 Lr: 0.00432
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[2023-12-25 10:53:34,818 INFO misc.py line 119 253097] Train: [39/100][383/510] Data 0.003 (0.132) Batch 1.169 (1.575) Remain 13:39:54 loss: 0.1665 Lr: 0.00432
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[2023-12-25 10:54:25,694 INFO misc.py line 119 253097] Train: [39/100][422/510] Data 0.007 (0.131) Batch 1.169 (1.550) Remain 13:25:48 loss: 0.2771 Lr: 0.00431
[2023-12-25 10:54:26,856 INFO misc.py line 119 253097] Train: [39/100][423/510] Data 0.004 (0.130) Batch 1.162 (1.549) Remain 13:25:18 loss: 0.1278 Lr: 0.00431
[2023-12-25 10:54:27,910 INFO misc.py line 119 253097] Train: [39/100][424/510] Data 0.004 (0.130) Batch 1.054 (1.548) Remain 13:24:40 loss: 0.2842 Lr: 0.00431
[2023-12-25 10:54:29,009 INFO misc.py line 119 253097] Train: [39/100][425/510] Data 0.004 (0.130) Batch 1.093 (1.547) Remain 13:24:04 loss: 0.1818 Lr: 0.00431
[2023-12-25 10:54:30,292 INFO misc.py line 119 253097] Train: [39/100][426/510] Data 0.010 (0.129) Batch 1.288 (1.546) Remain 13:23:44 loss: 0.4567 Lr: 0.00431
[2023-12-25 10:54:31,501 INFO misc.py line 119 253097] Train: [39/100][427/510] Data 0.004 (0.129) Batch 1.196 (1.545) Remain 13:23:16 loss: 0.1714 Lr: 0.00431
[2023-12-25 10:54:32,771 INFO misc.py line 119 253097] Train: [39/100][428/510] Data 0.018 (0.129) Batch 1.281 (1.544) Remain 13:22:55 loss: 0.1306 Lr: 0.00431
[2023-12-25 10:54:33,941 INFO misc.py line 119 253097] Train: [39/100][429/510] Data 0.008 (0.129) Batch 1.168 (1.544) Remain 13:22:26 loss: 0.2284 Lr: 0.00431
[2023-12-25 10:54:34,931 INFO misc.py line 119 253097] Train: [39/100][430/510] Data 0.009 (0.128) Batch 0.993 (1.542) Remain 13:21:45 loss: 0.2631 Lr: 0.00431
[2023-12-25 10:54:36,005 INFO misc.py line 119 253097] Train: [39/100][431/510] Data 0.006 (0.128) Batch 1.074 (1.541) Remain 13:21:09 loss: 0.1396 Lr: 0.00431
[2023-12-25 10:54:37,031 INFO misc.py line 119 253097] Train: [39/100][432/510] Data 0.005 (0.128) Batch 1.027 (1.540) Remain 13:20:30 loss: 0.3988 Lr: 0.00431
[2023-12-25 10:54:38,292 INFO misc.py line 119 253097] Train: [39/100][433/510] Data 0.005 (0.127) Batch 1.254 (1.539) Remain 13:20:08 loss: 0.1644 Lr: 0.00431
[2023-12-25 10:54:39,394 INFO misc.py line 119 253097] Train: [39/100][434/510] Data 0.011 (0.127) Batch 1.109 (1.538) Remain 13:19:35 loss: 0.1820 Lr: 0.00431
[2023-12-25 10:54:40,694 INFO misc.py line 119 253097] Train: [39/100][435/510] Data 0.004 (0.127) Batch 1.294 (1.538) Remain 13:19:16 loss: 0.2658 Lr: 0.00431
[2023-12-25 10:54:41,771 INFO misc.py line 119 253097] Train: [39/100][436/510] Data 0.011 (0.127) Batch 1.073 (1.537) Remain 13:18:41 loss: 0.1952 Lr: 0.00431
[2023-12-25 10:54:42,738 INFO misc.py line 119 253097] Train: [39/100][437/510] Data 0.015 (0.126) Batch 0.976 (1.535) Remain 13:17:59 loss: 0.0876 Lr: 0.00431
[2023-12-25 10:54:44,025 INFO misc.py line 119 253097] Train: [39/100][438/510] Data 0.006 (0.126) Batch 1.289 (1.535) Remain 13:17:40 loss: 0.1197 Lr: 0.00431
[2023-12-25 10:54:45,270 INFO misc.py line 119 253097] Train: [39/100][439/510] Data 0.004 (0.126) Batch 1.245 (1.534) Remain 13:17:18 loss: 0.1709 Lr: 0.00431
[2023-12-25 10:54:46,516 INFO misc.py line 119 253097] Train: [39/100][440/510] Data 0.004 (0.126) Batch 1.242 (1.534) Remain 13:16:55 loss: 0.3525 Lr: 0.00431
[2023-12-25 10:54:47,707 INFO misc.py line 119 253097] Train: [39/100][441/510] Data 0.007 (0.125) Batch 1.195 (1.533) Remain 13:16:30 loss: 0.1735 Lr: 0.00431
[2023-12-25 10:54:55,272 INFO misc.py line 119 253097] Train: [39/100][442/510] Data 0.004 (0.125) Batch 7.564 (1.547) Remain 13:23:36 loss: 0.3962 Lr: 0.00431
[2023-12-25 10:54:56,511 INFO misc.py line 119 253097] Train: [39/100][443/510] Data 0.004 (0.125) Batch 1.233 (1.546) Remain 13:23:13 loss: 0.1059 Lr: 0.00431
[2023-12-25 10:54:57,717 INFO misc.py line 119 253097] Train: [39/100][444/510] Data 0.010 (0.124) Batch 1.203 (1.545) Remain 13:22:47 loss: 0.2593 Lr: 0.00431
[2023-12-25 10:54:58,783 INFO misc.py line 119 253097] Train: [39/100][445/510] Data 0.014 (0.124) Batch 1.072 (1.544) Remain 13:22:12 loss: 0.4022 Lr: 0.00431
[2023-12-25 10:54:59,948 INFO misc.py line 119 253097] Train: [39/100][446/510] Data 0.007 (0.124) Batch 1.164 (1.543) Remain 13:21:44 loss: 0.2056 Lr: 0.00431
[2023-12-25 10:55:00,992 INFO misc.py line 119 253097] Train: [39/100][447/510] Data 0.008 (0.124) Batch 1.044 (1.542) Remain 13:21:07 loss: 0.2446 Lr: 0.00431
[2023-12-25 10:55:02,169 INFO misc.py line 119 253097] Train: [39/100][448/510] Data 0.008 (0.123) Batch 1.176 (1.541) Remain 13:20:40 loss: 0.2114 Lr: 0.00431
[2023-12-25 10:55:03,352 INFO misc.py line 119 253097] Train: [39/100][449/510] Data 0.009 (0.123) Batch 1.184 (1.540) Remain 13:20:13 loss: 0.3541 Lr: 0.00431
[2023-12-25 10:55:04,591 INFO misc.py line 119 253097] Train: [39/100][450/510] Data 0.008 (0.123) Batch 1.238 (1.540) Remain 13:19:51 loss: 0.1340 Lr: 0.00431
[2023-12-25 10:55:05,771 INFO misc.py line 119 253097] Train: [39/100][451/510] Data 0.009 (0.123) Batch 1.180 (1.539) Remain 13:19:24 loss: 0.1575 Lr: 0.00431
[2023-12-25 10:55:06,972 INFO misc.py line 119 253097] Train: [39/100][452/510] Data 0.009 (0.122) Batch 1.207 (1.538) Remain 13:19:00 loss: 0.2244 Lr: 0.00431
[2023-12-25 10:55:08,137 INFO misc.py line 119 253097] Train: [39/100][453/510] Data 0.003 (0.122) Batch 1.164 (1.537) Remain 13:18:32 loss: 0.2207 Lr: 0.00431
[2023-12-25 10:55:09,295 INFO misc.py line 119 253097] Train: [39/100][454/510] Data 0.004 (0.122) Batch 1.152 (1.536) Remain 13:18:04 loss: 0.3730 Lr: 0.00431
[2023-12-25 10:55:18,640 INFO misc.py line 119 253097] Train: [39/100][455/510] Data 0.010 (0.122) Batch 9.350 (1.554) Remain 13:27:01 loss: 0.2039 Lr: 0.00431
[2023-12-25 10:55:19,718 INFO misc.py line 119 253097] Train: [39/100][456/510] Data 0.005 (0.121) Batch 1.080 (1.553) Remain 13:26:27 loss: 0.1801 Lr: 0.00431
[2023-12-25 10:55:22,846 INFO misc.py line 119 253097] Train: [39/100][457/510] Data 0.003 (0.121) Batch 3.128 (1.556) Remain 13:28:14 loss: 0.1161 Lr: 0.00431
[2023-12-25 10:55:24,111 INFO misc.py line 119 253097] Train: [39/100][458/510] Data 0.004 (0.121) Batch 1.265 (1.556) Remain 13:27:52 loss: 0.2002 Lr: 0.00431
[2023-12-25 10:55:25,167 INFO misc.py line 119 253097] Train: [39/100][459/510] Data 0.003 (0.121) Batch 1.055 (1.554) Remain 13:27:16 loss: 0.1924 Lr: 0.00431
[2023-12-25 10:55:26,133 INFO misc.py line 119 253097] Train: [39/100][460/510] Data 0.004 (0.120) Batch 0.966 (1.553) Remain 13:26:35 loss: 0.2636 Lr: 0.00431
[2023-12-25 10:55:27,316 INFO misc.py line 119 253097] Train: [39/100][461/510] Data 0.004 (0.120) Batch 1.183 (1.552) Remain 13:26:08 loss: 0.2728 Lr: 0.00431
[2023-12-25 10:55:28,495 INFO misc.py line 119 253097] Train: [39/100][462/510] Data 0.004 (0.120) Batch 1.177 (1.551) Remain 13:25:41 loss: 0.2109 Lr: 0.00430
[2023-12-25 10:55:29,495 INFO misc.py line 119 253097] Train: [39/100][463/510] Data 0.007 (0.120) Batch 0.999 (1.550) Remain 13:25:02 loss: 0.1619 Lr: 0.00430
[2023-12-25 10:55:30,738 INFO misc.py line 119 253097] Train: [39/100][464/510] Data 0.007 (0.119) Batch 1.230 (1.550) Remain 13:24:39 loss: 0.2513 Lr: 0.00430
[2023-12-25 10:55:31,999 INFO misc.py line 119 253097] Train: [39/100][465/510] Data 0.020 (0.119) Batch 1.271 (1.549) Remain 13:24:18 loss: 0.2678 Lr: 0.00430
[2023-12-25 10:55:33,294 INFO misc.py line 119 253097] Train: [39/100][466/510] Data 0.011 (0.119) Batch 1.293 (1.548) Remain 13:24:00 loss: 0.1590 Lr: 0.00430
[2023-12-25 10:55:34,443 INFO misc.py line 119 253097] Train: [39/100][467/510] Data 0.013 (0.119) Batch 1.151 (1.548) Remain 13:23:31 loss: 0.1722 Lr: 0.00430
[2023-12-25 10:55:35,560 INFO misc.py line 119 253097] Train: [39/100][468/510] Data 0.011 (0.118) Batch 1.120 (1.547) Remain 13:23:01 loss: 0.0815 Lr: 0.00430
[2023-12-25 10:55:36,666 INFO misc.py line 119 253097] Train: [39/100][469/510] Data 0.008 (0.118) Batch 1.110 (1.546) Remain 13:22:30 loss: 0.1729 Lr: 0.00430
[2023-12-25 10:55:37,712 INFO misc.py line 119 253097] Train: [39/100][470/510] Data 0.005 (0.118) Batch 1.046 (1.545) Remain 13:21:56 loss: 0.1997 Lr: 0.00430
[2023-12-25 10:55:39,026 INFO misc.py line 119 253097] Train: [39/100][471/510] Data 0.005 (0.118) Batch 1.314 (1.544) Remain 13:21:39 loss: 0.1634 Lr: 0.00430
[2023-12-25 10:55:40,223 INFO misc.py line 119 253097] Train: [39/100][472/510] Data 0.005 (0.117) Batch 1.194 (1.543) Remain 13:21:14 loss: 0.2714 Lr: 0.00430
[2023-12-25 10:55:42,109 INFO misc.py line 119 253097] Train: [39/100][473/510] Data 0.007 (0.117) Batch 1.890 (1.544) Remain 13:21:35 loss: 0.1597 Lr: 0.00430
[2023-12-25 10:55:43,242 INFO misc.py line 119 253097] Train: [39/100][474/510] Data 0.005 (0.117) Batch 1.133 (1.543) Remain 13:21:07 loss: 0.2649 Lr: 0.00430
[2023-12-25 10:55:44,253 INFO misc.py line 119 253097] Train: [39/100][475/510] Data 0.004 (0.117) Batch 1.012 (1.542) Remain 13:20:30 loss: 0.0880 Lr: 0.00430
[2023-12-25 10:55:45,438 INFO misc.py line 119 253097] Train: [39/100][476/510] Data 0.004 (0.116) Batch 1.184 (1.541) Remain 13:20:05 loss: 0.2156 Lr: 0.00430
[2023-12-25 10:55:46,650 INFO misc.py line 119 253097] Train: [39/100][477/510] Data 0.006 (0.116) Batch 1.212 (1.541) Remain 13:19:42 loss: 0.2152 Lr: 0.00430
[2023-12-25 10:55:47,747 INFO misc.py line 119 253097] Train: [39/100][478/510] Data 0.005 (0.116) Batch 1.099 (1.540) Remain 13:19:11 loss: 0.2196 Lr: 0.00430
[2023-12-25 10:55:49,289 INFO misc.py line 119 253097] Train: [39/100][479/510] Data 0.004 (0.116) Batch 1.537 (1.540) Remain 13:19:09 loss: 0.2249 Lr: 0.00430
[2023-12-25 10:55:50,199 INFO misc.py line 119 253097] Train: [39/100][480/510] Data 0.008 (0.116) Batch 0.914 (1.538) Remain 13:18:27 loss: 0.1739 Lr: 0.00430
[2023-12-25 10:55:51,509 INFO misc.py line 119 253097] Train: [39/100][481/510] Data 0.003 (0.115) Batch 1.311 (1.538) Remain 13:18:11 loss: 0.2592 Lr: 0.00430
[2023-12-25 10:55:52,808 INFO misc.py line 119 253097] Train: [39/100][482/510] Data 0.003 (0.115) Batch 1.294 (1.537) Remain 13:17:53 loss: 0.2232 Lr: 0.00430
[2023-12-25 10:55:53,990 INFO misc.py line 119 253097] Train: [39/100][483/510] Data 0.008 (0.115) Batch 1.186 (1.537) Remain 13:17:29 loss: 0.1804 Lr: 0.00430
[2023-12-25 10:55:55,244 INFO misc.py line 119 253097] Train: [39/100][484/510] Data 0.004 (0.115) Batch 1.252 (1.536) Remain 13:17:09 loss: 0.2301 Lr: 0.00430
[2023-12-25 10:55:56,458 INFO misc.py line 119 253097] Train: [39/100][485/510] Data 0.005 (0.114) Batch 1.215 (1.535) Remain 13:16:47 loss: 0.1843 Lr: 0.00430
[2023-12-25 10:55:57,573 INFO misc.py line 119 253097] Train: [39/100][486/510] Data 0.005 (0.114) Batch 1.112 (1.535) Remain 13:16:18 loss: 0.2976 Lr: 0.00430
[2023-12-25 10:55:58,818 INFO misc.py line 119 253097] Train: [39/100][487/510] Data 0.008 (0.114) Batch 1.243 (1.534) Remain 13:15:57 loss: 0.1519 Lr: 0.00430
[2023-12-25 10:55:59,870 INFO misc.py line 119 253097] Train: [39/100][488/510] Data 0.009 (0.114) Batch 1.058 (1.533) Remain 13:15:25 loss: 0.2398 Lr: 0.00430
[2023-12-25 10:56:02,954 INFO misc.py line 119 253097] Train: [39/100][489/510] Data 0.004 (0.114) Batch 3.085 (1.536) Remain 13:17:03 loss: 0.1833 Lr: 0.00430
[2023-12-25 10:56:03,925 INFO misc.py line 119 253097] Train: [39/100][490/510] Data 0.004 (0.113) Batch 0.965 (1.535) Remain 13:16:25 loss: 0.1042 Lr: 0.00430
[2023-12-25 10:56:04,974 INFO misc.py line 119 253097] Train: [39/100][491/510] Data 0.009 (0.113) Batch 1.052 (1.534) Remain 13:15:53 loss: 0.2014 Lr: 0.00430
[2023-12-25 10:56:06,103 INFO misc.py line 119 253097] Train: [39/100][492/510] Data 0.007 (0.113) Batch 1.131 (1.533) Remain 13:15:26 loss: 0.1743 Lr: 0.00430
[2023-12-25 10:56:12,847 INFO misc.py line 119 253097] Train: [39/100][493/510] Data 0.004 (0.113) Batch 6.744 (1.544) Remain 13:20:55 loss: 0.1512 Lr: 0.00430
[2023-12-25 10:56:14,130 INFO misc.py line 119 253097] Train: [39/100][494/510] Data 0.003 (0.112) Batch 1.279 (1.543) Remain 13:20:37 loss: 0.1082 Lr: 0.00430
[2023-12-25 10:56:22,636 INFO misc.py line 119 253097] Train: [39/100][495/510] Data 0.008 (0.112) Batch 8.511 (1.557) Remain 13:27:56 loss: 0.3060 Lr: 0.00430
[2023-12-25 10:56:23,917 INFO misc.py line 119 253097] Train: [39/100][496/510] Data 0.004 (0.112) Batch 1.277 (1.557) Remain 13:27:37 loss: 0.1977 Lr: 0.00430
[2023-12-25 10:56:25,164 INFO misc.py line 119 253097] Train: [39/100][497/510] Data 0.007 (0.112) Batch 1.251 (1.556) Remain 13:27:16 loss: 0.2042 Lr: 0.00430
[2023-12-25 10:56:26,405 INFO misc.py line 119 253097] Train: [39/100][498/510] Data 0.003 (0.112) Batch 1.238 (1.556) Remain 13:26:54 loss: 0.3423 Lr: 0.00430
[2023-12-25 10:56:27,514 INFO misc.py line 119 253097] Train: [39/100][499/510] Data 0.007 (0.111) Batch 1.107 (1.555) Remain 13:26:25 loss: 0.2600 Lr: 0.00430
[2023-12-25 10:56:28,630 INFO misc.py line 119 253097] Train: [39/100][500/510] Data 0.009 (0.111) Batch 1.121 (1.554) Remain 13:25:56 loss: 0.2489 Lr: 0.00430
[2023-12-25 10:56:29,733 INFO misc.py line 119 253097] Train: [39/100][501/510] Data 0.004 (0.111) Batch 1.103 (1.553) Remain 13:25:26 loss: 0.6402 Lr: 0.00430
[2023-12-25 10:56:30,704 INFO misc.py line 119 253097] Train: [39/100][502/510] Data 0.004 (0.111) Batch 0.972 (1.552) Remain 13:24:48 loss: 0.2936 Lr: 0.00430
[2023-12-25 10:56:31,718 INFO misc.py line 119 253097] Train: [39/100][503/510] Data 0.003 (0.110) Batch 1.013 (1.551) Remain 13:24:13 loss: 0.3899 Lr: 0.00430
[2023-12-25 10:56:32,558 INFO misc.py line 119 253097] Train: [39/100][504/510] Data 0.005 (0.110) Batch 0.840 (1.549) Remain 13:23:28 loss: 0.2761 Lr: 0.00430
[2023-12-25 10:56:33,540 INFO misc.py line 119 253097] Train: [39/100][505/510] Data 0.005 (0.110) Batch 0.983 (1.548) Remain 13:22:51 loss: 0.1568 Lr: 0.00430
[2023-12-25 10:56:34,680 INFO misc.py line 119 253097] Train: [39/100][506/510] Data 0.004 (0.110) Batch 1.134 (1.547) Remain 13:22:24 loss: 0.3869 Lr: 0.00430
[2023-12-25 10:56:35,875 INFO misc.py line 119 253097] Train: [39/100][507/510] Data 0.009 (0.110) Batch 1.202 (1.547) Remain 13:22:01 loss: 0.2360 Lr: 0.00430
[2023-12-25 10:56:39,078 INFO misc.py line 119 253097] Train: [39/100][508/510] Data 0.003 (0.109) Batch 3.201 (1.550) Remain 13:23:41 loss: 0.2538 Lr: 0.00430
[2023-12-25 10:56:40,208 INFO misc.py line 119 253097] Train: [39/100][509/510] Data 0.005 (0.109) Batch 1.131 (1.549) Remain 13:23:14 loss: 0.2072 Lr: 0.00430
[2023-12-25 10:56:41,238 INFO misc.py line 119 253097] Train: [39/100][510/510] Data 0.003 (0.109) Batch 1.029 (1.548) Remain 13:22:41 loss: 0.2066 Lr: 0.00430
[2023-12-25 10:56:41,239 INFO misc.py line 136 253097] Train result: loss: 0.2178 
[2023-12-25 10:56:41,240 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 10:57:08,688 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6926 
[2023-12-25 10:57:09,048 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4504 
[2023-12-25 10:57:13,987 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5934 
[2023-12-25 10:57:14,513 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4059 
[2023-12-25 10:57:16,492 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8650 
[2023-12-25 10:57:16,917 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6861 
[2023-12-25 10:57:17,798 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3948 
[2023-12-25 10:57:18,351 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.6942 
[2023-12-25 10:57:20,156 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9379 
[2023-12-25 10:57:22,283 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.6219 
[2023-12-25 10:57:23,140 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3905 
[2023-12-25 10:57:23,565 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9429 
[2023-12-25 10:57:24,473 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.7316 
[2023-12-25 10:57:27,422 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8093 
[2023-12-25 10:57:27,894 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4407 
[2023-12-25 10:57:28,505 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5034 
[2023-12-25 10:57:29,205 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.5772 
[2023-12-25 10:57:30,568 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6314/0.7243/0.8874.
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9144/0.9522
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9818/0.9888
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8285/0.9615
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.1852/0.1958
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6317/0.6980
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.4908/0.5189
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8090/0.9175
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8956/0.9672
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5432/0.7783
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7469/0.8682
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6430/0.9229
[2023-12-25 10:57:30,569 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5380/0.6469
[2023-12-25 10:57:30,570 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 10:57:30,571 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 10:57:30,571 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 10:57:37,682 INFO misc.py line 119 253097] Train: [40/100][1/510] Data 4.215 (4.215) Batch 5.221 (5.221) Remain 45:06:45 loss: 0.1757 Lr: 0.00430
[2023-12-25 10:57:39,413 INFO misc.py line 119 253097] Train: [40/100][2/510] Data 0.003 (0.003) Batch 1.731 (1.731) Remain 14:57:16 loss: 0.1774 Lr: 0.00430
[2023-12-25 10:57:43,888 INFO misc.py line 119 253097] Train: [40/100][3/510] Data 0.005 (0.005) Batch 4.475 (4.475) Remain 38:39:55 loss: 0.2108 Lr: 0.00430
[2023-12-25 10:57:46,480 INFO misc.py line 119 253097] Train: [40/100][4/510] Data 1.408 (1.408) Batch 2.591 (2.591) Remain 22:23:28 loss: 0.3937 Lr: 0.00430
[2023-12-25 10:57:47,533 INFO misc.py line 119 253097] Train: [40/100][5/510] Data 0.004 (0.706) Batch 1.053 (1.822) Remain 15:44:44 loss: 0.2962 Lr: 0.00430
[2023-12-25 10:57:48,573 INFO misc.py line 119 253097] Train: [40/100][6/510] Data 0.004 (0.472) Batch 1.034 (1.560) Remain 13:28:30 loss: 0.1039 Lr: 0.00430
[2023-12-25 10:57:49,606 INFO misc.py line 119 253097] Train: [40/100][7/510] Data 0.012 (0.357) Batch 1.038 (1.429) Remain 12:20:54 loss: 0.2625 Lr: 0.00430
[2023-12-25 10:57:50,875 INFO misc.py line 119 253097] Train: [40/100][8/510] Data 0.005 (0.287) Batch 1.270 (1.397) Remain 12:04:23 loss: 0.2795 Lr: 0.00430
[2023-12-25 10:57:51,889 INFO misc.py line 119 253097] Train: [40/100][9/510] Data 0.003 (0.239) Batch 1.010 (1.333) Remain 11:30:53 loss: 0.1834 Lr: 0.00429
[2023-12-25 10:57:52,763 INFO misc.py line 119 253097] Train: [40/100][10/510] Data 0.007 (0.206) Batch 0.877 (1.268) Remain 10:57:08 loss: 0.4601 Lr: 0.00429
[2023-12-25 10:57:53,985 INFO misc.py line 119 253097] Train: [40/100][11/510] Data 0.004 (0.181) Batch 1.222 (1.262) Remain 10:54:09 loss: 0.0898 Lr: 0.00429
[2023-12-25 10:57:55,187 INFO misc.py line 119 253097] Train: [40/100][12/510] Data 0.005 (0.161) Batch 1.200 (1.255) Remain 10:50:34 loss: 0.1620 Lr: 0.00429
[2023-12-25 10:57:56,440 INFO misc.py line 119 253097] Train: [40/100][13/510] Data 0.006 (0.146) Batch 1.253 (1.255) Remain 10:50:26 loss: 0.2476 Lr: 0.00429
[2023-12-25 10:57:57,602 INFO misc.py line 119 253097] Train: [40/100][14/510] Data 0.006 (0.133) Batch 1.162 (1.247) Remain 10:46:03 loss: 0.2247 Lr: 0.00429
[2023-12-25 10:57:58,917 INFO misc.py line 119 253097] Train: [40/100][15/510] Data 0.006 (0.123) Batch 1.314 (1.252) Remain 10:48:56 loss: 0.0920 Lr: 0.00429
[2023-12-25 10:58:00,215 INFO misc.py line 119 253097] Train: [40/100][16/510] Data 0.007 (0.114) Batch 1.300 (1.256) Remain 10:50:48 loss: 0.2303 Lr: 0.00429
[2023-12-25 10:58:01,459 INFO misc.py line 119 253097] Train: [40/100][17/510] Data 0.005 (0.106) Batch 1.245 (1.255) Remain 10:50:23 loss: 0.1369 Lr: 0.00429
[2023-12-25 10:58:13,124 INFO misc.py line 119 253097] Train: [40/100][18/510] Data 10.324 (0.787) Batch 11.666 (1.949) Remain 16:50:00 loss: 0.1565 Lr: 0.00429
[2023-12-25 10:58:14,222 INFO misc.py line 119 253097] Train: [40/100][19/510] Data 0.004 (0.738) Batch 1.097 (1.896) Remain 16:22:23 loss: 0.1321 Lr: 0.00429
[2023-12-25 10:58:15,259 INFO misc.py line 119 253097] Train: [40/100][20/510] Data 0.004 (0.695) Batch 1.032 (1.845) Remain 15:56:02 loss: 0.1802 Lr: 0.00429
[2023-12-25 10:58:16,425 INFO misc.py line 119 253097] Train: [40/100][21/510] Data 0.009 (0.657) Batch 1.171 (1.808) Remain 15:36:35 loss: 0.2270 Lr: 0.00429
[2023-12-25 10:58:17,490 INFO misc.py line 119 253097] Train: [40/100][22/510] Data 0.005 (0.623) Batch 1.061 (1.768) Remain 15:16:12 loss: 0.1675 Lr: 0.00429
[2023-12-25 10:58:18,714 INFO misc.py line 119 253097] Train: [40/100][23/510] Data 0.008 (0.592) Batch 1.222 (1.741) Remain 15:02:01 loss: 0.1830 Lr: 0.00429
[2023-12-25 10:58:19,944 INFO misc.py line 119 253097] Train: [40/100][24/510] Data 0.010 (0.564) Batch 1.236 (1.717) Remain 14:49:32 loss: 0.1484 Lr: 0.00429
[2023-12-25 10:58:21,253 INFO misc.py line 119 253097] Train: [40/100][25/510] Data 0.004 (0.539) Batch 1.309 (1.698) Remain 14:39:54 loss: 0.1793 Lr: 0.00429
[2023-12-25 10:58:22,446 INFO misc.py line 119 253097] Train: [40/100][26/510] Data 0.005 (0.516) Batch 1.191 (1.676) Remain 14:28:26 loss: 0.2086 Lr: 0.00429
[2023-12-25 10:58:23,636 INFO misc.py line 119 253097] Train: [40/100][27/510] Data 0.007 (0.494) Batch 1.189 (1.656) Remain 14:17:54 loss: 0.2193 Lr: 0.00429
[2023-12-25 10:58:24,982 INFO misc.py line 119 253097] Train: [40/100][28/510] Data 0.007 (0.475) Batch 1.350 (1.644) Remain 14:11:32 loss: 0.1893 Lr: 0.00429
[2023-12-25 10:58:26,136 INFO misc.py line 119 253097] Train: [40/100][29/510] Data 0.003 (0.457) Batch 1.148 (1.625) Remain 14:01:38 loss: 0.2613 Lr: 0.00429
[2023-12-25 10:58:27,228 INFO misc.py line 119 253097] Train: [40/100][30/510] Data 0.008 (0.440) Batch 1.097 (1.605) Remain 13:51:29 loss: 0.1619 Lr: 0.00429
[2023-12-25 10:58:28,530 INFO misc.py line 119 253097] Train: [40/100][31/510] Data 0.004 (0.424) Batch 1.302 (1.594) Remain 13:45:51 loss: 0.1165 Lr: 0.00429
[2023-12-25 10:58:29,676 INFO misc.py line 119 253097] Train: [40/100][32/510] Data 0.003 (0.410) Batch 1.137 (1.579) Remain 13:37:39 loss: 0.2016 Lr: 0.00429
[2023-12-25 10:58:30,840 INFO misc.py line 119 253097] Train: [40/100][33/510] Data 0.012 (0.397) Batch 1.173 (1.565) Remain 13:30:37 loss: 0.2488 Lr: 0.00429
[2023-12-25 10:58:36,987 INFO misc.py line 119 253097] Train: [40/100][34/510] Data 5.283 (0.554) Batch 6.146 (1.713) Remain 14:47:08 loss: 0.1656 Lr: 0.00429
[2023-12-25 10:58:38,132 INFO misc.py line 119 253097] Train: [40/100][35/510] Data 0.004 (0.537) Batch 1.145 (1.695) Remain 14:37:55 loss: 0.1161 Lr: 0.00429
[2023-12-25 10:58:39,371 INFO misc.py line 119 253097] Train: [40/100][36/510] Data 0.004 (0.521) Batch 1.240 (1.681) Remain 14:30:45 loss: 0.1041 Lr: 0.00429
[2023-12-25 10:58:40,490 INFO misc.py line 119 253097] Train: [40/100][37/510] Data 0.003 (0.506) Batch 1.114 (1.665) Remain 14:22:05 loss: 0.2676 Lr: 0.00429
[2023-12-25 10:58:41,670 INFO misc.py line 119 253097] Train: [40/100][38/510] Data 0.008 (0.492) Batch 1.180 (1.651) Remain 14:14:53 loss: 0.1928 Lr: 0.00429
[2023-12-25 10:58:46,049 INFO misc.py line 119 253097] Train: [40/100][39/510] Data 0.009 (0.478) Batch 4.383 (1.727) Remain 14:54:09 loss: 0.3565 Lr: 0.00429
[2023-12-25 10:58:47,167 INFO misc.py line 119 253097] Train: [40/100][40/510] Data 0.004 (0.465) Batch 1.119 (1.710) Remain 14:45:37 loss: 0.2743 Lr: 0.00429
[2023-12-25 10:58:48,125 INFO misc.py line 119 253097] Train: [40/100][41/510] Data 0.003 (0.453) Batch 0.958 (1.690) Remain 14:35:20 loss: 0.2525 Lr: 0.00429
[2023-12-25 10:58:49,114 INFO misc.py line 119 253097] Train: [40/100][42/510] Data 0.004 (0.442) Batch 0.986 (1.672) Remain 14:25:57 loss: 0.1855 Lr: 0.00429
[2023-12-25 10:58:50,128 INFO misc.py line 119 253097] Train: [40/100][43/510] Data 0.007 (0.431) Batch 1.013 (1.656) Remain 14:17:23 loss: 0.2572 Lr: 0.00429
[2023-12-25 10:58:51,119 INFO misc.py line 119 253097] Train: [40/100][44/510] Data 0.009 (0.420) Batch 0.995 (1.640) Remain 14:09:00 loss: 0.1885 Lr: 0.00429
[2023-12-25 10:58:53,469 INFO misc.py line 119 253097] Train: [40/100][45/510] Data 1.110 (0.437) Batch 2.350 (1.657) Remain 14:17:44 loss: 0.2938 Lr: 0.00429
[2023-12-25 10:58:54,476 INFO misc.py line 119 253097] Train: [40/100][46/510] Data 0.004 (0.427) Batch 1.007 (1.642) Remain 14:09:53 loss: 0.1580 Lr: 0.00429
[2023-12-25 10:58:55,583 INFO misc.py line 119 253097] Train: [40/100][47/510] Data 0.004 (0.417) Batch 1.106 (1.629) Remain 14:03:34 loss: 0.1974 Lr: 0.00429
[2023-12-25 10:58:56,836 INFO misc.py line 119 253097] Train: [40/100][48/510] Data 0.005 (0.408) Batch 1.249 (1.621) Remain 13:59:10 loss: 0.1700 Lr: 0.00429
[2023-12-25 10:58:57,859 INFO misc.py line 119 253097] Train: [40/100][49/510] Data 0.008 (0.399) Batch 1.027 (1.608) Remain 13:52:27 loss: 0.1865 Lr: 0.00429
[2023-12-25 10:58:59,017 INFO misc.py line 119 253097] Train: [40/100][50/510] Data 0.004 (0.391) Batch 1.154 (1.598) Remain 13:47:25 loss: 0.3951 Lr: 0.00429
[2023-12-25 10:59:00,114 INFO misc.py line 119 253097] Train: [40/100][51/510] Data 0.009 (0.383) Batch 1.084 (1.588) Remain 13:41:51 loss: 0.1028 Lr: 0.00429
[2023-12-25 10:59:01,023 INFO misc.py line 119 253097] Train: [40/100][52/510] Data 0.022 (0.376) Batch 0.927 (1.574) Remain 13:34:50 loss: 0.2031 Lr: 0.00429
[2023-12-25 10:59:02,086 INFO misc.py line 119 253097] Train: [40/100][53/510] Data 0.004 (0.368) Batch 1.063 (1.564) Remain 13:29:32 loss: 0.1591 Lr: 0.00429
[2023-12-25 10:59:03,080 INFO misc.py line 119 253097] Train: [40/100][54/510] Data 0.003 (0.361) Batch 0.993 (1.553) Remain 13:23:42 loss: 0.1761 Lr: 0.00429
[2023-12-25 10:59:04,344 INFO misc.py line 119 253097] Train: [40/100][55/510] Data 0.005 (0.354) Batch 1.263 (1.547) Remain 13:20:48 loss: 0.3182 Lr: 0.00429
[2023-12-25 10:59:05,504 INFO misc.py line 119 253097] Train: [40/100][56/510] Data 0.005 (0.348) Batch 1.161 (1.540) Remain 13:17:00 loss: 0.2308 Lr: 0.00429
[2023-12-25 10:59:06,777 INFO misc.py line 119 253097] Train: [40/100][57/510] Data 0.005 (0.341) Batch 1.272 (1.535) Remain 13:14:24 loss: 0.1729 Lr: 0.00429
[2023-12-25 10:59:07,960 INFO misc.py line 119 253097] Train: [40/100][58/510] Data 0.006 (0.335) Batch 1.185 (1.529) Remain 13:11:05 loss: 0.1567 Lr: 0.00429
[2023-12-25 10:59:20,340 INFO misc.py line 119 253097] Train: [40/100][59/510] Data 0.004 (0.329) Batch 12.377 (1.722) Remain 14:51:19 loss: 0.3448 Lr: 0.00429
[2023-12-25 10:59:21,540 INFO misc.py line 119 253097] Train: [40/100][60/510] Data 0.007 (0.324) Batch 1.202 (1.713) Remain 14:46:34 loss: 0.1991 Lr: 0.00429
[2023-12-25 10:59:22,669 INFO misc.py line 119 253097] Train: [40/100][61/510] Data 0.004 (0.318) Batch 1.128 (1.703) Remain 14:41:19 loss: 0.2955 Lr: 0.00429
[2023-12-25 10:59:23,853 INFO misc.py line 119 253097] Train: [40/100][62/510] Data 0.005 (0.313) Batch 1.185 (1.694) Remain 14:36:45 loss: 0.1489 Lr: 0.00429
[2023-12-25 10:59:25,118 INFO misc.py line 119 253097] Train: [40/100][63/510] Data 0.004 (0.308) Batch 1.225 (1.687) Remain 14:32:41 loss: 0.3155 Lr: 0.00429
[2023-12-25 10:59:26,437 INFO misc.py line 119 253097] Train: [40/100][64/510] Data 0.043 (0.303) Batch 1.354 (1.681) Remain 14:29:50 loss: 0.2850 Lr: 0.00429
[2023-12-25 10:59:30,349 INFO misc.py line 119 253097] Train: [40/100][65/510] Data 0.009 (0.299) Batch 3.916 (1.717) Remain 14:48:27 loss: 0.2358 Lr: 0.00429
[2023-12-25 10:59:31,562 INFO misc.py line 119 253097] Train: [40/100][66/510] Data 0.004 (0.294) Batch 1.213 (1.709) Remain 14:44:17 loss: 0.1635 Lr: 0.00428
[2023-12-25 10:59:32,726 INFO misc.py line 119 253097] Train: [40/100][67/510] Data 0.005 (0.289) Batch 1.163 (1.701) Remain 14:39:50 loss: 0.3091 Lr: 0.00428
[2023-12-25 10:59:33,728 INFO misc.py line 119 253097] Train: [40/100][68/510] Data 0.005 (0.285) Batch 1.002 (1.690) Remain 14:34:15 loss: 0.1183 Lr: 0.00428
[2023-12-25 10:59:34,797 INFO misc.py line 119 253097] Train: [40/100][69/510] Data 0.005 (0.281) Batch 1.070 (1.680) Remain 14:29:22 loss: 0.5394 Lr: 0.00428
[2023-12-25 10:59:36,012 INFO misc.py line 119 253097] Train: [40/100][70/510] Data 0.004 (0.277) Batch 1.215 (1.673) Remain 14:25:45 loss: 0.2348 Lr: 0.00428
[2023-12-25 10:59:38,651 INFO misc.py line 119 253097] Train: [40/100][71/510] Data 0.002 (0.273) Batch 2.639 (1.688) Remain 14:33:04 loss: 0.2451 Lr: 0.00428
[2023-12-25 10:59:39,672 INFO misc.py line 119 253097] Train: [40/100][72/510] Data 0.004 (0.269) Batch 1.021 (1.678) Remain 14:28:02 loss: 0.3095 Lr: 0.00428
[2023-12-25 10:59:40,696 INFO misc.py line 119 253097] Train: [40/100][73/510] Data 0.003 (0.265) Batch 1.021 (1.669) Remain 14:23:09 loss: 0.1061 Lr: 0.00428
[2023-12-25 10:59:41,690 INFO misc.py line 119 253097] Train: [40/100][74/510] Data 0.006 (0.261) Batch 0.996 (1.659) Remain 14:18:14 loss: 0.2621 Lr: 0.00428
[2023-12-25 10:59:42,794 INFO misc.py line 119 253097] Train: [40/100][75/510] Data 0.005 (0.258) Batch 1.103 (1.651) Remain 14:14:13 loss: 0.1411 Lr: 0.00428
[2023-12-25 10:59:44,003 INFO misc.py line 119 253097] Train: [40/100][76/510] Data 0.004 (0.254) Batch 1.210 (1.645) Remain 14:11:03 loss: 0.1819 Lr: 0.00428
[2023-12-25 10:59:45,034 INFO misc.py line 119 253097] Train: [40/100][77/510] Data 0.004 (0.251) Batch 1.031 (1.637) Remain 14:06:44 loss: 0.1104 Lr: 0.00428
[2023-12-25 10:59:46,244 INFO misc.py line 119 253097] Train: [40/100][78/510] Data 0.004 (0.248) Batch 1.210 (1.631) Remain 14:03:46 loss: 0.1640 Lr: 0.00428
[2023-12-25 10:59:47,484 INFO misc.py line 119 253097] Train: [40/100][79/510] Data 0.003 (0.244) Batch 1.236 (1.626) Remain 14:01:02 loss: 0.2589 Lr: 0.00428
[2023-12-25 10:59:48,703 INFO misc.py line 119 253097] Train: [40/100][80/510] Data 0.007 (0.241) Batch 1.223 (1.621) Remain 13:58:18 loss: 0.2482 Lr: 0.00428
[2023-12-25 10:59:49,757 INFO misc.py line 119 253097] Train: [40/100][81/510] Data 0.004 (0.238) Batch 1.049 (1.614) Remain 13:54:29 loss: 0.2290 Lr: 0.00428
[2023-12-25 10:59:50,977 INFO misc.py line 119 253097] Train: [40/100][82/510] Data 0.009 (0.235) Batch 1.218 (1.609) Remain 13:51:52 loss: 0.1563 Lr: 0.00428
[2023-12-25 10:59:52,165 INFO misc.py line 119 253097] Train: [40/100][83/510] Data 0.013 (0.233) Batch 1.195 (1.603) Remain 13:49:10 loss: 0.1888 Lr: 0.00428
[2023-12-25 10:59:53,199 INFO misc.py line 119 253097] Train: [40/100][84/510] Data 0.004 (0.230) Batch 1.033 (1.596) Remain 13:45:30 loss: 0.2760 Lr: 0.00428
[2023-12-25 10:59:54,216 INFO misc.py line 119 253097] Train: [40/100][85/510] Data 0.006 (0.227) Batch 1.016 (1.589) Remain 13:41:49 loss: 0.2164 Lr: 0.00428
[2023-12-25 11:00:04,557 INFO misc.py line 119 253097] Train: [40/100][86/510] Data 0.006 (0.224) Batch 10.344 (1.695) Remain 14:36:19 loss: 0.1171 Lr: 0.00428
[2023-12-25 11:00:05,652 INFO misc.py line 119 253097] Train: [40/100][87/510] Data 0.004 (0.222) Batch 1.093 (1.688) Remain 14:32:35 loss: 0.4118 Lr: 0.00428
[2023-12-25 11:00:06,822 INFO misc.py line 119 253097] Train: [40/100][88/510] Data 0.006 (0.219) Batch 1.171 (1.682) Remain 14:29:25 loss: 0.2075 Lr: 0.00428
[2023-12-25 11:00:07,759 INFO misc.py line 119 253097] Train: [40/100][89/510] Data 0.004 (0.217) Batch 0.938 (1.673) Remain 14:24:55 loss: 0.2166 Lr: 0.00428
[2023-12-25 11:00:08,883 INFO misc.py line 119 253097] Train: [40/100][90/510] Data 0.004 (0.214) Batch 1.124 (1.667) Remain 14:21:38 loss: 0.2993 Lr: 0.00428
[2023-12-25 11:00:10,169 INFO misc.py line 119 253097] Train: [40/100][91/510] Data 0.003 (0.212) Batch 1.285 (1.662) Remain 14:19:22 loss: 0.1037 Lr: 0.00428
[2023-12-25 11:00:11,124 INFO misc.py line 119 253097] Train: [40/100][92/510] Data 0.005 (0.209) Batch 0.954 (1.654) Remain 14:15:13 loss: 0.0675 Lr: 0.00428
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[2023-12-25 11:00:13,319 INFO misc.py line 119 253097] Train: [40/100][94/510] Data 0.003 (0.205) Batch 1.063 (1.642) Remain 14:08:51 loss: 0.2467 Lr: 0.00428
[2023-12-25 11:00:14,511 INFO misc.py line 119 253097] Train: [40/100][95/510] Data 0.005 (0.203) Batch 1.188 (1.637) Remain 14:06:16 loss: 0.2060 Lr: 0.00428
[2023-12-25 11:00:15,747 INFO misc.py line 119 253097] Train: [40/100][96/510] Data 0.009 (0.201) Batch 1.239 (1.633) Remain 14:04:02 loss: 0.0833 Lr: 0.00428
[2023-12-25 11:00:16,954 INFO misc.py line 119 253097] Train: [40/100][97/510] Data 0.006 (0.199) Batch 1.205 (1.628) Remain 14:01:39 loss: 0.1119 Lr: 0.00428
[2023-12-25 11:00:18,222 INFO misc.py line 119 253097] Train: [40/100][98/510] Data 0.007 (0.197) Batch 1.258 (1.624) Remain 13:59:36 loss: 0.1714 Lr: 0.00428
[2023-12-25 11:00:19,347 INFO misc.py line 119 253097] Train: [40/100][99/510] Data 0.017 (0.195) Batch 1.139 (1.619) Remain 13:56:58 loss: 0.3188 Lr: 0.00428
[2023-12-25 11:00:20,315 INFO misc.py line 119 253097] Train: [40/100][100/510] Data 0.003 (0.193) Batch 0.967 (1.613) Remain 13:53:28 loss: 0.1556 Lr: 0.00428
[2023-12-25 11:00:21,412 INFO misc.py line 119 253097] Train: [40/100][101/510] Data 0.005 (0.191) Batch 1.096 (1.607) Remain 13:50:43 loss: 0.2000 Lr: 0.00428
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[2023-12-25 11:00:45,259 INFO misc.py line 119 253097] Train: [40/100][119/510] Data 0.004 (0.162) Batch 1.081 (1.564) Remain 13:27:35 loss: 0.1247 Lr: 0.00428
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[2023-12-25 11:01:03,097 INFO misc.py line 119 253097] Train: [40/100][128/510] Data 0.005 (0.151) Batch 1.095 (1.594) Remain 13:42:54 loss: 0.2527 Lr: 0.00427
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[2023-12-25 11:01:06,337 INFO misc.py line 119 253097] Train: [40/100][131/510] Data 0.019 (0.147) Batch 1.211 (1.582) Remain 13:36:36 loss: 0.1857 Lr: 0.00427
[2023-12-25 11:01:07,206 INFO misc.py line 119 253097] Train: [40/100][132/510] Data 0.009 (0.146) Batch 0.873 (1.576) Remain 13:33:44 loss: 0.2751 Lr: 0.00427
[2023-12-25 11:01:08,397 INFO misc.py line 119 253097] Train: [40/100][133/510] Data 0.005 (0.145) Batch 1.191 (1.573) Remain 13:32:10 loss: 0.1753 Lr: 0.00427
[2023-12-25 11:01:09,636 INFO misc.py line 119 253097] Train: [40/100][134/510] Data 0.006 (0.144) Batch 1.241 (1.571) Remain 13:30:50 loss: 0.1492 Lr: 0.00427
[2023-12-25 11:01:10,917 INFO misc.py line 119 253097] Train: [40/100][135/510] Data 0.003 (0.143) Batch 1.279 (1.568) Remain 13:29:40 loss: 0.2378 Lr: 0.00427
[2023-12-25 11:01:12,062 INFO misc.py line 119 253097] Train: [40/100][136/510] Data 0.010 (0.142) Batch 1.141 (1.565) Remain 13:27:59 loss: 0.1516 Lr: 0.00427
[2023-12-25 11:01:13,167 INFO misc.py line 119 253097] Train: [40/100][137/510] Data 0.011 (0.141) Batch 1.111 (1.562) Remain 13:26:13 loss: 0.2410 Lr: 0.00427
[2023-12-25 11:01:14,421 INFO misc.py line 119 253097] Train: [40/100][138/510] Data 0.006 (0.140) Batch 1.254 (1.559) Remain 13:25:00 loss: 0.1813 Lr: 0.00427
[2023-12-25 11:01:15,380 INFO misc.py line 119 253097] Train: [40/100][139/510] Data 0.004 (0.139) Batch 0.958 (1.555) Remain 13:22:42 loss: 0.1413 Lr: 0.00427
[2023-12-25 11:01:16,677 INFO misc.py line 119 253097] Train: [40/100][140/510] Data 0.006 (0.138) Batch 1.298 (1.553) Remain 13:21:42 loss: 0.1254 Lr: 0.00427
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[2023-12-25 11:01:19,106 INFO misc.py line 119 253097] Train: [40/100][142/510] Data 0.006 (0.136) Batch 1.291 (1.548) Remain 13:19:07 loss: 0.2453 Lr: 0.00427
[2023-12-25 11:01:20,149 INFO misc.py line 119 253097] Train: [40/100][143/510] Data 0.007 (0.135) Batch 1.017 (1.545) Remain 13:17:08 loss: 0.3199 Lr: 0.00427
[2023-12-25 11:01:21,217 INFO misc.py line 119 253097] Train: [40/100][144/510] Data 0.036 (0.135) Batch 1.098 (1.541) Remain 13:15:29 loss: 0.2341 Lr: 0.00427
[2023-12-25 11:01:22,305 INFO misc.py line 119 253097] Train: [40/100][145/510] Data 0.004 (0.134) Batch 1.086 (1.538) Remain 13:13:48 loss: 0.1373 Lr: 0.00427
[2023-12-25 11:01:23,473 INFO misc.py line 119 253097] Train: [40/100][146/510] Data 0.006 (0.133) Batch 1.165 (1.536) Remain 13:12:25 loss: 0.1834 Lr: 0.00427
[2023-12-25 11:01:29,070 INFO misc.py line 119 253097] Train: [40/100][147/510] Data 4.312 (0.162) Batch 5.602 (1.564) Remain 13:26:58 loss: 0.3331 Lr: 0.00427
[2023-12-25 11:01:30,376 INFO misc.py line 119 253097] Train: [40/100][148/510] Data 0.005 (0.161) Batch 1.306 (1.562) Remain 13:26:02 loss: 0.1391 Lr: 0.00427
[2023-12-25 11:01:31,435 INFO misc.py line 119 253097] Train: [40/100][149/510] Data 0.005 (0.160) Batch 1.058 (1.559) Remain 13:24:13 loss: 0.1866 Lr: 0.00427
[2023-12-25 11:01:32,483 INFO misc.py line 119 253097] Train: [40/100][150/510] Data 0.005 (0.159) Batch 1.047 (1.555) Remain 13:22:24 loss: 0.1856 Lr: 0.00427
[2023-12-25 11:01:33,319 INFO misc.py line 119 253097] Train: [40/100][151/510] Data 0.006 (0.158) Batch 0.837 (1.550) Remain 13:19:52 loss: 0.1948 Lr: 0.00427
[2023-12-25 11:01:35,153 INFO misc.py line 119 253097] Train: [40/100][152/510] Data 0.006 (0.157) Batch 1.803 (1.552) Remain 13:20:43 loss: 0.1819 Lr: 0.00427
[2023-12-25 11:01:36,653 INFO misc.py line 119 253097] Train: [40/100][153/510] Data 0.618 (0.160) Batch 1.532 (1.552) Remain 13:20:38 loss: 0.1551 Lr: 0.00427
[2023-12-25 11:01:37,893 INFO misc.py line 119 253097] Train: [40/100][154/510] Data 0.004 (0.159) Batch 1.236 (1.550) Remain 13:19:31 loss: 0.3329 Lr: 0.00427
[2023-12-25 11:01:38,839 INFO misc.py line 119 253097] Train: [40/100][155/510] Data 0.007 (0.158) Batch 0.948 (1.546) Remain 13:17:27 loss: 0.2025 Lr: 0.00427
[2023-12-25 11:01:40,147 INFO misc.py line 119 253097] Train: [40/100][156/510] Data 0.005 (0.157) Batch 1.286 (1.544) Remain 13:16:33 loss: 0.1910 Lr: 0.00427
[2023-12-25 11:01:41,335 INFO misc.py line 119 253097] Train: [40/100][157/510] Data 0.027 (0.156) Batch 1.206 (1.542) Remain 13:15:24 loss: 0.2411 Lr: 0.00427
[2023-12-25 11:01:42,421 INFO misc.py line 119 253097] Train: [40/100][158/510] Data 0.010 (0.155) Batch 1.091 (1.539) Remain 13:13:52 loss: 0.2155 Lr: 0.00427
[2023-12-25 11:01:43,578 INFO misc.py line 119 253097] Train: [40/100][159/510] Data 0.004 (0.154) Batch 1.156 (1.536) Remain 13:12:35 loss: 0.2041 Lr: 0.00427
[2023-12-25 11:01:44,776 INFO misc.py line 119 253097] Train: [40/100][160/510] Data 0.004 (0.153) Batch 1.197 (1.534) Remain 13:11:26 loss: 0.1494 Lr: 0.00427
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[2023-12-25 11:01:50,171 INFO misc.py line 119 253097] Train: [40/100][162/510] Data 0.006 (0.151) Batch 4.225 (1.549) Remain 13:18:56 loss: 0.1613 Lr: 0.00427
[2023-12-25 11:01:51,351 INFO misc.py line 119 253097] Train: [40/100][163/510] Data 0.004 (0.150) Batch 1.180 (1.547) Remain 13:17:43 loss: 0.1940 Lr: 0.00427
[2023-12-25 11:01:53,845 INFO misc.py line 119 253097] Train: [40/100][164/510] Data 1.820 (0.161) Batch 2.494 (1.553) Remain 13:20:44 loss: 0.1053 Lr: 0.00427
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[2023-12-25 11:01:59,280 INFO misc.py line 119 253097] Train: [40/100][169/510] Data 0.003 (0.156) Batch 1.069 (1.539) Remain 13:13:22 loss: 0.1760 Lr: 0.00427
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[2023-12-25 11:02:02,505 INFO misc.py line 119 253097] Train: [40/100][172/510] Data 0.006 (0.153) Batch 0.990 (1.530) Remain 13:09:03 loss: 0.1580 Lr: 0.00427
[2023-12-25 11:02:03,574 INFO misc.py line 119 253097] Train: [40/100][173/510] Data 0.005 (0.152) Batch 1.070 (1.528) Remain 13:07:38 loss: 0.1945 Lr: 0.00427
[2023-12-25 11:02:04,753 INFO misc.py line 119 253097] Train: [40/100][174/510] Data 0.004 (0.151) Batch 1.179 (1.526) Remain 13:06:33 loss: 0.2349 Lr: 0.00427
[2023-12-25 11:02:05,910 INFO misc.py line 119 253097] Train: [40/100][175/510] Data 0.003 (0.151) Batch 1.156 (1.523) Remain 13:05:25 loss: 0.1332 Lr: 0.00427
[2023-12-25 11:02:07,030 INFO misc.py line 119 253097] Train: [40/100][176/510] Data 0.004 (0.150) Batch 1.121 (1.521) Remain 13:04:12 loss: 0.2361 Lr: 0.00427
[2023-12-25 11:02:08,203 INFO misc.py line 119 253097] Train: [40/100][177/510] Data 0.003 (0.149) Batch 1.173 (1.519) Remain 13:03:08 loss: 0.2183 Lr: 0.00427
[2023-12-25 11:02:09,419 INFO misc.py line 119 253097] Train: [40/100][178/510] Data 0.004 (0.148) Batch 1.215 (1.517) Remain 13:02:13 loss: 0.2506 Lr: 0.00427
[2023-12-25 11:02:10,634 INFO misc.py line 119 253097] Train: [40/100][179/510] Data 0.004 (0.147) Batch 1.215 (1.516) Remain 13:01:18 loss: 0.2034 Lr: 0.00427
[2023-12-25 11:02:11,826 INFO misc.py line 119 253097] Train: [40/100][180/510] Data 0.005 (0.146) Batch 1.193 (1.514) Remain 13:00:20 loss: 0.1553 Lr: 0.00426
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[2023-12-25 11:02:15,374 INFO misc.py line 119 253097] Train: [40/100][183/510] Data 0.006 (0.144) Batch 1.193 (1.508) Remain 12:57:24 loss: 0.2336 Lr: 0.00426
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[2023-12-25 11:02:31,855 INFO misc.py line 119 253097] Train: [40/100][191/510] Data 2.016 (0.158) Batch 2.806 (1.532) Remain 13:09:19 loss: 0.2614 Lr: 0.00426
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[2023-12-25 11:04:22,780 INFO misc.py line 119 253097] Train: [40/100][262/510] Data 0.003 (0.118) Batch 1.258 (1.540) Remain 13:11:49 loss: 0.0999 Lr: 0.00425
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[2023-12-25 11:08:13,336 INFO misc.py line 119 253097] Train: [40/100][416/510] Data 0.008 (0.107) Batch 1.151 (1.524) Remain 12:59:40 loss: 0.1569 Lr: 0.00422
[2023-12-25 11:08:14,525 INFO misc.py line 119 253097] Train: [40/100][417/510] Data 0.004 (0.107) Batch 1.189 (1.523) Remain 12:59:13 loss: 0.1873 Lr: 0.00422
[2023-12-25 11:08:15,679 INFO misc.py line 119 253097] Train: [40/100][418/510] Data 0.005 (0.107) Batch 1.153 (1.522) Remain 12:58:44 loss: 0.2000 Lr: 0.00422
[2023-12-25 11:08:16,754 INFO misc.py line 119 253097] Train: [40/100][419/510] Data 0.005 (0.106) Batch 1.070 (1.521) Remain 12:58:10 loss: 0.4052 Lr: 0.00422
[2023-12-25 11:08:17,903 INFO misc.py line 119 253097] Train: [40/100][420/510] Data 0.010 (0.106) Batch 1.151 (1.520) Remain 12:57:41 loss: 0.1538 Lr: 0.00422
[2023-12-25 11:08:18,983 INFO misc.py line 119 253097] Train: [40/100][421/510] Data 0.008 (0.106) Batch 1.083 (1.519) Remain 12:57:07 loss: 0.3121 Lr: 0.00422
[2023-12-25 11:08:20,003 INFO misc.py line 119 253097] Train: [40/100][422/510] Data 0.005 (0.106) Batch 1.020 (1.518) Remain 12:56:29 loss: 0.2428 Lr: 0.00422
[2023-12-25 11:08:21,063 INFO misc.py line 119 253097] Train: [40/100][423/510] Data 0.005 (0.105) Batch 1.056 (1.517) Remain 12:55:54 loss: 0.0731 Lr: 0.00422
[2023-12-25 11:08:22,267 INFO misc.py line 119 253097] Train: [40/100][424/510] Data 0.008 (0.105) Batch 1.208 (1.516) Remain 12:55:30 loss: 0.2019 Lr: 0.00422
[2023-12-25 11:08:23,385 INFO misc.py line 119 253097] Train: [40/100][425/510] Data 0.004 (0.105) Batch 1.118 (1.515) Remain 12:54:59 loss: 0.2005 Lr: 0.00422
[2023-12-25 11:08:24,702 INFO misc.py line 119 253097] Train: [40/100][426/510] Data 0.004 (0.105) Batch 1.312 (1.515) Remain 12:54:43 loss: 0.2740 Lr: 0.00422
[2023-12-25 11:08:25,879 INFO misc.py line 119 253097] Train: [40/100][427/510] Data 0.011 (0.104) Batch 1.182 (1.514) Remain 12:54:17 loss: 0.1751 Lr: 0.00422
[2023-12-25 11:08:26,990 INFO misc.py line 119 253097] Train: [40/100][428/510] Data 0.006 (0.104) Batch 1.111 (1.513) Remain 12:53:47 loss: 0.2069 Lr: 0.00422
[2023-12-25 11:08:28,175 INFO misc.py line 119 253097] Train: [40/100][429/510] Data 0.004 (0.104) Batch 1.181 (1.512) Remain 12:53:21 loss: 0.3375 Lr: 0.00422
[2023-12-25 11:08:29,349 INFO misc.py line 119 253097] Train: [40/100][430/510] Data 0.009 (0.104) Batch 1.178 (1.512) Remain 12:52:56 loss: 0.3101 Lr: 0.00422
[2023-12-25 11:08:30,638 INFO misc.py line 119 253097] Train: [40/100][431/510] Data 0.005 (0.104) Batch 1.286 (1.511) Remain 12:52:38 loss: 0.2448 Lr: 0.00422
[2023-12-25 11:08:31,883 INFO misc.py line 119 253097] Train: [40/100][432/510] Data 0.009 (0.103) Batch 1.245 (1.510) Remain 12:52:18 loss: 0.1441 Lr: 0.00422
[2023-12-25 11:08:33,073 INFO misc.py line 119 253097] Train: [40/100][433/510] Data 0.009 (0.103) Batch 1.154 (1.510) Remain 12:51:51 loss: 0.1372 Lr: 0.00422
[2023-12-25 11:08:34,009 INFO misc.py line 119 253097] Train: [40/100][434/510] Data 0.044 (0.103) Batch 0.976 (1.508) Remain 12:51:11 loss: 0.2862 Lr: 0.00422
[2023-12-25 11:08:35,128 INFO misc.py line 119 253097] Train: [40/100][435/510] Data 0.004 (0.103) Batch 1.119 (1.507) Remain 12:50:42 loss: 0.1033 Lr: 0.00422
[2023-12-25 11:08:43,120 INFO misc.py line 119 253097] Train: [40/100][436/510] Data 0.004 (0.102) Batch 7.992 (1.522) Remain 12:58:20 loss: 0.2080 Lr: 0.00422
[2023-12-25 11:08:44,373 INFO misc.py line 119 253097] Train: [40/100][437/510] Data 0.004 (0.102) Batch 1.252 (1.522) Remain 12:57:59 loss: 0.2937 Lr: 0.00422
[2023-12-25 11:08:45,591 INFO misc.py line 119 253097] Train: [40/100][438/510] Data 0.005 (0.102) Batch 1.219 (1.521) Remain 12:57:36 loss: 0.3505 Lr: 0.00422
[2023-12-25 11:08:46,689 INFO misc.py line 119 253097] Train: [40/100][439/510] Data 0.003 (0.102) Batch 1.097 (1.520) Remain 12:57:05 loss: 0.1519 Lr: 0.00422
[2023-12-25 11:08:47,992 INFO misc.py line 119 253097] Train: [40/100][440/510] Data 0.004 (0.102) Batch 1.302 (1.520) Remain 12:56:48 loss: 0.1363 Lr: 0.00422
[2023-12-25 11:08:49,298 INFO misc.py line 119 253097] Train: [40/100][441/510] Data 0.005 (0.101) Batch 1.302 (1.519) Remain 12:56:31 loss: 0.1573 Lr: 0.00422
[2023-12-25 11:08:50,562 INFO misc.py line 119 253097] Train: [40/100][442/510] Data 0.010 (0.101) Batch 1.266 (1.519) Remain 12:56:12 loss: 0.1121 Lr: 0.00422
[2023-12-25 11:08:51,730 INFO misc.py line 119 253097] Train: [40/100][443/510] Data 0.008 (0.101) Batch 1.171 (1.518) Remain 12:55:46 loss: 0.3026 Lr: 0.00422
[2023-12-25 11:08:53,411 INFO misc.py line 119 253097] Train: [40/100][444/510] Data 0.005 (0.101) Batch 1.678 (1.518) Remain 12:55:56 loss: 0.1432 Lr: 0.00422
[2023-12-25 11:08:54,599 INFO misc.py line 119 253097] Train: [40/100][445/510] Data 0.007 (0.101) Batch 1.190 (1.517) Remain 12:55:32 loss: 0.1490 Lr: 0.00422
[2023-12-25 11:08:55,690 INFO misc.py line 119 253097] Train: [40/100][446/510] Data 0.005 (0.100) Batch 1.092 (1.516) Remain 12:55:01 loss: 0.3814 Lr: 0.00422
[2023-12-25 11:08:56,921 INFO misc.py line 119 253097] Train: [40/100][447/510] Data 0.004 (0.100) Batch 1.231 (1.516) Remain 12:54:40 loss: 0.1939 Lr: 0.00422
[2023-12-25 11:08:57,962 INFO misc.py line 119 253097] Train: [40/100][448/510] Data 0.005 (0.100) Batch 1.035 (1.515) Remain 12:54:05 loss: 0.1626 Lr: 0.00422
[2023-12-25 11:08:59,107 INFO misc.py line 119 253097] Train: [40/100][449/510] Data 0.011 (0.100) Batch 1.147 (1.514) Remain 12:53:38 loss: 0.2214 Lr: 0.00422
[2023-12-25 11:09:00,321 INFO misc.py line 119 253097] Train: [40/100][450/510] Data 0.009 (0.099) Batch 1.213 (1.513) Remain 12:53:16 loss: 0.0822 Lr: 0.00422
[2023-12-25 11:09:01,221 INFO misc.py line 119 253097] Train: [40/100][451/510] Data 0.010 (0.099) Batch 0.902 (1.512) Remain 12:52:33 loss: 0.2419 Lr: 0.00422
[2023-12-25 11:09:02,391 INFO misc.py line 119 253097] Train: [40/100][452/510] Data 0.008 (0.099) Batch 1.174 (1.511) Remain 12:52:08 loss: 0.2348 Lr: 0.00422
[2023-12-25 11:09:03,307 INFO misc.py line 119 253097] Train: [40/100][453/510] Data 0.004 (0.099) Batch 0.914 (1.510) Remain 12:51:26 loss: 0.2368 Lr: 0.00422
[2023-12-25 11:09:04,477 INFO misc.py line 119 253097] Train: [40/100][454/510] Data 0.006 (0.099) Batch 1.172 (1.509) Remain 12:51:01 loss: 0.1799 Lr: 0.00422
[2023-12-25 11:09:05,518 INFO misc.py line 119 253097] Train: [40/100][455/510] Data 0.005 (0.098) Batch 1.041 (1.508) Remain 12:50:28 loss: 0.3266 Lr: 0.00422
[2023-12-25 11:09:06,809 INFO misc.py line 119 253097] Train: [40/100][456/510] Data 0.003 (0.098) Batch 1.291 (1.508) Remain 12:50:12 loss: 0.1326 Lr: 0.00422
[2023-12-25 11:09:08,028 INFO misc.py line 119 253097] Train: [40/100][457/510] Data 0.004 (0.098) Batch 1.219 (1.507) Remain 12:49:51 loss: 0.1498 Lr: 0.00422
[2023-12-25 11:09:09,320 INFO misc.py line 119 253097] Train: [40/100][458/510] Data 0.004 (0.098) Batch 1.288 (1.506) Remain 12:49:35 loss: 0.1362 Lr: 0.00422
[2023-12-25 11:09:10,279 INFO misc.py line 119 253097] Train: [40/100][459/510] Data 0.009 (0.098) Batch 0.963 (1.505) Remain 12:48:57 loss: 0.3765 Lr: 0.00422
[2023-12-25 11:09:11,410 INFO misc.py line 119 253097] Train: [40/100][460/510] Data 0.003 (0.097) Batch 1.130 (1.504) Remain 12:48:30 loss: 0.1060 Lr: 0.00422
[2023-12-25 11:09:12,540 INFO misc.py line 119 253097] Train: [40/100][461/510] Data 0.004 (0.097) Batch 1.131 (1.504) Remain 12:48:04 loss: 0.3055 Lr: 0.00422
[2023-12-25 11:09:13,874 INFO misc.py line 119 253097] Train: [40/100][462/510] Data 0.003 (0.097) Batch 1.333 (1.503) Remain 12:47:51 loss: 0.1914 Lr: 0.00421
[2023-12-25 11:09:14,821 INFO misc.py line 119 253097] Train: [40/100][463/510] Data 0.004 (0.097) Batch 0.948 (1.502) Remain 12:47:12 loss: 0.1590 Lr: 0.00421
[2023-12-25 11:09:15,741 INFO misc.py line 119 253097] Train: [40/100][464/510] Data 0.004 (0.097) Batch 0.920 (1.501) Remain 12:46:32 loss: 0.1514 Lr: 0.00421
[2023-12-25 11:09:17,067 INFO misc.py line 119 253097] Train: [40/100][465/510] Data 0.003 (0.096) Batch 1.313 (1.500) Remain 12:46:18 loss: 0.1143 Lr: 0.00421
[2023-12-25 11:09:25,786 INFO misc.py line 119 253097] Train: [40/100][466/510] Data 0.016 (0.096) Batch 8.729 (1.516) Remain 12:54:15 loss: 0.2435 Lr: 0.00421
[2023-12-25 11:09:26,823 INFO misc.py line 119 253097] Train: [40/100][467/510] Data 0.007 (0.096) Batch 1.039 (1.515) Remain 12:53:42 loss: 0.1023 Lr: 0.00421
[2023-12-25 11:09:28,060 INFO misc.py line 119 253097] Train: [40/100][468/510] Data 0.005 (0.096) Batch 1.237 (1.514) Remain 12:53:22 loss: 0.1735 Lr: 0.00421
[2023-12-25 11:09:29,325 INFO misc.py line 119 253097] Train: [40/100][469/510] Data 0.004 (0.096) Batch 1.258 (1.514) Remain 12:53:04 loss: 0.4046 Lr: 0.00421
[2023-12-25 11:09:30,473 INFO misc.py line 119 253097] Train: [40/100][470/510] Data 0.011 (0.095) Batch 1.153 (1.513) Remain 12:52:39 loss: 0.1829 Lr: 0.00421
[2023-12-25 11:09:31,701 INFO misc.py line 119 253097] Train: [40/100][471/510] Data 0.008 (0.095) Batch 1.225 (1.512) Remain 12:52:18 loss: 0.2141 Lr: 0.00421
[2023-12-25 11:09:32,775 INFO misc.py line 119 253097] Train: [40/100][472/510] Data 0.009 (0.095) Batch 1.078 (1.511) Remain 12:51:48 loss: 0.2906 Lr: 0.00421
[2023-12-25 11:09:33,865 INFO misc.py line 119 253097] Train: [40/100][473/510] Data 0.005 (0.095) Batch 1.091 (1.511) Remain 12:51:19 loss: 0.2205 Lr: 0.00421
[2023-12-25 11:09:35,069 INFO misc.py line 119 253097] Train: [40/100][474/510] Data 0.005 (0.095) Batch 1.204 (1.510) Remain 12:50:58 loss: 0.2897 Lr: 0.00421
[2023-12-25 11:09:36,263 INFO misc.py line 119 253097] Train: [40/100][475/510] Data 0.006 (0.095) Batch 1.192 (1.509) Remain 12:50:36 loss: 0.2985 Lr: 0.00421
[2023-12-25 11:09:37,360 INFO misc.py line 119 253097] Train: [40/100][476/510] Data 0.007 (0.094) Batch 1.098 (1.508) Remain 12:50:08 loss: 0.1536 Lr: 0.00421
[2023-12-25 11:09:38,595 INFO misc.py line 119 253097] Train: [40/100][477/510] Data 0.006 (0.094) Batch 1.228 (1.508) Remain 12:49:48 loss: 0.2551 Lr: 0.00421
[2023-12-25 11:09:39,874 INFO misc.py line 119 253097] Train: [40/100][478/510] Data 0.013 (0.094) Batch 1.285 (1.507) Remain 12:49:32 loss: 0.1004 Lr: 0.00421
[2023-12-25 11:09:41,043 INFO misc.py line 119 253097] Train: [40/100][479/510] Data 0.006 (0.094) Batch 1.165 (1.507) Remain 12:49:09 loss: 0.2712 Lr: 0.00421
[2023-12-25 11:09:42,311 INFO misc.py line 119 253097] Train: [40/100][480/510] Data 0.010 (0.094) Batch 1.269 (1.506) Remain 12:48:52 loss: 0.2123 Lr: 0.00421
[2023-12-25 11:09:49,748 INFO misc.py line 119 253097] Train: [40/100][481/510] Data 0.009 (0.093) Batch 7.441 (1.519) Remain 12:55:11 loss: 0.1665 Lr: 0.00421
[2023-12-25 11:09:51,055 INFO misc.py line 119 253097] Train: [40/100][482/510] Data 0.005 (0.093) Batch 1.297 (1.518) Remain 12:54:55 loss: 0.1719 Lr: 0.00421
[2023-12-25 11:09:52,398 INFO misc.py line 119 253097] Train: [40/100][483/510] Data 0.015 (0.093) Batch 1.352 (1.518) Remain 12:54:43 loss: 0.1688 Lr: 0.00421
[2023-12-25 11:09:53,612 INFO misc.py line 119 253097] Train: [40/100][484/510] Data 0.004 (0.093) Batch 1.208 (1.517) Remain 12:54:22 loss: 0.2353 Lr: 0.00421
[2023-12-25 11:09:54,640 INFO misc.py line 119 253097] Train: [40/100][485/510] Data 0.011 (0.093) Batch 1.030 (1.516) Remain 12:53:49 loss: 0.1896 Lr: 0.00421
[2023-12-25 11:09:55,805 INFO misc.py line 119 253097] Train: [40/100][486/510] Data 0.009 (0.093) Batch 1.170 (1.515) Remain 12:53:26 loss: 0.2686 Lr: 0.00421
[2023-12-25 11:09:56,974 INFO misc.py line 119 253097] Train: [40/100][487/510] Data 0.004 (0.092) Batch 1.164 (1.515) Remain 12:53:02 loss: 0.2800 Lr: 0.00421
[2023-12-25 11:09:58,082 INFO misc.py line 119 253097] Train: [40/100][488/510] Data 0.009 (0.092) Batch 1.093 (1.514) Remain 12:52:34 loss: 0.2209 Lr: 0.00421
[2023-12-25 11:09:59,152 INFO misc.py line 119 253097] Train: [40/100][489/510] Data 0.025 (0.092) Batch 1.089 (1.513) Remain 12:52:06 loss: 0.1668 Lr: 0.00421
[2023-12-25 11:10:00,380 INFO misc.py line 119 253097] Train: [40/100][490/510] Data 0.005 (0.092) Batch 1.221 (1.512) Remain 12:51:46 loss: 0.1917 Lr: 0.00421
[2023-12-25 11:10:01,583 INFO misc.py line 119 253097] Train: [40/100][491/510] Data 0.012 (0.092) Batch 1.210 (1.512) Remain 12:51:25 loss: 0.1924 Lr: 0.00421
[2023-12-25 11:10:02,745 INFO misc.py line 119 253097] Train: [40/100][492/510] Data 0.004 (0.092) Batch 1.163 (1.511) Remain 12:51:02 loss: 0.2584 Lr: 0.00421
[2023-12-25 11:10:03,757 INFO misc.py line 119 253097] Train: [40/100][493/510] Data 0.004 (0.091) Batch 1.009 (1.510) Remain 12:50:29 loss: 0.1624 Lr: 0.00421
[2023-12-25 11:10:05,036 INFO misc.py line 119 253097] Train: [40/100][494/510] Data 0.006 (0.091) Batch 1.278 (1.509) Remain 12:50:13 loss: 0.2382 Lr: 0.00421
[2023-12-25 11:10:14,064 INFO misc.py line 119 253097] Train: [40/100][495/510] Data 0.007 (0.091) Batch 9.030 (1.525) Remain 12:58:00 loss: 0.1422 Lr: 0.00421
[2023-12-25 11:10:15,296 INFO misc.py line 119 253097] Train: [40/100][496/510] Data 0.005 (0.091) Batch 1.233 (1.524) Remain 12:57:40 loss: 0.3744 Lr: 0.00421
[2023-12-25 11:10:16,541 INFO misc.py line 119 253097] Train: [40/100][497/510] Data 0.004 (0.091) Batch 1.242 (1.524) Remain 12:57:21 loss: 0.2388 Lr: 0.00421
[2023-12-25 11:10:17,697 INFO misc.py line 119 253097] Train: [40/100][498/510] Data 0.007 (0.091) Batch 1.155 (1.523) Remain 12:56:57 loss: 0.1061 Lr: 0.00421
[2023-12-25 11:10:18,763 INFO misc.py line 119 253097] Train: [40/100][499/510] Data 0.007 (0.090) Batch 1.067 (1.522) Remain 12:56:27 loss: 0.1638 Lr: 0.00421
[2023-12-25 11:10:19,749 INFO misc.py line 119 253097] Train: [40/100][500/510] Data 0.007 (0.090) Batch 0.989 (1.521) Remain 12:55:53 loss: 0.1665 Lr: 0.00421
[2023-12-25 11:10:20,785 INFO misc.py line 119 253097] Train: [40/100][501/510] Data 0.004 (0.090) Batch 1.034 (1.520) Remain 12:55:21 loss: 0.1715 Lr: 0.00421
[2023-12-25 11:10:22,892 INFO misc.py line 119 253097] Train: [40/100][502/510] Data 0.815 (0.091) Batch 2.109 (1.521) Remain 12:55:56 loss: 0.0625 Lr: 0.00421
[2023-12-25 11:10:24,008 INFO misc.py line 119 253097] Train: [40/100][503/510] Data 0.005 (0.091) Batch 1.115 (1.520) Remain 12:55:29 loss: 0.1912 Lr: 0.00421
[2023-12-25 11:10:25,315 INFO misc.py line 119 253097] Train: [40/100][504/510] Data 0.004 (0.091) Batch 1.289 (1.520) Remain 12:55:14 loss: 0.2412 Lr: 0.00421
[2023-12-25 11:10:26,566 INFO misc.py line 119 253097] Train: [40/100][505/510] Data 0.023 (0.091) Batch 1.263 (1.519) Remain 12:54:57 loss: 0.2159 Lr: 0.00421
[2023-12-25 11:10:27,715 INFO misc.py line 119 253097] Train: [40/100][506/510] Data 0.011 (0.091) Batch 1.151 (1.519) Remain 12:54:33 loss: 0.4208 Lr: 0.00421
[2023-12-25 11:10:28,927 INFO misc.py line 119 253097] Train: [40/100][507/510] Data 0.009 (0.091) Batch 1.216 (1.518) Remain 12:54:13 loss: 0.3249 Lr: 0.00421
[2023-12-25 11:10:30,288 INFO misc.py line 119 253097] Train: [40/100][508/510] Data 0.489 (0.091) Batch 1.360 (1.518) Remain 12:54:02 loss: 0.1653 Lr: 0.00421
[2023-12-25 11:10:31,487 INFO misc.py line 119 253097] Train: [40/100][509/510] Data 0.007 (0.091) Batch 1.199 (1.517) Remain 12:53:41 loss: 0.1656 Lr: 0.00421
[2023-12-25 11:10:38,229 INFO misc.py line 119 253097] Train: [40/100][510/510] Data 5.379 (0.102) Batch 6.729 (1.527) Remain 12:58:54 loss: 0.2170 Lr: 0.00421
[2023-12-25 11:10:38,231 INFO misc.py line 136 253097] Train result: loss: 0.2118 
[2023-12-25 11:10:38,231 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 11:11:03,569 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7453 
[2023-12-25 11:11:03,929 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4300 
[2023-12-25 11:11:11,916 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5058 
[2023-12-25 11:11:12,441 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3472 
[2023-12-25 11:11:14,412 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7214 
[2023-12-25 11:11:14,837 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3632 
[2023-12-25 11:11:15,715 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0692 
[2023-12-25 11:11:16,281 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4682 
[2023-12-25 11:11:18,088 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0320 
[2023-12-25 11:11:20,211 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3384 
[2023-12-25 11:11:21,067 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2760 
[2023-12-25 11:11:21,492 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6224 
[2023-12-25 11:11:22,393 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.8659 
[2023-12-25 11:11:25,337 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8457 
[2023-12-25 11:11:25,805 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2118 
[2023-12-25 11:11:26,425 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3554 
[2023-12-25 11:11:27,123 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3414 
[2023-12-25 11:11:28,454 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6507/0.7161/0.8956.
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9173/0.9453
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9783/0.9913
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8354/0.9672
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2481/0.2592
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6271/0.6609
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6554/0.7919
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7993/0.8763
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8637/0.9526
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5260/0.5715
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7630/0.8644
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6753/0.7338
[2023-12-25 11:11:28,455 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5704/0.6956
[2023-12-25 11:11:28,456 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 11:11:28,457 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 11:11:28,457 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 11:12:01,451 INFO misc.py line 119 253097] Train: [41/100][1/510] Data 28.879 (28.879) Batch 30.659 (30.659) Remain 260:35:30 loss: 0.1518 Lr: 0.00421
[2023-12-25 11:12:02,621 INFO misc.py line 119 253097] Train: [41/100][2/510] Data 0.007 (0.007) Batch 1.171 (1.171) Remain 09:57:02 loss: 0.1801 Lr: 0.00421
[2023-12-25 11:12:03,901 INFO misc.py line 119 253097] Train: [41/100][3/510] Data 0.004 (0.004) Batch 1.277 (1.277) Remain 10:51:10 loss: 0.3314 Lr: 0.00421
[2023-12-25 11:12:05,017 INFO misc.py line 119 253097] Train: [41/100][4/510] Data 0.009 (0.009) Batch 1.120 (1.120) Remain 09:31:21 loss: 0.1663 Lr: 0.00421
[2023-12-25 11:12:06,123 INFO misc.py line 119 253097] Train: [41/100][5/510] Data 0.004 (0.006) Batch 1.098 (1.109) Remain 09:25:33 loss: 0.2225 Lr: 0.00421
[2023-12-25 11:12:07,204 INFO misc.py line 119 253097] Train: [41/100][6/510] Data 0.012 (0.008) Batch 1.084 (1.101) Remain 09:21:18 loss: 0.1474 Lr: 0.00421
[2023-12-25 11:12:08,314 INFO misc.py line 119 253097] Train: [41/100][7/510] Data 0.009 (0.008) Batch 1.111 (1.103) Remain 09:22:36 loss: 0.0931 Lr: 0.00421
[2023-12-25 11:12:09,487 INFO misc.py line 119 253097] Train: [41/100][8/510] Data 0.008 (0.008) Batch 1.162 (1.115) Remain 09:28:31 loss: 0.3155 Lr: 0.00420
[2023-12-25 11:12:10,524 INFO misc.py line 119 253097] Train: [41/100][9/510] Data 0.019 (0.010) Batch 1.052 (1.105) Remain 09:23:10 loss: 0.1163 Lr: 0.00420
[2023-12-25 11:12:11,745 INFO misc.py line 119 253097] Train: [41/100][10/510] Data 0.003 (0.009) Batch 1.219 (1.121) Remain 09:31:28 loss: 0.3117 Lr: 0.00420
[2023-12-25 11:12:12,937 INFO misc.py line 119 253097] Train: [41/100][11/510] Data 0.006 (0.009) Batch 1.193 (1.130) Remain 09:36:02 loss: 0.1202 Lr: 0.00420
[2023-12-25 11:12:14,124 INFO misc.py line 119 253097] Train: [41/100][12/510] Data 0.006 (0.008) Batch 1.187 (1.136) Remain 09:39:16 loss: 0.1950 Lr: 0.00420
[2023-12-25 11:12:15,400 INFO misc.py line 119 253097] Train: [41/100][13/510] Data 0.005 (0.008) Batch 1.273 (1.150) Remain 09:46:13 loss: 0.1962 Lr: 0.00420
[2023-12-25 11:12:16,441 INFO misc.py line 119 253097] Train: [41/100][14/510] Data 0.008 (0.008) Batch 1.043 (1.140) Remain 09:41:15 loss: 0.4366 Lr: 0.00420
[2023-12-25 11:12:17,508 INFO misc.py line 119 253097] Train: [41/100][15/510] Data 0.006 (0.008) Batch 1.068 (1.134) Remain 09:38:09 loss: 0.2673 Lr: 0.00420
[2023-12-25 11:12:18,804 INFO misc.py line 119 253097] Train: [41/100][16/510] Data 0.004 (0.007) Batch 1.295 (1.147) Remain 09:44:26 loss: 0.2587 Lr: 0.00420
[2023-12-25 11:12:19,791 INFO misc.py line 119 253097] Train: [41/100][17/510] Data 0.010 (0.008) Batch 0.989 (1.135) Remain 09:38:40 loss: 0.3412 Lr: 0.00420
[2023-12-25 11:12:20,711 INFO misc.py line 119 253097] Train: [41/100][18/510] Data 0.004 (0.007) Batch 0.918 (1.121) Remain 09:31:16 loss: 0.2387 Lr: 0.00420
[2023-12-25 11:12:21,768 INFO misc.py line 119 253097] Train: [41/100][19/510] Data 0.006 (0.007) Batch 1.058 (1.117) Remain 09:29:16 loss: 0.1488 Lr: 0.00420
[2023-12-25 11:12:22,883 INFO misc.py line 119 253097] Train: [41/100][20/510] Data 0.004 (0.007) Batch 1.116 (1.117) Remain 09:29:12 loss: 0.3136 Lr: 0.00420
[2023-12-25 11:12:24,034 INFO misc.py line 119 253097] Train: [41/100][21/510] Data 0.005 (0.007) Batch 1.151 (1.119) Remain 09:30:10 loss: 0.1125 Lr: 0.00420
[2023-12-25 11:12:32,437 INFO misc.py line 119 253097] Train: [41/100][22/510] Data 0.005 (0.007) Batch 8.403 (1.502) Remain 12:45:31 loss: 0.1718 Lr: 0.00420
[2023-12-25 11:12:33,648 INFO misc.py line 119 253097] Train: [41/100][23/510] Data 0.004 (0.007) Batch 1.211 (1.488) Remain 12:38:04 loss: 0.2955 Lr: 0.00420
[2023-12-25 11:12:34,823 INFO misc.py line 119 253097] Train: [41/100][24/510] Data 0.004 (0.007) Batch 1.175 (1.473) Remain 12:30:28 loss: 0.1525 Lr: 0.00420
[2023-12-25 11:12:35,822 INFO misc.py line 119 253097] Train: [41/100][25/510] Data 0.006 (0.007) Batch 0.999 (1.451) Remain 12:19:27 loss: 0.1516 Lr: 0.00420
[2023-12-25 11:12:36,920 INFO misc.py line 119 253097] Train: [41/100][26/510] Data 0.005 (0.006) Batch 1.099 (1.436) Remain 12:11:38 loss: 0.1526 Lr: 0.00420
[2023-12-25 11:12:38,144 INFO misc.py line 119 253097] Train: [41/100][27/510] Data 0.003 (0.006) Batch 1.223 (1.427) Remain 12:07:05 loss: 0.2479 Lr: 0.00420
[2023-12-25 11:12:39,264 INFO misc.py line 119 253097] Train: [41/100][28/510] Data 0.006 (0.006) Batch 1.120 (1.415) Remain 12:00:48 loss: 0.1687 Lr: 0.00420
[2023-12-25 11:12:40,511 INFO misc.py line 119 253097] Train: [41/100][29/510] Data 0.005 (0.006) Batch 1.247 (1.408) Remain 11:57:29 loss: 0.2741 Lr: 0.00420
[2023-12-25 11:12:41,659 INFO misc.py line 119 253097] Train: [41/100][30/510] Data 0.006 (0.006) Batch 1.149 (1.399) Remain 11:52:34 loss: 0.0872 Lr: 0.00420
[2023-12-25 11:12:42,831 INFO misc.py line 119 253097] Train: [41/100][31/510] Data 0.005 (0.006) Batch 1.173 (1.391) Remain 11:48:26 loss: 0.2068 Lr: 0.00420
[2023-12-25 11:12:44,082 INFO misc.py line 119 253097] Train: [41/100][32/510] Data 0.003 (0.006) Batch 1.247 (1.386) Remain 11:45:53 loss: 0.3502 Lr: 0.00420
[2023-12-25 11:12:45,367 INFO misc.py line 119 253097] Train: [41/100][33/510] Data 0.009 (0.006) Batch 1.285 (1.382) Remain 11:44:10 loss: 0.3122 Lr: 0.00420
[2023-12-25 11:12:46,491 INFO misc.py line 119 253097] Train: [41/100][34/510] Data 0.008 (0.006) Batch 1.122 (1.374) Remain 11:39:51 loss: 0.1226 Lr: 0.00420
[2023-12-25 11:12:47,602 INFO misc.py line 119 253097] Train: [41/100][35/510] Data 0.010 (0.006) Batch 1.113 (1.366) Remain 11:35:41 loss: 0.1720 Lr: 0.00420
[2023-12-25 11:12:48,702 INFO misc.py line 119 253097] Train: [41/100][36/510] Data 0.008 (0.006) Batch 1.103 (1.358) Remain 11:31:36 loss: 0.1003 Lr: 0.00420
[2023-12-25 11:12:49,768 INFO misc.py line 119 253097] Train: [41/100][37/510] Data 0.005 (0.006) Batch 1.056 (1.349) Remain 11:27:03 loss: 0.2329 Lr: 0.00420
[2023-12-25 11:12:50,812 INFO misc.py line 119 253097] Train: [41/100][38/510] Data 0.015 (0.007) Batch 1.054 (1.340) Remain 11:22:45 loss: 0.3309 Lr: 0.00420
[2023-12-25 11:12:51,919 INFO misc.py line 119 253097] Train: [41/100][39/510] Data 0.005 (0.007) Batch 1.105 (1.334) Remain 11:19:24 loss: 0.1529 Lr: 0.00420
[2023-12-25 11:12:53,208 INFO misc.py line 119 253097] Train: [41/100][40/510] Data 0.010 (0.007) Batch 1.286 (1.333) Remain 11:18:44 loss: 0.1416 Lr: 0.00420
[2023-12-25 11:12:54,461 INFO misc.py line 119 253097] Train: [41/100][41/510] Data 0.008 (0.007) Batch 1.256 (1.331) Remain 11:17:41 loss: 0.1360 Lr: 0.00420
[2023-12-25 11:12:55,618 INFO misc.py line 119 253097] Train: [41/100][42/510] Data 0.005 (0.007) Batch 1.155 (1.326) Remain 11:15:22 loss: 0.4579 Lr: 0.00420
[2023-12-25 11:12:56,719 INFO misc.py line 119 253097] Train: [41/100][43/510] Data 0.007 (0.007) Batch 1.101 (1.320) Remain 11:12:29 loss: 0.1450 Lr: 0.00420
[2023-12-25 11:12:57,753 INFO misc.py line 119 253097] Train: [41/100][44/510] Data 0.007 (0.007) Batch 1.035 (1.313) Remain 11:08:55 loss: 0.3427 Lr: 0.00420
[2023-12-25 11:12:58,979 INFO misc.py line 119 253097] Train: [41/100][45/510] Data 0.006 (0.007) Batch 1.220 (1.311) Remain 11:07:45 loss: 0.3058 Lr: 0.00420
[2023-12-25 11:13:00,054 INFO misc.py line 119 253097] Train: [41/100][46/510] Data 0.014 (0.007) Batch 1.083 (1.306) Remain 11:05:02 loss: 0.3123 Lr: 0.00420
[2023-12-25 11:13:01,315 INFO misc.py line 119 253097] Train: [41/100][47/510] Data 0.005 (0.007) Batch 1.258 (1.305) Remain 11:04:27 loss: 0.2426 Lr: 0.00420
[2023-12-25 11:13:02,395 INFO misc.py line 119 253097] Train: [41/100][48/510] Data 0.007 (0.007) Batch 1.084 (1.300) Remain 11:01:56 loss: 0.2859 Lr: 0.00420
[2023-12-25 11:13:03,699 INFO misc.py line 119 253097] Train: [41/100][49/510] Data 0.004 (0.007) Batch 1.296 (1.300) Remain 11:01:52 loss: 0.1479 Lr: 0.00420
[2023-12-25 11:13:04,892 INFO misc.py line 119 253097] Train: [41/100][50/510] Data 0.011 (0.007) Batch 1.200 (1.298) Remain 11:00:46 loss: 0.2201 Lr: 0.00420
[2023-12-25 11:13:05,901 INFO misc.py line 119 253097] Train: [41/100][51/510] Data 0.005 (0.007) Batch 1.010 (1.292) Remain 10:57:41 loss: 0.0945 Lr: 0.00420
[2023-12-25 11:13:11,157 INFO misc.py line 119 253097] Train: [41/100][52/510] Data 0.003 (0.007) Batch 5.253 (1.373) Remain 11:38:50 loss: 0.0872 Lr: 0.00420
[2023-12-25 11:13:12,435 INFO misc.py line 119 253097] Train: [41/100][53/510] Data 0.007 (0.007) Batch 1.277 (1.371) Remain 11:37:50 loss: 0.1909 Lr: 0.00420
[2023-12-25 11:13:13,564 INFO misc.py line 119 253097] Train: [41/100][54/510] Data 0.007 (0.007) Batch 1.130 (1.366) Remain 11:35:24 loss: 0.3014 Lr: 0.00420
[2023-12-25 11:13:14,787 INFO misc.py line 119 253097] Train: [41/100][55/510] Data 0.006 (0.007) Batch 1.220 (1.363) Remain 11:33:57 loss: 0.2677 Lr: 0.00420
[2023-12-25 11:13:15,953 INFO misc.py line 119 253097] Train: [41/100][56/510] Data 0.009 (0.007) Batch 1.169 (1.360) Remain 11:32:04 loss: 0.3003 Lr: 0.00420
[2023-12-25 11:13:17,154 INFO misc.py line 119 253097] Train: [41/100][57/510] Data 0.006 (0.007) Batch 1.199 (1.357) Remain 11:30:32 loss: 0.1768 Lr: 0.00420
[2023-12-25 11:13:20,088 INFO misc.py line 119 253097] Train: [41/100][58/510] Data 0.008 (0.007) Batch 2.938 (1.385) Remain 11:45:09 loss: 0.1913 Lr: 0.00420
[2023-12-25 11:13:21,261 INFO misc.py line 119 253097] Train: [41/100][59/510] Data 0.004 (0.007) Batch 1.171 (1.381) Remain 11:43:11 loss: 0.1050 Lr: 0.00420
[2023-12-25 11:13:22,421 INFO misc.py line 119 253097] Train: [41/100][60/510] Data 0.006 (0.007) Batch 1.161 (1.378) Remain 11:41:12 loss: 0.1681 Lr: 0.00420
[2023-12-25 11:13:23,744 INFO misc.py line 119 253097] Train: [41/100][61/510] Data 0.004 (0.007) Batch 1.323 (1.377) Remain 11:40:41 loss: 0.2181 Lr: 0.00420
[2023-12-25 11:13:24,941 INFO misc.py line 119 253097] Train: [41/100][62/510] Data 0.004 (0.007) Batch 1.194 (1.374) Remain 11:39:06 loss: 0.2215 Lr: 0.00420
[2023-12-25 11:13:26,204 INFO misc.py line 119 253097] Train: [41/100][63/510] Data 0.008 (0.007) Batch 1.263 (1.372) Remain 11:38:08 loss: 0.1457 Lr: 0.00420
[2023-12-25 11:13:28,475 INFO misc.py line 119 253097] Train: [41/100][64/510] Data 0.007 (0.007) Batch 2.274 (1.387) Remain 11:45:38 loss: 0.1147 Lr: 0.00419
[2023-12-25 11:13:29,740 INFO misc.py line 119 253097] Train: [41/100][65/510] Data 0.004 (0.007) Batch 1.265 (1.385) Remain 11:44:37 loss: 0.2612 Lr: 0.00419
[2023-12-25 11:13:30,842 INFO misc.py line 119 253097] Train: [41/100][66/510] Data 0.005 (0.007) Batch 1.096 (1.380) Remain 11:42:15 loss: 0.1625 Lr: 0.00419
[2023-12-25 11:13:32,037 INFO misc.py line 119 253097] Train: [41/100][67/510] Data 0.010 (0.007) Batch 1.198 (1.377) Remain 11:40:47 loss: 0.2193 Lr: 0.00419
[2023-12-25 11:13:33,200 INFO misc.py line 119 253097] Train: [41/100][68/510] Data 0.008 (0.007) Batch 1.166 (1.374) Remain 11:39:07 loss: 0.1312 Lr: 0.00419
[2023-12-25 11:13:35,100 INFO misc.py line 119 253097] Train: [41/100][69/510] Data 0.794 (0.019) Batch 1.900 (1.382) Remain 11:43:09 loss: 0.1603 Lr: 0.00419
[2023-12-25 11:13:36,229 INFO misc.py line 119 253097] Train: [41/100][70/510] Data 0.004 (0.018) Batch 1.129 (1.378) Remain 11:41:12 loss: 0.1245 Lr: 0.00419
[2023-12-25 11:13:37,191 INFO misc.py line 119 253097] Train: [41/100][71/510] Data 0.004 (0.018) Batch 0.963 (1.372) Remain 11:38:04 loss: 0.1173 Lr: 0.00419
[2023-12-25 11:13:38,361 INFO misc.py line 119 253097] Train: [41/100][72/510] Data 0.005 (0.018) Batch 1.170 (1.369) Remain 11:36:34 loss: 0.1680 Lr: 0.00419
[2023-12-25 11:13:39,566 INFO misc.py line 119 253097] Train: [41/100][73/510] Data 0.004 (0.018) Batch 1.204 (1.367) Remain 11:35:21 loss: 0.2414 Lr: 0.00419
[2023-12-25 11:13:40,823 INFO misc.py line 119 253097] Train: [41/100][74/510] Data 0.006 (0.018) Batch 1.255 (1.365) Remain 11:34:31 loss: 0.1474 Lr: 0.00419
[2023-12-25 11:13:44,498 INFO misc.py line 119 253097] Train: [41/100][75/510] Data 2.631 (0.054) Batch 3.677 (1.397) Remain 11:50:50 loss: 0.1323 Lr: 0.00419
[2023-12-25 11:13:45,636 INFO misc.py line 119 253097] Train: [41/100][76/510] Data 0.004 (0.053) Batch 1.138 (1.394) Remain 11:49:00 loss: 0.1019 Lr: 0.00419
[2023-12-25 11:13:46,783 INFO misc.py line 119 253097] Train: [41/100][77/510] Data 0.004 (0.053) Batch 1.148 (1.390) Remain 11:47:18 loss: 0.1280 Lr: 0.00419
[2023-12-25 11:13:47,908 INFO misc.py line 119 253097] Train: [41/100][78/510] Data 0.003 (0.052) Batch 1.124 (1.387) Remain 11:45:28 loss: 0.2367 Lr: 0.00419
[2023-12-25 11:13:49,019 INFO misc.py line 119 253097] Train: [41/100][79/510] Data 0.004 (0.051) Batch 1.109 (1.383) Remain 11:43:35 loss: 0.2514 Lr: 0.00419
[2023-12-25 11:13:50,200 INFO misc.py line 119 253097] Train: [41/100][80/510] Data 0.006 (0.051) Batch 1.182 (1.381) Remain 11:42:14 loss: 0.2349 Lr: 0.00419
[2023-12-25 11:13:53,368 INFO misc.py line 119 253097] Train: [41/100][81/510] Data 2.107 (0.077) Batch 3.165 (1.403) Remain 11:53:51 loss: 0.1400 Lr: 0.00419
[2023-12-25 11:13:54,574 INFO misc.py line 119 253097] Train: [41/100][82/510] Data 0.007 (0.076) Batch 1.207 (1.401) Remain 11:52:34 loss: 0.3051 Lr: 0.00419
[2023-12-25 11:13:55,815 INFO misc.py line 119 253097] Train: [41/100][83/510] Data 0.005 (0.075) Batch 1.243 (1.399) Remain 11:51:32 loss: 0.1522 Lr: 0.00419
[2023-12-25 11:13:57,080 INFO misc.py line 119 253097] Train: [41/100][84/510] Data 0.003 (0.074) Batch 1.260 (1.397) Remain 11:50:38 loss: 0.1304 Lr: 0.00419
[2023-12-25 11:13:58,157 INFO misc.py line 119 253097] Train: [41/100][85/510] Data 0.008 (0.074) Batch 1.082 (1.393) Remain 11:48:40 loss: 0.2500 Lr: 0.00419
[2023-12-25 11:13:59,305 INFO misc.py line 119 253097] Train: [41/100][86/510] Data 0.005 (0.073) Batch 1.146 (1.390) Remain 11:47:07 loss: 0.1428 Lr: 0.00419
[2023-12-25 11:14:00,624 INFO misc.py line 119 253097] Train: [41/100][87/510] Data 0.005 (0.072) Batch 1.320 (1.390) Remain 11:46:40 loss: 0.1694 Lr: 0.00419
[2023-12-25 11:14:06,794 INFO misc.py line 119 253097] Train: [41/100][88/510] Data 5.170 (0.132) Batch 6.169 (1.446) Remain 12:15:15 loss: 0.2584 Lr: 0.00419
[2023-12-25 11:14:07,995 INFO misc.py line 119 253097] Train: [41/100][89/510] Data 0.006 (0.130) Batch 1.202 (1.443) Remain 12:13:47 loss: 0.3533 Lr: 0.00419
[2023-12-25 11:14:08,870 INFO misc.py line 119 253097] Train: [41/100][90/510] Data 0.004 (0.129) Batch 0.876 (1.436) Remain 12:10:26 loss: 0.1364 Lr: 0.00419
[2023-12-25 11:14:09,874 INFO misc.py line 119 253097] Train: [41/100][91/510] Data 0.003 (0.128) Batch 0.998 (1.431) Remain 12:07:53 loss: 0.3262 Lr: 0.00419
[2023-12-25 11:14:11,096 INFO misc.py line 119 253097] Train: [41/100][92/510] Data 0.009 (0.126) Batch 1.227 (1.429) Remain 12:06:41 loss: 0.2146 Lr: 0.00419
[2023-12-25 11:14:12,100 INFO misc.py line 119 253097] Train: [41/100][93/510] Data 0.005 (0.125) Batch 1.003 (1.424) Remain 12:04:15 loss: 0.1559 Lr: 0.00419
[2023-12-25 11:14:13,329 INFO misc.py line 119 253097] Train: [41/100][94/510] Data 0.005 (0.124) Batch 1.231 (1.422) Remain 12:03:09 loss: 0.2137 Lr: 0.00419
[2023-12-25 11:14:14,614 INFO misc.py line 119 253097] Train: [41/100][95/510] Data 0.004 (0.122) Batch 1.281 (1.421) Remain 12:02:21 loss: 0.2952 Lr: 0.00419
[2023-12-25 11:14:15,939 INFO misc.py line 119 253097] Train: [41/100][96/510] Data 0.007 (0.121) Batch 1.326 (1.420) Remain 12:01:48 loss: 0.1078 Lr: 0.00419
[2023-12-25 11:14:17,133 INFO misc.py line 119 253097] Train: [41/100][97/510] Data 0.008 (0.120) Batch 1.189 (1.417) Remain 12:00:32 loss: 0.2246 Lr: 0.00419
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[2023-12-25 11:14:28,044 INFO misc.py line 119 253097] Train: [41/100][100/510] Data 8.224 (0.201) Batch 9.004 (1.486) Remain 12:35:24 loss: 0.1224 Lr: 0.00419
[2023-12-25 11:14:29,008 INFO misc.py line 119 253097] Train: [41/100][101/510] Data 0.005 (0.199) Batch 0.959 (1.481) Remain 12:32:38 loss: 0.4385 Lr: 0.00419
[2023-12-25 11:14:30,139 INFO misc.py line 119 253097] Train: [41/100][102/510] Data 0.009 (0.197) Batch 1.117 (1.477) Remain 12:30:45 loss: 0.2085 Lr: 0.00419
[2023-12-25 11:14:31,362 INFO misc.py line 119 253097] Train: [41/100][103/510] Data 0.023 (0.195) Batch 1.237 (1.475) Remain 12:29:30 loss: 0.1308 Lr: 0.00419
[2023-12-25 11:14:32,439 INFO misc.py line 119 253097] Train: [41/100][104/510] Data 0.008 (0.194) Batch 1.073 (1.471) Remain 12:27:28 loss: 0.2232 Lr: 0.00419
[2023-12-25 11:14:33,682 INFO misc.py line 119 253097] Train: [41/100][105/510] Data 0.012 (0.192) Batch 1.248 (1.468) Remain 12:26:20 loss: 0.1978 Lr: 0.00419
[2023-12-25 11:14:36,719 INFO misc.py line 119 253097] Train: [41/100][106/510] Data 2.014 (0.209) Batch 3.041 (1.484) Remain 12:34:04 loss: 0.1889 Lr: 0.00419
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[2023-12-25 11:14:39,128 INFO misc.py line 119 253097] Train: [41/100][108/510] Data 0.004 (0.206) Batch 1.277 (1.478) Remain 12:31:18 loss: 0.2932 Lr: 0.00419
[2023-12-25 11:14:40,280 INFO misc.py line 119 253097] Train: [41/100][109/510] Data 0.005 (0.204) Batch 1.153 (1.475) Remain 12:29:43 loss: 0.3384 Lr: 0.00419
[2023-12-25 11:14:41,421 INFO misc.py line 119 253097] Train: [41/100][110/510] Data 0.005 (0.202) Batch 1.140 (1.472) Remain 12:28:06 loss: 0.2925 Lr: 0.00419
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[2023-12-25 11:14:43,575 INFO misc.py line 119 253097] Train: [41/100][112/510] Data 0.005 (0.198) Batch 1.002 (1.465) Remain 12:24:22 loss: 0.3486 Lr: 0.00419
[2023-12-25 11:14:44,698 INFO misc.py line 119 253097] Train: [41/100][113/510] Data 0.005 (0.196) Batch 1.124 (1.462) Remain 12:22:46 loss: 0.2754 Lr: 0.00419
[2023-12-25 11:14:45,655 INFO misc.py line 119 253097] Train: [41/100][114/510] Data 0.004 (0.195) Batch 0.957 (1.457) Remain 12:20:26 loss: 0.0977 Lr: 0.00419
[2023-12-25 11:14:50,615 INFO misc.py line 119 253097] Train: [41/100][115/510] Data 0.004 (0.193) Batch 4.959 (1.489) Remain 12:36:18 loss: 0.1057 Lr: 0.00419
[2023-12-25 11:14:51,835 INFO misc.py line 119 253097] Train: [41/100][116/510] Data 0.005 (0.191) Batch 1.221 (1.486) Remain 12:35:04 loss: 0.1887 Lr: 0.00419
[2023-12-25 11:14:57,065 INFO misc.py line 119 253097] Train: [41/100][117/510] Data 0.005 (0.190) Batch 5.229 (1.519) Remain 12:51:43 loss: 0.1291 Lr: 0.00419
[2023-12-25 11:14:58,267 INFO misc.py line 119 253097] Train: [41/100][118/510] Data 0.006 (0.188) Batch 1.204 (1.516) Remain 12:50:18 loss: 0.1964 Lr: 0.00419
[2023-12-25 11:14:59,335 INFO misc.py line 119 253097] Train: [41/100][119/510] Data 0.004 (0.187) Batch 1.067 (1.512) Remain 12:48:19 loss: 0.1035 Lr: 0.00419
[2023-12-25 11:15:00,609 INFO misc.py line 119 253097] Train: [41/100][120/510] Data 0.005 (0.185) Batch 1.272 (1.510) Remain 12:47:14 loss: 0.0986 Lr: 0.00418
[2023-12-25 11:15:01,711 INFO misc.py line 119 253097] Train: [41/100][121/510] Data 0.006 (0.183) Batch 1.104 (1.507) Remain 12:45:28 loss: 0.2312 Lr: 0.00418
[2023-12-25 11:15:02,929 INFO misc.py line 119 253097] Train: [41/100][122/510] Data 0.005 (0.182) Batch 1.217 (1.504) Remain 12:44:12 loss: 0.1845 Lr: 0.00418
[2023-12-25 11:15:04,193 INFO misc.py line 119 253097] Train: [41/100][123/510] Data 0.006 (0.180) Batch 1.263 (1.502) Remain 12:43:09 loss: 0.1438 Lr: 0.00418
[2023-12-25 11:15:05,349 INFO misc.py line 119 253097] Train: [41/100][124/510] Data 0.007 (0.179) Batch 1.155 (1.500) Remain 12:41:40 loss: 0.1652 Lr: 0.00418
[2023-12-25 11:15:06,431 INFO misc.py line 119 253097] Train: [41/100][125/510] Data 0.009 (0.178) Batch 1.087 (1.496) Remain 12:39:56 loss: 0.2668 Lr: 0.00418
[2023-12-25 11:15:07,394 INFO misc.py line 119 253097] Train: [41/100][126/510] Data 0.004 (0.176) Batch 0.962 (1.492) Remain 12:37:42 loss: 0.1216 Lr: 0.00418
[2023-12-25 11:15:08,661 INFO misc.py line 119 253097] Train: [41/100][127/510] Data 0.004 (0.175) Batch 1.263 (1.490) Remain 12:36:44 loss: 0.1286 Lr: 0.00418
[2023-12-25 11:15:09,808 INFO misc.py line 119 253097] Train: [41/100][128/510] Data 0.008 (0.174) Batch 1.147 (1.487) Remain 12:35:19 loss: 0.1814 Lr: 0.00418
[2023-12-25 11:15:11,156 INFO misc.py line 119 253097] Train: [41/100][129/510] Data 0.007 (0.172) Batch 1.348 (1.486) Remain 12:34:44 loss: 0.1728 Lr: 0.00418
[2023-12-25 11:15:12,275 INFO misc.py line 119 253097] Train: [41/100][130/510] Data 0.008 (0.171) Batch 1.121 (1.483) Remain 12:33:15 loss: 0.1305 Lr: 0.00418
[2023-12-25 11:15:13,419 INFO misc.py line 119 253097] Train: [41/100][131/510] Data 0.007 (0.170) Batch 1.146 (1.481) Remain 12:31:53 loss: 0.1221 Lr: 0.00418
[2023-12-25 11:15:14,504 INFO misc.py line 119 253097] Train: [41/100][132/510] Data 0.005 (0.168) Batch 1.080 (1.478) Remain 12:30:17 loss: 0.2697 Lr: 0.00418
[2023-12-25 11:15:15,409 INFO misc.py line 119 253097] Train: [41/100][133/510] Data 0.009 (0.167) Batch 0.910 (1.473) Remain 12:28:03 loss: 0.0799 Lr: 0.00418
[2023-12-25 11:15:16,548 INFO misc.py line 119 253097] Train: [41/100][134/510] Data 0.004 (0.166) Batch 1.139 (1.471) Remain 12:26:43 loss: 0.1699 Lr: 0.00418
[2023-12-25 11:15:17,738 INFO misc.py line 119 253097] Train: [41/100][135/510] Data 0.004 (0.165) Batch 1.190 (1.468) Remain 12:25:37 loss: 0.1177 Lr: 0.00418
[2023-12-25 11:15:18,837 INFO misc.py line 119 253097] Train: [41/100][136/510] Data 0.005 (0.163) Batch 1.099 (1.466) Remain 12:24:11 loss: 0.1422 Lr: 0.00418
[2023-12-25 11:15:19,975 INFO misc.py line 119 253097] Train: [41/100][137/510] Data 0.004 (0.162) Batch 1.139 (1.463) Remain 12:22:55 loss: 0.1470 Lr: 0.00418
[2023-12-25 11:15:21,014 INFO misc.py line 119 253097] Train: [41/100][138/510] Data 0.004 (0.161) Batch 1.038 (1.460) Remain 12:21:18 loss: 0.0840 Lr: 0.00418
[2023-12-25 11:15:22,215 INFO misc.py line 119 253097] Train: [41/100][139/510] Data 0.005 (0.160) Batch 1.202 (1.458) Remain 12:20:18 loss: 0.1762 Lr: 0.00418
[2023-12-25 11:15:23,566 INFO misc.py line 119 253097] Train: [41/100][140/510] Data 0.004 (0.159) Batch 1.352 (1.457) Remain 12:19:53 loss: 0.2042 Lr: 0.00418
[2023-12-25 11:15:30,603 INFO misc.py line 119 253097] Train: [41/100][141/510] Data 0.004 (0.158) Batch 7.036 (1.498) Remain 12:40:23 loss: 0.1064 Lr: 0.00418
[2023-12-25 11:15:31,579 INFO misc.py line 119 253097] Train: [41/100][142/510] Data 0.004 (0.157) Batch 0.972 (1.494) Remain 12:38:26 loss: 0.1670 Lr: 0.00418
[2023-12-25 11:15:32,789 INFO misc.py line 119 253097] Train: [41/100][143/510] Data 0.008 (0.156) Batch 1.208 (1.492) Remain 12:37:23 loss: 0.1688 Lr: 0.00418
[2023-12-25 11:15:33,941 INFO misc.py line 119 253097] Train: [41/100][144/510] Data 0.010 (0.154) Batch 1.157 (1.490) Remain 12:36:09 loss: 0.2094 Lr: 0.00418
[2023-12-25 11:15:35,208 INFO misc.py line 119 253097] Train: [41/100][145/510] Data 0.004 (0.153) Batch 1.267 (1.488) Remain 12:35:20 loss: 0.1772 Lr: 0.00418
[2023-12-25 11:15:36,273 INFO misc.py line 119 253097] Train: [41/100][146/510] Data 0.005 (0.152) Batch 1.062 (1.485) Remain 12:33:47 loss: 0.0739 Lr: 0.00418
[2023-12-25 11:15:37,738 INFO misc.py line 119 253097] Train: [41/100][147/510] Data 0.008 (0.151) Batch 1.470 (1.485) Remain 12:33:42 loss: 0.1877 Lr: 0.00418
[2023-12-25 11:15:39,006 INFO misc.py line 119 253097] Train: [41/100][148/510] Data 0.003 (0.150) Batch 1.264 (1.483) Remain 12:32:54 loss: 0.4399 Lr: 0.00418
[2023-12-25 11:15:40,068 INFO misc.py line 119 253097] Train: [41/100][149/510] Data 0.007 (0.149) Batch 1.066 (1.481) Remain 12:31:26 loss: 0.2999 Lr: 0.00418
[2023-12-25 11:15:41,066 INFO misc.py line 119 253097] Train: [41/100][150/510] Data 0.004 (0.148) Batch 0.997 (1.477) Remain 12:29:44 loss: 0.2398 Lr: 0.00418
[2023-12-25 11:15:42,028 INFO misc.py line 119 253097] Train: [41/100][151/510] Data 0.006 (0.147) Batch 0.963 (1.474) Remain 12:27:57 loss: 0.2404 Lr: 0.00418
[2023-12-25 11:15:44,565 INFO misc.py line 119 253097] Train: [41/100][152/510] Data 0.005 (0.146) Batch 2.537 (1.481) Remain 12:31:33 loss: 0.1833 Lr: 0.00418
[2023-12-25 11:15:45,614 INFO misc.py line 119 253097] Train: [41/100][153/510] Data 0.005 (0.146) Batch 1.048 (1.478) Remain 12:30:03 loss: 0.2234 Lr: 0.00418
[2023-12-25 11:15:46,820 INFO misc.py line 119 253097] Train: [41/100][154/510] Data 0.005 (0.145) Batch 1.208 (1.476) Remain 12:29:07 loss: 0.1972 Lr: 0.00418
[2023-12-25 11:15:48,161 INFO misc.py line 119 253097] Train: [41/100][155/510] Data 0.003 (0.144) Batch 1.338 (1.475) Remain 12:28:38 loss: 0.1369 Lr: 0.00418
[2023-12-25 11:15:49,326 INFO misc.py line 119 253097] Train: [41/100][156/510] Data 0.007 (0.143) Batch 1.166 (1.473) Remain 12:27:35 loss: 0.1915 Lr: 0.00418
[2023-12-25 11:15:50,440 INFO misc.py line 119 253097] Train: [41/100][157/510] Data 0.006 (0.142) Batch 1.111 (1.471) Remain 12:26:22 loss: 0.1425 Lr: 0.00418
[2023-12-25 11:16:00,790 INFO misc.py line 119 253097] Train: [41/100][158/510] Data 0.009 (0.141) Batch 10.353 (1.528) Remain 12:55:25 loss: 0.2076 Lr: 0.00418
[2023-12-25 11:16:01,951 INFO misc.py line 119 253097] Train: [41/100][159/510] Data 0.006 (0.140) Batch 1.162 (1.526) Remain 12:54:12 loss: 0.1562 Lr: 0.00418
[2023-12-25 11:16:03,171 INFO misc.py line 119 253097] Train: [41/100][160/510] Data 0.004 (0.139) Batch 1.218 (1.524) Remain 12:53:11 loss: 0.1773 Lr: 0.00418
[2023-12-25 11:16:04,271 INFO misc.py line 119 253097] Train: [41/100][161/510] Data 0.006 (0.138) Batch 1.102 (1.521) Remain 12:51:48 loss: 0.1804 Lr: 0.00418
[2023-12-25 11:16:05,478 INFO misc.py line 119 253097] Train: [41/100][162/510] Data 0.004 (0.138) Batch 1.202 (1.519) Remain 12:50:45 loss: 0.1243 Lr: 0.00418
[2023-12-25 11:16:06,587 INFO misc.py line 119 253097] Train: [41/100][163/510] Data 0.008 (0.137) Batch 1.108 (1.517) Remain 12:49:26 loss: 0.2735 Lr: 0.00418
[2023-12-25 11:16:08,530 INFO misc.py line 119 253097] Train: [41/100][164/510] Data 0.010 (0.136) Batch 1.948 (1.519) Remain 12:50:46 loss: 0.2275 Lr: 0.00418
[2023-12-25 11:16:09,772 INFO misc.py line 119 253097] Train: [41/100][165/510] Data 0.004 (0.135) Batch 1.243 (1.518) Remain 12:49:52 loss: 0.1907 Lr: 0.00418
[2023-12-25 11:16:10,968 INFO misc.py line 119 253097] Train: [41/100][166/510] Data 0.003 (0.134) Batch 1.191 (1.516) Remain 12:48:50 loss: 0.2123 Lr: 0.00418
[2023-12-25 11:16:12,103 INFO misc.py line 119 253097] Train: [41/100][167/510] Data 0.008 (0.134) Batch 1.134 (1.513) Remain 12:47:37 loss: 0.1811 Lr: 0.00418
[2023-12-25 11:16:13,096 INFO misc.py line 119 253097] Train: [41/100][168/510] Data 0.009 (0.133) Batch 0.999 (1.510) Remain 12:46:01 loss: 0.2123 Lr: 0.00418
[2023-12-25 11:16:14,288 INFO misc.py line 119 253097] Train: [41/100][169/510] Data 0.003 (0.132) Batch 1.187 (1.508) Remain 12:45:00 loss: 0.3712 Lr: 0.00418
[2023-12-25 11:16:15,248 INFO misc.py line 119 253097] Train: [41/100][170/510] Data 0.008 (0.131) Batch 0.965 (1.505) Remain 12:43:20 loss: 0.1412 Lr: 0.00418
[2023-12-25 11:16:16,341 INFO misc.py line 119 253097] Train: [41/100][171/510] Data 0.004 (0.131) Batch 1.093 (1.503) Remain 12:42:03 loss: 0.3055 Lr: 0.00418
[2023-12-25 11:16:17,524 INFO misc.py line 119 253097] Train: [41/100][172/510] Data 0.004 (0.130) Batch 1.183 (1.501) Remain 12:41:04 loss: 0.1416 Lr: 0.00418
[2023-12-25 11:16:18,584 INFO misc.py line 119 253097] Train: [41/100][173/510] Data 0.004 (0.129) Batch 1.060 (1.498) Remain 12:39:44 loss: 0.1900 Lr: 0.00418
[2023-12-25 11:16:19,712 INFO misc.py line 119 253097] Train: [41/100][174/510] Data 0.003 (0.128) Batch 1.129 (1.496) Remain 12:38:37 loss: 0.3939 Lr: 0.00418
[2023-12-25 11:16:20,808 INFO misc.py line 119 253097] Train: [41/100][175/510] Data 0.003 (0.128) Batch 1.095 (1.494) Remain 12:37:24 loss: 0.3029 Lr: 0.00418
[2023-12-25 11:16:21,953 INFO misc.py line 119 253097] Train: [41/100][176/510] Data 0.003 (0.127) Batch 1.145 (1.492) Remain 12:36:22 loss: 0.1886 Lr: 0.00417
[2023-12-25 11:16:23,033 INFO misc.py line 119 253097] Train: [41/100][177/510] Data 0.003 (0.126) Batch 1.080 (1.489) Remain 12:35:08 loss: 0.1893 Lr: 0.00417
[2023-12-25 11:16:24,038 INFO misc.py line 119 253097] Train: [41/100][178/510] Data 0.003 (0.125) Batch 1.003 (1.487) Remain 12:33:42 loss: 0.1945 Lr: 0.00417
[2023-12-25 11:16:25,282 INFO misc.py line 119 253097] Train: [41/100][179/510] Data 0.004 (0.125) Batch 1.228 (1.485) Remain 12:32:56 loss: 0.1454 Lr: 0.00417
[2023-12-25 11:16:26,325 INFO misc.py line 119 253097] Train: [41/100][180/510] Data 0.021 (0.124) Batch 1.050 (1.483) Remain 12:31:40 loss: 0.4744 Lr: 0.00417
[2023-12-25 11:16:27,481 INFO misc.py line 119 253097] Train: [41/100][181/510] Data 0.015 (0.124) Batch 1.162 (1.481) Remain 12:30:43 loss: 0.2015 Lr: 0.00417
[2023-12-25 11:16:28,568 INFO misc.py line 119 253097] Train: [41/100][182/510] Data 0.008 (0.123) Batch 1.086 (1.479) Remain 12:29:35 loss: 0.1378 Lr: 0.00417
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[2023-12-25 11:16:30,966 INFO misc.py line 119 253097] Train: [41/100][184/510] Data 0.008 (0.122) Batch 1.175 (1.475) Remain 12:27:58 loss: 0.1933 Lr: 0.00417
[2023-12-25 11:16:32,344 INFO misc.py line 119 253097] Train: [41/100][185/510] Data 0.011 (0.121) Batch 1.385 (1.475) Remain 12:27:41 loss: 0.2576 Lr: 0.00417
[2023-12-25 11:16:33,683 INFO misc.py line 119 253097] Train: [41/100][186/510] Data 0.004 (0.120) Batch 1.335 (1.474) Remain 12:27:16 loss: 0.4109 Lr: 0.00417
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[2023-12-25 11:16:35,936 INFO misc.py line 119 253097] Train: [41/100][188/510] Data 0.015 (0.119) Batch 0.941 (1.470) Remain 12:25:20 loss: 0.1700 Lr: 0.00417
[2023-12-25 11:16:36,858 INFO misc.py line 119 253097] Train: [41/100][189/510] Data 0.003 (0.119) Batch 0.921 (1.468) Remain 12:23:49 loss: 0.2232 Lr: 0.00417
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[2023-12-25 11:16:39,149 INFO misc.py line 119 253097] Train: [41/100][191/510] Data 0.003 (0.117) Batch 1.329 (1.464) Remain 12:22:01 loss: 0.3226 Lr: 0.00417
[2023-12-25 11:16:40,179 INFO misc.py line 119 253097] Train: [41/100][192/510] Data 0.009 (0.117) Batch 1.033 (1.462) Remain 12:20:50 loss: 0.1763 Lr: 0.00417
[2023-12-25 11:16:47,275 INFO misc.py line 119 253097] Train: [41/100][193/510] Data 0.006 (0.116) Batch 7.098 (1.491) Remain 12:35:50 loss: 0.2381 Lr: 0.00417
[2023-12-25 11:16:48,466 INFO misc.py line 119 253097] Train: [41/100][194/510] Data 0.004 (0.116) Batch 1.190 (1.490) Remain 12:35:01 loss: 0.1610 Lr: 0.00417
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[2023-12-25 11:16:50,779 INFO misc.py line 119 253097] Train: [41/100][196/510] Data 0.005 (0.115) Batch 1.132 (1.486) Remain 12:33:13 loss: 0.2350 Lr: 0.00417
[2023-12-25 11:16:52,044 INFO misc.py line 119 253097] Train: [41/100][197/510] Data 0.004 (0.114) Batch 1.246 (1.485) Remain 12:32:34 loss: 0.1601 Lr: 0.00417
[2023-12-25 11:16:53,114 INFO misc.py line 119 253097] Train: [41/100][198/510] Data 0.023 (0.113) Batch 1.047 (1.483) Remain 12:31:24 loss: 0.3601 Lr: 0.00417
[2023-12-25 11:16:54,368 INFO misc.py line 119 253097] Train: [41/100][199/510] Data 0.046 (0.113) Batch 1.295 (1.482) Remain 12:30:53 loss: 0.1999 Lr: 0.00417
[2023-12-25 11:16:55,321 INFO misc.py line 119 253097] Train: [41/100][200/510] Data 0.005 (0.113) Batch 0.954 (1.479) Remain 12:29:30 loss: 0.0952 Lr: 0.00417
[2023-12-25 11:16:56,348 INFO misc.py line 119 253097] Train: [41/100][201/510] Data 0.005 (0.112) Batch 1.027 (1.477) Remain 12:28:20 loss: 0.2571 Lr: 0.00417
[2023-12-25 11:17:02,706 INFO misc.py line 119 253097] Train: [41/100][202/510] Data 5.159 (0.137) Batch 6.358 (1.502) Remain 12:40:44 loss: 0.0995 Lr: 0.00417
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[2023-12-25 11:17:15,787 INFO misc.py line 119 253097] Train: [41/100][213/510] Data 0.003 (0.131) Batch 1.255 (1.485) Remain 12:32:09 loss: 0.2308 Lr: 0.00417
[2023-12-25 11:17:18,113 INFO misc.py line 119 253097] Train: [41/100][214/510] Data 0.008 (0.130) Batch 2.331 (1.489) Remain 12:34:10 loss: 0.1822 Lr: 0.00417
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[2023-12-25 11:17:21,484 INFO misc.py line 119 253097] Train: [41/100][217/510] Data 0.004 (0.128) Batch 1.159 (1.484) Remain 12:31:29 loss: 0.1987 Lr: 0.00417
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[2023-12-25 11:17:27,255 INFO misc.py line 119 253097] Train: [41/100][222/510] Data 0.005 (0.125) Batch 1.246 (1.477) Remain 12:27:33 loss: 0.1369 Lr: 0.00417
[2023-12-25 11:17:28,405 INFO misc.py line 119 253097] Train: [41/100][223/510] Data 0.003 (0.125) Batch 1.150 (1.475) Remain 12:26:47 loss: 0.1817 Lr: 0.00417
[2023-12-25 11:17:29,474 INFO misc.py line 119 253097] Train: [41/100][224/510] Data 0.003 (0.124) Batch 1.068 (1.473) Remain 12:25:49 loss: 0.2729 Lr: 0.00417
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[2023-12-25 11:17:31,874 INFO misc.py line 119 253097] Train: [41/100][226/510] Data 0.004 (0.123) Batch 1.203 (1.471) Remain 12:24:31 loss: 0.2509 Lr: 0.00417
[2023-12-25 11:17:37,827 INFO misc.py line 119 253097] Train: [41/100][227/510] Data 0.009 (0.123) Batch 5.958 (1.491) Remain 12:34:38 loss: 0.3270 Lr: 0.00417
[2023-12-25 11:17:38,922 INFO misc.py line 119 253097] Train: [41/100][228/510] Data 0.004 (0.122) Batch 1.096 (1.489) Remain 12:33:43 loss: 0.1722 Lr: 0.00417
[2023-12-25 11:17:39,996 INFO misc.py line 119 253097] Train: [41/100][229/510] Data 0.003 (0.122) Batch 1.072 (1.487) Remain 12:32:46 loss: 0.2115 Lr: 0.00417
[2023-12-25 11:17:41,199 INFO misc.py line 119 253097] Train: [41/100][230/510] Data 0.007 (0.121) Batch 1.204 (1.486) Remain 12:32:07 loss: 0.1904 Lr: 0.00417
[2023-12-25 11:17:42,366 INFO misc.py line 119 253097] Train: [41/100][231/510] Data 0.005 (0.121) Batch 1.165 (1.485) Remain 12:31:22 loss: 0.1956 Lr: 0.00417
[2023-12-25 11:17:43,537 INFO misc.py line 119 253097] Train: [41/100][232/510] Data 0.007 (0.120) Batch 1.172 (1.483) Remain 12:30:39 loss: 0.4341 Lr: 0.00416
[2023-12-25 11:17:44,793 INFO misc.py line 119 253097] Train: [41/100][233/510] Data 0.006 (0.120) Batch 1.252 (1.482) Remain 12:30:07 loss: 0.1920 Lr: 0.00416
[2023-12-25 11:17:46,085 INFO misc.py line 119 253097] Train: [41/100][234/510] Data 0.009 (0.119) Batch 1.295 (1.481) Remain 12:29:41 loss: 0.3384 Lr: 0.00416
[2023-12-25 11:17:47,186 INFO misc.py line 119 253097] Train: [41/100][235/510] Data 0.006 (0.119) Batch 1.099 (1.480) Remain 12:28:50 loss: 0.2085 Lr: 0.00416
[2023-12-25 11:17:48,232 INFO misc.py line 119 253097] Train: [41/100][236/510] Data 0.008 (0.118) Batch 1.048 (1.478) Remain 12:27:52 loss: 0.0827 Lr: 0.00416
[2023-12-25 11:17:49,373 INFO misc.py line 119 253097] Train: [41/100][237/510] Data 0.006 (0.118) Batch 1.138 (1.476) Remain 12:27:07 loss: 0.2323 Lr: 0.00416
[2023-12-25 11:17:50,701 INFO misc.py line 119 253097] Train: [41/100][238/510] Data 0.009 (0.117) Batch 1.324 (1.476) Remain 12:26:45 loss: 0.0958 Lr: 0.00416
[2023-12-25 11:17:51,624 INFO misc.py line 119 253097] Train: [41/100][239/510] Data 0.013 (0.117) Batch 0.931 (1.473) Remain 12:25:34 loss: 0.3290 Lr: 0.00416
[2023-12-25 11:17:54,432 INFO misc.py line 119 253097] Train: [41/100][240/510] Data 0.005 (0.116) Batch 2.809 (1.479) Remain 12:28:23 loss: 0.1857 Lr: 0.00416
[2023-12-25 11:17:55,650 INFO misc.py line 119 253097] Train: [41/100][241/510] Data 0.004 (0.116) Batch 1.218 (1.478) Remain 12:27:49 loss: 0.1446 Lr: 0.00416
[2023-12-25 11:17:56,861 INFO misc.py line 119 253097] Train: [41/100][242/510] Data 0.005 (0.115) Batch 1.211 (1.477) Remain 12:27:13 loss: 0.1075 Lr: 0.00416
[2023-12-25 11:17:57,835 INFO misc.py line 119 253097] Train: [41/100][243/510] Data 0.004 (0.115) Batch 0.973 (1.475) Remain 12:26:08 loss: 0.1723 Lr: 0.00416
[2023-12-25 11:17:59,108 INFO misc.py line 119 253097] Train: [41/100][244/510] Data 0.004 (0.114) Batch 1.273 (1.474) Remain 12:25:41 loss: 0.1633 Lr: 0.00416
[2023-12-25 11:18:00,090 INFO misc.py line 119 253097] Train: [41/100][245/510] Data 0.004 (0.114) Batch 0.983 (1.472) Remain 12:24:38 loss: 0.0849 Lr: 0.00416
[2023-12-25 11:18:03,326 INFO misc.py line 119 253097] Train: [41/100][246/510] Data 0.004 (0.114) Batch 3.235 (1.479) Remain 12:28:17 loss: 0.3822 Lr: 0.00416
[2023-12-25 11:18:04,634 INFO misc.py line 119 253097] Train: [41/100][247/510] Data 0.004 (0.113) Batch 1.309 (1.478) Remain 12:27:54 loss: 0.3069 Lr: 0.00416
[2023-12-25 11:18:05,698 INFO misc.py line 119 253097] Train: [41/100][248/510] Data 0.004 (0.113) Batch 1.061 (1.477) Remain 12:27:01 loss: 0.2728 Lr: 0.00416
[2023-12-25 11:18:06,924 INFO misc.py line 119 253097] Train: [41/100][249/510] Data 0.007 (0.112) Batch 1.228 (1.476) Remain 12:26:29 loss: 0.1898 Lr: 0.00416
[2023-12-25 11:18:07,997 INFO misc.py line 119 253097] Train: [41/100][250/510] Data 0.006 (0.112) Batch 1.074 (1.474) Remain 12:25:38 loss: 0.2442 Lr: 0.00416
[2023-12-25 11:18:09,223 INFO misc.py line 119 253097] Train: [41/100][251/510] Data 0.005 (0.111) Batch 1.221 (1.473) Remain 12:25:05 loss: 0.1933 Lr: 0.00416
[2023-12-25 11:18:10,431 INFO misc.py line 119 253097] Train: [41/100][252/510] Data 0.010 (0.111) Batch 1.213 (1.472) Remain 12:24:32 loss: 0.4010 Lr: 0.00416
[2023-12-25 11:18:11,462 INFO misc.py line 119 253097] Train: [41/100][253/510] Data 0.005 (0.111) Batch 1.028 (1.470) Remain 12:23:37 loss: 0.1348 Lr: 0.00416
[2023-12-25 11:18:17,186 INFO misc.py line 119 253097] Train: [41/100][254/510] Data 0.008 (0.110) Batch 5.728 (1.487) Remain 12:32:10 loss: 0.1416 Lr: 0.00416
[2023-12-25 11:18:18,299 INFO misc.py line 119 253097] Train: [41/100][255/510] Data 0.004 (0.110) Batch 1.113 (1.486) Remain 12:31:24 loss: 0.1527 Lr: 0.00416
[2023-12-25 11:18:19,281 INFO misc.py line 119 253097] Train: [41/100][256/510] Data 0.005 (0.109) Batch 0.982 (1.484) Remain 12:30:22 loss: 0.1301 Lr: 0.00416
[2023-12-25 11:18:20,300 INFO misc.py line 119 253097] Train: [41/100][257/510] Data 0.005 (0.109) Batch 1.020 (1.482) Remain 12:29:25 loss: 0.4081 Lr: 0.00416
[2023-12-25 11:18:24,813 INFO misc.py line 119 253097] Train: [41/100][258/510] Data 0.004 (0.108) Batch 4.512 (1.494) Remain 12:35:24 loss: 0.2300 Lr: 0.00416
[2023-12-25 11:18:26,096 INFO misc.py line 119 253097] Train: [41/100][259/510] Data 0.005 (0.108) Batch 1.283 (1.493) Remain 12:34:57 loss: 0.2875 Lr: 0.00416
[2023-12-25 11:18:27,294 INFO misc.py line 119 253097] Train: [41/100][260/510] Data 0.006 (0.108) Batch 1.195 (1.492) Remain 12:34:21 loss: 0.1900 Lr: 0.00416
[2023-12-25 11:18:28,281 INFO misc.py line 119 253097] Train: [41/100][261/510] Data 0.008 (0.107) Batch 0.990 (1.490) Remain 12:33:20 loss: 0.2154 Lr: 0.00416
[2023-12-25 11:18:29,427 INFO misc.py line 119 253097] Train: [41/100][262/510] Data 0.004 (0.107) Batch 1.147 (1.489) Remain 12:32:39 loss: 0.1620 Lr: 0.00416
[2023-12-25 11:18:30,363 INFO misc.py line 119 253097] Train: [41/100][263/510] Data 0.004 (0.106) Batch 0.935 (1.486) Remain 12:31:33 loss: 0.1767 Lr: 0.00416
[2023-12-25 11:18:31,544 INFO misc.py line 119 253097] Train: [41/100][264/510] Data 0.005 (0.106) Batch 1.181 (1.485) Remain 12:30:56 loss: 0.2473 Lr: 0.00416
[2023-12-25 11:18:32,599 INFO misc.py line 119 253097] Train: [41/100][265/510] Data 0.004 (0.106) Batch 1.055 (1.484) Remain 12:30:04 loss: 0.1527 Lr: 0.00416
[2023-12-25 11:18:33,720 INFO misc.py line 119 253097] Train: [41/100][266/510] Data 0.005 (0.105) Batch 1.120 (1.482) Remain 12:29:21 loss: 0.2150 Lr: 0.00416
[2023-12-25 11:18:34,940 INFO misc.py line 119 253097] Train: [41/100][267/510] Data 0.007 (0.105) Batch 1.216 (1.481) Remain 12:28:49 loss: 0.2089 Lr: 0.00416
[2023-12-25 11:18:36,153 INFO misc.py line 119 253097] Train: [41/100][268/510] Data 0.010 (0.105) Batch 1.208 (1.480) Remain 12:28:16 loss: 0.1349 Lr: 0.00416
[2023-12-25 11:18:37,158 INFO misc.py line 119 253097] Train: [41/100][269/510] Data 0.015 (0.104) Batch 1.009 (1.478) Remain 12:27:21 loss: 0.1980 Lr: 0.00416
[2023-12-25 11:18:38,447 INFO misc.py line 119 253097] Train: [41/100][270/510] Data 0.011 (0.104) Batch 1.279 (1.478) Remain 12:26:57 loss: 0.1960 Lr: 0.00416
[2023-12-25 11:18:39,673 INFO misc.py line 119 253097] Train: [41/100][271/510] Data 0.021 (0.104) Batch 1.243 (1.477) Remain 12:26:29 loss: 0.2722 Lr: 0.00416
[2023-12-25 11:18:41,014 INFO misc.py line 119 253097] Train: [41/100][272/510] Data 0.005 (0.103) Batch 1.340 (1.476) Remain 12:26:12 loss: 0.3443 Lr: 0.00416
[2023-12-25 11:18:42,035 INFO misc.py line 119 253097] Train: [41/100][273/510] Data 0.004 (0.103) Batch 1.018 (1.475) Remain 12:25:19 loss: 0.2616 Lr: 0.00416
[2023-12-25 11:18:43,276 INFO misc.py line 119 253097] Train: [41/100][274/510] Data 0.007 (0.102) Batch 1.238 (1.474) Remain 12:24:51 loss: 0.1582 Lr: 0.00416
[2023-12-25 11:18:44,377 INFO misc.py line 119 253097] Train: [41/100][275/510] Data 0.011 (0.102) Batch 1.107 (1.472) Remain 12:24:08 loss: 0.2184 Lr: 0.00416
[2023-12-25 11:18:45,489 INFO misc.py line 119 253097] Train: [41/100][276/510] Data 0.005 (0.102) Batch 1.110 (1.471) Remain 12:23:27 loss: 0.1316 Lr: 0.00416
[2023-12-25 11:18:46,775 INFO misc.py line 119 253097] Train: [41/100][277/510] Data 0.007 (0.101) Batch 1.287 (1.470) Remain 12:23:05 loss: 0.1800 Lr: 0.00416
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[2023-12-25 11:18:49,009 INFO misc.py line 119 253097] Train: [41/100][279/510] Data 0.009 (0.101) Batch 1.101 (1.468) Remain 12:21:44 loss: 0.2109 Lr: 0.00416
[2023-12-25 11:18:50,127 INFO misc.py line 119 253097] Train: [41/100][280/510] Data 0.013 (0.100) Batch 1.106 (1.466) Remain 12:21:03 loss: 0.1867 Lr: 0.00416
[2023-12-25 11:18:51,458 INFO misc.py line 119 253097] Train: [41/100][281/510] Data 0.024 (0.100) Batch 1.350 (1.466) Remain 12:20:48 loss: 0.2023 Lr: 0.00416
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[2023-12-25 11:18:55,091 INFO misc.py line 119 253097] Train: [41/100][283/510] Data 0.012 (0.100) Batch 2.418 (1.469) Remain 12:22:01 loss: 0.1292 Lr: 0.00416
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[2023-12-25 11:19:00,955 INFO misc.py line 119 253097] Train: [41/100][288/510] Data 0.026 (0.098) Batch 1.061 (1.463) Remain 12:19:17 loss: 0.2031 Lr: 0.00415
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[2023-12-25 11:19:23,654 INFO misc.py line 119 253097] Train: [41/100][298/510] Data 3.056 (0.105) Batch 4.187 (1.491) Remain 12:32:51 loss: 0.1101 Lr: 0.00415
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[2023-12-25 11:19:25,764 INFO misc.py line 119 253097] Train: [41/100][300/510] Data 0.004 (0.104) Batch 1.021 (1.488) Remain 12:31:19 loss: 0.2126 Lr: 0.00415
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[2023-12-25 11:19:27,720 INFO misc.py line 119 253097] Train: [41/100][302/510] Data 0.005 (0.104) Batch 0.910 (1.484) Remain 12:29:33 loss: 0.1837 Lr: 0.00415
[2023-12-25 11:19:28,817 INFO misc.py line 119 253097] Train: [41/100][303/510] Data 0.003 (0.103) Batch 1.097 (1.483) Remain 12:28:52 loss: 0.1500 Lr: 0.00415
[2023-12-25 11:19:33,597 INFO misc.py line 119 253097] Train: [41/100][304/510] Data 3.747 (0.116) Batch 4.778 (1.494) Remain 12:34:22 loss: 0.1370 Lr: 0.00415
[2023-12-25 11:19:34,714 INFO misc.py line 119 253097] Train: [41/100][305/510] Data 0.008 (0.115) Batch 1.118 (1.493) Remain 12:33:43 loss: 0.1512 Lr: 0.00415
[2023-12-25 11:19:35,886 INFO misc.py line 119 253097] Train: [41/100][306/510] Data 0.005 (0.115) Batch 1.174 (1.492) Remain 12:33:09 loss: 0.1463 Lr: 0.00415
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[2023-12-25 11:20:47,823 INFO misc.py line 119 253097] Train: [41/100][342/510] Data 0.005 (0.117) Batch 1.020 (1.546) Remain 12:59:23 loss: 0.2006 Lr: 0.00415
[2023-12-25 11:20:49,014 INFO misc.py line 119 253097] Train: [41/100][343/510] Data 0.005 (0.117) Batch 1.191 (1.544) Remain 12:58:50 loss: 0.1934 Lr: 0.00414
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[2023-12-25 11:20:53,522 INFO misc.py line 119 253097] Train: [41/100][347/510] Data 0.004 (0.116) Batch 1.040 (1.540) Remain 12:56:17 loss: 0.1966 Lr: 0.00414
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[2023-12-25 11:20:58,402 INFO misc.py line 119 253097] Train: [41/100][351/510] Data 0.009 (0.114) Batch 1.224 (1.536) Remain 12:54:20 loss: 0.2883 Lr: 0.00414
[2023-12-25 11:20:59,512 INFO misc.py line 119 253097] Train: [41/100][352/510] Data 0.004 (0.114) Batch 1.111 (1.535) Remain 12:53:41 loss: 0.2520 Lr: 0.00414
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[2023-12-25 11:21:02,931 INFO misc.py line 119 253097] Train: [41/100][355/510] Data 0.008 (0.113) Batch 1.095 (1.531) Remain 12:51:55 loss: 0.2432 Lr: 0.00414
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[2023-12-25 11:21:05,367 INFO misc.py line 119 253097] Train: [41/100][357/510] Data 0.003 (0.112) Batch 1.180 (1.530) Remain 12:50:58 loss: 0.2217 Lr: 0.00414
[2023-12-25 11:21:06,514 INFO misc.py line 119 253097] Train: [41/100][358/510] Data 0.004 (0.112) Batch 1.139 (1.528) Remain 12:50:24 loss: 0.2893 Lr: 0.00414
[2023-12-25 11:21:07,794 INFO misc.py line 119 253097] Train: [41/100][359/510] Data 0.011 (0.112) Batch 1.286 (1.528) Remain 12:50:02 loss: 0.1842 Lr: 0.00414
[2023-12-25 11:21:08,955 INFO misc.py line 119 253097] Train: [41/100][360/510] Data 0.006 (0.112) Batch 1.159 (1.527) Remain 12:49:29 loss: 0.1947 Lr: 0.00414
[2023-12-25 11:21:21,193 INFO misc.py line 119 253097] Train: [41/100][361/510] Data 0.007 (0.111) Batch 12.242 (1.557) Remain 13:04:32 loss: 0.1911 Lr: 0.00414
[2023-12-25 11:21:22,481 INFO misc.py line 119 253097] Train: [41/100][362/510] Data 0.004 (0.111) Batch 1.284 (1.556) Remain 13:04:08 loss: 0.2677 Lr: 0.00414
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[2023-12-25 11:21:36,951 INFO misc.py line 119 253097] Train: [41/100][375/510] Data 0.009 (0.107) Batch 1.160 (1.540) Remain 12:55:58 loss: 0.5330 Lr: 0.00414
[2023-12-25 11:21:38,018 INFO misc.py line 119 253097] Train: [41/100][376/510] Data 0.027 (0.107) Batch 1.089 (1.539) Remain 12:55:20 loss: 0.2411 Lr: 0.00414
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[2023-12-25 11:21:41,315 INFO misc.py line 119 253097] Train: [41/100][379/510] Data 0.007 (0.106) Batch 1.175 (1.536) Remain 12:53:29 loss: 0.4301 Lr: 0.00414
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[2023-12-25 11:21:43,642 INFO misc.py line 119 253097] Train: [41/100][381/510] Data 0.011 (0.106) Batch 1.223 (1.534) Remain 12:52:26 loss: 0.2198 Lr: 0.00414
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[2023-12-25 11:21:46,085 INFO misc.py line 119 253097] Train: [41/100][383/510] Data 0.004 (0.105) Batch 1.220 (1.532) Remain 12:51:34 loss: 0.2454 Lr: 0.00414
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[2023-12-25 11:22:29,827 INFO misc.py line 119 253097] Train: [41/100][411/510] Data 0.007 (0.098) Batch 1.223 (1.534) Remain 12:51:54 loss: 0.1066 Lr: 0.00413
[2023-12-25 11:22:31,114 INFO misc.py line 119 253097] Train: [41/100][412/510] Data 0.003 (0.098) Batch 1.286 (1.534) Remain 12:51:34 loss: 0.2079 Lr: 0.00413
[2023-12-25 11:22:32,280 INFO misc.py line 119 253097] Train: [41/100][413/510] Data 0.004 (0.098) Batch 1.161 (1.533) Remain 12:51:05 loss: 0.1779 Lr: 0.00413
[2023-12-25 11:22:33,356 INFO misc.py line 119 253097] Train: [41/100][414/510] Data 0.009 (0.098) Batch 1.081 (1.532) Remain 12:50:30 loss: 0.3039 Lr: 0.00413
[2023-12-25 11:22:34,623 INFO misc.py line 119 253097] Train: [41/100][415/510] Data 0.004 (0.098) Batch 1.264 (1.531) Remain 12:50:09 loss: 0.2512 Lr: 0.00413
[2023-12-25 11:22:35,723 INFO misc.py line 119 253097] Train: [41/100][416/510] Data 0.007 (0.097) Batch 1.095 (1.530) Remain 12:49:36 loss: 0.4565 Lr: 0.00413
[2023-12-25 11:22:36,947 INFO misc.py line 119 253097] Train: [41/100][417/510] Data 0.013 (0.097) Batch 1.229 (1.529) Remain 12:49:12 loss: 0.2427 Lr: 0.00413
[2023-12-25 11:22:37,979 INFO misc.py line 119 253097] Train: [41/100][418/510] Data 0.007 (0.097) Batch 1.035 (1.528) Remain 12:48:35 loss: 0.2126 Lr: 0.00413
[2023-12-25 11:22:43,236 INFO misc.py line 119 253097] Train: [41/100][419/510] Data 0.004 (0.097) Batch 5.258 (1.537) Remain 12:53:04 loss: 0.1709 Lr: 0.00413
[2023-12-25 11:22:44,349 INFO misc.py line 119 253097] Train: [41/100][420/510] Data 0.003 (0.096) Batch 1.110 (1.536) Remain 12:52:31 loss: 0.2115 Lr: 0.00413
[2023-12-25 11:22:45,360 INFO misc.py line 119 253097] Train: [41/100][421/510] Data 0.005 (0.096) Batch 1.012 (1.535) Remain 12:51:52 loss: 0.3716 Lr: 0.00413
[2023-12-25 11:22:46,645 INFO misc.py line 119 253097] Train: [41/100][422/510] Data 0.005 (0.096) Batch 1.272 (1.534) Remain 12:51:32 loss: 0.3131 Lr: 0.00413
[2023-12-25 11:22:47,922 INFO misc.py line 119 253097] Train: [41/100][423/510] Data 0.018 (0.096) Batch 1.287 (1.533) Remain 12:51:12 loss: 0.1131 Lr: 0.00413
[2023-12-25 11:22:48,952 INFO misc.py line 119 253097] Train: [41/100][424/510] Data 0.007 (0.096) Batch 1.034 (1.532) Remain 12:50:35 loss: 0.2444 Lr: 0.00413
[2023-12-25 11:22:50,201 INFO misc.py line 119 253097] Train: [41/100][425/510] Data 0.004 (0.095) Batch 1.247 (1.532) Remain 12:50:13 loss: 0.0847 Lr: 0.00413
[2023-12-25 11:22:51,286 INFO misc.py line 119 253097] Train: [41/100][426/510] Data 0.007 (0.095) Batch 1.085 (1.530) Remain 12:49:40 loss: 0.4324 Lr: 0.00413
[2023-12-25 11:22:52,288 INFO misc.py line 119 253097] Train: [41/100][427/510] Data 0.006 (0.095) Batch 1.005 (1.529) Remain 12:49:01 loss: 0.1261 Lr: 0.00413
[2023-12-25 11:22:53,536 INFO misc.py line 119 253097] Train: [41/100][428/510] Data 0.004 (0.095) Batch 1.243 (1.529) Remain 12:48:39 loss: 0.2132 Lr: 0.00413
[2023-12-25 11:22:55,464 INFO misc.py line 119 253097] Train: [41/100][429/510] Data 0.844 (0.097) Batch 1.933 (1.529) Remain 12:49:06 loss: 0.2394 Lr: 0.00413
[2023-12-25 11:22:56,694 INFO misc.py line 119 253097] Train: [41/100][430/510] Data 0.003 (0.096) Batch 1.224 (1.529) Remain 12:48:43 loss: 0.1363 Lr: 0.00413
[2023-12-25 11:22:57,990 INFO misc.py line 119 253097] Train: [41/100][431/510] Data 0.010 (0.096) Batch 1.300 (1.528) Remain 12:48:25 loss: 0.2014 Lr: 0.00413
[2023-12-25 11:22:59,035 INFO misc.py line 119 253097] Train: [41/100][432/510] Data 0.006 (0.096) Batch 1.044 (1.527) Remain 12:47:50 loss: 0.1089 Lr: 0.00413
[2023-12-25 11:23:00,144 INFO misc.py line 119 253097] Train: [41/100][433/510] Data 0.008 (0.096) Batch 1.108 (1.526) Remain 12:47:19 loss: 0.0758 Lr: 0.00413
[2023-12-25 11:23:01,229 INFO misc.py line 119 253097] Train: [41/100][434/510] Data 0.008 (0.096) Batch 1.089 (1.525) Remain 12:46:47 loss: 0.1508 Lr: 0.00413
[2023-12-25 11:23:02,528 INFO misc.py line 119 253097] Train: [41/100][435/510] Data 0.005 (0.095) Batch 1.295 (1.525) Remain 12:46:29 loss: 0.1821 Lr: 0.00413
[2023-12-25 11:23:03,849 INFO misc.py line 119 253097] Train: [41/100][436/510] Data 0.010 (0.095) Batch 1.326 (1.524) Remain 12:46:14 loss: 0.1168 Lr: 0.00413
[2023-12-25 11:23:10,368 INFO misc.py line 119 253097] Train: [41/100][437/510] Data 0.004 (0.095) Batch 6.506 (1.536) Remain 12:51:58 loss: 0.2024 Lr: 0.00413
[2023-12-25 11:23:11,460 INFO misc.py line 119 253097] Train: [41/100][438/510] Data 0.017 (0.095) Batch 1.106 (1.535) Remain 12:51:27 loss: 0.2880 Lr: 0.00413
[2023-12-25 11:23:12,678 INFO misc.py line 119 253097] Train: [41/100][439/510] Data 0.003 (0.095) Batch 1.218 (1.534) Remain 12:51:03 loss: 0.2020 Lr: 0.00413
[2023-12-25 11:23:13,795 INFO misc.py line 119 253097] Train: [41/100][440/510] Data 0.004 (0.094) Batch 1.116 (1.533) Remain 12:50:33 loss: 0.1273 Lr: 0.00413
[2023-12-25 11:23:14,852 INFO misc.py line 119 253097] Train: [41/100][441/510] Data 0.004 (0.094) Batch 1.057 (1.532) Remain 12:49:59 loss: 0.1412 Lr: 0.00413
[2023-12-25 11:23:15,883 INFO misc.py line 119 253097] Train: [41/100][442/510] Data 0.004 (0.094) Batch 1.030 (1.531) Remain 12:49:23 loss: 0.1834 Lr: 0.00413
[2023-12-25 11:23:21,747 INFO misc.py line 119 253097] Train: [41/100][443/510] Data 0.005 (0.094) Batch 5.865 (1.541) Remain 12:54:18 loss: 0.1504 Lr: 0.00413
[2023-12-25 11:23:22,832 INFO misc.py line 119 253097] Train: [41/100][444/510] Data 0.004 (0.093) Batch 1.084 (1.540) Remain 12:53:46 loss: 0.1275 Lr: 0.00413
[2023-12-25 11:23:23,948 INFO misc.py line 119 253097] Train: [41/100][445/510] Data 0.004 (0.093) Batch 1.114 (1.539) Remain 12:53:15 loss: 0.1816 Lr: 0.00413
[2023-12-25 11:23:25,134 INFO misc.py line 119 253097] Train: [41/100][446/510] Data 0.006 (0.093) Batch 1.188 (1.538) Remain 12:52:50 loss: 0.2610 Lr: 0.00413
[2023-12-25 11:23:26,396 INFO misc.py line 119 253097] Train: [41/100][447/510] Data 0.004 (0.093) Batch 1.256 (1.537) Remain 12:52:29 loss: 0.2081 Lr: 0.00413
[2023-12-25 11:23:27,566 INFO misc.py line 119 253097] Train: [41/100][448/510] Data 0.011 (0.093) Batch 1.175 (1.536) Remain 12:52:03 loss: 0.1965 Lr: 0.00413
[2023-12-25 11:23:28,672 INFO misc.py line 119 253097] Train: [41/100][449/510] Data 0.006 (0.093) Batch 1.108 (1.535) Remain 12:51:32 loss: 0.1563 Lr: 0.00413
[2023-12-25 11:23:29,937 INFO misc.py line 119 253097] Train: [41/100][450/510] Data 0.003 (0.092) Batch 1.261 (1.535) Remain 12:51:12 loss: 0.1731 Lr: 0.00413
[2023-12-25 11:23:31,028 INFO misc.py line 119 253097] Train: [41/100][451/510] Data 0.006 (0.092) Batch 1.094 (1.534) Remain 12:50:41 loss: 0.2128 Lr: 0.00413
[2023-12-25 11:23:32,169 INFO misc.py line 119 253097] Train: [41/100][452/510] Data 0.005 (0.092) Batch 1.139 (1.533) Remain 12:50:13 loss: 0.1715 Lr: 0.00413
[2023-12-25 11:23:33,355 INFO misc.py line 119 253097] Train: [41/100][453/510] Data 0.007 (0.092) Batch 1.189 (1.532) Remain 12:49:49 loss: 0.1810 Lr: 0.00413
[2023-12-25 11:23:34,532 INFO misc.py line 119 253097] Train: [41/100][454/510] Data 0.003 (0.092) Batch 1.174 (1.531) Remain 12:49:23 loss: 0.0980 Lr: 0.00412
[2023-12-25 11:23:35,736 INFO misc.py line 119 253097] Train: [41/100][455/510] Data 0.007 (0.091) Batch 1.202 (1.531) Remain 12:49:00 loss: 0.3093 Lr: 0.00412
[2023-12-25 11:23:36,901 INFO misc.py line 119 253097] Train: [41/100][456/510] Data 0.008 (0.091) Batch 1.165 (1.530) Remain 12:48:34 loss: 0.1361 Lr: 0.00412
[2023-12-25 11:23:37,992 INFO misc.py line 119 253097] Train: [41/100][457/510] Data 0.008 (0.091) Batch 1.095 (1.529) Remain 12:48:03 loss: 0.2157 Lr: 0.00412
[2023-12-25 11:23:39,155 INFO misc.py line 119 253097] Train: [41/100][458/510] Data 0.004 (0.091) Batch 1.164 (1.528) Remain 12:47:38 loss: 0.1412 Lr: 0.00412
[2023-12-25 11:23:40,514 INFO misc.py line 119 253097] Train: [41/100][459/510] Data 0.004 (0.091) Batch 1.353 (1.528) Remain 12:47:25 loss: 0.1354 Lr: 0.00412
[2023-12-25 11:23:41,689 INFO misc.py line 119 253097] Train: [41/100][460/510] Data 0.010 (0.090) Batch 1.178 (1.527) Remain 12:47:00 loss: 0.2075 Lr: 0.00412
[2023-12-25 11:23:43,025 INFO misc.py line 119 253097] Train: [41/100][461/510] Data 0.007 (0.090) Batch 1.334 (1.526) Remain 12:46:46 loss: 0.2404 Lr: 0.00412
[2023-12-25 11:23:44,203 INFO misc.py line 119 253097] Train: [41/100][462/510] Data 0.009 (0.090) Batch 1.183 (1.526) Remain 12:46:22 loss: 0.2312 Lr: 0.00412
[2023-12-25 11:23:45,385 INFO misc.py line 119 253097] Train: [41/100][463/510] Data 0.004 (0.090) Batch 1.181 (1.525) Remain 12:45:58 loss: 0.1181 Lr: 0.00412
[2023-12-25 11:23:46,366 INFO misc.py line 119 253097] Train: [41/100][464/510] Data 0.004 (0.090) Batch 0.982 (1.524) Remain 12:45:21 loss: 0.1908 Lr: 0.00412
[2023-12-25 11:23:47,561 INFO misc.py line 119 253097] Train: [41/100][465/510] Data 0.004 (0.090) Batch 1.193 (1.523) Remain 12:44:57 loss: 0.2614 Lr: 0.00412
[2023-12-25 11:23:56,020 INFO misc.py line 119 253097] Train: [41/100][466/510] Data 0.005 (0.089) Batch 8.461 (1.538) Remain 12:52:27 loss: 0.1347 Lr: 0.00412
[2023-12-25 11:23:56,969 INFO misc.py line 119 253097] Train: [41/100][467/510] Data 0.003 (0.089) Batch 0.944 (1.537) Remain 12:51:47 loss: 0.1544 Lr: 0.00412
[2023-12-25 11:23:58,190 INFO misc.py line 119 253097] Train: [41/100][468/510] Data 0.008 (0.089) Batch 1.225 (1.536) Remain 12:51:26 loss: 0.2389 Lr: 0.00412
[2023-12-25 11:23:59,356 INFO misc.py line 119 253097] Train: [41/100][469/510] Data 0.004 (0.089) Batch 1.164 (1.535) Remain 12:51:00 loss: 0.2641 Lr: 0.00412
[2023-12-25 11:24:00,425 INFO misc.py line 119 253097] Train: [41/100][470/510] Data 0.006 (0.089) Batch 1.069 (1.534) Remain 12:50:28 loss: 0.1290 Lr: 0.00412
[2023-12-25 11:24:01,541 INFO misc.py line 119 253097] Train: [41/100][471/510] Data 0.007 (0.088) Batch 1.110 (1.533) Remain 12:50:00 loss: 0.1284 Lr: 0.00412
[2023-12-25 11:24:02,676 INFO misc.py line 119 253097] Train: [41/100][472/510] Data 0.013 (0.088) Batch 1.139 (1.533) Remain 12:49:33 loss: 0.3389 Lr: 0.00412
[2023-12-25 11:24:03,863 INFO misc.py line 119 253097] Train: [41/100][473/510] Data 0.008 (0.088) Batch 1.186 (1.532) Remain 12:49:09 loss: 0.2223 Lr: 0.00412
[2023-12-25 11:24:05,014 INFO misc.py line 119 253097] Train: [41/100][474/510] Data 0.009 (0.088) Batch 1.155 (1.531) Remain 12:48:43 loss: 0.2154 Lr: 0.00412
[2023-12-25 11:24:06,164 INFO misc.py line 119 253097] Train: [41/100][475/510] Data 0.005 (0.088) Batch 1.148 (1.530) Remain 12:48:17 loss: 0.1597 Lr: 0.00412
[2023-12-25 11:24:07,262 INFO misc.py line 119 253097] Train: [41/100][476/510] Data 0.007 (0.088) Batch 1.101 (1.529) Remain 12:47:48 loss: 0.1408 Lr: 0.00412
[2023-12-25 11:24:08,517 INFO misc.py line 119 253097] Train: [41/100][477/510] Data 0.005 (0.087) Batch 1.220 (1.529) Remain 12:47:27 loss: 0.1745 Lr: 0.00412
[2023-12-25 11:24:09,572 INFO misc.py line 119 253097] Train: [41/100][478/510] Data 0.041 (0.087) Batch 1.090 (1.528) Remain 12:46:58 loss: 0.1946 Lr: 0.00412
[2023-12-25 11:24:10,701 INFO misc.py line 119 253097] Train: [41/100][479/510] Data 0.005 (0.087) Batch 1.124 (1.527) Remain 12:46:31 loss: 0.1137 Lr: 0.00412
[2023-12-25 11:24:14,084 INFO misc.py line 119 253097] Train: [41/100][480/510] Data 0.010 (0.087) Batch 3.389 (1.531) Remain 12:48:27 loss: 0.4605 Lr: 0.00412
[2023-12-25 11:24:15,106 INFO misc.py line 119 253097] Train: [41/100][481/510] Data 0.004 (0.087) Batch 1.022 (1.530) Remain 12:47:53 loss: 0.1530 Lr: 0.00412
[2023-12-25 11:24:16,185 INFO misc.py line 119 253097] Train: [41/100][482/510] Data 0.004 (0.087) Batch 1.067 (1.529) Remain 12:47:23 loss: 0.2296 Lr: 0.00412
[2023-12-25 11:24:17,532 INFO misc.py line 119 253097] Train: [41/100][483/510] Data 0.016 (0.086) Batch 1.358 (1.528) Remain 12:47:10 loss: 0.1094 Lr: 0.00412
[2023-12-25 11:24:18,746 INFO misc.py line 119 253097] Train: [41/100][484/510] Data 0.006 (0.086) Batch 1.209 (1.528) Remain 12:46:49 loss: 0.1963 Lr: 0.00412
[2023-12-25 11:24:19,751 INFO misc.py line 119 253097] Train: [41/100][485/510] Data 0.010 (0.086) Batch 1.011 (1.527) Remain 12:46:15 loss: 0.2037 Lr: 0.00412
[2023-12-25 11:24:20,921 INFO misc.py line 119 253097] Train: [41/100][486/510] Data 0.004 (0.086) Batch 1.166 (1.526) Remain 12:45:51 loss: 0.0983 Lr: 0.00412
[2023-12-25 11:24:21,963 INFO misc.py line 119 253097] Train: [41/100][487/510] Data 0.008 (0.086) Batch 1.043 (1.525) Remain 12:45:19 loss: 0.3029 Lr: 0.00412
[2023-12-25 11:24:23,119 INFO misc.py line 119 253097] Train: [41/100][488/510] Data 0.008 (0.086) Batch 1.150 (1.524) Remain 12:44:55 loss: 0.1383 Lr: 0.00412
[2023-12-25 11:24:24,391 INFO misc.py line 119 253097] Train: [41/100][489/510] Data 0.014 (0.086) Batch 1.280 (1.524) Remain 12:44:38 loss: 0.3763 Lr: 0.00412
[2023-12-25 11:24:25,512 INFO misc.py line 119 253097] Train: [41/100][490/510] Data 0.006 (0.085) Batch 1.122 (1.523) Remain 12:44:12 loss: 0.2907 Lr: 0.00412
[2023-12-25 11:24:26,575 INFO misc.py line 119 253097] Train: [41/100][491/510] Data 0.004 (0.085) Batch 1.064 (1.522) Remain 12:43:42 loss: 0.4914 Lr: 0.00412
[2023-12-25 11:24:30,671 INFO misc.py line 119 253097] Train: [41/100][492/510] Data 0.004 (0.085) Batch 4.094 (1.527) Remain 12:46:19 loss: 0.1890 Lr: 0.00412
[2023-12-25 11:24:31,955 INFO misc.py line 119 253097] Train: [41/100][493/510] Data 0.005 (0.085) Batch 1.283 (1.527) Remain 12:46:02 loss: 0.1167 Lr: 0.00412
[2023-12-25 11:24:33,076 INFO misc.py line 119 253097] Train: [41/100][494/510] Data 0.007 (0.085) Batch 1.119 (1.526) Remain 12:45:36 loss: 0.0856 Lr: 0.00412
[2023-12-25 11:24:38,241 INFO misc.py line 119 253097] Train: [41/100][495/510] Data 0.008 (0.085) Batch 5.170 (1.533) Remain 12:49:17 loss: 0.1613 Lr: 0.00412
[2023-12-25 11:24:39,469 INFO misc.py line 119 253097] Train: [41/100][496/510] Data 0.004 (0.084) Batch 1.217 (1.533) Remain 12:48:56 loss: 0.1716 Lr: 0.00412
[2023-12-25 11:24:40,638 INFO misc.py line 119 253097] Train: [41/100][497/510] Data 0.016 (0.084) Batch 1.180 (1.532) Remain 12:48:33 loss: 0.1995 Lr: 0.00412
[2023-12-25 11:24:41,703 INFO misc.py line 119 253097] Train: [41/100][498/510] Data 0.005 (0.084) Batch 1.065 (1.531) Remain 12:48:03 loss: 0.2879 Lr: 0.00412
[2023-12-25 11:24:42,737 INFO misc.py line 119 253097] Train: [41/100][499/510] Data 0.004 (0.084) Batch 1.035 (1.530) Remain 12:47:32 loss: 0.2397 Lr: 0.00412
[2023-12-25 11:24:43,738 INFO misc.py line 119 253097] Train: [41/100][500/510] Data 0.004 (0.084) Batch 0.999 (1.529) Remain 12:46:58 loss: 0.1564 Lr: 0.00412
[2023-12-25 11:24:45,264 INFO misc.py line 119 253097] Train: [41/100][501/510] Data 0.006 (0.084) Batch 1.526 (1.529) Remain 12:46:56 loss: 0.1502 Lr: 0.00412
[2023-12-25 11:24:46,511 INFO misc.py line 119 253097] Train: [41/100][502/510] Data 0.006 (0.083) Batch 1.247 (1.528) Remain 12:46:38 loss: 0.2014 Lr: 0.00412
[2023-12-25 11:24:54,115 INFO misc.py line 119 253097] Train: [41/100][503/510] Data 0.006 (0.083) Batch 7.606 (1.540) Remain 12:52:42 loss: 0.1444 Lr: 0.00412
[2023-12-25 11:24:55,208 INFO misc.py line 119 253097] Train: [41/100][504/510] Data 0.004 (0.083) Batch 1.093 (1.540) Remain 12:52:14 loss: 0.1935 Lr: 0.00412
[2023-12-25 11:24:56,517 INFO misc.py line 119 253097] Train: [41/100][505/510] Data 0.004 (0.083) Batch 1.307 (1.539) Remain 12:51:58 loss: 0.2178 Lr: 0.00412
[2023-12-25 11:24:57,702 INFO misc.py line 119 253097] Train: [41/100][506/510] Data 0.005 (0.083) Batch 1.187 (1.538) Remain 12:51:35 loss: 0.1206 Lr: 0.00412
[2023-12-25 11:24:58,991 INFO misc.py line 119 253097] Train: [41/100][507/510] Data 0.003 (0.083) Batch 1.288 (1.538) Remain 12:51:19 loss: 0.3482 Lr: 0.00412
[2023-12-25 11:25:00,131 INFO misc.py line 119 253097] Train: [41/100][508/510] Data 0.004 (0.083) Batch 1.087 (1.537) Remain 12:50:51 loss: 0.3910 Lr: 0.00412
[2023-12-25 11:25:01,347 INFO misc.py line 119 253097] Train: [41/100][509/510] Data 0.058 (0.082) Batch 1.269 (1.536) Remain 12:50:33 loss: 0.1862 Lr: 0.00412
[2023-12-25 11:25:02,544 INFO misc.py line 119 253097] Train: [41/100][510/510] Data 0.004 (0.082) Batch 1.193 (1.536) Remain 12:50:11 loss: 0.2066 Lr: 0.00411
[2023-12-25 11:25:02,545 INFO misc.py line 136 253097] Train result: loss: 0.2038 
[2023-12-25 11:25:02,546 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 11:25:39,071 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.3316 
[2023-12-25 11:25:39,426 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4964 
[2023-12-25 11:25:44,360 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6164 
[2023-12-25 11:25:44,878 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3313 
[2023-12-25 11:25:46,865 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7287 
[2023-12-25 11:25:47,296 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4646 
[2023-12-25 11:25:48,174 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3036 
[2023-12-25 11:25:48,740 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3556 
[2023-12-25 11:25:50,551 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9453 
[2023-12-25 11:25:52,675 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3114 
[2023-12-25 11:25:53,534 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3541 
[2023-12-25 11:25:53,961 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8789 
[2023-12-25 11:25:54,871 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4488 
[2023-12-25 11:25:57,814 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9035 
[2023-12-25 11:25:58,279 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1645 
[2023-12-25 11:25:58,891 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4373 
[2023-12-25 11:25:59,596 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3710 
[2023-12-25 11:26:01,163 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6594/0.7538/0.8945.
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9172/0.9490
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9798/0.9876
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8415/0.9384
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3854/0.5229
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5983/0.6278
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5722/0.7260
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8048/0.8970
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9005/0.9512
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5683/0.7523
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7585/0.8346
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6544/0.8481
[2023-12-25 11:26:01,164 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5914/0.7651
[2023-12-25 11:26:01,165 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 11:26:01,167 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 11:26:01,167 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 11:26:21,861 INFO misc.py line 119 253097] Train: [42/100][1/510] Data 11.895 (11.895) Batch 18.754 (18.754) Remain 156:44:40 loss: 0.1507 Lr: 0.00411
[2023-12-25 11:26:23,014 INFO misc.py line 119 253097] Train: [42/100][2/510] Data 0.004 (0.004) Batch 1.154 (1.154) Remain 09:38:37 loss: 0.2681 Lr: 0.00411
[2023-12-25 11:26:25,602 INFO misc.py line 119 253097] Train: [42/100][3/510] Data 0.004 (0.004) Batch 2.585 (2.585) Remain 21:36:04 loss: 0.2757 Lr: 0.00411
[2023-12-25 11:26:26,614 INFO misc.py line 119 253097] Train: [42/100][4/510] Data 0.007 (0.007) Batch 1.011 (1.011) Remain 08:26:46 loss: 0.2785 Lr: 0.00411
[2023-12-25 11:26:27,771 INFO misc.py line 119 253097] Train: [42/100][5/510] Data 0.007 (0.007) Batch 1.161 (1.086) Remain 09:04:19 loss: 0.1653 Lr: 0.00411
[2023-12-25 11:26:28,901 INFO misc.py line 119 253097] Train: [42/100][6/510] Data 0.004 (0.006) Batch 1.128 (1.100) Remain 09:11:25 loss: 0.0981 Lr: 0.00411
[2023-12-25 11:26:30,120 INFO misc.py line 119 253097] Train: [42/100][7/510] Data 0.006 (0.006) Batch 1.208 (1.127) Remain 09:24:57 loss: 0.1805 Lr: 0.00411
[2023-12-25 11:26:31,146 INFO misc.py line 119 253097] Train: [42/100][8/510] Data 0.017 (0.008) Batch 1.036 (1.109) Remain 09:15:49 loss: 0.2454 Lr: 0.00411
[2023-12-25 11:26:32,275 INFO misc.py line 119 253097] Train: [42/100][9/510] Data 0.008 (0.008) Batch 1.132 (1.113) Remain 09:17:46 loss: 0.0813 Lr: 0.00411
[2023-12-25 11:26:33,548 INFO misc.py line 119 253097] Train: [42/100][10/510] Data 0.005 (0.008) Batch 1.266 (1.134) Remain 09:28:42 loss: 0.2319 Lr: 0.00411
[2023-12-25 11:26:34,566 INFO misc.py line 119 253097] Train: [42/100][11/510] Data 0.012 (0.008) Batch 1.022 (1.120) Remain 09:21:38 loss: 0.1613 Lr: 0.00411
[2023-12-25 11:26:36,546 INFO misc.py line 119 253097] Train: [42/100][12/510] Data 0.721 (0.087) Batch 1.983 (1.216) Remain 10:09:40 loss: 0.1363 Lr: 0.00411
[2023-12-25 11:26:37,802 INFO misc.py line 119 253097] Train: [42/100][13/510] Data 0.005 (0.079) Batch 1.256 (1.220) Remain 10:11:38 loss: 0.2912 Lr: 0.00411
[2023-12-25 11:26:38,886 INFO misc.py line 119 253097] Train: [42/100][14/510] Data 0.005 (0.072) Batch 1.084 (1.208) Remain 10:05:25 loss: 0.0687 Lr: 0.00411
[2023-12-25 11:26:39,930 INFO misc.py line 119 253097] Train: [42/100][15/510] Data 0.004 (0.067) Batch 1.041 (1.194) Remain 09:58:27 loss: 0.1967 Lr: 0.00411
[2023-12-25 11:26:40,933 INFO misc.py line 119 253097] Train: [42/100][16/510] Data 0.007 (0.062) Batch 1.007 (1.180) Remain 09:51:13 loss: 0.1156 Lr: 0.00411
[2023-12-25 11:26:41,979 INFO misc.py line 119 253097] Train: [42/100][17/510] Data 0.005 (0.058) Batch 1.041 (1.170) Remain 09:46:14 loss: 0.1599 Lr: 0.00411
[2023-12-25 11:26:43,066 INFO misc.py line 119 253097] Train: [42/100][18/510] Data 0.008 (0.055) Batch 1.093 (1.165) Remain 09:43:39 loss: 0.1291 Lr: 0.00411
[2023-12-25 11:26:44,169 INFO misc.py line 119 253097] Train: [42/100][19/510] Data 0.003 (0.051) Batch 1.098 (1.160) Remain 09:41:32 loss: 0.1239 Lr: 0.00411
[2023-12-25 11:26:50,078 INFO misc.py line 119 253097] Train: [42/100][20/510] Data 4.720 (0.326) Batch 5.912 (1.440) Remain 12:01:37 loss: 0.2401 Lr: 0.00411
[2023-12-25 11:26:51,221 INFO misc.py line 119 253097] Train: [42/100][21/510] Data 0.004 (0.308) Batch 1.143 (1.423) Remain 11:53:20 loss: 0.1653 Lr: 0.00411
[2023-12-25 11:26:55,025 INFO misc.py line 119 253097] Train: [42/100][22/510] Data 0.003 (0.292) Batch 3.804 (1.549) Remain 12:56:07 loss: 0.1504 Lr: 0.00411
[2023-12-25 11:27:08,070 INFO misc.py line 119 253097] Train: [42/100][23/510] Data 12.036 (0.879) Batch 13.045 (2.124) Remain 17:44:08 loss: 0.2237 Lr: 0.00411
[2023-12-25 11:27:09,071 INFO misc.py line 119 253097] Train: [42/100][24/510] Data 0.004 (0.838) Batch 1.001 (2.070) Remain 17:17:19 loss: 0.2056 Lr: 0.00411
[2023-12-25 11:27:10,276 INFO misc.py line 119 253097] Train: [42/100][25/510] Data 0.003 (0.800) Batch 1.205 (2.031) Remain 16:57:34 loss: 0.2320 Lr: 0.00411
[2023-12-25 11:27:11,306 INFO misc.py line 119 253097] Train: [42/100][26/510] Data 0.004 (0.765) Batch 1.030 (1.987) Remain 16:35:44 loss: 0.2645 Lr: 0.00411
[2023-12-25 11:27:12,543 INFO misc.py line 119 253097] Train: [42/100][27/510] Data 0.003 (0.733) Batch 1.230 (1.956) Remain 16:19:54 loss: 0.2364 Lr: 0.00411
[2023-12-25 11:27:13,833 INFO misc.py line 119 253097] Train: [42/100][28/510] Data 0.010 (0.704) Batch 1.279 (1.929) Remain 16:06:18 loss: 0.1643 Lr: 0.00411
[2023-12-25 11:27:15,064 INFO misc.py line 119 253097] Train: [42/100][29/510] Data 0.021 (0.678) Batch 1.239 (1.902) Remain 15:52:59 loss: 0.1200 Lr: 0.00411
[2023-12-25 11:27:16,193 INFO misc.py line 119 253097] Train: [42/100][30/510] Data 0.015 (0.654) Batch 1.137 (1.874) Remain 15:38:45 loss: 0.2364 Lr: 0.00411
[2023-12-25 11:27:17,211 INFO misc.py line 119 253097] Train: [42/100][31/510] Data 0.005 (0.630) Batch 1.004 (1.843) Remain 15:23:09 loss: 0.2590 Lr: 0.00411
[2023-12-25 11:27:18,413 INFO misc.py line 119 253097] Train: [42/100][32/510] Data 0.021 (0.609) Batch 1.217 (1.821) Remain 15:12:18 loss: 0.1930 Lr: 0.00411
[2023-12-25 11:27:19,476 INFO misc.py line 119 253097] Train: [42/100][33/510] Data 0.005 (0.589) Batch 1.038 (1.795) Remain 14:59:12 loss: 0.1665 Lr: 0.00411
[2023-12-25 11:27:20,602 INFO misc.py line 119 253097] Train: [42/100][34/510] Data 0.030 (0.571) Batch 1.149 (1.774) Remain 14:48:43 loss: 0.1161 Lr: 0.00411
[2023-12-25 11:27:21,722 INFO misc.py line 119 253097] Train: [42/100][35/510] Data 0.007 (0.554) Batch 1.124 (1.754) Remain 14:38:31 loss: 0.1047 Lr: 0.00411
[2023-12-25 11:27:22,922 INFO misc.py line 119 253097] Train: [42/100][36/510] Data 0.003 (0.537) Batch 1.177 (1.736) Remain 14:29:44 loss: 0.3209 Lr: 0.00411
[2023-12-25 11:27:24,104 INFO misc.py line 119 253097] Train: [42/100][37/510] Data 0.026 (0.522) Batch 1.188 (1.720) Remain 14:21:38 loss: 0.1706 Lr: 0.00411
[2023-12-25 11:27:25,364 INFO misc.py line 119 253097] Train: [42/100][38/510] Data 0.019 (0.507) Batch 1.265 (1.707) Remain 14:15:06 loss: 0.2157 Lr: 0.00411
[2023-12-25 11:27:26,447 INFO misc.py line 119 253097] Train: [42/100][39/510] Data 0.014 (0.494) Batch 1.081 (1.690) Remain 14:06:21 loss: 0.3263 Lr: 0.00411
[2023-12-25 11:27:27,547 INFO misc.py line 119 253097] Train: [42/100][40/510] Data 0.017 (0.481) Batch 1.100 (1.674) Remain 13:58:20 loss: 0.2437 Lr: 0.00411
[2023-12-25 11:27:35,777 INFO misc.py line 119 253097] Train: [42/100][41/510] Data 0.017 (0.469) Batch 8.243 (1.847) Remain 15:24:53 loss: 0.1998 Lr: 0.00411
[2023-12-25 11:27:36,972 INFO misc.py line 119 253097] Train: [42/100][42/510] Data 0.004 (0.457) Batch 1.195 (1.830) Remain 15:16:29 loss: 0.1532 Lr: 0.00411
[2023-12-25 11:27:38,083 INFO misc.py line 119 253097] Train: [42/100][43/510] Data 0.004 (0.445) Batch 1.112 (1.812) Remain 15:07:28 loss: 0.2922 Lr: 0.00411
[2023-12-25 11:27:39,079 INFO misc.py line 119 253097] Train: [42/100][44/510] Data 0.003 (0.435) Batch 0.995 (1.792) Remain 14:57:27 loss: 0.3017 Lr: 0.00411
[2023-12-25 11:27:40,137 INFO misc.py line 119 253097] Train: [42/100][45/510] Data 0.004 (0.424) Batch 1.058 (1.775) Remain 14:48:40 loss: 0.3011 Lr: 0.00411
[2023-12-25 11:27:41,357 INFO misc.py line 119 253097] Train: [42/100][46/510] Data 0.005 (0.415) Batch 1.219 (1.762) Remain 14:42:10 loss: 0.2187 Lr: 0.00411
[2023-12-25 11:27:42,478 INFO misc.py line 119 253097] Train: [42/100][47/510] Data 0.005 (0.405) Batch 1.120 (1.747) Remain 14:34:50 loss: 0.2203 Lr: 0.00411
[2023-12-25 11:27:43,756 INFO misc.py line 119 253097] Train: [42/100][48/510] Data 0.007 (0.396) Batch 1.264 (1.736) Remain 14:29:26 loss: 0.1438 Lr: 0.00411
[2023-12-25 11:27:44,997 INFO misc.py line 119 253097] Train: [42/100][49/510] Data 0.024 (0.388) Batch 1.245 (1.726) Remain 14:24:03 loss: 0.1820 Lr: 0.00411
[2023-12-25 11:27:45,965 INFO misc.py line 119 253097] Train: [42/100][50/510] Data 0.016 (0.380) Batch 0.978 (1.710) Remain 14:16:04 loss: 0.2015 Lr: 0.00411
[2023-12-25 11:27:47,104 INFO misc.py line 119 253097] Train: [42/100][51/510] Data 0.006 (0.373) Batch 1.140 (1.698) Remain 14:10:05 loss: 0.3673 Lr: 0.00411
[2023-12-25 11:27:48,199 INFO misc.py line 119 253097] Train: [42/100][52/510] Data 0.005 (0.365) Batch 1.095 (1.686) Remain 14:03:54 loss: 0.1559 Lr: 0.00411
[2023-12-25 11:27:49,158 INFO misc.py line 119 253097] Train: [42/100][53/510] Data 0.005 (0.358) Batch 0.960 (1.671) Remain 13:56:36 loss: 0.2099 Lr: 0.00411
[2023-12-25 11:27:50,309 INFO misc.py line 119 253097] Train: [42/100][54/510] Data 0.005 (0.351) Batch 1.152 (1.661) Remain 13:51:28 loss: 0.1793 Lr: 0.00411
[2023-12-25 11:27:51,477 INFO misc.py line 119 253097] Train: [42/100][55/510] Data 0.006 (0.344) Batch 1.167 (1.651) Remain 13:46:41 loss: 0.2783 Lr: 0.00410
[2023-12-25 11:27:52,394 INFO misc.py line 119 253097] Train: [42/100][56/510] Data 0.005 (0.338) Batch 0.917 (1.638) Remain 13:39:43 loss: 0.1283 Lr: 0.00410
[2023-12-25 11:27:53,679 INFO misc.py line 119 253097] Train: [42/100][57/510] Data 0.005 (0.332) Batch 1.283 (1.631) Remain 13:36:25 loss: 0.4482 Lr: 0.00410
[2023-12-25 11:27:54,760 INFO misc.py line 119 253097] Train: [42/100][58/510] Data 0.007 (0.326) Batch 1.081 (1.621) Remain 13:31:23 loss: 0.2270 Lr: 0.00410
[2023-12-25 11:27:55,823 INFO misc.py line 119 253097] Train: [42/100][59/510] Data 0.007 (0.320) Batch 1.061 (1.611) Remain 13:26:21 loss: 0.1771 Lr: 0.00410
[2023-12-25 11:27:57,096 INFO misc.py line 119 253097] Train: [42/100][60/510] Data 0.008 (0.315) Batch 1.273 (1.605) Remain 13:23:21 loss: 0.1166 Lr: 0.00410
[2023-12-25 11:28:01,214 INFO misc.py line 119 253097] Train: [42/100][61/510] Data 0.008 (0.309) Batch 4.123 (1.649) Remain 13:45:03 loss: 0.1509 Lr: 0.00410
[2023-12-25 11:28:02,983 INFO misc.py line 119 253097] Train: [42/100][62/510] Data 0.004 (0.304) Batch 1.768 (1.651) Remain 13:46:02 loss: 0.1287 Lr: 0.00410
[2023-12-25 11:28:04,239 INFO misc.py line 119 253097] Train: [42/100][63/510] Data 0.006 (0.299) Batch 1.250 (1.644) Remain 13:42:40 loss: 0.1585 Lr: 0.00410
[2023-12-25 11:28:05,315 INFO misc.py line 119 253097] Train: [42/100][64/510] Data 0.012 (0.295) Batch 1.074 (1.635) Remain 13:37:58 loss: 0.2001 Lr: 0.00410
[2023-12-25 11:28:06,565 INFO misc.py line 119 253097] Train: [42/100][65/510] Data 0.014 (0.290) Batch 1.258 (1.628) Remain 13:34:54 loss: 0.2082 Lr: 0.00410
[2023-12-25 11:28:07,683 INFO misc.py line 119 253097] Train: [42/100][66/510] Data 0.004 (0.286) Batch 1.115 (1.620) Remain 13:30:48 loss: 0.2496 Lr: 0.00410
[2023-12-25 11:28:08,728 INFO misc.py line 119 253097] Train: [42/100][67/510] Data 0.008 (0.281) Batch 1.042 (1.611) Remain 13:26:15 loss: 0.2480 Lr: 0.00410
[2023-12-25 11:28:09,775 INFO misc.py line 119 253097] Train: [42/100][68/510] Data 0.012 (0.277) Batch 1.052 (1.603) Remain 13:21:55 loss: 0.0853 Lr: 0.00410
[2023-12-25 11:28:11,013 INFO misc.py line 119 253097] Train: [42/100][69/510] Data 0.005 (0.273) Batch 1.235 (1.597) Remain 13:19:06 loss: 0.4218 Lr: 0.00410
[2023-12-25 11:28:12,165 INFO misc.py line 119 253097] Train: [42/100][70/510] Data 0.008 (0.269) Batch 1.151 (1.590) Remain 13:15:45 loss: 0.1469 Lr: 0.00410
[2023-12-25 11:28:13,442 INFO misc.py line 119 253097] Train: [42/100][71/510] Data 0.010 (0.265) Batch 1.280 (1.586) Remain 13:13:26 loss: 0.1393 Lr: 0.00410
[2023-12-25 11:28:14,721 INFO misc.py line 119 253097] Train: [42/100][72/510] Data 0.007 (0.261) Batch 1.281 (1.581) Remain 13:11:12 loss: 0.2660 Lr: 0.00410
[2023-12-25 11:28:16,018 INFO misc.py line 119 253097] Train: [42/100][73/510] Data 0.005 (0.258) Batch 1.298 (1.577) Remain 13:09:08 loss: 0.1762 Lr: 0.00410
[2023-12-25 11:28:17,257 INFO misc.py line 119 253097] Train: [42/100][74/510] Data 0.004 (0.254) Batch 1.234 (1.573) Remain 13:06:42 loss: 0.2051 Lr: 0.00410
[2023-12-25 11:28:18,517 INFO misc.py line 119 253097] Train: [42/100][75/510] Data 0.009 (0.251) Batch 1.265 (1.568) Remain 13:04:32 loss: 0.1580 Lr: 0.00410
[2023-12-25 11:28:19,753 INFO misc.py line 119 253097] Train: [42/100][76/510] Data 0.003 (0.247) Batch 1.233 (1.564) Remain 13:02:12 loss: 0.1579 Lr: 0.00410
[2023-12-25 11:28:20,960 INFO misc.py line 119 253097] Train: [42/100][77/510] Data 0.006 (0.244) Batch 1.206 (1.559) Remain 12:59:46 loss: 0.2780 Lr: 0.00410
[2023-12-25 11:28:22,062 INFO misc.py line 119 253097] Train: [42/100][78/510] Data 0.008 (0.241) Batch 1.104 (1.553) Remain 12:56:42 loss: 0.1564 Lr: 0.00410
[2023-12-25 11:28:23,130 INFO misc.py line 119 253097] Train: [42/100][79/510] Data 0.006 (0.238) Batch 1.045 (1.546) Remain 12:53:20 loss: 0.1801 Lr: 0.00410
[2023-12-25 11:28:24,337 INFO misc.py line 119 253097] Train: [42/100][80/510] Data 0.030 (0.235) Batch 1.205 (1.542) Remain 12:51:06 loss: 0.1668 Lr: 0.00410
[2023-12-25 11:28:25,402 INFO misc.py line 119 253097] Train: [42/100][81/510] Data 0.031 (0.233) Batch 1.090 (1.536) Remain 12:48:10 loss: 0.1378 Lr: 0.00410
[2023-12-25 11:28:26,616 INFO misc.py line 119 253097] Train: [42/100][82/510] Data 0.006 (0.230) Batch 1.210 (1.532) Remain 12:46:05 loss: 0.0601 Lr: 0.00410
[2023-12-25 11:28:27,854 INFO misc.py line 119 253097] Train: [42/100][83/510] Data 0.010 (0.227) Batch 1.243 (1.528) Remain 12:44:15 loss: 0.2978 Lr: 0.00410
[2023-12-25 11:28:29,067 INFO misc.py line 119 253097] Train: [42/100][84/510] Data 0.006 (0.224) Batch 1.215 (1.524) Remain 12:42:17 loss: 0.1561 Lr: 0.00410
[2023-12-25 11:28:40,299 INFO misc.py line 119 253097] Train: [42/100][85/510] Data 0.004 (0.222) Batch 11.232 (1.643) Remain 13:41:28 loss: 0.1323 Lr: 0.00410
[2023-12-25 11:28:41,503 INFO misc.py line 119 253097] Train: [42/100][86/510] Data 0.004 (0.219) Batch 1.204 (1.637) Remain 13:38:48 loss: 0.1285 Lr: 0.00410
[2023-12-25 11:28:42,583 INFO misc.py line 119 253097] Train: [42/100][87/510] Data 0.003 (0.216) Batch 1.079 (1.631) Remain 13:35:27 loss: 0.1286 Lr: 0.00410
[2023-12-25 11:28:43,627 INFO misc.py line 119 253097] Train: [42/100][88/510] Data 0.004 (0.214) Batch 1.041 (1.624) Remain 13:31:57 loss: 0.2751 Lr: 0.00410
[2023-12-25 11:28:44,779 INFO misc.py line 119 253097] Train: [42/100][89/510] Data 0.008 (0.211) Batch 1.155 (1.618) Remain 13:29:12 loss: 0.1103 Lr: 0.00410
[2023-12-25 11:28:46,104 INFO misc.py line 119 253097] Train: [42/100][90/510] Data 0.005 (0.209) Batch 1.321 (1.615) Remain 13:27:28 loss: 0.2383 Lr: 0.00410
[2023-12-25 11:28:47,400 INFO misc.py line 119 253097] Train: [42/100][91/510] Data 0.009 (0.207) Batch 1.300 (1.611) Remain 13:25:39 loss: 0.0996 Lr: 0.00410
[2023-12-25 11:28:48,426 INFO misc.py line 119 253097] Train: [42/100][92/510] Data 0.005 (0.205) Batch 1.026 (1.605) Remain 13:22:20 loss: 0.1530 Lr: 0.00410
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[2023-12-25 11:28:50,672 INFO misc.py line 119 253097] Train: [42/100][94/510] Data 0.009 (0.200) Batch 0.996 (1.594) Remain 13:16:59 loss: 0.1312 Lr: 0.00410
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[2023-12-25 11:28:58,988 INFO misc.py line 119 253097] Train: [42/100][101/510] Data 0.011 (0.186) Batch 1.112 (1.565) Remain 13:02:17 loss: 0.2075 Lr: 0.00410
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[2023-12-25 11:29:13,213 INFO misc.py line 119 253097] Train: [42/100][105/510] Data 0.005 (0.179) Batch 11.370 (1.643) Remain 13:41:13 loss: 0.0778 Lr: 0.00410
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[2023-12-25 11:29:16,517 INFO misc.py line 119 253097] Train: [42/100][108/510] Data 0.007 (0.174) Batch 0.989 (1.628) Remain 13:33:24 loss: 0.1628 Lr: 0.00410
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[2023-12-25 11:29:19,856 INFO misc.py line 119 253097] Train: [42/100][111/510] Data 0.004 (0.170) Batch 0.921 (1.613) Remain 13:26:10 loss: 0.2943 Lr: 0.00409
[2023-12-25 11:29:20,879 INFO misc.py line 119 253097] Train: [42/100][112/510] Data 0.005 (0.168) Batch 1.023 (1.608) Remain 13:23:26 loss: 0.3089 Lr: 0.00409
[2023-12-25 11:29:21,908 INFO misc.py line 119 253097] Train: [42/100][113/510] Data 0.006 (0.167) Batch 1.030 (1.603) Remain 13:20:47 loss: 0.1724 Lr: 0.00409
[2023-12-25 11:29:23,149 INFO misc.py line 119 253097] Train: [42/100][114/510] Data 0.004 (0.165) Batch 1.240 (1.600) Remain 13:19:07 loss: 0.1828 Lr: 0.00409
[2023-12-25 11:29:24,393 INFO misc.py line 119 253097] Train: [42/100][115/510] Data 0.005 (0.164) Batch 1.244 (1.596) Remain 13:17:31 loss: 0.2677 Lr: 0.00409
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[2023-12-25 11:29:27,824 INFO misc.py line 119 253097] Train: [42/100][118/510] Data 0.006 (0.160) Batch 1.137 (1.585) Remain 13:11:31 loss: 0.2339 Lr: 0.00409
[2023-12-25 11:29:29,057 INFO misc.py line 119 253097] Train: [42/100][119/510] Data 0.007 (0.158) Batch 1.237 (1.582) Remain 13:09:59 loss: 0.1780 Lr: 0.00409
[2023-12-25 11:29:30,383 INFO misc.py line 119 253097] Train: [42/100][120/510] Data 0.004 (0.157) Batch 1.326 (1.579) Remain 13:08:53 loss: 0.2665 Lr: 0.00409
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[2023-12-25 11:29:32,623 INFO misc.py line 119 253097] Train: [42/100][122/510] Data 0.011 (0.155) Batch 1.155 (1.572) Remain 13:04:57 loss: 0.1364 Lr: 0.00409
[2023-12-25 11:29:33,727 INFO misc.py line 119 253097] Train: [42/100][123/510] Data 0.007 (0.153) Batch 1.100 (1.568) Remain 13:02:58 loss: 0.2281 Lr: 0.00409
[2023-12-25 11:29:34,821 INFO misc.py line 119 253097] Train: [42/100][124/510] Data 0.011 (0.152) Batch 1.101 (1.564) Remain 13:01:00 loss: 0.1893 Lr: 0.00409
[2023-12-25 11:29:35,846 INFO misc.py line 119 253097] Train: [42/100][125/510] Data 0.005 (0.151) Batch 1.020 (1.559) Remain 12:58:45 loss: 0.1382 Lr: 0.00409
[2023-12-25 11:29:36,864 INFO misc.py line 119 253097] Train: [42/100][126/510] Data 0.010 (0.150) Batch 1.024 (1.555) Remain 12:56:33 loss: 0.3143 Lr: 0.00409
[2023-12-25 11:29:38,040 INFO misc.py line 119 253097] Train: [42/100][127/510] Data 0.004 (0.149) Batch 1.171 (1.552) Remain 12:54:59 loss: 0.4033 Lr: 0.00409
[2023-12-25 11:29:39,259 INFO misc.py line 119 253097] Train: [42/100][128/510] Data 0.009 (0.147) Batch 1.223 (1.549) Remain 12:53:39 loss: 0.1932 Lr: 0.00409
[2023-12-25 11:29:44,244 INFO misc.py line 119 253097] Train: [42/100][129/510] Data 0.004 (0.146) Batch 4.986 (1.577) Remain 13:07:14 loss: 0.2651 Lr: 0.00409
[2023-12-25 11:29:45,424 INFO misc.py line 119 253097] Train: [42/100][130/510] Data 0.003 (0.145) Batch 1.170 (1.573) Remain 13:05:37 loss: 0.1012 Lr: 0.00409
[2023-12-25 11:29:46,410 INFO misc.py line 119 253097] Train: [42/100][131/510] Data 0.013 (0.144) Batch 0.995 (1.569) Remain 13:03:20 loss: 0.1557 Lr: 0.00409
[2023-12-25 11:29:47,721 INFO misc.py line 119 253097] Train: [42/100][132/510] Data 0.004 (0.143) Batch 1.306 (1.567) Remain 13:02:17 loss: 0.2497 Lr: 0.00409
[2023-12-25 11:29:48,951 INFO misc.py line 119 253097] Train: [42/100][133/510] Data 0.009 (0.142) Batch 1.235 (1.564) Remain 13:00:59 loss: 0.1729 Lr: 0.00409
[2023-12-25 11:29:50,206 INFO misc.py line 119 253097] Train: [42/100][134/510] Data 0.004 (0.141) Batch 1.252 (1.562) Remain 12:59:46 loss: 0.3111 Lr: 0.00409
[2023-12-25 11:29:51,351 INFO misc.py line 119 253097] Train: [42/100][135/510] Data 0.007 (0.140) Batch 1.148 (1.559) Remain 12:58:11 loss: 0.1961 Lr: 0.00409
[2023-12-25 11:29:52,479 INFO misc.py line 119 253097] Train: [42/100][136/510] Data 0.005 (0.139) Batch 1.116 (1.555) Remain 12:56:30 loss: 0.3309 Lr: 0.00409
[2023-12-25 11:29:53,786 INFO misc.py line 119 253097] Train: [42/100][137/510] Data 0.017 (0.138) Batch 1.320 (1.554) Remain 12:55:35 loss: 0.1613 Lr: 0.00409
[2023-12-25 11:29:56,268 INFO misc.py line 119 253097] Train: [42/100][138/510] Data 0.003 (0.137) Batch 2.481 (1.561) Remain 12:59:00 loss: 0.1752 Lr: 0.00409
[2023-12-25 11:29:57,588 INFO misc.py line 119 253097] Train: [42/100][139/510] Data 0.004 (0.136) Batch 1.320 (1.559) Remain 12:58:05 loss: 0.1425 Lr: 0.00409
[2023-12-25 11:29:58,749 INFO misc.py line 119 253097] Train: [42/100][140/510] Data 0.004 (0.135) Batch 1.160 (1.556) Remain 12:56:37 loss: 0.1686 Lr: 0.00409
[2023-12-25 11:29:59,600 INFO misc.py line 119 253097] Train: [42/100][141/510] Data 0.005 (0.134) Batch 0.852 (1.551) Remain 12:54:02 loss: 0.1262 Lr: 0.00409
[2023-12-25 11:30:00,686 INFO misc.py line 119 253097] Train: [42/100][142/510] Data 0.004 (0.133) Batch 1.086 (1.547) Remain 12:52:20 loss: 0.2025 Lr: 0.00409
[2023-12-25 11:30:01,809 INFO misc.py line 119 253097] Train: [42/100][143/510] Data 0.005 (0.132) Batch 1.124 (1.544) Remain 12:50:48 loss: 0.2031 Lr: 0.00409
[2023-12-25 11:30:09,592 INFO misc.py line 119 253097] Train: [42/100][144/510] Data 0.004 (0.131) Batch 7.782 (1.589) Remain 13:12:52 loss: 0.1688 Lr: 0.00409
[2023-12-25 11:30:10,853 INFO misc.py line 119 253097] Train: [42/100][145/510] Data 0.004 (0.131) Batch 1.257 (1.586) Remain 13:11:40 loss: 0.2984 Lr: 0.00409
[2023-12-25 11:30:11,923 INFO misc.py line 119 253097] Train: [42/100][146/510] Data 0.008 (0.130) Batch 1.075 (1.583) Remain 13:09:51 loss: 0.1360 Lr: 0.00409
[2023-12-25 11:30:13,143 INFO misc.py line 119 253097] Train: [42/100][147/510] Data 0.003 (0.129) Batch 1.173 (1.580) Remain 13:08:24 loss: 0.1860 Lr: 0.00409
[2023-12-25 11:30:14,349 INFO misc.py line 119 253097] Train: [42/100][148/510] Data 0.051 (0.128) Batch 1.226 (1.577) Remain 13:07:10 loss: 0.3437 Lr: 0.00409
[2023-12-25 11:30:15,449 INFO misc.py line 119 253097] Train: [42/100][149/510] Data 0.032 (0.128) Batch 1.119 (1.574) Remain 13:05:34 loss: 0.1492 Lr: 0.00409
[2023-12-25 11:30:16,699 INFO misc.py line 119 253097] Train: [42/100][150/510] Data 0.013 (0.127) Batch 1.256 (1.572) Remain 13:04:28 loss: 0.3774 Lr: 0.00409
[2023-12-25 11:30:17,869 INFO misc.py line 119 253097] Train: [42/100][151/510] Data 0.007 (0.126) Batch 1.171 (1.569) Remain 13:03:05 loss: 0.1061 Lr: 0.00409
[2023-12-25 11:30:19,138 INFO misc.py line 119 253097] Train: [42/100][152/510] Data 0.006 (0.125) Batch 1.268 (1.567) Remain 13:02:03 loss: 0.2635 Lr: 0.00409
[2023-12-25 11:30:20,401 INFO misc.py line 119 253097] Train: [42/100][153/510] Data 0.008 (0.124) Batch 1.266 (1.565) Remain 13:01:01 loss: 0.2307 Lr: 0.00409
[2023-12-25 11:30:21,650 INFO misc.py line 119 253097] Train: [42/100][154/510] Data 0.005 (0.124) Batch 1.246 (1.563) Remain 12:59:56 loss: 0.1590 Lr: 0.00409
[2023-12-25 11:30:22,804 INFO misc.py line 119 253097] Train: [42/100][155/510] Data 0.007 (0.123) Batch 1.155 (1.561) Remain 12:58:34 loss: 0.2634 Lr: 0.00409
[2023-12-25 11:30:23,876 INFO misc.py line 119 253097] Train: [42/100][156/510] Data 0.007 (0.122) Batch 1.073 (1.557) Remain 12:56:57 loss: 0.2436 Lr: 0.00409
[2023-12-25 11:30:25,000 INFO misc.py line 119 253097] Train: [42/100][157/510] Data 0.006 (0.121) Batch 1.120 (1.555) Remain 12:55:31 loss: 0.1517 Lr: 0.00409
[2023-12-25 11:30:35,196 INFO misc.py line 119 253097] Train: [42/100][158/510] Data 0.010 (0.121) Batch 10.202 (1.610) Remain 13:23:19 loss: 0.1226 Lr: 0.00409
[2023-12-25 11:30:36,323 INFO misc.py line 119 253097] Train: [42/100][159/510] Data 0.004 (0.120) Batch 1.127 (1.607) Remain 13:21:45 loss: 0.3351 Lr: 0.00409
[2023-12-25 11:30:37,689 INFO misc.py line 119 253097] Train: [42/100][160/510] Data 0.004 (0.119) Batch 1.361 (1.606) Remain 13:20:56 loss: 0.3138 Lr: 0.00409
[2023-12-25 11:30:38,974 INFO misc.py line 119 253097] Train: [42/100][161/510] Data 0.009 (0.118) Batch 1.290 (1.604) Remain 13:19:55 loss: 0.2545 Lr: 0.00409
[2023-12-25 11:30:40,007 INFO misc.py line 119 253097] Train: [42/100][162/510] Data 0.005 (0.118) Batch 1.032 (1.600) Remain 13:18:05 loss: 0.2923 Lr: 0.00409
[2023-12-25 11:30:40,956 INFO misc.py line 119 253097] Train: [42/100][163/510] Data 0.006 (0.117) Batch 0.948 (1.596) Remain 13:16:02 loss: 0.2590 Lr: 0.00409
[2023-12-25 11:30:42,077 INFO misc.py line 119 253097] Train: [42/100][164/510] Data 0.007 (0.116) Batch 1.123 (1.593) Remain 13:14:32 loss: 0.3036 Lr: 0.00409
[2023-12-25 11:30:43,308 INFO misc.py line 119 253097] Train: [42/100][165/510] Data 0.004 (0.116) Batch 1.230 (1.591) Remain 13:13:24 loss: 0.3559 Lr: 0.00409
[2023-12-25 11:30:44,448 INFO misc.py line 119 253097] Train: [42/100][166/510] Data 0.005 (0.115) Batch 1.140 (1.588) Remain 13:11:59 loss: 0.3169 Lr: 0.00408
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[2023-12-25 11:30:46,788 INFO misc.py line 119 253097] Train: [42/100][168/510] Data 0.007 (0.114) Batch 1.282 (1.583) Remain 13:09:24 loss: 0.1666 Lr: 0.00408
[2023-12-25 11:30:47,958 INFO misc.py line 119 253097] Train: [42/100][169/510] Data 0.008 (0.113) Batch 1.173 (1.580) Remain 13:08:09 loss: 0.1490 Lr: 0.00408
[2023-12-25 11:30:49,037 INFO misc.py line 119 253097] Train: [42/100][170/510] Data 0.004 (0.112) Batch 1.079 (1.577) Remain 13:06:37 loss: 0.3592 Lr: 0.00408
[2023-12-25 11:30:50,147 INFO misc.py line 119 253097] Train: [42/100][171/510] Data 0.005 (0.112) Batch 1.100 (1.575) Remain 13:05:11 loss: 0.1239 Lr: 0.00408
[2023-12-25 11:30:51,343 INFO misc.py line 119 253097] Train: [42/100][172/510] Data 0.015 (0.111) Batch 1.199 (1.572) Remain 13:04:03 loss: 0.2264 Lr: 0.00408
[2023-12-25 11:30:52,516 INFO misc.py line 119 253097] Train: [42/100][173/510] Data 0.012 (0.111) Batch 1.176 (1.570) Remain 13:02:51 loss: 0.1909 Lr: 0.00408
[2023-12-25 11:31:06,601 INFO misc.py line 119 253097] Train: [42/100][174/510] Data 12.813 (0.185) Batch 14.089 (1.643) Remain 13:39:20 loss: 0.0709 Lr: 0.00408
[2023-12-25 11:31:07,800 INFO misc.py line 119 253097] Train: [42/100][175/510] Data 0.004 (0.184) Batch 1.199 (1.641) Remain 13:38:01 loss: 0.3619 Lr: 0.00408
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[2023-12-25 11:31:11,220 INFO misc.py line 119 253097] Train: [42/100][178/510] Data 0.005 (0.181) Batch 1.083 (1.632) Remain 13:33:39 loss: 0.1648 Lr: 0.00408
[2023-12-25 11:31:12,375 INFO misc.py line 119 253097] Train: [42/100][179/510] Data 0.004 (0.180) Batch 1.152 (1.629) Remain 13:32:16 loss: 0.2677 Lr: 0.00408
[2023-12-25 11:31:13,631 INFO misc.py line 119 253097] Train: [42/100][180/510] Data 0.006 (0.179) Batch 1.255 (1.627) Remain 13:31:11 loss: 0.2036 Lr: 0.00408
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[2023-12-25 11:31:19,383 INFO misc.py line 119 253097] Train: [42/100][185/510] Data 0.006 (0.174) Batch 1.141 (1.614) Remain 13:24:32 loss: 0.1689 Lr: 0.00408
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[2023-12-25 11:31:21,739 INFO misc.py line 119 253097] Train: [42/100][187/510] Data 0.010 (0.172) Batch 1.194 (1.609) Remain 13:22:06 loss: 0.2738 Lr: 0.00408
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[2023-12-25 11:31:24,230 INFO misc.py line 119 253097] Train: [42/100][189/510] Data 0.009 (0.170) Batch 1.212 (1.606) Remain 13:20:07 loss: 0.1540 Lr: 0.00408
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[2023-12-25 11:31:27,887 INFO misc.py line 119 253097] Train: [42/100][192/510] Data 0.005 (0.168) Batch 1.117 (1.599) Remain 13:16:58 loss: 0.1474 Lr: 0.00408
[2023-12-25 11:31:29,032 INFO misc.py line 119 253097] Train: [42/100][193/510] Data 0.007 (0.167) Batch 1.146 (1.597) Remain 13:15:45 loss: 0.1000 Lr: 0.00408
[2023-12-25 11:31:30,570 INFO misc.py line 119 253097] Train: [42/100][194/510] Data 0.007 (0.166) Batch 1.540 (1.597) Remain 13:15:34 loss: 0.1694 Lr: 0.00408
[2023-12-25 11:31:31,678 INFO misc.py line 119 253097] Train: [42/100][195/510] Data 0.005 (0.165) Batch 1.107 (1.594) Remain 13:14:17 loss: 0.2352 Lr: 0.00408
[2023-12-25 11:31:32,821 INFO misc.py line 119 253097] Train: [42/100][196/510] Data 0.005 (0.165) Batch 1.144 (1.592) Remain 13:13:05 loss: 0.1504 Lr: 0.00408
[2023-12-25 11:31:33,857 INFO misc.py line 119 253097] Train: [42/100][197/510] Data 0.004 (0.164) Batch 1.030 (1.589) Remain 13:11:37 loss: 0.2129 Lr: 0.00408
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[2023-12-25 11:31:36,148 INFO misc.py line 119 253097] Train: [42/100][199/510] Data 0.005 (0.162) Batch 1.328 (1.584) Remain 13:09:19 loss: 0.2484 Lr: 0.00408
[2023-12-25 11:31:37,405 INFO misc.py line 119 253097] Train: [42/100][200/510] Data 0.010 (0.161) Batch 1.261 (1.583) Remain 13:08:28 loss: 0.1939 Lr: 0.00408
[2023-12-25 11:31:38,526 INFO misc.py line 119 253097] Train: [42/100][201/510] Data 0.006 (0.161) Batch 1.114 (1.580) Remain 13:07:16 loss: 0.2303 Lr: 0.00408
[2023-12-25 11:31:39,559 INFO misc.py line 119 253097] Train: [42/100][202/510] Data 0.013 (0.160) Batch 1.029 (1.578) Remain 13:05:51 loss: 0.1155 Lr: 0.00408
[2023-12-25 11:31:40,525 INFO misc.py line 119 253097] Train: [42/100][203/510] Data 0.016 (0.159) Batch 0.979 (1.575) Remain 13:04:20 loss: 0.1539 Lr: 0.00408
[2023-12-25 11:31:41,597 INFO misc.py line 119 253097] Train: [42/100][204/510] Data 0.003 (0.158) Batch 1.072 (1.572) Remain 13:03:04 loss: 0.1673 Lr: 0.00408
[2023-12-25 11:31:42,713 INFO misc.py line 119 253097] Train: [42/100][205/510] Data 0.003 (0.158) Batch 1.115 (1.570) Remain 13:01:55 loss: 0.1681 Lr: 0.00408
[2023-12-25 11:31:43,821 INFO misc.py line 119 253097] Train: [42/100][206/510] Data 0.003 (0.157) Batch 1.108 (1.568) Remain 13:00:45 loss: 0.3577 Lr: 0.00408
[2023-12-25 11:31:44,989 INFO misc.py line 119 253097] Train: [42/100][207/510] Data 0.003 (0.156) Batch 1.167 (1.566) Remain 12:59:45 loss: 0.1789 Lr: 0.00408
[2023-12-25 11:31:54,126 INFO misc.py line 119 253097] Train: [42/100][208/510] Data 8.079 (0.195) Batch 9.138 (1.603) Remain 13:18:08 loss: 0.0629 Lr: 0.00408
[2023-12-25 11:31:55,148 INFO misc.py line 119 253097] Train: [42/100][209/510] Data 0.003 (0.194) Batch 1.022 (1.600) Remain 13:16:42 loss: 0.0784 Lr: 0.00408
[2023-12-25 11:31:56,065 INFO misc.py line 119 253097] Train: [42/100][210/510] Data 0.003 (0.193) Batch 0.914 (1.596) Remain 13:15:01 loss: 0.2391 Lr: 0.00408
[2023-12-25 11:31:57,314 INFO misc.py line 119 253097] Train: [42/100][211/510] Data 0.007 (0.192) Batch 1.251 (1.595) Remain 13:14:10 loss: 0.3680 Lr: 0.00408
[2023-12-25 11:31:58,607 INFO misc.py line 119 253097] Train: [42/100][212/510] Data 0.005 (0.191) Batch 1.290 (1.593) Remain 13:13:25 loss: 0.3030 Lr: 0.00408
[2023-12-25 11:32:01,644 INFO misc.py line 119 253097] Train: [42/100][213/510] Data 0.008 (0.190) Batch 3.041 (1.600) Remain 13:16:49 loss: 0.1432 Lr: 0.00408
[2023-12-25 11:32:02,890 INFO misc.py line 119 253097] Train: [42/100][214/510] Data 0.004 (0.189) Batch 1.242 (1.599) Remain 13:15:57 loss: 0.1378 Lr: 0.00408
[2023-12-25 11:32:04,065 INFO misc.py line 119 253097] Train: [42/100][215/510] Data 0.008 (0.188) Batch 1.179 (1.597) Remain 13:14:56 loss: 0.2972 Lr: 0.00408
[2023-12-25 11:32:05,132 INFO misc.py line 119 253097] Train: [42/100][216/510] Data 0.003 (0.188) Batch 1.054 (1.594) Remain 13:13:38 loss: 0.2974 Lr: 0.00408
[2023-12-25 11:32:06,195 INFO misc.py line 119 253097] Train: [42/100][217/510] Data 0.017 (0.187) Batch 1.074 (1.592) Remain 13:12:24 loss: 0.1894 Lr: 0.00408
[2023-12-25 11:32:07,446 INFO misc.py line 119 253097] Train: [42/100][218/510] Data 0.007 (0.186) Batch 1.248 (1.590) Remain 13:11:35 loss: 0.4890 Lr: 0.00408
[2023-12-25 11:32:08,621 INFO misc.py line 119 253097] Train: [42/100][219/510] Data 0.008 (0.185) Batch 1.178 (1.588) Remain 13:10:36 loss: 0.0946 Lr: 0.00408
[2023-12-25 11:32:11,118 INFO misc.py line 119 253097] Train: [42/100][220/510] Data 0.005 (0.184) Batch 2.498 (1.592) Remain 13:12:40 loss: 0.1550 Lr: 0.00408
[2023-12-25 11:32:12,242 INFO misc.py line 119 253097] Train: [42/100][221/510] Data 0.004 (0.183) Batch 1.125 (1.590) Remain 13:11:34 loss: 0.3311 Lr: 0.00407
[2023-12-25 11:32:13,424 INFO misc.py line 119 253097] Train: [42/100][222/510] Data 0.003 (0.183) Batch 1.181 (1.588) Remain 13:10:37 loss: 0.1813 Lr: 0.00407
[2023-12-25 11:32:14,468 INFO misc.py line 119 253097] Train: [42/100][223/510] Data 0.004 (0.182) Batch 1.041 (1.586) Remain 13:09:21 loss: 0.1587 Lr: 0.00407
[2023-12-25 11:32:22,321 INFO misc.py line 119 253097] Train: [42/100][224/510] Data 0.008 (0.181) Batch 7.854 (1.614) Remain 13:23:27 loss: 0.1663 Lr: 0.00407
[2023-12-25 11:32:23,390 INFO misc.py line 119 253097] Train: [42/100][225/510] Data 0.005 (0.180) Batch 1.070 (1.612) Remain 13:22:12 loss: 0.3664 Lr: 0.00407
[2023-12-25 11:32:24,343 INFO misc.py line 119 253097] Train: [42/100][226/510] Data 0.005 (0.179) Batch 0.953 (1.609) Remain 13:20:42 loss: 0.1207 Lr: 0.00407
[2023-12-25 11:32:25,584 INFO misc.py line 119 253097] Train: [42/100][227/510] Data 0.004 (0.179) Batch 1.241 (1.607) Remain 13:19:51 loss: 0.1764 Lr: 0.00407
[2023-12-25 11:32:28,348 INFO misc.py line 119 253097] Train: [42/100][228/510] Data 1.702 (0.185) Batch 2.765 (1.612) Remain 13:22:23 loss: 0.1521 Lr: 0.00407
[2023-12-25 11:32:29,638 INFO misc.py line 119 253097] Train: [42/100][229/510] Data 0.005 (0.185) Batch 1.290 (1.611) Remain 13:21:39 loss: 0.0901 Lr: 0.00407
[2023-12-25 11:32:30,879 INFO misc.py line 119 253097] Train: [42/100][230/510] Data 0.004 (0.184) Batch 1.242 (1.609) Remain 13:20:49 loss: 0.1950 Lr: 0.00407
[2023-12-25 11:32:31,965 INFO misc.py line 119 253097] Train: [42/100][231/510] Data 0.003 (0.183) Batch 1.084 (1.607) Remain 13:19:39 loss: 0.2333 Lr: 0.00407
[2023-12-25 11:32:32,992 INFO misc.py line 119 253097] Train: [42/100][232/510] Data 0.005 (0.182) Batch 1.021 (1.604) Remain 13:18:21 loss: 0.2093 Lr: 0.00407
[2023-12-25 11:32:34,134 INFO misc.py line 119 253097] Train: [42/100][233/510] Data 0.011 (0.182) Batch 1.142 (1.602) Remain 13:17:19 loss: 0.2251 Lr: 0.00407
[2023-12-25 11:32:44,040 INFO misc.py line 119 253097] Train: [42/100][234/510] Data 9.014 (0.220) Batch 9.914 (1.638) Remain 13:35:12 loss: 0.1950 Lr: 0.00407
[2023-12-25 11:32:45,142 INFO misc.py line 119 253097] Train: [42/100][235/510] Data 0.004 (0.219) Batch 1.078 (1.636) Remain 13:33:58 loss: 0.1992 Lr: 0.00407
[2023-12-25 11:32:46,349 INFO misc.py line 119 253097] Train: [42/100][236/510] Data 0.029 (0.218) Batch 1.227 (1.634) Remain 13:33:04 loss: 0.0720 Lr: 0.00407
[2023-12-25 11:32:47,339 INFO misc.py line 119 253097] Train: [42/100][237/510] Data 0.008 (0.217) Batch 0.994 (1.631) Remain 13:31:41 loss: 0.1079 Lr: 0.00407
[2023-12-25 11:32:48,592 INFO misc.py line 119 253097] Train: [42/100][238/510] Data 0.003 (0.216) Batch 1.254 (1.630) Remain 13:30:51 loss: 0.2066 Lr: 0.00407
[2023-12-25 11:32:49,768 INFO misc.py line 119 253097] Train: [42/100][239/510] Data 0.003 (0.215) Batch 1.174 (1.628) Remain 13:29:52 loss: 0.2116 Lr: 0.00407
[2023-12-25 11:32:50,897 INFO misc.py line 119 253097] Train: [42/100][240/510] Data 0.005 (0.214) Batch 1.123 (1.626) Remain 13:28:47 loss: 0.1631 Lr: 0.00407
[2023-12-25 11:32:51,724 INFO misc.py line 119 253097] Train: [42/100][241/510] Data 0.011 (0.214) Batch 0.835 (1.622) Remain 13:27:06 loss: 0.4040 Lr: 0.00407
[2023-12-25 11:32:52,863 INFO misc.py line 119 253097] Train: [42/100][242/510] Data 0.004 (0.213) Batch 1.139 (1.620) Remain 13:26:04 loss: 0.1820 Lr: 0.00407
[2023-12-25 11:32:54,174 INFO misc.py line 119 253097] Train: [42/100][243/510] Data 0.003 (0.212) Batch 1.307 (1.619) Remain 13:25:23 loss: 0.2527 Lr: 0.00407
[2023-12-25 11:32:55,272 INFO misc.py line 119 253097] Train: [42/100][244/510] Data 0.007 (0.211) Batch 1.097 (1.617) Remain 13:24:17 loss: 0.2083 Lr: 0.00407
[2023-12-25 11:32:56,445 INFO misc.py line 119 253097] Train: [42/100][245/510] Data 0.009 (0.210) Batch 1.171 (1.615) Remain 13:23:20 loss: 0.1704 Lr: 0.00407
[2023-12-25 11:32:57,556 INFO misc.py line 119 253097] Train: [42/100][246/510] Data 0.012 (0.209) Batch 1.118 (1.613) Remain 13:22:17 loss: 0.1061 Lr: 0.00407
[2023-12-25 11:32:58,659 INFO misc.py line 119 253097] Train: [42/100][247/510] Data 0.005 (0.208) Batch 1.102 (1.611) Remain 13:21:13 loss: 0.3471 Lr: 0.00407
[2023-12-25 11:32:59,738 INFO misc.py line 119 253097] Train: [42/100][248/510] Data 0.006 (0.208) Batch 1.079 (1.609) Remain 13:20:07 loss: 0.2401 Lr: 0.00407
[2023-12-25 11:33:00,834 INFO misc.py line 119 253097] Train: [42/100][249/510] Data 0.005 (0.207) Batch 1.093 (1.607) Remain 13:19:03 loss: 0.2152 Lr: 0.00407
[2023-12-25 11:33:01,967 INFO misc.py line 119 253097] Train: [42/100][250/510] Data 0.008 (0.206) Batch 1.133 (1.605) Remain 13:18:04 loss: 0.1013 Lr: 0.00407
[2023-12-25 11:33:06,759 INFO misc.py line 119 253097] Train: [42/100][251/510] Data 0.008 (0.205) Batch 4.796 (1.618) Remain 13:24:26 loss: 0.1732 Lr: 0.00407
[2023-12-25 11:33:08,009 INFO misc.py line 119 253097] Train: [42/100][252/510] Data 0.004 (0.204) Batch 1.247 (1.616) Remain 13:23:40 loss: 0.1656 Lr: 0.00407
[2023-12-25 11:33:09,149 INFO misc.py line 119 253097] Train: [42/100][253/510] Data 0.006 (0.204) Batch 1.131 (1.614) Remain 13:22:41 loss: 0.1183 Lr: 0.00407
[2023-12-25 11:33:10,424 INFO misc.py line 119 253097] Train: [42/100][254/510] Data 0.016 (0.203) Batch 1.286 (1.613) Remain 13:22:00 loss: 0.2727 Lr: 0.00407
[2023-12-25 11:33:11,623 INFO misc.py line 119 253097] Train: [42/100][255/510] Data 0.004 (0.202) Batch 1.194 (1.611) Remain 13:21:09 loss: 0.2291 Lr: 0.00407
[2023-12-25 11:33:12,779 INFO misc.py line 119 253097] Train: [42/100][256/510] Data 0.009 (0.201) Batch 1.161 (1.609) Remain 13:20:14 loss: 0.3537 Lr: 0.00407
[2023-12-25 11:33:13,921 INFO misc.py line 119 253097] Train: [42/100][257/510] Data 0.003 (0.201) Batch 1.142 (1.608) Remain 13:19:18 loss: 0.3095 Lr: 0.00407
[2023-12-25 11:33:14,825 INFO misc.py line 119 253097] Train: [42/100][258/510] Data 0.004 (0.200) Batch 0.904 (1.605) Remain 13:17:54 loss: 0.0917 Lr: 0.00407
[2023-12-25 11:33:15,965 INFO misc.py line 119 253097] Train: [42/100][259/510] Data 0.004 (0.199) Batch 1.108 (1.603) Remain 13:16:54 loss: 0.1765 Lr: 0.00407
[2023-12-25 11:33:17,016 INFO misc.py line 119 253097] Train: [42/100][260/510] Data 0.037 (0.198) Batch 1.083 (1.601) Remain 13:15:53 loss: 0.2209 Lr: 0.00407
[2023-12-25 11:33:18,209 INFO misc.py line 119 253097] Train: [42/100][261/510] Data 0.004 (0.198) Batch 1.192 (1.599) Remain 13:15:04 loss: 0.1913 Lr: 0.00407
[2023-12-25 11:33:19,493 INFO misc.py line 119 253097] Train: [42/100][262/510] Data 0.273 (0.198) Batch 1.260 (1.598) Remain 13:14:23 loss: 0.2194 Lr: 0.00407
[2023-12-25 11:33:20,738 INFO misc.py line 119 253097] Train: [42/100][263/510] Data 0.029 (0.197) Batch 1.266 (1.597) Remain 13:13:43 loss: 0.2232 Lr: 0.00407
[2023-12-25 11:33:21,749 INFO misc.py line 119 253097] Train: [42/100][264/510] Data 0.007 (0.197) Batch 1.011 (1.594) Remain 13:12:35 loss: 0.2969 Lr: 0.00407
[2023-12-25 11:33:22,840 INFO misc.py line 119 253097] Train: [42/100][265/510] Data 0.008 (0.196) Batch 1.095 (1.593) Remain 13:11:36 loss: 0.2816 Lr: 0.00407
[2023-12-25 11:33:23,959 INFO misc.py line 119 253097] Train: [42/100][266/510] Data 0.005 (0.195) Batch 1.119 (1.591) Remain 13:10:41 loss: 0.1228 Lr: 0.00407
[2023-12-25 11:33:25,137 INFO misc.py line 119 253097] Train: [42/100][267/510] Data 0.004 (0.194) Batch 1.175 (1.589) Remain 13:09:53 loss: 0.2064 Lr: 0.00407
[2023-12-25 11:33:26,599 INFO misc.py line 119 253097] Train: [42/100][268/510] Data 0.444 (0.195) Batch 1.465 (1.589) Remain 13:09:37 loss: 0.1181 Lr: 0.00407
[2023-12-25 11:33:27,785 INFO misc.py line 119 253097] Train: [42/100][269/510] Data 0.003 (0.195) Batch 1.186 (1.587) Remain 13:08:50 loss: 0.2198 Lr: 0.00407
[2023-12-25 11:33:29,039 INFO misc.py line 119 253097] Train: [42/100][270/510] Data 0.004 (0.194) Batch 1.253 (1.586) Remain 13:08:11 loss: 0.1752 Lr: 0.00407
[2023-12-25 11:33:30,179 INFO misc.py line 119 253097] Train: [42/100][271/510] Data 0.005 (0.193) Batch 1.139 (1.584) Remain 13:07:20 loss: 0.0760 Lr: 0.00407
[2023-12-25 11:33:31,259 INFO misc.py line 119 253097] Train: [42/100][272/510] Data 0.005 (0.192) Batch 1.078 (1.582) Remain 13:06:22 loss: 0.2861 Lr: 0.00407
[2023-12-25 11:33:32,524 INFO misc.py line 119 253097] Train: [42/100][273/510] Data 0.007 (0.192) Batch 1.262 (1.581) Remain 13:05:45 loss: 0.1221 Lr: 0.00407
[2023-12-25 11:33:33,486 INFO misc.py line 119 253097] Train: [42/100][274/510] Data 0.011 (0.191) Batch 0.968 (1.579) Remain 13:04:36 loss: 0.1729 Lr: 0.00407
[2023-12-25 11:33:34,713 INFO misc.py line 119 253097] Train: [42/100][275/510] Data 0.003 (0.190) Batch 1.222 (1.578) Remain 13:03:56 loss: 0.1659 Lr: 0.00407
[2023-12-25 11:33:38,350 INFO misc.py line 119 253097] Train: [42/100][276/510] Data 2.476 (0.199) Batch 3.642 (1.585) Remain 13:07:40 loss: 0.2982 Lr: 0.00406
[2023-12-25 11:33:39,475 INFO misc.py line 119 253097] Train: [42/100][277/510] Data 0.003 (0.198) Batch 1.125 (1.583) Remain 13:06:48 loss: 0.1272 Lr: 0.00406
[2023-12-25 11:33:40,670 INFO misc.py line 119 253097] Train: [42/100][278/510] Data 0.005 (0.197) Batch 1.189 (1.582) Remain 13:06:04 loss: 0.0795 Lr: 0.00406
[2023-12-25 11:33:41,809 INFO misc.py line 119 253097] Train: [42/100][279/510] Data 0.010 (0.197) Batch 1.145 (1.580) Remain 13:05:15 loss: 0.0964 Lr: 0.00406
[2023-12-25 11:33:42,959 INFO misc.py line 119 253097] Train: [42/100][280/510] Data 0.004 (0.196) Batch 1.147 (1.579) Remain 13:04:27 loss: 0.2155 Lr: 0.00406
[2023-12-25 11:33:44,048 INFO misc.py line 119 253097] Train: [42/100][281/510] Data 0.006 (0.195) Batch 1.093 (1.577) Remain 13:03:33 loss: 0.1366 Lr: 0.00406
[2023-12-25 11:33:44,934 INFO misc.py line 119 253097] Train: [42/100][282/510] Data 0.003 (0.195) Batch 0.885 (1.575) Remain 13:02:17 loss: 0.2557 Lr: 0.00406
[2023-12-25 11:33:46,071 INFO misc.py line 119 253097] Train: [42/100][283/510] Data 0.003 (0.194) Batch 1.136 (1.573) Remain 13:01:29 loss: 0.1773 Lr: 0.00406
[2023-12-25 11:33:47,179 INFO misc.py line 119 253097] Train: [42/100][284/510] Data 0.004 (0.193) Batch 1.108 (1.571) Remain 13:00:38 loss: 0.1698 Lr: 0.00406
[2023-12-25 11:33:48,347 INFO misc.py line 119 253097] Train: [42/100][285/510] Data 0.004 (0.193) Batch 1.167 (1.570) Remain 12:59:54 loss: 0.2555 Lr: 0.00406
[2023-12-25 11:33:49,505 INFO misc.py line 119 253097] Train: [42/100][286/510] Data 0.005 (0.192) Batch 1.159 (1.569) Remain 12:59:09 loss: 0.2583 Lr: 0.00406
[2023-12-25 11:33:50,656 INFO misc.py line 119 253097] Train: [42/100][287/510] Data 0.004 (0.191) Batch 1.150 (1.567) Remain 12:58:24 loss: 0.2035 Lr: 0.00406
[2023-12-25 11:33:51,869 INFO misc.py line 119 253097] Train: [42/100][288/510] Data 0.004 (0.191) Batch 1.209 (1.566) Remain 12:57:45 loss: 0.2206 Lr: 0.00406
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[2023-12-25 11:33:57,988 INFO misc.py line 119 253097] Train: [42/100][290/510] Data 3.990 (0.203) Batch 5.028 (1.576) Remain 13:02:52 loss: 0.3263 Lr: 0.00406
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[2023-12-25 11:34:02,383 INFO misc.py line 119 253097] Train: [42/100][292/510] Data 0.004 (0.202) Batch 3.279 (1.581) Remain 13:04:57 loss: 0.1777 Lr: 0.00406
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[2023-12-25 11:34:07,079 INFO misc.py line 119 253097] Train: [42/100][296/510] Data 0.009 (0.199) Batch 1.226 (1.575) Remain 13:02:05 loss: 0.2809 Lr: 0.00406
[2023-12-25 11:34:08,374 INFO misc.py line 119 253097] Train: [42/100][297/510] Data 0.008 (0.199) Batch 1.299 (1.574) Remain 13:01:36 loss: 0.2437 Lr: 0.00406
[2023-12-25 11:34:09,698 INFO misc.py line 119 253097] Train: [42/100][298/510] Data 0.004 (0.198) Batch 1.320 (1.573) Remain 13:01:08 loss: 0.1400 Lr: 0.00406
[2023-12-25 11:34:10,918 INFO misc.py line 119 253097] Train: [42/100][299/510] Data 0.008 (0.197) Batch 1.220 (1.572) Remain 13:00:31 loss: 0.1409 Lr: 0.00406
[2023-12-25 11:34:12,031 INFO misc.py line 119 253097] Train: [42/100][300/510] Data 0.006 (0.197) Batch 1.113 (1.570) Remain 12:59:44 loss: 0.2729 Lr: 0.00406
[2023-12-25 11:34:13,334 INFO misc.py line 119 253097] Train: [42/100][301/510] Data 0.007 (0.196) Batch 1.296 (1.570) Remain 12:59:15 loss: 0.3272 Lr: 0.00406
[2023-12-25 11:34:14,556 INFO misc.py line 119 253097] Train: [42/100][302/510] Data 0.015 (0.195) Batch 1.232 (1.568) Remain 12:58:39 loss: 0.2739 Lr: 0.00406
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[2023-12-25 11:34:45,954 INFO misc.py line 119 253097] Train: [42/100][322/510] Data 0.004 (0.183) Batch 7.382 (1.569) Remain 12:58:11 loss: 0.1767 Lr: 0.00406
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[2023-12-25 11:34:48,430 INFO misc.py line 119 253097] Train: [42/100][324/510] Data 0.003 (0.182) Batch 1.366 (1.566) Remain 12:57:06 loss: 0.1972 Lr: 0.00406
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[2023-12-25 11:34:53,208 INFO misc.py line 119 253097] Train: [42/100][328/510] Data 0.010 (0.180) Batch 1.285 (1.562) Remain 12:54:44 loss: 0.2111 Lr: 0.00406
[2023-12-25 11:34:54,155 INFO misc.py line 119 253097] Train: [42/100][329/510] Data 0.004 (0.180) Batch 0.946 (1.560) Remain 12:53:46 loss: 0.2424 Lr: 0.00406
[2023-12-25 11:34:55,350 INFO misc.py line 119 253097] Train: [42/100][330/510] Data 0.005 (0.179) Batch 1.195 (1.559) Remain 12:53:11 loss: 0.2809 Lr: 0.00406
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[2023-12-25 11:34:58,716 INFO misc.py line 119 253097] Train: [42/100][333/510] Data 0.008 (0.178) Batch 0.965 (1.555) Remain 12:51:09 loss: 0.1796 Lr: 0.00405
[2023-12-25 11:34:59,995 INFO misc.py line 119 253097] Train: [42/100][334/510] Data 0.003 (0.177) Batch 1.275 (1.554) Remain 12:50:42 loss: 0.1260 Lr: 0.00405
[2023-12-25 11:35:01,108 INFO misc.py line 119 253097] Train: [42/100][335/510] Data 0.007 (0.176) Batch 1.117 (1.553) Remain 12:50:01 loss: 0.2830 Lr: 0.00405
[2023-12-25 11:35:02,314 INFO misc.py line 119 253097] Train: [42/100][336/510] Data 0.005 (0.176) Batch 1.194 (1.552) Remain 12:49:27 loss: 0.2891 Lr: 0.00405
[2023-12-25 11:35:03,586 INFO misc.py line 119 253097] Train: [42/100][337/510] Data 0.016 (0.176) Batch 1.266 (1.551) Remain 12:49:00 loss: 0.1323 Lr: 0.00405
[2023-12-25 11:35:14,304 INFO misc.py line 119 253097] Train: [42/100][338/510] Data 0.022 (0.175) Batch 10.736 (1.578) Remain 13:02:35 loss: 0.1918 Lr: 0.00405
[2023-12-25 11:35:15,399 INFO misc.py line 119 253097] Train: [42/100][339/510] Data 0.005 (0.175) Batch 1.095 (1.577) Remain 13:01:50 loss: 0.3560 Lr: 0.00405
[2023-12-25 11:35:28,327 INFO misc.py line 119 253097] Train: [42/100][340/510] Data 0.004 (0.174) Batch 12.928 (1.610) Remain 13:18:31 loss: 0.2300 Lr: 0.00405
[2023-12-25 11:35:29,523 INFO misc.py line 119 253097] Train: [42/100][341/510] Data 0.004 (0.174) Batch 1.196 (1.609) Remain 13:17:53 loss: 0.1804 Lr: 0.00405
[2023-12-25 11:35:30,749 INFO misc.py line 119 253097] Train: [42/100][342/510] Data 0.003 (0.173) Batch 1.225 (1.608) Remain 13:17:17 loss: 0.1827 Lr: 0.00405
[2023-12-25 11:35:31,970 INFO misc.py line 119 253097] Train: [42/100][343/510] Data 0.006 (0.173) Batch 1.221 (1.607) Remain 13:16:42 loss: 0.1514 Lr: 0.00405
[2023-12-25 11:35:33,157 INFO misc.py line 119 253097] Train: [42/100][344/510] Data 0.005 (0.172) Batch 1.189 (1.606) Remain 13:16:04 loss: 0.1551 Lr: 0.00405
[2023-12-25 11:35:34,533 INFO misc.py line 119 253097] Train: [42/100][345/510] Data 0.003 (0.172) Batch 1.373 (1.605) Remain 13:15:42 loss: 0.1907 Lr: 0.00405
[2023-12-25 11:35:35,622 INFO misc.py line 119 253097] Train: [42/100][346/510] Data 0.007 (0.171) Batch 1.089 (1.604) Remain 13:14:56 loss: 0.2248 Lr: 0.00405
[2023-12-25 11:35:36,580 INFO misc.py line 119 253097] Train: [42/100][347/510] Data 0.007 (0.171) Batch 0.960 (1.602) Remain 13:13:58 loss: 0.3503 Lr: 0.00405
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[2023-12-25 11:35:39,075 INFO misc.py line 119 253097] Train: [42/100][349/510] Data 0.004 (0.170) Batch 1.265 (1.600) Remain 13:12:54 loss: 0.2349 Lr: 0.00405
[2023-12-25 11:35:40,186 INFO misc.py line 119 253097] Train: [42/100][350/510] Data 0.010 (0.169) Batch 1.108 (1.598) Remain 13:12:10 loss: 0.1411 Lr: 0.00405
[2023-12-25 11:35:41,423 INFO misc.py line 119 253097] Train: [42/100][351/510] Data 0.012 (0.169) Batch 1.243 (1.597) Remain 13:11:38 loss: 0.1222 Lr: 0.00405
[2023-12-25 11:35:42,741 INFO misc.py line 119 253097] Train: [42/100][352/510] Data 0.007 (0.168) Batch 1.319 (1.596) Remain 13:11:13 loss: 0.1353 Lr: 0.00405
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[2023-12-25 11:35:44,766 INFO misc.py line 119 253097] Train: [42/100][354/510] Data 0.015 (0.167) Batch 1.001 (1.593) Remain 13:09:31 loss: 0.1779 Lr: 0.00405
[2023-12-25 11:35:52,796 INFO misc.py line 119 253097] Train: [42/100][355/510] Data 5.993 (0.184) Batch 8.025 (1.611) Remain 13:18:33 loss: 0.0984 Lr: 0.00405
[2023-12-25 11:35:53,895 INFO misc.py line 119 253097] Train: [42/100][356/510] Data 0.010 (0.183) Batch 1.104 (1.610) Remain 13:17:48 loss: 0.1460 Lr: 0.00405
[2023-12-25 11:35:55,028 INFO misc.py line 119 253097] Train: [42/100][357/510] Data 0.005 (0.183) Batch 1.133 (1.609) Remain 13:17:07 loss: 0.2848 Lr: 0.00405
[2023-12-25 11:35:56,107 INFO misc.py line 119 253097] Train: [42/100][358/510] Data 0.005 (0.182) Batch 1.078 (1.607) Remain 13:16:20 loss: 0.2782 Lr: 0.00405
[2023-12-25 11:35:57,316 INFO misc.py line 119 253097] Train: [42/100][359/510] Data 0.007 (0.182) Batch 1.211 (1.606) Remain 13:15:46 loss: 0.1431 Lr: 0.00405
[2023-12-25 11:35:58,403 INFO misc.py line 119 253097] Train: [42/100][360/510] Data 0.005 (0.181) Batch 1.086 (1.604) Remain 13:15:01 loss: 0.3282 Lr: 0.00405
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[2023-12-25 11:36:01,510 INFO misc.py line 119 253097] Train: [42/100][363/510] Data 0.003 (0.180) Batch 0.934 (1.600) Remain 13:12:35 loss: 0.2520 Lr: 0.00405
[2023-12-25 11:36:02,619 INFO misc.py line 119 253097] Train: [42/100][364/510] Data 0.004 (0.179) Batch 1.109 (1.598) Remain 13:11:53 loss: 0.1727 Lr: 0.00405
[2023-12-25 11:36:03,688 INFO misc.py line 119 253097] Train: [42/100][365/510] Data 0.005 (0.179) Batch 1.069 (1.597) Remain 13:11:08 loss: 0.3044 Lr: 0.00405
[2023-12-25 11:36:04,871 INFO misc.py line 119 253097] Train: [42/100][366/510] Data 0.004 (0.178) Batch 1.183 (1.596) Remain 13:10:33 loss: 0.2344 Lr: 0.00405
[2023-12-25 11:36:06,057 INFO misc.py line 119 253097] Train: [42/100][367/510] Data 0.004 (0.178) Batch 1.185 (1.595) Remain 13:09:58 loss: 0.1142 Lr: 0.00405
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[2023-12-25 11:36:25,330 INFO misc.py line 119 253097] Train: [42/100][374/510] Data 0.003 (0.175) Batch 1.186 (1.617) Remain 13:20:36 loss: 0.1037 Lr: 0.00405
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[2023-12-25 11:36:27,676 INFO misc.py line 119 253097] Train: [42/100][376/510] Data 0.005 (0.174) Batch 1.241 (1.614) Remain 13:19:22 loss: 0.3798 Lr: 0.00405
[2023-12-25 11:36:28,763 INFO misc.py line 119 253097] Train: [42/100][377/510] Data 0.004 (0.173) Batch 1.087 (1.613) Remain 13:18:39 loss: 0.2931 Lr: 0.00405
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[2023-12-25 11:36:30,903 INFO misc.py line 119 253097] Train: [42/100][379/510] Data 0.006 (0.173) Batch 1.023 (1.610) Remain 13:17:10 loss: 0.1836 Lr: 0.00405
[2023-12-25 11:36:36,496 INFO misc.py line 119 253097] Train: [42/100][380/510] Data 4.363 (0.184) Batch 5.593 (1.620) Remain 13:22:22 loss: 0.3209 Lr: 0.00405
[2023-12-25 11:36:37,663 INFO misc.py line 119 253097] Train: [42/100][381/510] Data 0.004 (0.183) Batch 1.167 (1.619) Remain 13:21:45 loss: 0.1864 Lr: 0.00405
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[2023-12-25 11:37:05,629 INFO misc.py line 119 253097] Train: [42/100][406/510] Data 0.003 (0.172) Batch 1.194 (1.588) Remain 13:05:42 loss: 0.2294 Lr: 0.00404
[2023-12-25 11:37:06,457 INFO misc.py line 119 253097] Train: [42/100][407/510] Data 0.003 (0.172) Batch 0.826 (1.586) Remain 13:04:45 loss: 0.3936 Lr: 0.00404
[2023-12-25 11:37:10,623 INFO misc.py line 119 253097] Train: [42/100][408/510] Data 0.005 (0.171) Batch 4.162 (1.593) Remain 13:07:52 loss: 0.2423 Lr: 0.00404
[2023-12-25 11:37:11,938 INFO misc.py line 119 253097] Train: [42/100][409/510] Data 0.009 (0.171) Batch 1.313 (1.592) Remain 13:07:30 loss: 0.1246 Lr: 0.00404
[2023-12-25 11:37:12,936 INFO misc.py line 119 253097] Train: [42/100][410/510] Data 0.012 (0.171) Batch 1.005 (1.591) Remain 13:06:46 loss: 0.1909 Lr: 0.00404
[2023-12-25 11:37:14,140 INFO misc.py line 119 253097] Train: [42/100][411/510] Data 0.005 (0.170) Batch 1.199 (1.590) Remain 13:06:16 loss: 0.1002 Lr: 0.00404
[2023-12-25 11:37:15,176 INFO misc.py line 119 253097] Train: [42/100][412/510] Data 0.008 (0.170) Batch 1.037 (1.588) Remain 13:05:34 loss: 0.2850 Lr: 0.00404
[2023-12-25 11:37:16,323 INFO misc.py line 119 253097] Train: [42/100][413/510] Data 0.007 (0.169) Batch 1.144 (1.587) Remain 13:05:00 loss: 0.2054 Lr: 0.00404
[2023-12-25 11:37:17,458 INFO misc.py line 119 253097] Train: [42/100][414/510] Data 0.010 (0.169) Batch 1.136 (1.586) Remain 13:04:26 loss: 0.1054 Lr: 0.00404
[2023-12-25 11:37:18,552 INFO misc.py line 119 253097] Train: [42/100][415/510] Data 0.011 (0.169) Batch 1.099 (1.585) Remain 13:03:49 loss: 0.1573 Lr: 0.00404
[2023-12-25 11:37:23,081 INFO misc.py line 119 253097] Train: [42/100][416/510] Data 0.003 (0.168) Batch 4.528 (1.592) Remain 13:07:19 loss: 0.1366 Lr: 0.00404
[2023-12-25 11:37:24,109 INFO misc.py line 119 253097] Train: [42/100][417/510] Data 0.004 (0.168) Batch 1.028 (1.591) Remain 13:06:37 loss: 0.1734 Lr: 0.00404
[2023-12-25 11:37:25,241 INFO misc.py line 119 253097] Train: [42/100][418/510] Data 0.005 (0.167) Batch 1.133 (1.589) Remain 13:06:03 loss: 0.2327 Lr: 0.00404
[2023-12-25 11:37:26,313 INFO misc.py line 119 253097] Train: [42/100][419/510] Data 0.005 (0.167) Batch 1.072 (1.588) Remain 13:05:24 loss: 0.3776 Lr: 0.00404
[2023-12-25 11:37:27,454 INFO misc.py line 119 253097] Train: [42/100][420/510] Data 0.004 (0.167) Batch 1.142 (1.587) Remain 13:04:51 loss: 0.1994 Lr: 0.00404
[2023-12-25 11:37:28,509 INFO misc.py line 119 253097] Train: [42/100][421/510] Data 0.003 (0.166) Batch 1.053 (1.586) Remain 13:04:12 loss: 0.2017 Lr: 0.00404
[2023-12-25 11:37:29,758 INFO misc.py line 119 253097] Train: [42/100][422/510] Data 0.005 (0.166) Batch 1.246 (1.585) Remain 13:03:46 loss: 0.2049 Lr: 0.00404
[2023-12-25 11:37:30,804 INFO misc.py line 119 253097] Train: [42/100][423/510] Data 0.008 (0.165) Batch 1.050 (1.584) Remain 13:03:07 loss: 0.1108 Lr: 0.00404
[2023-12-25 11:37:31,765 INFO misc.py line 119 253097] Train: [42/100][424/510] Data 0.005 (0.165) Batch 0.961 (1.582) Remain 13:02:21 loss: 0.2157 Lr: 0.00404
[2023-12-25 11:37:32,859 INFO misc.py line 119 253097] Train: [42/100][425/510] Data 0.004 (0.165) Batch 1.093 (1.581) Remain 13:01:45 loss: 0.1545 Lr: 0.00404
[2023-12-25 11:37:38,731 INFO misc.py line 119 253097] Train: [42/100][426/510] Data 4.736 (0.175) Batch 5.872 (1.591) Remain 13:06:45 loss: 0.1436 Lr: 0.00404
[2023-12-25 11:37:39,746 INFO misc.py line 119 253097] Train: [42/100][427/510] Data 0.004 (0.175) Batch 1.016 (1.590) Remain 13:06:03 loss: 0.1767 Lr: 0.00404
[2023-12-25 11:37:40,904 INFO misc.py line 119 253097] Train: [42/100][428/510] Data 0.003 (0.175) Batch 1.157 (1.589) Remain 13:05:31 loss: 0.1300 Lr: 0.00404
[2023-12-25 11:37:42,154 INFO misc.py line 119 253097] Train: [42/100][429/510] Data 0.004 (0.174) Batch 1.251 (1.588) Remain 13:05:06 loss: 0.0985 Lr: 0.00404
[2023-12-25 11:37:43,378 INFO misc.py line 119 253097] Train: [42/100][430/510] Data 0.003 (0.174) Batch 1.220 (1.587) Remain 13:04:39 loss: 0.1211 Lr: 0.00404
[2023-12-25 11:37:44,593 INFO misc.py line 119 253097] Train: [42/100][431/510] Data 0.008 (0.173) Batch 1.214 (1.586) Remain 13:04:11 loss: 0.2491 Lr: 0.00404
[2023-12-25 11:37:45,660 INFO misc.py line 119 253097] Train: [42/100][432/510] Data 0.009 (0.173) Batch 1.073 (1.585) Remain 13:03:34 loss: 0.1757 Lr: 0.00404
[2023-12-25 11:37:46,874 INFO misc.py line 119 253097] Train: [42/100][433/510] Data 0.004 (0.173) Batch 1.184 (1.584) Remain 13:03:05 loss: 0.3245 Lr: 0.00404
[2023-12-25 11:37:48,010 INFO misc.py line 119 253097] Train: [42/100][434/510] Data 0.033 (0.172) Batch 1.161 (1.583) Remain 13:02:34 loss: 0.3109 Lr: 0.00404
[2023-12-25 11:37:49,242 INFO misc.py line 119 253097] Train: [42/100][435/510] Data 0.009 (0.172) Batch 1.237 (1.583) Remain 13:02:09 loss: 0.2628 Lr: 0.00404
[2023-12-25 11:37:50,301 INFO misc.py line 119 253097] Train: [42/100][436/510] Data 0.003 (0.172) Batch 1.059 (1.581) Remain 13:01:31 loss: 0.2767 Lr: 0.00404
[2023-12-25 11:37:51,266 INFO misc.py line 119 253097] Train: [42/100][437/510] Data 0.002 (0.171) Batch 0.965 (1.580) Remain 13:00:48 loss: 0.1716 Lr: 0.00404
[2023-12-25 11:37:52,370 INFO misc.py line 119 253097] Train: [42/100][438/510] Data 0.003 (0.171) Batch 1.104 (1.579) Remain 13:00:14 loss: 0.1811 Lr: 0.00404
[2023-12-25 11:37:53,428 INFO misc.py line 119 253097] Train: [42/100][439/510] Data 0.003 (0.170) Batch 1.057 (1.578) Remain 12:59:37 loss: 0.1240 Lr: 0.00404
[2023-12-25 11:37:54,477 INFO misc.py line 119 253097] Train: [42/100][440/510] Data 0.003 (0.170) Batch 1.050 (1.576) Remain 12:58:59 loss: 0.2278 Lr: 0.00403
[2023-12-25 11:37:55,466 INFO misc.py line 119 253097] Train: [42/100][441/510] Data 0.003 (0.170) Batch 0.989 (1.575) Remain 12:58:18 loss: 0.1006 Lr: 0.00403
[2023-12-25 11:37:56,572 INFO misc.py line 119 253097] Train: [42/100][442/510] Data 0.003 (0.169) Batch 1.105 (1.574) Remain 12:57:44 loss: 0.1512 Lr: 0.00403
[2023-12-25 11:37:57,825 INFO misc.py line 119 253097] Train: [42/100][443/510] Data 0.004 (0.169) Batch 1.253 (1.573) Remain 12:57:21 loss: 0.1181 Lr: 0.00403
[2023-12-25 11:37:58,932 INFO misc.py line 119 253097] Train: [42/100][444/510] Data 0.004 (0.169) Batch 1.107 (1.572) Remain 12:56:48 loss: 0.3154 Lr: 0.00403
[2023-12-25 11:38:00,167 INFO misc.py line 119 253097] Train: [42/100][445/510] Data 0.004 (0.168) Batch 1.233 (1.571) Remain 12:56:24 loss: 0.2685 Lr: 0.00403
[2023-12-25 11:38:02,936 INFO misc.py line 119 253097] Train: [42/100][446/510] Data 0.006 (0.168) Batch 2.771 (1.574) Remain 12:57:43 loss: 0.1624 Lr: 0.00403
[2023-12-25 11:38:04,254 INFO misc.py line 119 253097] Train: [42/100][447/510] Data 0.004 (0.167) Batch 1.318 (1.574) Remain 12:57:24 loss: 0.1945 Lr: 0.00403
[2023-12-25 11:38:05,565 INFO misc.py line 119 253097] Train: [42/100][448/510] Data 0.004 (0.167) Batch 1.308 (1.573) Remain 12:57:05 loss: 0.1837 Lr: 0.00403
[2023-12-25 11:38:06,907 INFO misc.py line 119 253097] Train: [42/100][449/510] Data 0.007 (0.167) Batch 1.342 (1.572) Remain 12:56:48 loss: 0.1176 Lr: 0.00403
[2023-12-25 11:38:07,954 INFO misc.py line 119 253097] Train: [42/100][450/510] Data 0.007 (0.166) Batch 1.048 (1.571) Remain 12:56:12 loss: 0.2534 Lr: 0.00403
[2023-12-25 11:38:08,920 INFO misc.py line 119 253097] Train: [42/100][451/510] Data 0.006 (0.166) Batch 0.968 (1.570) Remain 12:55:30 loss: 0.2192 Lr: 0.00403
[2023-12-25 11:38:11,094 INFO misc.py line 119 253097] Train: [42/100][452/510] Data 0.004 (0.166) Batch 2.173 (1.571) Remain 12:56:08 loss: 0.2457 Lr: 0.00403
[2023-12-25 11:38:12,272 INFO misc.py line 119 253097] Train: [42/100][453/510] Data 0.006 (0.165) Batch 1.180 (1.570) Remain 12:55:41 loss: 0.3141 Lr: 0.00403
[2023-12-25 11:38:13,512 INFO misc.py line 119 253097] Train: [42/100][454/510] Data 0.004 (0.165) Batch 1.235 (1.570) Remain 12:55:17 loss: 0.2793 Lr: 0.00403
[2023-12-25 11:38:14,718 INFO misc.py line 119 253097] Train: [42/100][455/510] Data 0.009 (0.165) Batch 1.211 (1.569) Remain 12:54:52 loss: 0.1174 Lr: 0.00403
[2023-12-25 11:38:15,864 INFO misc.py line 119 253097] Train: [42/100][456/510] Data 0.003 (0.164) Batch 1.142 (1.568) Remain 12:54:23 loss: 0.2193 Lr: 0.00403
[2023-12-25 11:38:16,895 INFO misc.py line 119 253097] Train: [42/100][457/510] Data 0.007 (0.164) Batch 1.035 (1.567) Remain 12:53:46 loss: 0.2845 Lr: 0.00403
[2023-12-25 11:38:17,959 INFO misc.py line 119 253097] Train: [42/100][458/510] Data 0.004 (0.164) Batch 1.063 (1.566) Remain 12:53:12 loss: 0.1426 Lr: 0.00403
[2023-12-25 11:38:19,213 INFO misc.py line 119 253097] Train: [42/100][459/510] Data 0.006 (0.163) Batch 1.250 (1.565) Remain 12:52:50 loss: 0.1437 Lr: 0.00403
[2023-12-25 11:38:20,318 INFO misc.py line 119 253097] Train: [42/100][460/510] Data 0.009 (0.163) Batch 1.106 (1.564) Remain 12:52:19 loss: 0.2830 Lr: 0.00403
[2023-12-25 11:38:21,427 INFO misc.py line 119 253097] Train: [42/100][461/510] Data 0.007 (0.162) Batch 1.112 (1.563) Remain 12:51:48 loss: 0.1737 Lr: 0.00403
[2023-12-25 11:38:22,443 INFO misc.py line 119 253097] Train: [42/100][462/510] Data 0.004 (0.162) Batch 1.012 (1.562) Remain 12:51:11 loss: 0.2384 Lr: 0.00403
[2023-12-25 11:38:23,691 INFO misc.py line 119 253097] Train: [42/100][463/510] Data 0.009 (0.162) Batch 1.253 (1.561) Remain 12:50:49 loss: 0.2183 Lr: 0.00403
[2023-12-25 11:38:24,671 INFO misc.py line 119 253097] Train: [42/100][464/510] Data 0.003 (0.161) Batch 0.979 (1.560) Remain 12:50:10 loss: 0.2112 Lr: 0.00403
[2023-12-25 11:38:26,775 INFO misc.py line 119 253097] Train: [42/100][465/510] Data 1.118 (0.164) Batch 2.104 (1.561) Remain 12:50:44 loss: 0.1347 Lr: 0.00403
[2023-12-25 11:38:27,818 INFO misc.py line 119 253097] Train: [42/100][466/510] Data 0.003 (0.163) Batch 1.038 (1.560) Remain 12:50:09 loss: 0.2929 Lr: 0.00403
[2023-12-25 11:38:28,943 INFO misc.py line 119 253097] Train: [42/100][467/510] Data 0.008 (0.163) Batch 1.128 (1.559) Remain 12:49:39 loss: 0.2636 Lr: 0.00403
[2023-12-25 11:38:29,864 INFO misc.py line 119 253097] Train: [42/100][468/510] Data 0.006 (0.163) Batch 0.922 (1.558) Remain 12:48:57 loss: 0.3536 Lr: 0.00403
[2023-12-25 11:38:30,916 INFO misc.py line 119 253097] Train: [42/100][469/510] Data 0.004 (0.162) Batch 1.052 (1.556) Remain 12:48:24 loss: 0.2637 Lr: 0.00403
[2023-12-25 11:38:32,038 INFO misc.py line 119 253097] Train: [42/100][470/510] Data 0.005 (0.162) Batch 1.124 (1.556) Remain 12:47:55 loss: 0.2294 Lr: 0.00403
[2023-12-25 11:38:32,943 INFO misc.py line 119 253097] Train: [42/100][471/510] Data 0.003 (0.161) Batch 0.905 (1.554) Remain 12:47:12 loss: 0.2652 Lr: 0.00403
[2023-12-25 11:38:34,587 INFO misc.py line 119 253097] Train: [42/100][472/510] Data 0.653 (0.163) Batch 1.644 (1.554) Remain 12:47:16 loss: 0.1591 Lr: 0.00403
[2023-12-25 11:38:35,599 INFO misc.py line 119 253097] Train: [42/100][473/510] Data 0.003 (0.162) Batch 1.011 (1.553) Remain 12:46:40 loss: 0.2888 Lr: 0.00403
[2023-12-25 11:38:36,800 INFO misc.py line 119 253097] Train: [42/100][474/510] Data 0.005 (0.162) Batch 1.199 (1.552) Remain 12:46:16 loss: 0.3183 Lr: 0.00403
[2023-12-25 11:38:37,961 INFO misc.py line 119 253097] Train: [42/100][475/510] Data 0.008 (0.162) Batch 1.161 (1.552) Remain 12:45:50 loss: 0.2147 Lr: 0.00403
[2023-12-25 11:38:39,122 INFO misc.py line 119 253097] Train: [42/100][476/510] Data 0.008 (0.161) Batch 1.163 (1.551) Remain 12:45:24 loss: 0.1220 Lr: 0.00403
[2023-12-25 11:38:40,349 INFO misc.py line 119 253097] Train: [42/100][477/510] Data 0.006 (0.161) Batch 1.227 (1.550) Remain 12:45:03 loss: 0.1851 Lr: 0.00403
[2023-12-25 11:38:41,426 INFO misc.py line 119 253097] Train: [42/100][478/510] Data 0.006 (0.161) Batch 1.074 (1.549) Remain 12:44:31 loss: 0.2563 Lr: 0.00403
[2023-12-25 11:38:42,558 INFO misc.py line 119 253097] Train: [42/100][479/510] Data 0.008 (0.160) Batch 1.136 (1.548) Remain 12:44:04 loss: 0.1390 Lr: 0.00403
[2023-12-25 11:38:43,827 INFO misc.py line 119 253097] Train: [42/100][480/510] Data 0.005 (0.160) Batch 1.270 (1.548) Remain 12:43:45 loss: 0.1608 Lr: 0.00403
[2023-12-25 11:38:44,883 INFO misc.py line 119 253097] Train: [42/100][481/510] Data 0.003 (0.160) Batch 1.056 (1.547) Remain 12:43:13 loss: 0.0973 Lr: 0.00403
[2023-12-25 11:38:46,147 INFO misc.py line 119 253097] Train: [42/100][482/510] Data 0.004 (0.159) Batch 1.264 (1.546) Remain 12:42:54 loss: 0.1428 Lr: 0.00403
[2023-12-25 11:38:47,277 INFO misc.py line 119 253097] Train: [42/100][483/510] Data 0.004 (0.159) Batch 1.124 (1.545) Remain 12:42:27 loss: 0.1320 Lr: 0.00403
[2023-12-25 11:38:48,442 INFO misc.py line 119 253097] Train: [42/100][484/510] Data 0.010 (0.159) Batch 1.161 (1.544) Remain 12:42:02 loss: 0.2297 Lr: 0.00403
[2023-12-25 11:38:49,605 INFO misc.py line 119 253097] Train: [42/100][485/510] Data 0.013 (0.158) Batch 1.162 (1.544) Remain 12:41:37 loss: 0.1830 Lr: 0.00403
[2023-12-25 11:38:57,807 INFO misc.py line 119 253097] Train: [42/100][486/510] Data 7.026 (0.173) Batch 8.212 (1.557) Remain 12:48:24 loss: 0.2609 Lr: 0.00403
[2023-12-25 11:38:58,917 INFO misc.py line 119 253097] Train: [42/100][487/510] Data 0.004 (0.172) Batch 1.110 (1.556) Remain 12:47:55 loss: 0.1885 Lr: 0.00403
[2023-12-25 11:39:00,130 INFO misc.py line 119 253097] Train: [42/100][488/510] Data 0.003 (0.172) Batch 1.213 (1.556) Remain 12:47:32 loss: 0.2174 Lr: 0.00403
[2023-12-25 11:39:01,185 INFO misc.py line 119 253097] Train: [42/100][489/510] Data 0.005 (0.172) Batch 1.053 (1.555) Remain 12:47:00 loss: 0.2162 Lr: 0.00403
[2023-12-25 11:39:02,319 INFO misc.py line 119 253097] Train: [42/100][490/510] Data 0.007 (0.171) Batch 1.136 (1.554) Remain 12:46:33 loss: 0.2420 Lr: 0.00403
[2023-12-25 11:39:03,492 INFO misc.py line 119 253097] Train: [42/100][491/510] Data 0.004 (0.171) Batch 1.173 (1.553) Remain 12:46:08 loss: 0.0937 Lr: 0.00403
[2023-12-25 11:39:04,472 INFO misc.py line 119 253097] Train: [42/100][492/510] Data 0.004 (0.170) Batch 0.980 (1.552) Remain 12:45:32 loss: 0.1644 Lr: 0.00403
[2023-12-25 11:39:06,164 INFO misc.py line 119 253097] Train: [42/100][493/510] Data 0.003 (0.170) Batch 1.196 (1.551) Remain 12:45:09 loss: 0.1336 Lr: 0.00403
[2023-12-25 11:39:07,439 INFO misc.py line 119 253097] Train: [42/100][494/510] Data 0.499 (0.171) Batch 1.767 (1.552) Remain 12:45:21 loss: 0.1436 Lr: 0.00403
[2023-12-25 11:39:08,587 INFO misc.py line 119 253097] Train: [42/100][495/510] Data 0.006 (0.170) Batch 1.151 (1.551) Remain 12:44:55 loss: 0.1273 Lr: 0.00402
[2023-12-25 11:39:09,771 INFO misc.py line 119 253097] Train: [42/100][496/510] Data 0.004 (0.170) Batch 1.184 (1.550) Remain 12:44:31 loss: 0.3469 Lr: 0.00402
[2023-12-25 11:39:10,976 INFO misc.py line 119 253097] Train: [42/100][497/510] Data 0.004 (0.170) Batch 1.203 (1.549) Remain 12:44:09 loss: 0.1726 Lr: 0.00402
[2023-12-25 11:39:12,177 INFO misc.py line 119 253097] Train: [42/100][498/510] Data 0.006 (0.169) Batch 1.198 (1.549) Remain 12:43:47 loss: 0.1981 Lr: 0.00402
[2023-12-25 11:39:13,436 INFO misc.py line 119 253097] Train: [42/100][499/510] Data 0.010 (0.169) Batch 1.258 (1.548) Remain 12:43:28 loss: 0.2567 Lr: 0.00402
[2023-12-25 11:39:14,390 INFO misc.py line 119 253097] Train: [42/100][500/510] Data 0.010 (0.169) Batch 0.961 (1.547) Remain 12:42:51 loss: 0.2651 Lr: 0.00402
[2023-12-25 11:39:15,359 INFO misc.py line 119 253097] Train: [42/100][501/510] Data 0.003 (0.169) Batch 0.969 (1.546) Remain 12:42:15 loss: 0.1733 Lr: 0.00402
[2023-12-25 11:39:16,521 INFO misc.py line 119 253097] Train: [42/100][502/510] Data 0.003 (0.168) Batch 1.162 (1.545) Remain 12:41:51 loss: 0.1995 Lr: 0.00402
[2023-12-25 11:39:17,601 INFO misc.py line 119 253097] Train: [42/100][503/510] Data 0.004 (0.168) Batch 1.080 (1.544) Remain 12:41:22 loss: 0.1812 Lr: 0.00402
[2023-12-25 11:39:18,861 INFO misc.py line 119 253097] Train: [42/100][504/510] Data 0.003 (0.168) Batch 1.259 (1.543) Remain 12:41:04 loss: 0.2374 Lr: 0.00402
[2023-12-25 11:39:19,942 INFO misc.py line 119 253097] Train: [42/100][505/510] Data 0.004 (0.167) Batch 1.079 (1.543) Remain 12:40:35 loss: 0.2488 Lr: 0.00402
[2023-12-25 11:39:21,069 INFO misc.py line 119 253097] Train: [42/100][506/510] Data 0.006 (0.167) Batch 1.121 (1.542) Remain 12:40:08 loss: 0.1547 Lr: 0.00402
[2023-12-25 11:39:22,313 INFO misc.py line 119 253097] Train: [42/100][507/510] Data 0.013 (0.167) Batch 1.249 (1.541) Remain 12:39:50 loss: 0.1804 Lr: 0.00402
[2023-12-25 11:39:26,886 INFO misc.py line 119 253097] Train: [42/100][508/510] Data 0.007 (0.166) Batch 4.575 (1.547) Remain 12:42:46 loss: 0.2422 Lr: 0.00402
[2023-12-25 11:39:31,346 INFO misc.py line 119 253097] Train: [42/100][509/510] Data 3.376 (0.173) Batch 4.463 (1.553) Remain 12:45:35 loss: 0.1844 Lr: 0.00402
[2023-12-25 11:39:32,531 INFO misc.py line 119 253097] Train: [42/100][510/510] Data 0.002 (0.172) Batch 1.183 (1.552) Remain 12:45:12 loss: 0.2083 Lr: 0.00402
[2023-12-25 11:39:32,532 INFO misc.py line 136 253097] Train result: loss: 0.2043 
[2023-12-25 11:39:32,532 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 11:40:00,359 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6995 
[2023-12-25 11:40:00,714 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3366 
[2023-12-25 11:40:05,657 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4004 
[2023-12-25 11:40:06,173 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3390 
[2023-12-25 11:40:08,144 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0057 
[2023-12-25 11:40:08,568 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4550 
[2023-12-25 11:40:09,448 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.4479 
[2023-12-25 11:40:10,010 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3502 
[2023-12-25 11:40:11,818 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8217 
[2023-12-25 11:40:13,966 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2331 
[2023-12-25 11:40:14,829 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2060 
[2023-12-25 11:40:15,253 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9607 
[2023-12-25 11:40:16,161 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.8722 
[2023-12-25 11:40:19,101 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9640 
[2023-12-25 11:40:19,568 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3134 
[2023-12-25 11:40:20,179 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3965 
[2023-12-25 11:40:20,880 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3278 
[2023-12-25 11:40:22,206 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6681/0.7304/0.8977.
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9260/0.9518
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9809/0.9895
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8436/0.9719
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2119/0.2243
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5724/0.5955
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6478/0.8263
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7979/0.9186
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9124/0.9550
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.7090/0.7418
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7400/0.7829
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7349/0.7562
[2023-12-25 11:40:22,206 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6086/0.7817
[2023-12-25 11:40:22,207 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 11:40:22,208 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 11:40:22,208 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 11:40:31,064 INFO misc.py line 119 253097] Train: [43/100][1/510] Data 5.343 (5.343) Batch 6.307 (6.307) Remain 51:49:28 loss: 0.3020 Lr: 0.00402
[2023-12-25 11:40:32,174 INFO misc.py line 119 253097] Train: [43/100][2/510] Data 0.004 (0.004) Batch 1.109 (1.109) Remain 09:06:48 loss: 0.1646 Lr: 0.00402
[2023-12-25 11:40:33,485 INFO misc.py line 119 253097] Train: [43/100][3/510] Data 0.027 (0.027) Batch 1.312 (1.312) Remain 10:46:55 loss: 0.3879 Lr: 0.00402
[2023-12-25 11:40:34,460 INFO misc.py line 119 253097] Train: [43/100][4/510] Data 0.003 (0.003) Batch 0.974 (0.974) Remain 08:00:19 loss: 0.1430 Lr: 0.00402
[2023-12-25 11:40:35,541 INFO misc.py line 119 253097] Train: [43/100][5/510] Data 0.004 (0.003) Batch 1.081 (1.028) Remain 08:26:40 loss: 0.1621 Lr: 0.00402
[2023-12-25 11:40:36,562 INFO misc.py line 119 253097] Train: [43/100][6/510] Data 0.003 (0.003) Batch 1.021 (1.025) Remain 08:25:26 loss: 0.5170 Lr: 0.00402
[2023-12-25 11:40:37,774 INFO misc.py line 119 253097] Train: [43/100][7/510] Data 0.005 (0.004) Batch 1.212 (1.072) Remain 08:48:24 loss: 0.1744 Lr: 0.00402
[2023-12-25 11:40:38,858 INFO misc.py line 119 253097] Train: [43/100][8/510] Data 0.004 (0.004) Batch 1.084 (1.075) Remain 08:49:35 loss: 0.1208 Lr: 0.00402
[2023-12-25 11:40:40,097 INFO misc.py line 119 253097] Train: [43/100][9/510] Data 0.004 (0.004) Batch 1.238 (1.102) Remain 09:03:00 loss: 0.1669 Lr: 0.00402
[2023-12-25 11:40:41,293 INFO misc.py line 119 253097] Train: [43/100][10/510] Data 0.005 (0.004) Batch 1.198 (1.115) Remain 09:09:43 loss: 0.1806 Lr: 0.00402
[2023-12-25 11:40:42,447 INFO misc.py line 119 253097] Train: [43/100][11/510] Data 0.004 (0.004) Batch 1.153 (1.120) Remain 09:12:02 loss: 0.1882 Lr: 0.00402
[2023-12-25 11:40:43,659 INFO misc.py line 119 253097] Train: [43/100][12/510] Data 0.005 (0.004) Batch 1.207 (1.130) Remain 09:16:45 loss: 0.2121 Lr: 0.00402
[2023-12-25 11:40:45,692 INFO misc.py line 119 253097] Train: [43/100][13/510] Data 0.905 (0.094) Batch 2.038 (1.221) Remain 10:01:29 loss: 0.3221 Lr: 0.00402
[2023-12-25 11:40:46,636 INFO misc.py line 119 253097] Train: [43/100][14/510] Data 0.005 (0.086) Batch 0.944 (1.195) Remain 09:49:04 loss: 0.2240 Lr: 0.00402
[2023-12-25 11:40:47,731 INFO misc.py line 119 253097] Train: [43/100][15/510] Data 0.004 (0.079) Batch 1.095 (1.187) Remain 09:44:56 loss: 0.1376 Lr: 0.00402
[2023-12-25 11:40:57,285 INFO misc.py line 119 253097] Train: [43/100][16/510] Data 0.005 (0.073) Batch 9.554 (1.831) Remain 15:02:03 loss: 0.1469 Lr: 0.00402
[2023-12-25 11:40:58,445 INFO misc.py line 119 253097] Train: [43/100][17/510] Data 0.004 (0.069) Batch 1.160 (1.783) Remain 14:38:24 loss: 0.1923 Lr: 0.00402
[2023-12-25 11:40:59,776 INFO misc.py line 119 253097] Train: [43/100][18/510] Data 0.004 (0.064) Batch 1.332 (1.753) Remain 14:23:33 loss: 0.2648 Lr: 0.00402
[2023-12-25 11:41:00,984 INFO misc.py line 119 253097] Train: [43/100][19/510] Data 0.004 (0.060) Batch 1.209 (1.719) Remain 14:06:47 loss: 0.3172 Lr: 0.00402
[2023-12-25 11:41:02,041 INFO misc.py line 119 253097] Train: [43/100][20/510] Data 0.003 (0.057) Batch 1.054 (1.680) Remain 13:47:29 loss: 0.1628 Lr: 0.00402
[2023-12-25 11:41:03,221 INFO misc.py line 119 253097] Train: [43/100][21/510] Data 0.007 (0.054) Batch 1.182 (1.652) Remain 13:33:49 loss: 0.3601 Lr: 0.00402
[2023-12-25 11:41:04,335 INFO misc.py line 119 253097] Train: [43/100][22/510] Data 0.004 (0.052) Batch 1.111 (1.623) Remain 13:19:46 loss: 0.2184 Lr: 0.00402
[2023-12-25 11:41:05,281 INFO misc.py line 119 253097] Train: [43/100][23/510] Data 0.008 (0.049) Batch 0.950 (1.590) Remain 13:03:09 loss: 0.2229 Lr: 0.00402
[2023-12-25 11:41:06,515 INFO misc.py line 119 253097] Train: [43/100][24/510] Data 0.003 (0.047) Batch 1.233 (1.573) Remain 12:54:46 loss: 0.2813 Lr: 0.00402
[2023-12-25 11:41:07,769 INFO misc.py line 119 253097] Train: [43/100][25/510] Data 0.004 (0.045) Batch 1.255 (1.558) Remain 12:47:38 loss: 0.1789 Lr: 0.00402
[2023-12-25 11:41:08,868 INFO misc.py line 119 253097] Train: [43/100][26/510] Data 0.003 (0.043) Batch 1.097 (1.538) Remain 12:37:43 loss: 0.1330 Lr: 0.00402
[2023-12-25 11:41:10,042 INFO misc.py line 119 253097] Train: [43/100][27/510] Data 0.006 (0.042) Batch 1.172 (1.523) Remain 12:30:11 loss: 0.1850 Lr: 0.00402
[2023-12-25 11:41:11,226 INFO misc.py line 119 253097] Train: [43/100][28/510] Data 0.006 (0.041) Batch 1.187 (1.510) Remain 12:23:32 loss: 0.1819 Lr: 0.00402
[2023-12-25 11:41:19,302 INFO misc.py line 119 253097] Train: [43/100][29/510] Data 7.177 (0.315) Batch 8.076 (1.762) Remain 14:27:55 loss: 0.3078 Lr: 0.00402
[2023-12-25 11:41:20,375 INFO misc.py line 119 253097] Train: [43/100][30/510] Data 0.002 (0.303) Batch 1.073 (1.737) Remain 14:15:19 loss: 0.2809 Lr: 0.00402
[2023-12-25 11:41:21,557 INFO misc.py line 119 253097] Train: [43/100][31/510] Data 0.003 (0.293) Batch 1.180 (1.717) Remain 14:05:30 loss: 0.1430 Lr: 0.00402
[2023-12-25 11:41:22,716 INFO misc.py line 119 253097] Train: [43/100][32/510] Data 0.005 (0.283) Batch 1.153 (1.697) Remain 13:55:54 loss: 0.1179 Lr: 0.00402
[2023-12-25 11:41:26,262 INFO misc.py line 119 253097] Train: [43/100][33/510] Data 0.011 (0.274) Batch 3.553 (1.759) Remain 14:26:20 loss: 0.1408 Lr: 0.00402
[2023-12-25 11:41:27,176 INFO misc.py line 119 253097] Train: [43/100][34/510] Data 0.003 (0.265) Batch 0.913 (1.732) Remain 14:12:52 loss: 0.2073 Lr: 0.00402
[2023-12-25 11:41:28,110 INFO misc.py line 119 253097] Train: [43/100][35/510] Data 0.004 (0.257) Batch 0.933 (1.707) Remain 14:00:32 loss: 0.2213 Lr: 0.00402
[2023-12-25 11:41:29,349 INFO misc.py line 119 253097] Train: [43/100][36/510] Data 0.006 (0.249) Batch 1.240 (1.693) Remain 13:53:32 loss: 0.2043 Lr: 0.00402
[2023-12-25 11:41:30,596 INFO misc.py line 119 253097] Train: [43/100][37/510] Data 0.005 (0.242) Batch 1.247 (1.680) Remain 13:47:03 loss: 0.1292 Lr: 0.00402
[2023-12-25 11:41:31,880 INFO misc.py line 119 253097] Train: [43/100][38/510] Data 0.005 (0.235) Batch 1.281 (1.668) Remain 13:41:25 loss: 0.1909 Lr: 0.00402
[2023-12-25 11:41:32,847 INFO misc.py line 119 253097] Train: [43/100][39/510] Data 0.008 (0.229) Batch 0.971 (1.649) Remain 13:31:51 loss: 0.1660 Lr: 0.00402
[2023-12-25 11:41:33,948 INFO misc.py line 119 253097] Train: [43/100][40/510] Data 0.004 (0.223) Batch 1.100 (1.634) Remain 13:24:31 loss: 0.3049 Lr: 0.00401
[2023-12-25 11:41:34,994 INFO misc.py line 119 253097] Train: [43/100][41/510] Data 0.005 (0.217) Batch 1.045 (1.619) Remain 13:16:51 loss: 0.3304 Lr: 0.00401
[2023-12-25 11:41:36,020 INFO misc.py line 119 253097] Train: [43/100][42/510] Data 0.006 (0.212) Batch 1.026 (1.603) Remain 13:09:21 loss: 0.2410 Lr: 0.00401
[2023-12-25 11:41:37,188 INFO misc.py line 119 253097] Train: [43/100][43/510] Data 0.005 (0.207) Batch 1.169 (1.593) Remain 13:03:59 loss: 0.1618 Lr: 0.00401
[2023-12-25 11:41:38,177 INFO misc.py line 119 253097] Train: [43/100][44/510] Data 0.004 (0.202) Batch 0.989 (1.578) Remain 12:56:43 loss: 0.2679 Lr: 0.00401
[2023-12-25 11:41:39,091 INFO misc.py line 119 253097] Train: [43/100][45/510] Data 0.004 (0.197) Batch 0.893 (1.562) Remain 12:48:39 loss: 0.1528 Lr: 0.00401
[2023-12-25 11:41:40,249 INFO misc.py line 119 253097] Train: [43/100][46/510] Data 0.025 (0.193) Batch 1.177 (1.553) Remain 12:44:14 loss: 0.2342 Lr: 0.00401
[2023-12-25 11:41:48,850 INFO misc.py line 119 253097] Train: [43/100][47/510] Data 0.155 (0.192) Batch 8.604 (1.713) Remain 14:03:05 loss: 0.1241 Lr: 0.00401
[2023-12-25 11:41:50,164 INFO misc.py line 119 253097] Train: [43/100][48/510] Data 0.003 (0.188) Batch 1.309 (1.704) Remain 13:58:39 loss: 0.1070 Lr: 0.00401
[2023-12-25 11:41:51,398 INFO misc.py line 119 253097] Train: [43/100][49/510] Data 0.007 (0.184) Batch 1.233 (1.694) Remain 13:53:35 loss: 0.3676 Lr: 0.00401
[2023-12-25 11:41:52,417 INFO misc.py line 119 253097] Train: [43/100][50/510] Data 0.009 (0.180) Batch 1.018 (1.679) Remain 13:46:28 loss: 0.2159 Lr: 0.00401
[2023-12-25 11:41:53,691 INFO misc.py line 119 253097] Train: [43/100][51/510] Data 0.010 (0.177) Batch 1.277 (1.671) Remain 13:42:19 loss: 0.1479 Lr: 0.00401
[2023-12-25 11:41:54,807 INFO misc.py line 119 253097] Train: [43/100][52/510] Data 0.007 (0.173) Batch 1.115 (1.660) Remain 13:36:42 loss: 0.1995 Lr: 0.00401
[2023-12-25 11:41:55,973 INFO misc.py line 119 253097] Train: [43/100][53/510] Data 0.009 (0.170) Batch 1.168 (1.650) Remain 13:31:50 loss: 0.2581 Lr: 0.00401
[2023-12-25 11:41:57,064 INFO misc.py line 119 253097] Train: [43/100][54/510] Data 0.007 (0.167) Batch 1.095 (1.639) Remain 13:26:27 loss: 0.1564 Lr: 0.00401
[2023-12-25 11:41:58,137 INFO misc.py line 119 253097] Train: [43/100][55/510] Data 0.004 (0.164) Batch 1.073 (1.628) Remain 13:21:04 loss: 0.2686 Lr: 0.00401
[2023-12-25 11:41:59,325 INFO misc.py line 119 253097] Train: [43/100][56/510] Data 0.004 (0.160) Batch 1.182 (1.620) Remain 13:16:54 loss: 0.1135 Lr: 0.00401
[2023-12-25 11:42:00,520 INFO misc.py line 119 253097] Train: [43/100][57/510] Data 0.009 (0.158) Batch 1.199 (1.612) Remain 13:13:03 loss: 0.0795 Lr: 0.00401
[2023-12-25 11:42:01,550 INFO misc.py line 119 253097] Train: [43/100][58/510] Data 0.005 (0.155) Batch 1.025 (1.601) Remain 13:07:46 loss: 0.3354 Lr: 0.00401
[2023-12-25 11:42:02,535 INFO misc.py line 119 253097] Train: [43/100][59/510] Data 0.011 (0.152) Batch 0.991 (1.590) Remain 13:02:23 loss: 0.1767 Lr: 0.00401
[2023-12-25 11:42:03,608 INFO misc.py line 119 253097] Train: [43/100][60/510] Data 0.004 (0.150) Batch 1.074 (1.581) Remain 12:57:54 loss: 0.2384 Lr: 0.00401
[2023-12-25 11:42:04,607 INFO misc.py line 119 253097] Train: [43/100][61/510] Data 0.003 (0.147) Batch 0.999 (1.571) Remain 12:52:56 loss: 0.1702 Lr: 0.00401
[2023-12-25 11:42:05,761 INFO misc.py line 119 253097] Train: [43/100][62/510] Data 0.004 (0.145) Batch 1.154 (1.564) Remain 12:49:25 loss: 0.2191 Lr: 0.00401
[2023-12-25 11:42:07,047 INFO misc.py line 119 253097] Train: [43/100][63/510] Data 0.004 (0.142) Batch 1.286 (1.559) Remain 12:47:07 loss: 0.1320 Lr: 0.00401
[2023-12-25 11:42:09,922 INFO misc.py line 119 253097] Train: [43/100][64/510] Data 0.004 (0.140) Batch 2.874 (1.581) Remain 12:57:42 loss: 0.1600 Lr: 0.00401
[2023-12-25 11:42:10,931 INFO misc.py line 119 253097] Train: [43/100][65/510] Data 0.005 (0.138) Batch 1.003 (1.572) Remain 12:53:05 loss: 0.1711 Lr: 0.00401
[2023-12-25 11:42:12,151 INFO misc.py line 119 253097] Train: [43/100][66/510] Data 0.012 (0.136) Batch 1.227 (1.566) Remain 12:50:22 loss: 0.1453 Lr: 0.00401
[2023-12-25 11:42:13,237 INFO misc.py line 119 253097] Train: [43/100][67/510] Data 0.004 (0.134) Batch 1.081 (1.559) Remain 12:46:36 loss: 0.2577 Lr: 0.00401
[2023-12-25 11:42:14,457 INFO misc.py line 119 253097] Train: [43/100][68/510] Data 0.011 (0.132) Batch 1.226 (1.553) Remain 12:44:04 loss: 0.1432 Lr: 0.00401
[2023-12-25 11:42:15,608 INFO misc.py line 119 253097] Train: [43/100][69/510] Data 0.004 (0.130) Batch 1.150 (1.547) Remain 12:41:02 loss: 0.4517 Lr: 0.00401
[2023-12-25 11:42:16,800 INFO misc.py line 119 253097] Train: [43/100][70/510] Data 0.005 (0.128) Batch 1.190 (1.542) Remain 12:38:23 loss: 0.2040 Lr: 0.00401
[2023-12-25 11:42:17,989 INFO misc.py line 119 253097] Train: [43/100][71/510] Data 0.007 (0.126) Batch 1.189 (1.537) Remain 12:35:48 loss: 0.1809 Lr: 0.00401
[2023-12-25 11:42:19,259 INFO misc.py line 119 253097] Train: [43/100][72/510] Data 0.007 (0.125) Batch 1.270 (1.533) Remain 12:33:52 loss: 0.1484 Lr: 0.00401
[2023-12-25 11:42:20,349 INFO misc.py line 119 253097] Train: [43/100][73/510] Data 0.007 (0.123) Batch 1.089 (1.527) Remain 12:30:44 loss: 0.2391 Lr: 0.00401
[2023-12-25 11:42:21,497 INFO misc.py line 119 253097] Train: [43/100][74/510] Data 0.007 (0.121) Batch 1.146 (1.521) Remain 12:28:04 loss: 0.2103 Lr: 0.00401
[2023-12-25 11:42:22,500 INFO misc.py line 119 253097] Train: [43/100][75/510] Data 0.009 (0.120) Batch 1.005 (1.514) Remain 12:24:31 loss: 0.1101 Lr: 0.00401
[2023-12-25 11:42:28,719 INFO misc.py line 119 253097] Train: [43/100][76/510] Data 0.008 (0.118) Batch 6.223 (1.579) Remain 12:56:13 loss: 0.1014 Lr: 0.00401
[2023-12-25 11:42:29,997 INFO misc.py line 119 253097] Train: [43/100][77/510] Data 0.002 (0.117) Batch 1.274 (1.574) Remain 12:54:10 loss: 0.2752 Lr: 0.00401
[2023-12-25 11:42:31,244 INFO misc.py line 119 253097] Train: [43/100][78/510] Data 0.007 (0.115) Batch 1.250 (1.570) Remain 12:52:01 loss: 0.3758 Lr: 0.00401
[2023-12-25 11:42:32,370 INFO misc.py line 119 253097] Train: [43/100][79/510] Data 0.004 (0.114) Batch 1.125 (1.564) Remain 12:49:07 loss: 0.1081 Lr: 0.00401
[2023-12-25 11:42:33,367 INFO misc.py line 119 253097] Train: [43/100][80/510] Data 0.006 (0.112) Batch 0.995 (1.557) Remain 12:45:27 loss: 0.1649 Lr: 0.00401
[2023-12-25 11:42:40,430 INFO misc.py line 119 253097] Train: [43/100][81/510] Data 0.007 (0.111) Batch 7.066 (1.627) Remain 13:20:09 loss: 0.1522 Lr: 0.00401
[2023-12-25 11:42:41,651 INFO misc.py line 119 253097] Train: [43/100][82/510] Data 0.004 (0.110) Batch 1.222 (1.622) Remain 13:17:36 loss: 0.2077 Lr: 0.00401
[2023-12-25 11:42:42,729 INFO misc.py line 119 253097] Train: [43/100][83/510] Data 0.003 (0.108) Batch 1.072 (1.615) Remain 13:14:11 loss: 0.1748 Lr: 0.00401
[2023-12-25 11:42:43,878 INFO misc.py line 119 253097] Train: [43/100][84/510] Data 0.009 (0.107) Batch 1.155 (1.610) Remain 13:11:22 loss: 0.2985 Lr: 0.00401
[2023-12-25 11:42:45,016 INFO misc.py line 119 253097] Train: [43/100][85/510] Data 0.004 (0.106) Batch 1.134 (1.604) Remain 13:08:29 loss: 0.1089 Lr: 0.00401
[2023-12-25 11:42:46,133 INFO misc.py line 119 253097] Train: [43/100][86/510] Data 0.007 (0.105) Batch 1.114 (1.598) Remain 13:05:34 loss: 0.1221 Lr: 0.00401
[2023-12-25 11:42:47,463 INFO misc.py line 119 253097] Train: [43/100][87/510] Data 0.010 (0.104) Batch 1.336 (1.595) Remain 13:04:00 loss: 0.2081 Lr: 0.00401
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[2023-12-25 11:42:49,998 INFO misc.py line 119 253097] Train: [43/100][89/510] Data 0.006 (0.101) Batch 1.310 (1.587) Remain 13:00:12 loss: 0.1467 Lr: 0.00401
[2023-12-25 11:42:51,018 INFO misc.py line 119 253097] Train: [43/100][90/510] Data 0.005 (0.100) Batch 1.021 (1.581) Remain 12:56:58 loss: 0.2466 Lr: 0.00401
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[2023-12-25 11:43:03,796 INFO misc.py line 119 253097] Train: [43/100][101/510] Data 0.004 (0.090) Batch 1.215 (1.534) Remain 12:33:32 loss: 0.2443 Lr: 0.00400
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[2023-12-25 11:43:15,789 INFO misc.py line 119 253097] Train: [43/100][104/510] Data 0.008 (0.087) Batch 1.087 (1.607) Remain 13:09:26 loss: 0.2647 Lr: 0.00400
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[2023-12-25 11:43:18,093 INFO misc.py line 119 253097] Train: [43/100][106/510] Data 0.004 (0.086) Batch 1.206 (1.598) Remain 13:04:50 loss: 0.2769 Lr: 0.00400
[2023-12-25 11:43:19,250 INFO misc.py line 119 253097] Train: [43/100][107/510] Data 0.048 (0.085) Batch 1.196 (1.594) Remain 13:02:55 loss: 0.1956 Lr: 0.00400
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[2023-12-25 11:43:22,874 INFO misc.py line 119 253097] Train: [43/100][110/510] Data 0.006 (0.083) Batch 1.136 (1.583) Remain 12:57:33 loss: 0.1984 Lr: 0.00400
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[2023-12-25 11:43:25,292 INFO misc.py line 119 253097] Train: [43/100][112/510] Data 0.015 (0.082) Batch 1.208 (1.576) Remain 12:54:06 loss: 0.1439 Lr: 0.00400
[2023-12-25 11:43:26,490 INFO misc.py line 119 253097] Train: [43/100][113/510] Data 0.010 (0.081) Batch 1.200 (1.573) Remain 12:52:23 loss: 0.3703 Lr: 0.00400
[2023-12-25 11:43:31,277 INFO misc.py line 119 253097] Train: [43/100][114/510] Data 0.008 (0.080) Batch 4.790 (1.602) Remain 13:06:36 loss: 0.3181 Lr: 0.00400
[2023-12-25 11:43:32,246 INFO misc.py line 119 253097] Train: [43/100][115/510] Data 0.004 (0.080) Batch 0.967 (1.596) Remain 13:03:47 loss: 0.5274 Lr: 0.00400
[2023-12-25 11:43:33,479 INFO misc.py line 119 253097] Train: [43/100][116/510] Data 0.006 (0.079) Batch 1.236 (1.593) Remain 13:02:12 loss: 0.2095 Lr: 0.00400
[2023-12-25 11:43:34,719 INFO misc.py line 119 253097] Train: [43/100][117/510] Data 0.004 (0.078) Batch 1.236 (1.590) Remain 13:00:38 loss: 0.2827 Lr: 0.00400
[2023-12-25 11:43:35,858 INFO misc.py line 119 253097] Train: [43/100][118/510] Data 0.008 (0.078) Batch 1.138 (1.586) Remain 12:58:40 loss: 0.2359 Lr: 0.00400
[2023-12-25 11:43:37,050 INFO misc.py line 119 253097] Train: [43/100][119/510] Data 0.009 (0.077) Batch 1.195 (1.582) Remain 12:57:00 loss: 0.1414 Lr: 0.00400
[2023-12-25 11:43:38,153 INFO misc.py line 119 253097] Train: [43/100][120/510] Data 0.005 (0.076) Batch 1.103 (1.578) Remain 12:54:57 loss: 0.1468 Lr: 0.00400
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[2023-12-25 11:43:40,577 INFO misc.py line 119 253097] Train: [43/100][122/510] Data 0.008 (0.075) Batch 1.237 (1.572) Remain 12:51:52 loss: 0.1326 Lr: 0.00400
[2023-12-25 11:43:41,749 INFO misc.py line 119 253097] Train: [43/100][123/510] Data 0.011 (0.075) Batch 1.175 (1.569) Remain 12:50:12 loss: 0.2221 Lr: 0.00400
[2023-12-25 11:43:42,937 INFO misc.py line 119 253097] Train: [43/100][124/510] Data 0.010 (0.074) Batch 1.192 (1.566) Remain 12:48:39 loss: 0.3215 Lr: 0.00400
[2023-12-25 11:43:43,932 INFO misc.py line 119 253097] Train: [43/100][125/510] Data 0.003 (0.074) Batch 0.995 (1.561) Remain 12:46:20 loss: 0.2782 Lr: 0.00400
[2023-12-25 11:43:44,865 INFO misc.py line 119 253097] Train: [43/100][126/510] Data 0.004 (0.073) Batch 0.933 (1.556) Remain 12:43:48 loss: 0.2233 Lr: 0.00400
[2023-12-25 11:43:46,005 INFO misc.py line 119 253097] Train: [43/100][127/510] Data 0.004 (0.073) Batch 1.140 (1.553) Remain 12:42:08 loss: 0.1073 Lr: 0.00400
[2023-12-25 11:43:47,076 INFO misc.py line 119 253097] Train: [43/100][128/510] Data 0.003 (0.072) Batch 1.071 (1.549) Remain 12:40:13 loss: 0.1714 Lr: 0.00400
[2023-12-25 11:43:48,003 INFO misc.py line 119 253097] Train: [43/100][129/510] Data 0.004 (0.071) Batch 0.927 (1.544) Remain 12:37:46 loss: 0.2138 Lr: 0.00400
[2023-12-25 11:43:49,229 INFO misc.py line 119 253097] Train: [43/100][130/510] Data 0.006 (0.071) Batch 1.226 (1.541) Remain 12:36:31 loss: 0.2742 Lr: 0.00400
[2023-12-25 11:43:50,458 INFO misc.py line 119 253097] Train: [43/100][131/510] Data 0.004 (0.070) Batch 1.229 (1.539) Remain 12:35:17 loss: 0.1366 Lr: 0.00400
[2023-12-25 11:43:51,558 INFO misc.py line 119 253097] Train: [43/100][132/510] Data 0.004 (0.070) Batch 1.099 (1.535) Remain 12:33:35 loss: 0.1437 Lr: 0.00400
[2023-12-25 11:43:52,613 INFO misc.py line 119 253097] Train: [43/100][133/510] Data 0.004 (0.069) Batch 1.055 (1.532) Remain 12:31:45 loss: 0.2299 Lr: 0.00400
[2023-12-25 11:43:53,628 INFO misc.py line 119 253097] Train: [43/100][134/510] Data 0.003 (0.069) Batch 1.015 (1.528) Remain 12:29:47 loss: 0.1081 Lr: 0.00400
[2023-12-25 11:43:54,693 INFO misc.py line 119 253097] Train: [43/100][135/510] Data 0.005 (0.068) Batch 1.064 (1.524) Remain 12:28:02 loss: 0.1461 Lr: 0.00400
[2023-12-25 11:43:55,844 INFO misc.py line 119 253097] Train: [43/100][136/510] Data 0.006 (0.068) Batch 1.151 (1.521) Remain 12:26:38 loss: 0.1706 Lr: 0.00400
[2023-12-25 11:43:57,023 INFO misc.py line 119 253097] Train: [43/100][137/510] Data 0.005 (0.067) Batch 1.180 (1.519) Remain 12:25:22 loss: 0.1657 Lr: 0.00400
[2023-12-25 11:43:58,097 INFO misc.py line 119 253097] Train: [43/100][138/510] Data 0.003 (0.067) Batch 1.053 (1.515) Remain 12:23:38 loss: 0.2006 Lr: 0.00400
[2023-12-25 11:43:59,176 INFO misc.py line 119 253097] Train: [43/100][139/510] Data 0.025 (0.067) Batch 1.100 (1.512) Remain 12:22:07 loss: 0.1950 Lr: 0.00400
[2023-12-25 11:44:00,304 INFO misc.py line 119 253097] Train: [43/100][140/510] Data 0.003 (0.066) Batch 1.127 (1.510) Remain 12:20:43 loss: 0.2415 Lr: 0.00400
[2023-12-25 11:44:01,383 INFO misc.py line 119 253097] Train: [43/100][141/510] Data 0.006 (0.066) Batch 1.080 (1.507) Remain 12:19:09 loss: 0.2209 Lr: 0.00400
[2023-12-25 11:44:02,629 INFO misc.py line 119 253097] Train: [43/100][142/510] Data 0.004 (0.065) Batch 1.245 (1.505) Remain 12:18:13 loss: 0.3698 Lr: 0.00400
[2023-12-25 11:44:03,865 INFO misc.py line 119 253097] Train: [43/100][143/510] Data 0.005 (0.065) Batch 1.237 (1.503) Remain 12:17:15 loss: 0.1342 Lr: 0.00400
[2023-12-25 11:44:05,002 INFO misc.py line 119 253097] Train: [43/100][144/510] Data 0.005 (0.064) Batch 1.137 (1.500) Remain 12:15:57 loss: 0.1898 Lr: 0.00400
[2023-12-25 11:44:06,085 INFO misc.py line 119 253097] Train: [43/100][145/510] Data 0.003 (0.064) Batch 1.083 (1.497) Remain 12:14:29 loss: 0.2183 Lr: 0.00400
[2023-12-25 11:44:07,317 INFO misc.py line 119 253097] Train: [43/100][146/510] Data 0.004 (0.064) Batch 1.232 (1.495) Remain 12:13:33 loss: 0.1688 Lr: 0.00400
[2023-12-25 11:44:08,523 INFO misc.py line 119 253097] Train: [43/100][147/510] Data 0.004 (0.063) Batch 1.206 (1.493) Remain 12:12:32 loss: 0.4425 Lr: 0.00400
[2023-12-25 11:44:09,566 INFO misc.py line 119 253097] Train: [43/100][148/510] Data 0.004 (0.063) Batch 1.038 (1.490) Remain 12:10:58 loss: 0.0880 Lr: 0.00400
[2023-12-25 11:44:18,255 INFO misc.py line 119 253097] Train: [43/100][149/510] Data 2.331 (0.078) Batch 8.694 (1.540) Remain 12:35:09 loss: 0.2029 Lr: 0.00399
[2023-12-25 11:44:19,330 INFO misc.py line 119 253097] Train: [43/100][150/510] Data 0.006 (0.078) Batch 1.077 (1.536) Remain 12:33:35 loss: 0.4673 Lr: 0.00399
[2023-12-25 11:44:20,433 INFO misc.py line 119 253097] Train: [43/100][151/510] Data 0.003 (0.077) Batch 1.102 (1.533) Remain 12:32:07 loss: 0.1541 Lr: 0.00399
[2023-12-25 11:44:21,440 INFO misc.py line 119 253097] Train: [43/100][152/510] Data 0.004 (0.077) Batch 1.007 (1.530) Remain 12:30:21 loss: 0.4118 Lr: 0.00399
[2023-12-25 11:44:22,663 INFO misc.py line 119 253097] Train: [43/100][153/510] Data 0.003 (0.076) Batch 1.224 (1.528) Remain 12:29:20 loss: 0.1660 Lr: 0.00399
[2023-12-25 11:44:23,866 INFO misc.py line 119 253097] Train: [43/100][154/510] Data 0.003 (0.076) Batch 1.196 (1.526) Remain 12:28:13 loss: 0.3058 Lr: 0.00399
[2023-12-25 11:44:24,995 INFO misc.py line 119 253097] Train: [43/100][155/510] Data 0.030 (0.076) Batch 1.135 (1.523) Remain 12:26:56 loss: 0.1076 Lr: 0.00399
[2023-12-25 11:44:26,192 INFO misc.py line 119 253097] Train: [43/100][156/510] Data 0.004 (0.075) Batch 1.198 (1.521) Remain 12:25:52 loss: 0.1577 Lr: 0.00399
[2023-12-25 11:44:27,261 INFO misc.py line 119 253097] Train: [43/100][157/510] Data 0.004 (0.075) Batch 1.063 (1.518) Remain 12:24:23 loss: 0.2130 Lr: 0.00399
[2023-12-25 11:44:28,478 INFO misc.py line 119 253097] Train: [43/100][158/510] Data 0.008 (0.074) Batch 1.219 (1.516) Remain 12:23:25 loss: 0.2114 Lr: 0.00399
[2023-12-25 11:44:29,531 INFO misc.py line 119 253097] Train: [43/100][159/510] Data 0.006 (0.074) Batch 1.055 (1.513) Remain 12:21:57 loss: 0.4504 Lr: 0.00399
[2023-12-25 11:44:30,726 INFO misc.py line 119 253097] Train: [43/100][160/510] Data 0.004 (0.073) Batch 1.192 (1.511) Remain 12:20:55 loss: 0.4311 Lr: 0.00399
[2023-12-25 11:44:31,855 INFO misc.py line 119 253097] Train: [43/100][161/510] Data 0.007 (0.073) Batch 1.131 (1.509) Remain 12:19:43 loss: 0.2165 Lr: 0.00399
[2023-12-25 11:44:33,131 INFO misc.py line 119 253097] Train: [43/100][162/510] Data 0.005 (0.072) Batch 1.276 (1.507) Remain 12:18:58 loss: 0.3903 Lr: 0.00399
[2023-12-25 11:44:34,092 INFO misc.py line 119 253097] Train: [43/100][163/510] Data 0.005 (0.072) Batch 0.963 (1.504) Remain 12:17:17 loss: 0.1778 Lr: 0.00399
[2023-12-25 11:44:35,382 INFO misc.py line 119 253097] Train: [43/100][164/510] Data 0.004 (0.072) Batch 1.278 (1.502) Remain 12:16:34 loss: 0.1687 Lr: 0.00399
[2023-12-25 11:44:36,660 INFO misc.py line 119 253097] Train: [43/100][165/510] Data 0.015 (0.071) Batch 1.284 (1.501) Remain 12:15:53 loss: 0.4012 Lr: 0.00399
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[2023-12-25 11:44:48,085 INFO misc.py line 119 253097] Train: [43/100][167/510] Data 9.221 (0.127) Batch 10.290 (1.552) Remain 12:41:01 loss: 0.2642 Lr: 0.00399
[2023-12-25 11:44:49,085 INFO misc.py line 119 253097] Train: [43/100][168/510] Data 0.004 (0.126) Batch 0.999 (1.549) Remain 12:39:21 loss: 0.1122 Lr: 0.00399
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[2023-12-25 11:44:56,667 INFO misc.py line 119 253097] Train: [43/100][173/510] Data 1.859 (0.133) Batch 2.999 (1.548) Remain 12:38:45 loss: 0.2311 Lr: 0.00399
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[2023-12-25 11:45:08,205 INFO misc.py line 119 253097] Train: [43/100][183/510] Data 0.003 (0.126) Batch 1.266 (1.526) Remain 12:27:46 loss: 0.3499 Lr: 0.00399
[2023-12-25 11:45:09,336 INFO misc.py line 119 253097] Train: [43/100][184/510] Data 0.006 (0.126) Batch 1.128 (1.524) Remain 12:26:39 loss: 0.2358 Lr: 0.00399
[2023-12-25 11:45:20,484 INFO misc.py line 119 253097] Train: [43/100][185/510] Data 9.936 (0.180) Batch 11.151 (1.577) Remain 12:52:33 loss: 0.0961 Lr: 0.00399
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[2023-12-25 11:45:22,829 INFO misc.py line 119 253097] Train: [43/100][187/510] Data 0.004 (0.178) Batch 1.251 (1.573) Remain 12:50:21 loss: 0.2683 Lr: 0.00399
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[2023-12-25 11:45:27,411 INFO misc.py line 119 253097] Train: [43/100][191/510] Data 0.003 (0.174) Batch 1.225 (1.563) Remain 12:45:47 loss: 0.1896 Lr: 0.00399
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[2023-12-25 11:45:39,616 INFO misc.py line 119 253097] Train: [43/100][202/510] Data 0.007 (0.165) Batch 0.902 (1.538) Remain 12:33:13 loss: 0.2419 Lr: 0.00399
[2023-12-25 11:45:50,103 INFO misc.py line 119 253097] Train: [43/100][203/510] Data 9.392 (0.211) Batch 10.488 (1.583) Remain 12:55:06 loss: 0.1062 Lr: 0.00398
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[2023-12-25 11:45:53,336 INFO misc.py line 119 253097] Train: [43/100][206/510] Data 0.003 (0.208) Batch 1.114 (1.576) Remain 12:51:22 loss: 0.3622 Lr: 0.00398
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[2023-12-25 11:45:58,224 INFO misc.py line 119 253097] Train: [43/100][209/510] Data 1.967 (0.214) Batch 2.776 (1.576) Remain 12:51:40 loss: 0.2151 Lr: 0.00398
[2023-12-25 11:45:59,328 INFO misc.py line 119 253097] Train: [43/100][210/510] Data 0.005 (0.213) Batch 1.104 (1.574) Remain 12:50:31 loss: 0.1122 Lr: 0.00398
[2023-12-25 11:46:00,475 INFO misc.py line 119 253097] Train: [43/100][211/510] Data 0.003 (0.212) Batch 1.144 (1.572) Remain 12:49:29 loss: 0.2023 Lr: 0.00398
[2023-12-25 11:46:01,588 INFO misc.py line 119 253097] Train: [43/100][212/510] Data 0.006 (0.211) Batch 1.112 (1.570) Remain 12:48:23 loss: 0.1677 Lr: 0.00398
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[2023-12-25 11:46:03,928 INFO misc.py line 119 253097] Train: [43/100][214/510] Data 0.006 (0.209) Batch 1.217 (1.566) Remain 12:46:29 loss: 0.1676 Lr: 0.00398
[2023-12-25 11:46:05,216 INFO misc.py line 119 253097] Train: [43/100][215/510] Data 0.006 (0.208) Batch 1.287 (1.565) Remain 12:45:48 loss: 0.2577 Lr: 0.00398
[2023-12-25 11:46:06,588 INFO misc.py line 119 253097] Train: [43/100][216/510] Data 0.007 (0.207) Batch 1.374 (1.564) Remain 12:45:21 loss: 0.2139 Lr: 0.00398
[2023-12-25 11:46:07,698 INFO misc.py line 119 253097] Train: [43/100][217/510] Data 0.005 (0.207) Batch 1.109 (1.562) Remain 12:44:17 loss: 0.2113 Lr: 0.00398
[2023-12-25 11:46:08,941 INFO misc.py line 119 253097] Train: [43/100][218/510] Data 0.005 (0.206) Batch 1.229 (1.560) Remain 12:43:30 loss: 0.2497 Lr: 0.00398
[2023-12-25 11:46:18,173 INFO misc.py line 119 253097] Train: [43/100][219/510] Data 8.130 (0.242) Batch 9.248 (1.596) Remain 13:00:53 loss: 0.4500 Lr: 0.00398
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[2023-12-25 11:46:20,356 INFO misc.py line 119 253097] Train: [43/100][221/510] Data 0.003 (0.240) Batch 1.148 (1.591) Remain 12:58:34 loss: 0.2211 Lr: 0.00398
[2023-12-25 11:46:21,472 INFO misc.py line 119 253097] Train: [43/100][222/510] Data 0.003 (0.239) Batch 1.116 (1.589) Remain 12:57:29 loss: 0.2051 Lr: 0.00398
[2023-12-25 11:46:22,582 INFO misc.py line 119 253097] Train: [43/100][223/510] Data 0.003 (0.238) Batch 1.108 (1.587) Remain 12:56:23 loss: 0.1271 Lr: 0.00398
[2023-12-25 11:46:23,883 INFO misc.py line 119 253097] Train: [43/100][224/510] Data 0.005 (0.237) Batch 1.302 (1.586) Remain 12:55:44 loss: 0.1490 Lr: 0.00398
[2023-12-25 11:46:28,163 INFO misc.py line 119 253097] Train: [43/100][225/510] Data 2.960 (0.249) Batch 4.280 (1.598) Remain 13:01:38 loss: 0.1414 Lr: 0.00398
[2023-12-25 11:46:29,198 INFO misc.py line 119 253097] Train: [43/100][226/510] Data 0.004 (0.248) Batch 1.035 (1.595) Remain 13:00:23 loss: 0.2041 Lr: 0.00398
[2023-12-25 11:46:30,338 INFO misc.py line 119 253097] Train: [43/100][227/510] Data 0.003 (0.247) Batch 1.141 (1.593) Remain 12:59:22 loss: 0.2790 Lr: 0.00398
[2023-12-25 11:46:31,545 INFO misc.py line 119 253097] Train: [43/100][228/510] Data 0.003 (0.246) Batch 1.207 (1.591) Remain 12:58:30 loss: 0.1237 Lr: 0.00398
[2023-12-25 11:46:32,569 INFO misc.py line 119 253097] Train: [43/100][229/510] Data 0.004 (0.245) Batch 1.024 (1.589) Remain 12:57:14 loss: 0.2283 Lr: 0.00398
[2023-12-25 11:46:33,955 INFO misc.py line 119 253097] Train: [43/100][230/510] Data 0.003 (0.244) Batch 1.386 (1.588) Remain 12:56:46 loss: 0.2004 Lr: 0.00398
[2023-12-25 11:46:35,126 INFO misc.py line 119 253097] Train: [43/100][231/510] Data 0.003 (0.243) Batch 1.169 (1.586) Remain 12:55:51 loss: 0.4712 Lr: 0.00398
[2023-12-25 11:46:36,407 INFO misc.py line 119 253097] Train: [43/100][232/510] Data 0.005 (0.242) Batch 1.283 (1.585) Remain 12:55:10 loss: 0.2629 Lr: 0.00398
[2023-12-25 11:46:37,487 INFO misc.py line 119 253097] Train: [43/100][233/510] Data 0.004 (0.241) Batch 1.080 (1.583) Remain 12:54:04 loss: 0.1857 Lr: 0.00398
[2023-12-25 11:46:38,521 INFO misc.py line 119 253097] Train: [43/100][234/510] Data 0.003 (0.240) Batch 1.029 (1.580) Remain 12:52:53 loss: 0.1849 Lr: 0.00398
[2023-12-25 11:46:39,620 INFO misc.py line 119 253097] Train: [43/100][235/510] Data 0.009 (0.239) Batch 1.100 (1.578) Remain 12:51:50 loss: 0.1387 Lr: 0.00398
[2023-12-25 11:46:40,717 INFO misc.py line 119 253097] Train: [43/100][236/510] Data 0.071 (0.238) Batch 1.099 (1.576) Remain 12:50:48 loss: 0.1854 Lr: 0.00398
[2023-12-25 11:46:42,030 INFO misc.py line 119 253097] Train: [43/100][237/510] Data 0.007 (0.237) Batch 1.311 (1.575) Remain 12:50:13 loss: 0.1923 Lr: 0.00398
[2023-12-25 11:46:43,292 INFO misc.py line 119 253097] Train: [43/100][238/510] Data 0.008 (0.236) Batch 1.260 (1.574) Remain 12:49:33 loss: 0.1440 Lr: 0.00398
[2023-12-25 11:46:44,549 INFO misc.py line 119 253097] Train: [43/100][239/510] Data 0.010 (0.235) Batch 1.262 (1.572) Remain 12:48:52 loss: 0.2561 Lr: 0.00398
[2023-12-25 11:46:45,916 INFO misc.py line 119 253097] Train: [43/100][240/510] Data 0.006 (0.234) Batch 1.367 (1.571) Remain 12:48:25 loss: 0.1807 Lr: 0.00398
[2023-12-25 11:46:56,961 INFO misc.py line 119 253097] Train: [43/100][241/510] Data 0.005 (0.233) Batch 11.046 (1.611) Remain 13:07:52 loss: 0.1756 Lr: 0.00398
[2023-12-25 11:46:57,922 INFO misc.py line 119 253097] Train: [43/100][242/510] Data 0.004 (0.232) Batch 0.960 (1.609) Remain 13:06:30 loss: 0.1179 Lr: 0.00398
[2023-12-25 11:46:59,126 INFO misc.py line 119 253097] Train: [43/100][243/510] Data 0.004 (0.231) Batch 1.204 (1.607) Remain 13:05:39 loss: 0.2077 Lr: 0.00398
[2023-12-25 11:47:00,411 INFO misc.py line 119 253097] Train: [43/100][244/510] Data 0.005 (0.230) Batch 1.284 (1.605) Remain 13:04:58 loss: 0.3257 Lr: 0.00398
[2023-12-25 11:47:04,303 INFO misc.py line 119 253097] Train: [43/100][245/510] Data 0.005 (0.229) Batch 3.893 (1.615) Remain 13:09:34 loss: 0.1858 Lr: 0.00398
[2023-12-25 11:47:05,279 INFO misc.py line 119 253097] Train: [43/100][246/510] Data 0.004 (0.228) Batch 0.976 (1.612) Remain 13:08:15 loss: 0.1087 Lr: 0.00398
[2023-12-25 11:47:06,425 INFO misc.py line 119 253097] Train: [43/100][247/510] Data 0.005 (0.227) Batch 1.146 (1.610) Remain 13:07:18 loss: 0.2751 Lr: 0.00398
[2023-12-25 11:47:07,560 INFO misc.py line 119 253097] Train: [43/100][248/510] Data 0.004 (0.226) Batch 1.136 (1.608) Remain 13:06:19 loss: 0.2812 Lr: 0.00398
[2023-12-25 11:47:08,838 INFO misc.py line 119 253097] Train: [43/100][249/510] Data 0.004 (0.226) Batch 1.272 (1.607) Remain 13:05:37 loss: 0.1987 Lr: 0.00398
[2023-12-25 11:47:10,070 INFO misc.py line 119 253097] Train: [43/100][250/510] Data 0.009 (0.225) Batch 1.237 (1.606) Remain 13:04:52 loss: 0.1724 Lr: 0.00398
[2023-12-25 11:47:11,258 INFO misc.py line 119 253097] Train: [43/100][251/510] Data 0.004 (0.224) Batch 1.183 (1.604) Remain 13:04:00 loss: 0.3341 Lr: 0.00398
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[2023-12-25 11:47:14,580 INFO misc.py line 119 253097] Train: [43/100][254/510] Data 0.005 (0.221) Batch 1.206 (1.598) Remain 13:01:02 loss: 0.2691 Lr: 0.00398
[2023-12-25 11:47:15,552 INFO misc.py line 119 253097] Train: [43/100][255/510] Data 0.004 (0.220) Batch 0.972 (1.595) Remain 12:59:48 loss: 0.1768 Lr: 0.00398
[2023-12-25 11:47:16,788 INFO misc.py line 119 253097] Train: [43/100][256/510] Data 0.005 (0.220) Batch 1.236 (1.594) Remain 12:59:04 loss: 0.2287 Lr: 0.00398
[2023-12-25 11:47:23,087 INFO misc.py line 119 253097] Train: [43/100][257/510] Data 0.004 (0.219) Batch 6.300 (1.613) Remain 13:08:06 loss: 0.1742 Lr: 0.00398
[2023-12-25 11:47:24,139 INFO misc.py line 119 253097] Train: [43/100][258/510] Data 0.003 (0.218) Batch 1.052 (1.610) Remain 13:07:00 loss: 0.1210 Lr: 0.00397
[2023-12-25 11:47:25,357 INFO misc.py line 119 253097] Train: [43/100][259/510] Data 0.003 (0.217) Batch 1.215 (1.609) Remain 13:06:13 loss: 0.1149 Lr: 0.00397
[2023-12-25 11:47:26,451 INFO misc.py line 119 253097] Train: [43/100][260/510] Data 0.005 (0.216) Batch 1.092 (1.607) Remain 13:05:12 loss: 0.2405 Lr: 0.00397
[2023-12-25 11:47:27,704 INFO misc.py line 119 253097] Train: [43/100][261/510] Data 0.008 (0.215) Batch 1.251 (1.605) Remain 13:04:30 loss: 0.1051 Lr: 0.00397
[2023-12-25 11:47:28,876 INFO misc.py line 119 253097] Train: [43/100][262/510] Data 0.010 (0.215) Batch 1.177 (1.604) Remain 13:03:40 loss: 0.0599 Lr: 0.00397
[2023-12-25 11:47:30,135 INFO misc.py line 119 253097] Train: [43/100][263/510] Data 0.005 (0.214) Batch 1.255 (1.602) Remain 13:02:59 loss: 0.1814 Lr: 0.00397
[2023-12-25 11:47:31,253 INFO misc.py line 119 253097] Train: [43/100][264/510] Data 0.009 (0.213) Batch 1.118 (1.601) Remain 13:02:03 loss: 0.2727 Lr: 0.00397
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[2023-12-25 11:47:33,506 INFO misc.py line 119 253097] Train: [43/100][266/510] Data 0.010 (0.211) Batch 1.141 (1.597) Remain 13:00:14 loss: 0.2012 Lr: 0.00397
[2023-12-25 11:47:34,694 INFO misc.py line 119 253097] Train: [43/100][267/510] Data 0.011 (0.211) Batch 1.191 (1.595) Remain 12:59:27 loss: 0.1632 Lr: 0.00397
[2023-12-25 11:47:35,781 INFO misc.py line 119 253097] Train: [43/100][268/510] Data 0.009 (0.210) Batch 1.091 (1.594) Remain 12:58:30 loss: 0.1786 Lr: 0.00397
[2023-12-25 11:47:36,941 INFO misc.py line 119 253097] Train: [43/100][269/510] Data 0.006 (0.209) Batch 1.160 (1.592) Remain 12:57:41 loss: 0.2204 Lr: 0.00397
[2023-12-25 11:47:38,000 INFO misc.py line 119 253097] Train: [43/100][270/510] Data 0.005 (0.208) Batch 1.061 (1.590) Remain 12:56:41 loss: 0.1661 Lr: 0.00397
[2023-12-25 11:47:39,202 INFO misc.py line 119 253097] Train: [43/100][271/510] Data 0.003 (0.208) Batch 1.193 (1.588) Remain 12:55:56 loss: 0.2786 Lr: 0.00397
[2023-12-25 11:47:48,605 INFO misc.py line 119 253097] Train: [43/100][272/510] Data 0.013 (0.207) Batch 9.412 (1.618) Remain 13:10:06 loss: 0.2583 Lr: 0.00397
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[2023-12-25 11:47:54,966 INFO misc.py line 119 253097] Train: [43/100][276/510] Data 0.006 (0.204) Batch 1.169 (1.617) Remain 13:09:48 loss: 0.1474 Lr: 0.00397
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[2023-12-25 11:47:59,875 INFO misc.py line 119 253097] Train: [43/100][280/510] Data 0.004 (0.201) Batch 1.150 (1.612) Remain 13:06:57 loss: 0.1281 Lr: 0.00397
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[2023-12-25 11:48:28,377 INFO misc.py line 119 253097] Train: [43/100][296/510] Data 0.010 (0.191) Batch 5.155 (1.621) Remain 13:11:03 loss: 0.2372 Lr: 0.00397
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[2023-12-25 11:48:34,527 INFO misc.py line 119 253097] Train: [43/100][301/510] Data 0.025 (0.188) Batch 1.171 (1.614) Remain 13:07:40 loss: 0.2134 Lr: 0.00397
[2023-12-25 11:48:35,645 INFO misc.py line 119 253097] Train: [43/100][302/510] Data 0.035 (0.187) Batch 1.147 (1.613) Remain 13:06:52 loss: 0.2148 Lr: 0.00397
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[2023-12-25 11:48:51,582 INFO misc.py line 119 253097] Train: [43/100][310/510] Data 6.165 (0.203) Batch 7.337 (1.622) Remain 13:11:29 loss: 0.0877 Lr: 0.00397
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[2023-12-25 11:48:54,989 INFO misc.py line 119 253097] Train: [43/100][313/510] Data 0.004 (0.201) Batch 1.222 (1.618) Remain 13:09:06 loss: 0.2247 Lr: 0.00396
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[2023-12-25 11:49:01,620 INFO misc.py line 119 253097] Train: [43/100][319/510] Data 0.007 (0.197) Batch 1.022 (1.608) Remain 13:04:12 loss: 0.2544 Lr: 0.00396
[2023-12-25 11:49:02,798 INFO misc.py line 119 253097] Train: [43/100][320/510] Data 0.005 (0.196) Batch 1.171 (1.607) Remain 13:03:30 loss: 0.1247 Lr: 0.00396
[2023-12-25 11:49:04,017 INFO misc.py line 119 253097] Train: [43/100][321/510] Data 0.012 (0.196) Batch 1.225 (1.605) Remain 13:02:53 loss: 0.1614 Lr: 0.00396
[2023-12-25 11:49:08,227 INFO misc.py line 119 253097] Train: [43/100][322/510] Data 2.924 (0.204) Batch 4.211 (1.614) Remain 13:06:50 loss: 0.1378 Lr: 0.00396
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[2023-12-25 11:49:11,818 INFO misc.py line 119 253097] Train: [43/100][325/510] Data 0.004 (0.202) Batch 0.993 (1.610) Remain 13:04:52 loss: 0.2292 Lr: 0.00396
[2023-12-25 11:49:13,019 INFO misc.py line 119 253097] Train: [43/100][326/510] Data 0.007 (0.202) Batch 1.204 (1.608) Remain 13:04:13 loss: 0.1241 Lr: 0.00396
[2023-12-25 11:49:14,315 INFO misc.py line 119 253097] Train: [43/100][327/510] Data 0.004 (0.201) Batch 1.281 (1.607) Remain 13:03:42 loss: 0.1126 Lr: 0.00396
[2023-12-25 11:49:15,503 INFO misc.py line 119 253097] Train: [43/100][328/510] Data 0.019 (0.201) Batch 1.199 (1.606) Remain 13:03:04 loss: 0.1449 Lr: 0.00396
[2023-12-25 11:49:16,497 INFO misc.py line 119 253097] Train: [43/100][329/510] Data 0.008 (0.200) Batch 0.999 (1.604) Remain 13:02:08 loss: 0.1767 Lr: 0.00396
[2023-12-25 11:49:17,589 INFO misc.py line 119 253097] Train: [43/100][330/510] Data 0.004 (0.200) Batch 1.092 (1.603) Remain 13:01:20 loss: 0.1283 Lr: 0.00396
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[2023-12-25 11:49:20,051 INFO misc.py line 119 253097] Train: [43/100][332/510] Data 0.004 (0.198) Batch 1.161 (1.601) Remain 13:00:11 loss: 0.2129 Lr: 0.00396
[2023-12-25 11:49:21,293 INFO misc.py line 119 253097] Train: [43/100][333/510] Data 0.004 (0.198) Batch 1.237 (1.599) Remain 12:59:37 loss: 0.2506 Lr: 0.00396
[2023-12-25 11:49:22,205 INFO misc.py line 119 253097] Train: [43/100][334/510] Data 0.009 (0.197) Batch 0.917 (1.597) Remain 12:58:35 loss: 0.1809 Lr: 0.00396
[2023-12-25 11:49:23,385 INFO misc.py line 119 253097] Train: [43/100][335/510] Data 0.003 (0.197) Batch 1.180 (1.596) Remain 12:57:57 loss: 0.2506 Lr: 0.00396
[2023-12-25 11:49:24,537 INFO misc.py line 119 253097] Train: [43/100][336/510] Data 0.003 (0.196) Batch 1.152 (1.595) Remain 12:57:16 loss: 0.1833 Lr: 0.00396
[2023-12-25 11:49:25,634 INFO misc.py line 119 253097] Train: [43/100][337/510] Data 0.003 (0.195) Batch 1.096 (1.593) Remain 12:56:31 loss: 0.1410 Lr: 0.00396
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[2023-12-25 11:49:32,623 INFO misc.py line 119 253097] Train: [43/100][339/510] Data 0.004 (0.194) Batch 5.959 (1.605) Remain 13:01:59 loss: 0.2242 Lr: 0.00396
[2023-12-25 11:49:33,789 INFO misc.py line 119 253097] Train: [43/100][340/510] Data 0.005 (0.194) Batch 1.166 (1.603) Remain 13:01:19 loss: 0.2615 Lr: 0.00396
[2023-12-25 11:49:34,959 INFO misc.py line 119 253097] Train: [43/100][341/510] Data 0.004 (0.193) Batch 1.164 (1.602) Remain 13:00:40 loss: 0.1493 Lr: 0.00396
[2023-12-25 11:49:35,976 INFO misc.py line 119 253097] Train: [43/100][342/510] Data 0.009 (0.193) Batch 1.017 (1.600) Remain 12:59:48 loss: 0.1999 Lr: 0.00396
[2023-12-25 11:49:37,053 INFO misc.py line 119 253097] Train: [43/100][343/510] Data 0.010 (0.192) Batch 1.082 (1.599) Remain 12:59:01 loss: 0.1504 Lr: 0.00396
[2023-12-25 11:49:38,219 INFO misc.py line 119 253097] Train: [43/100][344/510] Data 0.004 (0.192) Batch 1.162 (1.597) Remain 12:58:22 loss: 0.3082 Lr: 0.00396
[2023-12-25 11:49:39,420 INFO misc.py line 119 253097] Train: [43/100][345/510] Data 0.008 (0.191) Batch 1.198 (1.596) Remain 12:57:47 loss: 0.1557 Lr: 0.00396
[2023-12-25 11:49:40,549 INFO misc.py line 119 253097] Train: [43/100][346/510] Data 0.012 (0.190) Batch 1.137 (1.595) Remain 12:57:06 loss: 0.2857 Lr: 0.00396
[2023-12-25 11:49:41,603 INFO misc.py line 119 253097] Train: [43/100][347/510] Data 0.003 (0.190) Batch 1.048 (1.593) Remain 12:56:18 loss: 0.2068 Lr: 0.00396
[2023-12-25 11:49:42,619 INFO misc.py line 119 253097] Train: [43/100][348/510] Data 0.009 (0.189) Batch 1.022 (1.592) Remain 12:55:28 loss: 0.1620 Lr: 0.00396
[2023-12-25 11:49:47,317 INFO misc.py line 119 253097] Train: [43/100][349/510] Data 0.003 (0.189) Batch 4.698 (1.601) Remain 12:59:49 loss: 0.1805 Lr: 0.00396
[2023-12-25 11:49:48,473 INFO misc.py line 119 253097] Train: [43/100][350/510] Data 0.003 (0.188) Batch 1.157 (1.599) Remain 12:59:10 loss: 0.1693 Lr: 0.00396
[2023-12-25 11:49:51,256 INFO misc.py line 119 253097] Train: [43/100][351/510] Data 1.676 (0.193) Batch 2.782 (1.603) Remain 13:00:47 loss: 0.0793 Lr: 0.00396
[2023-12-25 11:49:52,218 INFO misc.py line 119 253097] Train: [43/100][352/510] Data 0.003 (0.192) Batch 0.962 (1.601) Remain 12:59:52 loss: 0.1702 Lr: 0.00396
[2023-12-25 11:49:53,360 INFO misc.py line 119 253097] Train: [43/100][353/510] Data 0.004 (0.192) Batch 1.142 (1.600) Remain 12:59:12 loss: 0.0762 Lr: 0.00396
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[2023-12-25 11:49:55,576 INFO misc.py line 119 253097] Train: [43/100][355/510] Data 0.004 (0.190) Batch 1.077 (1.597) Remain 12:57:47 loss: 0.3529 Lr: 0.00396
[2023-12-25 11:49:56,610 INFO misc.py line 119 253097] Train: [43/100][356/510] Data 0.004 (0.190) Batch 1.034 (1.595) Remain 12:56:59 loss: 0.1590 Lr: 0.00396
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[2023-12-25 11:49:58,738 INFO misc.py line 119 253097] Train: [43/100][358/510] Data 0.003 (0.189) Batch 1.005 (1.592) Remain 12:55:29 loss: 0.0551 Lr: 0.00396
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[2023-12-25 11:50:02,336 INFO misc.py line 119 253097] Train: [43/100][361/510] Data 0.006 (0.187) Batch 1.142 (1.589) Remain 12:53:48 loss: 0.1135 Lr: 0.00396
[2023-12-25 11:50:03,564 INFO misc.py line 119 253097] Train: [43/100][362/510] Data 0.003 (0.187) Batch 1.223 (1.588) Remain 12:53:16 loss: 0.4807 Lr: 0.00396
[2023-12-25 11:50:04,613 INFO misc.py line 119 253097] Train: [43/100][363/510] Data 0.008 (0.186) Batch 1.054 (1.586) Remain 12:52:31 loss: 0.2609 Lr: 0.00396
[2023-12-25 11:50:05,577 INFO misc.py line 119 253097] Train: [43/100][364/510] Data 0.004 (0.186) Batch 0.965 (1.585) Remain 12:51:39 loss: 0.3833 Lr: 0.00396
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[2023-12-25 11:50:07,935 INFO misc.py line 119 253097] Train: [43/100][366/510] Data 0.003 (0.185) Batch 1.132 (1.583) Remain 12:50:31 loss: 0.1851 Lr: 0.00395
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[2023-12-25 11:50:10,090 INFO misc.py line 119 253097] Train: [43/100][368/510] Data 0.004 (0.184) Batch 1.083 (1.580) Remain 12:49:07 loss: 0.1701 Lr: 0.00395
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[2023-12-25 11:51:04,011 INFO misc.py line 119 253097] Train: [43/100][401/510] Data 0.009 (0.169) Batch 0.969 (1.584) Remain 12:50:26 loss: 0.1566 Lr: 0.00395
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[2023-12-25 11:51:19,960 INFO misc.py line 119 253097] Train: [43/100][415/510] Data 0.009 (0.164) Batch 1.047 (1.569) Remain 12:42:43 loss: 0.2377 Lr: 0.00395
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[2023-12-25 11:51:34,968 INFO misc.py line 119 253097] Train: [43/100][425/510] Data 0.004 (0.169) Batch 1.197 (1.567) Remain 12:41:40 loss: 0.2018 Lr: 0.00394
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[2023-12-25 11:51:39,511 INFO misc.py line 119 253097] Train: [43/100][429/510] Data 0.008 (0.167) Batch 1.214 (1.563) Remain 12:39:35 loss: 0.2877 Lr: 0.00394
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[2023-12-25 11:51:49,320 INFO misc.py line 119 253097] Train: [43/100][435/510] Data 0.018 (0.165) Batch 1.211 (1.564) Remain 12:39:53 loss: 0.1982 Lr: 0.00394
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[2023-12-25 11:51:51,455 INFO misc.py line 119 253097] Train: [43/100][437/510] Data 0.008 (0.164) Batch 1.042 (1.562) Remain 12:38:45 loss: 0.3279 Lr: 0.00394
[2023-12-25 11:51:52,442 INFO misc.py line 119 253097] Train: [43/100][438/510] Data 0.006 (0.164) Batch 0.988 (1.561) Remain 12:38:05 loss: 0.2446 Lr: 0.00394
[2023-12-25 11:51:53,734 INFO misc.py line 119 253097] Train: [43/100][439/510] Data 0.004 (0.164) Batch 1.290 (1.560) Remain 12:37:45 loss: 0.2410 Lr: 0.00394
[2023-12-25 11:51:54,663 INFO misc.py line 119 253097] Train: [43/100][440/510] Data 0.007 (0.163) Batch 0.931 (1.559) Remain 12:37:02 loss: 0.2454 Lr: 0.00394
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[2023-12-25 11:51:57,042 INFO misc.py line 119 253097] Train: [43/100][442/510] Data 0.010 (0.163) Batch 1.029 (1.557) Remain 12:36:10 loss: 0.2096 Lr: 0.00394
[2023-12-25 11:51:58,212 INFO misc.py line 119 253097] Train: [43/100][443/510] Data 0.004 (0.162) Batch 1.165 (1.556) Remain 12:35:42 loss: 0.1363 Lr: 0.00394
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[2023-12-25 11:52:01,700 INFO misc.py line 119 253097] Train: [43/100][446/510] Data 0.007 (0.161) Batch 1.247 (1.554) Remain 12:34:20 loss: 0.3191 Lr: 0.00394
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[2023-12-25 11:52:03,775 INFO misc.py line 119 253097] Train: [43/100][448/510] Data 0.005 (0.161) Batch 1.100 (1.551) Remain 12:33:09 loss: 0.1478 Lr: 0.00394
[2023-12-25 11:52:05,019 INFO misc.py line 119 253097] Train: [43/100][449/510] Data 0.004 (0.160) Batch 1.242 (1.551) Remain 12:32:48 loss: 0.0998 Lr: 0.00394
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[2023-12-25 11:52:07,338 INFO misc.py line 119 253097] Train: [43/100][451/510] Data 0.005 (0.160) Batch 1.124 (1.549) Remain 12:31:54 loss: 0.1870 Lr: 0.00394
[2023-12-25 11:52:08,462 INFO misc.py line 119 253097] Train: [43/100][452/510] Data 0.007 (0.159) Batch 1.126 (1.548) Remain 12:31:25 loss: 0.1310 Lr: 0.00394
[2023-12-25 11:52:09,610 INFO misc.py line 119 253097] Train: [43/100][453/510] Data 0.006 (0.159) Batch 1.149 (1.547) Remain 12:30:57 loss: 0.1386 Lr: 0.00394
[2023-12-25 11:52:10,690 INFO misc.py line 119 253097] Train: [43/100][454/510] Data 0.005 (0.159) Batch 1.081 (1.546) Remain 12:30:26 loss: 0.1268 Lr: 0.00394
[2023-12-25 11:52:11,723 INFO misc.py line 119 253097] Train: [43/100][455/510] Data 0.004 (0.158) Batch 1.032 (1.545) Remain 12:29:51 loss: 0.1061 Lr: 0.00394
[2023-12-25 11:52:12,891 INFO misc.py line 119 253097] Train: [43/100][456/510] Data 0.005 (0.158) Batch 1.170 (1.544) Remain 12:29:25 loss: 0.2365 Lr: 0.00394
[2023-12-25 11:52:14,238 INFO misc.py line 119 253097] Train: [43/100][457/510] Data 0.003 (0.157) Batch 1.338 (1.543) Remain 12:29:10 loss: 0.1172 Lr: 0.00394
[2023-12-25 11:52:15,345 INFO misc.py line 119 253097] Train: [43/100][458/510] Data 0.013 (0.157) Batch 1.115 (1.543) Remain 12:28:42 loss: 0.1131 Lr: 0.00394
[2023-12-25 11:52:16,632 INFO misc.py line 119 253097] Train: [43/100][459/510] Data 0.004 (0.157) Batch 1.286 (1.542) Remain 12:28:24 loss: 0.2492 Lr: 0.00394
[2023-12-25 11:52:17,601 INFO misc.py line 119 253097] Train: [43/100][460/510] Data 0.005 (0.157) Batch 0.971 (1.541) Remain 12:27:46 loss: 0.1333 Lr: 0.00394
[2023-12-25 11:52:18,753 INFO misc.py line 119 253097] Train: [43/100][461/510] Data 0.003 (0.156) Batch 1.151 (1.540) Remain 12:27:19 loss: 0.2039 Lr: 0.00394
[2023-12-25 11:52:19,984 INFO misc.py line 119 253097] Train: [43/100][462/510] Data 0.005 (0.156) Batch 1.228 (1.539) Remain 12:26:58 loss: 0.1806 Lr: 0.00394
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[2023-12-25 11:52:23,376 INFO misc.py line 119 253097] Train: [43/100][465/510] Data 0.011 (0.155) Batch 1.230 (1.537) Remain 12:25:36 loss: 0.1832 Lr: 0.00394
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[2023-12-25 11:52:32,677 INFO misc.py line 119 253097] Train: [43/100][469/510] Data 0.033 (0.154) Batch 3.575 (1.543) Remain 12:28:47 loss: 0.3367 Lr: 0.00394
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[2023-12-25 11:52:35,018 INFO misc.py line 119 253097] Train: [43/100][471/510] Data 0.007 (0.153) Batch 1.261 (1.542) Remain 12:27:58 loss: 0.4718 Lr: 0.00394
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[2023-12-25 11:52:37,208 INFO misc.py line 119 253097] Train: [43/100][473/510] Data 0.004 (0.152) Batch 1.141 (1.540) Remain 12:26:59 loss: 0.0971 Lr: 0.00394
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[2023-12-25 11:52:39,108 INFO misc.py line 119 253097] Train: [43/100][475/510] Data 0.014 (0.152) Batch 0.968 (1.537) Remain 12:25:44 loss: 0.1100 Lr: 0.00393
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[2023-12-25 11:52:41,485 INFO misc.py line 119 253097] Train: [43/100][477/510] Data 0.006 (0.151) Batch 1.160 (1.536) Remain 12:24:58 loss: 0.1416 Lr: 0.00393
[2023-12-25 11:52:42,745 INFO misc.py line 119 253097] Train: [43/100][478/510] Data 0.004 (0.151) Batch 1.260 (1.535) Remain 12:24:39 loss: 0.1314 Lr: 0.00393
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[2023-12-25 11:52:44,935 INFO misc.py line 119 253097] Train: [43/100][480/510] Data 0.008 (0.150) Batch 1.013 (1.533) Remain 12:23:42 loss: 0.1561 Lr: 0.00393
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[2023-12-25 11:52:54,394 INFO misc.py line 119 253097] Train: [43/100][483/510] Data 0.004 (0.149) Batch 7.510 (1.544) Remain 12:28:32 loss: 0.2086 Lr: 0.00393
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[2023-12-25 11:53:25,101 INFO misc.py line 119 253097] Train: [43/100][506/510] Data 0.004 (0.143) Batch 5.394 (1.534) Remain 12:23:20 loss: 0.1420 Lr: 0.00393
[2023-12-25 11:53:26,298 INFO misc.py line 119 253097] Train: [43/100][507/510] Data 0.005 (0.142) Batch 1.192 (1.533) Remain 12:22:58 loss: 0.0870 Lr: 0.00393
[2023-12-25 11:53:27,351 INFO misc.py line 119 253097] Train: [43/100][508/510] Data 0.009 (0.142) Batch 1.059 (1.532) Remain 12:22:30 loss: 0.1400 Lr: 0.00393
[2023-12-25 11:53:28,654 INFO misc.py line 119 253097] Train: [43/100][509/510] Data 0.003 (0.142) Batch 1.297 (1.532) Remain 12:22:15 loss: 0.1529 Lr: 0.00393
[2023-12-25 11:53:29,604 INFO misc.py line 119 253097] Train: [43/100][510/510] Data 0.009 (0.142) Batch 0.955 (1.531) Remain 12:21:40 loss: 0.4685 Lr: 0.00393
[2023-12-25 11:53:29,605 INFO misc.py line 136 253097] Train result: loss: 0.2068 
[2023-12-25 11:53:29,605 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 11:53:59,549 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6104 
[2023-12-25 11:53:59,898 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4264 
[2023-12-25 11:54:04,846 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4964 
[2023-12-25 11:54:05,368 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3410 
[2023-12-25 11:54:07,348 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8417 
[2023-12-25 11:54:07,774 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.5689 
[2023-12-25 11:54:08,656 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9147 
[2023-12-25 11:54:09,223 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4137 
[2023-12-25 11:54:11,037 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9812 
[2023-12-25 11:54:13,173 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1957 
[2023-12-25 11:54:14,042 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2487 
[2023-12-25 11:54:14,468 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.5932 
[2023-12-25 11:54:15,371 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.8800 
[2023-12-25 11:54:18,315 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9219 
[2023-12-25 11:54:18,786 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2160 
[2023-12-25 11:54:19,404 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4447 
[2023-12-25 11:54:20,114 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3826 
[2023-12-25 11:54:21,472 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6698/0.7376/0.8994.
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9193/0.9503
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9825/0.9893
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8411/0.9704
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2591/0.2975
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5964/0.6176
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6407/0.8237
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8000/0.9201
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8836/0.9639
[2023-12-25 11:54:21,472 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6326/0.6863
[2023-12-25 11:54:21,473 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7802/0.8517
[2023-12-25 11:54:21,473 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7725/0.8194
[2023-12-25 11:54:21,473 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5992/0.6982
[2023-12-25 11:54:21,473 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 11:54:21,475 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 11:54:21,475 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 11:54:30,912 INFO misc.py line 119 253097] Train: [44/100][1/510] Data 2.974 (2.974) Batch 7.349 (7.349) Remain 59:20:31 loss: 0.1973 Lr: 0.00393
[2023-12-25 11:54:31,786 INFO misc.py line 119 253097] Train: [44/100][2/510] Data 0.005 (0.005) Batch 0.874 (0.874) Remain 07:03:17 loss: 0.2502 Lr: 0.00393
[2023-12-25 11:54:46,176 INFO misc.py line 119 253097] Train: [44/100][3/510] Data 13.229 (13.229) Batch 14.391 (14.391) Remain 116:11:38 loss: 0.2694 Lr: 0.00393
[2023-12-25 11:54:47,195 INFO misc.py line 119 253097] Train: [44/100][4/510] Data 0.004 (0.004) Batch 1.019 (1.019) Remain 08:13:30 loss: 0.1831 Lr: 0.00393
[2023-12-25 11:54:48,328 INFO misc.py line 119 253097] Train: [44/100][5/510] Data 0.004 (0.004) Batch 1.130 (1.074) Remain 08:40:19 loss: 0.2893 Lr: 0.00393
[2023-12-25 11:54:49,545 INFO misc.py line 119 253097] Train: [44/100][6/510] Data 0.008 (0.005) Batch 1.220 (1.123) Remain 09:03:56 loss: 0.1617 Lr: 0.00393
[2023-12-25 11:54:50,844 INFO misc.py line 119 253097] Train: [44/100][7/510] Data 0.004 (0.005) Batch 1.297 (1.166) Remain 09:24:58 loss: 0.1221 Lr: 0.00393
[2023-12-25 11:54:51,895 INFO misc.py line 119 253097] Train: [44/100][8/510] Data 0.006 (0.005) Batch 1.050 (1.143) Remain 09:13:38 loss: 0.4419 Lr: 0.00393
[2023-12-25 11:54:52,886 INFO misc.py line 119 253097] Train: [44/100][9/510] Data 0.007 (0.006) Batch 0.996 (1.118) Remain 09:01:44 loss: 0.3036 Lr: 0.00393
[2023-12-25 11:54:54,003 INFO misc.py line 119 253097] Train: [44/100][10/510] Data 0.003 (0.005) Batch 1.117 (1.118) Remain 09:01:37 loss: 0.3333 Lr: 0.00393
[2023-12-25 11:54:55,133 INFO misc.py line 119 253097] Train: [44/100][11/510] Data 0.003 (0.005) Batch 1.124 (1.119) Remain 09:01:58 loss: 0.1419 Lr: 0.00393
[2023-12-25 11:54:56,289 INFO misc.py line 119 253097] Train: [44/100][12/510] Data 0.008 (0.005) Batch 1.156 (1.123) Remain 09:03:57 loss: 0.1923 Lr: 0.00393
[2023-12-25 11:54:57,446 INFO misc.py line 119 253097] Train: [44/100][13/510] Data 0.010 (0.006) Batch 1.159 (1.127) Remain 09:05:40 loss: 0.2772 Lr: 0.00393
[2023-12-25 11:54:58,577 INFO misc.py line 119 253097] Train: [44/100][14/510] Data 0.007 (0.006) Batch 1.132 (1.127) Remain 09:05:52 loss: 0.3060 Lr: 0.00393
[2023-12-25 11:54:59,677 INFO misc.py line 119 253097] Train: [44/100][15/510] Data 0.007 (0.006) Batch 1.101 (1.125) Remain 09:04:47 loss: 0.2125 Lr: 0.00393
[2023-12-25 11:55:00,999 INFO misc.py line 119 253097] Train: [44/100][16/510] Data 0.005 (0.006) Batch 1.317 (1.140) Remain 09:11:54 loss: 0.2962 Lr: 0.00393
[2023-12-25 11:55:02,140 INFO misc.py line 119 253097] Train: [44/100][17/510] Data 0.010 (0.006) Batch 1.145 (1.140) Remain 09:12:04 loss: 0.1337 Lr: 0.00393
[2023-12-25 11:55:03,336 INFO misc.py line 119 253097] Train: [44/100][18/510] Data 0.006 (0.006) Batch 1.198 (1.144) Remain 09:13:55 loss: 0.1543 Lr: 0.00393
[2023-12-25 11:55:04,415 INFO misc.py line 119 253097] Train: [44/100][19/510] Data 0.005 (0.006) Batch 1.073 (1.140) Remain 09:11:44 loss: 0.2058 Lr: 0.00393
[2023-12-25 11:55:05,466 INFO misc.py line 119 253097] Train: [44/100][20/510] Data 0.010 (0.006) Batch 1.054 (1.135) Remain 09:09:17 loss: 0.1301 Lr: 0.00392
[2023-12-25 11:55:06,725 INFO misc.py line 119 253097] Train: [44/100][21/510] Data 0.007 (0.006) Batch 1.255 (1.141) Remain 09:12:31 loss: 0.1381 Lr: 0.00392
[2023-12-25 11:55:08,006 INFO misc.py line 119 253097] Train: [44/100][22/510] Data 0.011 (0.007) Batch 1.289 (1.149) Remain 09:16:15 loss: 0.1499 Lr: 0.00392
[2023-12-25 11:55:09,022 INFO misc.py line 119 253097] Train: [44/100][23/510] Data 0.004 (0.006) Batch 1.008 (1.142) Remain 09:12:50 loss: 0.1887 Lr: 0.00392
[2023-12-25 11:55:16,032 INFO misc.py line 119 253097] Train: [44/100][24/510] Data 0.011 (0.007) Batch 7.012 (1.422) Remain 11:28:09 loss: 0.1006 Lr: 0.00392
[2023-12-25 11:55:17,085 INFO misc.py line 119 253097] Train: [44/100][25/510] Data 0.008 (0.007) Batch 1.057 (1.405) Remain 11:20:06 loss: 0.1684 Lr: 0.00392
[2023-12-25 11:55:18,156 INFO misc.py line 119 253097] Train: [44/100][26/510] Data 0.005 (0.007) Batch 1.069 (1.390) Remain 11:13:01 loss: 0.2203 Lr: 0.00392
[2023-12-25 11:55:19,223 INFO misc.py line 119 253097] Train: [44/100][27/510] Data 0.007 (0.007) Batch 1.068 (1.377) Remain 11:06:29 loss: 0.1531 Lr: 0.00392
[2023-12-25 11:55:20,267 INFO misc.py line 119 253097] Train: [44/100][28/510] Data 0.005 (0.007) Batch 1.046 (1.364) Remain 11:00:03 loss: 0.2318 Lr: 0.00392
[2023-12-25 11:55:21,260 INFO misc.py line 119 253097] Train: [44/100][29/510] Data 0.003 (0.006) Batch 0.990 (1.349) Remain 10:53:05 loss: 0.0945 Lr: 0.00392
[2023-12-25 11:55:22,289 INFO misc.py line 119 253097] Train: [44/100][30/510] Data 0.007 (0.006) Batch 1.031 (1.338) Remain 10:47:21 loss: 0.3344 Lr: 0.00392
[2023-12-25 11:55:23,150 INFO misc.py line 119 253097] Train: [44/100][31/510] Data 0.005 (0.006) Batch 0.861 (1.321) Remain 10:39:06 loss: 0.1444 Lr: 0.00392
[2023-12-25 11:55:24,438 INFO misc.py line 119 253097] Train: [44/100][32/510] Data 0.004 (0.006) Batch 1.287 (1.319) Remain 10:38:31 loss: 0.2441 Lr: 0.00392
[2023-12-25 11:55:25,629 INFO misc.py line 119 253097] Train: [44/100][33/510] Data 0.005 (0.006) Batch 1.191 (1.315) Remain 10:36:26 loss: 0.1297 Lr: 0.00392
[2023-12-25 11:55:31,183 INFO misc.py line 119 253097] Train: [44/100][34/510] Data 4.393 (0.148) Batch 5.554 (1.452) Remain 11:42:35 loss: 0.2605 Lr: 0.00392
[2023-12-25 11:55:32,326 INFO misc.py line 119 253097] Train: [44/100][35/510] Data 0.004 (0.143) Batch 1.143 (1.442) Remain 11:37:53 loss: 0.1539 Lr: 0.00392
[2023-12-25 11:55:33,629 INFO misc.py line 119 253097] Train: [44/100][36/510] Data 0.004 (0.139) Batch 1.298 (1.438) Remain 11:35:45 loss: 0.2238 Lr: 0.00392
[2023-12-25 11:55:34,732 INFO misc.py line 119 253097] Train: [44/100][37/510] Data 0.010 (0.135) Batch 1.104 (1.428) Remain 11:30:58 loss: 0.4380 Lr: 0.00392
[2023-12-25 11:55:35,852 INFO misc.py line 119 253097] Train: [44/100][38/510] Data 0.009 (0.132) Batch 1.119 (1.419) Remain 11:26:41 loss: 0.0862 Lr: 0.00392
[2023-12-25 11:55:36,868 INFO misc.py line 119 253097] Train: [44/100][39/510] Data 0.009 (0.128) Batch 1.017 (1.408) Remain 11:21:15 loss: 0.1006 Lr: 0.00392
[2023-12-25 11:55:37,984 INFO misc.py line 119 253097] Train: [44/100][40/510] Data 0.009 (0.125) Batch 1.115 (1.400) Remain 11:17:23 loss: 0.1024 Lr: 0.00392
[2023-12-25 11:55:39,261 INFO misc.py line 119 253097] Train: [44/100][41/510] Data 0.009 (0.122) Batch 1.279 (1.397) Remain 11:15:49 loss: 0.1918 Lr: 0.00392
[2023-12-25 11:55:40,452 INFO misc.py line 119 253097] Train: [44/100][42/510] Data 0.008 (0.119) Batch 1.196 (1.392) Remain 11:13:18 loss: 0.1881 Lr: 0.00392
[2023-12-25 11:55:41,629 INFO misc.py line 119 253097] Train: [44/100][43/510] Data 0.003 (0.116) Batch 1.172 (1.386) Remain 11:10:38 loss: 0.3192 Lr: 0.00392
[2023-12-25 11:55:42,728 INFO misc.py line 119 253097] Train: [44/100][44/510] Data 0.008 (0.114) Batch 1.097 (1.379) Remain 11:07:12 loss: 0.1861 Lr: 0.00392
[2023-12-25 11:55:43,906 INFO misc.py line 119 253097] Train: [44/100][45/510] Data 0.009 (0.111) Batch 1.180 (1.374) Remain 11:04:53 loss: 0.1580 Lr: 0.00392
[2023-12-25 11:55:44,738 INFO misc.py line 119 253097] Train: [44/100][46/510] Data 0.006 (0.109) Batch 0.835 (1.362) Remain 10:58:47 loss: 0.2134 Lr: 0.00392
[2023-12-25 11:55:45,947 INFO misc.py line 119 253097] Train: [44/100][47/510] Data 0.004 (0.106) Batch 1.209 (1.358) Remain 10:57:05 loss: 0.2737 Lr: 0.00392
[2023-12-25 11:55:46,974 INFO misc.py line 119 253097] Train: [44/100][48/510] Data 0.004 (0.104) Batch 1.026 (1.351) Remain 10:53:29 loss: 0.1469 Lr: 0.00392
[2023-12-25 11:55:48,146 INFO misc.py line 119 253097] Train: [44/100][49/510] Data 0.006 (0.102) Batch 1.172 (1.347) Remain 10:51:35 loss: 0.1304 Lr: 0.00392
[2023-12-25 11:56:00,692 INFO misc.py line 119 253097] Train: [44/100][50/510] Data 0.005 (0.100) Batch 12.545 (1.585) Remain 12:46:48 loss: 0.2734 Lr: 0.00392
[2023-12-25 11:56:01,961 INFO misc.py line 119 253097] Train: [44/100][51/510] Data 0.006 (0.098) Batch 1.269 (1.579) Remain 12:43:35 loss: 0.2341 Lr: 0.00392
[2023-12-25 11:56:03,223 INFO misc.py line 119 253097] Train: [44/100][52/510] Data 0.005 (0.096) Batch 1.264 (1.572) Remain 12:40:28 loss: 0.1948 Lr: 0.00392
[2023-12-25 11:56:04,229 INFO misc.py line 119 253097] Train: [44/100][53/510] Data 0.004 (0.094) Batch 1.001 (1.561) Remain 12:34:54 loss: 0.2506 Lr: 0.00392
[2023-12-25 11:56:05,487 INFO misc.py line 119 253097] Train: [44/100][54/510] Data 0.008 (0.092) Batch 1.261 (1.555) Remain 12:32:02 loss: 0.1504 Lr: 0.00392
[2023-12-25 11:56:06,696 INFO misc.py line 119 253097] Train: [44/100][55/510] Data 0.005 (0.091) Batch 1.205 (1.548) Remain 12:28:45 loss: 0.1846 Lr: 0.00392
[2023-12-25 11:56:20,065 INFO misc.py line 119 253097] Train: [44/100][56/510] Data 0.009 (0.089) Batch 13.375 (1.771) Remain 14:16:38 loss: 0.1395 Lr: 0.00392
[2023-12-25 11:56:21,187 INFO misc.py line 119 253097] Train: [44/100][57/510] Data 0.005 (0.088) Batch 1.121 (1.759) Remain 14:10:47 loss: 0.1554 Lr: 0.00392
[2023-12-25 11:56:22,475 INFO misc.py line 119 253097] Train: [44/100][58/510] Data 0.004 (0.086) Batch 1.285 (1.751) Remain 14:06:35 loss: 0.1880 Lr: 0.00392
[2023-12-25 11:56:23,466 INFO misc.py line 119 253097] Train: [44/100][59/510] Data 0.007 (0.085) Batch 0.995 (1.737) Remain 14:00:01 loss: 0.2221 Lr: 0.00392
[2023-12-25 11:56:24,551 INFO misc.py line 119 253097] Train: [44/100][60/510] Data 0.003 (0.083) Batch 1.082 (1.726) Remain 13:54:26 loss: 0.1267 Lr: 0.00392
[2023-12-25 11:56:25,688 INFO misc.py line 119 253097] Train: [44/100][61/510] Data 0.007 (0.082) Batch 1.140 (1.716) Remain 13:49:31 loss: 0.1290 Lr: 0.00392
[2023-12-25 11:56:26,929 INFO misc.py line 119 253097] Train: [44/100][62/510] Data 0.004 (0.081) Batch 1.240 (1.708) Remain 13:45:36 loss: 0.1150 Lr: 0.00392
[2023-12-25 11:56:28,143 INFO misc.py line 119 253097] Train: [44/100][63/510] Data 0.005 (0.079) Batch 1.213 (1.699) Remain 13:41:35 loss: 0.3131 Lr: 0.00392
[2023-12-25 11:56:29,385 INFO misc.py line 119 253097] Train: [44/100][64/510] Data 0.005 (0.078) Batch 1.239 (1.692) Remain 13:37:54 loss: 0.1907 Lr: 0.00392
[2023-12-25 11:56:30,337 INFO misc.py line 119 253097] Train: [44/100][65/510] Data 0.008 (0.077) Batch 0.956 (1.680) Remain 13:32:08 loss: 0.2028 Lr: 0.00392
[2023-12-25 11:56:31,604 INFO misc.py line 119 253097] Train: [44/100][66/510] Data 0.005 (0.076) Batch 1.267 (1.673) Remain 13:28:57 loss: 0.2166 Lr: 0.00392
[2023-12-25 11:56:32,873 INFO misc.py line 119 253097] Train: [44/100][67/510] Data 0.003 (0.075) Batch 1.268 (1.667) Remain 13:25:51 loss: 0.1845 Lr: 0.00392
[2023-12-25 11:56:41,875 INFO misc.py line 119 253097] Train: [44/100][68/510] Data 0.005 (0.074) Batch 9.002 (1.780) Remain 14:20:22 loss: 0.2190 Lr: 0.00392
[2023-12-25 11:56:43,090 INFO misc.py line 119 253097] Train: [44/100][69/510] Data 0.005 (0.073) Batch 1.213 (1.771) Remain 14:16:12 loss: 0.1646 Lr: 0.00392
[2023-12-25 11:56:44,083 INFO misc.py line 119 253097] Train: [44/100][70/510] Data 0.006 (0.072) Batch 0.996 (1.760) Remain 14:10:34 loss: 0.1494 Lr: 0.00392
[2023-12-25 11:56:45,330 INFO misc.py line 119 253097] Train: [44/100][71/510] Data 0.003 (0.071) Batch 1.246 (1.752) Remain 14:06:53 loss: 0.1631 Lr: 0.00392
[2023-12-25 11:56:46,475 INFO misc.py line 119 253097] Train: [44/100][72/510] Data 0.005 (0.070) Batch 1.146 (1.743) Remain 14:02:37 loss: 0.3988 Lr: 0.00392
[2023-12-25 11:56:47,579 INFO misc.py line 119 253097] Train: [44/100][73/510] Data 0.003 (0.069) Batch 1.104 (1.734) Remain 13:58:10 loss: 0.1805 Lr: 0.00392
[2023-12-25 11:56:52,368 INFO misc.py line 119 253097] Train: [44/100][74/510] Data 0.003 (0.068) Batch 4.789 (1.777) Remain 14:18:56 loss: 0.1088 Lr: 0.00391
[2023-12-25 11:56:53,564 INFO misc.py line 119 253097] Train: [44/100][75/510] Data 0.003 (0.067) Batch 1.196 (1.769) Remain 14:15:00 loss: 0.1093 Lr: 0.00391
[2023-12-25 11:56:54,855 INFO misc.py line 119 253097] Train: [44/100][76/510] Data 0.003 (0.066) Batch 1.285 (1.763) Remain 14:11:46 loss: 0.1805 Lr: 0.00391
[2023-12-25 11:56:56,040 INFO misc.py line 119 253097] Train: [44/100][77/510] Data 0.010 (0.065) Batch 1.185 (1.755) Remain 14:07:58 loss: 0.0940 Lr: 0.00391
[2023-12-25 11:56:57,165 INFO misc.py line 119 253097] Train: [44/100][78/510] Data 0.010 (0.065) Batch 1.128 (1.746) Remain 14:03:54 loss: 0.2958 Lr: 0.00391
[2023-12-25 11:56:58,335 INFO misc.py line 119 253097] Train: [44/100][79/510] Data 0.006 (0.064) Batch 1.164 (1.739) Remain 14:00:10 loss: 0.2142 Lr: 0.00391
[2023-12-25 11:57:00,534 INFO misc.py line 119 253097] Train: [44/100][80/510] Data 0.011 (0.063) Batch 2.208 (1.745) Remain 14:03:05 loss: 0.1004 Lr: 0.00391
[2023-12-25 11:57:01,679 INFO misc.py line 119 253097] Train: [44/100][81/510] Data 0.003 (0.062) Batch 1.143 (1.737) Remain 13:59:19 loss: 0.1401 Lr: 0.00391
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[2023-12-25 11:58:16,769 INFO misc.py line 119 253097] Train: [44/100][128/510] Data 10.322 (0.124) Batch 11.550 (1.685) Remain 13:32:39 loss: 0.4470 Lr: 0.00390
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[2023-12-25 11:58:24,980 INFO misc.py line 119 253097] Train: [44/100][135/510] Data 0.005 (0.117) Batch 1.351 (1.658) Remain 13:19:20 loss: 0.1449 Lr: 0.00390
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[2023-12-25 12:00:14,461 INFO misc.py line 119 253097] Train: [44/100][209/510] Data 0.003 (0.096) Batch 1.263 (1.594) Remain 12:46:32 loss: 0.3378 Lr: 0.00389
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[2023-12-25 12:00:23,248 INFO misc.py line 119 253097] Train: [44/100][211/510] Data 0.016 (0.095) Batch 7.570 (1.621) Remain 12:59:27 loss: 0.0794 Lr: 0.00389
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[2023-12-25 12:00:25,592 INFO misc.py line 119 253097] Train: [44/100][213/510] Data 0.003 (0.095) Batch 1.140 (1.616) Remain 12:57:19 loss: 0.1806 Lr: 0.00389
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[2023-12-25 12:00:27,658 INFO misc.py line 119 253097] Train: [44/100][215/510] Data 0.003 (0.094) Batch 0.878 (1.611) Remain 12:54:38 loss: 0.2190 Lr: 0.00389
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[2023-12-25 12:00:31,728 INFO misc.py line 119 253097] Train: [44/100][219/510] Data 0.003 (0.092) Batch 0.851 (1.600) Remain 12:49:15 loss: 0.1529 Lr: 0.00389
[2023-12-25 12:00:32,711 INFO misc.py line 119 253097] Train: [44/100][220/510] Data 0.003 (0.092) Batch 0.979 (1.597) Remain 12:47:51 loss: 0.1941 Lr: 0.00389
[2023-12-25 12:00:33,839 INFO misc.py line 119 253097] Train: [44/100][221/510] Data 0.007 (0.091) Batch 1.132 (1.595) Remain 12:46:47 loss: 0.2070 Lr: 0.00389
[2023-12-25 12:00:35,178 INFO misc.py line 119 253097] Train: [44/100][222/510] Data 0.003 (0.091) Batch 1.332 (1.594) Remain 12:46:11 loss: 0.1143 Lr: 0.00389
[2023-12-25 12:00:36,007 INFO misc.py line 119 253097] Train: [44/100][223/510] Data 0.009 (0.090) Batch 0.835 (1.590) Remain 12:44:30 loss: 0.1993 Lr: 0.00389
[2023-12-25 12:00:37,023 INFO misc.py line 119 253097] Train: [44/100][224/510] Data 0.004 (0.090) Batch 1.016 (1.588) Remain 12:43:14 loss: 0.3188 Lr: 0.00389
[2023-12-25 12:00:38,129 INFO misc.py line 119 253097] Train: [44/100][225/510] Data 0.003 (0.090) Batch 1.107 (1.585) Remain 12:42:10 loss: 0.4550 Lr: 0.00389
[2023-12-25 12:00:41,419 INFO misc.py line 119 253097] Train: [44/100][226/510] Data 0.003 (0.089) Batch 3.289 (1.593) Remain 12:45:48 loss: 0.1780 Lr: 0.00389
[2023-12-25 12:00:42,611 INFO misc.py line 119 253097] Train: [44/100][227/510] Data 0.004 (0.089) Batch 1.192 (1.591) Remain 12:44:55 loss: 0.1777 Lr: 0.00389
[2023-12-25 12:00:46,355 INFO misc.py line 119 253097] Train: [44/100][228/510] Data 2.375 (0.099) Batch 3.744 (1.601) Remain 12:49:30 loss: 0.0756 Lr: 0.00389
[2023-12-25 12:00:47,432 INFO misc.py line 119 253097] Train: [44/100][229/510] Data 0.003 (0.099) Batch 1.056 (1.598) Remain 12:48:19 loss: 0.1616 Lr: 0.00389
[2023-12-25 12:00:48,576 INFO misc.py line 119 253097] Train: [44/100][230/510] Data 0.024 (0.098) Batch 1.160 (1.596) Remain 12:47:21 loss: 0.1760 Lr: 0.00389
[2023-12-25 12:00:49,620 INFO misc.py line 119 253097] Train: [44/100][231/510] Data 0.008 (0.098) Batch 1.048 (1.594) Remain 12:46:10 loss: 0.3487 Lr: 0.00389
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[2023-12-25 12:00:52,024 INFO misc.py line 119 253097] Train: [44/100][233/510] Data 0.005 (0.097) Batch 1.223 (1.591) Remain 12:44:29 loss: 0.1786 Lr: 0.00389
[2023-12-25 12:00:53,271 INFO misc.py line 119 253097] Train: [44/100][234/510] Data 0.004 (0.097) Batch 1.243 (1.589) Remain 12:43:44 loss: 0.2001 Lr: 0.00389
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[2023-12-25 12:00:57,877 INFO misc.py line 119 253097] Train: [44/100][238/510] Data 0.008 (0.095) Batch 1.041 (1.582) Remain 12:40:03 loss: 0.1559 Lr: 0.00388
[2023-12-25 12:00:58,922 INFO misc.py line 119 253097] Train: [44/100][239/510] Data 0.007 (0.095) Batch 1.045 (1.579) Remain 12:38:56 loss: 0.1420 Lr: 0.00388
[2023-12-25 12:01:00,131 INFO misc.py line 119 253097] Train: [44/100][240/510] Data 0.007 (0.094) Batch 1.199 (1.578) Remain 12:38:08 loss: 0.1960 Lr: 0.00388
[2023-12-25 12:01:06,189 INFO misc.py line 119 253097] Train: [44/100][241/510] Data 0.016 (0.094) Batch 6.070 (1.597) Remain 12:47:10 loss: 0.1662 Lr: 0.00388
[2023-12-25 12:01:07,229 INFO misc.py line 119 253097] Train: [44/100][242/510] Data 0.004 (0.094) Batch 1.041 (1.594) Remain 12:46:02 loss: 0.2390 Lr: 0.00388
[2023-12-25 12:01:08,304 INFO misc.py line 119 253097] Train: [44/100][243/510] Data 0.003 (0.093) Batch 1.074 (1.592) Remain 12:44:58 loss: 0.1284 Lr: 0.00388
[2023-12-25 12:01:09,535 INFO misc.py line 119 253097] Train: [44/100][244/510] Data 0.005 (0.093) Batch 1.232 (1.591) Remain 12:44:13 loss: 0.2183 Lr: 0.00388
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[2023-12-25 12:01:11,924 INFO misc.py line 119 253097] Train: [44/100][246/510] Data 0.008 (0.092) Batch 1.125 (1.587) Remain 12:42:36 loss: 0.1759 Lr: 0.00388
[2023-12-25 12:01:13,249 INFO misc.py line 119 253097] Train: [44/100][247/510] Data 0.004 (0.092) Batch 1.326 (1.586) Remain 12:42:03 loss: 0.1298 Lr: 0.00388
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[2023-12-25 12:01:15,592 INFO misc.py line 119 253097] Train: [44/100][249/510] Data 0.019 (0.091) Batch 1.082 (1.583) Remain 12:40:23 loss: 0.2675 Lr: 0.00388
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[2023-12-25 12:01:23,233 INFO misc.py line 119 253097] Train: [44/100][251/510] Data 0.003 (0.091) Batch 1.089 (1.601) Remain 12:49:00 loss: 0.1950 Lr: 0.00388
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[2023-12-25 12:01:25,461 INFO misc.py line 119 253097] Train: [44/100][253/510] Data 0.003 (0.090) Batch 1.091 (1.597) Remain 12:47:04 loss: 0.1486 Lr: 0.00388
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[2023-12-25 12:01:27,432 INFO misc.py line 119 253097] Train: [44/100][255/510] Data 0.003 (0.089) Batch 0.922 (1.592) Remain 12:44:41 loss: 0.4514 Lr: 0.00388
[2023-12-25 12:01:28,622 INFO misc.py line 119 253097] Train: [44/100][256/510] Data 0.004 (0.089) Batch 1.187 (1.591) Remain 12:43:53 loss: 0.1299 Lr: 0.00388
[2023-12-25 12:01:29,680 INFO misc.py line 119 253097] Train: [44/100][257/510] Data 0.007 (0.089) Batch 1.046 (1.589) Remain 12:42:50 loss: 0.2512 Lr: 0.00388
[2023-12-25 12:01:30,973 INFO misc.py line 119 253097] Train: [44/100][258/510] Data 0.019 (0.088) Batch 1.305 (1.587) Remain 12:42:16 loss: 0.1150 Lr: 0.00388
[2023-12-25 12:01:32,219 INFO misc.py line 119 253097] Train: [44/100][259/510] Data 0.007 (0.088) Batch 1.215 (1.586) Remain 12:41:33 loss: 0.3708 Lr: 0.00388
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[2023-12-25 12:01:38,080 INFO misc.py line 119 253097] Train: [44/100][264/510] Data 0.005 (0.087) Batch 0.960 (1.578) Remain 12:37:40 loss: 0.2192 Lr: 0.00388
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[2023-12-25 12:03:58,143 INFO misc.py line 119 253097] Train: [44/100][348/510] Data 4.205 (0.079) Batch 4.949 (1.600) Remain 12:45:52 loss: 0.1285 Lr: 0.00386
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[2023-12-25 12:05:08,731 INFO misc.py line 119 253097] Train: [44/100][395/510] Data 4.500 (0.084) Batch 5.649 (1.588) Remain 12:39:00 loss: 0.1180 Lr: 0.00386
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[2023-12-25 12:05:23,043 INFO misc.py line 119 253097] Train: [44/100][403/510] Data 0.007 (0.095) Batch 1.093 (1.592) Remain 12:40:41 loss: 0.2389 Lr: 0.00385
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[2023-12-25 12:06:07,187 INFO misc.py line 119 253097] Train: [44/100][430/510] Data 5.609 (0.103) Batch 9.519 (1.595) Remain 12:41:17 loss: 0.0881 Lr: 0.00385
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[2023-12-25 12:06:20,353 INFO misc.py line 119 253097] Train: [44/100][442/510] Data 0.024 (0.101) Batch 1.035 (1.581) Remain 12:34:28 loss: 0.1521 Lr: 0.00385
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[2023-12-25 12:06:25,965 INFO misc.py line 119 253097] Train: [44/100][446/510] Data 0.012 (0.102) Batch 1.185 (1.580) Remain 12:33:36 loss: 0.1877 Lr: 0.00385
[2023-12-25 12:06:27,173 INFO misc.py line 119 253097] Train: [44/100][447/510] Data 0.005 (0.102) Batch 1.208 (1.579) Remain 12:33:10 loss: 0.1337 Lr: 0.00385
[2023-12-25 12:06:28,452 INFO misc.py line 119 253097] Train: [44/100][448/510] Data 0.006 (0.101) Batch 1.273 (1.578) Remain 12:32:49 loss: 0.1483 Lr: 0.00385
[2023-12-25 12:06:29,920 INFO misc.py line 119 253097] Train: [44/100][449/510] Data 0.011 (0.101) Batch 1.474 (1.578) Remain 12:32:41 loss: 0.2032 Lr: 0.00385
[2023-12-25 12:06:39,164 INFO misc.py line 119 253097] Train: [44/100][450/510] Data 0.004 (0.101) Batch 9.242 (1.595) Remain 12:40:50 loss: 0.0782 Lr: 0.00384
[2023-12-25 12:06:40,369 INFO misc.py line 119 253097] Train: [44/100][451/510] Data 0.006 (0.101) Batch 1.207 (1.594) Remain 12:40:23 loss: 0.1349 Lr: 0.00384
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[2023-12-25 12:06:42,386 INFO misc.py line 119 253097] Train: [44/100][453/510] Data 0.004 (0.100) Batch 0.931 (1.592) Remain 12:39:06 loss: 0.3637 Lr: 0.00384
[2023-12-25 12:06:43,493 INFO misc.py line 119 253097] Train: [44/100][454/510] Data 0.005 (0.100) Batch 1.099 (1.590) Remain 12:38:33 loss: 0.1606 Lr: 0.00384
[2023-12-25 12:06:44,579 INFO misc.py line 119 253097] Train: [44/100][455/510] Data 0.014 (0.100) Batch 1.095 (1.589) Remain 12:38:00 loss: 0.2108 Lr: 0.00384
[2023-12-25 12:06:45,898 INFO misc.py line 119 253097] Train: [44/100][456/510] Data 0.004 (0.100) Batch 1.319 (1.589) Remain 12:37:41 loss: 0.3504 Lr: 0.00384
[2023-12-25 12:06:46,855 INFO misc.py line 119 253097] Train: [44/100][457/510] Data 0.004 (0.099) Batch 0.956 (1.587) Remain 12:37:00 loss: 0.1373 Lr: 0.00384
[2023-12-25 12:06:47,964 INFO misc.py line 119 253097] Train: [44/100][458/510] Data 0.004 (0.099) Batch 1.110 (1.586) Remain 12:36:28 loss: 0.1655 Lr: 0.00384
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[2023-12-25 12:06:51,243 INFO misc.py line 119 253097] Train: [44/100][461/510] Data 0.006 (0.099) Batch 1.012 (1.583) Remain 12:34:51 loss: 0.1831 Lr: 0.00384
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[2023-12-25 12:07:04,511 INFO misc.py line 119 253097] Train: [44/100][473/510] Data 0.008 (0.096) Batch 0.940 (1.571) Remain 12:28:43 loss: 0.1304 Lr: 0.00384
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[2023-12-25 12:07:41,010 INFO misc.py line 119 253097] Train: [44/100][500/510] Data 0.009 (0.093) Batch 1.201 (1.559) Remain 12:22:21 loss: 0.1007 Lr: 0.00384
[2023-12-25 12:07:42,299 INFO misc.py line 119 253097] Train: [44/100][501/510] Data 0.005 (0.093) Batch 1.289 (1.558) Remain 12:22:04 loss: 0.2480 Lr: 0.00384
[2023-12-25 12:07:43,500 INFO misc.py line 119 253097] Train: [44/100][502/510] Data 0.004 (0.093) Batch 1.197 (1.558) Remain 12:21:41 loss: 0.2719 Lr: 0.00384
[2023-12-25 12:07:44,521 INFO misc.py line 119 253097] Train: [44/100][503/510] Data 0.008 (0.093) Batch 1.021 (1.557) Remain 12:21:09 loss: 0.2441 Lr: 0.00384
[2023-12-25 12:07:45,595 INFO misc.py line 119 253097] Train: [44/100][504/510] Data 0.008 (0.092) Batch 1.077 (1.556) Remain 12:20:40 loss: 0.2009 Lr: 0.00383
[2023-12-25 12:07:46,835 INFO misc.py line 119 253097] Train: [44/100][505/510] Data 0.005 (0.092) Batch 1.242 (1.555) Remain 12:20:21 loss: 0.0644 Lr: 0.00383
[2023-12-25 12:07:47,974 INFO misc.py line 119 253097] Train: [44/100][506/510] Data 0.003 (0.092) Batch 1.135 (1.554) Remain 12:19:55 loss: 0.1432 Lr: 0.00383
[2023-12-25 12:07:49,158 INFO misc.py line 119 253097] Train: [44/100][507/510] Data 0.007 (0.092) Batch 1.187 (1.554) Remain 12:19:33 loss: 0.2818 Lr: 0.00383
[2023-12-25 12:07:50,328 INFO misc.py line 119 253097] Train: [44/100][508/510] Data 0.004 (0.092) Batch 1.170 (1.553) Remain 12:19:10 loss: 0.2996 Lr: 0.00383
[2023-12-25 12:07:51,561 INFO misc.py line 119 253097] Train: [44/100][509/510] Data 0.004 (0.092) Batch 1.230 (1.552) Remain 12:18:50 loss: 0.1937 Lr: 0.00383
[2023-12-25 12:07:58,303 INFO misc.py line 119 253097] Train: [44/100][510/510] Data 0.006 (0.091) Batch 6.744 (1.562) Remain 12:23:41 loss: 0.0842 Lr: 0.00383
[2023-12-25 12:07:58,303 INFO misc.py line 136 253097] Train result: loss: 0.1951 
[2023-12-25 12:07:58,304 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 12:08:24,707 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6488 
[2023-12-25 12:08:25,062 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3006 
[2023-12-25 12:08:31,570 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3487 
[2023-12-25 12:08:32,096 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4079 
[2023-12-25 12:08:34,072 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9520 
[2023-12-25 12:08:34,497 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4132 
[2023-12-25 12:08:35,380 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9427 
[2023-12-25 12:08:35,936 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2709 
[2023-12-25 12:08:37,757 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9285 
[2023-12-25 12:08:39,880 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2123 
[2023-12-25 12:08:40,745 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3991 
[2023-12-25 12:08:41,169 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8343 
[2023-12-25 12:08:42,076 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5801 
[2023-12-25 12:08:45,026 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7673 
[2023-12-25 12:08:45,495 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1491 
[2023-12-25 12:08:46,109 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3778 
[2023-12-25 12:08:46,811 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4735 
[2023-12-25 12:08:48,517 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6736/0.7431/0.8969.
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9171/0.9378
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9806/0.9895
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8469/0.9584
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0002/0.0035
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3140/0.3585
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5907/0.6086
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6465/0.8286
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8105/0.8861
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9118/0.9643
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6179/0.6851
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7454/0.8149
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7776/0.8466
[2023-12-25 12:08:48,518 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5970/0.7788
[2023-12-25 12:08:48,519 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 12:08:48,520 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 12:08:48,520 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 12:08:58,590 INFO misc.py line 119 253097] Train: [45/100][1/510] Data 3.070 (3.070) Batch 7.542 (7.542) Remain 59:49:45 loss: 0.3277 Lr: 0.00383
[2023-12-25 12:08:59,707 INFO misc.py line 119 253097] Train: [45/100][2/510] Data 0.004 (0.004) Batch 1.117 (1.117) Remain 08:51:31 loss: 0.1413 Lr: 0.00383
[2023-12-25 12:09:13,585 INFO misc.py line 119 253097] Train: [45/100][3/510] Data 1.301 (1.301) Batch 13.877 (13.877) Remain 110:04:53 loss: 0.1744 Lr: 0.00383
[2023-12-25 12:09:14,748 INFO misc.py line 119 253097] Train: [45/100][4/510] Data 0.005 (0.005) Batch 1.164 (1.164) Remain 09:14:06 loss: 0.1914 Lr: 0.00383
[2023-12-25 12:09:15,841 INFO misc.py line 119 253097] Train: [45/100][5/510] Data 0.004 (0.004) Batch 1.092 (1.128) Remain 08:56:49 loss: 0.2137 Lr: 0.00383
[2023-12-25 12:09:16,883 INFO misc.py line 119 253097] Train: [45/100][6/510] Data 0.006 (0.005) Batch 1.043 (1.100) Remain 08:43:17 loss: 0.2349 Lr: 0.00383
[2023-12-25 12:09:18,001 INFO misc.py line 119 253097] Train: [45/100][7/510] Data 0.005 (0.005) Batch 1.118 (1.104) Remain 08:45:25 loss: 0.1069 Lr: 0.00383
[2023-12-25 12:09:19,116 INFO misc.py line 119 253097] Train: [45/100][8/510] Data 0.005 (0.005) Batch 1.113 (1.106) Remain 08:46:16 loss: 0.1621 Lr: 0.00383
[2023-12-25 12:09:20,386 INFO misc.py line 119 253097] Train: [45/100][9/510] Data 0.007 (0.005) Batch 1.272 (1.134) Remain 08:59:26 loss: 0.1773 Lr: 0.00383
[2023-12-25 12:09:21,520 INFO misc.py line 119 253097] Train: [45/100][10/510] Data 0.004 (0.005) Batch 1.133 (1.134) Remain 08:59:21 loss: 0.2250 Lr: 0.00383
[2023-12-25 12:09:22,781 INFO misc.py line 119 253097] Train: [45/100][11/510] Data 0.005 (0.005) Batch 1.256 (1.149) Remain 09:06:37 loss: 0.1794 Lr: 0.00383
[2023-12-25 12:09:23,953 INFO misc.py line 119 253097] Train: [45/100][12/510] Data 0.011 (0.006) Batch 1.175 (1.152) Remain 09:08:00 loss: 0.2029 Lr: 0.00383
[2023-12-25 12:09:25,228 INFO misc.py line 119 253097] Train: [45/100][13/510] Data 0.007 (0.006) Batch 1.278 (1.164) Remain 09:13:59 loss: 0.2126 Lr: 0.00383
[2023-12-25 12:09:26,471 INFO misc.py line 119 253097] Train: [45/100][14/510] Data 0.004 (0.006) Batch 1.242 (1.171) Remain 09:17:20 loss: 0.0806 Lr: 0.00383
[2023-12-25 12:09:27,596 INFO misc.py line 119 253097] Train: [45/100][15/510] Data 0.005 (0.006) Batch 1.119 (1.167) Remain 09:15:14 loss: 0.1809 Lr: 0.00383
[2023-12-25 12:09:29,368 INFO misc.py line 119 253097] Train: [45/100][16/510] Data 0.435 (0.039) Batch 1.778 (1.214) Remain 09:37:35 loss: 0.1174 Lr: 0.00383
[2023-12-25 12:09:30,610 INFO misc.py line 119 253097] Train: [45/100][17/510] Data 0.004 (0.036) Batch 1.230 (1.215) Remain 09:38:07 loss: 0.2261 Lr: 0.00383
[2023-12-25 12:09:31,767 INFO misc.py line 119 253097] Train: [45/100][18/510] Data 0.017 (0.035) Batch 1.166 (1.212) Remain 09:36:32 loss: 0.1677 Lr: 0.00383
[2023-12-25 12:09:32,761 INFO misc.py line 119 253097] Train: [45/100][19/510] Data 0.007 (0.033) Batch 0.997 (1.199) Remain 09:30:08 loss: 0.2017 Lr: 0.00383
[2023-12-25 12:09:34,066 INFO misc.py line 119 253097] Train: [45/100][20/510] Data 0.004 (0.031) Batch 1.305 (1.205) Remain 09:33:05 loss: 0.1205 Lr: 0.00383
[2023-12-25 12:09:35,316 INFO misc.py line 119 253097] Train: [45/100][21/510] Data 0.004 (0.030) Batch 1.247 (1.207) Remain 09:34:11 loss: 0.1437 Lr: 0.00383
[2023-12-25 12:09:36,520 INFO misc.py line 119 253097] Train: [45/100][22/510] Data 0.006 (0.029) Batch 1.204 (1.207) Remain 09:34:06 loss: 0.1255 Lr: 0.00383
[2023-12-25 12:09:37,810 INFO misc.py line 119 253097] Train: [45/100][23/510] Data 0.006 (0.028) Batch 1.293 (1.211) Remain 09:36:06 loss: 0.1226 Lr: 0.00383
[2023-12-25 12:09:39,893 INFO misc.py line 119 253097] Train: [45/100][24/510] Data 0.004 (0.026) Batch 2.083 (1.253) Remain 09:55:49 loss: 0.0853 Lr: 0.00383
[2023-12-25 12:09:41,066 INFO misc.py line 119 253097] Train: [45/100][25/510] Data 0.004 (0.025) Batch 1.174 (1.249) Remain 09:54:05 loss: 0.1117 Lr: 0.00383
[2023-12-25 12:09:43,697 INFO misc.py line 119 253097] Train: [45/100][26/510] Data 0.003 (0.024) Batch 2.631 (1.309) Remain 10:22:38 loss: 0.1984 Lr: 0.00383
[2023-12-25 12:09:44,737 INFO misc.py line 119 253097] Train: [45/100][27/510] Data 0.004 (0.024) Batch 1.037 (1.298) Remain 10:17:13 loss: 0.1294 Lr: 0.00383
[2023-12-25 12:09:45,690 INFO misc.py line 119 253097] Train: [45/100][28/510] Data 0.007 (0.023) Batch 0.954 (1.284) Remain 10:10:39 loss: 0.1748 Lr: 0.00383
[2023-12-25 12:09:46,906 INFO misc.py line 119 253097] Train: [45/100][29/510] Data 0.005 (0.022) Batch 1.217 (1.282) Remain 10:09:25 loss: 0.3949 Lr: 0.00383
[2023-12-25 12:09:48,690 INFO misc.py line 119 253097] Train: [45/100][30/510] Data 0.004 (0.022) Batch 1.778 (1.300) Remain 10:18:08 loss: 0.2228 Lr: 0.00383
[2023-12-25 12:09:49,733 INFO misc.py line 119 253097] Train: [45/100][31/510] Data 0.011 (0.021) Batch 1.045 (1.291) Remain 10:13:47 loss: 0.2126 Lr: 0.00383
[2023-12-25 12:09:51,010 INFO misc.py line 119 253097] Train: [45/100][32/510] Data 0.009 (0.021) Batch 1.281 (1.291) Remain 10:13:36 loss: 0.1198 Lr: 0.00383
[2023-12-25 12:09:52,380 INFO misc.py line 119 253097] Train: [45/100][33/510] Data 0.004 (0.020) Batch 1.366 (1.293) Remain 10:14:46 loss: 0.2622 Lr: 0.00383
[2023-12-25 12:09:53,464 INFO misc.py line 119 253097] Train: [45/100][34/510] Data 0.008 (0.020) Batch 1.088 (1.286) Remain 10:11:36 loss: 0.1133 Lr: 0.00383
[2023-12-25 12:09:54,668 INFO misc.py line 119 253097] Train: [45/100][35/510] Data 0.005 (0.019) Batch 1.199 (1.284) Remain 10:10:17 loss: 0.2028 Lr: 0.00383
[2023-12-25 12:09:55,946 INFO misc.py line 119 253097] Train: [45/100][36/510] Data 0.009 (0.019) Batch 1.279 (1.284) Remain 10:10:12 loss: 0.2007 Lr: 0.00383
[2023-12-25 12:09:56,979 INFO misc.py line 119 253097] Train: [45/100][37/510] Data 0.009 (0.019) Batch 1.038 (1.276) Remain 10:06:44 loss: 0.1913 Lr: 0.00383
[2023-12-25 12:09:58,034 INFO misc.py line 119 253097] Train: [45/100][38/510] Data 0.004 (0.018) Batch 1.054 (1.270) Remain 10:03:42 loss: 0.1570 Lr: 0.00383
[2023-12-25 12:09:59,308 INFO misc.py line 119 253097] Train: [45/100][39/510] Data 0.005 (0.018) Batch 1.273 (1.270) Remain 10:03:43 loss: 0.2293 Lr: 0.00383
[2023-12-25 12:10:00,318 INFO misc.py line 119 253097] Train: [45/100][40/510] Data 0.006 (0.018) Batch 1.009 (1.263) Remain 10:00:20 loss: 0.2747 Lr: 0.00383
[2023-12-25 12:10:01,430 INFO misc.py line 119 253097] Train: [45/100][41/510] Data 0.006 (0.017) Batch 1.115 (1.259) Remain 09:58:28 loss: 0.1344 Lr: 0.00383
[2023-12-25 12:10:02,497 INFO misc.py line 119 253097] Train: [45/100][42/510] Data 0.004 (0.017) Batch 1.067 (1.254) Remain 09:56:06 loss: 0.1690 Lr: 0.00383
[2023-12-25 12:10:03,565 INFO misc.py line 119 253097] Train: [45/100][43/510] Data 0.004 (0.017) Batch 1.069 (1.250) Remain 09:53:52 loss: 0.1248 Lr: 0.00383
[2023-12-25 12:10:29,556 INFO misc.py line 119 253097] Train: [45/100][44/510] Data 0.003 (0.016) Batch 25.991 (1.853) Remain 14:40:39 loss: 0.1433 Lr: 0.00383
[2023-12-25 12:10:30,741 INFO misc.py line 119 253097] Train: [45/100][45/510] Data 0.004 (0.016) Batch 1.185 (1.837) Remain 14:33:04 loss: 0.2491 Lr: 0.00383
[2023-12-25 12:10:31,882 INFO misc.py line 119 253097] Train: [45/100][46/510] Data 0.004 (0.016) Batch 1.141 (1.821) Remain 14:25:20 loss: 0.1490 Lr: 0.00383
[2023-12-25 12:10:33,198 INFO misc.py line 119 253097] Train: [45/100][47/510] Data 0.004 (0.015) Batch 1.312 (1.809) Remain 14:19:49 loss: 0.3420 Lr: 0.00382
[2023-12-25 12:10:34,106 INFO misc.py line 119 253097] Train: [45/100][48/510] Data 0.008 (0.015) Batch 0.912 (1.789) Remain 14:10:19 loss: 0.1076 Lr: 0.00382
[2023-12-25 12:10:35,192 INFO misc.py line 119 253097] Train: [45/100][49/510] Data 0.003 (0.015) Batch 1.086 (1.774) Remain 14:03:01 loss: 0.3675 Lr: 0.00382
[2023-12-25 12:10:36,541 INFO misc.py line 119 253097] Train: [45/100][50/510] Data 0.003 (0.015) Batch 1.344 (1.765) Remain 13:58:39 loss: 0.1068 Lr: 0.00382
[2023-12-25 12:10:37,591 INFO misc.py line 119 253097] Train: [45/100][51/510] Data 0.008 (0.015) Batch 1.049 (1.750) Remain 13:51:32 loss: 0.1314 Lr: 0.00382
[2023-12-25 12:10:38,786 INFO misc.py line 119 253097] Train: [45/100][52/510] Data 0.008 (0.014) Batch 1.198 (1.739) Remain 13:46:09 loss: 0.1485 Lr: 0.00382
[2023-12-25 12:10:40,030 INFO misc.py line 119 253097] Train: [45/100][53/510] Data 0.005 (0.014) Batch 1.242 (1.729) Remain 13:41:24 loss: 0.1877 Lr: 0.00382
[2023-12-25 12:10:41,230 INFO misc.py line 119 253097] Train: [45/100][54/510] Data 0.008 (0.014) Batch 1.197 (1.718) Remain 13:36:25 loss: 0.1725 Lr: 0.00382
[2023-12-25 12:10:42,497 INFO misc.py line 119 253097] Train: [45/100][55/510] Data 0.011 (0.014) Batch 1.271 (1.710) Remain 13:32:18 loss: 0.1482 Lr: 0.00382
[2023-12-25 12:10:43,786 INFO misc.py line 119 253097] Train: [45/100][56/510] Data 0.006 (0.014) Batch 1.289 (1.702) Remain 13:28:30 loss: 0.0997 Lr: 0.00382
[2023-12-25 12:10:44,806 INFO misc.py line 119 253097] Train: [45/100][57/510] Data 0.006 (0.014) Batch 1.022 (1.689) Remain 13:22:29 loss: 0.3505 Lr: 0.00382
[2023-12-25 12:10:45,995 INFO misc.py line 119 253097] Train: [45/100][58/510] Data 0.005 (0.014) Batch 1.190 (1.680) Remain 13:18:09 loss: 0.2354 Lr: 0.00382
[2023-12-25 12:10:47,593 INFO misc.py line 119 253097] Train: [45/100][59/510] Data 0.704 (0.026) Batch 1.598 (1.679) Remain 13:17:25 loss: 0.5416 Lr: 0.00382
[2023-12-25 12:10:48,754 INFO misc.py line 119 253097] Train: [45/100][60/510] Data 0.004 (0.026) Batch 1.154 (1.670) Remain 13:13:01 loss: 0.3230 Lr: 0.00382
[2023-12-25 12:10:49,986 INFO misc.py line 119 253097] Train: [45/100][61/510] Data 0.011 (0.025) Batch 1.237 (1.662) Remain 13:09:27 loss: 0.1556 Lr: 0.00382
[2023-12-25 12:10:51,117 INFO misc.py line 119 253097] Train: [45/100][62/510] Data 0.010 (0.025) Batch 1.133 (1.653) Remain 13:05:09 loss: 0.1388 Lr: 0.00382
[2023-12-25 12:10:52,280 INFO misc.py line 119 253097] Train: [45/100][63/510] Data 0.004 (0.025) Batch 1.160 (1.645) Remain 13:01:13 loss: 0.1542 Lr: 0.00382
[2023-12-25 12:10:53,447 INFO misc.py line 119 253097] Train: [45/100][64/510] Data 0.008 (0.024) Batch 1.169 (1.637) Remain 12:57:29 loss: 0.5862 Lr: 0.00382
[2023-12-25 12:10:58,268 INFO misc.py line 119 253097] Train: [45/100][65/510] Data 3.824 (0.086) Batch 4.823 (1.688) Remain 13:21:52 loss: 0.2304 Lr: 0.00382
[2023-12-25 12:10:59,330 INFO misc.py line 119 253097] Train: [45/100][66/510] Data 0.005 (0.084) Batch 1.059 (1.678) Remain 13:17:05 loss: 0.2792 Lr: 0.00382
[2023-12-25 12:11:00,513 INFO misc.py line 119 253097] Train: [45/100][67/510] Data 0.007 (0.083) Batch 1.185 (1.671) Remain 13:13:24 loss: 0.2832 Lr: 0.00382
[2023-12-25 12:11:01,811 INFO misc.py line 119 253097] Train: [45/100][68/510] Data 0.004 (0.082) Batch 1.298 (1.665) Remain 13:10:39 loss: 0.4249 Lr: 0.00382
[2023-12-25 12:11:02,873 INFO misc.py line 119 253097] Train: [45/100][69/510] Data 0.004 (0.081) Batch 1.058 (1.656) Remain 13:06:16 loss: 0.2549 Lr: 0.00382
[2023-12-25 12:11:03,993 INFO misc.py line 119 253097] Train: [45/100][70/510] Data 0.007 (0.080) Batch 1.119 (1.648) Remain 13:02:26 loss: 0.1453 Lr: 0.00382
[2023-12-25 12:11:05,121 INFO misc.py line 119 253097] Train: [45/100][71/510] Data 0.009 (0.079) Batch 1.133 (1.640) Remain 12:58:48 loss: 0.3091 Lr: 0.00382
[2023-12-25 12:11:06,375 INFO misc.py line 119 253097] Train: [45/100][72/510] Data 0.004 (0.078) Batch 1.249 (1.635) Remain 12:56:05 loss: 0.1193 Lr: 0.00382
[2023-12-25 12:11:07,598 INFO misc.py line 119 253097] Train: [45/100][73/510] Data 0.010 (0.077) Batch 1.227 (1.629) Remain 12:53:18 loss: 0.2602 Lr: 0.00382
[2023-12-25 12:11:08,776 INFO misc.py line 119 253097] Train: [45/100][74/510] Data 0.004 (0.076) Batch 1.176 (1.622) Remain 12:50:15 loss: 0.1802 Lr: 0.00382
[2023-12-25 12:11:09,964 INFO misc.py line 119 253097] Train: [45/100][75/510] Data 0.007 (0.075) Batch 1.191 (1.616) Remain 12:47:22 loss: 0.0641 Lr: 0.00382
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[2023-12-25 12:12:26,774 INFO misc.py line 119 253097] Train: [45/100][128/510] Data 0.010 (0.179) Batch 2.137 (1.546) Remain 12:12:21 loss: 0.1617 Lr: 0.00381
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[2023-12-25 12:12:28,987 INFO misc.py line 119 253097] Train: [45/100][130/510] Data 0.004 (0.176) Batch 1.121 (1.539) Remain 12:09:02 loss: 0.2008 Lr: 0.00381
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[2023-12-25 12:12:31,294 INFO misc.py line 119 253097] Train: [45/100][132/510] Data 0.003 (0.173) Batch 1.139 (1.533) Remain 12:06:09 loss: 0.1421 Lr: 0.00381
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[2023-12-25 12:14:19,189 INFO misc.py line 119 253097] Train: [45/100][200/510] Data 2.633 (0.130) Batch 3.625 (1.551) Remain 12:13:14 loss: 0.1477 Lr: 0.00380
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[2023-12-25 12:14:28,911 INFO misc.py line 119 253097] Train: [45/100][207/510] Data 1.303 (0.135) Batch 2.625 (1.546) Remain 12:10:25 loss: 0.3387 Lr: 0.00379
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[2023-12-25 12:14:32,178 INFO misc.py line 119 253097] Train: [45/100][210/510] Data 0.003 (0.133) Batch 0.935 (1.539) Remain 12:07:13 loss: 0.2589 Lr: 0.00379
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[2023-12-25 12:14:35,795 INFO misc.py line 119 253097] Train: [45/100][213/510] Data 0.009 (0.131) Batch 1.102 (1.534) Remain 12:04:53 loss: 0.1914 Lr: 0.00379
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[2023-12-25 12:14:37,870 INFO misc.py line 119 253097] Train: [45/100][215/510] Data 0.006 (0.130) Batch 0.886 (1.530) Remain 12:02:37 loss: 0.1161 Lr: 0.00379
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[2023-12-25 12:14:41,283 INFO misc.py line 119 253097] Train: [45/100][218/510] Data 0.004 (0.128) Batch 1.227 (1.524) Remain 11:59:58 loss: 0.1422 Lr: 0.00379
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[2023-12-25 12:14:43,361 INFO misc.py line 119 253097] Train: [45/100][220/510] Data 0.007 (0.127) Batch 0.936 (1.520) Remain 11:57:48 loss: 0.0792 Lr: 0.00379
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[2023-12-25 12:14:46,687 INFO misc.py line 119 253097] Train: [45/100][223/510] Data 0.010 (0.125) Batch 0.968 (1.514) Remain 11:55:05 loss: 0.1049 Lr: 0.00379
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[2023-12-25 12:14:48,981 INFO misc.py line 119 253097] Train: [45/100][225/510] Data 0.006 (0.124) Batch 1.122 (1.511) Remain 11:53:28 loss: 0.1918 Lr: 0.00379
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[2023-12-25 12:14:56,818 INFO misc.py line 119 253097] Train: [45/100][232/510] Data 0.008 (0.121) Batch 1.173 (1.499) Remain 11:47:38 loss: 0.1026 Lr: 0.00379
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[2023-12-25 12:15:07,778 INFO misc.py line 119 253097] Train: [45/100][236/510] Data 0.038 (0.119) Batch 7.552 (1.520) Remain 11:57:36 loss: 0.1407 Lr: 0.00379
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[2023-12-25 12:15:21,324 INFO misc.py line 119 253097] Train: [45/100][243/510] Data 0.003 (0.116) Batch 6.574 (1.532) Remain 12:03:08 loss: 0.1349 Lr: 0.00379
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[2023-12-25 12:15:23,819 INFO misc.py line 119 253097] Train: [45/100][245/510] Data 0.005 (0.115) Batch 1.146 (1.530) Remain 12:01:58 loss: 0.1487 Lr: 0.00379
[2023-12-25 12:15:25,133 INFO misc.py line 119 253097] Train: [45/100][246/510] Data 0.008 (0.114) Batch 1.318 (1.529) Remain 12:01:32 loss: 0.1810 Lr: 0.00379
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[2023-12-25 12:15:27,442 INFO misc.py line 119 253097] Train: [45/100][248/510] Data 0.010 (0.113) Batch 1.124 (1.526) Remain 12:00:02 loss: 0.2306 Lr: 0.00379
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[2023-12-25 12:15:29,943 INFO misc.py line 119 253097] Train: [45/100][250/510] Data 0.011 (0.113) Batch 1.278 (1.524) Remain 11:58:55 loss: 0.1571 Lr: 0.00379
[2023-12-25 12:15:30,983 INFO misc.py line 119 253097] Train: [45/100][251/510] Data 0.015 (0.112) Batch 1.041 (1.522) Remain 11:57:58 loss: 0.2199 Lr: 0.00379
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[2023-12-25 12:15:34,249 INFO misc.py line 119 253097] Train: [45/100][254/510] Data 0.009 (0.111) Batch 1.056 (1.517) Remain 11:55:28 loss: 0.2288 Lr: 0.00379
[2023-12-25 12:15:35,275 INFO misc.py line 119 253097] Train: [45/100][255/510] Data 0.005 (0.111) Batch 1.022 (1.515) Remain 11:54:31 loss: 0.1471 Lr: 0.00379
[2023-12-25 12:15:41,149 INFO misc.py line 119 253097] Train: [45/100][256/510] Data 0.008 (0.110) Batch 5.877 (1.532) Remain 12:02:38 loss: 0.1359 Lr: 0.00379
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[2023-12-25 12:17:21,940 INFO misc.py line 119 253097] Train: [45/100][319/510] Data 0.008 (0.089) Batch 1.068 (1.545) Remain 12:07:24 loss: 0.2726 Lr: 0.00377
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[2023-12-25 12:17:28,887 INFO misc.py line 119 253097] Train: [45/100][325/510] Data 0.004 (0.088) Batch 1.172 (1.538) Remain 12:03:50 loss: 0.1468 Lr: 0.00377
[2023-12-25 12:17:30,011 INFO misc.py line 119 253097] Train: [45/100][326/510] Data 0.007 (0.087) Batch 1.127 (1.537) Remain 12:03:13 loss: 0.3248 Lr: 0.00377
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[2023-12-25 12:17:39,116 INFO misc.py line 119 253097] Train: [45/100][334/510] Data 0.007 (0.086) Batch 1.177 (1.527) Remain 11:58:29 loss: 0.1535 Lr: 0.00377
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[2023-12-25 12:17:41,318 INFO misc.py line 119 253097] Train: [45/100][336/510] Data 0.035 (0.085) Batch 1.096 (1.525) Remain 11:57:13 loss: 0.2173 Lr: 0.00377
[2023-12-25 12:17:42,569 INFO misc.py line 119 253097] Train: [45/100][337/510] Data 0.005 (0.085) Batch 1.253 (1.524) Remain 11:56:49 loss: 0.1027 Lr: 0.00377
[2023-12-25 12:17:43,679 INFO misc.py line 119 253097] Train: [45/100][338/510] Data 0.003 (0.085) Batch 1.110 (1.523) Remain 11:56:12 loss: 0.3158 Lr: 0.00377
[2023-12-25 12:17:44,911 INFO misc.py line 119 253097] Train: [45/100][339/510] Data 0.004 (0.084) Batch 1.230 (1.522) Remain 11:55:46 loss: 0.2080 Lr: 0.00377
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[2023-12-25 12:17:47,180 INFO misc.py line 119 253097] Train: [45/100][341/510] Data 0.004 (0.084) Batch 0.973 (1.520) Remain 11:54:38 loss: 0.2124 Lr: 0.00377
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[2023-12-25 12:17:58,353 INFO misc.py line 119 253097] Train: [45/100][343/510] Data 0.003 (0.083) Batch 4.972 (1.543) Remain 12:05:51 loss: 0.1384 Lr: 0.00377
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[2023-12-25 12:18:16,827 INFO misc.py line 119 253097] Train: [45/100][359/510] Data 0.009 (0.080) Batch 1.088 (1.526) Remain 11:57:13 loss: 0.2470 Lr: 0.00377
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[2023-12-25 12:18:59,961 INFO misc.py line 119 253097] Train: [45/100][389/510] Data 0.011 (0.074) Batch 1.200 (1.519) Remain 11:53:14 loss: 0.2415 Lr: 0.00376
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[2023-12-25 12:19:25,064 INFO misc.py line 119 253097] Train: [45/100][406/510] Data 0.011 (0.072) Batch 6.190 (1.517) Remain 11:51:58 loss: 0.2623 Lr: 0.00376
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[2023-12-25 12:19:28,377 INFO misc.py line 119 253097] Train: [45/100][409/510] Data 0.004 (0.071) Batch 1.156 (1.514) Remain 11:50:28 loss: 0.1040 Lr: 0.00376
[2023-12-25 12:19:37,231 INFO misc.py line 119 253097] Train: [45/100][410/510] Data 7.543 (0.089) Batch 8.855 (1.532) Remain 11:58:54 loss: 0.3122 Lr: 0.00376
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[2023-12-25 12:19:51,715 INFO misc.py line 119 253097] Train: [45/100][423/510] Data 0.006 (0.087) Batch 0.958 (1.519) Remain 11:52:30 loss: 0.1445 Lr: 0.00375
[2023-12-25 12:19:52,593 INFO misc.py line 119 253097] Train: [45/100][424/510] Data 0.004 (0.087) Batch 0.873 (1.518) Remain 11:51:45 loss: 0.1032 Lr: 0.00375
[2023-12-25 12:19:53,721 INFO misc.py line 119 253097] Train: [45/100][425/510] Data 0.009 (0.086) Batch 1.132 (1.517) Remain 11:51:18 loss: 0.2794 Lr: 0.00375
[2023-12-25 12:19:54,945 INFO misc.py line 119 253097] Train: [45/100][426/510] Data 0.005 (0.086) Batch 1.225 (1.516) Remain 11:50:57 loss: 0.1071 Lr: 0.00375
[2023-12-25 12:19:57,244 INFO misc.py line 119 253097] Train: [45/100][427/510] Data 1.273 (0.089) Batch 2.299 (1.518) Remain 11:51:47 loss: 0.2219 Lr: 0.00375
[2023-12-25 12:20:02,638 INFO misc.py line 119 253097] Train: [45/100][428/510] Data 0.004 (0.089) Batch 5.393 (1.527) Remain 11:56:02 loss: 0.1361 Lr: 0.00375
[2023-12-25 12:20:03,765 INFO misc.py line 119 253097] Train: [45/100][429/510] Data 0.004 (0.089) Batch 1.126 (1.526) Remain 11:55:34 loss: 0.1295 Lr: 0.00375
[2023-12-25 12:20:04,976 INFO misc.py line 119 253097] Train: [45/100][430/510] Data 0.005 (0.088) Batch 1.210 (1.526) Remain 11:55:12 loss: 0.0778 Lr: 0.00375
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[2023-12-25 12:20:10,569 INFO misc.py line 119 253097] Train: [45/100][435/510] Data 0.008 (0.087) Batch 0.889 (1.521) Remain 11:52:52 loss: 0.1594 Lr: 0.00375
[2023-12-25 12:20:15,162 INFO misc.py line 119 253097] Train: [45/100][436/510] Data 3.322 (0.095) Batch 4.595 (1.528) Remain 11:56:10 loss: 0.2306 Lr: 0.00375
[2023-12-25 12:20:16,169 INFO misc.py line 119 253097] Train: [45/100][437/510] Data 0.004 (0.095) Batch 1.007 (1.527) Remain 11:55:35 loss: 0.1671 Lr: 0.00375
[2023-12-25 12:20:17,330 INFO misc.py line 119 253097] Train: [45/100][438/510] Data 0.004 (0.095) Batch 1.161 (1.526) Remain 11:55:09 loss: 0.1763 Lr: 0.00375
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[2023-12-25 12:20:19,586 INFO misc.py line 119 253097] Train: [45/100][440/510] Data 0.003 (0.094) Batch 1.146 (1.524) Remain 11:54:15 loss: 0.1851 Lr: 0.00375
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[2023-12-25 12:20:24,480 INFO misc.py line 119 253097] Train: [45/100][442/510] Data 2.558 (0.100) Batch 3.834 (1.528) Remain 11:56:10 loss: 0.2998 Lr: 0.00375
[2023-12-25 12:20:25,566 INFO misc.py line 119 253097] Train: [45/100][443/510] Data 0.005 (0.099) Batch 1.086 (1.527) Remain 11:55:41 loss: 0.1453 Lr: 0.00375
[2023-12-25 12:20:26,482 INFO misc.py line 119 253097] Train: [45/100][444/510] Data 0.004 (0.099) Batch 0.915 (1.526) Remain 11:55:00 loss: 0.0958 Lr: 0.00375
[2023-12-25 12:20:27,631 INFO misc.py line 119 253097] Train: [45/100][445/510] Data 0.005 (0.099) Batch 1.149 (1.525) Remain 11:54:35 loss: 0.5395 Lr: 0.00375
[2023-12-25 12:20:28,696 INFO misc.py line 119 253097] Train: [45/100][446/510] Data 0.005 (0.099) Batch 1.066 (1.524) Remain 11:54:04 loss: 0.1502 Lr: 0.00375
[2023-12-25 12:20:29,771 INFO misc.py line 119 253097] Train: [45/100][447/510] Data 0.004 (0.098) Batch 1.075 (1.523) Remain 11:53:34 loss: 0.1111 Lr: 0.00375
[2023-12-25 12:20:33,780 INFO misc.py line 119 253097] Train: [45/100][448/510] Data 2.823 (0.105) Batch 4.010 (1.529) Remain 11:56:09 loss: 0.2823 Lr: 0.00375
[2023-12-25 12:20:34,943 INFO misc.py line 119 253097] Train: [45/100][449/510] Data 0.003 (0.104) Batch 1.163 (1.528) Remain 11:55:45 loss: 0.1199 Lr: 0.00375
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[2023-12-25 12:20:37,455 INFO misc.py line 119 253097] Train: [45/100][451/510] Data 0.007 (0.104) Batch 1.240 (1.526) Remain 11:55:08 loss: 0.1298 Lr: 0.00375
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[2023-12-25 12:20:39,703 INFO misc.py line 119 253097] Train: [45/100][453/510] Data 0.005 (0.103) Batch 1.050 (1.525) Remain 11:54:14 loss: 0.1804 Lr: 0.00375
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[2023-12-25 12:20:41,948 INFO misc.py line 119 253097] Train: [45/100][455/510] Data 0.008 (0.103) Batch 1.074 (1.523) Remain 11:53:21 loss: 0.1046 Lr: 0.00375
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[2023-12-25 12:21:44,659 INFO misc.py line 119 253097] Train: [45/100][494/510] Data 0.003 (0.096) Batch 5.175 (1.530) Remain 11:55:32 loss: 0.0989 Lr: 0.00374
[2023-12-25 12:21:45,723 INFO misc.py line 119 253097] Train: [45/100][495/510] Data 0.005 (0.096) Batch 1.062 (1.529) Remain 11:55:03 loss: 0.1704 Lr: 0.00374
[2023-12-25 12:21:47,786 INFO misc.py line 119 253097] Train: [45/100][496/510] Data 0.739 (0.098) Batch 2.065 (1.530) Remain 11:55:32 loss: 0.2157 Lr: 0.00374
[2023-12-25 12:21:49,074 INFO misc.py line 119 253097] Train: [45/100][497/510] Data 0.004 (0.097) Batch 1.288 (1.529) Remain 11:55:17 loss: 0.1882 Lr: 0.00374
[2023-12-25 12:21:50,292 INFO misc.py line 119 253097] Train: [45/100][498/510] Data 0.004 (0.097) Batch 1.208 (1.529) Remain 11:54:57 loss: 0.3764 Lr: 0.00374
[2023-12-25 12:21:51,503 INFO misc.py line 119 253097] Train: [45/100][499/510] Data 0.013 (0.097) Batch 1.215 (1.528) Remain 11:54:38 loss: 0.3006 Lr: 0.00374
[2023-12-25 12:21:52,409 INFO misc.py line 119 253097] Train: [45/100][500/510] Data 0.010 (0.097) Batch 0.909 (1.527) Remain 11:54:02 loss: 0.3655 Lr: 0.00374
[2023-12-25 12:21:53,441 INFO misc.py line 119 253097] Train: [45/100][501/510] Data 0.007 (0.097) Batch 1.033 (1.526) Remain 11:53:32 loss: 0.1262 Lr: 0.00374
[2023-12-25 12:21:54,730 INFO misc.py line 119 253097] Train: [45/100][502/510] Data 0.005 (0.096) Batch 1.289 (1.525) Remain 11:53:17 loss: 0.1524 Lr: 0.00374
[2023-12-25 12:21:55,854 INFO misc.py line 119 253097] Train: [45/100][503/510] Data 0.006 (0.096) Batch 1.123 (1.525) Remain 11:52:53 loss: 0.2114 Lr: 0.00374
[2023-12-25 12:21:57,021 INFO misc.py line 119 253097] Train: [45/100][504/510] Data 0.006 (0.096) Batch 1.167 (1.524) Remain 11:52:32 loss: 0.2107 Lr: 0.00374
[2023-12-25 12:21:58,127 INFO misc.py line 119 253097] Train: [45/100][505/510] Data 0.007 (0.096) Batch 1.108 (1.523) Remain 11:52:07 loss: 0.2141 Lr: 0.00374
[2023-12-25 12:21:59,168 INFO misc.py line 119 253097] Train: [45/100][506/510] Data 0.005 (0.096) Batch 1.042 (1.522) Remain 11:51:39 loss: 0.4057 Lr: 0.00374
[2023-12-25 12:22:00,432 INFO misc.py line 119 253097] Train: [45/100][507/510] Data 0.004 (0.096) Batch 1.260 (1.522) Remain 11:51:23 loss: 0.1697 Lr: 0.00374
[2023-12-25 12:22:01,507 INFO misc.py line 119 253097] Train: [45/100][508/510] Data 0.009 (0.095) Batch 1.077 (1.521) Remain 11:50:56 loss: 0.1725 Lr: 0.00374
[2023-12-25 12:22:02,655 INFO misc.py line 119 253097] Train: [45/100][509/510] Data 0.004 (0.095) Batch 1.148 (1.520) Remain 11:50:34 loss: 0.1641 Lr: 0.00374
[2023-12-25 12:22:03,950 INFO misc.py line 119 253097] Train: [45/100][510/510] Data 0.005 (0.095) Batch 1.294 (1.519) Remain 11:50:20 loss: 0.1226 Lr: 0.00374
[2023-12-25 12:22:03,951 INFO misc.py line 136 253097] Train result: loss: 0.1997 
[2023-12-25 12:22:03,951 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 12:22:35,848 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4815 
[2023-12-25 12:22:36,215 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3391 
[2023-12-25 12:22:41,156 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3760 
[2023-12-25 12:22:41,683 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2299 
[2023-12-25 12:22:43,660 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6000 
[2023-12-25 12:22:44,084 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4109 
[2023-12-25 12:22:44,961 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3218 
[2023-12-25 12:22:45,519 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3659 
[2023-12-25 12:22:47,339 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1003 
[2023-12-25 12:22:49,472 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3195 
[2023-12-25 12:22:50,332 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2634 
[2023-12-25 12:22:50,754 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.5861 
[2023-12-25 12:22:51,655 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.9532 
[2023-12-25 12:22:54,606 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8153 
[2023-12-25 12:22:55,074 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1880 
[2023-12-25 12:22:55,684 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5072 
[2023-12-25 12:22:56,384 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3361 
[2023-12-25 12:22:57,802 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6626/0.7393/0.8961.
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9228/0.9468
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9820/0.9925
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8398/0.9478
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3637/0.4870
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5630/0.5755
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6230/0.7616
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8161/0.9088
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9058/0.9654
[2023-12-25 12:22:57,802 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5606/0.6038
[2023-12-25 12:22:57,803 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7550/0.8684
[2023-12-25 12:22:57,803 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6992/0.8297
[2023-12-25 12:22:57,803 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5828/0.7236
[2023-12-25 12:22:57,803 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 12:22:57,805 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 12:22:57,805 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 12:23:10,306 INFO misc.py line 119 253097] Train: [46/100][1/510] Data 2.445 (2.445) Batch 10.051 (10.051) Remain 78:18:29 loss: 0.2986 Lr: 0.00374
[2023-12-25 12:23:11,414 INFO misc.py line 119 253097] Train: [46/100][2/510] Data 0.005 (0.005) Batch 1.108 (1.108) Remain 08:37:50 loss: 0.1075 Lr: 0.00374
[2023-12-25 12:23:12,560 INFO misc.py line 119 253097] Train: [46/100][3/510] Data 0.005 (0.005) Batch 1.147 (1.147) Remain 08:55:57 loss: 0.1364 Lr: 0.00374
[2023-12-25 12:23:19,542 INFO misc.py line 119 253097] Train: [46/100][4/510] Data 0.003 (0.003) Batch 6.982 (6.982) Remain 54:23:49 loss: 0.2406 Lr: 0.00374
[2023-12-25 12:23:20,782 INFO misc.py line 119 253097] Train: [46/100][5/510] Data 0.004 (0.004) Batch 1.233 (4.108) Remain 32:00:07 loss: 0.1339 Lr: 0.00374
[2023-12-25 12:23:22,063 INFO misc.py line 119 253097] Train: [46/100][6/510] Data 0.009 (0.005) Batch 1.287 (3.168) Remain 24:40:32 loss: 0.3064 Lr: 0.00374
[2023-12-25 12:23:23,291 INFO misc.py line 119 253097] Train: [46/100][7/510] Data 0.004 (0.005) Batch 1.226 (2.682) Remain 20:53:36 loss: 0.2508 Lr: 0.00374
[2023-12-25 12:23:24,623 INFO misc.py line 119 253097] Train: [46/100][8/510] Data 0.006 (0.005) Batch 1.323 (2.410) Remain 18:46:28 loss: 0.2715 Lr: 0.00374
[2023-12-25 12:23:25,711 INFO misc.py line 119 253097] Train: [46/100][9/510] Data 0.015 (0.007) Batch 1.100 (2.192) Remain 17:04:23 loss: 0.1987 Lr: 0.00374
[2023-12-25 12:23:26,920 INFO misc.py line 119 253097] Train: [46/100][10/510] Data 0.003 (0.006) Batch 1.206 (2.051) Remain 15:58:31 loss: 0.1253 Lr: 0.00374
[2023-12-25 12:23:28,115 INFO misc.py line 119 253097] Train: [46/100][11/510] Data 0.007 (0.006) Batch 1.195 (1.944) Remain 15:08:27 loss: 0.0826 Lr: 0.00374
[2023-12-25 12:23:29,360 INFO misc.py line 119 253097] Train: [46/100][12/510] Data 0.007 (0.006) Batch 1.247 (1.867) Remain 14:32:15 loss: 0.2023 Lr: 0.00374
[2023-12-25 12:23:30,513 INFO misc.py line 119 253097] Train: [46/100][13/510] Data 0.004 (0.006) Batch 1.150 (1.795) Remain 13:58:43 loss: 0.0884 Lr: 0.00374
[2023-12-25 12:23:38,660 INFO misc.py line 119 253097] Train: [46/100][14/510] Data 0.008 (0.006) Batch 8.151 (2.373) Remain 18:28:41 loss: 0.1372 Lr: 0.00374
[2023-12-25 12:23:39,796 INFO misc.py line 119 253097] Train: [46/100][15/510] Data 0.004 (0.006) Batch 1.136 (2.270) Remain 17:40:30 loss: 0.0919 Lr: 0.00374
[2023-12-25 12:23:41,124 INFO misc.py line 119 253097] Train: [46/100][16/510] Data 0.003 (0.006) Batch 1.328 (2.197) Remain 17:06:37 loss: 0.2140 Lr: 0.00373
[2023-12-25 12:23:42,273 INFO misc.py line 119 253097] Train: [46/100][17/510] Data 0.003 (0.006) Batch 1.143 (2.122) Remain 16:31:24 loss: 0.2137 Lr: 0.00373
[2023-12-25 12:23:43,360 INFO misc.py line 119 253097] Train: [46/100][18/510] Data 0.009 (0.006) Batch 1.089 (2.053) Remain 15:59:11 loss: 0.1455 Lr: 0.00373
[2023-12-25 12:23:44,214 INFO misc.py line 119 253097] Train: [46/100][19/510] Data 0.008 (0.006) Batch 0.858 (1.978) Remain 15:24:15 loss: 0.4103 Lr: 0.00373
[2023-12-25 12:23:45,087 INFO misc.py line 119 253097] Train: [46/100][20/510] Data 0.004 (0.006) Batch 0.873 (1.913) Remain 14:53:51 loss: 0.2099 Lr: 0.00373
[2023-12-25 12:23:46,260 INFO misc.py line 119 253097] Train: [46/100][21/510] Data 0.003 (0.006) Batch 1.173 (1.872) Remain 14:34:36 loss: 0.2205 Lr: 0.00373
[2023-12-25 12:23:47,506 INFO misc.py line 119 253097] Train: [46/100][22/510] Data 0.004 (0.006) Batch 1.243 (1.839) Remain 14:19:06 loss: 0.2685 Lr: 0.00373
[2023-12-25 12:23:48,689 INFO misc.py line 119 253097] Train: [46/100][23/510] Data 0.007 (0.006) Batch 1.182 (1.806) Remain 14:03:43 loss: 0.1560 Lr: 0.00373
[2023-12-25 12:23:49,915 INFO misc.py line 119 253097] Train: [46/100][24/510] Data 0.008 (0.006) Batch 1.225 (1.779) Remain 13:50:45 loss: 0.2285 Lr: 0.00373
[2023-12-25 12:23:51,091 INFO misc.py line 119 253097] Train: [46/100][25/510] Data 0.009 (0.006) Batch 1.179 (1.751) Remain 13:38:00 loss: 0.2019 Lr: 0.00373
[2023-12-25 12:23:52,288 INFO misc.py line 119 253097] Train: [46/100][26/510] Data 0.005 (0.006) Batch 1.195 (1.727) Remain 13:26:41 loss: 0.0987 Lr: 0.00373
[2023-12-25 12:23:53,485 INFO misc.py line 119 253097] Train: [46/100][27/510] Data 0.007 (0.006) Batch 1.200 (1.705) Remain 13:16:24 loss: 0.1495 Lr: 0.00373
[2023-12-25 12:23:54,669 INFO misc.py line 119 253097] Train: [46/100][28/510] Data 0.004 (0.006) Batch 1.181 (1.684) Remain 13:06:34 loss: 0.1717 Lr: 0.00373
[2023-12-25 12:23:55,489 INFO misc.py line 119 253097] Train: [46/100][29/510] Data 0.007 (0.006) Batch 0.824 (1.651) Remain 12:51:05 loss: 0.1695 Lr: 0.00373
[2023-12-25 12:23:56,568 INFO misc.py line 119 253097] Train: [46/100][30/510] Data 0.003 (0.006) Batch 1.078 (1.630) Remain 12:41:09 loss: 0.1949 Lr: 0.00373
[2023-12-25 12:23:57,763 INFO misc.py line 119 253097] Train: [46/100][31/510] Data 0.004 (0.006) Batch 1.197 (1.614) Remain 12:33:54 loss: 0.3622 Lr: 0.00373
[2023-12-25 12:23:58,950 INFO misc.py line 119 253097] Train: [46/100][32/510] Data 0.003 (0.006) Batch 1.186 (1.600) Remain 12:26:58 loss: 0.1265 Lr: 0.00373
[2023-12-25 12:24:00,301 INFO misc.py line 119 253097] Train: [46/100][33/510] Data 0.005 (0.006) Batch 1.301 (1.590) Remain 12:22:17 loss: 0.1854 Lr: 0.00373
[2023-12-25 12:24:01,360 INFO misc.py line 119 253097] Train: [46/100][34/510] Data 0.054 (0.007) Batch 1.108 (1.574) Remain 12:15:00 loss: 0.1570 Lr: 0.00373
[2023-12-25 12:24:02,355 INFO misc.py line 119 253097] Train: [46/100][35/510] Data 0.005 (0.007) Batch 0.998 (1.556) Remain 12:06:34 loss: 0.1654 Lr: 0.00373
[2023-12-25 12:24:05,068 INFO misc.py line 119 253097] Train: [46/100][36/510] Data 0.003 (0.007) Batch 2.712 (1.591) Remain 12:22:54 loss: 0.1517 Lr: 0.00373
[2023-12-25 12:24:06,352 INFO misc.py line 119 253097] Train: [46/100][37/510] Data 0.004 (0.007) Batch 1.281 (1.582) Remain 12:18:37 loss: 0.3890 Lr: 0.00373
[2023-12-25 12:24:11,187 INFO misc.py line 119 253097] Train: [46/100][38/510] Data 0.007 (0.007) Batch 4.838 (1.675) Remain 13:02:01 loss: 0.2075 Lr: 0.00373
[2023-12-25 12:24:12,458 INFO misc.py line 119 253097] Train: [46/100][39/510] Data 0.004 (0.007) Batch 1.270 (1.664) Remain 12:56:44 loss: 0.4780 Lr: 0.00373
[2023-12-25 12:24:13,622 INFO misc.py line 119 253097] Train: [46/100][40/510] Data 0.004 (0.007) Batch 1.165 (1.650) Remain 12:50:25 loss: 0.1157 Lr: 0.00373
[2023-12-25 12:24:14,736 INFO misc.py line 119 253097] Train: [46/100][41/510] Data 0.004 (0.007) Batch 1.114 (1.636) Remain 12:43:48 loss: 0.2298 Lr: 0.00373
[2023-12-25 12:24:16,060 INFO misc.py line 119 253097] Train: [46/100][42/510] Data 0.003 (0.007) Batch 1.323 (1.628) Remain 12:40:02 loss: 0.1021 Lr: 0.00373
[2023-12-25 12:24:17,148 INFO misc.py line 119 253097] Train: [46/100][43/510] Data 0.004 (0.007) Batch 1.089 (1.615) Remain 12:33:43 loss: 0.1409 Lr: 0.00373
[2023-12-25 12:24:18,254 INFO misc.py line 119 253097] Train: [46/100][44/510] Data 0.003 (0.006) Batch 1.102 (1.602) Remain 12:27:51 loss: 0.3561 Lr: 0.00373
[2023-12-25 12:24:19,476 INFO misc.py line 119 253097] Train: [46/100][45/510] Data 0.007 (0.006) Batch 1.222 (1.593) Remain 12:23:36 loss: 0.1658 Lr: 0.00373
[2023-12-25 12:24:20,643 INFO misc.py line 119 253097] Train: [46/100][46/510] Data 0.007 (0.006) Batch 1.166 (1.583) Remain 12:18:56 loss: 0.1367 Lr: 0.00373
[2023-12-25 12:24:21,695 INFO misc.py line 119 253097] Train: [46/100][47/510] Data 0.007 (0.006) Batch 1.051 (1.571) Remain 12:13:16 loss: 0.1319 Lr: 0.00373
[2023-12-25 12:24:22,846 INFO misc.py line 119 253097] Train: [46/100][48/510] Data 0.008 (0.007) Batch 1.150 (1.562) Remain 12:08:52 loss: 0.2627 Lr: 0.00373
[2023-12-25 12:24:26,118 INFO misc.py line 119 253097] Train: [46/100][49/510] Data 0.009 (0.007) Batch 1.146 (1.553) Remain 12:04:38 loss: 0.2234 Lr: 0.00373
[2023-12-25 12:24:30,146 INFO misc.py line 119 253097] Train: [46/100][50/510] Data 2.135 (0.052) Batch 6.160 (1.651) Remain 12:50:21 loss: 0.1223 Lr: 0.00373
[2023-12-25 12:24:31,132 INFO misc.py line 119 253097] Train: [46/100][51/510] Data 0.003 (0.051) Batch 0.984 (1.637) Remain 12:43:51 loss: 0.1173 Lr: 0.00373
[2023-12-25 12:24:34,322 INFO misc.py line 119 253097] Train: [46/100][52/510] Data 0.005 (0.050) Batch 3.191 (1.669) Remain 12:58:37 loss: 0.2218 Lr: 0.00373
[2023-12-25 12:24:35,522 INFO misc.py line 119 253097] Train: [46/100][53/510] Data 0.005 (0.049) Batch 1.201 (1.659) Remain 12:54:13 loss: 0.2136 Lr: 0.00373
[2023-12-25 12:24:36,622 INFO misc.py line 119 253097] Train: [46/100][54/510] Data 0.004 (0.048) Batch 1.099 (1.648) Remain 12:49:04 loss: 0.3468 Lr: 0.00373
[2023-12-25 12:24:37,698 INFO misc.py line 119 253097] Train: [46/100][55/510] Data 0.005 (0.047) Batch 1.077 (1.637) Remain 12:43:55 loss: 0.1122 Lr: 0.00373
[2023-12-25 12:24:38,778 INFO misc.py line 119 253097] Train: [46/100][56/510] Data 0.004 (0.046) Batch 1.080 (1.627) Remain 12:38:59 loss: 0.1871 Lr: 0.00373
[2023-12-25 12:24:39,902 INFO misc.py line 119 253097] Train: [46/100][57/510] Data 0.005 (0.046) Batch 1.122 (1.617) Remain 12:34:35 loss: 0.1168 Lr: 0.00373
[2023-12-25 12:24:41,079 INFO misc.py line 119 253097] Train: [46/100][58/510] Data 0.006 (0.045) Batch 1.179 (1.609) Remain 12:30:50 loss: 0.2322 Lr: 0.00373
[2023-12-25 12:24:42,321 INFO misc.py line 119 253097] Train: [46/100][59/510] Data 0.005 (0.044) Batch 1.238 (1.603) Remain 12:27:43 loss: 0.1847 Lr: 0.00373
[2023-12-25 12:24:44,358 INFO misc.py line 119 253097] Train: [46/100][60/510] Data 0.842 (0.058) Batch 2.042 (1.610) Remain 12:31:17 loss: 0.0957 Lr: 0.00373
[2023-12-25 12:24:45,578 INFO misc.py line 119 253097] Train: [46/100][61/510] Data 0.004 (0.057) Batch 1.220 (1.604) Remain 12:28:07 loss: 0.1522 Lr: 0.00373
[2023-12-25 12:24:50,298 INFO misc.py line 119 253097] Train: [46/100][62/510] Data 0.004 (0.056) Batch 4.720 (1.657) Remain 12:52:44 loss: 0.1192 Lr: 0.00373
[2023-12-25 12:24:51,462 INFO misc.py line 119 253097] Train: [46/100][63/510] Data 0.004 (0.056) Batch 1.163 (1.648) Remain 12:48:52 loss: 0.1746 Lr: 0.00373
[2023-12-25 12:24:52,411 INFO misc.py line 119 253097] Train: [46/100][64/510] Data 0.004 (0.055) Batch 0.949 (1.637) Remain 12:43:30 loss: 0.2001 Lr: 0.00373
[2023-12-25 12:24:53,433 INFO misc.py line 119 253097] Train: [46/100][65/510] Data 0.005 (0.054) Batch 1.021 (1.627) Remain 12:38:50 loss: 0.1814 Lr: 0.00373
[2023-12-25 12:24:59,041 INFO misc.py line 119 253097] Train: [46/100][66/510] Data 0.004 (0.053) Batch 5.609 (1.690) Remain 13:08:17 loss: 0.1807 Lr: 0.00373
[2023-12-25 12:25:00,201 INFO misc.py line 119 253097] Train: [46/100][67/510] Data 0.004 (0.052) Batch 1.160 (1.682) Remain 13:04:24 loss: 0.1067 Lr: 0.00373
[2023-12-25 12:25:01,467 INFO misc.py line 119 253097] Train: [46/100][68/510] Data 0.004 (0.052) Batch 1.261 (1.675) Remain 13:01:21 loss: 0.2110 Lr: 0.00373
[2023-12-25 12:25:02,543 INFO misc.py line 119 253097] Train: [46/100][69/510] Data 0.009 (0.051) Batch 1.077 (1.666) Remain 12:57:05 loss: 0.1768 Lr: 0.00372
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[2023-12-25 12:25:30,449 INFO misc.py line 119 253097] Train: [46/100][90/510] Data 0.012 (0.070) Batch 2.398 (1.585) Remain 12:18:34 loss: 0.1132 Lr: 0.00372
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[2023-12-25 12:25:34,936 INFO misc.py line 119 253097] Train: [46/100][94/510] Data 0.010 (0.068) Batch 1.119 (1.565) Remain 12:08:58 loss: 0.1267 Lr: 0.00372
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[2023-12-25 12:25:46,364 INFO misc.py line 119 253097] Train: [46/100][96/510] Data 0.009 (0.066) Batch 10.209 (1.654) Remain 12:50:29 loss: 0.1484 Lr: 0.00372
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[2023-12-25 12:25:52,431 INFO misc.py line 119 253097] Train: [46/100][101/510] Data 0.004 (0.063) Batch 1.229 (1.631) Remain 12:39:54 loss: 0.1412 Lr: 0.00372
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[2023-12-25 12:25:56,949 INFO misc.py line 119 253097] Train: [46/100][105/510] Data 0.003 (0.061) Batch 1.081 (1.612) Remain 12:30:36 loss: 0.3076 Lr: 0.00372
[2023-12-25 12:25:58,183 INFO misc.py line 119 253097] Train: [46/100][106/510] Data 0.009 (0.060) Batch 1.237 (1.608) Remain 12:28:52 loss: 0.2335 Lr: 0.00372
[2023-12-25 12:25:59,424 INFO misc.py line 119 253097] Train: [46/100][107/510] Data 0.008 (0.060) Batch 1.244 (1.604) Remain 12:27:13 loss: 0.1809 Lr: 0.00372
[2023-12-25 12:26:00,470 INFO misc.py line 119 253097] Train: [46/100][108/510] Data 0.004 (0.059) Batch 1.042 (1.599) Remain 12:24:42 loss: 0.1192 Lr: 0.00372
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[2023-12-25 12:26:02,840 INFO misc.py line 119 253097] Train: [46/100][110/510] Data 0.007 (0.058) Batch 1.119 (1.591) Remain 12:21:02 loss: 0.1539 Lr: 0.00372
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[2023-12-25 12:26:07,329 INFO misc.py line 119 253097] Train: [46/100][114/510] Data 0.006 (0.056) Batch 1.196 (1.574) Remain 12:13:04 loss: 0.1120 Lr: 0.00372
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[2023-12-25 12:26:12,875 INFO misc.py line 119 253097] Train: [46/100][119/510] Data 0.004 (0.054) Batch 1.091 (1.554) Remain 12:03:35 loss: 0.1982 Lr: 0.00372
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[2023-12-25 12:26:16,233 INFO misc.py line 119 253097] Train: [46/100][122/510] Data 0.004 (0.053) Batch 1.038 (1.543) Remain 11:58:25 loss: 0.2932 Lr: 0.00371
[2023-12-25 12:26:17,348 INFO misc.py line 119 253097] Train: [46/100][123/510] Data 0.004 (0.053) Batch 1.111 (1.540) Remain 11:56:43 loss: 0.1592 Lr: 0.00371
[2023-12-25 12:26:18,579 INFO misc.py line 119 253097] Train: [46/100][124/510] Data 0.010 (0.052) Batch 1.231 (1.537) Remain 11:55:30 loss: 0.1822 Lr: 0.00371
[2023-12-25 12:26:24,210 INFO misc.py line 119 253097] Train: [46/100][125/510] Data 0.008 (0.052) Batch 5.636 (1.571) Remain 12:11:07 loss: 0.1997 Lr: 0.00371
[2023-12-25 12:26:26,258 INFO misc.py line 119 253097] Train: [46/100][126/510] Data 0.004 (0.052) Batch 2.046 (1.575) Remain 12:12:53 loss: 0.1181 Lr: 0.00371
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[2023-12-25 12:26:33,474 INFO misc.py line 119 253097] Train: [46/100][131/510] Data 0.015 (0.059) Batch 1.657 (1.570) Remain 12:10:22 loss: 0.1617 Lr: 0.00371
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[2023-12-25 12:28:08,402 INFO misc.py line 119 253097] Train: [46/100][193/510] Data 0.013 (0.042) Batch 0.961 (1.557) Remain 12:02:54 loss: 0.1979 Lr: 0.00370
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[2023-12-25 12:28:11,818 INFO misc.py line 119 253097] Train: [46/100][196/510] Data 0.005 (0.041) Batch 1.224 (1.551) Remain 11:59:49 loss: 0.1624 Lr: 0.00370
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[2023-12-25 12:28:14,189 INFO misc.py line 119 253097] Train: [46/100][198/510] Data 0.004 (0.041) Batch 1.179 (1.547) Remain 11:58:01 loss: 0.3495 Lr: 0.00370
[2023-12-25 12:28:15,298 INFO misc.py line 119 253097] Train: [46/100][199/510] Data 0.004 (0.041) Batch 1.107 (1.545) Remain 11:56:57 loss: 0.1183 Lr: 0.00370
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[2023-12-25 12:28:17,541 INFO misc.py line 119 253097] Train: [46/100][201/510] Data 0.003 (0.040) Batch 1.036 (1.540) Remain 11:54:56 loss: 0.1851 Lr: 0.00370
[2023-12-25 12:28:18,631 INFO misc.py line 119 253097] Train: [46/100][202/510] Data 0.003 (0.040) Batch 1.089 (1.538) Remain 11:53:51 loss: 0.1073 Lr: 0.00370
[2023-12-25 12:28:27,666 INFO misc.py line 119 253097] Train: [46/100][203/510] Data 0.004 (0.040) Batch 9.033 (1.576) Remain 12:11:13 loss: 0.1746 Lr: 0.00370
[2023-12-25 12:28:28,766 INFO misc.py line 119 253097] Train: [46/100][204/510] Data 0.006 (0.040) Batch 1.102 (1.573) Remain 12:10:06 loss: 0.1345 Lr: 0.00370
[2023-12-25 12:28:29,849 INFO misc.py line 119 253097] Train: [46/100][205/510] Data 0.005 (0.039) Batch 1.083 (1.571) Remain 12:08:57 loss: 0.1039 Lr: 0.00370
[2023-12-25 12:28:31,706 INFO misc.py line 119 253097] Train: [46/100][206/510] Data 0.744 (0.043) Batch 1.857 (1.572) Remain 12:09:34 loss: 0.1575 Lr: 0.00370
[2023-12-25 12:28:32,829 INFO misc.py line 119 253097] Train: [46/100][207/510] Data 0.003 (0.043) Batch 1.122 (1.570) Remain 12:08:31 loss: 0.1966 Lr: 0.00370
[2023-12-25 12:28:34,122 INFO misc.py line 119 253097] Train: [46/100][208/510] Data 0.004 (0.043) Batch 1.293 (1.569) Remain 12:07:52 loss: 0.2001 Lr: 0.00370
[2023-12-25 12:28:35,075 INFO misc.py line 119 253097] Train: [46/100][209/510] Data 0.004 (0.042) Batch 0.953 (1.566) Remain 12:06:27 loss: 0.1698 Lr: 0.00370
[2023-12-25 12:28:36,257 INFO misc.py line 119 253097] Train: [46/100][210/510] Data 0.004 (0.042) Batch 1.181 (1.564) Remain 12:05:34 loss: 0.1356 Lr: 0.00370
[2023-12-25 12:28:37,353 INFO misc.py line 119 253097] Train: [46/100][211/510] Data 0.005 (0.042) Batch 1.094 (1.561) Remain 12:04:30 loss: 0.3875 Lr: 0.00370
[2023-12-25 12:28:38,529 INFO misc.py line 119 253097] Train: [46/100][212/510] Data 0.008 (0.042) Batch 1.179 (1.560) Remain 12:03:37 loss: 0.2136 Lr: 0.00370
[2023-12-25 12:28:39,691 INFO misc.py line 119 253097] Train: [46/100][213/510] Data 0.004 (0.042) Batch 1.163 (1.558) Remain 12:02:43 loss: 0.2232 Lr: 0.00370
[2023-12-25 12:28:40,851 INFO misc.py line 119 253097] Train: [46/100][214/510] Data 0.003 (0.041) Batch 1.159 (1.556) Remain 12:01:49 loss: 0.1821 Lr: 0.00370
[2023-12-25 12:28:41,893 INFO misc.py line 119 253097] Train: [46/100][215/510] Data 0.004 (0.041) Batch 1.037 (1.553) Remain 12:00:39 loss: 0.3200 Lr: 0.00370
[2023-12-25 12:28:43,141 INFO misc.py line 119 253097] Train: [46/100][216/510] Data 0.010 (0.041) Batch 1.251 (1.552) Remain 11:59:58 loss: 0.1494 Lr: 0.00370
[2023-12-25 12:28:44,444 INFO misc.py line 119 253097] Train: [46/100][217/510] Data 0.008 (0.041) Batch 1.306 (1.551) Remain 11:59:24 loss: 0.2796 Lr: 0.00370
[2023-12-25 12:28:45,704 INFO misc.py line 119 253097] Train: [46/100][218/510] Data 0.004 (0.041) Batch 1.257 (1.549) Remain 11:58:45 loss: 0.2239 Lr: 0.00370
[2023-12-25 12:28:46,901 INFO misc.py line 119 253097] Train: [46/100][219/510] Data 0.008 (0.041) Batch 1.201 (1.548) Remain 11:57:58 loss: 0.1626 Lr: 0.00370
[2023-12-25 12:28:48,151 INFO misc.py line 119 253097] Train: [46/100][220/510] Data 0.004 (0.040) Batch 1.250 (1.547) Remain 11:57:19 loss: 0.1631 Lr: 0.00370
[2023-12-25 12:28:49,323 INFO misc.py line 119 253097] Train: [46/100][221/510] Data 0.003 (0.040) Batch 1.171 (1.545) Remain 11:56:29 loss: 0.1610 Lr: 0.00370
[2023-12-25 12:28:50,496 INFO misc.py line 119 253097] Train: [46/100][222/510] Data 0.004 (0.040) Batch 1.170 (1.543) Remain 11:55:40 loss: 0.1402 Lr: 0.00370
[2023-12-25 12:28:51,418 INFO misc.py line 119 253097] Train: [46/100][223/510] Data 0.008 (0.040) Batch 0.926 (1.540) Remain 11:54:20 loss: 0.2659 Lr: 0.00370
[2023-12-25 12:28:52,629 INFO misc.py line 119 253097] Train: [46/100][224/510] Data 0.004 (0.040) Batch 1.210 (1.539) Remain 11:53:37 loss: 0.1521 Lr: 0.00370
[2023-12-25 12:28:53,489 INFO misc.py line 119 253097] Train: [46/100][225/510] Data 0.005 (0.040) Batch 0.860 (1.536) Remain 11:52:11 loss: 0.2267 Lr: 0.00370
[2023-12-25 12:28:54,522 INFO misc.py line 119 253097] Train: [46/100][226/510] Data 0.005 (0.040) Batch 1.031 (1.533) Remain 11:51:06 loss: 0.2310 Lr: 0.00370
[2023-12-25 12:28:55,772 INFO misc.py line 119 253097] Train: [46/100][227/510] Data 0.007 (0.039) Batch 1.249 (1.532) Remain 11:50:29 loss: 0.1822 Lr: 0.00370
[2023-12-25 12:28:57,465 INFO misc.py line 119 253097] Train: [46/100][228/510] Data 0.008 (0.039) Batch 1.696 (1.533) Remain 11:50:48 loss: 0.2421 Lr: 0.00369
[2023-12-25 12:28:58,586 INFO misc.py line 119 253097] Train: [46/100][229/510] Data 0.007 (0.039) Batch 1.122 (1.531) Remain 11:49:56 loss: 0.3437 Lr: 0.00369
[2023-12-25 12:28:59,614 INFO misc.py line 119 253097] Train: [46/100][230/510] Data 0.005 (0.039) Batch 1.025 (1.529) Remain 11:48:52 loss: 0.4574 Lr: 0.00369
[2023-12-25 12:29:00,557 INFO misc.py line 119 253097] Train: [46/100][231/510] Data 0.006 (0.039) Batch 0.946 (1.526) Remain 11:47:40 loss: 0.1493 Lr: 0.00369
[2023-12-25 12:29:01,784 INFO misc.py line 119 253097] Train: [46/100][232/510] Data 0.004 (0.039) Batch 1.227 (1.525) Remain 11:47:02 loss: 0.1657 Lr: 0.00369
[2023-12-25 12:29:09,425 INFO misc.py line 119 253097] Train: [46/100][233/510] Data 6.639 (0.067) Batch 7.640 (1.552) Remain 11:59:20 loss: 0.2174 Lr: 0.00369
[2023-12-25 12:29:10,608 INFO misc.py line 119 253097] Train: [46/100][234/510] Data 0.005 (0.067) Batch 1.183 (1.550) Remain 11:58:34 loss: 0.3189 Lr: 0.00369
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[2023-12-25 12:29:14,203 INFO misc.py line 119 253097] Train: [46/100][237/510] Data 0.005 (0.066) Batch 1.208 (1.545) Remain 11:56:24 loss: 0.2421 Lr: 0.00369
[2023-12-25 12:29:15,135 INFO misc.py line 119 253097] Train: [46/100][238/510] Data 0.006 (0.066) Batch 0.936 (1.543) Remain 11:55:10 loss: 0.2651 Lr: 0.00369
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[2023-12-25 12:29:18,543 INFO misc.py line 119 253097] Train: [46/100][241/510] Data 0.004 (0.065) Batch 1.176 (1.538) Remain 11:52:42 loss: 0.2729 Lr: 0.00369
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[2023-12-25 12:29:21,922 INFO misc.py line 119 253097] Train: [46/100][244/510] Data 0.004 (0.064) Batch 0.998 (1.533) Remain 11:50:16 loss: 0.2379 Lr: 0.00369
[2023-12-25 12:29:23,076 INFO misc.py line 119 253097] Train: [46/100][245/510] Data 0.004 (0.064) Batch 1.154 (1.531) Remain 11:49:31 loss: 0.3934 Lr: 0.00369
[2023-12-25 12:29:24,370 INFO misc.py line 119 253097] Train: [46/100][246/510] Data 0.004 (0.064) Batch 1.291 (1.530) Remain 11:49:02 loss: 0.2678 Lr: 0.00369
[2023-12-25 12:29:29,251 INFO misc.py line 119 253097] Train: [46/100][247/510] Data 0.009 (0.064) Batch 4.882 (1.544) Remain 11:55:22 loss: 0.2187 Lr: 0.00369
[2023-12-25 12:29:30,733 INFO misc.py line 119 253097] Train: [46/100][248/510] Data 0.754 (0.067) Batch 1.483 (1.544) Remain 11:55:14 loss: 0.4290 Lr: 0.00369
[2023-12-25 12:29:31,847 INFO misc.py line 119 253097] Train: [46/100][249/510] Data 0.004 (0.066) Batch 1.115 (1.542) Remain 11:54:24 loss: 0.1877 Lr: 0.00369
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[2023-12-25 12:29:34,199 INFO misc.py line 119 253097] Train: [46/100][251/510] Data 0.011 (0.066) Batch 1.271 (1.539) Remain 11:52:58 loss: 0.2069 Lr: 0.00369
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[2023-12-25 12:33:43,218 INFO misc.py line 119 253097] Train: [46/100][414/510] Data 0.005 (0.064) Batch 1.130 (1.534) Remain 11:46:45 loss: 0.2545 Lr: 0.00366
[2023-12-25 12:33:44,348 INFO misc.py line 119 253097] Train: [46/100][415/510] Data 0.004 (0.064) Batch 1.121 (1.533) Remain 11:46:16 loss: 0.1250 Lr: 0.00366
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[2023-12-25 12:33:46,806 INFO misc.py line 119 253097] Train: [46/100][417/510] Data 0.016 (0.064) Batch 1.148 (1.532) Remain 11:45:33 loss: 0.1925 Lr: 0.00366
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[2023-12-25 12:33:52,466 INFO misc.py line 119 253097] Train: [46/100][422/510] Data 0.005 (0.063) Batch 1.251 (1.527) Remain 11:43:14 loss: 0.1969 Lr: 0.00366
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[2023-12-25 12:33:55,613 INFO misc.py line 119 253097] Train: [46/100][425/510] Data 0.019 (0.063) Batch 0.988 (1.524) Remain 11:41:35 loss: 0.1815 Lr: 0.00366
[2023-12-25 12:34:05,436 INFO misc.py line 119 253097] Train: [46/100][426/510] Data 8.653 (0.083) Batch 9.823 (1.543) Remain 11:50:35 loss: 0.1245 Lr: 0.00366
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[2023-12-25 12:34:12,429 INFO misc.py line 119 253097] Train: [46/100][429/510] Data 0.004 (0.082) Batch 4.734 (1.549) Remain 11:53:04 loss: 0.1275 Lr: 0.00366
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[2023-12-25 12:34:21,710 INFO misc.py line 119 253097] Train: [46/100][437/510] Data 0.008 (0.081) Batch 1.176 (1.542) Remain 11:49:34 loss: 0.2021 Lr: 0.00366
[2023-12-25 12:34:22,944 INFO misc.py line 119 253097] Train: [46/100][438/510] Data 0.007 (0.081) Batch 1.227 (1.541) Remain 11:49:12 loss: 0.1636 Lr: 0.00366
[2023-12-25 12:34:24,041 INFO misc.py line 119 253097] Train: [46/100][439/510] Data 0.015 (0.081) Batch 1.105 (1.540) Remain 11:48:43 loss: 0.2004 Lr: 0.00365
[2023-12-25 12:34:25,014 INFO misc.py line 119 253097] Train: [46/100][440/510] Data 0.006 (0.081) Batch 0.976 (1.539) Remain 11:48:06 loss: 0.2279 Lr: 0.00365
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[2023-12-25 12:34:27,481 INFO misc.py line 119 253097] Train: [46/100][442/510] Data 0.005 (0.080) Batch 1.290 (1.537) Remain 11:47:24 loss: 0.1988 Lr: 0.00365
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[2023-12-25 12:34:30,401 INFO misc.py line 119 253097] Train: [46/100][444/510] Data 0.008 (0.081) Batch 1.259 (1.537) Remain 11:47:11 loss: 0.1186 Lr: 0.00365
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[2023-12-25 12:34:32,823 INFO misc.py line 119 253097] Train: [46/100][446/510] Data 0.007 (0.080) Batch 1.247 (1.536) Remain 11:46:27 loss: 0.1324 Lr: 0.00365
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[2023-12-25 12:34:37,334 INFO misc.py line 119 253097] Train: [46/100][450/510] Data 0.004 (0.080) Batch 1.110 (1.532) Remain 11:44:41 loss: 0.1093 Lr: 0.00365
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[2023-12-25 12:34:39,408 INFO misc.py line 119 253097] Train: [46/100][452/510] Data 0.008 (0.079) Batch 1.003 (1.530) Remain 11:43:37 loss: 0.1978 Lr: 0.00365
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[2023-12-25 12:34:41,731 INFO misc.py line 119 253097] Train: [46/100][454/510] Data 0.006 (0.079) Batch 1.024 (1.528) Remain 11:42:48 loss: 0.1745 Lr: 0.00365
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[2023-12-25 12:35:25,756 INFO misc.py line 119 253097] Train: [46/100][488/510] Data 0.009 (0.074) Batch 1.234 (1.512) Remain 11:34:26 loss: 0.2506 Lr: 0.00365
[2023-12-25 12:35:26,982 INFO misc.py line 119 253097] Train: [46/100][489/510] Data 0.004 (0.074) Batch 1.226 (1.511) Remain 11:34:08 loss: 0.2908 Lr: 0.00365
[2023-12-25 12:35:28,334 INFO misc.py line 119 253097] Train: [46/100][490/510] Data 0.003 (0.074) Batch 1.352 (1.511) Remain 11:33:58 loss: 0.1712 Lr: 0.00365
[2023-12-25 12:35:29,938 INFO misc.py line 119 253097] Train: [46/100][491/510] Data 0.003 (0.074) Batch 1.187 (1.510) Remain 11:33:38 loss: 0.1321 Lr: 0.00365
[2023-12-25 12:35:31,138 INFO misc.py line 119 253097] Train: [46/100][492/510] Data 0.421 (0.074) Batch 1.613 (1.510) Remain 11:33:42 loss: 0.1758 Lr: 0.00364
[2023-12-25 12:35:36,867 INFO misc.py line 119 253097] Train: [46/100][493/510] Data 4.865 (0.084) Batch 5.734 (1.519) Remain 11:37:38 loss: 0.1617 Lr: 0.00364
[2023-12-25 12:35:37,877 INFO misc.py line 119 253097] Train: [46/100][494/510] Data 0.003 (0.084) Batch 1.010 (1.518) Remain 11:37:08 loss: 0.1183 Lr: 0.00364
[2023-12-25 12:35:39,047 INFO misc.py line 119 253097] Train: [46/100][495/510] Data 0.002 (0.084) Batch 1.165 (1.517) Remain 11:36:47 loss: 0.2202 Lr: 0.00364
[2023-12-25 12:35:40,039 INFO misc.py line 119 253097] Train: [46/100][496/510] Data 0.008 (0.084) Batch 0.997 (1.516) Remain 11:36:16 loss: 0.4002 Lr: 0.00364
[2023-12-25 12:35:40,953 INFO misc.py line 119 253097] Train: [46/100][497/510] Data 0.004 (0.083) Batch 0.913 (1.515) Remain 11:35:41 loss: 0.1351 Lr: 0.00364
[2023-12-25 12:35:42,055 INFO misc.py line 119 253097] Train: [46/100][498/510] Data 0.004 (0.083) Batch 1.101 (1.514) Remain 11:35:17 loss: 0.1917 Lr: 0.00364
[2023-12-25 12:35:43,339 INFO misc.py line 119 253097] Train: [46/100][499/510] Data 0.005 (0.083) Batch 1.278 (1.514) Remain 11:35:02 loss: 0.2218 Lr: 0.00364
[2023-12-25 12:35:44,529 INFO misc.py line 119 253097] Train: [46/100][500/510] Data 0.011 (0.083) Batch 1.197 (1.513) Remain 11:34:43 loss: 0.1881 Lr: 0.00364
[2023-12-25 12:35:45,442 INFO misc.py line 119 253097] Train: [46/100][501/510] Data 0.005 (0.083) Batch 0.912 (1.512) Remain 11:34:08 loss: 0.1283 Lr: 0.00364
[2023-12-25 12:35:46,673 INFO misc.py line 119 253097] Train: [46/100][502/510] Data 0.006 (0.083) Batch 1.229 (1.511) Remain 11:33:51 loss: 0.1626 Lr: 0.00364
[2023-12-25 12:35:47,813 INFO misc.py line 119 253097] Train: [46/100][503/510] Data 0.007 (0.083) Batch 1.139 (1.510) Remain 11:33:29 loss: 0.1378 Lr: 0.00364
[2023-12-25 12:35:49,032 INFO misc.py line 119 253097] Train: [46/100][504/510] Data 0.008 (0.082) Batch 1.220 (1.510) Remain 11:33:12 loss: 0.1812 Lr: 0.00364
[2023-12-25 12:35:50,143 INFO misc.py line 119 253097] Train: [46/100][505/510] Data 0.007 (0.082) Batch 1.114 (1.509) Remain 11:32:48 loss: 0.0809 Lr: 0.00364
[2023-12-25 12:35:51,122 INFO misc.py line 119 253097] Train: [46/100][506/510] Data 0.004 (0.082) Batch 0.979 (1.508) Remain 11:32:18 loss: 0.2545 Lr: 0.00364
[2023-12-25 12:35:52,130 INFO misc.py line 119 253097] Train: [46/100][507/510] Data 0.004 (0.082) Batch 1.009 (1.507) Remain 11:31:49 loss: 0.3662 Lr: 0.00364
[2023-12-25 12:35:53,222 INFO misc.py line 119 253097] Train: [46/100][508/510] Data 0.004 (0.082) Batch 1.091 (1.506) Remain 11:31:25 loss: 0.2237 Lr: 0.00364
[2023-12-25 12:35:54,160 INFO misc.py line 119 253097] Train: [46/100][509/510] Data 0.004 (0.082) Batch 0.938 (1.505) Remain 11:30:52 loss: 0.2010 Lr: 0.00364
[2023-12-25 12:35:55,263 INFO misc.py line 119 253097] Train: [46/100][510/510] Data 0.004 (0.081) Batch 1.105 (1.504) Remain 11:30:29 loss: 0.2752 Lr: 0.00364
[2023-12-25 12:35:55,264 INFO misc.py line 136 253097] Train result: loss: 0.1974 
[2023-12-25 12:35:55,264 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 12:36:23,669 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7493 
[2023-12-25 12:36:24,030 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3238 
[2023-12-25 12:36:28,959 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3435 
[2023-12-25 12:36:29,480 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5063 
[2023-12-25 12:36:31,454 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9640 
[2023-12-25 12:36:31,877 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3584 
[2023-12-25 12:36:32,755 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.4184 
[2023-12-25 12:36:33,317 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.5566 
[2023-12-25 12:36:35,133 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.4923 
[2023-12-25 12:36:37,265 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.5144 
[2023-12-25 12:36:38,123 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3575 
[2023-12-25 12:36:38,548 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7800 
[2023-12-25 12:36:39,454 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6156 
[2023-12-25 12:36:42,395 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9689 
[2023-12-25 12:36:42,862 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2449 
[2023-12-25 12:36:43,478 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5463 
[2023-12-25 12:36:44,181 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3309 
[2023-12-25 12:36:45,476 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6442/0.7010/0.8891.
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9136/0.9446
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9812/0.9908
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8171/0.9678
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2571/0.2812
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5599/0.5710
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5665/0.5875
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8122/0.8929
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9067/0.9644
[2023-12-25 12:36:45,476 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5750/0.6083
[2023-12-25 12:36:45,477 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7568/0.8385
[2023-12-25 12:36:45,477 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6470/0.7210
[2023-12-25 12:36:45,477 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5819/0.7452
[2023-12-25 12:36:45,477 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 12:36:45,478 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 12:36:45,478 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 12:37:02,513 INFO misc.py line 119 253097] Train: [47/100][1/510] Data 2.801 (2.801) Batch 14.776 (14.776) Remain 113:01:53 loss: 0.1357 Lr: 0.00364
[2023-12-25 12:37:03,828 INFO misc.py line 119 253097] Train: [47/100][2/510] Data 0.007 (0.007) Batch 1.315 (1.315) Remain 10:03:23 loss: 0.3258 Lr: 0.00364
[2023-12-25 12:37:05,104 INFO misc.py line 119 253097] Train: [47/100][3/510] Data 0.007 (0.007) Batch 1.262 (1.262) Remain 09:39:09 loss: 0.1565 Lr: 0.00364
[2023-12-25 12:37:06,342 INFO misc.py line 119 253097] Train: [47/100][4/510] Data 0.023 (0.023) Batch 1.251 (1.251) Remain 09:34:20 loss: 0.1428 Lr: 0.00364
[2023-12-25 12:37:07,477 INFO misc.py line 119 253097] Train: [47/100][5/510] Data 0.007 (0.015) Batch 1.135 (1.193) Remain 09:07:38 loss: 0.1122 Lr: 0.00364
[2023-12-25 12:37:08,596 INFO misc.py line 119 253097] Train: [47/100][6/510] Data 0.007 (0.013) Batch 1.121 (1.169) Remain 08:56:29 loss: 0.1414 Lr: 0.00364
[2023-12-25 12:37:11,487 INFO misc.py line 119 253097] Train: [47/100][7/510] Data 0.006 (0.011) Batch 2.892 (1.600) Remain 12:14:08 loss: 0.1106 Lr: 0.00364
[2023-12-25 12:37:12,756 INFO misc.py line 119 253097] Train: [47/100][8/510] Data 0.006 (0.010) Batch 1.269 (1.534) Remain 11:43:44 loss: 0.2091 Lr: 0.00364
[2023-12-25 12:37:14,146 INFO misc.py line 119 253097] Train: [47/100][9/510] Data 0.005 (0.009) Batch 1.390 (1.510) Remain 11:32:44 loss: 0.0952 Lr: 0.00364
[2023-12-25 12:37:15,362 INFO misc.py line 119 253097] Train: [47/100][10/510] Data 0.005 (0.009) Batch 1.215 (1.468) Remain 11:13:23 loss: 0.1869 Lr: 0.00364
[2023-12-25 12:37:16,483 INFO misc.py line 119 253097] Train: [47/100][11/510] Data 0.006 (0.008) Batch 1.123 (1.425) Remain 10:53:35 loss: 0.3496 Lr: 0.00364
[2023-12-25 12:37:17,647 INFO misc.py line 119 253097] Train: [47/100][12/510] Data 0.004 (0.008) Batch 1.163 (1.395) Remain 10:40:15 loss: 0.2851 Lr: 0.00364
[2023-12-25 12:37:19,089 INFO misc.py line 119 253097] Train: [47/100][13/510] Data 0.005 (0.007) Batch 1.441 (1.400) Remain 10:42:20 loss: 0.2211 Lr: 0.00364
[2023-12-25 12:37:20,296 INFO misc.py line 119 253097] Train: [47/100][14/510] Data 0.004 (0.007) Batch 1.206 (1.382) Remain 10:34:12 loss: 0.1834 Lr: 0.00364
[2023-12-25 12:37:29,383 INFO misc.py line 119 253097] Train: [47/100][15/510] Data 0.006 (0.007) Batch 9.089 (2.025) Remain 15:28:47 loss: 0.1761 Lr: 0.00364
[2023-12-25 12:37:30,513 INFO misc.py line 119 253097] Train: [47/100][16/510] Data 0.004 (0.007) Batch 1.128 (1.956) Remain 14:57:08 loss: 0.0999 Lr: 0.00364
[2023-12-25 12:37:31,662 INFO misc.py line 119 253097] Train: [47/100][17/510] Data 0.005 (0.007) Batch 1.151 (1.898) Remain 14:30:43 loss: 0.2014 Lr: 0.00364
[2023-12-25 12:37:32,897 INFO misc.py line 119 253097] Train: [47/100][18/510] Data 0.004 (0.007) Batch 1.236 (1.854) Remain 14:10:26 loss: 0.2017 Lr: 0.00364
[2023-12-25 12:37:34,123 INFO misc.py line 119 253097] Train: [47/100][19/510] Data 0.004 (0.006) Batch 1.218 (1.814) Remain 13:52:10 loss: 0.1534 Lr: 0.00364
[2023-12-25 12:37:35,206 INFO misc.py line 119 253097] Train: [47/100][20/510] Data 0.011 (0.007) Batch 1.089 (1.772) Remain 13:32:35 loss: 0.1358 Lr: 0.00364
[2023-12-25 12:37:36,286 INFO misc.py line 119 253097] Train: [47/100][21/510] Data 0.005 (0.007) Batch 1.078 (1.733) Remain 13:14:52 loss: 0.1087 Lr: 0.00364
[2023-12-25 12:37:37,338 INFO misc.py line 119 253097] Train: [47/100][22/510] Data 0.008 (0.007) Batch 1.056 (1.697) Remain 12:58:30 loss: 0.3064 Lr: 0.00364
[2023-12-25 12:37:38,493 INFO misc.py line 119 253097] Train: [47/100][23/510] Data 0.004 (0.007) Batch 1.154 (1.670) Remain 12:46:00 loss: 0.1793 Lr: 0.00364
[2023-12-25 12:37:39,606 INFO misc.py line 119 253097] Train: [47/100][24/510] Data 0.005 (0.006) Batch 1.113 (1.644) Remain 12:33:48 loss: 0.0905 Lr: 0.00364
[2023-12-25 12:37:40,907 INFO misc.py line 119 253097] Train: [47/100][25/510] Data 0.004 (0.006) Batch 1.302 (1.628) Remain 12:26:39 loss: 0.0887 Lr: 0.00364
[2023-12-25 12:37:42,069 INFO misc.py line 119 253097] Train: [47/100][26/510] Data 0.004 (0.006) Batch 1.155 (1.608) Remain 12:17:11 loss: 0.1705 Lr: 0.00364
[2023-12-25 12:37:43,165 INFO misc.py line 119 253097] Train: [47/100][27/510] Data 0.011 (0.006) Batch 1.101 (1.586) Remain 12:07:28 loss: 0.4862 Lr: 0.00364
[2023-12-25 12:37:44,253 INFO misc.py line 119 253097] Train: [47/100][28/510] Data 0.006 (0.006) Batch 1.091 (1.567) Remain 11:58:21 loss: 0.1293 Lr: 0.00364
[2023-12-25 12:37:45,414 INFO misc.py line 119 253097] Train: [47/100][29/510] Data 0.004 (0.006) Batch 1.138 (1.550) Remain 11:50:46 loss: 0.1093 Lr: 0.00364
[2023-12-25 12:37:52,526 INFO misc.py line 119 253097] Train: [47/100][30/510] Data 6.090 (0.232) Batch 7.135 (1.757) Remain 13:25:34 loss: 0.1237 Lr: 0.00364
[2023-12-25 12:37:53,339 INFO misc.py line 119 253097] Train: [47/100][31/510] Data 0.004 (0.224) Batch 0.813 (1.723) Remain 13:10:05 loss: 0.3782 Lr: 0.00364
[2023-12-25 12:37:54,526 INFO misc.py line 119 253097] Train: [47/100][32/510] Data 0.004 (0.216) Batch 1.187 (1.705) Remain 13:01:35 loss: 0.1546 Lr: 0.00364
[2023-12-25 12:37:55,697 INFO misc.py line 119 253097] Train: [47/100][33/510] Data 0.004 (0.209) Batch 1.169 (1.687) Remain 12:53:23 loss: 0.2616 Lr: 0.00364
[2023-12-25 12:37:56,940 INFO misc.py line 119 253097] Train: [47/100][34/510] Data 0.005 (0.202) Batch 1.244 (1.673) Remain 12:46:48 loss: 0.1335 Lr: 0.00364
[2023-12-25 12:37:58,073 INFO misc.py line 119 253097] Train: [47/100][35/510] Data 0.005 (0.196) Batch 1.132 (1.656) Remain 12:39:01 loss: 0.2335 Lr: 0.00363
[2023-12-25 12:37:59,365 INFO misc.py line 119 253097] Train: [47/100][36/510] Data 0.006 (0.190) Batch 1.273 (1.644) Remain 12:33:41 loss: 0.1753 Lr: 0.00363
[2023-12-25 12:38:00,631 INFO misc.py line 119 253097] Train: [47/100][37/510] Data 0.024 (0.186) Batch 1.287 (1.634) Remain 12:28:50 loss: 0.1505 Lr: 0.00363
[2023-12-25 12:38:03,902 INFO misc.py line 119 253097] Train: [47/100][38/510] Data 1.989 (0.237) Batch 3.270 (1.680) Remain 12:50:14 loss: 0.3618 Lr: 0.00363
[2023-12-25 12:38:05,060 INFO misc.py line 119 253097] Train: [47/100][39/510] Data 0.004 (0.231) Batch 1.159 (1.666) Remain 12:43:34 loss: 0.1146 Lr: 0.00363
[2023-12-25 12:38:06,141 INFO misc.py line 119 253097] Train: [47/100][40/510] Data 0.003 (0.224) Batch 1.080 (1.650) Remain 12:36:17 loss: 0.1003 Lr: 0.00363
[2023-12-25 12:38:07,236 INFO misc.py line 119 253097] Train: [47/100][41/510] Data 0.004 (0.219) Batch 1.095 (1.635) Remain 12:29:33 loss: 0.2852 Lr: 0.00363
[2023-12-25 12:38:08,406 INFO misc.py line 119 253097] Train: [47/100][42/510] Data 0.005 (0.213) Batch 1.170 (1.624) Remain 12:24:04 loss: 0.1177 Lr: 0.00363
[2023-12-25 12:38:09,401 INFO misc.py line 119 253097] Train: [47/100][43/510] Data 0.006 (0.208) Batch 0.996 (1.608) Remain 12:16:51 loss: 0.2476 Lr: 0.00363
[2023-12-25 12:38:10,544 INFO misc.py line 119 253097] Train: [47/100][44/510] Data 0.003 (0.203) Batch 1.143 (1.597) Remain 12:11:37 loss: 0.1161 Lr: 0.00363
[2023-12-25 12:38:11,714 INFO misc.py line 119 253097] Train: [47/100][45/510] Data 0.005 (0.198) Batch 1.170 (1.586) Remain 12:06:56 loss: 0.1540 Lr: 0.00363
[2023-12-25 12:38:12,798 INFO misc.py line 119 253097] Train: [47/100][46/510] Data 0.003 (0.194) Batch 1.083 (1.575) Remain 12:01:33 loss: 0.2553 Lr: 0.00363
[2023-12-25 12:38:14,068 INFO misc.py line 119 253097] Train: [47/100][47/510] Data 0.004 (0.189) Batch 1.270 (1.568) Remain 11:58:21 loss: 0.1218 Lr: 0.00363
[2023-12-25 12:38:15,238 INFO misc.py line 119 253097] Train: [47/100][48/510] Data 0.004 (0.185) Batch 1.171 (1.559) Remain 11:54:17 loss: 0.1856 Lr: 0.00363
[2023-12-25 12:38:16,573 INFO misc.py line 119 253097] Train: [47/100][49/510] Data 0.003 (0.181) Batch 1.333 (1.554) Remain 11:52:01 loss: 0.1789 Lr: 0.00363
[2023-12-25 12:38:17,856 INFO misc.py line 119 253097] Train: [47/100][50/510] Data 0.005 (0.178) Batch 1.284 (1.548) Remain 11:49:21 loss: 0.2832 Lr: 0.00363
[2023-12-25 12:38:32,943 INFO misc.py line 119 253097] Train: [47/100][51/510] Data 0.004 (0.174) Batch 15.085 (1.830) Remain 13:58:32 loss: 0.2801 Lr: 0.00363
[2023-12-25 12:38:34,201 INFO misc.py line 119 253097] Train: [47/100][52/510] Data 0.007 (0.171) Batch 1.260 (1.819) Remain 13:53:10 loss: 0.2028 Lr: 0.00363
[2023-12-25 12:38:35,421 INFO misc.py line 119 253097] Train: [47/100][53/510] Data 0.004 (0.167) Batch 1.214 (1.807) Remain 13:47:36 loss: 0.3291 Lr: 0.00363
[2023-12-25 12:38:36,490 INFO misc.py line 119 253097] Train: [47/100][54/510] Data 0.010 (0.164) Batch 1.072 (1.792) Remain 13:40:59 loss: 0.0893 Lr: 0.00363
[2023-12-25 12:38:37,572 INFO misc.py line 119 253097] Train: [47/100][55/510] Data 0.006 (0.161) Batch 1.085 (1.779) Remain 13:34:43 loss: 0.2283 Lr: 0.00363
[2023-12-25 12:38:38,772 INFO misc.py line 119 253097] Train: [47/100][56/510] Data 0.004 (0.158) Batch 1.198 (1.768) Remain 13:29:40 loss: 0.1950 Lr: 0.00363
[2023-12-25 12:38:39,817 INFO misc.py line 119 253097] Train: [47/100][57/510] Data 0.006 (0.155) Batch 1.044 (1.754) Remain 13:23:30 loss: 0.1446 Lr: 0.00363
[2023-12-25 12:38:40,895 INFO misc.py line 119 253097] Train: [47/100][58/510] Data 0.008 (0.153) Batch 1.077 (1.742) Remain 13:17:50 loss: 0.1396 Lr: 0.00363
[2023-12-25 12:38:42,210 INFO misc.py line 119 253097] Train: [47/100][59/510] Data 0.008 (0.150) Batch 1.320 (1.734) Remain 13:14:21 loss: 0.2515 Lr: 0.00363
[2023-12-25 12:38:43,375 INFO misc.py line 119 253097] Train: [47/100][60/510] Data 0.003 (0.147) Batch 1.163 (1.724) Remain 13:09:44 loss: 0.3405 Lr: 0.00363
[2023-12-25 12:38:44,639 INFO misc.py line 119 253097] Train: [47/100][61/510] Data 0.007 (0.145) Batch 1.241 (1.716) Remain 13:05:53 loss: 0.1593 Lr: 0.00363
[2023-12-25 12:38:45,614 INFO misc.py line 119 253097] Train: [47/100][62/510] Data 0.029 (0.143) Batch 0.999 (1.704) Remain 13:00:17 loss: 0.3316 Lr: 0.00363
[2023-12-25 12:38:46,847 INFO misc.py line 119 253097] Train: [47/100][63/510] Data 0.005 (0.141) Batch 1.233 (1.696) Remain 12:56:40 loss: 0.1221 Lr: 0.00363
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[2023-12-25 12:40:28,251 INFO misc.py line 119 253097] Train: [47/100][125/510] Data 0.005 (0.125) Batch 1.264 (1.665) Remain 12:40:52 loss: 0.1881 Lr: 0.00362
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[2023-12-25 12:42:20,525 INFO misc.py line 119 253097] Train: [47/100][200/510] Data 0.005 (0.125) Batch 1.083 (1.601) Remain 12:09:36 loss: 0.3602 Lr: 0.00360
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[2023-12-25 12:42:22,919 INFO misc.py line 119 253097] Train: [47/100][202/510] Data 0.003 (0.124) Batch 1.218 (1.597) Remain 12:07:42 loss: 0.0985 Lr: 0.00360
[2023-12-25 12:42:23,764 INFO misc.py line 119 253097] Train: [47/100][203/510] Data 0.007 (0.123) Batch 0.847 (1.593) Remain 12:05:58 loss: 0.1013 Lr: 0.00360
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[2023-12-25 12:42:26,117 INFO misc.py line 119 253097] Train: [47/100][205/510] Data 0.008 (0.122) Batch 1.015 (1.589) Remain 12:04:01 loss: 0.3103 Lr: 0.00360
[2023-12-25 12:42:27,300 INFO misc.py line 119 253097] Train: [47/100][206/510] Data 0.012 (0.121) Batch 1.185 (1.587) Remain 12:03:05 loss: 0.1807 Lr: 0.00360
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[2023-12-25 12:42:29,744 INFO misc.py line 119 253097] Train: [47/100][208/510] Data 0.004 (0.120) Batch 1.193 (1.584) Remain 12:01:23 loss: 0.1282 Lr: 0.00360
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[2023-12-25 12:42:31,984 INFO misc.py line 119 253097] Train: [47/100][210/510] Data 0.008 (0.119) Batch 1.111 (1.579) Remain 11:59:19 loss: 0.2230 Lr: 0.00360
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[2023-12-25 12:42:34,288 INFO misc.py line 119 253097] Train: [47/100][212/510] Data 0.005 (0.118) Batch 1.178 (1.575) Remain 11:57:24 loss: 0.2189 Lr: 0.00360
[2023-12-25 12:42:35,490 INFO misc.py line 119 253097] Train: [47/100][213/510] Data 0.005 (0.118) Batch 1.200 (1.573) Remain 11:56:34 loss: 0.2743 Lr: 0.00360
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[2023-12-25 12:42:41,969 INFO misc.py line 119 253097] Train: [47/100][215/510] Data 0.005 (0.117) Batch 5.468 (1.589) Remain 12:03:40 loss: 0.2062 Lr: 0.00360
[2023-12-25 12:42:43,149 INFO misc.py line 119 253097] Train: [47/100][216/510] Data 0.006 (0.116) Batch 1.180 (1.587) Remain 12:02:46 loss: 0.2283 Lr: 0.00360
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[2023-12-25 12:42:47,298 INFO misc.py line 119 253097] Train: [47/100][219/510] Data 0.004 (0.115) Batch 2.156 (1.584) Remain 12:01:24 loss: 0.1332 Lr: 0.00360
[2023-12-25 12:42:48,520 INFO misc.py line 119 253097] Train: [47/100][220/510] Data 0.005 (0.114) Batch 1.223 (1.583) Remain 12:00:37 loss: 0.1166 Lr: 0.00360
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[2023-12-25 12:42:51,807 INFO misc.py line 119 253097] Train: [47/100][223/510] Data 0.009 (0.113) Batch 1.108 (1.576) Remain 11:57:31 loss: 0.1466 Lr: 0.00360
[2023-12-25 12:42:52,910 INFO misc.py line 119 253097] Train: [47/100][224/510] Data 0.007 (0.112) Batch 1.107 (1.574) Remain 11:56:31 loss: 0.1764 Lr: 0.00360
[2023-12-25 12:42:54,176 INFO misc.py line 119 253097] Train: [47/100][225/510] Data 0.004 (0.112) Batch 1.265 (1.572) Remain 11:55:51 loss: 0.1737 Lr: 0.00360
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[2023-12-25 12:42:57,564 INFO misc.py line 119 253097] Train: [47/100][228/510] Data 0.006 (0.110) Batch 1.135 (1.567) Remain 11:53:05 loss: 0.1827 Lr: 0.00360
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[2023-12-25 12:43:02,180 INFO misc.py line 119 253097] Train: [47/100][232/510] Data 0.009 (0.108) Batch 1.258 (1.559) Remain 11:49:42 loss: 0.1110 Lr: 0.00360
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[2023-12-25 12:43:04,463 INFO misc.py line 119 253097] Train: [47/100][234/510] Data 0.009 (0.108) Batch 1.172 (1.556) Remain 11:48:00 loss: 0.2824 Lr: 0.00360
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[2023-12-25 12:43:09,114 INFO misc.py line 119 253097] Train: [47/100][238/510] Data 0.008 (0.106) Batch 1.122 (1.549) Remain 11:44:51 loss: 0.1307 Lr: 0.00360
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[2023-12-25 12:43:57,148 INFO misc.py line 119 253097] Train: [47/100][272/510] Data 0.006 (0.093) Batch 1.180 (1.532) Remain 11:36:09 loss: 0.1291 Lr: 0.00359
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[2023-12-25 12:44:46,354 INFO misc.py line 119 253097] Train: [47/100][297/510] Data 7.172 (0.110) Batch 8.365 (1.569) Remain 11:52:22 loss: 0.3439 Lr: 0.00358
[2023-12-25 12:44:47,610 INFO misc.py line 119 253097] Train: [47/100][298/510] Data 0.004 (0.110) Batch 1.251 (1.568) Remain 11:51:51 loss: 0.1397 Lr: 0.00358
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[2023-12-25 12:44:49,868 INFO misc.py line 119 253097] Train: [47/100][300/510] Data 0.005 (0.109) Batch 1.141 (1.565) Remain 11:50:27 loss: 0.2019 Lr: 0.00358
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[2023-12-25 12:44:54,464 INFO misc.py line 119 253097] Train: [47/100][304/510] Data 0.007 (0.108) Batch 1.117 (1.559) Remain 11:47:50 loss: 0.2423 Lr: 0.00358
[2023-12-25 12:44:55,490 INFO misc.py line 119 253097] Train: [47/100][305/510] Data 0.009 (0.107) Batch 1.032 (1.558) Remain 11:47:01 loss: 0.1219 Lr: 0.00358
[2023-12-25 12:44:56,538 INFO misc.py line 119 253097] Train: [47/100][306/510] Data 0.005 (0.107) Batch 1.043 (1.556) Remain 11:46:14 loss: 0.1157 Lr: 0.00358
[2023-12-25 12:44:57,682 INFO misc.py line 119 253097] Train: [47/100][307/510] Data 0.011 (0.107) Batch 1.147 (1.555) Remain 11:45:35 loss: 0.2034 Lr: 0.00358
[2023-12-25 12:44:58,839 INFO misc.py line 119 253097] Train: [47/100][308/510] Data 0.006 (0.106) Batch 1.154 (1.553) Remain 11:44:58 loss: 0.2639 Lr: 0.00358
[2023-12-25 12:44:59,909 INFO misc.py line 119 253097] Train: [47/100][309/510] Data 0.010 (0.106) Batch 1.075 (1.552) Remain 11:44:14 loss: 0.3164 Lr: 0.00358
[2023-12-25 12:45:01,024 INFO misc.py line 119 253097] Train: [47/100][310/510] Data 0.004 (0.106) Batch 1.108 (1.550) Remain 11:43:33 loss: 0.0773 Lr: 0.00358
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[2023-12-25 12:45:03,341 INFO misc.py line 119 253097] Train: [47/100][312/510] Data 0.009 (0.105) Batch 0.981 (1.548) Remain 11:42:22 loss: 0.1998 Lr: 0.00358
[2023-12-25 12:45:04,408 INFO misc.py line 119 253097] Train: [47/100][313/510] Data 0.003 (0.105) Batch 1.064 (1.546) Remain 11:41:37 loss: 0.1815 Lr: 0.00358
[2023-12-25 12:45:05,562 INFO misc.py line 119 253097] Train: [47/100][314/510] Data 0.007 (0.105) Batch 1.156 (1.545) Remain 11:41:02 loss: 0.0915 Lr: 0.00358
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[2023-12-25 12:45:07,683 INFO misc.py line 119 253097] Train: [47/100][316/510] Data 0.006 (0.104) Batch 1.027 (1.542) Remain 11:39:35 loss: 0.1421 Lr: 0.00358
[2023-12-25 12:45:08,968 INFO misc.py line 119 253097] Train: [47/100][317/510] Data 0.003 (0.104) Batch 1.284 (1.541) Remain 11:39:11 loss: 0.1306 Lr: 0.00358
[2023-12-25 12:45:11,122 INFO misc.py line 119 253097] Train: [47/100][318/510] Data 0.005 (0.103) Batch 2.153 (1.543) Remain 11:40:02 loss: 0.2057 Lr: 0.00358
[2023-12-25 12:45:12,355 INFO misc.py line 119 253097] Train: [47/100][319/510] Data 0.004 (0.103) Batch 1.234 (1.542) Remain 11:39:34 loss: 0.2541 Lr: 0.00358
[2023-12-25 12:45:13,252 INFO misc.py line 119 253097] Train: [47/100][320/510] Data 0.003 (0.103) Batch 0.897 (1.540) Remain 11:38:37 loss: 0.1727 Lr: 0.00358
[2023-12-25 12:45:14,448 INFO misc.py line 119 253097] Train: [47/100][321/510] Data 0.004 (0.102) Batch 1.188 (1.539) Remain 11:38:05 loss: 0.1143 Lr: 0.00358
[2023-12-25 12:45:15,735 INFO misc.py line 119 253097] Train: [47/100][322/510] Data 0.012 (0.102) Batch 1.293 (1.538) Remain 11:37:43 loss: 0.1085 Lr: 0.00358
[2023-12-25 12:45:16,902 INFO misc.py line 119 253097] Train: [47/100][323/510] Data 0.006 (0.102) Batch 1.163 (1.537) Remain 11:37:09 loss: 0.1358 Lr: 0.00358
[2023-12-25 12:45:18,034 INFO misc.py line 119 253097] Train: [47/100][324/510] Data 0.011 (0.101) Batch 1.134 (1.536) Remain 11:36:34 loss: 0.1808 Lr: 0.00358
[2023-12-25 12:45:19,318 INFO misc.py line 119 253097] Train: [47/100][325/510] Data 0.008 (0.101) Batch 1.288 (1.535) Remain 11:36:11 loss: 0.1816 Lr: 0.00358
[2023-12-25 12:45:20,458 INFO misc.py line 119 253097] Train: [47/100][326/510] Data 0.004 (0.101) Batch 1.139 (1.534) Remain 11:35:36 loss: 0.1191 Lr: 0.00358
[2023-12-25 12:45:21,660 INFO misc.py line 119 253097] Train: [47/100][327/510] Data 0.005 (0.101) Batch 1.200 (1.533) Remain 11:35:07 loss: 0.1428 Lr: 0.00358
[2023-12-25 12:45:22,742 INFO misc.py line 119 253097] Train: [47/100][328/510] Data 0.008 (0.100) Batch 1.077 (1.531) Remain 11:34:27 loss: 0.1129 Lr: 0.00358
[2023-12-25 12:45:24,931 INFO misc.py line 119 253097] Train: [47/100][329/510] Data 0.014 (0.100) Batch 2.198 (1.533) Remain 11:35:21 loss: 0.1213 Lr: 0.00358
[2023-12-25 12:45:26,009 INFO misc.py line 119 253097] Train: [47/100][330/510] Data 0.004 (0.100) Batch 1.078 (1.532) Remain 11:34:42 loss: 0.1369 Lr: 0.00358
[2023-12-25 12:45:27,028 INFO misc.py line 119 253097] Train: [47/100][331/510] Data 0.004 (0.099) Batch 1.019 (1.530) Remain 11:33:58 loss: 0.2720 Lr: 0.00358
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[2023-12-25 12:45:37,569 INFO misc.py line 119 253097] Train: [47/100][335/510] Data 0.013 (0.098) Batch 6.977 (1.544) Remain 11:39:54 loss: 0.2898 Lr: 0.00358
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[2023-12-25 12:45:41,288 INFO misc.py line 119 253097] Train: [47/100][337/510] Data 0.004 (0.098) Batch 1.340 (1.546) Remain 11:40:42 loss: 0.1505 Lr: 0.00358
[2023-12-25 12:45:42,304 INFO misc.py line 119 253097] Train: [47/100][338/510] Data 0.007 (0.097) Batch 1.015 (1.544) Remain 11:39:57 loss: 0.3091 Lr: 0.00358
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[2023-12-25 12:45:44,373 INFO misc.py line 119 253097] Train: [47/100][340/510] Data 0.003 (0.097) Batch 1.101 (1.541) Remain 11:38:32 loss: 0.1229 Lr: 0.00358
[2023-12-25 12:45:45,567 INFO misc.py line 119 253097] Train: [47/100][341/510] Data 0.005 (0.097) Batch 1.194 (1.540) Remain 11:38:03 loss: 0.3476 Lr: 0.00358
[2023-12-25 12:45:46,418 INFO misc.py line 119 253097] Train: [47/100][342/510] Data 0.004 (0.096) Batch 0.851 (1.538) Remain 11:37:06 loss: 0.1417 Lr: 0.00358
[2023-12-25 12:45:47,592 INFO misc.py line 119 253097] Train: [47/100][343/510] Data 0.003 (0.096) Batch 1.175 (1.537) Remain 11:36:35 loss: 0.1903 Lr: 0.00358
[2023-12-25 12:45:48,841 INFO misc.py line 119 253097] Train: [47/100][344/510] Data 0.003 (0.096) Batch 1.247 (1.536) Remain 11:36:11 loss: 0.1559 Lr: 0.00358
[2023-12-25 12:45:50,058 INFO misc.py line 119 253097] Train: [47/100][345/510] Data 0.005 (0.096) Batch 1.219 (1.535) Remain 11:35:44 loss: 0.0912 Lr: 0.00358
[2023-12-25 12:45:51,041 INFO misc.py line 119 253097] Train: [47/100][346/510] Data 0.003 (0.095) Batch 0.982 (1.533) Remain 11:34:58 loss: 0.2209 Lr: 0.00358
[2023-12-25 12:45:52,062 INFO misc.py line 119 253097] Train: [47/100][347/510] Data 0.004 (0.095) Batch 1.021 (1.532) Remain 11:34:16 loss: 0.1036 Lr: 0.00358
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[2023-12-25 12:45:54,393 INFO misc.py line 119 253097] Train: [47/100][349/510] Data 0.004 (0.095) Batch 1.217 (1.530) Remain 11:33:16 loss: 0.3018 Lr: 0.00357
[2023-12-25 12:45:55,535 INFO misc.py line 119 253097] Train: [47/100][350/510] Data 0.003 (0.094) Batch 1.141 (1.529) Remain 11:32:44 loss: 0.1359 Lr: 0.00357
[2023-12-25 12:45:56,612 INFO misc.py line 119 253097] Train: [47/100][351/510] Data 0.004 (0.094) Batch 1.078 (1.527) Remain 11:32:07 loss: 0.1625 Lr: 0.00357
[2023-12-25 12:45:57,636 INFO misc.py line 119 253097] Train: [47/100][352/510] Data 0.004 (0.094) Batch 1.023 (1.526) Remain 11:31:26 loss: 0.1345 Lr: 0.00357
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[2023-12-25 12:47:17,742 INFO misc.py line 119 253097] Train: [47/100][400/510] Data 0.004 (0.092) Batch 1.079 (1.543) Remain 11:38:02 loss: 0.2440 Lr: 0.00357
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[2023-12-25 12:47:41,658 INFO misc.py line 119 253097] Train: [47/100][413/510] Data 0.005 (0.089) Batch 1.013 (1.553) Remain 11:41:57 loss: 0.4269 Lr: 0.00356
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[2023-12-25 12:48:03,058 INFO misc.py line 119 253097] Train: [47/100][431/510] Data 0.019 (0.086) Batch 1.238 (1.537) Remain 11:34:34 loss: 0.1549 Lr: 0.00356
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[2023-12-25 12:48:05,287 INFO misc.py line 119 253097] Train: [47/100][433/510] Data 0.012 (0.085) Batch 1.181 (1.535) Remain 11:33:38 loss: 0.3390 Lr: 0.00356
[2023-12-25 12:48:06,498 INFO misc.py line 119 253097] Train: [47/100][434/510] Data 0.009 (0.085) Batch 1.214 (1.535) Remain 11:33:16 loss: 0.2151 Lr: 0.00356
[2023-12-25 12:48:07,527 INFO misc.py line 119 253097] Train: [47/100][435/510] Data 0.006 (0.085) Batch 1.029 (1.533) Remain 11:32:43 loss: 0.1638 Lr: 0.00356
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[2023-12-25 12:48:31,130 INFO misc.py line 119 253097] Train: [47/100][450/510] Data 0.005 (0.096) Batch 1.168 (1.535) Remain 11:32:56 loss: 0.3856 Lr: 0.00356
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[2023-12-25 12:48:36,861 INFO misc.py line 119 253097] Train: [47/100][455/510] Data 0.006 (0.096) Batch 1.085 (1.530) Remain 11:30:52 loss: 0.0955 Lr: 0.00355
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[2023-12-25 12:49:18,105 INFO misc.py line 119 253097] Train: [47/100][482/510] Data 0.006 (0.090) Batch 1.246 (1.530) Remain 11:30:06 loss: 0.2071 Lr: 0.00355
[2023-12-25 12:49:19,457 INFO misc.py line 119 253097] Train: [47/100][483/510] Data 0.004 (0.090) Batch 1.349 (1.530) Remain 11:29:55 loss: 0.0852 Lr: 0.00355
[2023-12-25 12:49:20,647 INFO misc.py line 119 253097] Train: [47/100][484/510] Data 0.008 (0.090) Batch 1.193 (1.529) Remain 11:29:34 loss: 0.2264 Lr: 0.00355
[2023-12-25 12:49:21,921 INFO misc.py line 119 253097] Train: [47/100][485/510] Data 0.005 (0.090) Batch 1.271 (1.529) Remain 11:29:18 loss: 0.1065 Lr: 0.00355
[2023-12-25 12:49:23,199 INFO misc.py line 119 253097] Train: [47/100][486/510] Data 0.008 (0.090) Batch 1.272 (1.528) Remain 11:29:02 loss: 0.1711 Lr: 0.00355
[2023-12-25 12:49:24,574 INFO misc.py line 119 253097] Train: [47/100][487/510] Data 0.015 (0.090) Batch 1.380 (1.528) Remain 11:28:52 loss: 0.2539 Lr: 0.00355
[2023-12-25 12:49:25,822 INFO misc.py line 119 253097] Train: [47/100][488/510] Data 0.009 (0.089) Batch 1.244 (1.527) Remain 11:28:35 loss: 0.1512 Lr: 0.00355
[2023-12-25 12:49:26,806 INFO misc.py line 119 253097] Train: [47/100][489/510] Data 0.014 (0.089) Batch 0.995 (1.526) Remain 11:28:04 loss: 0.2398 Lr: 0.00355
[2023-12-25 12:49:28,042 INFO misc.py line 119 253097] Train: [47/100][490/510] Data 0.003 (0.089) Batch 1.234 (1.526) Remain 11:27:46 loss: 0.1724 Lr: 0.00355
[2023-12-25 12:49:29,092 INFO misc.py line 119 253097] Train: [47/100][491/510] Data 0.005 (0.089) Batch 1.050 (1.525) Remain 11:27:18 loss: 0.1694 Lr: 0.00355
[2023-12-25 12:49:30,186 INFO misc.py line 119 253097] Train: [47/100][492/510] Data 0.005 (0.089) Batch 1.093 (1.524) Remain 11:26:53 loss: 0.0903 Lr: 0.00355
[2023-12-25 12:49:31,120 INFO misc.py line 119 253097] Train: [47/100][493/510] Data 0.006 (0.089) Batch 0.936 (1.523) Remain 11:26:19 loss: 0.3400 Lr: 0.00355
[2023-12-25 12:49:32,281 INFO misc.py line 119 253097] Train: [47/100][494/510] Data 0.004 (0.088) Batch 1.162 (1.522) Remain 11:25:58 loss: 0.1446 Lr: 0.00355
[2023-12-25 12:49:33,376 INFO misc.py line 119 253097] Train: [47/100][495/510] Data 0.004 (0.088) Batch 1.091 (1.521) Remain 11:25:32 loss: 0.1334 Lr: 0.00355
[2023-12-25 12:49:34,512 INFO misc.py line 119 253097] Train: [47/100][496/510] Data 0.008 (0.088) Batch 1.134 (1.520) Remain 11:25:10 loss: 0.1519 Lr: 0.00355
[2023-12-25 12:49:35,645 INFO misc.py line 119 253097] Train: [47/100][497/510] Data 0.009 (0.088) Batch 1.135 (1.519) Remain 11:24:47 loss: 0.1293 Lr: 0.00355
[2023-12-25 12:49:36,736 INFO misc.py line 119 253097] Train: [47/100][498/510] Data 0.008 (0.088) Batch 1.094 (1.518) Remain 11:24:22 loss: 0.1139 Lr: 0.00355
[2023-12-25 12:49:37,714 INFO misc.py line 119 253097] Train: [47/100][499/510] Data 0.004 (0.088) Batch 0.978 (1.517) Remain 11:23:51 loss: 0.1646 Lr: 0.00355
[2023-12-25 12:49:38,771 INFO misc.py line 119 253097] Train: [47/100][500/510] Data 0.004 (0.087) Batch 1.055 (1.516) Remain 11:23:25 loss: 0.1123 Lr: 0.00355
[2023-12-25 12:49:39,927 INFO misc.py line 119 253097] Train: [47/100][501/510] Data 0.006 (0.087) Batch 1.158 (1.516) Remain 11:23:04 loss: 0.1013 Lr: 0.00355
[2023-12-25 12:49:47,705 INFO misc.py line 119 253097] Train: [47/100][502/510] Data 6.613 (0.100) Batch 7.773 (1.528) Remain 11:28:41 loss: 0.1242 Lr: 0.00355
[2023-12-25 12:49:48,927 INFO misc.py line 119 253097] Train: [47/100][503/510] Data 0.008 (0.100) Batch 1.222 (1.528) Remain 11:28:23 loss: 0.1340 Lr: 0.00355
[2023-12-25 12:49:50,012 INFO misc.py line 119 253097] Train: [47/100][504/510] Data 0.008 (0.100) Batch 1.089 (1.527) Remain 11:27:58 loss: 0.1097 Lr: 0.00355
[2023-12-25 12:49:51,257 INFO misc.py line 119 253097] Train: [47/100][505/510] Data 0.005 (0.100) Batch 1.240 (1.526) Remain 11:27:41 loss: 0.1385 Lr: 0.00355
[2023-12-25 12:49:52,388 INFO misc.py line 119 253097] Train: [47/100][506/510] Data 0.009 (0.100) Batch 1.136 (1.525) Remain 11:27:18 loss: 0.1893 Lr: 0.00354
[2023-12-25 12:49:53,689 INFO misc.py line 119 253097] Train: [47/100][507/510] Data 0.005 (0.099) Batch 1.301 (1.525) Remain 11:27:05 loss: 0.1285 Lr: 0.00354
[2023-12-25 12:49:54,928 INFO misc.py line 119 253097] Train: [47/100][508/510] Data 0.004 (0.099) Batch 1.235 (1.524) Remain 11:26:48 loss: 0.1082 Lr: 0.00354
[2023-12-25 12:49:56,055 INFO misc.py line 119 253097] Train: [47/100][509/510] Data 0.008 (0.099) Batch 1.132 (1.524) Remain 11:26:25 loss: 0.1649 Lr: 0.00354
[2023-12-25 12:49:57,076 INFO misc.py line 119 253097] Train: [47/100][510/510] Data 0.003 (0.099) Batch 1.021 (1.523) Remain 11:25:57 loss: 0.4043 Lr: 0.00354
[2023-12-25 12:49:57,077 INFO misc.py line 136 253097] Train result: loss: 0.1819 
[2023-12-25 12:49:57,078 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 12:50:26,204 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5421 
[2023-12-25 12:50:26,553 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.6718 
[2023-12-25 12:50:31,493 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6784 
[2023-12-25 12:50:32,014 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5723 
[2023-12-25 12:50:33,987 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0604 
[2023-12-25 12:50:34,420 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.6856 
[2023-12-25 12:50:35,301 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2227 
[2023-12-25 12:50:35,864 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4854 
[2023-12-25 12:50:37,681 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.3607 
[2023-12-25 12:50:39,808 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1545 
[2023-12-25 12:50:40,662 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2969 
[2023-12-25 12:50:41,088 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9829 
[2023-12-25 12:50:41,989 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4523 
[2023-12-25 12:50:44,927 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8657 
[2023-12-25 12:50:45,394 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3745 
[2023-12-25 12:50:46,002 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5164 
[2023-12-25 12:50:46,705 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4430 
[2023-12-25 12:50:48,594 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6467/0.7154/0.8901.
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9185/0.9513
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9817/0.9892
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8131/0.9713
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2548/0.2692
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5962/0.6218
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6122/0.6937
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8273/0.8943
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9056/0.9577
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5504/0.5956
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7610/0.8293
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6207/0.8526
[2023-12-25 12:50:48,595 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5652/0.6735
[2023-12-25 12:50:48,596 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 12:50:48,597 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 12:50:48,597 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 12:50:55,484 INFO misc.py line 119 253097] Train: [48/100][1/510] Data 3.997 (3.997) Batch 4.871 (4.871) Remain 36:34:28 loss: 0.4127 Lr: 0.00354
[2023-12-25 12:51:01,827 INFO misc.py line 119 253097] Train: [48/100][2/510] Data 5.065 (5.065) Batch 6.343 (6.343) Remain 47:37:06 loss: 0.3228 Lr: 0.00354
[2023-12-25 12:51:03,085 INFO misc.py line 119 253097] Train: [48/100][3/510] Data 0.008 (0.008) Batch 1.256 (1.256) Remain 09:25:58 loss: 0.1583 Lr: 0.00354
[2023-12-25 12:51:04,258 INFO misc.py line 119 253097] Train: [48/100][4/510] Data 0.005 (0.005) Batch 1.171 (1.171) Remain 08:47:33 loss: 0.1498 Lr: 0.00354
[2023-12-25 12:51:05,441 INFO misc.py line 119 253097] Train: [48/100][5/510] Data 0.006 (0.006) Batch 1.186 (1.179) Remain 08:50:49 loss: 0.2150 Lr: 0.00354
[2023-12-25 12:51:06,626 INFO misc.py line 119 253097] Train: [48/100][6/510] Data 0.004 (0.005) Batch 1.183 (1.180) Remain 08:51:32 loss: 0.1832 Lr: 0.00354
[2023-12-25 12:51:07,751 INFO misc.py line 119 253097] Train: [48/100][7/510] Data 0.009 (0.006) Batch 1.126 (1.167) Remain 08:45:28 loss: 0.2393 Lr: 0.00354
[2023-12-25 12:51:08,890 INFO misc.py line 119 253097] Train: [48/100][8/510] Data 0.004 (0.006) Batch 1.134 (1.160) Remain 08:42:29 loss: 0.1268 Lr: 0.00354
[2023-12-25 12:51:09,919 INFO misc.py line 119 253097] Train: [48/100][9/510] Data 0.010 (0.006) Batch 1.032 (1.139) Remain 08:32:51 loss: 0.1071 Lr: 0.00354
[2023-12-25 12:51:10,964 INFO misc.py line 119 253097] Train: [48/100][10/510] Data 0.007 (0.006) Batch 1.043 (1.125) Remain 08:26:39 loss: 0.1137 Lr: 0.00354
[2023-12-25 12:51:12,017 INFO misc.py line 119 253097] Train: [48/100][11/510] Data 0.009 (0.007) Batch 1.056 (1.116) Remain 08:22:43 loss: 0.1560 Lr: 0.00354
[2023-12-25 12:51:13,199 INFO misc.py line 119 253097] Train: [48/100][12/510] Data 0.007 (0.007) Batch 1.180 (1.123) Remain 08:25:54 loss: 0.2629 Lr: 0.00354
[2023-12-25 12:51:14,256 INFO misc.py line 119 253097] Train: [48/100][13/510] Data 0.008 (0.007) Batch 1.060 (1.117) Remain 08:23:02 loss: 0.2638 Lr: 0.00354
[2023-12-25 12:51:15,407 INFO misc.py line 119 253097] Train: [48/100][14/510] Data 0.006 (0.007) Batch 1.139 (1.119) Remain 08:23:56 loss: 0.1622 Lr: 0.00354
[2023-12-25 12:51:16,565 INFO misc.py line 119 253097] Train: [48/100][15/510] Data 0.016 (0.008) Batch 1.169 (1.123) Remain 08:25:47 loss: 0.1257 Lr: 0.00354
[2023-12-25 12:51:17,784 INFO misc.py line 119 253097] Train: [48/100][16/510] Data 0.005 (0.007) Batch 1.220 (1.131) Remain 08:29:06 loss: 0.2616 Lr: 0.00354
[2023-12-25 12:51:21,238 INFO misc.py line 119 253097] Train: [48/100][17/510] Data 0.004 (0.007) Batch 3.453 (1.297) Remain 09:43:46 loss: 0.1648 Lr: 0.00354
[2023-12-25 12:51:22,346 INFO misc.py line 119 253097] Train: [48/100][18/510] Data 0.005 (0.007) Batch 1.108 (1.284) Remain 09:38:05 loss: 0.1584 Lr: 0.00354
[2023-12-25 12:51:28,995 INFO misc.py line 119 253097] Train: [48/100][19/510] Data 0.005 (0.007) Batch 6.650 (1.619) Remain 12:09:03 loss: 0.1549 Lr: 0.00354
[2023-12-25 12:51:30,186 INFO misc.py line 119 253097] Train: [48/100][20/510] Data 0.004 (0.007) Batch 1.191 (1.594) Remain 11:57:40 loss: 0.2202 Lr: 0.00354
[2023-12-25 12:51:31,386 INFO misc.py line 119 253097] Train: [48/100][21/510] Data 0.004 (0.007) Batch 1.197 (1.572) Remain 11:47:42 loss: 0.1576 Lr: 0.00354
[2023-12-25 12:51:32,505 INFO misc.py line 119 253097] Train: [48/100][22/510] Data 0.007 (0.007) Batch 1.118 (1.548) Remain 11:36:56 loss: 0.2682 Lr: 0.00354
[2023-12-25 12:51:33,616 INFO misc.py line 119 253097] Train: [48/100][23/510] Data 0.009 (0.007) Batch 1.115 (1.527) Remain 11:27:09 loss: 0.2090 Lr: 0.00354
[2023-12-25 12:51:34,591 INFO misc.py line 119 253097] Train: [48/100][24/510] Data 0.004 (0.007) Batch 0.975 (1.500) Remain 11:15:18 loss: 0.2250 Lr: 0.00354
[2023-12-25 12:51:38,071 INFO misc.py line 119 253097] Train: [48/100][25/510] Data 2.400 (0.115) Batch 3.477 (1.590) Remain 11:55:43 loss: 0.2018 Lr: 0.00354
[2023-12-25 12:51:39,156 INFO misc.py line 119 253097] Train: [48/100][26/510] Data 0.007 (0.111) Batch 1.087 (1.568) Remain 11:45:51 loss: 0.2544 Lr: 0.00354
[2023-12-25 12:51:40,242 INFO misc.py line 119 253097] Train: [48/100][27/510] Data 0.005 (0.106) Batch 1.087 (1.548) Remain 11:36:48 loss: 0.1691 Lr: 0.00354
[2023-12-25 12:51:41,190 INFO misc.py line 119 253097] Train: [48/100][28/510] Data 0.004 (0.102) Batch 0.948 (1.524) Remain 11:25:57 loss: 0.2138 Lr: 0.00354
[2023-12-25 12:51:42,215 INFO misc.py line 119 253097] Train: [48/100][29/510] Data 0.005 (0.098) Batch 1.024 (1.505) Remain 11:17:17 loss: 0.1272 Lr: 0.00354
[2023-12-25 12:51:43,230 INFO misc.py line 119 253097] Train: [48/100][30/510] Data 0.004 (0.095) Batch 1.015 (1.487) Remain 11:09:05 loss: 0.1751 Lr: 0.00354
[2023-12-25 12:51:44,343 INFO misc.py line 119 253097] Train: [48/100][31/510] Data 0.004 (0.092) Batch 1.114 (1.474) Remain 11:03:04 loss: 0.2337 Lr: 0.00354
[2023-12-25 12:51:45,540 INFO misc.py line 119 253097] Train: [48/100][32/510] Data 0.003 (0.089) Batch 1.196 (1.464) Remain 10:58:45 loss: 0.0922 Lr: 0.00354
[2023-12-25 12:51:46,811 INFO misc.py line 119 253097] Train: [48/100][33/510] Data 0.003 (0.086) Batch 1.271 (1.458) Remain 10:55:50 loss: 0.2536 Lr: 0.00354
[2023-12-25 12:51:47,898 INFO misc.py line 119 253097] Train: [48/100][34/510] Data 0.005 (0.083) Batch 1.088 (1.446) Remain 10:50:26 loss: 0.2895 Lr: 0.00354
[2023-12-25 12:51:49,023 INFO misc.py line 119 253097] Train: [48/100][35/510] Data 0.003 (0.081) Batch 1.123 (1.436) Remain 10:45:53 loss: 0.2273 Lr: 0.00354
[2023-12-25 12:51:50,118 INFO misc.py line 119 253097] Train: [48/100][36/510] Data 0.005 (0.078) Batch 1.097 (1.425) Remain 10:41:14 loss: 0.1409 Lr: 0.00354
[2023-12-25 12:51:51,272 INFO misc.py line 119 253097] Train: [48/100][37/510] Data 0.003 (0.076) Batch 1.153 (1.417) Remain 10:37:37 loss: 0.1163 Lr: 0.00354
[2023-12-25 12:51:52,299 INFO misc.py line 119 253097] Train: [48/100][38/510] Data 0.004 (0.074) Batch 1.021 (1.406) Remain 10:32:30 loss: 0.1739 Lr: 0.00354
[2023-12-25 12:51:53,445 INFO misc.py line 119 253097] Train: [48/100][39/510] Data 0.010 (0.072) Batch 1.149 (1.399) Remain 10:29:15 loss: 0.4263 Lr: 0.00354
[2023-12-25 12:51:54,576 INFO misc.py line 119 253097] Train: [48/100][40/510] Data 0.007 (0.070) Batch 1.124 (1.391) Remain 10:25:54 loss: 0.2036 Lr: 0.00354
[2023-12-25 12:51:55,807 INFO misc.py line 119 253097] Train: [48/100][41/510] Data 0.013 (0.069) Batch 1.234 (1.387) Remain 10:24:01 loss: 0.1107 Lr: 0.00354
[2023-12-25 12:51:56,892 INFO misc.py line 119 253097] Train: [48/100][42/510] Data 0.012 (0.067) Batch 1.092 (1.380) Remain 10:20:35 loss: 0.1600 Lr: 0.00354
[2023-12-25 12:51:58,154 INFO misc.py line 119 253097] Train: [48/100][43/510] Data 0.004 (0.066) Batch 1.261 (1.377) Remain 10:19:13 loss: 0.1448 Lr: 0.00354
[2023-12-25 12:51:59,114 INFO misc.py line 119 253097] Train: [48/100][44/510] Data 0.005 (0.064) Batch 0.960 (1.367) Remain 10:14:38 loss: 0.1451 Lr: 0.00354
[2023-12-25 12:52:00,142 INFO misc.py line 119 253097] Train: [48/100][45/510] Data 0.004 (0.063) Batch 1.027 (1.358) Remain 10:10:58 loss: 0.3105 Lr: 0.00354
[2023-12-25 12:52:01,476 INFO misc.py line 119 253097] Train: [48/100][46/510] Data 0.006 (0.062) Batch 1.332 (1.358) Remain 10:10:40 loss: 0.1920 Lr: 0.00354
[2023-12-25 12:52:02,729 INFO misc.py line 119 253097] Train: [48/100][47/510] Data 0.008 (0.060) Batch 1.257 (1.356) Remain 10:09:37 loss: 0.1008 Lr: 0.00354
[2023-12-25 12:52:04,071 INFO misc.py line 119 253097] Train: [48/100][48/510] Data 0.004 (0.059) Batch 1.339 (1.355) Remain 10:09:26 loss: 0.2722 Lr: 0.00354
[2023-12-25 12:52:05,219 INFO misc.py line 119 253097] Train: [48/100][49/510] Data 0.007 (0.058) Batch 1.146 (1.351) Remain 10:07:22 loss: 0.2520 Lr: 0.00353
[2023-12-25 12:52:06,307 INFO misc.py line 119 253097] Train: [48/100][50/510] Data 0.010 (0.057) Batch 1.088 (1.345) Remain 10:04:50 loss: 0.2309 Lr: 0.00353
[2023-12-25 12:52:11,425 INFO misc.py line 119 253097] Train: [48/100][51/510] Data 4.352 (0.147) Batch 5.124 (1.424) Remain 10:40:12 loss: 0.0634 Lr: 0.00353
[2023-12-25 12:52:12,691 INFO misc.py line 119 253097] Train: [48/100][52/510] Data 0.003 (0.144) Batch 1.264 (1.421) Remain 10:38:42 loss: 0.1708 Lr: 0.00353
[2023-12-25 12:52:13,930 INFO misc.py line 119 253097] Train: [48/100][53/510] Data 0.006 (0.141) Batch 1.239 (1.417) Remain 10:37:03 loss: 0.1732 Lr: 0.00353
[2023-12-25 12:52:15,121 INFO misc.py line 119 253097] Train: [48/100][54/510] Data 0.006 (0.138) Batch 1.188 (1.412) Remain 10:35:00 loss: 0.1572 Lr: 0.00353
[2023-12-25 12:52:16,122 INFO misc.py line 119 253097] Train: [48/100][55/510] Data 0.010 (0.136) Batch 1.000 (1.404) Remain 10:31:25 loss: 0.1598 Lr: 0.00353
[2023-12-25 12:52:17,267 INFO misc.py line 119 253097] Train: [48/100][56/510] Data 0.011 (0.133) Batch 1.151 (1.400) Remain 10:29:14 loss: 0.1128 Lr: 0.00353
[2023-12-25 12:52:18,285 INFO misc.py line 119 253097] Train: [48/100][57/510] Data 0.004 (0.131) Batch 1.010 (1.392) Remain 10:25:58 loss: 0.2369 Lr: 0.00353
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[2023-12-25 12:53:32,168 INFO misc.py line 119 253097] Train: [48/100][111/510] Data 0.007 (0.105) Batch 2.876 (1.380) Remain 10:19:19 loss: 0.4972 Lr: 0.00352
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[2023-12-25 12:53:44,443 INFO misc.py line 119 253097] Train: [48/100][115/510] Data 7.235 (0.166) Batch 8.529 (1.441) Remain 10:46:16 loss: 0.0569 Lr: 0.00352
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[2023-12-25 12:56:09,899 INFO misc.py line 119 253097] Train: [48/100][211/510] Data 0.006 (0.108) Batch 7.094 (1.475) Remain 10:59:19 loss: 0.0923 Lr: 0.00350
[2023-12-25 12:56:11,111 INFO misc.py line 119 253097] Train: [48/100][212/510] Data 0.005 (0.108) Batch 1.212 (1.474) Remain 10:58:44 loss: 0.2855 Lr: 0.00350
[2023-12-25 12:56:12,315 INFO misc.py line 119 253097] Train: [48/100][213/510] Data 0.005 (0.107) Batch 1.205 (1.473) Remain 10:58:08 loss: 0.1413 Lr: 0.00350
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[2023-12-25 12:56:14,551 INFO misc.py line 119 253097] Train: [48/100][215/510] Data 0.003 (0.106) Batch 1.078 (1.469) Remain 10:56:36 loss: 0.1076 Lr: 0.00350
[2023-12-25 12:56:15,823 INFO misc.py line 119 253097] Train: [48/100][216/510] Data 0.004 (0.106) Batch 1.270 (1.468) Remain 10:56:09 loss: 0.1295 Lr: 0.00350
[2023-12-25 12:56:28,710 INFO misc.py line 119 253097] Train: [48/100][217/510] Data 0.007 (0.105) Batch 12.890 (1.522) Remain 11:19:59 loss: 0.1999 Lr: 0.00350
[2023-12-25 12:56:29,765 INFO misc.py line 119 253097] Train: [48/100][218/510] Data 0.004 (0.105) Batch 1.047 (1.519) Remain 11:18:58 loss: 0.1415 Lr: 0.00350
[2023-12-25 12:56:31,007 INFO misc.py line 119 253097] Train: [48/100][219/510] Data 0.012 (0.104) Batch 1.247 (1.518) Remain 11:18:23 loss: 0.3316 Lr: 0.00350
[2023-12-25 12:56:32,272 INFO misc.py line 119 253097] Train: [48/100][220/510] Data 0.008 (0.104) Batch 1.268 (1.517) Remain 11:17:50 loss: 0.2409 Lr: 0.00350
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[2023-12-25 12:56:35,745 INFO misc.py line 119 253097] Train: [48/100][223/510] Data 0.003 (0.103) Batch 1.107 (1.512) Remain 11:15:34 loss: 0.3480 Lr: 0.00350
[2023-12-25 12:56:36,918 INFO misc.py line 119 253097] Train: [48/100][224/510] Data 0.004 (0.102) Batch 1.172 (1.511) Remain 11:14:51 loss: 0.0898 Lr: 0.00350
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[2023-12-25 12:56:46,228 INFO misc.py line 119 253097] Train: [48/100][232/510] Data 0.004 (0.099) Batch 1.255 (1.498) Remain 11:09:14 loss: 0.2178 Lr: 0.00350
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[2023-12-25 12:58:37,972 INFO misc.py line 119 253097] Train: [48/100][308/510] Data 0.006 (0.076) Batch 0.997 (1.491) Remain 11:04:14 loss: 0.1088 Lr: 0.00349
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[2023-12-25 12:58:52,036 INFO misc.py line 119 253097] Train: [48/100][312/510] Data 3.097 (0.085) Batch 4.420 (1.518) Remain 11:15:48 loss: 0.1586 Lr: 0.00348
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[2023-12-25 12:58:55,538 INFO misc.py line 119 253097] Train: [48/100][315/510] Data 0.009 (0.084) Batch 1.142 (1.514) Remain 11:14:13 loss: 0.1189 Lr: 0.00348
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[2023-12-25 12:59:21,888 INFO misc.py line 119 253097] Train: [48/100][331/510] Data 0.005 (0.080) Batch 8.938 (1.521) Remain 11:16:42 loss: 0.2812 Lr: 0.00348
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[2023-12-25 12:59:34,924 INFO misc.py line 119 253097] Train: [48/100][339/510] Data 3.074 (0.087) Batch 4.259 (1.523) Remain 11:17:39 loss: 0.0752 Lr: 0.00348
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[2023-12-25 12:59:40,315 INFO misc.py line 119 253097] Train: [48/100][342/510] Data 0.004 (0.087) Batch 3.107 (1.526) Remain 11:18:39 loss: 0.1324 Lr: 0.00348
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[2023-12-25 13:03:01,559 INFO misc.py line 119 253097] Train: [48/100][476/510] Data 0.013 (0.069) Batch 1.128 (1.519) Remain 11:12:14 loss: 0.2269 Lr: 0.00345
[2023-12-25 13:03:02,789 INFO misc.py line 119 253097] Train: [48/100][477/510] Data 0.005 (0.069) Batch 1.228 (1.518) Remain 11:11:56 loss: 0.1429 Lr: 0.00345
[2023-12-25 13:03:03,831 INFO misc.py line 119 253097] Train: [48/100][478/510] Data 0.009 (0.069) Batch 1.044 (1.517) Remain 11:11:28 loss: 0.2922 Lr: 0.00345
[2023-12-25 13:03:05,154 INFO misc.py line 119 253097] Train: [48/100][479/510] Data 0.007 (0.069) Batch 1.323 (1.517) Remain 11:11:16 loss: 0.1776 Lr: 0.00345
[2023-12-25 13:03:13,039 INFO misc.py line 119 253097] Train: [48/100][480/510] Data 0.007 (0.068) Batch 7.886 (1.530) Remain 11:17:09 loss: 0.0998 Lr: 0.00345
[2023-12-25 13:03:14,103 INFO misc.py line 119 253097] Train: [48/100][481/510] Data 0.004 (0.068) Batch 1.065 (1.529) Remain 11:16:42 loss: 0.1128 Lr: 0.00345
[2023-12-25 13:03:15,164 INFO misc.py line 119 253097] Train: [48/100][482/510] Data 0.004 (0.068) Batch 1.060 (1.528) Remain 11:16:14 loss: 0.1920 Lr: 0.00345
[2023-12-25 13:03:16,371 INFO misc.py line 119 253097] Train: [48/100][483/510] Data 0.004 (0.068) Batch 1.207 (1.528) Remain 11:15:55 loss: 0.2685 Lr: 0.00345
[2023-12-25 13:03:17,645 INFO misc.py line 119 253097] Train: [48/100][484/510] Data 0.003 (0.068) Batch 1.269 (1.527) Remain 11:15:39 loss: 0.1440 Lr: 0.00345
[2023-12-25 13:03:18,934 INFO misc.py line 119 253097] Train: [48/100][485/510] Data 0.009 (0.068) Batch 1.285 (1.527) Remain 11:15:24 loss: 0.2476 Lr: 0.00345
[2023-12-25 13:03:20,071 INFO misc.py line 119 253097] Train: [48/100][486/510] Data 0.013 (0.068) Batch 1.139 (1.526) Remain 11:15:01 loss: 0.2091 Lr: 0.00345
[2023-12-25 13:03:21,201 INFO misc.py line 119 253097] Train: [48/100][487/510] Data 0.011 (0.068) Batch 1.133 (1.525) Remain 11:14:38 loss: 0.1704 Lr: 0.00345
[2023-12-25 13:03:22,269 INFO misc.py line 119 253097] Train: [48/100][488/510] Data 0.008 (0.067) Batch 1.071 (1.524) Remain 11:14:12 loss: 0.1310 Lr: 0.00345
[2023-12-25 13:03:23,430 INFO misc.py line 119 253097] Train: [48/100][489/510] Data 0.006 (0.067) Batch 1.159 (1.523) Remain 11:13:50 loss: 0.1235 Lr: 0.00345
[2023-12-25 13:03:24,582 INFO misc.py line 119 253097] Train: [48/100][490/510] Data 0.007 (0.067) Batch 1.152 (1.523) Remain 11:13:29 loss: 0.2095 Lr: 0.00345
[2023-12-25 13:03:25,800 INFO misc.py line 119 253097] Train: [48/100][491/510] Data 0.008 (0.067) Batch 1.217 (1.522) Remain 11:13:10 loss: 0.1010 Lr: 0.00345
[2023-12-25 13:03:35,453 INFO misc.py line 119 253097] Train: [48/100][492/510] Data 0.008 (0.067) Batch 9.658 (1.539) Remain 11:20:30 loss: 0.1321 Lr: 0.00345
[2023-12-25 13:03:36,471 INFO misc.py line 119 253097] Train: [48/100][493/510] Data 0.005 (0.067) Batch 1.017 (1.538) Remain 11:20:01 loss: 0.1197 Lr: 0.00345
[2023-12-25 13:03:37,708 INFO misc.py line 119 253097] Train: [48/100][494/510] Data 0.004 (0.067) Batch 1.237 (1.537) Remain 11:19:43 loss: 0.1527 Lr: 0.00345
[2023-12-25 13:03:38,841 INFO misc.py line 119 253097] Train: [48/100][495/510] Data 0.004 (0.067) Batch 1.132 (1.536) Remain 11:19:20 loss: 0.1644 Lr: 0.00345
[2023-12-25 13:03:40,077 INFO misc.py line 119 253097] Train: [48/100][496/510] Data 0.005 (0.066) Batch 1.235 (1.535) Remain 11:19:02 loss: 0.3670 Lr: 0.00345
[2023-12-25 13:03:41,358 INFO misc.py line 119 253097] Train: [48/100][497/510] Data 0.006 (0.066) Batch 1.283 (1.535) Remain 11:18:47 loss: 0.1004 Lr: 0.00345
[2023-12-25 13:03:42,484 INFO misc.py line 119 253097] Train: [48/100][498/510] Data 0.005 (0.066) Batch 1.120 (1.534) Remain 11:18:23 loss: 0.1231 Lr: 0.00345
[2023-12-25 13:03:43,653 INFO misc.py line 119 253097] Train: [48/100][499/510] Data 0.010 (0.066) Batch 1.169 (1.533) Remain 11:18:02 loss: 0.3515 Lr: 0.00345
[2023-12-25 13:03:44,753 INFO misc.py line 119 253097] Train: [48/100][500/510] Data 0.011 (0.066) Batch 1.106 (1.533) Remain 11:17:38 loss: 0.1296 Lr: 0.00345
[2023-12-25 13:03:45,961 INFO misc.py line 119 253097] Train: [48/100][501/510] Data 0.004 (0.066) Batch 1.209 (1.532) Remain 11:17:19 loss: 0.3139 Lr: 0.00345
[2023-12-25 13:03:47,082 INFO misc.py line 119 253097] Train: [48/100][502/510] Data 0.003 (0.066) Batch 1.120 (1.531) Remain 11:16:55 loss: 0.2496 Lr: 0.00345
[2023-12-25 13:03:48,323 INFO misc.py line 119 253097] Train: [48/100][503/510] Data 0.004 (0.066) Batch 1.235 (1.530) Remain 11:16:38 loss: 0.1091 Lr: 0.00345
[2023-12-25 13:03:49,358 INFO misc.py line 119 253097] Train: [48/100][504/510] Data 0.010 (0.065) Batch 1.042 (1.529) Remain 11:16:11 loss: 0.2991 Lr: 0.00345
[2023-12-25 13:03:50,645 INFO misc.py line 119 253097] Train: [48/100][505/510] Data 0.004 (0.065) Batch 1.283 (1.529) Remain 11:15:56 loss: 0.1627 Lr: 0.00345
[2023-12-25 13:03:51,849 INFO misc.py line 119 253097] Train: [48/100][506/510] Data 0.007 (0.065) Batch 1.207 (1.528) Remain 11:15:38 loss: 0.3387 Lr: 0.00345
[2023-12-25 13:03:53,155 INFO misc.py line 119 253097] Train: [48/100][507/510] Data 0.004 (0.065) Batch 1.305 (1.528) Remain 11:15:24 loss: 0.2433 Lr: 0.00345
[2023-12-25 13:03:54,356 INFO misc.py line 119 253097] Train: [48/100][508/510] Data 0.005 (0.065) Batch 1.196 (1.527) Remain 11:15:05 loss: 0.1691 Lr: 0.00345
[2023-12-25 13:03:55,496 INFO misc.py line 119 253097] Train: [48/100][509/510] Data 0.010 (0.065) Batch 1.142 (1.526) Remain 11:14:44 loss: 0.2064 Lr: 0.00345
[2023-12-25 13:04:00,179 INFO misc.py line 119 253097] Train: [48/100][510/510] Data 0.008 (0.065) Batch 4.687 (1.533) Remain 11:17:28 loss: 0.1566 Lr: 0.00345
[2023-12-25 13:04:00,179 INFO misc.py line 136 253097] Train result: loss: 0.1850 
[2023-12-25 13:04:00,180 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 13:04:27,627 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5459 
[2023-12-25 13:04:27,974 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2960 
[2023-12-25 13:04:34,310 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4519 
[2023-12-25 13:04:34,838 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.6439 
[2023-12-25 13:04:36,810 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.5691 
[2023-12-25 13:04:37,243 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4053 
[2023-12-25 13:04:38,131 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1798 
[2023-12-25 13:04:38,691 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4841 
[2023-12-25 13:04:40,506 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.3982 
[2023-12-25 13:04:42,627 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1686 
[2023-12-25 13:04:43,486 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.4024 
[2023-12-25 13:04:43,910 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7786 
[2023-12-25 13:04:44,818 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5123 
[2023-12-25 13:04:47,766 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9058 
[2023-12-25 13:04:48,232 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2350 
[2023-12-25 13:04:48,861 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5243 
[2023-12-25 13:04:49,569 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3893 
[2023-12-25 13:04:51,190 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6225/0.7001/0.8813.
[2023-12-25 13:04:51,190 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.8901/0.9394
[2023-12-25 13:04:51,190 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9459/0.9544
[2023-12-25 13:04:51,190 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8316/0.9689
[2023-12-25 13:04:51,190 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 13:04:51,190 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3341/0.3903
[2023-12-25 13:04:51,190 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.4613/0.4731
[2023-12-25 13:04:51,190 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6314/0.7462
[2023-12-25 13:04:51,191 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8106/0.8890
[2023-12-25 13:04:51,191 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8110/0.8505
[2023-12-25 13:04:51,191 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4698/0.4956
[2023-12-25 13:04:51,191 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7751/0.8780
[2023-12-25 13:04:51,191 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.6072/0.8542
[2023-12-25 13:04:51,191 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5237/0.6611
[2023-12-25 13:04:51,191 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 13:04:51,192 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 13:04:51,192 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 13:05:00,271 INFO misc.py line 119 253097] Train: [49/100][1/510] Data 5.484 (5.484) Batch 6.549 (6.549) Remain 48:14:46 loss: 0.1105 Lr: 0.00345
[2023-12-25 13:05:01,253 INFO misc.py line 119 253097] Train: [49/100][2/510] Data 0.005 (0.005) Batch 0.983 (0.983) Remain 07:14:30 loss: 0.1167 Lr: 0.00345
[2023-12-25 13:05:02,522 INFO misc.py line 119 253097] Train: [49/100][3/510] Data 0.003 (0.003) Batch 1.269 (1.269) Remain 09:20:41 loss: 0.1538 Lr: 0.00345
[2023-12-25 13:05:03,858 INFO misc.py line 119 253097] Train: [49/100][4/510] Data 0.004 (0.004) Batch 1.336 (1.336) Remain 09:50:19 loss: 0.0968 Lr: 0.00345
[2023-12-25 13:05:08,710 INFO misc.py line 119 253097] Train: [49/100][5/510] Data 0.004 (0.004) Batch 4.852 (3.094) Remain 22:47:10 loss: 0.1570 Lr: 0.00345
[2023-12-25 13:05:09,792 INFO misc.py line 119 253097] Train: [49/100][6/510] Data 0.005 (0.004) Batch 1.075 (2.421) Remain 17:49:45 loss: 0.1735 Lr: 0.00345
[2023-12-25 13:05:10,894 INFO misc.py line 119 253097] Train: [49/100][7/510] Data 0.012 (0.006) Batch 1.102 (2.091) Remain 15:24:04 loss: 0.1579 Lr: 0.00345
[2023-12-25 13:05:11,865 INFO misc.py line 119 253097] Train: [49/100][8/510] Data 0.011 (0.007) Batch 0.978 (1.869) Remain 13:45:40 loss: 0.1381 Lr: 0.00345
[2023-12-25 13:05:12,899 INFO misc.py line 119 253097] Train: [49/100][9/510] Data 0.005 (0.007) Batch 1.033 (1.729) Remain 12:44:04 loss: 0.4460 Lr: 0.00344
[2023-12-25 13:05:13,965 INFO misc.py line 119 253097] Train: [49/100][10/510] Data 0.006 (0.007) Batch 1.068 (1.635) Remain 12:02:19 loss: 0.2360 Lr: 0.00344
[2023-12-25 13:05:14,962 INFO misc.py line 119 253097] Train: [49/100][11/510] Data 0.003 (0.006) Batch 0.995 (1.555) Remain 11:26:57 loss: 0.1591 Lr: 0.00344
[2023-12-25 13:05:15,994 INFO misc.py line 119 253097] Train: [49/100][12/510] Data 0.006 (0.006) Batch 1.033 (1.497) Remain 11:01:18 loss: 0.2393 Lr: 0.00344
[2023-12-25 13:05:17,263 INFO misc.py line 119 253097] Train: [49/100][13/510] Data 0.004 (0.006) Batch 1.269 (1.474) Remain 10:51:12 loss: 0.1945 Lr: 0.00344
[2023-12-25 13:05:18,693 INFO misc.py line 119 253097] Train: [49/100][14/510] Data 0.004 (0.006) Batch 1.426 (1.470) Remain 10:49:15 loss: 0.0807 Lr: 0.00344
[2023-12-25 13:05:19,672 INFO misc.py line 119 253097] Train: [49/100][15/510] Data 0.009 (0.006) Batch 0.982 (1.429) Remain 10:31:16 loss: 0.2319 Lr: 0.00344
[2023-12-25 13:05:20,827 INFO misc.py line 119 253097] Train: [49/100][16/510] Data 0.007 (0.006) Batch 1.156 (1.408) Remain 10:21:57 loss: 0.1174 Lr: 0.00344
[2023-12-25 13:05:36,186 INFO misc.py line 119 253097] Train: [49/100][17/510] Data 0.005 (0.006) Batch 15.361 (2.405) Remain 17:42:09 loss: 0.0989 Lr: 0.00344
[2023-12-25 13:05:37,370 INFO misc.py line 119 253097] Train: [49/100][18/510] Data 0.003 (0.006) Batch 1.183 (2.323) Remain 17:06:09 loss: 0.2864 Lr: 0.00344
[2023-12-25 13:05:38,605 INFO misc.py line 119 253097] Train: [49/100][19/510] Data 0.004 (0.006) Batch 1.230 (2.255) Remain 16:35:56 loss: 0.1790 Lr: 0.00344
[2023-12-25 13:05:39,801 INFO misc.py line 119 253097] Train: [49/100][20/510] Data 0.009 (0.006) Batch 1.196 (2.193) Remain 16:08:24 loss: 0.1903 Lr: 0.00344
[2023-12-25 13:05:40,843 INFO misc.py line 119 253097] Train: [49/100][21/510] Data 0.009 (0.006) Batch 1.046 (2.129) Remain 15:40:14 loss: 0.1224 Lr: 0.00344
[2023-12-25 13:05:42,072 INFO misc.py line 119 253097] Train: [49/100][22/510] Data 0.004 (0.006) Batch 1.229 (2.082) Remain 15:19:17 loss: 0.1061 Lr: 0.00344
[2023-12-25 13:05:43,022 INFO misc.py line 119 253097] Train: [49/100][23/510] Data 0.005 (0.006) Batch 0.950 (2.025) Remain 14:54:15 loss: 0.2868 Lr: 0.00344
[2023-12-25 13:05:44,207 INFO misc.py line 119 253097] Train: [49/100][24/510] Data 0.004 (0.006) Batch 1.185 (1.985) Remain 14:36:33 loss: 0.2063 Lr: 0.00344
[2023-12-25 13:05:45,269 INFO misc.py line 119 253097] Train: [49/100][25/510] Data 0.004 (0.006) Batch 1.062 (1.943) Remain 14:18:00 loss: 0.1864 Lr: 0.00344
[2023-12-25 13:05:46,422 INFO misc.py line 119 253097] Train: [49/100][26/510] Data 0.004 (0.006) Batch 1.153 (1.909) Remain 14:02:49 loss: 0.1441 Lr: 0.00344
[2023-12-25 13:05:47,468 INFO misc.py line 119 253097] Train: [49/100][27/510] Data 0.004 (0.006) Batch 1.042 (1.873) Remain 13:46:50 loss: 0.2291 Lr: 0.00344
[2023-12-25 13:05:48,599 INFO misc.py line 119 253097] Train: [49/100][28/510] Data 0.009 (0.006) Batch 1.133 (1.843) Remain 13:33:44 loss: 0.1718 Lr: 0.00344
[2023-12-25 13:05:49,793 INFO misc.py line 119 253097] Train: [49/100][29/510] Data 0.006 (0.006) Batch 1.196 (1.818) Remain 13:22:43 loss: 0.1265 Lr: 0.00344
[2023-12-25 13:05:56,516 INFO misc.py line 119 253097] Train: [49/100][30/510] Data 0.004 (0.006) Batch 6.723 (2.000) Remain 14:42:53 loss: 0.1063 Lr: 0.00344
[2023-12-25 13:05:57,589 INFO misc.py line 119 253097] Train: [49/100][31/510] Data 0.004 (0.006) Batch 1.069 (1.967) Remain 14:28:11 loss: 0.2493 Lr: 0.00344
[2023-12-25 13:05:58,632 INFO misc.py line 119 253097] Train: [49/100][32/510] Data 0.008 (0.006) Batch 1.044 (1.935) Remain 14:14:06 loss: 0.1145 Lr: 0.00344
[2023-12-25 13:05:59,650 INFO misc.py line 119 253097] Train: [49/100][33/510] Data 0.007 (0.006) Batch 1.017 (1.904) Remain 14:00:34 loss: 0.2396 Lr: 0.00344
[2023-12-25 13:06:00,937 INFO misc.py line 119 253097] Train: [49/100][34/510] Data 0.009 (0.006) Batch 1.291 (1.884) Remain 13:51:49 loss: 0.1282 Lr: 0.00344
[2023-12-25 13:06:02,048 INFO misc.py line 119 253097] Train: [49/100][35/510] Data 0.004 (0.006) Batch 1.106 (1.860) Remain 13:41:03 loss: 0.1720 Lr: 0.00344
[2023-12-25 13:06:03,062 INFO misc.py line 119 253097] Train: [49/100][36/510] Data 0.009 (0.006) Batch 1.019 (1.835) Remain 13:29:46 loss: 0.1415 Lr: 0.00344
[2023-12-25 13:06:04,202 INFO misc.py line 119 253097] Train: [49/100][37/510] Data 0.004 (0.006) Batch 1.140 (1.814) Remain 13:20:43 loss: 0.1867 Lr: 0.00344
[2023-12-25 13:06:05,205 INFO misc.py line 119 253097] Train: [49/100][38/510] Data 0.004 (0.006) Batch 0.999 (1.791) Remain 13:10:25 loss: 0.0827 Lr: 0.00344
[2023-12-25 13:06:06,350 INFO misc.py line 119 253097] Train: [49/100][39/510] Data 0.007 (0.006) Batch 1.148 (1.773) Remain 13:02:30 loss: 0.3538 Lr: 0.00344
[2023-12-25 13:06:07,617 INFO misc.py line 119 253097] Train: [49/100][40/510] Data 0.004 (0.006) Batch 1.266 (1.759) Remain 12:56:25 loss: 0.2105 Lr: 0.00344
[2023-12-25 13:06:08,780 INFO misc.py line 119 253097] Train: [49/100][41/510] Data 0.005 (0.006) Batch 1.164 (1.744) Remain 12:49:29 loss: 0.1450 Lr: 0.00344
[2023-12-25 13:06:09,777 INFO misc.py line 119 253097] Train: [49/100][42/510] Data 0.004 (0.006) Batch 0.998 (1.724) Remain 12:41:01 loss: 0.2535 Lr: 0.00344
[2023-12-25 13:06:10,743 INFO misc.py line 119 253097] Train: [49/100][43/510] Data 0.005 (0.006) Batch 0.966 (1.706) Remain 12:32:36 loss: 0.1207 Lr: 0.00344
[2023-12-25 13:06:11,886 INFO misc.py line 119 253097] Train: [49/100][44/510] Data 0.004 (0.006) Batch 1.143 (1.692) Remain 12:26:32 loss: 0.1941 Lr: 0.00344
[2023-12-25 13:06:13,186 INFO misc.py line 119 253097] Train: [49/100][45/510] Data 0.004 (0.006) Batch 1.300 (1.682) Remain 12:22:23 loss: 0.4642 Lr: 0.00344
[2023-12-25 13:06:14,537 INFO misc.py line 119 253097] Train: [49/100][46/510] Data 0.005 (0.006) Batch 1.351 (1.675) Remain 12:18:57 loss: 0.3097 Lr: 0.00344
[2023-12-25 13:06:22,364 INFO misc.py line 119 253097] Train: [49/100][47/510] Data 0.004 (0.006) Batch 7.827 (1.815) Remain 13:20:37 loss: 0.2050 Lr: 0.00344
[2023-12-25 13:06:23,524 INFO misc.py line 119 253097] Train: [49/100][48/510] Data 0.004 (0.006) Batch 1.160 (1.800) Remain 13:14:10 loss: 0.1775 Lr: 0.00344
[2023-12-25 13:06:24,760 INFO misc.py line 119 253097] Train: [49/100][49/510] Data 0.007 (0.006) Batch 1.235 (1.788) Remain 13:08:43 loss: 0.2335 Lr: 0.00344
[2023-12-25 13:06:25,814 INFO misc.py line 119 253097] Train: [49/100][50/510] Data 0.006 (0.006) Batch 1.055 (1.772) Remain 13:01:48 loss: 0.1253 Lr: 0.00344
[2023-12-25 13:06:27,092 INFO misc.py line 119 253097] Train: [49/100][51/510] Data 0.004 (0.006) Batch 1.270 (1.762) Remain 12:57:10 loss: 0.1840 Lr: 0.00344
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[2023-12-25 13:07:22,335 INFO misc.py line 119 253097] Train: [49/100][86/510] Data 6.701 (0.087) Batch 8.034 (1.684) Remain 12:22:07 loss: 0.0622 Lr: 0.00343
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[2023-12-25 13:07:55,521 INFO misc.py line 119 253097] Train: [49/100][109/510] Data 4.249 (0.110) Batch 8.256 (1.632) Remain 11:58:24 loss: 0.0893 Lr: 0.00343
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[2023-12-25 13:09:34,959 INFO misc.py line 119 253097] Train: [49/100][172/510] Data 0.004 (0.184) Batch 1.121 (1.612) Remain 11:47:53 loss: 0.0800 Lr: 0.00341
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[2023-12-25 13:09:47,079 INFO misc.py line 119 253097] Train: [49/100][178/510] Data 0.005 (0.178) Batch 1.147 (1.626) Remain 11:53:52 loss: 0.2988 Lr: 0.00341
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[2023-12-25 13:09:49,421 INFO misc.py line 119 253097] Train: [49/100][180/510] Data 0.004 (0.176) Batch 1.147 (1.621) Remain 11:51:34 loss: 0.2976 Lr: 0.00341
[2023-12-25 13:09:50,644 INFO misc.py line 119 253097] Train: [49/100][181/510] Data 0.006 (0.175) Batch 1.225 (1.619) Remain 11:50:33 loss: 0.1189 Lr: 0.00341
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[2023-12-25 13:10:00,169 INFO misc.py line 119 253097] Train: [49/100][186/510] Data 0.005 (0.171) Batch 1.169 (1.626) Remain 11:53:51 loss: 0.1963 Lr: 0.00341
[2023-12-25 13:10:01,204 INFO misc.py line 119 253097] Train: [49/100][187/510] Data 0.005 (0.170) Batch 1.036 (1.623) Remain 11:52:25 loss: 0.0849 Lr: 0.00341
[2023-12-25 13:10:02,522 INFO misc.py line 119 253097] Train: [49/100][188/510] Data 0.004 (0.169) Batch 1.316 (1.622) Remain 11:51:40 loss: 0.2111 Lr: 0.00341
[2023-12-25 13:10:03,738 INFO misc.py line 119 253097] Train: [49/100][189/510] Data 0.005 (0.168) Batch 1.218 (1.619) Remain 11:50:41 loss: 0.1153 Lr: 0.00341
[2023-12-25 13:10:04,818 INFO misc.py line 119 253097] Train: [49/100][190/510] Data 0.004 (0.167) Batch 1.075 (1.617) Remain 11:49:23 loss: 0.1065 Lr: 0.00341
[2023-12-25 13:10:05,939 INFO misc.py line 119 253097] Train: [49/100][191/510] Data 0.009 (0.166) Batch 1.125 (1.614) Remain 11:48:12 loss: 0.1760 Lr: 0.00341
[2023-12-25 13:10:07,063 INFO misc.py line 119 253097] Train: [49/100][192/510] Data 0.006 (0.165) Batch 1.124 (1.611) Remain 11:47:02 loss: 0.2726 Lr: 0.00341
[2023-12-25 13:10:08,335 INFO misc.py line 119 253097] Train: [49/100][193/510] Data 0.007 (0.165) Batch 1.273 (1.610) Remain 11:46:14 loss: 0.1676 Lr: 0.00341
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[2023-12-25 13:10:11,812 INFO misc.py line 119 253097] Train: [49/100][196/510] Data 0.009 (0.162) Batch 1.042 (1.603) Remain 11:43:05 loss: 0.1310 Lr: 0.00341
[2023-12-25 13:10:13,080 INFO misc.py line 119 253097] Train: [49/100][197/510] Data 0.004 (0.161) Batch 1.260 (1.601) Remain 11:42:17 loss: 0.3870 Lr: 0.00341
[2023-12-25 13:10:18,334 INFO misc.py line 119 253097] Train: [49/100][198/510] Data 4.100 (0.182) Batch 5.263 (1.620) Remain 11:50:29 loss: 0.2501 Lr: 0.00341
[2023-12-25 13:10:19,558 INFO misc.py line 119 253097] Train: [49/100][199/510] Data 0.003 (0.181) Batch 1.223 (1.618) Remain 11:49:34 loss: 0.1042 Lr: 0.00341
[2023-12-25 13:10:27,382 INFO misc.py line 119 253097] Train: [49/100][200/510] Data 0.004 (0.180) Batch 7.825 (1.649) Remain 12:03:22 loss: 0.2132 Lr: 0.00341
[2023-12-25 13:10:28,684 INFO misc.py line 119 253097] Train: [49/100][201/510] Data 0.004 (0.179) Batch 1.298 (1.647) Remain 12:02:34 loss: 0.0949 Lr: 0.00341
[2023-12-25 13:10:29,822 INFO misc.py line 119 253097] Train: [49/100][202/510] Data 0.008 (0.178) Batch 1.137 (1.645) Remain 12:01:25 loss: 0.1310 Lr: 0.00341
[2023-12-25 13:10:30,817 INFO misc.py line 119 253097] Train: [49/100][203/510] Data 0.008 (0.177) Batch 0.999 (1.641) Remain 11:59:58 loss: 0.2827 Lr: 0.00341
[2023-12-25 13:10:31,931 INFO misc.py line 119 253097] Train: [49/100][204/510] Data 0.004 (0.176) Batch 1.110 (1.639) Remain 11:58:47 loss: 0.2798 Lr: 0.00341
[2023-12-25 13:10:32,853 INFO misc.py line 119 253097] Train: [49/100][205/510] Data 0.009 (0.176) Batch 0.926 (1.635) Remain 11:57:12 loss: 0.1459 Lr: 0.00341
[2023-12-25 13:10:33,895 INFO misc.py line 119 253097] Train: [49/100][206/510] Data 0.005 (0.175) Batch 1.043 (1.632) Remain 11:55:54 loss: 0.1248 Lr: 0.00341
[2023-12-25 13:10:35,177 INFO misc.py line 119 253097] Train: [49/100][207/510] Data 0.004 (0.174) Batch 1.281 (1.631) Remain 11:55:07 loss: 0.1469 Lr: 0.00341
[2023-12-25 13:10:36,476 INFO misc.py line 119 253097] Train: [49/100][208/510] Data 0.005 (0.173) Batch 1.300 (1.629) Remain 11:54:23 loss: 0.3362 Lr: 0.00341
[2023-12-25 13:10:37,456 INFO misc.py line 119 253097] Train: [49/100][209/510] Data 0.004 (0.172) Batch 0.980 (1.626) Remain 11:52:58 loss: 0.1441 Lr: 0.00341
[2023-12-25 13:10:38,561 INFO misc.py line 119 253097] Train: [49/100][210/510] Data 0.004 (0.171) Batch 1.101 (1.623) Remain 11:51:50 loss: 0.1297 Lr: 0.00341
[2023-12-25 13:10:39,639 INFO misc.py line 119 253097] Train: [49/100][211/510] Data 0.008 (0.171) Batch 1.079 (1.621) Remain 11:50:40 loss: 0.0909 Lr: 0.00341
[2023-12-25 13:10:40,902 INFO misc.py line 119 253097] Train: [49/100][212/510] Data 0.006 (0.170) Batch 1.261 (1.619) Remain 11:49:53 loss: 0.1240 Lr: 0.00341
[2023-12-25 13:10:42,027 INFO misc.py line 119 253097] Train: [49/100][213/510] Data 0.009 (0.169) Batch 1.129 (1.617) Remain 11:48:50 loss: 0.2122 Lr: 0.00341
[2023-12-25 13:10:43,060 INFO misc.py line 119 253097] Train: [49/100][214/510] Data 0.004 (0.168) Batch 1.033 (1.614) Remain 11:47:35 loss: 0.2148 Lr: 0.00341
[2023-12-25 13:10:44,403 INFO misc.py line 119 253097] Train: [49/100][215/510] Data 0.004 (0.168) Batch 1.343 (1.613) Remain 11:47:00 loss: 0.1433 Lr: 0.00341
[2023-12-25 13:10:45,624 INFO misc.py line 119 253097] Train: [49/100][216/510] Data 0.005 (0.167) Batch 1.223 (1.611) Remain 11:46:10 loss: 0.1970 Lr: 0.00340
[2023-12-25 13:10:46,700 INFO misc.py line 119 253097] Train: [49/100][217/510] Data 0.004 (0.166) Batch 1.074 (1.608) Remain 11:45:03 loss: 0.1605 Lr: 0.00340
[2023-12-25 13:10:47,702 INFO misc.py line 119 253097] Train: [49/100][218/510] Data 0.005 (0.165) Batch 1.002 (1.605) Remain 11:43:47 loss: 0.1480 Lr: 0.00340
[2023-12-25 13:10:48,856 INFO misc.py line 119 253097] Train: [49/100][219/510] Data 0.006 (0.165) Batch 1.156 (1.603) Remain 11:42:51 loss: 0.2313 Lr: 0.00340
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[2023-12-25 13:10:52,298 INFO misc.py line 119 253097] Train: [49/100][222/510] Data 0.007 (0.162) Batch 1.080 (1.597) Remain 11:40:01 loss: 0.2027 Lr: 0.00340
[2023-12-25 13:10:53,191 INFO misc.py line 119 253097] Train: [49/100][223/510] Data 0.004 (0.162) Batch 0.895 (1.594) Remain 11:38:36 loss: 0.2094 Lr: 0.00340
[2023-12-25 13:10:54,484 INFO misc.py line 119 253097] Train: [49/100][224/510] Data 0.003 (0.161) Batch 1.288 (1.593) Remain 11:37:58 loss: 0.3250 Lr: 0.00340
[2023-12-25 13:10:55,500 INFO misc.py line 119 253097] Train: [49/100][225/510] Data 0.079 (0.161) Batch 1.015 (1.590) Remain 11:36:48 loss: 0.0821 Lr: 0.00340
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[2023-12-25 13:11:07,644 INFO misc.py line 119 253097] Train: [49/100][227/510] Data 0.008 (0.159) Batch 10.996 (1.630) Remain 11:54:17 loss: 0.1486 Lr: 0.00340
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[2023-12-25 13:11:09,782 INFO misc.py line 119 253097] Train: [49/100][229/510] Data 0.005 (0.158) Batch 1.063 (1.625) Remain 11:52:03 loss: 0.1971 Lr: 0.00340
[2023-12-25 13:11:10,833 INFO misc.py line 119 253097] Train: [49/100][230/510] Data 0.004 (0.157) Batch 1.051 (1.623) Remain 11:50:55 loss: 0.1000 Lr: 0.00340
[2023-12-25 13:11:11,823 INFO misc.py line 119 253097] Train: [49/100][231/510] Data 0.004 (0.156) Batch 0.989 (1.620) Remain 11:49:41 loss: 0.1283 Lr: 0.00340
[2023-12-25 13:11:12,974 INFO misc.py line 119 253097] Train: [49/100][232/510] Data 0.005 (0.156) Batch 1.151 (1.618) Remain 11:48:45 loss: 0.1619 Lr: 0.00340
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[2023-12-25 13:11:15,350 INFO misc.py line 119 253097] Train: [49/100][234/510] Data 0.004 (0.154) Batch 1.229 (1.614) Remain 11:47:04 loss: 0.2691 Lr: 0.00340
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[2023-12-25 13:13:28,370 INFO misc.py line 119 253097] Train: [49/100][312/510] Data 6.200 (0.137) Batch 7.438 (1.637) Remain 11:55:03 loss: 0.2523 Lr: 0.00339
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[2023-12-25 13:13:46,205 INFO misc.py line 119 253097] Train: [49/100][318/510] Data 11.002 (0.170) Batch 12.226 (1.662) Remain 12:06:00 loss: 0.1240 Lr: 0.00339
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[2023-12-25 13:15:36,784 INFO misc.py line 119 253097] Train: [49/100][397/510] Data 0.010 (0.179) Batch 1.195 (1.610) Remain 11:40:52 loss: 0.1817 Lr: 0.00337
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[2023-12-25 13:15:41,166 INFO misc.py line 119 253097] Train: [49/100][401/510] Data 0.007 (0.177) Batch 1.066 (1.605) Remain 11:38:31 loss: 0.1166 Lr: 0.00337
[2023-12-25 13:15:42,300 INFO misc.py line 119 253097] Train: [49/100][402/510] Data 0.007 (0.177) Batch 1.138 (1.603) Remain 11:37:59 loss: 0.1588 Lr: 0.00337
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[2023-12-25 13:15:49,229 INFO misc.py line 119 253097] Train: [49/100][408/510] Data 0.007 (0.174) Batch 1.103 (1.597) Remain 11:34:55 loss: 0.2830 Lr: 0.00337
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[2023-12-25 13:15:58,894 INFO misc.py line 119 253097] Train: [49/100][417/510] Data 0.008 (0.171) Batch 0.911 (1.585) Remain 11:29:44 loss: 0.3154 Lr: 0.00337
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[2023-12-25 13:16:06,850 INFO misc.py line 119 253097] Train: [49/100][419/510] Data 0.004 (0.170) Batch 6.704 (1.597) Remain 11:34:41 loss: 0.1537 Lr: 0.00337
[2023-12-25 13:16:07,872 INFO misc.py line 119 253097] Train: [49/100][420/510] Data 0.005 (0.169) Batch 1.023 (1.596) Remain 11:34:04 loss: 0.1364 Lr: 0.00337
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[2023-12-25 13:17:18,503 INFO misc.py line 119 253097] Train: [49/100][470/510] Data 0.014 (0.152) Batch 1.198 (1.576) Remain 11:24:13 loss: 0.1836 Lr: 0.00336
[2023-12-25 13:17:19,756 INFO misc.py line 119 253097] Train: [49/100][471/510] Data 0.007 (0.152) Batch 1.255 (1.575) Remain 11:23:54 loss: 0.2241 Lr: 0.00336
[2023-12-25 13:17:20,873 INFO misc.py line 119 253097] Train: [49/100][472/510] Data 0.005 (0.151) Batch 1.111 (1.574) Remain 11:23:27 loss: 0.2363 Lr: 0.00336
[2023-12-25 13:17:22,003 INFO misc.py line 119 253097] Train: [49/100][473/510] Data 0.012 (0.151) Batch 1.134 (1.573) Remain 11:23:01 loss: 0.1255 Lr: 0.00336
[2023-12-25 13:17:23,216 INFO misc.py line 119 253097] Train: [49/100][474/510] Data 0.007 (0.151) Batch 1.199 (1.573) Remain 11:22:38 loss: 0.1316 Lr: 0.00336
[2023-12-25 13:17:24,531 INFO misc.py line 119 253097] Train: [49/100][475/510] Data 0.021 (0.150) Batch 1.326 (1.572) Remain 11:22:23 loss: 0.2754 Lr: 0.00336
[2023-12-25 13:17:25,740 INFO misc.py line 119 253097] Train: [49/100][476/510] Data 0.010 (0.150) Batch 1.210 (1.571) Remain 11:22:02 loss: 0.1132 Lr: 0.00335
[2023-12-25 13:17:26,964 INFO misc.py line 119 253097] Train: [49/100][477/510] Data 0.009 (0.150) Batch 1.223 (1.571) Remain 11:21:41 loss: 0.2193 Lr: 0.00335
[2023-12-25 13:17:28,067 INFO misc.py line 119 253097] Train: [49/100][478/510] Data 0.010 (0.149) Batch 1.107 (1.570) Remain 11:21:14 loss: 0.1563 Lr: 0.00335
[2023-12-25 13:17:29,189 INFO misc.py line 119 253097] Train: [49/100][479/510] Data 0.006 (0.149) Batch 1.122 (1.569) Remain 11:20:48 loss: 0.1051 Lr: 0.00335
[2023-12-25 13:17:30,289 INFO misc.py line 119 253097] Train: [49/100][480/510] Data 0.005 (0.149) Batch 1.097 (1.568) Remain 11:20:21 loss: 0.1710 Lr: 0.00335
[2023-12-25 13:17:31,589 INFO misc.py line 119 253097] Train: [49/100][481/510] Data 0.009 (0.149) Batch 1.304 (1.567) Remain 11:20:05 loss: 0.2061 Lr: 0.00335
[2023-12-25 13:17:32,763 INFO misc.py line 119 253097] Train: [49/100][482/510] Data 0.005 (0.148) Batch 1.172 (1.566) Remain 11:19:42 loss: 0.2252 Lr: 0.00335
[2023-12-25 13:17:33,926 INFO misc.py line 119 253097] Train: [49/100][483/510] Data 0.007 (0.148) Batch 1.158 (1.565) Remain 11:19:18 loss: 0.1945 Lr: 0.00335
[2023-12-25 13:17:35,007 INFO misc.py line 119 253097] Train: [49/100][484/510] Data 0.011 (0.148) Batch 1.088 (1.564) Remain 11:18:51 loss: 0.1497 Lr: 0.00335
[2023-12-25 13:17:36,214 INFO misc.py line 119 253097] Train: [49/100][485/510] Data 0.005 (0.147) Batch 1.206 (1.564) Remain 11:18:30 loss: 0.2878 Lr: 0.00335
[2023-12-25 13:17:37,492 INFO misc.py line 119 253097] Train: [49/100][486/510] Data 0.004 (0.147) Batch 1.278 (1.563) Remain 11:18:13 loss: 0.1171 Lr: 0.00335
[2023-12-25 13:17:38,776 INFO misc.py line 119 253097] Train: [49/100][487/510] Data 0.005 (0.147) Batch 1.284 (1.563) Remain 11:17:56 loss: 0.1569 Lr: 0.00335
[2023-12-25 13:17:39,971 INFO misc.py line 119 253097] Train: [49/100][488/510] Data 0.004 (0.147) Batch 1.191 (1.562) Remain 11:17:35 loss: 0.2508 Lr: 0.00335
[2023-12-25 13:17:41,097 INFO misc.py line 119 253097] Train: [49/100][489/510] Data 0.008 (0.146) Batch 1.127 (1.561) Remain 11:17:10 loss: 0.2125 Lr: 0.00335
[2023-12-25 13:17:42,222 INFO misc.py line 119 253097] Train: [49/100][490/510] Data 0.007 (0.146) Batch 1.122 (1.560) Remain 11:16:45 loss: 0.3318 Lr: 0.00335
[2023-12-25 13:17:43,374 INFO misc.py line 119 253097] Train: [49/100][491/510] Data 0.010 (0.146) Batch 1.157 (1.559) Remain 11:16:22 loss: 0.2298 Lr: 0.00335
[2023-12-25 13:17:44,538 INFO misc.py line 119 253097] Train: [49/100][492/510] Data 0.005 (0.145) Batch 1.161 (1.558) Remain 11:15:59 loss: 0.1388 Lr: 0.00335
[2023-12-25 13:17:49,306 INFO misc.py line 119 253097] Train: [49/100][493/510] Data 0.009 (0.145) Batch 4.771 (1.565) Remain 11:18:48 loss: 0.1429 Lr: 0.00335
[2023-12-25 13:17:53,759 INFO misc.py line 119 253097] Train: [49/100][494/510] Data 0.005 (0.145) Batch 4.454 (1.571) Remain 11:21:20 loss: 0.0911 Lr: 0.00335
[2023-12-25 13:17:55,011 INFO misc.py line 119 253097] Train: [49/100][495/510] Data 0.005 (0.145) Batch 1.252 (1.570) Remain 11:21:01 loss: 0.1460 Lr: 0.00335
[2023-12-25 13:17:55,874 INFO misc.py line 119 253097] Train: [49/100][496/510] Data 0.004 (0.144) Batch 0.863 (1.569) Remain 11:20:22 loss: 0.2831 Lr: 0.00335
[2023-12-25 13:17:57,077 INFO misc.py line 119 253097] Train: [49/100][497/510] Data 0.004 (0.144) Batch 1.202 (1.568) Remain 11:20:02 loss: 0.1809 Lr: 0.00335
[2023-12-25 13:17:58,224 INFO misc.py line 119 253097] Train: [49/100][498/510] Data 0.004 (0.144) Batch 1.148 (1.567) Remain 11:19:38 loss: 0.1049 Lr: 0.00335
[2023-12-25 13:17:59,442 INFO misc.py line 119 253097] Train: [49/100][499/510] Data 0.003 (0.143) Batch 1.218 (1.566) Remain 11:19:18 loss: 0.2827 Lr: 0.00335
[2023-12-25 13:18:00,693 INFO misc.py line 119 253097] Train: [49/100][500/510] Data 0.003 (0.143) Batch 1.248 (1.566) Remain 11:19:00 loss: 0.1729 Lr: 0.00335
[2023-12-25 13:18:01,786 INFO misc.py line 119 253097] Train: [49/100][501/510] Data 0.006 (0.143) Batch 1.097 (1.565) Remain 11:18:34 loss: 0.0938 Lr: 0.00335
[2023-12-25 13:18:02,974 INFO misc.py line 119 253097] Train: [49/100][502/510] Data 0.003 (0.143) Batch 1.183 (1.564) Remain 11:18:12 loss: 0.2221 Lr: 0.00335
[2023-12-25 13:18:04,079 INFO misc.py line 119 253097] Train: [49/100][503/510] Data 0.008 (0.142) Batch 1.105 (1.563) Remain 11:17:47 loss: 0.1781 Lr: 0.00335
[2023-12-25 13:18:05,151 INFO misc.py line 119 253097] Train: [49/100][504/510] Data 0.008 (0.142) Batch 1.076 (1.562) Remain 11:17:20 loss: 0.1734 Lr: 0.00335
[2023-12-25 13:18:06,332 INFO misc.py line 119 253097] Train: [49/100][505/510] Data 0.004 (0.142) Batch 1.179 (1.561) Remain 11:16:59 loss: 0.2685 Lr: 0.00335
[2023-12-25 13:18:07,523 INFO misc.py line 119 253097] Train: [49/100][506/510] Data 0.006 (0.141) Batch 1.193 (1.561) Remain 11:16:38 loss: 0.1917 Lr: 0.00335
[2023-12-25 13:18:08,549 INFO misc.py line 119 253097] Train: [49/100][507/510] Data 0.004 (0.141) Batch 1.021 (1.560) Remain 11:16:08 loss: 0.1034 Lr: 0.00335
[2023-12-25 13:18:09,552 INFO misc.py line 119 253097] Train: [49/100][508/510] Data 0.009 (0.141) Batch 1.003 (1.558) Remain 11:15:38 loss: 0.0781 Lr: 0.00335
[2023-12-25 13:18:10,731 INFO misc.py line 119 253097] Train: [49/100][509/510] Data 0.009 (0.141) Batch 1.185 (1.558) Remain 11:15:17 loss: 0.1089 Lr: 0.00335
[2023-12-25 13:18:11,906 INFO misc.py line 119 253097] Train: [49/100][510/510] Data 0.003 (0.140) Batch 1.173 (1.557) Remain 11:14:56 loss: 0.2176 Lr: 0.00335
[2023-12-25 13:18:11,907 INFO misc.py line 136 253097] Train result: loss: 0.1867 
[2023-12-25 13:18:11,908 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 13:18:38,946 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6617 
[2023-12-25 13:18:39,302 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3813 
[2023-12-25 13:18:44,248 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4228 
[2023-12-25 13:18:44,773 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3869 
[2023-12-25 13:18:46,748 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7867 
[2023-12-25 13:18:47,173 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3794 
[2023-12-25 13:18:48,053 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0211 
[2023-12-25 13:18:48,607 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2270 
[2023-12-25 13:18:50,427 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.5043 
[2023-12-25 13:18:52,554 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2444 
[2023-12-25 13:18:53,416 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3410 
[2023-12-25 13:18:53,842 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7925 
[2023-12-25 13:18:54,746 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.7735 
[2023-12-25 13:18:57,690 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7115 
[2023-12-25 13:18:58,158 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2675 
[2023-12-25 13:18:58,779 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3697 
[2023-12-25 13:18:59,488 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2674 
[2023-12-25 13:19:01,157 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6688/0.7270/0.9016.
[2023-12-25 13:19:01,157 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9196/0.9522
[2023-12-25 13:19:01,157 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9836/0.9901
[2023-12-25 13:19:01,157 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8420/0.9736
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3717/0.4045
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6007/0.6127
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7389/0.8447
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7992/0.8903
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9030/0.9598
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4795/0.5089
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7633/0.8408
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7054/0.7533
[2023-12-25 13:19:01,158 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5875/0.7208
[2023-12-25 13:19:01,158 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 13:19:01,161 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 13:19:01,161 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 13:19:09,536 INFO misc.py line 119 253097] Train: [50/100][1/510] Data 3.410 (3.410) Batch 6.782 (6.782) Remain 48:59:57 loss: 0.1932 Lr: 0.00335
[2023-12-25 13:19:13,044 INFO misc.py line 119 253097] Train: [50/100][2/510] Data 0.312 (0.312) Batch 3.507 (3.507) Remain 25:20:19 loss: 0.3603 Lr: 0.00335
[2023-12-25 13:19:14,147 INFO misc.py line 119 253097] Train: [50/100][3/510] Data 0.005 (0.005) Batch 1.103 (1.103) Remain 07:58:10 loss: 0.1439 Lr: 0.00335
[2023-12-25 13:19:15,515 INFO misc.py line 119 253097] Train: [50/100][4/510] Data 0.230 (0.230) Batch 1.366 (1.366) Remain 09:52:15 loss: 0.1253 Lr: 0.00335
[2023-12-25 13:19:16,634 INFO misc.py line 119 253097] Train: [50/100][5/510] Data 0.007 (0.118) Batch 1.120 (1.243) Remain 08:58:53 loss: 0.1097 Lr: 0.00335
[2023-12-25 13:19:17,755 INFO misc.py line 119 253097] Train: [50/100][6/510] Data 0.004 (0.080) Batch 1.117 (1.201) Remain 08:40:34 loss: 0.1686 Lr: 0.00335
[2023-12-25 13:19:18,748 INFO misc.py line 119 253097] Train: [50/100][7/510] Data 0.008 (0.062) Batch 0.998 (1.150) Remain 08:18:32 loss: 0.2464 Lr: 0.00335
[2023-12-25 13:19:19,967 INFO misc.py line 119 253097] Train: [50/100][8/510] Data 0.003 (0.050) Batch 1.213 (1.163) Remain 08:23:56 loss: 0.1083 Lr: 0.00335
[2023-12-25 13:19:21,110 INFO misc.py line 119 253097] Train: [50/100][9/510] Data 0.010 (0.044) Batch 1.149 (1.161) Remain 08:22:55 loss: 0.1291 Lr: 0.00335
[2023-12-25 13:19:22,285 INFO misc.py line 119 253097] Train: [50/100][10/510] Data 0.003 (0.038) Batch 1.175 (1.163) Remain 08:23:47 loss: 0.3604 Lr: 0.00335
[2023-12-25 13:19:23,439 INFO misc.py line 119 253097] Train: [50/100][11/510] Data 0.004 (0.034) Batch 1.150 (1.161) Remain 08:23:06 loss: 0.1929 Lr: 0.00335
[2023-12-25 13:19:24,665 INFO misc.py line 119 253097] Train: [50/100][12/510] Data 0.008 (0.031) Batch 1.226 (1.168) Remain 08:26:12 loss: 0.2766 Lr: 0.00335
[2023-12-25 13:19:25,953 INFO misc.py line 119 253097] Train: [50/100][13/510] Data 0.008 (0.028) Batch 1.287 (1.180) Remain 08:31:19 loss: 0.1798 Lr: 0.00335
[2023-12-25 13:19:26,888 INFO misc.py line 119 253097] Train: [50/100][14/510] Data 0.008 (0.027) Batch 0.940 (1.158) Remain 08:21:51 loss: 0.1955 Lr: 0.00335
[2023-12-25 13:19:32,770 INFO misc.py line 119 253097] Train: [50/100][15/510] Data 0.003 (0.025) Batch 5.882 (1.552) Remain 11:12:22 loss: 0.3060 Lr: 0.00335
[2023-12-25 13:19:33,925 INFO misc.py line 119 253097] Train: [50/100][16/510] Data 0.004 (0.023) Batch 1.155 (1.521) Remain 10:59:06 loss: 0.2446 Lr: 0.00335
[2023-12-25 13:19:35,108 INFO misc.py line 119 253097] Train: [50/100][17/510] Data 0.003 (0.022) Batch 1.184 (1.497) Remain 10:48:39 loss: 0.1714 Lr: 0.00334
[2023-12-25 13:19:36,080 INFO misc.py line 119 253097] Train: [50/100][18/510] Data 0.003 (0.020) Batch 0.970 (1.462) Remain 10:33:24 loss: 0.3695 Lr: 0.00334
[2023-12-25 13:19:37,351 INFO misc.py line 119 253097] Train: [50/100][19/510] Data 0.004 (0.019) Batch 1.271 (1.450) Remain 10:28:12 loss: 0.1412 Lr: 0.00334
[2023-12-25 13:19:38,600 INFO misc.py line 119 253097] Train: [50/100][20/510] Data 0.006 (0.019) Batch 1.244 (1.438) Remain 10:22:55 loss: 0.0991 Lr: 0.00334
[2023-12-25 13:19:39,832 INFO misc.py line 119 253097] Train: [50/100][21/510] Data 0.010 (0.018) Batch 1.236 (1.427) Remain 10:18:02 loss: 0.1480 Lr: 0.00334
[2023-12-25 13:19:41,033 INFO misc.py line 119 253097] Train: [50/100][22/510] Data 0.007 (0.018) Batch 1.200 (1.415) Remain 10:12:50 loss: 0.1524 Lr: 0.00334
[2023-12-25 13:19:42,080 INFO misc.py line 119 253097] Train: [50/100][23/510] Data 0.007 (0.017) Batch 1.045 (1.396) Remain 10:04:48 loss: 0.4753 Lr: 0.00334
[2023-12-25 13:19:43,231 INFO misc.py line 119 253097] Train: [50/100][24/510] Data 0.010 (0.017) Batch 1.153 (1.385) Remain 09:59:45 loss: 0.3409 Lr: 0.00334
[2023-12-25 13:19:44,490 INFO misc.py line 119 253097] Train: [50/100][25/510] Data 0.007 (0.016) Batch 1.260 (1.379) Remain 09:57:16 loss: 0.2564 Lr: 0.00334
[2023-12-25 13:19:45,733 INFO misc.py line 119 253097] Train: [50/100][26/510] Data 0.006 (0.016) Batch 1.245 (1.373) Remain 09:54:42 loss: 0.2121 Lr: 0.00334
[2023-12-25 13:19:53,473 INFO misc.py line 119 253097] Train: [50/100][27/510] Data 6.644 (0.292) Batch 7.742 (1.639) Remain 11:49:35 loss: 0.0593 Lr: 0.00334
[2023-12-25 13:19:54,458 INFO misc.py line 119 253097] Train: [50/100][28/510] Data 0.004 (0.280) Batch 0.984 (1.612) Remain 11:38:13 loss: 0.2557 Lr: 0.00334
[2023-12-25 13:19:55,668 INFO misc.py line 119 253097] Train: [50/100][29/510] Data 0.004 (0.270) Batch 1.211 (1.597) Remain 11:31:31 loss: 0.1708 Lr: 0.00334
[2023-12-25 13:19:56,699 INFO misc.py line 119 253097] Train: [50/100][30/510] Data 0.004 (0.260) Batch 1.030 (1.576) Remain 11:22:24 loss: 0.1326 Lr: 0.00334
[2023-12-25 13:19:57,897 INFO misc.py line 119 253097] Train: [50/100][31/510] Data 0.004 (0.251) Batch 1.198 (1.563) Remain 11:16:32 loss: 0.2030 Lr: 0.00334
[2023-12-25 13:19:59,195 INFO misc.py line 119 253097] Train: [50/100][32/510] Data 0.003 (0.242) Batch 1.297 (1.553) Remain 11:12:32 loss: 0.1681 Lr: 0.00334
[2023-12-25 13:20:00,367 INFO misc.py line 119 253097] Train: [50/100][33/510] Data 0.005 (0.234) Batch 1.173 (1.541) Remain 11:07:01 loss: 0.1225 Lr: 0.00334
[2023-12-25 13:20:01,619 INFO misc.py line 119 253097] Train: [50/100][34/510] Data 0.004 (0.227) Batch 1.247 (1.531) Remain 11:02:54 loss: 0.1083 Lr: 0.00334
[2023-12-25 13:20:02,726 INFO misc.py line 119 253097] Train: [50/100][35/510] Data 0.009 (0.220) Batch 1.110 (1.518) Remain 10:57:10 loss: 0.1499 Lr: 0.00334
[2023-12-25 13:20:03,947 INFO misc.py line 119 253097] Train: [50/100][36/510] Data 0.007 (0.214) Batch 1.221 (1.509) Remain 10:53:15 loss: 0.1447 Lr: 0.00334
[2023-12-25 13:20:04,905 INFO misc.py line 119 253097] Train: [50/100][37/510] Data 0.007 (0.208) Batch 0.961 (1.493) Remain 10:46:14 loss: 0.1651 Lr: 0.00334
[2023-12-25 13:20:05,853 INFO misc.py line 119 253097] Train: [50/100][38/510] Data 0.004 (0.202) Batch 0.948 (1.477) Remain 10:39:29 loss: 0.2298 Lr: 0.00334
[2023-12-25 13:20:07,170 INFO misc.py line 119 253097] Train: [50/100][39/510] Data 0.003 (0.196) Batch 1.316 (1.473) Remain 10:37:31 loss: 0.2060 Lr: 0.00334
[2023-12-25 13:20:08,332 INFO misc.py line 119 253097] Train: [50/100][40/510] Data 0.004 (0.191) Batch 1.158 (1.464) Remain 10:33:48 loss: 0.1028 Lr: 0.00334
[2023-12-25 13:20:09,374 INFO misc.py line 119 253097] Train: [50/100][41/510] Data 0.009 (0.186) Batch 1.040 (1.453) Remain 10:28:57 loss: 0.1329 Lr: 0.00334
[2023-12-25 13:20:10,574 INFO misc.py line 119 253097] Train: [50/100][42/510] Data 0.011 (0.182) Batch 1.206 (1.447) Remain 10:26:11 loss: 0.2165 Lr: 0.00334
[2023-12-25 13:20:12,363 INFO misc.py line 119 253097] Train: [50/100][43/510] Data 0.004 (0.177) Batch 1.786 (1.455) Remain 10:29:50 loss: 0.1435 Lr: 0.00334
[2023-12-25 13:20:13,621 INFO misc.py line 119 253097] Train: [50/100][44/510] Data 0.007 (0.173) Batch 1.261 (1.451) Remain 10:27:45 loss: 0.1376 Lr: 0.00334
[2023-12-25 13:20:14,875 INFO misc.py line 119 253097] Train: [50/100][45/510] Data 0.007 (0.169) Batch 1.248 (1.446) Remain 10:25:39 loss: 0.0700 Lr: 0.00334
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[2023-12-25 13:23:49,854 INFO misc.py line 119 253097] Train: [50/100][183/510] Data 0.007 (0.137) Batch 1.251 (1.532) Remain 10:59:19 loss: 0.1739 Lr: 0.00331
[2023-12-25 13:23:51,101 INFO misc.py line 119 253097] Train: [50/100][184/510] Data 0.005 (0.136) Batch 1.243 (1.530) Remain 10:58:36 loss: 0.2450 Lr: 0.00331
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[2023-12-25 13:23:53,284 INFO misc.py line 119 253097] Train: [50/100][186/510] Data 0.009 (0.135) Batch 1.132 (1.525) Remain 10:56:23 loss: 0.2771 Lr: 0.00331
[2023-12-25 13:23:54,396 INFO misc.py line 119 253097] Train: [50/100][187/510] Data 0.055 (0.134) Batch 1.161 (1.523) Remain 10:55:30 loss: 0.1613 Lr: 0.00331
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[2023-12-25 13:23:56,566 INFO misc.py line 119 253097] Train: [50/100][189/510] Data 0.008 (0.133) Batch 1.099 (1.518) Remain 10:53:25 loss: 0.1205 Lr: 0.00331
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[2023-12-25 13:24:06,655 INFO misc.py line 119 253097] Train: [50/100][192/510] Data 0.009 (0.131) Batch 7.820 (1.548) Remain 11:05:57 loss: 0.1127 Lr: 0.00331
[2023-12-25 13:24:07,900 INFO misc.py line 119 253097] Train: [50/100][193/510] Data 0.005 (0.130) Batch 1.244 (1.546) Remain 11:05:14 loss: 0.0951 Lr: 0.00331
[2023-12-25 13:24:09,156 INFO misc.py line 119 253097] Train: [50/100][194/510] Data 0.004 (0.130) Batch 1.257 (1.545) Remain 11:04:34 loss: 0.2123 Lr: 0.00331
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[2023-12-25 13:24:15,996 INFO misc.py line 119 253097] Train: [50/100][200/510] Data 0.004 (0.126) Batch 1.008 (1.532) Remain 10:59:06 loss: 0.2303 Lr: 0.00331
[2023-12-25 13:24:17,180 INFO misc.py line 119 253097] Train: [50/100][201/510] Data 0.004 (0.125) Batch 1.178 (1.530) Remain 10:58:19 loss: 0.1220 Lr: 0.00331
[2023-12-25 13:24:18,128 INFO misc.py line 119 253097] Train: [50/100][202/510] Data 0.009 (0.125) Batch 0.954 (1.528) Remain 10:57:02 loss: 0.1457 Lr: 0.00331
[2023-12-25 13:24:19,249 INFO misc.py line 119 253097] Train: [50/100][203/510] Data 0.003 (0.124) Batch 1.121 (1.526) Remain 10:56:08 loss: 0.1023 Lr: 0.00331
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[2023-12-25 13:24:52,734 INFO misc.py line 119 253097] Train: [50/100][228/510] Data 0.005 (0.111) Batch 1.036 (1.505) Remain 10:46:37 loss: 0.2270 Lr: 0.00330
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[2023-12-25 13:25:53,981 INFO misc.py line 119 253097] Train: [50/100][254/510] Data 0.004 (0.187) Batch 1.150 (1.593) Remain 11:23:48 loss: 0.1434 Lr: 0.00330
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[2023-12-25 13:27:25,641 INFO misc.py line 119 253097] Train: [50/100][324/510] Data 0.005 (0.160) Batch 0.928 (1.531) Remain 10:55:28 loss: 0.0895 Lr: 0.00329
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[2023-12-25 13:30:52,911 INFO misc.py line 119 253097] Train: [50/100][464/510] Data 0.010 (0.130) Batch 1.252 (1.516) Remain 10:45:21 loss: 0.2526 Lr: 0.00326
[2023-12-25 13:30:54,116 INFO misc.py line 119 253097] Train: [50/100][465/510] Data 0.004 (0.130) Batch 1.201 (1.515) Remain 10:45:02 loss: 0.1235 Lr: 0.00326
[2023-12-25 13:30:55,356 INFO misc.py line 119 253097] Train: [50/100][466/510] Data 0.008 (0.129) Batch 1.244 (1.514) Remain 10:44:46 loss: 0.1412 Lr: 0.00326
[2023-12-25 13:30:56,286 INFO misc.py line 119 253097] Train: [50/100][467/510] Data 0.004 (0.129) Batch 0.931 (1.513) Remain 10:44:12 loss: 0.3143 Lr: 0.00326
[2023-12-25 13:30:57,197 INFO misc.py line 119 253097] Train: [50/100][468/510] Data 0.003 (0.129) Batch 0.910 (1.512) Remain 10:43:37 loss: 0.1291 Lr: 0.00326
[2023-12-25 13:30:58,507 INFO misc.py line 119 253097] Train: [50/100][469/510] Data 0.004 (0.128) Batch 1.309 (1.511) Remain 10:43:25 loss: 0.2815 Lr: 0.00326
[2023-12-25 13:30:59,777 INFO misc.py line 119 253097] Train: [50/100][470/510] Data 0.006 (0.128) Batch 1.271 (1.511) Remain 10:43:10 loss: 0.1547 Lr: 0.00326
[2023-12-25 13:31:00,909 INFO misc.py line 119 253097] Train: [50/100][471/510] Data 0.005 (0.128) Batch 1.127 (1.510) Remain 10:42:48 loss: 0.0903 Lr: 0.00326
[2023-12-25 13:31:02,137 INFO misc.py line 119 253097] Train: [50/100][472/510] Data 0.008 (0.128) Batch 1.233 (1.510) Remain 10:42:31 loss: 0.1405 Lr: 0.00326
[2023-12-25 13:31:14,668 INFO misc.py line 119 253097] Train: [50/100][473/510] Data 0.003 (0.127) Batch 12.525 (1.533) Remain 10:52:28 loss: 0.0780 Lr: 0.00326
[2023-12-25 13:31:15,670 INFO misc.py line 119 253097] Train: [50/100][474/510] Data 0.010 (0.127) Batch 1.004 (1.532) Remain 10:51:58 loss: 0.2327 Lr: 0.00326
[2023-12-25 13:31:16,620 INFO misc.py line 119 253097] Train: [50/100][475/510] Data 0.008 (0.127) Batch 0.954 (1.531) Remain 10:51:25 loss: 0.1651 Lr: 0.00326
[2023-12-25 13:31:17,878 INFO misc.py line 119 253097] Train: [50/100][476/510] Data 0.003 (0.127) Batch 1.254 (1.530) Remain 10:51:08 loss: 0.2830 Lr: 0.00326
[2023-12-25 13:31:19,052 INFO misc.py line 119 253097] Train: [50/100][477/510] Data 0.008 (0.126) Batch 1.179 (1.529) Remain 10:50:48 loss: 0.0984 Lr: 0.00326
[2023-12-25 13:31:20,227 INFO misc.py line 119 253097] Train: [50/100][478/510] Data 0.003 (0.126) Batch 1.171 (1.529) Remain 10:50:27 loss: 0.1207 Lr: 0.00326
[2023-12-25 13:31:21,502 INFO misc.py line 119 253097] Train: [50/100][479/510] Data 0.007 (0.126) Batch 1.274 (1.528) Remain 10:50:12 loss: 0.2213 Lr: 0.00326
[2023-12-25 13:31:22,564 INFO misc.py line 119 253097] Train: [50/100][480/510] Data 0.007 (0.126) Batch 1.066 (1.527) Remain 10:49:46 loss: 0.1722 Lr: 0.00326
[2023-12-25 13:31:23,567 INFO misc.py line 119 253097] Train: [50/100][481/510] Data 0.004 (0.125) Batch 1.001 (1.526) Remain 10:49:16 loss: 0.1533 Lr: 0.00326
[2023-12-25 13:31:24,750 INFO misc.py line 119 253097] Train: [50/100][482/510] Data 0.006 (0.125) Batch 1.180 (1.525) Remain 10:48:56 loss: 0.1589 Lr: 0.00326
[2023-12-25 13:31:25,914 INFO misc.py line 119 253097] Train: [50/100][483/510] Data 0.008 (0.125) Batch 1.168 (1.525) Remain 10:48:36 loss: 0.2746 Lr: 0.00325
[2023-12-25 13:31:26,978 INFO misc.py line 119 253097] Train: [50/100][484/510] Data 0.005 (0.125) Batch 1.063 (1.524) Remain 10:48:10 loss: 0.1160 Lr: 0.00325
[2023-12-25 13:31:28,264 INFO misc.py line 119 253097] Train: [50/100][485/510] Data 0.007 (0.124) Batch 1.287 (1.523) Remain 10:47:56 loss: 0.0914 Lr: 0.00325
[2023-12-25 13:31:29,308 INFO misc.py line 119 253097] Train: [50/100][486/510] Data 0.006 (0.124) Batch 1.034 (1.522) Remain 10:47:28 loss: 0.2172 Lr: 0.00325
[2023-12-25 13:31:30,150 INFO misc.py line 119 253097] Train: [50/100][487/510] Data 0.015 (0.124) Batch 0.852 (1.521) Remain 10:46:51 loss: 0.2055 Lr: 0.00325
[2023-12-25 13:31:31,370 INFO misc.py line 119 253097] Train: [50/100][488/510] Data 0.005 (0.124) Batch 1.219 (1.520) Remain 10:46:34 loss: 0.2724 Lr: 0.00325
[2023-12-25 13:31:32,425 INFO misc.py line 119 253097] Train: [50/100][489/510] Data 0.005 (0.123) Batch 1.055 (1.519) Remain 10:46:08 loss: 0.2682 Lr: 0.00325
[2023-12-25 13:31:33,592 INFO misc.py line 119 253097] Train: [50/100][490/510] Data 0.005 (0.123) Batch 1.164 (1.518) Remain 10:45:48 loss: 0.3709 Lr: 0.00325
[2023-12-25 13:31:34,759 INFO misc.py line 119 253097] Train: [50/100][491/510] Data 0.008 (0.123) Batch 1.170 (1.518) Remain 10:45:28 loss: 0.1745 Lr: 0.00325
[2023-12-25 13:31:35,960 INFO misc.py line 119 253097] Train: [50/100][492/510] Data 0.006 (0.123) Batch 1.200 (1.517) Remain 10:45:10 loss: 0.1482 Lr: 0.00325
[2023-12-25 13:31:36,979 INFO misc.py line 119 253097] Train: [50/100][493/510] Data 0.005 (0.122) Batch 1.018 (1.516) Remain 10:44:43 loss: 0.1344 Lr: 0.00325
[2023-12-25 13:31:38,126 INFO misc.py line 119 253097] Train: [50/100][494/510] Data 0.006 (0.122) Batch 1.143 (1.515) Remain 10:44:22 loss: 0.2327 Lr: 0.00325
[2023-12-25 13:31:39,405 INFO misc.py line 119 253097] Train: [50/100][495/510] Data 0.011 (0.122) Batch 1.284 (1.515) Remain 10:44:08 loss: 0.1166 Lr: 0.00325
[2023-12-25 13:31:40,506 INFO misc.py line 119 253097] Train: [50/100][496/510] Data 0.005 (0.122) Batch 1.101 (1.514) Remain 10:43:45 loss: 0.1417 Lr: 0.00325
[2023-12-25 13:31:41,642 INFO misc.py line 119 253097] Train: [50/100][497/510] Data 0.004 (0.122) Batch 1.136 (1.513) Remain 10:43:24 loss: 0.1216 Lr: 0.00325
[2023-12-25 13:31:54,007 INFO misc.py line 119 253097] Train: [50/100][498/510] Data 0.003 (0.121) Batch 12.365 (1.535) Remain 10:52:42 loss: 0.1172 Lr: 0.00325
[2023-12-25 13:31:54,992 INFO misc.py line 119 253097] Train: [50/100][499/510] Data 0.005 (0.121) Batch 0.985 (1.534) Remain 10:52:12 loss: 0.1335 Lr: 0.00325
[2023-12-25 13:31:56,174 INFO misc.py line 119 253097] Train: [50/100][500/510] Data 0.005 (0.121) Batch 1.182 (1.533) Remain 10:51:53 loss: 0.0952 Lr: 0.00325
[2023-12-25 13:31:57,372 INFO misc.py line 119 253097] Train: [50/100][501/510] Data 0.004 (0.121) Batch 1.195 (1.533) Remain 10:51:34 loss: 0.0893 Lr: 0.00325
[2023-12-25 13:31:58,333 INFO misc.py line 119 253097] Train: [50/100][502/510] Data 0.009 (0.120) Batch 0.964 (1.531) Remain 10:51:03 loss: 0.0709 Lr: 0.00325
[2023-12-25 13:31:59,476 INFO misc.py line 119 253097] Train: [50/100][503/510] Data 0.005 (0.120) Batch 1.143 (1.531) Remain 10:50:42 loss: 0.3843 Lr: 0.00325
[2023-12-25 13:32:04,129 INFO misc.py line 119 253097] Train: [50/100][504/510] Data 0.005 (0.120) Batch 4.653 (1.537) Remain 10:53:19 loss: 0.1393 Lr: 0.00325
[2023-12-25 13:32:05,424 INFO misc.py line 119 253097] Train: [50/100][505/510] Data 0.004 (0.120) Batch 1.261 (1.536) Remain 10:53:04 loss: 0.2071 Lr: 0.00325
[2023-12-25 13:32:06,466 INFO misc.py line 119 253097] Train: [50/100][506/510] Data 0.038 (0.120) Batch 1.071 (1.535) Remain 10:52:39 loss: 0.0553 Lr: 0.00325
[2023-12-25 13:32:07,657 INFO misc.py line 119 253097] Train: [50/100][507/510] Data 0.009 (0.119) Batch 1.192 (1.535) Remain 10:52:20 loss: 0.1552 Lr: 0.00325
[2023-12-25 13:32:08,776 INFO misc.py line 119 253097] Train: [50/100][508/510] Data 0.008 (0.119) Batch 1.119 (1.534) Remain 10:51:57 loss: 0.1492 Lr: 0.00325
[2023-12-25 13:32:09,974 INFO misc.py line 119 253097] Train: [50/100][509/510] Data 0.009 (0.119) Batch 1.197 (1.533) Remain 10:51:39 loss: 0.2802 Lr: 0.00325
[2023-12-25 13:32:11,219 INFO misc.py line 119 253097] Train: [50/100][510/510] Data 0.010 (0.119) Batch 1.245 (1.533) Remain 10:51:23 loss: 0.1516 Lr: 0.00325
[2023-12-25 13:32:11,219 INFO misc.py line 136 253097] Train result: loss: 0.1855 
[2023-12-25 13:32:11,219 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 13:32:39,645 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6393 
[2023-12-25 13:32:39,997 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4168 
[2023-12-25 13:32:44,950 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4795 
[2023-12-25 13:32:45,467 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4677 
[2023-12-25 13:32:47,455 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8552 
[2023-12-25 13:32:47,880 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3828 
[2023-12-25 13:32:48,759 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0446 
[2023-12-25 13:32:49,322 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3604 
[2023-12-25 13:32:51,132 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.7815 
[2023-12-25 13:32:53,252 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2712 
[2023-12-25 13:32:54,111 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3104 
[2023-12-25 13:32:54,536 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7006 
[2023-12-25 13:32:55,436 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.9727 
[2023-12-25 13:32:58,377 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9699 
[2023-12-25 13:32:58,847 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1791 
[2023-12-25 13:32:59,469 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.6394 
[2023-12-25 13:33:00,173 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3392 
[2023-12-25 13:33:01,636 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6358/0.7143/0.8927.
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9301/0.9532
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9812/0.9905
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8331/0.9646
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2782/0.2923
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5345/0.5490
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6363/0.7692
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7838/0.8559
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8967/0.9582
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4636/0.4769
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7503/0.8549
[2023-12-25 13:33:01,636 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.5979/0.9237
[2023-12-25 13:33:01,637 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5798/0.6973
[2023-12-25 13:33:01,637 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 13:33:01,639 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 13:33:01,639 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 13:33:19,184 INFO misc.py line 119 253097] Train: [51/100][1/510] Data 13.613 (13.613) Batch 14.876 (14.876) Remain 105:21:50 loss: 0.2954 Lr: 0.00325
[2023-12-25 13:33:20,424 INFO misc.py line 119 253097] Train: [51/100][2/510] Data 0.004 (0.004) Batch 1.210 (1.210) Remain 08:34:22 loss: 0.1401 Lr: 0.00325
[2023-12-25 13:33:21,572 INFO misc.py line 119 253097] Train: [51/100][3/510] Data 0.034 (0.034) Batch 1.178 (1.178) Remain 08:20:23 loss: 0.2069 Lr: 0.00325
[2023-12-25 13:33:22,830 INFO misc.py line 119 253097] Train: [51/100][4/510] Data 0.004 (0.004) Batch 1.257 (1.257) Remain 08:54:09 loss: 0.1973 Lr: 0.00325
[2023-12-25 13:33:24,080 INFO misc.py line 119 253097] Train: [51/100][5/510] Data 0.005 (0.004) Batch 1.249 (1.253) Remain 08:52:29 loss: 0.2221 Lr: 0.00325
[2023-12-25 13:33:25,033 INFO misc.py line 119 253097] Train: [51/100][6/510] Data 0.006 (0.005) Batch 0.954 (1.153) Remain 08:10:02 loss: 0.1166 Lr: 0.00325
[2023-12-25 13:33:26,169 INFO misc.py line 119 253097] Train: [51/100][7/510] Data 0.006 (0.005) Batch 1.135 (1.149) Remain 08:08:07 loss: 0.1309 Lr: 0.00325
[2023-12-25 13:33:27,174 INFO misc.py line 119 253097] Train: [51/100][8/510] Data 0.006 (0.005) Batch 1.006 (1.120) Remain 07:55:57 loss: 0.1604 Lr: 0.00325
[2023-12-25 13:33:28,228 INFO misc.py line 119 253097] Train: [51/100][9/510] Data 0.005 (0.005) Batch 1.053 (1.109) Remain 07:51:11 loss: 0.2357 Lr: 0.00325
[2023-12-25 13:33:29,351 INFO misc.py line 119 253097] Train: [51/100][10/510] Data 0.006 (0.005) Batch 1.124 (1.111) Remain 07:52:04 loss: 0.1688 Lr: 0.00325
[2023-12-25 13:33:30,348 INFO misc.py line 119 253097] Train: [51/100][11/510] Data 0.004 (0.005) Batch 0.997 (1.097) Remain 07:46:01 loss: 0.1596 Lr: 0.00325
[2023-12-25 13:33:31,588 INFO misc.py line 119 253097] Train: [51/100][12/510] Data 0.004 (0.005) Batch 1.240 (1.113) Remain 07:52:44 loss: 0.1246 Lr: 0.00325
[2023-12-25 13:33:32,721 INFO misc.py line 119 253097] Train: [51/100][13/510] Data 0.004 (0.005) Batch 1.133 (1.115) Remain 07:53:35 loss: 0.1381 Lr: 0.00325
[2023-12-25 13:33:33,797 INFO misc.py line 119 253097] Train: [51/100][14/510] Data 0.004 (0.005) Batch 1.076 (1.111) Remain 07:52:04 loss: 0.1035 Lr: 0.00325
[2023-12-25 13:33:34,965 INFO misc.py line 119 253097] Train: [51/100][15/510] Data 0.004 (0.005) Batch 1.167 (1.116) Remain 07:54:01 loss: 0.0869 Lr: 0.00325
[2023-12-25 13:33:37,655 INFO misc.py line 119 253097] Train: [51/100][16/510] Data 0.005 (0.005) Batch 2.689 (1.237) Remain 08:45:23 loss: 0.1432 Lr: 0.00325
[2023-12-25 13:33:38,904 INFO misc.py line 119 253097] Train: [51/100][17/510] Data 0.006 (0.005) Batch 1.251 (1.238) Remain 08:45:48 loss: 0.2171 Lr: 0.00325
[2023-12-25 13:33:40,073 INFO misc.py line 119 253097] Train: [51/100][18/510] Data 0.004 (0.005) Batch 1.164 (1.233) Remain 08:43:41 loss: 0.2021 Lr: 0.00325
[2023-12-25 13:33:41,193 INFO misc.py line 119 253097] Train: [51/100][19/510] Data 0.008 (0.005) Batch 1.123 (1.226) Remain 08:40:45 loss: 0.1122 Lr: 0.00325
[2023-12-25 13:33:42,235 INFO misc.py line 119 253097] Train: [51/100][20/510] Data 0.005 (0.005) Batch 1.043 (1.215) Remain 08:36:09 loss: 0.1594 Lr: 0.00325
[2023-12-25 13:33:43,474 INFO misc.py line 119 253097] Train: [51/100][21/510] Data 0.005 (0.005) Batch 1.240 (1.217) Remain 08:36:42 loss: 0.1575 Lr: 0.00325
[2023-12-25 13:33:49,602 INFO misc.py line 119 253097] Train: [51/100][22/510] Data 0.003 (0.005) Batch 6.127 (1.475) Remain 10:26:25 loss: 0.1298 Lr: 0.00325
[2023-12-25 13:33:50,912 INFO misc.py line 119 253097] Train: [51/100][23/510] Data 0.004 (0.005) Batch 1.309 (1.467) Remain 10:22:52 loss: 0.1458 Lr: 0.00325
[2023-12-25 13:33:51,977 INFO misc.py line 119 253097] Train: [51/100][24/510] Data 0.007 (0.005) Batch 1.064 (1.448) Remain 10:14:42 loss: 0.1491 Lr: 0.00325
[2023-12-25 13:33:53,254 INFO misc.py line 119 253097] Train: [51/100][25/510] Data 0.006 (0.005) Batch 1.275 (1.440) Remain 10:11:21 loss: 0.0985 Lr: 0.00324
[2023-12-25 13:33:54,223 INFO misc.py line 119 253097] Train: [51/100][26/510] Data 0.008 (0.005) Batch 0.974 (1.420) Remain 10:02:43 loss: 0.1872 Lr: 0.00324
[2023-12-25 13:33:55,481 INFO misc.py line 119 253097] Train: [51/100][27/510] Data 0.003 (0.005) Batch 1.252 (1.413) Remain 09:59:44 loss: 0.1704 Lr: 0.00324
[2023-12-25 13:33:56,673 INFO misc.py line 119 253097] Train: [51/100][28/510] Data 0.010 (0.005) Batch 1.192 (1.404) Remain 09:55:57 loss: 0.2732 Lr: 0.00324
[2023-12-25 13:33:57,923 INFO misc.py line 119 253097] Train: [51/100][29/510] Data 0.009 (0.005) Batch 1.251 (1.398) Remain 09:53:26 loss: 0.1058 Lr: 0.00324
[2023-12-25 13:33:59,215 INFO misc.py line 119 253097] Train: [51/100][30/510] Data 0.009 (0.006) Batch 1.298 (1.394) Remain 09:51:50 loss: 0.1070 Lr: 0.00324
[2023-12-25 13:34:00,357 INFO misc.py line 119 253097] Train: [51/100][31/510] Data 0.004 (0.005) Batch 1.140 (1.385) Remain 09:47:57 loss: 0.2584 Lr: 0.00324
[2023-12-25 13:34:01,448 INFO misc.py line 119 253097] Train: [51/100][32/510] Data 0.006 (0.005) Batch 1.091 (1.375) Remain 09:43:38 loss: 0.1382 Lr: 0.00324
[2023-12-25 13:34:02,696 INFO misc.py line 119 253097] Train: [51/100][33/510] Data 0.006 (0.005) Batch 1.245 (1.371) Remain 09:41:46 loss: 0.1842 Lr: 0.00324
[2023-12-25 13:34:03,824 INFO misc.py line 119 253097] Train: [51/100][34/510] Data 0.009 (0.006) Batch 1.111 (1.362) Remain 09:38:11 loss: 0.2774 Lr: 0.00324
[2023-12-25 13:34:05,120 INFO misc.py line 119 253097] Train: [51/100][35/510] Data 0.026 (0.006) Batch 1.295 (1.360) Remain 09:37:16 loss: 0.2556 Lr: 0.00324
[2023-12-25 13:34:06,262 INFO misc.py line 119 253097] Train: [51/100][36/510] Data 0.028 (0.007) Batch 1.164 (1.354) Remain 09:34:44 loss: 0.1252 Lr: 0.00324
[2023-12-25 13:34:07,583 INFO misc.py line 119 253097] Train: [51/100][37/510] Data 0.004 (0.007) Batch 1.319 (1.353) Remain 09:34:16 loss: 0.1522 Lr: 0.00324
[2023-12-25 13:34:08,689 INFO misc.py line 119 253097] Train: [51/100][38/510] Data 0.007 (0.007) Batch 1.107 (1.346) Remain 09:31:16 loss: 0.2199 Lr: 0.00324
[2023-12-25 13:34:09,586 INFO misc.py line 119 253097] Train: [51/100][39/510] Data 0.005 (0.007) Batch 0.896 (1.334) Remain 09:25:56 loss: 0.1543 Lr: 0.00324
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[2023-12-25 13:45:02,107 INFO misc.py line 119 253097] Train: [51/100][458/510] Data 0.004 (0.032) Batch 0.970 (1.540) Remain 10:42:35 loss: 0.2040 Lr: 0.00316
[2023-12-25 13:45:03,145 INFO misc.py line 119 253097] Train: [51/100][459/510] Data 0.004 (0.032) Batch 1.038 (1.539) Remain 10:42:06 loss: 0.1444 Lr: 0.00316
[2023-12-25 13:45:04,316 INFO misc.py line 119 253097] Train: [51/100][460/510] Data 0.003 (0.032) Batch 1.171 (1.538) Remain 10:41:44 loss: 0.1687 Lr: 0.00316
[2023-12-25 13:45:05,414 INFO misc.py line 119 253097] Train: [51/100][461/510] Data 0.005 (0.032) Batch 1.098 (1.537) Remain 10:41:19 loss: 0.0985 Lr: 0.00316
[2023-12-25 13:45:06,738 INFO misc.py line 119 253097] Train: [51/100][462/510] Data 0.004 (0.032) Batch 1.320 (1.536) Remain 10:41:05 loss: 0.1278 Lr: 0.00316
[2023-12-25 13:45:07,892 INFO misc.py line 119 253097] Train: [51/100][463/510] Data 0.007 (0.032) Batch 1.153 (1.535) Remain 10:40:43 loss: 0.3878 Lr: 0.00316
[2023-12-25 13:45:09,235 INFO misc.py line 119 253097] Train: [51/100][464/510] Data 0.009 (0.032) Batch 1.348 (1.535) Remain 10:40:31 loss: 0.1358 Lr: 0.00316
[2023-12-25 13:45:18,934 INFO misc.py line 119 253097] Train: [51/100][465/510] Data 0.004 (0.032) Batch 9.699 (1.553) Remain 10:47:52 loss: 0.0901 Lr: 0.00316
[2023-12-25 13:45:20,133 INFO misc.py line 119 253097] Train: [51/100][466/510] Data 0.004 (0.031) Batch 1.198 (1.552) Remain 10:47:31 loss: 0.1804 Lr: 0.00316
[2023-12-25 13:45:21,426 INFO misc.py line 119 253097] Train: [51/100][467/510] Data 0.004 (0.031) Batch 1.290 (1.551) Remain 10:47:16 loss: 0.1454 Lr: 0.00316
[2023-12-25 13:45:22,617 INFO misc.py line 119 253097] Train: [51/100][468/510] Data 0.008 (0.031) Batch 1.189 (1.551) Remain 10:46:55 loss: 0.1734 Lr: 0.00316
[2023-12-25 13:45:23,931 INFO misc.py line 119 253097] Train: [51/100][469/510] Data 0.009 (0.031) Batch 1.318 (1.550) Remain 10:46:41 loss: 0.1310 Lr: 0.00316
[2023-12-25 13:45:25,064 INFO misc.py line 119 253097] Train: [51/100][470/510] Data 0.004 (0.031) Batch 1.128 (1.549) Remain 10:46:16 loss: 0.2028 Lr: 0.00316
[2023-12-25 13:45:26,505 INFO misc.py line 119 253097] Train: [51/100][471/510] Data 0.009 (0.031) Batch 1.443 (1.549) Remain 10:46:09 loss: 0.1894 Lr: 0.00316
[2023-12-25 13:45:27,562 INFO misc.py line 119 253097] Train: [51/100][472/510] Data 0.007 (0.031) Batch 1.056 (1.548) Remain 10:45:41 loss: 0.2180 Lr: 0.00316
[2023-12-25 13:45:28,696 INFO misc.py line 119 253097] Train: [51/100][473/510] Data 0.008 (0.031) Batch 1.133 (1.547) Remain 10:45:18 loss: 0.1973 Lr: 0.00316
[2023-12-25 13:45:29,617 INFO misc.py line 119 253097] Train: [51/100][474/510] Data 0.009 (0.031) Batch 0.924 (1.546) Remain 10:44:43 loss: 0.1377 Lr: 0.00316
[2023-12-25 13:45:30,785 INFO misc.py line 119 253097] Train: [51/100][475/510] Data 0.006 (0.031) Batch 1.170 (1.545) Remain 10:44:22 loss: 0.1170 Lr: 0.00316
[2023-12-25 13:45:31,822 INFO misc.py line 119 253097] Train: [51/100][476/510] Data 0.003 (0.031) Batch 1.037 (1.544) Remain 10:43:53 loss: 0.2340 Lr: 0.00316
[2023-12-25 13:45:33,064 INFO misc.py line 119 253097] Train: [51/100][477/510] Data 0.005 (0.031) Batch 1.242 (1.543) Remain 10:43:36 loss: 0.1534 Lr: 0.00316
[2023-12-25 13:45:34,012 INFO misc.py line 119 253097] Train: [51/100][478/510] Data 0.005 (0.031) Batch 0.947 (1.542) Remain 10:43:03 loss: 0.1497 Lr: 0.00316
[2023-12-25 13:45:35,023 INFO misc.py line 119 253097] Train: [51/100][479/510] Data 0.005 (0.031) Batch 1.013 (1.541) Remain 10:42:33 loss: 0.1542 Lr: 0.00316
[2023-12-25 13:45:36,109 INFO misc.py line 119 253097] Train: [51/100][480/510] Data 0.004 (0.031) Batch 1.086 (1.540) Remain 10:42:08 loss: 0.1645 Lr: 0.00316
[2023-12-25 13:45:37,160 INFO misc.py line 119 253097] Train: [51/100][481/510] Data 0.004 (0.031) Batch 1.052 (1.539) Remain 10:41:41 loss: 0.1429 Lr: 0.00316
[2023-12-25 13:45:38,350 INFO misc.py line 119 253097] Train: [51/100][482/510] Data 0.004 (0.031) Batch 1.188 (1.538) Remain 10:41:21 loss: 0.1595 Lr: 0.00316
[2023-12-25 13:45:39,653 INFO misc.py line 119 253097] Train: [51/100][483/510] Data 0.006 (0.031) Batch 1.293 (1.538) Remain 10:41:07 loss: 0.1119 Lr: 0.00316
[2023-12-25 13:45:40,858 INFO misc.py line 119 253097] Train: [51/100][484/510] Data 0.015 (0.031) Batch 1.211 (1.537) Remain 10:40:48 loss: 0.1838 Lr: 0.00316
[2023-12-25 13:45:42,113 INFO misc.py line 119 253097] Train: [51/100][485/510] Data 0.009 (0.030) Batch 1.260 (1.536) Remain 10:40:32 loss: 0.2663 Lr: 0.00316
[2023-12-25 13:45:43,401 INFO misc.py line 119 253097] Train: [51/100][486/510] Data 0.004 (0.030) Batch 1.282 (1.536) Remain 10:40:18 loss: 0.3845 Lr: 0.00316
[2023-12-25 13:45:44,624 INFO misc.py line 119 253097] Train: [51/100][487/510] Data 0.010 (0.030) Batch 1.229 (1.535) Remain 10:40:00 loss: 0.1469 Lr: 0.00316
[2023-12-25 13:45:45,627 INFO misc.py line 119 253097] Train: [51/100][488/510] Data 0.003 (0.030) Batch 1.002 (1.534) Remain 10:39:31 loss: 0.1280 Lr: 0.00316
[2023-12-25 13:45:52,446 INFO misc.py line 119 253097] Train: [51/100][489/510] Data 0.004 (0.030) Batch 6.819 (1.545) Remain 10:44:02 loss: 0.1833 Lr: 0.00315
[2023-12-25 13:45:53,710 INFO misc.py line 119 253097] Train: [51/100][490/510] Data 0.004 (0.030) Batch 1.259 (1.544) Remain 10:43:45 loss: 0.2297 Lr: 0.00315
[2023-12-25 13:45:55,083 INFO misc.py line 119 253097] Train: [51/100][491/510] Data 0.009 (0.030) Batch 1.374 (1.544) Remain 10:43:35 loss: 0.1328 Lr: 0.00315
[2023-12-25 13:45:56,228 INFO misc.py line 119 253097] Train: [51/100][492/510] Data 0.008 (0.030) Batch 1.149 (1.543) Remain 10:43:13 loss: 0.1353 Lr: 0.00315
[2023-12-25 13:45:57,377 INFO misc.py line 119 253097] Train: [51/100][493/510] Data 0.004 (0.030) Batch 1.148 (1.542) Remain 10:42:52 loss: 0.1105 Lr: 0.00315
[2023-12-25 13:45:58,645 INFO misc.py line 119 253097] Train: [51/100][494/510] Data 0.004 (0.030) Batch 1.265 (1.542) Remain 10:42:36 loss: 0.1247 Lr: 0.00315
[2023-12-25 13:45:59,863 INFO misc.py line 119 253097] Train: [51/100][495/510] Data 0.008 (0.030) Batch 1.211 (1.541) Remain 10:42:18 loss: 0.0896 Lr: 0.00315
[2023-12-25 13:46:01,135 INFO misc.py line 119 253097] Train: [51/100][496/510] Data 0.015 (0.030) Batch 1.283 (1.541) Remain 10:42:03 loss: 0.1976 Lr: 0.00315
[2023-12-25 13:46:02,367 INFO misc.py line 119 253097] Train: [51/100][497/510] Data 0.003 (0.030) Batch 1.225 (1.540) Remain 10:41:46 loss: 0.4735 Lr: 0.00315
[2023-12-25 13:46:03,652 INFO misc.py line 119 253097] Train: [51/100][498/510] Data 0.010 (0.030) Batch 1.292 (1.540) Remain 10:41:31 loss: 0.1460 Lr: 0.00315
[2023-12-25 13:46:04,871 INFO misc.py line 119 253097] Train: [51/100][499/510] Data 0.003 (0.030) Batch 1.215 (1.539) Remain 10:41:14 loss: 0.1210 Lr: 0.00315
[2023-12-25 13:46:06,000 INFO misc.py line 119 253097] Train: [51/100][500/510] Data 0.007 (0.030) Batch 1.127 (1.538) Remain 10:40:51 loss: 0.0977 Lr: 0.00315
[2023-12-25 13:46:07,281 INFO misc.py line 119 253097] Train: [51/100][501/510] Data 0.009 (0.030) Batch 1.287 (1.538) Remain 10:40:37 loss: 0.2031 Lr: 0.00315
[2023-12-25 13:46:08,435 INFO misc.py line 119 253097] Train: [51/100][502/510] Data 0.003 (0.030) Batch 1.151 (1.537) Remain 10:40:16 loss: 0.3836 Lr: 0.00315
[2023-12-25 13:46:09,572 INFO misc.py line 119 253097] Train: [51/100][503/510] Data 0.006 (0.030) Batch 1.134 (1.536) Remain 10:39:55 loss: 0.1864 Lr: 0.00315
[2023-12-25 13:46:10,667 INFO misc.py line 119 253097] Train: [51/100][504/510] Data 0.009 (0.030) Batch 1.097 (1.535) Remain 10:39:31 loss: 0.2209 Lr: 0.00315
[2023-12-25 13:46:11,934 INFO misc.py line 119 253097] Train: [51/100][505/510] Data 0.008 (0.030) Batch 1.267 (1.535) Remain 10:39:16 loss: 0.1723 Lr: 0.00315
[2023-12-25 13:46:13,059 INFO misc.py line 119 253097] Train: [51/100][506/510] Data 0.008 (0.029) Batch 1.126 (1.534) Remain 10:38:54 loss: 0.3774 Lr: 0.00315
[2023-12-25 13:46:16,738 INFO misc.py line 119 253097] Train: [51/100][507/510] Data 0.006 (0.029) Batch 3.682 (1.538) Remain 10:40:39 loss: 0.1557 Lr: 0.00315
[2023-12-25 13:46:17,722 INFO misc.py line 119 253097] Train: [51/100][508/510] Data 0.004 (0.029) Batch 0.984 (1.537) Remain 10:40:10 loss: 0.2552 Lr: 0.00315
[2023-12-25 13:46:18,950 INFO misc.py line 119 253097] Train: [51/100][509/510] Data 0.003 (0.029) Batch 1.228 (1.536) Remain 10:39:54 loss: 0.1191 Lr: 0.00315
[2023-12-25 13:46:20,233 INFO misc.py line 119 253097] Train: [51/100][510/510] Data 0.003 (0.029) Batch 1.281 (1.536) Remain 10:39:40 loss: 0.1456 Lr: 0.00315
[2023-12-25 13:46:20,235 INFO misc.py line 136 253097] Train result: loss: 0.1885 
[2023-12-25 13:46:20,236 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 13:46:47,969 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4021 
[2023-12-25 13:46:48,322 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.5334 
[2023-12-25 13:46:54,085 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6347 
[2023-12-25 13:46:54,608 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.5946 
[2023-12-25 13:46:56,581 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0458 
[2023-12-25 13:46:57,010 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4065 
[2023-12-25 13:46:57,889 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1373 
[2023-12-25 13:46:58,442 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4255 
[2023-12-25 13:47:00,250 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9484 
[2023-12-25 13:47:02,380 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.5476 
[2023-12-25 13:47:03,234 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3641 
[2023-12-25 13:47:03,661 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9101 
[2023-12-25 13:47:04,560 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4437 
[2023-12-25 13:47:07,502 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8205 
[2023-12-25 13:47:07,970 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4285 
[2023-12-25 13:47:08,582 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5987 
[2023-12-25 13:47:09,281 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3338 
[2023-12-25 13:47:10,558 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6183/0.6876/0.8852.
[2023-12-25 13:47:10,558 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9217/0.9452
[2023-12-25 13:47:10,558 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9834/0.9912
[2023-12-25 13:47:10,558 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8039/0.9699
[2023-12-25 13:47:10,558 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 13:47:10,558 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3295/0.3521
[2023-12-25 13:47:10,558 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5842/0.6064
[2023-12-25 13:47:10,558 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5079/0.5237
[2023-12-25 13:47:10,559 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7888/0.8633
[2023-12-25 13:47:10,559 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9008/0.9461
[2023-12-25 13:47:10,559 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.3027/0.3061
[2023-12-25 13:47:10,559 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7432/0.8147
[2023-12-25 13:47:10,559 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.5914/0.9062
[2023-12-25 13:47:10,559 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5806/0.7148
[2023-12-25 13:47:10,559 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 13:47:10,561 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 13:47:10,561 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 13:47:16,830 INFO misc.py line 119 253097] Train: [52/100][1/510] Data 2.649 (2.649) Batch 3.395 (3.395) Remain 23:34:01 loss: 0.1338 Lr: 0.00315
[2023-12-25 13:47:18,598 INFO misc.py line 119 253097] Train: [52/100][2/510] Data 0.453 (0.453) Batch 1.772 (1.772) Remain 12:18:04 loss: 0.1942 Lr: 0.00315
[2023-12-25 13:47:21,010 INFO misc.py line 119 253097] Train: [52/100][3/510] Data 1.562 (1.562) Batch 2.415 (2.415) Remain 16:45:33 loss: 0.1901 Lr: 0.00315
[2023-12-25 13:47:22,223 INFO misc.py line 119 253097] Train: [52/100][4/510] Data 0.005 (0.005) Batch 1.212 (1.212) Remain 08:24:44 loss: 0.2918 Lr: 0.00315
[2023-12-25 13:47:23,190 INFO misc.py line 119 253097] Train: [52/100][5/510] Data 0.005 (0.005) Batch 0.966 (1.089) Remain 07:33:31 loss: 0.1459 Lr: 0.00315
[2023-12-25 13:47:24,246 INFO misc.py line 119 253097] Train: [52/100][6/510] Data 0.007 (0.006) Batch 1.059 (1.079) Remain 07:29:22 loss: 0.2343 Lr: 0.00315
[2023-12-25 13:47:25,358 INFO misc.py line 119 253097] Train: [52/100][7/510] Data 0.004 (0.005) Batch 1.111 (1.087) Remain 07:32:38 loss: 0.0945 Lr: 0.00315
[2023-12-25 13:47:26,694 INFO misc.py line 119 253097] Train: [52/100][8/510] Data 0.005 (0.005) Batch 1.331 (1.136) Remain 07:52:57 loss: 0.2472 Lr: 0.00315
[2023-12-25 13:47:29,091 INFO misc.py line 119 253097] Train: [52/100][9/510] Data 1.468 (0.249) Batch 2.402 (1.347) Remain 09:20:46 loss: 0.1651 Lr: 0.00315
[2023-12-25 13:47:30,146 INFO misc.py line 119 253097] Train: [52/100][10/510] Data 0.004 (0.214) Batch 1.047 (1.304) Remain 09:02:55 loss: 0.1981 Lr: 0.00315
[2023-12-25 13:47:31,225 INFO misc.py line 119 253097] Train: [52/100][11/510] Data 0.013 (0.189) Batch 1.087 (1.277) Remain 08:51:35 loss: 0.1882 Lr: 0.00315
[2023-12-25 13:47:32,318 INFO misc.py line 119 253097] Train: [52/100][12/510] Data 0.005 (0.168) Batch 1.093 (1.256) Remain 08:43:04 loss: 0.2319 Lr: 0.00315
[2023-12-25 13:47:33,337 INFO misc.py line 119 253097] Train: [52/100][13/510] Data 0.005 (0.152) Batch 1.020 (1.233) Remain 08:33:12 loss: 0.2623 Lr: 0.00315
[2023-12-25 13:47:34,353 INFO misc.py line 119 253097] Train: [52/100][14/510] Data 0.005 (0.139) Batch 1.015 (1.213) Remain 08:24:57 loss: 0.2008 Lr: 0.00315
[2023-12-25 13:47:40,735 INFO misc.py line 119 253097] Train: [52/100][15/510] Data 5.234 (0.563) Batch 6.383 (1.644) Remain 11:24:15 loss: 0.3176 Lr: 0.00315
[2023-12-25 13:47:41,944 INFO misc.py line 119 253097] Train: [52/100][16/510] Data 0.003 (0.520) Batch 1.208 (1.610) Remain 11:10:15 loss: 0.2591 Lr: 0.00315
[2023-12-25 13:47:43,169 INFO misc.py line 119 253097] Train: [52/100][17/510] Data 0.005 (0.483) Batch 1.222 (1.583) Remain 10:58:41 loss: 0.1185 Lr: 0.00315
[2023-12-25 13:47:44,358 INFO misc.py line 119 253097] Train: [52/100][18/510] Data 0.009 (0.452) Batch 1.186 (1.556) Remain 10:47:39 loss: 0.2404 Lr: 0.00315
[2023-12-25 13:47:45,666 INFO misc.py line 119 253097] Train: [52/100][19/510] Data 0.011 (0.424) Batch 1.303 (1.540) Remain 10:41:01 loss: 0.1804 Lr: 0.00315
[2023-12-25 13:47:46,805 INFO misc.py line 119 253097] Train: [52/100][20/510] Data 0.017 (0.400) Batch 1.151 (1.517) Remain 10:31:29 loss: 0.1724 Lr: 0.00315
[2023-12-25 13:47:47,933 INFO misc.py line 119 253097] Train: [52/100][21/510] Data 0.004 (0.378) Batch 1.127 (1.496) Remain 10:22:25 loss: 0.3414 Lr: 0.00315
[2023-12-25 13:47:49,136 INFO misc.py line 119 253097] Train: [52/100][22/510] Data 0.006 (0.359) Batch 1.204 (1.480) Remain 10:16:01 loss: 0.1133 Lr: 0.00315
[2023-12-25 13:47:50,329 INFO misc.py line 119 253097] Train: [52/100][23/510] Data 0.004 (0.341) Batch 1.188 (1.466) Remain 10:09:55 loss: 0.2316 Lr: 0.00315
[2023-12-25 13:47:51,584 INFO misc.py line 119 253097] Train: [52/100][24/510] Data 0.009 (0.325) Batch 1.259 (1.456) Remain 10:05:47 loss: 0.2521 Lr: 0.00315
[2023-12-25 13:47:52,707 INFO misc.py line 119 253097] Train: [52/100][25/510] Data 0.006 (0.311) Batch 1.123 (1.441) Remain 09:59:28 loss: 0.3779 Lr: 0.00315
[2023-12-25 13:47:53,789 INFO misc.py line 119 253097] Train: [52/100][26/510] Data 0.005 (0.297) Batch 1.084 (1.425) Remain 09:52:58 loss: 0.2347 Lr: 0.00315
[2023-12-25 13:47:54,676 INFO misc.py line 119 253097] Train: [52/100][27/510] Data 0.004 (0.285) Batch 0.887 (1.403) Remain 09:43:37 loss: 0.1128 Lr: 0.00315
[2023-12-25 13:47:58,642 INFO misc.py line 119 253097] Train: [52/100][28/510] Data 2.731 (0.383) Batch 3.967 (1.505) Remain 10:26:16 loss: 0.1683 Lr: 0.00315
[2023-12-25 13:47:59,532 INFO misc.py line 119 253097] Train: [52/100][29/510] Data 0.003 (0.368) Batch 0.889 (1.482) Remain 10:16:22 loss: 0.3010 Lr: 0.00315
[2023-12-25 13:48:00,636 INFO misc.py line 119 253097] Train: [52/100][30/510] Data 0.004 (0.355) Batch 1.105 (1.468) Remain 10:10:33 loss: 0.1353 Lr: 0.00314
[2023-12-25 13:48:01,765 INFO misc.py line 119 253097] Train: [52/100][31/510] Data 0.005 (0.342) Batch 1.124 (1.455) Remain 10:05:25 loss: 0.3673 Lr: 0.00314
[2023-12-25 13:48:08,687 INFO misc.py line 119 253097] Train: [52/100][32/510] Data 5.679 (0.526) Batch 6.927 (1.644) Remain 11:23:53 loss: 0.1158 Lr: 0.00314
[2023-12-25 13:48:09,721 INFO misc.py line 119 253097] Train: [52/100][33/510] Data 0.003 (0.509) Batch 1.033 (1.624) Remain 11:15:23 loss: 0.1586 Lr: 0.00314
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[2023-12-25 13:48:49,625 INFO misc.py line 119 253097] Train: [52/100][55/510] Data 7.365 (0.579) Batch 8.368 (1.704) Remain 11:48:12 loss: 0.0957 Lr: 0.00314
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[2023-12-25 13:49:00,162 INFO misc.py line 119 253097] Train: [52/100][64/510] Data 0.006 (0.494) Batch 1.172 (1.625) Remain 11:15:15 loss: 0.1407 Lr: 0.00314
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[2023-12-25 13:49:02,371 INFO misc.py line 119 253097] Train: [52/100][66/510] Data 0.008 (0.479) Batch 1.053 (1.609) Remain 11:08:20 loss: 0.1359 Lr: 0.00314
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[2023-12-25 13:49:04,836 INFO misc.py line 119 253097] Train: [52/100][68/510] Data 0.009 (0.464) Batch 1.249 (1.597) Remain 11:03:28 loss: 0.1182 Lr: 0.00314
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[2023-12-25 13:49:07,172 INFO misc.py line 119 253097] Train: [52/100][70/510] Data 0.005 (0.450) Batch 1.157 (1.584) Remain 10:58:05 loss: 0.1638 Lr: 0.00314
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[2023-12-25 13:49:16,478 INFO misc.py line 119 253097] Train: [52/100][73/510] Data 0.003 (0.431) Batch 6.863 (1.650) Remain 11:25:02 loss: 0.3292 Lr: 0.00314
[2023-12-25 13:49:17,686 INFO misc.py line 119 253097] Train: [52/100][74/510] Data 0.004 (0.425) Batch 1.208 (1.643) Remain 11:22:25 loss: 0.1255 Lr: 0.00314
[2023-12-25 13:49:22,883 INFO misc.py line 119 253097] Train: [52/100][75/510] Data 3.892 (0.474) Batch 5.196 (1.693) Remain 11:42:53 loss: 0.1081 Lr: 0.00314
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[2023-12-25 13:49:27,606 INFO misc.py line 119 253097] Train: [52/100][79/510] Data 0.010 (0.449) Batch 1.140 (1.666) Remain 11:31:35 loss: 0.1945 Lr: 0.00314
[2023-12-25 13:49:28,634 INFO misc.py line 119 253097] Train: [52/100][80/510] Data 0.005 (0.443) Batch 1.022 (1.657) Remain 11:28:05 loss: 0.1652 Lr: 0.00314
[2023-12-25 13:49:29,716 INFO misc.py line 119 253097] Train: [52/100][81/510] Data 0.011 (0.438) Batch 1.085 (1.650) Remain 11:25:00 loss: 0.2360 Lr: 0.00314
[2023-12-25 13:49:30,856 INFO misc.py line 119 253097] Train: [52/100][82/510] Data 0.009 (0.432) Batch 1.141 (1.644) Remain 11:22:18 loss: 0.1589 Lr: 0.00313
[2023-12-25 13:49:31,932 INFO misc.py line 119 253097] Train: [52/100][83/510] Data 0.008 (0.427) Batch 1.076 (1.636) Remain 11:19:19 loss: 0.1077 Lr: 0.00313
[2023-12-25 13:49:33,063 INFO misc.py line 119 253097] Train: [52/100][84/510] Data 0.008 (0.422) Batch 1.132 (1.630) Remain 11:16:43 loss: 0.3182 Lr: 0.00313
[2023-12-25 13:49:34,157 INFO misc.py line 119 253097] Train: [52/100][85/510] Data 0.007 (0.417) Batch 1.097 (1.624) Remain 11:13:59 loss: 0.2868 Lr: 0.00313
[2023-12-25 13:49:35,426 INFO misc.py line 119 253097] Train: [52/100][86/510] Data 0.004 (0.412) Batch 1.244 (1.619) Remain 11:12:04 loss: 0.2157 Lr: 0.00313
[2023-12-25 13:49:36,618 INFO misc.py line 119 253097] Train: [52/100][87/510] Data 0.029 (0.407) Batch 1.208 (1.614) Remain 11:10:00 loss: 0.0948 Lr: 0.00313
[2023-12-25 13:49:38,849 INFO misc.py line 119 253097] Train: [52/100][88/510] Data 0.013 (0.403) Batch 2.239 (1.622) Remain 11:13:02 loss: 0.2104 Lr: 0.00313
[2023-12-25 13:49:46,097 INFO misc.py line 119 253097] Train: [52/100][89/510] Data 0.005 (0.398) Batch 7.249 (1.687) Remain 11:40:09 loss: 0.3193 Lr: 0.00313
[2023-12-25 13:49:47,398 INFO misc.py line 119 253097] Train: [52/100][90/510] Data 0.004 (0.393) Batch 1.301 (1.683) Remain 11:38:17 loss: 0.2846 Lr: 0.00313
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[2023-12-25 13:50:01,873 INFO misc.py line 119 253097] Train: [52/100][103/510] Data 0.011 (0.343) Batch 0.969 (1.609) Remain 11:07:14 loss: 0.2737 Lr: 0.00313
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[2023-12-25 13:50:19,988 INFO misc.py line 119 253097] Train: [52/100][112/510] Data 4.750 (0.373) Batch 5.802 (1.642) Remain 11:20:49 loss: 0.0808 Lr: 0.00313
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[2023-12-25 13:52:10,471 INFO misc.py line 119 253097] Train: [52/100][175/510] Data 12.788 (0.408) Batch 13.969 (1.683) Remain 11:36:01 loss: 0.4868 Lr: 0.00312
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[2023-12-25 13:54:18,248 INFO misc.py line 119 253097] Train: [52/100][264/510] Data 0.005 (0.271) Batch 1.028 (1.599) Remain 10:58:46 loss: 0.1060 Lr: 0.00310
[2023-12-25 13:54:29,299 INFO misc.py line 119 253097] Train: [52/100][265/510] Data 0.008 (0.270) Batch 11.055 (1.635) Remain 11:13:37 loss: 0.1508 Lr: 0.00310
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[2023-12-25 13:54:31,692 INFO misc.py line 119 253097] Train: [52/100][267/510] Data 0.009 (0.268) Batch 1.188 (1.631) Remain 11:12:12 loss: 0.1044 Lr: 0.00310
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[2023-12-25 13:54:34,014 INFO misc.py line 119 253097] Train: [52/100][269/510] Data 0.010 (0.266) Batch 1.258 (1.628) Remain 11:10:41 loss: 0.1833 Lr: 0.00310
[2023-12-25 13:54:35,151 INFO misc.py line 119 253097] Train: [52/100][270/510] Data 0.006 (0.265) Batch 1.131 (1.626) Remain 11:09:54 loss: 0.1113 Lr: 0.00310
[2023-12-25 13:54:36,397 INFO misc.py line 119 253097] Train: [52/100][271/510] Data 0.011 (0.264) Batch 1.248 (1.625) Remain 11:09:17 loss: 0.3023 Lr: 0.00310
[2023-12-25 13:54:37,609 INFO misc.py line 119 253097] Train: [52/100][272/510] Data 0.008 (0.263) Batch 1.211 (1.623) Remain 11:08:37 loss: 0.0774 Lr: 0.00310
[2023-12-25 13:54:38,718 INFO misc.py line 119 253097] Train: [52/100][273/510] Data 0.009 (0.262) Batch 1.111 (1.621) Remain 11:07:49 loss: 0.2013 Lr: 0.00310
[2023-12-25 13:54:39,930 INFO misc.py line 119 253097] Train: [52/100][274/510] Data 0.008 (0.261) Batch 1.212 (1.620) Remain 11:07:10 loss: 0.2073 Lr: 0.00310
[2023-12-25 13:54:40,938 INFO misc.py line 119 253097] Train: [52/100][275/510] Data 0.008 (0.260) Batch 1.012 (1.617) Remain 11:06:13 loss: 0.0837 Lr: 0.00310
[2023-12-25 13:54:42,098 INFO misc.py line 119 253097] Train: [52/100][276/510] Data 0.004 (0.259) Batch 1.159 (1.616) Remain 11:05:30 loss: 0.1082 Lr: 0.00310
[2023-12-25 13:54:43,223 INFO misc.py line 119 253097] Train: [52/100][277/510] Data 0.008 (0.258) Batch 1.122 (1.614) Remain 11:04:44 loss: 0.1206 Lr: 0.00310
[2023-12-25 13:54:44,408 INFO misc.py line 119 253097] Train: [52/100][278/510] Data 0.008 (0.257) Batch 1.188 (1.612) Remain 11:04:04 loss: 0.1779 Lr: 0.00310
[2023-12-25 13:54:45,415 INFO misc.py line 119 253097] Train: [52/100][279/510] Data 0.006 (0.257) Batch 1.003 (1.610) Remain 11:03:08 loss: 0.3343 Lr: 0.00310
[2023-12-25 13:54:46,471 INFO misc.py line 119 253097] Train: [52/100][280/510] Data 0.009 (0.256) Batch 1.059 (1.608) Remain 11:02:17 loss: 0.3132 Lr: 0.00310
[2023-12-25 13:54:54,361 INFO misc.py line 119 253097] Train: [52/100][281/510] Data 0.006 (0.255) Batch 7.892 (1.631) Remain 11:11:34 loss: 0.1317 Lr: 0.00310
[2023-12-25 13:54:57,693 INFO misc.py line 119 253097] Train: [52/100][282/510] Data 0.004 (0.254) Batch 3.331 (1.637) Remain 11:14:03 loss: 0.1193 Lr: 0.00310
[2023-12-25 13:54:58,677 INFO misc.py line 119 253097] Train: [52/100][283/510] Data 0.005 (0.253) Batch 0.981 (1.635) Remain 11:13:03 loss: 0.1661 Lr: 0.00310
[2023-12-25 13:54:59,836 INFO misc.py line 119 253097] Train: [52/100][284/510] Data 0.008 (0.252) Batch 1.160 (1.633) Remain 11:12:20 loss: 0.1201 Lr: 0.00310
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[2023-12-25 13:55:02,191 INFO misc.py line 119 253097] Train: [52/100][286/510] Data 0.003 (0.250) Batch 1.099 (1.630) Remain 11:10:57 loss: 0.1849 Lr: 0.00310
[2023-12-25 13:55:03,494 INFO misc.py line 119 253097] Train: [52/100][287/510] Data 0.008 (0.250) Batch 1.302 (1.628) Remain 11:10:27 loss: 0.1176 Lr: 0.00310
[2023-12-25 13:55:04,639 INFO misc.py line 119 253097] Train: [52/100][288/510] Data 0.009 (0.249) Batch 1.144 (1.627) Remain 11:09:44 loss: 0.2050 Lr: 0.00309
[2023-12-25 13:55:05,848 INFO misc.py line 119 253097] Train: [52/100][289/510] Data 0.009 (0.248) Batch 1.210 (1.625) Remain 11:09:06 loss: 0.0864 Lr: 0.00309
[2023-12-25 13:55:06,780 INFO misc.py line 119 253097] Train: [52/100][290/510] Data 0.007 (0.247) Batch 0.935 (1.623) Remain 11:08:05 loss: 0.1288 Lr: 0.00309
[2023-12-25 13:55:07,899 INFO misc.py line 119 253097] Train: [52/100][291/510] Data 0.004 (0.246) Batch 1.120 (1.621) Remain 11:07:20 loss: 0.1249 Lr: 0.00309
[2023-12-25 13:55:09,158 INFO misc.py line 119 253097] Train: [52/100][292/510] Data 0.004 (0.245) Batch 1.259 (1.620) Remain 11:06:48 loss: 0.1366 Lr: 0.00309
[2023-12-25 13:55:10,316 INFO misc.py line 119 253097] Train: [52/100][293/510] Data 0.004 (0.244) Batch 1.157 (1.618) Remain 11:06:07 loss: 0.1560 Lr: 0.00309
[2023-12-25 13:55:11,640 INFO misc.py line 119 253097] Train: [52/100][294/510] Data 0.004 (0.244) Batch 1.319 (1.617) Remain 11:05:40 loss: 0.1245 Lr: 0.00309
[2023-12-25 13:55:12,699 INFO misc.py line 119 253097] Train: [52/100][295/510] Data 0.010 (0.243) Batch 1.062 (1.615) Remain 11:04:51 loss: 0.2050 Lr: 0.00309
[2023-12-25 13:55:14,577 INFO misc.py line 119 253097] Train: [52/100][296/510] Data 0.797 (0.245) Batch 1.881 (1.616) Remain 11:05:12 loss: 0.0698 Lr: 0.00309
[2023-12-25 13:55:15,721 INFO misc.py line 119 253097] Train: [52/100][297/510] Data 0.004 (0.244) Batch 1.140 (1.615) Remain 11:04:30 loss: 0.0920 Lr: 0.00309
[2023-12-25 13:55:17,026 INFO misc.py line 119 253097] Train: [52/100][298/510] Data 0.008 (0.243) Batch 1.302 (1.614) Remain 11:04:02 loss: 0.1875 Lr: 0.00309
[2023-12-25 13:55:18,170 INFO misc.py line 119 253097] Train: [52/100][299/510] Data 0.012 (0.242) Batch 1.146 (1.612) Remain 11:03:22 loss: 0.1388 Lr: 0.00309
[2023-12-25 13:55:19,367 INFO misc.py line 119 253097] Train: [52/100][300/510] Data 0.008 (0.242) Batch 1.202 (1.611) Remain 11:02:46 loss: 0.2391 Lr: 0.00309
[2023-12-25 13:55:23,377 INFO misc.py line 119 253097] Train: [52/100][301/510] Data 3.176 (0.251) Batch 4.010 (1.619) Remain 11:06:03 loss: 0.1263 Lr: 0.00309
[2023-12-25 13:55:28,107 INFO misc.py line 119 253097] Train: [52/100][302/510] Data 0.004 (0.251) Batch 4.729 (1.629) Remain 11:10:18 loss: 0.1552 Lr: 0.00309
[2023-12-25 13:55:29,211 INFO misc.py line 119 253097] Train: [52/100][303/510] Data 0.005 (0.250) Batch 1.103 (1.627) Remain 11:09:33 loss: 0.1512 Lr: 0.00309
[2023-12-25 13:55:30,208 INFO misc.py line 119 253097] Train: [52/100][304/510] Data 0.006 (0.249) Batch 0.998 (1.625) Remain 11:08:40 loss: 0.0757 Lr: 0.00309
[2023-12-25 13:55:31,237 INFO misc.py line 119 253097] Train: [52/100][305/510] Data 0.007 (0.248) Batch 1.028 (1.623) Remain 11:07:50 loss: 0.1609 Lr: 0.00309
[2023-12-25 13:55:32,393 INFO misc.py line 119 253097] Train: [52/100][306/510] Data 0.005 (0.247) Batch 1.156 (1.622) Remain 11:07:10 loss: 0.1055 Lr: 0.00309
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[2023-12-25 13:55:35,750 INFO misc.py line 119 253097] Train: [52/100][309/510] Data 0.008 (0.245) Batch 0.962 (1.617) Remain 11:05:04 loss: 0.2160 Lr: 0.00309
[2023-12-25 13:55:36,860 INFO misc.py line 119 253097] Train: [52/100][310/510] Data 0.003 (0.244) Batch 1.109 (1.615) Remain 11:04:21 loss: 0.1492 Lr: 0.00309
[2023-12-25 13:55:37,928 INFO misc.py line 119 253097] Train: [52/100][311/510] Data 0.005 (0.243) Batch 1.066 (1.613) Remain 11:03:36 loss: 0.1241 Lr: 0.00309
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[2023-12-25 13:55:41,571 INFO misc.py line 119 253097] Train: [52/100][314/510] Data 0.009 (0.241) Batch 1.077 (1.610) Remain 11:01:56 loss: 0.1741 Lr: 0.00309
[2023-12-25 13:55:42,759 INFO misc.py line 119 253097] Train: [52/100][315/510] Data 0.007 (0.240) Batch 1.189 (1.608) Remain 11:01:21 loss: 0.1338 Lr: 0.00309
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[2023-12-25 13:55:46,191 INFO misc.py line 119 253097] Train: [52/100][318/510] Data 0.007 (0.238) Batch 1.117 (1.604) Remain 10:59:27 loss: 0.2790 Lr: 0.00309
[2023-12-25 13:55:47,146 INFO misc.py line 119 253097] Train: [52/100][319/510] Data 0.008 (0.237) Batch 0.960 (1.602) Remain 10:58:35 loss: 0.1809 Lr: 0.00309
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[2023-12-25 13:57:36,347 INFO misc.py line 119 253097] Train: [52/100][385/510] Data 4.544 (0.209) Batch 5.606 (1.611) Remain 11:00:34 loss: 0.1375 Lr: 0.00308
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[2023-12-25 13:57:56,341 INFO misc.py line 119 253097] Train: [52/100][402/510] Data 0.005 (0.201) Batch 0.994 (1.592) Remain 10:52:31 loss: 0.1116 Lr: 0.00307
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[2023-12-25 13:58:18,568 INFO misc.py line 119 253097] Train: [52/100][412/510] Data 10.434 (0.222) Batch 11.705 (1.608) Remain 10:58:34 loss: 0.3652 Lr: 0.00307
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[2023-12-25 13:59:09,263 INFO misc.py line 119 253097] Train: [52/100][452/510] Data 0.004 (0.211) Batch 1.301 (1.577) Remain 10:45:06 loss: 0.2429 Lr: 0.00306
[2023-12-25 13:59:10,489 INFO misc.py line 119 253097] Train: [52/100][453/510] Data 0.008 (0.210) Batch 1.221 (1.577) Remain 10:44:45 loss: 0.1760 Lr: 0.00306
[2023-12-25 13:59:11,681 INFO misc.py line 119 253097] Train: [52/100][454/510] Data 0.011 (0.210) Batch 1.200 (1.576) Remain 10:44:23 loss: 0.1590 Lr: 0.00306
[2023-12-25 13:59:12,962 INFO misc.py line 119 253097] Train: [52/100][455/510] Data 0.004 (0.209) Batch 1.275 (1.575) Remain 10:44:05 loss: 0.1359 Lr: 0.00306
[2023-12-25 13:59:14,015 INFO misc.py line 119 253097] Train: [52/100][456/510] Data 0.010 (0.209) Batch 1.054 (1.574) Remain 10:43:35 loss: 0.1864 Lr: 0.00306
[2023-12-25 13:59:15,205 INFO misc.py line 119 253097] Train: [52/100][457/510] Data 0.009 (0.208) Batch 1.189 (1.573) Remain 10:43:12 loss: 0.2408 Lr: 0.00306
[2023-12-25 13:59:16,335 INFO misc.py line 119 253097] Train: [52/100][458/510] Data 0.010 (0.208) Batch 1.126 (1.572) Remain 10:42:47 loss: 0.1371 Lr: 0.00306
[2023-12-25 13:59:20,140 INFO misc.py line 119 253097] Train: [52/100][459/510] Data 0.014 (0.208) Batch 3.813 (1.577) Remain 10:44:46 loss: 0.1389 Lr: 0.00306
[2023-12-25 13:59:21,275 INFO misc.py line 119 253097] Train: [52/100][460/510] Data 0.005 (0.207) Batch 1.136 (1.576) Remain 10:44:21 loss: 0.0884 Lr: 0.00306
[2023-12-25 13:59:22,554 INFO misc.py line 119 253097] Train: [52/100][461/510] Data 0.007 (0.207) Batch 1.274 (1.575) Remain 10:44:03 loss: 0.2343 Lr: 0.00306
[2023-12-25 13:59:23,832 INFO misc.py line 119 253097] Train: [52/100][462/510] Data 0.010 (0.206) Batch 1.283 (1.575) Remain 10:43:46 loss: 0.0978 Lr: 0.00306
[2023-12-25 13:59:25,075 INFO misc.py line 119 253097] Train: [52/100][463/510] Data 0.004 (0.206) Batch 1.242 (1.574) Remain 10:43:26 loss: 0.0976 Lr: 0.00306
[2023-12-25 13:59:26,097 INFO misc.py line 119 253097] Train: [52/100][464/510] Data 0.005 (0.205) Batch 1.019 (1.573) Remain 10:42:55 loss: 0.1614 Lr: 0.00306
[2023-12-25 13:59:27,326 INFO misc.py line 119 253097] Train: [52/100][465/510] Data 0.007 (0.205) Batch 1.232 (1.572) Remain 10:42:36 loss: 0.2473 Lr: 0.00306
[2023-12-25 13:59:28,552 INFO misc.py line 119 253097] Train: [52/100][466/510] Data 0.004 (0.204) Batch 1.224 (1.571) Remain 10:42:16 loss: 0.1225 Lr: 0.00306
[2023-12-25 13:59:29,829 INFO misc.py line 119 253097] Train: [52/100][467/510] Data 0.006 (0.204) Batch 1.274 (1.571) Remain 10:41:58 loss: 0.0678 Lr: 0.00306
[2023-12-25 13:59:35,964 INFO misc.py line 119 253097] Train: [52/100][468/510] Data 0.009 (0.204) Batch 6.141 (1.581) Remain 10:45:58 loss: 0.2611 Lr: 0.00306
[2023-12-25 13:59:37,154 INFO misc.py line 119 253097] Train: [52/100][469/510] Data 0.003 (0.203) Batch 1.189 (1.580) Remain 10:45:36 loss: 0.1216 Lr: 0.00306
[2023-12-25 13:59:38,304 INFO misc.py line 119 253097] Train: [52/100][470/510] Data 0.005 (0.203) Batch 1.150 (1.579) Remain 10:45:11 loss: 0.1248 Lr: 0.00306
[2023-12-25 13:59:39,338 INFO misc.py line 119 253097] Train: [52/100][471/510] Data 0.004 (0.202) Batch 1.034 (1.578) Remain 10:44:41 loss: 0.2505 Lr: 0.00306
[2023-12-25 13:59:40,455 INFO misc.py line 119 253097] Train: [52/100][472/510] Data 0.004 (0.202) Batch 1.117 (1.577) Remain 10:44:16 loss: 0.2746 Lr: 0.00306
[2023-12-25 13:59:41,560 INFO misc.py line 119 253097] Train: [52/100][473/510] Data 0.004 (0.202) Batch 1.105 (1.576) Remain 10:43:49 loss: 0.1359 Lr: 0.00306
[2023-12-25 13:59:42,841 INFO misc.py line 119 253097] Train: [52/100][474/510] Data 0.004 (0.201) Batch 1.279 (1.575) Remain 10:43:32 loss: 0.0800 Lr: 0.00306
[2023-12-25 13:59:43,936 INFO misc.py line 119 253097] Train: [52/100][475/510] Data 0.007 (0.201) Batch 1.098 (1.574) Remain 10:43:06 loss: 0.2248 Lr: 0.00306
[2023-12-25 13:59:44,999 INFO misc.py line 119 253097] Train: [52/100][476/510] Data 0.004 (0.200) Batch 1.059 (1.573) Remain 10:42:38 loss: 0.1318 Lr: 0.00306
[2023-12-25 13:59:46,021 INFO misc.py line 119 253097] Train: [52/100][477/510] Data 0.009 (0.200) Batch 1.024 (1.572) Remain 10:42:08 loss: 0.1887 Lr: 0.00306
[2023-12-25 13:59:47,264 INFO misc.py line 119 253097] Train: [52/100][478/510] Data 0.006 (0.199) Batch 1.245 (1.571) Remain 10:41:49 loss: 0.1781 Lr: 0.00306
[2023-12-25 13:59:48,197 INFO misc.py line 119 253097] Train: [52/100][479/510] Data 0.005 (0.199) Batch 0.927 (1.570) Remain 10:41:15 loss: 0.1495 Lr: 0.00306
[2023-12-25 13:59:49,249 INFO misc.py line 119 253097] Train: [52/100][480/510] Data 0.010 (0.199) Batch 1.057 (1.569) Remain 10:40:47 loss: 0.3230 Lr: 0.00306
[2023-12-25 13:59:50,396 INFO misc.py line 119 253097] Train: [52/100][481/510] Data 0.005 (0.198) Batch 1.148 (1.568) Remain 10:40:24 loss: 0.1895 Lr: 0.00306
[2023-12-25 13:59:51,574 INFO misc.py line 119 253097] Train: [52/100][482/510] Data 0.004 (0.198) Batch 1.176 (1.567) Remain 10:40:02 loss: 0.1357 Lr: 0.00306
[2023-12-25 13:59:52,701 INFO misc.py line 119 253097] Train: [52/100][483/510] Data 0.007 (0.197) Batch 1.129 (1.566) Remain 10:39:38 loss: 0.1595 Lr: 0.00306
[2023-12-25 13:59:53,677 INFO misc.py line 119 253097] Train: [52/100][484/510] Data 0.005 (0.197) Batch 0.975 (1.565) Remain 10:39:06 loss: 0.4309 Lr: 0.00306
[2023-12-25 13:59:57,429 INFO misc.py line 119 253097] Train: [52/100][485/510] Data 0.007 (0.197) Batch 3.754 (1.569) Remain 10:40:56 loss: 0.1975 Lr: 0.00306
[2023-12-25 13:59:58,747 INFO misc.py line 119 253097] Train: [52/100][486/510] Data 0.004 (0.196) Batch 1.317 (1.569) Remain 10:40:42 loss: 0.0871 Lr: 0.00306
[2023-12-25 13:59:59,939 INFO misc.py line 119 253097] Train: [52/100][487/510] Data 0.004 (0.196) Batch 1.187 (1.568) Remain 10:40:21 loss: 0.3816 Lr: 0.00306
[2023-12-25 14:00:01,123 INFO misc.py line 119 253097] Train: [52/100][488/510] Data 0.010 (0.195) Batch 1.188 (1.567) Remain 10:40:00 loss: 0.2032 Lr: 0.00306
[2023-12-25 14:00:02,365 INFO misc.py line 119 253097] Train: [52/100][489/510] Data 0.005 (0.195) Batch 1.243 (1.567) Remain 10:39:42 loss: 0.2881 Lr: 0.00306
[2023-12-25 14:00:03,419 INFO misc.py line 119 253097] Train: [52/100][490/510] Data 0.004 (0.195) Batch 1.055 (1.566) Remain 10:39:15 loss: 0.1619 Lr: 0.00306
[2023-12-25 14:00:04,638 INFO misc.py line 119 253097] Train: [52/100][491/510] Data 0.003 (0.194) Batch 1.211 (1.565) Remain 10:38:55 loss: 0.1503 Lr: 0.00306
[2023-12-25 14:00:05,923 INFO misc.py line 119 253097] Train: [52/100][492/510] Data 0.012 (0.194) Batch 1.294 (1.564) Remain 10:38:40 loss: 0.1426 Lr: 0.00306
[2023-12-25 14:00:07,105 INFO misc.py line 119 253097] Train: [52/100][493/510] Data 0.004 (0.194) Batch 1.179 (1.563) Remain 10:38:19 loss: 0.1198 Lr: 0.00305
[2023-12-25 14:00:08,151 INFO misc.py line 119 253097] Train: [52/100][494/510] Data 0.007 (0.193) Batch 1.046 (1.562) Remain 10:37:52 loss: 0.1532 Lr: 0.00305
[2023-12-25 14:00:09,373 INFO misc.py line 119 253097] Train: [52/100][495/510] Data 0.007 (0.193) Batch 1.224 (1.562) Remain 10:37:34 loss: 0.1579 Lr: 0.00305
[2023-12-25 14:00:10,453 INFO misc.py line 119 253097] Train: [52/100][496/510] Data 0.004 (0.192) Batch 1.075 (1.561) Remain 10:37:08 loss: 0.3760 Lr: 0.00305
[2023-12-25 14:00:11,626 INFO misc.py line 119 253097] Train: [52/100][497/510] Data 0.008 (0.192) Batch 1.175 (1.560) Remain 10:36:47 loss: 0.1730 Lr: 0.00305
[2023-12-25 14:00:12,829 INFO misc.py line 119 253097] Train: [52/100][498/510] Data 0.007 (0.192) Batch 1.195 (1.559) Remain 10:36:28 loss: 0.1885 Lr: 0.00305
[2023-12-25 14:00:14,086 INFO misc.py line 119 253097] Train: [52/100][499/510] Data 0.016 (0.191) Batch 1.267 (1.559) Remain 10:36:12 loss: 0.0928 Lr: 0.00305
[2023-12-25 14:00:15,229 INFO misc.py line 119 253097] Train: [52/100][500/510] Data 0.005 (0.191) Batch 1.143 (1.558) Remain 10:35:50 loss: 0.1939 Lr: 0.00305
[2023-12-25 14:00:16,493 INFO misc.py line 119 253097] Train: [52/100][501/510] Data 0.005 (0.191) Batch 1.264 (1.557) Remain 10:35:34 loss: 0.2693 Lr: 0.00305
[2023-12-25 14:00:17,500 INFO misc.py line 119 253097] Train: [52/100][502/510] Data 0.004 (0.190) Batch 1.002 (1.556) Remain 10:35:05 loss: 0.1984 Lr: 0.00305
[2023-12-25 14:00:18,485 INFO misc.py line 119 253097] Train: [52/100][503/510] Data 0.009 (0.190) Batch 0.991 (1.555) Remain 10:34:36 loss: 0.0941 Lr: 0.00305
[2023-12-25 14:00:19,507 INFO misc.py line 119 253097] Train: [52/100][504/510] Data 0.003 (0.189) Batch 1.016 (1.554) Remain 10:34:08 loss: 0.2278 Lr: 0.00305
[2023-12-25 14:00:20,549 INFO misc.py line 119 253097] Train: [52/100][505/510] Data 0.009 (0.189) Batch 1.042 (1.553) Remain 10:33:41 loss: 0.1619 Lr: 0.00305
[2023-12-25 14:00:21,699 INFO misc.py line 119 253097] Train: [52/100][506/510] Data 0.009 (0.189) Batch 1.155 (1.552) Remain 10:33:20 loss: 0.0779 Lr: 0.00305
[2023-12-25 14:00:22,782 INFO misc.py line 119 253097] Train: [52/100][507/510] Data 0.004 (0.188) Batch 1.083 (1.551) Remain 10:32:56 loss: 0.1263 Lr: 0.00305
[2023-12-25 14:00:23,957 INFO misc.py line 119 253097] Train: [52/100][508/510] Data 0.004 (0.188) Batch 1.171 (1.550) Remain 10:32:36 loss: 0.0615 Lr: 0.00305
[2023-12-25 14:00:24,879 INFO misc.py line 119 253097] Train: [52/100][509/510] Data 0.009 (0.188) Batch 0.926 (1.549) Remain 10:32:04 loss: 0.1203 Lr: 0.00305
[2023-12-25 14:00:26,084 INFO misc.py line 119 253097] Train: [52/100][510/510] Data 0.005 (0.187) Batch 1.205 (1.548) Remain 10:31:46 loss: 0.1943 Lr: 0.00305
[2023-12-25 14:00:26,084 INFO misc.py line 136 253097] Train result: loss: 0.1777 
[2023-12-25 14:00:26,085 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 14:00:53,205 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.9185 
[2023-12-25 14:00:53,552 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3551 
[2023-12-25 14:00:58,490 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.6081 
[2023-12-25 14:00:59,007 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4297 
[2023-12-25 14:01:00,984 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0371 
[2023-12-25 14:01:01,409 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4396 
[2023-12-25 14:01:02,286 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2437 
[2023-12-25 14:01:02,843 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4215 
[2023-12-25 14:01:04,648 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1190 
[2023-12-25 14:01:06,775 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1789 
[2023-12-25 14:01:07,630 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2070 
[2023-12-25 14:01:08,061 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0685 
[2023-12-25 14:01:08,962 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5098 
[2023-12-25 14:01:11,905 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9624 
[2023-12-25 14:01:12,371 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3388 
[2023-12-25 14:01:12,982 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4888 
[2023-12-25 14:01:13,684 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4439 
[2023-12-25 14:01:14,918 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6632/0.7243/0.8955.
[2023-12-25 14:01:14,918 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9146/0.9429
[2023-12-25 14:01:14,918 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9807/0.9907
[2023-12-25 14:01:14,918 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8311/0.9766
[2023-12-25 14:01:14,919 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 14:01:14,919 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.1779/0.1886
[2023-12-25 14:01:14,919 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6149/0.6335
[2023-12-25 14:01:14,919 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6557/0.7179
[2023-12-25 14:01:14,919 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7980/0.9268
[2023-12-25 14:01:14,920 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9193/0.9559
[2023-12-25 14:01:14,920 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6503/0.6904
[2023-12-25 14:01:14,920 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7609/0.8423
[2023-12-25 14:01:14,920 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7404/0.8447
[2023-12-25 14:01:14,920 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5781/0.7051
[2023-12-25 14:01:14,921 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 14:01:14,922 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 14:01:14,922 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 14:01:27,533 INFO misc.py line 119 253097] Train: [53/100][1/510] Data 8.798 (8.798) Batch 10.050 (10.050) Remain 68:20:16 loss: 0.2901 Lr: 0.00305
[2023-12-25 14:01:28,640 INFO misc.py line 119 253097] Train: [53/100][2/510] Data 0.004 (0.004) Batch 1.106 (1.106) Remain 07:31:24 loss: 0.0928 Lr: 0.00305
[2023-12-25 14:01:29,733 INFO misc.py line 119 253097] Train: [53/100][3/510] Data 0.006 (0.006) Batch 1.094 (1.094) Remain 07:26:21 loss: 0.1811 Lr: 0.00305
[2023-12-25 14:01:34,898 INFO misc.py line 119 253097] Train: [53/100][4/510] Data 0.005 (0.005) Batch 5.165 (5.165) Remain 35:06:54 loss: 0.0940 Lr: 0.00305
[2023-12-25 14:01:36,169 INFO misc.py line 119 253097] Train: [53/100][5/510] Data 0.005 (0.005) Batch 1.271 (3.218) Remain 21:52:42 loss: 0.1334 Lr: 0.00305
[2023-12-25 14:01:37,400 INFO misc.py line 119 253097] Train: [53/100][6/510] Data 0.004 (0.005) Batch 1.230 (2.555) Remain 17:22:17 loss: 0.1138 Lr: 0.00305
[2023-12-25 14:01:38,670 INFO misc.py line 119 253097] Train: [53/100][7/510] Data 0.006 (0.005) Batch 1.271 (2.234) Remain 15:11:18 loss: 0.1569 Lr: 0.00305
[2023-12-25 14:01:39,766 INFO misc.py line 119 253097] Train: [53/100][8/510] Data 0.005 (0.005) Batch 1.089 (2.005) Remain 13:37:48 loss: 0.1364 Lr: 0.00305
[2023-12-25 14:01:40,842 INFO misc.py line 119 253097] Train: [53/100][9/510] Data 0.011 (0.006) Batch 1.083 (1.851) Remain 12:35:05 loss: 0.2980 Lr: 0.00305
[2023-12-25 14:01:42,768 INFO misc.py line 119 253097] Train: [53/100][10/510] Data 0.007 (0.006) Batch 1.927 (1.862) Remain 12:39:27 loss: 0.0997 Lr: 0.00305
[2023-12-25 14:01:43,937 INFO misc.py line 119 253097] Train: [53/100][11/510] Data 0.005 (0.006) Batch 1.167 (1.775) Remain 12:04:00 loss: 0.1645 Lr: 0.00305
[2023-12-25 14:01:45,039 INFO misc.py line 119 253097] Train: [53/100][12/510] Data 0.005 (0.006) Batch 1.097 (1.700) Remain 11:33:16 loss: 0.1624 Lr: 0.00305
[2023-12-25 14:01:58,322 INFO misc.py line 119 253097] Train: [53/100][13/510] Data 0.011 (0.006) Batch 13.288 (2.859) Remain 19:25:47 loss: 0.1182 Lr: 0.00305
[2023-12-25 14:01:59,357 INFO misc.py line 119 253097] Train: [53/100][14/510] Data 0.005 (0.006) Batch 1.036 (2.693) Remain 18:18:11 loss: 0.1260 Lr: 0.00305
[2023-12-25 14:02:00,474 INFO misc.py line 119 253097] Train: [53/100][15/510] Data 0.003 (0.006) Batch 1.116 (2.562) Remain 17:24:33 loss: 0.1343 Lr: 0.00305
[2023-12-25 14:02:01,589 INFO misc.py line 119 253097] Train: [53/100][16/510] Data 0.004 (0.006) Batch 1.113 (2.450) Remain 16:39:05 loss: 0.1307 Lr: 0.00305
[2023-12-25 14:02:02,678 INFO misc.py line 119 253097] Train: [53/100][17/510] Data 0.005 (0.006) Batch 1.091 (2.353) Remain 15:59:28 loss: 0.1235 Lr: 0.00305
[2023-12-25 14:02:03,910 INFO misc.py line 119 253097] Train: [53/100][18/510] Data 0.003 (0.005) Batch 1.232 (2.279) Remain 15:28:57 loss: 0.1528 Lr: 0.00305
[2023-12-25 14:02:05,096 INFO misc.py line 119 253097] Train: [53/100][19/510] Data 0.004 (0.005) Batch 1.185 (2.210) Remain 15:01:02 loss: 0.1482 Lr: 0.00305
[2023-12-25 14:02:06,281 INFO misc.py line 119 253097] Train: [53/100][20/510] Data 0.005 (0.005) Batch 1.186 (2.150) Remain 14:36:27 loss: 0.1755 Lr: 0.00305
[2023-12-25 14:02:07,252 INFO misc.py line 119 253097] Train: [53/100][21/510] Data 0.003 (0.005) Batch 0.968 (2.084) Remain 14:09:39 loss: 0.1378 Lr: 0.00305
[2023-12-25 14:02:08,315 INFO misc.py line 119 253097] Train: [53/100][22/510] Data 0.007 (0.005) Batch 1.066 (2.031) Remain 13:47:46 loss: 0.3757 Lr: 0.00305
[2023-12-25 14:02:09,359 INFO misc.py line 119 253097] Train: [53/100][23/510] Data 0.003 (0.005) Batch 1.043 (1.981) Remain 13:27:36 loss: 0.1462 Lr: 0.00305
[2023-12-25 14:02:10,580 INFO misc.py line 119 253097] Train: [53/100][24/510] Data 0.004 (0.005) Batch 1.220 (1.945) Remain 13:12:48 loss: 0.3510 Lr: 0.00305
[2023-12-25 14:02:11,849 INFO misc.py line 119 253097] Train: [53/100][25/510] Data 0.006 (0.005) Batch 1.269 (1.914) Remain 13:00:14 loss: 0.2876 Lr: 0.00305
[2023-12-25 14:02:13,128 INFO misc.py line 119 253097] Train: [53/100][26/510] Data 0.005 (0.005) Batch 1.279 (1.887) Remain 12:48:57 loss: 0.1429 Lr: 0.00305
[2023-12-25 14:02:14,193 INFO misc.py line 119 253097] Train: [53/100][27/510] Data 0.005 (0.005) Batch 1.061 (1.852) Remain 12:34:54 loss: 0.1619 Lr: 0.00305
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[2023-12-25 14:11:30,404 INFO misc.py line 119 253097] Train: [53/100][389/510] Data 0.014 (0.107) Batch 1.220 (1.556) Remain 10:24:49 loss: 0.2415 Lr: 0.00298
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[2023-12-25 14:11:37,480 INFO misc.py line 119 253097] Train: [53/100][395/510] Data 0.015 (0.105) Batch 1.307 (1.550) Remain 10:22:20 loss: 0.2552 Lr: 0.00297
[2023-12-25 14:11:38,545 INFO misc.py line 119 253097] Train: [53/100][396/510] Data 0.011 (0.105) Batch 1.071 (1.549) Remain 10:21:49 loss: 0.0885 Lr: 0.00297
[2023-12-25 14:11:43,605 INFO misc.py line 119 253097] Train: [53/100][397/510] Data 3.829 (0.114) Batch 5.060 (1.558) Remain 10:25:22 loss: 0.3736 Lr: 0.00297
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[2023-12-25 14:11:45,749 INFO misc.py line 119 253097] Train: [53/100][399/510] Data 0.005 (0.114) Batch 1.202 (1.556) Remain 10:24:20 loss: 0.1268 Lr: 0.00297
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[2023-12-25 14:13:02,399 INFO misc.py line 119 253097] Train: [53/100][446/510] Data 0.007 (0.105) Batch 1.125 (1.564) Remain 10:26:18 loss: 0.1677 Lr: 0.00296
[2023-12-25 14:13:03,371 INFO misc.py line 119 253097] Train: [53/100][447/510] Data 0.006 (0.104) Batch 0.974 (1.562) Remain 10:25:45 loss: 0.2617 Lr: 0.00296
[2023-12-25 14:13:04,325 INFO misc.py line 119 253097] Train: [53/100][448/510] Data 0.005 (0.104) Batch 0.954 (1.561) Remain 10:25:11 loss: 0.1370 Lr: 0.00296
[2023-12-25 14:13:05,504 INFO misc.py line 119 253097] Train: [53/100][449/510] Data 0.005 (0.104) Batch 1.179 (1.560) Remain 10:24:48 loss: 0.1454 Lr: 0.00296
[2023-12-25 14:13:06,478 INFO misc.py line 119 253097] Train: [53/100][450/510] Data 0.004 (0.104) Batch 0.974 (1.559) Remain 10:24:15 loss: 0.2095 Lr: 0.00296
[2023-12-25 14:13:07,499 INFO misc.py line 119 253097] Train: [53/100][451/510] Data 0.004 (0.104) Batch 1.021 (1.558) Remain 10:23:45 loss: 0.2287 Lr: 0.00296
[2023-12-25 14:13:08,612 INFO misc.py line 119 253097] Train: [53/100][452/510] Data 0.005 (0.103) Batch 1.114 (1.557) Remain 10:23:20 loss: 0.1949 Lr: 0.00296
[2023-12-25 14:13:09,624 INFO misc.py line 119 253097] Train: [53/100][453/510] Data 0.004 (0.103) Batch 1.013 (1.555) Remain 10:22:49 loss: 0.2374 Lr: 0.00296
[2023-12-25 14:13:10,859 INFO misc.py line 119 253097] Train: [53/100][454/510] Data 0.004 (0.103) Batch 1.234 (1.555) Remain 10:22:30 loss: 0.1305 Lr: 0.00296
[2023-12-25 14:13:12,032 INFO misc.py line 119 253097] Train: [53/100][455/510] Data 0.005 (0.103) Batch 1.174 (1.554) Remain 10:22:09 loss: 0.1721 Lr: 0.00296
[2023-12-25 14:13:13,252 INFO misc.py line 119 253097] Train: [53/100][456/510] Data 0.004 (0.102) Batch 1.220 (1.553) Remain 10:21:49 loss: 0.1324 Lr: 0.00296
[2023-12-25 14:13:14,567 INFO misc.py line 119 253097] Train: [53/100][457/510] Data 0.003 (0.102) Batch 1.312 (1.552) Remain 10:21:35 loss: 0.2997 Lr: 0.00296
[2023-12-25 14:13:15,594 INFO misc.py line 119 253097] Train: [53/100][458/510] Data 0.006 (0.102) Batch 1.029 (1.551) Remain 10:21:06 loss: 0.1212 Lr: 0.00296
[2023-12-25 14:13:16,689 INFO misc.py line 119 253097] Train: [53/100][459/510] Data 0.005 (0.102) Batch 1.093 (1.550) Remain 10:20:40 loss: 0.1612 Lr: 0.00296
[2023-12-25 14:13:17,893 INFO misc.py line 119 253097] Train: [53/100][460/510] Data 0.007 (0.102) Batch 1.202 (1.550) Remain 10:20:20 loss: 0.1001 Lr: 0.00296
[2023-12-25 14:13:19,037 INFO misc.py line 119 253097] Train: [53/100][461/510] Data 0.009 (0.101) Batch 1.146 (1.549) Remain 10:19:57 loss: 0.1856 Lr: 0.00296
[2023-12-25 14:13:29,065 INFO misc.py line 119 253097] Train: [53/100][462/510] Data 0.008 (0.101) Batch 10.030 (1.567) Remain 10:27:20 loss: 0.1184 Lr: 0.00296
[2023-12-25 14:13:30,205 INFO misc.py line 119 253097] Train: [53/100][463/510] Data 0.005 (0.101) Batch 1.141 (1.566) Remain 10:26:56 loss: 0.2028 Lr: 0.00296
[2023-12-25 14:13:31,228 INFO misc.py line 119 253097] Train: [53/100][464/510] Data 0.004 (0.101) Batch 1.024 (1.565) Remain 10:26:26 loss: 0.1044 Lr: 0.00296
[2023-12-25 14:13:32,363 INFO misc.py line 119 253097] Train: [53/100][465/510] Data 0.003 (0.101) Batch 1.135 (1.564) Remain 10:26:02 loss: 0.1364 Lr: 0.00296
[2023-12-25 14:13:33,424 INFO misc.py line 119 253097] Train: [53/100][466/510] Data 0.004 (0.100) Batch 1.060 (1.563) Remain 10:25:34 loss: 0.1457 Lr: 0.00296
[2023-12-25 14:13:34,577 INFO misc.py line 119 253097] Train: [53/100][467/510] Data 0.005 (0.100) Batch 1.153 (1.562) Remain 10:25:12 loss: 0.1009 Lr: 0.00296
[2023-12-25 14:13:35,741 INFO misc.py line 119 253097] Train: [53/100][468/510] Data 0.005 (0.100) Batch 1.165 (1.561) Remain 10:24:50 loss: 0.1318 Lr: 0.00296
[2023-12-25 14:13:36,971 INFO misc.py line 119 253097] Train: [53/100][469/510] Data 0.003 (0.100) Batch 1.227 (1.561) Remain 10:24:31 loss: 0.1620 Lr: 0.00296
[2023-12-25 14:13:38,145 INFO misc.py line 119 253097] Train: [53/100][470/510] Data 0.008 (0.100) Batch 1.176 (1.560) Remain 10:24:09 loss: 0.1033 Lr: 0.00296
[2023-12-25 14:13:39,437 INFO misc.py line 119 253097] Train: [53/100][471/510] Data 0.004 (0.099) Batch 1.293 (1.559) Remain 10:23:54 loss: 0.2074 Lr: 0.00296
[2023-12-25 14:13:40,582 INFO misc.py line 119 253097] Train: [53/100][472/510] Data 0.003 (0.099) Batch 1.145 (1.558) Remain 10:23:31 loss: 0.1125 Lr: 0.00296
[2023-12-25 14:13:41,723 INFO misc.py line 119 253097] Train: [53/100][473/510] Data 0.004 (0.099) Batch 1.133 (1.557) Remain 10:23:08 loss: 0.2653 Lr: 0.00296
[2023-12-25 14:13:42,808 INFO misc.py line 119 253097] Train: [53/100][474/510] Data 0.013 (0.099) Batch 1.093 (1.556) Remain 10:22:43 loss: 0.1812 Lr: 0.00296
[2023-12-25 14:13:43,976 INFO misc.py line 119 253097] Train: [53/100][475/510] Data 0.004 (0.099) Batch 1.164 (1.556) Remain 10:22:21 loss: 0.2806 Lr: 0.00296
[2023-12-25 14:13:44,943 INFO misc.py line 119 253097] Train: [53/100][476/510] Data 0.008 (0.098) Batch 0.968 (1.554) Remain 10:21:50 loss: 0.0917 Lr: 0.00296
[2023-12-25 14:13:46,169 INFO misc.py line 119 253097] Train: [53/100][477/510] Data 0.007 (0.098) Batch 1.229 (1.554) Remain 10:21:32 loss: 0.2219 Lr: 0.00296
[2023-12-25 14:13:47,487 INFO misc.py line 119 253097] Train: [53/100][478/510] Data 0.004 (0.098) Batch 1.318 (1.553) Remain 10:21:19 loss: 0.2828 Lr: 0.00296
[2023-12-25 14:13:49,395 INFO misc.py line 119 253097] Train: [53/100][479/510] Data 0.006 (0.098) Batch 1.907 (1.554) Remain 10:21:35 loss: 0.2062 Lr: 0.00296
[2023-12-25 14:13:50,492 INFO misc.py line 119 253097] Train: [53/100][480/510] Data 0.005 (0.098) Batch 1.098 (1.553) Remain 10:21:10 loss: 0.0850 Lr: 0.00296
[2023-12-25 14:13:51,800 INFO misc.py line 119 253097] Train: [53/100][481/510] Data 0.004 (0.097) Batch 1.308 (1.552) Remain 10:20:56 loss: 0.1768 Lr: 0.00296
[2023-12-25 14:13:52,863 INFO misc.py line 119 253097] Train: [53/100][482/510] Data 0.005 (0.097) Batch 1.063 (1.551) Remain 10:20:30 loss: 0.1904 Lr: 0.00296
[2023-12-25 14:13:53,932 INFO misc.py line 119 253097] Train: [53/100][483/510] Data 0.005 (0.097) Batch 1.068 (1.550) Remain 10:20:05 loss: 0.1537 Lr: 0.00296
[2023-12-25 14:13:55,075 INFO misc.py line 119 253097] Train: [53/100][484/510] Data 0.005 (0.097) Batch 1.134 (1.550) Remain 10:19:42 loss: 0.1347 Lr: 0.00296
[2023-12-25 14:13:56,346 INFO misc.py line 119 253097] Train: [53/100][485/510] Data 0.014 (0.097) Batch 1.280 (1.549) Remain 10:19:27 loss: 0.2107 Lr: 0.00296
[2023-12-25 14:13:57,460 INFO misc.py line 119 253097] Train: [53/100][486/510] Data 0.005 (0.096) Batch 1.111 (1.548) Remain 10:19:04 loss: 0.2070 Lr: 0.00296
[2023-12-25 14:14:05,633 INFO misc.py line 119 253097] Train: [53/100][487/510] Data 6.916 (0.111) Batch 8.177 (1.562) Remain 10:24:31 loss: 0.1660 Lr: 0.00296
[2023-12-25 14:14:06,883 INFO misc.py line 119 253097] Train: [53/100][488/510] Data 0.005 (0.110) Batch 1.250 (1.561) Remain 10:24:14 loss: 0.1348 Lr: 0.00296
[2023-12-25 14:14:08,067 INFO misc.py line 119 253097] Train: [53/100][489/510] Data 0.004 (0.110) Batch 1.182 (1.560) Remain 10:23:54 loss: 0.1225 Lr: 0.00296
[2023-12-25 14:14:09,090 INFO misc.py line 119 253097] Train: [53/100][490/510] Data 0.005 (0.110) Batch 1.024 (1.559) Remain 10:23:26 loss: 0.1393 Lr: 0.00296
[2023-12-25 14:14:10,323 INFO misc.py line 119 253097] Train: [53/100][491/510] Data 0.004 (0.110) Batch 1.234 (1.559) Remain 10:23:08 loss: 0.1077 Lr: 0.00296
[2023-12-25 14:14:11,493 INFO misc.py line 119 253097] Train: [53/100][492/510] Data 0.004 (0.109) Batch 1.168 (1.558) Remain 10:22:48 loss: 0.1536 Lr: 0.00296
[2023-12-25 14:14:12,538 INFO misc.py line 119 253097] Train: [53/100][493/510] Data 0.006 (0.109) Batch 1.041 (1.557) Remain 10:22:21 loss: 0.1100 Lr: 0.00296
[2023-12-25 14:14:13,625 INFO misc.py line 119 253097] Train: [53/100][494/510] Data 0.009 (0.109) Batch 1.091 (1.556) Remain 10:21:57 loss: 0.1163 Lr: 0.00296
[2023-12-25 14:14:14,913 INFO misc.py line 119 253097] Train: [53/100][495/510] Data 0.005 (0.109) Batch 1.286 (1.555) Remain 10:21:42 loss: 0.1744 Lr: 0.00296
[2023-12-25 14:14:16,148 INFO misc.py line 119 253097] Train: [53/100][496/510] Data 0.008 (0.109) Batch 1.234 (1.555) Remain 10:21:25 loss: 0.1725 Lr: 0.00296
[2023-12-25 14:14:17,434 INFO misc.py line 119 253097] Train: [53/100][497/510] Data 0.008 (0.108) Batch 1.287 (1.554) Remain 10:21:10 loss: 0.1090 Lr: 0.00295
[2023-12-25 14:14:18,522 INFO misc.py line 119 253097] Train: [53/100][498/510] Data 0.007 (0.108) Batch 1.091 (1.553) Remain 10:20:46 loss: 0.1174 Lr: 0.00295
[2023-12-25 14:14:19,737 INFO misc.py line 119 253097] Train: [53/100][499/510] Data 0.004 (0.108) Batch 1.207 (1.552) Remain 10:20:28 loss: 0.1126 Lr: 0.00295
[2023-12-25 14:14:20,896 INFO misc.py line 119 253097] Train: [53/100][500/510] Data 0.011 (0.108) Batch 1.167 (1.552) Remain 10:20:08 loss: 0.2116 Lr: 0.00295
[2023-12-25 14:14:22,113 INFO misc.py line 119 253097] Train: [53/100][501/510] Data 0.004 (0.108) Batch 1.216 (1.551) Remain 10:19:50 loss: 0.2989 Lr: 0.00295
[2023-12-25 14:14:23,155 INFO misc.py line 119 253097] Train: [53/100][502/510] Data 0.005 (0.107) Batch 1.037 (1.550) Remain 10:19:24 loss: 0.0980 Lr: 0.00295
[2023-12-25 14:14:24,084 INFO misc.py line 119 253097] Train: [53/100][503/510] Data 0.010 (0.107) Batch 0.934 (1.549) Remain 10:18:53 loss: 0.1862 Lr: 0.00295
[2023-12-25 14:14:28,925 INFO misc.py line 119 253097] Train: [53/100][504/510] Data 0.005 (0.107) Batch 4.842 (1.555) Remain 10:21:29 loss: 0.2120 Lr: 0.00295
[2023-12-25 14:14:30,165 INFO misc.py line 119 253097] Train: [53/100][505/510] Data 0.004 (0.107) Batch 1.239 (1.555) Remain 10:21:12 loss: 0.1880 Lr: 0.00295
[2023-12-25 14:14:31,445 INFO misc.py line 119 253097] Train: [53/100][506/510] Data 0.006 (0.107) Batch 1.281 (1.554) Remain 10:20:57 loss: 0.1609 Lr: 0.00295
[2023-12-25 14:14:32,495 INFO misc.py line 119 253097] Train: [53/100][507/510] Data 0.004 (0.106) Batch 1.048 (1.553) Remain 10:20:32 loss: 0.1174 Lr: 0.00295
[2023-12-25 14:14:33,598 INFO misc.py line 119 253097] Train: [53/100][508/510] Data 0.007 (0.106) Batch 1.102 (1.552) Remain 10:20:09 loss: 0.2662 Lr: 0.00295
[2023-12-25 14:14:34,883 INFO misc.py line 119 253097] Train: [53/100][509/510] Data 0.008 (0.106) Batch 1.290 (1.552) Remain 10:19:55 loss: 0.1421 Lr: 0.00295
[2023-12-25 14:14:36,030 INFO misc.py line 119 253097] Train: [53/100][510/510] Data 0.003 (0.106) Batch 1.136 (1.551) Remain 10:19:34 loss: 0.1160 Lr: 0.00295
[2023-12-25 14:14:36,031 INFO misc.py line 136 253097] Train result: loss: 0.1699 
[2023-12-25 14:14:36,031 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 14:15:04,848 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5533 
[2023-12-25 14:15:05,205 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4026 
[2023-12-25 14:15:10,148 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4484 
[2023-12-25 14:15:10,682 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4147 
[2023-12-25 14:15:12,661 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8026 
[2023-12-25 14:15:13,085 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4279 
[2023-12-25 14:15:13,966 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9717 
[2023-12-25 14:15:14,520 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2815 
[2023-12-25 14:15:16,337 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.3625 
[2023-12-25 14:15:18,468 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2208 
[2023-12-25 14:15:19,343 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2845 
[2023-12-25 14:15:19,774 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9433 
[2023-12-25 14:15:20,686 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6828 
[2023-12-25 14:15:23,627 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9552 
[2023-12-25 14:15:24,094 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2422 
[2023-12-25 14:15:24,718 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3877 
[2023-12-25 14:15:25,437 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4332 
[2023-12-25 14:15:26,690 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6620/0.7200/0.8936.
[2023-12-25 14:15:26,690 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9186/0.9415
[2023-12-25 14:15:26,690 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9803/0.9889
[2023-12-25 14:15:26,690 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8221/0.9720
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3620/0.4350
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5250/0.5354
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6465/0.6998
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7977/0.8797
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8894/0.9700
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5357/0.5550
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7700/0.8502
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7671/0.8156
[2023-12-25 14:15:26,691 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5910/0.7167
[2023-12-25 14:15:26,691 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 14:15:26,693 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 14:15:26,693 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 14:15:42,980 INFO misc.py line 119 253097] Train: [54/100][1/510] Data 2.065 (2.065) Batch 13.021 (13.021) Remain 86:41:43 loss: 0.2672 Lr: 0.00295
[2023-12-25 14:15:44,142 INFO misc.py line 119 253097] Train: [54/100][2/510] Data 0.005 (0.005) Batch 1.161 (1.161) Remain 07:43:37 loss: 0.2140 Lr: 0.00295
[2023-12-25 14:15:45,442 INFO misc.py line 119 253097] Train: [54/100][3/510] Data 0.006 (0.006) Batch 1.303 (1.303) Remain 08:40:20 loss: 0.1991 Lr: 0.00295
[2023-12-25 14:15:46,605 INFO misc.py line 119 253097] Train: [54/100][4/510] Data 0.004 (0.004) Batch 1.159 (1.159) Remain 07:42:55 loss: 0.2266 Lr: 0.00295
[2023-12-25 14:15:47,899 INFO misc.py line 119 253097] Train: [54/100][5/510] Data 0.008 (0.006) Batch 1.298 (1.228) Remain 08:10:35 loss: 0.1005 Lr: 0.00295
[2023-12-25 14:15:48,954 INFO misc.py line 119 253097] Train: [54/100][6/510] Data 0.005 (0.006) Batch 1.055 (1.170) Remain 07:47:27 loss: 0.2021 Lr: 0.00295
[2023-12-25 14:15:50,146 INFO misc.py line 119 253097] Train: [54/100][7/510] Data 0.003 (0.005) Batch 1.188 (1.175) Remain 07:49:08 loss: 0.1766 Lr: 0.00295
[2023-12-25 14:15:51,249 INFO misc.py line 119 253097] Train: [54/100][8/510] Data 0.009 (0.006) Batch 1.104 (1.161) Remain 07:43:28 loss: 0.0741 Lr: 0.00295
[2023-12-25 14:15:52,043 INFO misc.py line 119 253097] Train: [54/100][9/510] Data 0.008 (0.006) Batch 0.799 (1.100) Remain 07:19:23 loss: 0.1271 Lr: 0.00295
[2023-12-25 14:15:53,070 INFO misc.py line 119 253097] Train: [54/100][10/510] Data 0.003 (0.006) Batch 1.026 (1.090) Remain 07:15:06 loss: 0.0915 Lr: 0.00295
[2023-12-25 14:15:54,208 INFO misc.py line 119 253097] Train: [54/100][11/510] Data 0.004 (0.006) Batch 1.139 (1.096) Remain 07:17:33 loss: 0.0809 Lr: 0.00295
[2023-12-25 14:15:55,340 INFO misc.py line 119 253097] Train: [54/100][12/510] Data 0.004 (0.005) Batch 1.131 (1.100) Remain 07:19:06 loss: 0.2326 Lr: 0.00295
[2023-12-25 14:15:56,276 INFO misc.py line 119 253097] Train: [54/100][13/510] Data 0.004 (0.005) Batch 0.937 (1.083) Remain 07:12:35 loss: 0.1864 Lr: 0.00295
[2023-12-25 14:16:00,147 INFO misc.py line 119 253097] Train: [54/100][14/510] Data 0.003 (0.005) Batch 3.870 (1.337) Remain 08:53:44 loss: 0.3608 Lr: 0.00295
[2023-12-25 14:16:01,203 INFO misc.py line 119 253097] Train: [54/100][15/510] Data 0.004 (0.005) Batch 1.056 (1.313) Remain 08:44:21 loss: 0.0990 Lr: 0.00295
[2023-12-25 14:16:02,355 INFO misc.py line 119 253097] Train: [54/100][16/510] Data 0.004 (0.005) Batch 1.150 (1.301) Remain 08:39:19 loss: 0.1270 Lr: 0.00295
[2023-12-25 14:16:03,408 INFO misc.py line 119 253097] Train: [54/100][17/510] Data 0.007 (0.005) Batch 1.056 (1.283) Remain 08:32:19 loss: 0.2828 Lr: 0.00295
[2023-12-25 14:16:07,967 INFO misc.py line 119 253097] Train: [54/100][18/510] Data 0.002 (0.005) Batch 4.557 (1.502) Remain 09:59:25 loss: 0.0968 Lr: 0.00295
[2023-12-25 14:16:09,041 INFO misc.py line 119 253097] Train: [54/100][19/510] Data 0.005 (0.005) Batch 1.075 (1.475) Remain 09:48:45 loss: 0.0614 Lr: 0.00295
[2023-12-25 14:16:10,119 INFO misc.py line 119 253097] Train: [54/100][20/510] Data 0.004 (0.005) Batch 1.077 (1.452) Remain 09:39:23 loss: 0.1593 Lr: 0.00295
[2023-12-25 14:16:11,368 INFO misc.py line 119 253097] Train: [54/100][21/510] Data 0.005 (0.005) Batch 1.246 (1.440) Remain 09:34:49 loss: 0.2396 Lr: 0.00295
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[2023-12-25 14:22:29,342 INFO misc.py line 119 253097] Train: [54/100][262/510] Data 0.004 (0.155) Batch 1.224 (1.559) Remain 10:16:11 loss: 0.2547 Lr: 0.00290
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[2023-12-25 14:22:31,935 INFO misc.py line 119 253097] Train: [54/100][264/510] Data 0.003 (0.154) Batch 1.293 (1.557) Remain 10:15:20 loss: 0.1149 Lr: 0.00290
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[2023-12-25 14:22:36,852 INFO misc.py line 119 253097] Train: [54/100][268/510] Data 0.008 (0.152) Batch 1.206 (1.552) Remain 10:13:16 loss: 0.2465 Lr: 0.00290
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[2023-12-25 14:22:38,896 INFO misc.py line 119 253097] Train: [54/100][270/510] Data 0.011 (0.151) Batch 0.887 (1.549) Remain 10:11:39 loss: 0.0944 Lr: 0.00290
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[2023-12-25 14:22:42,369 INFO misc.py line 119 253097] Train: [54/100][273/510] Data 0.010 (0.149) Batch 0.987 (1.544) Remain 10:09:52 loss: 0.2556 Lr: 0.00290
[2023-12-25 14:22:43,539 INFO misc.py line 119 253097] Train: [54/100][274/510] Data 0.006 (0.149) Batch 1.171 (1.543) Remain 10:09:17 loss: 0.2109 Lr: 0.00290
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[2023-12-25 14:22:53,224 INFO misc.py line 119 253097] Train: [54/100][281/510] Data 0.006 (0.152) Batch 1.145 (1.539) Remain 10:07:31 loss: 0.1217 Lr: 0.00290
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[2023-12-25 14:22:58,985 INFO misc.py line 119 253097] Train: [54/100][286/510] Data 0.006 (0.149) Batch 1.036 (1.532) Remain 10:04:42 loss: 0.1265 Lr: 0.00290
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[2023-12-25 14:23:20,581 INFO misc.py line 119 253097] Train: [54/100][305/510] Data 0.007 (0.140) Batch 1.217 (1.507) Remain 09:54:25 loss: 0.1946 Lr: 0.00289
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[2023-12-25 14:25:17,739 INFO misc.py line 119 253097] Train: [54/100][372/510] Data 8.502 (0.201) Batch 9.608 (1.551) Remain 10:09:59 loss: 0.1220 Lr: 0.00288
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[2023-12-25 14:25:29,467 INFO misc.py line 119 253097] Train: [54/100][382/510] Data 0.008 (0.195) Batch 1.335 (1.541) Remain 10:05:48 loss: 0.2182 Lr: 0.00288
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[2023-12-25 14:25:31,787 INFO misc.py line 119 253097] Train: [54/100][384/510] Data 0.004 (0.194) Batch 1.243 (1.539) Remain 10:04:57 loss: 0.1489 Lr: 0.00288
[2023-12-25 14:25:38,280 INFO misc.py line 119 253097] Train: [54/100][385/510] Data 5.472 (0.208) Batch 6.499 (1.552) Remain 10:10:02 loss: 0.1393 Lr: 0.00288
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[2023-12-25 14:25:43,227 INFO misc.py line 119 253097] Train: [54/100][389/510] Data 0.004 (0.207) Batch 1.135 (1.549) Remain 10:08:39 loss: 0.1698 Lr: 0.00288
[2023-12-25 14:25:44,338 INFO misc.py line 119 253097] Train: [54/100][390/510] Data 0.003 (0.206) Batch 1.108 (1.548) Remain 10:08:10 loss: 0.1160 Lr: 0.00288
[2023-12-25 14:25:45,423 INFO misc.py line 119 253097] Train: [54/100][391/510] Data 0.005 (0.206) Batch 1.086 (1.546) Remain 10:07:41 loss: 0.1042 Lr: 0.00288
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[2023-12-25 14:25:50,980 INFO misc.py line 119 253097] Train: [54/100][393/510] Data 3.327 (0.213) Batch 4.390 (1.553) Remain 10:10:06 loss: 0.1208 Lr: 0.00288
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[2023-12-25 14:26:02,695 INFO misc.py line 119 253097] Train: [54/100][399/510] Data 1.740 (0.214) Batch 6.218 (1.559) Remain 10:12:20 loss: 0.1476 Lr: 0.00287
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[2023-12-25 14:26:55,330 INFO misc.py line 119 253097] Train: [54/100][440/510] Data 0.005 (0.195) Batch 0.882 (1.533) Remain 10:01:09 loss: 0.2636 Lr: 0.00287
[2023-12-25 14:26:56,409 INFO misc.py line 119 253097] Train: [54/100][441/510] Data 0.004 (0.195) Batch 1.079 (1.532) Remain 10:00:43 loss: 0.1986 Lr: 0.00287
[2023-12-25 14:26:57,455 INFO misc.py line 119 253097] Train: [54/100][442/510] Data 0.003 (0.194) Batch 1.045 (1.531) Remain 10:00:16 loss: 0.2918 Lr: 0.00287
[2023-12-25 14:26:58,715 INFO misc.py line 119 253097] Train: [54/100][443/510] Data 0.005 (0.194) Batch 1.258 (1.530) Remain 10:00:00 loss: 0.2324 Lr: 0.00287
[2023-12-25 14:26:59,925 INFO misc.py line 119 253097] Train: [54/100][444/510] Data 0.006 (0.193) Batch 1.207 (1.529) Remain 09:59:41 loss: 0.1426 Lr: 0.00287
[2023-12-25 14:27:00,857 INFO misc.py line 119 253097] Train: [54/100][445/510] Data 0.009 (0.193) Batch 0.937 (1.528) Remain 09:59:08 loss: 0.1162 Lr: 0.00287
[2023-12-25 14:27:02,192 INFO misc.py line 119 253097] Train: [54/100][446/510] Data 0.005 (0.192) Batch 1.330 (1.528) Remain 09:58:56 loss: 0.2924 Lr: 0.00287
[2023-12-25 14:27:03,368 INFO misc.py line 119 253097] Train: [54/100][447/510] Data 0.009 (0.192) Batch 1.150 (1.527) Remain 09:58:34 loss: 0.1597 Lr: 0.00287
[2023-12-25 14:27:04,583 INFO misc.py line 119 253097] Train: [54/100][448/510] Data 0.036 (0.192) Batch 1.240 (1.526) Remain 09:58:17 loss: 0.1973 Lr: 0.00287
[2023-12-25 14:27:05,704 INFO misc.py line 119 253097] Train: [54/100][449/510] Data 0.011 (0.191) Batch 1.127 (1.525) Remain 09:57:55 loss: 0.1113 Lr: 0.00287
[2023-12-25 14:27:06,840 INFO misc.py line 119 253097] Train: [54/100][450/510] Data 0.005 (0.191) Batch 1.130 (1.524) Remain 09:57:32 loss: 0.0859 Lr: 0.00286
[2023-12-25 14:27:07,792 INFO misc.py line 119 253097] Train: [54/100][451/510] Data 0.011 (0.190) Batch 0.960 (1.523) Remain 09:57:01 loss: 0.2300 Lr: 0.00286
[2023-12-25 14:27:08,943 INFO misc.py line 119 253097] Train: [54/100][452/510] Data 0.003 (0.190) Batch 1.151 (1.522) Remain 09:56:40 loss: 0.0955 Lr: 0.00286
[2023-12-25 14:27:09,825 INFO misc.py line 119 253097] Train: [54/100][453/510] Data 0.003 (0.190) Batch 0.881 (1.521) Remain 09:56:05 loss: 0.1893 Lr: 0.00286
[2023-12-25 14:27:11,113 INFO misc.py line 119 253097] Train: [54/100][454/510] Data 0.003 (0.189) Batch 1.284 (1.520) Remain 09:55:51 loss: 0.2781 Lr: 0.00286
[2023-12-25 14:27:12,208 INFO misc.py line 119 253097] Train: [54/100][455/510] Data 0.007 (0.189) Batch 1.099 (1.519) Remain 09:55:28 loss: 0.1487 Lr: 0.00286
[2023-12-25 14:27:16,696 INFO misc.py line 119 253097] Train: [54/100][456/510] Data 0.003 (0.188) Batch 4.488 (1.526) Remain 09:58:01 loss: 0.1230 Lr: 0.00286
[2023-12-25 14:27:17,924 INFO misc.py line 119 253097] Train: [54/100][457/510] Data 0.003 (0.188) Batch 1.228 (1.525) Remain 09:57:44 loss: 0.1814 Lr: 0.00286
[2023-12-25 14:27:19,026 INFO misc.py line 119 253097] Train: [54/100][458/510] Data 0.003 (0.188) Batch 1.102 (1.524) Remain 09:57:20 loss: 0.2331 Lr: 0.00286
[2023-12-25 14:27:20,106 INFO misc.py line 119 253097] Train: [54/100][459/510] Data 0.003 (0.187) Batch 1.080 (1.523) Remain 09:56:56 loss: 0.4909 Lr: 0.00286
[2023-12-25 14:27:21,187 INFO misc.py line 119 253097] Train: [54/100][460/510] Data 0.004 (0.187) Batch 1.080 (1.522) Remain 09:56:31 loss: 0.2665 Lr: 0.00286
[2023-12-25 14:27:25,147 INFO misc.py line 119 253097] Train: [54/100][461/510] Data 0.004 (0.186) Batch 3.961 (1.528) Remain 09:58:35 loss: 0.1449 Lr: 0.00286
[2023-12-25 14:27:26,154 INFO misc.py line 119 253097] Train: [54/100][462/510] Data 0.004 (0.186) Batch 1.008 (1.527) Remain 09:58:07 loss: 0.1500 Lr: 0.00286
[2023-12-25 14:27:27,336 INFO misc.py line 119 253097] Train: [54/100][463/510] Data 0.004 (0.186) Batch 1.175 (1.526) Remain 09:57:47 loss: 0.1083 Lr: 0.00286
[2023-12-25 14:27:28,529 INFO misc.py line 119 253097] Train: [54/100][464/510] Data 0.010 (0.185) Batch 1.199 (1.525) Remain 09:57:29 loss: 0.1711 Lr: 0.00286
[2023-12-25 14:27:29,804 INFO misc.py line 119 253097] Train: [54/100][465/510] Data 0.004 (0.185) Batch 1.275 (1.525) Remain 09:57:15 loss: 0.1714 Lr: 0.00286
[2023-12-25 14:27:30,940 INFO misc.py line 119 253097] Train: [54/100][466/510] Data 0.004 (0.184) Batch 1.133 (1.524) Remain 09:56:54 loss: 0.1069 Lr: 0.00286
[2023-12-25 14:27:32,049 INFO misc.py line 119 253097] Train: [54/100][467/510] Data 0.007 (0.184) Batch 1.109 (1.523) Remain 09:56:31 loss: 0.3962 Lr: 0.00286
[2023-12-25 14:27:33,267 INFO misc.py line 119 253097] Train: [54/100][468/510] Data 0.007 (0.184) Batch 1.220 (1.522) Remain 09:56:14 loss: 0.1549 Lr: 0.00286
[2023-12-25 14:27:34,500 INFO misc.py line 119 253097] Train: [54/100][469/510] Data 0.005 (0.183) Batch 1.229 (1.522) Remain 09:55:58 loss: 0.2519 Lr: 0.00286
[2023-12-25 14:27:35,596 INFO misc.py line 119 253097] Train: [54/100][470/510] Data 0.009 (0.183) Batch 1.096 (1.521) Remain 09:55:35 loss: 0.2516 Lr: 0.00286
[2023-12-25 14:27:36,673 INFO misc.py line 119 253097] Train: [54/100][471/510] Data 0.011 (0.183) Batch 1.082 (1.520) Remain 09:55:11 loss: 0.2570 Lr: 0.00286
[2023-12-25 14:27:38,025 INFO misc.py line 119 253097] Train: [54/100][472/510] Data 0.006 (0.182) Batch 1.348 (1.519) Remain 09:55:01 loss: 0.1066 Lr: 0.00286
[2023-12-25 14:27:39,246 INFO misc.py line 119 253097] Train: [54/100][473/510] Data 0.008 (0.182) Batch 1.225 (1.519) Remain 09:54:45 loss: 0.1455 Lr: 0.00286
[2023-12-25 14:27:40,524 INFO misc.py line 119 253097] Train: [54/100][474/510] Data 0.005 (0.181) Batch 1.279 (1.518) Remain 09:54:32 loss: 0.1313 Lr: 0.00286
[2023-12-25 14:27:41,608 INFO misc.py line 119 253097] Train: [54/100][475/510] Data 0.004 (0.181) Batch 1.084 (1.517) Remain 09:54:08 loss: 0.1276 Lr: 0.00286
[2023-12-25 14:27:42,703 INFO misc.py line 119 253097] Train: [54/100][476/510] Data 0.004 (0.181) Batch 1.095 (1.516) Remain 09:53:46 loss: 0.1486 Lr: 0.00286
[2023-12-25 14:27:43,870 INFO misc.py line 119 253097] Train: [54/100][477/510] Data 0.003 (0.180) Batch 1.165 (1.516) Remain 09:53:27 loss: 0.1970 Lr: 0.00286
[2023-12-25 14:27:45,094 INFO misc.py line 119 253097] Train: [54/100][478/510] Data 0.005 (0.180) Batch 1.224 (1.515) Remain 09:53:11 loss: 0.2331 Lr: 0.00286
[2023-12-25 14:27:47,763 INFO misc.py line 119 253097] Train: [54/100][479/510] Data 1.322 (0.182) Batch 2.668 (1.517) Remain 09:54:06 loss: 0.1533 Lr: 0.00286
[2023-12-25 14:27:49,027 INFO misc.py line 119 253097] Train: [54/100][480/510] Data 0.006 (0.182) Batch 1.263 (1.517) Remain 09:53:52 loss: 0.1636 Lr: 0.00286
[2023-12-25 14:27:50,172 INFO misc.py line 119 253097] Train: [54/100][481/510] Data 0.007 (0.182) Batch 1.145 (1.516) Remain 09:53:33 loss: 0.2859 Lr: 0.00286
[2023-12-25 14:27:51,288 INFO misc.py line 119 253097] Train: [54/100][482/510] Data 0.008 (0.181) Batch 1.115 (1.515) Remain 09:53:11 loss: 0.1280 Lr: 0.00286
[2023-12-25 14:27:52,539 INFO misc.py line 119 253097] Train: [54/100][483/510] Data 0.009 (0.181) Batch 1.256 (1.515) Remain 09:52:57 loss: 0.1760 Lr: 0.00286
[2023-12-25 14:27:54,352 INFO misc.py line 119 253097] Train: [54/100][484/510] Data 0.004 (0.180) Batch 1.814 (1.515) Remain 09:53:10 loss: 0.2953 Lr: 0.00286
[2023-12-25 14:27:55,418 INFO misc.py line 119 253097] Train: [54/100][485/510] Data 0.003 (0.180) Batch 1.061 (1.514) Remain 09:52:47 loss: 0.0806 Lr: 0.00286
[2023-12-25 14:27:56,623 INFO misc.py line 119 253097] Train: [54/100][486/510] Data 0.008 (0.180) Batch 1.209 (1.514) Remain 09:52:30 loss: 0.3308 Lr: 0.00286
[2023-12-25 14:28:00,734 INFO misc.py line 119 253097] Train: [54/100][487/510] Data 0.003 (0.179) Batch 4.109 (1.519) Remain 09:54:35 loss: 0.0988 Lr: 0.00286
[2023-12-25 14:28:01,743 INFO misc.py line 119 253097] Train: [54/100][488/510] Data 0.005 (0.179) Batch 1.010 (1.518) Remain 09:54:09 loss: 0.2323 Lr: 0.00286
[2023-12-25 14:28:03,019 INFO misc.py line 119 253097] Train: [54/100][489/510] Data 0.003 (0.179) Batch 1.270 (1.518) Remain 09:53:55 loss: 0.2778 Lr: 0.00286
[2023-12-25 14:28:04,144 INFO misc.py line 119 253097] Train: [54/100][490/510] Data 0.010 (0.178) Batch 1.127 (1.517) Remain 09:53:35 loss: 0.2489 Lr: 0.00286
[2023-12-25 14:28:05,093 INFO misc.py line 119 253097] Train: [54/100][491/510] Data 0.007 (0.178) Batch 0.953 (1.516) Remain 09:53:06 loss: 0.1219 Lr: 0.00286
[2023-12-25 14:28:06,194 INFO misc.py line 119 253097] Train: [54/100][492/510] Data 0.003 (0.178) Batch 1.101 (1.515) Remain 09:52:45 loss: 0.2096 Lr: 0.00286
[2023-12-25 14:28:10,251 INFO misc.py line 119 253097] Train: [54/100][493/510] Data 0.003 (0.177) Batch 4.056 (1.520) Remain 09:54:45 loss: 0.1962 Lr: 0.00286
[2023-12-25 14:28:11,371 INFO misc.py line 119 253097] Train: [54/100][494/510] Data 0.004 (0.177) Batch 1.119 (1.519) Remain 09:54:24 loss: 0.2532 Lr: 0.00286
[2023-12-25 14:28:12,664 INFO misc.py line 119 253097] Train: [54/100][495/510] Data 0.006 (0.177) Batch 1.293 (1.519) Remain 09:54:12 loss: 0.0870 Lr: 0.00286
[2023-12-25 14:28:13,736 INFO misc.py line 119 253097] Train: [54/100][496/510] Data 0.006 (0.176) Batch 1.074 (1.518) Remain 09:53:49 loss: 0.2161 Lr: 0.00286
[2023-12-25 14:28:14,760 INFO misc.py line 119 253097] Train: [54/100][497/510] Data 0.004 (0.176) Batch 1.021 (1.517) Remain 09:53:24 loss: 0.2097 Lr: 0.00286
[2023-12-25 14:28:16,038 INFO misc.py line 119 253097] Train: [54/100][498/510] Data 0.007 (0.176) Batch 1.280 (1.516) Remain 09:53:11 loss: 0.2833 Lr: 0.00286
[2023-12-25 14:28:17,257 INFO misc.py line 119 253097] Train: [54/100][499/510] Data 0.006 (0.175) Batch 1.215 (1.516) Remain 09:52:56 loss: 0.2035 Lr: 0.00286
[2023-12-25 14:28:18,440 INFO misc.py line 119 253097] Train: [54/100][500/510] Data 0.009 (0.175) Batch 1.186 (1.515) Remain 09:52:38 loss: 0.1129 Lr: 0.00286
[2023-12-25 14:28:19,463 INFO misc.py line 119 253097] Train: [54/100][501/510] Data 0.005 (0.174) Batch 1.018 (1.514) Remain 09:52:14 loss: 0.1609 Lr: 0.00286
[2023-12-25 14:28:24,843 INFO misc.py line 119 253097] Train: [54/100][502/510] Data 0.011 (0.174) Batch 5.385 (1.522) Remain 09:55:14 loss: 0.2197 Lr: 0.00285
[2023-12-25 14:28:25,969 INFO misc.py line 119 253097] Train: [54/100][503/510] Data 0.007 (0.174) Batch 1.126 (1.521) Remain 09:54:54 loss: 0.1933 Lr: 0.00285
[2023-12-25 14:28:27,089 INFO misc.py line 119 253097] Train: [54/100][504/510] Data 0.005 (0.173) Batch 1.121 (1.520) Remain 09:54:34 loss: 0.2031 Lr: 0.00285
[2023-12-25 14:28:28,125 INFO misc.py line 119 253097] Train: [54/100][505/510] Data 0.004 (0.173) Batch 1.036 (1.519) Remain 09:54:10 loss: 0.1071 Lr: 0.00285
[2023-12-25 14:28:35,158 INFO misc.py line 119 253097] Train: [54/100][506/510] Data 0.005 (0.173) Batch 7.032 (1.530) Remain 09:58:25 loss: 0.1321 Lr: 0.00285
[2023-12-25 14:28:36,243 INFO misc.py line 119 253097] Train: [54/100][507/510] Data 0.006 (0.172) Batch 1.087 (1.529) Remain 09:58:03 loss: 0.0971 Lr: 0.00285
[2023-12-25 14:28:37,616 INFO misc.py line 119 253097] Train: [54/100][508/510] Data 0.004 (0.172) Batch 1.368 (1.529) Remain 09:57:54 loss: 0.1567 Lr: 0.00285
[2023-12-25 14:28:38,759 INFO misc.py line 119 253097] Train: [54/100][509/510] Data 0.010 (0.172) Batch 1.143 (1.528) Remain 09:57:34 loss: 0.1872 Lr: 0.00285
[2023-12-25 14:28:39,940 INFO misc.py line 119 253097] Train: [54/100][510/510] Data 0.010 (0.172) Batch 1.187 (1.528) Remain 09:57:17 loss: 0.2686 Lr: 0.00285
[2023-12-25 14:28:39,940 INFO misc.py line 136 253097] Train result: loss: 0.1713 
[2023-12-25 14:28:39,941 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 14:29:06,525 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7168 
[2023-12-25 14:29:06,876 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3917 
[2023-12-25 14:29:11,818 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3769 
[2023-12-25 14:29:12,333 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3470 
[2023-12-25 14:29:14,307 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8897 
[2023-12-25 14:29:14,731 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4684 
[2023-12-25 14:29:15,609 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0258 
[2023-12-25 14:29:16,162 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3455 
[2023-12-25 14:29:17,967 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.3074 
[2023-12-25 14:29:20,093 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2408 
[2023-12-25 14:29:20,954 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2400 
[2023-12-25 14:29:21,383 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7189 
[2023-12-25 14:29:22,281 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.7740 
[2023-12-25 14:29:25,226 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7688 
[2023-12-25 14:29:25,702 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1775 
[2023-12-25 14:29:26,312 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4757 
[2023-12-25 14:29:27,016 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2345 
[2023-12-25 14:29:28,411 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6749/0.7444/0.8974.
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9178/0.9482
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9809/0.9913
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8345/0.9642
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3356/0.4301
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5849/0.6039
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6599/0.7675
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7983/0.9220
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8997/0.9469
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6656/0.7244
[2023-12-25 14:29:28,411 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7580/0.8350
[2023-12-25 14:29:28,412 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7376/0.8276
[2023-12-25 14:29:28,412 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6012/0.7156
[2023-12-25 14:29:28,412 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 14:29:28,414 INFO misc.py line 165 253097] Currently Best mIoU: 0.6817
[2023-12-25 14:29:28,414 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 14:29:34,782 INFO misc.py line 119 253097] Train: [55/100][1/510] Data 3.725 (3.725) Batch 4.629 (4.629) Remain 30:09:55 loss: 0.2389 Lr: 0.00285
[2023-12-25 14:29:36,661 INFO misc.py line 119 253097] Train: [55/100][2/510] Data 0.009 (0.009) Batch 1.883 (1.883) Remain 12:16:22 loss: 0.2383 Lr: 0.00285
[2023-12-25 14:29:37,567 INFO misc.py line 119 253097] Train: [55/100][3/510] Data 0.004 (0.004) Batch 0.905 (0.905) Remain 05:53:59 loss: 0.1372 Lr: 0.00285
[2023-12-25 14:29:38,810 INFO misc.py line 119 253097] Train: [55/100][4/510] Data 0.004 (0.004) Batch 1.243 (1.243) Remain 08:05:59 loss: 0.1369 Lr: 0.00285
[2023-12-25 14:29:40,142 INFO misc.py line 119 253097] Train: [55/100][5/510] Data 0.004 (0.004) Batch 1.332 (1.287) Remain 08:23:15 loss: 0.1872 Lr: 0.00285
[2023-12-25 14:29:41,400 INFO misc.py line 119 253097] Train: [55/100][6/510] Data 0.006 (0.005) Batch 1.256 (1.277) Remain 08:19:09 loss: 0.1371 Lr: 0.00285
[2023-12-25 14:29:50,994 INFO misc.py line 119 253097] Train: [55/100][7/510] Data 0.007 (0.005) Batch 9.596 (3.357) Remain 21:52:04 loss: 0.2177 Lr: 0.00285
[2023-12-25 14:29:52,043 INFO misc.py line 119 253097] Train: [55/100][8/510] Data 0.005 (0.005) Batch 1.048 (2.895) Remain 18:51:32 loss: 0.1922 Lr: 0.00285
[2023-12-25 14:29:53,104 INFO misc.py line 119 253097] Train: [55/100][9/510] Data 0.006 (0.005) Batch 1.063 (2.590) Remain 16:52:10 loss: 0.1233 Lr: 0.00285
[2023-12-25 14:29:54,266 INFO misc.py line 119 253097] Train: [55/100][10/510] Data 0.003 (0.005) Batch 1.161 (2.386) Remain 15:32:22 loss: 0.2501 Lr: 0.00285
[2023-12-25 14:29:55,392 INFO misc.py line 119 253097] Train: [55/100][11/510] Data 0.005 (0.005) Batch 1.127 (2.228) Remain 14:30:50 loss: 0.0815 Lr: 0.00285
[2023-12-25 14:29:56,564 INFO misc.py line 119 253097] Train: [55/100][12/510] Data 0.005 (0.005) Batch 1.172 (2.111) Remain 13:44:55 loss: 0.2089 Lr: 0.00285
[2023-12-25 14:29:57,762 INFO misc.py line 119 253097] Train: [55/100][13/510] Data 0.005 (0.005) Batch 1.198 (2.020) Remain 13:09:12 loss: 0.3606 Lr: 0.00285
[2023-12-25 14:29:58,901 INFO misc.py line 119 253097] Train: [55/100][14/510] Data 0.005 (0.005) Batch 1.139 (1.939) Remain 12:37:52 loss: 0.0852 Lr: 0.00285
[2023-12-25 14:30:00,153 INFO misc.py line 119 253097] Train: [55/100][15/510] Data 0.005 (0.005) Batch 1.247 (1.882) Remain 12:15:18 loss: 0.3441 Lr: 0.00285
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[2023-12-25 14:36:31,645 INFO misc.py line 119 253097] Train: [55/100][263/510] Data 6.563 (0.140) Batch 7.543 (1.593) Remain 10:15:43 loss: 0.0544 Lr: 0.00280
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[2023-12-25 14:36:33,779 INFO misc.py line 119 253097] Train: [55/100][265/510] Data 0.004 (0.139) Batch 1.117 (1.589) Remain 10:14:07 loss: 0.1966 Lr: 0.00280
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[2023-12-25 14:36:53,919 INFO misc.py line 119 253097] Train: [55/100][277/510] Data 6.099 (0.157) Batch 7.448 (1.593) Remain 10:15:19 loss: 0.0940 Lr: 0.00280
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[2023-12-25 14:36:56,160 INFO misc.py line 119 253097] Train: [55/100][279/510] Data 0.007 (0.156) Batch 1.075 (1.589) Remain 10:13:56 loss: 0.1279 Lr: 0.00280
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[2023-12-25 14:36:58,534 INFO misc.py line 119 253097] Train: [55/100][281/510] Data 0.004 (0.155) Batch 1.223 (1.586) Remain 10:12:46 loss: 0.1242 Lr: 0.00280
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[2023-12-25 14:37:00,766 INFO misc.py line 119 253097] Train: [55/100][283/510] Data 0.009 (0.154) Batch 1.099 (1.583) Remain 10:11:25 loss: 0.2017 Lr: 0.00280
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[2023-12-25 14:37:03,984 INFO misc.py line 119 253097] Train: [55/100][286/510] Data 0.005 (0.152) Batch 0.957 (1.577) Remain 10:09:15 loss: 0.2454 Lr: 0.00280
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[2023-12-25 14:37:11,793 INFO misc.py line 119 253097] Train: [55/100][288/510] Data 5.273 (0.170) Batch 6.462 (1.594) Remain 10:15:30 loss: 0.1539 Lr: 0.00280
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[2023-12-25 14:41:06,780 INFO misc.py line 119 253097] Train: [55/100][434/510] Data 0.004 (0.130) Batch 1.191 (1.599) Remain 10:13:40 loss: 0.2245 Lr: 0.00277
[2023-12-25 14:41:07,794 INFO misc.py line 119 253097] Train: [55/100][435/510] Data 0.003 (0.129) Batch 1.013 (1.598) Remain 10:13:08 loss: 0.1523 Lr: 0.00277
[2023-12-25 14:41:09,010 INFO misc.py line 119 253097] Train: [55/100][436/510] Data 0.004 (0.129) Batch 1.216 (1.597) Remain 10:12:46 loss: 0.1064 Lr: 0.00277
[2023-12-25 14:41:10,183 INFO misc.py line 119 253097] Train: [55/100][437/510] Data 0.004 (0.129) Batch 1.173 (1.596) Remain 10:12:22 loss: 0.2100 Lr: 0.00277
[2023-12-25 14:41:11,313 INFO misc.py line 119 253097] Train: [55/100][438/510] Data 0.004 (0.129) Batch 1.130 (1.595) Remain 10:11:55 loss: 0.2969 Lr: 0.00277
[2023-12-25 14:41:12,363 INFO misc.py line 119 253097] Train: [55/100][439/510] Data 0.005 (0.128) Batch 1.050 (1.594) Remain 10:11:25 loss: 0.0950 Lr: 0.00277
[2023-12-25 14:41:13,436 INFO misc.py line 119 253097] Train: [55/100][440/510] Data 0.004 (0.128) Batch 1.072 (1.592) Remain 10:10:56 loss: 0.1248 Lr: 0.00277
[2023-12-25 14:41:14,602 INFO misc.py line 119 253097] Train: [55/100][441/510] Data 0.006 (0.128) Batch 1.166 (1.591) Remain 10:10:32 loss: 0.2319 Lr: 0.00277
[2023-12-25 14:41:15,666 INFO misc.py line 119 253097] Train: [55/100][442/510] Data 0.004 (0.127) Batch 1.064 (1.590) Remain 10:10:03 loss: 0.1205 Lr: 0.00277
[2023-12-25 14:41:16,875 INFO misc.py line 119 253097] Train: [55/100][443/510] Data 0.005 (0.127) Batch 1.208 (1.589) Remain 10:09:41 loss: 0.0951 Lr: 0.00277
[2023-12-25 14:41:18,232 INFO misc.py line 119 253097] Train: [55/100][444/510] Data 0.005 (0.127) Batch 1.359 (1.589) Remain 10:09:28 loss: 0.1215 Lr: 0.00277
[2023-12-25 14:41:19,282 INFO misc.py line 119 253097] Train: [55/100][445/510] Data 0.003 (0.127) Batch 1.049 (1.588) Remain 10:08:58 loss: 0.1876 Lr: 0.00277
[2023-12-25 14:41:20,393 INFO misc.py line 119 253097] Train: [55/100][446/510] Data 0.004 (0.126) Batch 1.104 (1.586) Remain 10:08:31 loss: 0.1265 Lr: 0.00277
[2023-12-25 14:41:21,396 INFO misc.py line 119 253097] Train: [55/100][447/510] Data 0.011 (0.126) Batch 1.009 (1.585) Remain 10:08:00 loss: 0.2504 Lr: 0.00277
[2023-12-25 14:41:22,586 INFO misc.py line 119 253097] Train: [55/100][448/510] Data 0.005 (0.126) Batch 1.187 (1.584) Remain 10:07:38 loss: 0.2324 Lr: 0.00277
[2023-12-25 14:41:23,615 INFO misc.py line 119 253097] Train: [55/100][449/510] Data 0.008 (0.126) Batch 1.033 (1.583) Remain 10:07:07 loss: 0.0811 Lr: 0.00277
[2023-12-25 14:41:24,890 INFO misc.py line 119 253097] Train: [55/100][450/510] Data 0.004 (0.125) Batch 1.275 (1.582) Remain 10:06:50 loss: 0.1248 Lr: 0.00277
[2023-12-25 14:41:25,913 INFO misc.py line 119 253097] Train: [55/100][451/510] Data 0.005 (0.125) Batch 1.020 (1.581) Remain 10:06:20 loss: 0.1984 Lr: 0.00277
[2023-12-25 14:41:26,968 INFO misc.py line 119 253097] Train: [55/100][452/510] Data 0.007 (0.125) Batch 1.056 (1.580) Remain 10:05:51 loss: 0.1094 Lr: 0.00277
[2023-12-25 14:41:28,033 INFO misc.py line 119 253097] Train: [55/100][453/510] Data 0.005 (0.125) Batch 1.062 (1.579) Remain 10:05:23 loss: 0.2192 Lr: 0.00277
[2023-12-25 14:41:29,266 INFO misc.py line 119 253097] Train: [55/100][454/510] Data 0.010 (0.124) Batch 1.239 (1.578) Remain 10:05:04 loss: 0.1348 Lr: 0.00277
[2023-12-25 14:41:30,939 INFO misc.py line 119 253097] Train: [55/100][455/510] Data 0.003 (0.124) Batch 1.672 (1.578) Remain 10:05:07 loss: 0.1018 Lr: 0.00276
[2023-12-25 14:41:31,949 INFO misc.py line 119 253097] Train: [55/100][456/510] Data 0.004 (0.124) Batch 1.009 (1.577) Remain 10:04:37 loss: 0.1396 Lr: 0.00276
[2023-12-25 14:41:32,943 INFO misc.py line 119 253097] Train: [55/100][457/510] Data 0.006 (0.123) Batch 0.996 (1.576) Remain 10:04:06 loss: 0.1880 Lr: 0.00276
[2023-12-25 14:41:33,972 INFO misc.py line 119 253097] Train: [55/100][458/510] Data 0.003 (0.123) Batch 1.025 (1.575) Remain 10:03:36 loss: 0.1531 Lr: 0.00276
[2023-12-25 14:41:41,202 INFO misc.py line 119 253097] Train: [55/100][459/510] Data 0.008 (0.123) Batch 7.234 (1.587) Remain 10:08:20 loss: 0.0703 Lr: 0.00276
[2023-12-25 14:41:42,145 INFO misc.py line 119 253097] Train: [55/100][460/510] Data 0.004 (0.123) Batch 0.943 (1.586) Remain 10:07:46 loss: 0.1169 Lr: 0.00276
[2023-12-25 14:41:43,388 INFO misc.py line 119 253097] Train: [55/100][461/510] Data 0.005 (0.122) Batch 1.243 (1.585) Remain 10:07:27 loss: 0.2148 Lr: 0.00276
[2023-12-25 14:41:44,644 INFO misc.py line 119 253097] Train: [55/100][462/510] Data 0.004 (0.122) Batch 1.255 (1.584) Remain 10:07:09 loss: 0.1458 Lr: 0.00276
[2023-12-25 14:41:45,860 INFO misc.py line 119 253097] Train: [55/100][463/510] Data 0.006 (0.122) Batch 1.216 (1.583) Remain 10:06:49 loss: 0.2033 Lr: 0.00276
[2023-12-25 14:41:47,841 INFO misc.py line 119 253097] Train: [55/100][464/510] Data 0.716 (0.123) Batch 1.981 (1.584) Remain 10:07:08 loss: 0.1130 Lr: 0.00276
[2023-12-25 14:41:49,003 INFO misc.py line 119 253097] Train: [55/100][465/510] Data 0.005 (0.123) Batch 1.163 (1.583) Remain 10:06:45 loss: 0.1748 Lr: 0.00276
[2023-12-25 14:41:49,980 INFO misc.py line 119 253097] Train: [55/100][466/510] Data 0.004 (0.123) Batch 0.977 (1.582) Remain 10:06:13 loss: 0.0930 Lr: 0.00276
[2023-12-25 14:41:51,102 INFO misc.py line 119 253097] Train: [55/100][467/510] Data 0.004 (0.122) Batch 1.121 (1.581) Remain 10:05:49 loss: 0.4641 Lr: 0.00276
[2023-12-25 14:41:52,239 INFO misc.py line 119 253097] Train: [55/100][468/510] Data 0.005 (0.122) Batch 1.138 (1.580) Remain 10:05:25 loss: 0.1861 Lr: 0.00276
[2023-12-25 14:41:53,252 INFO misc.py line 119 253097] Train: [55/100][469/510] Data 0.004 (0.122) Batch 1.014 (1.579) Remain 10:04:56 loss: 0.1780 Lr: 0.00276
[2023-12-25 14:41:54,467 INFO misc.py line 119 253097] Train: [55/100][470/510] Data 0.003 (0.122) Batch 1.214 (1.578) Remain 10:04:36 loss: 0.1714 Lr: 0.00276
[2023-12-25 14:41:55,636 INFO misc.py line 119 253097] Train: [55/100][471/510] Data 0.004 (0.121) Batch 1.167 (1.577) Remain 10:04:15 loss: 0.2040 Lr: 0.00276
[2023-12-25 14:41:56,804 INFO misc.py line 119 253097] Train: [55/100][472/510] Data 0.006 (0.121) Batch 1.158 (1.576) Remain 10:03:53 loss: 0.2906 Lr: 0.00276
[2023-12-25 14:41:57,797 INFO misc.py line 119 253097] Train: [55/100][473/510] Data 0.016 (0.121) Batch 1.003 (1.575) Remain 10:03:23 loss: 0.1063 Lr: 0.00276
[2023-12-25 14:41:59,121 INFO misc.py line 119 253097] Train: [55/100][474/510] Data 0.006 (0.121) Batch 1.325 (1.574) Remain 10:03:09 loss: 0.1756 Lr: 0.00276
[2023-12-25 14:42:00,003 INFO misc.py line 119 253097] Train: [55/100][475/510] Data 0.005 (0.120) Batch 0.881 (1.573) Remain 10:02:34 loss: 0.1272 Lr: 0.00276
[2023-12-25 14:42:01,351 INFO misc.py line 119 253097] Train: [55/100][476/510] Data 0.006 (0.120) Batch 1.349 (1.572) Remain 10:02:21 loss: 0.1534 Lr: 0.00276
[2023-12-25 14:42:02,381 INFO misc.py line 119 253097] Train: [55/100][477/510] Data 0.005 (0.120) Batch 1.031 (1.571) Remain 10:01:54 loss: 0.2186 Lr: 0.00276
[2023-12-25 14:42:03,623 INFO misc.py line 119 253097] Train: [55/100][478/510] Data 0.004 (0.120) Batch 1.237 (1.571) Remain 10:01:36 loss: 0.1466 Lr: 0.00276
[2023-12-25 14:42:04,474 INFO misc.py line 119 253097] Train: [55/100][479/510] Data 0.009 (0.119) Batch 0.855 (1.569) Remain 10:01:00 loss: 0.3155 Lr: 0.00276
[2023-12-25 14:42:05,748 INFO misc.py line 119 253097] Train: [55/100][480/510] Data 0.004 (0.119) Batch 1.271 (1.569) Remain 10:00:44 loss: 0.1233 Lr: 0.00276
[2023-12-25 14:42:06,873 INFO misc.py line 119 253097] Train: [55/100][481/510] Data 0.007 (0.119) Batch 1.124 (1.568) Remain 10:00:21 loss: 0.1222 Lr: 0.00276
[2023-12-25 14:42:08,090 INFO misc.py line 119 253097] Train: [55/100][482/510] Data 0.009 (0.119) Batch 1.220 (1.567) Remain 10:00:03 loss: 0.1255 Lr: 0.00276
[2023-12-25 14:42:09,345 INFO misc.py line 119 253097] Train: [55/100][483/510] Data 0.004 (0.119) Batch 1.255 (1.566) Remain 09:59:46 loss: 0.1739 Lr: 0.00276
[2023-12-25 14:42:10,431 INFO misc.py line 119 253097] Train: [55/100][484/510] Data 0.005 (0.118) Batch 1.084 (1.565) Remain 09:59:22 loss: 0.1574 Lr: 0.00276
[2023-12-25 14:42:11,725 INFO misc.py line 119 253097] Train: [55/100][485/510] Data 0.007 (0.118) Batch 1.291 (1.565) Remain 09:59:07 loss: 0.2386 Lr: 0.00276
[2023-12-25 14:42:12,865 INFO misc.py line 119 253097] Train: [55/100][486/510] Data 0.010 (0.118) Batch 1.145 (1.564) Remain 09:58:45 loss: 0.1162 Lr: 0.00276
[2023-12-25 14:42:14,108 INFO misc.py line 119 253097] Train: [55/100][487/510] Data 0.005 (0.118) Batch 1.238 (1.563) Remain 09:58:28 loss: 0.1136 Lr: 0.00276
[2023-12-25 14:42:16,785 INFO misc.py line 119 253097] Train: [55/100][488/510] Data 1.421 (0.120) Batch 2.682 (1.565) Remain 09:59:20 loss: 0.1297 Lr: 0.00276
[2023-12-25 14:42:17,799 INFO misc.py line 119 253097] Train: [55/100][489/510] Data 0.006 (0.120) Batch 1.015 (1.564) Remain 09:58:52 loss: 0.1838 Lr: 0.00276
[2023-12-25 14:42:18,946 INFO misc.py line 119 253097] Train: [55/100][490/510] Data 0.004 (0.120) Batch 1.146 (1.563) Remain 09:58:31 loss: 0.0886 Lr: 0.00276
[2023-12-25 14:42:20,005 INFO misc.py line 119 253097] Train: [55/100][491/510] Data 0.005 (0.120) Batch 1.059 (1.562) Remain 09:58:06 loss: 0.1515 Lr: 0.00276
[2023-12-25 14:42:23,401 INFO misc.py line 119 253097] Train: [55/100][492/510] Data 0.004 (0.119) Batch 3.396 (1.566) Remain 09:59:30 loss: 0.3031 Lr: 0.00276
[2023-12-25 14:42:24,455 INFO misc.py line 119 253097] Train: [55/100][493/510] Data 0.004 (0.119) Batch 1.051 (1.565) Remain 09:59:04 loss: 0.2558 Lr: 0.00276
[2023-12-25 14:42:25,553 INFO misc.py line 119 253097] Train: [55/100][494/510] Data 0.007 (0.119) Batch 1.101 (1.564) Remain 09:58:41 loss: 0.1309 Lr: 0.00276
[2023-12-25 14:42:43,494 INFO misc.py line 119 253097] Train: [55/100][495/510] Data 0.005 (0.119) Batch 17.937 (1.597) Remain 10:11:24 loss: 0.1752 Lr: 0.00276
[2023-12-25 14:42:44,589 INFO misc.py line 119 253097] Train: [55/100][496/510] Data 0.009 (0.118) Batch 1.095 (1.596) Remain 10:10:59 loss: 0.1072 Lr: 0.00276
[2023-12-25 14:42:45,840 INFO misc.py line 119 253097] Train: [55/100][497/510] Data 0.008 (0.118) Batch 1.251 (1.596) Remain 10:10:41 loss: 0.0961 Lr: 0.00276
[2023-12-25 14:42:46,974 INFO misc.py line 119 253097] Train: [55/100][498/510] Data 0.008 (0.118) Batch 1.132 (1.595) Remain 10:10:18 loss: 0.1595 Lr: 0.00276
[2023-12-25 14:42:48,237 INFO misc.py line 119 253097] Train: [55/100][499/510] Data 0.009 (0.118) Batch 1.262 (1.594) Remain 10:10:01 loss: 0.2944 Lr: 0.00276
[2023-12-25 14:42:49,431 INFO misc.py line 119 253097] Train: [55/100][500/510] Data 0.010 (0.118) Batch 1.194 (1.593) Remain 10:09:41 loss: 0.1618 Lr: 0.00276
[2023-12-25 14:42:50,258 INFO misc.py line 119 253097] Train: [55/100][501/510] Data 0.010 (0.117) Batch 0.835 (1.592) Remain 10:09:04 loss: 0.1068 Lr: 0.00276
[2023-12-25 14:42:51,359 INFO misc.py line 119 253097] Train: [55/100][502/510] Data 0.003 (0.117) Batch 1.101 (1.591) Remain 10:08:40 loss: 0.2521 Lr: 0.00276
[2023-12-25 14:42:52,666 INFO misc.py line 119 253097] Train: [55/100][503/510] Data 0.003 (0.117) Batch 1.304 (1.590) Remain 10:08:26 loss: 0.3198 Lr: 0.00276
[2023-12-25 14:42:53,832 INFO misc.py line 119 253097] Train: [55/100][504/510] Data 0.006 (0.117) Batch 1.163 (1.589) Remain 10:08:04 loss: 0.4159 Lr: 0.00276
[2023-12-25 14:42:55,067 INFO misc.py line 119 253097] Train: [55/100][505/510] Data 0.008 (0.116) Batch 1.240 (1.589) Remain 10:07:47 loss: 0.1949 Lr: 0.00276
[2023-12-25 14:42:56,077 INFO misc.py line 119 253097] Train: [55/100][506/510] Data 0.004 (0.116) Batch 1.002 (1.587) Remain 10:07:19 loss: 0.1492 Lr: 0.00276
[2023-12-25 14:42:57,226 INFO misc.py line 119 253097] Train: [55/100][507/510] Data 0.011 (0.116) Batch 1.157 (1.587) Remain 10:06:57 loss: 0.1502 Lr: 0.00275
[2023-12-25 14:42:58,381 INFO misc.py line 119 253097] Train: [55/100][508/510] Data 0.003 (0.116) Batch 1.153 (1.586) Remain 10:06:36 loss: 0.2622 Lr: 0.00275
[2023-12-25 14:42:59,398 INFO misc.py line 119 253097] Train: [55/100][509/510] Data 0.006 (0.116) Batch 1.019 (1.585) Remain 10:06:09 loss: 0.1926 Lr: 0.00275
[2023-12-25 14:43:00,543 INFO misc.py line 119 253097] Train: [55/100][510/510] Data 0.003 (0.115) Batch 1.144 (1.584) Remain 10:05:47 loss: 0.1376 Lr: 0.00275
[2023-12-25 14:43:00,543 INFO misc.py line 136 253097] Train result: loss: 0.1704 
[2023-12-25 14:43:00,543 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 14:43:27,841 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4362 
[2023-12-25 14:43:28,188 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2860 
[2023-12-25 14:43:33,121 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3492 
[2023-12-25 14:43:33,639 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2889 
[2023-12-25 14:43:35,617 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7818 
[2023-12-25 14:43:36,043 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2198 
[2023-12-25 14:43:36,925 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0760 
[2023-12-25 14:43:37,485 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3886 
[2023-12-25 14:43:39,295 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6046 
[2023-12-25 14:43:41,421 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2074 
[2023-12-25 14:43:42,276 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2521 
[2023-12-25 14:43:42,705 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.1124 
[2023-12-25 14:43:43,604 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5805 
[2023-12-25 14:43:46,542 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7601 
[2023-12-25 14:43:47,009 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.1817 
[2023-12-25 14:43:47,636 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4373 
[2023-12-25 14:43:48,345 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2669 
[2023-12-25 14:43:49,924 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.7000/0.7692/0.9021.
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9198/0.9377
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9811/0.9924
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8428/0.9617
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.4271/0.5502
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6067/0.6233
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6541/0.7702
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8220/0.9069
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9222/0.9613
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.7752/0.8278
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7632/0.8444
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7735/0.8767
[2023-12-25 14:43:49,924 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6119/0.7473
[2023-12-25 14:43:49,925 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 14:43:49,927 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.7000
[2023-12-25 14:43:49,927 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 14:43:49,928 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 14:44:01,625 INFO misc.py line 119 253097] Train: [56/100][1/510] Data 1.749 (1.749) Batch 6.726 (6.726) Remain 42:52:28 loss: 0.1940 Lr: 0.00275
[2023-12-25 14:44:02,897 INFO misc.py line 119 253097] Train: [56/100][2/510] Data 0.004 (0.004) Batch 1.269 (1.269) Remain 08:05:24 loss: 0.3311 Lr: 0.00275
[2023-12-25 14:44:04,148 INFO misc.py line 119 253097] Train: [56/100][3/510] Data 0.007 (0.007) Batch 1.252 (1.252) Remain 07:58:41 loss: 0.5249 Lr: 0.00275
[2023-12-25 14:44:05,194 INFO misc.py line 119 253097] Train: [56/100][4/510] Data 0.007 (0.007) Batch 1.043 (1.043) Remain 06:38:57 loss: 0.1177 Lr: 0.00275
[2023-12-25 14:44:06,252 INFO misc.py line 119 253097] Train: [56/100][5/510] Data 0.009 (0.008) Batch 1.062 (1.053) Remain 06:42:34 loss: 0.2867 Lr: 0.00275
[2023-12-25 14:44:07,532 INFO misc.py line 119 253097] Train: [56/100][6/510] Data 0.006 (0.007) Batch 1.275 (1.127) Remain 07:10:50 loss: 0.1357 Lr: 0.00275
[2023-12-25 14:44:08,724 INFO misc.py line 119 253097] Train: [56/100][7/510] Data 0.011 (0.008) Batch 1.193 (1.143) Remain 07:17:10 loss: 0.1728 Lr: 0.00275
[2023-12-25 14:44:09,733 INFO misc.py line 119 253097] Train: [56/100][8/510] Data 0.009 (0.008) Batch 1.014 (1.117) Remain 07:07:16 loss: 0.2711 Lr: 0.00275
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[2023-12-25 14:55:16,294 INFO misc.py line 119 253097] Train: [56/100][427/510] Data 0.003 (0.072) Batch 1.106 (1.585) Remain 09:55:04 loss: 0.1469 Lr: 0.00267
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[2023-12-25 14:55:18,465 INFO misc.py line 119 253097] Train: [56/100][429/510] Data 0.005 (0.072) Batch 1.158 (1.583) Remain 09:54:08 loss: 0.1347 Lr: 0.00267
[2023-12-25 14:55:19,767 INFO misc.py line 119 253097] Train: [56/100][430/510] Data 0.003 (0.071) Batch 1.301 (1.582) Remain 09:53:52 loss: 0.0973 Lr: 0.00267
[2023-12-25 14:55:21,049 INFO misc.py line 119 253097] Train: [56/100][431/510] Data 0.004 (0.071) Batch 1.281 (1.582) Remain 09:53:34 loss: 0.1576 Lr: 0.00267
[2023-12-25 14:55:22,430 INFO misc.py line 119 253097] Train: [56/100][432/510] Data 0.004 (0.071) Batch 1.376 (1.581) Remain 09:53:22 loss: 0.1868 Lr: 0.00267
[2023-12-25 14:55:23,648 INFO misc.py line 119 253097] Train: [56/100][433/510] Data 0.009 (0.071) Batch 1.222 (1.580) Remain 09:53:02 loss: 0.1004 Lr: 0.00267
[2023-12-25 14:55:24,871 INFO misc.py line 119 253097] Train: [56/100][434/510] Data 0.005 (0.071) Batch 1.224 (1.579) Remain 09:52:41 loss: 0.2431 Lr: 0.00267
[2023-12-25 14:55:25,939 INFO misc.py line 119 253097] Train: [56/100][435/510] Data 0.005 (0.071) Batch 1.066 (1.578) Remain 09:52:13 loss: 0.2306 Lr: 0.00267
[2023-12-25 14:55:39,979 INFO misc.py line 119 253097] Train: [56/100][436/510] Data 0.007 (0.071) Batch 14.044 (1.607) Remain 10:03:00 loss: 0.2202 Lr: 0.00267
[2023-12-25 14:55:41,259 INFO misc.py line 119 253097] Train: [56/100][437/510] Data 0.004 (0.070) Batch 1.278 (1.606) Remain 10:02:41 loss: 0.1630 Lr: 0.00267
[2023-12-25 14:55:42,507 INFO misc.py line 119 253097] Train: [56/100][438/510] Data 0.005 (0.070) Batch 1.248 (1.605) Remain 10:02:21 loss: 0.1133 Lr: 0.00267
[2023-12-25 14:55:43,592 INFO misc.py line 119 253097] Train: [56/100][439/510] Data 0.004 (0.070) Batch 1.076 (1.604) Remain 10:01:52 loss: 0.1813 Lr: 0.00267
[2023-12-25 14:55:44,891 INFO misc.py line 119 253097] Train: [56/100][440/510] Data 0.014 (0.070) Batch 1.309 (1.604) Remain 10:01:35 loss: 0.1169 Lr: 0.00267
[2023-12-25 14:55:46,160 INFO misc.py line 119 253097] Train: [56/100][441/510] Data 0.003 (0.070) Batch 1.267 (1.603) Remain 10:01:16 loss: 0.3851 Lr: 0.00267
[2023-12-25 14:55:47,478 INFO misc.py line 119 253097] Train: [56/100][442/510] Data 0.005 (0.070) Batch 1.314 (1.602) Remain 10:01:00 loss: 0.1803 Lr: 0.00267
[2023-12-25 14:55:48,426 INFO misc.py line 119 253097] Train: [56/100][443/510] Data 0.010 (0.070) Batch 0.953 (1.601) Remain 10:00:25 loss: 0.1152 Lr: 0.00267
[2023-12-25 14:55:49,644 INFO misc.py line 119 253097] Train: [56/100][444/510] Data 0.005 (0.069) Batch 1.217 (1.600) Remain 10:00:04 loss: 0.3447 Lr: 0.00267
[2023-12-25 14:55:50,919 INFO misc.py line 119 253097] Train: [56/100][445/510] Data 0.006 (0.069) Batch 1.275 (1.599) Remain 09:59:46 loss: 0.1318 Lr: 0.00267
[2023-12-25 14:55:52,274 INFO misc.py line 119 253097] Train: [56/100][446/510] Data 0.006 (0.069) Batch 1.351 (1.598) Remain 09:59:31 loss: 0.2135 Lr: 0.00267
[2023-12-25 14:55:53,296 INFO misc.py line 119 253097] Train: [56/100][447/510] Data 0.010 (0.069) Batch 1.023 (1.597) Remain 09:59:01 loss: 0.1462 Lr: 0.00267
[2023-12-25 14:55:54,344 INFO misc.py line 119 253097] Train: [56/100][448/510] Data 0.010 (0.069) Batch 1.051 (1.596) Remain 09:58:31 loss: 0.1222 Lr: 0.00267
[2023-12-25 14:55:55,643 INFO misc.py line 119 253097] Train: [56/100][449/510] Data 0.006 (0.069) Batch 1.301 (1.595) Remain 09:58:15 loss: 0.2701 Lr: 0.00267
[2023-12-25 14:55:57,007 INFO misc.py line 119 253097] Train: [56/100][450/510] Data 0.005 (0.069) Batch 1.362 (1.595) Remain 09:58:02 loss: 0.1860 Lr: 0.00267
[2023-12-25 14:56:03,341 INFO misc.py line 119 253097] Train: [56/100][451/510] Data 4.992 (0.080) Batch 6.334 (1.605) Remain 10:01:58 loss: 0.1426 Lr: 0.00267
[2023-12-25 14:56:04,503 INFO misc.py line 119 253097] Train: [56/100][452/510] Data 0.007 (0.079) Batch 1.164 (1.604) Remain 10:01:34 loss: 0.2518 Lr: 0.00267
[2023-12-25 14:56:05,687 INFO misc.py line 119 253097] Train: [56/100][453/510] Data 0.004 (0.079) Batch 1.185 (1.603) Remain 10:01:12 loss: 0.1710 Lr: 0.00267
[2023-12-25 14:56:06,621 INFO misc.py line 119 253097] Train: [56/100][454/510] Data 0.005 (0.079) Batch 0.933 (1.602) Remain 10:00:37 loss: 0.2079 Lr: 0.00267
[2023-12-25 14:56:07,889 INFO misc.py line 119 253097] Train: [56/100][455/510] Data 0.005 (0.079) Batch 1.268 (1.601) Remain 10:00:18 loss: 0.2722 Lr: 0.00267
[2023-12-25 14:56:09,209 INFO misc.py line 119 253097] Train: [56/100][456/510] Data 0.005 (0.079) Batch 1.316 (1.601) Remain 10:00:03 loss: 0.2196 Lr: 0.00267
[2023-12-25 14:56:10,532 INFO misc.py line 119 253097] Train: [56/100][457/510] Data 0.008 (0.079) Batch 1.294 (1.600) Remain 09:59:46 loss: 0.1767 Lr: 0.00267
[2023-12-25 14:56:11,486 INFO misc.py line 119 253097] Train: [56/100][458/510] Data 0.037 (0.078) Batch 0.986 (1.599) Remain 09:59:14 loss: 0.1776 Lr: 0.00267
[2023-12-25 14:56:12,680 INFO misc.py line 119 253097] Train: [56/100][459/510] Data 0.004 (0.078) Batch 1.190 (1.598) Remain 09:58:52 loss: 0.1009 Lr: 0.00267
[2023-12-25 14:56:13,639 INFO misc.py line 119 253097] Train: [56/100][460/510] Data 0.009 (0.078) Batch 0.962 (1.596) Remain 09:58:19 loss: 0.0982 Lr: 0.00267
[2023-12-25 14:56:14,747 INFO misc.py line 119 253097] Train: [56/100][461/510] Data 0.005 (0.078) Batch 1.111 (1.595) Remain 09:57:54 loss: 0.1731 Lr: 0.00267
[2023-12-25 14:56:16,022 INFO misc.py line 119 253097] Train: [56/100][462/510] Data 0.003 (0.078) Batch 1.275 (1.595) Remain 09:57:37 loss: 0.2107 Lr: 0.00267
[2023-12-25 14:56:17,241 INFO misc.py line 119 253097] Train: [56/100][463/510] Data 0.004 (0.078) Batch 1.212 (1.594) Remain 09:57:16 loss: 0.2529 Lr: 0.00266
[2023-12-25 14:56:18,474 INFO misc.py line 119 253097] Train: [56/100][464/510] Data 0.011 (0.078) Batch 1.240 (1.593) Remain 09:56:57 loss: 0.2398 Lr: 0.00266
[2023-12-25 14:56:19,727 INFO misc.py line 119 253097] Train: [56/100][465/510] Data 0.004 (0.077) Batch 1.253 (1.592) Remain 09:56:39 loss: 0.3304 Lr: 0.00266
[2023-12-25 14:56:21,034 INFO misc.py line 119 253097] Train: [56/100][466/510] Data 0.005 (0.077) Batch 1.305 (1.592) Remain 09:56:24 loss: 0.2120 Lr: 0.00266
[2023-12-25 14:56:22,259 INFO misc.py line 119 253097] Train: [56/100][467/510] Data 0.006 (0.077) Batch 1.227 (1.591) Remain 09:56:05 loss: 0.1355 Lr: 0.00266
[2023-12-25 14:56:23,473 INFO misc.py line 119 253097] Train: [56/100][468/510] Data 0.004 (0.077) Batch 1.207 (1.590) Remain 09:55:44 loss: 0.1329 Lr: 0.00266
[2023-12-25 14:56:24,667 INFO misc.py line 119 253097] Train: [56/100][469/510] Data 0.011 (0.077) Batch 1.200 (1.589) Remain 09:55:24 loss: 0.1828 Lr: 0.00266
[2023-12-25 14:56:25,748 INFO misc.py line 119 253097] Train: [56/100][470/510] Data 0.005 (0.077) Batch 1.082 (1.588) Remain 09:54:58 loss: 0.2247 Lr: 0.00266
[2023-12-25 14:56:33,695 INFO misc.py line 119 253097] Train: [56/100][471/510] Data 0.004 (0.076) Batch 7.947 (1.602) Remain 10:00:02 loss: 0.0697 Lr: 0.00266
[2023-12-25 14:56:35,379 INFO misc.py line 119 253097] Train: [56/100][472/510] Data 0.598 (0.078) Batch 1.683 (1.602) Remain 10:00:04 loss: 0.0952 Lr: 0.00266
[2023-12-25 14:56:36,509 INFO misc.py line 119 253097] Train: [56/100][473/510] Data 0.005 (0.077) Batch 1.127 (1.601) Remain 09:59:40 loss: 0.1282 Lr: 0.00266
[2023-12-25 14:56:37,589 INFO misc.py line 119 253097] Train: [56/100][474/510] Data 0.008 (0.077) Batch 1.083 (1.600) Remain 09:59:14 loss: 0.2023 Lr: 0.00266
[2023-12-25 14:56:38,573 INFO misc.py line 119 253097] Train: [56/100][475/510] Data 0.004 (0.077) Batch 0.982 (1.598) Remain 09:58:43 loss: 0.1259 Lr: 0.00266
[2023-12-25 14:56:39,697 INFO misc.py line 119 253097] Train: [56/100][476/510] Data 0.008 (0.077) Batch 1.124 (1.597) Remain 09:58:18 loss: 0.1666 Lr: 0.00266
[2023-12-25 14:56:40,615 INFO misc.py line 119 253097] Train: [56/100][477/510] Data 0.006 (0.077) Batch 0.920 (1.596) Remain 09:57:45 loss: 0.1215 Lr: 0.00266
[2023-12-25 14:56:41,674 INFO misc.py line 119 253097] Train: [56/100][478/510] Data 0.003 (0.077) Batch 1.054 (1.595) Remain 09:57:17 loss: 0.1353 Lr: 0.00266
[2023-12-25 14:56:42,798 INFO misc.py line 119 253097] Train: [56/100][479/510] Data 0.009 (0.077) Batch 1.127 (1.594) Remain 09:56:54 loss: 0.1076 Lr: 0.00266
[2023-12-25 14:56:44,011 INFO misc.py line 119 253097] Train: [56/100][480/510] Data 0.006 (0.076) Batch 1.215 (1.593) Remain 09:56:34 loss: 0.1243 Lr: 0.00266
[2023-12-25 14:56:45,093 INFO misc.py line 119 253097] Train: [56/100][481/510] Data 0.004 (0.076) Batch 1.076 (1.592) Remain 09:56:08 loss: 0.1217 Lr: 0.00266
[2023-12-25 14:56:46,207 INFO misc.py line 119 253097] Train: [56/100][482/510] Data 0.009 (0.076) Batch 1.117 (1.591) Remain 09:55:45 loss: 0.0823 Lr: 0.00266
[2023-12-25 14:56:47,364 INFO misc.py line 119 253097] Train: [56/100][483/510] Data 0.007 (0.076) Batch 1.156 (1.590) Remain 09:55:23 loss: 0.0819 Lr: 0.00266
[2023-12-25 14:56:48,429 INFO misc.py line 119 253097] Train: [56/100][484/510] Data 0.008 (0.076) Batch 1.064 (1.589) Remain 09:54:57 loss: 0.2442 Lr: 0.00266
[2023-12-25 14:56:57,447 INFO misc.py line 119 253097] Train: [56/100][485/510] Data 0.009 (0.076) Batch 9.021 (1.604) Remain 10:00:41 loss: 0.2714 Lr: 0.00266
[2023-12-25 14:56:58,664 INFO misc.py line 119 253097] Train: [56/100][486/510] Data 0.006 (0.076) Batch 1.219 (1.604) Remain 10:00:22 loss: 0.1718 Lr: 0.00266
[2023-12-25 14:56:59,771 INFO misc.py line 119 253097] Train: [56/100][487/510] Data 0.004 (0.075) Batch 1.106 (1.603) Remain 09:59:57 loss: 0.1059 Lr: 0.00266
[2023-12-25 14:57:00,905 INFO misc.py line 119 253097] Train: [56/100][488/510] Data 0.005 (0.075) Batch 1.134 (1.602) Remain 09:59:34 loss: 0.1508 Lr: 0.00266
[2023-12-25 14:57:02,110 INFO misc.py line 119 253097] Train: [56/100][489/510] Data 0.004 (0.075) Batch 1.205 (1.601) Remain 09:59:14 loss: 0.1366 Lr: 0.00266
[2023-12-25 14:57:03,395 INFO misc.py line 119 253097] Train: [56/100][490/510] Data 0.004 (0.075) Batch 1.266 (1.600) Remain 09:58:57 loss: 0.1103 Lr: 0.00266
[2023-12-25 14:57:04,619 INFO misc.py line 119 253097] Train: [56/100][491/510] Data 0.023 (0.075) Batch 1.237 (1.599) Remain 09:58:39 loss: 0.1277 Lr: 0.00266
[2023-12-25 14:57:05,855 INFO misc.py line 119 253097] Train: [56/100][492/510] Data 0.010 (0.075) Batch 1.242 (1.599) Remain 09:58:21 loss: 0.1319 Lr: 0.00266
[2023-12-25 14:57:07,047 INFO misc.py line 119 253097] Train: [56/100][493/510] Data 0.004 (0.075) Batch 1.184 (1.598) Remain 09:58:00 loss: 0.1194 Lr: 0.00266
[2023-12-25 14:57:08,262 INFO misc.py line 119 253097] Train: [56/100][494/510] Data 0.011 (0.074) Batch 1.210 (1.597) Remain 09:57:41 loss: 0.1797 Lr: 0.00266
[2023-12-25 14:57:09,370 INFO misc.py line 119 253097] Train: [56/100][495/510] Data 0.017 (0.074) Batch 1.120 (1.596) Remain 09:57:17 loss: 0.1093 Lr: 0.00266
[2023-12-25 14:57:10,531 INFO misc.py line 119 253097] Train: [56/100][496/510] Data 0.005 (0.074) Batch 1.122 (1.595) Remain 09:56:54 loss: 0.1710 Lr: 0.00266
[2023-12-25 14:57:11,671 INFO misc.py line 119 253097] Train: [56/100][497/510] Data 0.044 (0.074) Batch 1.176 (1.594) Remain 09:56:33 loss: 0.2254 Lr: 0.00266
[2023-12-25 14:57:12,717 INFO misc.py line 119 253097] Train: [56/100][498/510] Data 0.007 (0.074) Batch 1.048 (1.593) Remain 09:56:07 loss: 0.1761 Lr: 0.00266
[2023-12-25 14:57:13,782 INFO misc.py line 119 253097] Train: [56/100][499/510] Data 0.006 (0.074) Batch 1.067 (1.592) Remain 09:55:42 loss: 0.0685 Lr: 0.00266
[2023-12-25 14:57:14,917 INFO misc.py line 119 253097] Train: [56/100][500/510] Data 0.004 (0.074) Batch 1.134 (1.591) Remain 09:55:19 loss: 0.1425 Lr: 0.00266
[2023-12-25 14:57:16,085 INFO misc.py line 119 253097] Train: [56/100][501/510] Data 0.005 (0.074) Batch 1.169 (1.590) Remain 09:54:59 loss: 0.2019 Lr: 0.00266
[2023-12-25 14:57:17,089 INFO misc.py line 119 253097] Train: [56/100][502/510] Data 0.004 (0.073) Batch 1.001 (1.589) Remain 09:54:31 loss: 0.1030 Lr: 0.00266
[2023-12-25 14:57:18,185 INFO misc.py line 119 253097] Train: [56/100][503/510] Data 0.007 (0.073) Batch 1.099 (1.588) Remain 09:54:07 loss: 0.1727 Lr: 0.00266
[2023-12-25 14:57:19,153 INFO misc.py line 119 253097] Train: [56/100][504/510] Data 0.004 (0.073) Batch 0.967 (1.587) Remain 09:53:38 loss: 0.1867 Lr: 0.00266
[2023-12-25 14:57:20,218 INFO misc.py line 119 253097] Train: [56/100][505/510] Data 0.004 (0.073) Batch 1.065 (1.586) Remain 09:53:13 loss: 0.1091 Lr: 0.00266
[2023-12-25 14:57:21,295 INFO misc.py line 119 253097] Train: [56/100][506/510] Data 0.005 (0.073) Batch 1.078 (1.585) Remain 09:52:49 loss: 0.1071 Lr: 0.00266
[2023-12-25 14:57:22,323 INFO misc.py line 119 253097] Train: [56/100][507/510] Data 0.003 (0.073) Batch 1.028 (1.584) Remain 09:52:22 loss: 0.1164 Lr: 0.00266
[2023-12-25 14:57:25,446 INFO misc.py line 119 253097] Train: [56/100][508/510] Data 0.003 (0.073) Batch 3.123 (1.587) Remain 09:53:29 loss: 0.1346 Lr: 0.00266
[2023-12-25 14:57:26,541 INFO misc.py line 119 253097] Train: [56/100][509/510] Data 0.004 (0.072) Batch 1.095 (1.586) Remain 09:53:06 loss: 0.1236 Lr: 0.00266
[2023-12-25 14:57:27,682 INFO misc.py line 119 253097] Train: [56/100][510/510] Data 0.004 (0.072) Batch 1.139 (1.585) Remain 09:52:44 loss: 0.2494 Lr: 0.00266
[2023-12-25 14:57:27,683 INFO misc.py line 136 253097] Train result: loss: 0.1712 
[2023-12-25 14:57:27,683 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 14:57:54,646 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6074 
[2023-12-25 14:57:55,008 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3281 
[2023-12-25 14:58:00,885 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4075 
[2023-12-25 14:58:01,411 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3162 
[2023-12-25 14:58:03,379 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6138 
[2023-12-25 14:58:03,814 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3418 
[2023-12-25 14:58:04,697 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2686 
[2023-12-25 14:58:05,257 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4016 
[2023-12-25 14:58:07,068 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8166 
[2023-12-25 14:58:09,200 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4450 
[2023-12-25 14:58:10,062 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2872 
[2023-12-25 14:58:10,487 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8740 
[2023-12-25 14:58:11,387 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 1.3981 
[2023-12-25 14:58:14,328 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 1.0153 
[2023-12-25 14:58:14,798 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3456 
[2023-12-25 14:58:15,410 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5303 
[2023-12-25 14:58:16,113 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2353 
[2023-12-25 14:58:17,386 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6917/0.7494/0.9012.
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9227/0.9441
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9820/0.9911
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8368/0.9706
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3248/0.3708
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6020/0.6197
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7201/0.8013
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8081/0.8932
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9166/0.9623
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.7338/0.7657
[2023-12-25 14:58:17,386 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7810/0.9027
[2023-12-25 14:58:17,387 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7813/0.8552
[2023-12-25 14:58:17,387 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5830/0.6655
[2023-12-25 14:58:17,387 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 14:58:17,388 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 14:58:17,388 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 14:58:27,230 INFO misc.py line 119 253097] Train: [57/100][1/510] Data 4.399 (4.399) Batch 6.574 (6.574) Remain 40:58:34 loss: 0.3461 Lr: 0.00266
[2023-12-25 14:58:28,392 INFO misc.py line 119 253097] Train: [57/100][2/510] Data 0.037 (0.037) Batch 1.162 (1.162) Remain 07:14:23 loss: 0.1357 Lr: 0.00266
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[2023-12-25 14:58:41,532 INFO misc.py line 119 253097] Train: [57/100][5/510] Data 9.670 (4.836) Batch 10.793 (6.025) Remain 37:32:48 loss: 0.1513 Lr: 0.00265
[2023-12-25 14:58:42,580 INFO misc.py line 119 253097] Train: [57/100][6/510] Data 0.005 (3.226) Batch 1.047 (4.366) Remain 27:12:18 loss: 0.2293 Lr: 0.00265
[2023-12-25 14:58:45,679 INFO misc.py line 119 253097] Train: [57/100][7/510] Data 0.004 (2.420) Batch 3.100 (4.049) Remain 25:13:56 loss: 0.1235 Lr: 0.00265
[2023-12-25 14:58:46,737 INFO misc.py line 119 253097] Train: [57/100][8/510] Data 0.004 (1.937) Batch 1.057 (3.451) Remain 21:30:10 loss: 0.3058 Lr: 0.00265
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[2023-12-25 15:09:34,795 INFO misc.py line 119 253097] Train: [57/100][421/510] Data 0.455 (0.202) Batch 1.602 (1.592) Remain 09:44:06 loss: 0.0932 Lr: 0.00257
[2023-12-25 15:09:42,448 INFO misc.py line 119 253097] Train: [57/100][422/510] Data 0.007 (0.201) Batch 7.655 (1.606) Remain 09:49:23 loss: 0.2076 Lr: 0.00257
[2023-12-25 15:09:43,691 INFO misc.py line 119 253097] Train: [57/100][423/510] Data 0.007 (0.201) Batch 1.243 (1.605) Remain 09:49:02 loss: 0.1228 Lr: 0.00257
[2023-12-25 15:09:44,895 INFO misc.py line 119 253097] Train: [57/100][424/510] Data 0.005 (0.200) Batch 1.201 (1.604) Remain 09:48:40 loss: 0.0999 Lr: 0.00257
[2023-12-25 15:09:46,176 INFO misc.py line 119 253097] Train: [57/100][425/510] Data 0.008 (0.200) Batch 1.281 (1.604) Remain 09:48:21 loss: 0.2425 Lr: 0.00257
[2023-12-25 15:09:47,390 INFO misc.py line 119 253097] Train: [57/100][426/510] Data 0.008 (0.199) Batch 1.217 (1.603) Remain 09:47:59 loss: 0.1662 Lr: 0.00257
[2023-12-25 15:09:48,558 INFO misc.py line 119 253097] Train: [57/100][427/510] Data 0.005 (0.199) Batch 1.163 (1.602) Remain 09:47:35 loss: 0.0987 Lr: 0.00257
[2023-12-25 15:09:49,622 INFO misc.py line 119 253097] Train: [57/100][428/510] Data 0.010 (0.199) Batch 1.067 (1.600) Remain 09:47:06 loss: 0.1237 Lr: 0.00257
[2023-12-25 15:09:50,865 INFO misc.py line 119 253097] Train: [57/100][429/510] Data 0.007 (0.198) Batch 1.241 (1.599) Remain 09:46:46 loss: 0.1819 Lr: 0.00257
[2023-12-25 15:09:51,938 INFO misc.py line 119 253097] Train: [57/100][430/510] Data 0.011 (0.198) Batch 1.079 (1.598) Remain 09:46:17 loss: 0.1355 Lr: 0.00257
[2023-12-25 15:09:53,141 INFO misc.py line 119 253097] Train: [57/100][431/510] Data 0.004 (0.197) Batch 1.201 (1.597) Remain 09:45:55 loss: 0.2067 Lr: 0.00257
[2023-12-25 15:09:54,384 INFO misc.py line 119 253097] Train: [57/100][432/510] Data 0.006 (0.197) Batch 1.244 (1.597) Remain 09:45:35 loss: 0.1080 Lr: 0.00257
[2023-12-25 15:09:55,358 INFO misc.py line 119 253097] Train: [57/100][433/510] Data 0.004 (0.196) Batch 0.974 (1.595) Remain 09:45:02 loss: 0.1812 Lr: 0.00257
[2023-12-25 15:09:56,408 INFO misc.py line 119 253097] Train: [57/100][434/510] Data 0.005 (0.196) Batch 1.050 (1.594) Remain 09:44:32 loss: 0.1701 Lr: 0.00257
[2023-12-25 15:09:57,648 INFO misc.py line 119 253097] Train: [57/100][435/510] Data 0.005 (0.195) Batch 1.237 (1.593) Remain 09:44:13 loss: 0.1084 Lr: 0.00257
[2023-12-25 15:09:58,677 INFO misc.py line 119 253097] Train: [57/100][436/510] Data 0.008 (0.195) Batch 1.011 (1.592) Remain 09:43:42 loss: 0.1344 Lr: 0.00257
[2023-12-25 15:09:59,808 INFO misc.py line 119 253097] Train: [57/100][437/510] Data 0.027 (0.195) Batch 1.153 (1.591) Remain 09:43:18 loss: 0.1728 Lr: 0.00257
[2023-12-25 15:10:01,038 INFO misc.py line 119 253097] Train: [57/100][438/510] Data 0.005 (0.194) Batch 1.225 (1.590) Remain 09:42:58 loss: 0.0917 Lr: 0.00257
[2023-12-25 15:10:02,153 INFO misc.py line 119 253097] Train: [57/100][439/510] Data 0.009 (0.194) Batch 1.116 (1.589) Remain 09:42:32 loss: 0.4228 Lr: 0.00257
[2023-12-25 15:10:03,311 INFO misc.py line 119 253097] Train: [57/100][440/510] Data 0.008 (0.193) Batch 1.158 (1.588) Remain 09:42:09 loss: 0.4421 Lr: 0.00257
[2023-12-25 15:10:04,325 INFO misc.py line 119 253097] Train: [57/100][441/510] Data 0.007 (0.193) Batch 1.012 (1.586) Remain 09:41:38 loss: 0.2766 Lr: 0.00257
[2023-12-25 15:10:05,462 INFO misc.py line 119 253097] Train: [57/100][442/510] Data 0.008 (0.192) Batch 1.142 (1.585) Remain 09:41:15 loss: 0.1262 Lr: 0.00257
[2023-12-25 15:10:06,615 INFO misc.py line 119 253097] Train: [57/100][443/510] Data 0.005 (0.192) Batch 1.154 (1.584) Remain 09:40:51 loss: 0.1116 Lr: 0.00257
[2023-12-25 15:10:07,753 INFO misc.py line 119 253097] Train: [57/100][444/510] Data 0.003 (0.192) Batch 1.133 (1.583) Remain 09:40:27 loss: 0.1253 Lr: 0.00257
[2023-12-25 15:10:08,895 INFO misc.py line 119 253097] Train: [57/100][445/510] Data 0.008 (0.191) Batch 1.143 (1.582) Remain 09:40:04 loss: 0.1573 Lr: 0.00257
[2023-12-25 15:10:10,071 INFO misc.py line 119 253097] Train: [57/100][446/510] Data 0.008 (0.191) Batch 1.180 (1.581) Remain 09:39:42 loss: 0.1240 Lr: 0.00257
[2023-12-25 15:10:11,129 INFO misc.py line 119 253097] Train: [57/100][447/510] Data 0.003 (0.190) Batch 1.058 (1.580) Remain 09:39:15 loss: 0.1426 Lr: 0.00257
[2023-12-25 15:10:12,263 INFO misc.py line 119 253097] Train: [57/100][448/510] Data 0.003 (0.190) Batch 1.132 (1.579) Remain 09:38:51 loss: 0.0832 Lr: 0.00257
[2023-12-25 15:10:15,749 INFO misc.py line 119 253097] Train: [57/100][449/510] Data 2.753 (0.196) Batch 3.488 (1.584) Remain 09:40:24 loss: 0.2425 Lr: 0.00257
[2023-12-25 15:10:22,309 INFO misc.py line 119 253097] Train: [57/100][450/510] Data 0.002 (0.195) Batch 6.559 (1.595) Remain 09:44:27 loss: 0.1450 Lr: 0.00257
[2023-12-25 15:10:23,482 INFO misc.py line 119 253097] Train: [57/100][451/510] Data 0.005 (0.195) Batch 1.173 (1.594) Remain 09:44:04 loss: 0.2155 Lr: 0.00257
[2023-12-25 15:10:24,428 INFO misc.py line 119 253097] Train: [57/100][452/510] Data 0.004 (0.194) Batch 0.947 (1.592) Remain 09:43:31 loss: 0.1095 Lr: 0.00257
[2023-12-25 15:10:25,551 INFO misc.py line 119 253097] Train: [57/100][453/510] Data 0.003 (0.194) Batch 1.111 (1.591) Remain 09:43:06 loss: 0.3588 Lr: 0.00257
[2023-12-25 15:10:26,797 INFO misc.py line 119 253097] Train: [57/100][454/510] Data 0.015 (0.194) Batch 1.247 (1.590) Remain 09:42:48 loss: 0.1610 Lr: 0.00257
[2023-12-25 15:10:27,903 INFO misc.py line 119 253097] Train: [57/100][455/510] Data 0.014 (0.193) Batch 1.113 (1.589) Remain 09:42:23 loss: 0.1029 Lr: 0.00257
[2023-12-25 15:10:29,071 INFO misc.py line 119 253097] Train: [57/100][456/510] Data 0.008 (0.193) Batch 1.171 (1.588) Remain 09:42:01 loss: 0.1536 Lr: 0.00257
[2023-12-25 15:10:30,251 INFO misc.py line 119 253097] Train: [57/100][457/510] Data 0.004 (0.192) Batch 1.179 (1.588) Remain 09:41:39 loss: 0.1302 Lr: 0.00257
[2023-12-25 15:10:31,570 INFO misc.py line 119 253097] Train: [57/100][458/510] Data 0.006 (0.192) Batch 1.321 (1.587) Remain 09:41:25 loss: 0.1166 Lr: 0.00257
[2023-12-25 15:10:32,859 INFO misc.py line 119 253097] Train: [57/100][459/510] Data 0.004 (0.192) Batch 1.289 (1.586) Remain 09:41:09 loss: 0.1216 Lr: 0.00257
[2023-12-25 15:10:33,880 INFO misc.py line 119 253097] Train: [57/100][460/510] Data 0.004 (0.191) Batch 1.021 (1.585) Remain 09:40:40 loss: 0.1273 Lr: 0.00257
[2023-12-25 15:10:35,047 INFO misc.py line 119 253097] Train: [57/100][461/510] Data 0.005 (0.191) Batch 1.164 (1.584) Remain 09:40:18 loss: 0.1956 Lr: 0.00257
[2023-12-25 15:10:38,155 INFO misc.py line 119 253097] Train: [57/100][462/510] Data 0.008 (0.190) Batch 3.111 (1.588) Remain 09:41:30 loss: 0.2512 Lr: 0.00257
[2023-12-25 15:10:40,981 INFO misc.py line 119 253097] Train: [57/100][463/510] Data 0.004 (0.190) Batch 2.826 (1.590) Remain 09:42:28 loss: 0.1252 Lr: 0.00257
[2023-12-25 15:10:42,190 INFO misc.py line 119 253097] Train: [57/100][464/510] Data 0.003 (0.190) Batch 1.208 (1.589) Remain 09:42:08 loss: 0.2059 Lr: 0.00257
[2023-12-25 15:10:43,236 INFO misc.py line 119 253097] Train: [57/100][465/510] Data 0.004 (0.189) Batch 1.047 (1.588) Remain 09:41:40 loss: 0.1513 Lr: 0.00257
[2023-12-25 15:10:44,491 INFO misc.py line 119 253097] Train: [57/100][466/510] Data 0.003 (0.189) Batch 1.251 (1.587) Remain 09:41:23 loss: 0.1255 Lr: 0.00257
[2023-12-25 15:10:45,652 INFO misc.py line 119 253097] Train: [57/100][467/510] Data 0.008 (0.188) Batch 1.160 (1.587) Remain 09:41:01 loss: 0.2396 Lr: 0.00257
[2023-12-25 15:10:51,192 INFO misc.py line 119 253097] Train: [57/100][468/510] Data 0.008 (0.188) Batch 5.544 (1.595) Remain 09:44:06 loss: 0.1500 Lr: 0.00257
[2023-12-25 15:10:52,357 INFO misc.py line 119 253097] Train: [57/100][469/510] Data 0.004 (0.188) Batch 1.163 (1.594) Remain 09:43:45 loss: 0.1384 Lr: 0.00257
[2023-12-25 15:10:53,379 INFO misc.py line 119 253097] Train: [57/100][470/510] Data 0.005 (0.187) Batch 1.020 (1.593) Remain 09:43:16 loss: 0.1954 Lr: 0.00257
[2023-12-25 15:10:54,188 INFO misc.py line 119 253097] Train: [57/100][471/510] Data 0.007 (0.187) Batch 0.812 (1.591) Remain 09:42:38 loss: 0.1721 Lr: 0.00256
[2023-12-25 15:10:55,210 INFO misc.py line 119 253097] Train: [57/100][472/510] Data 0.005 (0.186) Batch 1.023 (1.590) Remain 09:42:09 loss: 0.1873 Lr: 0.00256
[2023-12-25 15:10:56,483 INFO misc.py line 119 253097] Train: [57/100][473/510] Data 0.003 (0.186) Batch 1.271 (1.589) Remain 09:41:53 loss: 0.0958 Lr: 0.00256
[2023-12-25 15:10:57,670 INFO misc.py line 119 253097] Train: [57/100][474/510] Data 0.006 (0.186) Batch 1.187 (1.589) Remain 09:41:33 loss: 0.0733 Lr: 0.00256
[2023-12-25 15:10:58,755 INFO misc.py line 119 253097] Train: [57/100][475/510] Data 0.005 (0.185) Batch 1.081 (1.587) Remain 09:41:07 loss: 0.1650 Lr: 0.00256
[2023-12-25 15:11:00,079 INFO misc.py line 119 253097] Train: [57/100][476/510] Data 0.010 (0.185) Batch 1.329 (1.587) Remain 09:40:54 loss: 0.1012 Lr: 0.00256
[2023-12-25 15:11:01,183 INFO misc.py line 119 253097] Train: [57/100][477/510] Data 0.005 (0.184) Batch 1.105 (1.586) Remain 09:40:30 loss: 0.1519 Lr: 0.00256
[2023-12-25 15:11:02,285 INFO misc.py line 119 253097] Train: [57/100][478/510] Data 0.004 (0.184) Batch 1.100 (1.585) Remain 09:40:06 loss: 0.4591 Lr: 0.00256
[2023-12-25 15:11:03,490 INFO misc.py line 119 253097] Train: [57/100][479/510] Data 0.007 (0.184) Batch 1.203 (1.584) Remain 09:39:47 loss: 0.4137 Lr: 0.00256
[2023-12-25 15:11:10,609 INFO misc.py line 119 253097] Train: [57/100][480/510] Data 0.009 (0.183) Batch 7.124 (1.596) Remain 09:44:00 loss: 0.1199 Lr: 0.00256
[2023-12-25 15:11:11,824 INFO misc.py line 119 253097] Train: [57/100][481/510] Data 0.003 (0.183) Batch 1.215 (1.595) Remain 09:43:41 loss: 0.1321 Lr: 0.00256
[2023-12-25 15:11:13,114 INFO misc.py line 119 253097] Train: [57/100][482/510] Data 0.004 (0.183) Batch 1.288 (1.594) Remain 09:43:25 loss: 0.2502 Lr: 0.00256
[2023-12-25 15:11:14,388 INFO misc.py line 119 253097] Train: [57/100][483/510] Data 0.006 (0.182) Batch 1.271 (1.594) Remain 09:43:09 loss: 0.1200 Lr: 0.00256
[2023-12-25 15:11:15,609 INFO misc.py line 119 253097] Train: [57/100][484/510] Data 0.008 (0.182) Batch 1.226 (1.593) Remain 09:42:51 loss: 0.1458 Lr: 0.00256
[2023-12-25 15:11:16,455 INFO misc.py line 119 253097] Train: [57/100][485/510] Data 0.003 (0.182) Batch 0.846 (1.591) Remain 09:42:15 loss: 0.1755 Lr: 0.00256
[2023-12-25 15:11:17,589 INFO misc.py line 119 253097] Train: [57/100][486/510] Data 0.003 (0.181) Batch 1.133 (1.590) Remain 09:41:53 loss: 0.1277 Lr: 0.00256
[2023-12-25 15:11:18,847 INFO misc.py line 119 253097] Train: [57/100][487/510] Data 0.004 (0.181) Batch 1.252 (1.590) Remain 09:41:36 loss: 0.1338 Lr: 0.00256
[2023-12-25 15:11:20,013 INFO misc.py line 119 253097] Train: [57/100][488/510] Data 0.010 (0.180) Batch 1.159 (1.589) Remain 09:41:15 loss: 0.2060 Lr: 0.00256
[2023-12-25 15:11:21,001 INFO misc.py line 119 253097] Train: [57/100][489/510] Data 0.016 (0.180) Batch 1.000 (1.587) Remain 09:40:46 loss: 0.3383 Lr: 0.00256
[2023-12-25 15:11:22,054 INFO misc.py line 119 253097] Train: [57/100][490/510] Data 0.004 (0.180) Batch 1.051 (1.586) Remain 09:40:21 loss: 0.1469 Lr: 0.00256
[2023-12-25 15:11:23,370 INFO misc.py line 119 253097] Train: [57/100][491/510] Data 0.006 (0.179) Batch 1.317 (1.586) Remain 09:40:07 loss: 0.1295 Lr: 0.00256
[2023-12-25 15:11:24,655 INFO misc.py line 119 253097] Train: [57/100][492/510] Data 0.005 (0.179) Batch 1.271 (1.585) Remain 09:39:51 loss: 0.1745 Lr: 0.00256
[2023-12-25 15:11:25,897 INFO misc.py line 119 253097] Train: [57/100][493/510] Data 0.020 (0.179) Batch 1.256 (1.585) Remain 09:39:35 loss: 0.1982 Lr: 0.00256
[2023-12-25 15:11:26,974 INFO misc.py line 119 253097] Train: [57/100][494/510] Data 0.004 (0.178) Batch 1.074 (1.583) Remain 09:39:10 loss: 0.1426 Lr: 0.00256
[2023-12-25 15:11:28,057 INFO misc.py line 119 253097] Train: [57/100][495/510] Data 0.007 (0.178) Batch 1.084 (1.582) Remain 09:38:47 loss: 0.1788 Lr: 0.00256
[2023-12-25 15:11:30,119 INFO misc.py line 119 253097] Train: [57/100][496/510] Data 0.007 (0.178) Batch 2.059 (1.583) Remain 09:39:06 loss: 0.2320 Lr: 0.00256
[2023-12-25 15:11:31,144 INFO misc.py line 119 253097] Train: [57/100][497/510] Data 0.010 (0.177) Batch 1.027 (1.582) Remain 09:38:40 loss: 0.1671 Lr: 0.00256
[2023-12-25 15:11:32,382 INFO misc.py line 119 253097] Train: [57/100][498/510] Data 0.008 (0.177) Batch 1.239 (1.582) Remain 09:38:23 loss: 0.1782 Lr: 0.00256
[2023-12-25 15:11:33,569 INFO misc.py line 119 253097] Train: [57/100][499/510] Data 0.006 (0.177) Batch 1.179 (1.581) Remain 09:38:04 loss: 0.3884 Lr: 0.00256
[2023-12-25 15:11:34,628 INFO misc.py line 119 253097] Train: [57/100][500/510] Data 0.015 (0.176) Batch 1.063 (1.580) Remain 09:37:39 loss: 0.1493 Lr: 0.00256
[2023-12-25 15:11:42,356 INFO misc.py line 119 253097] Train: [57/100][501/510] Data 0.010 (0.176) Batch 7.733 (1.592) Remain 09:42:09 loss: 0.2044 Lr: 0.00256
[2023-12-25 15:11:43,373 INFO misc.py line 119 253097] Train: [57/100][502/510] Data 0.006 (0.176) Batch 1.018 (1.591) Remain 09:41:42 loss: 0.2697 Lr: 0.00256
[2023-12-25 15:11:44,471 INFO misc.py line 119 253097] Train: [57/100][503/510] Data 0.004 (0.175) Batch 1.099 (1.590) Remain 09:41:19 loss: 0.0906 Lr: 0.00256
[2023-12-25 15:11:45,561 INFO misc.py line 119 253097] Train: [57/100][504/510] Data 0.004 (0.175) Batch 1.090 (1.589) Remain 09:40:55 loss: 0.1738 Lr: 0.00256
[2023-12-25 15:11:46,691 INFO misc.py line 119 253097] Train: [57/100][505/510] Data 0.004 (0.175) Batch 1.130 (1.588) Remain 09:40:34 loss: 0.1978 Lr: 0.00256
[2023-12-25 15:11:47,983 INFO misc.py line 119 253097] Train: [57/100][506/510] Data 0.004 (0.174) Batch 1.287 (1.587) Remain 09:40:19 loss: 0.1937 Lr: 0.00256
[2023-12-25 15:11:49,087 INFO misc.py line 119 253097] Train: [57/100][507/510] Data 0.010 (0.174) Batch 1.104 (1.587) Remain 09:39:56 loss: 0.1178 Lr: 0.00256
[2023-12-25 15:11:50,193 INFO misc.py line 119 253097] Train: [57/100][508/510] Data 0.009 (0.174) Batch 1.110 (1.586) Remain 09:39:34 loss: 0.1726 Lr: 0.00256
[2023-12-25 15:11:51,105 INFO misc.py line 119 253097] Train: [57/100][509/510] Data 0.005 (0.173) Batch 0.911 (1.584) Remain 09:39:03 loss: 0.2431 Lr: 0.00256
[2023-12-25 15:11:52,349 INFO misc.py line 119 253097] Train: [57/100][510/510] Data 0.006 (0.173) Batch 1.245 (1.584) Remain 09:38:47 loss: 0.1633 Lr: 0.00256
[2023-12-25 15:11:52,350 INFO misc.py line 136 253097] Train result: loss: 0.1669 
[2023-12-25 15:11:52,350 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 15:12:21,350 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4941 
[2023-12-25 15:12:21,715 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3721 
[2023-12-25 15:12:26,644 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4398 
[2023-12-25 15:12:27,160 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3068 
[2023-12-25 15:12:29,134 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7734 
[2023-12-25 15:12:29,558 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3431 
[2023-12-25 15:12:30,437 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1250 
[2023-12-25 15:12:30,988 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3908 
[2023-12-25 15:12:32,809 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9622 
[2023-12-25 15:12:34,928 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1295 
[2023-12-25 15:12:35,780 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3394 
[2023-12-25 15:12:36,210 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0795 
[2023-12-25 15:12:37,108 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4346 
[2023-12-25 15:12:40,066 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8761 
[2023-12-25 15:12:40,540 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2375 
[2023-12-25 15:12:41,147 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5864 
[2023-12-25 15:12:41,846 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3548 
[2023-12-25 15:12:42,990 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6856/0.7510/0.8967.
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9082/0.9516
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9815/0.9921
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8325/0.9671
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3579/0.4574
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5878/0.6005
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6288/0.7164
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8024/0.9058
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9109/0.9481
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.7847/0.8301
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7807/0.8679
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7594/0.8626
[2023-12-25 15:12:42,990 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5777/0.6635
[2023-12-25 15:12:42,991 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 15:12:42,992 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 15:12:42,992 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 15:12:50,561 INFO misc.py line 119 253097] Train: [58/100][1/510] Data 2.371 (2.371) Batch 4.914 (4.914) Remain 29:55:48 loss: 0.1427 Lr: 0.00256
[2023-12-25 15:12:51,729 INFO misc.py line 119 253097] Train: [58/100][2/510] Data 0.004 (0.004) Batch 1.168 (1.168) Remain 07:06:45 loss: 0.1513 Lr: 0.00256
[2023-12-25 15:12:52,779 INFO misc.py line 119 253097] Train: [58/100][3/510] Data 0.004 (0.004) Batch 1.049 (1.049) Remain 06:23:23 loss: 0.1737 Lr: 0.00256
[2023-12-25 15:12:56,035 INFO misc.py line 119 253097] Train: [58/100][4/510] Data 2.114 (2.114) Batch 3.257 (3.257) Remain 19:50:03 loss: 0.1575 Lr: 0.00256
[2023-12-25 15:12:57,290 INFO misc.py line 119 253097] Train: [58/100][5/510] Data 0.004 (1.059) Batch 1.256 (2.256) Remain 13:44:29 loss: 0.0861 Lr: 0.00256
[2023-12-25 15:12:58,440 INFO misc.py line 119 253097] Train: [58/100][6/510] Data 0.003 (0.707) Batch 1.147 (1.886) Remain 11:29:18 loss: 0.0886 Lr: 0.00256
[2023-12-25 15:12:59,670 INFO misc.py line 119 253097] Train: [58/100][7/510] Data 0.006 (0.532) Batch 1.232 (1.723) Remain 10:29:29 loss: 0.1788 Lr: 0.00256
[2023-12-25 15:13:00,918 INFO misc.py line 119 253097] Train: [58/100][8/510] Data 0.005 (0.426) Batch 1.249 (1.628) Remain 09:54:49 loss: 0.1772 Lr: 0.00256
[2023-12-25 15:13:02,052 INFO misc.py line 119 253097] Train: [58/100][9/510] Data 0.003 (0.356) Batch 1.131 (1.545) Remain 09:24:30 loss: 0.1537 Lr: 0.00256
[2023-12-25 15:13:03,119 INFO misc.py line 119 253097] Train: [58/100][10/510] Data 0.007 (0.306) Batch 1.069 (1.477) Remain 08:59:38 loss: 0.1332 Lr: 0.00256
[2023-12-25 15:13:04,082 INFO misc.py line 119 253097] Train: [58/100][11/510] Data 0.006 (0.269) Batch 0.964 (1.413) Remain 08:36:11 loss: 0.1510 Lr: 0.00256
[2023-12-25 15:13:05,025 INFO misc.py line 119 253097] Train: [58/100][12/510] Data 0.004 (0.239) Batch 0.940 (1.360) Remain 08:16:58 loss: 0.1342 Lr: 0.00256
[2023-12-25 15:13:08,579 INFO misc.py line 119 253097] Train: [58/100][13/510] Data 0.007 (0.216) Batch 3.557 (1.580) Remain 09:37:11 loss: 0.1283 Lr: 0.00255
[2023-12-25 15:13:09,507 INFO misc.py line 119 253097] Train: [58/100][14/510] Data 0.004 (0.197) Batch 0.926 (1.521) Remain 09:15:27 loss: 0.0970 Lr: 0.00255
[2023-12-25 15:13:10,624 INFO misc.py line 119 253097] Train: [58/100][15/510] Data 0.006 (0.181) Batch 1.114 (1.487) Remain 09:03:02 loss: 0.1453 Lr: 0.00255
[2023-12-25 15:13:11,842 INFO misc.py line 119 253097] Train: [58/100][16/510] Data 0.009 (0.167) Batch 1.222 (1.466) Remain 08:55:35 loss: 0.0862 Lr: 0.00255
[2023-12-25 15:13:13,046 INFO misc.py line 119 253097] Train: [58/100][17/510] Data 0.004 (0.156) Batch 1.203 (1.448) Remain 08:48:41 loss: 0.1030 Lr: 0.00255
[2023-12-25 15:13:14,227 INFO misc.py line 119 253097] Train: [58/100][18/510] Data 0.005 (0.146) Batch 1.181 (1.430) Remain 08:42:09 loss: 0.1380 Lr: 0.00255
[2023-12-25 15:13:15,304 INFO misc.py line 119 253097] Train: [58/100][19/510] Data 0.006 (0.137) Batch 1.074 (1.408) Remain 08:34:00 loss: 0.1620 Lr: 0.00255
[2023-12-25 15:13:16,344 INFO misc.py line 119 253097] Train: [58/100][20/510] Data 0.010 (0.130) Batch 1.043 (1.386) Remain 08:26:09 loss: 0.1775 Lr: 0.00255
[2023-12-25 15:13:17,378 INFO misc.py line 119 253097] Train: [58/100][21/510] Data 0.006 (0.123) Batch 1.032 (1.366) Remain 08:18:57 loss: 0.1507 Lr: 0.00255
[2023-12-25 15:13:18,391 INFO misc.py line 119 253097] Train: [58/100][22/510] Data 0.007 (0.117) Batch 1.014 (1.348) Remain 08:12:10 loss: 0.1557 Lr: 0.00255
[2023-12-25 15:13:19,659 INFO misc.py line 119 253097] Train: [58/100][23/510] Data 0.006 (0.111) Batch 1.268 (1.344) Remain 08:10:41 loss: 0.2203 Lr: 0.00255
[2023-12-25 15:13:20,877 INFO misc.py line 119 253097] Train: [58/100][24/510] Data 0.006 (0.106) Batch 1.221 (1.338) Remain 08:08:31 loss: 0.1185 Lr: 0.00255
[2023-12-25 15:13:21,905 INFO misc.py line 119 253097] Train: [58/100][25/510] Data 0.004 (0.101) Batch 1.027 (1.324) Remain 08:03:20 loss: 0.1184 Lr: 0.00255
[2023-12-25 15:13:23,194 INFO misc.py line 119 253097] Train: [58/100][26/510] Data 0.004 (0.097) Batch 1.286 (1.322) Remain 08:02:43 loss: 0.1551 Lr: 0.00255
[2023-12-25 15:13:24,326 INFO misc.py line 119 253097] Train: [58/100][27/510] Data 0.007 (0.093) Batch 1.131 (1.314) Remain 07:59:47 loss: 0.2346 Lr: 0.00255
[2023-12-25 15:13:25,513 INFO misc.py line 119 253097] Train: [58/100][28/510] Data 0.007 (0.090) Batch 1.189 (1.309) Remain 07:57:56 loss: 0.0823 Lr: 0.00255
[2023-12-25 15:13:26,774 INFO misc.py line 119 253097] Train: [58/100][29/510] Data 0.006 (0.087) Batch 1.259 (1.307) Remain 07:57:12 loss: 0.2306 Lr: 0.00255
[2023-12-25 15:13:34,070 INFO misc.py line 119 253097] Train: [58/100][30/510] Data 0.009 (0.084) Batch 7.300 (1.529) Remain 09:18:11 loss: 0.2227 Lr: 0.00255
[2023-12-25 15:13:35,198 INFO misc.py line 119 253097] Train: [58/100][31/510] Data 0.008 (0.081) Batch 1.128 (1.515) Remain 09:12:56 loss: 0.1276 Lr: 0.00255
[2023-12-25 15:13:36,328 INFO misc.py line 119 253097] Train: [58/100][32/510] Data 0.005 (0.078) Batch 1.130 (1.502) Remain 09:08:04 loss: 0.1015 Lr: 0.00255
[2023-12-25 15:13:37,477 INFO misc.py line 119 253097] Train: [58/100][33/510] Data 0.005 (0.076) Batch 1.149 (1.490) Remain 09:03:44 loss: 0.1443 Lr: 0.00255
[2023-12-25 15:13:38,705 INFO misc.py line 119 253097] Train: [58/100][34/510] Data 0.005 (0.074) Batch 1.228 (1.481) Remain 09:00:38 loss: 0.4901 Lr: 0.00255
[2023-12-25 15:13:39,860 INFO misc.py line 119 253097] Train: [58/100][35/510] Data 0.005 (0.072) Batch 1.155 (1.471) Remain 08:56:53 loss: 0.1419 Lr: 0.00255
[2023-12-25 15:13:40,947 INFO misc.py line 119 253097] Train: [58/100][36/510] Data 0.005 (0.070) Batch 1.088 (1.460) Remain 08:52:37 loss: 0.1512 Lr: 0.00255
[2023-12-25 15:13:42,355 INFO misc.py line 119 253097] Train: [58/100][37/510] Data 0.003 (0.068) Batch 1.407 (1.458) Remain 08:52:02 loss: 0.1206 Lr: 0.00255
[2023-12-25 15:13:43,329 INFO misc.py line 119 253097] Train: [58/100][38/510] Data 0.004 (0.066) Batch 0.974 (1.444) Remain 08:46:58 loss: 0.0728 Lr: 0.00255
[2023-12-25 15:13:44,624 INFO misc.py line 119 253097] Train: [58/100][39/510] Data 0.005 (0.064) Batch 1.295 (1.440) Remain 08:45:25 loss: 0.0889 Lr: 0.00255
[2023-12-25 15:13:45,745 INFO misc.py line 119 253097] Train: [58/100][40/510] Data 0.004 (0.062) Batch 1.123 (1.432) Remain 08:42:16 loss: 0.0820 Lr: 0.00255
[2023-12-25 15:13:46,928 INFO misc.py line 119 253097] Train: [58/100][41/510] Data 0.003 (0.061) Batch 1.182 (1.425) Remain 08:39:51 loss: 0.3081 Lr: 0.00255
[2023-12-25 15:13:48,417 INFO misc.py line 119 253097] Train: [58/100][42/510] Data 0.246 (0.066) Batch 1.489 (1.427) Remain 08:40:26 loss: 0.0788 Lr: 0.00255
[2023-12-25 15:13:49,506 INFO misc.py line 119 253097] Train: [58/100][43/510] Data 0.004 (0.064) Batch 1.083 (1.418) Remain 08:37:16 loss: 0.1991 Lr: 0.00255
[2023-12-25 15:13:50,524 INFO misc.py line 119 253097] Train: [58/100][44/510] Data 0.010 (0.063) Batch 1.024 (1.408) Remain 08:33:44 loss: 0.1678 Lr: 0.00255
[2023-12-25 15:13:51,810 INFO misc.py line 119 253097] Train: [58/100][45/510] Data 0.005 (0.061) Batch 1.286 (1.406) Remain 08:32:39 loss: 0.1044 Lr: 0.00255
[2023-12-25 15:13:52,914 INFO misc.py line 119 253097] Train: [58/100][46/510] Data 0.003 (0.060) Batch 1.104 (1.399) Remain 08:30:05 loss: 0.1466 Lr: 0.00255
[2023-12-25 15:13:56,456 INFO misc.py line 119 253097] Train: [58/100][47/510] Data 0.005 (0.059) Batch 3.537 (1.447) Remain 08:47:47 loss: 0.1292 Lr: 0.00255
[2023-12-25 15:13:57,735 INFO misc.py line 119 253097] Train: [58/100][48/510] Data 0.008 (0.058) Batch 1.283 (1.443) Remain 08:46:25 loss: 0.0877 Lr: 0.00255
[2023-12-25 15:13:58,980 INFO misc.py line 119 253097] Train: [58/100][49/510] Data 0.004 (0.057) Batch 1.242 (1.439) Remain 08:44:48 loss: 0.1438 Lr: 0.00255
[2023-12-25 15:13:59,735 INFO misc.py line 119 253097] Train: [58/100][50/510] Data 0.008 (0.055) Batch 0.758 (1.425) Remain 08:39:30 loss: 0.0892 Lr: 0.00255
[2023-12-25 15:14:00,961 INFO misc.py line 119 253097] Train: [58/100][51/510] Data 0.004 (0.054) Batch 1.227 (1.420) Remain 08:37:58 loss: 0.1063 Lr: 0.00255
[2023-12-25 15:14:02,177 INFO misc.py line 119 253097] Train: [58/100][52/510] Data 0.003 (0.053) Batch 1.215 (1.416) Remain 08:36:25 loss: 0.2633 Lr: 0.00255
[2023-12-25 15:14:03,402 INFO misc.py line 119 253097] Train: [58/100][53/510] Data 0.005 (0.052) Batch 1.225 (1.412) Remain 08:35:00 loss: 0.1552 Lr: 0.00255
[2023-12-25 15:14:04,569 INFO misc.py line 119 253097] Train: [58/100][54/510] Data 0.004 (0.051) Batch 1.165 (1.408) Remain 08:33:13 loss: 0.0951 Lr: 0.00255
[2023-12-25 15:14:05,658 INFO misc.py line 119 253097] Train: [58/100][55/510] Data 0.007 (0.051) Batch 1.091 (1.402) Remain 08:30:58 loss: 0.1934 Lr: 0.00255
[2023-12-25 15:14:06,923 INFO misc.py line 119 253097] Train: [58/100][56/510] Data 0.004 (0.050) Batch 1.261 (1.399) Remain 08:29:59 loss: 0.0673 Lr: 0.00255
[2023-12-25 15:14:07,994 INFO misc.py line 119 253097] Train: [58/100][57/510] Data 0.007 (0.049) Batch 1.069 (1.393) Remain 08:27:44 loss: 0.1605 Lr: 0.00255
[2023-12-25 15:14:09,191 INFO misc.py line 119 253097] Train: [58/100][58/510] Data 0.010 (0.048) Batch 1.199 (1.389) Remain 08:26:25 loss: 0.1873 Lr: 0.00255
[2023-12-25 15:14:10,448 INFO misc.py line 119 253097] Train: [58/100][59/510] Data 0.007 (0.047) Batch 1.251 (1.387) Remain 08:25:30 loss: 0.0836 Lr: 0.00255
[2023-12-25 15:14:11,630 INFO misc.py line 119 253097] Train: [58/100][60/510] Data 0.013 (0.047) Batch 1.189 (1.383) Remain 08:24:13 loss: 0.0733 Lr: 0.00255
[2023-12-25 15:14:12,691 INFO misc.py line 119 253097] Train: [58/100][61/510] Data 0.006 (0.046) Batch 1.064 (1.378) Remain 08:22:11 loss: 0.1181 Lr: 0.00255
[2023-12-25 15:14:13,881 INFO misc.py line 119 253097] Train: [58/100][62/510] Data 0.003 (0.045) Batch 1.189 (1.375) Remain 08:21:00 loss: 0.1619 Lr: 0.00255
[2023-12-25 15:14:22,331 INFO misc.py line 119 253097] Train: [58/100][63/510] Data 0.004 (0.045) Batch 8.450 (1.493) Remain 09:03:57 loss: 0.0915 Lr: 0.00255
[2023-12-25 15:14:23,403 INFO misc.py line 119 253097] Train: [58/100][64/510] Data 0.004 (0.044) Batch 1.071 (1.486) Remain 09:01:25 loss: 0.1483 Lr: 0.00255
[2023-12-25 15:14:26,835 INFO misc.py line 119 253097] Train: [58/100][65/510] Data 0.004 (0.043) Batch 3.432 (1.517) Remain 09:12:50 loss: 0.2259 Lr: 0.00254
[2023-12-25 15:14:27,913 INFO misc.py line 119 253097] Train: [58/100][66/510] Data 0.004 (0.043) Batch 1.077 (1.510) Remain 09:10:15 loss: 0.0860 Lr: 0.00254
[2023-12-25 15:14:29,084 INFO misc.py line 119 253097] Train: [58/100][67/510] Data 0.005 (0.042) Batch 1.173 (1.505) Remain 09:08:19 loss: 0.2515 Lr: 0.00254
[2023-12-25 15:14:30,185 INFO misc.py line 119 253097] Train: [58/100][68/510] Data 0.003 (0.042) Batch 1.100 (1.499) Remain 09:06:01 loss: 0.1269 Lr: 0.00254
[2023-12-25 15:14:31,176 INFO misc.py line 119 253097] Train: [58/100][69/510] Data 0.005 (0.041) Batch 0.992 (1.491) Remain 09:03:11 loss: 0.1248 Lr: 0.00254
[2023-12-25 15:14:32,412 INFO misc.py line 119 253097] Train: [58/100][70/510] Data 0.005 (0.041) Batch 1.236 (1.487) Remain 09:01:47 loss: 0.3110 Lr: 0.00254
[2023-12-25 15:14:33,499 INFO misc.py line 119 253097] Train: [58/100][71/510] Data 0.004 (0.040) Batch 1.087 (1.481) Remain 08:59:37 loss: 0.1187 Lr: 0.00254
[2023-12-25 15:14:34,690 INFO misc.py line 119 253097] Train: [58/100][72/510] Data 0.004 (0.039) Batch 1.187 (1.477) Remain 08:58:02 loss: 0.1077 Lr: 0.00254
[2023-12-25 15:14:35,720 INFO misc.py line 119 253097] Train: [58/100][73/510] Data 0.008 (0.039) Batch 1.034 (1.471) Remain 08:55:42 loss: 0.1374 Lr: 0.00254
[2023-12-25 15:14:37,002 INFO misc.py line 119 253097] Train: [58/100][74/510] Data 0.004 (0.039) Batch 1.281 (1.468) Remain 08:54:43 loss: 0.1790 Lr: 0.00254
[2023-12-25 15:14:38,201 INFO misc.py line 119 253097] Train: [58/100][75/510] Data 0.005 (0.038) Batch 1.199 (1.464) Remain 08:53:19 loss: 0.1562 Lr: 0.00254
[2023-12-25 15:14:39,265 INFO misc.py line 119 253097] Train: [58/100][76/510] Data 0.005 (0.038) Batch 1.060 (1.459) Remain 08:51:17 loss: 0.1505 Lr: 0.00254
[2023-12-25 15:14:40,623 INFO misc.py line 119 253097] Train: [58/100][77/510] Data 0.008 (0.037) Batch 1.360 (1.457) Remain 08:50:46 loss: 0.1162 Lr: 0.00254
[2023-12-25 15:14:41,858 INFO misc.py line 119 253097] Train: [58/100][78/510] Data 0.007 (0.037) Batch 1.233 (1.454) Remain 08:49:40 loss: 0.1215 Lr: 0.00254
[2023-12-25 15:14:42,941 INFO misc.py line 119 253097] Train: [58/100][79/510] Data 0.008 (0.036) Batch 1.084 (1.449) Remain 08:47:52 loss: 0.1035 Lr: 0.00254
[2023-12-25 15:14:44,111 INFO misc.py line 119 253097] Train: [58/100][80/510] Data 0.008 (0.036) Batch 1.122 (1.445) Remain 08:46:17 loss: 0.1309 Lr: 0.00254
[2023-12-25 15:14:45,345 INFO misc.py line 119 253097] Train: [58/100][81/510] Data 0.055 (0.036) Batch 1.285 (1.443) Remain 08:45:31 loss: 0.1358 Lr: 0.00254
[2023-12-25 15:14:46,541 INFO misc.py line 119 253097] Train: [58/100][82/510] Data 0.005 (0.036) Batch 1.196 (1.440) Remain 08:44:21 loss: 0.1542 Lr: 0.00254
[2023-12-25 15:14:47,762 INFO misc.py line 119 253097] Train: [58/100][83/510] Data 0.004 (0.036) Batch 1.217 (1.437) Remain 08:43:19 loss: 0.1351 Lr: 0.00254
[2023-12-25 15:14:48,834 INFO misc.py line 119 253097] Train: [58/100][84/510] Data 0.009 (0.035) Batch 1.077 (1.433) Remain 08:41:40 loss: 0.1774 Lr: 0.00254
[2023-12-25 15:14:55,579 INFO misc.py line 119 253097] Train: [58/100][85/510] Data 0.004 (0.035) Batch 6.746 (1.498) Remain 09:05:14 loss: 0.0942 Lr: 0.00254
[2023-12-25 15:14:56,742 INFO misc.py line 119 253097] Train: [58/100][86/510] Data 0.003 (0.034) Batch 1.163 (1.494) Remain 09:03:45 loss: 0.1342 Lr: 0.00254
[2023-12-25 15:14:57,472 INFO misc.py line 119 253097] Train: [58/100][87/510] Data 0.003 (0.034) Batch 0.730 (1.484) Remain 09:00:24 loss: 0.2530 Lr: 0.00254
[2023-12-25 15:14:58,463 INFO misc.py line 119 253097] Train: [58/100][88/510] Data 0.003 (0.034) Batch 0.991 (1.479) Remain 08:58:16 loss: 0.1300 Lr: 0.00254
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[2023-12-25 15:15:00,833 INFO misc.py line 119 253097] Train: [58/100][90/510] Data 0.004 (0.033) Batch 1.128 (1.472) Remain 08:55:46 loss: 0.1823 Lr: 0.00254
[2023-12-25 15:15:02,120 INFO misc.py line 119 253097] Train: [58/100][91/510] Data 0.004 (0.033) Batch 1.287 (1.470) Remain 08:54:58 loss: 0.1638 Lr: 0.00254
[2023-12-25 15:15:03,374 INFO misc.py line 119 253097] Train: [58/100][92/510] Data 0.004 (0.032) Batch 1.252 (1.467) Remain 08:54:03 loss: 0.1798 Lr: 0.00254
[2023-12-25 15:15:04,552 INFO misc.py line 119 253097] Train: [58/100][93/510] Data 0.005 (0.032) Batch 1.179 (1.464) Remain 08:52:52 loss: 0.1319 Lr: 0.00254
[2023-12-25 15:15:05,643 INFO misc.py line 119 253097] Train: [58/100][94/510] Data 0.005 (0.032) Batch 1.088 (1.460) Remain 08:51:20 loss: 0.1241 Lr: 0.00254
[2023-12-25 15:15:06,817 INFO misc.py line 119 253097] Train: [58/100][95/510] Data 0.008 (0.031) Batch 1.171 (1.457) Remain 08:50:10 loss: 0.1280 Lr: 0.00254
[2023-12-25 15:15:07,823 INFO misc.py line 119 253097] Train: [58/100][96/510] Data 0.010 (0.031) Batch 1.005 (1.452) Remain 08:48:23 loss: 0.2002 Lr: 0.00254
[2023-12-25 15:15:08,837 INFO misc.py line 119 253097] Train: [58/100][97/510] Data 0.011 (0.031) Batch 1.015 (1.447) Remain 08:46:40 loss: 0.0689 Lr: 0.00254
[2023-12-25 15:15:09,923 INFO misc.py line 119 253097] Train: [58/100][98/510] Data 0.010 (0.031) Batch 1.086 (1.444) Remain 08:45:15 loss: 0.0798 Lr: 0.00254
[2023-12-25 15:15:11,210 INFO misc.py line 119 253097] Train: [58/100][99/510] Data 0.009 (0.031) Batch 1.293 (1.442) Remain 08:44:40 loss: 0.2267 Lr: 0.00254
[2023-12-25 15:15:12,361 INFO misc.py line 119 253097] Train: [58/100][100/510] Data 0.004 (0.030) Batch 1.150 (1.439) Remain 08:43:33 loss: 0.2277 Lr: 0.00254
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[2023-12-25 15:23:09,270 INFO misc.py line 119 253097] Train: [58/100][416/510] Data 0.005 (0.137) Batch 1.075 (1.493) Remain 08:55:14 loss: 0.3243 Lr: 0.00248
[2023-12-25 15:23:20,131 INFO misc.py line 119 253097] Train: [58/100][417/510] Data 0.004 (0.137) Batch 10.862 (1.515) Remain 09:03:19 loss: 0.1611 Lr: 0.00248
[2023-12-25 15:23:21,309 INFO misc.py line 119 253097] Train: [58/100][418/510] Data 0.004 (0.136) Batch 1.176 (1.515) Remain 09:03:00 loss: 0.1242 Lr: 0.00248
[2023-12-25 15:23:22,348 INFO misc.py line 119 253097] Train: [58/100][419/510] Data 0.007 (0.136) Batch 1.036 (1.513) Remain 09:02:34 loss: 0.0815 Lr: 0.00248
[2023-12-25 15:23:23,612 INFO misc.py line 119 253097] Train: [58/100][420/510] Data 0.009 (0.136) Batch 1.264 (1.513) Remain 09:02:19 loss: 0.1063 Lr: 0.00248
[2023-12-25 15:23:24,872 INFO misc.py line 119 253097] Train: [58/100][421/510] Data 0.010 (0.135) Batch 1.266 (1.512) Remain 09:02:05 loss: 0.1795 Lr: 0.00248
[2023-12-25 15:23:26,051 INFO misc.py line 119 253097] Train: [58/100][422/510] Data 0.003 (0.135) Batch 1.152 (1.511) Remain 09:01:45 loss: 0.1886 Lr: 0.00248
[2023-12-25 15:23:27,114 INFO misc.py line 119 253097] Train: [58/100][423/510] Data 0.031 (0.135) Batch 1.085 (1.510) Remain 09:01:22 loss: 0.1521 Lr: 0.00248
[2023-12-25 15:23:28,031 INFO misc.py line 119 253097] Train: [58/100][424/510] Data 0.008 (0.135) Batch 0.922 (1.509) Remain 09:00:50 loss: 0.1132 Lr: 0.00248
[2023-12-25 15:23:29,147 INFO misc.py line 119 253097] Train: [58/100][425/510] Data 0.004 (0.134) Batch 1.115 (1.508) Remain 09:00:29 loss: 0.2638 Lr: 0.00248
[2023-12-25 15:23:30,154 INFO misc.py line 119 253097] Train: [58/100][426/510] Data 0.005 (0.134) Batch 1.007 (1.507) Remain 09:00:02 loss: 0.2018 Lr: 0.00248
[2023-12-25 15:23:32,134 INFO misc.py line 119 253097] Train: [58/100][427/510] Data 0.005 (0.134) Batch 1.981 (1.508) Remain 09:00:24 loss: 0.1871 Lr: 0.00248
[2023-12-25 15:23:33,247 INFO misc.py line 119 253097] Train: [58/100][428/510] Data 0.003 (0.133) Batch 1.112 (1.507) Remain 09:00:03 loss: 0.1343 Lr: 0.00248
[2023-12-25 15:23:34,462 INFO misc.py line 119 253097] Train: [58/100][429/510] Data 0.004 (0.133) Batch 1.209 (1.506) Remain 08:59:46 loss: 0.2828 Lr: 0.00248
[2023-12-25 15:23:37,241 INFO misc.py line 119 253097] Train: [58/100][430/510] Data 0.010 (0.133) Batch 2.785 (1.509) Remain 09:00:49 loss: 0.1104 Lr: 0.00247
[2023-12-25 15:23:38,458 INFO misc.py line 119 253097] Train: [58/100][431/510] Data 0.004 (0.132) Batch 1.218 (1.509) Remain 09:00:33 loss: 0.1297 Lr: 0.00247
[2023-12-25 15:23:39,712 INFO misc.py line 119 253097] Train: [58/100][432/510] Data 0.003 (0.132) Batch 1.250 (1.508) Remain 09:00:18 loss: 0.0693 Lr: 0.00247
[2023-12-25 15:23:41,028 INFO misc.py line 119 253097] Train: [58/100][433/510] Data 0.008 (0.132) Batch 1.315 (1.508) Remain 09:00:07 loss: 0.3777 Lr: 0.00247
[2023-12-25 15:23:44,105 INFO misc.py line 119 253097] Train: [58/100][434/510] Data 1.933 (0.136) Batch 3.082 (1.511) Remain 09:01:24 loss: 0.1408 Lr: 0.00247
[2023-12-25 15:23:45,224 INFO misc.py line 119 253097] Train: [58/100][435/510] Data 0.003 (0.136) Batch 1.120 (1.510) Remain 09:01:03 loss: 0.1477 Lr: 0.00247
[2023-12-25 15:23:46,375 INFO misc.py line 119 253097] Train: [58/100][436/510] Data 0.003 (0.135) Batch 1.151 (1.509) Remain 09:00:44 loss: 0.1089 Lr: 0.00247
[2023-12-25 15:23:47,647 INFO misc.py line 119 253097] Train: [58/100][437/510] Data 0.003 (0.135) Batch 1.271 (1.509) Remain 09:00:31 loss: 0.1351 Lr: 0.00247
[2023-12-25 15:23:48,724 INFO misc.py line 119 253097] Train: [58/100][438/510] Data 0.004 (0.135) Batch 1.031 (1.508) Remain 09:00:05 loss: 0.2875 Lr: 0.00247
[2023-12-25 15:23:49,815 INFO misc.py line 119 253097] Train: [58/100][439/510] Data 0.050 (0.135) Batch 1.130 (1.507) Remain 08:59:45 loss: 0.1614 Lr: 0.00247
[2023-12-25 15:23:51,152 INFO misc.py line 119 253097] Train: [58/100][440/510] Data 0.011 (0.134) Batch 1.344 (1.507) Remain 08:59:36 loss: 0.1672 Lr: 0.00247
[2023-12-25 15:23:52,002 INFO misc.py line 119 253097] Train: [58/100][441/510] Data 0.004 (0.134) Batch 0.851 (1.505) Remain 08:59:02 loss: 0.2692 Lr: 0.00247
[2023-12-25 15:23:53,114 INFO misc.py line 119 253097] Train: [58/100][442/510] Data 0.003 (0.134) Batch 1.111 (1.504) Remain 08:58:41 loss: 0.2213 Lr: 0.00247
[2023-12-25 15:23:54,131 INFO misc.py line 119 253097] Train: [58/100][443/510] Data 0.003 (0.133) Batch 1.017 (1.503) Remain 08:58:16 loss: 0.2248 Lr: 0.00247
[2023-12-25 15:23:55,343 INFO misc.py line 119 253097] Train: [58/100][444/510] Data 0.003 (0.133) Batch 1.211 (1.502) Remain 08:58:00 loss: 0.1034 Lr: 0.00247
[2023-12-25 15:23:56,479 INFO misc.py line 119 253097] Train: [58/100][445/510] Data 0.004 (0.133) Batch 1.137 (1.502) Remain 08:57:41 loss: 0.2102 Lr: 0.00247
[2023-12-25 15:23:57,652 INFO misc.py line 119 253097] Train: [58/100][446/510] Data 0.003 (0.133) Batch 1.172 (1.501) Remain 08:57:24 loss: 0.1400 Lr: 0.00247
[2023-12-25 15:23:58,678 INFO misc.py line 119 253097] Train: [58/100][447/510] Data 0.004 (0.132) Batch 1.026 (1.500) Remain 08:56:59 loss: 0.2451 Lr: 0.00247
[2023-12-25 15:23:59,764 INFO misc.py line 119 253097] Train: [58/100][448/510] Data 0.003 (0.132) Batch 1.084 (1.499) Remain 08:56:38 loss: 0.1491 Lr: 0.00247
[2023-12-25 15:24:00,761 INFO misc.py line 119 253097] Train: [58/100][449/510] Data 0.004 (0.132) Batch 0.999 (1.498) Remain 08:56:12 loss: 0.0971 Lr: 0.00247
[2023-12-25 15:24:02,004 INFO misc.py line 119 253097] Train: [58/100][450/510] Data 0.003 (0.131) Batch 1.238 (1.497) Remain 08:55:58 loss: 0.1024 Lr: 0.00247
[2023-12-25 15:24:03,271 INFO misc.py line 119 253097] Train: [58/100][451/510] Data 0.007 (0.131) Batch 1.266 (1.497) Remain 08:55:45 loss: 0.2780 Lr: 0.00247
[2023-12-25 15:24:04,366 INFO misc.py line 119 253097] Train: [58/100][452/510] Data 0.008 (0.131) Batch 1.094 (1.496) Remain 08:55:25 loss: 0.2397 Lr: 0.00247
[2023-12-25 15:24:05,389 INFO misc.py line 119 253097] Train: [58/100][453/510] Data 0.010 (0.131) Batch 1.026 (1.495) Remain 08:55:01 loss: 0.1195 Lr: 0.00247
[2023-12-25 15:24:06,680 INFO misc.py line 119 253097] Train: [58/100][454/510] Data 0.007 (0.130) Batch 1.292 (1.494) Remain 08:54:50 loss: 0.0989 Lr: 0.00247
[2023-12-25 15:24:07,417 INFO misc.py line 119 253097] Train: [58/100][455/510] Data 0.005 (0.130) Batch 0.739 (1.493) Remain 08:54:12 loss: 0.1963 Lr: 0.00247
[2023-12-25 15:24:08,497 INFO misc.py line 119 253097] Train: [58/100][456/510] Data 0.003 (0.130) Batch 1.080 (1.492) Remain 08:53:51 loss: 0.2270 Lr: 0.00247
[2023-12-25 15:24:09,633 INFO misc.py line 119 253097] Train: [58/100][457/510] Data 0.006 (0.130) Batch 1.135 (1.491) Remain 08:53:33 loss: 0.2175 Lr: 0.00247
[2023-12-25 15:24:10,764 INFO misc.py line 119 253097] Train: [58/100][458/510] Data 0.004 (0.129) Batch 1.131 (1.490) Remain 08:53:14 loss: 0.1441 Lr: 0.00247
[2023-12-25 15:24:11,977 INFO misc.py line 119 253097] Train: [58/100][459/510] Data 0.008 (0.129) Batch 1.213 (1.489) Remain 08:53:00 loss: 0.0789 Lr: 0.00247
[2023-12-25 15:24:17,050 INFO misc.py line 119 253097] Train: [58/100][460/510] Data 0.004 (0.129) Batch 5.072 (1.497) Remain 08:55:47 loss: 0.1094 Lr: 0.00247
[2023-12-25 15:24:18,183 INFO misc.py line 119 253097] Train: [58/100][461/510] Data 0.005 (0.128) Batch 1.134 (1.497) Remain 08:55:28 loss: 0.0928 Lr: 0.00247
[2023-12-25 15:24:19,299 INFO misc.py line 119 253097] Train: [58/100][462/510] Data 0.003 (0.128) Batch 1.114 (1.496) Remain 08:55:09 loss: 0.0834 Lr: 0.00247
[2023-12-25 15:24:20,503 INFO misc.py line 119 253097] Train: [58/100][463/510] Data 0.006 (0.128) Batch 1.206 (1.495) Remain 08:54:54 loss: 0.1119 Lr: 0.00247
[2023-12-25 15:24:21,607 INFO misc.py line 119 253097] Train: [58/100][464/510] Data 0.004 (0.128) Batch 1.103 (1.494) Remain 08:54:34 loss: 0.0960 Lr: 0.00247
[2023-12-25 15:24:22,826 INFO misc.py line 119 253097] Train: [58/100][465/510] Data 0.004 (0.127) Batch 1.217 (1.494) Remain 08:54:20 loss: 0.3193 Lr: 0.00247
[2023-12-25 15:24:24,037 INFO misc.py line 119 253097] Train: [58/100][466/510] Data 0.007 (0.127) Batch 1.213 (1.493) Remain 08:54:05 loss: 0.2084 Lr: 0.00247
[2023-12-25 15:24:25,134 INFO misc.py line 119 253097] Train: [58/100][467/510] Data 0.005 (0.127) Batch 1.097 (1.492) Remain 08:53:45 loss: 0.1319 Lr: 0.00247
[2023-12-25 15:24:26,287 INFO misc.py line 119 253097] Train: [58/100][468/510] Data 0.006 (0.127) Batch 1.149 (1.491) Remain 08:53:28 loss: 0.1158 Lr: 0.00247
[2023-12-25 15:24:27,422 INFO misc.py line 119 253097] Train: [58/100][469/510] Data 0.009 (0.126) Batch 1.126 (1.491) Remain 08:53:10 loss: 0.0918 Lr: 0.00247
[2023-12-25 15:24:28,469 INFO misc.py line 119 253097] Train: [58/100][470/510] Data 0.018 (0.126) Batch 1.061 (1.490) Remain 08:52:48 loss: 0.1815 Lr: 0.00247
[2023-12-25 15:24:29,529 INFO misc.py line 119 253097] Train: [58/100][471/510] Data 0.004 (0.126) Batch 1.059 (1.489) Remain 08:52:27 loss: 0.1514 Lr: 0.00247
[2023-12-25 15:24:30,763 INFO misc.py line 119 253097] Train: [58/100][472/510] Data 0.004 (0.126) Batch 1.235 (1.488) Remain 08:52:14 loss: 0.1399 Lr: 0.00247
[2023-12-25 15:24:31,897 INFO misc.py line 119 253097] Train: [58/100][473/510] Data 0.003 (0.125) Batch 1.130 (1.487) Remain 08:51:56 loss: 0.1324 Lr: 0.00247
[2023-12-25 15:24:36,909 INFO misc.py line 119 253097] Train: [58/100][474/510] Data 4.188 (0.134) Batch 5.016 (1.495) Remain 08:54:36 loss: 0.2362 Lr: 0.00247
[2023-12-25 15:24:38,093 INFO misc.py line 119 253097] Train: [58/100][475/510] Data 0.003 (0.134) Batch 1.179 (1.494) Remain 08:54:20 loss: 0.1697 Lr: 0.00247
[2023-12-25 15:24:39,256 INFO misc.py line 119 253097] Train: [58/100][476/510] Data 0.009 (0.133) Batch 1.164 (1.494) Remain 08:54:03 loss: 0.0836 Lr: 0.00247
[2023-12-25 15:24:40,532 INFO misc.py line 119 253097] Train: [58/100][477/510] Data 0.007 (0.133) Batch 1.277 (1.493) Remain 08:53:52 loss: 0.1317 Lr: 0.00247
[2023-12-25 15:24:41,835 INFO misc.py line 119 253097] Train: [58/100][478/510] Data 0.005 (0.133) Batch 1.297 (1.493) Remain 08:53:42 loss: 0.1013 Lr: 0.00247
[2023-12-25 15:24:42,942 INFO misc.py line 119 253097] Train: [58/100][479/510] Data 0.012 (0.133) Batch 1.110 (1.492) Remain 08:53:23 loss: 0.1012 Lr: 0.00247
[2023-12-25 15:24:44,064 INFO misc.py line 119 253097] Train: [58/100][480/510] Data 0.009 (0.132) Batch 1.122 (1.491) Remain 08:53:05 loss: 0.3811 Lr: 0.00247
[2023-12-25 15:24:45,260 INFO misc.py line 119 253097] Train: [58/100][481/510] Data 0.008 (0.132) Batch 1.201 (1.491) Remain 08:52:50 loss: 0.1407 Lr: 0.00247
[2023-12-25 15:24:51,747 INFO misc.py line 119 253097] Train: [58/100][482/510] Data 5.727 (0.144) Batch 6.487 (1.501) Remain 08:56:32 loss: 0.1037 Lr: 0.00246
[2023-12-25 15:24:53,050 INFO misc.py line 119 253097] Train: [58/100][483/510] Data 0.003 (0.143) Batch 1.296 (1.501) Remain 08:56:22 loss: 0.3124 Lr: 0.00246
[2023-12-25 15:24:54,094 INFO misc.py line 119 253097] Train: [58/100][484/510] Data 0.011 (0.143) Batch 1.046 (1.500) Remain 08:56:00 loss: 0.1861 Lr: 0.00246
[2023-12-25 15:24:55,291 INFO misc.py line 119 253097] Train: [58/100][485/510] Data 0.010 (0.143) Batch 1.197 (1.499) Remain 08:55:45 loss: 0.1031 Lr: 0.00246
[2023-12-25 15:24:56,514 INFO misc.py line 119 253097] Train: [58/100][486/510] Data 0.009 (0.143) Batch 1.223 (1.498) Remain 08:55:31 loss: 0.1161 Lr: 0.00246
[2023-12-25 15:24:57,669 INFO misc.py line 119 253097] Train: [58/100][487/510] Data 0.009 (0.142) Batch 1.152 (1.498) Remain 08:55:14 loss: 0.1437 Lr: 0.00246
[2023-12-25 15:24:58,795 INFO misc.py line 119 253097] Train: [58/100][488/510] Data 0.011 (0.142) Batch 1.132 (1.497) Remain 08:54:57 loss: 0.1741 Lr: 0.00246
[2023-12-25 15:24:59,816 INFO misc.py line 119 253097] Train: [58/100][489/510] Data 0.006 (0.142) Batch 1.021 (1.496) Remain 08:54:34 loss: 0.1398 Lr: 0.00246
[2023-12-25 15:25:01,044 INFO misc.py line 119 253097] Train: [58/100][490/510] Data 0.006 (0.142) Batch 1.225 (1.495) Remain 08:54:21 loss: 0.1159 Lr: 0.00246
[2023-12-25 15:25:02,376 INFO misc.py line 119 253097] Train: [58/100][491/510] Data 0.008 (0.141) Batch 1.334 (1.495) Remain 08:54:12 loss: 0.0949 Lr: 0.00246
[2023-12-25 15:25:03,293 INFO misc.py line 119 253097] Train: [58/100][492/510] Data 0.007 (0.141) Batch 0.920 (1.494) Remain 08:53:46 loss: 0.2179 Lr: 0.00246
[2023-12-25 15:25:04,471 INFO misc.py line 119 253097] Train: [58/100][493/510] Data 0.004 (0.141) Batch 1.177 (1.493) Remain 08:53:30 loss: 0.1586 Lr: 0.00246
[2023-12-25 15:25:05,583 INFO misc.py line 119 253097] Train: [58/100][494/510] Data 0.005 (0.140) Batch 1.113 (1.492) Remain 08:53:12 loss: 0.1118 Lr: 0.00246
[2023-12-25 15:25:11,747 INFO misc.py line 119 253097] Train: [58/100][495/510] Data 0.003 (0.140) Batch 6.164 (1.502) Remain 08:56:34 loss: 0.0620 Lr: 0.00246
[2023-12-25 15:25:12,942 INFO misc.py line 119 253097] Train: [58/100][496/510] Data 0.004 (0.140) Batch 1.195 (1.501) Remain 08:56:19 loss: 0.1897 Lr: 0.00246
[2023-12-25 15:25:14,059 INFO misc.py line 119 253097] Train: [58/100][497/510] Data 0.003 (0.140) Batch 1.116 (1.501) Remain 08:56:01 loss: 0.1212 Lr: 0.00246
[2023-12-25 15:25:15,536 INFO misc.py line 119 253097] Train: [58/100][498/510] Data 0.004 (0.139) Batch 1.452 (1.500) Remain 08:55:58 loss: 0.2368 Lr: 0.00246
[2023-12-25 15:25:16,765 INFO misc.py line 119 253097] Train: [58/100][499/510] Data 0.028 (0.139) Batch 1.251 (1.500) Remain 08:55:45 loss: 0.1625 Lr: 0.00246
[2023-12-25 15:25:18,024 INFO misc.py line 119 253097] Train: [58/100][500/510] Data 0.006 (0.139) Batch 1.257 (1.499) Remain 08:55:33 loss: 0.2519 Lr: 0.00246
[2023-12-25 15:25:25,377 INFO misc.py line 119 253097] Train: [58/100][501/510] Data 0.008 (0.139) Batch 7.349 (1.511) Remain 08:59:43 loss: 0.2452 Lr: 0.00246
[2023-12-25 15:25:26,511 INFO misc.py line 119 253097] Train: [58/100][502/510] Data 0.013 (0.138) Batch 1.138 (1.510) Remain 08:59:26 loss: 0.2690 Lr: 0.00246
[2023-12-25 15:25:27,701 INFO misc.py line 119 253097] Train: [58/100][503/510] Data 0.009 (0.138) Batch 1.189 (1.510) Remain 08:59:11 loss: 0.3024 Lr: 0.00246
[2023-12-25 15:25:28,854 INFO misc.py line 119 253097] Train: [58/100][504/510] Data 0.010 (0.138) Batch 1.157 (1.509) Remain 08:58:54 loss: 0.1114 Lr: 0.00246
[2023-12-25 15:25:30,032 INFO misc.py line 119 253097] Train: [58/100][505/510] Data 0.006 (0.138) Batch 1.180 (1.508) Remain 08:58:38 loss: 0.1434 Lr: 0.00246
[2023-12-25 15:25:31,209 INFO misc.py line 119 253097] Train: [58/100][506/510] Data 0.004 (0.137) Batch 1.175 (1.508) Remain 08:58:23 loss: 0.0967 Lr: 0.00246
[2023-12-25 15:25:32,467 INFO misc.py line 119 253097] Train: [58/100][507/510] Data 0.006 (0.137) Batch 1.256 (1.507) Remain 08:58:11 loss: 0.3249 Lr: 0.00246
[2023-12-25 15:25:33,612 INFO misc.py line 119 253097] Train: [58/100][508/510] Data 0.008 (0.137) Batch 1.142 (1.507) Remain 08:57:54 loss: 0.2345 Lr: 0.00246
[2023-12-25 15:25:34,654 INFO misc.py line 119 253097] Train: [58/100][509/510] Data 0.011 (0.137) Batch 1.023 (1.506) Remain 08:57:32 loss: 0.1235 Lr: 0.00246
[2023-12-25 15:25:35,647 INFO misc.py line 119 253097] Train: [58/100][510/510] Data 0.030 (0.136) Batch 1.015 (1.505) Remain 08:57:09 loss: 0.1621 Lr: 0.00246
[2023-12-25 15:25:35,647 INFO misc.py line 136 253097] Train result: loss: 0.1607 
[2023-12-25 15:25:35,647 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 15:26:03,142 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7827 
[2023-12-25 15:26:03,490 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3664 
[2023-12-25 15:26:08,414 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3514 
[2023-12-25 15:26:08,941 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2824 
[2023-12-25 15:26:10,916 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8950 
[2023-12-25 15:26:11,342 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3692 
[2023-12-25 15:26:12,221 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1988 
[2023-12-25 15:26:12,773 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3885 
[2023-12-25 15:26:14,579 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0183 
[2023-12-25 15:26:16,700 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2969 
[2023-12-25 15:26:17,563 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2366 
[2023-12-25 15:26:17,992 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7380 
[2023-12-25 15:26:18,892 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5913 
[2023-12-25 15:26:21,834 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9484 
[2023-12-25 15:26:22,305 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2792 
[2023-12-25 15:26:22,920 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3897 
[2023-12-25 15:26:23,621 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3550 
[2023-12-25 15:26:25,038 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6689/0.7400/0.8993.
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9142/0.9514
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9813/0.9905
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8422/0.9641
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0183/0.0939
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2683/0.3091
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6105/0.6288
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6445/0.7917
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8171/0.8966
[2023-12-25 15:26:25,038 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8975/0.9592
[2023-12-25 15:26:25,039 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5831/0.6343
[2023-12-25 15:26:25,039 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7808/0.8684
[2023-12-25 15:26:25,039 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7527/0.8220
[2023-12-25 15:26:25,039 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5848/0.7100
[2023-12-25 15:26:25,039 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 15:26:25,041 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 15:26:25,041 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 15:26:31,358 INFO misc.py line 119 253097] Train: [59/100][1/510] Data 2.076 (2.076) Batch 2.697 (2.697) Remain 16:02:39 loss: 0.1337 Lr: 0.00246
[2023-12-25 15:26:32,664 INFO misc.py line 119 253097] Train: [59/100][2/510] Data 0.009 (0.009) Batch 1.306 (1.306) Remain 07:46:21 loss: 0.1140 Lr: 0.00246
[2023-12-25 15:26:33,887 INFO misc.py line 119 253097] Train: [59/100][3/510] Data 0.059 (0.059) Batch 1.222 (1.222) Remain 07:16:08 loss: 0.1666 Lr: 0.00246
[2023-12-25 15:26:35,043 INFO misc.py line 119 253097] Train: [59/100][4/510] Data 0.005 (0.005) Batch 1.157 (1.157) Remain 06:52:54 loss: 0.1387 Lr: 0.00246
[2023-12-25 15:26:36,037 INFO misc.py line 119 253097] Train: [59/100][5/510] Data 0.004 (0.005) Batch 0.994 (1.076) Remain 06:23:53 loss: 0.1188 Lr: 0.00246
[2023-12-25 15:26:37,045 INFO misc.py line 119 253097] Train: [59/100][6/510] Data 0.005 (0.005) Batch 1.007 (1.053) Remain 06:15:41 loss: 0.1217 Lr: 0.00246
[2023-12-25 15:26:38,273 INFO misc.py line 119 253097] Train: [59/100][7/510] Data 0.006 (0.005) Batch 1.230 (1.097) Remain 06:31:27 loss: 0.2040 Lr: 0.00246
[2023-12-25 15:26:41,502 INFO misc.py line 119 253097] Train: [59/100][8/510] Data 0.004 (0.005) Batch 3.228 (1.523) Remain 09:03:34 loss: 0.0990 Lr: 0.00246
[2023-12-25 15:26:47,048 INFO misc.py line 119 253097] Train: [59/100][9/510] Data 0.003 (0.005) Batch 5.545 (2.193) Remain 13:02:44 loss: 0.3119 Lr: 0.00246
[2023-12-25 15:26:48,007 INFO misc.py line 119 253097] Train: [59/100][10/510] Data 0.005 (0.005) Batch 0.959 (2.017) Remain 11:59:45 loss: 0.2883 Lr: 0.00246
[2023-12-25 15:26:49,375 INFO misc.py line 119 253097] Train: [59/100][11/510] Data 0.440 (0.059) Batch 1.369 (1.936) Remain 11:30:49 loss: 0.1298 Lr: 0.00246
[2023-12-25 15:26:50,433 INFO misc.py line 119 253097] Train: [59/100][12/510] Data 0.004 (0.053) Batch 1.059 (1.839) Remain 10:56:00 loss: 0.1536 Lr: 0.00246
[2023-12-25 15:26:51,524 INFO misc.py line 119 253097] Train: [59/100][13/510] Data 0.003 (0.048) Batch 1.090 (1.764) Remain 10:29:16 loss: 0.0993 Lr: 0.00246
[2023-12-25 15:26:52,611 INFO misc.py line 119 253097] Train: [59/100][14/510] Data 0.005 (0.044) Batch 1.088 (1.702) Remain 10:07:19 loss: 0.1223 Lr: 0.00246
[2023-12-25 15:26:54,247 INFO misc.py line 119 253097] Train: [59/100][15/510] Data 0.003 (0.041) Batch 1.632 (1.696) Remain 10:05:12 loss: 0.1603 Lr: 0.00246
[2023-12-25 15:26:55,365 INFO misc.py line 119 253097] Train: [59/100][16/510] Data 0.008 (0.038) Batch 1.121 (1.652) Remain 09:49:24 loss: 0.0718 Lr: 0.00246
[2023-12-25 15:26:56,510 INFO misc.py line 119 253097] Train: [59/100][17/510] Data 0.004 (0.036) Batch 1.144 (1.616) Remain 09:36:25 loss: 0.1687 Lr: 0.00246
[2023-12-25 15:26:57,591 INFO misc.py line 119 253097] Train: [59/100][18/510] Data 0.005 (0.034) Batch 1.082 (1.580) Remain 09:23:41 loss: 0.1052 Lr: 0.00246
[2023-12-25 15:26:58,596 INFO misc.py line 119 253097] Train: [59/100][19/510] Data 0.004 (0.032) Batch 1.004 (1.544) Remain 09:10:48 loss: 0.1625 Lr: 0.00246
[2023-12-25 15:27:09,742 INFO misc.py line 119 253097] Train: [59/100][20/510] Data 0.005 (0.030) Batch 11.149 (2.109) Remain 12:32:17 loss: 0.1803 Lr: 0.00246
[2023-12-25 15:27:10,852 INFO misc.py line 119 253097] Train: [59/100][21/510] Data 0.003 (0.029) Batch 1.105 (2.053) Remain 12:12:21 loss: 0.2870 Lr: 0.00246
[2023-12-25 15:27:12,114 INFO misc.py line 119 253097] Train: [59/100][22/510] Data 0.008 (0.028) Batch 1.263 (2.012) Remain 11:57:30 loss: 0.1108 Lr: 0.00246
[2023-12-25 15:27:13,369 INFO misc.py line 119 253097] Train: [59/100][23/510] Data 0.007 (0.027) Batch 1.254 (1.974) Remain 11:43:56 loss: 0.1466 Lr: 0.00246
[2023-12-25 15:27:14,477 INFO misc.py line 119 253097] Train: [59/100][24/510] Data 0.008 (0.026) Batch 1.110 (1.933) Remain 11:29:14 loss: 0.1795 Lr: 0.00246
[2023-12-25 15:27:15,669 INFO misc.py line 119 253097] Train: [59/100][25/510] Data 0.006 (0.025) Batch 1.190 (1.899) Remain 11:17:10 loss: 0.1345 Lr: 0.00245
[2023-12-25 15:27:16,940 INFO misc.py line 119 253097] Train: [59/100][26/510] Data 0.008 (0.024) Batch 1.275 (1.872) Remain 11:07:27 loss: 0.1384 Lr: 0.00245
[2023-12-25 15:27:18,067 INFO misc.py line 119 253097] Train: [59/100][27/510] Data 0.005 (0.023) Batch 1.125 (1.841) Remain 10:56:20 loss: 0.0762 Lr: 0.00245
[2023-12-25 15:27:19,250 INFO misc.py line 119 253097] Train: [59/100][28/510] Data 0.006 (0.023) Batch 1.183 (1.814) Remain 10:46:55 loss: 0.2436 Lr: 0.00245
[2023-12-25 15:27:20,436 INFO misc.py line 119 253097] Train: [59/100][29/510] Data 0.006 (0.022) Batch 1.183 (1.790) Remain 10:38:14 loss: 0.2761 Lr: 0.00245
[2023-12-25 15:27:21,660 INFO misc.py line 119 253097] Train: [59/100][30/510] Data 0.008 (0.021) Batch 1.224 (1.769) Remain 10:30:44 loss: 0.1810 Lr: 0.00245
[2023-12-25 15:27:22,704 INFO misc.py line 119 253097] Train: [59/100][31/510] Data 0.008 (0.021) Batch 1.045 (1.743) Remain 10:21:28 loss: 0.1591 Lr: 0.00245
[2023-12-25 15:27:23,802 INFO misc.py line 119 253097] Train: [59/100][32/510] Data 0.008 (0.020) Batch 1.102 (1.721) Remain 10:13:33 loss: 0.1305 Lr: 0.00245
[2023-12-25 15:27:24,901 INFO misc.py line 119 253097] Train: [59/100][33/510] Data 0.005 (0.020) Batch 1.094 (1.700) Remain 10:06:04 loss: 0.2052 Lr: 0.00245
[2023-12-25 15:27:26,164 INFO misc.py line 119 253097] Train: [59/100][34/510] Data 0.010 (0.020) Batch 1.264 (1.686) Remain 10:01:01 loss: 0.1557 Lr: 0.00245
[2023-12-25 15:27:27,240 INFO misc.py line 119 253097] Train: [59/100][35/510] Data 0.012 (0.019) Batch 1.080 (1.667) Remain 09:54:14 loss: 0.1656 Lr: 0.00245
[2023-12-25 15:27:28,328 INFO misc.py line 119 253097] Train: [59/100][36/510] Data 0.006 (0.019) Batch 1.081 (1.650) Remain 09:47:53 loss: 0.1869 Lr: 0.00245
[2023-12-25 15:27:32,485 INFO misc.py line 119 253097] Train: [59/100][37/510] Data 0.012 (0.019) Batch 4.164 (1.723) Remain 10:14:13 loss: 0.1784 Lr: 0.00245
[2023-12-25 15:27:33,755 INFO misc.py line 119 253097] Train: [59/100][38/510] Data 0.005 (0.018) Batch 1.248 (1.710) Remain 10:09:21 loss: 0.1320 Lr: 0.00245
[2023-12-25 15:27:34,724 INFO misc.py line 119 253097] Train: [59/100][39/510] Data 0.027 (0.019) Batch 0.992 (1.690) Remain 10:02:12 loss: 0.1410 Lr: 0.00245
[2023-12-25 15:27:36,010 INFO misc.py line 119 253097] Train: [59/100][40/510] Data 0.004 (0.018) Batch 1.282 (1.679) Remain 09:58:15 loss: 0.1202 Lr: 0.00245
[2023-12-25 15:27:36,947 INFO misc.py line 119 253097] Train: [59/100][41/510] Data 0.007 (0.018) Batch 0.942 (1.660) Remain 09:51:19 loss: 0.1504 Lr: 0.00245
[2023-12-25 15:27:38,131 INFO misc.py line 119 253097] Train: [59/100][42/510] Data 0.004 (0.018) Batch 1.179 (1.647) Remain 09:46:53 loss: 0.1387 Lr: 0.00245
[2023-12-25 15:27:39,317 INFO misc.py line 119 253097] Train: [59/100][43/510] Data 0.009 (0.017) Batch 1.189 (1.636) Remain 09:42:47 loss: 0.0752 Lr: 0.00245
[2023-12-25 15:27:40,567 INFO misc.py line 119 253097] Train: [59/100][44/510] Data 0.005 (0.017) Batch 1.248 (1.626) Remain 09:39:23 loss: 0.1689 Lr: 0.00245
[2023-12-25 15:27:41,671 INFO misc.py line 119 253097] Train: [59/100][45/510] Data 0.006 (0.017) Batch 1.103 (1.614) Remain 09:34:55 loss: 0.1332 Lr: 0.00245
[2023-12-25 15:27:42,838 INFO misc.py line 119 253097] Train: [59/100][46/510] Data 0.007 (0.017) Batch 1.168 (1.603) Remain 09:31:12 loss: 0.2147 Lr: 0.00245
[2023-12-25 15:27:43,803 INFO misc.py line 119 253097] Train: [59/100][47/510] Data 0.006 (0.016) Batch 0.969 (1.589) Remain 09:26:02 loss: 0.2081 Lr: 0.00245
[2023-12-25 15:27:45,000 INFO misc.py line 119 253097] Train: [59/100][48/510] Data 0.003 (0.016) Batch 1.188 (1.580) Remain 09:22:50 loss: 0.1198 Lr: 0.00245
[2023-12-25 15:27:47,747 INFO misc.py line 119 253097] Train: [59/100][49/510] Data 0.012 (0.016) Batch 2.754 (1.606) Remain 09:31:54 loss: 0.1105 Lr: 0.00245
[2023-12-25 15:27:48,870 INFO misc.py line 119 253097] Train: [59/100][50/510] Data 0.005 (0.016) Batch 1.124 (1.595) Remain 09:28:13 loss: 0.2293 Lr: 0.00245
[2023-12-25 15:27:57,340 INFO misc.py line 119 253097] Train: [59/100][51/510] Data 0.004 (0.015) Batch 8.469 (1.739) Remain 10:19:12 loss: 0.1313 Lr: 0.00245
[2023-12-25 15:27:58,393 INFO misc.py line 119 253097] Train: [59/100][52/510] Data 0.005 (0.015) Batch 1.052 (1.725) Remain 10:14:11 loss: 0.0906 Lr: 0.00245
[2023-12-25 15:27:59,439 INFO misc.py line 119 253097] Train: [59/100][53/510] Data 0.005 (0.015) Batch 1.046 (1.711) Remain 10:09:19 loss: 0.1736 Lr: 0.00245
[2023-12-25 15:28:00,619 INFO misc.py line 119 253097] Train: [59/100][54/510] Data 0.005 (0.015) Batch 1.182 (1.701) Remain 10:05:36 loss: 0.2016 Lr: 0.00245
[2023-12-25 15:28:01,657 INFO misc.py line 119 253097] Train: [59/100][55/510] Data 0.004 (0.015) Batch 1.038 (1.688) Remain 10:01:02 loss: 0.1202 Lr: 0.00245
[2023-12-25 15:28:02,603 INFO misc.py line 119 253097] Train: [59/100][56/510] Data 0.004 (0.014) Batch 0.946 (1.674) Remain 09:56:01 loss: 0.1863 Lr: 0.00245
[2023-12-25 15:28:06,576 INFO misc.py line 119 253097] Train: [59/100][57/510] Data 0.005 (0.014) Batch 3.974 (1.716) Remain 10:11:09 loss: 0.2231 Lr: 0.00245
[2023-12-25 15:28:07,741 INFO misc.py line 119 253097] Train: [59/100][58/510] Data 0.004 (0.014) Batch 1.165 (1.706) Remain 10:07:33 loss: 0.1763 Lr: 0.00245
[2023-12-25 15:28:08,802 INFO misc.py line 119 253097] Train: [59/100][59/510] Data 0.004 (0.014) Batch 1.060 (1.695) Remain 10:03:25 loss: 0.2009 Lr: 0.00245
[2023-12-25 15:28:09,935 INFO misc.py line 119 253097] Train: [59/100][60/510] Data 0.004 (0.014) Batch 1.133 (1.685) Remain 09:59:52 loss: 0.1962 Lr: 0.00245
[2023-12-25 15:28:10,891 INFO misc.py line 119 253097] Train: [59/100][61/510] Data 0.004 (0.014) Batch 0.957 (1.673) Remain 09:55:23 loss: 0.0516 Lr: 0.00245
[2023-12-25 15:28:11,911 INFO misc.py line 119 253097] Train: [59/100][62/510] Data 0.003 (0.013) Batch 1.018 (1.661) Remain 09:51:24 loss: 0.0842 Lr: 0.00245
[2023-12-25 15:28:12,862 INFO misc.py line 119 253097] Train: [59/100][63/510] Data 0.005 (0.013) Batch 0.952 (1.650) Remain 09:47:10 loss: 0.0975 Lr: 0.00245
[2023-12-25 15:28:14,112 INFO misc.py line 119 253097] Train: [59/100][64/510] Data 0.003 (0.013) Batch 1.241 (1.643) Remain 09:44:45 loss: 0.1516 Lr: 0.00245
[2023-12-25 15:28:15,133 INFO misc.py line 119 253097] Train: [59/100][65/510] Data 0.013 (0.013) Batch 1.030 (1.633) Remain 09:41:12 loss: 0.0896 Lr: 0.00245
[2023-12-25 15:28:19,944 INFO misc.py line 119 253097] Train: [59/100][66/510] Data 3.665 (0.071) Batch 4.812 (1.683) Remain 09:59:08 loss: 0.0616 Lr: 0.00245
[2023-12-25 15:28:23,290 INFO misc.py line 119 253097] Train: [59/100][67/510] Data 0.004 (0.070) Batch 3.346 (1.709) Remain 10:08:21 loss: 0.1155 Lr: 0.00245
[2023-12-25 15:28:24,357 INFO misc.py line 119 253097] Train: [59/100][68/510] Data 0.007 (0.069) Batch 1.067 (1.700) Remain 10:04:48 loss: 0.1086 Lr: 0.00245
[2023-12-25 15:28:25,601 INFO misc.py line 119 253097] Train: [59/100][69/510] Data 0.004 (0.068) Batch 1.243 (1.693) Remain 10:02:19 loss: 0.1026 Lr: 0.00245
[2023-12-25 15:28:28,866 INFO misc.py line 119 253097] Train: [59/100][70/510] Data 0.005 (0.067) Batch 3.264 (1.716) Remain 10:10:38 loss: 0.1737 Lr: 0.00245
[2023-12-25 15:28:29,743 INFO misc.py line 119 253097] Train: [59/100][71/510] Data 0.008 (0.066) Batch 0.861 (1.704) Remain 10:06:08 loss: 0.2540 Lr: 0.00245
[2023-12-25 15:28:31,022 INFO misc.py line 119 253097] Train: [59/100][72/510] Data 0.021 (0.066) Batch 1.293 (1.698) Remain 10:03:59 loss: 0.2790 Lr: 0.00245
[2023-12-25 15:28:32,178 INFO misc.py line 119 253097] Train: [59/100][73/510] Data 0.008 (0.065) Batch 1.161 (1.690) Remain 10:01:14 loss: 0.1539 Lr: 0.00245
[2023-12-25 15:28:33,256 INFO misc.py line 119 253097] Train: [59/100][74/510] Data 0.003 (0.064) Batch 1.071 (1.681) Remain 09:58:06 loss: 0.1626 Lr: 0.00245
[2023-12-25 15:28:34,426 INFO misc.py line 119 253097] Train: [59/100][75/510] Data 0.010 (0.063) Batch 1.176 (1.674) Remain 09:55:35 loss: 0.1798 Lr: 0.00245
[2023-12-25 15:28:35,651 INFO misc.py line 119 253097] Train: [59/100][76/510] Data 0.003 (0.062) Batch 1.224 (1.668) Remain 09:53:22 loss: 0.2340 Lr: 0.00245
[2023-12-25 15:28:36,672 INFO misc.py line 119 253097] Train: [59/100][77/510] Data 0.004 (0.061) Batch 1.021 (1.659) Remain 09:50:13 loss: 0.1368 Lr: 0.00244
[2023-12-25 15:28:37,956 INFO misc.py line 119 253097] Train: [59/100][78/510] Data 0.004 (0.061) Batch 1.282 (1.654) Remain 09:48:25 loss: 0.1178 Lr: 0.00244
[2023-12-25 15:28:38,921 INFO misc.py line 119 253097] Train: [59/100][79/510] Data 0.006 (0.060) Batch 0.966 (1.645) Remain 09:45:09 loss: 0.1279 Lr: 0.00244
[2023-12-25 15:28:40,221 INFO misc.py line 119 253097] Train: [59/100][80/510] Data 0.005 (0.059) Batch 1.301 (1.641) Remain 09:43:32 loss: 0.2047 Lr: 0.00244
[2023-12-25 15:28:41,545 INFO misc.py line 119 253097] Train: [59/100][81/510] Data 0.004 (0.059) Batch 1.324 (1.637) Remain 09:42:04 loss: 0.1437 Lr: 0.00244
[2023-12-25 15:29:02,131 INFO misc.py line 119 253097] Train: [59/100][82/510] Data 0.004 (0.058) Batch 20.581 (1.876) Remain 11:07:19 loss: 0.1224 Lr: 0.00244
[2023-12-25 15:29:03,239 INFO misc.py line 119 253097] Train: [59/100][83/510] Data 0.010 (0.057) Batch 1.100 (1.867) Remain 11:03:50 loss: 0.1498 Lr: 0.00244
[2023-12-25 15:29:04,435 INFO misc.py line 119 253097] Train: [59/100][84/510] Data 0.018 (0.057) Batch 1.211 (1.859) Remain 11:00:55 loss: 0.1985 Lr: 0.00244
[2023-12-25 15:29:05,672 INFO misc.py line 119 253097] Train: [59/100][85/510] Data 0.003 (0.056) Batch 1.232 (1.851) Remain 10:58:11 loss: 0.2612 Lr: 0.00244
[2023-12-25 15:29:06,780 INFO misc.py line 119 253097] Train: [59/100][86/510] Data 0.009 (0.056) Batch 1.106 (1.842) Remain 10:54:57 loss: 0.1354 Lr: 0.00244
[2023-12-25 15:29:07,956 INFO misc.py line 119 253097] Train: [59/100][87/510] Data 0.010 (0.055) Batch 1.179 (1.834) Remain 10:52:07 loss: 0.2963 Lr: 0.00244
[2023-12-25 15:29:11,536 INFO misc.py line 119 253097] Train: [59/100][88/510] Data 0.007 (0.054) Batch 3.583 (1.855) Remain 10:59:24 loss: 0.2008 Lr: 0.00244
[2023-12-25 15:29:12,477 INFO misc.py line 119 253097] Train: [59/100][89/510] Data 0.003 (0.054) Batch 0.928 (1.844) Remain 10:55:32 loss: 0.1759 Lr: 0.00244
[2023-12-25 15:29:13,800 INFO misc.py line 119 253097] Train: [59/100][90/510] Data 0.017 (0.053) Batch 1.332 (1.838) Remain 10:53:25 loss: 0.0960 Lr: 0.00244
[2023-12-25 15:29:14,739 INFO misc.py line 119 253097] Train: [59/100][91/510] Data 0.008 (0.053) Batch 0.943 (1.828) Remain 10:49:46 loss: 0.1715 Lr: 0.00244
[2023-12-25 15:29:16,059 INFO misc.py line 119 253097] Train: [59/100][92/510] Data 0.003 (0.052) Batch 1.316 (1.822) Remain 10:47:42 loss: 0.1492 Lr: 0.00244
[2023-12-25 15:29:16,904 INFO misc.py line 119 253097] Train: [59/100][93/510] Data 0.008 (0.052) Batch 0.849 (1.811) Remain 10:43:49 loss: 0.2105 Lr: 0.00244
[2023-12-25 15:29:17,973 INFO misc.py line 119 253097] Train: [59/100][94/510] Data 0.003 (0.051) Batch 1.068 (1.803) Remain 10:40:54 loss: 0.1240 Lr: 0.00244
[2023-12-25 15:29:19,043 INFO misc.py line 119 253097] Train: [59/100][95/510] Data 0.004 (0.051) Batch 1.069 (1.795) Remain 10:38:02 loss: 0.0935 Lr: 0.00244
[2023-12-25 15:29:20,246 INFO misc.py line 119 253097] Train: [59/100][96/510] Data 0.004 (0.050) Batch 1.205 (1.789) Remain 10:35:44 loss: 0.1270 Lr: 0.00244
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[2023-12-25 15:30:48,368 INFO misc.py line 119 253097] Train: [59/100][153/510] Data 6.969 (0.080) Batch 8.044 (1.697) Remain 10:01:20 loss: 0.0611 Lr: 0.00243
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[2023-12-25 15:37:14,991 INFO misc.py line 119 253097] Train: [59/100][411/510] Data 0.007 (0.113) Batch 1.166 (1.571) Remain 09:10:12 loss: 0.1172 Lr: 0.00238
[2023-12-25 15:37:16,178 INFO misc.py line 119 253097] Train: [59/100][412/510] Data 0.004 (0.113) Batch 1.184 (1.570) Remain 09:09:50 loss: 0.1161 Lr: 0.00238
[2023-12-25 15:37:17,082 INFO misc.py line 119 253097] Train: [59/100][413/510] Data 0.007 (0.112) Batch 0.906 (1.569) Remain 09:09:15 loss: 0.0869 Lr: 0.00238
[2023-12-25 15:37:18,062 INFO misc.py line 119 253097] Train: [59/100][414/510] Data 0.008 (0.112) Batch 0.981 (1.567) Remain 09:08:43 loss: 0.1008 Lr: 0.00238
[2023-12-25 15:37:19,290 INFO misc.py line 119 253097] Train: [59/100][415/510] Data 0.003 (0.112) Batch 1.228 (1.567) Remain 09:08:24 loss: 0.1209 Lr: 0.00238
[2023-12-25 15:37:20,469 INFO misc.py line 119 253097] Train: [59/100][416/510] Data 0.004 (0.112) Batch 1.178 (1.566) Remain 09:08:03 loss: 0.1068 Lr: 0.00238
[2023-12-25 15:37:21,567 INFO misc.py line 119 253097] Train: [59/100][417/510] Data 0.005 (0.111) Batch 1.098 (1.564) Remain 09:07:38 loss: 0.1954 Lr: 0.00238
[2023-12-25 15:37:22,578 INFO misc.py line 119 253097] Train: [59/100][418/510] Data 0.005 (0.111) Batch 1.008 (1.563) Remain 09:07:08 loss: 0.1550 Lr: 0.00238
[2023-12-25 15:37:23,734 INFO misc.py line 119 253097] Train: [59/100][419/510] Data 0.007 (0.111) Batch 1.159 (1.562) Remain 09:06:46 loss: 0.1289 Lr: 0.00238
[2023-12-25 15:37:24,808 INFO misc.py line 119 253097] Train: [59/100][420/510] Data 0.003 (0.111) Batch 1.074 (1.561) Remain 09:06:20 loss: 0.1022 Lr: 0.00238
[2023-12-25 15:37:25,928 INFO misc.py line 119 253097] Train: [59/100][421/510] Data 0.003 (0.110) Batch 1.120 (1.560) Remain 09:05:56 loss: 0.1373 Lr: 0.00238
[2023-12-25 15:37:27,168 INFO misc.py line 119 253097] Train: [59/100][422/510] Data 0.007 (0.110) Batch 1.236 (1.559) Remain 09:05:38 loss: 0.1415 Lr: 0.00238
[2023-12-25 15:37:28,413 INFO misc.py line 119 253097] Train: [59/100][423/510] Data 0.009 (0.110) Batch 1.243 (1.558) Remain 09:05:21 loss: 0.1651 Lr: 0.00238
[2023-12-25 15:37:29,549 INFO misc.py line 119 253097] Train: [59/100][424/510] Data 0.009 (0.110) Batch 1.137 (1.557) Remain 09:04:58 loss: 0.1607 Lr: 0.00238
[2023-12-25 15:37:30,771 INFO misc.py line 119 253097] Train: [59/100][425/510] Data 0.008 (0.109) Batch 1.227 (1.557) Remain 09:04:40 loss: 0.1681 Lr: 0.00238
[2023-12-25 15:37:32,007 INFO misc.py line 119 253097] Train: [59/100][426/510] Data 0.003 (0.109) Batch 1.234 (1.556) Remain 09:04:23 loss: 0.3059 Lr: 0.00238
[2023-12-25 15:37:33,109 INFO misc.py line 119 253097] Train: [59/100][427/510] Data 0.006 (0.109) Batch 1.100 (1.555) Remain 09:03:59 loss: 0.1528 Lr: 0.00238
[2023-12-25 15:37:34,227 INFO misc.py line 119 253097] Train: [59/100][428/510] Data 0.006 (0.109) Batch 1.117 (1.554) Remain 09:03:35 loss: 0.2688 Lr: 0.00238
[2023-12-25 15:37:35,517 INFO misc.py line 119 253097] Train: [59/100][429/510] Data 0.008 (0.108) Batch 1.293 (1.553) Remain 09:03:21 loss: 0.1245 Lr: 0.00238
[2023-12-25 15:37:36,768 INFO misc.py line 119 253097] Train: [59/100][430/510] Data 0.005 (0.108) Batch 1.253 (1.552) Remain 09:03:05 loss: 0.1151 Lr: 0.00238
[2023-12-25 15:37:45,916 INFO misc.py line 119 253097] Train: [59/100][431/510] Data 0.003 (0.108) Batch 9.147 (1.570) Remain 09:09:16 loss: 0.1033 Lr: 0.00238
[2023-12-25 15:37:47,244 INFO misc.py line 119 253097] Train: [59/100][432/510] Data 0.004 (0.108) Batch 1.326 (1.570) Remain 09:09:02 loss: 0.2170 Lr: 0.00238
[2023-12-25 15:37:48,274 INFO misc.py line 119 253097] Train: [59/100][433/510] Data 0.006 (0.107) Batch 1.033 (1.568) Remain 09:08:34 loss: 0.1315 Lr: 0.00238
[2023-12-25 15:37:49,327 INFO misc.py line 119 253097] Train: [59/100][434/510] Data 0.004 (0.107) Batch 1.053 (1.567) Remain 09:08:08 loss: 0.2299 Lr: 0.00238
[2023-12-25 15:37:50,390 INFO misc.py line 119 253097] Train: [59/100][435/510] Data 0.003 (0.107) Batch 1.055 (1.566) Remain 09:07:41 loss: 0.0981 Lr: 0.00238
[2023-12-25 15:37:51,620 INFO misc.py line 119 253097] Train: [59/100][436/510] Data 0.011 (0.107) Batch 1.231 (1.565) Remain 09:07:23 loss: 0.0826 Lr: 0.00238
[2023-12-25 15:37:52,705 INFO misc.py line 119 253097] Train: [59/100][437/510] Data 0.011 (0.107) Batch 1.087 (1.564) Remain 09:06:59 loss: 0.1818 Lr: 0.00238
[2023-12-25 15:37:53,651 INFO misc.py line 119 253097] Train: [59/100][438/510] Data 0.007 (0.106) Batch 0.950 (1.563) Remain 09:06:28 loss: 0.1576 Lr: 0.00238
[2023-12-25 15:37:54,568 INFO misc.py line 119 253097] Train: [59/100][439/510] Data 0.005 (0.106) Batch 0.917 (1.561) Remain 09:05:55 loss: 0.1476 Lr: 0.00238
[2023-12-25 15:37:55,583 INFO misc.py line 119 253097] Train: [59/100][440/510] Data 0.004 (0.106) Batch 1.015 (1.560) Remain 09:05:27 loss: 0.1982 Lr: 0.00238
[2023-12-25 15:37:56,748 INFO misc.py line 119 253097] Train: [59/100][441/510] Data 0.004 (0.106) Batch 1.165 (1.559) Remain 09:05:07 loss: 0.1969 Lr: 0.00238
[2023-12-25 15:37:57,914 INFO misc.py line 119 253097] Train: [59/100][442/510] Data 0.004 (0.105) Batch 1.161 (1.558) Remain 09:04:46 loss: 0.1176 Lr: 0.00238
[2023-12-25 15:38:07,649 INFO misc.py line 119 253097] Train: [59/100][443/510] Data 0.009 (0.105) Batch 9.740 (1.577) Remain 09:11:15 loss: 0.1094 Lr: 0.00238
[2023-12-25 15:38:08,597 INFO misc.py line 119 253097] Train: [59/100][444/510] Data 0.004 (0.105) Batch 0.948 (1.575) Remain 09:10:43 loss: 0.0605 Lr: 0.00237
[2023-12-25 15:38:09,846 INFO misc.py line 119 253097] Train: [59/100][445/510] Data 0.003 (0.105) Batch 1.242 (1.575) Remain 09:10:26 loss: 0.2110 Lr: 0.00237
[2023-12-25 15:38:11,045 INFO misc.py line 119 253097] Train: [59/100][446/510] Data 0.010 (0.105) Batch 1.199 (1.574) Remain 09:10:06 loss: 0.1277 Lr: 0.00237
[2023-12-25 15:38:12,256 INFO misc.py line 119 253097] Train: [59/100][447/510] Data 0.010 (0.104) Batch 1.214 (1.573) Remain 09:09:48 loss: 0.1827 Lr: 0.00237
[2023-12-25 15:38:13,261 INFO misc.py line 119 253097] Train: [59/100][448/510] Data 0.007 (0.104) Batch 1.008 (1.572) Remain 09:09:20 loss: 0.1013 Lr: 0.00237
[2023-12-25 15:38:14,550 INFO misc.py line 119 253097] Train: [59/100][449/510] Data 0.003 (0.104) Batch 1.289 (1.571) Remain 09:09:05 loss: 0.1876 Lr: 0.00237
[2023-12-25 15:38:15,877 INFO misc.py line 119 253097] Train: [59/100][450/510] Data 0.004 (0.104) Batch 1.328 (1.570) Remain 09:08:52 loss: 0.0916 Lr: 0.00237
[2023-12-25 15:38:16,990 INFO misc.py line 119 253097] Train: [59/100][451/510] Data 0.003 (0.103) Batch 1.111 (1.569) Remain 09:08:29 loss: 0.3437 Lr: 0.00237
[2023-12-25 15:38:21,721 INFO misc.py line 119 253097] Train: [59/100][452/510] Data 0.006 (0.103) Batch 4.730 (1.576) Remain 09:10:55 loss: 0.3211 Lr: 0.00237
[2023-12-25 15:38:22,797 INFO misc.py line 119 253097] Train: [59/100][453/510] Data 0.006 (0.103) Batch 1.077 (1.575) Remain 09:10:30 loss: 0.2025 Lr: 0.00237
[2023-12-25 15:38:24,044 INFO misc.py line 119 253097] Train: [59/100][454/510] Data 0.005 (0.103) Batch 1.243 (1.575) Remain 09:10:13 loss: 0.2048 Lr: 0.00237
[2023-12-25 15:38:25,161 INFO misc.py line 119 253097] Train: [59/100][455/510] Data 0.009 (0.103) Batch 1.122 (1.574) Remain 09:09:50 loss: 0.0879 Lr: 0.00237
[2023-12-25 15:38:26,363 INFO misc.py line 119 253097] Train: [59/100][456/510] Data 0.004 (0.102) Batch 1.202 (1.573) Remain 09:09:32 loss: 0.2029 Lr: 0.00237
[2023-12-25 15:38:27,679 INFO misc.py line 119 253097] Train: [59/100][457/510] Data 0.003 (0.102) Batch 1.313 (1.572) Remain 09:09:18 loss: 0.1816 Lr: 0.00237
[2023-12-25 15:38:28,862 INFO misc.py line 119 253097] Train: [59/100][458/510] Data 0.007 (0.102) Batch 1.164 (1.571) Remain 09:08:58 loss: 0.1540 Lr: 0.00237
[2023-12-25 15:38:29,861 INFO misc.py line 119 253097] Train: [59/100][459/510] Data 0.026 (0.102) Batch 1.014 (1.570) Remain 09:08:30 loss: 0.2501 Lr: 0.00237
[2023-12-25 15:38:30,955 INFO misc.py line 119 253097] Train: [59/100][460/510] Data 0.010 (0.102) Batch 1.100 (1.569) Remain 09:08:07 loss: 0.1250 Lr: 0.00237
[2023-12-25 15:38:32,146 INFO misc.py line 119 253097] Train: [59/100][461/510] Data 0.004 (0.101) Batch 1.184 (1.568) Remain 09:07:48 loss: 0.0704 Lr: 0.00237
[2023-12-25 15:38:34,589 INFO misc.py line 119 253097] Train: [59/100][462/510] Data 0.011 (0.101) Batch 2.451 (1.570) Remain 09:08:27 loss: 0.1845 Lr: 0.00237
[2023-12-25 15:38:43,048 INFO misc.py line 119 253097] Train: [59/100][463/510] Data 0.004 (0.101) Batch 8.459 (1.585) Remain 09:13:39 loss: 0.2823 Lr: 0.00237
[2023-12-25 15:38:44,144 INFO misc.py line 119 253097] Train: [59/100][464/510] Data 0.004 (0.101) Batch 1.091 (1.584) Remain 09:13:15 loss: 0.2291 Lr: 0.00237
[2023-12-25 15:38:45,424 INFO misc.py line 119 253097] Train: [59/100][465/510] Data 0.010 (0.101) Batch 1.283 (1.583) Remain 09:13:00 loss: 0.1401 Lr: 0.00237
[2023-12-25 15:38:46,332 INFO misc.py line 119 253097] Train: [59/100][466/510] Data 0.007 (0.100) Batch 0.910 (1.582) Remain 09:12:28 loss: 0.1541 Lr: 0.00237
[2023-12-25 15:38:47,704 INFO misc.py line 119 253097] Train: [59/100][467/510] Data 0.004 (0.100) Batch 1.368 (1.581) Remain 09:12:17 loss: 0.1422 Lr: 0.00237
[2023-12-25 15:38:48,835 INFO misc.py line 119 253097] Train: [59/100][468/510] Data 0.008 (0.100) Batch 1.131 (1.581) Remain 09:11:55 loss: 0.2516 Lr: 0.00237
[2023-12-25 15:38:50,060 INFO misc.py line 119 253097] Train: [59/100][469/510] Data 0.008 (0.100) Batch 1.228 (1.580) Remain 09:11:37 loss: 0.1390 Lr: 0.00237
[2023-12-25 15:38:51,206 INFO misc.py line 119 253097] Train: [59/100][470/510] Data 0.007 (0.099) Batch 1.148 (1.579) Remain 09:11:16 loss: 0.1228 Lr: 0.00237
[2023-12-25 15:38:52,426 INFO misc.py line 119 253097] Train: [59/100][471/510] Data 0.004 (0.099) Batch 1.219 (1.578) Remain 09:10:59 loss: 0.0930 Lr: 0.00237
[2023-12-25 15:38:53,435 INFO misc.py line 119 253097] Train: [59/100][472/510] Data 0.004 (0.099) Batch 1.008 (1.577) Remain 09:10:32 loss: 0.1846 Lr: 0.00237
[2023-12-25 15:38:54,681 INFO misc.py line 119 253097] Train: [59/100][473/510] Data 0.006 (0.099) Batch 1.241 (1.576) Remain 09:10:15 loss: 0.2403 Lr: 0.00237
[2023-12-25 15:38:55,751 INFO misc.py line 119 253097] Train: [59/100][474/510] Data 0.012 (0.099) Batch 1.074 (1.575) Remain 09:09:51 loss: 0.0856 Lr: 0.00237
[2023-12-25 15:38:56,905 INFO misc.py line 119 253097] Train: [59/100][475/510] Data 0.007 (0.099) Batch 1.157 (1.574) Remain 09:09:31 loss: 0.2290 Lr: 0.00237
[2023-12-25 15:38:57,910 INFO misc.py line 119 253097] Train: [59/100][476/510] Data 0.005 (0.098) Batch 1.000 (1.573) Remain 09:09:04 loss: 0.1993 Lr: 0.00237
[2023-12-25 15:39:05,193 INFO misc.py line 119 253097] Train: [59/100][477/510] Data 6.182 (0.111) Batch 7.287 (1.585) Remain 09:13:15 loss: 0.1308 Lr: 0.00237
[2023-12-25 15:39:06,337 INFO misc.py line 119 253097] Train: [59/100][478/510] Data 0.005 (0.111) Batch 1.145 (1.584) Remain 09:12:54 loss: 0.1272 Lr: 0.00237
[2023-12-25 15:39:07,301 INFO misc.py line 119 253097] Train: [59/100][479/510] Data 0.004 (0.111) Batch 0.962 (1.583) Remain 09:12:25 loss: 0.1098 Lr: 0.00237
[2023-12-25 15:39:08,317 INFO misc.py line 119 253097] Train: [59/100][480/510] Data 0.006 (0.110) Batch 1.018 (1.582) Remain 09:11:59 loss: 0.1310 Lr: 0.00237
[2023-12-25 15:39:09,454 INFO misc.py line 119 253097] Train: [59/100][481/510] Data 0.004 (0.110) Batch 1.137 (1.581) Remain 09:11:37 loss: 0.1318 Lr: 0.00237
[2023-12-25 15:39:10,516 INFO misc.py line 119 253097] Train: [59/100][482/510] Data 0.005 (0.110) Batch 1.063 (1.580) Remain 09:11:13 loss: 0.1164 Lr: 0.00237
[2023-12-25 15:39:11,527 INFO misc.py line 119 253097] Train: [59/100][483/510] Data 0.003 (0.110) Batch 1.009 (1.578) Remain 09:10:47 loss: 0.2802 Lr: 0.00237
[2023-12-25 15:39:12,592 INFO misc.py line 119 253097] Train: [59/100][484/510] Data 0.005 (0.110) Batch 1.066 (1.577) Remain 09:10:23 loss: 0.2992 Lr: 0.00237
[2023-12-25 15:39:14,123 INFO misc.py line 119 253097] Train: [59/100][485/510] Data 0.005 (0.109) Batch 1.532 (1.577) Remain 09:10:19 loss: 0.2152 Lr: 0.00237
[2023-12-25 15:39:15,223 INFO misc.py line 119 253097] Train: [59/100][486/510] Data 0.004 (0.109) Batch 1.096 (1.576) Remain 09:09:57 loss: 0.1004 Lr: 0.00237
[2023-12-25 15:39:16,423 INFO misc.py line 119 253097] Train: [59/100][487/510] Data 0.008 (0.109) Batch 1.200 (1.575) Remain 09:09:39 loss: 0.1985 Lr: 0.00237
[2023-12-25 15:39:17,657 INFO misc.py line 119 253097] Train: [59/100][488/510] Data 0.007 (0.109) Batch 1.233 (1.575) Remain 09:09:23 loss: 0.1066 Lr: 0.00237
[2023-12-25 15:39:18,589 INFO misc.py line 119 253097] Train: [59/100][489/510] Data 0.008 (0.109) Batch 0.934 (1.573) Remain 09:08:54 loss: 0.1183 Lr: 0.00237
[2023-12-25 15:39:19,749 INFO misc.py line 119 253097] Train: [59/100][490/510] Data 0.006 (0.108) Batch 1.155 (1.573) Remain 09:08:34 loss: 0.0952 Lr: 0.00237
[2023-12-25 15:39:20,729 INFO misc.py line 119 253097] Train: [59/100][491/510] Data 0.013 (0.108) Batch 0.988 (1.571) Remain 09:08:07 loss: 0.1282 Lr: 0.00237
[2023-12-25 15:39:21,803 INFO misc.py line 119 253097] Train: [59/100][492/510] Data 0.003 (0.108) Batch 1.074 (1.570) Remain 09:07:44 loss: 0.1520 Lr: 0.00237
[2023-12-25 15:39:22,913 INFO misc.py line 119 253097] Train: [59/100][493/510] Data 0.003 (0.108) Batch 1.109 (1.569) Remain 09:07:23 loss: 0.3309 Lr: 0.00237
[2023-12-25 15:39:24,101 INFO misc.py line 119 253097] Train: [59/100][494/510] Data 0.005 (0.107) Batch 1.189 (1.569) Remain 09:07:05 loss: 0.1145 Lr: 0.00237
[2023-12-25 15:39:25,212 INFO misc.py line 119 253097] Train: [59/100][495/510] Data 0.004 (0.107) Batch 1.109 (1.568) Remain 09:06:44 loss: 0.1244 Lr: 0.00237
[2023-12-25 15:39:26,410 INFO misc.py line 119 253097] Train: [59/100][496/510] Data 0.005 (0.107) Batch 1.199 (1.567) Remain 09:06:27 loss: 0.1229 Lr: 0.00237
[2023-12-25 15:39:27,745 INFO misc.py line 119 253097] Train: [59/100][497/510] Data 0.004 (0.107) Batch 1.335 (1.567) Remain 09:06:16 loss: 0.2188 Lr: 0.00236
[2023-12-25 15:39:32,824 INFO misc.py line 119 253097] Train: [59/100][498/510] Data 0.004 (0.107) Batch 5.079 (1.574) Remain 09:08:43 loss: 0.1438 Lr: 0.00236
[2023-12-25 15:39:34,073 INFO misc.py line 119 253097] Train: [59/100][499/510] Data 0.004 (0.106) Batch 1.248 (1.573) Remain 09:08:27 loss: 0.1557 Lr: 0.00236
[2023-12-25 15:39:35,026 INFO misc.py line 119 253097] Train: [59/100][500/510] Data 0.005 (0.106) Batch 0.953 (1.572) Remain 09:08:00 loss: 0.0794 Lr: 0.00236
[2023-12-25 15:39:36,172 INFO misc.py line 119 253097] Train: [59/100][501/510] Data 0.005 (0.106) Batch 1.148 (1.571) Remain 09:07:40 loss: 0.0780 Lr: 0.00236
[2023-12-25 15:39:37,421 INFO misc.py line 119 253097] Train: [59/100][502/510] Data 0.003 (0.106) Batch 1.247 (1.570) Remain 09:07:25 loss: 0.1511 Lr: 0.00236
[2023-12-25 15:39:38,567 INFO misc.py line 119 253097] Train: [59/100][503/510] Data 0.005 (0.106) Batch 1.137 (1.569) Remain 09:07:05 loss: 0.1301 Lr: 0.00236
[2023-12-25 15:39:39,815 INFO misc.py line 119 253097] Train: [59/100][504/510] Data 0.015 (0.105) Batch 1.260 (1.569) Remain 09:06:51 loss: 0.3022 Lr: 0.00236
[2023-12-25 15:39:43,314 INFO misc.py line 119 253097] Train: [59/100][505/510] Data 0.003 (0.105) Batch 3.498 (1.573) Remain 09:08:10 loss: 0.1629 Lr: 0.00236
[2023-12-25 15:39:44,510 INFO misc.py line 119 253097] Train: [59/100][506/510] Data 0.004 (0.105) Batch 1.195 (1.572) Remain 09:07:52 loss: 0.2147 Lr: 0.00236
[2023-12-25 15:39:47,768 INFO misc.py line 119 253097] Train: [59/100][507/510] Data 1.967 (0.109) Batch 3.259 (1.575) Remain 09:09:01 loss: 0.0899 Lr: 0.00236
[2023-12-25 15:39:49,034 INFO misc.py line 119 253097] Train: [59/100][508/510] Data 0.003 (0.109) Batch 1.267 (1.575) Remain 09:08:46 loss: 0.1055 Lr: 0.00236
[2023-12-25 15:39:50,315 INFO misc.py line 119 253097] Train: [59/100][509/510] Data 0.004 (0.108) Batch 1.275 (1.574) Remain 09:08:33 loss: 0.1830 Lr: 0.00236
[2023-12-25 15:39:51,528 INFO misc.py line 119 253097] Train: [59/100][510/510] Data 0.009 (0.108) Batch 1.217 (1.573) Remain 09:08:16 loss: 0.1089 Lr: 0.00236
[2023-12-25 15:39:51,528 INFO misc.py line 136 253097] Train result: loss: 0.1590 
[2023-12-25 15:39:51,529 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 15:40:17,869 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6589 
[2023-12-25 15:40:18,225 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3804 
[2023-12-25 15:40:23,161 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3251 
[2023-12-25 15:40:23,686 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3750 
[2023-12-25 15:40:25,666 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9161 
[2023-12-25 15:40:26,093 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2795 
[2023-12-25 15:40:26,973 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2665 
[2023-12-25 15:40:27,527 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2311 
[2023-12-25 15:40:29,344 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.4617 
[2023-12-25 15:40:31,473 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4874 
[2023-12-25 15:40:32,334 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2463 
[2023-12-25 15:40:32,762 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.1455 
[2023-12-25 15:40:33,669 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4571 
[2023-12-25 15:40:36,612 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8435 
[2023-12-25 15:40:37,083 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2386 
[2023-12-25 15:40:37,713 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.5121 
[2023-12-25 15:40:38,416 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2914 
[2023-12-25 15:40:39,681 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6643/0.7256/0.8968.
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9222/0.9489
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9814/0.9883
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8324/0.9704
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3028/0.3468
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5563/0.5714
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.5911/0.6395
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7968/0.8923
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9190/0.9638
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6055/0.6910
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7731/0.8413
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7553/0.8093
[2023-12-25 15:40:39,682 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6004/0.7694
[2023-12-25 15:40:39,683 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 15:40:39,684 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 15:40:39,684 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 15:40:47,789 INFO misc.py line 119 253097] Train: [60/100][1/510] Data 4.971 (4.971) Batch 6.149 (6.149) Remain 35:42:52 loss: 0.1052 Lr: 0.00236
[2023-12-25 15:40:50,741 INFO misc.py line 119 253097] Train: [60/100][2/510] Data 2.133 (2.133) Batch 2.951 (2.951) Remain 17:08:25 loss: 0.2044 Lr: 0.00236
[2023-12-25 15:40:59,629 INFO misc.py line 119 253097] Train: [60/100][3/510] Data 0.005 (0.005) Batch 8.888 (8.888) Remain 51:37:01 loss: 0.2408 Lr: 0.00236
[2023-12-25 15:41:00,677 INFO misc.py line 119 253097] Train: [60/100][4/510] Data 0.004 (0.004) Batch 1.048 (1.048) Remain 06:05:07 loss: 0.0831 Lr: 0.00236
[2023-12-25 15:41:05,706 INFO misc.py line 119 253097] Train: [60/100][5/510] Data 0.004 (0.004) Batch 5.030 (3.039) Remain 17:38:52 loss: 0.2269 Lr: 0.00236
[2023-12-25 15:41:06,807 INFO misc.py line 119 253097] Train: [60/100][6/510] Data 0.003 (0.004) Batch 1.101 (2.393) Remain 13:53:46 loss: 0.3305 Lr: 0.00236
[2023-12-25 15:41:07,985 INFO misc.py line 119 253097] Train: [60/100][7/510] Data 0.003 (0.004) Batch 1.177 (2.089) Remain 12:07:50 loss: 0.1511 Lr: 0.00236
[2023-12-25 15:41:09,231 INFO misc.py line 119 253097] Train: [60/100][8/510] Data 0.004 (0.004) Batch 1.246 (1.921) Remain 11:09:04 loss: 0.1124 Lr: 0.00236
[2023-12-25 15:41:10,337 INFO misc.py line 119 253097] Train: [60/100][9/510] Data 0.003 (0.004) Batch 1.102 (1.784) Remain 10:21:30 loss: 0.2293 Lr: 0.00236
[2023-12-25 15:41:11,532 INFO misc.py line 119 253097] Train: [60/100][10/510] Data 0.007 (0.004) Batch 1.199 (1.701) Remain 09:52:21 loss: 0.1256 Lr: 0.00236
[2023-12-25 15:41:12,636 INFO misc.py line 119 253097] Train: [60/100][11/510] Data 0.003 (0.004) Batch 1.101 (1.626) Remain 09:26:12 loss: 0.2902 Lr: 0.00236
[2023-12-25 15:41:13,832 INFO misc.py line 119 253097] Train: [60/100][12/510] Data 0.007 (0.004) Batch 1.199 (1.578) Remain 09:09:40 loss: 0.2544 Lr: 0.00236
[2023-12-25 15:41:14,787 INFO misc.py line 119 253097] Train: [60/100][13/510] Data 0.004 (0.004) Batch 0.956 (1.516) Remain 08:47:58 loss: 0.1902 Lr: 0.00236
[2023-12-25 15:41:15,991 INFO misc.py line 119 253097] Train: [60/100][14/510] Data 0.003 (0.004) Batch 1.204 (1.488) Remain 08:38:04 loss: 0.1841 Lr: 0.00236
[2023-12-25 15:41:16,992 INFO misc.py line 119 253097] Train: [60/100][15/510] Data 0.004 (0.004) Batch 1.000 (1.447) Remain 08:23:53 loss: 0.0803 Lr: 0.00236
[2023-12-25 15:41:18,235 INFO misc.py line 119 253097] Train: [60/100][16/510] Data 0.004 (0.004) Batch 1.242 (1.431) Remain 08:18:23 loss: 0.2261 Lr: 0.00236
[2023-12-25 15:41:19,386 INFO misc.py line 119 253097] Train: [60/100][17/510] Data 0.006 (0.004) Batch 1.151 (1.411) Remain 08:11:23 loss: 0.1206 Lr: 0.00236
[2023-12-25 15:41:23,190 INFO misc.py line 119 253097] Train: [60/100][18/510] Data 2.582 (0.176) Batch 3.806 (1.571) Remain 09:06:56 loss: 0.1262 Lr: 0.00236
[2023-12-25 15:41:24,375 INFO misc.py line 119 253097] Train: [60/100][19/510] Data 0.003 (0.165) Batch 1.185 (1.547) Remain 08:58:31 loss: 0.1583 Lr: 0.00236
[2023-12-25 15:41:25,416 INFO misc.py line 119 253097] Train: [60/100][20/510] Data 0.005 (0.156) Batch 1.041 (1.517) Remain 08:48:08 loss: 0.1948 Lr: 0.00236
[2023-12-25 15:41:26,563 INFO misc.py line 119 253097] Train: [60/100][21/510] Data 0.003 (0.147) Batch 1.147 (1.496) Remain 08:40:58 loss: 0.1864 Lr: 0.00236
[2023-12-25 15:41:27,679 INFO misc.py line 119 253097] Train: [60/100][22/510] Data 0.004 (0.140) Batch 1.116 (1.476) Remain 08:33:58 loss: 0.1696 Lr: 0.00236
[2023-12-25 15:41:28,748 INFO misc.py line 119 253097] Train: [60/100][23/510] Data 0.003 (0.133) Batch 1.069 (1.456) Remain 08:26:50 loss: 0.1023 Lr: 0.00236
[2023-12-25 15:41:29,879 INFO misc.py line 119 253097] Train: [60/100][24/510] Data 0.004 (0.127) Batch 1.130 (1.440) Remain 08:21:25 loss: 0.1105 Lr: 0.00236
[2023-12-25 15:41:31,154 INFO misc.py line 119 253097] Train: [60/100][25/510] Data 0.005 (0.121) Batch 1.275 (1.433) Remain 08:18:46 loss: 0.4141 Lr: 0.00236
[2023-12-25 15:41:37,446 INFO misc.py line 119 253097] Train: [60/100][26/510] Data 5.237 (0.344) Batch 6.292 (1.644) Remain 09:32:17 loss: 0.0730 Lr: 0.00236
[2023-12-25 15:41:38,706 INFO misc.py line 119 253097] Train: [60/100][27/510] Data 0.005 (0.330) Batch 1.262 (1.628) Remain 09:26:42 loss: 0.1013 Lr: 0.00236
[2023-12-25 15:41:39,888 INFO misc.py line 119 253097] Train: [60/100][28/510] Data 0.003 (0.317) Batch 1.162 (1.610) Remain 09:20:11 loss: 0.1169 Lr: 0.00236
[2023-12-25 15:41:41,123 INFO misc.py line 119 253097] Train: [60/100][29/510] Data 0.023 (0.305) Batch 1.254 (1.596) Remain 09:15:24 loss: 0.2371 Lr: 0.00236
[2023-12-25 15:41:48,365 INFO misc.py line 119 253097] Train: [60/100][30/510] Data 6.014 (0.517) Batch 7.241 (1.805) Remain 10:28:09 loss: 0.2346 Lr: 0.00236
[2023-12-25 15:41:49,595 INFO misc.py line 119 253097] Train: [60/100][31/510] Data 0.004 (0.498) Batch 1.222 (1.784) Remain 10:20:52 loss: 0.1258 Lr: 0.00236
[2023-12-25 15:41:50,827 INFO misc.py line 119 253097] Train: [60/100][32/510] Data 0.013 (0.482) Batch 1.240 (1.765) Remain 10:14:18 loss: 0.1122 Lr: 0.00236
[2023-12-25 15:41:51,975 INFO misc.py line 119 253097] Train: [60/100][33/510] Data 0.005 (0.466) Batch 1.149 (1.745) Remain 10:07:07 loss: 0.1617 Lr: 0.00236
[2023-12-25 15:41:53,054 INFO misc.py line 119 253097] Train: [60/100][34/510] Data 0.004 (0.451) Batch 1.080 (1.723) Remain 09:59:38 loss: 0.3222 Lr: 0.00236
[2023-12-25 15:41:54,007 INFO misc.py line 119 253097] Train: [60/100][35/510] Data 0.003 (0.437) Batch 0.952 (1.699) Remain 09:51:13 loss: 0.1176 Lr: 0.00236
[2023-12-25 15:41:55,155 INFO misc.py line 119 253097] Train: [60/100][36/510] Data 0.004 (0.424) Batch 1.149 (1.683) Remain 09:45:23 loss: 0.1400 Lr: 0.00236
[2023-12-25 15:41:56,415 INFO misc.py line 119 253097] Train: [60/100][37/510] Data 0.004 (0.411) Batch 1.258 (1.670) Remain 09:41:01 loss: 0.1499 Lr: 0.00236
[2023-12-25 15:41:57,538 INFO misc.py line 119 253097] Train: [60/100][38/510] Data 0.004 (0.400) Batch 1.119 (1.654) Remain 09:35:31 loss: 0.4056 Lr: 0.00236
[2023-12-25 15:41:58,676 INFO misc.py line 119 253097] Train: [60/100][39/510] Data 0.008 (0.389) Batch 1.138 (1.640) Remain 09:30:29 loss: 0.0686 Lr: 0.00235
[2023-12-25 15:42:01,287 INFO misc.py line 119 253097] Train: [60/100][40/510] Data 0.009 (0.379) Batch 2.616 (1.666) Remain 09:39:38 loss: 0.0590 Lr: 0.00235
[2023-12-25 15:42:02,413 INFO misc.py line 119 253097] Train: [60/100][41/510] Data 0.004 (0.369) Batch 1.126 (1.652) Remain 09:34:39 loss: 0.1958 Lr: 0.00235
[2023-12-25 15:42:03,483 INFO misc.py line 119 253097] Train: [60/100][42/510] Data 0.005 (0.359) Batch 1.071 (1.637) Remain 09:29:27 loss: 0.2710 Lr: 0.00235
[2023-12-25 15:42:04,884 INFO misc.py line 119 253097] Train: [60/100][43/510] Data 0.003 (0.350) Batch 1.188 (1.626) Remain 09:25:31 loss: 0.0688 Lr: 0.00235
[2023-12-25 15:42:06,111 INFO misc.py line 119 253097] Train: [60/100][44/510] Data 0.216 (0.347) Batch 1.437 (1.621) Remain 09:23:53 loss: 0.1592 Lr: 0.00235
[2023-12-25 15:42:07,305 INFO misc.py line 119 253097] Train: [60/100][45/510] Data 0.006 (0.339) Batch 1.196 (1.611) Remain 09:20:20 loss: 0.1792 Lr: 0.00235
[2023-12-25 15:42:08,469 INFO misc.py line 119 253097] Train: [60/100][46/510] Data 0.003 (0.331) Batch 1.160 (1.601) Remain 09:16:40 loss: 0.0943 Lr: 0.00235
[2023-12-25 15:42:09,635 INFO misc.py line 119 253097] Train: [60/100][47/510] Data 0.008 (0.324) Batch 1.170 (1.591) Remain 09:13:14 loss: 0.0675 Lr: 0.00235
[2023-12-25 15:42:10,758 INFO misc.py line 119 253097] Train: [60/100][48/510] Data 0.004 (0.317) Batch 1.123 (1.581) Remain 09:09:35 loss: 0.1485 Lr: 0.00235
[2023-12-25 15:42:11,749 INFO misc.py line 119 253097] Train: [60/100][49/510] Data 0.004 (0.310) Batch 0.988 (1.568) Remain 09:05:05 loss: 0.1559 Lr: 0.00235
[2023-12-25 15:42:12,975 INFO misc.py line 119 253097] Train: [60/100][50/510] Data 0.007 (0.304) Batch 1.223 (1.560) Remain 09:02:30 loss: 0.1092 Lr: 0.00235
[2023-12-25 15:42:14,007 INFO misc.py line 119 253097] Train: [60/100][51/510] Data 0.009 (0.297) Batch 1.034 (1.549) Remain 08:58:40 loss: 0.1905 Lr: 0.00235
[2023-12-25 15:42:15,302 INFO misc.py line 119 253097] Train: [60/100][52/510] Data 0.008 (0.292) Batch 1.296 (1.544) Remain 08:56:50 loss: 0.1614 Lr: 0.00235
[2023-12-25 15:42:16,453 INFO misc.py line 119 253097] Train: [60/100][53/510] Data 0.006 (0.286) Batch 1.153 (1.536) Remain 08:54:06 loss: 0.1170 Lr: 0.00235
[2023-12-25 15:42:17,654 INFO misc.py line 119 253097] Train: [60/100][54/510] Data 0.005 (0.280) Batch 1.196 (1.530) Remain 08:51:45 loss: 0.3425 Lr: 0.00235
[2023-12-25 15:42:18,720 INFO misc.py line 119 253097] Train: [60/100][55/510] Data 0.013 (0.275) Batch 1.069 (1.521) Remain 08:48:39 loss: 0.1984 Lr: 0.00235
[2023-12-25 15:42:19,931 INFO misc.py line 119 253097] Train: [60/100][56/510] Data 0.006 (0.270) Batch 1.213 (1.515) Remain 08:46:36 loss: 0.1007 Lr: 0.00235
[2023-12-25 15:42:21,061 INFO misc.py line 119 253097] Train: [60/100][57/510] Data 0.004 (0.265) Batch 1.123 (1.508) Remain 08:44:03 loss: 0.1966 Lr: 0.00235
[2023-12-25 15:42:22,863 INFO misc.py line 119 253097] Train: [60/100][58/510] Data 0.011 (0.261) Batch 1.810 (1.513) Remain 08:45:56 loss: 0.1330 Lr: 0.00235
[2023-12-25 15:42:23,927 INFO misc.py line 119 253097] Train: [60/100][59/510] Data 0.003 (0.256) Batch 1.059 (1.505) Remain 08:43:05 loss: 0.1354 Lr: 0.00235
[2023-12-25 15:42:25,161 INFO misc.py line 119 253097] Train: [60/100][60/510] Data 0.008 (0.252) Batch 1.234 (1.500) Remain 08:41:24 loss: 0.2636 Lr: 0.00235
[2023-12-25 15:42:26,496 INFO misc.py line 119 253097] Train: [60/100][61/510] Data 0.008 (0.247) Batch 1.330 (1.498) Remain 08:40:22 loss: 0.1453 Lr: 0.00235
[2023-12-25 15:42:28,696 INFO misc.py line 119 253097] Train: [60/100][62/510] Data 0.014 (0.243) Batch 2.210 (1.510) Remain 08:44:32 loss: 0.0957 Lr: 0.00235
[2023-12-25 15:42:29,772 INFO misc.py line 119 253097] Train: [60/100][63/510] Data 0.004 (0.239) Batch 1.077 (1.502) Remain 08:42:00 loss: 0.0956 Lr: 0.00235
[2023-12-25 15:42:31,058 INFO misc.py line 119 253097] Train: [60/100][64/510] Data 0.003 (0.236) Batch 1.281 (1.499) Remain 08:40:43 loss: 0.1712 Lr: 0.00235
[2023-12-25 15:42:32,001 INFO misc.py line 119 253097] Train: [60/100][65/510] Data 0.007 (0.232) Batch 0.948 (1.490) Remain 08:37:36 loss: 0.1458 Lr: 0.00235
[2023-12-25 15:42:33,143 INFO misc.py line 119 253097] Train: [60/100][66/510] Data 0.003 (0.228) Batch 1.140 (1.484) Remain 08:35:39 loss: 0.2093 Lr: 0.00235
[2023-12-25 15:42:34,261 INFO misc.py line 119 253097] Train: [60/100][67/510] Data 0.005 (0.225) Batch 1.118 (1.479) Remain 08:33:38 loss: 0.1007 Lr: 0.00235
[2023-12-25 15:42:35,430 INFO misc.py line 119 253097] Train: [60/100][68/510] Data 0.005 (0.221) Batch 1.165 (1.474) Remain 08:31:56 loss: 0.1038 Lr: 0.00235
[2023-12-25 15:42:36,734 INFO misc.py line 119 253097] Train: [60/100][69/510] Data 0.009 (0.218) Batch 1.306 (1.471) Remain 08:31:02 loss: 0.1429 Lr: 0.00235
[2023-12-25 15:42:39,027 INFO misc.py line 119 253097] Train: [60/100][70/510] Data 0.007 (0.215) Batch 2.297 (1.484) Remain 08:35:17 loss: 0.3134 Lr: 0.00235
[2023-12-25 15:42:40,859 INFO misc.py line 119 253097] Train: [60/100][71/510] Data 0.727 (0.223) Batch 1.831 (1.489) Remain 08:37:02 loss: 0.0818 Lr: 0.00235
[2023-12-25 15:42:45,990 INFO misc.py line 119 253097] Train: [60/100][72/510] Data 0.004 (0.219) Batch 5.126 (1.541) Remain 08:55:19 loss: 0.1282 Lr: 0.00235
[2023-12-25 15:42:47,210 INFO misc.py line 119 253097] Train: [60/100][73/510] Data 0.009 (0.216) Batch 1.218 (1.537) Remain 08:53:41 loss: 0.1024 Lr: 0.00235
[2023-12-25 15:42:53,230 INFO misc.py line 119 253097] Train: [60/100][74/510] Data 0.013 (0.214) Batch 6.027 (1.600) Remain 09:15:37 loss: 0.1552 Lr: 0.00235
[2023-12-25 15:42:54,340 INFO misc.py line 119 253097] Train: [60/100][75/510] Data 0.004 (0.211) Batch 1.110 (1.593) Remain 09:13:14 loss: 0.1025 Lr: 0.00235
[2023-12-25 15:42:55,657 INFO misc.py line 119 253097] Train: [60/100][76/510] Data 0.004 (0.208) Batch 1.318 (1.589) Remain 09:11:54 loss: 0.1426 Lr: 0.00235
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[2023-12-25 15:42:57,952 INFO misc.py line 119 253097] Train: [60/100][78/510] Data 0.009 (0.202) Batch 1.169 (1.578) Remain 09:07:45 loss: 0.1417 Lr: 0.00235
[2023-12-25 15:42:59,176 INFO misc.py line 119 253097] Train: [60/100][79/510] Data 0.004 (0.200) Batch 1.224 (1.573) Remain 09:06:06 loss: 0.1049 Lr: 0.00235
[2023-12-25 15:43:00,278 INFO misc.py line 119 253097] Train: [60/100][80/510] Data 0.004 (0.197) Batch 1.100 (1.567) Remain 09:03:57 loss: 0.1623 Lr: 0.00235
[2023-12-25 15:43:01,468 INFO misc.py line 119 253097] Train: [60/100][81/510] Data 0.006 (0.195) Batch 1.189 (1.562) Remain 09:02:15 loss: 0.0933 Lr: 0.00235
[2023-12-25 15:43:02,568 INFO misc.py line 119 253097] Train: [60/100][82/510] Data 0.007 (0.192) Batch 1.096 (1.556) Remain 09:00:10 loss: 0.1668 Lr: 0.00235
[2023-12-25 15:43:03,644 INFO misc.py line 119 253097] Train: [60/100][83/510] Data 0.009 (0.190) Batch 1.081 (1.550) Remain 08:58:05 loss: 0.1128 Lr: 0.00235
[2023-12-25 15:43:04,724 INFO misc.py line 119 253097] Train: [60/100][84/510] Data 0.006 (0.188) Batch 1.079 (1.544) Remain 08:56:02 loss: 0.2958 Lr: 0.00235
[2023-12-25 15:43:05,754 INFO misc.py line 119 253097] Train: [60/100][85/510] Data 0.006 (0.186) Batch 1.025 (1.538) Remain 08:53:49 loss: 0.0871 Lr: 0.00235
[2023-12-25 15:43:07,102 INFO misc.py line 119 253097] Train: [60/100][86/510] Data 0.012 (0.184) Batch 1.353 (1.536) Remain 08:53:01 loss: 0.1011 Lr: 0.00235
[2023-12-25 15:43:08,316 INFO misc.py line 119 253097] Train: [60/100][87/510] Data 0.008 (0.181) Batch 1.215 (1.532) Remain 08:51:40 loss: 0.2195 Lr: 0.00235
[2023-12-25 15:43:09,583 INFO misc.py line 119 253097] Train: [60/100][88/510] Data 0.006 (0.179) Batch 1.270 (1.529) Remain 08:50:34 loss: 0.1949 Lr: 0.00235
[2023-12-25 15:43:10,543 INFO misc.py line 119 253097] Train: [60/100][89/510] Data 0.004 (0.177) Batch 0.960 (1.522) Remain 08:48:15 loss: 0.1257 Lr: 0.00235
[2023-12-25 15:43:11,647 INFO misc.py line 119 253097] Train: [60/100][90/510] Data 0.004 (0.175) Batch 1.080 (1.517) Remain 08:46:27 loss: 0.2104 Lr: 0.00235
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[2023-12-25 15:51:09,311 INFO misc.py line 119 253097] Train: [60/100][406/510] Data 0.003 (0.094) Batch 1.254 (1.513) Remain 08:36:59 loss: 0.1387 Lr: 0.00229
[2023-12-25 15:51:10,414 INFO misc.py line 119 253097] Train: [60/100][407/510] Data 0.009 (0.094) Batch 1.105 (1.512) Remain 08:36:37 loss: 0.1966 Lr: 0.00229
[2023-12-25 15:51:12,737 INFO misc.py line 119 253097] Train: [60/100][408/510] Data 0.005 (0.094) Batch 2.322 (1.514) Remain 08:37:16 loss: 0.1495 Lr: 0.00229
[2023-12-25 15:51:14,038 INFO misc.py line 119 253097] Train: [60/100][409/510] Data 0.007 (0.094) Batch 1.301 (1.513) Remain 08:37:04 loss: 0.1242 Lr: 0.00228
[2023-12-25 15:51:14,986 INFO misc.py line 119 253097] Train: [60/100][410/510] Data 0.007 (0.093) Batch 0.950 (1.512) Remain 08:36:34 loss: 0.0707 Lr: 0.00228
[2023-12-25 15:51:16,071 INFO misc.py line 119 253097] Train: [60/100][411/510] Data 0.004 (0.093) Batch 1.085 (1.511) Remain 08:36:11 loss: 0.1377 Lr: 0.00228
[2023-12-25 15:51:21,736 INFO misc.py line 119 253097] Train: [60/100][412/510] Data 0.004 (0.093) Batch 5.663 (1.521) Remain 08:39:38 loss: 0.1987 Lr: 0.00228
[2023-12-25 15:51:23,060 INFO misc.py line 119 253097] Train: [60/100][413/510] Data 0.006 (0.093) Batch 1.324 (1.521) Remain 08:39:26 loss: 0.1273 Lr: 0.00228
[2023-12-25 15:51:24,263 INFO misc.py line 119 253097] Train: [60/100][414/510] Data 0.005 (0.093) Batch 1.199 (1.520) Remain 08:39:09 loss: 0.1609 Lr: 0.00228
[2023-12-25 15:51:25,462 INFO misc.py line 119 253097] Train: [60/100][415/510] Data 0.009 (0.092) Batch 1.201 (1.519) Remain 08:38:52 loss: 0.0695 Lr: 0.00228
[2023-12-25 15:51:26,596 INFO misc.py line 119 253097] Train: [60/100][416/510] Data 0.007 (0.092) Batch 1.107 (1.518) Remain 08:38:30 loss: 0.1556 Lr: 0.00228
[2023-12-25 15:51:27,842 INFO misc.py line 119 253097] Train: [60/100][417/510] Data 0.034 (0.092) Batch 1.244 (1.517) Remain 08:38:14 loss: 0.1161 Lr: 0.00228
[2023-12-25 15:51:28,975 INFO misc.py line 119 253097] Train: [60/100][418/510] Data 0.036 (0.092) Batch 1.163 (1.516) Remain 08:37:55 loss: 0.0758 Lr: 0.00228
[2023-12-25 15:51:30,292 INFO misc.py line 119 253097] Train: [60/100][419/510] Data 0.007 (0.092) Batch 1.290 (1.516) Remain 08:37:43 loss: 0.1803 Lr: 0.00228
[2023-12-25 15:51:31,417 INFO misc.py line 119 253097] Train: [60/100][420/510] Data 0.033 (0.091) Batch 1.154 (1.515) Remain 08:37:23 loss: 0.1382 Lr: 0.00228
[2023-12-25 15:51:32,608 INFO misc.py line 119 253097] Train: [60/100][421/510] Data 0.004 (0.091) Batch 1.167 (1.514) Remain 08:37:05 loss: 0.2362 Lr: 0.00228
[2023-12-25 15:51:33,813 INFO misc.py line 119 253097] Train: [60/100][422/510] Data 0.030 (0.091) Batch 1.220 (1.514) Remain 08:36:49 loss: 0.1635 Lr: 0.00228
[2023-12-25 15:51:34,831 INFO misc.py line 119 253097] Train: [60/100][423/510] Data 0.015 (0.091) Batch 1.016 (1.512) Remain 08:36:23 loss: 0.0880 Lr: 0.00228
[2023-12-25 15:51:36,047 INFO misc.py line 119 253097] Train: [60/100][424/510] Data 0.016 (0.091) Batch 1.228 (1.512) Remain 08:36:08 loss: 0.0979 Lr: 0.00228
[2023-12-25 15:51:36,986 INFO misc.py line 119 253097] Train: [60/100][425/510] Data 0.005 (0.091) Batch 0.939 (1.510) Remain 08:35:38 loss: 0.1658 Lr: 0.00228
[2023-12-25 15:51:45,377 INFO misc.py line 119 253097] Train: [60/100][426/510] Data 0.004 (0.090) Batch 8.391 (1.527) Remain 08:41:10 loss: 0.2051 Lr: 0.00228
[2023-12-25 15:51:46,503 INFO misc.py line 119 253097] Train: [60/100][427/510] Data 0.004 (0.090) Batch 1.127 (1.526) Remain 08:40:49 loss: 0.1210 Lr: 0.00228
[2023-12-25 15:51:47,735 INFO misc.py line 119 253097] Train: [60/100][428/510] Data 0.003 (0.090) Batch 1.230 (1.525) Remain 08:40:34 loss: 0.1233 Lr: 0.00228
[2023-12-25 15:51:49,005 INFO misc.py line 119 253097] Train: [60/100][429/510] Data 0.006 (0.090) Batch 1.268 (1.524) Remain 08:40:20 loss: 0.1867 Lr: 0.00228
[2023-12-25 15:51:49,995 INFO misc.py line 119 253097] Train: [60/100][430/510] Data 0.006 (0.090) Batch 0.994 (1.523) Remain 08:39:53 loss: 0.1065 Lr: 0.00228
[2023-12-25 15:51:51,245 INFO misc.py line 119 253097] Train: [60/100][431/510] Data 0.004 (0.089) Batch 1.249 (1.522) Remain 08:39:38 loss: 0.1502 Lr: 0.00228
[2023-12-25 15:51:52,411 INFO misc.py line 119 253097] Train: [60/100][432/510] Data 0.005 (0.089) Batch 1.165 (1.522) Remain 08:39:20 loss: 0.1158 Lr: 0.00228
[2023-12-25 15:51:53,704 INFO misc.py line 119 253097] Train: [60/100][433/510] Data 0.004 (0.089) Batch 1.290 (1.521) Remain 08:39:07 loss: 0.1951 Lr: 0.00228
[2023-12-25 15:51:54,773 INFO misc.py line 119 253097] Train: [60/100][434/510] Data 0.009 (0.089) Batch 1.068 (1.520) Remain 08:38:44 loss: 0.2112 Lr: 0.00228
[2023-12-25 15:51:56,008 INFO misc.py line 119 253097] Train: [60/100][435/510] Data 0.009 (0.089) Batch 1.235 (1.519) Remain 08:38:29 loss: 0.1188 Lr: 0.00228
[2023-12-25 15:51:57,189 INFO misc.py line 119 253097] Train: [60/100][436/510] Data 0.010 (0.088) Batch 1.182 (1.519) Remain 08:38:11 loss: 0.1391 Lr: 0.00228
[2023-12-25 15:51:58,286 INFO misc.py line 119 253097] Train: [60/100][437/510] Data 0.009 (0.088) Batch 1.102 (1.518) Remain 08:37:50 loss: 0.1237 Lr: 0.00228
[2023-12-25 15:51:59,549 INFO misc.py line 119 253097] Train: [60/100][438/510] Data 0.003 (0.088) Batch 1.205 (1.517) Remain 08:37:34 loss: 0.0967 Lr: 0.00228
[2023-12-25 15:52:00,656 INFO misc.py line 119 253097] Train: [60/100][439/510] Data 0.061 (0.088) Batch 1.159 (1.516) Remain 08:37:16 loss: 0.1135 Lr: 0.00228
[2023-12-25 15:52:01,839 INFO misc.py line 119 253097] Train: [60/100][440/510] Data 0.010 (0.088) Batch 1.185 (1.515) Remain 08:36:59 loss: 0.0853 Lr: 0.00228
[2023-12-25 15:52:02,941 INFO misc.py line 119 253097] Train: [60/100][441/510] Data 0.008 (0.088) Batch 1.104 (1.514) Remain 08:36:38 loss: 0.0919 Lr: 0.00228
[2023-12-25 15:52:08,614 INFO misc.py line 119 253097] Train: [60/100][442/510] Data 0.005 (0.087) Batch 5.675 (1.524) Remain 08:39:50 loss: 0.1412 Lr: 0.00228
[2023-12-25 15:52:09,879 INFO misc.py line 119 253097] Train: [60/100][443/510] Data 0.003 (0.087) Batch 1.264 (1.523) Remain 08:39:37 loss: 0.0890 Lr: 0.00228
[2023-12-25 15:52:10,839 INFO misc.py line 119 253097] Train: [60/100][444/510] Data 0.004 (0.087) Batch 0.960 (1.522) Remain 08:39:09 loss: 0.1678 Lr: 0.00228
[2023-12-25 15:52:12,031 INFO misc.py line 119 253097] Train: [60/100][445/510] Data 0.004 (0.087) Batch 1.193 (1.521) Remain 08:38:52 loss: 0.1793 Lr: 0.00228
[2023-12-25 15:52:13,272 INFO misc.py line 119 253097] Train: [60/100][446/510] Data 0.003 (0.087) Batch 1.174 (1.520) Remain 08:38:35 loss: 0.2274 Lr: 0.00228
[2023-12-25 15:52:14,287 INFO misc.py line 119 253097] Train: [60/100][447/510] Data 0.070 (0.087) Batch 1.082 (1.520) Remain 08:38:13 loss: 0.1074 Lr: 0.00228
[2023-12-25 15:52:16,360 INFO misc.py line 119 253097] Train: [60/100][448/510] Data 0.003 (0.086) Batch 2.066 (1.521) Remain 08:38:37 loss: 0.1003 Lr: 0.00228
[2023-12-25 15:52:17,447 INFO misc.py line 119 253097] Train: [60/100][449/510] Data 0.010 (0.086) Batch 1.088 (1.520) Remain 08:38:15 loss: 0.3410 Lr: 0.00228
[2023-12-25 15:52:18,562 INFO misc.py line 119 253097] Train: [60/100][450/510] Data 0.008 (0.086) Batch 1.120 (1.519) Remain 08:37:55 loss: 0.2671 Lr: 0.00228
[2023-12-25 15:52:19,561 INFO misc.py line 119 253097] Train: [60/100][451/510] Data 0.005 (0.086) Batch 0.993 (1.518) Remain 08:37:30 loss: 0.1300 Lr: 0.00228
[2023-12-25 15:52:20,703 INFO misc.py line 119 253097] Train: [60/100][452/510] Data 0.010 (0.086) Batch 1.148 (1.517) Remain 08:37:12 loss: 0.2588 Lr: 0.00228
[2023-12-25 15:52:21,784 INFO misc.py line 119 253097] Train: [60/100][453/510] Data 0.003 (0.086) Batch 1.082 (1.516) Remain 08:36:50 loss: 0.1246 Lr: 0.00228
[2023-12-25 15:52:22,743 INFO misc.py line 119 253097] Train: [60/100][454/510] Data 0.003 (0.085) Batch 0.957 (1.515) Remain 08:36:23 loss: 0.1033 Lr: 0.00228
[2023-12-25 15:52:24,128 INFO misc.py line 119 253097] Train: [60/100][455/510] Data 0.005 (0.085) Batch 1.379 (1.514) Remain 08:36:16 loss: 0.1131 Lr: 0.00228
[2023-12-25 15:52:38,009 INFO misc.py line 119 253097] Train: [60/100][456/510] Data 12.587 (0.113) Batch 13.888 (1.542) Remain 08:45:33 loss: 0.3181 Lr: 0.00228
[2023-12-25 15:52:39,020 INFO misc.py line 119 253097] Train: [60/100][457/510] Data 0.004 (0.113) Batch 1.011 (1.541) Remain 08:45:07 loss: 0.2870 Lr: 0.00228
[2023-12-25 15:52:40,241 INFO misc.py line 119 253097] Train: [60/100][458/510] Data 0.004 (0.112) Batch 1.210 (1.540) Remain 08:44:51 loss: 0.3533 Lr: 0.00228
[2023-12-25 15:52:41,269 INFO misc.py line 119 253097] Train: [60/100][459/510] Data 0.015 (0.112) Batch 1.040 (1.539) Remain 08:44:27 loss: 0.1417 Lr: 0.00228
[2023-12-25 15:52:42,441 INFO misc.py line 119 253097] Train: [60/100][460/510] Data 0.003 (0.112) Batch 1.171 (1.538) Remain 08:44:09 loss: 0.2039 Lr: 0.00228
[2023-12-25 15:52:43,584 INFO misc.py line 119 253097] Train: [60/100][461/510] Data 0.004 (0.112) Batch 1.142 (1.537) Remain 08:43:50 loss: 0.1075 Lr: 0.00228
[2023-12-25 15:52:47,407 INFO misc.py line 119 253097] Train: [60/100][462/510] Data 2.460 (0.117) Batch 3.823 (1.542) Remain 08:45:30 loss: 0.1104 Lr: 0.00227
[2023-12-25 15:52:48,832 INFO misc.py line 119 253097] Train: [60/100][463/510] Data 0.005 (0.117) Batch 1.423 (1.542) Remain 08:45:23 loss: 0.0953 Lr: 0.00227
[2023-12-25 15:52:49,950 INFO misc.py line 119 253097] Train: [60/100][464/510] Data 0.008 (0.116) Batch 1.103 (1.541) Remain 08:45:02 loss: 0.3112 Lr: 0.00227
[2023-12-25 15:52:50,926 INFO misc.py line 119 253097] Train: [60/100][465/510] Data 0.023 (0.116) Batch 0.987 (1.540) Remain 08:44:36 loss: 0.0991 Lr: 0.00227
[2023-12-25 15:52:51,979 INFO misc.py line 119 253097] Train: [60/100][466/510] Data 0.011 (0.116) Batch 1.060 (1.539) Remain 08:44:14 loss: 0.1454 Lr: 0.00227
[2023-12-25 15:52:53,077 INFO misc.py line 119 253097] Train: [60/100][467/510] Data 0.003 (0.116) Batch 1.096 (1.538) Remain 08:43:53 loss: 0.1011 Lr: 0.00227
[2023-12-25 15:52:54,180 INFO misc.py line 119 253097] Train: [60/100][468/510] Data 0.005 (0.115) Batch 1.105 (1.537) Remain 08:43:32 loss: 0.2301 Lr: 0.00227
[2023-12-25 15:52:55,307 INFO misc.py line 119 253097] Train: [60/100][469/510] Data 0.005 (0.115) Batch 1.121 (1.536) Remain 08:43:12 loss: 0.1163 Lr: 0.00227
[2023-12-25 15:52:56,630 INFO misc.py line 119 253097] Train: [60/100][470/510] Data 0.010 (0.115) Batch 1.330 (1.535) Remain 08:43:02 loss: 0.1186 Lr: 0.00227
[2023-12-25 15:52:57,649 INFO misc.py line 119 253097] Train: [60/100][471/510] Data 0.004 (0.115) Batch 0.997 (1.534) Remain 08:42:37 loss: 0.1671 Lr: 0.00227
[2023-12-25 15:52:58,880 INFO misc.py line 119 253097] Train: [60/100][472/510] Data 0.026 (0.114) Batch 1.246 (1.534) Remain 08:42:23 loss: 0.0915 Lr: 0.00227
[2023-12-25 15:52:59,967 INFO misc.py line 119 253097] Train: [60/100][473/510] Data 0.011 (0.114) Batch 1.092 (1.533) Remain 08:42:02 loss: 0.1746 Lr: 0.00227
[2023-12-25 15:53:01,235 INFO misc.py line 119 253097] Train: [60/100][474/510] Data 0.006 (0.114) Batch 1.269 (1.532) Remain 08:41:49 loss: 0.0971 Lr: 0.00227
[2023-12-25 15:53:02,335 INFO misc.py line 119 253097] Train: [60/100][475/510] Data 0.004 (0.114) Batch 1.095 (1.531) Remain 08:41:28 loss: 0.1725 Lr: 0.00227
[2023-12-25 15:53:03,499 INFO misc.py line 119 253097] Train: [60/100][476/510] Data 0.009 (0.114) Batch 1.169 (1.530) Remain 08:41:11 loss: 0.1669 Lr: 0.00227
[2023-12-25 15:53:04,553 INFO misc.py line 119 253097] Train: [60/100][477/510] Data 0.004 (0.113) Batch 1.055 (1.529) Remain 08:40:49 loss: 0.1283 Lr: 0.00227
[2023-12-25 15:53:13,367 INFO misc.py line 119 253097] Train: [60/100][478/510] Data 0.004 (0.113) Batch 8.813 (1.545) Remain 08:46:01 loss: 0.0871 Lr: 0.00227
[2023-12-25 15:53:14,724 INFO misc.py line 119 253097] Train: [60/100][479/510] Data 0.005 (0.113) Batch 1.352 (1.544) Remain 08:45:51 loss: 0.2596 Lr: 0.00227
[2023-12-25 15:53:15,846 INFO misc.py line 119 253097] Train: [60/100][480/510] Data 0.010 (0.113) Batch 1.124 (1.543) Remain 08:45:32 loss: 0.1179 Lr: 0.00227
[2023-12-25 15:53:17,007 INFO misc.py line 119 253097] Train: [60/100][481/510] Data 0.008 (0.112) Batch 1.164 (1.543) Remain 08:45:14 loss: 0.0815 Lr: 0.00227
[2023-12-25 15:53:18,226 INFO misc.py line 119 253097] Train: [60/100][482/510] Data 0.004 (0.112) Batch 1.218 (1.542) Remain 08:44:59 loss: 0.2798 Lr: 0.00227
[2023-12-25 15:53:19,280 INFO misc.py line 119 253097] Train: [60/100][483/510] Data 0.004 (0.112) Batch 1.050 (1.541) Remain 08:44:36 loss: 0.1934 Lr: 0.00227
[2023-12-25 15:53:20,507 INFO misc.py line 119 253097] Train: [60/100][484/510] Data 0.009 (0.112) Batch 1.225 (1.540) Remain 08:44:21 loss: 0.1962 Lr: 0.00227
[2023-12-25 15:53:21,488 INFO misc.py line 119 253097] Train: [60/100][485/510] Data 0.011 (0.112) Batch 0.987 (1.539) Remain 08:43:56 loss: 0.2512 Lr: 0.00227
[2023-12-25 15:53:22,552 INFO misc.py line 119 253097] Train: [60/100][486/510] Data 0.006 (0.111) Batch 1.065 (1.538) Remain 08:43:35 loss: 0.1080 Lr: 0.00227
[2023-12-25 15:53:23,644 INFO misc.py line 119 253097] Train: [60/100][487/510] Data 0.004 (0.111) Batch 1.091 (1.537) Remain 08:43:14 loss: 0.1341 Lr: 0.00227
[2023-12-25 15:53:24,643 INFO misc.py line 119 253097] Train: [60/100][488/510] Data 0.005 (0.111) Batch 1.000 (1.536) Remain 08:42:50 loss: 0.1442 Lr: 0.00227
[2023-12-25 15:53:25,721 INFO misc.py line 119 253097] Train: [60/100][489/510] Data 0.004 (0.111) Batch 1.076 (1.535) Remain 08:42:29 loss: 0.2279 Lr: 0.00227
[2023-12-25 15:53:26,794 INFO misc.py line 119 253097] Train: [60/100][490/510] Data 0.006 (0.110) Batch 1.074 (1.534) Remain 08:42:08 loss: 0.3725 Lr: 0.00227
[2023-12-25 15:53:27,920 INFO misc.py line 119 253097] Train: [60/100][491/510] Data 0.005 (0.110) Batch 1.126 (1.533) Remain 08:41:50 loss: 0.1293 Lr: 0.00227
[2023-12-25 15:53:29,068 INFO misc.py line 119 253097] Train: [60/100][492/510] Data 0.005 (0.110) Batch 1.149 (1.533) Remain 08:41:32 loss: 0.0989 Lr: 0.00227
[2023-12-25 15:53:30,096 INFO misc.py line 119 253097] Train: [60/100][493/510] Data 0.004 (0.110) Batch 1.028 (1.532) Remain 08:41:09 loss: 0.1083 Lr: 0.00227
[2023-12-25 15:53:31,275 INFO misc.py line 119 253097] Train: [60/100][494/510] Data 0.003 (0.110) Batch 1.179 (1.531) Remain 08:40:53 loss: 0.1843 Lr: 0.00227
[2023-12-25 15:53:32,213 INFO misc.py line 119 253097] Train: [60/100][495/510] Data 0.004 (0.109) Batch 0.939 (1.530) Remain 08:40:27 loss: 0.1183 Lr: 0.00227
[2023-12-25 15:53:33,376 INFO misc.py line 119 253097] Train: [60/100][496/510] Data 0.003 (0.109) Batch 1.163 (1.529) Remain 08:40:10 loss: 0.1828 Lr: 0.00227
[2023-12-25 15:53:34,397 INFO misc.py line 119 253097] Train: [60/100][497/510] Data 0.004 (0.109) Batch 1.021 (1.528) Remain 08:39:48 loss: 0.1549 Lr: 0.00227
[2023-12-25 15:53:35,568 INFO misc.py line 119 253097] Train: [60/100][498/510] Data 0.004 (0.109) Batch 1.171 (1.527) Remain 08:39:32 loss: 0.1137 Lr: 0.00227
[2023-12-25 15:53:36,754 INFO misc.py line 119 253097] Train: [60/100][499/510] Data 0.005 (0.109) Batch 1.186 (1.526) Remain 08:39:16 loss: 0.1037 Lr: 0.00227
[2023-12-25 15:53:38,273 INFO misc.py line 119 253097] Train: [60/100][500/510] Data 0.004 (0.108) Batch 1.175 (1.526) Remain 08:39:00 loss: 0.2637 Lr: 0.00227
[2023-12-25 15:53:39,394 INFO misc.py line 119 253097] Train: [60/100][501/510] Data 0.347 (0.109) Batch 1.459 (1.526) Remain 08:38:56 loss: 0.1542 Lr: 0.00227
[2023-12-25 15:53:40,426 INFO misc.py line 119 253097] Train: [60/100][502/510] Data 0.010 (0.109) Batch 1.033 (1.525) Remain 08:38:34 loss: 0.2010 Lr: 0.00227
[2023-12-25 15:53:49,887 INFO misc.py line 119 253097] Train: [60/100][503/510] Data 0.008 (0.108) Batch 9.465 (1.541) Remain 08:43:57 loss: 0.0987 Lr: 0.00227
[2023-12-25 15:53:50,867 INFO misc.py line 119 253097] Train: [60/100][504/510] Data 0.005 (0.108) Batch 0.982 (1.539) Remain 08:43:32 loss: 0.1879 Lr: 0.00227
[2023-12-25 15:53:52,004 INFO misc.py line 119 253097] Train: [60/100][505/510] Data 0.003 (0.108) Batch 1.136 (1.539) Remain 08:43:15 loss: 0.1069 Lr: 0.00227
[2023-12-25 15:53:53,010 INFO misc.py line 119 253097] Train: [60/100][506/510] Data 0.003 (0.108) Batch 1.007 (1.538) Remain 08:42:51 loss: 0.1764 Lr: 0.00227
[2023-12-25 15:53:54,105 INFO misc.py line 119 253097] Train: [60/100][507/510] Data 0.003 (0.108) Batch 1.095 (1.537) Remain 08:42:32 loss: 0.1819 Lr: 0.00227
[2023-12-25 15:53:55,254 INFO misc.py line 119 253097] Train: [60/100][508/510] Data 0.004 (0.107) Batch 1.149 (1.536) Remain 08:42:15 loss: 0.1867 Lr: 0.00227
[2023-12-25 15:53:56,449 INFO misc.py line 119 253097] Train: [60/100][509/510] Data 0.003 (0.107) Batch 1.194 (1.535) Remain 08:41:59 loss: 0.1641 Lr: 0.00227
[2023-12-25 15:53:57,570 INFO misc.py line 119 253097] Train: [60/100][510/510] Data 0.004 (0.107) Batch 1.121 (1.534) Remain 08:41:41 loss: 0.1615 Lr: 0.00227
[2023-12-25 15:53:57,571 INFO misc.py line 136 253097] Train result: loss: 0.1573 
[2023-12-25 15:53:57,571 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 15:54:25,170 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7060 
[2023-12-25 15:54:25,521 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3139 
[2023-12-25 15:54:32,428 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4483 
[2023-12-25 15:54:32,960 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3592 
[2023-12-25 15:54:34,937 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9453 
[2023-12-25 15:54:35,364 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3376 
[2023-12-25 15:54:36,244 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2186 
[2023-12-25 15:54:36,800 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3335 
[2023-12-25 15:54:38,608 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1336 
[2023-12-25 15:54:40,733 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3279 
[2023-12-25 15:54:41,590 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3443 
[2023-12-25 15:54:42,016 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8912 
[2023-12-25 15:54:42,919 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.7986 
[2023-12-25 15:54:45,861 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8721 
[2023-12-25 15:54:46,330 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4172 
[2023-12-25 15:54:46,944 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4847 
[2023-12-25 15:54:47,646 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4499 
[2023-12-25 15:54:49,246 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6614/0.7207/0.9008.
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9260/0.9628
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9806/0.9910
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8423/0.9685
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0011/0.0057
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2638/0.2924
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6297/0.6581
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6284/0.7456
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8202/0.9090
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9113/0.9687
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4967/0.5127
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7682/0.8570
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7376/0.7894
[2023-12-25 15:54:49,247 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5928/0.7082
[2023-12-25 15:54:49,248 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 15:54:49,250 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 15:54:49,250 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 15:54:56,463 INFO misc.py line 119 253097] Train: [61/100][1/510] Data 3.426 (3.426) Batch 4.306 (4.306) Remain 24:23:58 loss: 0.2191 Lr: 0.00227
[2023-12-25 15:55:00,684 INFO misc.py line 119 253097] Train: [61/100][2/510] Data 3.298 (3.298) Batch 4.226 (4.226) Remain 23:56:43 loss: 0.1896 Lr: 0.00227
[2023-12-25 15:55:11,329 INFO misc.py line 119 253097] Train: [61/100][3/510] Data 0.004 (0.004) Batch 10.645 (10.645) Remain 60:18:45 loss: 0.1555 Lr: 0.00227
[2023-12-25 15:55:12,410 INFO misc.py line 119 253097] Train: [61/100][4/510] Data 0.005 (0.005) Batch 1.080 (1.080) Remain 06:07:07 loss: 0.1771 Lr: 0.00227
[2023-12-25 15:55:13,674 INFO misc.py line 119 253097] Train: [61/100][5/510] Data 0.005 (0.005) Batch 1.265 (1.173) Remain 06:38:37 loss: 0.1132 Lr: 0.00226
[2023-12-25 15:55:14,749 INFO misc.py line 119 253097] Train: [61/100][6/510] Data 0.004 (0.005) Batch 1.075 (1.140) Remain 06:27:32 loss: 0.1896 Lr: 0.00226
[2023-12-25 15:55:15,980 INFO misc.py line 119 253097] Train: [61/100][7/510] Data 0.004 (0.004) Batch 1.226 (1.162) Remain 06:34:47 loss: 0.1406 Lr: 0.00226
[2023-12-25 15:55:16,986 INFO misc.py line 119 253097] Train: [61/100][8/510] Data 0.008 (0.005) Batch 1.007 (1.131) Remain 06:24:16 loss: 0.2621 Lr: 0.00226
[2023-12-25 15:55:18,012 INFO misc.py line 119 253097] Train: [61/100][9/510] Data 0.007 (0.005) Batch 1.025 (1.113) Remain 06:18:16 loss: 0.1617 Lr: 0.00226
[2023-12-25 15:55:19,087 INFO misc.py line 119 253097] Train: [61/100][10/510] Data 0.008 (0.006) Batch 1.073 (1.107) Remain 06:16:19 loss: 0.2509 Lr: 0.00226
[2023-12-25 15:55:20,351 INFO misc.py line 119 253097] Train: [61/100][11/510] Data 0.010 (0.006) Batch 1.271 (1.128) Remain 06:23:14 loss: 0.1166 Lr: 0.00226
[2023-12-25 15:55:21,315 INFO misc.py line 119 253097] Train: [61/100][12/510] Data 0.004 (0.006) Batch 0.964 (1.110) Remain 06:17:01 loss: 0.1041 Lr: 0.00226
[2023-12-25 15:55:22,389 INFO misc.py line 119 253097] Train: [61/100][13/510] Data 0.004 (0.006) Batch 1.072 (1.106) Remain 06:15:43 loss: 0.1524 Lr: 0.00226
[2023-12-25 15:55:23,389 INFO misc.py line 119 253097] Train: [61/100][14/510] Data 0.006 (0.006) Batch 1.000 (1.096) Remain 06:12:26 loss: 0.1148 Lr: 0.00226
[2023-12-25 15:55:24,469 INFO misc.py line 119 253097] Train: [61/100][15/510] Data 0.006 (0.006) Batch 1.079 (1.095) Remain 06:11:55 loss: 0.1724 Lr: 0.00226
[2023-12-25 15:55:25,889 INFO misc.py line 119 253097] Train: [61/100][16/510] Data 0.006 (0.006) Batch 1.222 (1.105) Remain 06:15:14 loss: 0.2210 Lr: 0.00226
[2023-12-25 15:55:27,180 INFO misc.py line 119 253097] Train: [61/100][17/510] Data 0.205 (0.020) Batch 1.485 (1.132) Remain 06:24:27 loss: 0.1546 Lr: 0.00226
[2023-12-25 15:55:28,267 INFO misc.py line 119 253097] Train: [61/100][18/510] Data 0.011 (0.020) Batch 1.092 (1.129) Remain 06:23:32 loss: 0.1324 Lr: 0.00226
[2023-12-25 15:55:29,293 INFO misc.py line 119 253097] Train: [61/100][19/510] Data 0.006 (0.019) Batch 1.029 (1.123) Remain 06:21:23 loss: 0.1529 Lr: 0.00226
[2023-12-25 15:55:30,550 INFO misc.py line 119 253097] Train: [61/100][20/510] Data 0.003 (0.018) Batch 1.257 (1.131) Remain 06:24:03 loss: 0.2340 Lr: 0.00226
[2023-12-25 15:55:31,474 INFO misc.py line 119 253097] Train: [61/100][21/510] Data 0.003 (0.017) Batch 0.923 (1.119) Remain 06:20:06 loss: 0.2071 Lr: 0.00226
[2023-12-25 15:55:38,807 INFO misc.py line 119 253097] Train: [61/100][22/510] Data 0.005 (0.016) Batch 7.334 (1.446) Remain 08:11:11 loss: 0.1235 Lr: 0.00226
[2023-12-25 15:55:39,947 INFO misc.py line 119 253097] Train: [61/100][23/510] Data 0.003 (0.016) Batch 1.137 (1.431) Remain 08:05:54 loss: 0.1888 Lr: 0.00226
[2023-12-25 15:55:41,271 INFO misc.py line 119 253097] Train: [61/100][24/510] Data 0.007 (0.015) Batch 1.323 (1.426) Remain 08:04:08 loss: 0.1695 Lr: 0.00226
[2023-12-25 15:55:42,416 INFO misc.py line 119 253097] Train: [61/100][25/510] Data 0.008 (0.015) Batch 1.147 (1.413) Remain 07:59:49 loss: 0.0918 Lr: 0.00226
[2023-12-25 15:55:43,484 INFO misc.py line 119 253097] Train: [61/100][26/510] Data 0.006 (0.014) Batch 1.066 (1.398) Remain 07:54:40 loss: 0.4254 Lr: 0.00226
[2023-12-25 15:55:44,500 INFO misc.py line 119 253097] Train: [61/100][27/510] Data 0.008 (0.014) Batch 1.014 (1.382) Remain 07:49:13 loss: 0.1841 Lr: 0.00226
[2023-12-25 15:55:47,387 INFO misc.py line 119 253097] Train: [61/100][28/510] Data 0.010 (0.014) Batch 2.893 (1.442) Remain 08:09:43 loss: 0.1153 Lr: 0.00226
[2023-12-25 15:55:48,492 INFO misc.py line 119 253097] Train: [61/100][29/510] Data 0.004 (0.014) Batch 1.100 (1.429) Remain 08:05:14 loss: 0.1548 Lr: 0.00226
[2023-12-25 15:55:49,503 INFO misc.py line 119 253097] Train: [61/100][30/510] Data 0.009 (0.013) Batch 1.007 (1.414) Remain 07:59:54 loss: 0.3158 Lr: 0.00226
[2023-12-25 15:55:50,626 INFO misc.py line 119 253097] Train: [61/100][31/510] Data 0.012 (0.013) Batch 1.128 (1.403) Remain 07:56:25 loss: 0.1038 Lr: 0.00226
[2023-12-25 15:56:03,034 INFO misc.py line 119 253097] Train: [61/100][32/510] Data 11.280 (0.402) Batch 12.411 (1.783) Remain 10:05:14 loss: 0.1004 Lr: 0.00226
[2023-12-25 15:56:04,087 INFO misc.py line 119 253097] Train: [61/100][33/510] Data 0.004 (0.389) Batch 1.052 (1.759) Remain 09:56:56 loss: 0.1551 Lr: 0.00226
[2023-12-25 15:56:05,194 INFO misc.py line 119 253097] Train: [61/100][34/510] Data 0.005 (0.376) Batch 1.109 (1.738) Remain 09:49:47 loss: 0.0988 Lr: 0.00226
[2023-12-25 15:56:06,285 INFO misc.py line 119 253097] Train: [61/100][35/510] Data 0.004 (0.365) Batch 1.090 (1.717) Remain 09:42:53 loss: 0.1041 Lr: 0.00226
[2023-12-25 15:56:07,554 INFO misc.py line 119 253097] Train: [61/100][36/510] Data 0.006 (0.354) Batch 1.270 (1.704) Remain 09:38:16 loss: 0.4024 Lr: 0.00226
[2023-12-25 15:56:08,816 INFO misc.py line 119 253097] Train: [61/100][37/510] Data 0.003 (0.343) Batch 1.259 (1.691) Remain 09:33:48 loss: 0.1436 Lr: 0.00226
[2023-12-25 15:56:10,010 INFO misc.py line 119 253097] Train: [61/100][38/510] Data 0.006 (0.334) Batch 1.196 (1.677) Remain 09:28:58 loss: 0.1350 Lr: 0.00226
[2023-12-25 15:56:11,280 INFO misc.py line 119 253097] Train: [61/100][39/510] Data 0.004 (0.325) Batch 1.270 (1.665) Remain 09:25:07 loss: 0.1410 Lr: 0.00226
[2023-12-25 15:56:12,355 INFO misc.py line 119 253097] Train: [61/100][40/510] Data 0.004 (0.316) Batch 1.073 (1.649) Remain 09:19:39 loss: 0.1241 Lr: 0.00226
[2023-12-25 15:56:13,493 INFO misc.py line 119 253097] Train: [61/100][41/510] Data 0.007 (0.308) Batch 1.132 (1.636) Remain 09:15:00 loss: 0.1496 Lr: 0.00226
[2023-12-25 15:56:14,760 INFO misc.py line 119 253097] Train: [61/100][42/510] Data 0.013 (0.300) Batch 1.272 (1.626) Remain 09:11:49 loss: 0.1658 Lr: 0.00226
[2023-12-25 15:56:15,854 INFO misc.py line 119 253097] Train: [61/100][43/510] Data 0.007 (0.293) Batch 1.096 (1.613) Remain 09:07:17 loss: 0.1701 Lr: 0.00226
[2023-12-25 15:56:17,071 INFO misc.py line 119 253097] Train: [61/100][44/510] Data 0.005 (0.286) Batch 1.216 (1.603) Remain 09:03:59 loss: 0.1233 Lr: 0.00226
[2023-12-25 15:56:18,086 INFO misc.py line 119 253097] Train: [61/100][45/510] Data 0.006 (0.279) Batch 1.016 (1.589) Remain 08:59:12 loss: 0.1195 Lr: 0.00226
[2023-12-25 15:56:27,786 INFO misc.py line 119 253097] Train: [61/100][46/510] Data 0.005 (0.273) Batch 9.701 (1.778) Remain 10:03:11 loss: 0.0818 Lr: 0.00226
[2023-12-25 15:56:29,109 INFO misc.py line 119 253097] Train: [61/100][47/510] Data 0.003 (0.267) Batch 1.319 (1.768) Remain 09:59:37 loss: 0.1088 Lr: 0.00226
[2023-12-25 15:56:30,285 INFO misc.py line 119 253097] Train: [61/100][48/510] Data 0.007 (0.261) Batch 1.176 (1.755) Remain 09:55:07 loss: 0.1023 Lr: 0.00226
[2023-12-25 15:56:31,477 INFO misc.py line 119 253097] Train: [61/100][49/510] Data 0.007 (0.255) Batch 1.194 (1.742) Remain 09:50:58 loss: 0.2038 Lr: 0.00226
[2023-12-25 15:56:32,521 INFO misc.py line 119 253097] Train: [61/100][50/510] Data 0.005 (0.250) Batch 1.044 (1.727) Remain 09:45:54 loss: 0.1472 Lr: 0.00226
[2023-12-25 15:56:33,482 INFO misc.py line 119 253097] Train: [61/100][51/510] Data 0.005 (0.245) Batch 0.961 (1.712) Remain 09:40:27 loss: 0.1443 Lr: 0.00226
[2023-12-25 15:56:34,690 INFO misc.py line 119 253097] Train: [61/100][52/510] Data 0.004 (0.240) Batch 1.209 (1.701) Remain 09:36:57 loss: 0.2113 Lr: 0.00226
[2023-12-25 15:56:36,018 INFO misc.py line 119 253097] Train: [61/100][53/510] Data 0.003 (0.235) Batch 1.324 (1.694) Remain 09:34:21 loss: 0.0994 Lr: 0.00226
[2023-12-25 15:56:36,907 INFO misc.py line 119 253097] Train: [61/100][54/510] Data 0.008 (0.231) Batch 0.893 (1.678) Remain 09:29:00 loss: 0.2686 Lr: 0.00226
[2023-12-25 15:56:38,004 INFO misc.py line 119 253097] Train: [61/100][55/510] Data 0.003 (0.227) Batch 1.077 (1.666) Remain 09:25:03 loss: 0.1345 Lr: 0.00226
[2023-12-25 15:56:39,089 INFO misc.py line 119 253097] Train: [61/100][56/510] Data 0.025 (0.223) Batch 1.106 (1.656) Remain 09:21:26 loss: 0.2721 Lr: 0.00226
[2023-12-25 15:56:40,326 INFO misc.py line 119 253097] Train: [61/100][57/510] Data 0.003 (0.219) Batch 1.233 (1.648) Remain 09:18:45 loss: 0.1401 Lr: 0.00226
[2023-12-25 15:56:51,515 INFO misc.py line 119 253097] Train: [61/100][58/510] Data 0.007 (0.215) Batch 11.191 (1.822) Remain 10:17:33 loss: 0.1896 Lr: 0.00225
[2023-12-25 15:56:52,659 INFO misc.py line 119 253097] Train: [61/100][59/510] Data 0.007 (0.211) Batch 1.146 (1.809) Remain 10:13:26 loss: 0.0712 Lr: 0.00225
[2023-12-25 15:56:53,812 INFO misc.py line 119 253097] Train: [61/100][60/510] Data 0.003 (0.207) Batch 1.153 (1.798) Remain 10:09:30 loss: 0.1132 Lr: 0.00225
[2023-12-25 15:56:54,899 INFO misc.py line 119 253097] Train: [61/100][61/510] Data 0.003 (0.204) Batch 1.086 (1.786) Remain 10:05:18 loss: 0.1360 Lr: 0.00225
[2023-12-25 15:56:56,016 INFO misc.py line 119 253097] Train: [61/100][62/510] Data 0.005 (0.201) Batch 1.118 (1.774) Remain 10:01:26 loss: 0.1071 Lr: 0.00225
[2023-12-25 15:56:57,158 INFO misc.py line 119 253097] Train: [61/100][63/510] Data 0.004 (0.197) Batch 1.141 (1.764) Remain 09:57:50 loss: 0.0627 Lr: 0.00225
[2023-12-25 15:56:58,270 INFO misc.py line 119 253097] Train: [61/100][64/510] Data 0.006 (0.194) Batch 1.112 (1.753) Remain 09:54:11 loss: 0.0740 Lr: 0.00225
[2023-12-25 15:56:59,253 INFO misc.py line 119 253097] Train: [61/100][65/510] Data 0.004 (0.191) Batch 0.983 (1.741) Remain 09:49:57 loss: 0.2316 Lr: 0.00225
[2023-12-25 15:57:00,448 INFO misc.py line 119 253097] Train: [61/100][66/510] Data 0.004 (0.188) Batch 1.194 (1.732) Remain 09:46:58 loss: 0.2324 Lr: 0.00225
[2023-12-25 15:57:01,549 INFO misc.py line 119 253097] Train: [61/100][67/510] Data 0.005 (0.185) Batch 1.094 (1.722) Remain 09:43:34 loss: 0.1584 Lr: 0.00225
[2023-12-25 15:57:02,834 INFO misc.py line 119 253097] Train: [61/100][68/510] Data 0.012 (0.183) Batch 1.288 (1.715) Remain 09:41:17 loss: 0.1083 Lr: 0.00225
[2023-12-25 15:57:03,903 INFO misc.py line 119 253097] Train: [61/100][69/510] Data 0.010 (0.180) Batch 1.070 (1.706) Remain 09:37:56 loss: 0.2714 Lr: 0.00225
[2023-12-25 15:57:05,137 INFO misc.py line 119 253097] Train: [61/100][70/510] Data 0.008 (0.177) Batch 1.231 (1.699) Remain 09:35:30 loss: 0.1773 Lr: 0.00225
[2023-12-25 15:57:06,411 INFO misc.py line 119 253097] Train: [61/100][71/510] Data 0.012 (0.175) Batch 1.275 (1.692) Remain 09:33:22 loss: 0.1117 Lr: 0.00225
[2023-12-25 15:57:07,627 INFO misc.py line 119 253097] Train: [61/100][72/510] Data 0.010 (0.173) Batch 1.218 (1.685) Remain 09:31:00 loss: 0.1670 Lr: 0.00225
[2023-12-25 15:57:08,704 INFO misc.py line 119 253097] Train: [61/100][73/510] Data 0.009 (0.170) Batch 1.069 (1.677) Remain 09:28:00 loss: 0.1134 Lr: 0.00225
[2023-12-25 15:57:09,978 INFO misc.py line 119 253097] Train: [61/100][74/510] Data 0.017 (0.168) Batch 1.258 (1.671) Remain 09:25:58 loss: 0.2443 Lr: 0.00225
[2023-12-25 15:57:11,137 INFO misc.py line 119 253097] Train: [61/100][75/510] Data 0.033 (0.166) Batch 1.184 (1.664) Remain 09:23:39 loss: 0.2875 Lr: 0.00225
[2023-12-25 15:57:12,176 INFO misc.py line 119 253097] Train: [61/100][76/510] Data 0.008 (0.164) Batch 1.040 (1.655) Remain 09:20:44 loss: 0.1482 Lr: 0.00225
[2023-12-25 15:57:13,422 INFO misc.py line 119 253097] Train: [61/100][77/510] Data 0.007 (0.162) Batch 1.246 (1.650) Remain 09:18:50 loss: 0.1695 Lr: 0.00225
[2023-12-25 15:57:14,534 INFO misc.py line 119 253097] Train: [61/100][78/510] Data 0.007 (0.160) Batch 1.108 (1.643) Remain 09:16:21 loss: 0.1583 Lr: 0.00225
[2023-12-25 15:57:15,708 INFO misc.py line 119 253097] Train: [61/100][79/510] Data 0.011 (0.158) Batch 1.181 (1.637) Remain 09:14:16 loss: 0.1451 Lr: 0.00225
[2023-12-25 15:57:16,804 INFO misc.py line 119 253097] Train: [61/100][80/510] Data 0.005 (0.156) Batch 1.095 (1.630) Remain 09:11:52 loss: 0.2190 Lr: 0.00225
[2023-12-25 15:57:17,830 INFO misc.py line 119 253097] Train: [61/100][81/510] Data 0.005 (0.154) Batch 1.024 (1.622) Remain 09:09:12 loss: 0.1738 Lr: 0.00225
[2023-12-25 15:57:19,014 INFO misc.py line 119 253097] Train: [61/100][82/510] Data 0.007 (0.152) Batch 1.185 (1.616) Remain 09:07:18 loss: 0.2905 Lr: 0.00225
[2023-12-25 15:57:20,287 INFO misc.py line 119 253097] Train: [61/100][83/510] Data 0.006 (0.150) Batch 1.274 (1.612) Remain 09:05:50 loss: 0.2241 Lr: 0.00225
[2023-12-25 15:57:21,591 INFO misc.py line 119 253097] Train: [61/100][84/510] Data 0.006 (0.148) Batch 1.305 (1.608) Remain 09:04:31 loss: 0.1201 Lr: 0.00225
[2023-12-25 15:57:22,898 INFO misc.py line 119 253097] Train: [61/100][85/510] Data 0.004 (0.147) Batch 1.307 (1.605) Remain 09:03:15 loss: 0.3168 Lr: 0.00225
[2023-12-25 15:57:24,041 INFO misc.py line 119 253097] Train: [61/100][86/510] Data 0.003 (0.145) Batch 1.139 (1.599) Remain 09:01:20 loss: 0.1246 Lr: 0.00225
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[2023-12-25 16:07:51,617 INFO misc.py line 119 253097] Train: [61/100][506/510] Data 0.003 (0.113) Batch 0.909 (1.512) Remain 08:21:09 loss: 0.2220 Lr: 0.00217
[2023-12-25 16:07:52,839 INFO misc.py line 119 253097] Train: [61/100][507/510] Data 0.003 (0.113) Batch 1.222 (1.511) Remain 08:20:56 loss: 0.1122 Lr: 0.00217
[2023-12-25 16:07:53,831 INFO misc.py line 119 253097] Train: [61/100][508/510] Data 0.003 (0.113) Batch 0.993 (1.510) Remain 08:20:35 loss: 0.2121 Lr: 0.00217
[2023-12-25 16:07:54,913 INFO misc.py line 119 253097] Train: [61/100][509/510] Data 0.003 (0.113) Batch 1.082 (1.509) Remain 08:20:16 loss: 0.1552 Lr: 0.00217
[2023-12-25 16:07:56,142 INFO misc.py line 119 253097] Train: [61/100][510/510] Data 0.004 (0.113) Batch 1.227 (1.509) Remain 08:20:04 loss: 0.3329 Lr: 0.00217
[2023-12-25 16:07:56,143 INFO misc.py line 136 253097] Train result: loss: 0.1609 
[2023-12-25 16:07:56,143 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 16:08:27,758 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5385 
[2023-12-25 16:08:28,120 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4015 
[2023-12-25 16:08:33,073 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3722 
[2023-12-25 16:08:33,598 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2938 
[2023-12-25 16:08:35,577 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6287 
[2023-12-25 16:08:36,001 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3273 
[2023-12-25 16:08:36,888 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3327 
[2023-12-25 16:08:37,452 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3690 
[2023-12-25 16:08:39,260 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6019 
[2023-12-25 16:08:41,389 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2435 
[2023-12-25 16:08:42,249 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3359 
[2023-12-25 16:08:42,687 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9053 
[2023-12-25 16:08:43,590 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4444 
[2023-12-25 16:08:46,541 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.6911 
[2023-12-25 16:08:47,014 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2362 
[2023-12-25 16:08:47,626 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3306 
[2023-12-25 16:08:48,328 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2773 
[2023-12-25 16:08:49,695 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6677/0.7371/0.8974.
[2023-12-25 16:08:49,695 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9074/0.9573
[2023-12-25 16:08:49,695 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9818/0.9867
[2023-12-25 16:08:49,695 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8449/0.9538
[2023-12-25 16:08:49,695 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 16:08:49,695 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.4368/0.5789
[2023-12-25 16:08:49,696 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5886/0.6149
[2023-12-25 16:08:49,696 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6943/0.7893
[2023-12-25 16:08:49,696 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8086/0.9004
[2023-12-25 16:08:49,696 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9130/0.9688
[2023-12-25 16:08:49,696 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.4344/0.4427
[2023-12-25 16:08:49,696 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7600/0.8811
[2023-12-25 16:08:49,696 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7577/0.8549
[2023-12-25 16:08:49,696 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5524/0.6535
[2023-12-25 16:08:49,696 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 16:08:49,698 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 16:08:49,698 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 16:08:58,220 INFO misc.py line 119 253097] Train: [62/100][1/510] Data 3.122 (3.122) Batch 6.366 (6.366) Remain 35:10:12 loss: 0.1790 Lr: 0.00217
[2023-12-25 16:08:59,507 INFO misc.py line 119 253097] Train: [62/100][2/510] Data 0.448 (0.448) Batch 1.287 (1.287) Remain 07:06:32 loss: 0.1148 Lr: 0.00217
[2023-12-25 16:09:00,708 INFO misc.py line 119 253097] Train: [62/100][3/510] Data 0.004 (0.004) Batch 1.192 (1.192) Remain 06:34:58 loss: 0.1402 Lr: 0.00217
[2023-12-25 16:09:01,726 INFO misc.py line 119 253097] Train: [62/100][4/510] Data 0.014 (0.014) Batch 1.023 (1.023) Remain 05:39:06 loss: 0.1153 Lr: 0.00217
[2023-12-25 16:09:02,969 INFO misc.py line 119 253097] Train: [62/100][5/510] Data 0.009 (0.011) Batch 1.236 (1.130) Remain 06:14:26 loss: 0.1605 Lr: 0.00217
[2023-12-25 16:09:04,006 INFO misc.py line 119 253097] Train: [62/100][6/510] Data 0.016 (0.013) Batch 1.044 (1.101) Remain 06:04:58 loss: 0.1962 Lr: 0.00217
[2023-12-25 16:09:04,975 INFO misc.py line 119 253097] Train: [62/100][7/510] Data 0.008 (0.012) Batch 0.973 (1.069) Remain 05:54:20 loss: 0.1015 Lr: 0.00217
[2023-12-25 16:09:05,953 INFO misc.py line 119 253097] Train: [62/100][8/510] Data 0.006 (0.011) Batch 0.979 (1.051) Remain 05:48:19 loss: 0.1893 Lr: 0.00217
[2023-12-25 16:09:07,018 INFO misc.py line 119 253097] Train: [62/100][9/510] Data 0.003 (0.009) Batch 1.065 (1.053) Remain 05:49:03 loss: 0.1551 Lr: 0.00217
[2023-12-25 16:09:08,187 INFO misc.py line 119 253097] Train: [62/100][10/510] Data 0.004 (0.009) Batch 1.166 (1.070) Remain 05:54:22 loss: 0.1350 Lr: 0.00217
[2023-12-25 16:09:09,337 INFO misc.py line 119 253097] Train: [62/100][11/510] Data 0.007 (0.008) Batch 1.152 (1.080) Remain 05:57:47 loss: 0.1152 Lr: 0.00217
[2023-12-25 16:09:10,520 INFO misc.py line 119 253097] Train: [62/100][12/510] Data 0.003 (0.008) Batch 1.183 (1.091) Remain 06:01:34 loss: 0.1294 Lr: 0.00217
[2023-12-25 16:09:11,744 INFO misc.py line 119 253097] Train: [62/100][13/510] Data 0.003 (0.007) Batch 1.223 (1.105) Remain 06:05:55 loss: 0.1479 Lr: 0.00217
[2023-12-25 16:09:13,742 INFO misc.py line 119 253097] Train: [62/100][14/510] Data 0.004 (0.007) Batch 1.999 (1.186) Remain 06:32:51 loss: 0.2371 Lr: 0.00217
[2023-12-25 16:09:22,924 INFO misc.py line 119 253097] Train: [62/100][15/510] Data 0.003 (0.007) Batch 9.181 (1.852) Remain 10:13:32 loss: 0.1755 Lr: 0.00217
[2023-12-25 16:09:24,080 INFO misc.py line 119 253097] Train: [62/100][16/510] Data 0.003 (0.006) Batch 1.155 (1.799) Remain 09:55:45 loss: 0.1514 Lr: 0.00217
[2023-12-25 16:09:25,094 INFO misc.py line 119 253097] Train: [62/100][17/510] Data 0.004 (0.006) Batch 1.014 (1.743) Remain 09:37:08 loss: 0.1539 Lr: 0.00217
[2023-12-25 16:09:26,392 INFO misc.py line 119 253097] Train: [62/100][18/510] Data 0.005 (0.006) Batch 1.294 (1.713) Remain 09:27:13 loss: 0.1709 Lr: 0.00217
[2023-12-25 16:09:27,509 INFO misc.py line 119 253097] Train: [62/100][19/510] Data 0.009 (0.006) Batch 1.117 (1.675) Remain 09:14:52 loss: 0.2540 Lr: 0.00217
[2023-12-25 16:09:28,600 INFO misc.py line 119 253097] Train: [62/100][20/510] Data 0.019 (0.007) Batch 1.090 (1.641) Remain 09:03:25 loss: 0.1559 Lr: 0.00217
[2023-12-25 16:09:43,176 INFO misc.py line 119 253097] Train: [62/100][21/510] Data 0.009 (0.007) Batch 14.581 (2.360) Remain 13:01:28 loss: 0.2072 Lr: 0.00217
[2023-12-25 16:09:44,330 INFO misc.py line 119 253097] Train: [62/100][22/510] Data 0.004 (0.007) Batch 1.155 (2.296) Remain 12:40:25 loss: 0.2165 Lr: 0.00217
[2023-12-25 16:09:45,388 INFO misc.py line 119 253097] Train: [62/100][23/510] Data 0.003 (0.007) Batch 1.058 (2.235) Remain 12:19:53 loss: 0.1274 Lr: 0.00217
[2023-12-25 16:09:46,335 INFO misc.py line 119 253097] Train: [62/100][24/510] Data 0.004 (0.007) Batch 0.947 (2.173) Remain 11:59:33 loss: 0.1583 Lr: 0.00217
[2023-12-25 16:09:47,615 INFO misc.py line 119 253097] Train: [62/100][25/510] Data 0.003 (0.007) Batch 1.280 (2.133) Remain 11:46:04 loss: 0.1506 Lr: 0.00217
[2023-12-25 16:09:48,663 INFO misc.py line 119 253097] Train: [62/100][26/510] Data 0.003 (0.006) Batch 1.048 (2.085) Remain 11:30:25 loss: 0.2527 Lr: 0.00217
[2023-12-25 16:09:49,879 INFO misc.py line 119 253097] Train: [62/100][27/510] Data 0.003 (0.006) Batch 1.215 (2.049) Remain 11:18:22 loss: 0.1618 Lr: 0.00217
[2023-12-25 16:09:50,978 INFO misc.py line 119 253097] Train: [62/100][28/510] Data 0.006 (0.006) Batch 1.100 (2.011) Remain 11:05:46 loss: 0.1164 Lr: 0.00216
[2023-12-25 16:09:52,146 INFO misc.py line 119 253097] Train: [62/100][29/510] Data 0.004 (0.006) Batch 1.166 (1.979) Remain 10:54:59 loss: 0.2558 Lr: 0.00216
[2023-12-25 16:09:53,401 INFO misc.py line 119 253097] Train: [62/100][30/510] Data 0.005 (0.006) Batch 1.252 (1.952) Remain 10:46:02 loss: 0.1718 Lr: 0.00216
[2023-12-25 16:09:54,613 INFO misc.py line 119 253097] Train: [62/100][31/510] Data 0.007 (0.006) Batch 1.212 (1.925) Remain 10:37:16 loss: 0.1459 Lr: 0.00216
[2023-12-25 16:09:55,443 INFO misc.py line 119 253097] Train: [62/100][32/510] Data 0.008 (0.006) Batch 0.835 (1.888) Remain 10:24:47 loss: 0.1889 Lr: 0.00216
[2023-12-25 16:09:56,552 INFO misc.py line 119 253097] Train: [62/100][33/510] Data 0.003 (0.006) Batch 1.108 (1.862) Remain 10:16:09 loss: 0.1233 Lr: 0.00216
[2023-12-25 16:10:02,030 INFO misc.py line 119 253097] Train: [62/100][34/510] Data 0.005 (0.006) Batch 5.479 (1.978) Remain 10:54:44 loss: 0.2003 Lr: 0.00216
[2023-12-25 16:10:03,330 INFO misc.py line 119 253097] Train: [62/100][35/510] Data 0.003 (0.006) Batch 1.299 (1.957) Remain 10:47:40 loss: 0.0757 Lr: 0.00216
[2023-12-25 16:10:04,702 INFO misc.py line 119 253097] Train: [62/100][36/510] Data 0.006 (0.006) Batch 1.368 (1.939) Remain 10:41:44 loss: 0.0885 Lr: 0.00216
[2023-12-25 16:10:05,781 INFO misc.py line 119 253097] Train: [62/100][37/510] Data 0.008 (0.006) Batch 1.084 (1.914) Remain 10:33:22 loss: 0.3274 Lr: 0.00216
[2023-12-25 16:10:06,982 INFO misc.py line 119 253097] Train: [62/100][38/510] Data 0.004 (0.006) Batch 1.196 (1.894) Remain 10:26:33 loss: 0.0936 Lr: 0.00216
[2023-12-25 16:10:08,114 INFO misc.py line 119 253097] Train: [62/100][39/510] Data 0.009 (0.006) Batch 1.137 (1.873) Remain 10:19:34 loss: 0.1428 Lr: 0.00216
[2023-12-25 16:10:09,287 INFO misc.py line 119 253097] Train: [62/100][40/510] Data 0.005 (0.006) Batch 1.169 (1.854) Remain 10:13:14 loss: 0.1086 Lr: 0.00216
[2023-12-25 16:10:10,580 INFO misc.py line 119 253097] Train: [62/100][41/510] Data 0.008 (0.006) Batch 1.294 (1.839) Remain 10:08:20 loss: 0.1112 Lr: 0.00216
[2023-12-25 16:10:11,831 INFO misc.py line 119 253097] Train: [62/100][42/510] Data 0.007 (0.006) Batch 1.249 (1.824) Remain 10:03:18 loss: 0.1456 Lr: 0.00216
[2023-12-25 16:10:14,051 INFO misc.py line 119 253097] Train: [62/100][43/510] Data 0.010 (0.006) Batch 1.196 (1.808) Remain 09:58:04 loss: 0.2329 Lr: 0.00216
[2023-12-25 16:10:14,987 INFO misc.py line 119 253097] Train: [62/100][44/510] Data 1.034 (0.031) Batch 1.965 (1.812) Remain 09:59:19 loss: 0.1122 Lr: 0.00216
[2023-12-25 16:10:15,944 INFO misc.py line 119 253097] Train: [62/100][45/510] Data 0.004 (0.031) Batch 0.954 (1.792) Remain 09:52:32 loss: 0.0725 Lr: 0.00216
[2023-12-25 16:10:16,827 INFO misc.py line 119 253097] Train: [62/100][46/510] Data 0.007 (0.030) Batch 0.886 (1.770) Remain 09:45:32 loss: 0.1013 Lr: 0.00216
[2023-12-25 16:10:17,916 INFO misc.py line 119 253097] Train: [62/100][47/510] Data 0.004 (0.029) Batch 1.080 (1.755) Remain 09:40:19 loss: 0.1112 Lr: 0.00216
[2023-12-25 16:10:19,222 INFO misc.py line 119 253097] Train: [62/100][48/510] Data 0.014 (0.029) Batch 1.314 (1.745) Remain 09:37:03 loss: 0.0849 Lr: 0.00216
[2023-12-25 16:10:20,264 INFO misc.py line 119 253097] Train: [62/100][49/510] Data 0.005 (0.029) Batch 1.044 (1.730) Remain 09:31:59 loss: 0.1446 Lr: 0.00216
[2023-12-25 16:10:23,366 INFO misc.py line 119 253097] Train: [62/100][50/510] Data 0.003 (0.028) Batch 3.101 (1.759) Remain 09:41:36 loss: 0.1079 Lr: 0.00216
[2023-12-25 16:10:24,560 INFO misc.py line 119 253097] Train: [62/100][51/510] Data 0.004 (0.028) Batch 1.194 (1.747) Remain 09:37:41 loss: 0.2371 Lr: 0.00216
[2023-12-25 16:10:25,645 INFO misc.py line 119 253097] Train: [62/100][52/510] Data 0.003 (0.027) Batch 1.085 (1.734) Remain 09:33:11 loss: 0.1987 Lr: 0.00216
[2023-12-25 16:10:26,945 INFO misc.py line 119 253097] Train: [62/100][53/510] Data 0.004 (0.027) Batch 1.298 (1.725) Remain 09:30:16 loss: 0.2807 Lr: 0.00216
[2023-12-25 16:10:30,083 INFO misc.py line 119 253097] Train: [62/100][54/510] Data 0.006 (0.026) Batch 3.141 (1.753) Remain 09:39:25 loss: 0.1432 Lr: 0.00216
[2023-12-25 16:10:31,205 INFO misc.py line 119 253097] Train: [62/100][55/510] Data 0.003 (0.026) Batch 1.107 (1.740) Remain 09:35:17 loss: 0.2588 Lr: 0.00216
[2023-12-25 16:10:32,189 INFO misc.py line 119 253097] Train: [62/100][56/510] Data 0.018 (0.026) Batch 0.999 (1.726) Remain 09:30:38 loss: 0.0905 Lr: 0.00216
[2023-12-25 16:10:33,396 INFO misc.py line 119 253097] Train: [62/100][57/510] Data 0.003 (0.025) Batch 1.200 (1.717) Remain 09:27:23 loss: 0.0902 Lr: 0.00216
[2023-12-25 16:10:34,494 INFO misc.py line 119 253097] Train: [62/100][58/510] Data 0.010 (0.025) Batch 1.098 (1.705) Remain 09:23:38 loss: 0.2850 Lr: 0.00216
[2023-12-25 16:10:35,630 INFO misc.py line 119 253097] Train: [62/100][59/510] Data 0.010 (0.025) Batch 1.137 (1.695) Remain 09:20:15 loss: 0.2244 Lr: 0.00216
[2023-12-25 16:10:38,656 INFO misc.py line 119 253097] Train: [62/100][60/510] Data 0.009 (0.024) Batch 3.032 (1.719) Remain 09:27:59 loss: 0.1280 Lr: 0.00216
[2023-12-25 16:10:39,877 INFO misc.py line 119 253097] Train: [62/100][61/510] Data 0.003 (0.024) Batch 1.211 (1.710) Remain 09:25:04 loss: 0.1176 Lr: 0.00216
[2023-12-25 16:10:41,274 INFO misc.py line 119 253097] Train: [62/100][62/510] Data 0.013 (0.024) Batch 1.401 (1.705) Remain 09:23:18 loss: 0.2023 Lr: 0.00216
[2023-12-25 16:10:42,382 INFO misc.py line 119 253097] Train: [62/100][63/510] Data 0.009 (0.024) Batch 1.097 (1.694) Remain 09:19:56 loss: 0.3042 Lr: 0.00216
[2023-12-25 16:10:43,564 INFO misc.py line 119 253097] Train: [62/100][64/510] Data 0.020 (0.024) Batch 1.195 (1.686) Remain 09:17:12 loss: 0.1043 Lr: 0.00216
[2023-12-25 16:10:44,657 INFO misc.py line 119 253097] Train: [62/100][65/510] Data 0.008 (0.023) Batch 1.096 (1.677) Remain 09:14:01 loss: 0.1279 Lr: 0.00216
[2023-12-25 16:10:45,695 INFO misc.py line 119 253097] Train: [62/100][66/510] Data 0.005 (0.023) Batch 1.029 (1.666) Remain 09:10:36 loss: 0.1485 Lr: 0.00216
[2023-12-25 16:10:46,718 INFO misc.py line 119 253097] Train: [62/100][67/510] Data 0.013 (0.023) Batch 1.032 (1.657) Remain 09:07:17 loss: 0.0607 Lr: 0.00216
[2023-12-25 16:10:47,869 INFO misc.py line 119 253097] Train: [62/100][68/510] Data 0.005 (0.023) Batch 1.144 (1.649) Remain 09:04:40 loss: 0.1171 Lr: 0.00216
[2023-12-25 16:10:53,964 INFO misc.py line 119 253097] Train: [62/100][69/510] Data 0.013 (0.022) Batch 6.101 (1.716) Remain 09:26:55 loss: 0.2205 Lr: 0.00216
[2023-12-25 16:10:55,101 INFO misc.py line 119 253097] Train: [62/100][70/510] Data 0.005 (0.022) Batch 1.135 (1.707) Remain 09:24:01 loss: 0.0808 Lr: 0.00216
[2023-12-25 16:10:56,192 INFO misc.py line 119 253097] Train: [62/100][71/510] Data 0.007 (0.022) Batch 1.093 (1.698) Remain 09:21:01 loss: 0.0672 Lr: 0.00216
[2023-12-25 16:10:57,306 INFO misc.py line 119 253097] Train: [62/100][72/510] Data 0.005 (0.022) Batch 1.116 (1.690) Remain 09:18:11 loss: 0.2266 Lr: 0.00216
[2023-12-25 16:10:58,430 INFO misc.py line 119 253097] Train: [62/100][73/510] Data 0.004 (0.021) Batch 1.123 (1.682) Remain 09:15:29 loss: 0.1691 Lr: 0.00216
[2023-12-25 16:10:59,574 INFO misc.py line 119 253097] Train: [62/100][74/510] Data 0.003 (0.021) Batch 1.141 (1.674) Remain 09:12:57 loss: 0.1342 Lr: 0.00216
[2023-12-25 16:11:00,619 INFO misc.py line 119 253097] Train: [62/100][75/510] Data 0.007 (0.021) Batch 1.048 (1.666) Remain 09:10:03 loss: 0.0860 Lr: 0.00216
[2023-12-25 16:11:01,784 INFO misc.py line 119 253097] Train: [62/100][76/510] Data 0.004 (0.021) Batch 1.165 (1.659) Remain 09:07:45 loss: 0.1618 Lr: 0.00216
[2023-12-25 16:11:02,973 INFO misc.py line 119 253097] Train: [62/100][77/510] Data 0.004 (0.020) Batch 1.183 (1.652) Remain 09:05:36 loss: 0.2632 Lr: 0.00216
[2023-12-25 16:11:03,965 INFO misc.py line 119 253097] Train: [62/100][78/510] Data 0.009 (0.020) Batch 0.997 (1.644) Remain 09:02:42 loss: 0.2729 Lr: 0.00216
[2023-12-25 16:11:05,409 INFO misc.py line 119 253097] Train: [62/100][79/510] Data 0.004 (0.020) Batch 1.244 (1.638) Remain 09:00:56 loss: 0.1823 Lr: 0.00216
[2023-12-25 16:11:06,659 INFO misc.py line 119 253097] Train: [62/100][80/510] Data 0.204 (0.023) Batch 1.450 (1.636) Remain 09:00:06 loss: 0.0740 Lr: 0.00216
[2023-12-25 16:11:07,835 INFO misc.py line 119 253097] Train: [62/100][81/510] Data 0.005 (0.022) Batch 1.174 (1.630) Remain 08:58:07 loss: 0.1311 Lr: 0.00216
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[2023-12-25 16:21:50,742 INFO misc.py line 119 253097] Train: [62/100][500/510] Data 0.003 (0.094) Batch 1.058 (1.549) Remain 08:20:42 loss: 0.2102 Lr: 0.00208
[2023-12-25 16:21:51,853 INFO misc.py line 119 253097] Train: [62/100][501/510] Data 0.004 (0.094) Batch 1.105 (1.548) Remain 08:20:23 loss: 0.1241 Lr: 0.00208
[2023-12-25 16:21:54,440 INFO misc.py line 119 253097] Train: [62/100][502/510] Data 0.009 (0.094) Batch 2.593 (1.551) Remain 08:21:02 loss: 0.0972 Lr: 0.00208
[2023-12-25 16:21:55,450 INFO misc.py line 119 253097] Train: [62/100][503/510] Data 0.004 (0.094) Batch 1.010 (1.550) Remain 08:20:40 loss: 0.0680 Lr: 0.00208
[2023-12-25 16:21:57,364 INFO misc.py line 119 253097] Train: [62/100][504/510] Data 0.003 (0.093) Batch 1.908 (1.550) Remain 08:20:52 loss: 0.1894 Lr: 0.00208
[2023-12-25 16:21:58,451 INFO misc.py line 119 253097] Train: [62/100][505/510] Data 0.008 (0.093) Batch 1.087 (1.549) Remain 08:20:33 loss: 0.3510 Lr: 0.00208
[2023-12-25 16:21:59,550 INFO misc.py line 119 253097] Train: [62/100][506/510] Data 0.008 (0.093) Batch 1.100 (1.548) Remain 08:20:14 loss: 0.0860 Lr: 0.00208
[2023-12-25 16:22:00,793 INFO misc.py line 119 253097] Train: [62/100][507/510] Data 0.007 (0.093) Batch 1.220 (1.548) Remain 08:20:00 loss: 0.1503 Lr: 0.00208
[2023-12-25 16:22:01,904 INFO misc.py line 119 253097] Train: [62/100][508/510] Data 0.031 (0.093) Batch 1.136 (1.547) Remain 08:19:42 loss: 0.1101 Lr: 0.00208
[2023-12-25 16:22:08,285 INFO misc.py line 119 253097] Train: [62/100][509/510] Data 0.005 (0.093) Batch 6.381 (1.556) Remain 08:22:46 loss: 0.1629 Lr: 0.00208
[2023-12-25 16:22:09,301 INFO misc.py line 119 253097] Train: [62/100][510/510] Data 0.005 (0.092) Batch 1.017 (1.555) Remain 08:22:24 loss: 0.2421 Lr: 0.00208
[2023-12-25 16:22:09,301 INFO misc.py line 136 253097] Train result: loss: 0.1526 
[2023-12-25 16:22:09,301 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 16:22:37,677 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6934 
[2023-12-25 16:22:38,031 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3778 
[2023-12-25 16:22:44,362 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3934 
[2023-12-25 16:22:44,881 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4181 
[2023-12-25 16:22:46,848 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0603 
[2023-12-25 16:22:47,277 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3345 
[2023-12-25 16:22:48,154 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9538 
[2023-12-25 16:22:48,716 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4493 
[2023-12-25 16:22:50,522 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9083 
[2023-12-25 16:22:52,644 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3902 
[2023-12-25 16:22:53,504 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2738 
[2023-12-25 16:22:53,931 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8549 
[2023-12-25 16:22:54,835 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6278 
[2023-12-25 16:22:57,772 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 1.0254 
[2023-12-25 16:22:58,242 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3743 
[2023-12-25 16:22:58,861 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3901 
[2023-12-25 16:22:59,575 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2939 
[2023-12-25 16:23:01,040 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6737/0.7241/0.9018.
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9150/0.9501
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9821/0.9910
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8249/0.9811
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.1921/0.1975
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6271/0.6544
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7576/0.8335
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8138/0.9070
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9098/0.9548
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5230/0.5380
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7888/0.8666
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8253/0.8598
[2023-12-25 16:23:01,041 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5991/0.6790
[2023-12-25 16:23:01,042 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 16:23:01,043 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 16:23:01,043 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 16:23:09,441 INFO misc.py line 119 253097] Train: [63/100][1/510] Data 5.248 (5.248) Batch 6.133 (6.133) Remain 33:00:51 loss: 0.1821 Lr: 0.00208
[2023-12-25 16:23:12,264 INFO misc.py line 119 253097] Train: [63/100][2/510] Data 0.003 (0.003) Batch 2.821 (2.821) Remain 15:11:09 loss: 0.1129 Lr: 0.00208
[2023-12-25 16:23:13,447 INFO misc.py line 119 253097] Train: [63/100][3/510] Data 0.006 (0.006) Batch 1.184 (1.184) Remain 06:22:29 loss: 0.1260 Lr: 0.00207
[2023-12-25 16:23:14,544 INFO misc.py line 119 253097] Train: [63/100][4/510] Data 0.005 (0.005) Batch 1.098 (1.098) Remain 05:54:27 loss: 0.1202 Lr: 0.00207
[2023-12-25 16:23:15,790 INFO misc.py line 119 253097] Train: [63/100][5/510] Data 0.003 (0.004) Batch 1.245 (1.171) Remain 06:18:10 loss: 0.1330 Lr: 0.00207
[2023-12-25 16:23:17,015 INFO misc.py line 119 253097] Train: [63/100][6/510] Data 0.005 (0.004) Batch 1.211 (1.184) Remain 06:22:26 loss: 0.3092 Lr: 0.00207
[2023-12-25 16:23:18,434 INFO misc.py line 119 253097] Train: [63/100][7/510] Data 0.019 (0.008) Batch 1.221 (1.194) Remain 06:25:24 loss: 0.0898 Lr: 0.00207
[2023-12-25 16:23:19,477 INFO misc.py line 119 253097] Train: [63/100][8/510] Data 0.216 (0.050) Batch 1.256 (1.206) Remain 06:29:23 loss: 0.1374 Lr: 0.00207
[2023-12-25 16:23:20,520 INFO misc.py line 119 253097] Train: [63/100][9/510] Data 0.004 (0.042) Batch 1.038 (1.178) Remain 06:20:18 loss: 0.1882 Lr: 0.00207
[2023-12-25 16:23:21,649 INFO misc.py line 119 253097] Train: [63/100][10/510] Data 0.010 (0.037) Batch 1.129 (1.171) Remain 06:18:03 loss: 0.1842 Lr: 0.00207
[2023-12-25 16:23:22,701 INFO misc.py line 119 253097] Train: [63/100][11/510] Data 0.009 (0.034) Batch 1.056 (1.157) Remain 06:13:24 loss: 0.1197 Lr: 0.00207
[2023-12-25 16:23:23,887 INFO misc.py line 119 253097] Train: [63/100][12/510] Data 0.005 (0.031) Batch 1.186 (1.160) Remain 06:14:25 loss: 0.2420 Lr: 0.00207
[2023-12-25 16:23:25,088 INFO misc.py line 119 253097] Train: [63/100][13/510] Data 0.005 (0.028) Batch 1.200 (1.164) Remain 06:15:42 loss: 0.1318 Lr: 0.00207
[2023-12-25 16:23:26,139 INFO misc.py line 119 253097] Train: [63/100][14/510] Data 0.005 (0.026) Batch 1.046 (1.153) Remain 06:12:13 loss: 0.1248 Lr: 0.00207
[2023-12-25 16:23:27,463 INFO misc.py line 119 253097] Train: [63/100][15/510] Data 0.011 (0.025) Batch 1.330 (1.168) Remain 06:16:57 loss: 0.0920 Lr: 0.00207
[2023-12-25 16:23:28,572 INFO misc.py line 119 253097] Train: [63/100][16/510] Data 0.004 (0.023) Batch 1.106 (1.163) Remain 06:15:23 loss: 0.1057 Lr: 0.00207
[2023-12-25 16:23:34,075 INFO misc.py line 119 253097] Train: [63/100][17/510] Data 4.696 (0.357) Batch 5.507 (1.473) Remain 07:55:29 loss: 0.0580 Lr: 0.00207
[2023-12-25 16:23:35,313 INFO misc.py line 119 253097] Train: [63/100][18/510] Data 0.004 (0.333) Batch 1.238 (1.458) Remain 07:50:24 loss: 0.1572 Lr: 0.00207
[2023-12-25 16:23:36,413 INFO misc.py line 119 253097] Train: [63/100][19/510] Data 0.004 (0.313) Batch 1.091 (1.435) Remain 07:42:59 loss: 0.0912 Lr: 0.00207
[2023-12-25 16:23:37,449 INFO misc.py line 119 253097] Train: [63/100][20/510] Data 0.013 (0.295) Batch 1.043 (1.412) Remain 07:35:31 loss: 0.1820 Lr: 0.00207
[2023-12-25 16:23:38,547 INFO misc.py line 119 253097] Train: [63/100][21/510] Data 0.006 (0.279) Batch 1.096 (1.394) Remain 07:29:50 loss: 0.1076 Lr: 0.00207
[2023-12-25 16:23:39,501 INFO misc.py line 119 253097] Train: [63/100][22/510] Data 0.009 (0.265) Batch 0.959 (1.371) Remain 07:22:25 loss: 0.1477 Lr: 0.00207
[2023-12-25 16:23:40,631 INFO misc.py line 119 253097] Train: [63/100][23/510] Data 0.005 (0.252) Batch 1.129 (1.359) Remain 07:18:28 loss: 0.1702 Lr: 0.00207
[2023-12-25 16:23:41,921 INFO misc.py line 119 253097] Train: [63/100][24/510] Data 0.006 (0.240) Batch 1.289 (1.356) Remain 07:17:23 loss: 0.2123 Lr: 0.00207
[2023-12-25 16:23:43,139 INFO misc.py line 119 253097] Train: [63/100][25/510] Data 0.006 (0.230) Batch 1.219 (1.350) Remain 07:15:21 loss: 0.1067 Lr: 0.00207
[2023-12-25 16:23:44,339 INFO misc.py line 119 253097] Train: [63/100][26/510] Data 0.006 (0.220) Batch 1.197 (1.343) Remain 07:13:11 loss: 0.1796 Lr: 0.00207
[2023-12-25 16:23:45,585 INFO misc.py line 119 253097] Train: [63/100][27/510] Data 0.008 (0.211) Batch 1.232 (1.338) Remain 07:11:40 loss: 0.1408 Lr: 0.00207
[2023-12-25 16:23:46,806 INFO misc.py line 119 253097] Train: [63/100][28/510] Data 0.024 (0.203) Batch 1.235 (1.334) Remain 07:10:19 loss: 0.0895 Lr: 0.00207
[2023-12-25 16:23:47,894 INFO misc.py line 119 253097] Train: [63/100][29/510] Data 0.008 (0.196) Batch 1.092 (1.325) Remain 07:07:17 loss: 0.1984 Lr: 0.00207
[2023-12-25 16:23:49,012 INFO misc.py line 119 253097] Train: [63/100][30/510] Data 0.004 (0.189) Batch 1.111 (1.317) Remain 07:04:43 loss: 0.1864 Lr: 0.00207
[2023-12-25 16:23:50,094 INFO misc.py line 119 253097] Train: [63/100][31/510] Data 0.010 (0.182) Batch 1.083 (1.309) Remain 07:02:00 loss: 0.0816 Lr: 0.00207
[2023-12-25 16:23:57,226 INFO misc.py line 119 253097] Train: [63/100][32/510] Data 0.009 (0.177) Batch 7.129 (1.509) Remain 08:06:43 loss: 0.1306 Lr: 0.00207
[2023-12-25 16:23:58,421 INFO misc.py line 119 253097] Train: [63/100][33/510] Data 0.012 (0.171) Batch 1.202 (1.499) Remain 08:03:23 loss: 0.0912 Lr: 0.00207
[2023-12-25 16:23:59,517 INFO misc.py line 119 253097] Train: [63/100][34/510] Data 0.005 (0.166) Batch 1.097 (1.486) Remain 07:59:10 loss: 0.0873 Lr: 0.00207
[2023-12-25 16:24:00,719 INFO misc.py line 119 253097] Train: [63/100][35/510] Data 0.004 (0.161) Batch 1.202 (1.477) Remain 07:56:17 loss: 0.2012 Lr: 0.00207
[2023-12-25 16:24:01,954 INFO misc.py line 119 253097] Train: [63/100][36/510] Data 0.004 (0.156) Batch 1.236 (1.470) Remain 07:53:54 loss: 0.1154 Lr: 0.00207
[2023-12-25 16:24:03,052 INFO misc.py line 119 253097] Train: [63/100][37/510] Data 0.003 (0.151) Batch 1.097 (1.459) Remain 07:50:20 loss: 0.3296 Lr: 0.00207
[2023-12-25 16:24:04,304 INFO misc.py line 119 253097] Train: [63/100][38/510] Data 0.003 (0.147) Batch 1.252 (1.453) Remain 07:48:25 loss: 0.2975 Lr: 0.00207
[2023-12-25 16:24:05,580 INFO misc.py line 119 253097] Train: [63/100][39/510] Data 0.003 (0.143) Batch 1.265 (1.448) Remain 07:46:42 loss: 0.1550 Lr: 0.00207
[2023-12-25 16:24:06,783 INFO misc.py line 119 253097] Train: [63/100][40/510] Data 0.015 (0.140) Batch 1.215 (1.442) Remain 07:44:39 loss: 0.1043 Lr: 0.00207
[2023-12-25 16:24:08,085 INFO misc.py line 119 253097] Train: [63/100][41/510] Data 0.003 (0.136) Batch 1.268 (1.437) Remain 07:43:09 loss: 0.1343 Lr: 0.00207
[2023-12-25 16:24:09,262 INFO misc.py line 119 253097] Train: [63/100][42/510] Data 0.037 (0.134) Batch 1.206 (1.431) Remain 07:41:13 loss: 0.0817 Lr: 0.00207
[2023-12-25 16:24:10,249 INFO misc.py line 119 253097] Train: [63/100][43/510] Data 0.008 (0.130) Batch 0.991 (1.420) Remain 07:37:39 loss: 0.1287 Lr: 0.00207
[2023-12-25 16:24:11,430 INFO misc.py line 119 253097] Train: [63/100][44/510] Data 0.004 (0.127) Batch 1.181 (1.414) Remain 07:35:45 loss: 0.0798 Lr: 0.00207
[2023-12-25 16:24:12,608 INFO misc.py line 119 253097] Train: [63/100][45/510] Data 0.006 (0.124) Batch 1.179 (1.409) Remain 07:33:55 loss: 0.1288 Lr: 0.00207
[2023-12-25 16:24:13,760 INFO misc.py line 119 253097] Train: [63/100][46/510] Data 0.004 (0.122) Batch 1.153 (1.403) Remain 07:31:58 loss: 0.2265 Lr: 0.00207
[2023-12-25 16:24:14,818 INFO misc.py line 119 253097] Train: [63/100][47/510] Data 0.003 (0.119) Batch 1.058 (1.395) Remain 07:29:26 loss: 0.1673 Lr: 0.00207
[2023-12-25 16:24:15,957 INFO misc.py line 119 253097] Train: [63/100][48/510] Data 0.003 (0.116) Batch 1.138 (1.389) Remain 07:27:34 loss: 0.1670 Lr: 0.00207
[2023-12-25 16:24:16,919 INFO misc.py line 119 253097] Train: [63/100][49/510] Data 0.003 (0.114) Batch 0.962 (1.380) Remain 07:24:33 loss: 0.2346 Lr: 0.00207
[2023-12-25 16:24:18,096 INFO misc.py line 119 253097] Train: [63/100][50/510] Data 0.003 (0.112) Batch 1.177 (1.376) Remain 07:23:08 loss: 0.1237 Lr: 0.00207
[2023-12-25 16:24:19,430 INFO misc.py line 119 253097] Train: [63/100][51/510] Data 0.004 (0.109) Batch 1.334 (1.375) Remain 07:22:50 loss: 0.1227 Lr: 0.00207
[2023-12-25 16:24:20,612 INFO misc.py line 119 253097] Train: [63/100][52/510] Data 0.005 (0.107) Batch 1.181 (1.371) Remain 07:21:32 loss: 0.1067 Lr: 0.00207
[2023-12-25 16:24:21,767 INFO misc.py line 119 253097] Train: [63/100][53/510] Data 0.005 (0.105) Batch 1.152 (1.366) Remain 07:20:07 loss: 0.1318 Lr: 0.00207
[2023-12-25 16:24:22,879 INFO misc.py line 119 253097] Train: [63/100][54/510] Data 0.007 (0.103) Batch 1.116 (1.361) Remain 07:18:30 loss: 0.1971 Lr: 0.00207
[2023-12-25 16:24:24,019 INFO misc.py line 119 253097] Train: [63/100][55/510] Data 0.003 (0.101) Batch 1.116 (1.357) Remain 07:16:58 loss: 0.1317 Lr: 0.00207
[2023-12-25 16:24:32,918 INFO misc.py line 119 253097] Train: [63/100][56/510] Data 0.028 (0.100) Batch 8.922 (1.499) Remain 08:02:55 loss: 0.1593 Lr: 0.00207
[2023-12-25 16:24:33,971 INFO misc.py line 119 253097] Train: [63/100][57/510] Data 0.005 (0.098) Batch 1.046 (1.491) Remain 08:00:11 loss: 0.1013 Lr: 0.00206
[2023-12-25 16:24:35,255 INFO misc.py line 119 253097] Train: [63/100][58/510] Data 0.012 (0.097) Batch 1.292 (1.487) Remain 07:59:00 loss: 0.2056 Lr: 0.00206
[2023-12-25 16:24:36,478 INFO misc.py line 119 253097] Train: [63/100][59/510] Data 0.004 (0.095) Batch 1.221 (1.483) Remain 07:57:26 loss: 0.0792 Lr: 0.00206
[2023-12-25 16:24:37,567 INFO misc.py line 119 253097] Train: [63/100][60/510] Data 0.006 (0.093) Batch 1.086 (1.476) Remain 07:55:10 loss: 0.1157 Lr: 0.00206
[2023-12-25 16:24:38,689 INFO misc.py line 119 253097] Train: [63/100][61/510] Data 0.008 (0.092) Batch 1.123 (1.470) Remain 07:53:11 loss: 0.1775 Lr: 0.00206
[2023-12-25 16:24:39,734 INFO misc.py line 119 253097] Train: [63/100][62/510] Data 0.008 (0.090) Batch 1.047 (1.462) Remain 07:50:51 loss: 0.1493 Lr: 0.00206
[2023-12-25 16:24:40,972 INFO misc.py line 119 253097] Train: [63/100][63/510] Data 0.007 (0.089) Batch 1.239 (1.459) Remain 07:49:38 loss: 0.1311 Lr: 0.00206
[2023-12-25 16:24:42,129 INFO misc.py line 119 253097] Train: [63/100][64/510] Data 0.005 (0.088) Batch 1.155 (1.454) Remain 07:48:00 loss: 0.1998 Lr: 0.00206
[2023-12-25 16:24:43,224 INFO misc.py line 119 253097] Train: [63/100][65/510] Data 0.006 (0.086) Batch 1.095 (1.448) Remain 07:46:07 loss: 0.2642 Lr: 0.00206
[2023-12-25 16:24:44,172 INFO misc.py line 119 253097] Train: [63/100][66/510] Data 0.007 (0.085) Batch 0.951 (1.440) Remain 07:43:33 loss: 0.1776 Lr: 0.00206
[2023-12-25 16:24:45,465 INFO misc.py line 119 253097] Train: [63/100][67/510] Data 0.004 (0.084) Batch 1.283 (1.438) Remain 07:42:45 loss: 0.2100 Lr: 0.00206
[2023-12-25 16:24:46,633 INFO misc.py line 119 253097] Train: [63/100][68/510] Data 0.014 (0.083) Batch 1.171 (1.434) Remain 07:41:24 loss: 0.1543 Lr: 0.00206
[2023-12-25 16:24:47,774 INFO misc.py line 119 253097] Train: [63/100][69/510] Data 0.010 (0.082) Batch 1.139 (1.429) Remain 07:39:56 loss: 0.2064 Lr: 0.00206
[2023-12-25 16:24:49,012 INFO misc.py line 119 253097] Train: [63/100][70/510] Data 0.013 (0.081) Batch 1.241 (1.426) Remain 07:39:01 loss: 0.1752 Lr: 0.00206
[2023-12-25 16:24:50,198 INFO misc.py line 119 253097] Train: [63/100][71/510] Data 0.009 (0.080) Batch 1.187 (1.423) Remain 07:37:51 loss: 0.1160 Lr: 0.00206
[2023-12-25 16:24:51,386 INFO misc.py line 119 253097] Train: [63/100][72/510] Data 0.008 (0.079) Batch 1.175 (1.419) Remain 07:36:41 loss: 0.1525 Lr: 0.00206
[2023-12-25 16:24:52,543 INFO misc.py line 119 253097] Train: [63/100][73/510] Data 0.021 (0.078) Batch 1.170 (1.416) Remain 07:35:30 loss: 0.2490 Lr: 0.00206
[2023-12-25 16:24:53,865 INFO misc.py line 119 253097] Train: [63/100][74/510] Data 0.008 (0.077) Batch 1.323 (1.414) Remain 07:35:04 loss: 0.3342 Lr: 0.00206
[2023-12-25 16:24:54,949 INFO misc.py line 119 253097] Train: [63/100][75/510] Data 0.007 (0.076) Batch 1.082 (1.410) Remain 07:33:33 loss: 0.1563 Lr: 0.00206
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[2023-12-25 16:29:19,025 INFO misc.py line 119 253097] Train: [63/100][250/510] Data 0.003 (0.060) Batch 7.495 (1.480) Remain 07:51:53 loss: 0.0941 Lr: 0.00203
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[2023-12-25 16:35:29,750 INFO misc.py line 119 253097] Train: [63/100][494/510] Data 0.003 (0.065) Batch 1.080 (1.500) Remain 07:52:01 loss: 0.0921 Lr: 0.00198
[2023-12-25 16:35:30,671 INFO misc.py line 119 253097] Train: [63/100][495/510] Data 0.008 (0.065) Batch 0.927 (1.498) Remain 07:51:37 loss: 0.0996 Lr: 0.00198
[2023-12-25 16:35:31,742 INFO misc.py line 119 253097] Train: [63/100][496/510] Data 0.003 (0.065) Batch 1.071 (1.498) Remain 07:51:19 loss: 0.1915 Lr: 0.00198
[2023-12-25 16:35:32,914 INFO misc.py line 119 253097] Train: [63/100][497/510] Data 0.003 (0.065) Batch 1.172 (1.497) Remain 07:51:05 loss: 0.1720 Lr: 0.00198
[2023-12-25 16:35:34,038 INFO misc.py line 119 253097] Train: [63/100][498/510] Data 0.003 (0.065) Batch 1.124 (1.496) Remain 07:50:50 loss: 0.1294 Lr: 0.00198
[2023-12-25 16:35:35,090 INFO misc.py line 119 253097] Train: [63/100][499/510] Data 0.004 (0.065) Batch 1.039 (1.495) Remain 07:50:31 loss: 0.0855 Lr: 0.00198
[2023-12-25 16:35:36,165 INFO misc.py line 119 253097] Train: [63/100][500/510] Data 0.016 (0.065) Batch 1.087 (1.494) Remain 07:50:14 loss: 0.2230 Lr: 0.00198
[2023-12-25 16:35:37,304 INFO misc.py line 119 253097] Train: [63/100][501/510] Data 0.004 (0.065) Batch 1.138 (1.494) Remain 07:49:59 loss: 0.0887 Lr: 0.00198
[2023-12-25 16:35:48,108 INFO misc.py line 119 253097] Train: [63/100][502/510] Data 0.003 (0.064) Batch 1.109 (1.493) Remain 07:49:43 loss: 0.1550 Lr: 0.00198
[2023-12-25 16:35:49,123 INFO misc.py line 119 253097] Train: [63/100][503/510] Data 9.700 (0.084) Batch 10.710 (1.511) Remain 07:55:29 loss: 0.1633 Lr: 0.00198
[2023-12-25 16:35:50,273 INFO misc.py line 119 253097] Train: [63/100][504/510] Data 0.004 (0.084) Batch 1.151 (1.511) Remain 07:55:14 loss: 0.1094 Lr: 0.00198
[2023-12-25 16:35:51,361 INFO misc.py line 119 253097] Train: [63/100][505/510] Data 0.004 (0.083) Batch 1.085 (1.510) Remain 07:54:57 loss: 0.2617 Lr: 0.00198
[2023-12-25 16:35:52,512 INFO misc.py line 119 253097] Train: [63/100][506/510] Data 0.006 (0.083) Batch 1.153 (1.509) Remain 07:54:42 loss: 0.1260 Lr: 0.00198
[2023-12-25 16:35:53,633 INFO misc.py line 119 253097] Train: [63/100][507/510] Data 0.004 (0.083) Batch 1.121 (1.508) Remain 07:54:26 loss: 0.0962 Lr: 0.00198
[2023-12-25 16:35:54,619 INFO misc.py line 119 253097] Train: [63/100][508/510] Data 0.004 (0.083) Batch 0.986 (1.507) Remain 07:54:05 loss: 0.1915 Lr: 0.00198
[2023-12-25 16:35:55,620 INFO misc.py line 119 253097] Train: [63/100][509/510] Data 0.004 (0.083) Batch 1.001 (1.506) Remain 07:53:44 loss: 0.1820 Lr: 0.00198
[2023-12-25 16:36:04,454 INFO misc.py line 119 253097] Train: [63/100][510/510] Data 0.004 (0.083) Batch 8.835 (1.521) Remain 07:58:16 loss: 0.1469 Lr: 0.00198
[2023-12-25 16:36:04,455 INFO misc.py line 136 253097] Train result: loss: 0.1535 
[2023-12-25 16:36:04,455 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 16:36:32,676 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4807 
[2023-12-25 16:36:33,028 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3257 
[2023-12-25 16:36:38,516 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3418 
[2023-12-25 16:36:39,047 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4262 
[2023-12-25 16:36:41,014 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9055 
[2023-12-25 16:36:41,438 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2839 
[2023-12-25 16:36:42,320 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3798 
[2023-12-25 16:36:42,873 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2867 
[2023-12-25 16:36:44,681 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.4765 
[2023-12-25 16:36:46,808 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1045 
[2023-12-25 16:36:47,664 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2887 
[2023-12-25 16:36:48,088 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7991 
[2023-12-25 16:36:48,988 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6026 
[2023-12-25 16:36:51,939 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9080 
[2023-12-25 16:36:52,407 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3543 
[2023-12-25 16:36:53,016 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3130 
[2023-12-25 16:36:53,715 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3248 
[2023-12-25 16:36:55,247 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6905/0.7559/0.9047.
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9234/0.9519
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9831/0.9905
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8477/0.9711
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3218/0.3528
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6319/0.6502
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7673/0.8637
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8105/0.8854
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9159/0.9622
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5945/0.7428
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7627/0.8333
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8185/0.8842
[2023-12-25 16:36:55,248 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5991/0.7383
[2023-12-25 16:36:55,249 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 16:36:55,250 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 16:36:55,250 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 16:37:07,461 INFO misc.py line 119 253097] Train: [64/100][1/510] Data 5.704 (5.704) Batch 9.890 (9.890) Remain 51:50:21 loss: 0.0940 Lr: 0.00198
[2023-12-25 16:37:08,628 INFO misc.py line 119 253097] Train: [64/100][2/510] Data 0.003 (0.003) Batch 1.167 (1.167) Remain 06:06:51 loss: 0.1334 Lr: 0.00198
[2023-12-25 16:37:09,736 INFO misc.py line 119 253097] Train: [64/100][3/510] Data 0.004 (0.004) Batch 1.109 (1.109) Remain 05:48:45 loss: 0.1482 Lr: 0.00198
[2023-12-25 16:37:10,847 INFO misc.py line 119 253097] Train: [64/100][4/510] Data 0.003 (0.003) Batch 1.111 (1.111) Remain 05:49:15 loss: 0.0788 Lr: 0.00198
[2023-12-25 16:37:14,138 INFO misc.py line 119 253097] Train: [64/100][5/510] Data 0.004 (0.003) Batch 3.292 (2.201) Remain 11:32:05 loss: 0.0959 Lr: 0.00198
[2023-12-25 16:37:15,261 INFO misc.py line 119 253097] Train: [64/100][6/510] Data 0.003 (0.003) Batch 1.123 (1.842) Remain 09:39:01 loss: 0.1254 Lr: 0.00198
[2023-12-25 16:37:16,507 INFO misc.py line 119 253097] Train: [64/100][7/510] Data 0.004 (0.004) Batch 1.244 (1.692) Remain 08:52:03 loss: 0.1483 Lr: 0.00198
[2023-12-25 16:37:17,809 INFO misc.py line 119 253097] Train: [64/100][8/510] Data 0.005 (0.004) Batch 1.301 (1.614) Remain 08:27:25 loss: 0.1128 Lr: 0.00198
[2023-12-25 16:37:18,982 INFO misc.py line 119 253097] Train: [64/100][9/510] Data 0.006 (0.004) Batch 1.171 (1.540) Remain 08:04:11 loss: 0.1754 Lr: 0.00198
[2023-12-25 16:37:20,076 INFO misc.py line 119 253097] Train: [64/100][10/510] Data 0.008 (0.005) Batch 1.093 (1.476) Remain 07:44:05 loss: 0.0928 Lr: 0.00198
[2023-12-25 16:37:21,199 INFO misc.py line 119 253097] Train: [64/100][11/510] Data 0.009 (0.005) Batch 1.124 (1.432) Remain 07:30:12 loss: 0.2573 Lr: 0.00198
[2023-12-25 16:37:22,404 INFO misc.py line 119 253097] Train: [64/100][12/510] Data 0.008 (0.005) Batch 1.207 (1.407) Remain 07:22:19 loss: 0.1928 Lr: 0.00198
[2023-12-25 16:37:23,352 INFO misc.py line 119 253097] Train: [64/100][13/510] Data 0.005 (0.005) Batch 0.946 (1.361) Remain 07:07:49 loss: 0.1165 Lr: 0.00198
[2023-12-25 16:37:24,431 INFO misc.py line 119 253097] Train: [64/100][14/510] Data 0.008 (0.006) Batch 1.082 (1.336) Remain 06:59:49 loss: 0.0926 Lr: 0.00198
[2023-12-25 16:37:25,632 INFO misc.py line 119 253097] Train: [64/100][15/510] Data 0.005 (0.006) Batch 1.200 (1.325) Remain 06:56:15 loss: 0.0931 Lr: 0.00198
[2023-12-25 16:37:26,548 INFO misc.py line 119 253097] Train: [64/100][16/510] Data 0.004 (0.005) Batch 0.916 (1.293) Remain 06:46:21 loss: 0.3330 Lr: 0.00198
[2023-12-25 16:37:27,809 INFO misc.py line 119 253097] Train: [64/100][17/510] Data 0.004 (0.005) Batch 1.261 (1.291) Remain 06:45:37 loss: 0.3131 Lr: 0.00198
[2023-12-25 16:37:29,021 INFO misc.py line 119 253097] Train: [64/100][18/510] Data 0.003 (0.005) Batch 1.212 (1.286) Remain 06:43:57 loss: 0.1382 Lr: 0.00198
[2023-12-25 16:37:30,105 INFO misc.py line 119 253097] Train: [64/100][19/510] Data 0.004 (0.005) Batch 1.083 (1.273) Remain 06:39:57 loss: 0.1125 Lr: 0.00198
[2023-12-25 16:37:31,290 INFO misc.py line 119 253097] Train: [64/100][20/510] Data 0.004 (0.005) Batch 1.185 (1.268) Remain 06:38:18 loss: 0.1186 Lr: 0.00198
[2023-12-25 16:37:33,769 INFO misc.py line 119 253097] Train: [64/100][21/510] Data 0.004 (0.005) Batch 2.474 (1.335) Remain 06:59:20 loss: 0.1236 Lr: 0.00198
[2023-12-25 16:37:34,918 INFO misc.py line 119 253097] Train: [64/100][22/510] Data 0.010 (0.005) Batch 1.155 (1.325) Remain 06:56:20 loss: 0.0898 Lr: 0.00198
[2023-12-25 16:37:35,935 INFO misc.py line 119 253097] Train: [64/100][23/510] Data 0.003 (0.005) Batch 1.016 (1.310) Remain 06:51:28 loss: 0.2546 Lr: 0.00198
[2023-12-25 16:37:37,197 INFO misc.py line 119 253097] Train: [64/100][24/510] Data 0.004 (0.005) Batch 1.258 (1.307) Remain 06:50:40 loss: 0.1235 Lr: 0.00198
[2023-12-25 16:37:38,952 INFO misc.py line 119 253097] Train: [64/100][25/510] Data 0.008 (0.005) Batch 1.757 (1.328) Remain 06:57:03 loss: 0.1737 Lr: 0.00198
[2023-12-25 16:37:40,138 INFO misc.py line 119 253097] Train: [64/100][26/510] Data 0.008 (0.005) Batch 1.189 (1.322) Remain 06:55:08 loss: 0.0639 Lr: 0.00198
[2023-12-25 16:37:44,845 INFO misc.py line 119 253097] Train: [64/100][27/510] Data 0.003 (0.005) Batch 4.707 (1.463) Remain 07:39:24 loss: 0.1348 Lr: 0.00198
[2023-12-25 16:37:45,980 INFO misc.py line 119 253097] Train: [64/100][28/510] Data 0.003 (0.005) Batch 1.135 (1.450) Remain 07:35:16 loss: 0.1266 Lr: 0.00198
[2023-12-25 16:37:47,124 INFO misc.py line 119 253097] Train: [64/100][29/510] Data 0.003 (0.005) Batch 1.144 (1.438) Remain 07:31:33 loss: 0.2770 Lr: 0.00198
[2023-12-25 16:37:48,295 INFO misc.py line 119 253097] Train: [64/100][30/510] Data 0.005 (0.005) Batch 1.171 (1.428) Remain 07:28:25 loss: 0.1197 Lr: 0.00198
[2023-12-25 16:37:49,629 INFO misc.py line 119 253097] Train: [64/100][31/510] Data 0.004 (0.005) Batch 1.333 (1.425) Remain 07:27:20 loss: 0.1870 Lr: 0.00198
[2023-12-25 16:37:50,696 INFO misc.py line 119 253097] Train: [64/100][32/510] Data 0.004 (0.005) Batch 1.056 (1.412) Remain 07:23:19 loss: 0.0760 Lr: 0.00198
[2023-12-25 16:37:51,968 INFO misc.py line 119 253097] Train: [64/100][33/510] Data 0.015 (0.005) Batch 1.280 (1.408) Remain 07:21:55 loss: 0.2119 Lr: 0.00198
[2023-12-25 16:37:56,181 INFO misc.py line 119 253097] Train: [64/100][34/510] Data 0.006 (0.005) Batch 4.215 (1.498) Remain 07:50:19 loss: 0.1873 Lr: 0.00198
[2023-12-25 16:37:57,344 INFO misc.py line 119 253097] Train: [64/100][35/510] Data 0.005 (0.005) Batch 1.162 (1.488) Remain 07:47:00 loss: 0.1618 Lr: 0.00198
[2023-12-25 16:37:58,291 INFO misc.py line 119 253097] Train: [64/100][36/510] Data 0.007 (0.005) Batch 0.950 (1.471) Remain 07:41:51 loss: 0.1069 Lr: 0.00197
[2023-12-25 16:37:59,518 INFO misc.py line 119 253097] Train: [64/100][37/510] Data 0.004 (0.005) Batch 1.228 (1.464) Remain 07:39:35 loss: 0.1259 Lr: 0.00197
[2023-12-25 16:38:00,693 INFO misc.py line 119 253097] Train: [64/100][38/510] Data 0.003 (0.005) Batch 1.175 (1.456) Remain 07:36:58 loss: 0.1640 Lr: 0.00197
[2023-12-25 16:38:01,753 INFO misc.py line 119 253097] Train: [64/100][39/510] Data 0.003 (0.005) Batch 1.056 (1.445) Remain 07:33:27 loss: 0.0932 Lr: 0.00197
[2023-12-25 16:38:02,988 INFO misc.py line 119 253097] Train: [64/100][40/510] Data 0.006 (0.005) Batch 1.234 (1.439) Remain 07:31:38 loss: 0.2561 Lr: 0.00197
[2023-12-25 16:38:03,991 INFO misc.py line 119 253097] Train: [64/100][41/510] Data 0.008 (0.005) Batch 1.008 (1.428) Remain 07:28:03 loss: 0.1549 Lr: 0.00197
[2023-12-25 16:38:05,169 INFO misc.py line 119 253097] Train: [64/100][42/510] Data 0.003 (0.005) Batch 1.176 (1.421) Remain 07:26:00 loss: 0.1191 Lr: 0.00197
[2023-12-25 16:38:06,397 INFO misc.py line 119 253097] Train: [64/100][43/510] Data 0.005 (0.005) Batch 1.229 (1.417) Remain 07:24:28 loss: 0.1785 Lr: 0.00197
[2023-12-25 16:38:07,480 INFO misc.py line 119 253097] Train: [64/100][44/510] Data 0.004 (0.005) Batch 1.081 (1.408) Remain 07:21:53 loss: 0.0951 Lr: 0.00197
[2023-12-25 16:38:08,563 INFO misc.py line 119 253097] Train: [64/100][45/510] Data 0.006 (0.005) Batch 1.083 (1.401) Remain 07:19:26 loss: 0.1524 Lr: 0.00197
[2023-12-25 16:38:09,745 INFO misc.py line 119 253097] Train: [64/100][46/510] Data 0.005 (0.005) Batch 1.184 (1.396) Remain 07:17:49 loss: 0.1007 Lr: 0.00197
[2023-12-25 16:38:10,954 INFO misc.py line 119 253097] Train: [64/100][47/510] Data 0.004 (0.005) Batch 1.205 (1.391) Remain 07:16:26 loss: 0.0970 Lr: 0.00197
[2023-12-25 16:38:20,933 INFO misc.py line 119 253097] Train: [64/100][48/510] Data 0.007 (0.005) Batch 9.982 (1.582) Remain 08:16:18 loss: 0.0835 Lr: 0.00197
[2023-12-25 16:38:22,053 INFO misc.py line 119 253097] Train: [64/100][49/510] Data 0.004 (0.005) Batch 1.120 (1.572) Remain 08:13:08 loss: 0.3625 Lr: 0.00197
[2023-12-25 16:38:23,205 INFO misc.py line 119 253097] Train: [64/100][50/510] Data 0.004 (0.005) Batch 1.104 (1.562) Remain 08:09:59 loss: 0.0950 Lr: 0.00197
[2023-12-25 16:38:24,435 INFO misc.py line 119 253097] Train: [64/100][51/510] Data 0.052 (0.006) Batch 1.273 (1.556) Remain 08:08:04 loss: 0.0837 Lr: 0.00197
[2023-12-25 16:38:25,717 INFO misc.py line 119 253097] Train: [64/100][52/510] Data 0.010 (0.006) Batch 1.287 (1.551) Remain 08:06:19 loss: 0.1211 Lr: 0.00197
[2023-12-25 16:38:26,739 INFO misc.py line 119 253097] Train: [64/100][53/510] Data 0.005 (0.006) Batch 1.017 (1.540) Remain 08:02:57 loss: 0.1778 Lr: 0.00197
[2023-12-25 16:38:31,329 INFO misc.py line 119 253097] Train: [64/100][54/510] Data 0.009 (0.006) Batch 4.595 (1.600) Remain 08:21:42 loss: 0.1324 Lr: 0.00197
[2023-12-25 16:38:32,620 INFO misc.py line 119 253097] Train: [64/100][55/510] Data 0.005 (0.006) Batch 1.291 (1.594) Remain 08:19:49 loss: 0.1383 Lr: 0.00197
[2023-12-25 16:38:33,860 INFO misc.py line 119 253097] Train: [64/100][56/510] Data 0.004 (0.006) Batch 1.240 (1.587) Remain 08:17:42 loss: 0.1598 Lr: 0.00197
[2023-12-25 16:38:35,076 INFO misc.py line 119 253097] Train: [64/100][57/510] Data 0.004 (0.006) Batch 1.211 (1.580) Remain 08:15:29 loss: 0.2695 Lr: 0.00197
[2023-12-25 16:38:36,340 INFO misc.py line 119 253097] Train: [64/100][58/510] Data 0.009 (0.006) Batch 1.269 (1.575) Remain 08:13:41 loss: 0.2190 Lr: 0.00197
[2023-12-25 16:38:37,427 INFO misc.py line 119 253097] Train: [64/100][59/510] Data 0.004 (0.006) Batch 1.087 (1.566) Remain 08:10:56 loss: 0.1839 Lr: 0.00197
[2023-12-25 16:38:38,526 INFO misc.py line 119 253097] Train: [64/100][60/510] Data 0.004 (0.006) Batch 1.100 (1.558) Remain 08:08:20 loss: 0.0790 Lr: 0.00197
[2023-12-25 16:38:39,635 INFO misc.py line 119 253097] Train: [64/100][61/510] Data 0.005 (0.006) Batch 1.108 (1.550) Remain 08:05:53 loss: 0.0762 Lr: 0.00197
[2023-12-25 16:38:40,824 INFO misc.py line 119 253097] Train: [64/100][62/510] Data 0.004 (0.006) Batch 1.190 (1.544) Remain 08:03:56 loss: 0.3710 Lr: 0.00197
[2023-12-25 16:38:45,854 INFO misc.py line 119 253097] Train: [64/100][63/510] Data 3.753 (0.069) Batch 5.028 (1.602) Remain 08:22:07 loss: 0.1158 Lr: 0.00197
[2023-12-25 16:38:47,192 INFO misc.py line 119 253097] Train: [64/100][64/510] Data 0.005 (0.068) Batch 1.334 (1.598) Remain 08:20:43 loss: 0.1688 Lr: 0.00197
[2023-12-25 16:38:48,498 INFO misc.py line 119 253097] Train: [64/100][65/510] Data 0.010 (0.067) Batch 1.311 (1.593) Remain 08:19:15 loss: 0.0987 Lr: 0.00197
[2023-12-25 16:38:49,486 INFO misc.py line 119 253097] Train: [64/100][66/510] Data 0.004 (0.066) Batch 0.988 (1.583) Remain 08:16:12 loss: 0.1527 Lr: 0.00197
[2023-12-25 16:38:50,795 INFO misc.py line 119 253097] Train: [64/100][67/510] Data 0.003 (0.065) Batch 1.304 (1.579) Remain 08:14:49 loss: 0.1973 Lr: 0.00197
[2023-12-25 16:38:51,821 INFO misc.py line 119 253097] Train: [64/100][68/510] Data 0.009 (0.064) Batch 1.028 (1.570) Remain 08:12:08 loss: 0.2281 Lr: 0.00197
[2023-12-25 16:39:00,627 INFO misc.py line 119 253097] Train: [64/100][69/510] Data 0.006 (0.063) Batch 8.807 (1.680) Remain 08:46:28 loss: 0.2087 Lr: 0.00197
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[2023-12-25 16:49:23,013 INFO misc.py line 119 253097] Train: [64/100][488/510] Data 0.008 (0.107) Batch 1.218 (1.512) Remain 07:43:11 loss: 0.1106 Lr: 0.00189
[2023-12-25 16:49:24,156 INFO misc.py line 119 253097] Train: [64/100][489/510] Data 0.005 (0.107) Batch 1.139 (1.511) Remain 07:42:56 loss: 0.0887 Lr: 0.00189
[2023-12-25 16:49:25,373 INFO misc.py line 119 253097] Train: [64/100][490/510] Data 0.009 (0.107) Batch 1.221 (1.511) Remain 07:42:43 loss: 0.1308 Lr: 0.00189
[2023-12-25 16:49:26,653 INFO misc.py line 119 253097] Train: [64/100][491/510] Data 0.005 (0.107) Batch 1.277 (1.510) Remain 07:42:33 loss: 0.0875 Lr: 0.00189
[2023-12-25 16:49:27,725 INFO misc.py line 119 253097] Train: [64/100][492/510] Data 0.008 (0.107) Batch 1.076 (1.509) Remain 07:42:15 loss: 0.1251 Lr: 0.00189
[2023-12-25 16:49:28,729 INFO misc.py line 119 253097] Train: [64/100][493/510] Data 0.004 (0.106) Batch 0.998 (1.508) Remain 07:41:54 loss: 0.0647 Lr: 0.00189
[2023-12-25 16:49:29,762 INFO misc.py line 119 253097] Train: [64/100][494/510] Data 0.010 (0.106) Batch 1.037 (1.507) Remain 07:41:35 loss: 0.1441 Lr: 0.00189
[2023-12-25 16:49:30,857 INFO misc.py line 119 253097] Train: [64/100][495/510] Data 0.007 (0.106) Batch 1.093 (1.506) Remain 07:41:18 loss: 0.1288 Lr: 0.00189
[2023-12-25 16:49:31,718 INFO misc.py line 119 253097] Train: [64/100][496/510] Data 0.008 (0.106) Batch 0.866 (1.505) Remain 07:40:53 loss: 0.0821 Lr: 0.00189
[2023-12-25 16:49:39,738 INFO misc.py line 119 253097] Train: [64/100][497/510] Data 0.003 (0.106) Batch 8.019 (1.518) Remain 07:44:54 loss: 0.2639 Lr: 0.00189
[2023-12-25 16:49:40,814 INFO misc.py line 119 253097] Train: [64/100][498/510] Data 0.005 (0.105) Batch 1.077 (1.517) Remain 07:44:36 loss: 0.1779 Lr: 0.00189
[2023-12-25 16:49:41,888 INFO misc.py line 119 253097] Train: [64/100][499/510] Data 0.004 (0.105) Batch 1.074 (1.516) Remain 07:44:18 loss: 0.1298 Lr: 0.00189
[2023-12-25 16:49:43,124 INFO misc.py line 119 253097] Train: [64/100][500/510] Data 0.004 (0.105) Batch 1.234 (1.516) Remain 07:44:06 loss: 0.1597 Lr: 0.00189
[2023-12-25 16:49:44,042 INFO misc.py line 119 253097] Train: [64/100][501/510] Data 0.005 (0.105) Batch 0.919 (1.515) Remain 07:43:42 loss: 0.1687 Lr: 0.00189
[2023-12-25 16:49:45,180 INFO misc.py line 119 253097] Train: [64/100][502/510] Data 0.004 (0.105) Batch 1.138 (1.514) Remain 07:43:27 loss: 0.1223 Lr: 0.00189
[2023-12-25 16:49:46,411 INFO misc.py line 119 253097] Train: [64/100][503/510] Data 0.004 (0.104) Batch 1.229 (1.513) Remain 07:43:15 loss: 0.2105 Lr: 0.00189
[2023-12-25 16:49:47,669 INFO misc.py line 119 253097] Train: [64/100][504/510] Data 0.005 (0.104) Batch 1.254 (1.513) Remain 07:43:04 loss: 0.1185 Lr: 0.00189
[2023-12-25 16:49:48,610 INFO misc.py line 119 253097] Train: [64/100][505/510] Data 0.009 (0.104) Batch 0.945 (1.512) Remain 07:42:42 loss: 0.1724 Lr: 0.00189
[2023-12-25 16:49:49,946 INFO misc.py line 119 253097] Train: [64/100][506/510] Data 0.005 (0.104) Batch 1.280 (1.511) Remain 07:42:32 loss: 0.1299 Lr: 0.00189
[2023-12-25 16:49:50,905 INFO misc.py line 119 253097] Train: [64/100][507/510] Data 0.062 (0.104) Batch 1.017 (1.510) Remain 07:42:12 loss: 0.1293 Lr: 0.00189
[2023-12-25 16:49:52,131 INFO misc.py line 119 253097] Train: [64/100][508/510] Data 0.004 (0.104) Batch 1.218 (1.510) Remain 07:42:00 loss: 0.1296 Lr: 0.00189
[2023-12-25 16:50:04,798 INFO misc.py line 119 253097] Train: [64/100][509/510] Data 0.012 (0.103) Batch 12.675 (1.532) Remain 07:48:44 loss: 0.3183 Lr: 0.00189
[2023-12-25 16:50:05,959 INFO misc.py line 119 253097] Train: [64/100][510/510] Data 0.004 (0.103) Batch 1.157 (1.531) Remain 07:48:29 loss: 0.0873 Lr: 0.00189
[2023-12-25 16:50:05,960 INFO misc.py line 136 253097] Train result: loss: 0.1467 
[2023-12-25 16:50:05,961 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 16:50:35,625 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5146 
[2023-12-25 16:50:35,972 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2628 
[2023-12-25 16:50:40,919 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3304 
[2023-12-25 16:50:41,435 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2903 
[2023-12-25 16:50:43,414 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6229 
[2023-12-25 16:50:43,838 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3103 
[2023-12-25 16:50:44,718 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0699 
[2023-12-25 16:50:45,271 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3522 
[2023-12-25 16:50:47,077 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9044 
[2023-12-25 16:50:49,207 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3436 
[2023-12-25 16:50:50,066 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2657 
[2023-12-25 16:50:50,489 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.1473 
[2023-12-25 16:50:51,390 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5341 
[2023-12-25 16:50:54,324 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7236 
[2023-12-25 16:50:54,793 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3781 
[2023-12-25 16:50:55,402 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4238 
[2023-12-25 16:50:56,102 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2549 
[2023-12-25 16:50:57,691 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6778/0.7631/0.9015.
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9171/0.9482
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9814/0.9918
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8434/0.9617
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.4381/0.5598
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6145/0.6493
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6528/0.7191
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7970/0.8672
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9247/0.9646
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5193/0.8202
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7829/0.8742
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7471/0.8548
[2023-12-25 16:50:57,691 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5930/0.7091
[2023-12-25 16:50:57,692 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 16:50:57,693 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 16:50:57,693 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 16:51:22,096 INFO misc.py line 119 253097] Train: [65/100][1/510] Data 4.792 (4.792) Batch 22.399 (22.399) Remain 114:13:48 loss: 0.1216 Lr: 0.00189
[2023-12-25 16:51:23,173 INFO misc.py line 119 253097] Train: [65/100][2/510] Data 0.005 (0.005) Batch 1.078 (1.078) Remain 05:29:58 loss: 0.0646 Lr: 0.00189
[2023-12-25 16:51:24,312 INFO misc.py line 119 253097] Train: [65/100][3/510] Data 0.003 (0.003) Batch 1.138 (1.138) Remain 05:48:18 loss: 0.1875 Lr: 0.00189
[2023-12-25 16:51:25,406 INFO misc.py line 119 253097] Train: [65/100][4/510] Data 0.004 (0.004) Batch 1.082 (1.082) Remain 05:31:06 loss: 0.0968 Lr: 0.00189
[2023-12-25 16:51:26,512 INFO misc.py line 119 253097] Train: [65/100][5/510] Data 0.016 (0.010) Batch 1.119 (1.100) Remain 05:36:38 loss: 0.0995 Lr: 0.00189
[2023-12-25 16:51:27,546 INFO misc.py line 119 253097] Train: [65/100][6/510] Data 0.003 (0.008) Batch 1.033 (1.078) Remain 05:29:45 loss: 0.1150 Lr: 0.00189
[2023-12-25 16:51:30,227 INFO misc.py line 119 253097] Train: [65/100][7/510] Data 0.003 (0.007) Batch 2.681 (1.479) Remain 07:32:19 loss: 0.0813 Lr: 0.00189
[2023-12-25 16:51:31,503 INFO misc.py line 119 253097] Train: [65/100][8/510] Data 0.004 (0.006) Batch 1.274 (1.438) Remain 07:19:48 loss: 0.3301 Lr: 0.00189
[2023-12-25 16:51:32,670 INFO misc.py line 119 253097] Train: [65/100][9/510] Data 0.005 (0.006) Batch 1.165 (1.392) Remain 07:05:52 loss: 0.1368 Lr: 0.00189
[2023-12-25 16:51:33,715 INFO misc.py line 119 253097] Train: [65/100][10/510] Data 0.008 (0.006) Batch 1.049 (1.343) Remain 06:50:50 loss: 0.1729 Lr: 0.00189
[2023-12-25 16:51:34,879 INFO misc.py line 119 253097] Train: [65/100][11/510] Data 0.004 (0.006) Batch 1.161 (1.321) Remain 06:43:50 loss: 0.1046 Lr: 0.00189
[2023-12-25 16:51:35,870 INFO misc.py line 119 253097] Train: [65/100][12/510] Data 0.007 (0.006) Batch 0.989 (1.284) Remain 06:32:34 loss: 0.1427 Lr: 0.00189
[2023-12-25 16:51:37,024 INFO misc.py line 119 253097] Train: [65/100][13/510] Data 0.009 (0.006) Batch 1.155 (1.271) Remain 06:28:36 loss: 0.1150 Lr: 0.00189
[2023-12-25 16:51:38,239 INFO misc.py line 119 253097] Train: [65/100][14/510] Data 0.007 (0.006) Batch 1.211 (1.265) Remain 06:26:55 loss: 0.1031 Lr: 0.00189
[2023-12-25 16:51:39,406 INFO misc.py line 119 253097] Train: [65/100][15/510] Data 0.012 (0.007) Batch 1.173 (1.258) Remain 06:24:33 loss: 0.0776 Lr: 0.00189
[2023-12-25 16:51:40,339 INFO misc.py line 119 253097] Train: [65/100][16/510] Data 0.005 (0.007) Batch 0.934 (1.233) Remain 06:16:55 loss: 0.0886 Lr: 0.00189
[2023-12-25 16:51:41,511 INFO misc.py line 119 253097] Train: [65/100][17/510] Data 0.004 (0.006) Batch 1.172 (1.229) Remain 06:15:35 loss: 0.2895 Lr: 0.00189
[2023-12-25 16:51:42,679 INFO misc.py line 119 253097] Train: [65/100][18/510] Data 0.003 (0.006) Batch 1.165 (1.224) Remain 06:14:15 loss: 0.1039 Lr: 0.00189
[2023-12-25 16:51:43,759 INFO misc.py line 119 253097] Train: [65/100][19/510] Data 0.007 (0.006) Batch 1.082 (1.215) Remain 06:11:31 loss: 0.1107 Lr: 0.00189
[2023-12-25 16:51:45,005 INFO misc.py line 119 253097] Train: [65/100][20/510] Data 0.005 (0.006) Batch 1.246 (1.217) Remain 06:12:02 loss: 0.2663 Lr: 0.00189
[2023-12-25 16:51:46,255 INFO misc.py line 119 253097] Train: [65/100][21/510] Data 0.006 (0.006) Batch 1.251 (1.219) Remain 06:12:35 loss: 0.1251 Lr: 0.00189
[2023-12-25 16:51:47,524 INFO misc.py line 119 253097] Train: [65/100][22/510] Data 0.004 (0.006) Batch 1.265 (1.221) Remain 06:13:18 loss: 0.1922 Lr: 0.00188
[2023-12-25 16:51:48,660 INFO misc.py line 119 253097] Train: [65/100][23/510] Data 0.008 (0.006) Batch 1.140 (1.217) Remain 06:12:03 loss: 0.1762 Lr: 0.00188
[2023-12-25 16:51:49,876 INFO misc.py line 119 253097] Train: [65/100][24/510] Data 0.004 (0.006) Batch 1.216 (1.217) Remain 06:12:00 loss: 0.1161 Lr: 0.00188
[2023-12-25 16:51:55,855 INFO misc.py line 119 253097] Train: [65/100][25/510] Data 0.004 (0.006) Batch 5.979 (1.434) Remain 07:18:08 loss: 0.1063 Lr: 0.00188
[2023-12-25 16:51:56,874 INFO misc.py line 119 253097] Train: [65/100][26/510] Data 0.004 (0.006) Batch 1.019 (1.416) Remain 07:12:36 loss: 0.1124 Lr: 0.00188
[2023-12-25 16:51:58,138 INFO misc.py line 119 253097] Train: [65/100][27/510] Data 0.004 (0.006) Batch 1.261 (1.409) Remain 07:10:36 loss: 0.0617 Lr: 0.00188
[2023-12-25 16:51:59,239 INFO misc.py line 119 253097] Train: [65/100][28/510] Data 0.006 (0.006) Batch 1.104 (1.397) Remain 07:06:51 loss: 0.0799 Lr: 0.00188
[2023-12-25 16:52:00,425 INFO misc.py line 119 253097] Train: [65/100][29/510] Data 0.004 (0.006) Batch 1.185 (1.389) Remain 07:04:20 loss: 0.2528 Lr: 0.00188
[2023-12-25 16:52:01,498 INFO misc.py line 119 253097] Train: [65/100][30/510] Data 0.005 (0.006) Batch 1.070 (1.377) Remain 07:00:42 loss: 0.1772 Lr: 0.00188
[2023-12-25 16:52:02,701 INFO misc.py line 119 253097] Train: [65/100][31/510] Data 0.008 (0.006) Batch 1.201 (1.371) Remain 06:58:46 loss: 0.1453 Lr: 0.00188
[2023-12-25 16:52:03,913 INFO misc.py line 119 253097] Train: [65/100][32/510] Data 0.010 (0.006) Batch 1.217 (1.366) Remain 06:57:08 loss: 0.0943 Lr: 0.00188
[2023-12-25 16:52:04,857 INFO misc.py line 119 253097] Train: [65/100][33/510] Data 0.003 (0.006) Batch 0.942 (1.351) Remain 06:52:48 loss: 0.1349 Lr: 0.00188
[2023-12-25 16:52:05,964 INFO misc.py line 119 253097] Train: [65/100][34/510] Data 0.007 (0.006) Batch 1.109 (1.344) Remain 06:50:23 loss: 0.0997 Lr: 0.00188
[2023-12-25 16:52:07,024 INFO misc.py line 119 253097] Train: [65/100][35/510] Data 0.004 (0.006) Batch 1.059 (1.335) Remain 06:47:39 loss: 0.0798 Lr: 0.00188
[2023-12-25 16:52:08,252 INFO misc.py line 119 253097] Train: [65/100][36/510] Data 0.006 (0.006) Batch 1.225 (1.331) Remain 06:46:36 loss: 0.0957 Lr: 0.00188
[2023-12-25 16:52:09,466 INFO misc.py line 119 253097] Train: [65/100][37/510] Data 0.008 (0.006) Batch 1.212 (1.328) Remain 06:45:30 loss: 0.3478 Lr: 0.00188
[2023-12-25 16:52:10,602 INFO misc.py line 119 253097] Train: [65/100][38/510] Data 0.010 (0.006) Batch 1.104 (1.321) Remain 06:43:32 loss: 0.0931 Lr: 0.00188
[2023-12-25 16:52:11,572 INFO misc.py line 119 253097] Train: [65/100][39/510] Data 0.043 (0.007) Batch 1.005 (1.313) Remain 06:40:49 loss: 0.1923 Lr: 0.00188
[2023-12-25 16:52:12,381 INFO misc.py line 119 253097] Train: [65/100][40/510] Data 0.007 (0.007) Batch 0.811 (1.299) Remain 06:36:40 loss: 0.2432 Lr: 0.00188
[2023-12-25 16:52:13,363 INFO misc.py line 119 253097] Train: [65/100][41/510] Data 0.005 (0.007) Batch 0.983 (1.291) Remain 06:34:06 loss: 0.1039 Lr: 0.00188
[2023-12-25 16:52:14,476 INFO misc.py line 119 253097] Train: [65/100][42/510] Data 0.004 (0.007) Batch 1.111 (1.286) Remain 06:32:41 loss: 0.1034 Lr: 0.00188
[2023-12-25 16:52:30,618 INFO misc.py line 119 253097] Train: [65/100][43/510] Data 0.006 (0.007) Batch 16.143 (1.658) Remain 08:26:03 loss: 0.1424 Lr: 0.00188
[2023-12-25 16:52:31,913 INFO misc.py line 119 253097] Train: [65/100][44/510] Data 0.005 (0.007) Batch 1.295 (1.649) Remain 08:23:19 loss: 0.0975 Lr: 0.00188
[2023-12-25 16:52:32,974 INFO misc.py line 119 253097] Train: [65/100][45/510] Data 0.004 (0.007) Batch 1.058 (1.635) Remain 08:19:00 loss: 0.1306 Lr: 0.00188
[2023-12-25 16:52:34,148 INFO misc.py line 119 253097] Train: [65/100][46/510] Data 0.006 (0.007) Batch 1.174 (1.624) Remain 08:15:42 loss: 0.2084 Lr: 0.00188
[2023-12-25 16:52:35,324 INFO misc.py line 119 253097] Train: [65/100][47/510] Data 0.007 (0.007) Batch 1.180 (1.614) Remain 08:12:35 loss: 0.1094 Lr: 0.00188
[2023-12-25 16:52:36,241 INFO misc.py line 119 253097] Train: [65/100][48/510] Data 0.003 (0.007) Batch 0.916 (1.598) Remain 08:07:50 loss: 0.2260 Lr: 0.00188
[2023-12-25 16:52:39,723 INFO misc.py line 119 253097] Train: [65/100][49/510] Data 0.003 (0.007) Batch 3.481 (1.639) Remain 08:20:18 loss: 0.2393 Lr: 0.00188
[2023-12-25 16:52:40,956 INFO misc.py line 119 253097] Train: [65/100][50/510] Data 0.005 (0.007) Batch 1.229 (1.631) Remain 08:17:36 loss: 0.2096 Lr: 0.00188
[2023-12-25 16:52:42,257 INFO misc.py line 119 253097] Train: [65/100][51/510] Data 0.008 (0.007) Batch 1.306 (1.624) Remain 08:15:31 loss: 0.0737 Lr: 0.00188
[2023-12-25 16:52:43,502 INFO misc.py line 119 253097] Train: [65/100][52/510] Data 0.003 (0.007) Batch 1.240 (1.616) Remain 08:13:06 loss: 0.1700 Lr: 0.00188
[2023-12-25 16:52:44,561 INFO misc.py line 119 253097] Train: [65/100][53/510] Data 0.009 (0.007) Batch 1.064 (1.605) Remain 08:09:42 loss: 0.2371 Lr: 0.00188
[2023-12-25 16:52:45,712 INFO misc.py line 119 253097] Train: [65/100][54/510] Data 0.004 (0.007) Batch 1.147 (1.596) Remain 08:06:56 loss: 0.1226 Lr: 0.00188
[2023-12-25 16:52:46,841 INFO misc.py line 119 253097] Train: [65/100][55/510] Data 0.008 (0.007) Batch 1.130 (1.587) Remain 08:04:10 loss: 0.0923 Lr: 0.00188
[2023-12-25 16:52:48,083 INFO misc.py line 119 253097] Train: [65/100][56/510] Data 0.006 (0.007) Batch 1.244 (1.581) Remain 08:02:10 loss: 0.1719 Lr: 0.00188
[2023-12-25 16:52:49,234 INFO misc.py line 119 253097] Train: [65/100][57/510] Data 0.005 (0.007) Batch 1.152 (1.573) Remain 07:59:44 loss: 0.1914 Lr: 0.00188
[2023-12-25 16:52:50,395 INFO misc.py line 119 253097] Train: [65/100][58/510] Data 0.003 (0.006) Batch 1.161 (1.565) Remain 07:57:25 loss: 0.3916 Lr: 0.00188
[2023-12-25 16:52:55,069 INFO misc.py line 119 253097] Train: [65/100][59/510] Data 0.003 (0.006) Batch 4.673 (1.621) Remain 08:14:19 loss: 0.1204 Lr: 0.00188
[2023-12-25 16:52:56,097 INFO misc.py line 119 253097] Train: [65/100][60/510] Data 0.006 (0.006) Batch 1.029 (1.610) Remain 08:11:07 loss: 0.1912 Lr: 0.00188
[2023-12-25 16:52:57,316 INFO misc.py line 119 253097] Train: [65/100][61/510] Data 0.004 (0.006) Batch 1.219 (1.604) Remain 08:09:02 loss: 0.1373 Lr: 0.00188
[2023-12-25 16:52:58,199 INFO misc.py line 119 253097] Train: [65/100][62/510] Data 0.003 (0.006) Batch 0.882 (1.591) Remain 08:05:17 loss: 0.0806 Lr: 0.00188
[2023-12-25 16:52:59,378 INFO misc.py line 119 253097] Train: [65/100][63/510] Data 0.005 (0.006) Batch 1.180 (1.584) Remain 08:03:10 loss: 0.2341 Lr: 0.00188
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[2023-12-25 16:56:40,421 INFO misc.py line 119 253097] Train: [65/100][204/510] Data 0.004 (0.170) Batch 0.995 (1.573) Remain 07:55:53 loss: 0.2055 Lr: 0.00185
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[2023-12-25 16:56:46,458 INFO misc.py line 119 253097] Train: [65/100][209/510] Data 0.005 (0.166) Batch 1.189 (1.564) Remain 07:53:04 loss: 0.1110 Lr: 0.00185
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[2023-12-25 16:56:49,867 INFO misc.py line 119 253097] Train: [65/100][212/510] Data 0.004 (0.164) Batch 1.152 (1.558) Remain 07:51:08 loss: 0.2264 Lr: 0.00185
[2023-12-25 16:56:50,829 INFO misc.py line 119 253097] Train: [65/100][213/510] Data 0.004 (0.163) Batch 0.963 (1.555) Remain 07:50:15 loss: 0.1448 Lr: 0.00185
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[2023-12-25 16:56:53,138 INFO misc.py line 119 253097] Train: [65/100][215/510] Data 0.005 (0.161) Batch 1.182 (1.551) Remain 07:49:04 loss: 0.0623 Lr: 0.00185
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[2023-12-25 16:56:59,212 INFO misc.py line 119 253097] Train: [65/100][217/510] Data 4.113 (0.179) Batch 4.996 (1.565) Remain 07:53:12 loss: 0.0854 Lr: 0.00185
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[2023-12-25 16:57:02,482 INFO misc.py line 119 253097] Train: [65/100][220/510] Data 0.005 (0.177) Batch 0.979 (1.558) Remain 07:51:09 loss: 0.0734 Lr: 0.00185
[2023-12-25 16:57:03,377 INFO misc.py line 119 253097] Train: [65/100][221/510] Data 0.010 (0.176) Batch 0.897 (1.555) Remain 07:50:12 loss: 0.1972 Lr: 0.00185
[2023-12-25 16:57:08,747 INFO misc.py line 119 253097] Train: [65/100][222/510] Data 0.003 (0.175) Batch 5.369 (1.573) Remain 07:55:26 loss: 0.1079 Lr: 0.00185
[2023-12-25 16:57:09,979 INFO misc.py line 119 253097] Train: [65/100][223/510] Data 0.003 (0.174) Batch 1.232 (1.571) Remain 07:54:57 loss: 0.2309 Lr: 0.00185
[2023-12-25 16:57:11,164 INFO misc.py line 119 253097] Train: [65/100][224/510] Data 0.003 (0.174) Batch 1.182 (1.569) Remain 07:54:23 loss: 0.0975 Lr: 0.00185
[2023-12-25 16:57:12,454 INFO misc.py line 119 253097] Train: [65/100][225/510] Data 0.006 (0.173) Batch 1.279 (1.568) Remain 07:53:58 loss: 0.1144 Lr: 0.00185
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[2023-12-25 16:57:18,377 INFO misc.py line 119 253097] Train: [65/100][230/510] Data 0.008 (0.169) Batch 1.124 (1.560) Remain 07:51:18 loss: 0.2637 Lr: 0.00185
[2023-12-25 16:57:19,562 INFO misc.py line 119 253097] Train: [65/100][231/510] Data 0.010 (0.169) Batch 1.185 (1.558) Remain 07:50:46 loss: 0.0924 Lr: 0.00185
[2023-12-25 16:57:20,852 INFO misc.py line 119 253097] Train: [65/100][232/510] Data 0.007 (0.168) Batch 1.291 (1.557) Remain 07:50:24 loss: 0.0905 Lr: 0.00185
[2023-12-25 16:57:22,075 INFO misc.py line 119 253097] Train: [65/100][233/510] Data 0.005 (0.167) Batch 1.219 (1.555) Remain 07:49:55 loss: 0.1492 Lr: 0.00185
[2023-12-25 16:57:26,144 INFO misc.py line 119 253097] Train: [65/100][234/510] Data 0.010 (0.166) Batch 4.075 (1.566) Remain 07:53:12 loss: 0.0863 Lr: 0.00185
[2023-12-25 16:57:27,392 INFO misc.py line 119 253097] Train: [65/100][235/510] Data 0.003 (0.166) Batch 1.246 (1.565) Remain 07:52:45 loss: 0.1128 Lr: 0.00185
[2023-12-25 16:57:28,561 INFO misc.py line 119 253097] Train: [65/100][236/510] Data 0.005 (0.165) Batch 1.171 (1.563) Remain 07:52:13 loss: 0.0881 Lr: 0.00185
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[2023-12-25 16:57:32,045 INFO misc.py line 119 253097] Train: [65/100][239/510] Data 0.009 (0.163) Batch 0.987 (1.558) Remain 07:50:35 loss: 0.1477 Lr: 0.00185
[2023-12-25 16:57:36,204 INFO misc.py line 119 253097] Train: [65/100][240/510] Data 0.004 (0.162) Batch 4.159 (1.569) Remain 07:53:53 loss: 0.1464 Lr: 0.00185
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[2023-12-25 16:57:43,977 INFO misc.py line 119 253097] Train: [65/100][246/510] Data 0.006 (0.158) Batch 1.028 (1.562) Remain 07:51:41 loss: 0.0885 Lr: 0.00184
[2023-12-25 16:57:45,035 INFO misc.py line 119 253097] Train: [65/100][247/510] Data 0.007 (0.158) Batch 1.061 (1.560) Remain 07:51:02 loss: 0.1046 Lr: 0.00184
[2023-12-25 16:57:46,123 INFO misc.py line 119 253097] Train: [65/100][248/510] Data 0.005 (0.157) Batch 1.088 (1.558) Remain 07:50:25 loss: 0.1087 Lr: 0.00184
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[2023-12-25 17:03:53,959 INFO misc.py line 119 253097] Train: [65/100][482/510] Data 0.006 (0.112) Batch 1.205 (1.565) Remain 07:46:19 loss: 0.0686 Lr: 0.00180
[2023-12-25 17:03:55,052 INFO misc.py line 119 253097] Train: [65/100][483/510] Data 0.006 (0.112) Batch 1.071 (1.564) Remain 07:45:59 loss: 0.0726 Lr: 0.00180
[2023-12-25 17:03:56,169 INFO misc.py line 119 253097] Train: [65/100][484/510] Data 0.028 (0.112) Batch 1.139 (1.563) Remain 07:45:42 loss: 0.1187 Lr: 0.00180
[2023-12-25 17:03:57,326 INFO misc.py line 119 253097] Train: [65/100][485/510] Data 0.007 (0.112) Batch 1.160 (1.562) Remain 07:45:25 loss: 0.1591 Lr: 0.00180
[2023-12-25 17:03:58,290 INFO misc.py line 119 253097] Train: [65/100][486/510] Data 0.005 (0.112) Batch 0.963 (1.561) Remain 07:45:01 loss: 0.1496 Lr: 0.00180
[2023-12-25 17:03:59,428 INFO misc.py line 119 253097] Train: [65/100][487/510] Data 0.004 (0.111) Batch 1.139 (1.560) Remain 07:44:44 loss: 0.0863 Lr: 0.00180
[2023-12-25 17:04:00,317 INFO misc.py line 119 253097] Train: [65/100][488/510] Data 0.005 (0.111) Batch 0.887 (1.559) Remain 07:44:18 loss: 0.0922 Lr: 0.00180
[2023-12-25 17:04:01,393 INFO misc.py line 119 253097] Train: [65/100][489/510] Data 0.006 (0.111) Batch 1.078 (1.558) Remain 07:43:59 loss: 0.0873 Lr: 0.00180
[2023-12-25 17:04:02,640 INFO misc.py line 119 253097] Train: [65/100][490/510] Data 0.004 (0.111) Batch 1.243 (1.557) Remain 07:43:45 loss: 0.1556 Lr: 0.00180
[2023-12-25 17:04:03,798 INFO misc.py line 119 253097] Train: [65/100][491/510] Data 0.008 (0.110) Batch 1.162 (1.556) Remain 07:43:29 loss: 0.0681 Lr: 0.00180
[2023-12-25 17:04:04,840 INFO misc.py line 119 253097] Train: [65/100][492/510] Data 0.005 (0.110) Batch 1.042 (1.555) Remain 07:43:09 loss: 0.1069 Lr: 0.00180
[2023-12-25 17:04:06,030 INFO misc.py line 119 253097] Train: [65/100][493/510] Data 0.005 (0.110) Batch 1.186 (1.555) Remain 07:42:54 loss: 0.0907 Lr: 0.00180
[2023-12-25 17:04:07,190 INFO misc.py line 119 253097] Train: [65/100][494/510] Data 0.009 (0.110) Batch 1.160 (1.554) Remain 07:42:38 loss: 0.1438 Lr: 0.00180
[2023-12-25 17:04:08,336 INFO misc.py line 119 253097] Train: [65/100][495/510] Data 0.009 (0.110) Batch 1.151 (1.553) Remain 07:42:22 loss: 0.2164 Lr: 0.00180
[2023-12-25 17:04:09,618 INFO misc.py line 119 253097] Train: [65/100][496/510] Data 0.004 (0.109) Batch 1.280 (1.552) Remain 07:42:11 loss: 0.1045 Lr: 0.00180
[2023-12-25 17:04:10,981 INFO misc.py line 119 253097] Train: [65/100][497/510] Data 0.005 (0.109) Batch 1.357 (1.552) Remain 07:42:02 loss: 0.1043 Lr: 0.00180
[2023-12-25 17:04:12,117 INFO misc.py line 119 253097] Train: [65/100][498/510] Data 0.012 (0.109) Batch 1.123 (1.551) Remain 07:41:45 loss: 0.0934 Lr: 0.00180
[2023-12-25 17:04:13,126 INFO misc.py line 119 253097] Train: [65/100][499/510] Data 0.024 (0.109) Batch 1.028 (1.550) Remain 07:41:24 loss: 0.0966 Lr: 0.00180
[2023-12-25 17:04:14,450 INFO misc.py line 119 253097] Train: [65/100][500/510] Data 0.004 (0.109) Batch 1.321 (1.550) Remain 07:41:15 loss: 0.1131 Lr: 0.00180
[2023-12-25 17:04:15,704 INFO misc.py line 119 253097] Train: [65/100][501/510] Data 0.008 (0.108) Batch 1.259 (1.549) Remain 07:41:03 loss: 0.1026 Lr: 0.00180
[2023-12-25 17:04:16,864 INFO misc.py line 119 253097] Train: [65/100][502/510] Data 0.004 (0.108) Batch 1.080 (1.548) Remain 07:40:44 loss: 0.1085 Lr: 0.00180
[2023-12-25 17:04:17,891 INFO misc.py line 119 253097] Train: [65/100][503/510] Data 0.084 (0.108) Batch 1.103 (1.547) Remain 07:40:27 loss: 0.1804 Lr: 0.00180
[2023-12-25 17:04:19,026 INFO misc.py line 119 253097] Train: [65/100][504/510] Data 0.007 (0.108) Batch 1.136 (1.546) Remain 07:40:11 loss: 0.1946 Lr: 0.00180
[2023-12-25 17:04:20,308 INFO misc.py line 119 253097] Train: [65/100][505/510] Data 0.007 (0.108) Batch 1.280 (1.546) Remain 07:40:00 loss: 0.0681 Lr: 0.00180
[2023-12-25 17:04:21,400 INFO misc.py line 119 253097] Train: [65/100][506/510] Data 0.007 (0.108) Batch 1.094 (1.545) Remain 07:39:42 loss: 0.2222 Lr: 0.00180
[2023-12-25 17:04:22,561 INFO misc.py line 119 253097] Train: [65/100][507/510] Data 0.006 (0.107) Batch 1.157 (1.544) Remain 07:39:27 loss: 0.1069 Lr: 0.00180
[2023-12-25 17:04:23,484 INFO misc.py line 119 253097] Train: [65/100][508/510] Data 0.009 (0.107) Batch 0.924 (1.543) Remain 07:39:03 loss: 0.3152 Lr: 0.00180
[2023-12-25 17:04:24,698 INFO misc.py line 119 253097] Train: [65/100][509/510] Data 0.009 (0.107) Batch 1.219 (1.542) Remain 07:38:50 loss: 0.1736 Lr: 0.00180
[2023-12-25 17:04:25,724 INFO misc.py line 119 253097] Train: [65/100][510/510] Data 0.003 (0.107) Batch 1.023 (1.541) Remain 07:38:31 loss: 0.1033 Lr: 0.00180
[2023-12-25 17:04:25,725 INFO misc.py line 136 253097] Train result: loss: 0.1441 
[2023-12-25 17:04:25,726 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 17:04:54,830 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4632 
[2023-12-25 17:04:55,193 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4710 
[2023-12-25 17:05:00,135 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4234 
[2023-12-25 17:05:00,656 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3656 
[2023-12-25 17:05:02,634 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.5980 
[2023-12-25 17:05:03,064 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4546 
[2023-12-25 17:05:03,949 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9565 
[2023-12-25 17:05:04,512 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3426 
[2023-12-25 17:05:06,318 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8444 
[2023-12-25 17:05:08,440 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3558 
[2023-12-25 17:05:09,313 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3030 
[2023-12-25 17:05:09,739 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9656 
[2023-12-25 17:05:10,644 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 1.0907 
[2023-12-25 17:05:13,601 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7352 
[2023-12-25 17:05:14,067 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2518 
[2023-12-25 17:05:14,694 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4467 
[2023-12-25 17:05:15,405 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3148 
[2023-12-25 17:05:16,816 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6780/0.7415/0.8986.
[2023-12-25 17:05:16,816 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9198/0.9502
[2023-12-25 17:05:16,816 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9814/0.9888
[2023-12-25 17:05:16,816 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8422/0.9622
[2023-12-25 17:05:16,816 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 17:05:16,816 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3717/0.4811
[2023-12-25 17:05:16,816 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6302/0.6534
[2023-12-25 17:05:16,817 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6371/0.7367
[2023-12-25 17:05:16,817 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8207/0.8858
[2023-12-25 17:05:16,817 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9074/0.9619
[2023-12-25 17:05:16,817 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6108/0.6326
[2023-12-25 17:05:16,817 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7641/0.8776
[2023-12-25 17:05:16,817 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7473/0.8264
[2023-12-25 17:05:16,817 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5819/0.6831
[2023-12-25 17:05:16,817 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 17:05:16,818 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 17:05:16,818 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 17:05:28,775 INFO misc.py line 119 253097] Train: [66/100][1/510] Data 5.922 (5.922) Batch 9.493 (9.493) Remain 47:03:55 loss: 0.1157 Lr: 0.00180
[2023-12-25 17:05:30,060 INFO misc.py line 119 253097] Train: [66/100][2/510] Data 0.006 (0.006) Batch 1.286 (1.286) Remain 06:22:38 loss: 0.1737 Lr: 0.00180
[2023-12-25 17:05:31,917 INFO misc.py line 119 253097] Train: [66/100][3/510] Data 0.005 (0.005) Batch 1.853 (1.853) Remain 09:11:14 loss: 0.1512 Lr: 0.00180
[2023-12-25 17:05:33,058 INFO misc.py line 119 253097] Train: [66/100][4/510] Data 0.009 (0.009) Batch 1.145 (1.145) Remain 05:40:37 loss: 0.0938 Lr: 0.00180
[2023-12-25 17:05:34,207 INFO misc.py line 119 253097] Train: [66/100][5/510] Data 0.004 (0.007) Batch 1.149 (1.147) Remain 05:41:10 loss: 0.1268 Lr: 0.00180
[2023-12-25 17:05:35,409 INFO misc.py line 119 253097] Train: [66/100][6/510] Data 0.004 (0.006) Batch 1.196 (1.163) Remain 05:45:58 loss: 0.1013 Lr: 0.00180
[2023-12-25 17:05:38,164 INFO misc.py line 119 253097] Train: [66/100][7/510] Data 0.010 (0.007) Batch 2.761 (1.563) Remain 07:44:46 loss: 0.1069 Lr: 0.00180
[2023-12-25 17:05:39,246 INFO misc.py line 119 253097] Train: [66/100][8/510] Data 0.003 (0.006) Batch 1.078 (1.466) Remain 07:15:54 loss: 0.0783 Lr: 0.00180
[2023-12-25 17:05:40,410 INFO misc.py line 119 253097] Train: [66/100][9/510] Data 0.007 (0.006) Batch 1.095 (1.404) Remain 06:57:29 loss: 0.1753 Lr: 0.00180
[2023-12-25 17:05:47,364 INFO misc.py line 119 253097] Train: [66/100][10/510] Data 0.077 (0.016) Batch 7.026 (2.207) Remain 10:56:16 loss: 0.1223 Lr: 0.00180
[2023-12-25 17:05:48,453 INFO misc.py line 119 253097] Train: [66/100][11/510] Data 0.006 (0.015) Batch 1.089 (2.067) Remain 10:14:41 loss: 0.1944 Lr: 0.00180
[2023-12-25 17:05:49,682 INFO misc.py line 119 253097] Train: [66/100][12/510] Data 0.005 (0.014) Batch 1.225 (1.974) Remain 09:46:50 loss: 0.1671 Lr: 0.00180
[2023-12-25 17:05:50,833 INFO misc.py line 119 253097] Train: [66/100][13/510] Data 0.008 (0.013) Batch 1.155 (1.892) Remain 09:22:27 loss: 0.1586 Lr: 0.00179
[2023-12-25 17:05:51,798 INFO misc.py line 119 253097] Train: [66/100][14/510] Data 0.004 (0.012) Batch 0.966 (1.808) Remain 08:57:23 loss: 0.1537 Lr: 0.00179
[2023-12-25 17:05:53,038 INFO misc.py line 119 253097] Train: [66/100][15/510] Data 0.003 (0.012) Batch 1.238 (1.760) Remain 08:43:15 loss: 0.1317 Lr: 0.00179
[2023-12-25 17:05:54,120 INFO misc.py line 119 253097] Train: [66/100][16/510] Data 0.005 (0.011) Batch 1.083 (1.708) Remain 08:27:45 loss: 0.0993 Lr: 0.00179
[2023-12-25 17:05:55,309 INFO misc.py line 119 253097] Train: [66/100][17/510] Data 0.003 (0.011) Batch 1.189 (1.671) Remain 08:16:41 loss: 0.1201 Lr: 0.00179
[2023-12-25 17:05:56,467 INFO misc.py line 119 253097] Train: [66/100][18/510] Data 0.005 (0.010) Batch 1.157 (1.637) Remain 08:06:29 loss: 0.0788 Lr: 0.00179
[2023-12-25 17:05:57,677 INFO misc.py line 119 253097] Train: [66/100][19/510] Data 0.005 (0.010) Batch 1.211 (1.610) Remain 07:58:32 loss: 0.0813 Lr: 0.00179
[2023-12-25 17:05:58,891 INFO misc.py line 119 253097] Train: [66/100][20/510] Data 0.004 (0.010) Batch 1.215 (1.587) Remain 07:51:36 loss: 0.0734 Lr: 0.00179
[2023-12-25 17:06:00,225 INFO misc.py line 119 253097] Train: [66/100][21/510] Data 0.003 (0.009) Batch 1.295 (1.571) Remain 07:46:46 loss: 0.2882 Lr: 0.00179
[2023-12-25 17:06:01,442 INFO misc.py line 119 253097] Train: [66/100][22/510] Data 0.041 (0.011) Batch 1.246 (1.554) Remain 07:41:39 loss: 0.1249 Lr: 0.00179
[2023-12-25 17:06:02,521 INFO misc.py line 119 253097] Train: [66/100][23/510] Data 0.013 (0.011) Batch 1.088 (1.530) Remain 07:34:42 loss: 0.0877 Lr: 0.00179
[2023-12-25 17:06:03,852 INFO misc.py line 119 253097] Train: [66/100][24/510] Data 0.003 (0.011) Batch 1.276 (1.518) Remain 07:31:05 loss: 0.1748 Lr: 0.00179
[2023-12-25 17:06:12,509 INFO misc.py line 119 253097] Train: [66/100][25/510] Data 7.449 (0.349) Batch 8.712 (1.845) Remain 09:08:12 loss: 0.1400 Lr: 0.00179
[2023-12-25 17:06:13,464 INFO misc.py line 119 253097] Train: [66/100][26/510] Data 0.004 (0.334) Batch 0.951 (1.806) Remain 08:56:38 loss: 0.0723 Lr: 0.00179
[2023-12-25 17:06:14,591 INFO misc.py line 119 253097] Train: [66/100][27/510] Data 0.007 (0.320) Batch 1.110 (1.777) Remain 08:47:59 loss: 0.1928 Lr: 0.00179
[2023-12-25 17:06:15,522 INFO misc.py line 119 253097] Train: [66/100][28/510] Data 0.024 (0.308) Batch 0.951 (1.744) Remain 08:38:08 loss: 0.1131 Lr: 0.00179
[2023-12-25 17:06:16,730 INFO misc.py line 119 253097] Train: [66/100][29/510] Data 0.003 (0.297) Batch 1.206 (1.724) Remain 08:31:57 loss: 0.1539 Lr: 0.00179
[2023-12-25 17:06:17,830 INFO misc.py line 119 253097] Train: [66/100][30/510] Data 0.006 (0.286) Batch 1.102 (1.701) Remain 08:25:05 loss: 0.1256 Lr: 0.00179
[2023-12-25 17:06:19,031 INFO misc.py line 119 253097] Train: [66/100][31/510] Data 0.004 (0.276) Batch 1.201 (1.683) Remain 08:19:45 loss: 0.1955 Lr: 0.00179
[2023-12-25 17:06:20,155 INFO misc.py line 119 253097] Train: [66/100][32/510] Data 0.004 (0.266) Batch 1.124 (1.664) Remain 08:14:00 loss: 0.1269 Lr: 0.00179
[2023-12-25 17:06:21,115 INFO misc.py line 119 253097] Train: [66/100][33/510] Data 0.004 (0.258) Batch 0.936 (1.639) Remain 08:06:47 loss: 0.1355 Lr: 0.00179
[2023-12-25 17:06:22,297 INFO misc.py line 119 253097] Train: [66/100][34/510] Data 0.028 (0.250) Batch 1.205 (1.625) Remain 08:02:35 loss: 0.1649 Lr: 0.00179
[2023-12-25 17:06:23,379 INFO misc.py line 119 253097] Train: [66/100][35/510] Data 0.004 (0.242) Batch 1.081 (1.608) Remain 07:57:30 loss: 0.2168 Lr: 0.00179
[2023-12-25 17:06:24,349 INFO misc.py line 119 253097] Train: [66/100][36/510] Data 0.006 (0.235) Batch 0.972 (1.589) Remain 07:51:45 loss: 0.0896 Lr: 0.00179
[2023-12-25 17:06:25,586 INFO misc.py line 119 253097] Train: [66/100][37/510] Data 0.005 (0.229) Batch 1.237 (1.579) Remain 07:48:39 loss: 0.1650 Lr: 0.00179
[2023-12-25 17:06:26,708 INFO misc.py line 119 253097] Train: [66/100][38/510] Data 0.005 (0.222) Batch 1.122 (1.566) Remain 07:44:45 loss: 0.1639 Lr: 0.00179
[2023-12-25 17:06:27,793 INFO misc.py line 119 253097] Train: [66/100][39/510] Data 0.005 (0.216) Batch 1.063 (1.552) Remain 07:40:35 loss: 0.1306 Lr: 0.00179
[2023-12-25 17:06:28,997 INFO misc.py line 119 253097] Train: [66/100][40/510] Data 0.028 (0.211) Batch 1.193 (1.542) Remain 07:37:41 loss: 0.1324 Lr: 0.00179
[2023-12-25 17:06:30,181 INFO misc.py line 119 253097] Train: [66/100][41/510] Data 0.038 (0.206) Batch 1.208 (1.533) Remain 07:35:03 loss: 0.1134 Lr: 0.00179
[2023-12-25 17:06:31,378 INFO misc.py line 119 253097] Train: [66/100][42/510] Data 0.014 (0.202) Batch 1.202 (1.525) Remain 07:32:30 loss: 0.1351 Lr: 0.00179
[2023-12-25 17:06:32,476 INFO misc.py line 119 253097] Train: [66/100][43/510] Data 0.009 (0.197) Batch 1.102 (1.514) Remain 07:29:21 loss: 0.1339 Lr: 0.00179
[2023-12-25 17:06:33,683 INFO misc.py line 119 253097] Train: [66/100][44/510] Data 0.005 (0.192) Batch 1.207 (1.507) Remain 07:27:06 loss: 0.1306 Lr: 0.00179
[2023-12-25 17:06:34,760 INFO misc.py line 119 253097] Train: [66/100][45/510] Data 0.004 (0.188) Batch 1.076 (1.496) Remain 07:24:02 loss: 0.1067 Lr: 0.00179
[2023-12-25 17:06:36,039 INFO misc.py line 119 253097] Train: [66/100][46/510] Data 0.005 (0.183) Batch 1.280 (1.491) Remain 07:22:31 loss: 0.1828 Lr: 0.00179
[2023-12-25 17:06:37,250 INFO misc.py line 119 253097] Train: [66/100][47/510] Data 0.003 (0.179) Batch 1.205 (1.485) Remain 07:20:34 loss: 0.1461 Lr: 0.00179
[2023-12-25 17:06:38,368 INFO misc.py line 119 253097] Train: [66/100][48/510] Data 0.009 (0.175) Batch 1.124 (1.477) Remain 07:18:09 loss: 0.1520 Lr: 0.00179
[2023-12-25 17:06:47,467 INFO misc.py line 119 253097] Train: [66/100][49/510] Data 0.004 (0.172) Batch 9.099 (1.642) Remain 08:07:18 loss: 0.1002 Lr: 0.00179
[2023-12-25 17:06:48,629 INFO misc.py line 119 253097] Train: [66/100][50/510] Data 0.004 (0.168) Batch 1.162 (1.632) Remain 08:04:14 loss: 0.1251 Lr: 0.00179
[2023-12-25 17:06:49,768 INFO misc.py line 119 253097] Train: [66/100][51/510] Data 0.003 (0.165) Batch 1.139 (1.622) Remain 08:01:09 loss: 0.0839 Lr: 0.00179
[2023-12-25 17:06:50,806 INFO misc.py line 119 253097] Train: [66/100][52/510] Data 0.004 (0.161) Batch 1.038 (1.610) Remain 07:57:36 loss: 0.2266 Lr: 0.00179
[2023-12-25 17:06:51,808 INFO misc.py line 119 253097] Train: [66/100][53/510] Data 0.003 (0.158) Batch 1.002 (1.598) Remain 07:53:58 loss: 0.0996 Lr: 0.00179
[2023-12-25 17:06:52,761 INFO misc.py line 119 253097] Train: [66/100][54/510] Data 0.003 (0.155) Batch 0.952 (1.585) Remain 07:50:11 loss: 0.0861 Lr: 0.00179
[2023-12-25 17:06:53,960 INFO misc.py line 119 253097] Train: [66/100][55/510] Data 0.004 (0.152) Batch 1.200 (1.578) Remain 07:47:57 loss: 0.2296 Lr: 0.00179
[2023-12-25 17:06:55,259 INFO misc.py line 119 253097] Train: [66/100][56/510] Data 0.004 (0.150) Batch 1.296 (1.573) Remain 07:46:21 loss: 0.2997 Lr: 0.00179
[2023-12-25 17:06:56,366 INFO misc.py line 119 253097] Train: [66/100][57/510] Data 0.006 (0.147) Batch 1.108 (1.564) Remain 07:43:47 loss: 0.1198 Lr: 0.00179
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[2023-12-25 17:17:48,649 INFO misc.py line 119 253097] Train: [66/100][476/510] Data 0.005 (0.065) Batch 12.197 (1.558) Remain 07:31:01 loss: 0.2373 Lr: 0.00171
[2023-12-25 17:17:49,770 INFO misc.py line 119 253097] Train: [66/100][477/510] Data 0.004 (0.065) Batch 1.120 (1.557) Remain 07:30:43 loss: 0.1309 Lr: 0.00171
[2023-12-25 17:17:50,962 INFO misc.py line 119 253097] Train: [66/100][478/510] Data 0.004 (0.065) Batch 1.193 (1.556) Remain 07:30:28 loss: 0.1184 Lr: 0.00171
[2023-12-25 17:17:52,119 INFO misc.py line 119 253097] Train: [66/100][479/510] Data 0.004 (0.065) Batch 1.157 (1.555) Remain 07:30:12 loss: 0.2020 Lr: 0.00171
[2023-12-25 17:17:53,333 INFO misc.py line 119 253097] Train: [66/100][480/510] Data 0.004 (0.065) Batch 1.193 (1.554) Remain 07:29:58 loss: 0.2338 Lr: 0.00171
[2023-12-25 17:17:54,492 INFO misc.py line 119 253097] Train: [66/100][481/510] Data 0.025 (0.065) Batch 1.179 (1.554) Remain 07:29:42 loss: 0.1285 Lr: 0.00171
[2023-12-25 17:17:55,757 INFO misc.py line 119 253097] Train: [66/100][482/510] Data 0.004 (0.064) Batch 1.233 (1.553) Remain 07:29:29 loss: 0.1207 Lr: 0.00171
[2023-12-25 17:17:56,807 INFO misc.py line 119 253097] Train: [66/100][483/510] Data 0.037 (0.064) Batch 1.044 (1.552) Remain 07:29:09 loss: 0.0962 Lr: 0.00171
[2023-12-25 17:17:57,870 INFO misc.py line 119 253097] Train: [66/100][484/510] Data 0.042 (0.064) Batch 1.072 (1.551) Remain 07:28:50 loss: 0.1831 Lr: 0.00171
[2023-12-25 17:17:59,132 INFO misc.py line 119 253097] Train: [66/100][485/510] Data 0.033 (0.064) Batch 1.288 (1.550) Remain 07:28:39 loss: 0.1558 Lr: 0.00171
[2023-12-25 17:18:00,203 INFO misc.py line 119 253097] Train: [66/100][486/510] Data 0.007 (0.064) Batch 1.073 (1.549) Remain 07:28:21 loss: 0.1496 Lr: 0.00171
[2023-12-25 17:18:01,273 INFO misc.py line 119 253097] Train: [66/100][487/510] Data 0.004 (0.064) Batch 1.067 (1.548) Remain 07:28:02 loss: 0.1515 Lr: 0.00171
[2023-12-25 17:18:02,345 INFO misc.py line 119 253097] Train: [66/100][488/510] Data 0.007 (0.064) Batch 1.077 (1.547) Remain 07:27:43 loss: 0.1700 Lr: 0.00171
[2023-12-25 17:18:09,483 INFO misc.py line 119 253097] Train: [66/100][489/510] Data 0.003 (0.064) Batch 7.137 (1.559) Remain 07:31:02 loss: 0.2235 Lr: 0.00171
[2023-12-25 17:18:10,740 INFO misc.py line 119 253097] Train: [66/100][490/510] Data 0.004 (0.064) Batch 1.258 (1.558) Remain 07:30:49 loss: 0.1683 Lr: 0.00171
[2023-12-25 17:18:12,073 INFO misc.py line 119 253097] Train: [66/100][491/510] Data 0.003 (0.064) Batch 1.166 (1.557) Remain 07:30:34 loss: 0.2704 Lr: 0.00171
[2023-12-25 17:18:13,030 INFO misc.py line 119 253097] Train: [66/100][492/510] Data 0.170 (0.064) Batch 1.124 (1.556) Remain 07:30:17 loss: 0.0848 Lr: 0.00171
[2023-12-25 17:18:14,202 INFO misc.py line 119 253097] Train: [66/100][493/510] Data 0.003 (0.064) Batch 1.155 (1.556) Remain 07:30:01 loss: 0.1052 Lr: 0.00171
[2023-12-25 17:18:15,260 INFO misc.py line 119 253097] Train: [66/100][494/510] Data 0.020 (0.064) Batch 1.075 (1.555) Remain 07:29:42 loss: 0.0953 Lr: 0.00171
[2023-12-25 17:18:16,466 INFO misc.py line 119 253097] Train: [66/100][495/510] Data 0.003 (0.063) Batch 1.206 (1.554) Remain 07:29:29 loss: 0.1234 Lr: 0.00171
[2023-12-25 17:18:17,602 INFO misc.py line 119 253097] Train: [66/100][496/510] Data 0.004 (0.063) Batch 1.136 (1.553) Remain 07:29:12 loss: 0.2353 Lr: 0.00171
[2023-12-25 17:18:18,817 INFO misc.py line 119 253097] Train: [66/100][497/510] Data 0.003 (0.063) Batch 1.210 (1.552) Remain 07:28:59 loss: 0.0816 Lr: 0.00171
[2023-12-25 17:18:19,880 INFO misc.py line 119 253097] Train: [66/100][498/510] Data 0.008 (0.063) Batch 1.067 (1.551) Remain 07:28:40 loss: 0.1587 Lr: 0.00171
[2023-12-25 17:18:20,928 INFO misc.py line 119 253097] Train: [66/100][499/510] Data 0.004 (0.063) Batch 1.048 (1.550) Remain 07:28:21 loss: 0.3200 Lr: 0.00171
[2023-12-25 17:18:22,182 INFO misc.py line 119 253097] Train: [66/100][500/510] Data 0.004 (0.063) Batch 1.251 (1.550) Remain 07:28:09 loss: 0.0820 Lr: 0.00171
[2023-12-25 17:18:23,442 INFO misc.py line 119 253097] Train: [66/100][501/510] Data 0.007 (0.063) Batch 1.259 (1.549) Remain 07:27:57 loss: 0.1584 Lr: 0.00171
[2023-12-25 17:18:24,672 INFO misc.py line 119 253097] Train: [66/100][502/510] Data 0.007 (0.063) Batch 1.231 (1.549) Remain 07:27:45 loss: 0.1154 Lr: 0.00171
[2023-12-25 17:18:25,638 INFO misc.py line 119 253097] Train: [66/100][503/510] Data 0.007 (0.063) Batch 0.969 (1.547) Remain 07:27:23 loss: 0.1104 Lr: 0.00171
[2023-12-25 17:18:26,818 INFO misc.py line 119 253097] Train: [66/100][504/510] Data 0.003 (0.062) Batch 1.175 (1.547) Remain 07:27:09 loss: 0.2228 Lr: 0.00171
[2023-12-25 17:18:27,825 INFO misc.py line 119 253097] Train: [66/100][505/510] Data 0.008 (0.062) Batch 1.007 (1.546) Remain 07:26:48 loss: 0.1421 Lr: 0.00171
[2023-12-25 17:18:29,040 INFO misc.py line 119 253097] Train: [66/100][506/510] Data 0.008 (0.062) Batch 1.214 (1.545) Remain 07:26:35 loss: 0.1070 Lr: 0.00171
[2023-12-25 17:18:30,175 INFO misc.py line 119 253097] Train: [66/100][507/510] Data 0.010 (0.062) Batch 1.142 (1.544) Remain 07:26:20 loss: 0.1744 Lr: 0.00171
[2023-12-25 17:18:31,333 INFO misc.py line 119 253097] Train: [66/100][508/510] Data 0.003 (0.062) Batch 1.154 (1.543) Remain 07:26:05 loss: 0.1008 Lr: 0.00171
[2023-12-25 17:18:32,381 INFO misc.py line 119 253097] Train: [66/100][509/510] Data 0.007 (0.062) Batch 1.046 (1.542) Remain 07:25:47 loss: 0.1120 Lr: 0.00171
[2023-12-25 17:18:33,629 INFO misc.py line 119 253097] Train: [66/100][510/510] Data 0.009 (0.062) Batch 1.250 (1.542) Remain 07:25:35 loss: 0.0997 Lr: 0.00171
[2023-12-25 17:18:33,629 INFO misc.py line 136 253097] Train result: loss: 0.1398 
[2023-12-25 17:18:33,630 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 17:19:05,742 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6140 
[2023-12-25 17:19:06,116 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3770 
[2023-12-25 17:19:11,068 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4217 
[2023-12-25 17:19:11,584 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4046 
[2023-12-25 17:19:13,559 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.1125 
[2023-12-25 17:19:13,985 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4140 
[2023-12-25 17:19:14,863 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0709 
[2023-12-25 17:19:15,420 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2359 
[2023-12-25 17:19:17,231 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0643 
[2023-12-25 17:19:19,352 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1888 
[2023-12-25 17:19:20,208 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3035 
[2023-12-25 17:19:20,643 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.1145 
[2023-12-25 17:19:21,546 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3197 
[2023-12-25 17:19:24,494 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8025 
[2023-12-25 17:19:24,974 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2396 
[2023-12-25 17:19:25,584 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3726 
[2023-12-25 17:19:26,287 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4168 
[2023-12-25 17:19:27,723 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6881/0.7496/0.9018.
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9221/0.9462
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9803/0.9915
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8437/0.9628
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3339/0.3824
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6193/0.6468
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6967/0.7745
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7909/0.8381
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9062/0.9504
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6825/0.7589
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7659/0.8466
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8006/0.8687
[2023-12-25 17:19:27,724 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6026/0.7778
[2023-12-25 17:19:27,725 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 17:19:27,725 INFO misc.py line 165 253097] Currently Best mIoU: 0.7000
[2023-12-25 17:19:27,726 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 17:19:36,893 INFO misc.py line 119 253097] Train: [67/100][1/510] Data 2.232 (2.232) Batch 7.402 (7.402) Remain 35:39:03 loss: 0.0673 Lr: 0.00171
[2023-12-25 17:19:37,985 INFO misc.py line 119 253097] Train: [67/100][2/510] Data 0.058 (0.058) Batch 1.098 (1.098) Remain 05:17:08 loss: 0.0776 Lr: 0.00171
[2023-12-25 17:19:39,419 INFO misc.py line 119 253097] Train: [67/100][3/510] Data 0.004 (0.004) Batch 1.426 (1.426) Remain 06:52:06 loss: 0.1016 Lr: 0.00171
[2023-12-25 17:19:43,074 INFO misc.py line 119 253097] Train: [67/100][4/510] Data 0.012 (0.012) Batch 3.662 (3.662) Remain 17:38:11 loss: 0.2968 Lr: 0.00171
[2023-12-25 17:19:44,139 INFO misc.py line 119 253097] Train: [67/100][5/510] Data 0.005 (0.008) Batch 1.065 (2.364) Remain 11:22:58 loss: 0.0788 Lr: 0.00171
[2023-12-25 17:19:45,204 INFO misc.py line 119 253097] Train: [67/100][6/510] Data 0.003 (0.007) Batch 1.065 (1.931) Remain 09:17:50 loss: 0.1400 Lr: 0.00171
[2023-12-25 17:19:46,311 INFO misc.py line 119 253097] Train: [67/100][7/510] Data 0.004 (0.006) Batch 1.106 (1.725) Remain 08:18:13 loss: 0.0593 Lr: 0.00171
[2023-12-25 17:19:47,516 INFO misc.py line 119 253097] Train: [67/100][8/510] Data 0.004 (0.006) Batch 1.184 (1.616) Remain 07:46:56 loss: 0.1683 Lr: 0.00171
[2023-12-25 17:19:48,742 INFO misc.py line 119 253097] Train: [67/100][9/510] Data 0.027 (0.009) Batch 1.225 (1.551) Remain 07:28:02 loss: 0.1098 Lr: 0.00171
[2023-12-25 17:19:49,893 INFO misc.py line 119 253097] Train: [67/100][10/510] Data 0.028 (0.012) Batch 1.174 (1.497) Remain 07:12:27 loss: 0.0859 Lr: 0.00171
[2023-12-25 17:19:51,045 INFO misc.py line 119 253097] Train: [67/100][11/510] Data 0.005 (0.011) Batch 1.153 (1.454) Remain 07:00:00 loss: 0.1273 Lr: 0.00171
[2023-12-25 17:19:52,331 INFO misc.py line 119 253097] Train: [67/100][12/510] Data 0.003 (0.010) Batch 1.287 (1.436) Remain 06:54:36 loss: 0.0887 Lr: 0.00170
[2023-12-25 17:19:53,657 INFO misc.py line 119 253097] Train: [67/100][13/510] Data 0.004 (0.010) Batch 1.325 (1.425) Remain 06:51:22 loss: 0.1212 Lr: 0.00170
[2023-12-25 17:19:54,832 INFO misc.py line 119 253097] Train: [67/100][14/510] Data 0.004 (0.009) Batch 1.172 (1.402) Remain 06:44:42 loss: 0.1856 Lr: 0.00170
[2023-12-25 17:19:55,967 INFO misc.py line 119 253097] Train: [67/100][15/510] Data 0.010 (0.009) Batch 1.138 (1.380) Remain 06:38:20 loss: 0.2209 Lr: 0.00170
[2023-12-25 17:19:57,019 INFO misc.py line 119 253097] Train: [67/100][16/510] Data 0.006 (0.009) Batch 1.053 (1.354) Remain 06:31:03 loss: 0.1172 Lr: 0.00170
[2023-12-25 17:19:58,093 INFO misc.py line 119 253097] Train: [67/100][17/510] Data 0.005 (0.009) Batch 1.049 (1.333) Remain 06:24:44 loss: 0.0926 Lr: 0.00170
[2023-12-25 17:19:59,006 INFO misc.py line 119 253097] Train: [67/100][18/510] Data 0.030 (0.010) Batch 0.937 (1.306) Remain 06:17:05 loss: 0.0775 Lr: 0.00170
[2023-12-25 17:20:00,163 INFO misc.py line 119 253097] Train: [67/100][19/510] Data 0.007 (0.010) Batch 1.159 (1.297) Remain 06:14:25 loss: 0.1498 Lr: 0.00170
[2023-12-25 17:20:01,323 INFO misc.py line 119 253097] Train: [67/100][20/510] Data 0.004 (0.009) Batch 1.157 (1.289) Remain 06:12:01 loss: 0.0838 Lr: 0.00170
[2023-12-25 17:20:12,307 INFO misc.py line 119 253097] Train: [67/100][21/510] Data 10.105 (0.570) Batch 10.981 (1.827) Remain 08:47:25 loss: 0.1667 Lr: 0.00170
[2023-12-25 17:20:13,581 INFO misc.py line 119 253097] Train: [67/100][22/510] Data 0.011 (0.541) Batch 1.277 (1.798) Remain 08:39:02 loss: 0.0912 Lr: 0.00170
[2023-12-25 17:20:14,673 INFO misc.py line 119 253097] Train: [67/100][23/510] Data 0.007 (0.514) Batch 1.093 (1.763) Remain 08:28:49 loss: 0.2702 Lr: 0.00170
[2023-12-25 17:20:15,837 INFO misc.py line 119 253097] Train: [67/100][24/510] Data 0.005 (0.490) Batch 1.161 (1.734) Remain 08:20:31 loss: 0.0930 Lr: 0.00170
[2023-12-25 17:20:16,967 INFO misc.py line 119 253097] Train: [67/100][25/510] Data 0.009 (0.468) Batch 1.135 (1.707) Remain 08:12:38 loss: 0.0710 Lr: 0.00170
[2023-12-25 17:20:18,227 INFO misc.py line 119 253097] Train: [67/100][26/510] Data 0.004 (0.448) Batch 1.259 (1.688) Remain 08:06:59 loss: 0.1201 Lr: 0.00170
[2023-12-25 17:20:19,426 INFO misc.py line 119 253097] Train: [67/100][27/510] Data 0.005 (0.429) Batch 1.197 (1.667) Remain 08:01:03 loss: 0.2019 Lr: 0.00170
[2023-12-25 17:20:20,568 INFO misc.py line 119 253097] Train: [67/100][28/510] Data 0.007 (0.413) Batch 1.111 (1.645) Remain 07:54:36 loss: 0.0807 Lr: 0.00170
[2023-12-25 17:20:21,740 INFO misc.py line 119 253097] Train: [67/100][29/510] Data 0.037 (0.398) Batch 1.206 (1.628) Remain 07:49:42 loss: 0.1058 Lr: 0.00170
[2023-12-25 17:20:23,032 INFO misc.py line 119 253097] Train: [67/100][30/510] Data 0.003 (0.384) Batch 1.286 (1.615) Remain 07:46:01 loss: 0.1158 Lr: 0.00170
[2023-12-25 17:20:24,223 INFO misc.py line 119 253097] Train: [67/100][31/510] Data 0.009 (0.370) Batch 1.195 (1.600) Remain 07:41:40 loss: 0.0830 Lr: 0.00170
[2023-12-25 17:20:25,353 INFO misc.py line 119 253097] Train: [67/100][32/510] Data 0.006 (0.358) Batch 1.120 (1.584) Remain 07:36:52 loss: 0.1932 Lr: 0.00170
[2023-12-25 17:20:26,543 INFO misc.py line 119 253097] Train: [67/100][33/510] Data 0.016 (0.346) Batch 1.200 (1.571) Remain 07:33:09 loss: 0.1965 Lr: 0.00170
[2023-12-25 17:20:27,685 INFO misc.py line 119 253097] Train: [67/100][34/510] Data 0.006 (0.335) Batch 1.137 (1.557) Remain 07:29:05 loss: 0.1960 Lr: 0.00170
[2023-12-25 17:20:28,840 INFO misc.py line 119 253097] Train: [67/100][35/510] Data 0.010 (0.325) Batch 1.159 (1.545) Remain 07:25:28 loss: 0.2451 Lr: 0.00170
[2023-12-25 17:20:30,002 INFO misc.py line 119 253097] Train: [67/100][36/510] Data 0.007 (0.315) Batch 1.136 (1.532) Remain 07:21:53 loss: 0.2072 Lr: 0.00170
[2023-12-25 17:20:31,120 INFO misc.py line 119 253097] Train: [67/100][37/510] Data 0.031 (0.307) Batch 1.124 (1.520) Remain 07:18:24 loss: 0.1704 Lr: 0.00170
[2023-12-25 17:20:45,147 INFO misc.py line 119 253097] Train: [67/100][38/510] Data 12.852 (0.666) Batch 14.049 (1.878) Remain 09:01:36 loss: 0.0918 Lr: 0.00170
[2023-12-25 17:20:46,219 INFO misc.py line 119 253097] Train: [67/100][39/510] Data 0.004 (0.647) Batch 1.072 (1.856) Remain 08:55:06 loss: 0.0759 Lr: 0.00170
[2023-12-25 17:20:47,522 INFO misc.py line 119 253097] Train: [67/100][40/510] Data 0.004 (0.630) Batch 1.301 (1.841) Remain 08:50:45 loss: 0.1469 Lr: 0.00170
[2023-12-25 17:20:48,884 INFO misc.py line 119 253097] Train: [67/100][41/510] Data 0.006 (0.613) Batch 1.362 (1.828) Remain 08:47:05 loss: 0.1300 Lr: 0.00170
[2023-12-25 17:20:50,197 INFO misc.py line 119 253097] Train: [67/100][42/510] Data 0.006 (0.598) Batch 1.300 (1.815) Remain 08:43:09 loss: 0.1741 Lr: 0.00170
[2023-12-25 17:20:51,457 INFO misc.py line 119 253097] Train: [67/100][43/510] Data 0.020 (0.583) Batch 1.274 (1.801) Remain 08:39:14 loss: 0.1506 Lr: 0.00170
[2023-12-25 17:20:52,473 INFO misc.py line 119 253097] Train: [67/100][44/510] Data 0.004 (0.569) Batch 1.014 (1.782) Remain 08:33:40 loss: 0.2078 Lr: 0.00170
[2023-12-25 17:20:53,739 INFO misc.py line 119 253097] Train: [67/100][45/510] Data 0.006 (0.556) Batch 1.265 (1.770) Remain 08:30:05 loss: 0.1133 Lr: 0.00170
[2023-12-25 17:20:54,838 INFO misc.py line 119 253097] Train: [67/100][46/510] Data 0.006 (0.543) Batch 1.095 (1.754) Remain 08:25:32 loss: 0.1912 Lr: 0.00170
[2023-12-25 17:20:55,911 INFO misc.py line 119 253097] Train: [67/100][47/510] Data 0.011 (0.531) Batch 1.078 (1.739) Remain 08:21:05 loss: 0.1829 Lr: 0.00170
[2023-12-25 17:20:57,082 INFO misc.py line 119 253097] Train: [67/100][48/510] Data 0.006 (0.519) Batch 1.165 (1.726) Remain 08:17:23 loss: 0.1138 Lr: 0.00170
[2023-12-25 17:20:58,364 INFO misc.py line 119 253097] Train: [67/100][49/510] Data 0.012 (0.508) Batch 1.289 (1.716) Remain 08:14:37 loss: 0.0888 Lr: 0.00170
[2023-12-25 17:20:59,627 INFO misc.py line 119 253097] Train: [67/100][50/510] Data 0.005 (0.497) Batch 1.261 (1.707) Remain 08:11:48 loss: 0.0991 Lr: 0.00170
[2023-12-25 17:21:00,811 INFO misc.py line 119 253097] Train: [67/100][51/510] Data 0.007 (0.487) Batch 1.177 (1.696) Remain 08:08:35 loss: 0.1003 Lr: 0.00170
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[2023-12-25 17:31:57,604 INFO misc.py line 119 253097] Train: [67/100][470/510] Data 0.006 (0.082) Batch 1.014 (1.581) Remain 07:24:26 loss: 0.1280 Lr: 0.00163
[2023-12-25 17:31:58,805 INFO misc.py line 119 253097] Train: [67/100][471/510] Data 0.007 (0.082) Batch 1.200 (1.580) Remain 07:24:11 loss: 0.2430 Lr: 0.00163
[2023-12-25 17:32:00,078 INFO misc.py line 119 253097] Train: [67/100][472/510] Data 0.008 (0.082) Batch 1.275 (1.579) Remain 07:23:58 loss: 0.2246 Lr: 0.00163
[2023-12-25 17:32:01,316 INFO misc.py line 119 253097] Train: [67/100][473/510] Data 0.007 (0.082) Batch 1.238 (1.579) Remain 07:23:44 loss: 0.1222 Lr: 0.00162
[2023-12-25 17:32:02,555 INFO misc.py line 119 253097] Train: [67/100][474/510] Data 0.008 (0.082) Batch 1.239 (1.578) Remain 07:23:31 loss: 0.0979 Lr: 0.00162
[2023-12-25 17:32:09,150 INFO misc.py line 119 253097] Train: [67/100][475/510] Data 0.007 (0.082) Batch 6.597 (1.588) Remain 07:26:28 loss: 0.0949 Lr: 0.00162
[2023-12-25 17:32:10,307 INFO misc.py line 119 253097] Train: [67/100][476/510] Data 0.006 (0.081) Batch 1.158 (1.588) Remain 07:26:11 loss: 0.1220 Lr: 0.00162
[2023-12-25 17:32:11,279 INFO misc.py line 119 253097] Train: [67/100][477/510] Data 0.005 (0.081) Batch 0.972 (1.586) Remain 07:25:48 loss: 0.0850 Lr: 0.00162
[2023-12-25 17:32:12,304 INFO misc.py line 119 253097] Train: [67/100][478/510] Data 0.004 (0.081) Batch 1.025 (1.585) Remain 07:25:26 loss: 0.1034 Lr: 0.00162
[2023-12-25 17:32:13,393 INFO misc.py line 119 253097] Train: [67/100][479/510] Data 0.004 (0.081) Batch 1.087 (1.584) Remain 07:25:07 loss: 0.1675 Lr: 0.00162
[2023-12-25 17:32:14,516 INFO misc.py line 119 253097] Train: [67/100][480/510] Data 0.006 (0.081) Batch 1.094 (1.583) Remain 07:24:48 loss: 0.2564 Lr: 0.00162
[2023-12-25 17:32:15,645 INFO misc.py line 119 253097] Train: [67/100][481/510] Data 0.034 (0.081) Batch 1.120 (1.582) Remain 07:24:30 loss: 0.1192 Lr: 0.00162
[2023-12-25 17:32:16,773 INFO misc.py line 119 253097] Train: [67/100][482/510] Data 0.044 (0.081) Batch 1.164 (1.581) Remain 07:24:14 loss: 0.0629 Lr: 0.00162
[2023-12-25 17:32:17,811 INFO misc.py line 119 253097] Train: [67/100][483/510] Data 0.008 (0.081) Batch 1.039 (1.580) Remain 07:23:53 loss: 0.0861 Lr: 0.00162
[2023-12-25 17:32:19,207 INFO misc.py line 119 253097] Train: [67/100][484/510] Data 0.006 (0.080) Batch 1.397 (1.580) Remain 07:23:45 loss: 0.2809 Lr: 0.00162
[2023-12-25 17:32:20,492 INFO misc.py line 119 253097] Train: [67/100][485/510] Data 0.004 (0.080) Batch 1.282 (1.579) Remain 07:23:33 loss: 0.0866 Lr: 0.00162
[2023-12-25 17:32:21,597 INFO misc.py line 119 253097] Train: [67/100][486/510] Data 0.008 (0.080) Batch 1.068 (1.578) Remain 07:23:14 loss: 0.1081 Lr: 0.00162
[2023-12-25 17:32:23,276 INFO misc.py line 119 253097] Train: [67/100][487/510] Data 0.045 (0.080) Batch 1.716 (1.578) Remain 07:23:17 loss: 0.2149 Lr: 0.00162
[2023-12-25 17:32:24,442 INFO misc.py line 119 253097] Train: [67/100][488/510] Data 0.007 (0.080) Batch 1.168 (1.577) Remain 07:23:01 loss: 0.1176 Lr: 0.00162
[2023-12-25 17:32:25,605 INFO misc.py line 119 253097] Train: [67/100][489/510] Data 0.006 (0.080) Batch 1.160 (1.577) Remain 07:22:45 loss: 0.1526 Lr: 0.00162
[2023-12-25 17:32:26,750 INFO misc.py line 119 253097] Train: [67/100][490/510] Data 0.009 (0.080) Batch 1.148 (1.576) Remain 07:22:29 loss: 0.0880 Lr: 0.00162
[2023-12-25 17:32:27,907 INFO misc.py line 119 253097] Train: [67/100][491/510] Data 0.007 (0.079) Batch 1.153 (1.575) Remain 07:22:13 loss: 0.1262 Lr: 0.00162
[2023-12-25 17:32:28,970 INFO misc.py line 119 253097] Train: [67/100][492/510] Data 0.008 (0.079) Batch 1.030 (1.574) Remain 07:21:53 loss: 0.1141 Lr: 0.00162
[2023-12-25 17:32:30,048 INFO misc.py line 119 253097] Train: [67/100][493/510] Data 0.041 (0.079) Batch 1.110 (1.573) Remain 07:21:35 loss: 0.2447 Lr: 0.00162
[2023-12-25 17:32:31,290 INFO misc.py line 119 253097] Train: [67/100][494/510] Data 0.010 (0.079) Batch 1.245 (1.572) Remain 07:21:22 loss: 0.1842 Lr: 0.00162
[2023-12-25 17:32:32,397 INFO misc.py line 119 253097] Train: [67/100][495/510] Data 0.006 (0.079) Batch 1.110 (1.571) Remain 07:21:05 loss: 0.1172 Lr: 0.00162
[2023-12-25 17:32:33,441 INFO misc.py line 119 253097] Train: [67/100][496/510] Data 0.004 (0.079) Batch 1.040 (1.570) Remain 07:20:45 loss: 0.0864 Lr: 0.00162
[2023-12-25 17:32:34,722 INFO misc.py line 119 253097] Train: [67/100][497/510] Data 0.008 (0.079) Batch 1.284 (1.569) Remain 07:20:34 loss: 0.1046 Lr: 0.00162
[2023-12-25 17:32:35,875 INFO misc.py line 119 253097] Train: [67/100][498/510] Data 0.004 (0.078) Batch 1.152 (1.569) Remain 07:20:18 loss: 0.1136 Lr: 0.00162
[2023-12-25 17:32:36,967 INFO misc.py line 119 253097] Train: [67/100][499/510] Data 0.006 (0.078) Batch 1.089 (1.568) Remain 07:20:00 loss: 0.1839 Lr: 0.00162
[2023-12-25 17:32:38,279 INFO misc.py line 119 253097] Train: [67/100][500/510] Data 0.009 (0.078) Batch 1.317 (1.567) Remain 07:19:50 loss: 0.0473 Lr: 0.00162
[2023-12-25 17:32:39,151 INFO misc.py line 119 253097] Train: [67/100][501/510] Data 0.005 (0.078) Batch 0.871 (1.566) Remain 07:19:25 loss: 0.1266 Lr: 0.00162
[2023-12-25 17:32:40,212 INFO misc.py line 119 253097] Train: [67/100][502/510] Data 0.004 (0.078) Batch 1.062 (1.565) Remain 07:19:06 loss: 0.1476 Lr: 0.00162
[2023-12-25 17:32:41,370 INFO misc.py line 119 253097] Train: [67/100][503/510] Data 0.003 (0.078) Batch 1.157 (1.564) Remain 07:18:51 loss: 0.0875 Lr: 0.00162
[2023-12-25 17:32:42,653 INFO misc.py line 119 253097] Train: [67/100][504/510] Data 0.005 (0.078) Batch 1.284 (1.563) Remain 07:18:40 loss: 0.1507 Lr: 0.00162
[2023-12-25 17:32:43,790 INFO misc.py line 119 253097] Train: [67/100][505/510] Data 0.003 (0.077) Batch 1.135 (1.563) Remain 07:18:24 loss: 0.1153 Lr: 0.00162
[2023-12-25 17:32:45,036 INFO misc.py line 119 253097] Train: [67/100][506/510] Data 0.005 (0.077) Batch 1.239 (1.562) Remain 07:18:12 loss: 0.1221 Lr: 0.00162
[2023-12-25 17:32:46,170 INFO misc.py line 119 253097] Train: [67/100][507/510] Data 0.011 (0.077) Batch 1.141 (1.561) Remain 07:17:56 loss: 0.0691 Lr: 0.00162
[2023-12-25 17:32:47,457 INFO misc.py line 119 253097] Train: [67/100][508/510] Data 0.004 (0.077) Batch 1.283 (1.560) Remain 07:17:45 loss: 0.3773 Lr: 0.00162
[2023-12-25 17:32:48,512 INFO misc.py line 119 253097] Train: [67/100][509/510] Data 0.008 (0.077) Batch 1.059 (1.559) Remain 07:17:27 loss: 0.1361 Lr: 0.00162
[2023-12-25 17:32:52,750 INFO misc.py line 119 253097] Train: [67/100][510/510] Data 0.004 (0.077) Batch 4.238 (1.565) Remain 07:18:55 loss: 0.1337 Lr: 0.00162
[2023-12-25 17:32:52,751 INFO misc.py line 136 253097] Train result: loss: 0.1429 
[2023-12-25 17:32:52,752 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 17:33:23,442 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4393 
[2023-12-25 17:33:23,789 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2408 
[2023-12-25 17:33:28,722 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.2982 
[2023-12-25 17:33:29,243 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2936 
[2023-12-25 17:33:31,218 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.6067 
[2023-12-25 17:33:31,653 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2324 
[2023-12-25 17:33:32,542 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1101 
[2023-12-25 17:33:33,097 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.4471 
[2023-12-25 17:33:34,910 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6782 
[2023-12-25 17:33:37,028 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2062 
[2023-12-25 17:33:37,883 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2381 
[2023-12-25 17:33:38,309 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7601 
[2023-12-25 17:33:39,208 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6903 
[2023-12-25 17:33:42,149 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.6757 
[2023-12-25 17:33:42,637 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3638 
[2023-12-25 17:33:43,245 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3547 
[2023-12-25 17:33:43,948 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2676 
[2023-12-25 17:33:45,329 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.7192/0.7778/0.9101.
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9175/0.9519
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9810/0.9908
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8559/0.9726
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.5040/0.5913
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6274/0.6498
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7413/0.8585
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8191/0.8911
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9211/0.9588
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.7725/0.8188
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7844/0.8818
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8238/0.8535
[2023-12-25 17:33:45,330 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6015/0.6928
[2023-12-25 17:33:45,331 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 17:33:45,333 INFO misc.py line 160 253097] Best validation mIoU updated to: 0.7192
[2023-12-25 17:33:45,333 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 17:33:45,333 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 17:33:54,066 INFO misc.py line 119 253097] Train: [68/100][1/510] Data 2.737 (2.737) Batch 3.601 (3.601) Remain 16:49:58 loss: 0.1454 Lr: 0.00162
[2023-12-25 17:33:55,320 INFO misc.py line 119 253097] Train: [68/100][2/510] Data 0.009 (0.009) Batch 1.191 (1.191) Remain 05:34:04 loss: 0.1139 Lr: 0.00162
[2023-12-25 17:33:57,630 INFO misc.py line 119 253097] Train: [68/100][3/510] Data 0.178 (0.178) Batch 2.377 (2.377) Remain 11:06:30 loss: 0.1495 Lr: 0.00162
[2023-12-25 17:34:02,175 INFO misc.py line 119 253097] Train: [68/100][4/510] Data 3.397 (3.397) Batch 4.545 (4.545) Remain 21:14:28 loss: 0.2587 Lr: 0.00162
[2023-12-25 17:34:03,264 INFO misc.py line 119 253097] Train: [68/100][5/510] Data 0.004 (1.701) Batch 1.088 (2.816) Remain 13:09:42 loss: 0.0753 Lr: 0.00162
[2023-12-25 17:34:04,345 INFO misc.py line 119 253097] Train: [68/100][6/510] Data 0.005 (1.135) Batch 1.080 (2.237) Remain 10:27:21 loss: 0.1500 Lr: 0.00162
[2023-12-25 17:34:13,333 INFO misc.py line 119 253097] Train: [68/100][7/510] Data 0.007 (0.853) Batch 8.991 (3.926) Remain 18:20:42 loss: 0.2389 Lr: 0.00162
[2023-12-25 17:34:14,503 INFO misc.py line 119 253097] Train: [68/100][8/510] Data 0.004 (0.683) Batch 1.170 (3.375) Remain 15:46:07 loss: 0.1330 Lr: 0.00162
[2023-12-25 17:34:15,635 INFO misc.py line 119 253097] Train: [68/100][9/510] Data 0.004 (0.570) Batch 1.132 (3.001) Remain 14:01:17 loss: 0.1749 Lr: 0.00162
[2023-12-25 17:34:16,516 INFO misc.py line 119 253097] Train: [68/100][10/510] Data 0.004 (0.489) Batch 0.882 (2.698) Remain 12:36:22 loss: 0.1893 Lr: 0.00162
[2023-12-25 17:34:17,697 INFO misc.py line 119 253097] Train: [68/100][11/510] Data 0.003 (0.429) Batch 1.180 (2.508) Remain 11:43:08 loss: 0.0946 Lr: 0.00162
[2023-12-25 17:34:18,773 INFO misc.py line 119 253097] Train: [68/100][12/510] Data 0.004 (0.381) Batch 1.076 (2.349) Remain 10:58:28 loss: 0.1153 Lr: 0.00162
[2023-12-25 17:34:19,696 INFO misc.py line 119 253097] Train: [68/100][13/510] Data 0.004 (0.344) Batch 0.875 (2.202) Remain 10:17:07 loss: 0.1994 Lr: 0.00162
[2023-12-25 17:34:20,942 INFO misc.py line 119 253097] Train: [68/100][14/510] Data 0.052 (0.317) Batch 1.258 (2.116) Remain 09:53:02 loss: 0.1311 Lr: 0.00162
[2023-12-25 17:34:22,127 INFO misc.py line 119 253097] Train: [68/100][15/510] Data 0.040 (0.294) Batch 1.221 (2.041) Remain 09:32:06 loss: 0.0866 Lr: 0.00162
[2023-12-25 17:34:23,331 INFO misc.py line 119 253097] Train: [68/100][16/510] Data 0.004 (0.272) Batch 1.203 (1.977) Remain 09:13:58 loss: 0.1098 Lr: 0.00162
[2023-12-25 17:34:24,400 INFO misc.py line 119 253097] Train: [68/100][17/510] Data 0.006 (0.253) Batch 1.064 (1.912) Remain 08:55:40 loss: 0.1141 Lr: 0.00162
[2023-12-25 17:34:25,646 INFO misc.py line 119 253097] Train: [68/100][18/510] Data 0.011 (0.237) Batch 1.249 (1.867) Remain 08:43:15 loss: 0.0649 Lr: 0.00162
[2023-12-25 17:34:26,976 INFO misc.py line 119 253097] Train: [68/100][19/510] Data 0.008 (0.222) Batch 1.335 (1.834) Remain 08:33:53 loss: 0.1354 Lr: 0.00162
[2023-12-25 17:34:28,132 INFO misc.py line 119 253097] Train: [68/100][20/510] Data 0.004 (0.209) Batch 1.148 (1.794) Remain 08:22:33 loss: 0.0925 Lr: 0.00162
[2023-12-25 17:34:29,444 INFO misc.py line 119 253097] Train: [68/100][21/510] Data 0.012 (0.198) Batch 1.304 (1.767) Remain 08:14:54 loss: 0.2375 Lr: 0.00161
[2023-12-25 17:34:30,650 INFO misc.py line 119 253097] Train: [68/100][22/510] Data 0.019 (0.189) Batch 1.217 (1.738) Remain 08:06:47 loss: 0.1092 Lr: 0.00161
[2023-12-25 17:34:31,878 INFO misc.py line 119 253097] Train: [68/100][23/510] Data 0.008 (0.180) Batch 1.232 (1.712) Remain 07:59:39 loss: 0.1464 Lr: 0.00161
[2023-12-25 17:34:33,092 INFO misc.py line 119 253097] Train: [68/100][24/510] Data 0.005 (0.172) Batch 1.214 (1.689) Remain 07:52:59 loss: 0.2383 Lr: 0.00161
[2023-12-25 17:34:42,018 INFO misc.py line 119 253097] Train: [68/100][25/510] Data 5.075 (0.395) Batch 8.925 (2.018) Remain 09:25:05 loss: 0.0662 Lr: 0.00161
[2023-12-25 17:34:43,056 INFO misc.py line 119 253097] Train: [68/100][26/510] Data 0.004 (0.378) Batch 1.039 (1.975) Remain 09:13:08 loss: 0.1086 Lr: 0.00161
[2023-12-25 17:34:44,143 INFO misc.py line 119 253097] Train: [68/100][27/510] Data 0.003 (0.362) Batch 1.036 (1.936) Remain 09:02:09 loss: 0.1321 Lr: 0.00161
[2023-12-25 17:34:45,273 INFO misc.py line 119 253097] Train: [68/100][28/510] Data 0.054 (0.350) Batch 1.180 (1.906) Remain 08:53:39 loss: 0.1061 Lr: 0.00161
[2023-12-25 17:34:46,367 INFO misc.py line 119 253097] Train: [68/100][29/510] Data 0.004 (0.336) Batch 1.094 (1.874) Remain 08:44:52 loss: 0.0838 Lr: 0.00161
[2023-12-25 17:34:47,502 INFO misc.py line 119 253097] Train: [68/100][30/510] Data 0.005 (0.324) Batch 1.135 (1.847) Remain 08:37:11 loss: 0.1191 Lr: 0.00161
[2023-12-25 17:34:48,513 INFO misc.py line 119 253097] Train: [68/100][31/510] Data 0.004 (0.313) Batch 1.011 (1.817) Remain 08:28:47 loss: 0.1174 Lr: 0.00161
[2023-12-25 17:34:49,754 INFO misc.py line 119 253097] Train: [68/100][32/510] Data 0.005 (0.302) Batch 1.241 (1.797) Remain 08:23:12 loss: 0.2842 Lr: 0.00161
[2023-12-25 17:34:50,996 INFO misc.py line 119 253097] Train: [68/100][33/510] Data 0.005 (0.292) Batch 1.238 (1.779) Remain 08:17:57 loss: 0.0837 Lr: 0.00161
[2023-12-25 17:34:52,156 INFO misc.py line 119 253097] Train: [68/100][34/510] Data 0.008 (0.283) Batch 1.162 (1.759) Remain 08:12:21 loss: 0.0972 Lr: 0.00161
[2023-12-25 17:34:53,414 INFO misc.py line 119 253097] Train: [68/100][35/510] Data 0.006 (0.274) Batch 1.256 (1.743) Remain 08:07:55 loss: 0.2083 Lr: 0.00161
[2023-12-25 17:34:54,464 INFO misc.py line 119 253097] Train: [68/100][36/510] Data 0.008 (0.266) Batch 1.053 (1.722) Remain 08:02:02 loss: 0.1841 Lr: 0.00161
[2023-12-25 17:34:55,625 INFO misc.py line 119 253097] Train: [68/100][37/510] Data 0.006 (0.259) Batch 1.162 (1.706) Remain 07:57:24 loss: 0.2341 Lr: 0.00161
[2023-12-25 17:34:56,900 INFO misc.py line 119 253097] Train: [68/100][38/510] Data 0.004 (0.251) Batch 1.275 (1.693) Remain 07:53:55 loss: 0.2512 Lr: 0.00161
[2023-12-25 17:34:58,080 INFO misc.py line 119 253097] Train: [68/100][39/510] Data 0.005 (0.244) Batch 1.179 (1.679) Remain 07:49:54 loss: 0.2479 Lr: 0.00161
[2023-12-25 17:34:59,257 INFO misc.py line 119 253097] Train: [68/100][40/510] Data 0.005 (0.238) Batch 1.175 (1.665) Remain 07:46:03 loss: 0.0972 Lr: 0.00161
[2023-12-25 17:35:08,059 INFO misc.py line 119 253097] Train: [68/100][41/510] Data 7.594 (0.432) Batch 8.806 (1.853) Remain 08:38:36 loss: 0.1307 Lr: 0.00161
[2023-12-25 17:35:09,174 INFO misc.py line 119 253097] Train: [68/100][42/510] Data 0.004 (0.421) Batch 1.115 (1.834) Remain 08:33:16 loss: 0.1557 Lr: 0.00161
[2023-12-25 17:35:10,304 INFO misc.py line 119 253097] Train: [68/100][43/510] Data 0.004 (0.410) Batch 1.121 (1.817) Remain 08:28:15 loss: 0.1672 Lr: 0.00161
[2023-12-25 17:35:11,554 INFO misc.py line 119 253097] Train: [68/100][44/510] Data 0.013 (0.401) Batch 1.256 (1.803) Remain 08:24:24 loss: 0.1306 Lr: 0.00161
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[2023-12-25 17:45:59,032 INFO misc.py line 119 253097] Train: [68/100][463/510] Data 0.010 (0.128) Batch 1.100 (1.568) Remain 07:07:47 loss: 0.0922 Lr: 0.00154
[2023-12-25 17:46:00,388 INFO misc.py line 119 253097] Train: [68/100][464/510] Data 0.005 (0.128) Batch 1.357 (1.568) Remain 07:07:38 loss: 0.0693 Lr: 0.00154
[2023-12-25 17:46:01,548 INFO misc.py line 119 253097] Train: [68/100][465/510] Data 0.005 (0.128) Batch 1.160 (1.567) Remain 07:07:22 loss: 0.1677 Lr: 0.00154
[2023-12-25 17:46:02,780 INFO misc.py line 119 253097] Train: [68/100][466/510] Data 0.005 (0.127) Batch 1.204 (1.566) Remain 07:07:08 loss: 0.1133 Lr: 0.00154
[2023-12-25 17:46:04,072 INFO misc.py line 119 253097] Train: [68/100][467/510] Data 0.033 (0.127) Batch 1.316 (1.566) Remain 07:06:57 loss: 0.1074 Lr: 0.00154
[2023-12-25 17:46:05,245 INFO misc.py line 119 253097] Train: [68/100][468/510] Data 0.008 (0.127) Batch 1.177 (1.565) Remain 07:06:42 loss: 0.1603 Lr: 0.00154
[2023-12-25 17:46:06,217 INFO misc.py line 119 253097] Train: [68/100][469/510] Data 0.005 (0.127) Batch 0.972 (1.563) Remain 07:06:20 loss: 0.1249 Lr: 0.00154
[2023-12-25 17:46:07,463 INFO misc.py line 119 253097] Train: [68/100][470/510] Data 0.005 (0.126) Batch 1.243 (1.563) Remain 07:06:07 loss: 0.1133 Lr: 0.00154
[2023-12-25 17:46:08,506 INFO misc.py line 119 253097] Train: [68/100][471/510] Data 0.008 (0.126) Batch 1.039 (1.562) Remain 07:05:47 loss: 0.1092 Lr: 0.00154
[2023-12-25 17:46:09,707 INFO misc.py line 119 253097] Train: [68/100][472/510] Data 0.011 (0.126) Batch 1.205 (1.561) Remain 07:05:33 loss: 0.4134 Lr: 0.00154
[2023-12-25 17:46:11,003 INFO misc.py line 119 253097] Train: [68/100][473/510] Data 0.007 (0.126) Batch 1.296 (1.560) Remain 07:05:22 loss: 0.1506 Lr: 0.00154
[2023-12-25 17:46:12,136 INFO misc.py line 119 253097] Train: [68/100][474/510] Data 0.007 (0.125) Batch 1.125 (1.559) Remain 07:05:06 loss: 0.1199 Lr: 0.00154
[2023-12-25 17:46:13,292 INFO misc.py line 119 253097] Train: [68/100][475/510] Data 0.015 (0.125) Batch 1.161 (1.559) Remain 07:04:50 loss: 0.1359 Lr: 0.00154
[2023-12-25 17:46:14,531 INFO misc.py line 119 253097] Train: [68/100][476/510] Data 0.010 (0.125) Batch 1.243 (1.558) Remain 07:04:38 loss: 0.1174 Lr: 0.00154
[2023-12-25 17:46:24,686 INFO misc.py line 119 253097] Train: [68/100][477/510] Data 0.007 (0.125) Batch 10.156 (1.576) Remain 07:09:33 loss: 0.0781 Lr: 0.00154
[2023-12-25 17:46:25,958 INFO misc.py line 119 253097] Train: [68/100][478/510] Data 0.006 (0.124) Batch 1.274 (1.575) Remain 07:09:21 loss: 0.2436 Lr: 0.00154
[2023-12-25 17:46:27,209 INFO misc.py line 119 253097] Train: [68/100][479/510] Data 0.004 (0.124) Batch 1.251 (1.575) Remain 07:09:08 loss: 0.0732 Lr: 0.00154
[2023-12-25 17:46:28,329 INFO misc.py line 119 253097] Train: [68/100][480/510] Data 0.004 (0.124) Batch 1.117 (1.574) Remain 07:08:51 loss: 0.1117 Lr: 0.00154
[2023-12-25 17:46:29,339 INFO misc.py line 119 253097] Train: [68/100][481/510] Data 0.008 (0.124) Batch 0.981 (1.573) Remain 07:08:29 loss: 0.1421 Lr: 0.00154
[2023-12-25 17:46:30,369 INFO misc.py line 119 253097] Train: [68/100][482/510] Data 0.037 (0.123) Batch 1.062 (1.571) Remain 07:08:10 loss: 0.0621 Lr: 0.00154
[2023-12-25 17:46:31,384 INFO misc.py line 119 253097] Train: [68/100][483/510] Data 0.005 (0.123) Batch 1.014 (1.570) Remain 07:07:49 loss: 0.0765 Lr: 0.00154
[2023-12-25 17:46:32,535 INFO misc.py line 119 253097] Train: [68/100][484/510] Data 0.007 (0.123) Batch 1.152 (1.569) Remain 07:07:34 loss: 0.1207 Lr: 0.00154
[2023-12-25 17:46:33,778 INFO misc.py line 119 253097] Train: [68/100][485/510] Data 0.005 (0.123) Batch 1.237 (1.569) Remain 07:07:21 loss: 0.0948 Lr: 0.00154
[2023-12-25 17:46:34,741 INFO misc.py line 119 253097] Train: [68/100][486/510] Data 0.011 (0.122) Batch 0.970 (1.568) Remain 07:06:59 loss: 0.1026 Lr: 0.00154
[2023-12-25 17:46:35,904 INFO misc.py line 119 253097] Train: [68/100][487/510] Data 0.005 (0.122) Batch 1.163 (1.567) Remain 07:06:44 loss: 0.1047 Lr: 0.00154
[2023-12-25 17:46:36,869 INFO misc.py line 119 253097] Train: [68/100][488/510] Data 0.004 (0.122) Batch 0.964 (1.565) Remain 07:06:22 loss: 0.0974 Lr: 0.00153
[2023-12-25 17:46:38,970 INFO misc.py line 119 253097] Train: [68/100][489/510] Data 0.005 (0.122) Batch 2.099 (1.567) Remain 07:06:38 loss: 0.0854 Lr: 0.00153
[2023-12-25 17:46:40,201 INFO misc.py line 119 253097] Train: [68/100][490/510] Data 0.007 (0.121) Batch 1.234 (1.566) Remain 07:06:26 loss: 0.0949 Lr: 0.00153
[2023-12-25 17:46:41,242 INFO misc.py line 119 253097] Train: [68/100][491/510] Data 0.005 (0.121) Batch 1.036 (1.565) Remain 07:06:06 loss: 0.1962 Lr: 0.00153
[2023-12-25 17:46:42,386 INFO misc.py line 119 253097] Train: [68/100][492/510] Data 0.009 (0.121) Batch 1.149 (1.564) Remain 07:05:51 loss: 0.1138 Lr: 0.00153
[2023-12-25 17:46:43,668 INFO misc.py line 119 253097] Train: [68/100][493/510] Data 0.005 (0.121) Batch 1.280 (1.563) Remain 07:05:40 loss: 0.1809 Lr: 0.00153
[2023-12-25 17:46:45,997 INFO misc.py line 119 253097] Train: [68/100][494/510] Data 0.007 (0.121) Batch 2.332 (1.565) Remain 07:06:04 loss: 0.1704 Lr: 0.00153
[2023-12-25 17:46:51,031 INFO misc.py line 119 253097] Train: [68/100][495/510] Data 0.004 (0.120) Batch 5.032 (1.572) Remain 07:07:57 loss: 0.1597 Lr: 0.00153
[2023-12-25 17:46:52,272 INFO misc.py line 119 253097] Train: [68/100][496/510] Data 0.006 (0.120) Batch 1.243 (1.571) Remain 07:07:45 loss: 0.0855 Lr: 0.00153
[2023-12-25 17:46:53,464 INFO misc.py line 119 253097] Train: [68/100][497/510] Data 0.003 (0.120) Batch 1.192 (1.571) Remain 07:07:31 loss: 0.0992 Lr: 0.00153
[2023-12-25 17:46:54,827 INFO misc.py line 119 253097] Train: [68/100][498/510] Data 0.004 (0.120) Batch 1.359 (1.570) Remain 07:07:22 loss: 0.1502 Lr: 0.00153
[2023-12-25 17:46:55,860 INFO misc.py line 119 253097] Train: [68/100][499/510] Data 0.009 (0.119) Batch 1.037 (1.569) Remain 07:07:03 loss: 0.0603 Lr: 0.00153
[2023-12-25 17:46:57,105 INFO misc.py line 119 253097] Train: [68/100][500/510] Data 0.004 (0.119) Batch 1.244 (1.568) Remain 07:06:51 loss: 0.1076 Lr: 0.00153
[2023-12-25 17:46:58,342 INFO misc.py line 119 253097] Train: [68/100][501/510] Data 0.005 (0.119) Batch 1.236 (1.568) Remain 07:06:38 loss: 0.0695 Lr: 0.00153
[2023-12-25 17:46:59,305 INFO misc.py line 119 253097] Train: [68/100][502/510] Data 0.007 (0.119) Batch 0.966 (1.566) Remain 07:06:17 loss: 0.1788 Lr: 0.00153
[2023-12-25 17:47:00,581 INFO misc.py line 119 253097] Train: [68/100][503/510] Data 0.003 (0.118) Batch 1.269 (1.566) Remain 07:06:06 loss: 0.1040 Lr: 0.00153
[2023-12-25 17:47:07,517 INFO misc.py line 119 253097] Train: [68/100][504/510] Data 0.010 (0.118) Batch 6.943 (1.577) Remain 07:08:59 loss: 0.1359 Lr: 0.00153
[2023-12-25 17:47:08,643 INFO misc.py line 119 253097] Train: [68/100][505/510] Data 0.003 (0.118) Batch 1.125 (1.576) Remain 07:08:43 loss: 0.1201 Lr: 0.00153
[2023-12-25 17:47:09,756 INFO misc.py line 119 253097] Train: [68/100][506/510] Data 0.004 (0.118) Batch 1.113 (1.575) Remain 07:08:27 loss: 0.0992 Lr: 0.00153
[2023-12-25 17:47:10,868 INFO misc.py line 119 253097] Train: [68/100][507/510] Data 0.005 (0.118) Batch 1.113 (1.574) Remain 07:08:10 loss: 0.2005 Lr: 0.00153
[2023-12-25 17:47:11,990 INFO misc.py line 119 253097] Train: [68/100][508/510] Data 0.005 (0.117) Batch 1.120 (1.573) Remain 07:07:54 loss: 0.1040 Lr: 0.00153
[2023-12-25 17:47:13,252 INFO misc.py line 119 253097] Train: [68/100][509/510] Data 0.006 (0.117) Batch 1.254 (1.572) Remain 07:07:42 loss: 0.2967 Lr: 0.00153
[2023-12-25 17:47:14,431 INFO misc.py line 119 253097] Train: [68/100][510/510] Data 0.013 (0.117) Batch 1.182 (1.572) Remain 07:07:28 loss: 0.1894 Lr: 0.00153
[2023-12-25 17:47:14,431 INFO misc.py line 136 253097] Train result: loss: 0.1403 
[2023-12-25 17:47:14,432 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 17:47:43,837 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6507 
[2023-12-25 17:47:44,200 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3850 
[2023-12-25 17:47:50,377 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5182 
[2023-12-25 17:47:50,897 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3749 
[2023-12-25 17:47:52,868 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8808 
[2023-12-25 17:47:53,313 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3855 
[2023-12-25 17:47:54,192 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3481 
[2023-12-25 17:47:54,745 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3582 
[2023-12-25 17:47:56,565 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1770 
[2023-12-25 17:47:58,689 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.6412 
[2023-12-25 17:47:59,546 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3212 
[2023-12-25 17:47:59,970 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.1031 
[2023-12-25 17:48:00,872 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6420 
[2023-12-25 17:48:03,824 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9659 
[2023-12-25 17:48:04,291 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4202 
[2023-12-25 17:48:04,901 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4877 
[2023-12-25 17:48:05,600 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4082 
[2023-12-25 17:48:07,066 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6771/0.7318/0.8961.
[2023-12-25 17:48:07,066 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9195/0.9465
[2023-12-25 17:48:07,066 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9831/0.9905
[2023-12-25 17:48:07,066 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8329/0.9670
[2023-12-25 17:48:07,066 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 17:48:07,066 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3066/0.3399
[2023-12-25 17:48:07,066 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6031/0.6192
[2023-12-25 17:48:07,066 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6315/0.6545
[2023-12-25 17:48:07,066 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7841/0.8971
[2023-12-25 17:48:07,067 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8947/0.9236
[2023-12-25 17:48:07,067 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.7256/0.7503
[2023-12-25 17:48:07,067 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7532/0.8725
[2023-12-25 17:48:07,067 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7753/0.8361
[2023-12-25 17:48:07,067 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5930/0.7163
[2023-12-25 17:48:07,067 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 17:48:07,068 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 17:48:07,068 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 17:48:24,365 INFO misc.py line 119 253097] Train: [69/100][1/510] Data 8.478 (8.478) Batch 15.091 (15.091) Remain 68:24:26 loss: 0.0930 Lr: 0.00153
[2023-12-25 17:48:25,329 INFO misc.py line 119 253097] Train: [69/100][2/510] Data 0.005 (0.005) Batch 0.965 (0.965) Remain 04:22:27 loss: 0.0693 Lr: 0.00153
[2023-12-25 17:48:26,615 INFO misc.py line 119 253097] Train: [69/100][3/510] Data 0.004 (0.004) Batch 1.286 (1.286) Remain 05:49:40 loss: 0.1334 Lr: 0.00153
[2023-12-25 17:48:27,850 INFO misc.py line 119 253097] Train: [69/100][4/510] Data 0.005 (0.005) Batch 1.235 (1.235) Remain 05:35:53 loss: 0.1778 Lr: 0.00153
[2023-12-25 17:48:29,109 INFO misc.py line 119 253097] Train: [69/100][5/510] Data 0.005 (0.005) Batch 1.237 (1.236) Remain 05:36:06 loss: 0.1367 Lr: 0.00153
[2023-12-25 17:48:30,374 INFO misc.py line 119 253097] Train: [69/100][6/510] Data 0.026 (0.012) Batch 1.288 (1.253) Remain 05:40:46 loss: 0.1385 Lr: 0.00153
[2023-12-25 17:48:31,618 INFO misc.py line 119 253097] Train: [69/100][7/510] Data 0.004 (0.010) Batch 1.244 (1.251) Remain 05:40:07 loss: 0.1084 Lr: 0.00153
[2023-12-25 17:48:32,892 INFO misc.py line 119 253097] Train: [69/100][8/510] Data 0.003 (0.008) Batch 1.273 (1.255) Remain 05:41:19 loss: 0.0848 Lr: 0.00153
[2023-12-25 17:48:34,231 INFO misc.py line 119 253097] Train: [69/100][9/510] Data 0.004 (0.008) Batch 1.334 (1.269) Remain 05:44:51 loss: 0.1016 Lr: 0.00153
[2023-12-25 17:48:35,568 INFO misc.py line 119 253097] Train: [69/100][10/510] Data 0.010 (0.008) Batch 1.324 (1.276) Remain 05:46:59 loss: 0.1149 Lr: 0.00153
[2023-12-25 17:48:36,765 INFO misc.py line 119 253097] Train: [69/100][11/510] Data 0.022 (0.010) Batch 1.212 (1.268) Remain 05:44:45 loss: 0.1723 Lr: 0.00153
[2023-12-25 17:48:37,861 INFO misc.py line 119 253097] Train: [69/100][12/510] Data 0.007 (0.009) Batch 1.096 (1.249) Remain 05:39:32 loss: 0.1361 Lr: 0.00153
[2023-12-25 17:48:38,824 INFO misc.py line 119 253097] Train: [69/100][13/510] Data 0.007 (0.009) Batch 0.965 (1.221) Remain 05:31:47 loss: 0.2003 Lr: 0.00153
[2023-12-25 17:48:39,852 INFO misc.py line 119 253097] Train: [69/100][14/510] Data 0.005 (0.009) Batch 1.029 (1.203) Remain 05:27:01 loss: 0.1356 Lr: 0.00153
[2023-12-25 17:48:41,088 INFO misc.py line 119 253097] Train: [69/100][15/510] Data 0.005 (0.008) Batch 1.234 (1.206) Remain 05:27:42 loss: 0.1744 Lr: 0.00153
[2023-12-25 17:48:43,594 INFO misc.py line 119 253097] Train: [69/100][16/510] Data 1.665 (0.136) Batch 2.508 (1.306) Remain 05:54:53 loss: 0.0446 Lr: 0.00153
[2023-12-25 17:48:44,885 INFO misc.py line 119 253097] Train: [69/100][17/510] Data 0.005 (0.126) Batch 1.288 (1.305) Remain 05:54:30 loss: 0.1492 Lr: 0.00153
[2023-12-25 17:48:46,033 INFO misc.py line 119 253097] Train: [69/100][18/510] Data 0.009 (0.119) Batch 1.148 (1.294) Remain 05:51:38 loss: 0.1633 Lr: 0.00153
[2023-12-25 17:48:47,205 INFO misc.py line 119 253097] Train: [69/100][19/510] Data 0.010 (0.112) Batch 1.176 (1.287) Remain 05:49:37 loss: 0.1162 Lr: 0.00153
[2023-12-25 17:48:48,535 INFO misc.py line 119 253097] Train: [69/100][20/510] Data 0.004 (0.106) Batch 1.286 (1.287) Remain 05:49:34 loss: 0.1194 Lr: 0.00153
[2023-12-25 17:48:55,481 INFO misc.py line 119 253097] Train: [69/100][21/510] Data 0.049 (0.102) Batch 6.991 (1.604) Remain 07:15:38 loss: 0.1251 Lr: 0.00153
[2023-12-25 17:48:56,496 INFO misc.py line 119 253097] Train: [69/100][22/510] Data 0.004 (0.097) Batch 1.015 (1.573) Remain 07:07:11 loss: 0.1075 Lr: 0.00153
[2023-12-25 17:48:57,600 INFO misc.py line 119 253097] Train: [69/100][23/510] Data 0.003 (0.093) Batch 1.103 (1.549) Remain 07:00:47 loss: 0.1576 Lr: 0.00153
[2023-12-25 17:48:58,820 INFO misc.py line 119 253097] Train: [69/100][24/510] Data 0.005 (0.088) Batch 1.216 (1.533) Remain 06:56:27 loss: 0.1375 Lr: 0.00153
[2023-12-25 17:48:59,909 INFO misc.py line 119 253097] Train: [69/100][25/510] Data 0.008 (0.085) Batch 1.094 (1.513) Remain 06:51:00 loss: 0.1218 Lr: 0.00153
[2023-12-25 17:49:00,862 INFO misc.py line 119 253097] Train: [69/100][26/510] Data 0.003 (0.081) Batch 0.952 (1.489) Remain 06:44:21 loss: 0.2866 Lr: 0.00153
[2023-12-25 17:49:02,123 INFO misc.py line 119 253097] Train: [69/100][27/510] Data 0.004 (0.078) Batch 1.256 (1.479) Remain 06:41:42 loss: 0.1932 Lr: 0.00153
[2023-12-25 17:49:03,313 INFO misc.py line 119 253097] Train: [69/100][28/510] Data 0.008 (0.075) Batch 1.189 (1.468) Remain 06:38:31 loss: 0.1927 Lr: 0.00153
[2023-12-25 17:49:04,428 INFO misc.py line 119 253097] Train: [69/100][29/510] Data 0.010 (0.073) Batch 1.120 (1.454) Remain 06:34:51 loss: 0.1551 Lr: 0.00153
[2023-12-25 17:49:05,503 INFO misc.py line 119 253097] Train: [69/100][30/510] Data 0.005 (0.070) Batch 1.069 (1.440) Remain 06:30:57 loss: 0.2412 Lr: 0.00153
[2023-12-25 17:49:06,417 INFO misc.py line 119 253097] Train: [69/100][31/510] Data 0.012 (0.068) Batch 0.921 (1.421) Remain 06:25:54 loss: 0.1018 Lr: 0.00153
[2023-12-25 17:49:07,436 INFO misc.py line 119 253097] Train: [69/100][32/510] Data 0.005 (0.066) Batch 0.980 (1.406) Remain 06:21:45 loss: 0.2009 Lr: 0.00153
[2023-12-25 17:49:08,497 INFO misc.py line 119 253097] Train: [69/100][33/510] Data 0.042 (0.065) Batch 1.099 (1.396) Remain 06:18:57 loss: 0.1102 Lr: 0.00153
[2023-12-25 17:49:09,520 INFO misc.py line 119 253097] Train: [69/100][34/510] Data 0.006 (0.063) Batch 1.025 (1.384) Remain 06:15:40 loss: 0.0869 Lr: 0.00153
[2023-12-25 17:49:10,594 INFO misc.py line 119 253097] Train: [69/100][35/510] Data 0.004 (0.061) Batch 1.072 (1.374) Remain 06:13:00 loss: 0.2245 Lr: 0.00153
[2023-12-25 17:49:11,760 INFO misc.py line 119 253097] Train: [69/100][36/510] Data 0.006 (0.060) Batch 1.168 (1.368) Remain 06:11:17 loss: 0.1415 Lr: 0.00153
[2023-12-25 17:49:12,841 INFO misc.py line 119 253097] Train: [69/100][37/510] Data 0.004 (0.058) Batch 1.079 (1.360) Remain 06:08:57 loss: 0.1372 Lr: 0.00152
[2023-12-25 17:49:13,945 INFO misc.py line 119 253097] Train: [69/100][38/510] Data 0.005 (0.057) Batch 1.103 (1.352) Remain 06:06:56 loss: 0.1270 Lr: 0.00152
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[2023-12-25 17:52:37,216 INFO misc.py line 119 253097] Train: [69/100][170/510] Data 6.245 (0.103) Batch 7.573 (1.501) Remain 06:43:54 loss: 0.2364 Lr: 0.00150
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[2023-12-25 17:52:43,149 INFO misc.py line 119 253097] Train: [69/100][175/510] Data 0.004 (0.100) Batch 1.184 (1.491) Remain 06:41:19 loss: 0.1064 Lr: 0.00150
[2023-12-25 17:52:44,356 INFO misc.py line 119 253097] Train: [69/100][176/510] Data 0.013 (0.099) Batch 1.212 (1.490) Remain 06:40:51 loss: 0.1565 Lr: 0.00150
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[2023-12-25 17:52:46,554 INFO misc.py line 119 253097] Train: [69/100][178/510] Data 0.005 (0.098) Batch 0.979 (1.485) Remain 06:39:36 loss: 0.2210 Lr: 0.00150
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[2023-12-25 17:53:12,382 INFO misc.py line 119 253097] Train: [69/100][197/510] Data 0.006 (0.089) Batch 1.197 (1.473) Remain 06:35:49 loss: 0.0722 Lr: 0.00150
[2023-12-25 17:53:13,615 INFO misc.py line 119 253097] Train: [69/100][198/510] Data 0.004 (0.089) Batch 1.233 (1.472) Remain 06:35:28 loss: 0.1550 Lr: 0.00150
[2023-12-25 17:53:14,634 INFO misc.py line 119 253097] Train: [69/100][199/510] Data 0.004 (0.088) Batch 1.017 (1.469) Remain 06:34:49 loss: 0.1562 Lr: 0.00150
[2023-12-25 17:53:23,247 INFO misc.py line 119 253097] Train: [69/100][200/510] Data 7.559 (0.126) Batch 8.585 (1.506) Remain 06:44:30 loss: 0.1089 Lr: 0.00150
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[2023-12-25 17:53:36,657 INFO misc.py line 119 253097] Train: [69/100][211/510] Data 0.167 (0.121) Batch 1.390 (1.491) Remain 06:40:11 loss: 0.3428 Lr: 0.00150
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[2023-12-25 17:55:45,539 INFO misc.py line 119 253097] Train: [69/100][303/510] Data 0.005 (0.120) Batch 1.062 (1.463) Remain 06:30:33 loss: 0.1146 Lr: 0.00148
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[2023-12-25 17:59:55,754 INFO misc.py line 119 253097] Train: [69/100][457/510] Data 0.004 (0.107) Batch 1.082 (1.518) Remain 06:41:18 loss: 0.1157 Lr: 0.00145
[2023-12-25 17:59:56,891 INFO misc.py line 119 253097] Train: [69/100][458/510] Data 0.005 (0.106) Batch 1.136 (1.517) Remain 06:41:04 loss: 0.1041 Lr: 0.00145
[2023-12-25 17:59:57,908 INFO misc.py line 119 253097] Train: [69/100][459/510] Data 0.005 (0.106) Batch 1.016 (1.516) Remain 06:40:45 loss: 0.2305 Lr: 0.00145
[2023-12-25 17:59:59,032 INFO misc.py line 119 253097] Train: [69/100][460/510] Data 0.005 (0.106) Batch 1.124 (1.515) Remain 06:40:29 loss: 0.1199 Lr: 0.00145
[2023-12-25 18:00:00,381 INFO misc.py line 119 253097] Train: [69/100][461/510] Data 0.006 (0.106) Batch 1.344 (1.515) Remain 06:40:22 loss: 0.1144 Lr: 0.00145
[2023-12-25 18:00:01,652 INFO misc.py line 119 253097] Train: [69/100][462/510] Data 0.011 (0.106) Batch 1.277 (1.514) Remain 06:40:12 loss: 0.1005 Lr: 0.00145
[2023-12-25 18:00:02,809 INFO misc.py line 119 253097] Train: [69/100][463/510] Data 0.005 (0.105) Batch 1.155 (1.513) Remain 06:39:58 loss: 0.1065 Lr: 0.00145
[2023-12-25 18:00:04,083 INFO misc.py line 119 253097] Train: [69/100][464/510] Data 0.006 (0.105) Batch 1.277 (1.513) Remain 06:39:49 loss: 0.1053 Lr: 0.00145
[2023-12-25 18:00:05,068 INFO misc.py line 119 253097] Train: [69/100][465/510] Data 0.004 (0.105) Batch 0.985 (1.512) Remain 06:39:29 loss: 0.0879 Lr: 0.00145
[2023-12-25 18:00:05,977 INFO misc.py line 119 253097] Train: [69/100][466/510] Data 0.004 (0.105) Batch 0.908 (1.510) Remain 06:39:07 loss: 0.1658 Lr: 0.00145
[2023-12-25 18:00:07,102 INFO misc.py line 119 253097] Train: [69/100][467/510] Data 0.006 (0.104) Batch 1.126 (1.510) Remain 06:38:52 loss: 0.1321 Lr: 0.00145
[2023-12-25 18:00:08,233 INFO misc.py line 119 253097] Train: [69/100][468/510] Data 0.005 (0.104) Batch 1.130 (1.509) Remain 06:38:38 loss: 0.0737 Lr: 0.00145
[2023-12-25 18:00:09,421 INFO misc.py line 119 253097] Train: [69/100][469/510] Data 0.005 (0.104) Batch 1.189 (1.508) Remain 06:38:25 loss: 0.0918 Lr: 0.00145
[2023-12-25 18:00:17,751 INFO misc.py line 119 253097] Train: [69/100][470/510] Data 7.229 (0.119) Batch 8.331 (1.523) Remain 06:42:15 loss: 0.1712 Lr: 0.00145
[2023-12-25 18:00:18,922 INFO misc.py line 119 253097] Train: [69/100][471/510] Data 0.004 (0.119) Batch 1.170 (1.522) Remain 06:42:02 loss: 0.1310 Lr: 0.00145
[2023-12-25 18:00:20,040 INFO misc.py line 119 253097] Train: [69/100][472/510] Data 0.004 (0.119) Batch 1.117 (1.521) Remain 06:41:47 loss: 0.2472 Lr: 0.00145
[2023-12-25 18:00:21,213 INFO misc.py line 119 253097] Train: [69/100][473/510] Data 0.004 (0.119) Batch 1.175 (1.520) Remain 06:41:34 loss: 0.1552 Lr: 0.00145
[2023-12-25 18:00:22,520 INFO misc.py line 119 253097] Train: [69/100][474/510] Data 0.004 (0.118) Batch 1.301 (1.520) Remain 06:41:25 loss: 0.1232 Lr: 0.00145
[2023-12-25 18:00:23,678 INFO misc.py line 119 253097] Train: [69/100][475/510] Data 0.010 (0.118) Batch 1.164 (1.519) Remain 06:41:11 loss: 0.1109 Lr: 0.00145
[2023-12-25 18:00:24,844 INFO misc.py line 119 253097] Train: [69/100][476/510] Data 0.003 (0.118) Batch 1.165 (1.518) Remain 06:40:58 loss: 0.1468 Lr: 0.00145
[2023-12-25 18:00:25,992 INFO misc.py line 119 253097] Train: [69/100][477/510] Data 0.005 (0.118) Batch 1.144 (1.518) Remain 06:40:44 loss: 0.1298 Lr: 0.00145
[2023-12-25 18:00:27,166 INFO misc.py line 119 253097] Train: [69/100][478/510] Data 0.009 (0.117) Batch 1.173 (1.517) Remain 06:40:31 loss: 0.1232 Lr: 0.00145
[2023-12-25 18:00:28,282 INFO misc.py line 119 253097] Train: [69/100][479/510] Data 0.010 (0.117) Batch 1.122 (1.516) Remain 06:40:16 loss: 0.1109 Lr: 0.00145
[2023-12-25 18:00:29,611 INFO misc.py line 119 253097] Train: [69/100][480/510] Data 0.004 (0.117) Batch 1.321 (1.516) Remain 06:40:08 loss: 0.1002 Lr: 0.00145
[2023-12-25 18:00:30,842 INFO misc.py line 119 253097] Train: [69/100][481/510] Data 0.012 (0.117) Batch 1.214 (1.515) Remain 06:39:57 loss: 0.0780 Lr: 0.00145
[2023-12-25 18:00:31,898 INFO misc.py line 119 253097] Train: [69/100][482/510] Data 0.029 (0.116) Batch 1.079 (1.514) Remain 06:39:41 loss: 0.1033 Lr: 0.00145
[2023-12-25 18:00:33,188 INFO misc.py line 119 253097] Train: [69/100][483/510] Data 0.006 (0.116) Batch 1.287 (1.514) Remain 06:39:32 loss: 0.1954 Lr: 0.00145
[2023-12-25 18:00:34,105 INFO misc.py line 119 253097] Train: [69/100][484/510] Data 0.008 (0.116) Batch 0.920 (1.512) Remain 06:39:11 loss: 0.1107 Lr: 0.00145
[2023-12-25 18:00:35,153 INFO misc.py line 119 253097] Train: [69/100][485/510] Data 0.005 (0.116) Batch 1.050 (1.511) Remain 06:38:54 loss: 0.2462 Lr: 0.00145
[2023-12-25 18:00:36,260 INFO misc.py line 119 253097] Train: [69/100][486/510] Data 0.003 (0.116) Batch 1.106 (1.511) Remain 06:38:39 loss: 0.1358 Lr: 0.00145
[2023-12-25 18:00:37,417 INFO misc.py line 119 253097] Train: [69/100][487/510] Data 0.005 (0.115) Batch 1.157 (1.510) Remain 06:38:26 loss: 0.1513 Lr: 0.00145
[2023-12-25 18:00:38,592 INFO misc.py line 119 253097] Train: [69/100][488/510] Data 0.003 (0.115) Batch 1.175 (1.509) Remain 06:38:14 loss: 0.1267 Lr: 0.00145
[2023-12-25 18:00:39,631 INFO misc.py line 119 253097] Train: [69/100][489/510] Data 0.004 (0.115) Batch 1.038 (1.508) Remain 06:37:57 loss: 0.2874 Lr: 0.00145
[2023-12-25 18:00:40,682 INFO misc.py line 119 253097] Train: [69/100][490/510] Data 0.005 (0.115) Batch 1.052 (1.507) Remain 06:37:40 loss: 0.0856 Lr: 0.00145
[2023-12-25 18:00:41,841 INFO misc.py line 119 253097] Train: [69/100][491/510] Data 0.004 (0.114) Batch 1.160 (1.507) Remain 06:37:28 loss: 0.1396 Lr: 0.00145
[2023-12-25 18:00:42,858 INFO misc.py line 119 253097] Train: [69/100][492/510] Data 0.003 (0.114) Batch 1.017 (1.506) Remain 06:37:10 loss: 0.1139 Lr: 0.00145
[2023-12-25 18:00:43,905 INFO misc.py line 119 253097] Train: [69/100][493/510] Data 0.004 (0.114) Batch 1.047 (1.505) Remain 06:36:54 loss: 0.0760 Lr: 0.00145
[2023-12-25 18:00:45,172 INFO misc.py line 119 253097] Train: [69/100][494/510] Data 0.004 (0.114) Batch 1.265 (1.504) Remain 06:36:45 loss: 0.2713 Lr: 0.00145
[2023-12-25 18:00:46,278 INFO misc.py line 119 253097] Train: [69/100][495/510] Data 0.006 (0.114) Batch 1.107 (1.503) Remain 06:36:30 loss: 0.1480 Lr: 0.00145
[2023-12-25 18:00:47,532 INFO misc.py line 119 253097] Train: [69/100][496/510] Data 0.005 (0.113) Batch 1.254 (1.503) Remain 06:36:21 loss: 0.1101 Lr: 0.00145
[2023-12-25 18:00:48,486 INFO misc.py line 119 253097] Train: [69/100][497/510] Data 0.004 (0.113) Batch 0.955 (1.502) Remain 06:36:02 loss: 0.1577 Lr: 0.00145
[2023-12-25 18:00:49,663 INFO misc.py line 119 253097] Train: [69/100][498/510] Data 0.004 (0.113) Batch 1.176 (1.501) Remain 06:35:50 loss: 0.0987 Lr: 0.00145
[2023-12-25 18:00:50,919 INFO misc.py line 119 253097] Train: [69/100][499/510] Data 0.004 (0.113) Batch 1.251 (1.501) Remain 06:35:40 loss: 0.0847 Lr: 0.00145
[2023-12-25 18:00:52,199 INFO misc.py line 119 253097] Train: [69/100][500/510] Data 0.010 (0.112) Batch 1.282 (1.500) Remain 06:35:32 loss: 0.1702 Lr: 0.00145
[2023-12-25 18:00:55,238 INFO misc.py line 119 253097] Train: [69/100][501/510] Data 0.008 (0.112) Batch 3.042 (1.503) Remain 06:36:20 loss: 0.1161 Lr: 0.00145
[2023-12-25 18:00:56,334 INFO misc.py line 119 253097] Train: [69/100][502/510] Data 0.004 (0.112) Batch 1.096 (1.502) Remain 06:36:05 loss: 0.0909 Lr: 0.00145
[2023-12-25 18:00:57,581 INFO misc.py line 119 253097] Train: [69/100][503/510] Data 0.004 (0.112) Batch 1.246 (1.502) Remain 06:35:55 loss: 0.1749 Lr: 0.00145
[2023-12-25 18:01:01,348 INFO misc.py line 119 253097] Train: [69/100][504/510] Data 0.006 (0.112) Batch 3.768 (1.506) Remain 06:37:06 loss: 0.2299 Lr: 0.00145
[2023-12-25 18:01:02,457 INFO misc.py line 119 253097] Train: [69/100][505/510] Data 0.005 (0.111) Batch 1.109 (1.506) Remain 06:36:51 loss: 0.1630 Lr: 0.00145
[2023-12-25 18:01:03,462 INFO misc.py line 119 253097] Train: [69/100][506/510] Data 0.004 (0.111) Batch 1.004 (1.505) Remain 06:36:34 loss: 0.1850 Lr: 0.00145
[2023-12-25 18:01:04,558 INFO misc.py line 119 253097] Train: [69/100][507/510] Data 0.005 (0.111) Batch 1.098 (1.504) Remain 06:36:20 loss: 0.1313 Lr: 0.00145
[2023-12-25 18:01:05,670 INFO misc.py line 119 253097] Train: [69/100][508/510] Data 0.003 (0.111) Batch 1.113 (1.503) Remain 06:36:06 loss: 0.1571 Lr: 0.00145
[2023-12-25 18:01:06,940 INFO misc.py line 119 253097] Train: [69/100][509/510] Data 0.003 (0.111) Batch 1.267 (1.503) Remain 06:35:57 loss: 0.3546 Lr: 0.00145
[2023-12-25 18:01:08,282 INFO misc.py line 119 253097] Train: [69/100][510/510] Data 0.005 (0.110) Batch 1.340 (1.502) Remain 06:35:51 loss: 0.1365 Lr: 0.00145
[2023-12-25 18:01:08,283 INFO misc.py line 136 253097] Train result: loss: 0.1408 
[2023-12-25 18:01:08,283 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 18:01:34,614 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.8010 
[2023-12-25 18:01:34,963 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.4207 
[2023-12-25 18:01:39,924 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4114 
[2023-12-25 18:01:40,441 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3470 
[2023-12-25 18:01:42,419 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0685 
[2023-12-25 18:01:42,846 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4576 
[2023-12-25 18:01:43,725 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.4473 
[2023-12-25 18:01:44,280 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2810 
[2023-12-25 18:01:46,091 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9587 
[2023-12-25 18:01:48,216 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1702 
[2023-12-25 18:01:49,082 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2865 
[2023-12-25 18:01:49,509 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8622 
[2023-12-25 18:01:50,413 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5220 
[2023-12-25 18:01:53,366 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9917 
[2023-12-25 18:01:53,837 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2937 
[2023-12-25 18:01:54,448 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3753 
[2023-12-25 18:01:55,154 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3640 
[2023-12-25 18:01:56,647 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6677/0.7311/0.8994.
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9192/0.9484
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9818/0.9908
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8452/0.9723
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2094/0.2186
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6337/0.6541
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6637/0.7591
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7986/0.9041
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9173/0.9608
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6366/0.6719
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7522/0.8409
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7335/0.8506
[2023-12-25 18:01:56,647 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5895/0.7324
[2023-12-25 18:01:56,648 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 18:01:56,650 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 18:01:56,650 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 18:02:08,522 INFO misc.py line 119 253097] Train: [70/100][1/510] Data 3.677 (3.677) Batch 9.171 (9.171) Remain 40:16:23 loss: 0.1178 Lr: 0.00145
[2023-12-25 18:02:09,925 INFO misc.py line 119 253097] Train: [70/100][2/510] Data 0.177 (0.177) Batch 1.406 (1.406) Remain 06:10:21 loss: 0.0909 Lr: 0.00145
[2023-12-25 18:02:11,091 INFO misc.py line 119 253097] Train: [70/100][3/510] Data 0.004 (0.004) Batch 1.166 (1.166) Remain 05:07:18 loss: 0.1556 Lr: 0.00145
[2023-12-25 18:02:12,266 INFO misc.py line 119 253097] Train: [70/100][4/510] Data 0.004 (0.004) Batch 1.173 (1.173) Remain 05:08:59 loss: 0.1896 Lr: 0.00144
[2023-12-25 18:02:13,387 INFO misc.py line 119 253097] Train: [70/100][5/510] Data 0.007 (0.006) Batch 1.120 (1.147) Remain 05:02:02 loss: 0.0992 Lr: 0.00144
[2023-12-25 18:02:14,659 INFO misc.py line 119 253097] Train: [70/100][6/510] Data 0.008 (0.006) Batch 1.274 (1.189) Remain 05:13:11 loss: 0.1494 Lr: 0.00144
[2023-12-25 18:02:15,854 INFO misc.py line 119 253097] Train: [70/100][7/510] Data 0.006 (0.006) Batch 1.145 (1.178) Remain 05:10:17 loss: 0.2045 Lr: 0.00144
[2023-12-25 18:02:16,955 INFO misc.py line 119 253097] Train: [70/100][8/510] Data 0.055 (0.016) Batch 1.152 (1.173) Remain 05:08:54 loss: 0.3193 Lr: 0.00144
[2023-12-25 18:02:18,183 INFO misc.py line 119 253097] Train: [70/100][9/510] Data 0.004 (0.014) Batch 1.228 (1.182) Remain 05:11:17 loss: 0.1497 Lr: 0.00144
[2023-12-25 18:02:19,359 INFO misc.py line 119 253097] Train: [70/100][10/510] Data 0.004 (0.013) Batch 1.168 (1.180) Remain 05:10:45 loss: 0.1557 Lr: 0.00144
[2023-12-25 18:02:20,484 INFO misc.py line 119 253097] Train: [70/100][11/510] Data 0.012 (0.012) Batch 1.128 (1.174) Remain 05:09:01 loss: 0.0701 Lr: 0.00144
[2023-12-25 18:02:21,609 INFO misc.py line 119 253097] Train: [70/100][12/510] Data 0.009 (0.012) Batch 1.128 (1.169) Remain 05:07:40 loss: 0.0969 Lr: 0.00144
[2023-12-25 18:02:22,500 INFO misc.py line 119 253097] Train: [70/100][13/510] Data 0.006 (0.011) Batch 0.892 (1.141) Remain 05:00:22 loss: 0.1236 Lr: 0.00144
[2023-12-25 18:02:23,513 INFO misc.py line 119 253097] Train: [70/100][14/510] Data 0.004 (0.011) Batch 1.013 (1.129) Remain 04:57:18 loss: 0.1233 Lr: 0.00144
[2023-12-25 18:02:24,677 INFO misc.py line 119 253097] Train: [70/100][15/510] Data 0.004 (0.010) Batch 1.163 (1.132) Remain 04:58:01 loss: 0.2230 Lr: 0.00144
[2023-12-25 18:02:25,945 INFO misc.py line 119 253097] Train: [70/100][16/510] Data 0.005 (0.010) Batch 1.264 (1.142) Remain 05:00:40 loss: 0.1099 Lr: 0.00144
[2023-12-25 18:02:26,932 INFO misc.py line 119 253097] Train: [70/100][17/510] Data 0.011 (0.010) Batch 0.993 (1.132) Remain 04:57:50 loss: 0.2168 Lr: 0.00144
[2023-12-25 18:02:27,969 INFO misc.py line 119 253097] Train: [70/100][18/510] Data 0.004 (0.009) Batch 1.030 (1.125) Remain 04:56:02 loss: 0.1047 Lr: 0.00144
[2023-12-25 18:02:29,247 INFO misc.py line 119 253097] Train: [70/100][19/510] Data 0.011 (0.009) Batch 1.280 (1.135) Remain 04:58:34 loss: 0.1657 Lr: 0.00144
[2023-12-25 18:02:30,383 INFO misc.py line 119 253097] Train: [70/100][20/510] Data 0.008 (0.009) Batch 1.134 (1.134) Remain 04:58:32 loss: 0.1411 Lr: 0.00144
[2023-12-25 18:02:31,547 INFO misc.py line 119 253097] Train: [70/100][21/510] Data 0.011 (0.010) Batch 1.161 (1.136) Remain 04:58:54 loss: 0.1354 Lr: 0.00144
[2023-12-25 18:02:32,766 INFO misc.py line 119 253097] Train: [70/100][22/510] Data 0.014 (0.010) Batch 1.229 (1.141) Remain 05:00:10 loss: 0.0889 Lr: 0.00144
[2023-12-25 18:02:34,074 INFO misc.py line 119 253097] Train: [70/100][23/510] Data 0.004 (0.009) Batch 1.304 (1.149) Remain 05:02:18 loss: 0.0687 Lr: 0.00144
[2023-12-25 18:02:35,043 INFO misc.py line 119 253097] Train: [70/100][24/510] Data 0.008 (0.009) Batch 0.972 (1.141) Remain 05:00:04 loss: 0.0925 Lr: 0.00144
[2023-12-25 18:02:36,091 INFO misc.py line 119 253097] Train: [70/100][25/510] Data 0.005 (0.009) Batch 1.049 (1.136) Remain 04:58:57 loss: 0.1043 Lr: 0.00144
[2023-12-25 18:02:37,282 INFO misc.py line 119 253097] Train: [70/100][26/510] Data 0.004 (0.009) Batch 1.190 (1.139) Remain 04:59:33 loss: 0.1509 Lr: 0.00144
[2023-12-25 18:02:38,355 INFO misc.py line 119 253097] Train: [70/100][27/510] Data 0.005 (0.009) Batch 1.065 (1.136) Remain 04:58:44 loss: 0.1497 Lr: 0.00144
[2023-12-25 18:02:39,546 INFO misc.py line 119 253097] Train: [70/100][28/510] Data 0.014 (0.009) Batch 1.200 (1.138) Remain 04:59:23 loss: 0.1130 Lr: 0.00144
[2023-12-25 18:02:40,655 INFO misc.py line 119 253097] Train: [70/100][29/510] Data 0.004 (0.009) Batch 1.109 (1.137) Remain 04:59:04 loss: 0.1054 Lr: 0.00144
[2023-12-25 18:02:41,964 INFO misc.py line 119 253097] Train: [70/100][30/510] Data 0.004 (0.009) Batch 1.309 (1.143) Remain 05:00:43 loss: 0.0869 Lr: 0.00144
[2023-12-25 18:02:47,149 INFO misc.py line 119 253097] Train: [70/100][31/510] Data 4.381 (0.165) Batch 5.185 (1.288) Remain 05:38:40 loss: 0.0687 Lr: 0.00144
[2023-12-25 18:02:48,215 INFO misc.py line 119 253097] Train: [70/100][32/510] Data 0.004 (0.159) Batch 1.066 (1.280) Remain 05:36:38 loss: 0.1860 Lr: 0.00144
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[2023-12-25 18:12:25,659 INFO misc.py line 119 253097] Train: [70/100][384/510] Data 6.842 (0.153) Batch 8.145 (1.613) Remain 06:54:42 loss: 0.0640 Lr: 0.00138
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[2023-12-25 18:12:44,424 INFO misc.py line 119 253097] Train: [70/100][400/510] Data 0.009 (0.147) Batch 0.972 (1.595) Remain 06:49:43 loss: 0.2099 Lr: 0.00138
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[2023-12-25 18:13:59,811 INFO misc.py line 119 253097] Train: [70/100][451/510] Data 0.009 (0.131) Batch 1.011 (1.582) Remain 06:44:57 loss: 0.0913 Lr: 0.00137
[2023-12-25 18:14:00,875 INFO misc.py line 119 253097] Train: [70/100][452/510] Data 0.005 (0.131) Batch 1.058 (1.581) Remain 06:44:37 loss: 0.2006 Lr: 0.00137
[2023-12-25 18:14:01,644 INFO misc.py line 119 253097] Train: [70/100][453/510] Data 0.011 (0.130) Batch 0.775 (1.579) Remain 06:44:08 loss: 0.1710 Lr: 0.00137
[2023-12-25 18:14:02,667 INFO misc.py line 119 253097] Train: [70/100][454/510] Data 0.004 (0.130) Batch 1.023 (1.578) Remain 06:43:48 loss: 0.1204 Lr: 0.00137
[2023-12-25 18:14:09,264 INFO misc.py line 119 253097] Train: [70/100][455/510] Data 0.005 (0.130) Batch 6.598 (1.589) Remain 06:46:37 loss: 0.1172 Lr: 0.00137
[2023-12-25 18:14:10,417 INFO misc.py line 119 253097] Train: [70/100][456/510] Data 0.005 (0.130) Batch 1.153 (1.588) Remain 06:46:20 loss: 0.2775 Lr: 0.00137
[2023-12-25 18:14:11,308 INFO misc.py line 119 253097] Train: [70/100][457/510] Data 0.003 (0.129) Batch 0.890 (1.586) Remain 06:45:55 loss: 0.0999 Lr: 0.00137
[2023-12-25 18:14:12,643 INFO misc.py line 119 253097] Train: [70/100][458/510] Data 0.004 (0.129) Batch 1.336 (1.586) Remain 06:45:45 loss: 0.2520 Lr: 0.00137
[2023-12-25 18:14:15,087 INFO misc.py line 119 253097] Train: [70/100][459/510] Data 0.004 (0.129) Batch 2.443 (1.588) Remain 06:46:12 loss: 0.1206 Lr: 0.00137
[2023-12-25 18:14:18,584 INFO misc.py line 119 253097] Train: [70/100][460/510] Data 0.004 (0.129) Batch 3.496 (1.592) Remain 06:47:15 loss: 0.0860 Lr: 0.00137
[2023-12-25 18:14:19,921 INFO misc.py line 119 253097] Train: [70/100][461/510] Data 0.005 (0.128) Batch 1.332 (1.591) Remain 06:47:05 loss: 0.1589 Lr: 0.00137
[2023-12-25 18:14:20,934 INFO misc.py line 119 253097] Train: [70/100][462/510] Data 0.010 (0.128) Batch 1.019 (1.590) Remain 06:46:44 loss: 0.0948 Lr: 0.00137
[2023-12-25 18:14:22,021 INFO misc.py line 119 253097] Train: [70/100][463/510] Data 0.004 (0.128) Batch 1.086 (1.589) Remain 06:46:26 loss: 0.1103 Lr: 0.00137
[2023-12-25 18:14:23,376 INFO misc.py line 119 253097] Train: [70/100][464/510] Data 0.005 (0.127) Batch 1.343 (1.588) Remain 06:46:16 loss: 0.1274 Lr: 0.00137
[2023-12-25 18:14:24,398 INFO misc.py line 119 253097] Train: [70/100][465/510] Data 0.016 (0.127) Batch 1.030 (1.587) Remain 06:45:56 loss: 0.2416 Lr: 0.00137
[2023-12-25 18:14:25,526 INFO misc.py line 119 253097] Train: [70/100][466/510] Data 0.008 (0.127) Batch 1.118 (1.586) Remain 06:45:38 loss: 0.1966 Lr: 0.00137
[2023-12-25 18:14:26,838 INFO misc.py line 119 253097] Train: [70/100][467/510] Data 0.020 (0.127) Batch 1.326 (1.586) Remain 06:45:28 loss: 0.1205 Lr: 0.00137
[2023-12-25 18:14:27,859 INFO misc.py line 119 253097] Train: [70/100][468/510] Data 0.004 (0.126) Batch 1.015 (1.584) Remain 06:45:08 loss: 0.1240 Lr: 0.00137
[2023-12-25 18:14:29,145 INFO misc.py line 119 253097] Train: [70/100][469/510] Data 0.010 (0.126) Batch 1.292 (1.584) Remain 06:44:57 loss: 0.1528 Lr: 0.00137
[2023-12-25 18:14:30,438 INFO misc.py line 119 253097] Train: [70/100][470/510] Data 0.005 (0.126) Batch 1.290 (1.583) Remain 06:44:45 loss: 0.1839 Lr: 0.00137
[2023-12-25 18:14:31,690 INFO misc.py line 119 253097] Train: [70/100][471/510] Data 0.009 (0.126) Batch 1.251 (1.582) Remain 06:44:33 loss: 0.3075 Lr: 0.00137
[2023-12-25 18:14:33,000 INFO misc.py line 119 253097] Train: [70/100][472/510] Data 0.009 (0.125) Batch 1.315 (1.582) Remain 06:44:23 loss: 0.1632 Lr: 0.00137
[2023-12-25 18:14:34,289 INFO misc.py line 119 253097] Train: [70/100][473/510] Data 0.005 (0.125) Batch 1.289 (1.581) Remain 06:44:11 loss: 0.0837 Lr: 0.00137
[2023-12-25 18:14:35,450 INFO misc.py line 119 253097] Train: [70/100][474/510] Data 0.005 (0.125) Batch 1.157 (1.580) Remain 06:43:56 loss: 0.0937 Lr: 0.00137
[2023-12-25 18:14:36,710 INFO misc.py line 119 253097] Train: [70/100][475/510] Data 0.008 (0.125) Batch 1.262 (1.580) Remain 06:43:44 loss: 0.0933 Lr: 0.00137
[2023-12-25 18:14:38,761 INFO misc.py line 119 253097] Train: [70/100][476/510] Data 0.916 (0.126) Batch 2.054 (1.581) Remain 06:43:58 loss: 0.1355 Lr: 0.00137
[2023-12-25 18:14:39,881 INFO misc.py line 119 253097] Train: [70/100][477/510] Data 0.003 (0.126) Batch 1.118 (1.580) Remain 06:43:41 loss: 0.0611 Lr: 0.00137
[2023-12-25 18:14:40,942 INFO misc.py line 119 253097] Train: [70/100][478/510] Data 0.006 (0.126) Batch 1.061 (1.579) Remain 06:43:23 loss: 0.1101 Lr: 0.00137
[2023-12-25 18:14:42,233 INFO misc.py line 119 253097] Train: [70/100][479/510] Data 0.005 (0.126) Batch 1.288 (1.578) Remain 06:43:12 loss: 0.1885 Lr: 0.00137
[2023-12-25 18:14:43,141 INFO misc.py line 119 253097] Train: [70/100][480/510] Data 0.009 (0.125) Batch 0.911 (1.577) Remain 06:42:49 loss: 0.1244 Lr: 0.00137
[2023-12-25 18:14:44,113 INFO misc.py line 119 253097] Train: [70/100][481/510] Data 0.005 (0.125) Batch 0.973 (1.575) Remain 06:42:28 loss: 0.0832 Lr: 0.00137
[2023-12-25 18:14:45,298 INFO misc.py line 119 253097] Train: [70/100][482/510] Data 0.003 (0.125) Batch 1.185 (1.575) Remain 06:42:14 loss: 0.1011 Lr: 0.00137
[2023-12-25 18:14:46,549 INFO misc.py line 119 253097] Train: [70/100][483/510] Data 0.003 (0.125) Batch 1.250 (1.574) Remain 06:42:02 loss: 0.0754 Lr: 0.00137
[2023-12-25 18:14:50,072 INFO misc.py line 119 253097] Train: [70/100][484/510] Data 0.003 (0.124) Batch 3.512 (1.578) Remain 06:43:02 loss: 0.0800 Lr: 0.00137
[2023-12-25 18:14:54,116 INFO misc.py line 119 253097] Train: [70/100][485/510] Data 0.014 (0.124) Batch 4.055 (1.583) Remain 06:44:20 loss: 0.1008 Lr: 0.00137
[2023-12-25 18:14:55,094 INFO misc.py line 119 253097] Train: [70/100][486/510] Data 0.004 (0.124) Batch 0.979 (1.582) Remain 06:43:59 loss: 0.2366 Lr: 0.00137
[2023-12-25 18:14:56,411 INFO misc.py line 119 253097] Train: [70/100][487/510] Data 0.004 (0.124) Batch 1.315 (1.581) Remain 06:43:49 loss: 0.1127 Lr: 0.00137
[2023-12-25 18:14:57,566 INFO misc.py line 119 253097] Train: [70/100][488/510] Data 0.005 (0.123) Batch 1.157 (1.580) Remain 06:43:34 loss: 0.0907 Lr: 0.00137
[2023-12-25 18:15:02,694 INFO misc.py line 119 253097] Train: [70/100][489/510] Data 0.004 (0.123) Batch 5.128 (1.588) Remain 06:45:24 loss: 0.1111 Lr: 0.00136
[2023-12-25 18:15:03,905 INFO misc.py line 119 253097] Train: [70/100][490/510] Data 0.003 (0.123) Batch 1.208 (1.587) Remain 06:45:11 loss: 0.2489 Lr: 0.00136
[2023-12-25 18:15:05,138 INFO misc.py line 119 253097] Train: [70/100][491/510] Data 0.007 (0.123) Batch 1.236 (1.586) Remain 06:44:58 loss: 0.1079 Lr: 0.00136
[2023-12-25 18:15:06,319 INFO misc.py line 119 253097] Train: [70/100][492/510] Data 0.004 (0.122) Batch 1.181 (1.585) Remain 06:44:44 loss: 0.1306 Lr: 0.00136
[2023-12-25 18:15:07,587 INFO misc.py line 119 253097] Train: [70/100][493/510] Data 0.005 (0.122) Batch 1.268 (1.585) Remain 06:44:32 loss: 0.0742 Lr: 0.00136
[2023-12-25 18:15:08,698 INFO misc.py line 119 253097] Train: [70/100][494/510] Data 0.004 (0.122) Batch 1.106 (1.584) Remain 06:44:16 loss: 0.0867 Lr: 0.00136
[2023-12-25 18:15:09,926 INFO misc.py line 119 253097] Train: [70/100][495/510] Data 0.009 (0.122) Batch 1.229 (1.583) Remain 06:44:03 loss: 0.2358 Lr: 0.00136
[2023-12-25 18:15:11,204 INFO misc.py line 119 253097] Train: [70/100][496/510] Data 0.008 (0.121) Batch 1.279 (1.582) Remain 06:43:52 loss: 0.1621 Lr: 0.00136
[2023-12-25 18:15:12,202 INFO misc.py line 119 253097] Train: [70/100][497/510] Data 0.006 (0.121) Batch 1.000 (1.581) Remain 06:43:32 loss: 0.1822 Lr: 0.00136
[2023-12-25 18:15:13,316 INFO misc.py line 119 253097] Train: [70/100][498/510] Data 0.005 (0.121) Batch 1.109 (1.580) Remain 06:43:16 loss: 0.2417 Lr: 0.00136
[2023-12-25 18:15:14,345 INFO misc.py line 119 253097] Train: [70/100][499/510] Data 0.010 (0.121) Batch 1.032 (1.579) Remain 06:42:58 loss: 0.2775 Lr: 0.00136
[2023-12-25 18:15:15,556 INFO misc.py line 119 253097] Train: [70/100][500/510] Data 0.008 (0.121) Batch 1.210 (1.578) Remain 06:42:45 loss: 0.1365 Lr: 0.00136
[2023-12-25 18:15:16,683 INFO misc.py line 119 253097] Train: [70/100][501/510] Data 0.007 (0.120) Batch 1.132 (1.577) Remain 06:42:29 loss: 0.1131 Lr: 0.00136
[2023-12-25 18:15:25,382 INFO misc.py line 119 253097] Train: [70/100][502/510] Data 0.004 (0.120) Batch 8.697 (1.592) Remain 06:46:06 loss: 0.1284 Lr: 0.00136
[2023-12-25 18:15:26,553 INFO misc.py line 119 253097] Train: [70/100][503/510] Data 0.005 (0.120) Batch 1.170 (1.591) Remain 06:45:52 loss: 0.0918 Lr: 0.00136
[2023-12-25 18:15:27,756 INFO misc.py line 119 253097] Train: [70/100][504/510] Data 0.006 (0.120) Batch 1.203 (1.590) Remain 06:45:38 loss: 0.1067 Lr: 0.00136
[2023-12-25 18:15:29,001 INFO misc.py line 119 253097] Train: [70/100][505/510] Data 0.005 (0.119) Batch 1.241 (1.589) Remain 06:45:26 loss: 0.1648 Lr: 0.00136
[2023-12-25 18:15:30,240 INFO misc.py line 119 253097] Train: [70/100][506/510] Data 0.009 (0.119) Batch 1.237 (1.589) Remain 06:45:14 loss: 0.0957 Lr: 0.00136
[2023-12-25 18:15:31,273 INFO misc.py line 119 253097] Train: [70/100][507/510] Data 0.011 (0.119) Batch 1.039 (1.588) Remain 06:44:55 loss: 0.1412 Lr: 0.00136
[2023-12-25 18:15:32,409 INFO misc.py line 119 253097] Train: [70/100][508/510] Data 0.006 (0.119) Batch 1.134 (1.587) Remain 06:44:40 loss: 0.1432 Lr: 0.00136
[2023-12-25 18:15:33,652 INFO misc.py line 119 253097] Train: [70/100][509/510] Data 0.007 (0.119) Batch 1.242 (1.586) Remain 06:44:28 loss: 0.1329 Lr: 0.00136
[2023-12-25 18:15:34,917 INFO misc.py line 119 253097] Train: [70/100][510/510] Data 0.008 (0.118) Batch 1.263 (1.585) Remain 06:44:17 loss: 0.1009 Lr: 0.00136
[2023-12-25 18:15:34,917 INFO misc.py line 136 253097] Train result: loss: 0.1374 
[2023-12-25 18:15:34,918 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 18:16:03,235 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.3929 
[2023-12-25 18:16:03,585 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3351 
[2023-12-25 18:16:08,526 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4369 
[2023-12-25 18:16:09,047 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3473 
[2023-12-25 18:16:11,016 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9453 
[2023-12-25 18:16:11,445 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3487 
[2023-12-25 18:16:12,328 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2294 
[2023-12-25 18:16:12,895 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3582 
[2023-12-25 18:16:14,702 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9025 
[2023-12-25 18:16:16,820 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1611 
[2023-12-25 18:16:17,678 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3178 
[2023-12-25 18:16:18,106 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9715 
[2023-12-25 18:16:19,014 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.8152 
[2023-12-25 18:16:21,959 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8792 
[2023-12-25 18:16:22,426 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.4297 
[2023-12-25 18:16:23,036 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4564 
[2023-12-25 18:16:23,735 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3957 
[2023-12-25 18:16:25,244 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6648/0.7287/0.8970.
[2023-12-25 18:16:25,244 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9189/0.9471
[2023-12-25 18:16:25,244 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9816/0.9883
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8325/0.9797
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3072/0.3331
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6298/0.6509
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6569/0.7383
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8065/0.8804
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.8972/0.9553
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5459/0.5744
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7518/0.8273
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7348/0.9003
[2023-12-25 18:16:25,245 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5788/0.6984
[2023-12-25 18:16:25,245 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 18:16:25,247 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 18:16:25,247 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 18:16:34,814 INFO misc.py line 119 253097] Train: [71/100][1/510] Data 6.247 (6.247) Batch 7.419 (7.419) Remain 31:31:40 loss: 0.1010 Lr: 0.00136
[2023-12-25 18:16:36,115 INFO misc.py line 119 253097] Train: [71/100][2/510] Data 0.007 (0.007) Batch 1.299 (1.299) Remain 05:31:10 loss: 0.0887 Lr: 0.00136
[2023-12-25 18:16:37,208 INFO misc.py line 119 253097] Train: [71/100][3/510] Data 0.008 (0.008) Batch 1.092 (1.092) Remain 04:38:31 loss: 0.1348 Lr: 0.00136
[2023-12-25 18:16:38,459 INFO misc.py line 119 253097] Train: [71/100][4/510] Data 0.010 (0.010) Batch 1.253 (1.253) Remain 05:19:18 loss: 0.1952 Lr: 0.00136
[2023-12-25 18:16:39,554 INFO misc.py line 119 253097] Train: [71/100][5/510] Data 0.007 (0.009) Batch 1.092 (1.172) Remain 04:58:50 loss: 0.1947 Lr: 0.00136
[2023-12-25 18:16:40,621 INFO misc.py line 119 253097] Train: [71/100][6/510] Data 0.011 (0.009) Batch 1.073 (1.139) Remain 04:50:21 loss: 0.3238 Lr: 0.00136
[2023-12-25 18:16:42,398 INFO misc.py line 119 253097] Train: [71/100][7/510] Data 0.005 (0.008) Batch 1.777 (1.299) Remain 05:30:59 loss: 0.1440 Lr: 0.00136
[2023-12-25 18:16:43,513 INFO misc.py line 119 253097] Train: [71/100][8/510] Data 0.005 (0.008) Batch 1.109 (1.261) Remain 05:21:18 loss: 0.1664 Lr: 0.00136
[2023-12-25 18:16:44,558 INFO misc.py line 119 253097] Train: [71/100][9/510] Data 0.010 (0.008) Batch 1.051 (1.226) Remain 05:12:23 loss: 0.1777 Lr: 0.00136
[2023-12-25 18:16:45,672 INFO misc.py line 119 253097] Train: [71/100][10/510] Data 0.004 (0.008) Batch 1.113 (1.210) Remain 05:08:17 loss: 0.1338 Lr: 0.00136
[2023-12-25 18:16:46,675 INFO misc.py line 119 253097] Train: [71/100][11/510] Data 0.005 (0.007) Batch 0.998 (1.183) Remain 05:01:30 loss: 0.2586 Lr: 0.00136
[2023-12-25 18:16:47,534 INFO misc.py line 119 253097] Train: [71/100][12/510] Data 0.010 (0.008) Batch 0.865 (1.148) Remain 04:52:28 loss: 0.2230 Lr: 0.00136
[2023-12-25 18:16:48,649 INFO misc.py line 119 253097] Train: [71/100][13/510] Data 0.006 (0.007) Batch 1.114 (1.144) Remain 04:51:35 loss: 0.0959 Lr: 0.00136
[2023-12-25 18:17:04,707 INFO misc.py line 119 253097] Train: [71/100][14/510] Data 14.842 (1.356) Batch 16.059 (2.500) Remain 10:37:00 loss: 0.1454 Lr: 0.00136
[2023-12-25 18:17:05,984 INFO misc.py line 119 253097] Train: [71/100][15/510] Data 0.004 (1.243) Batch 1.271 (2.398) Remain 10:10:51 loss: 0.0771 Lr: 0.00136
[2023-12-25 18:17:07,137 INFO misc.py line 119 253097] Train: [71/100][16/510] Data 0.011 (1.149) Batch 1.150 (2.302) Remain 09:46:21 loss: 0.0696 Lr: 0.00136
[2023-12-25 18:17:08,295 INFO misc.py line 119 253097] Train: [71/100][17/510] Data 0.014 (1.068) Batch 1.164 (2.221) Remain 09:25:37 loss: 0.1049 Lr: 0.00136
[2023-12-25 18:17:09,415 INFO misc.py line 119 253097] Train: [71/100][18/510] Data 0.008 (0.997) Batch 1.119 (2.147) Remain 09:06:52 loss: 0.2101 Lr: 0.00136
[2023-12-25 18:17:10,565 INFO misc.py line 119 253097] Train: [71/100][19/510] Data 0.009 (0.935) Batch 1.154 (2.085) Remain 08:51:01 loss: 0.0787 Lr: 0.00136
[2023-12-25 18:17:11,632 INFO misc.py line 119 253097] Train: [71/100][20/510] Data 0.005 (0.880) Batch 1.064 (2.025) Remain 08:35:41 loss: 0.1107 Lr: 0.00136
[2023-12-25 18:17:12,742 INFO misc.py line 119 253097] Train: [71/100][21/510] Data 0.008 (0.832) Batch 1.115 (1.974) Remain 08:22:46 loss: 0.1124 Lr: 0.00136
[2023-12-25 18:17:13,945 INFO misc.py line 119 253097] Train: [71/100][22/510] Data 0.004 (0.788) Batch 1.201 (1.934) Remain 08:12:22 loss: 0.1248 Lr: 0.00136
[2023-12-25 18:17:15,208 INFO misc.py line 119 253097] Train: [71/100][23/510] Data 0.005 (0.749) Batch 1.263 (1.900) Remain 08:03:48 loss: 0.1275 Lr: 0.00136
[2023-12-25 18:17:16,491 INFO misc.py line 119 253097] Train: [71/100][24/510] Data 0.007 (0.714) Batch 1.282 (1.871) Remain 07:56:16 loss: 0.1367 Lr: 0.00136
[2023-12-25 18:17:17,721 INFO misc.py line 119 253097] Train: [71/100][25/510] Data 0.009 (0.682) Batch 1.231 (1.842) Remain 07:48:50 loss: 0.1943 Lr: 0.00136
[2023-12-25 18:17:18,909 INFO misc.py line 119 253097] Train: [71/100][26/510] Data 0.007 (0.653) Batch 1.190 (1.813) Remain 07:41:36 loss: 0.1132 Lr: 0.00136
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[2023-12-25 18:20:37,141 INFO misc.py line 119 253097] Train: [71/100][162/510] Data 0.003 (0.141) Batch 1.043 (1.509) Remain 06:20:43 loss: 0.1449 Lr: 0.00134
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[2023-12-25 18:20:39,330 INFO misc.py line 119 253097] Train: [71/100][164/510] Data 0.005 (0.139) Batch 0.989 (1.504) Remain 06:19:22 loss: 0.0939 Lr: 0.00133
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[2023-12-25 18:20:48,972 INFO misc.py line 119 253097] Train: [71/100][172/510] Data 0.018 (0.135) Batch 1.195 (1.490) Remain 06:15:36 loss: 0.1816 Lr: 0.00133
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[2023-12-25 18:21:04,352 INFO misc.py line 119 253097] Train: [71/100][178/510] Data 0.023 (0.130) Batch 0.988 (1.527) Remain 06:24:44 loss: 0.0932 Lr: 0.00133
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[2023-12-25 18:21:12,242 INFO misc.py line 119 253097] Train: [71/100][184/510] Data 0.004 (0.126) Batch 2.237 (1.520) Remain 06:22:49 loss: 0.0760 Lr: 0.00133
[2023-12-25 18:21:13,389 INFO misc.py line 119 253097] Train: [71/100][185/510] Data 0.004 (0.126) Batch 1.148 (1.518) Remain 06:22:17 loss: 0.1961 Lr: 0.00133
[2023-12-25 18:21:14,487 INFO misc.py line 119 253097] Train: [71/100][186/510] Data 0.003 (0.125) Batch 1.097 (1.515) Remain 06:21:40 loss: 0.3374 Lr: 0.00133
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[2023-12-25 18:21:16,584 INFO misc.py line 119 253097] Train: [71/100][188/510] Data 0.006 (0.124) Batch 1.083 (1.510) Remain 06:20:21 loss: 0.2248 Lr: 0.00133
[2023-12-25 18:21:18,817 INFO misc.py line 119 253097] Train: [71/100][189/510] Data 1.436 (0.131) Batch 2.233 (1.514) Remain 06:21:18 loss: 0.1055 Lr: 0.00133
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[2023-12-25 18:21:25,313 INFO misc.py line 119 253097] Train: [71/100][193/510] Data 0.007 (0.128) Batch 3.226 (1.516) Remain 06:21:47 loss: 0.1573 Lr: 0.00133
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[2023-12-25 18:23:27,349 INFO misc.py line 119 253097] Train: [71/100][272/510] Data 0.008 (0.102) Batch 1.078 (1.525) Remain 06:21:53 loss: 0.1250 Lr: 0.00132
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[2023-12-25 18:23:34,260 INFO misc.py line 119 253097] Train: [71/100][278/510] Data 0.006 (0.100) Batch 1.098 (1.517) Remain 06:19:41 loss: 0.1948 Lr: 0.00132
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[2023-12-25 18:23:36,340 INFO misc.py line 119 253097] Train: [71/100][280/510] Data 0.003 (0.099) Batch 0.961 (1.513) Remain 06:18:47 loss: 0.2025 Lr: 0.00132
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[2023-12-25 18:24:02,809 INFO misc.py line 119 253097] Train: [71/100][291/510] Data 13.660 (0.143) Batch 14.763 (1.547) Remain 06:27:02 loss: 0.1894 Lr: 0.00131
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[2023-12-25 18:25:49,390 INFO misc.py line 119 253097] Train: [71/100][362/510] Data 0.004 (0.116) Batch 1.187 (1.538) Remain 06:22:56 loss: 0.1764 Lr: 0.00130
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[2023-12-25 18:26:03,200 INFO misc.py line 119 253097] Train: [71/100][367/510] Data 0.004 (0.115) Batch 9.487 (1.555) Remain 06:26:59 loss: 0.1145 Lr: 0.00130
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[2023-12-25 18:26:18,449 INFO misc.py line 119 253097] Train: [71/100][381/510] Data 0.003 (0.111) Batch 0.872 (1.538) Remain 06:22:20 loss: 0.0706 Lr: 0.00130
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[2023-12-25 18:26:22,434 INFO misc.py line 119 253097] Train: [71/100][383/510] Data 0.003 (0.110) Batch 0.944 (1.540) Remain 06:22:53 loss: 0.1739 Lr: 0.00130
[2023-12-25 18:26:23,413 INFO misc.py line 119 253097] Train: [71/100][384/510] Data 0.003 (0.110) Batch 0.980 (1.539) Remain 06:22:29 loss: 0.0880 Lr: 0.00130
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[2023-12-25 18:26:29,195 INFO misc.py line 119 253097] Train: [71/100][389/510] Data 0.005 (0.108) Batch 1.246 (1.534) Remain 06:21:08 loss: 0.0758 Lr: 0.00130
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[2023-12-25 18:26:31,176 INFO misc.py line 119 253097] Train: [71/100][391/510] Data 0.005 (0.108) Batch 0.952 (1.531) Remain 06:20:23 loss: 0.1121 Lr: 0.00130
[2023-12-25 18:26:32,114 INFO misc.py line 119 253097] Train: [71/100][392/510] Data 0.003 (0.108) Batch 0.938 (1.529) Remain 06:19:59 loss: 0.2894 Lr: 0.00130
[2023-12-25 18:26:39,972 INFO misc.py line 119 253097] Train: [71/100][393/510] Data 6.911 (0.125) Batch 7.857 (1.546) Remain 06:23:59 loss: 0.0770 Lr: 0.00130
[2023-12-25 18:26:41,160 INFO misc.py line 119 253097] Train: [71/100][394/510] Data 0.004 (0.125) Batch 1.186 (1.545) Remain 06:23:44 loss: 0.1300 Lr: 0.00130
[2023-12-25 18:26:42,292 INFO misc.py line 119 253097] Train: [71/100][395/510] Data 0.005 (0.124) Batch 1.129 (1.544) Remain 06:23:27 loss: 0.2414 Lr: 0.00130
[2023-12-25 18:26:43,549 INFO misc.py line 119 253097] Train: [71/100][396/510] Data 0.008 (0.124) Batch 1.259 (1.543) Remain 06:23:14 loss: 0.1475 Lr: 0.00130
[2023-12-25 18:26:44,563 INFO misc.py line 119 253097] Train: [71/100][397/510] Data 0.007 (0.124) Batch 1.015 (1.542) Remain 06:22:53 loss: 0.1503 Lr: 0.00130
[2023-12-25 18:26:45,648 INFO misc.py line 119 253097] Train: [71/100][398/510] Data 0.005 (0.123) Batch 1.087 (1.540) Remain 06:22:34 loss: 0.1019 Lr: 0.00130
[2023-12-25 18:26:46,788 INFO misc.py line 119 253097] Train: [71/100][399/510] Data 0.003 (0.123) Batch 1.136 (1.539) Remain 06:22:17 loss: 0.1352 Lr: 0.00130
[2023-12-25 18:26:49,118 INFO misc.py line 119 253097] Train: [71/100][400/510] Data 0.007 (0.123) Batch 2.334 (1.541) Remain 06:22:46 loss: 0.0761 Lr: 0.00130
[2023-12-25 18:26:50,256 INFO misc.py line 119 253097] Train: [71/100][401/510] Data 0.003 (0.123) Batch 1.138 (1.540) Remain 06:22:29 loss: 0.0900 Lr: 0.00130
[2023-12-25 18:26:51,406 INFO misc.py line 119 253097] Train: [71/100][402/510] Data 0.003 (0.122) Batch 1.150 (1.539) Remain 06:22:13 loss: 0.1180 Lr: 0.00130
[2023-12-25 18:26:52,575 INFO misc.py line 119 253097] Train: [71/100][403/510] Data 0.003 (0.122) Batch 1.168 (1.538) Remain 06:21:57 loss: 0.0693 Lr: 0.00130
[2023-12-25 18:26:53,596 INFO misc.py line 119 253097] Train: [71/100][404/510] Data 0.003 (0.122) Batch 1.021 (1.537) Remain 06:21:37 loss: 0.1002 Lr: 0.00130
[2023-12-25 18:27:00,290 INFO misc.py line 119 253097] Train: [71/100][405/510] Data 5.505 (0.135) Batch 6.695 (1.550) Remain 06:24:46 loss: 0.1176 Lr: 0.00130
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[2023-12-25 18:27:07,177 INFO misc.py line 119 253097] Train: [71/100][411/510] Data 0.012 (0.133) Batch 1.029 (1.544) Remain 06:23:08 loss: 0.0977 Lr: 0.00130
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[2023-12-25 18:27:22,456 INFO misc.py line 119 253097] Train: [71/100][417/510] Data 8.588 (0.152) Batch 9.803 (1.559) Remain 06:26:36 loss: 0.1583 Lr: 0.00129
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[2023-12-25 18:28:00,282 INFO misc.py line 119 253097] Train: [71/100][443/510] Data 5.224 (0.155) Batch 6.319 (1.552) Remain 06:24:24 loss: 0.0729 Lr: 0.00129
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[2023-12-25 18:28:02,295 INFO misc.py line 119 253097] Train: [71/100][445/510] Data 0.003 (0.155) Batch 0.904 (1.550) Remain 06:23:44 loss: 0.1458 Lr: 0.00129
[2023-12-25 18:28:03,634 INFO misc.py line 119 253097] Train: [71/100][446/510] Data 0.004 (0.154) Batch 1.339 (1.550) Remain 06:23:36 loss: 0.0894 Lr: 0.00129
[2023-12-25 18:28:04,769 INFO misc.py line 119 253097] Train: [71/100][447/510] Data 0.005 (0.154) Batch 1.134 (1.549) Remain 06:23:20 loss: 0.0896 Lr: 0.00129
[2023-12-25 18:28:05,895 INFO misc.py line 119 253097] Train: [71/100][448/510] Data 0.006 (0.154) Batch 1.127 (1.548) Remain 06:23:05 loss: 0.3038 Lr: 0.00129
[2023-12-25 18:28:12,141 INFO misc.py line 119 253097] Train: [71/100][449/510] Data 4.954 (0.164) Batch 6.246 (1.558) Remain 06:25:40 loss: 0.1081 Lr: 0.00129
[2023-12-25 18:28:13,339 INFO misc.py line 119 253097] Train: [71/100][450/510] Data 0.003 (0.164) Batch 1.198 (1.557) Remain 06:25:26 loss: 0.1371 Lr: 0.00129
[2023-12-25 18:28:14,309 INFO misc.py line 119 253097] Train: [71/100][451/510] Data 0.004 (0.164) Batch 0.970 (1.556) Remain 06:25:05 loss: 0.1310 Lr: 0.00129
[2023-12-25 18:28:15,348 INFO misc.py line 119 253097] Train: [71/100][452/510] Data 0.005 (0.163) Batch 1.039 (1.555) Remain 06:24:46 loss: 0.2371 Lr: 0.00129
[2023-12-25 18:28:16,275 INFO misc.py line 119 253097] Train: [71/100][453/510] Data 0.004 (0.163) Batch 0.927 (1.553) Remain 06:24:24 loss: 0.1020 Lr: 0.00129
[2023-12-25 18:28:17,373 INFO misc.py line 119 253097] Train: [71/100][454/510] Data 0.004 (0.163) Batch 1.097 (1.552) Remain 06:24:08 loss: 0.2602 Lr: 0.00129
[2023-12-25 18:28:18,568 INFO misc.py line 119 253097] Train: [71/100][455/510] Data 0.005 (0.162) Batch 1.190 (1.552) Remain 06:23:54 loss: 0.0935 Lr: 0.00129
[2023-12-25 18:28:19,645 INFO misc.py line 119 253097] Train: [71/100][456/510] Data 0.011 (0.162) Batch 1.079 (1.551) Remain 06:23:37 loss: 0.1737 Lr: 0.00129
[2023-12-25 18:28:20,640 INFO misc.py line 119 253097] Train: [71/100][457/510] Data 0.008 (0.162) Batch 0.998 (1.549) Remain 06:23:17 loss: 0.1425 Lr: 0.00129
[2023-12-25 18:28:21,860 INFO misc.py line 119 253097] Train: [71/100][458/510] Data 0.007 (0.161) Batch 1.220 (1.549) Remain 06:23:05 loss: 0.1415 Lr: 0.00129
[2023-12-25 18:28:22,804 INFO misc.py line 119 253097] Train: [71/100][459/510] Data 0.006 (0.161) Batch 0.946 (1.547) Remain 06:22:44 loss: 0.1094 Lr: 0.00129
[2023-12-25 18:28:24,102 INFO misc.py line 119 253097] Train: [71/100][460/510] Data 0.003 (0.161) Batch 1.298 (1.547) Remain 06:22:34 loss: 0.1115 Lr: 0.00129
[2023-12-25 18:28:25,266 INFO misc.py line 119 253097] Train: [71/100][461/510] Data 0.004 (0.160) Batch 1.160 (1.546) Remain 06:22:20 loss: 0.2026 Lr: 0.00129
[2023-12-25 18:28:28,842 INFO misc.py line 119 253097] Train: [71/100][462/510] Data 0.008 (0.160) Batch 3.580 (1.550) Remain 06:23:24 loss: 0.2488 Lr: 0.00129
[2023-12-25 18:28:30,176 INFO misc.py line 119 253097] Train: [71/100][463/510] Data 0.004 (0.160) Batch 1.334 (1.550) Remain 06:23:16 loss: 0.0923 Lr: 0.00129
[2023-12-25 18:28:35,257 INFO misc.py line 119 253097] Train: [71/100][464/510] Data 3.799 (0.167) Batch 5.082 (1.558) Remain 06:25:08 loss: 0.2225 Lr: 0.00129
[2023-12-25 18:28:36,364 INFO misc.py line 119 253097] Train: [71/100][465/510] Data 0.004 (0.167) Batch 1.107 (1.557) Remain 06:24:52 loss: 0.1598 Lr: 0.00129
[2023-12-25 18:28:37,469 INFO misc.py line 119 253097] Train: [71/100][466/510] Data 0.003 (0.167) Batch 1.103 (1.556) Remain 06:24:36 loss: 0.1475 Lr: 0.00129
[2023-12-25 18:28:38,530 INFO misc.py line 119 253097] Train: [71/100][467/510] Data 0.005 (0.166) Batch 1.062 (1.555) Remain 06:24:19 loss: 0.1089 Lr: 0.00129
[2023-12-25 18:28:39,406 INFO misc.py line 119 253097] Train: [71/100][468/510] Data 0.004 (0.166) Batch 0.877 (1.553) Remain 06:23:55 loss: 0.1817 Lr: 0.00129
[2023-12-25 18:28:40,553 INFO misc.py line 119 253097] Train: [71/100][469/510] Data 0.004 (0.166) Batch 1.147 (1.552) Remain 06:23:41 loss: 0.2069 Lr: 0.00129
[2023-12-25 18:28:42,263 INFO misc.py line 119 253097] Train: [71/100][470/510] Data 0.713 (0.167) Batch 1.709 (1.553) Remain 06:23:44 loss: 0.0947 Lr: 0.00129
[2023-12-25 18:28:43,142 INFO misc.py line 119 253097] Train: [71/100][471/510] Data 0.005 (0.167) Batch 0.881 (1.551) Remain 06:23:22 loss: 0.1226 Lr: 0.00129
[2023-12-25 18:28:44,135 INFO misc.py line 119 253097] Train: [71/100][472/510] Data 0.003 (0.166) Batch 0.993 (1.550) Remain 06:23:02 loss: 0.1329 Lr: 0.00129
[2023-12-25 18:28:45,264 INFO misc.py line 119 253097] Train: [71/100][473/510] Data 0.003 (0.166) Batch 1.128 (1.549) Remain 06:22:47 loss: 0.0897 Lr: 0.00129
[2023-12-25 18:28:46,395 INFO misc.py line 119 253097] Train: [71/100][474/510] Data 0.007 (0.165) Batch 1.130 (1.548) Remain 06:22:33 loss: 0.1432 Lr: 0.00129
[2023-12-25 18:28:47,495 INFO misc.py line 119 253097] Train: [71/100][475/510] Data 0.005 (0.165) Batch 1.101 (1.547) Remain 06:22:17 loss: 0.0968 Lr: 0.00129
[2023-12-25 18:28:48,669 INFO misc.py line 119 253097] Train: [71/100][476/510] Data 0.004 (0.165) Batch 1.171 (1.546) Remain 06:22:04 loss: 0.0821 Lr: 0.00128
[2023-12-25 18:28:49,524 INFO misc.py line 119 253097] Train: [71/100][477/510] Data 0.007 (0.164) Batch 0.858 (1.545) Remain 06:21:41 loss: 0.2046 Lr: 0.00128
[2023-12-25 18:28:50,834 INFO misc.py line 119 253097] Train: [71/100][478/510] Data 0.004 (0.164) Batch 1.282 (1.544) Remain 06:21:31 loss: 0.0967 Lr: 0.00128
[2023-12-25 18:28:52,078 INFO misc.py line 119 253097] Train: [71/100][479/510] Data 0.032 (0.164) Batch 1.272 (1.544) Remain 06:21:21 loss: 0.1595 Lr: 0.00128
[2023-12-25 18:28:53,187 INFO misc.py line 119 253097] Train: [71/100][480/510] Data 0.005 (0.164) Batch 1.108 (1.543) Remain 06:21:06 loss: 0.1328 Lr: 0.00128
[2023-12-25 18:28:54,325 INFO misc.py line 119 253097] Train: [71/100][481/510] Data 0.006 (0.163) Batch 1.138 (1.542) Remain 06:20:52 loss: 0.1601 Lr: 0.00128
[2023-12-25 18:28:55,522 INFO misc.py line 119 253097] Train: [71/100][482/510] Data 0.005 (0.163) Batch 1.198 (1.541) Remain 06:20:40 loss: 0.1079 Lr: 0.00128
[2023-12-25 18:28:56,740 INFO misc.py line 119 253097] Train: [71/100][483/510] Data 0.004 (0.163) Batch 1.219 (1.541) Remain 06:20:28 loss: 0.0860 Lr: 0.00128
[2023-12-25 18:28:57,930 INFO misc.py line 119 253097] Train: [71/100][484/510] Data 0.004 (0.162) Batch 1.187 (1.540) Remain 06:20:16 loss: 0.1006 Lr: 0.00128
[2023-12-25 18:28:59,029 INFO misc.py line 119 253097] Train: [71/100][485/510] Data 0.007 (0.162) Batch 1.101 (1.539) Remain 06:20:01 loss: 0.1111 Lr: 0.00128
[2023-12-25 18:28:59,966 INFO misc.py line 119 253097] Train: [71/100][486/510] Data 0.004 (0.162) Batch 0.937 (1.538) Remain 06:19:41 loss: 0.0655 Lr: 0.00128
[2023-12-25 18:29:01,098 INFO misc.py line 119 253097] Train: [71/100][487/510] Data 0.004 (0.161) Batch 1.132 (1.537) Remain 06:19:27 loss: 0.1429 Lr: 0.00128
[2023-12-25 18:29:02,102 INFO misc.py line 119 253097] Train: [71/100][488/510] Data 0.004 (0.161) Batch 1.004 (1.536) Remain 06:19:09 loss: 0.1558 Lr: 0.00128
[2023-12-25 18:29:03,143 INFO misc.py line 119 253097] Train: [71/100][489/510] Data 0.004 (0.161) Batch 1.040 (1.535) Remain 06:18:52 loss: 0.3548 Lr: 0.00128
[2023-12-25 18:29:04,401 INFO misc.py line 119 253097] Train: [71/100][490/510] Data 0.006 (0.160) Batch 1.255 (1.534) Remain 06:18:42 loss: 0.0996 Lr: 0.00128
[2023-12-25 18:29:05,573 INFO misc.py line 119 253097] Train: [71/100][491/510] Data 0.007 (0.160) Batch 1.175 (1.534) Remain 06:18:30 loss: 0.1501 Lr: 0.00128
[2023-12-25 18:29:11,325 INFO misc.py line 119 253097] Train: [71/100][492/510] Data 0.004 (0.160) Batch 5.752 (1.542) Remain 06:20:36 loss: 0.1492 Lr: 0.00128
[2023-12-25 18:29:12,426 INFO misc.py line 119 253097] Train: [71/100][493/510] Data 0.005 (0.159) Batch 1.101 (1.541) Remain 06:20:21 loss: 0.1354 Lr: 0.00128
[2023-12-25 18:29:13,360 INFO misc.py line 119 253097] Train: [71/100][494/510] Data 0.004 (0.159) Batch 0.934 (1.540) Remain 06:20:01 loss: 0.0972 Lr: 0.00128
[2023-12-25 18:29:14,523 INFO misc.py line 119 253097] Train: [71/100][495/510] Data 0.005 (0.159) Batch 1.163 (1.539) Remain 06:19:48 loss: 0.0882 Lr: 0.00128
[2023-12-25 18:29:19,071 INFO misc.py line 119 253097] Train: [71/100][496/510] Data 3.464 (0.165) Batch 4.548 (1.545) Remain 06:21:17 loss: 0.0894 Lr: 0.00128
[2023-12-25 18:29:20,246 INFO misc.py line 119 253097] Train: [71/100][497/510] Data 0.004 (0.165) Batch 1.175 (1.545) Remain 06:21:05 loss: 0.1441 Lr: 0.00128
[2023-12-25 18:29:21,459 INFO misc.py line 119 253097] Train: [71/100][498/510] Data 0.003 (0.165) Batch 1.212 (1.544) Remain 06:20:53 loss: 0.2000 Lr: 0.00128
[2023-12-25 18:29:22,650 INFO misc.py line 119 253097] Train: [71/100][499/510] Data 0.005 (0.164) Batch 1.179 (1.543) Remain 06:20:41 loss: 0.0990 Lr: 0.00128
[2023-12-25 18:29:23,754 INFO misc.py line 119 253097] Train: [71/100][500/510] Data 0.015 (0.164) Batch 1.111 (1.542) Remain 06:20:26 loss: 0.1191 Lr: 0.00128
[2023-12-25 18:29:24,837 INFO misc.py line 119 253097] Train: [71/100][501/510] Data 0.009 (0.164) Batch 1.087 (1.541) Remain 06:20:11 loss: 0.1196 Lr: 0.00128
[2023-12-25 18:29:25,997 INFO misc.py line 119 253097] Train: [71/100][502/510] Data 0.004 (0.163) Batch 1.154 (1.541) Remain 06:19:58 loss: 0.1309 Lr: 0.00128
[2023-12-25 18:29:26,803 INFO misc.py line 119 253097] Train: [71/100][503/510] Data 0.010 (0.163) Batch 0.812 (1.539) Remain 06:19:35 loss: 0.1099 Lr: 0.00128
[2023-12-25 18:29:27,851 INFO misc.py line 119 253097] Train: [71/100][504/510] Data 0.003 (0.163) Batch 1.047 (1.538) Remain 06:19:19 loss: 0.1064 Lr: 0.00128
[2023-12-25 18:29:29,024 INFO misc.py line 119 253097] Train: [71/100][505/510] Data 0.004 (0.163) Batch 1.174 (1.537) Remain 06:19:07 loss: 0.0814 Lr: 0.00128
[2023-12-25 18:29:29,996 INFO misc.py line 119 253097] Train: [71/100][506/510] Data 0.003 (0.162) Batch 0.972 (1.536) Remain 06:18:49 loss: 0.1073 Lr: 0.00128
[2023-12-25 18:29:31,244 INFO misc.py line 119 253097] Train: [71/100][507/510] Data 0.003 (0.162) Batch 1.242 (1.536) Remain 06:18:38 loss: 0.2916 Lr: 0.00128
[2023-12-25 18:29:32,429 INFO misc.py line 119 253097] Train: [71/100][508/510] Data 0.009 (0.162) Batch 1.187 (1.535) Remain 06:18:27 loss: 0.1270 Lr: 0.00128
[2023-12-25 18:29:33,526 INFO misc.py line 119 253097] Train: [71/100][509/510] Data 0.007 (0.161) Batch 1.095 (1.534) Remain 06:18:12 loss: 0.1183 Lr: 0.00128
[2023-12-25 18:29:34,645 INFO misc.py line 119 253097] Train: [71/100][510/510] Data 0.009 (0.161) Batch 1.124 (1.533) Remain 06:17:59 loss: 0.2152 Lr: 0.00128
[2023-12-25 18:29:34,645 INFO misc.py line 136 253097] Train result: loss: 0.1371 
[2023-12-25 18:29:34,646 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 18:30:03,038 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4000 
[2023-12-25 18:30:03,391 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2548 
[2023-12-25 18:30:08,325 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.2911 
[2023-12-25 18:30:08,842 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2663 
[2023-12-25 18:30:10,820 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7031 
[2023-12-25 18:30:11,251 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3796 
[2023-12-25 18:30:12,131 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0721 
[2023-12-25 18:30:12,684 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3492 
[2023-12-25 18:30:14,499 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.7821 
[2023-12-25 18:30:16,636 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1316 
[2023-12-25 18:30:17,508 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2991 
[2023-12-25 18:30:17,931 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6551 
[2023-12-25 18:30:18,835 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6456 
[2023-12-25 18:30:21,778 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7088 
[2023-12-25 18:30:22,248 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2674 
[2023-12-25 18:30:22,859 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.2945 
[2023-12-25 18:30:23,588 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3308 
[2023-12-25 18:30:24,978 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6888/0.7626/0.9062.
[2023-12-25 18:30:24,978 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9167/0.9469
[2023-12-25 18:30:24,978 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9825/0.9886
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8544/0.9675
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.4077/0.4803
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6318/0.6567
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6950/0.8702
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8155/0.9109
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9162/0.9714
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6096/0.7032
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7873/0.8619
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7345/0.8432
[2023-12-25 18:30:24,979 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6038/0.7125
[2023-12-25 18:30:24,979 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 18:30:24,981 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 18:30:24,981 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 18:30:34,422 INFO misc.py line 119 253097] Train: [72/100][1/510] Data 6.391 (6.391) Batch 7.315 (7.315) Remain 30:03:06 loss: 0.1216 Lr: 0.00128
[2023-12-25 18:30:35,919 INFO misc.py line 119 253097] Train: [72/100][2/510] Data 0.273 (0.273) Batch 1.497 (1.497) Remain 06:09:00 loss: 0.2848 Lr: 0.00128
[2023-12-25 18:30:37,034 INFO misc.py line 119 253097] Train: [72/100][3/510] Data 0.003 (0.003) Batch 1.115 (1.115) Remain 04:34:50 loss: 0.0695 Lr: 0.00128
[2023-12-25 18:30:38,152 INFO misc.py line 119 253097] Train: [72/100][4/510] Data 0.003 (0.003) Batch 1.119 (1.119) Remain 04:35:41 loss: 0.1124 Lr: 0.00128
[2023-12-25 18:30:39,249 INFO misc.py line 119 253097] Train: [72/100][5/510] Data 0.004 (0.004) Batch 1.096 (1.107) Remain 04:32:53 loss: 0.1227 Lr: 0.00128
[2023-12-25 18:30:40,323 INFO misc.py line 119 253097] Train: [72/100][6/510] Data 0.004 (0.004) Batch 1.074 (1.096) Remain 04:30:06 loss: 0.1314 Lr: 0.00128
[2023-12-25 18:30:41,416 INFO misc.py line 119 253097] Train: [72/100][7/510] Data 0.004 (0.004) Batch 1.093 (1.095) Remain 04:29:54 loss: 0.0852 Lr: 0.00128
[2023-12-25 18:30:42,391 INFO misc.py line 119 253097] Train: [72/100][8/510] Data 0.004 (0.004) Batch 0.974 (1.071) Remain 04:23:55 loss: 0.0808 Lr: 0.00128
[2023-12-25 18:30:43,675 INFO misc.py line 119 253097] Train: [72/100][9/510] Data 0.004 (0.004) Batch 1.284 (1.107) Remain 04:32:38 loss: 0.1035 Lr: 0.00128
[2023-12-25 18:30:44,709 INFO misc.py line 119 253097] Train: [72/100][10/510] Data 0.005 (0.004) Batch 1.033 (1.096) Remain 04:30:01 loss: 0.1442 Lr: 0.00128
[2023-12-25 18:30:45,932 INFO misc.py line 119 253097] Train: [72/100][11/510] Data 0.005 (0.004) Batch 1.220 (1.112) Remain 04:33:49 loss: 0.1715 Lr: 0.00128
[2023-12-25 18:30:47,003 INFO misc.py line 119 253097] Train: [72/100][12/510] Data 0.008 (0.005) Batch 1.072 (1.107) Remain 04:32:42 loss: 0.1434 Lr: 0.00128
[2023-12-25 18:30:48,170 INFO misc.py line 119 253097] Train: [72/100][13/510] Data 0.009 (0.005) Batch 1.170 (1.114) Remain 04:34:14 loss: 0.1153 Lr: 0.00128
[2023-12-25 18:30:49,355 INFO misc.py line 119 253097] Train: [72/100][14/510] Data 0.004 (0.005) Batch 1.186 (1.120) Remain 04:35:50 loss: 0.0959 Lr: 0.00128
[2023-12-25 18:30:50,572 INFO misc.py line 119 253097] Train: [72/100][15/510] Data 0.004 (0.005) Batch 1.210 (1.128) Remain 04:37:39 loss: 0.1842 Lr: 0.00128
[2023-12-25 18:30:53,716 INFO misc.py line 119 253097] Train: [72/100][16/510] Data 0.011 (0.005) Batch 3.150 (1.283) Remain 05:15:57 loss: 0.1862 Lr: 0.00128
[2023-12-25 18:30:54,845 INFO misc.py line 119 253097] Train: [72/100][17/510] Data 0.005 (0.005) Batch 1.130 (1.272) Remain 05:13:14 loss: 0.1016 Lr: 0.00128
[2023-12-25 18:30:55,836 INFO misc.py line 119 253097] Train: [72/100][18/510] Data 0.003 (0.005) Batch 0.991 (1.253) Remain 05:08:35 loss: 0.0856 Lr: 0.00128
[2023-12-25 18:30:56,849 INFO misc.py line 119 253097] Train: [72/100][19/510] Data 0.004 (0.005) Batch 1.014 (1.238) Remain 05:04:53 loss: 0.1184 Lr: 0.00128
[2023-12-25 18:30:57,930 INFO misc.py line 119 253097] Train: [72/100][20/510] Data 0.004 (0.005) Batch 1.080 (1.229) Remain 05:02:34 loss: 0.0938 Lr: 0.00128
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[2023-12-25 18:35:06,494 INFO misc.py line 119 253097] Train: [72/100][178/510] Data 0.008 (0.083) Batch 1.074 (1.540) Remain 06:14:58 loss: 0.0985 Lr: 0.00125
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[2023-12-25 18:41:48,239 INFO misc.py line 119 253097] Train: [72/100][439/510] Data 0.007 (0.075) Batch 1.306 (1.539) Remain 06:08:12 loss: 0.0975 Lr: 0.00121
[2023-12-25 18:41:49,312 INFO misc.py line 119 253097] Train: [72/100][440/510] Data 0.007 (0.075) Batch 1.073 (1.538) Remain 06:07:55 loss: 0.0953 Lr: 0.00121
[2023-12-25 18:41:50,479 INFO misc.py line 119 253097] Train: [72/100][441/510] Data 0.008 (0.075) Batch 1.165 (1.538) Remain 06:07:42 loss: 0.1166 Lr: 0.00121
[2023-12-25 18:41:51,640 INFO misc.py line 119 253097] Train: [72/100][442/510] Data 0.010 (0.075) Batch 1.168 (1.537) Remain 06:07:28 loss: 0.1300 Lr: 0.00121
[2023-12-25 18:42:02,036 INFO misc.py line 119 253097] Train: [72/100][443/510] Data 0.002 (0.075) Batch 10.395 (1.557) Remain 06:12:15 loss: 0.1146 Lr: 0.00121
[2023-12-25 18:42:04,375 INFO misc.py line 119 253097] Train: [72/100][444/510] Data 0.004 (0.075) Batch 2.337 (1.559) Remain 06:12:39 loss: 0.2247 Lr: 0.00121
[2023-12-25 18:42:05,636 INFO misc.py line 119 253097] Train: [72/100][445/510] Data 0.005 (0.074) Batch 1.260 (1.558) Remain 06:12:28 loss: 0.0944 Lr: 0.00121
[2023-12-25 18:42:06,781 INFO misc.py line 119 253097] Train: [72/100][446/510] Data 0.006 (0.074) Batch 1.143 (1.557) Remain 06:12:13 loss: 0.2011 Lr: 0.00121
[2023-12-25 18:42:07,826 INFO misc.py line 119 253097] Train: [72/100][447/510] Data 0.009 (0.074) Batch 1.044 (1.556) Remain 06:11:55 loss: 0.0870 Lr: 0.00121
[2023-12-25 18:42:09,010 INFO misc.py line 119 253097] Train: [72/100][448/510] Data 0.009 (0.074) Batch 1.184 (1.555) Remain 06:11:41 loss: 0.3039 Lr: 0.00121
[2023-12-25 18:42:10,236 INFO misc.py line 119 253097] Train: [72/100][449/510] Data 0.010 (0.074) Batch 1.232 (1.554) Remain 06:11:29 loss: 0.1028 Lr: 0.00121
[2023-12-25 18:42:15,461 INFO misc.py line 119 253097] Train: [72/100][450/510] Data 0.004 (0.074) Batch 5.225 (1.562) Remain 06:13:25 loss: 0.1249 Lr: 0.00121
[2023-12-25 18:42:16,790 INFO misc.py line 119 253097] Train: [72/100][451/510] Data 0.003 (0.074) Batch 1.325 (1.562) Remain 06:13:16 loss: 0.2148 Lr: 0.00121
[2023-12-25 18:42:17,990 INFO misc.py line 119 253097] Train: [72/100][452/510] Data 0.008 (0.073) Batch 1.203 (1.561) Remain 06:13:03 loss: 0.0790 Lr: 0.00121
[2023-12-25 18:42:19,254 INFO misc.py line 119 253097] Train: [72/100][453/510] Data 0.005 (0.073) Batch 1.260 (1.560) Remain 06:12:52 loss: 0.0711 Lr: 0.00121
[2023-12-25 18:42:20,170 INFO misc.py line 119 253097] Train: [72/100][454/510] Data 0.008 (0.073) Batch 0.921 (1.559) Remain 06:12:30 loss: 0.1149 Lr: 0.00121
[2023-12-25 18:42:21,316 INFO misc.py line 119 253097] Train: [72/100][455/510] Data 0.004 (0.073) Batch 1.146 (1.558) Remain 06:12:15 loss: 0.1510 Lr: 0.00121
[2023-12-25 18:42:22,581 INFO misc.py line 119 253097] Train: [72/100][456/510] Data 0.004 (0.073) Batch 1.262 (1.557) Remain 06:12:05 loss: 0.0760 Lr: 0.00121
[2023-12-25 18:42:23,805 INFO misc.py line 119 253097] Train: [72/100][457/510] Data 0.007 (0.073) Batch 1.225 (1.557) Remain 06:11:53 loss: 0.2361 Lr: 0.00121
[2023-12-25 18:42:24,879 INFO misc.py line 119 253097] Train: [72/100][458/510] Data 0.006 (0.073) Batch 1.069 (1.556) Remain 06:11:36 loss: 0.0738 Lr: 0.00121
[2023-12-25 18:42:26,117 INFO misc.py line 119 253097] Train: [72/100][459/510] Data 0.010 (0.072) Batch 1.245 (1.555) Remain 06:11:24 loss: 0.1319 Lr: 0.00121
[2023-12-25 18:42:27,136 INFO misc.py line 119 253097] Train: [72/100][460/510] Data 0.004 (0.072) Batch 1.016 (1.554) Remain 06:11:06 loss: 0.2462 Lr: 0.00121
[2023-12-25 18:42:28,971 INFO misc.py line 119 253097] Train: [72/100][461/510] Data 0.007 (0.072) Batch 1.831 (1.554) Remain 06:11:13 loss: 0.0798 Lr: 0.00121
[2023-12-25 18:42:30,085 INFO misc.py line 119 253097] Train: [72/100][462/510] Data 0.010 (0.072) Batch 1.118 (1.553) Remain 06:10:58 loss: 0.0581 Lr: 0.00121
[2023-12-25 18:42:31,117 INFO misc.py line 119 253097] Train: [72/100][463/510] Data 0.006 (0.072) Batch 1.031 (1.552) Remain 06:10:40 loss: 0.0894 Lr: 0.00121
[2023-12-25 18:42:32,231 INFO misc.py line 119 253097] Train: [72/100][464/510] Data 0.007 (0.072) Batch 1.116 (1.551) Remain 06:10:25 loss: 0.1802 Lr: 0.00121
[2023-12-25 18:42:33,441 INFO misc.py line 119 253097] Train: [72/100][465/510] Data 0.006 (0.072) Batch 1.195 (1.551) Remain 06:10:12 loss: 0.1660 Lr: 0.00121
[2023-12-25 18:42:34,464 INFO misc.py line 119 253097] Train: [72/100][466/510] Data 0.020 (0.071) Batch 1.034 (1.550) Remain 06:09:55 loss: 0.1277 Lr: 0.00121
[2023-12-25 18:42:35,725 INFO misc.py line 119 253097] Train: [72/100][467/510] Data 0.008 (0.071) Batch 1.248 (1.549) Remain 06:09:44 loss: 0.4610 Lr: 0.00121
[2023-12-25 18:42:36,817 INFO misc.py line 119 253097] Train: [72/100][468/510] Data 0.021 (0.071) Batch 1.101 (1.548) Remain 06:09:29 loss: 0.1639 Lr: 0.00121
[2023-12-25 18:42:37,750 INFO misc.py line 119 253097] Train: [72/100][469/510] Data 0.013 (0.071) Batch 0.941 (1.547) Remain 06:09:08 loss: 0.1645 Lr: 0.00121
[2023-12-25 18:42:46,081 INFO misc.py line 119 253097] Train: [72/100][470/510] Data 0.006 (0.071) Batch 8.332 (1.561) Remain 06:12:35 loss: 0.1190 Lr: 0.00121
[2023-12-25 18:42:47,179 INFO misc.py line 119 253097] Train: [72/100][471/510] Data 0.003 (0.071) Batch 1.095 (1.560) Remain 06:12:19 loss: 0.1349 Lr: 0.00121
[2023-12-25 18:42:48,306 INFO misc.py line 119 253097] Train: [72/100][472/510] Data 0.007 (0.071) Batch 1.128 (1.559) Remain 06:12:04 loss: 0.1325 Lr: 0.00121
[2023-12-25 18:42:49,321 INFO misc.py line 119 253097] Train: [72/100][473/510] Data 0.006 (0.070) Batch 1.016 (1.558) Remain 06:11:46 loss: 0.1817 Lr: 0.00121
[2023-12-25 18:42:50,340 INFO misc.py line 119 253097] Train: [72/100][474/510] Data 0.007 (0.070) Batch 1.020 (1.557) Remain 06:11:28 loss: 0.1081 Lr: 0.00120
[2023-12-25 18:42:51,481 INFO misc.py line 119 253097] Train: [72/100][475/510] Data 0.004 (0.070) Batch 1.140 (1.556) Remain 06:11:14 loss: 0.1152 Lr: 0.00120
[2023-12-25 18:42:52,447 INFO misc.py line 119 253097] Train: [72/100][476/510] Data 0.004 (0.070) Batch 0.967 (1.555) Remain 06:10:55 loss: 0.0943 Lr: 0.00120
[2023-12-25 18:42:53,608 INFO misc.py line 119 253097] Train: [72/100][477/510] Data 0.003 (0.070) Batch 1.159 (1.554) Remain 06:10:41 loss: 0.1536 Lr: 0.00120
[2023-12-25 18:42:54,719 INFO misc.py line 119 253097] Train: [72/100][478/510] Data 0.005 (0.070) Batch 1.112 (1.553) Remain 06:10:26 loss: 0.1278 Lr: 0.00120
[2023-12-25 18:42:55,866 INFO misc.py line 119 253097] Train: [72/100][479/510] Data 0.004 (0.070) Batch 1.147 (1.552) Remain 06:10:13 loss: 0.3033 Lr: 0.00120
[2023-12-25 18:42:56,893 INFO misc.py line 119 253097] Train: [72/100][480/510] Data 0.005 (0.070) Batch 1.028 (1.551) Remain 06:09:55 loss: 0.0715 Lr: 0.00120
[2023-12-25 18:42:57,975 INFO misc.py line 119 253097] Train: [72/100][481/510] Data 0.004 (0.069) Batch 1.080 (1.550) Remain 06:09:40 loss: 0.1321 Lr: 0.00120
[2023-12-25 18:42:59,294 INFO misc.py line 119 253097] Train: [72/100][482/510] Data 0.006 (0.069) Batch 1.320 (1.550) Remain 06:09:31 loss: 0.0979 Lr: 0.00120
[2023-12-25 18:43:00,377 INFO misc.py line 119 253097] Train: [72/100][483/510] Data 0.004 (0.069) Batch 1.081 (1.549) Remain 06:09:16 loss: 0.1312 Lr: 0.00120
[2023-12-25 18:43:01,242 INFO misc.py line 119 253097] Train: [72/100][484/510] Data 0.006 (0.069) Batch 0.867 (1.547) Remain 06:08:54 loss: 0.0800 Lr: 0.00120
[2023-12-25 18:43:02,445 INFO misc.py line 119 253097] Train: [72/100][485/510] Data 0.004 (0.069) Batch 1.202 (1.546) Remain 06:08:42 loss: 0.0834 Lr: 0.00120
[2023-12-25 18:43:03,521 INFO misc.py line 119 253097] Train: [72/100][486/510] Data 0.005 (0.069) Batch 1.076 (1.546) Remain 06:08:27 loss: 0.1328 Lr: 0.00120
[2023-12-25 18:43:04,735 INFO misc.py line 119 253097] Train: [72/100][487/510] Data 0.005 (0.069) Batch 1.215 (1.545) Remain 06:08:15 loss: 0.1398 Lr: 0.00120
[2023-12-25 18:43:05,855 INFO misc.py line 119 253097] Train: [72/100][488/510] Data 0.004 (0.068) Batch 1.119 (1.544) Remain 06:08:01 loss: 0.1562 Lr: 0.00120
[2023-12-25 18:43:07,130 INFO misc.py line 119 253097] Train: [72/100][489/510] Data 0.004 (0.068) Batch 1.275 (1.543) Remain 06:07:52 loss: 0.0706 Lr: 0.00120
[2023-12-25 18:43:08,195 INFO misc.py line 119 253097] Train: [72/100][490/510] Data 0.004 (0.068) Batch 1.062 (1.542) Remain 06:07:36 loss: 0.0789 Lr: 0.00120
[2023-12-25 18:43:09,301 INFO misc.py line 119 253097] Train: [72/100][491/510] Data 0.007 (0.068) Batch 1.103 (1.542) Remain 06:07:22 loss: 0.1185 Lr: 0.00120
[2023-12-25 18:43:13,188 INFO misc.py line 119 253097] Train: [72/100][492/510] Data 2.954 (0.074) Batch 3.893 (1.546) Remain 06:08:29 loss: 0.1033 Lr: 0.00120
[2023-12-25 18:43:14,197 INFO misc.py line 119 253097] Train: [72/100][493/510] Data 0.004 (0.074) Batch 1.003 (1.545) Remain 06:08:11 loss: 0.1495 Lr: 0.00120
[2023-12-25 18:43:15,305 INFO misc.py line 119 253097] Train: [72/100][494/510] Data 0.009 (0.074) Batch 1.111 (1.544) Remain 06:07:57 loss: 0.0727 Lr: 0.00120
[2023-12-25 18:43:16,550 INFO misc.py line 119 253097] Train: [72/100][495/510] Data 0.006 (0.074) Batch 1.246 (1.544) Remain 06:07:47 loss: 0.0760 Lr: 0.00120
[2023-12-25 18:43:17,770 INFO misc.py line 119 253097] Train: [72/100][496/510] Data 0.005 (0.073) Batch 1.214 (1.543) Remain 06:07:36 loss: 0.0801 Lr: 0.00120
[2023-12-25 18:43:19,025 INFO misc.py line 119 253097] Train: [72/100][497/510] Data 0.012 (0.073) Batch 1.262 (1.542) Remain 06:07:26 loss: 0.0966 Lr: 0.00120
[2023-12-25 18:43:20,222 INFO misc.py line 119 253097] Train: [72/100][498/510] Data 0.005 (0.073) Batch 1.195 (1.542) Remain 06:07:15 loss: 0.1052 Lr: 0.00120
[2023-12-25 18:43:21,359 INFO misc.py line 119 253097] Train: [72/100][499/510] Data 0.007 (0.073) Batch 1.139 (1.541) Remain 06:07:02 loss: 0.2706 Lr: 0.00120
[2023-12-25 18:43:22,574 INFO misc.py line 119 253097] Train: [72/100][500/510] Data 0.005 (0.073) Batch 1.216 (1.540) Remain 06:06:51 loss: 0.0711 Lr: 0.00120
[2023-12-25 18:43:23,519 INFO misc.py line 119 253097] Train: [72/100][501/510] Data 0.004 (0.073) Batch 0.944 (1.539) Remain 06:06:32 loss: 0.1406 Lr: 0.00120
[2023-12-25 18:43:24,667 INFO misc.py line 119 253097] Train: [72/100][502/510] Data 0.004 (0.073) Batch 1.149 (1.538) Remain 06:06:19 loss: 0.1297 Lr: 0.00120
[2023-12-25 18:43:25,959 INFO misc.py line 119 253097] Train: [72/100][503/510] Data 0.004 (0.072) Batch 1.287 (1.538) Remain 06:06:11 loss: 0.1538 Lr: 0.00120
[2023-12-25 18:43:30,419 INFO misc.py line 119 253097] Train: [72/100][504/510] Data 0.008 (0.072) Batch 4.464 (1.544) Remain 06:07:33 loss: 0.2019 Lr: 0.00120
[2023-12-25 18:43:31,386 INFO misc.py line 119 253097] Train: [72/100][505/510] Data 0.005 (0.072) Batch 0.962 (1.543) Remain 06:07:14 loss: 0.1095 Lr: 0.00120
[2023-12-25 18:43:32,518 INFO misc.py line 119 253097] Train: [72/100][506/510] Data 0.010 (0.072) Batch 1.137 (1.542) Remain 06:07:01 loss: 0.0665 Lr: 0.00120
[2023-12-25 18:43:33,702 INFO misc.py line 119 253097] Train: [72/100][507/510] Data 0.004 (0.072) Batch 1.181 (1.541) Remain 06:06:50 loss: 0.1006 Lr: 0.00120
[2023-12-25 18:43:35,011 INFO misc.py line 119 253097] Train: [72/100][508/510] Data 0.008 (0.072) Batch 1.312 (1.541) Remain 06:06:42 loss: 0.1055 Lr: 0.00120
[2023-12-25 18:43:36,175 INFO misc.py line 119 253097] Train: [72/100][509/510] Data 0.004 (0.072) Batch 1.160 (1.540) Remain 06:06:29 loss: 0.0844 Lr: 0.00120
[2023-12-25 18:43:38,542 INFO misc.py line 119 253097] Train: [72/100][510/510] Data 0.008 (0.072) Batch 2.370 (1.541) Remain 06:06:51 loss: 0.1514 Lr: 0.00120
[2023-12-25 18:43:38,543 INFO misc.py line 136 253097] Train result: loss: 0.1331 
[2023-12-25 18:43:38,543 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 18:44:09,074 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5467 
[2023-12-25 18:44:09,420 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2677 
[2023-12-25 18:44:15,292 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3048 
[2023-12-25 18:44:15,812 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3986 
[2023-12-25 18:44:17,784 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7592 
[2023-12-25 18:44:18,207 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2557 
[2023-12-25 18:44:19,086 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0611 
[2023-12-25 18:44:19,652 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2855 
[2023-12-25 18:44:21,465 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6506 
[2023-12-25 18:44:23,587 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.0950 
[2023-12-25 18:44:24,445 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2810 
[2023-12-25 18:44:24,873 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6494 
[2023-12-25 18:44:25,784 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 1.0323 
[2023-12-25 18:44:28,728 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8476 
[2023-12-25 18:44:29,195 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2388 
[2023-12-25 18:44:29,811 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3744 
[2023-12-25 18:44:30,516 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2856 
[2023-12-25 18:44:31,886 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6864/0.7513/0.9049.
[2023-12-25 18:44:31,887 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9255/0.9484
[2023-12-25 18:44:31,887 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9830/0.9890
[2023-12-25 18:44:31,887 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8465/0.9674
[2023-12-25 18:44:31,887 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 18:44:31,887 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2970/0.3362
[2023-12-25 18:44:31,887 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6302/0.6655
[2023-12-25 18:44:31,887 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7008/0.8840
[2023-12-25 18:44:31,888 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7903/0.8986
[2023-12-25 18:44:31,888 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9139/0.9696
[2023-12-25 18:44:31,888 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6680/0.7188
[2023-12-25 18:44:31,888 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7828/0.8591
[2023-12-25 18:44:31,888 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7712/0.8020
[2023-12-25 18:44:31,888 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6136/0.7283
[2023-12-25 18:44:31,889 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 18:44:31,890 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 18:44:31,890 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 18:44:41,380 INFO misc.py line 119 253097] Train: [73/100][1/510] Data 5.969 (5.969) Batch 7.184 (7.184) Remain 28:29:44 loss: 0.1101 Lr: 0.00120
[2023-12-25 18:44:43,981 INFO misc.py line 119 253097] Train: [73/100][2/510] Data 1.478 (1.478) Batch 2.602 (2.602) Remain 10:19:13 loss: 0.1383 Lr: 0.00120
[2023-12-25 18:44:45,083 INFO misc.py line 119 253097] Train: [73/100][3/510] Data 0.005 (0.005) Batch 1.101 (1.101) Remain 04:22:04 loss: 0.1202 Lr: 0.00120
[2023-12-25 18:44:46,309 INFO misc.py line 119 253097] Train: [73/100][4/510] Data 0.004 (0.004) Batch 1.226 (1.226) Remain 04:51:43 loss: 0.0942 Lr: 0.00120
[2023-12-25 18:44:54,154 INFO misc.py line 119 253097] Train: [73/100][5/510] Data 0.004 (0.004) Batch 7.846 (4.536) Remain 17:59:10 loss: 0.1319 Lr: 0.00120
[2023-12-25 18:44:55,297 INFO misc.py line 119 253097] Train: [73/100][6/510] Data 0.005 (0.004) Batch 1.137 (3.403) Remain 13:29:36 loss: 0.1682 Lr: 0.00120
[2023-12-25 18:44:56,547 INFO misc.py line 119 253097] Train: [73/100][7/510] Data 0.009 (0.006) Batch 1.251 (2.865) Remain 11:21:32 loss: 0.1894 Lr: 0.00120
[2023-12-25 18:44:57,754 INFO misc.py line 119 253097] Train: [73/100][8/510] Data 0.008 (0.006) Batch 1.211 (2.534) Remain 10:02:47 loss: 0.1435 Lr: 0.00120
[2023-12-25 18:44:58,974 INFO misc.py line 119 253097] Train: [73/100][9/510] Data 0.005 (0.006) Batch 1.220 (2.315) Remain 09:10:39 loss: 0.1569 Lr: 0.00120
[2023-12-25 18:45:00,255 INFO misc.py line 119 253097] Train: [73/100][10/510] Data 0.005 (0.006) Batch 1.281 (2.168) Remain 08:35:30 loss: 0.1495 Lr: 0.00120
[2023-12-25 18:45:01,419 INFO misc.py line 119 253097] Train: [73/100][11/510] Data 0.003 (0.005) Batch 1.163 (2.042) Remain 08:05:36 loss: 0.0953 Lr: 0.00120
[2023-12-25 18:45:02,581 INFO misc.py line 119 253097] Train: [73/100][12/510] Data 0.006 (0.005) Batch 1.160 (1.944) Remain 07:42:17 loss: 0.1015 Lr: 0.00120
[2023-12-25 18:45:03,873 INFO misc.py line 119 253097] Train: [73/100][13/510] Data 0.007 (0.006) Batch 1.290 (1.879) Remain 07:26:42 loss: 0.1027 Lr: 0.00120
[2023-12-25 18:45:05,050 INFO misc.py line 119 253097] Train: [73/100][14/510] Data 0.008 (0.006) Batch 1.181 (1.815) Remain 07:11:35 loss: 0.1232 Lr: 0.00120
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[2023-12-25 18:46:03,502 INFO misc.py line 119 253097] Train: [73/100][49/510] Data 0.009 (0.095) Batch 1.125 (1.705) Remain 06:44:20 loss: 0.0792 Lr: 0.00119
[2023-12-25 18:46:04,705 INFO misc.py line 119 253097] Train: [73/100][50/510] Data 0.003 (0.093) Batch 1.201 (1.694) Remain 06:41:46 loss: 0.2764 Lr: 0.00119
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[2023-12-25 18:46:07,164 INFO misc.py line 119 253097] Train: [73/100][52/510] Data 0.004 (0.089) Batch 1.239 (1.675) Remain 06:37:10 loss: 0.1520 Lr: 0.00119
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[2023-12-25 18:46:38,529 INFO misc.py line 119 253097] Train: [73/100][72/510] Data 5.062 (0.139) Batch 6.372 (1.644) Remain 06:29:20 loss: 0.0966 Lr: 0.00119
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[2023-12-25 18:48:31,918 INFO misc.py line 119 253097] Train: [73/100][145/510] Data 4.000 (0.099) Batch 5.101 (1.597) Remain 06:16:19 loss: 0.1048 Lr: 0.00118
[2023-12-25 18:48:32,946 INFO misc.py line 119 253097] Train: [73/100][146/510] Data 0.004 (0.098) Batch 1.029 (1.593) Remain 06:15:21 loss: 0.1398 Lr: 0.00118
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[2023-12-25 18:48:35,046 INFO misc.py line 119 253097] Train: [73/100][148/510] Data 0.003 (0.097) Batch 1.123 (1.586) Remain 06:13:32 loss: 0.1004 Lr: 0.00118
[2023-12-25 18:48:36,283 INFO misc.py line 119 253097] Train: [73/100][149/510] Data 0.004 (0.096) Batch 1.237 (1.584) Remain 06:12:57 loss: 0.0973 Lr: 0.00118
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[2023-12-25 18:48:38,724 INFO misc.py line 119 253097] Train: [73/100][151/510] Data 0.007 (0.095) Batch 1.142 (1.579) Remain 06:11:44 loss: 0.1331 Lr: 0.00118
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[2023-12-25 18:48:41,113 INFO misc.py line 119 253097] Train: [73/100][153/510] Data 0.010 (0.094) Batch 1.231 (1.574) Remain 06:10:28 loss: 0.2272 Lr: 0.00118
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[2023-12-25 18:48:48,707 INFO misc.py line 119 253097] Train: [73/100][160/510] Data 0.008 (0.090) Batch 0.893 (1.552) Remain 06:05:10 loss: 0.1238 Lr: 0.00117
[2023-12-25 18:48:49,916 INFO misc.py line 119 253097] Train: [73/100][161/510] Data 0.003 (0.090) Batch 1.209 (1.550) Remain 06:04:38 loss: 0.1381 Lr: 0.00117
[2023-12-25 18:48:51,074 INFO misc.py line 119 253097] Train: [73/100][162/510] Data 0.003 (0.089) Batch 1.157 (1.547) Remain 06:04:02 loss: 0.1131 Lr: 0.00117
[2023-12-25 18:48:52,189 INFO misc.py line 119 253097] Train: [73/100][163/510] Data 0.004 (0.088) Batch 1.116 (1.544) Remain 06:03:22 loss: 0.0913 Lr: 0.00117
[2023-12-25 18:48:53,424 INFO misc.py line 119 253097] Train: [73/100][164/510] Data 0.002 (0.088) Batch 1.231 (1.542) Remain 06:02:53 loss: 0.0865 Lr: 0.00117
[2023-12-25 18:48:54,633 INFO misc.py line 119 253097] Train: [73/100][165/510] Data 0.007 (0.087) Batch 1.209 (1.540) Remain 06:02:22 loss: 0.1258 Lr: 0.00117
[2023-12-25 18:48:55,738 INFO misc.py line 119 253097] Train: [73/100][166/510] Data 0.008 (0.087) Batch 1.107 (1.538) Remain 06:01:43 loss: 0.0783 Lr: 0.00117
[2023-12-25 18:48:57,717 INFO misc.py line 119 253097] Train: [73/100][167/510] Data 0.792 (0.091) Batch 1.980 (1.540) Remain 06:02:20 loss: 0.1521 Lr: 0.00117
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[2023-12-25 18:49:04,132 INFO misc.py line 119 253097] Train: [73/100][173/510] Data 0.009 (0.088) Batch 1.092 (1.524) Remain 05:58:16 loss: 0.1657 Lr: 0.00117
[2023-12-25 18:49:05,410 INFO misc.py line 119 253097] Train: [73/100][174/510] Data 0.007 (0.088) Batch 1.281 (1.522) Remain 05:57:54 loss: 0.0843 Lr: 0.00117
[2023-12-25 18:49:06,697 INFO misc.py line 119 253097] Train: [73/100][175/510] Data 0.004 (0.087) Batch 1.283 (1.521) Remain 05:57:33 loss: 0.0965 Lr: 0.00117
[2023-12-25 18:49:07,996 INFO misc.py line 119 253097] Train: [73/100][176/510] Data 0.008 (0.087) Batch 1.301 (1.520) Remain 05:57:14 loss: 0.1863 Lr: 0.00117
[2023-12-25 18:49:09,103 INFO misc.py line 119 253097] Train: [73/100][177/510] Data 0.007 (0.086) Batch 1.106 (1.517) Remain 05:56:39 loss: 0.1426 Lr: 0.00117
[2023-12-25 18:49:10,137 INFO misc.py line 119 253097] Train: [73/100][178/510] Data 0.008 (0.086) Batch 1.034 (1.515) Remain 05:55:58 loss: 0.0886 Lr: 0.00117
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[2023-12-25 18:49:12,480 INFO misc.py line 119 253097] Train: [73/100][180/510] Data 0.010 (0.085) Batch 1.204 (1.511) Remain 05:55:00 loss: 0.2197 Lr: 0.00117
[2023-12-25 18:49:13,676 INFO misc.py line 119 253097] Train: [73/100][181/510] Data 0.008 (0.085) Batch 1.195 (1.509) Remain 05:54:34 loss: 0.2482 Lr: 0.00117
[2023-12-25 18:49:14,793 INFO misc.py line 119 253097] Train: [73/100][182/510] Data 0.009 (0.084) Batch 1.121 (1.507) Remain 05:54:02 loss: 0.1221 Lr: 0.00117
[2023-12-25 18:49:15,854 INFO misc.py line 119 253097] Train: [73/100][183/510] Data 0.004 (0.084) Batch 1.059 (1.504) Remain 05:53:25 loss: 0.1408 Lr: 0.00117
[2023-12-25 18:49:16,889 INFO misc.py line 119 253097] Train: [73/100][184/510] Data 0.007 (0.083) Batch 1.032 (1.502) Remain 05:52:47 loss: 0.0680 Lr: 0.00117
[2023-12-25 18:49:17,979 INFO misc.py line 119 253097] Train: [73/100][185/510] Data 0.010 (0.083) Batch 1.094 (1.499) Remain 05:52:14 loss: 0.1543 Lr: 0.00117
[2023-12-25 18:49:19,157 INFO misc.py line 119 253097] Train: [73/100][186/510] Data 0.007 (0.082) Batch 1.180 (1.498) Remain 05:51:48 loss: 0.1526 Lr: 0.00117
[2023-12-25 18:49:20,432 INFO misc.py line 119 253097] Train: [73/100][187/510] Data 0.003 (0.082) Batch 1.251 (1.496) Remain 05:51:27 loss: 0.0693 Lr: 0.00117
[2023-12-25 18:49:21,472 INFO misc.py line 119 253097] Train: [73/100][188/510] Data 0.031 (0.082) Batch 1.059 (1.494) Remain 05:50:53 loss: 0.0879 Lr: 0.00117
[2023-12-25 18:49:22,572 INFO misc.py line 119 253097] Train: [73/100][189/510] Data 0.009 (0.081) Batch 1.101 (1.492) Remain 05:50:21 loss: 0.0746 Lr: 0.00117
[2023-12-25 18:49:23,766 INFO misc.py line 119 253097] Train: [73/100][190/510] Data 0.007 (0.081) Batch 1.176 (1.490) Remain 05:49:56 loss: 0.0944 Lr: 0.00117
[2023-12-25 18:49:24,710 INFO misc.py line 119 253097] Train: [73/100][191/510] Data 0.026 (0.081) Batch 0.964 (1.487) Remain 05:49:15 loss: 0.0639 Lr: 0.00117
[2023-12-25 18:49:29,028 INFO misc.py line 119 253097] Train: [73/100][192/510] Data 3.153 (0.097) Batch 4.319 (1.502) Remain 05:52:45 loss: 0.1084 Lr: 0.00117
[2023-12-25 18:49:30,190 INFO misc.py line 119 253097] Train: [73/100][193/510] Data 0.004 (0.096) Batch 1.162 (1.501) Remain 05:52:18 loss: 0.0932 Lr: 0.00117
[2023-12-25 18:49:31,352 INFO misc.py line 119 253097] Train: [73/100][194/510] Data 0.004 (0.096) Batch 1.162 (1.499) Remain 05:51:51 loss: 0.2641 Lr: 0.00117
[2023-12-25 18:49:32,370 INFO misc.py line 119 253097] Train: [73/100][195/510] Data 0.005 (0.095) Batch 1.019 (1.496) Remain 05:51:15 loss: 0.1013 Lr: 0.00117
[2023-12-25 18:49:33,519 INFO misc.py line 119 253097] Train: [73/100][196/510] Data 0.004 (0.095) Batch 1.150 (1.494) Remain 05:50:48 loss: 0.0896 Lr: 0.00117
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[2023-12-25 18:49:35,764 INFO misc.py line 119 253097] Train: [73/100][198/510] Data 0.004 (0.094) Batch 1.137 (1.491) Remain 05:49:51 loss: 0.1095 Lr: 0.00117
[2023-12-25 18:49:36,949 INFO misc.py line 119 253097] Train: [73/100][199/510] Data 0.004 (0.094) Batch 1.185 (1.489) Remain 05:49:28 loss: 0.1637 Lr: 0.00117
[2023-12-25 18:49:38,233 INFO misc.py line 119 253097] Train: [73/100][200/510] Data 0.004 (0.093) Batch 1.278 (1.488) Remain 05:49:11 loss: 0.1840 Lr: 0.00117
[2023-12-25 18:49:39,441 INFO misc.py line 119 253097] Train: [73/100][201/510] Data 0.010 (0.093) Batch 1.214 (1.487) Remain 05:48:50 loss: 0.1292 Lr: 0.00117
[2023-12-25 18:49:40,509 INFO misc.py line 119 253097] Train: [73/100][202/510] Data 0.004 (0.092) Batch 1.066 (1.485) Remain 05:48:19 loss: 0.0704 Lr: 0.00117
[2023-12-25 18:49:41,837 INFO misc.py line 119 253097] Train: [73/100][203/510] Data 0.006 (0.092) Batch 1.327 (1.484) Remain 05:48:06 loss: 0.1721 Lr: 0.00117
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[2023-12-25 18:49:55,865 INFO misc.py line 119 253097] Train: [73/100][215/510] Data 0.005 (0.087) Batch 1.157 (1.466) Remain 05:43:38 loss: 0.1641 Lr: 0.00117
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[2023-12-25 18:50:06,012 INFO misc.py line 119 253097] Train: [73/100][217/510] Data 7.882 (0.123) Batch 9.157 (1.500) Remain 05:51:29 loss: 0.2187 Lr: 0.00117
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[2023-12-25 18:50:49,618 INFO misc.py line 119 253097] Train: [73/100][243/510] Data 0.004 (0.110) Batch 1.185 (1.519) Remain 05:55:20 loss: 0.1483 Lr: 0.00116
[2023-12-25 18:50:50,838 INFO misc.py line 119 253097] Train: [73/100][244/510] Data 0.004 (0.110) Batch 1.195 (1.518) Remain 05:55:00 loss: 0.1768 Lr: 0.00116
[2023-12-25 18:50:53,669 INFO misc.py line 119 253097] Train: [73/100][245/510] Data 0.029 (0.109) Batch 2.857 (1.523) Remain 05:56:16 loss: 0.0933 Lr: 0.00116
[2023-12-25 18:50:54,816 INFO misc.py line 119 253097] Train: [73/100][246/510] Data 0.004 (0.109) Batch 1.146 (1.522) Remain 05:55:53 loss: 0.1089 Lr: 0.00116
[2023-12-25 18:50:55,798 INFO misc.py line 119 253097] Train: [73/100][247/510] Data 0.004 (0.109) Batch 0.983 (1.519) Remain 05:55:20 loss: 0.1058 Lr: 0.00116
[2023-12-25 18:50:56,893 INFO misc.py line 119 253097] Train: [73/100][248/510] Data 0.003 (0.108) Batch 1.095 (1.518) Remain 05:54:54 loss: 0.1227 Lr: 0.00116
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[2023-12-25 18:51:01,468 INFO misc.py line 119 253097] Train: [73/100][252/510] Data 0.012 (0.107) Batch 1.086 (1.512) Remain 05:53:24 loss: 0.1419 Lr: 0.00116
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[2023-12-25 18:51:06,983 INFO misc.py line 119 253097] Train: [73/100][255/510] Data 0.004 (0.105) Batch 3.035 (1.515) Remain 05:54:14 loss: 0.1282 Lr: 0.00116
[2023-12-25 18:51:07,951 INFO misc.py line 119 253097] Train: [73/100][256/510] Data 0.006 (0.105) Batch 0.970 (1.513) Remain 05:53:42 loss: 0.0718 Lr: 0.00116
[2023-12-25 18:51:09,185 INFO misc.py line 119 253097] Train: [73/100][257/510] Data 0.003 (0.105) Batch 1.233 (1.512) Remain 05:53:25 loss: 0.1192 Lr: 0.00116
[2023-12-25 18:51:10,302 INFO misc.py line 119 253097] Train: [73/100][258/510] Data 0.006 (0.104) Batch 1.118 (1.511) Remain 05:53:02 loss: 0.1369 Lr: 0.00116
[2023-12-25 18:51:11,600 INFO misc.py line 119 253097] Train: [73/100][259/510] Data 0.003 (0.104) Batch 1.293 (1.510) Remain 05:52:49 loss: 0.1114 Lr: 0.00116
[2023-12-25 18:51:18,609 INFO misc.py line 119 253097] Train: [73/100][260/510] Data 5.708 (0.126) Batch 7.013 (1.531) Remain 05:57:47 loss: 0.0909 Lr: 0.00116
[2023-12-25 18:51:19,779 INFO misc.py line 119 253097] Train: [73/100][261/510] Data 0.004 (0.125) Batch 1.170 (1.530) Remain 05:57:26 loss: 0.1475 Lr: 0.00116
[2023-12-25 18:51:20,847 INFO misc.py line 119 253097] Train: [73/100][262/510] Data 0.004 (0.125) Batch 1.067 (1.528) Remain 05:57:00 loss: 0.1156 Lr: 0.00116
[2023-12-25 18:51:22,098 INFO misc.py line 119 253097] Train: [73/100][263/510] Data 0.005 (0.124) Batch 1.251 (1.527) Remain 05:56:43 loss: 0.1141 Lr: 0.00116
[2023-12-25 18:51:23,186 INFO misc.py line 119 253097] Train: [73/100][264/510] Data 0.004 (0.124) Batch 1.087 (1.525) Remain 05:56:18 loss: 0.1465 Lr: 0.00116
[2023-12-25 18:51:24,085 INFO misc.py line 119 253097] Train: [73/100][265/510] Data 0.005 (0.123) Batch 0.900 (1.523) Remain 05:55:43 loss: 0.0888 Lr: 0.00116
[2023-12-25 18:51:25,009 INFO misc.py line 119 253097] Train: [73/100][266/510] Data 0.004 (0.123) Batch 0.923 (1.521) Remain 05:55:10 loss: 0.1050 Lr: 0.00116
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[2023-12-25 18:51:28,535 INFO misc.py line 119 253097] Train: [73/100][269/510] Data 0.003 (0.121) Batch 1.054 (1.517) Remain 05:54:10 loss: 0.0977 Lr: 0.00116
[2023-12-25 18:51:29,835 INFO misc.py line 119 253097] Train: [73/100][270/510] Data 0.010 (0.121) Batch 1.305 (1.516) Remain 05:53:58 loss: 0.1069 Lr: 0.00116
[2023-12-25 18:51:30,946 INFO misc.py line 119 253097] Train: [73/100][271/510] Data 0.004 (0.121) Batch 1.110 (1.514) Remain 05:53:35 loss: 0.1567 Lr: 0.00116
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[2023-12-25 18:51:33,512 INFO misc.py line 119 253097] Train: [73/100][273/510] Data 0.003 (0.120) Batch 1.252 (1.513) Remain 05:53:08 loss: 0.0570 Lr: 0.00116
[2023-12-25 18:51:34,523 INFO misc.py line 119 253097] Train: [73/100][274/510] Data 0.010 (0.119) Batch 1.013 (1.511) Remain 05:52:40 loss: 0.0957 Lr: 0.00116
[2023-12-25 18:51:35,715 INFO misc.py line 119 253097] Train: [73/100][275/510] Data 0.006 (0.119) Batch 1.192 (1.510) Remain 05:52:22 loss: 0.1688 Lr: 0.00116
[2023-12-25 18:51:36,735 INFO misc.py line 119 253097] Train: [73/100][276/510] Data 0.007 (0.119) Batch 1.018 (1.508) Remain 05:51:56 loss: 0.1222 Lr: 0.00116
[2023-12-25 18:51:41,733 INFO misc.py line 119 253097] Train: [73/100][277/510] Data 0.009 (0.118) Batch 5.003 (1.521) Remain 05:54:53 loss: 0.1154 Lr: 0.00116
[2023-12-25 18:51:47,627 INFO misc.py line 119 253097] Train: [73/100][278/510] Data 4.779 (0.135) Batch 5.893 (1.537) Remain 05:58:34 loss: 0.0581 Lr: 0.00116
[2023-12-25 18:51:48,676 INFO misc.py line 119 253097] Train: [73/100][279/510] Data 0.005 (0.135) Batch 1.049 (1.535) Remain 05:58:08 loss: 0.1182 Lr: 0.00116
[2023-12-25 18:51:49,700 INFO misc.py line 119 253097] Train: [73/100][280/510] Data 0.004 (0.134) Batch 1.025 (1.533) Remain 05:57:40 loss: 0.0689 Lr: 0.00116
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[2023-12-25 18:51:52,988 INFO misc.py line 119 253097] Train: [73/100][283/510] Data 0.005 (0.133) Batch 1.076 (1.528) Remain 05:56:30 loss: 0.1326 Lr: 0.00116
[2023-12-25 18:51:55,247 INFO misc.py line 119 253097] Train: [73/100][284/510] Data 1.067 (0.136) Batch 2.259 (1.531) Remain 05:57:05 loss: 0.1858 Lr: 0.00116
[2023-12-25 18:51:56,401 INFO misc.py line 119 253097] Train: [73/100][285/510] Data 0.003 (0.136) Batch 1.154 (1.529) Remain 05:56:45 loss: 0.2199 Lr: 0.00116
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[2023-12-25 18:51:59,735 INFO misc.py line 119 253097] Train: [73/100][288/510] Data 0.003 (0.134) Batch 1.013 (1.525) Remain 05:55:38 loss: 0.0918 Lr: 0.00115
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[2023-12-25 18:52:02,081 INFO misc.py line 119 253097] Train: [73/100][290/510] Data 0.008 (0.133) Batch 1.208 (1.523) Remain 05:55:01 loss: 0.2862 Lr: 0.00115
[2023-12-25 18:52:03,192 INFO misc.py line 119 253097] Train: [73/100][291/510] Data 0.004 (0.133) Batch 1.106 (1.521) Remain 05:54:39 loss: 0.0843 Lr: 0.00115
[2023-12-25 18:52:04,308 INFO misc.py line 119 253097] Train: [73/100][292/510] Data 0.009 (0.132) Batch 1.118 (1.520) Remain 05:54:18 loss: 0.0877 Lr: 0.00115
[2023-12-25 18:52:05,458 INFO misc.py line 119 253097] Train: [73/100][293/510] Data 0.008 (0.132) Batch 1.154 (1.519) Remain 05:53:59 loss: 0.1277 Lr: 0.00115
[2023-12-25 18:52:09,987 INFO misc.py line 119 253097] Train: [73/100][294/510] Data 3.388 (0.143) Batch 4.529 (1.529) Remain 05:56:22 loss: 0.1223 Lr: 0.00115
[2023-12-25 18:52:11,174 INFO misc.py line 119 253097] Train: [73/100][295/510] Data 0.005 (0.143) Batch 1.187 (1.528) Remain 05:56:04 loss: 0.1827 Lr: 0.00115
[2023-12-25 18:52:12,340 INFO misc.py line 119 253097] Train: [73/100][296/510] Data 0.004 (0.142) Batch 1.167 (1.526) Remain 05:55:46 loss: 0.1288 Lr: 0.00115
[2023-12-25 18:52:13,362 INFO misc.py line 119 253097] Train: [73/100][297/510] Data 0.004 (0.142) Batch 1.021 (1.525) Remain 05:55:20 loss: 0.1340 Lr: 0.00115
[2023-12-25 18:52:14,432 INFO misc.py line 119 253097] Train: [73/100][298/510] Data 0.005 (0.141) Batch 1.070 (1.523) Remain 05:54:57 loss: 0.1598 Lr: 0.00115
[2023-12-25 18:52:15,456 INFO misc.py line 119 253097] Train: [73/100][299/510] Data 0.005 (0.141) Batch 1.025 (1.522) Remain 05:54:32 loss: 0.1224 Lr: 0.00115
[2023-12-25 18:52:20,391 INFO misc.py line 119 253097] Train: [73/100][300/510] Data 0.004 (0.140) Batch 4.935 (1.533) Remain 05:57:11 loss: 0.1344 Lr: 0.00115
[2023-12-25 18:52:21,476 INFO misc.py line 119 253097] Train: [73/100][301/510] Data 0.004 (0.140) Batch 1.078 (1.531) Remain 05:56:48 loss: 0.1925 Lr: 0.00115
[2023-12-25 18:52:22,692 INFO misc.py line 119 253097] Train: [73/100][302/510] Data 0.011 (0.140) Batch 1.218 (1.530) Remain 05:56:32 loss: 0.0913 Lr: 0.00115
[2023-12-25 18:52:23,830 INFO misc.py line 119 253097] Train: [73/100][303/510] Data 0.009 (0.139) Batch 1.139 (1.529) Remain 05:56:12 loss: 0.0702 Lr: 0.00115
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[2023-12-25 18:52:25,951 INFO misc.py line 119 253097] Train: [73/100][305/510] Data 0.005 (0.138) Batch 1.177 (1.526) Remain 05:55:26 loss: 0.1871 Lr: 0.00115
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[2023-12-25 18:52:30,779 INFO misc.py line 119 253097] Train: [73/100][308/510] Data 0.003 (0.137) Batch 1.203 (1.527) Remain 05:55:33 loss: 0.1479 Lr: 0.00115
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[2023-12-25 18:52:54,974 INFO misc.py line 119 253097] Train: [73/100][322/510] Data 0.006 (0.131) Batch 1.064 (1.536) Remain 05:57:15 loss: 0.1506 Lr: 0.00115
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[2023-12-25 18:53:00,542 INFO misc.py line 119 253097] Train: [73/100][324/510] Data 3.135 (0.140) Batch 4.325 (1.543) Remain 05:59:00 loss: 0.1227 Lr: 0.00115
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[2023-12-25 18:55:46,311 INFO misc.py line 119 253097] Train: [73/100][433/510] Data 0.003 (0.107) Batch 1.006 (1.538) Remain 05:54:53 loss: 0.1312 Lr: 0.00113
[2023-12-25 18:55:47,516 INFO misc.py line 119 253097] Train: [73/100][434/510] Data 0.004 (0.106) Batch 1.207 (1.537) Remain 05:54:40 loss: 0.1421 Lr: 0.00113
[2023-12-25 18:55:48,574 INFO misc.py line 119 253097] Train: [73/100][435/510] Data 0.003 (0.106) Batch 1.058 (1.536) Remain 05:54:23 loss: 0.0947 Lr: 0.00113
[2023-12-25 18:55:49,586 INFO misc.py line 119 253097] Train: [73/100][436/510] Data 0.004 (0.106) Batch 1.010 (1.535) Remain 05:54:05 loss: 0.1109 Lr: 0.00113
[2023-12-25 18:55:50,893 INFO misc.py line 119 253097] Train: [73/100][437/510] Data 0.006 (0.106) Batch 1.293 (1.534) Remain 05:53:56 loss: 0.1097 Lr: 0.00113
[2023-12-25 18:55:52,131 INFO misc.py line 119 253097] Train: [73/100][438/510] Data 0.020 (0.105) Batch 1.252 (1.533) Remain 05:53:45 loss: 0.0861 Lr: 0.00113
[2023-12-25 18:55:53,501 INFO misc.py line 119 253097] Train: [73/100][439/510] Data 0.006 (0.105) Batch 1.351 (1.533) Remain 05:53:38 loss: 0.1073 Lr: 0.00113
[2023-12-25 18:55:54,557 INFO misc.py line 119 253097] Train: [73/100][440/510] Data 0.025 (0.105) Batch 1.078 (1.532) Remain 05:53:22 loss: 0.2316 Lr: 0.00113
[2023-12-25 18:55:55,605 INFO misc.py line 119 253097] Train: [73/100][441/510] Data 0.004 (0.105) Batch 1.048 (1.531) Remain 05:53:05 loss: 0.1120 Lr: 0.00113
[2023-12-25 18:56:02,643 INFO misc.py line 119 253097] Train: [73/100][442/510] Data 0.003 (0.105) Batch 7.037 (1.543) Remain 05:55:57 loss: 0.0993 Lr: 0.00113
[2023-12-25 18:56:03,837 INFO misc.py line 119 253097] Train: [73/100][443/510] Data 0.005 (0.104) Batch 1.193 (1.543) Remain 05:55:45 loss: 0.1503 Lr: 0.00113
[2023-12-25 18:56:04,936 INFO misc.py line 119 253097] Train: [73/100][444/510] Data 0.006 (0.104) Batch 1.100 (1.542) Remain 05:55:29 loss: 0.0996 Lr: 0.00113
[2023-12-25 18:56:06,122 INFO misc.py line 119 253097] Train: [73/100][445/510] Data 0.004 (0.104) Batch 1.187 (1.541) Remain 05:55:17 loss: 0.0725 Lr: 0.00113
[2023-12-25 18:56:07,289 INFO misc.py line 119 253097] Train: [73/100][446/510] Data 0.004 (0.104) Batch 1.164 (1.540) Remain 05:55:03 loss: 0.2099 Lr: 0.00113
[2023-12-25 18:56:08,480 INFO misc.py line 119 253097] Train: [73/100][447/510] Data 0.007 (0.103) Batch 1.191 (1.539) Remain 05:54:51 loss: 0.1369 Lr: 0.00113
[2023-12-25 18:56:09,592 INFO misc.py line 119 253097] Train: [73/100][448/510] Data 0.008 (0.103) Batch 1.110 (1.538) Remain 05:54:36 loss: 0.1699 Lr: 0.00113
[2023-12-25 18:56:10,814 INFO misc.py line 119 253097] Train: [73/100][449/510] Data 0.008 (0.103) Batch 1.220 (1.538) Remain 05:54:25 loss: 0.1398 Lr: 0.00113
[2023-12-25 18:56:11,822 INFO misc.py line 119 253097] Train: [73/100][450/510] Data 0.009 (0.103) Batch 1.011 (1.536) Remain 05:54:07 loss: 0.1018 Lr: 0.00113
[2023-12-25 18:56:12,967 INFO misc.py line 119 253097] Train: [73/100][451/510] Data 0.007 (0.103) Batch 1.148 (1.535) Remain 05:53:53 loss: 0.1181 Lr: 0.00113
[2023-12-25 18:56:13,985 INFO misc.py line 119 253097] Train: [73/100][452/510] Data 0.004 (0.102) Batch 1.014 (1.534) Remain 05:53:36 loss: 0.2183 Lr: 0.00113
[2023-12-25 18:56:15,245 INFO misc.py line 119 253097] Train: [73/100][453/510] Data 0.008 (0.102) Batch 1.260 (1.534) Remain 05:53:26 loss: 0.1827 Lr: 0.00113
[2023-12-25 18:56:16,412 INFO misc.py line 119 253097] Train: [73/100][454/510] Data 0.007 (0.102) Batch 1.170 (1.533) Remain 05:53:13 loss: 0.2206 Lr: 0.00113
[2023-12-25 18:56:17,526 INFO misc.py line 119 253097] Train: [73/100][455/510] Data 0.005 (0.102) Batch 1.114 (1.532) Remain 05:52:59 loss: 0.2073 Lr: 0.00113
[2023-12-25 18:56:18,765 INFO misc.py line 119 253097] Train: [73/100][456/510] Data 0.004 (0.101) Batch 1.234 (1.531) Remain 05:52:48 loss: 0.1655 Lr: 0.00113
[2023-12-25 18:56:19,839 INFO misc.py line 119 253097] Train: [73/100][457/510] Data 0.009 (0.101) Batch 1.076 (1.530) Remain 05:52:33 loss: 0.1909 Lr: 0.00113
[2023-12-25 18:56:21,570 INFO misc.py line 119 253097] Train: [73/100][458/510] Data 0.008 (0.101) Batch 1.735 (1.531) Remain 05:52:37 loss: 0.1228 Lr: 0.00113
[2023-12-25 18:56:22,850 INFO misc.py line 119 253097] Train: [73/100][459/510] Data 0.004 (0.101) Batch 1.280 (1.530) Remain 05:52:28 loss: 0.1278 Lr: 0.00113
[2023-12-25 18:56:24,083 INFO misc.py line 119 253097] Train: [73/100][460/510] Data 0.005 (0.101) Batch 1.233 (1.530) Remain 05:52:18 loss: 0.1196 Lr: 0.00113
[2023-12-25 18:56:25,079 INFO misc.py line 119 253097] Train: [73/100][461/510] Data 0.005 (0.100) Batch 0.997 (1.528) Remain 05:52:00 loss: 0.1419 Lr: 0.00113
[2023-12-25 18:56:26,119 INFO misc.py line 119 253097] Train: [73/100][462/510] Data 0.003 (0.100) Batch 1.039 (1.527) Remain 05:51:44 loss: 0.1878 Lr: 0.00113
[2023-12-25 18:56:27,279 INFO misc.py line 119 253097] Train: [73/100][463/510] Data 0.005 (0.100) Batch 1.161 (1.527) Remain 05:51:31 loss: 0.0638 Lr: 0.00113
[2023-12-25 18:56:28,259 INFO misc.py line 119 253097] Train: [73/100][464/510] Data 0.004 (0.100) Batch 0.979 (1.525) Remain 05:51:13 loss: 0.2013 Lr: 0.00113
[2023-12-25 18:56:30,191 INFO misc.py line 119 253097] Train: [73/100][465/510] Data 0.006 (0.100) Batch 1.928 (1.526) Remain 05:51:24 loss: 0.1192 Lr: 0.00113
[2023-12-25 18:56:31,332 INFO misc.py line 119 253097] Train: [73/100][466/510] Data 0.009 (0.099) Batch 1.146 (1.525) Remain 05:51:11 loss: 0.0955 Lr: 0.00113
[2023-12-25 18:56:32,389 INFO misc.py line 119 253097] Train: [73/100][467/510] Data 0.005 (0.099) Batch 1.057 (1.524) Remain 05:50:56 loss: 0.1052 Lr: 0.00113
[2023-12-25 18:56:33,653 INFO misc.py line 119 253097] Train: [73/100][468/510] Data 0.004 (0.099) Batch 1.263 (1.524) Remain 05:50:46 loss: 0.1647 Lr: 0.00113
[2023-12-25 18:56:34,865 INFO misc.py line 119 253097] Train: [73/100][469/510] Data 0.007 (0.099) Batch 1.215 (1.523) Remain 05:50:36 loss: 0.1220 Lr: 0.00113
[2023-12-25 18:56:36,016 INFO misc.py line 119 253097] Train: [73/100][470/510] Data 0.003 (0.099) Batch 1.151 (1.522) Remain 05:50:23 loss: 0.1264 Lr: 0.00113
[2023-12-25 18:56:38,000 INFO misc.py line 119 253097] Train: [73/100][471/510] Data 0.003 (0.098) Batch 1.984 (1.523) Remain 05:50:35 loss: 0.1032 Lr: 0.00113
[2023-12-25 18:56:39,091 INFO misc.py line 119 253097] Train: [73/100][472/510] Data 0.004 (0.098) Batch 1.088 (1.522) Remain 05:50:21 loss: 0.1289 Lr: 0.00113
[2023-12-25 18:56:40,260 INFO misc.py line 119 253097] Train: [73/100][473/510] Data 0.007 (0.098) Batch 1.172 (1.522) Remain 05:50:09 loss: 0.1720 Lr: 0.00113
[2023-12-25 18:56:42,043 INFO misc.py line 119 253097] Train: [73/100][474/510] Data 0.004 (0.098) Batch 1.777 (1.522) Remain 05:50:15 loss: 0.1605 Lr: 0.00113
[2023-12-25 18:56:44,624 INFO misc.py line 119 253097] Train: [73/100][475/510] Data 0.011 (0.098) Batch 2.588 (1.524) Remain 05:50:45 loss: 0.0874 Lr: 0.00113
[2023-12-25 18:56:45,772 INFO misc.py line 119 253097] Train: [73/100][476/510] Data 0.004 (0.097) Batch 1.146 (1.524) Remain 05:50:32 loss: 0.1193 Lr: 0.00113
[2023-12-25 18:56:46,854 INFO misc.py line 119 253097] Train: [73/100][477/510] Data 0.006 (0.097) Batch 1.082 (1.523) Remain 05:50:18 loss: 0.1362 Lr: 0.00113
[2023-12-25 18:56:48,135 INFO misc.py line 119 253097] Train: [73/100][478/510] Data 0.005 (0.097) Batch 1.283 (1.522) Remain 05:50:09 loss: 0.2176 Lr: 0.00113
[2023-12-25 18:56:49,450 INFO misc.py line 119 253097] Train: [73/100][479/510] Data 0.003 (0.097) Batch 1.313 (1.522) Remain 05:50:02 loss: 0.1432 Lr: 0.00113
[2023-12-25 18:56:50,554 INFO misc.py line 119 253097] Train: [73/100][480/510] Data 0.007 (0.097) Batch 1.102 (1.521) Remain 05:49:48 loss: 0.1237 Lr: 0.00113
[2023-12-25 18:56:51,698 INFO misc.py line 119 253097] Train: [73/100][481/510] Data 0.009 (0.096) Batch 1.144 (1.520) Remain 05:49:35 loss: 0.0598 Lr: 0.00113
[2023-12-25 18:56:52,982 INFO misc.py line 119 253097] Train: [73/100][482/510] Data 0.007 (0.096) Batch 1.268 (1.520) Remain 05:49:27 loss: 0.1384 Lr: 0.00113
[2023-12-25 18:56:54,065 INFO misc.py line 119 253097] Train: [73/100][483/510] Data 0.023 (0.096) Batch 1.097 (1.519) Remain 05:49:13 loss: 0.1218 Lr: 0.00113
[2023-12-25 18:56:56,784 INFO misc.py line 119 253097] Train: [73/100][484/510] Data 0.008 (0.096) Batch 2.724 (1.521) Remain 05:49:46 loss: 0.1381 Lr: 0.00112
[2023-12-25 18:56:58,095 INFO misc.py line 119 253097] Train: [73/100][485/510] Data 0.003 (0.096) Batch 1.310 (1.521) Remain 05:49:39 loss: 0.1650 Lr: 0.00112
[2023-12-25 18:56:59,248 INFO misc.py line 119 253097] Train: [73/100][486/510] Data 0.004 (0.096) Batch 1.153 (1.520) Remain 05:49:26 loss: 0.0842 Lr: 0.00112
[2023-12-25 18:57:08,878 INFO misc.py line 119 253097] Train: [73/100][487/510] Data 8.480 (0.113) Batch 9.631 (1.537) Remain 05:53:16 loss: 0.0896 Lr: 0.00112
[2023-12-25 18:57:10,028 INFO misc.py line 119 253097] Train: [73/100][488/510] Data 0.003 (0.113) Batch 1.149 (1.536) Remain 05:53:04 loss: 0.1305 Lr: 0.00112
[2023-12-25 18:57:11,245 INFO misc.py line 119 253097] Train: [73/100][489/510] Data 0.005 (0.112) Batch 1.215 (1.535) Remain 05:52:53 loss: 0.1153 Lr: 0.00112
[2023-12-25 18:57:12,271 INFO misc.py line 119 253097] Train: [73/100][490/510] Data 0.006 (0.112) Batch 1.026 (1.534) Remain 05:52:37 loss: 0.1459 Lr: 0.00112
[2023-12-25 18:57:13,266 INFO misc.py line 119 253097] Train: [73/100][491/510] Data 0.005 (0.112) Batch 0.996 (1.533) Remain 05:52:20 loss: 0.0851 Lr: 0.00112
[2023-12-25 18:57:14,616 INFO misc.py line 119 253097] Train: [73/100][492/510] Data 0.004 (0.112) Batch 1.331 (1.533) Remain 05:52:13 loss: 0.1379 Lr: 0.00112
[2023-12-25 18:57:15,936 INFO misc.py line 119 253097] Train: [73/100][493/510] Data 0.024 (0.112) Batch 1.303 (1.532) Remain 05:52:05 loss: 0.1189 Lr: 0.00112
[2023-12-25 18:57:17,108 INFO misc.py line 119 253097] Train: [73/100][494/510] Data 0.040 (0.111) Batch 1.208 (1.532) Remain 05:51:54 loss: 0.1356 Lr: 0.00112
[2023-12-25 18:57:18,345 INFO misc.py line 119 253097] Train: [73/100][495/510] Data 0.004 (0.111) Batch 1.234 (1.531) Remain 05:51:44 loss: 0.0959 Lr: 0.00112
[2023-12-25 18:57:19,570 INFO misc.py line 119 253097] Train: [73/100][496/510] Data 0.008 (0.111) Batch 1.227 (1.530) Remain 05:51:34 loss: 0.1505 Lr: 0.00112
[2023-12-25 18:57:20,781 INFO misc.py line 119 253097] Train: [73/100][497/510] Data 0.006 (0.111) Batch 1.207 (1.530) Remain 05:51:24 loss: 0.1483 Lr: 0.00112
[2023-12-25 18:57:21,952 INFO misc.py line 119 253097] Train: [73/100][498/510] Data 0.010 (0.111) Batch 1.177 (1.529) Remain 05:51:13 loss: 0.3327 Lr: 0.00112
[2023-12-25 18:57:23,254 INFO misc.py line 119 253097] Train: [73/100][499/510] Data 0.004 (0.110) Batch 1.301 (1.529) Remain 05:51:05 loss: 0.0664 Lr: 0.00112
[2023-12-25 18:57:24,493 INFO misc.py line 119 253097] Train: [73/100][500/510] Data 0.005 (0.110) Batch 1.231 (1.528) Remain 05:50:55 loss: 0.1253 Lr: 0.00112
[2023-12-25 18:57:25,618 INFO misc.py line 119 253097] Train: [73/100][501/510] Data 0.013 (0.110) Batch 1.126 (1.527) Remain 05:50:42 loss: 0.1546 Lr: 0.00112
[2023-12-25 18:57:26,755 INFO misc.py line 119 253097] Train: [73/100][502/510] Data 0.011 (0.110) Batch 1.144 (1.526) Remain 05:50:30 loss: 0.1950 Lr: 0.00112
[2023-12-25 18:57:27,856 INFO misc.py line 119 253097] Train: [73/100][503/510] Data 0.004 (0.110) Batch 1.099 (1.526) Remain 05:50:17 loss: 0.1828 Lr: 0.00112
[2023-12-25 18:57:29,052 INFO misc.py line 119 253097] Train: [73/100][504/510] Data 0.007 (0.109) Batch 1.191 (1.525) Remain 05:50:06 loss: 0.1946 Lr: 0.00112
[2023-12-25 18:57:30,011 INFO misc.py line 119 253097] Train: [73/100][505/510] Data 0.011 (0.109) Batch 0.966 (1.524) Remain 05:49:49 loss: 0.0669 Lr: 0.00112
[2023-12-25 18:57:31,295 INFO misc.py line 119 253097] Train: [73/100][506/510] Data 0.004 (0.109) Batch 1.282 (1.523) Remain 05:49:41 loss: 0.1650 Lr: 0.00112
[2023-12-25 18:57:32,355 INFO misc.py line 119 253097] Train: [73/100][507/510] Data 0.007 (0.109) Batch 1.060 (1.522) Remain 05:49:27 loss: 0.1988 Lr: 0.00112
[2023-12-25 18:57:33,371 INFO misc.py line 119 253097] Train: [73/100][508/510] Data 0.006 (0.109) Batch 1.017 (1.521) Remain 05:49:12 loss: 0.0898 Lr: 0.00112
[2023-12-25 18:57:34,326 INFO misc.py line 119 253097] Train: [73/100][509/510] Data 0.006 (0.108) Batch 0.956 (1.520) Remain 05:48:55 loss: 0.1801 Lr: 0.00112
[2023-12-25 18:57:35,453 INFO misc.py line 119 253097] Train: [73/100][510/510] Data 0.004 (0.108) Batch 1.127 (1.519) Remain 05:48:43 loss: 0.1342 Lr: 0.00112
[2023-12-25 18:57:35,454 INFO misc.py line 136 253097] Train result: loss: 0.1336 
[2023-12-25 18:57:35,455 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 18:58:07,435 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4041 
[2023-12-25 18:58:07,790 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3253 
[2023-12-25 18:58:12,719 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3569 
[2023-12-25 18:58:13,244 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.2847 
[2023-12-25 18:58:15,214 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8107 
[2023-12-25 18:58:15,648 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4218 
[2023-12-25 18:58:16,526 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1439 
[2023-12-25 18:58:17,083 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2989 
[2023-12-25 18:58:18,895 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8066 
[2023-12-25 18:58:21,014 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2182 
[2023-12-25 18:58:21,868 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2737 
[2023-12-25 18:58:22,296 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8192 
[2023-12-25 18:58:23,197 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5130 
[2023-12-25 18:58:26,142 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9191 
[2023-12-25 18:58:26,609 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2104 
[2023-12-25 18:58:27,223 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4325 
[2023-12-25 18:58:27,922 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4004 
[2023-12-25 18:58:29,422 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6965/0.7563/0.9046.
[2023-12-25 18:58:29,422 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9208/0.9442
[2023-12-25 18:58:29,422 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9822/0.9917
[2023-12-25 18:58:29,422 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8432/0.9745
[2023-12-25 18:58:29,422 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 18:58:29,422 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3675/0.4205
[2023-12-25 18:58:29,422 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6068/0.6271
[2023-12-25 18:58:29,423 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6984/0.8114
[2023-12-25 18:58:29,423 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.7992/0.9060
[2023-12-25 18:58:29,423 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9251/0.9704
[2023-12-25 18:58:29,423 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6983/0.7300
[2023-12-25 18:58:29,423 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7728/0.8567
[2023-12-25 18:58:29,423 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8218/0.8824
[2023-12-25 18:58:29,423 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6179/0.7169
[2023-12-25 18:58:29,423 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 18:58:29,424 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 18:58:29,424 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 18:58:40,787 INFO misc.py line 119 253097] Train: [74/100][1/510] Data 2.537 (2.537) Batch 8.220 (8.220) Remain 31:26:19 loss: 0.1846 Lr: 0.00112
[2023-12-25 18:58:44,580 INFO misc.py line 119 253097] Train: [74/100][2/510] Data 2.583 (2.583) Batch 3.794 (3.794) Remain 14:30:31 loss: 0.1345 Lr: 0.00112
[2023-12-25 18:58:45,829 INFO misc.py line 119 253097] Train: [74/100][3/510] Data 0.004 (0.004) Batch 1.249 (1.249) Remain 04:46:34 loss: 0.0698 Lr: 0.00112
[2023-12-25 18:58:46,803 INFO misc.py line 119 253097] Train: [74/100][4/510] Data 0.004 (0.004) Batch 0.973 (0.973) Remain 03:43:19 loss: 0.1427 Lr: 0.00112
[2023-12-25 18:58:47,893 INFO misc.py line 119 253097] Train: [74/100][5/510] Data 0.004 (0.004) Batch 1.090 (1.032) Remain 03:56:44 loss: 0.1537 Lr: 0.00112
[2023-12-25 18:58:48,998 INFO misc.py line 119 253097] Train: [74/100][6/510] Data 0.005 (0.004) Batch 1.104 (1.056) Remain 04:02:15 loss: 0.1085 Lr: 0.00112
[2023-12-25 18:58:50,297 INFO misc.py line 119 253097] Train: [74/100][7/510] Data 0.005 (0.005) Batch 1.297 (1.116) Remain 04:16:03 loss: 0.0746 Lr: 0.00112
[2023-12-25 18:58:51,572 INFO misc.py line 119 253097] Train: [74/100][8/510] Data 0.007 (0.005) Batch 1.275 (1.148) Remain 04:23:19 loss: 0.1433 Lr: 0.00112
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[2023-12-25 19:04:52,891 INFO misc.py line 119 253097] Train: [74/100][253/510] Data 0.004 (0.118) Batch 1.076 (1.468) Remain 05:30:46 loss: 0.1928 Lr: 0.00108
[2023-12-25 19:04:54,142 INFO misc.py line 119 253097] Train: [74/100][254/510] Data 0.006 (0.118) Batch 1.249 (1.467) Remain 05:30:32 loss: 0.1015 Lr: 0.00108
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[2023-12-25 19:04:56,572 INFO misc.py line 119 253097] Train: [74/100][256/510] Data 0.009 (0.117) Batch 1.169 (1.465) Remain 05:30:02 loss: 0.2404 Lr: 0.00108
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[2023-12-25 19:05:02,212 INFO misc.py line 119 253097] Train: [74/100][261/510] Data 0.008 (0.115) Batch 1.107 (1.459) Remain 05:28:27 loss: 0.1120 Lr: 0.00108
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[2023-12-25 19:05:08,646 INFO misc.py line 119 253097] Train: [74/100][264/510] Data 0.006 (0.114) Batch 4.237 (1.467) Remain 05:30:09 loss: 0.1312 Lr: 0.00108
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[2023-12-25 19:05:15,496 INFO misc.py line 119 253097] Train: [74/100][270/510] Data 0.008 (0.111) Batch 1.020 (1.459) Remain 05:28:21 loss: 0.1508 Lr: 0.00108
[2023-12-25 19:05:16,598 INFO misc.py line 119 253097] Train: [74/100][271/510] Data 0.010 (0.111) Batch 1.108 (1.458) Remain 05:28:02 loss: 0.1264 Lr: 0.00108
[2023-12-25 19:05:24,915 INFO misc.py line 119 253097] Train: [74/100][272/510] Data 7.003 (0.136) Batch 8.317 (1.484) Remain 05:33:45 loss: 0.0975 Lr: 0.00108
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[2023-12-25 19:05:27,440 INFO misc.py line 119 253097] Train: [74/100][274/510] Data 0.004 (0.136) Batch 1.202 (1.482) Remain 05:33:20 loss: 0.0855 Lr: 0.00108
[2023-12-25 19:05:28,690 INFO misc.py line 119 253097] Train: [74/100][275/510] Data 0.009 (0.135) Batch 1.252 (1.481) Remain 05:33:07 loss: 0.2117 Lr: 0.00108
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[2023-12-25 19:05:31,047 INFO misc.py line 119 253097] Train: [74/100][277/510] Data 0.010 (0.134) Batch 1.113 (1.479) Remain 05:32:34 loss: 0.0859 Lr: 0.00108
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[2023-12-25 19:05:33,320 INFO misc.py line 119 253097] Train: [74/100][279/510] Data 0.009 (0.133) Batch 1.162 (1.476) Remain 05:31:58 loss: 0.1124 Lr: 0.00108
[2023-12-25 19:05:34,404 INFO misc.py line 119 253097] Train: [74/100][280/510] Data 0.008 (0.133) Batch 1.083 (1.475) Remain 05:31:37 loss: 0.1510 Lr: 0.00108
[2023-12-25 19:05:35,635 INFO misc.py line 119 253097] Train: [74/100][281/510] Data 0.008 (0.132) Batch 1.234 (1.474) Remain 05:31:24 loss: 0.1598 Lr: 0.00108
[2023-12-25 19:05:36,876 INFO misc.py line 119 253097] Train: [74/100][282/510] Data 0.005 (0.132) Batch 1.242 (1.473) Remain 05:31:11 loss: 0.1565 Lr: 0.00108
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[2023-12-25 19:05:40,973 INFO misc.py line 119 253097] Train: [74/100][286/510] Data 0.003 (0.130) Batch 1.014 (1.467) Remain 05:29:40 loss: 0.1624 Lr: 0.00108
[2023-12-25 19:05:42,041 INFO misc.py line 119 253097] Train: [74/100][287/510] Data 0.004 (0.130) Batch 1.068 (1.466) Remain 05:29:19 loss: 0.1261 Lr: 0.00108
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[2023-12-25 19:09:20,044 INFO misc.py line 119 253097] Train: [74/100][427/510] Data 0.005 (0.152) Batch 1.111 (1.496) Remain 05:32:38 loss: 0.1239 Lr: 0.00106
[2023-12-25 19:09:21,229 INFO misc.py line 119 253097] Train: [74/100][428/510] Data 0.009 (0.151) Batch 1.186 (1.495) Remain 05:32:26 loss: 0.1644 Lr: 0.00106
[2023-12-25 19:09:22,444 INFO misc.py line 119 253097] Train: [74/100][429/510] Data 0.007 (0.151) Batch 1.218 (1.494) Remain 05:32:16 loss: 0.1367 Lr: 0.00106
[2023-12-25 19:09:23,807 INFO misc.py line 119 253097] Train: [74/100][430/510] Data 0.004 (0.151) Batch 1.363 (1.494) Remain 05:32:11 loss: 0.1544 Lr: 0.00106
[2023-12-25 19:09:25,074 INFO misc.py line 119 253097] Train: [74/100][431/510] Data 0.004 (0.150) Batch 1.264 (1.494) Remain 05:32:02 loss: 0.1210 Lr: 0.00106
[2023-12-25 19:09:26,207 INFO misc.py line 119 253097] Train: [74/100][432/510] Data 0.007 (0.150) Batch 1.136 (1.493) Remain 05:31:49 loss: 0.1067 Lr: 0.00106
[2023-12-25 19:09:27,452 INFO misc.py line 119 253097] Train: [74/100][433/510] Data 0.004 (0.150) Batch 1.242 (1.492) Remain 05:31:40 loss: 0.1073 Lr: 0.00106
[2023-12-25 19:09:28,642 INFO misc.py line 119 253097] Train: [74/100][434/510] Data 0.007 (0.149) Batch 1.191 (1.491) Remain 05:31:29 loss: 0.1694 Lr: 0.00106
[2023-12-25 19:09:31,970 INFO misc.py line 119 253097] Train: [74/100][435/510] Data 0.006 (0.149) Batch 3.331 (1.496) Remain 05:32:25 loss: 0.1117 Lr: 0.00106
[2023-12-25 19:09:33,071 INFO misc.py line 119 253097] Train: [74/100][436/510] Data 0.004 (0.149) Batch 1.101 (1.495) Remain 05:32:11 loss: 0.1136 Lr: 0.00106
[2023-12-25 19:09:34,046 INFO misc.py line 119 253097] Train: [74/100][437/510] Data 0.004 (0.148) Batch 0.974 (1.494) Remain 05:31:53 loss: 0.1310 Lr: 0.00106
[2023-12-25 19:09:35,239 INFO misc.py line 119 253097] Train: [74/100][438/510] Data 0.005 (0.148) Batch 1.191 (1.493) Remain 05:31:43 loss: 0.0907 Lr: 0.00106
[2023-12-25 19:09:36,380 INFO misc.py line 119 253097] Train: [74/100][439/510] Data 0.008 (0.148) Batch 1.144 (1.492) Remain 05:31:31 loss: 0.1035 Lr: 0.00106
[2023-12-25 19:09:37,471 INFO misc.py line 119 253097] Train: [74/100][440/510] Data 0.004 (0.147) Batch 1.091 (1.491) Remain 05:31:17 loss: 0.1010 Lr: 0.00105
[2023-12-25 19:09:43,668 INFO misc.py line 119 253097] Train: [74/100][441/510] Data 0.004 (0.147) Batch 6.197 (1.502) Remain 05:33:38 loss: 0.1013 Lr: 0.00105
[2023-12-25 19:09:44,624 INFO misc.py line 119 253097] Train: [74/100][442/510] Data 0.004 (0.147) Batch 0.956 (1.501) Remain 05:33:20 loss: 0.0743 Lr: 0.00105
[2023-12-25 19:09:45,815 INFO misc.py line 119 253097] Train: [74/100][443/510] Data 0.004 (0.146) Batch 1.192 (1.500) Remain 05:33:10 loss: 0.1271 Lr: 0.00105
[2023-12-25 19:09:46,926 INFO misc.py line 119 253097] Train: [74/100][444/510] Data 0.003 (0.146) Batch 1.110 (1.499) Remain 05:32:56 loss: 0.1561 Lr: 0.00105
[2023-12-25 19:09:48,161 INFO misc.py line 119 253097] Train: [74/100][445/510] Data 0.004 (0.146) Batch 1.234 (1.498) Remain 05:32:47 loss: 0.0677 Lr: 0.00105
[2023-12-25 19:09:49,416 INFO misc.py line 119 253097] Train: [74/100][446/510] Data 0.005 (0.145) Batch 1.256 (1.498) Remain 05:32:38 loss: 0.0675 Lr: 0.00105
[2023-12-25 19:09:50,655 INFO misc.py line 119 253097] Train: [74/100][447/510] Data 0.003 (0.145) Batch 1.229 (1.497) Remain 05:32:28 loss: 0.0886 Lr: 0.00105
[2023-12-25 19:09:51,875 INFO misc.py line 119 253097] Train: [74/100][448/510] Data 0.013 (0.145) Batch 1.228 (1.497) Remain 05:32:19 loss: 0.1294 Lr: 0.00105
[2023-12-25 19:09:57,720 INFO misc.py line 119 253097] Train: [74/100][449/510] Data 4.736 (0.155) Batch 5.845 (1.506) Remain 05:34:27 loss: 0.0791 Lr: 0.00105
[2023-12-25 19:09:58,896 INFO misc.py line 119 253097] Train: [74/100][450/510] Data 0.006 (0.155) Batch 1.177 (1.506) Remain 05:34:16 loss: 0.0834 Lr: 0.00105
[2023-12-25 19:10:00,030 INFO misc.py line 119 253097] Train: [74/100][451/510] Data 0.004 (0.154) Batch 1.133 (1.505) Remain 05:34:03 loss: 0.1109 Lr: 0.00105
[2023-12-25 19:10:01,056 INFO misc.py line 119 253097] Train: [74/100][452/510] Data 0.004 (0.154) Batch 1.026 (1.504) Remain 05:33:48 loss: 0.1516 Lr: 0.00105
[2023-12-25 19:10:02,306 INFO misc.py line 119 253097] Train: [74/100][453/510] Data 0.005 (0.154) Batch 1.251 (1.503) Remain 05:33:39 loss: 0.0716 Lr: 0.00105
[2023-12-25 19:10:09,633 INFO misc.py line 119 253097] Train: [74/100][454/510] Data 0.003 (0.153) Batch 7.326 (1.516) Remain 05:36:29 loss: 0.0832 Lr: 0.00105
[2023-12-25 19:10:10,827 INFO misc.py line 119 253097] Train: [74/100][455/510] Data 0.005 (0.153) Batch 1.195 (1.515) Remain 05:36:18 loss: 0.0699 Lr: 0.00105
[2023-12-25 19:10:11,921 INFO misc.py line 119 253097] Train: [74/100][456/510] Data 0.004 (0.153) Batch 1.095 (1.515) Remain 05:36:04 loss: 0.1804 Lr: 0.00105
[2023-12-25 19:10:12,937 INFO misc.py line 119 253097] Train: [74/100][457/510] Data 0.004 (0.152) Batch 1.016 (1.513) Remain 05:35:48 loss: 0.2104 Lr: 0.00105
[2023-12-25 19:10:14,045 INFO misc.py line 119 253097] Train: [74/100][458/510] Data 0.004 (0.152) Batch 1.106 (1.513) Remain 05:35:35 loss: 0.0921 Lr: 0.00105
[2023-12-25 19:10:15,166 INFO misc.py line 119 253097] Train: [74/100][459/510] Data 0.006 (0.152) Batch 1.123 (1.512) Remain 05:35:22 loss: 0.0995 Lr: 0.00105
[2023-12-25 19:10:16,212 INFO misc.py line 119 253097] Train: [74/100][460/510] Data 0.004 (0.151) Batch 1.045 (1.511) Remain 05:35:07 loss: 0.0886 Lr: 0.00105
[2023-12-25 19:10:19,913 INFO misc.py line 119 253097] Train: [74/100][461/510] Data 2.398 (0.156) Batch 3.699 (1.515) Remain 05:36:09 loss: 0.0866 Lr: 0.00105
[2023-12-25 19:10:21,025 INFO misc.py line 119 253097] Train: [74/100][462/510] Data 0.007 (0.156) Batch 1.114 (1.515) Remain 05:35:56 loss: 0.0770 Lr: 0.00105
[2023-12-25 19:10:22,159 INFO misc.py line 119 253097] Train: [74/100][463/510] Data 0.004 (0.156) Batch 1.134 (1.514) Remain 05:35:43 loss: 0.2194 Lr: 0.00105
[2023-12-25 19:10:23,303 INFO misc.py line 119 253097] Train: [74/100][464/510] Data 0.005 (0.155) Batch 1.145 (1.513) Remain 05:35:31 loss: 0.0997 Lr: 0.00105
[2023-12-25 19:10:24,264 INFO misc.py line 119 253097] Train: [74/100][465/510] Data 0.003 (0.155) Batch 0.960 (1.512) Remain 05:35:13 loss: 0.1240 Lr: 0.00105
[2023-12-25 19:10:27,631 INFO misc.py line 119 253097] Train: [74/100][466/510] Data 0.005 (0.155) Batch 3.367 (1.516) Remain 05:36:05 loss: 0.2154 Lr: 0.00105
[2023-12-25 19:10:28,785 INFO misc.py line 119 253097] Train: [74/100][467/510] Data 0.005 (0.154) Batch 1.154 (1.515) Remain 05:35:53 loss: 0.1355 Lr: 0.00105
[2023-12-25 19:10:29,811 INFO misc.py line 119 253097] Train: [74/100][468/510] Data 0.004 (0.154) Batch 1.026 (1.514) Remain 05:35:38 loss: 0.1186 Lr: 0.00105
[2023-12-25 19:10:30,798 INFO misc.py line 119 253097] Train: [74/100][469/510] Data 0.004 (0.154) Batch 0.986 (1.513) Remain 05:35:21 loss: 0.1430 Lr: 0.00105
[2023-12-25 19:10:31,926 INFO misc.py line 119 253097] Train: [74/100][470/510] Data 0.004 (0.153) Batch 1.128 (1.512) Remain 05:35:09 loss: 0.0975 Lr: 0.00105
[2023-12-25 19:10:33,155 INFO misc.py line 119 253097] Train: [74/100][471/510] Data 0.005 (0.153) Batch 1.230 (1.511) Remain 05:34:59 loss: 0.1610 Lr: 0.00105
[2023-12-25 19:10:34,346 INFO misc.py line 119 253097] Train: [74/100][472/510] Data 0.004 (0.153) Batch 1.191 (1.511) Remain 05:34:49 loss: 0.1570 Lr: 0.00105
[2023-12-25 19:10:35,422 INFO misc.py line 119 253097] Train: [74/100][473/510] Data 0.004 (0.152) Batch 1.076 (1.510) Remain 05:34:35 loss: 0.1405 Lr: 0.00105
[2023-12-25 19:10:36,588 INFO misc.py line 119 253097] Train: [74/100][474/510] Data 0.005 (0.152) Batch 1.165 (1.509) Remain 05:34:24 loss: 0.1060 Lr: 0.00105
[2023-12-25 19:10:37,765 INFO misc.py line 119 253097] Train: [74/100][475/510] Data 0.006 (0.152) Batch 1.177 (1.508) Remain 05:34:13 loss: 0.1645 Lr: 0.00105
[2023-12-25 19:10:38,867 INFO misc.py line 119 253097] Train: [74/100][476/510] Data 0.005 (0.152) Batch 1.103 (1.507) Remain 05:34:00 loss: 0.1237 Lr: 0.00105
[2023-12-25 19:10:41,995 INFO misc.py line 119 253097] Train: [74/100][477/510] Data 1.897 (0.155) Batch 3.129 (1.511) Remain 05:34:44 loss: 0.0866 Lr: 0.00105
[2023-12-25 19:10:43,007 INFO misc.py line 119 253097] Train: [74/100][478/510] Data 0.003 (0.155) Batch 1.011 (1.510) Remain 05:34:28 loss: 0.2329 Lr: 0.00105
[2023-12-25 19:10:44,289 INFO misc.py line 119 253097] Train: [74/100][479/510] Data 0.004 (0.155) Batch 1.283 (1.509) Remain 05:34:21 loss: 0.0974 Lr: 0.00105
[2023-12-25 19:10:45,344 INFO misc.py line 119 253097] Train: [74/100][480/510] Data 0.003 (0.154) Batch 1.048 (1.508) Remain 05:34:06 loss: 0.1868 Lr: 0.00105
[2023-12-25 19:10:46,639 INFO misc.py line 119 253097] Train: [74/100][481/510] Data 0.010 (0.154) Batch 1.301 (1.508) Remain 05:33:59 loss: 0.0713 Lr: 0.00105
[2023-12-25 19:10:51,284 INFO misc.py line 119 253097] Train: [74/100][482/510] Data 0.004 (0.154) Batch 4.639 (1.515) Remain 05:35:24 loss: 0.1271 Lr: 0.00105
[2023-12-25 19:10:52,297 INFO misc.py line 119 253097] Train: [74/100][483/510] Data 0.011 (0.153) Batch 1.018 (1.513) Remain 05:35:09 loss: 0.1090 Lr: 0.00105
[2023-12-25 19:10:53,608 INFO misc.py line 119 253097] Train: [74/100][484/510] Data 0.005 (0.153) Batch 1.311 (1.513) Remain 05:35:02 loss: 0.2044 Lr: 0.00105
[2023-12-25 19:10:54,804 INFO misc.py line 119 253097] Train: [74/100][485/510] Data 0.005 (0.153) Batch 1.197 (1.512) Remain 05:34:52 loss: 0.0750 Lr: 0.00105
[2023-12-25 19:10:56,679 INFO misc.py line 119 253097] Train: [74/100][486/510] Data 0.004 (0.152) Batch 1.875 (1.513) Remain 05:35:00 loss: 0.1005 Lr: 0.00105
[2023-12-25 19:10:57,883 INFO misc.py line 119 253097] Train: [74/100][487/510] Data 0.003 (0.152) Batch 1.203 (1.513) Remain 05:34:50 loss: 0.2315 Lr: 0.00105
[2023-12-25 19:10:59,008 INFO misc.py line 119 253097] Train: [74/100][488/510] Data 0.005 (0.152) Batch 1.122 (1.512) Remain 05:34:38 loss: 0.1173 Lr: 0.00105
[2023-12-25 19:11:00,176 INFO misc.py line 119 253097] Train: [74/100][489/510] Data 0.007 (0.152) Batch 1.169 (1.511) Remain 05:34:27 loss: 0.1534 Lr: 0.00105
[2023-12-25 19:11:01,424 INFO misc.py line 119 253097] Train: [74/100][490/510] Data 0.006 (0.151) Batch 1.250 (1.510) Remain 05:34:18 loss: 0.0686 Lr: 0.00105
[2023-12-25 19:11:02,612 INFO misc.py line 119 253097] Train: [74/100][491/510] Data 0.004 (0.151) Batch 1.184 (1.510) Remain 05:34:08 loss: 0.1080 Lr: 0.00105
[2023-12-25 19:11:03,801 INFO misc.py line 119 253097] Train: [74/100][492/510] Data 0.008 (0.151) Batch 1.190 (1.509) Remain 05:33:58 loss: 0.1771 Lr: 0.00105
[2023-12-25 19:11:04,843 INFO misc.py line 119 253097] Train: [74/100][493/510] Data 0.007 (0.150) Batch 1.042 (1.508) Remain 05:33:44 loss: 0.1892 Lr: 0.00105
[2023-12-25 19:11:06,079 INFO misc.py line 119 253097] Train: [74/100][494/510] Data 0.008 (0.150) Batch 1.239 (1.508) Remain 05:33:35 loss: 0.1206 Lr: 0.00105
[2023-12-25 19:11:07,018 INFO misc.py line 119 253097] Train: [74/100][495/510] Data 0.004 (0.150) Batch 0.938 (1.506) Remain 05:33:18 loss: 0.1443 Lr: 0.00105
[2023-12-25 19:11:08,053 INFO misc.py line 119 253097] Train: [74/100][496/510] Data 0.006 (0.149) Batch 1.036 (1.506) Remain 05:33:04 loss: 0.0988 Lr: 0.00105
[2023-12-25 19:11:09,294 INFO misc.py line 119 253097] Train: [74/100][497/510] Data 0.004 (0.149) Batch 1.241 (1.505) Remain 05:32:55 loss: 0.0997 Lr: 0.00105
[2023-12-25 19:11:10,376 INFO misc.py line 119 253097] Train: [74/100][498/510] Data 0.006 (0.149) Batch 1.078 (1.504) Remain 05:32:42 loss: 0.1458 Lr: 0.00105
[2023-12-25 19:11:11,625 INFO misc.py line 119 253097] Train: [74/100][499/510] Data 0.008 (0.149) Batch 1.246 (1.504) Remain 05:32:34 loss: 0.1906 Lr: 0.00105
[2023-12-25 19:11:12,893 INFO misc.py line 119 253097] Train: [74/100][500/510] Data 0.011 (0.148) Batch 1.271 (1.503) Remain 05:32:26 loss: 0.0967 Lr: 0.00105
[2023-12-25 19:11:13,975 INFO misc.py line 119 253097] Train: [74/100][501/510] Data 0.008 (0.148) Batch 1.083 (1.502) Remain 05:32:13 loss: 0.0974 Lr: 0.00105
[2023-12-25 19:11:15,166 INFO misc.py line 119 253097] Train: [74/100][502/510] Data 0.006 (0.148) Batch 1.190 (1.502) Remain 05:32:04 loss: 0.2376 Lr: 0.00105
[2023-12-25 19:11:24,299 INFO misc.py line 119 253097] Train: [74/100][503/510] Data 7.895 (0.163) Batch 9.136 (1.517) Remain 05:35:25 loss: 0.0882 Lr: 0.00105
[2023-12-25 19:11:25,464 INFO misc.py line 119 253097] Train: [74/100][504/510] Data 0.003 (0.163) Batch 1.165 (1.516) Remain 05:35:14 loss: 0.1736 Lr: 0.00105
[2023-12-25 19:11:26,473 INFO misc.py line 119 253097] Train: [74/100][505/510] Data 0.003 (0.163) Batch 1.008 (1.515) Remain 05:34:59 loss: 0.0907 Lr: 0.00105
[2023-12-25 19:11:27,579 INFO misc.py line 119 253097] Train: [74/100][506/510] Data 0.004 (0.162) Batch 1.105 (1.514) Remain 05:34:47 loss: 0.1823 Lr: 0.00105
[2023-12-25 19:11:28,583 INFO misc.py line 119 253097] Train: [74/100][507/510] Data 0.004 (0.162) Batch 1.005 (1.513) Remain 05:34:32 loss: 0.1063 Lr: 0.00105
[2023-12-25 19:11:29,808 INFO misc.py line 119 253097] Train: [74/100][508/510] Data 0.003 (0.162) Batch 1.224 (1.513) Remain 05:34:23 loss: 0.2190 Lr: 0.00104
[2023-12-25 19:11:30,851 INFO misc.py line 119 253097] Train: [74/100][509/510] Data 0.004 (0.161) Batch 1.043 (1.512) Remain 05:34:09 loss: 0.1475 Lr: 0.00104
[2023-12-25 19:11:31,865 INFO misc.py line 119 253097] Train: [74/100][510/510] Data 0.004 (0.161) Batch 1.013 (1.511) Remain 05:33:54 loss: 0.0637 Lr: 0.00104
[2023-12-25 19:11:31,866 INFO misc.py line 136 253097] Train result: loss: 0.1315 
[2023-12-25 19:11:31,866 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 19:12:01,964 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.3674 
[2023-12-25 19:12:02,326 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2671 
[2023-12-25 19:12:07,313 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.2925 
[2023-12-25 19:12:07,829 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3701 
[2023-12-25 19:12:09,798 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7216 
[2023-12-25 19:12:10,222 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2671 
[2023-12-25 19:12:11,098 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0898 
[2023-12-25 19:12:11,651 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3070 
[2023-12-25 19:12:13,459 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8706 
[2023-12-25 19:12:15,580 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3184 
[2023-12-25 19:12:16,433 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2695 
[2023-12-25 19:12:16,862 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6856 
[2023-12-25 19:12:17,759 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3284 
[2023-12-25 19:12:20,705 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7926 
[2023-12-25 19:12:21,177 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2548 
[2023-12-25 19:12:21,787 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4131 
[2023-12-25 19:12:22,492 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3570 
[2023-12-25 19:12:23,743 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6928/0.7554/0.9060.
[2023-12-25 19:12:23,743 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9207/0.9440
[2023-12-25 19:12:23,743 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9836/0.9910
[2023-12-25 19:12:23,743 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8487/0.9681
[2023-12-25 19:12:23,743 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 19:12:23,743 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3355/0.3778
[2023-12-25 19:12:23,743 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6355/0.6594
[2023-12-25 19:12:23,743 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7129/0.8221
[2023-12-25 19:12:23,743 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8095/0.9058
[2023-12-25 19:12:23,744 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9134/0.9560
[2023-12-25 19:12:23,744 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6328/0.6781
[2023-12-25 19:12:23,744 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7780/0.8587
[2023-12-25 19:12:23,744 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8236/0.9129
[2023-12-25 19:12:23,744 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6116/0.7461
[2023-12-25 19:12:23,744 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 19:12:23,746 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 19:12:23,746 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 19:12:36,509 INFO misc.py line 119 253097] Train: [75/100][1/510] Data 3.541 (3.541) Batch 10.759 (10.759) Remain 39:37:28 loss: 0.1179 Lr: 0.00104
[2023-12-25 19:12:37,676 INFO misc.py line 119 253097] Train: [75/100][2/510] Data 0.006 (0.006) Batch 1.169 (1.169) Remain 04:18:18 loss: 0.0877 Lr: 0.00104
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[2023-12-25 19:20:16,924 INFO misc.py line 119 253097] Train: [75/100][288/510] Data 9.908 (0.112) Batch 10.915 (1.576) Remain 05:40:46 loss: 0.1083 Lr: 0.00100
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[2023-12-25 19:21:24,629 INFO misc.py line 119 253097] Train: [75/100][331/510] Data 8.509 (0.124) Batch 9.504 (1.576) Remain 05:39:35 loss: 0.0934 Lr: 0.00100
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[2023-12-25 19:22:29,795 INFO misc.py line 119 253097] Train: [75/100][384/510] Data 0.009 (0.108) Batch 1.089 (1.528) Remain 05:27:51 loss: 0.1012 Lr: 0.00099
[2023-12-25 19:22:31,009 INFO misc.py line 119 253097] Train: [75/100][385/510] Data 0.005 (0.108) Batch 1.173 (1.527) Remain 05:27:38 loss: 0.2472 Lr: 0.00099
[2023-12-25 19:22:32,194 INFO misc.py line 119 253097] Train: [75/100][386/510] Data 0.045 (0.108) Batch 1.222 (1.526) Remain 05:27:26 loss: 0.2404 Lr: 0.00099
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[2023-12-25 19:22:49,447 INFO misc.py line 119 253097] Train: [75/100][392/510] Data 0.006 (0.106) Batch 11.804 (1.547) Remain 05:31:45 loss: 0.1051 Lr: 0.00099
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[2023-12-25 19:22:52,701 INFO misc.py line 119 253097] Train: [75/100][395/510] Data 0.003 (0.105) Batch 1.105 (1.543) Remain 05:30:54 loss: 0.0984 Lr: 0.00099
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[2023-12-25 19:23:30,472 INFO misc.py line 119 253097] Train: [75/100][421/510] Data 0.007 (0.099) Batch 1.112 (1.538) Remain 05:29:02 loss: 0.2002 Lr: 0.00098
[2023-12-25 19:23:31,562 INFO misc.py line 119 253097] Train: [75/100][422/510] Data 0.004 (0.099) Batch 1.087 (1.537) Remain 05:28:47 loss: 0.1691 Lr: 0.00098
[2023-12-25 19:23:32,640 INFO misc.py line 119 253097] Train: [75/100][423/510] Data 0.006 (0.099) Batch 1.082 (1.536) Remain 05:28:31 loss: 0.1301 Lr: 0.00098
[2023-12-25 19:23:33,804 INFO misc.py line 119 253097] Train: [75/100][424/510] Data 0.004 (0.099) Batch 1.161 (1.535) Remain 05:28:18 loss: 0.0904 Lr: 0.00098
[2023-12-25 19:23:34,974 INFO misc.py line 119 253097] Train: [75/100][425/510] Data 0.007 (0.098) Batch 1.170 (1.534) Remain 05:28:06 loss: 0.0781 Lr: 0.00098
[2023-12-25 19:23:36,155 INFO misc.py line 119 253097] Train: [75/100][426/510] Data 0.007 (0.098) Batch 1.180 (1.533) Remain 05:27:53 loss: 0.1245 Lr: 0.00098
[2023-12-25 19:23:37,250 INFO misc.py line 119 253097] Train: [75/100][427/510] Data 0.008 (0.098) Batch 1.096 (1.532) Remain 05:27:39 loss: 0.2048 Lr: 0.00098
[2023-12-25 19:23:38,407 INFO misc.py line 119 253097] Train: [75/100][428/510] Data 0.006 (0.098) Batch 1.159 (1.531) Remain 05:27:26 loss: 0.1073 Lr: 0.00098
[2023-12-25 19:23:49,047 INFO misc.py line 119 253097] Train: [75/100][429/510] Data 0.005 (0.097) Batch 10.639 (1.552) Remain 05:31:59 loss: 0.1368 Lr: 0.00098
[2023-12-25 19:23:50,198 INFO misc.py line 119 253097] Train: [75/100][430/510] Data 0.006 (0.097) Batch 1.153 (1.551) Remain 05:31:45 loss: 0.0678 Lr: 0.00098
[2023-12-25 19:23:51,190 INFO misc.py line 119 253097] Train: [75/100][431/510] Data 0.004 (0.097) Batch 0.992 (1.550) Remain 05:31:27 loss: 0.1086 Lr: 0.00098
[2023-12-25 19:23:52,411 INFO misc.py line 119 253097] Train: [75/100][432/510] Data 0.004 (0.097) Batch 1.219 (1.549) Remain 05:31:15 loss: 0.0770 Lr: 0.00098
[2023-12-25 19:23:53,581 INFO misc.py line 119 253097] Train: [75/100][433/510] Data 0.007 (0.097) Batch 1.172 (1.549) Remain 05:31:02 loss: 0.1274 Lr: 0.00098
[2023-12-25 19:23:54,948 INFO misc.py line 119 253097] Train: [75/100][434/510] Data 0.005 (0.096) Batch 1.367 (1.548) Remain 05:30:55 loss: 0.0950 Lr: 0.00098
[2023-12-25 19:23:56,077 INFO misc.py line 119 253097] Train: [75/100][435/510] Data 0.004 (0.096) Batch 1.123 (1.547) Remain 05:30:41 loss: 0.0933 Lr: 0.00098
[2023-12-25 19:23:57,197 INFO misc.py line 119 253097] Train: [75/100][436/510] Data 0.010 (0.096) Batch 1.124 (1.546) Remain 05:30:27 loss: 0.1500 Lr: 0.00098
[2023-12-25 19:23:58,318 INFO misc.py line 119 253097] Train: [75/100][437/510] Data 0.006 (0.096) Batch 1.114 (1.545) Remain 05:30:13 loss: 0.0689 Lr: 0.00098
[2023-12-25 19:23:59,625 INFO misc.py line 119 253097] Train: [75/100][438/510] Data 0.012 (0.096) Batch 1.313 (1.545) Remain 05:30:05 loss: 0.0962 Lr: 0.00098
[2023-12-25 19:24:00,903 INFO misc.py line 119 253097] Train: [75/100][439/510] Data 0.007 (0.095) Batch 1.233 (1.544) Remain 05:29:54 loss: 0.1128 Lr: 0.00098
[2023-12-25 19:24:01,859 INFO misc.py line 119 253097] Train: [75/100][440/510] Data 0.051 (0.095) Batch 1.002 (1.543) Remain 05:29:36 loss: 0.1147 Lr: 0.00098
[2023-12-25 19:24:03,053 INFO misc.py line 119 253097] Train: [75/100][441/510] Data 0.005 (0.095) Batch 1.191 (1.542) Remain 05:29:25 loss: 0.0829 Lr: 0.00098
[2023-12-25 19:24:04,245 INFO misc.py line 119 253097] Train: [75/100][442/510] Data 0.008 (0.095) Batch 1.196 (1.541) Remain 05:29:13 loss: 0.1126 Lr: 0.00098
[2023-12-25 19:24:08,324 INFO misc.py line 119 253097] Train: [75/100][443/510] Data 2.898 (0.101) Batch 4.079 (1.547) Remain 05:30:25 loss: 0.1676 Lr: 0.00098
[2023-12-25 19:24:09,534 INFO misc.py line 119 253097] Train: [75/100][444/510] Data 0.004 (0.101) Batch 1.209 (1.546) Remain 05:30:14 loss: 0.1113 Lr: 0.00098
[2023-12-25 19:24:10,704 INFO misc.py line 119 253097] Train: [75/100][445/510] Data 0.004 (0.101) Batch 1.172 (1.545) Remain 05:30:02 loss: 0.0772 Lr: 0.00098
[2023-12-25 19:24:17,287 INFO misc.py line 119 253097] Train: [75/100][446/510] Data 0.003 (0.101) Batch 6.581 (1.557) Remain 05:32:26 loss: 0.1804 Lr: 0.00098
[2023-12-25 19:24:18,536 INFO misc.py line 119 253097] Train: [75/100][447/510] Data 0.005 (0.100) Batch 1.246 (1.556) Remain 05:32:15 loss: 0.1132 Lr: 0.00098
[2023-12-25 19:24:19,776 INFO misc.py line 119 253097] Train: [75/100][448/510] Data 0.008 (0.100) Batch 1.239 (1.555) Remain 05:32:04 loss: 0.1020 Lr: 0.00098
[2023-12-25 19:24:20,915 INFO misc.py line 119 253097] Train: [75/100][449/510] Data 0.008 (0.100) Batch 1.140 (1.554) Remain 05:31:51 loss: 0.0700 Lr: 0.00098
[2023-12-25 19:24:22,143 INFO misc.py line 119 253097] Train: [75/100][450/510] Data 0.009 (0.100) Batch 1.227 (1.554) Remain 05:31:40 loss: 0.1518 Lr: 0.00098
[2023-12-25 19:24:23,369 INFO misc.py line 119 253097] Train: [75/100][451/510] Data 0.008 (0.100) Batch 1.221 (1.553) Remain 05:31:29 loss: 0.1944 Lr: 0.00098
[2023-12-25 19:24:24,537 INFO misc.py line 119 253097] Train: [75/100][452/510] Data 0.014 (0.099) Batch 1.177 (1.552) Remain 05:31:17 loss: 0.1827 Lr: 0.00098
[2023-12-25 19:24:25,597 INFO misc.py line 119 253097] Train: [75/100][453/510] Data 0.007 (0.099) Batch 1.055 (1.551) Remain 05:31:01 loss: 0.1514 Lr: 0.00098
[2023-12-25 19:24:26,788 INFO misc.py line 119 253097] Train: [75/100][454/510] Data 0.009 (0.099) Batch 1.196 (1.550) Remain 05:30:49 loss: 0.2171 Lr: 0.00098
[2023-12-25 19:24:27,738 INFO misc.py line 119 253097] Train: [75/100][455/510] Data 0.004 (0.099) Batch 0.950 (1.549) Remain 05:30:31 loss: 0.1023 Lr: 0.00098
[2023-12-25 19:24:29,007 INFO misc.py line 119 253097] Train: [75/100][456/510] Data 0.004 (0.098) Batch 1.265 (1.548) Remain 05:30:21 loss: 0.1200 Lr: 0.00098
[2023-12-25 19:24:30,067 INFO misc.py line 119 253097] Train: [75/100][457/510] Data 0.009 (0.098) Batch 1.061 (1.547) Remain 05:30:06 loss: 0.0750 Lr: 0.00098
[2023-12-25 19:24:30,955 INFO misc.py line 119 253097] Train: [75/100][458/510] Data 0.007 (0.098) Batch 0.891 (1.546) Remain 05:29:46 loss: 0.1052 Lr: 0.00098
[2023-12-25 19:24:32,136 INFO misc.py line 119 253097] Train: [75/100][459/510] Data 0.004 (0.098) Batch 1.181 (1.545) Remain 05:29:34 loss: 0.0948 Lr: 0.00098
[2023-12-25 19:24:33,389 INFO misc.py line 119 253097] Train: [75/100][460/510] Data 0.004 (0.098) Batch 1.248 (1.544) Remain 05:29:24 loss: 0.1239 Lr: 0.00098
[2023-12-25 19:24:34,475 INFO misc.py line 119 253097] Train: [75/100][461/510] Data 0.009 (0.097) Batch 1.091 (1.543) Remain 05:29:10 loss: 0.0939 Lr: 0.00098
[2023-12-25 19:24:35,494 INFO misc.py line 119 253097] Train: [75/100][462/510] Data 0.005 (0.097) Batch 1.017 (1.542) Remain 05:28:54 loss: 0.1151 Lr: 0.00098
[2023-12-25 19:24:36,861 INFO misc.py line 119 253097] Train: [75/100][463/510] Data 0.005 (0.097) Batch 1.368 (1.542) Remain 05:28:48 loss: 0.1306 Lr: 0.00098
[2023-12-25 19:24:38,031 INFO misc.py line 119 253097] Train: [75/100][464/510] Data 0.005 (0.097) Batch 1.168 (1.541) Remain 05:28:36 loss: 0.1031 Lr: 0.00098
[2023-12-25 19:24:49,922 INFO misc.py line 119 253097] Train: [75/100][465/510] Data 10.695 (0.120) Batch 11.894 (1.563) Remain 05:33:21 loss: 0.1173 Lr: 0.00098
[2023-12-25 19:24:51,072 INFO misc.py line 119 253097] Train: [75/100][466/510] Data 0.003 (0.120) Batch 1.148 (1.562) Remain 05:33:08 loss: 0.0935 Lr: 0.00098
[2023-12-25 19:24:52,192 INFO misc.py line 119 253097] Train: [75/100][467/510] Data 0.005 (0.119) Batch 1.121 (1.561) Remain 05:32:54 loss: 0.0814 Lr: 0.00098
[2023-12-25 19:24:53,541 INFO misc.py line 119 253097] Train: [75/100][468/510] Data 0.004 (0.119) Batch 1.350 (1.561) Remain 05:32:47 loss: 0.1413 Lr: 0.00098
[2023-12-25 19:24:54,811 INFO misc.py line 119 253097] Train: [75/100][469/510] Data 0.003 (0.119) Batch 1.262 (1.560) Remain 05:32:37 loss: 0.1011 Lr: 0.00098
[2023-12-25 19:24:55,927 INFO misc.py line 119 253097] Train: [75/100][470/510] Data 0.011 (0.119) Batch 1.123 (1.559) Remain 05:32:23 loss: 0.1531 Lr: 0.00098
[2023-12-25 19:24:56,851 INFO misc.py line 119 253097] Train: [75/100][471/510] Data 0.003 (0.118) Batch 0.923 (1.558) Remain 05:32:04 loss: 0.0865 Lr: 0.00098
[2023-12-25 19:24:58,143 INFO misc.py line 119 253097] Train: [75/100][472/510] Data 0.005 (0.118) Batch 1.280 (1.557) Remain 05:31:55 loss: 0.0954 Lr: 0.00098
[2023-12-25 19:24:59,307 INFO misc.py line 119 253097] Train: [75/100][473/510] Data 0.017 (0.118) Batch 1.173 (1.557) Remain 05:31:43 loss: 0.1384 Lr: 0.00098
[2023-12-25 19:25:00,323 INFO misc.py line 119 253097] Train: [75/100][474/510] Data 0.009 (0.118) Batch 1.010 (1.555) Remain 05:31:27 loss: 0.1931 Lr: 0.00098
[2023-12-25 19:25:01,631 INFO misc.py line 119 253097] Train: [75/100][475/510] Data 0.013 (0.117) Batch 1.315 (1.555) Remain 05:31:19 loss: 0.0605 Lr: 0.00098
[2023-12-25 19:25:02,851 INFO misc.py line 119 253097] Train: [75/100][476/510] Data 0.006 (0.117) Batch 1.222 (1.554) Remain 05:31:08 loss: 0.0772 Lr: 0.00098
[2023-12-25 19:25:04,026 INFO misc.py line 119 253097] Train: [75/100][477/510] Data 0.011 (0.117) Batch 1.168 (1.553) Remain 05:30:56 loss: 0.1437 Lr: 0.00098
[2023-12-25 19:25:05,236 INFO misc.py line 119 253097] Train: [75/100][478/510] Data 0.011 (0.117) Batch 1.216 (1.553) Remain 05:30:46 loss: 0.1177 Lr: 0.00098
[2023-12-25 19:25:06,361 INFO misc.py line 119 253097] Train: [75/100][479/510] Data 0.005 (0.117) Batch 1.121 (1.552) Remain 05:30:33 loss: 0.1332 Lr: 0.00097
[2023-12-25 19:25:14,362 INFO misc.py line 119 253097] Train: [75/100][480/510] Data 0.009 (0.116) Batch 8.003 (1.565) Remain 05:33:24 loss: 0.1620 Lr: 0.00097
[2023-12-25 19:25:15,370 INFO misc.py line 119 253097] Train: [75/100][481/510] Data 0.007 (0.116) Batch 1.005 (1.564) Remain 05:33:07 loss: 0.1440 Lr: 0.00097
[2023-12-25 19:25:16,404 INFO misc.py line 119 253097] Train: [75/100][482/510] Data 0.011 (0.116) Batch 1.040 (1.563) Remain 05:32:52 loss: 0.0845 Lr: 0.00097
[2023-12-25 19:25:17,518 INFO misc.py line 119 253097] Train: [75/100][483/510] Data 0.005 (0.116) Batch 1.114 (1.562) Remain 05:32:38 loss: 0.1304 Lr: 0.00097
[2023-12-25 19:25:18,787 INFO misc.py line 119 253097] Train: [75/100][484/510] Data 0.004 (0.115) Batch 1.264 (1.561) Remain 05:32:29 loss: 0.1028 Lr: 0.00097
[2023-12-25 19:25:20,061 INFO misc.py line 119 253097] Train: [75/100][485/510] Data 0.009 (0.115) Batch 1.254 (1.561) Remain 05:32:19 loss: 0.0989 Lr: 0.00097
[2023-12-25 19:25:21,042 INFO misc.py line 119 253097] Train: [75/100][486/510] Data 0.029 (0.115) Batch 1.006 (1.560) Remain 05:32:03 loss: 0.1381 Lr: 0.00097
[2023-12-25 19:25:22,275 INFO misc.py line 119 253097] Train: [75/100][487/510] Data 0.004 (0.115) Batch 1.229 (1.559) Remain 05:31:53 loss: 0.1329 Lr: 0.00097
[2023-12-25 19:25:25,848 INFO misc.py line 119 253097] Train: [75/100][488/510] Data 0.008 (0.115) Batch 3.571 (1.563) Remain 05:32:44 loss: 0.0981 Lr: 0.00097
[2023-12-25 19:25:26,922 INFO misc.py line 119 253097] Train: [75/100][489/510] Data 0.011 (0.114) Batch 1.080 (1.562) Remain 05:32:30 loss: 0.0977 Lr: 0.00097
[2023-12-25 19:25:28,095 INFO misc.py line 119 253097] Train: [75/100][490/510] Data 0.005 (0.114) Batch 1.173 (1.561) Remain 05:32:18 loss: 0.1224 Lr: 0.00097
[2023-12-25 19:25:29,365 INFO misc.py line 119 253097] Train: [75/100][491/510] Data 0.004 (0.114) Batch 1.265 (1.561) Remain 05:32:09 loss: 0.1445 Lr: 0.00097
[2023-12-25 19:25:30,584 INFO misc.py line 119 253097] Train: [75/100][492/510] Data 0.009 (0.114) Batch 1.223 (1.560) Remain 05:31:58 loss: 0.0766 Lr: 0.00097
[2023-12-25 19:25:31,855 INFO misc.py line 119 253097] Train: [75/100][493/510] Data 0.005 (0.113) Batch 1.272 (1.559) Remain 05:31:49 loss: 0.1677 Lr: 0.00097
[2023-12-25 19:25:32,950 INFO misc.py line 119 253097] Train: [75/100][494/510] Data 0.004 (0.113) Batch 1.091 (1.559) Remain 05:31:35 loss: 0.0907 Lr: 0.00097
[2023-12-25 19:25:33,965 INFO misc.py line 119 253097] Train: [75/100][495/510] Data 0.008 (0.113) Batch 1.018 (1.557) Remain 05:31:20 loss: 0.1570 Lr: 0.00097
[2023-12-25 19:25:34,992 INFO misc.py line 119 253097] Train: [75/100][496/510] Data 0.004 (0.113) Batch 1.019 (1.556) Remain 05:31:04 loss: 0.2134 Lr: 0.00097
[2023-12-25 19:25:35,948 INFO misc.py line 119 253097] Train: [75/100][497/510] Data 0.014 (0.113) Batch 0.964 (1.555) Remain 05:30:48 loss: 0.1692 Lr: 0.00097
[2023-12-25 19:25:37,116 INFO misc.py line 119 253097] Train: [75/100][498/510] Data 0.005 (0.112) Batch 1.168 (1.554) Remain 05:30:36 loss: 0.1491 Lr: 0.00097
[2023-12-25 19:25:38,297 INFO misc.py line 119 253097] Train: [75/100][499/510] Data 0.005 (0.112) Batch 1.182 (1.554) Remain 05:30:25 loss: 0.1570 Lr: 0.00097
[2023-12-25 19:25:39,378 INFO misc.py line 119 253097] Train: [75/100][500/510] Data 0.004 (0.112) Batch 1.082 (1.553) Remain 05:30:11 loss: 0.1011 Lr: 0.00097
[2023-12-25 19:25:40,562 INFO misc.py line 119 253097] Train: [75/100][501/510] Data 0.003 (0.112) Batch 1.181 (1.552) Remain 05:30:00 loss: 0.1210 Lr: 0.00097
[2023-12-25 19:25:41,869 INFO misc.py line 119 253097] Train: [75/100][502/510] Data 0.006 (0.111) Batch 1.306 (1.551) Remain 05:29:52 loss: 0.0901 Lr: 0.00097
[2023-12-25 19:25:48,902 INFO misc.py line 119 253097] Train: [75/100][503/510] Data 5.966 (0.123) Batch 7.036 (1.562) Remain 05:32:11 loss: 0.0790 Lr: 0.00097
[2023-12-25 19:25:50,051 INFO misc.py line 119 253097] Train: [75/100][504/510] Data 0.004 (0.123) Batch 1.146 (1.562) Remain 05:31:59 loss: 0.0991 Lr: 0.00097
[2023-12-25 19:25:51,066 INFO misc.py line 119 253097] Train: [75/100][505/510] Data 0.007 (0.123) Batch 1.017 (1.560) Remain 05:31:43 loss: 0.1143 Lr: 0.00097
[2023-12-25 19:25:52,200 INFO misc.py line 119 253097] Train: [75/100][506/510] Data 0.004 (0.122) Batch 1.133 (1.560) Remain 05:31:31 loss: 0.1025 Lr: 0.00097
[2023-12-25 19:25:53,475 INFO misc.py line 119 253097] Train: [75/100][507/510] Data 0.005 (0.122) Batch 1.270 (1.559) Remain 05:31:22 loss: 0.1065 Lr: 0.00097
[2023-12-25 19:25:54,456 INFO misc.py line 119 253097] Train: [75/100][508/510] Data 0.011 (0.122) Batch 0.985 (1.558) Remain 05:31:06 loss: 0.2695 Lr: 0.00097
[2023-12-25 19:25:55,434 INFO misc.py line 119 253097] Train: [75/100][509/510] Data 0.005 (0.122) Batch 0.979 (1.557) Remain 05:30:50 loss: 0.1614 Lr: 0.00097
[2023-12-25 19:25:56,546 INFO misc.py line 119 253097] Train: [75/100][510/510] Data 0.004 (0.122) Batch 1.106 (1.556) Remain 05:30:37 loss: 0.0707 Lr: 0.00097
[2023-12-25 19:25:56,546 INFO misc.py line 136 253097] Train result: loss: 0.1242 
[2023-12-25 19:25:56,546 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 19:26:29,966 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6121 
[2023-12-25 19:26:30,312 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2525 
[2023-12-25 19:26:35,244 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3379 
[2023-12-25 19:26:35,772 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4342 
[2023-12-25 19:26:37,745 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9393 
[2023-12-25 19:26:38,179 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2627 
[2023-12-25 19:26:39,059 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 0.9692 
[2023-12-25 19:26:39,618 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2370 
[2023-12-25 19:26:41,435 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8114 
[2023-12-25 19:26:43,558 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3086 
[2023-12-25 19:26:44,416 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2963 
[2023-12-25 19:26:44,852 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7780 
[2023-12-25 19:26:45,756 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.8591 
[2023-12-25 19:26:48,708 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7876 
[2023-12-25 19:26:49,184 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3655 
[2023-12-25 19:26:49,794 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3871 
[2023-12-25 19:26:50,494 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2845 
[2023-12-25 19:26:51,950 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6823/0.7384/0.9048.
[2023-12-25 19:26:51,950 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9209/0.9434
[2023-12-25 19:26:51,950 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9830/0.9898
[2023-12-25 19:26:51,950 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8452/0.9731
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2769/0.3147
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6419/0.6691
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7044/0.8380
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8250/0.9066
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9113/0.9566
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5497/0.5768
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7810/0.8716
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8160/0.8397
[2023-12-25 19:26:51,951 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6147/0.7203
[2023-12-25 19:26:51,952 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 19:26:51,953 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 19:26:51,954 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 19:27:02,447 INFO misc.py line 119 253097] Train: [76/100][1/510] Data 7.059 (7.059) Batch 7.824 (7.824) Remain 27:42:27 loss: 0.0917 Lr: 0.00097
[2023-12-25 19:27:03,408 INFO misc.py line 119 253097] Train: [76/100][2/510] Data 0.004 (0.004) Batch 0.955 (0.955) Remain 03:22:55 loss: 0.1334 Lr: 0.00097
[2023-12-25 19:27:14,817 INFO misc.py line 119 253097] Train: [76/100][3/510] Data 0.011 (0.011) Batch 11.415 (11.415) Remain 40:25:05 loss: 0.0887 Lr: 0.00097
[2023-12-25 19:27:15,847 INFO misc.py line 119 253097] Train: [76/100][4/510] Data 0.004 (0.004) Batch 1.030 (1.030) Remain 03:38:43 loss: 0.2549 Lr: 0.00097
[2023-12-25 19:27:17,116 INFO misc.py line 119 253097] Train: [76/100][5/510] Data 0.004 (0.004) Batch 1.264 (1.147) Remain 04:03:34 loss: 0.0889 Lr: 0.00097
[2023-12-25 19:27:18,345 INFO misc.py line 119 253097] Train: [76/100][6/510] Data 0.011 (0.006) Batch 1.231 (1.175) Remain 04:09:31 loss: 0.1467 Lr: 0.00097
[2023-12-25 19:27:19,615 INFO misc.py line 119 253097] Train: [76/100][7/510] Data 0.008 (0.007) Batch 1.272 (1.199) Remain 04:14:40 loss: 0.1082 Lr: 0.00097
[2023-12-25 19:27:20,773 INFO misc.py line 119 253097] Train: [76/100][8/510] Data 0.006 (0.007) Batch 1.155 (1.190) Remain 04:12:47 loss: 0.4116 Lr: 0.00097
[2023-12-25 19:27:21,992 INFO misc.py line 119 253097] Train: [76/100][9/510] Data 0.009 (0.007) Batch 1.217 (1.195) Remain 04:13:43 loss: 0.1213 Lr: 0.00097
[2023-12-25 19:27:23,108 INFO misc.py line 119 253097] Train: [76/100][10/510] Data 0.010 (0.007) Batch 1.122 (1.184) Remain 04:11:29 loss: 0.2666 Lr: 0.00097
[2023-12-25 19:27:24,349 INFO misc.py line 119 253097] Train: [76/100][11/510] Data 0.005 (0.007) Batch 1.242 (1.192) Remain 04:12:59 loss: 0.1816 Lr: 0.00097
[2023-12-25 19:27:25,489 INFO misc.py line 119 253097] Train: [76/100][12/510] Data 0.004 (0.007) Batch 1.124 (1.184) Remain 04:11:22 loss: 0.1026 Lr: 0.00097
[2023-12-25 19:27:26,614 INFO misc.py line 119 253097] Train: [76/100][13/510] Data 0.019 (0.008) Batch 1.138 (1.179) Remain 04:10:22 loss: 0.0767 Lr: 0.00097
[2023-12-25 19:27:27,652 INFO misc.py line 119 253097] Train: [76/100][14/510] Data 0.007 (0.008) Batch 1.037 (1.166) Remain 04:07:36 loss: 0.1178 Lr: 0.00097
[2023-12-25 19:27:28,815 INFO misc.py line 119 253097] Train: [76/100][15/510] Data 0.008 (0.008) Batch 1.160 (1.166) Remain 04:07:28 loss: 0.1235 Lr: 0.00097
[2023-12-25 19:27:29,908 INFO misc.py line 119 253097] Train: [76/100][16/510] Data 0.011 (0.008) Batch 1.099 (1.161) Remain 04:06:21 loss: 0.1321 Lr: 0.00097
[2023-12-25 19:27:30,931 INFO misc.py line 119 253097] Train: [76/100][17/510] Data 0.005 (0.008) Batch 1.024 (1.151) Remain 04:04:15 loss: 0.1412 Lr: 0.00097
[2023-12-25 19:27:32,092 INFO misc.py line 119 253097] Train: [76/100][18/510] Data 0.006 (0.008) Batch 1.160 (1.152) Remain 04:04:22 loss: 0.0844 Lr: 0.00097
[2023-12-25 19:27:33,309 INFO misc.py line 119 253097] Train: [76/100][19/510] Data 0.005 (0.008) Batch 1.218 (1.156) Remain 04:05:14 loss: 0.1691 Lr: 0.00097
[2023-12-25 19:27:34,441 INFO misc.py line 119 253097] Train: [76/100][20/510] Data 0.003 (0.007) Batch 1.132 (1.154) Remain 04:04:55 loss: 0.1545 Lr: 0.00097
[2023-12-25 19:27:35,512 INFO misc.py line 119 253097] Train: [76/100][21/510] Data 0.004 (0.007) Batch 1.061 (1.149) Remain 04:03:48 loss: 0.1446 Lr: 0.00097
[2023-12-25 19:27:36,535 INFO misc.py line 119 253097] Train: [76/100][22/510] Data 0.013 (0.007) Batch 1.032 (1.143) Remain 04:02:28 loss: 0.1116 Lr: 0.00097
[2023-12-25 19:27:37,677 INFO misc.py line 119 253097] Train: [76/100][23/510] Data 0.004 (0.007) Batch 1.143 (1.143) Remain 04:02:27 loss: 0.1013 Lr: 0.00097
[2023-12-25 19:27:38,581 INFO misc.py line 119 253097] Train: [76/100][24/510] Data 0.004 (0.007) Batch 0.904 (1.132) Remain 04:00:00 loss: 0.1389 Lr: 0.00097
[2023-12-25 19:27:39,608 INFO misc.py line 119 253097] Train: [76/100][25/510] Data 0.004 (0.007) Batch 1.028 (1.127) Remain 03:59:00 loss: 0.1073 Lr: 0.00097
[2023-12-25 19:27:40,831 INFO misc.py line 119 253097] Train: [76/100][26/510] Data 0.003 (0.007) Batch 1.217 (1.131) Remain 03:59:48 loss: 0.0709 Lr: 0.00097
[2023-12-25 19:27:42,072 INFO misc.py line 119 253097] Train: [76/100][27/510] Data 0.009 (0.007) Batch 1.246 (1.136) Remain 04:00:48 loss: 0.1317 Lr: 0.00097
[2023-12-25 19:27:43,158 INFO misc.py line 119 253097] Train: [76/100][28/510] Data 0.003 (0.007) Batch 1.086 (1.134) Remain 04:00:22 loss: 0.0607 Lr: 0.00097
[2023-12-25 19:27:44,445 INFO misc.py line 119 253097] Train: [76/100][29/510] Data 0.004 (0.007) Batch 1.287 (1.140) Remain 04:01:36 loss: 0.1137 Lr: 0.00097
[2023-12-25 19:27:45,684 INFO misc.py line 119 253097] Train: [76/100][30/510] Data 0.004 (0.007) Batch 1.227 (1.143) Remain 04:02:15 loss: 0.0963 Lr: 0.00097
[2023-12-25 19:27:46,895 INFO misc.py line 119 253097] Train: [76/100][31/510] Data 0.016 (0.007) Batch 1.220 (1.146) Remain 04:02:49 loss: 0.2589 Lr: 0.00097
[2023-12-25 19:27:47,977 INFO misc.py line 119 253097] Train: [76/100][32/510] Data 0.007 (0.007) Batch 1.078 (1.143) Remain 04:02:19 loss: 0.0906 Lr: 0.00097
[2023-12-25 19:27:49,031 INFO misc.py line 119 253097] Train: [76/100][33/510] Data 0.013 (0.007) Batch 1.057 (1.140) Remain 04:01:41 loss: 0.1652 Lr: 0.00097
[2023-12-25 19:27:50,192 INFO misc.py line 119 253097] Train: [76/100][34/510] Data 0.008 (0.007) Batch 1.162 (1.141) Remain 04:01:49 loss: 0.1281 Lr: 0.00097
[2023-12-25 19:27:51,429 INFO misc.py line 119 253097] Train: [76/100][35/510] Data 0.007 (0.007) Batch 1.237 (1.144) Remain 04:02:26 loss: 0.1614 Lr: 0.00097
[2023-12-25 19:27:52,588 INFO misc.py line 119 253097] Train: [76/100][36/510] Data 0.008 (0.007) Batch 1.158 (1.144) Remain 04:02:30 loss: 0.0842 Lr: 0.00097
[2023-12-25 19:27:53,836 INFO misc.py line 119 253097] Train: [76/100][37/510] Data 0.009 (0.007) Batch 1.244 (1.147) Remain 04:03:06 loss: 0.1667 Lr: 0.00097
[2023-12-25 19:27:54,974 INFO misc.py line 119 253097] Train: [76/100][38/510] Data 0.011 (0.007) Batch 1.138 (1.147) Remain 04:03:02 loss: 0.0974 Lr: 0.00097
[2023-12-25 19:27:56,264 INFO misc.py line 119 253097] Train: [76/100][39/510] Data 0.011 (0.007) Batch 1.293 (1.151) Remain 04:03:52 loss: 0.1083 Lr: 0.00096
[2023-12-25 19:27:57,472 INFO misc.py line 119 253097] Train: [76/100][40/510] Data 0.009 (0.007) Batch 1.196 (1.152) Remain 04:04:07 loss: 0.1169 Lr: 0.00096
[2023-12-25 19:27:58,523 INFO misc.py line 119 253097] Train: [76/100][41/510] Data 0.020 (0.008) Batch 1.063 (1.150) Remain 04:03:35 loss: 0.1050 Lr: 0.00096
[2023-12-25 19:27:59,696 INFO misc.py line 119 253097] Train: [76/100][42/510] Data 0.008 (0.008) Batch 1.173 (1.151) Remain 04:03:42 loss: 0.1136 Lr: 0.00096
[2023-12-25 19:28:00,823 INFO misc.py line 119 253097] Train: [76/100][43/510] Data 0.007 (0.008) Batch 1.131 (1.150) Remain 04:03:35 loss: 0.1199 Lr: 0.00096
[2023-12-25 19:28:02,023 INFO misc.py line 119 253097] Train: [76/100][44/510] Data 0.005 (0.008) Batch 1.192 (1.151) Remain 04:03:47 loss: 0.2010 Lr: 0.00096
[2023-12-25 19:28:03,208 INFO misc.py line 119 253097] Train: [76/100][45/510] Data 0.012 (0.008) Batch 1.185 (1.152) Remain 04:03:56 loss: 0.1422 Lr: 0.00096
[2023-12-25 19:28:04,298 INFO misc.py line 119 253097] Train: [76/100][46/510] Data 0.012 (0.008) Batch 1.096 (1.151) Remain 04:03:38 loss: 0.2570 Lr: 0.00096
[2023-12-25 19:28:05,529 INFO misc.py line 119 253097] Train: [76/100][47/510] Data 0.006 (0.008) Batch 1.229 (1.152) Remain 04:03:59 loss: 0.1370 Lr: 0.00096
[2023-12-25 19:28:06,549 INFO misc.py line 119 253097] Train: [76/100][48/510] Data 0.007 (0.008) Batch 1.023 (1.150) Remain 04:03:22 loss: 0.2218 Lr: 0.00096
[2023-12-25 19:28:07,791 INFO misc.py line 119 253097] Train: [76/100][49/510] Data 0.005 (0.008) Batch 1.243 (1.152) Remain 04:03:46 loss: 0.0865 Lr: 0.00096
[2023-12-25 19:28:08,801 INFO misc.py line 119 253097] Train: [76/100][50/510] Data 0.004 (0.008) Batch 1.006 (1.149) Remain 04:03:06 loss: 0.0828 Lr: 0.00096
[2023-12-25 19:28:19,391 INFO misc.py line 119 253097] Train: [76/100][51/510] Data 9.632 (0.208) Batch 10.594 (1.345) Remain 04:44:43 loss: 0.1035 Lr: 0.00096
[2023-12-25 19:28:20,662 INFO misc.py line 119 253097] Train: [76/100][52/510] Data 0.003 (0.204) Batch 1.272 (1.344) Remain 04:44:23 loss: 0.1084 Lr: 0.00096
[2023-12-25 19:28:21,850 INFO misc.py line 119 253097] Train: [76/100][53/510] Data 0.004 (0.200) Batch 1.187 (1.341) Remain 04:43:42 loss: 0.1076 Lr: 0.00096
[2023-12-25 19:28:23,083 INFO misc.py line 119 253097] Train: [76/100][54/510] Data 0.004 (0.196) Batch 1.211 (1.338) Remain 04:43:08 loss: 0.1587 Lr: 0.00096
[2023-12-25 19:28:24,067 INFO misc.py line 119 253097] Train: [76/100][55/510] Data 0.026 (0.193) Batch 1.004 (1.332) Remain 04:41:45 loss: 0.1147 Lr: 0.00096
[2023-12-25 19:28:25,421 INFO misc.py line 119 253097] Train: [76/100][56/510] Data 0.007 (0.189) Batch 1.355 (1.332) Remain 04:41:49 loss: 0.0826 Lr: 0.00096
[2023-12-25 19:28:26,502 INFO misc.py line 119 253097] Train: [76/100][57/510] Data 0.006 (0.186) Batch 1.082 (1.327) Remain 04:40:49 loss: 0.1297 Lr: 0.00096
[2023-12-25 19:28:27,540 INFO misc.py line 119 253097] Train: [76/100][58/510] Data 0.004 (0.183) Batch 1.035 (1.322) Remain 04:39:41 loss: 0.1373 Lr: 0.00096
[2023-12-25 19:28:28,821 INFO misc.py line 119 253097] Train: [76/100][59/510] Data 0.008 (0.180) Batch 1.285 (1.322) Remain 04:39:31 loss: 0.1198 Lr: 0.00096
[2023-12-25 19:28:29,989 INFO misc.py line 119 253097] Train: [76/100][60/510] Data 0.004 (0.176) Batch 1.168 (1.319) Remain 04:38:55 loss: 0.1258 Lr: 0.00096
[2023-12-25 19:28:31,022 INFO misc.py line 119 253097] Train: [76/100][61/510] Data 0.003 (0.173) Batch 1.033 (1.314) Remain 04:37:51 loss: 0.1121 Lr: 0.00096
[2023-12-25 19:28:31,984 INFO misc.py line 119 253097] Train: [76/100][62/510] Data 0.003 (0.171) Batch 0.961 (1.308) Remain 04:36:34 loss: 0.0626 Lr: 0.00096
[2023-12-25 19:28:46,630 INFO misc.py line 119 253097] Train: [76/100][63/510] Data 13.303 (0.389) Batch 14.646 (1.530) Remain 05:23:33 loss: 0.0488 Lr: 0.00096
[2023-12-25 19:28:47,541 INFO misc.py line 119 253097] Train: [76/100][64/510] Data 0.005 (0.383) Batch 0.911 (1.520) Remain 05:21:23 loss: 0.1118 Lr: 0.00096
[2023-12-25 19:28:48,659 INFO misc.py line 119 253097] Train: [76/100][65/510] Data 0.004 (0.377) Batch 1.117 (1.514) Remain 05:19:59 loss: 0.1656 Lr: 0.00096
[2023-12-25 19:28:49,883 INFO misc.py line 119 253097] Train: [76/100][66/510] Data 0.005 (0.371) Batch 1.222 (1.509) Remain 05:18:59 loss: 0.1867 Lr: 0.00096
[2023-12-25 19:28:51,124 INFO misc.py line 119 253097] Train: [76/100][67/510] Data 0.007 (0.365) Batch 1.243 (1.505) Remain 05:18:05 loss: 0.1331 Lr: 0.00096
[2023-12-25 19:28:52,329 INFO misc.py line 119 253097] Train: [76/100][68/510] Data 0.005 (0.360) Batch 1.207 (1.500) Remain 05:17:05 loss: 0.0866 Lr: 0.00096
[2023-12-25 19:28:53,590 INFO misc.py line 119 253097] Train: [76/100][69/510] Data 0.003 (0.355) Batch 1.236 (1.496) Remain 05:16:13 loss: 0.1450 Lr: 0.00096
[2023-12-25 19:28:54,756 INFO misc.py line 119 253097] Train: [76/100][70/510] Data 0.029 (0.350) Batch 1.187 (1.492) Remain 05:15:13 loss: 0.1465 Lr: 0.00096
[2023-12-25 19:28:56,094 INFO misc.py line 119 253097] Train: [76/100][71/510] Data 0.008 (0.345) Batch 1.331 (1.489) Remain 05:14:41 loss: 0.2303 Lr: 0.00096
[2023-12-25 19:28:57,258 INFO misc.py line 119 253097] Train: [76/100][72/510] Data 0.014 (0.340) Batch 1.173 (1.485) Remain 05:13:42 loss: 0.0944 Lr: 0.00096
[2023-12-25 19:28:58,197 INFO misc.py line 119 253097] Train: [76/100][73/510] Data 0.005 (0.335) Batch 0.941 (1.477) Remain 05:12:02 loss: 0.3206 Lr: 0.00096
[2023-12-25 19:28:59,274 INFO misc.py line 119 253097] Train: [76/100][74/510] Data 0.003 (0.330) Batch 1.077 (1.471) Remain 05:10:49 loss: 0.1454 Lr: 0.00096
[2023-12-25 19:29:00,537 INFO misc.py line 119 253097] Train: [76/100][75/510] Data 0.003 (0.326) Batch 1.260 (1.468) Remain 05:10:10 loss: 0.1208 Lr: 0.00096
[2023-12-25 19:29:01,571 INFO misc.py line 119 253097] Train: [76/100][76/510] Data 0.008 (0.322) Batch 1.036 (1.462) Remain 05:08:54 loss: 0.0957 Lr: 0.00096
[2023-12-25 19:29:02,675 INFO misc.py line 119 253097] Train: [76/100][77/510] Data 0.004 (0.317) Batch 1.100 (1.457) Remain 05:07:50 loss: 0.0699 Lr: 0.00096
[2023-12-25 19:29:03,841 INFO misc.py line 119 253097] Train: [76/100][78/510] Data 0.009 (0.313) Batch 1.167 (1.454) Remain 05:07:00 loss: 0.1946 Lr: 0.00096
[2023-12-25 19:29:04,846 INFO misc.py line 119 253097] Train: [76/100][79/510] Data 0.008 (0.309) Batch 1.006 (1.448) Remain 05:05:43 loss: 0.1913 Lr: 0.00096
[2023-12-25 19:29:05,870 INFO misc.py line 119 253097] Train: [76/100][80/510] Data 0.007 (0.305) Batch 1.028 (1.442) Remain 05:04:33 loss: 0.0663 Lr: 0.00096
[2023-12-25 19:29:07,108 INFO misc.py line 119 253097] Train: [76/100][81/510] Data 0.003 (0.301) Batch 1.234 (1.440) Remain 05:03:58 loss: 0.1290 Lr: 0.00096
[2023-12-25 19:29:08,302 INFO misc.py line 119 253097] Train: [76/100][82/510] Data 0.007 (0.298) Batch 1.197 (1.437) Remain 05:03:17 loss: 0.1229 Lr: 0.00096
[2023-12-25 19:29:09,477 INFO misc.py line 119 253097] Train: [76/100][83/510] Data 0.005 (0.294) Batch 1.175 (1.433) Remain 05:02:34 loss: 0.0717 Lr: 0.00096
[2023-12-25 19:29:10,773 INFO misc.py line 119 253097] Train: [76/100][84/510] Data 0.005 (0.290) Batch 1.296 (1.432) Remain 05:02:12 loss: 0.1801 Lr: 0.00096
[2023-12-25 19:29:11,850 INFO misc.py line 119 253097] Train: [76/100][85/510] Data 0.005 (0.287) Batch 1.078 (1.427) Remain 05:01:15 loss: 0.0879 Lr: 0.00096
[2023-12-25 19:29:12,900 INFO misc.py line 119 253097] Train: [76/100][86/510] Data 0.004 (0.283) Batch 1.049 (1.423) Remain 05:00:16 loss: 0.1525 Lr: 0.00096
[2023-12-25 19:29:23,092 INFO misc.py line 119 253097] Train: [76/100][87/510] Data 8.879 (0.386) Batch 10.192 (1.527) Remain 05:22:17 loss: 0.0651 Lr: 0.00096
[2023-12-25 19:29:24,193 INFO misc.py line 119 253097] Train: [76/100][88/510] Data 0.004 (0.381) Batch 1.101 (1.522) Remain 05:21:12 loss: 0.0903 Lr: 0.00096
[2023-12-25 19:29:25,310 INFO misc.py line 119 253097] Train: [76/100][89/510] Data 0.004 (0.377) Batch 1.106 (1.517) Remain 05:20:09 loss: 0.1059 Lr: 0.00096
[2023-12-25 19:29:26,416 INFO misc.py line 119 253097] Train: [76/100][90/510] Data 0.016 (0.373) Batch 1.115 (1.513) Remain 05:19:09 loss: 0.1383 Lr: 0.00096
[2023-12-25 19:29:27,705 INFO misc.py line 119 253097] Train: [76/100][91/510] Data 0.006 (0.369) Batch 1.283 (1.510) Remain 05:18:35 loss: 0.1541 Lr: 0.00096
[2023-12-25 19:29:28,849 INFO misc.py line 119 253097] Train: [76/100][92/510] Data 0.013 (0.365) Batch 1.147 (1.506) Remain 05:17:41 loss: 0.1766 Lr: 0.00096
[2023-12-25 19:29:30,128 INFO misc.py line 119 253097] Train: [76/100][93/510] Data 0.010 (0.361) Batch 1.281 (1.503) Remain 05:17:08 loss: 0.1173 Lr: 0.00096
[2023-12-25 19:29:31,256 INFO misc.py line 119 253097] Train: [76/100][94/510] Data 0.007 (0.357) Batch 1.112 (1.499) Remain 05:16:12 loss: 0.1012 Lr: 0.00096
[2023-12-25 19:29:32,520 INFO misc.py line 119 253097] Train: [76/100][95/510] Data 0.024 (0.353) Batch 1.283 (1.497) Remain 05:15:41 loss: 0.0707 Lr: 0.00096
[2023-12-25 19:29:33,767 INFO misc.py line 119 253097] Train: [76/100][96/510] Data 0.005 (0.349) Batch 1.246 (1.494) Remain 05:15:06 loss: 0.2937 Lr: 0.00096
[2023-12-25 19:29:34,930 INFO misc.py line 119 253097] Train: [76/100][97/510] Data 0.005 (0.346) Batch 1.160 (1.491) Remain 05:14:19 loss: 0.1703 Lr: 0.00096
[2023-12-25 19:29:36,147 INFO misc.py line 119 253097] Train: [76/100][98/510] Data 0.008 (0.342) Batch 1.213 (1.488) Remain 05:13:41 loss: 0.0584 Lr: 0.00096
[2023-12-25 19:29:37,298 INFO misc.py line 119 253097] Train: [76/100][99/510] Data 0.012 (0.339) Batch 1.159 (1.484) Remain 05:12:56 loss: 0.1244 Lr: 0.00096
[2023-12-25 19:29:38,596 INFO misc.py line 119 253097] Train: [76/100][100/510] Data 0.004 (0.335) Batch 1.286 (1.482) Remain 05:12:28 loss: 0.0810 Lr: 0.00096
[2023-12-25 19:29:39,778 INFO misc.py line 119 253097] Train: [76/100][101/510] Data 0.016 (0.332) Batch 1.188 (1.479) Remain 05:11:49 loss: 0.0659 Lr: 0.00096
[2023-12-25 19:29:40,809 INFO misc.py line 119 253097] Train: [76/100][102/510] Data 0.011 (0.329) Batch 1.036 (1.475) Remain 05:10:51 loss: 0.1167 Lr: 0.00096
[2023-12-25 19:29:54,958 INFO misc.py line 119 253097] Train: [76/100][103/510] Data 12.865 (0.454) Batch 14.150 (1.601) Remain 05:37:33 loss: 0.1680 Lr: 0.00096
[2023-12-25 19:29:55,937 INFO misc.py line 119 253097] Train: [76/100][104/510] Data 0.003 (0.450) Batch 0.978 (1.595) Remain 05:36:13 loss: 0.1053 Lr: 0.00096
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[2023-12-25 19:29:59,446 INFO misc.py line 119 253097] Train: [76/100][107/510] Data 0.003 (0.437) Batch 1.231 (1.583) Remain 05:33:33 loss: 0.1372 Lr: 0.00096
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[2023-12-25 19:30:01,786 INFO misc.py line 119 253097] Train: [76/100][109/510] Data 0.011 (0.429) Batch 1.162 (1.575) Remain 05:31:51 loss: 0.1198 Lr: 0.00095
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[2023-12-25 19:30:06,600 INFO misc.py line 119 253097] Train: [76/100][113/510] Data 0.010 (0.414) Batch 1.274 (1.562) Remain 05:28:53 loss: 0.1126 Lr: 0.00095
[2023-12-25 19:30:07,824 INFO misc.py line 119 253097] Train: [76/100][114/510] Data 0.013 (0.410) Batch 1.226 (1.559) Remain 05:28:14 loss: 0.1788 Lr: 0.00095
[2023-12-25 19:30:09,097 INFO misc.py line 119 253097] Train: [76/100][115/510] Data 0.011 (0.406) Batch 1.276 (1.556) Remain 05:27:40 loss: 0.0835 Lr: 0.00095
[2023-12-25 19:30:10,172 INFO misc.py line 119 253097] Train: [76/100][116/510] Data 0.006 (0.403) Batch 1.077 (1.552) Remain 05:26:45 loss: 0.0832 Lr: 0.00095
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[2023-12-25 19:30:12,481 INFO misc.py line 119 253097] Train: [76/100][118/510] Data 0.006 (0.396) Batch 1.214 (1.545) Remain 05:25:14 loss: 0.2077 Lr: 0.00095
[2023-12-25 19:30:13,771 INFO misc.py line 119 253097] Train: [76/100][119/510] Data 0.008 (0.393) Batch 1.291 (1.543) Remain 05:24:45 loss: 0.0913 Lr: 0.00095
[2023-12-25 19:30:14,748 INFO misc.py line 119 253097] Train: [76/100][120/510] Data 0.007 (0.389) Batch 0.979 (1.538) Remain 05:23:43 loss: 0.0914 Lr: 0.00095
[2023-12-25 19:30:15,957 INFO misc.py line 119 253097] Train: [76/100][121/510] Data 0.005 (0.386) Batch 1.209 (1.535) Remain 05:23:06 loss: 0.1107 Lr: 0.00095
[2023-12-25 19:30:16,908 INFO misc.py line 119 253097] Train: [76/100][122/510] Data 0.006 (0.383) Batch 0.951 (1.530) Remain 05:22:03 loss: 0.1305 Lr: 0.00095
[2023-12-25 19:30:18,195 INFO misc.py line 119 253097] Train: [76/100][123/510] Data 0.004 (0.380) Batch 1.287 (1.528) Remain 05:21:35 loss: 0.1582 Lr: 0.00095
[2023-12-25 19:30:19,279 INFO misc.py line 119 253097] Train: [76/100][124/510] Data 0.004 (0.377) Batch 1.085 (1.524) Remain 05:20:48 loss: 0.0625 Lr: 0.00095
[2023-12-25 19:30:20,621 INFO misc.py line 119 253097] Train: [76/100][125/510] Data 0.005 (0.374) Batch 1.342 (1.523) Remain 05:20:27 loss: 0.1159 Lr: 0.00095
[2023-12-25 19:30:26,597 INFO misc.py line 119 253097] Train: [76/100][126/510] Data 0.004 (0.371) Batch 5.976 (1.559) Remain 05:28:03 loss: 0.1154 Lr: 0.00095
[2023-12-25 19:30:27,705 INFO misc.py line 119 253097] Train: [76/100][127/510] Data 0.005 (0.368) Batch 1.108 (1.556) Remain 05:27:15 loss: 0.1282 Lr: 0.00095
[2023-12-25 19:30:28,718 INFO misc.py line 119 253097] Train: [76/100][128/510] Data 0.004 (0.365) Batch 1.012 (1.551) Remain 05:26:19 loss: 0.2067 Lr: 0.00095
[2023-12-25 19:30:29,867 INFO misc.py line 119 253097] Train: [76/100][129/510] Data 0.005 (0.362) Batch 1.150 (1.548) Remain 05:25:37 loss: 0.0708 Lr: 0.00095
[2023-12-25 19:30:30,872 INFO misc.py line 119 253097] Train: [76/100][130/510] Data 0.004 (0.359) Batch 1.006 (1.544) Remain 05:24:42 loss: 0.0710 Lr: 0.00095
[2023-12-25 19:30:32,116 INFO misc.py line 119 253097] Train: [76/100][131/510] Data 0.003 (0.356) Batch 1.243 (1.541) Remain 05:24:10 loss: 0.1124 Lr: 0.00095
[2023-12-25 19:30:33,308 INFO misc.py line 119 253097] Train: [76/100][132/510] Data 0.005 (0.354) Batch 1.193 (1.539) Remain 05:23:35 loss: 0.1586 Lr: 0.00095
[2023-12-25 19:30:34,642 INFO misc.py line 119 253097] Train: [76/100][133/510] Data 0.004 (0.351) Batch 1.315 (1.537) Remain 05:23:11 loss: 0.1591 Lr: 0.00095
[2023-12-25 19:30:35,931 INFO misc.py line 119 253097] Train: [76/100][134/510] Data 0.023 (0.348) Batch 1.308 (1.535) Remain 05:22:48 loss: 0.0527 Lr: 0.00095
[2023-12-25 19:30:36,981 INFO misc.py line 119 253097] Train: [76/100][135/510] Data 0.004 (0.346) Batch 1.049 (1.532) Remain 05:22:00 loss: 0.2384 Lr: 0.00095
[2023-12-25 19:30:38,252 INFO misc.py line 119 253097] Train: [76/100][136/510] Data 0.004 (0.343) Batch 1.271 (1.530) Remain 05:21:34 loss: 0.2723 Lr: 0.00095
[2023-12-25 19:30:39,347 INFO misc.py line 119 253097] Train: [76/100][137/510] Data 0.004 (0.341) Batch 1.095 (1.526) Remain 05:20:51 loss: 0.2223 Lr: 0.00095
[2023-12-25 19:30:40,511 INFO misc.py line 119 253097] Train: [76/100][138/510] Data 0.003 (0.338) Batch 1.160 (1.524) Remain 05:20:16 loss: 0.0848 Lr: 0.00095
[2023-12-25 19:30:41,468 INFO misc.py line 119 253097] Train: [76/100][139/510] Data 0.008 (0.336) Batch 0.962 (1.519) Remain 05:19:22 loss: 0.0726 Lr: 0.00095
[2023-12-25 19:30:49,205 INFO misc.py line 119 253097] Train: [76/100][140/510] Data 6.756 (0.383) Batch 7.736 (1.565) Remain 05:28:53 loss: 0.3134 Lr: 0.00095
[2023-12-25 19:30:50,456 INFO misc.py line 119 253097] Train: [76/100][141/510] Data 0.004 (0.380) Batch 1.251 (1.563) Remain 05:28:22 loss: 0.0793 Lr: 0.00095
[2023-12-25 19:30:51,446 INFO misc.py line 119 253097] Train: [76/100][142/510] Data 0.005 (0.377) Batch 0.990 (1.558) Remain 05:27:29 loss: 0.1622 Lr: 0.00095
[2023-12-25 19:30:52,506 INFO misc.py line 119 253097] Train: [76/100][143/510] Data 0.004 (0.375) Batch 1.061 (1.555) Remain 05:26:42 loss: 0.0675 Lr: 0.00095
[2023-12-25 19:30:53,667 INFO misc.py line 119 253097] Train: [76/100][144/510] Data 0.004 (0.372) Batch 1.160 (1.552) Remain 05:26:06 loss: 0.1418 Lr: 0.00095
[2023-12-25 19:30:54,875 INFO misc.py line 119 253097] Train: [76/100][145/510] Data 0.005 (0.369) Batch 1.208 (1.550) Remain 05:25:33 loss: 0.1646 Lr: 0.00095
[2023-12-25 19:30:55,930 INFO misc.py line 119 253097] Train: [76/100][146/510] Data 0.005 (0.367) Batch 1.055 (1.546) Remain 05:24:48 loss: 0.0927 Lr: 0.00095
[2023-12-25 19:30:57,180 INFO misc.py line 119 253097] Train: [76/100][147/510] Data 0.005 (0.364) Batch 1.246 (1.544) Remain 05:24:20 loss: 0.1515 Lr: 0.00095
[2023-12-25 19:30:58,394 INFO misc.py line 119 253097] Train: [76/100][148/510] Data 0.008 (0.362) Batch 1.218 (1.542) Remain 05:23:51 loss: 0.0833 Lr: 0.00095
[2023-12-25 19:30:59,312 INFO misc.py line 119 253097] Train: [76/100][149/510] Data 0.006 (0.359) Batch 0.919 (1.538) Remain 05:22:55 loss: 0.1371 Lr: 0.00095
[2023-12-25 19:31:00,604 INFO misc.py line 119 253097] Train: [76/100][150/510] Data 0.003 (0.357) Batch 1.289 (1.536) Remain 05:22:32 loss: 0.1022 Lr: 0.00095
[2023-12-25 19:31:01,784 INFO misc.py line 119 253097] Train: [76/100][151/510] Data 0.007 (0.355) Batch 1.178 (1.534) Remain 05:22:00 loss: 0.0976 Lr: 0.00095
[2023-12-25 19:31:02,771 INFO misc.py line 119 253097] Train: [76/100][152/510] Data 0.010 (0.352) Batch 0.991 (1.530) Remain 05:21:13 loss: 0.1773 Lr: 0.00095
[2023-12-25 19:31:03,989 INFO misc.py line 119 253097] Train: [76/100][153/510] Data 0.005 (0.350) Batch 1.220 (1.528) Remain 05:20:45 loss: 0.1777 Lr: 0.00095
[2023-12-25 19:31:11,125 INFO misc.py line 119 253097] Train: [76/100][154/510] Data 5.845 (0.386) Batch 7.135 (1.565) Remain 05:28:32 loss: 0.0718 Lr: 0.00095
[2023-12-25 19:31:12,251 INFO misc.py line 119 253097] Train: [76/100][155/510] Data 0.005 (0.384) Batch 1.128 (1.562) Remain 05:27:54 loss: 0.1349 Lr: 0.00095
[2023-12-25 19:31:13,393 INFO misc.py line 119 253097] Train: [76/100][156/510] Data 0.003 (0.381) Batch 1.140 (1.559) Remain 05:27:18 loss: 0.1566 Lr: 0.00095
[2023-12-25 19:31:14,563 INFO misc.py line 119 253097] Train: [76/100][157/510] Data 0.005 (0.379) Batch 1.170 (1.557) Remain 05:26:44 loss: 0.1639 Lr: 0.00095
[2023-12-25 19:31:15,795 INFO misc.py line 119 253097] Train: [76/100][158/510] Data 0.004 (0.376) Batch 1.228 (1.555) Remain 05:26:16 loss: 0.1174 Lr: 0.00095
[2023-12-25 19:31:16,937 INFO misc.py line 119 253097] Train: [76/100][159/510] Data 0.008 (0.374) Batch 1.146 (1.552) Remain 05:25:41 loss: 0.1264 Lr: 0.00095
[2023-12-25 19:31:18,194 INFO misc.py line 119 253097] Train: [76/100][160/510] Data 0.004 (0.372) Batch 1.255 (1.550) Remain 05:25:16 loss: 0.1198 Lr: 0.00095
[2023-12-25 19:31:19,305 INFO misc.py line 119 253097] Train: [76/100][161/510] Data 0.006 (0.369) Batch 1.110 (1.547) Remain 05:24:39 loss: 0.1890 Lr: 0.00095
[2023-12-25 19:31:20,361 INFO misc.py line 119 253097] Train: [76/100][162/510] Data 0.007 (0.367) Batch 1.056 (1.544) Remain 05:23:59 loss: 0.2031 Lr: 0.00095
[2023-12-25 19:31:21,406 INFO misc.py line 119 253097] Train: [76/100][163/510] Data 0.007 (0.365) Batch 1.045 (1.541) Remain 05:23:18 loss: 0.2284 Lr: 0.00095
[2023-12-25 19:31:22,591 INFO misc.py line 119 253097] Train: [76/100][164/510] Data 0.008 (0.363) Batch 1.187 (1.539) Remain 05:22:49 loss: 0.1079 Lr: 0.00095
[2023-12-25 19:31:23,815 INFO misc.py line 119 253097] Train: [76/100][165/510] Data 0.006 (0.360) Batch 1.226 (1.537) Remain 05:22:23 loss: 0.1458 Lr: 0.00095
[2023-12-25 19:31:25,044 INFO misc.py line 119 253097] Train: [76/100][166/510] Data 0.004 (0.358) Batch 1.222 (1.535) Remain 05:21:57 loss: 0.0731 Lr: 0.00095
[2023-12-25 19:31:26,239 INFO misc.py line 119 253097] Train: [76/100][167/510] Data 0.012 (0.356) Batch 1.203 (1.533) Remain 05:21:30 loss: 0.1065 Lr: 0.00095
[2023-12-25 19:31:27,408 INFO misc.py line 119 253097] Train: [76/100][168/510] Data 0.004 (0.354) Batch 1.169 (1.531) Remain 05:21:01 loss: 0.1782 Lr: 0.00095
[2023-12-25 19:31:28,535 INFO misc.py line 119 253097] Train: [76/100][169/510] Data 0.004 (0.352) Batch 1.126 (1.528) Remain 05:20:29 loss: 0.1184 Lr: 0.00095
[2023-12-25 19:31:37,413 INFO misc.py line 119 253097] Train: [76/100][170/510] Data 7.604 (0.395) Batch 8.878 (1.572) Remain 05:29:41 loss: 0.1119 Lr: 0.00095
[2023-12-25 19:31:38,517 INFO misc.py line 119 253097] Train: [76/100][171/510] Data 0.004 (0.393) Batch 1.104 (1.570) Remain 05:29:04 loss: 0.1329 Lr: 0.00095
[2023-12-25 19:31:39,628 INFO misc.py line 119 253097] Train: [76/100][172/510] Data 0.003 (0.391) Batch 1.111 (1.567) Remain 05:28:28 loss: 0.1194 Lr: 0.00095
[2023-12-25 19:31:40,650 INFO misc.py line 119 253097] Train: [76/100][173/510] Data 0.004 (0.388) Batch 1.023 (1.564) Remain 05:27:47 loss: 0.0789 Lr: 0.00095
[2023-12-25 19:31:41,925 INFO misc.py line 119 253097] Train: [76/100][174/510] Data 0.003 (0.386) Batch 1.270 (1.562) Remain 05:27:23 loss: 0.0810 Lr: 0.00095
[2023-12-25 19:31:42,991 INFO misc.py line 119 253097] Train: [76/100][175/510] Data 0.008 (0.384) Batch 1.063 (1.559) Remain 05:26:45 loss: 0.0939 Lr: 0.00095
[2023-12-25 19:31:44,078 INFO misc.py line 119 253097] Train: [76/100][176/510] Data 0.011 (0.382) Batch 1.093 (1.556) Remain 05:26:10 loss: 0.2371 Lr: 0.00095
[2023-12-25 19:31:45,187 INFO misc.py line 119 253097] Train: [76/100][177/510] Data 0.005 (0.380) Batch 1.109 (1.554) Remain 05:25:36 loss: 0.1435 Lr: 0.00095
[2023-12-25 19:31:46,513 INFO misc.py line 119 253097] Train: [76/100][178/510] Data 0.004 (0.378) Batch 1.322 (1.553) Remain 05:25:18 loss: 0.1900 Lr: 0.00095
[2023-12-25 19:31:47,765 INFO misc.py line 119 253097] Train: [76/100][179/510] Data 0.009 (0.375) Batch 1.256 (1.551) Remain 05:24:55 loss: 0.1658 Lr: 0.00095
[2023-12-25 19:31:48,743 INFO misc.py line 119 253097] Train: [76/100][180/510] Data 0.004 (0.373) Batch 0.979 (1.548) Remain 05:24:13 loss: 0.1066 Lr: 0.00094
[2023-12-25 19:31:49,723 INFO misc.py line 119 253097] Train: [76/100][181/510] Data 0.004 (0.371) Batch 0.979 (1.544) Remain 05:23:31 loss: 0.1377 Lr: 0.00094
[2023-12-25 19:31:50,985 INFO misc.py line 119 253097] Train: [76/100][182/510] Data 0.005 (0.369) Batch 1.261 (1.543) Remain 05:23:10 loss: 0.2068 Lr: 0.00094
[2023-12-25 19:31:52,213 INFO misc.py line 119 253097] Train: [76/100][183/510] Data 0.005 (0.367) Batch 1.227 (1.541) Remain 05:22:46 loss: 0.1642 Lr: 0.00094
[2023-12-25 19:31:53,459 INFO misc.py line 119 253097] Train: [76/100][184/510] Data 0.007 (0.365) Batch 1.248 (1.539) Remain 05:22:24 loss: 0.2256 Lr: 0.00094
[2023-12-25 19:31:54,800 INFO misc.py line 119 253097] Train: [76/100][185/510] Data 0.006 (0.363) Batch 1.342 (1.538) Remain 05:22:09 loss: 0.1549 Lr: 0.00094
[2023-12-25 19:31:56,025 INFO misc.py line 119 253097] Train: [76/100][186/510] Data 0.004 (0.361) Batch 1.224 (1.537) Remain 05:21:46 loss: 0.0852 Lr: 0.00094
[2023-12-25 19:32:03,768 INFO misc.py line 119 253097] Train: [76/100][187/510] Data 0.006 (0.359) Batch 7.744 (1.570) Remain 05:28:48 loss: 0.2051 Lr: 0.00094
[2023-12-25 19:32:05,011 INFO misc.py line 119 253097] Train: [76/100][188/510] Data 0.004 (0.357) Batch 1.244 (1.569) Remain 05:28:25 loss: 0.0717 Lr: 0.00094
[2023-12-25 19:32:06,022 INFO misc.py line 119 253097] Train: [76/100][189/510] Data 0.003 (0.356) Batch 1.011 (1.566) Remain 05:27:45 loss: 0.0781 Lr: 0.00094
[2023-12-25 19:32:07,061 INFO misc.py line 119 253097] Train: [76/100][190/510] Data 0.003 (0.354) Batch 1.039 (1.563) Remain 05:27:08 loss: 0.0726 Lr: 0.00094
[2023-12-25 19:32:08,225 INFO misc.py line 119 253097] Train: [76/100][191/510] Data 0.003 (0.352) Batch 1.163 (1.561) Remain 05:26:40 loss: 0.1415 Lr: 0.00094
[2023-12-25 19:32:09,272 INFO misc.py line 119 253097] Train: [76/100][192/510] Data 0.005 (0.350) Batch 1.047 (1.558) Remain 05:26:04 loss: 0.0951 Lr: 0.00094
[2023-12-25 19:32:10,270 INFO misc.py line 119 253097] Train: [76/100][193/510] Data 0.004 (0.348) Batch 0.998 (1.555) Remain 05:25:26 loss: 0.1227 Lr: 0.00094
[2023-12-25 19:32:11,479 INFO misc.py line 119 253097] Train: [76/100][194/510] Data 0.004 (0.346) Batch 1.208 (1.553) Remain 05:25:01 loss: 0.1047 Lr: 0.00094
[2023-12-25 19:32:12,698 INFO misc.py line 119 253097] Train: [76/100][195/510] Data 0.005 (0.345) Batch 1.220 (1.551) Remain 05:24:38 loss: 0.1030 Lr: 0.00094
[2023-12-25 19:32:13,852 INFO misc.py line 119 253097] Train: [76/100][196/510] Data 0.004 (0.343) Batch 1.153 (1.549) Remain 05:24:11 loss: 0.2443 Lr: 0.00094
[2023-12-25 19:32:15,137 INFO misc.py line 119 253097] Train: [76/100][197/510] Data 0.005 (0.341) Batch 1.286 (1.548) Remain 05:23:52 loss: 0.1077 Lr: 0.00094
[2023-12-25 19:32:16,168 INFO misc.py line 119 253097] Train: [76/100][198/510] Data 0.004 (0.339) Batch 1.030 (1.545) Remain 05:23:17 loss: 0.0683 Lr: 0.00094
[2023-12-25 19:32:17,195 INFO misc.py line 119 253097] Train: [76/100][199/510] Data 0.004 (0.338) Batch 1.027 (1.543) Remain 05:22:42 loss: 0.1540 Lr: 0.00094
[2023-12-25 19:32:18,524 INFO misc.py line 119 253097] Train: [76/100][200/510] Data 0.004 (0.336) Batch 1.326 (1.542) Remain 05:22:27 loss: 0.1863 Lr: 0.00094
[2023-12-25 19:32:19,796 INFO misc.py line 119 253097] Train: [76/100][201/510] Data 0.007 (0.334) Batch 1.273 (1.540) Remain 05:22:09 loss: 0.1284 Lr: 0.00094
[2023-12-25 19:32:20,951 INFO misc.py line 119 253097] Train: [76/100][202/510] Data 0.006 (0.333) Batch 1.156 (1.538) Remain 05:21:43 loss: 0.0967 Lr: 0.00094
[2023-12-25 19:32:21,964 INFO misc.py line 119 253097] Train: [76/100][203/510] Data 0.005 (0.331) Batch 1.009 (1.536) Remain 05:21:08 loss: 0.0863 Lr: 0.00094
[2023-12-25 19:32:23,079 INFO misc.py line 119 253097] Train: [76/100][204/510] Data 0.009 (0.329) Batch 1.117 (1.534) Remain 05:20:40 loss: 0.1060 Lr: 0.00094
[2023-12-25 19:32:24,198 INFO misc.py line 119 253097] Train: [76/100][205/510] Data 0.007 (0.328) Batch 1.122 (1.532) Remain 05:20:13 loss: 0.1363 Lr: 0.00094
[2023-12-25 19:32:25,335 INFO misc.py line 119 253097] Train: [76/100][206/510] Data 0.005 (0.326) Batch 1.131 (1.530) Remain 05:19:47 loss: 0.2544 Lr: 0.00094
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[2023-12-25 19:32:32,942 INFO misc.py line 119 253097] Train: [76/100][213/510] Data 0.004 (0.315) Batch 1.009 (1.515) Remain 05:16:32 loss: 0.1999 Lr: 0.00094
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[2023-12-25 19:32:53,591 INFO misc.py line 119 253097] Train: [76/100][229/510] Data 0.004 (0.294) Batch 1.156 (1.499) Remain 05:12:48 loss: 0.0920 Lr: 0.00094
[2023-12-25 19:32:54,816 INFO misc.py line 119 253097] Train: [76/100][230/510] Data 0.004 (0.292) Batch 1.220 (1.498) Remain 05:12:32 loss: 0.0891 Lr: 0.00094
[2023-12-25 19:33:00,730 INFO misc.py line 119 253097] Train: [76/100][231/510] Data 4.607 (0.311) Batch 5.920 (1.517) Remain 05:16:33 loss: 0.0653 Lr: 0.00094
[2023-12-25 19:33:01,978 INFO misc.py line 119 253097] Train: [76/100][232/510] Data 0.004 (0.310) Batch 1.248 (1.516) Remain 05:16:17 loss: 0.0965 Lr: 0.00094
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[2023-12-25 19:33:04,162 INFO misc.py line 119 253097] Train: [76/100][234/510] Data 0.008 (0.307) Batch 0.998 (1.512) Remain 05:15:28 loss: 0.1308 Lr: 0.00094
[2023-12-25 19:33:05,284 INFO misc.py line 119 253097] Train: [76/100][235/510] Data 0.004 (0.306) Batch 1.121 (1.511) Remain 05:15:05 loss: 0.0975 Lr: 0.00094
[2023-12-25 19:33:06,498 INFO misc.py line 119 253097] Train: [76/100][236/510] Data 0.004 (0.305) Batch 1.211 (1.509) Remain 05:14:47 loss: 0.1039 Lr: 0.00094
[2023-12-25 19:33:07,362 INFO misc.py line 119 253097] Train: [76/100][237/510] Data 0.007 (0.303) Batch 0.864 (1.507) Remain 05:14:11 loss: 0.0790 Lr: 0.00094
[2023-12-25 19:33:08,253 INFO misc.py line 119 253097] Train: [76/100][238/510] Data 0.007 (0.302) Batch 0.891 (1.504) Remain 05:13:37 loss: 0.0874 Lr: 0.00094
[2023-12-25 19:33:16,683 INFO misc.py line 119 253097] Train: [76/100][239/510] Data 0.007 (0.301) Batch 8.432 (1.533) Remain 05:19:43 loss: 0.0821 Lr: 0.00094
[2023-12-25 19:33:17,948 INFO misc.py line 119 253097] Train: [76/100][240/510] Data 0.005 (0.300) Batch 1.266 (1.532) Remain 05:19:27 loss: 0.1778 Lr: 0.00094
[2023-12-25 19:33:19,153 INFO misc.py line 119 253097] Train: [76/100][241/510] Data 0.004 (0.298) Batch 1.203 (1.531) Remain 05:19:08 loss: 0.1340 Lr: 0.00094
[2023-12-25 19:33:20,255 INFO misc.py line 119 253097] Train: [76/100][242/510] Data 0.007 (0.297) Batch 1.104 (1.529) Remain 05:18:45 loss: 0.0665 Lr: 0.00094
[2023-12-25 19:33:21,186 INFO misc.py line 119 253097] Train: [76/100][243/510] Data 0.004 (0.296) Batch 0.931 (1.527) Remain 05:18:12 loss: 0.0867 Lr: 0.00094
[2023-12-25 19:33:22,418 INFO misc.py line 119 253097] Train: [76/100][244/510] Data 0.005 (0.295) Batch 1.232 (1.525) Remain 05:17:55 loss: 0.1653 Lr: 0.00094
[2023-12-25 19:33:23,664 INFO misc.py line 119 253097] Train: [76/100][245/510] Data 0.005 (0.294) Batch 1.245 (1.524) Remain 05:17:39 loss: 0.1089 Lr: 0.00094
[2023-12-25 19:33:24,680 INFO misc.py line 119 253097] Train: [76/100][246/510] Data 0.005 (0.292) Batch 1.016 (1.522) Remain 05:17:11 loss: 0.0920 Lr: 0.00094
[2023-12-25 19:33:25,866 INFO misc.py line 119 253097] Train: [76/100][247/510] Data 0.006 (0.291) Batch 1.188 (1.521) Remain 05:16:53 loss: 0.1966 Lr: 0.00094
[2023-12-25 19:33:26,976 INFO misc.py line 119 253097] Train: [76/100][248/510] Data 0.006 (0.290) Batch 1.110 (1.519) Remain 05:16:30 loss: 0.2264 Lr: 0.00094
[2023-12-25 19:33:27,818 INFO misc.py line 119 253097] Train: [76/100][249/510] Data 0.004 (0.289) Batch 0.842 (1.516) Remain 05:15:54 loss: 0.1514 Lr: 0.00094
[2023-12-25 19:33:29,010 INFO misc.py line 119 253097] Train: [76/100][250/510] Data 0.005 (0.288) Batch 1.187 (1.515) Remain 05:15:36 loss: 0.0983 Lr: 0.00094
[2023-12-25 19:33:33,246 INFO misc.py line 119 253097] Train: [76/100][251/510] Data 0.010 (0.287) Batch 4.241 (1.526) Remain 05:17:52 loss: 0.0745 Lr: 0.00094
[2023-12-25 19:33:34,415 INFO misc.py line 119 253097] Train: [76/100][252/510] Data 0.004 (0.286) Batch 1.165 (1.524) Remain 05:17:32 loss: 0.1502 Lr: 0.00093
[2023-12-25 19:33:39,619 INFO misc.py line 119 253097] Train: [76/100][253/510] Data 0.008 (0.284) Batch 5.208 (1.539) Remain 05:20:35 loss: 0.0997 Lr: 0.00093
[2023-12-25 19:33:40,763 INFO misc.py line 119 253097] Train: [76/100][254/510] Data 0.004 (0.283) Batch 1.146 (1.538) Remain 05:20:14 loss: 0.1194 Lr: 0.00093
[2023-12-25 19:33:41,715 INFO misc.py line 119 253097] Train: [76/100][255/510] Data 0.003 (0.282) Batch 0.951 (1.535) Remain 05:19:43 loss: 0.1995 Lr: 0.00093
[2023-12-25 19:33:42,705 INFO misc.py line 119 253097] Train: [76/100][256/510] Data 0.003 (0.281) Batch 0.987 (1.533) Remain 05:19:15 loss: 0.0703 Lr: 0.00093
[2023-12-25 19:33:43,755 INFO misc.py line 119 253097] Train: [76/100][257/510] Data 0.007 (0.280) Batch 1.053 (1.531) Remain 05:18:49 loss: 0.0877 Lr: 0.00093
[2023-12-25 19:33:44,897 INFO misc.py line 119 253097] Train: [76/100][258/510] Data 0.004 (0.279) Batch 1.138 (1.530) Remain 05:18:29 loss: 0.1247 Lr: 0.00093
[2023-12-25 19:33:45,953 INFO misc.py line 119 253097] Train: [76/100][259/510] Data 0.009 (0.278) Batch 1.056 (1.528) Remain 05:18:04 loss: 0.2353 Lr: 0.00093
[2023-12-25 19:33:47,030 INFO misc.py line 119 253097] Train: [76/100][260/510] Data 0.008 (0.277) Batch 1.079 (1.526) Remain 05:17:41 loss: 0.0690 Lr: 0.00093
[2023-12-25 19:33:48,922 INFO misc.py line 119 253097] Train: [76/100][261/510] Data 0.005 (0.276) Batch 1.894 (1.528) Remain 05:17:57 loss: 0.0896 Lr: 0.00093
[2023-12-25 19:33:49,968 INFO misc.py line 119 253097] Train: [76/100][262/510] Data 0.004 (0.275) Batch 1.047 (1.526) Remain 05:17:32 loss: 0.1339 Lr: 0.00093
[2023-12-25 19:33:51,170 INFO misc.py line 119 253097] Train: [76/100][263/510] Data 0.003 (0.274) Batch 1.202 (1.524) Remain 05:17:15 loss: 0.1462 Lr: 0.00093
[2023-12-25 19:33:52,301 INFO misc.py line 119 253097] Train: [76/100][264/510] Data 0.003 (0.273) Batch 1.131 (1.523) Remain 05:16:55 loss: 0.0872 Lr: 0.00093
[2023-12-25 19:33:53,319 INFO misc.py line 119 253097] Train: [76/100][265/510] Data 0.003 (0.272) Batch 1.018 (1.521) Remain 05:16:29 loss: 0.1519 Lr: 0.00093
[2023-12-25 19:33:56,915 INFO misc.py line 119 253097] Train: [76/100][266/510] Data 0.003 (0.271) Batch 3.595 (1.529) Remain 05:18:06 loss: 0.0547 Lr: 0.00093
[2023-12-25 19:34:01,002 INFO misc.py line 119 253097] Train: [76/100][267/510] Data 0.004 (0.270) Batch 4.089 (1.539) Remain 05:20:06 loss: 0.1140 Lr: 0.00093
[2023-12-25 19:34:02,050 INFO misc.py line 119 253097] Train: [76/100][268/510] Data 0.003 (0.269) Batch 1.047 (1.537) Remain 05:19:41 loss: 0.1430 Lr: 0.00093
[2023-12-25 19:34:03,207 INFO misc.py line 119 253097] Train: [76/100][269/510] Data 0.003 (0.268) Batch 1.157 (1.535) Remain 05:19:22 loss: 0.0488 Lr: 0.00093
[2023-12-25 19:34:04,269 INFO misc.py line 119 253097] Train: [76/100][270/510] Data 0.004 (0.267) Batch 1.062 (1.534) Remain 05:18:58 loss: 0.1662 Lr: 0.00093
[2023-12-25 19:34:05,527 INFO misc.py line 119 253097] Train: [76/100][271/510] Data 0.004 (0.266) Batch 1.254 (1.532) Remain 05:18:43 loss: 0.1817 Lr: 0.00093
[2023-12-25 19:34:06,570 INFO misc.py line 119 253097] Train: [76/100][272/510] Data 0.008 (0.265) Batch 1.042 (1.531) Remain 05:18:19 loss: 0.1950 Lr: 0.00093
[2023-12-25 19:34:07,883 INFO misc.py line 119 253097] Train: [76/100][273/510] Data 0.009 (0.264) Batch 1.317 (1.530) Remain 05:18:08 loss: 0.0894 Lr: 0.00093
[2023-12-25 19:34:08,852 INFO misc.py line 119 253097] Train: [76/100][274/510] Data 0.005 (0.263) Batch 0.971 (1.528) Remain 05:17:40 loss: 0.0975 Lr: 0.00093
[2023-12-25 19:34:09,954 INFO misc.py line 119 253097] Train: [76/100][275/510] Data 0.003 (0.262) Batch 1.099 (1.526) Remain 05:17:19 loss: 0.2048 Lr: 0.00093
[2023-12-25 19:34:11,117 INFO misc.py line 119 253097] Train: [76/100][276/510] Data 0.006 (0.261) Batch 1.161 (1.525) Remain 05:17:01 loss: 0.2932 Lr: 0.00093
[2023-12-25 19:34:12,225 INFO misc.py line 119 253097] Train: [76/100][277/510] Data 0.007 (0.260) Batch 1.106 (1.523) Remain 05:16:40 loss: 0.1928 Lr: 0.00093
[2023-12-25 19:34:17,507 INFO misc.py line 119 253097] Train: [76/100][278/510] Data 0.010 (0.259) Batch 5.288 (1.537) Remain 05:19:30 loss: 0.0816 Lr: 0.00093
[2023-12-25 19:34:18,840 INFO misc.py line 119 253097] Train: [76/100][279/510] Data 0.004 (0.258) Batch 1.315 (1.536) Remain 05:19:18 loss: 0.1358 Lr: 0.00093
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[2023-12-25 19:34:21,367 INFO misc.py line 119 253097] Train: [76/100][281/510] Data 0.019 (0.256) Batch 1.285 (1.534) Remain 05:18:51 loss: 0.1761 Lr: 0.00093
[2023-12-25 19:34:22,571 INFO misc.py line 119 253097] Train: [76/100][282/510] Data 0.009 (0.255) Batch 1.207 (1.533) Remain 05:18:35 loss: 0.1453 Lr: 0.00093
[2023-12-25 19:34:23,569 INFO misc.py line 119 253097] Train: [76/100][283/510] Data 0.006 (0.255) Batch 0.999 (1.531) Remain 05:18:10 loss: 0.1030 Lr: 0.00093
[2023-12-25 19:34:27,573 INFO misc.py line 119 253097] Train: [76/100][284/510] Data 0.006 (0.254) Batch 4.006 (1.540) Remain 05:19:58 loss: 0.0873 Lr: 0.00093
[2023-12-25 19:34:28,829 INFO misc.py line 119 253097] Train: [76/100][285/510] Data 0.004 (0.253) Batch 1.255 (1.539) Remain 05:19:44 loss: 0.1263 Lr: 0.00093
[2023-12-25 19:34:30,865 INFO misc.py line 119 253097] Train: [76/100][286/510] Data 0.005 (0.252) Batch 2.034 (1.541) Remain 05:20:04 loss: 0.1757 Lr: 0.00093
[2023-12-25 19:34:32,003 INFO misc.py line 119 253097] Train: [76/100][287/510] Data 0.008 (0.251) Batch 1.138 (1.539) Remain 05:19:45 loss: 0.1088 Lr: 0.00093
[2023-12-25 19:34:33,266 INFO misc.py line 119 253097] Train: [76/100][288/510] Data 0.007 (0.250) Batch 1.265 (1.538) Remain 05:19:31 loss: 0.1441 Lr: 0.00093
[2023-12-25 19:34:34,377 INFO misc.py line 119 253097] Train: [76/100][289/510] Data 0.006 (0.249) Batch 1.111 (1.537) Remain 05:19:11 loss: 0.2037 Lr: 0.00093
[2023-12-25 19:34:35,519 INFO misc.py line 119 253097] Train: [76/100][290/510] Data 0.005 (0.249) Batch 1.103 (1.535) Remain 05:18:51 loss: 0.0747 Lr: 0.00093
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[2023-12-25 19:34:47,069 INFO misc.py line 119 253097] Train: [76/100][300/510] Data 0.004 (0.240) Batch 1.251 (1.523) Remain 05:15:57 loss: 0.0650 Lr: 0.00093
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[2023-12-25 19:34:49,662 INFO misc.py line 119 253097] Train: [76/100][302/510] Data 0.007 (0.239) Batch 1.260 (1.521) Remain 05:15:35 loss: 0.1739 Lr: 0.00093
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[2023-12-25 19:35:06,692 INFO misc.py line 119 253097] Train: [76/100][304/510] Data 14.625 (0.286) Batch 15.846 (1.568) Remain 05:25:11 loss: 0.0787 Lr: 0.00093
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[2023-12-25 19:35:08,837 INFO misc.py line 119 253097] Train: [76/100][306/510] Data 0.004 (0.284) Batch 1.007 (1.564) Remain 05:24:27 loss: 0.0957 Lr: 0.00093
[2023-12-25 19:35:09,999 INFO misc.py line 119 253097] Train: [76/100][307/510] Data 0.003 (0.283) Batch 1.161 (1.563) Remain 05:24:09 loss: 0.1729 Lr: 0.00093
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[2023-12-25 19:36:39,121 INFO misc.py line 119 253097] Train: [76/100][363/510] Data 11.573 (0.272) Batch 12.859 (1.568) Remain 05:23:36 loss: 0.1828 Lr: 0.00092
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[2023-12-25 19:36:42,541 INFO misc.py line 119 253097] Train: [76/100][366/510] Data 0.007 (0.270) Batch 1.051 (1.564) Remain 05:22:47 loss: 0.1115 Lr: 0.00092
[2023-12-25 19:36:43,756 INFO misc.py line 119 253097] Train: [76/100][367/510] Data 0.015 (0.269) Batch 1.223 (1.563) Remain 05:22:34 loss: 0.1458 Lr: 0.00092
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[2023-12-25 19:37:05,563 INFO misc.py line 119 253097] Train: [76/100][380/510] Data 0.008 (0.260) Batch 0.987 (1.567) Remain 05:23:03 loss: 0.0793 Lr: 0.00092
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[2023-12-25 19:37:57,131 INFO misc.py line 119 253097] Train: [76/100][416/510] Data 0.007 (0.238) Batch 1.265 (1.555) Remain 05:19:42 loss: 0.0885 Lr: 0.00091
[2023-12-25 19:38:00,358 INFO misc.py line 119 253097] Train: [76/100][417/510] Data 0.011 (0.238) Batch 3.235 (1.559) Remain 05:20:30 loss: 0.0710 Lr: 0.00091
[2023-12-25 19:38:01,428 INFO misc.py line 119 253097] Train: [76/100][418/510] Data 0.005 (0.237) Batch 1.064 (1.558) Remain 05:20:14 loss: 0.0672 Lr: 0.00091
[2023-12-25 19:38:02,612 INFO misc.py line 119 253097] Train: [76/100][419/510] Data 0.010 (0.236) Batch 1.189 (1.557) Remain 05:20:01 loss: 0.1075 Lr: 0.00091
[2023-12-25 19:38:03,693 INFO misc.py line 119 253097] Train: [76/100][420/510] Data 0.005 (0.236) Batch 1.078 (1.556) Remain 05:19:46 loss: 0.0942 Lr: 0.00091
[2023-12-25 19:38:04,902 INFO misc.py line 119 253097] Train: [76/100][421/510] Data 0.008 (0.235) Batch 1.211 (1.555) Remain 05:19:34 loss: 0.0963 Lr: 0.00091
[2023-12-25 19:38:05,983 INFO misc.py line 119 253097] Train: [76/100][422/510] Data 0.006 (0.235) Batch 1.084 (1.554) Remain 05:19:18 loss: 0.0732 Lr: 0.00091
[2023-12-25 19:38:07,251 INFO misc.py line 119 253097] Train: [76/100][423/510] Data 0.003 (0.234) Batch 1.266 (1.553) Remain 05:19:08 loss: 0.1414 Lr: 0.00091
[2023-12-25 19:38:08,504 INFO misc.py line 119 253097] Train: [76/100][424/510] Data 0.005 (0.234) Batch 1.250 (1.553) Remain 05:18:58 loss: 0.1183 Lr: 0.00091
[2023-12-25 19:38:09,569 INFO misc.py line 119 253097] Train: [76/100][425/510] Data 0.009 (0.233) Batch 1.070 (1.552) Remain 05:18:42 loss: 0.2219 Lr: 0.00091
[2023-12-25 19:38:10,812 INFO misc.py line 119 253097] Train: [76/100][426/510] Data 0.004 (0.233) Batch 1.242 (1.551) Remain 05:18:32 loss: 0.1117 Lr: 0.00091
[2023-12-25 19:38:12,120 INFO misc.py line 119 253097] Train: [76/100][427/510] Data 0.004 (0.232) Batch 1.309 (1.550) Remain 05:18:23 loss: 0.0907 Lr: 0.00091
[2023-12-25 19:38:13,202 INFO misc.py line 119 253097] Train: [76/100][428/510] Data 0.003 (0.232) Batch 1.082 (1.549) Remain 05:18:08 loss: 0.0838 Lr: 0.00091
[2023-12-25 19:38:14,494 INFO misc.py line 119 253097] Train: [76/100][429/510] Data 0.004 (0.231) Batch 1.292 (1.549) Remain 05:17:59 loss: 0.0961 Lr: 0.00091
[2023-12-25 19:38:15,787 INFO misc.py line 119 253097] Train: [76/100][430/510] Data 0.003 (0.231) Batch 1.290 (1.548) Remain 05:17:50 loss: 0.1767 Lr: 0.00091
[2023-12-25 19:38:16,947 INFO misc.py line 119 253097] Train: [76/100][431/510] Data 0.007 (0.230) Batch 1.163 (1.547) Remain 05:17:37 loss: 0.3260 Lr: 0.00091
[2023-12-25 19:38:18,027 INFO misc.py line 119 253097] Train: [76/100][432/510] Data 0.004 (0.229) Batch 1.076 (1.546) Remain 05:17:22 loss: 0.2014 Lr: 0.00091
[2023-12-25 19:38:19,301 INFO misc.py line 119 253097] Train: [76/100][433/510] Data 0.008 (0.229) Batch 1.273 (1.545) Remain 05:17:13 loss: 0.1140 Lr: 0.00091
[2023-12-25 19:38:20,280 INFO misc.py line 119 253097] Train: [76/100][434/510] Data 0.009 (0.228) Batch 0.984 (1.544) Remain 05:16:55 loss: 0.1516 Lr: 0.00091
[2023-12-25 19:38:21,361 INFO misc.py line 119 253097] Train: [76/100][435/510] Data 0.003 (0.228) Batch 1.081 (1.543) Remain 05:16:41 loss: 0.0995 Lr: 0.00091
[2023-12-25 19:38:28,495 INFO misc.py line 119 253097] Train: [76/100][436/510] Data 0.004 (0.227) Batch 7.133 (1.556) Remain 05:19:18 loss: 0.1359 Lr: 0.00091
[2023-12-25 19:38:30,544 INFO misc.py line 119 253097] Train: [76/100][437/510] Data 0.005 (0.227) Batch 2.043 (1.557) Remain 05:19:30 loss: 0.1549 Lr: 0.00091
[2023-12-25 19:38:31,521 INFO misc.py line 119 253097] Train: [76/100][438/510] Data 0.011 (0.226) Batch 0.984 (1.556) Remain 05:19:13 loss: 0.0706 Lr: 0.00091
[2023-12-25 19:38:32,474 INFO misc.py line 119 253097] Train: [76/100][439/510] Data 0.004 (0.226) Batch 0.954 (1.554) Remain 05:18:54 loss: 0.3297 Lr: 0.00091
[2023-12-25 19:38:33,489 INFO misc.py line 119 253097] Train: [76/100][440/510] Data 0.004 (0.225) Batch 1.010 (1.553) Remain 05:18:37 loss: 0.0939 Lr: 0.00091
[2023-12-25 19:38:34,570 INFO misc.py line 119 253097] Train: [76/100][441/510] Data 0.009 (0.225) Batch 1.086 (1.552) Remain 05:18:22 loss: 0.2112 Lr: 0.00091
[2023-12-25 19:38:35,616 INFO misc.py line 119 253097] Train: [76/100][442/510] Data 0.004 (0.224) Batch 1.043 (1.551) Remain 05:18:07 loss: 0.0748 Lr: 0.00091
[2023-12-25 19:38:37,716 INFO misc.py line 119 253097] Train: [76/100][443/510] Data 0.006 (0.224) Batch 2.103 (1.552) Remain 05:18:20 loss: 0.1475 Lr: 0.00091
[2023-12-25 19:38:38,744 INFO misc.py line 119 253097] Train: [76/100][444/510] Data 0.004 (0.223) Batch 1.027 (1.551) Remain 05:18:04 loss: 0.0986 Lr: 0.00091
[2023-12-25 19:38:39,880 INFO misc.py line 119 253097] Train: [76/100][445/510] Data 0.005 (0.223) Batch 1.134 (1.550) Remain 05:17:51 loss: 0.1200 Lr: 0.00091
[2023-12-25 19:38:40,991 INFO misc.py line 119 253097] Train: [76/100][446/510] Data 0.007 (0.222) Batch 1.111 (1.549) Remain 05:17:37 loss: 0.1041 Lr: 0.00091
[2023-12-25 19:38:42,159 INFO misc.py line 119 253097] Train: [76/100][447/510] Data 0.008 (0.222) Batch 1.170 (1.548) Remain 05:17:25 loss: 0.0881 Lr: 0.00091
[2023-12-25 19:38:43,333 INFO misc.py line 119 253097] Train: [76/100][448/510] Data 0.005 (0.221) Batch 1.176 (1.547) Remain 05:17:13 loss: 0.0874 Lr: 0.00091
[2023-12-25 19:38:44,529 INFO misc.py line 119 253097] Train: [76/100][449/510] Data 0.003 (0.221) Batch 1.191 (1.546) Remain 05:17:02 loss: 0.1565 Lr: 0.00091
[2023-12-25 19:38:45,651 INFO misc.py line 119 253097] Train: [76/100][450/510] Data 0.008 (0.220) Batch 1.121 (1.545) Remain 05:16:49 loss: 0.1469 Lr: 0.00091
[2023-12-25 19:38:46,777 INFO misc.py line 119 253097] Train: [76/100][451/510] Data 0.009 (0.220) Batch 1.128 (1.545) Remain 05:16:36 loss: 0.0797 Lr: 0.00091
[2023-12-25 19:38:47,976 INFO misc.py line 119 253097] Train: [76/100][452/510] Data 0.006 (0.220) Batch 1.202 (1.544) Remain 05:16:25 loss: 0.1147 Lr: 0.00091
[2023-12-25 19:38:48,875 INFO misc.py line 119 253097] Train: [76/100][453/510] Data 0.005 (0.219) Batch 0.900 (1.542) Remain 05:16:06 loss: 0.1118 Lr: 0.00091
[2023-12-25 19:38:50,055 INFO misc.py line 119 253097] Train: [76/100][454/510] Data 0.003 (0.219) Batch 1.179 (1.542) Remain 05:15:54 loss: 0.1157 Lr: 0.00091
[2023-12-25 19:38:51,142 INFO misc.py line 119 253097] Train: [76/100][455/510] Data 0.004 (0.218) Batch 1.089 (1.541) Remain 05:15:40 loss: 0.2230 Lr: 0.00091
[2023-12-25 19:38:52,242 INFO misc.py line 119 253097] Train: [76/100][456/510] Data 0.003 (0.218) Batch 1.099 (1.540) Remain 05:15:27 loss: 0.1712 Lr: 0.00091
[2023-12-25 19:38:56,808 INFO misc.py line 119 253097] Train: [76/100][457/510] Data 0.004 (0.217) Batch 4.564 (1.546) Remain 05:16:47 loss: 0.1314 Lr: 0.00091
[2023-12-25 19:38:58,045 INFO misc.py line 119 253097] Train: [76/100][458/510] Data 0.006 (0.217) Batch 1.238 (1.546) Remain 05:16:37 loss: 0.0760 Lr: 0.00091
[2023-12-25 19:38:59,221 INFO misc.py line 119 253097] Train: [76/100][459/510] Data 0.004 (0.216) Batch 1.176 (1.545) Remain 05:16:26 loss: 0.1127 Lr: 0.00091
[2023-12-25 19:39:00,311 INFO misc.py line 119 253097] Train: [76/100][460/510] Data 0.004 (0.216) Batch 1.091 (1.544) Remain 05:16:12 loss: 0.0863 Lr: 0.00091
[2023-12-25 19:39:07,400 INFO misc.py line 119 253097] Train: [76/100][461/510] Data 0.004 (0.215) Batch 7.088 (1.556) Remain 05:18:39 loss: 0.1639 Lr: 0.00091
[2023-12-25 19:39:08,659 INFO misc.py line 119 253097] Train: [76/100][462/510] Data 0.004 (0.215) Batch 1.257 (1.555) Remain 05:18:30 loss: 0.1343 Lr: 0.00091
[2023-12-25 19:39:09,615 INFO misc.py line 119 253097] Train: [76/100][463/510] Data 0.007 (0.214) Batch 0.959 (1.554) Remain 05:18:12 loss: 0.0963 Lr: 0.00091
[2023-12-25 19:39:10,886 INFO misc.py line 119 253097] Train: [76/100][464/510] Data 0.003 (0.214) Batch 1.266 (1.553) Remain 05:18:03 loss: 0.1236 Lr: 0.00091
[2023-12-25 19:39:11,866 INFO misc.py line 119 253097] Train: [76/100][465/510] Data 0.008 (0.213) Batch 0.983 (1.552) Remain 05:17:46 loss: 0.0693 Lr: 0.00091
[2023-12-25 19:39:13,182 INFO misc.py line 119 253097] Train: [76/100][466/510] Data 0.006 (0.213) Batch 1.312 (1.552) Remain 05:17:39 loss: 0.1217 Lr: 0.00090
[2023-12-25 19:39:14,324 INFO misc.py line 119 253097] Train: [76/100][467/510] Data 0.009 (0.213) Batch 1.146 (1.551) Remain 05:17:26 loss: 0.0935 Lr: 0.00090
[2023-12-25 19:39:15,491 INFO misc.py line 119 253097] Train: [76/100][468/510] Data 0.005 (0.212) Batch 1.168 (1.550) Remain 05:17:15 loss: 0.1158 Lr: 0.00090
[2023-12-25 19:39:16,783 INFO misc.py line 119 253097] Train: [76/100][469/510] Data 0.004 (0.212) Batch 1.293 (1.549) Remain 05:17:06 loss: 0.1007 Lr: 0.00090
[2023-12-25 19:39:17,919 INFO misc.py line 119 253097] Train: [76/100][470/510] Data 0.004 (0.211) Batch 1.135 (1.548) Remain 05:16:54 loss: 0.0960 Lr: 0.00090
[2023-12-25 19:39:19,157 INFO misc.py line 119 253097] Train: [76/100][471/510] Data 0.004 (0.211) Batch 1.232 (1.548) Remain 05:16:44 loss: 0.1467 Lr: 0.00090
[2023-12-25 19:39:20,414 INFO misc.py line 119 253097] Train: [76/100][472/510] Data 0.010 (0.210) Batch 1.264 (1.547) Remain 05:16:35 loss: 0.0675 Lr: 0.00090
[2023-12-25 19:39:21,631 INFO misc.py line 119 253097] Train: [76/100][473/510] Data 0.003 (0.210) Batch 1.217 (1.546) Remain 05:16:25 loss: 0.2039 Lr: 0.00090
[2023-12-25 19:39:22,694 INFO misc.py line 119 253097] Train: [76/100][474/510] Data 0.003 (0.209) Batch 1.063 (1.545) Remain 05:16:11 loss: 0.1001 Lr: 0.00090
[2023-12-25 19:39:23,988 INFO misc.py line 119 253097] Train: [76/100][475/510] Data 0.003 (0.209) Batch 1.294 (1.545) Remain 05:16:03 loss: 0.0591 Lr: 0.00090
[2023-12-25 19:39:25,116 INFO misc.py line 119 253097] Train: [76/100][476/510] Data 0.004 (0.209) Batch 1.129 (1.544) Remain 05:15:50 loss: 0.1200 Lr: 0.00090
[2023-12-25 19:39:26,269 INFO misc.py line 119 253097] Train: [76/100][477/510] Data 0.003 (0.208) Batch 1.152 (1.543) Remain 05:15:39 loss: 0.0690 Lr: 0.00090
[2023-12-25 19:39:27,490 INFO misc.py line 119 253097] Train: [76/100][478/510] Data 0.003 (0.208) Batch 1.218 (1.542) Remain 05:15:29 loss: 0.1591 Lr: 0.00090
[2023-12-25 19:39:28,661 INFO misc.py line 119 253097] Train: [76/100][479/510] Data 0.007 (0.207) Batch 1.174 (1.542) Remain 05:15:18 loss: 0.1552 Lr: 0.00090
[2023-12-25 19:39:29,781 INFO misc.py line 119 253097] Train: [76/100][480/510] Data 0.003 (0.207) Batch 1.116 (1.541) Remain 05:15:05 loss: 0.0720 Lr: 0.00090
[2023-12-25 19:39:30,925 INFO misc.py line 119 253097] Train: [76/100][481/510] Data 0.008 (0.206) Batch 1.149 (1.540) Remain 05:14:53 loss: 0.1040 Lr: 0.00090
[2023-12-25 19:39:32,065 INFO misc.py line 119 253097] Train: [76/100][482/510] Data 0.005 (0.206) Batch 1.139 (1.539) Remain 05:14:42 loss: 0.1848 Lr: 0.00090
[2023-12-25 19:39:36,846 INFO misc.py line 119 253097] Train: [76/100][483/510] Data 3.577 (0.213) Batch 4.779 (1.546) Remain 05:16:03 loss: 0.0849 Lr: 0.00090
[2023-12-25 19:39:38,091 INFO misc.py line 119 253097] Train: [76/100][484/510] Data 0.006 (0.213) Batch 1.242 (1.545) Remain 05:15:54 loss: 0.0615 Lr: 0.00090
[2023-12-25 19:39:39,253 INFO misc.py line 119 253097] Train: [76/100][485/510] Data 0.008 (0.212) Batch 1.167 (1.544) Remain 05:15:42 loss: 0.1316 Lr: 0.00090
[2023-12-25 19:39:40,378 INFO misc.py line 119 253097] Train: [76/100][486/510] Data 0.004 (0.212) Batch 1.125 (1.544) Remain 05:15:30 loss: 0.1852 Lr: 0.00090
[2023-12-25 19:39:41,481 INFO misc.py line 119 253097] Train: [76/100][487/510] Data 0.003 (0.211) Batch 1.101 (1.543) Remain 05:15:17 loss: 0.1226 Lr: 0.00090
[2023-12-25 19:39:42,560 INFO misc.py line 119 253097] Train: [76/100][488/510] Data 0.005 (0.211) Batch 1.080 (1.542) Remain 05:15:04 loss: 0.1161 Lr: 0.00090
[2023-12-25 19:39:43,560 INFO misc.py line 119 253097] Train: [76/100][489/510] Data 0.005 (0.211) Batch 0.999 (1.541) Remain 05:14:49 loss: 0.1027 Lr: 0.00090
[2023-12-25 19:39:44,747 INFO misc.py line 119 253097] Train: [76/100][490/510] Data 0.006 (0.210) Batch 1.188 (1.540) Remain 05:14:39 loss: 0.0959 Lr: 0.00090
[2023-12-25 19:39:45,956 INFO misc.py line 119 253097] Train: [76/100][491/510] Data 0.005 (0.210) Batch 1.205 (1.539) Remain 05:14:29 loss: 0.1979 Lr: 0.00090
[2023-12-25 19:39:47,033 INFO misc.py line 119 253097] Train: [76/100][492/510] Data 0.009 (0.209) Batch 1.079 (1.538) Remain 05:14:16 loss: 0.1443 Lr: 0.00090
[2023-12-25 19:39:48,092 INFO misc.py line 119 253097] Train: [76/100][493/510] Data 0.008 (0.209) Batch 1.062 (1.537) Remain 05:14:02 loss: 0.1225 Lr: 0.00090
[2023-12-25 19:39:49,113 INFO misc.py line 119 253097] Train: [76/100][494/510] Data 0.004 (0.208) Batch 1.020 (1.536) Remain 05:13:48 loss: 0.0942 Lr: 0.00090
[2023-12-25 19:40:00,238 INFO misc.py line 119 253097] Train: [76/100][495/510] Data 9.862 (0.228) Batch 11.125 (1.556) Remain 05:17:45 loss: 0.0640 Lr: 0.00090
[2023-12-25 19:40:01,421 INFO misc.py line 119 253097] Train: [76/100][496/510] Data 0.004 (0.228) Batch 1.181 (1.555) Remain 05:17:34 loss: 0.2328 Lr: 0.00090
[2023-12-25 19:40:02,557 INFO misc.py line 119 253097] Train: [76/100][497/510] Data 0.009 (0.227) Batch 1.139 (1.554) Remain 05:17:22 loss: 0.1475 Lr: 0.00090
[2023-12-25 19:40:03,811 INFO misc.py line 119 253097] Train: [76/100][498/510] Data 0.004 (0.227) Batch 1.250 (1.554) Remain 05:17:13 loss: 0.1072 Lr: 0.00090
[2023-12-25 19:40:05,114 INFO misc.py line 119 253097] Train: [76/100][499/510] Data 0.009 (0.226) Batch 1.290 (1.553) Remain 05:17:05 loss: 0.0851 Lr: 0.00090
[2023-12-25 19:40:06,256 INFO misc.py line 119 253097] Train: [76/100][500/510] Data 0.021 (0.226) Batch 1.158 (1.552) Remain 05:16:54 loss: 0.2046 Lr: 0.00090
[2023-12-25 19:40:07,303 INFO misc.py line 119 253097] Train: [76/100][501/510] Data 0.004 (0.225) Batch 1.048 (1.551) Remain 05:16:40 loss: 0.1879 Lr: 0.00090
[2023-12-25 19:40:08,422 INFO misc.py line 119 253097] Train: [76/100][502/510] Data 0.005 (0.225) Batch 1.120 (1.550) Remain 05:16:28 loss: 0.1034 Lr: 0.00090
[2023-12-25 19:40:09,537 INFO misc.py line 119 253097] Train: [76/100][503/510] Data 0.003 (0.225) Batch 1.111 (1.549) Remain 05:16:15 loss: 0.1944 Lr: 0.00090
[2023-12-25 19:40:10,682 INFO misc.py line 119 253097] Train: [76/100][504/510] Data 0.006 (0.224) Batch 1.148 (1.549) Remain 05:16:04 loss: 0.1460 Lr: 0.00090
[2023-12-25 19:40:11,426 INFO misc.py line 119 253097] Train: [76/100][505/510] Data 0.003 (0.224) Batch 0.745 (1.547) Remain 05:15:43 loss: 0.1971 Lr: 0.00090
[2023-12-25 19:40:12,465 INFO misc.py line 119 253097] Train: [76/100][506/510] Data 0.003 (0.223) Batch 1.038 (1.546) Remain 05:15:29 loss: 0.0584 Lr: 0.00090
[2023-12-25 19:40:13,502 INFO misc.py line 119 253097] Train: [76/100][507/510] Data 0.004 (0.223) Batch 1.036 (1.545) Remain 05:15:15 loss: 0.1262 Lr: 0.00090
[2023-12-25 19:40:14,665 INFO misc.py line 119 253097] Train: [76/100][508/510] Data 0.004 (0.222) Batch 1.164 (1.544) Remain 05:15:04 loss: 0.0709 Lr: 0.00090
[2023-12-25 19:40:20,238 INFO misc.py line 119 253097] Train: [76/100][509/510] Data 0.003 (0.222) Batch 5.572 (1.552) Remain 05:16:40 loss: 0.0946 Lr: 0.00090
[2023-12-25 19:40:21,463 INFO misc.py line 119 253097] Train: [76/100][510/510] Data 0.004 (0.221) Batch 1.221 (1.552) Remain 05:16:31 loss: 0.0997 Lr: 0.00090
[2023-12-25 19:40:21,464 INFO misc.py line 136 253097] Train result: loss: 0.1308 
[2023-12-25 19:40:21,465 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 19:40:51,754 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6528 
[2023-12-25 19:40:52,110 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3408 
[2023-12-25 19:40:59,323 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4415 
[2023-12-25 19:40:59,839 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4441 
[2023-12-25 19:41:01,823 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8333 
[2023-12-25 19:41:02,267 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3164 
[2023-12-25 19:41:03,146 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1635 
[2023-12-25 19:41:03,705 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2912 
[2023-12-25 19:41:05,522 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8013 
[2023-12-25 19:41:07,647 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2278 
[2023-12-25 19:41:08,505 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3058 
[2023-12-25 19:41:08,928 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7015 
[2023-12-25 19:41:09,835 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3768 
[2023-12-25 19:41:12,781 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8384 
[2023-12-25 19:41:13,247 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3679 
[2023-12-25 19:41:13,857 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3714 
[2023-12-25 19:41:14,556 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4117 
[2023-12-25 19:41:16,124 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6931/0.7577/0.9077.
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9171/0.9478
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9831/0.9914
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8498/0.9688
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3056/0.3395
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6428/0.6634
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7242/0.8306
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8227/0.9012
[2023-12-25 19:41:16,124 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9211/0.9677
[2023-12-25 19:41:16,125 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6539/0.7684
[2023-12-25 19:41:16,125 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7909/0.8929
[2023-12-25 19:41:16,125 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7796/0.8568
[2023-12-25 19:41:16,125 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6190/0.7216
[2023-12-25 19:41:16,125 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 19:41:16,127 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 19:41:16,127 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 19:41:53,285 INFO misc.py line 119 253097] Train: [77/100][1/510] Data 34.123 (34.123) Batch 35.123 (35.123) Remain 119:24:24 loss: 0.0638 Lr: 0.00090
[2023-12-25 19:41:54,126 INFO misc.py line 119 253097] Train: [77/100][2/510] Data 0.003 (0.003) Batch 0.837 (0.837) Remain 02:50:40 loss: 0.1291 Lr: 0.00090
[2023-12-25 19:41:55,343 INFO misc.py line 119 253097] Train: [77/100][3/510] Data 0.007 (0.007) Batch 1.186 (1.186) Remain 04:01:52 loss: 0.1839 Lr: 0.00090
[2023-12-25 19:41:56,444 INFO misc.py line 119 253097] Train: [77/100][4/510] Data 0.039 (0.039) Batch 1.134 (1.134) Remain 03:51:11 loss: 0.3274 Lr: 0.00090
[2023-12-25 19:41:57,706 INFO misc.py line 119 253097] Train: [77/100][5/510] Data 0.006 (0.023) Batch 1.263 (1.198) Remain 04:04:22 loss: 0.0970 Lr: 0.00090
[2023-12-25 19:41:58,812 INFO misc.py line 119 253097] Train: [77/100][6/510] Data 0.005 (0.017) Batch 1.100 (1.165) Remain 03:57:38 loss: 0.1145 Lr: 0.00090
[2023-12-25 19:41:59,957 INFO misc.py line 119 253097] Train: [77/100][7/510] Data 0.012 (0.015) Batch 1.147 (1.161) Remain 03:56:39 loss: 0.2394 Lr: 0.00090
[2023-12-25 19:42:01,225 INFO misc.py line 119 253097] Train: [77/100][8/510] Data 0.010 (0.014) Batch 1.271 (1.183) Remain 04:01:08 loss: 0.1050 Lr: 0.00090
[2023-12-25 19:42:02,353 INFO misc.py line 119 253097] Train: [77/100][9/510] Data 0.007 (0.013) Batch 1.120 (1.172) Remain 03:58:59 loss: 0.1094 Lr: 0.00090
[2023-12-25 19:42:03,458 INFO misc.py line 119 253097] Train: [77/100][10/510] Data 0.014 (0.013) Batch 1.101 (1.162) Remain 03:56:53 loss: 0.1147 Lr: 0.00090
[2023-12-25 19:42:04,780 INFO misc.py line 119 253097] Train: [77/100][11/510] Data 0.020 (0.014) Batch 1.335 (1.184) Remain 04:01:17 loss: 0.1115 Lr: 0.00090
[2023-12-25 19:42:06,050 INFO misc.py line 119 253097] Train: [77/100][12/510] Data 0.005 (0.013) Batch 1.270 (1.193) Remain 04:03:13 loss: 0.2019 Lr: 0.00090
[2023-12-25 19:42:07,130 INFO misc.py line 119 253097] Train: [77/100][13/510] Data 0.007 (0.012) Batch 1.081 (1.182) Remain 04:00:54 loss: 0.1199 Lr: 0.00090
[2023-12-25 19:42:08,246 INFO misc.py line 119 253097] Train: [77/100][14/510] Data 0.003 (0.012) Batch 1.113 (1.176) Remain 03:59:37 loss: 0.1035 Lr: 0.00090
[2023-12-25 19:42:09,420 INFO misc.py line 119 253097] Train: [77/100][15/510] Data 0.006 (0.011) Batch 1.164 (1.175) Remain 03:59:23 loss: 0.1755 Lr: 0.00090
[2023-12-25 19:42:11,443 INFO misc.py line 119 253097] Train: [77/100][16/510] Data 0.017 (0.012) Batch 2.036 (1.241) Remain 04:12:51 loss: 0.0865 Lr: 0.00090
[2023-12-25 19:42:12,548 INFO misc.py line 119 253097] Train: [77/100][17/510] Data 0.007 (0.011) Batch 1.102 (1.231) Remain 04:10:48 loss: 0.0952 Lr: 0.00090
[2023-12-25 19:42:13,731 INFO misc.py line 119 253097] Train: [77/100][18/510] Data 0.008 (0.011) Batch 1.186 (1.228) Remain 04:10:10 loss: 0.1230 Lr: 0.00090
[2023-12-25 19:42:14,726 INFO misc.py line 119 253097] Train: [77/100][19/510] Data 0.006 (0.011) Batch 0.995 (1.214) Remain 04:07:11 loss: 0.0860 Lr: 0.00090
[2023-12-25 19:42:15,895 INFO misc.py line 119 253097] Train: [77/100][20/510] Data 0.004 (0.010) Batch 1.168 (1.211) Remain 04:06:37 loss: 0.0892 Lr: 0.00090
[2023-12-25 19:42:28,725 INFO misc.py line 119 253097] Train: [77/100][21/510] Data 0.005 (0.010) Batch 12.830 (1.856) Remain 06:18:03 loss: 0.1393 Lr: 0.00090
[2023-12-25 19:42:30,023 INFO misc.py line 119 253097] Train: [77/100][22/510] Data 0.006 (0.010) Batch 1.300 (1.827) Remain 06:12:03 loss: 0.0699 Lr: 0.00090
[2023-12-25 19:42:31,221 INFO misc.py line 119 253097] Train: [77/100][23/510] Data 0.003 (0.010) Batch 1.194 (1.795) Remain 06:05:35 loss: 0.0766 Lr: 0.00090
[2023-12-25 19:42:32,435 INFO misc.py line 119 253097] Train: [77/100][24/510] Data 0.007 (0.009) Batch 1.210 (1.768) Remain 05:59:52 loss: 0.0894 Lr: 0.00090
[2023-12-25 19:42:33,700 INFO misc.py line 119 253097] Train: [77/100][25/510] Data 0.011 (0.009) Batch 1.269 (1.745) Remain 05:55:14 loss: 0.1460 Lr: 0.00090
[2023-12-25 19:42:34,722 INFO misc.py line 119 253097] Train: [77/100][26/510] Data 0.007 (0.009) Batch 1.026 (1.714) Remain 05:48:50 loss: 0.1610 Lr: 0.00090
[2023-12-25 19:42:35,874 INFO misc.py line 119 253097] Train: [77/100][27/510] Data 0.003 (0.009) Batch 1.149 (1.690) Remain 05:44:01 loss: 0.1316 Lr: 0.00090
[2023-12-25 19:42:41,953 INFO misc.py line 119 253097] Train: [77/100][28/510] Data 0.007 (0.009) Batch 6.082 (1.866) Remain 06:19:45 loss: 0.1193 Lr: 0.00089
[2023-12-25 19:42:43,020 INFO misc.py line 119 253097] Train: [77/100][29/510] Data 0.003 (0.009) Batch 1.066 (1.835) Remain 06:13:28 loss: 0.2007 Lr: 0.00089
[2023-12-25 19:42:44,184 INFO misc.py line 119 253097] Train: [77/100][30/510] Data 0.004 (0.009) Batch 1.164 (1.810) Remain 06:08:22 loss: 0.0760 Lr: 0.00089
[2023-12-25 19:42:45,443 INFO misc.py line 119 253097] Train: [77/100][31/510] Data 0.005 (0.009) Batch 1.255 (1.790) Remain 06:04:18 loss: 0.1502 Lr: 0.00089
[2023-12-25 19:42:46,360 INFO misc.py line 119 253097] Train: [77/100][32/510] Data 0.009 (0.009) Batch 0.922 (1.760) Remain 05:58:11 loss: 0.0671 Lr: 0.00089
[2023-12-25 19:42:47,517 INFO misc.py line 119 253097] Train: [77/100][33/510] Data 0.004 (0.008) Batch 1.157 (1.740) Remain 05:54:03 loss: 0.0935 Lr: 0.00089
[2023-12-25 19:42:49,602 INFO misc.py line 119 253097] Train: [77/100][34/510] Data 0.004 (0.008) Batch 2.085 (1.751) Remain 05:56:17 loss: 0.1090 Lr: 0.00089
[2023-12-25 19:42:50,680 INFO misc.py line 119 253097] Train: [77/100][35/510] Data 0.004 (0.008) Batch 1.078 (1.730) Remain 05:51:58 loss: 0.0737 Lr: 0.00089
[2023-12-25 19:42:51,914 INFO misc.py line 119 253097] Train: [77/100][36/510] Data 0.004 (0.008) Batch 1.231 (1.715) Remain 05:48:52 loss: 0.0926 Lr: 0.00089
[2023-12-25 19:42:53,053 INFO misc.py line 119 253097] Train: [77/100][37/510] Data 0.007 (0.008) Batch 1.140 (1.698) Remain 05:45:24 loss: 0.1141 Lr: 0.00089
[2023-12-25 19:42:54,067 INFO misc.py line 119 253097] Train: [77/100][38/510] Data 0.007 (0.008) Batch 1.009 (1.679) Remain 05:41:22 loss: 0.0606 Lr: 0.00089
[2023-12-25 19:42:55,301 INFO misc.py line 119 253097] Train: [77/100][39/510] Data 0.013 (0.008) Batch 1.240 (1.666) Remain 05:38:52 loss: 0.1844 Lr: 0.00089
[2023-12-25 19:42:56,567 INFO misc.py line 119 253097] Train: [77/100][40/510] Data 0.005 (0.008) Batch 1.263 (1.656) Remain 05:36:37 loss: 0.1934 Lr: 0.00089
[2023-12-25 19:42:57,734 INFO misc.py line 119 253097] Train: [77/100][41/510] Data 0.009 (0.008) Batch 1.165 (1.643) Remain 05:33:58 loss: 0.0775 Lr: 0.00089
[2023-12-25 19:42:59,045 INFO misc.py line 119 253097] Train: [77/100][42/510] Data 0.010 (0.008) Batch 1.317 (1.634) Remain 05:32:14 loss: 0.0735 Lr: 0.00089
[2023-12-25 19:43:00,227 INFO misc.py line 119 253097] Train: [77/100][43/510] Data 0.003 (0.008) Batch 1.181 (1.623) Remain 05:29:55 loss: 0.1345 Lr: 0.00089
[2023-12-25 19:43:01,470 INFO misc.py line 119 253097] Train: [77/100][44/510] Data 0.005 (0.008) Batch 1.239 (1.614) Remain 05:27:59 loss: 0.0903 Lr: 0.00089
[2023-12-25 19:43:02,593 INFO misc.py line 119 253097] Train: [77/100][45/510] Data 0.008 (0.008) Batch 1.124 (1.602) Remain 05:25:35 loss: 0.0850 Lr: 0.00089
[2023-12-25 19:43:03,918 INFO misc.py line 119 253097] Train: [77/100][46/510] Data 0.008 (0.008) Batch 1.329 (1.596) Remain 05:24:16 loss: 0.1282 Lr: 0.00089
[2023-12-25 19:43:05,206 INFO misc.py line 119 253097] Train: [77/100][47/510] Data 0.004 (0.008) Batch 1.286 (1.589) Remain 05:22:48 loss: 0.0858 Lr: 0.00089
[2023-12-25 19:43:06,514 INFO misc.py line 119 253097] Train: [77/100][48/510] Data 0.007 (0.008) Batch 1.305 (1.582) Remain 05:21:30 loss: 0.2017 Lr: 0.00089
[2023-12-25 19:43:07,727 INFO misc.py line 119 253097] Train: [77/100][49/510] Data 0.010 (0.008) Batch 1.217 (1.574) Remain 05:19:52 loss: 0.1288 Lr: 0.00089
[2023-12-25 19:43:08,771 INFO misc.py line 119 253097] Train: [77/100][50/510] Data 0.006 (0.008) Batch 1.044 (1.563) Remain 05:17:32 loss: 0.1521 Lr: 0.00089
[2023-12-25 19:43:09,815 INFO misc.py line 119 253097] Train: [77/100][51/510] Data 0.006 (0.008) Batch 1.039 (1.552) Remain 05:15:18 loss: 0.1428 Lr: 0.00089
[2023-12-25 19:43:11,038 INFO misc.py line 119 253097] Train: [77/100][52/510] Data 0.011 (0.008) Batch 1.225 (1.545) Remain 05:13:55 loss: 0.2340 Lr: 0.00089
[2023-12-25 19:43:12,907 INFO misc.py line 119 253097] Train: [77/100][53/510] Data 0.906 (0.026) Batch 1.867 (1.552) Remain 05:15:12 loss: 0.0811 Lr: 0.00089
[2023-12-25 19:43:14,097 INFO misc.py line 119 253097] Train: [77/100][54/510] Data 0.011 (0.026) Batch 1.198 (1.545) Remain 05:13:45 loss: 0.0811 Lr: 0.00089
[2023-12-25 19:43:15,246 INFO misc.py line 119 253097] Train: [77/100][55/510] Data 0.004 (0.025) Batch 1.149 (1.537) Remain 05:12:11 loss: 0.1317 Lr: 0.00089
[2023-12-25 19:43:16,430 INFO misc.py line 119 253097] Train: [77/100][56/510] Data 0.004 (0.025) Batch 1.184 (1.531) Remain 05:10:48 loss: 0.0978 Lr: 0.00089
[2023-12-25 19:43:17,575 INFO misc.py line 119 253097] Train: [77/100][57/510] Data 0.004 (0.024) Batch 1.145 (1.523) Remain 05:09:20 loss: 0.0730 Lr: 0.00089
[2023-12-25 19:43:24,321 INFO misc.py line 119 253097] Train: [77/100][58/510] Data 5.569 (0.125) Batch 6.746 (1.618) Remain 05:28:35 loss: 0.1313 Lr: 0.00089
[2023-12-25 19:43:25,458 INFO misc.py line 119 253097] Train: [77/100][59/510] Data 0.003 (0.123) Batch 1.137 (1.610) Remain 05:26:49 loss: 0.1929 Lr: 0.00089
[2023-12-25 19:43:26,470 INFO misc.py line 119 253097] Train: [77/100][60/510] Data 0.003 (0.121) Batch 1.010 (1.599) Remain 05:24:39 loss: 0.1726 Lr: 0.00089
[2023-12-25 19:43:27,636 INFO misc.py line 119 253097] Train: [77/100][61/510] Data 0.005 (0.119) Batch 1.166 (1.592) Remain 05:23:07 loss: 0.1277 Lr: 0.00089
[2023-12-25 19:43:28,773 INFO misc.py line 119 253097] Train: [77/100][62/510] Data 0.005 (0.117) Batch 1.138 (1.584) Remain 05:21:31 loss: 0.1045 Lr: 0.00089
[2023-12-25 19:43:29,781 INFO misc.py line 119 253097] Train: [77/100][63/510] Data 0.004 (0.115) Batch 1.008 (1.575) Remain 05:19:33 loss: 0.1144 Lr: 0.00089
[2023-12-25 19:43:32,773 INFO misc.py line 119 253097] Train: [77/100][64/510] Data 0.004 (0.113) Batch 2.993 (1.598) Remain 05:24:14 loss: 0.1215 Lr: 0.00089
[2023-12-25 19:43:33,760 INFO misc.py line 119 253097] Train: [77/100][65/510] Data 0.003 (0.111) Batch 0.986 (1.588) Remain 05:22:12 loss: 0.0751 Lr: 0.00089
[2023-12-25 19:43:34,918 INFO misc.py line 119 253097] Train: [77/100][66/510] Data 0.005 (0.110) Batch 1.158 (1.581) Remain 05:20:48 loss: 0.0877 Lr: 0.00089
[2023-12-25 19:43:36,131 INFO misc.py line 119 253097] Train: [77/100][67/510] Data 0.004 (0.108) Batch 1.205 (1.575) Remain 05:19:35 loss: 0.0724 Lr: 0.00089
[2023-12-25 19:43:37,286 INFO misc.py line 119 253097] Train: [77/100][68/510] Data 0.013 (0.107) Batch 1.162 (1.569) Remain 05:18:16 loss: 0.2094 Lr: 0.00089
[2023-12-25 19:43:42,078 INFO misc.py line 119 253097] Train: [77/100][69/510] Data 0.005 (0.105) Batch 4.793 (1.618) Remain 05:28:09 loss: 0.1065 Lr: 0.00089
[2023-12-25 19:43:43,099 INFO misc.py line 119 253097] Train: [77/100][70/510] Data 0.004 (0.104) Batch 1.021 (1.609) Remain 05:26:19 loss: 0.1268 Lr: 0.00089
[2023-12-25 19:43:44,232 INFO misc.py line 119 253097] Train: [77/100][71/510] Data 0.004 (0.102) Batch 1.131 (1.602) Remain 05:24:52 loss: 0.0563 Lr: 0.00089
[2023-12-25 19:43:45,470 INFO misc.py line 119 253097] Train: [77/100][72/510] Data 0.005 (0.101) Batch 1.204 (1.596) Remain 05:23:40 loss: 0.1872 Lr: 0.00089
[2023-12-25 19:43:46,729 INFO misc.py line 119 253097] Train: [77/100][73/510] Data 0.040 (0.100) Batch 1.290 (1.592) Remain 05:22:45 loss: 0.0918 Lr: 0.00089
[2023-12-25 19:43:47,997 INFO misc.py line 119 253097] Train: [77/100][74/510] Data 0.009 (0.099) Batch 1.268 (1.587) Remain 05:21:48 loss: 0.2421 Lr: 0.00089
[2023-12-25 19:43:49,245 INFO misc.py line 119 253097] Train: [77/100][75/510] Data 0.008 (0.097) Batch 1.247 (1.582) Remain 05:20:49 loss: 0.1566 Lr: 0.00089
[2023-12-25 19:43:50,373 INFO misc.py line 119 253097] Train: [77/100][76/510] Data 0.009 (0.096) Batch 1.132 (1.576) Remain 05:19:32 loss: 0.2783 Lr: 0.00089
[2023-12-25 19:43:51,470 INFO misc.py line 119 253097] Train: [77/100][77/510] Data 0.006 (0.095) Batch 1.095 (1.570) Remain 05:18:12 loss: 0.2035 Lr: 0.00089
[2023-12-25 19:43:52,702 INFO misc.py line 119 253097] Train: [77/100][78/510] Data 0.008 (0.094) Batch 1.233 (1.565) Remain 05:17:16 loss: 0.1946 Lr: 0.00089
[2023-12-25 19:43:53,933 INFO misc.py line 119 253097] Train: [77/100][79/510] Data 0.007 (0.093) Batch 1.231 (1.561) Remain 05:16:20 loss: 0.1983 Lr: 0.00089
[2023-12-25 19:43:55,148 INFO misc.py line 119 253097] Train: [77/100][80/510] Data 0.007 (0.091) Batch 1.216 (1.556) Remain 05:15:24 loss: 0.1538 Lr: 0.00089
[2023-12-25 19:43:56,405 INFO misc.py line 119 253097] Train: [77/100][81/510] Data 0.006 (0.090) Batch 1.253 (1.552) Remain 05:14:36 loss: 0.1335 Lr: 0.00089
[2023-12-25 19:43:57,668 INFO misc.py line 119 253097] Train: [77/100][82/510] Data 0.010 (0.089) Batch 1.263 (1.549) Remain 05:13:50 loss: 0.0997 Lr: 0.00089
[2023-12-25 19:43:58,915 INFO misc.py line 119 253097] Train: [77/100][83/510] Data 0.010 (0.088) Batch 1.253 (1.545) Remain 05:13:03 loss: 0.0934 Lr: 0.00089
[2023-12-25 19:44:00,106 INFO misc.py line 119 253097] Train: [77/100][84/510] Data 0.005 (0.087) Batch 1.165 (1.540) Remain 05:12:04 loss: 0.1233 Lr: 0.00089
[2023-12-25 19:44:01,407 INFO misc.py line 119 253097] Train: [77/100][85/510] Data 0.030 (0.087) Batch 1.327 (1.538) Remain 05:11:31 loss: 0.1127 Lr: 0.00089
[2023-12-25 19:44:02,659 INFO misc.py line 119 253097] Train: [77/100][86/510] Data 0.004 (0.086) Batch 1.251 (1.534) Remain 05:10:48 loss: 0.1204 Lr: 0.00089
[2023-12-25 19:44:03,725 INFO misc.py line 119 253097] Train: [77/100][87/510] Data 0.005 (0.085) Batch 1.066 (1.529) Remain 05:09:39 loss: 0.2372 Lr: 0.00089
[2023-12-25 19:44:04,928 INFO misc.py line 119 253097] Train: [77/100][88/510] Data 0.005 (0.084) Batch 1.204 (1.525) Remain 05:08:51 loss: 0.0590 Lr: 0.00089
[2023-12-25 19:44:06,066 INFO misc.py line 119 253097] Train: [77/100][89/510] Data 0.003 (0.083) Batch 1.138 (1.520) Remain 05:07:54 loss: 0.0738 Lr: 0.00089
[2023-12-25 19:44:07,231 INFO misc.py line 119 253097] Train: [77/100][90/510] Data 0.004 (0.082) Batch 1.163 (1.516) Remain 05:07:03 loss: 0.1066 Lr: 0.00089
[2023-12-25 19:44:08,414 INFO misc.py line 119 253097] Train: [77/100][91/510] Data 0.006 (0.081) Batch 1.182 (1.513) Remain 05:06:15 loss: 0.1031 Lr: 0.00089
[2023-12-25 19:44:09,493 INFO misc.py line 119 253097] Train: [77/100][92/510] Data 0.007 (0.080) Batch 1.079 (1.508) Remain 05:05:15 loss: 0.1528 Lr: 0.00089
[2023-12-25 19:44:10,621 INFO misc.py line 119 253097] Train: [77/100][93/510] Data 0.007 (0.079) Batch 1.131 (1.503) Remain 05:04:22 loss: 0.1090 Lr: 0.00089
[2023-12-25 19:44:11,694 INFO misc.py line 119 253097] Train: [77/100][94/510] Data 0.004 (0.079) Batch 1.069 (1.499) Remain 05:03:23 loss: 0.1453 Lr: 0.00089
[2023-12-25 19:44:12,659 INFO misc.py line 119 253097] Train: [77/100][95/510] Data 0.007 (0.078) Batch 0.969 (1.493) Remain 05:02:11 loss: 0.1158 Lr: 0.00089
[2023-12-25 19:44:15,069 INFO misc.py line 119 253097] Train: [77/100][96/510] Data 0.005 (0.077) Batch 2.409 (1.503) Remain 05:04:09 loss: 0.1008 Lr: 0.00089
[2023-12-25 19:44:16,187 INFO misc.py line 119 253097] Train: [77/100][97/510] Data 0.005 (0.076) Batch 1.119 (1.499) Remain 05:03:18 loss: 0.1720 Lr: 0.00089
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[2023-12-25 19:44:45,512 INFO misc.py line 119 253097] Train: [77/100][119/510] Data 0.009 (0.063) Batch 0.940 (1.467) Remain 04:56:24 loss: 0.1336 Lr: 0.00088
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[2023-12-25 19:45:07,985 INFO misc.py line 119 253097] Train: [77/100][129/510] Data 0.007 (0.059) Batch 12.315 (1.529) Remain 05:08:39 loss: 0.0877 Lr: 0.00088
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[2023-12-25 19:45:11,487 INFO misc.py line 119 253097] Train: [77/100][132/510] Data 0.003 (0.057) Batch 1.173 (1.521) Remain 05:06:53 loss: 0.0770 Lr: 0.00088
[2023-12-25 19:45:12,462 INFO misc.py line 119 253097] Train: [77/100][133/510] Data 0.004 (0.057) Batch 0.975 (1.517) Remain 05:06:01 loss: 0.3345 Lr: 0.00088
[2023-12-25 19:45:13,554 INFO misc.py line 119 253097] Train: [77/100][134/510] Data 0.004 (0.056) Batch 1.081 (1.513) Remain 05:05:19 loss: 0.0647 Lr: 0.00088
[2023-12-25 19:45:20,765 INFO misc.py line 119 253097] Train: [77/100][135/510] Data 0.014 (0.056) Batch 7.222 (1.556) Remain 05:14:01 loss: 0.0915 Lr: 0.00088
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[2023-12-25 19:45:27,503 INFO misc.py line 119 253097] Train: [77/100][141/510] Data 0.003 (0.054) Batch 1.064 (1.538) Remain 05:10:03 loss: 0.3274 Lr: 0.00088
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[2023-12-25 19:45:29,805 INFO misc.py line 119 253097] Train: [77/100][143/510] Data 0.008 (0.053) Batch 1.251 (1.532) Remain 05:08:54 loss: 0.1089 Lr: 0.00088
[2023-12-25 19:45:30,857 INFO misc.py line 119 253097] Train: [77/100][144/510] Data 0.005 (0.053) Batch 1.048 (1.529) Remain 05:08:10 loss: 0.1307 Lr: 0.00088
[2023-12-25 19:45:31,820 INFO misc.py line 119 253097] Train: [77/100][145/510] Data 0.008 (0.053) Batch 0.967 (1.525) Remain 05:07:21 loss: 0.0921 Lr: 0.00088
[2023-12-25 19:45:33,087 INFO misc.py line 119 253097] Train: [77/100][146/510] Data 0.004 (0.052) Batch 1.264 (1.523) Remain 05:06:58 loss: 0.0721 Lr: 0.00088
[2023-12-25 19:45:34,413 INFO misc.py line 119 253097] Train: [77/100][147/510] Data 0.007 (0.052) Batch 1.326 (1.522) Remain 05:06:39 loss: 0.2382 Lr: 0.00088
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[2023-12-25 19:45:39,079 INFO misc.py line 119 253097] Train: [77/100][151/510] Data 0.005 (0.051) Batch 1.129 (1.512) Remain 05:04:38 loss: 0.2778 Lr: 0.00088
[2023-12-25 19:45:40,164 INFO misc.py line 119 253097] Train: [77/100][152/510] Data 0.004 (0.050) Batch 1.083 (1.509) Remain 05:04:01 loss: 0.0871 Lr: 0.00088
[2023-12-25 19:45:41,342 INFO misc.py line 119 253097] Train: [77/100][153/510] Data 0.005 (0.050) Batch 1.180 (1.507) Remain 05:03:33 loss: 0.1233 Lr: 0.00088
[2023-12-25 19:45:42,490 INFO misc.py line 119 253097] Train: [77/100][154/510] Data 0.003 (0.050) Batch 1.145 (1.504) Remain 05:03:03 loss: 0.1505 Lr: 0.00088
[2023-12-25 19:45:43,630 INFO misc.py line 119 253097] Train: [77/100][155/510] Data 0.007 (0.049) Batch 1.138 (1.502) Remain 05:02:32 loss: 0.2115 Lr: 0.00088
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[2023-12-25 19:45:48,168 INFO misc.py line 119 253097] Train: [77/100][159/510] Data 0.009 (0.048) Batch 1.041 (1.493) Remain 05:00:33 loss: 0.1419 Lr: 0.00088
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[2023-12-25 19:45:50,395 INFO misc.py line 119 253097] Train: [77/100][161/510] Data 0.012 (0.048) Batch 1.029 (1.488) Remain 04:59:31 loss: 0.0849 Lr: 0.00088
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[2023-12-25 19:45:52,743 INFO misc.py line 119 253097] Train: [77/100][163/510] Data 0.003 (0.048) Batch 1.205 (1.484) Remain 04:58:41 loss: 0.1775 Lr: 0.00088
[2023-12-25 19:45:53,968 INFO misc.py line 119 253097] Train: [77/100][164/510] Data 0.004 (0.047) Batch 1.225 (1.482) Remain 04:58:20 loss: 0.1864 Lr: 0.00088
[2023-12-25 19:45:55,223 INFO misc.py line 119 253097] Train: [77/100][165/510] Data 0.004 (0.047) Batch 1.256 (1.481) Remain 04:58:02 loss: 0.1001 Lr: 0.00088
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[2023-12-25 19:46:01,277 INFO misc.py line 119 253097] Train: [77/100][168/510] Data 0.004 (0.046) Batch 3.715 (1.491) Remain 04:59:56 loss: 0.0926 Lr: 0.00088
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[2023-12-25 19:46:04,858 INFO misc.py line 119 253097] Train: [77/100][171/510] Data 0.003 (0.046) Batch 1.152 (1.485) Remain 04:58:47 loss: 0.0854 Lr: 0.00088
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[2023-12-25 19:46:07,161 INFO misc.py line 119 253097] Train: [77/100][173/510] Data 0.004 (0.045) Batch 1.112 (1.481) Remain 04:57:57 loss: 0.1212 Lr: 0.00088
[2023-12-25 19:46:11,146 INFO misc.py line 119 253097] Train: [77/100][174/510] Data 2.862 (0.062) Batch 3.985 (1.496) Remain 05:00:52 loss: 0.0825 Lr: 0.00087
[2023-12-25 19:46:12,280 INFO misc.py line 119 253097] Train: [77/100][175/510] Data 0.003 (0.061) Batch 1.134 (1.494) Remain 05:00:25 loss: 0.0729 Lr: 0.00087
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[2023-12-25 19:47:48,742 INFO misc.py line 119 253097] Train: [77/100][236/510] Data 0.004 (0.082) Batch 4.668 (1.517) Remain 05:03:28 loss: 0.0772 Lr: 0.00087
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[2023-12-25 19:47:59,702 INFO misc.py line 119 253097] Train: [77/100][242/510] Data 0.003 (0.080) Batch 5.083 (1.525) Remain 05:04:52 loss: 0.1697 Lr: 0.00087
[2023-12-25 19:48:00,771 INFO misc.py line 119 253097] Train: [77/100][243/510] Data 0.004 (0.080) Batch 1.066 (1.523) Remain 05:04:28 loss: 0.0747 Lr: 0.00087
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[2023-12-25 19:48:04,324 INFO misc.py line 119 253097] Train: [77/100][246/510] Data 0.009 (0.079) Batch 1.200 (1.519) Remain 05:03:33 loss: 0.0780 Lr: 0.00087
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[2023-12-25 19:48:17,449 INFO misc.py line 119 253097] Train: [77/100][255/510] Data 0.009 (0.076) Batch 3.821 (1.516) Remain 05:02:54 loss: 0.1424 Lr: 0.00086
[2023-12-25 19:48:18,460 INFO misc.py line 119 253097] Train: [77/100][256/510] Data 0.005 (0.076) Batch 1.011 (1.514) Remain 05:02:28 loss: 0.1999 Lr: 0.00086
[2023-12-25 19:48:19,593 INFO misc.py line 119 253097] Train: [77/100][257/510] Data 0.006 (0.076) Batch 1.133 (1.513) Remain 05:02:09 loss: 0.0903 Lr: 0.00086
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[2023-12-25 19:48:25,984 INFO misc.py line 119 253097] Train: [77/100][259/510] Data 0.004 (0.075) Batch 1.306 (1.526) Remain 05:04:43 loss: 0.1620 Lr: 0.00086
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[2023-12-25 19:48:28,714 INFO misc.py line 119 253097] Train: [77/100][261/510] Data 0.362 (0.076) Batch 1.624 (1.525) Remain 05:04:25 loss: 0.2131 Lr: 0.00086
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[2023-12-25 19:48:30,939 INFO misc.py line 119 253097] Train: [77/100][263/510] Data 0.008 (0.076) Batch 1.066 (1.522) Remain 05:03:44 loss: 0.0949 Lr: 0.00086
[2023-12-25 19:48:32,109 INFO misc.py line 119 253097] Train: [77/100][264/510] Data 0.010 (0.075) Batch 1.176 (1.520) Remain 05:03:27 loss: 0.1546 Lr: 0.00086
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[2023-12-25 19:48:42,338 INFO misc.py line 119 253097] Train: [77/100][268/510] Data 0.003 (0.074) Batch 7.242 (1.536) Remain 05:06:28 loss: 0.0895 Lr: 0.00086
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[2023-12-25 19:48:49,350 INFO misc.py line 119 253097] Train: [77/100][274/510] Data 0.004 (0.073) Batch 1.114 (1.528) Remain 05:04:41 loss: 0.1829 Lr: 0.00086
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[2023-12-25 19:48:51,683 INFO misc.py line 119 253097] Train: [77/100][276/510] Data 0.012 (0.072) Batch 1.172 (1.525) Remain 05:04:07 loss: 0.1153 Lr: 0.00086
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[2023-12-25 19:50:46,461 INFO misc.py line 119 253097] Train: [77/100][354/510] Data 0.003 (0.064) Batch 1.065 (1.513) Remain 04:59:46 loss: 0.1084 Lr: 0.00085
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[2023-12-25 19:52:35,641 INFO misc.py line 119 253097] Train: [77/100][411/510] Data 0.006 (0.096) Batch 1.130 (1.569) Remain 05:09:24 loss: 0.1442 Lr: 0.00084
[2023-12-25 19:52:36,586 INFO misc.py line 119 253097] Train: [77/100][412/510] Data 0.004 (0.096) Batch 0.944 (1.568) Remain 05:09:05 loss: 0.1059 Lr: 0.00084
[2023-12-25 19:52:37,740 INFO misc.py line 119 253097] Train: [77/100][413/510] Data 0.005 (0.096) Batch 1.155 (1.567) Remain 05:08:51 loss: 0.1022 Lr: 0.00084
[2023-12-25 19:52:38,732 INFO misc.py line 119 253097] Train: [77/100][414/510] Data 0.004 (0.096) Batch 0.991 (1.566) Remain 05:08:33 loss: 0.1686 Lr: 0.00084
[2023-12-25 19:52:39,847 INFO misc.py line 119 253097] Train: [77/100][415/510] Data 0.005 (0.096) Batch 1.116 (1.564) Remain 05:08:19 loss: 0.0952 Lr: 0.00084
[2023-12-25 19:52:40,962 INFO misc.py line 119 253097] Train: [77/100][416/510] Data 0.005 (0.095) Batch 1.114 (1.563) Remain 05:08:04 loss: 0.0530 Lr: 0.00084
[2023-12-25 19:52:41,998 INFO misc.py line 119 253097] Train: [77/100][417/510] Data 0.005 (0.095) Batch 1.037 (1.562) Remain 05:07:48 loss: 0.0892 Lr: 0.00084
[2023-12-25 19:52:43,186 INFO misc.py line 119 253097] Train: [77/100][418/510] Data 0.003 (0.095) Batch 1.189 (1.561) Remain 05:07:35 loss: 0.1455 Lr: 0.00084
[2023-12-25 19:52:44,426 INFO misc.py line 119 253097] Train: [77/100][419/510] Data 0.003 (0.095) Batch 1.235 (1.560) Remain 05:07:25 loss: 0.1199 Lr: 0.00084
[2023-12-25 19:52:45,710 INFO misc.py line 119 253097] Train: [77/100][420/510] Data 0.009 (0.094) Batch 1.285 (1.560) Remain 05:07:15 loss: 0.0802 Lr: 0.00084
[2023-12-25 19:52:46,921 INFO misc.py line 119 253097] Train: [77/100][421/510] Data 0.007 (0.094) Batch 1.213 (1.559) Remain 05:07:04 loss: 0.1529 Lr: 0.00084
[2023-12-25 19:52:48,138 INFO misc.py line 119 253097] Train: [77/100][422/510] Data 0.005 (0.094) Batch 1.210 (1.558) Remain 05:06:52 loss: 0.1171 Lr: 0.00084
[2023-12-25 19:52:49,251 INFO misc.py line 119 253097] Train: [77/100][423/510] Data 0.013 (0.094) Batch 1.121 (1.557) Remain 05:06:39 loss: 0.1132 Lr: 0.00084
[2023-12-25 19:52:50,406 INFO misc.py line 119 253097] Train: [77/100][424/510] Data 0.004 (0.094) Batch 1.155 (1.556) Remain 05:06:26 loss: 0.1263 Lr: 0.00084
[2023-12-25 19:52:51,565 INFO misc.py line 119 253097] Train: [77/100][425/510] Data 0.005 (0.093) Batch 1.154 (1.555) Remain 05:06:13 loss: 0.0915 Lr: 0.00084
[2023-12-25 19:52:52,606 INFO misc.py line 119 253097] Train: [77/100][426/510] Data 0.009 (0.093) Batch 1.029 (1.554) Remain 05:05:57 loss: 0.1069 Lr: 0.00084
[2023-12-25 19:52:53,820 INFO misc.py line 119 253097] Train: [77/100][427/510] Data 0.022 (0.093) Batch 1.230 (1.553) Remain 05:05:46 loss: 0.1259 Lr: 0.00084
[2023-12-25 19:52:54,871 INFO misc.py line 119 253097] Train: [77/100][428/510] Data 0.005 (0.093) Batch 1.053 (1.552) Remain 05:05:31 loss: 0.0941 Lr: 0.00084
[2023-12-25 19:52:55,845 INFO misc.py line 119 253097] Train: [77/100][429/510] Data 0.003 (0.093) Batch 0.973 (1.551) Remain 05:05:13 loss: 0.0871 Lr: 0.00084
[2023-12-25 19:52:57,103 INFO misc.py line 119 253097] Train: [77/100][430/510] Data 0.004 (0.092) Batch 1.254 (1.550) Remain 05:05:03 loss: 0.0702 Lr: 0.00084
[2023-12-25 19:52:58,174 INFO misc.py line 119 253097] Train: [77/100][431/510] Data 0.008 (0.092) Batch 1.074 (1.549) Remain 05:04:49 loss: 0.0920 Lr: 0.00084
[2023-12-25 19:52:59,081 INFO misc.py line 119 253097] Train: [77/100][432/510] Data 0.004 (0.092) Batch 0.907 (1.547) Remain 05:04:29 loss: 0.0775 Lr: 0.00084
[2023-12-25 19:53:00,425 INFO misc.py line 119 253097] Train: [77/100][433/510] Data 0.005 (0.092) Batch 1.344 (1.547) Remain 05:04:22 loss: 0.1256 Lr: 0.00084
[2023-12-25 19:53:01,719 INFO misc.py line 119 253097] Train: [77/100][434/510] Data 0.004 (0.092) Batch 1.291 (1.546) Remain 05:04:14 loss: 0.1118 Lr: 0.00084
[2023-12-25 19:53:09,891 INFO misc.py line 119 253097] Train: [77/100][435/510] Data 7.269 (0.108) Batch 8.176 (1.562) Remain 05:07:13 loss: 0.0794 Lr: 0.00084
[2023-12-25 19:53:10,886 INFO misc.py line 119 253097] Train: [77/100][436/510] Data 0.004 (0.108) Batch 0.994 (1.560) Remain 05:06:56 loss: 0.1036 Lr: 0.00084
[2023-12-25 19:53:12,099 INFO misc.py line 119 253097] Train: [77/100][437/510] Data 0.004 (0.108) Batch 1.213 (1.559) Remain 05:06:45 loss: 0.1034 Lr: 0.00084
[2023-12-25 19:53:13,442 INFO misc.py line 119 253097] Train: [77/100][438/510] Data 0.005 (0.108) Batch 1.343 (1.559) Remain 05:06:38 loss: 0.1137 Lr: 0.00084
[2023-12-25 19:53:14,532 INFO misc.py line 119 253097] Train: [77/100][439/510] Data 0.006 (0.107) Batch 1.088 (1.558) Remain 05:06:24 loss: 0.0634 Lr: 0.00084
[2023-12-25 19:53:15,707 INFO misc.py line 119 253097] Train: [77/100][440/510] Data 0.007 (0.107) Batch 1.178 (1.557) Remain 05:06:12 loss: 0.1548 Lr: 0.00084
[2023-12-25 19:53:18,573 INFO misc.py line 119 253097] Train: [77/100][441/510] Data 1.703 (0.111) Batch 2.866 (1.560) Remain 05:06:45 loss: 0.0976 Lr: 0.00084
[2023-12-25 19:53:19,737 INFO misc.py line 119 253097] Train: [77/100][442/510] Data 0.005 (0.110) Batch 1.165 (1.559) Remain 05:06:33 loss: 0.1065 Lr: 0.00084
[2023-12-25 19:53:21,020 INFO misc.py line 119 253097] Train: [77/100][443/510] Data 0.004 (0.110) Batch 1.276 (1.558) Remain 05:06:24 loss: 0.1279 Lr: 0.00084
[2023-12-25 19:53:22,154 INFO misc.py line 119 253097] Train: [77/100][444/510] Data 0.012 (0.110) Batch 1.141 (1.557) Remain 05:06:11 loss: 0.0699 Lr: 0.00084
[2023-12-25 19:53:23,383 INFO misc.py line 119 253097] Train: [77/100][445/510] Data 0.006 (0.110) Batch 1.226 (1.557) Remain 05:06:01 loss: 0.1145 Lr: 0.00084
[2023-12-25 19:53:24,595 INFO misc.py line 119 253097] Train: [77/100][446/510] Data 0.007 (0.110) Batch 1.214 (1.556) Remain 05:05:50 loss: 0.1056 Lr: 0.00084
[2023-12-25 19:53:25,850 INFO misc.py line 119 253097] Train: [77/100][447/510] Data 0.006 (0.109) Batch 1.214 (1.555) Remain 05:05:40 loss: 0.0791 Lr: 0.00084
[2023-12-25 19:53:27,159 INFO misc.py line 119 253097] Train: [77/100][448/510] Data 0.047 (0.109) Batch 1.348 (1.555) Remain 05:05:33 loss: 0.2716 Lr: 0.00084
[2023-12-25 19:53:28,290 INFO misc.py line 119 253097] Train: [77/100][449/510] Data 0.008 (0.109) Batch 1.136 (1.554) Remain 05:05:20 loss: 0.1735 Lr: 0.00084
[2023-12-25 19:53:29,556 INFO misc.py line 119 253097] Train: [77/100][450/510] Data 0.003 (0.109) Batch 1.258 (1.553) Remain 05:05:11 loss: 0.1033 Lr: 0.00084
[2023-12-25 19:53:30,590 INFO misc.py line 119 253097] Train: [77/100][451/510] Data 0.011 (0.108) Batch 1.038 (1.552) Remain 05:04:56 loss: 0.1882 Lr: 0.00084
[2023-12-25 19:53:31,903 INFO misc.py line 119 253097] Train: [77/100][452/510] Data 0.009 (0.108) Batch 1.309 (1.551) Remain 05:04:48 loss: 0.1262 Lr: 0.00084
[2023-12-25 19:53:32,849 INFO misc.py line 119 253097] Train: [77/100][453/510] Data 0.012 (0.108) Batch 0.953 (1.550) Remain 05:04:30 loss: 0.1022 Lr: 0.00084
[2023-12-25 19:53:34,067 INFO misc.py line 119 253097] Train: [77/100][454/510] Data 0.005 (0.108) Batch 1.218 (1.549) Remain 05:04:20 loss: 0.1806 Lr: 0.00084
[2023-12-25 19:53:35,142 INFO misc.py line 119 253097] Train: [77/100][455/510] Data 0.005 (0.108) Batch 1.075 (1.548) Remain 05:04:06 loss: 0.1577 Lr: 0.00084
[2023-12-25 19:53:39,851 INFO misc.py line 119 253097] Train: [77/100][456/510] Data 0.005 (0.107) Batch 4.711 (1.555) Remain 05:05:27 loss: 0.0942 Lr: 0.00084
[2023-12-25 19:53:40,992 INFO misc.py line 119 253097] Train: [77/100][457/510] Data 0.004 (0.107) Batch 1.141 (1.554) Remain 05:05:15 loss: 0.1519 Lr: 0.00084
[2023-12-25 19:53:42,127 INFO misc.py line 119 253097] Train: [77/100][458/510] Data 0.003 (0.107) Batch 1.134 (1.553) Remain 05:05:02 loss: 0.0813 Lr: 0.00084
[2023-12-25 19:53:43,293 INFO misc.py line 119 253097] Train: [77/100][459/510] Data 0.004 (0.107) Batch 1.166 (1.553) Remain 05:04:51 loss: 0.0656 Lr: 0.00084
[2023-12-25 19:53:44,450 INFO misc.py line 119 253097] Train: [77/100][460/510] Data 0.005 (0.106) Batch 1.155 (1.552) Remain 05:04:39 loss: 0.1000 Lr: 0.00084
[2023-12-25 19:53:45,677 INFO misc.py line 119 253097] Train: [77/100][461/510] Data 0.007 (0.106) Batch 1.229 (1.551) Remain 05:04:29 loss: 0.0764 Lr: 0.00084
[2023-12-25 19:53:50,262 INFO misc.py line 119 253097] Train: [77/100][462/510] Data 0.005 (0.106) Batch 4.584 (1.558) Remain 05:05:45 loss: 0.1239 Lr: 0.00084
[2023-12-25 19:53:51,526 INFO misc.py line 119 253097] Train: [77/100][463/510] Data 0.006 (0.106) Batch 1.265 (1.557) Remain 05:05:36 loss: 0.1365 Lr: 0.00084
[2023-12-25 19:53:52,738 INFO misc.py line 119 253097] Train: [77/100][464/510] Data 0.004 (0.106) Batch 1.213 (1.556) Remain 05:05:26 loss: 0.1197 Lr: 0.00084
[2023-12-25 19:53:53,933 INFO misc.py line 119 253097] Train: [77/100][465/510] Data 0.004 (0.105) Batch 1.194 (1.555) Remain 05:05:15 loss: 0.1831 Lr: 0.00084
[2023-12-25 19:53:54,997 INFO misc.py line 119 253097] Train: [77/100][466/510] Data 0.005 (0.105) Batch 1.064 (1.554) Remain 05:05:01 loss: 0.1238 Lr: 0.00084
[2023-12-25 19:53:56,111 INFO misc.py line 119 253097] Train: [77/100][467/510] Data 0.004 (0.105) Batch 1.114 (1.553) Remain 05:04:48 loss: 0.0955 Lr: 0.00084
[2023-12-25 19:53:58,434 INFO misc.py line 119 253097] Train: [77/100][468/510] Data 0.005 (0.105) Batch 2.323 (1.555) Remain 05:05:06 loss: 0.0972 Lr: 0.00083
[2023-12-25 19:53:59,609 INFO misc.py line 119 253097] Train: [77/100][469/510] Data 0.005 (0.105) Batch 1.172 (1.554) Remain 05:04:55 loss: 0.1329 Lr: 0.00083
[2023-12-25 19:54:00,854 INFO misc.py line 119 253097] Train: [77/100][470/510] Data 0.007 (0.104) Batch 1.211 (1.554) Remain 05:04:45 loss: 0.1316 Lr: 0.00083
[2023-12-25 19:54:02,181 INFO misc.py line 119 253097] Train: [77/100][471/510] Data 0.042 (0.104) Batch 1.364 (1.553) Remain 05:04:38 loss: 0.1115 Lr: 0.00083
[2023-12-25 19:54:03,416 INFO misc.py line 119 253097] Train: [77/100][472/510] Data 0.005 (0.104) Batch 1.236 (1.552) Remain 05:04:29 loss: 0.1234 Lr: 0.00083
[2023-12-25 19:54:04,584 INFO misc.py line 119 253097] Train: [77/100][473/510] Data 0.004 (0.104) Batch 1.167 (1.552) Remain 05:04:18 loss: 0.0682 Lr: 0.00083
[2023-12-25 19:54:05,688 INFO misc.py line 119 253097] Train: [77/100][474/510] Data 0.005 (0.104) Batch 1.103 (1.551) Remain 05:04:05 loss: 0.1426 Lr: 0.00083
[2023-12-25 19:54:06,885 INFO misc.py line 119 253097] Train: [77/100][475/510] Data 0.006 (0.103) Batch 1.194 (1.550) Remain 05:03:55 loss: 0.1433 Lr: 0.00083
[2023-12-25 19:54:08,001 INFO misc.py line 119 253097] Train: [77/100][476/510] Data 0.009 (0.103) Batch 1.117 (1.549) Remain 05:03:42 loss: 0.0537 Lr: 0.00083
[2023-12-25 19:54:09,063 INFO misc.py line 119 253097] Train: [77/100][477/510] Data 0.007 (0.103) Batch 1.057 (1.548) Remain 05:03:28 loss: 0.1217 Lr: 0.00083
[2023-12-25 19:54:10,296 INFO misc.py line 119 253097] Train: [77/100][478/510] Data 0.012 (0.103) Batch 1.236 (1.547) Remain 05:03:19 loss: 0.1991 Lr: 0.00083
[2023-12-25 19:54:11,530 INFO misc.py line 119 253097] Train: [77/100][479/510] Data 0.008 (0.103) Batch 1.239 (1.547) Remain 05:03:10 loss: 0.2184 Lr: 0.00083
[2023-12-25 19:54:12,601 INFO misc.py line 119 253097] Train: [77/100][480/510] Data 0.004 (0.102) Batch 1.067 (1.546) Remain 05:02:57 loss: 0.1466 Lr: 0.00083
[2023-12-25 19:54:13,907 INFO misc.py line 119 253097] Train: [77/100][481/510] Data 0.008 (0.102) Batch 1.299 (1.545) Remain 05:02:49 loss: 0.1178 Lr: 0.00083
[2023-12-25 19:54:15,068 INFO misc.py line 119 253097] Train: [77/100][482/510] Data 0.015 (0.102) Batch 1.163 (1.544) Remain 05:02:38 loss: 0.0848 Lr: 0.00083
[2023-12-25 19:54:16,106 INFO misc.py line 119 253097] Train: [77/100][483/510] Data 0.013 (0.102) Batch 1.041 (1.543) Remain 05:02:24 loss: 0.1148 Lr: 0.00083
[2023-12-25 19:54:16,979 INFO misc.py line 119 253097] Train: [77/100][484/510] Data 0.010 (0.102) Batch 0.879 (1.542) Remain 05:02:06 loss: 0.0826 Lr: 0.00083
[2023-12-25 19:54:17,984 INFO misc.py line 119 253097] Train: [77/100][485/510] Data 0.004 (0.101) Batch 1.005 (1.541) Remain 05:01:52 loss: 0.1803 Lr: 0.00083
[2023-12-25 19:54:19,070 INFO misc.py line 119 253097] Train: [77/100][486/510] Data 0.004 (0.101) Batch 1.085 (1.540) Remain 05:01:39 loss: 0.1410 Lr: 0.00083
[2023-12-25 19:54:20,297 INFO misc.py line 119 253097] Train: [77/100][487/510] Data 0.005 (0.101) Batch 1.223 (1.539) Remain 05:01:30 loss: 0.0871 Lr: 0.00083
[2023-12-25 19:54:21,458 INFO misc.py line 119 253097] Train: [77/100][488/510] Data 0.010 (0.101) Batch 1.162 (1.538) Remain 05:01:19 loss: 0.2898 Lr: 0.00083
[2023-12-25 19:54:22,709 INFO misc.py line 119 253097] Train: [77/100][489/510] Data 0.010 (0.101) Batch 1.251 (1.538) Remain 05:01:11 loss: 0.0723 Lr: 0.00083
[2023-12-25 19:54:23,771 INFO misc.py line 119 253097] Train: [77/100][490/510] Data 0.008 (0.100) Batch 1.059 (1.537) Remain 05:00:58 loss: 0.0934 Lr: 0.00083
[2023-12-25 19:54:26,301 INFO misc.py line 119 253097] Train: [77/100][491/510] Data 0.011 (0.100) Batch 2.537 (1.539) Remain 05:01:20 loss: 0.1365 Lr: 0.00083
[2023-12-25 19:54:27,378 INFO misc.py line 119 253097] Train: [77/100][492/510] Data 0.005 (0.100) Batch 1.077 (1.538) Remain 05:01:08 loss: 0.0758 Lr: 0.00083
[2023-12-25 19:54:28,572 INFO misc.py line 119 253097] Train: [77/100][493/510] Data 0.005 (0.100) Batch 1.194 (1.537) Remain 05:00:58 loss: 0.0980 Lr: 0.00083
[2023-12-25 19:54:29,773 INFO misc.py line 119 253097] Train: [77/100][494/510] Data 0.006 (0.100) Batch 1.201 (1.537) Remain 05:00:48 loss: 0.0805 Lr: 0.00083
[2023-12-25 19:54:30,818 INFO misc.py line 119 253097] Train: [77/100][495/510] Data 0.004 (0.099) Batch 1.045 (1.536) Remain 05:00:35 loss: 0.1166 Lr: 0.00083
[2023-12-25 19:54:31,906 INFO misc.py line 119 253097] Train: [77/100][496/510] Data 0.004 (0.099) Batch 1.088 (1.535) Remain 05:00:23 loss: 0.0857 Lr: 0.00083
[2023-12-25 19:54:32,957 INFO misc.py line 119 253097] Train: [77/100][497/510] Data 0.004 (0.099) Batch 1.051 (1.534) Remain 05:00:10 loss: 0.1295 Lr: 0.00083
[2023-12-25 19:54:33,931 INFO misc.py line 119 253097] Train: [77/100][498/510] Data 0.005 (0.099) Batch 0.975 (1.533) Remain 04:59:55 loss: 0.1195 Lr: 0.00083
[2023-12-25 19:54:35,190 INFO misc.py line 119 253097] Train: [77/100][499/510] Data 0.004 (0.099) Batch 1.260 (1.532) Remain 04:59:47 loss: 0.0873 Lr: 0.00083
[2023-12-25 19:54:36,354 INFO misc.py line 119 253097] Train: [77/100][500/510] Data 0.003 (0.098) Batch 1.158 (1.531) Remain 04:59:37 loss: 0.1212 Lr: 0.00083
[2023-12-25 19:54:37,421 INFO misc.py line 119 253097] Train: [77/100][501/510] Data 0.009 (0.098) Batch 1.067 (1.530) Remain 04:59:24 loss: 0.0907 Lr: 0.00083
[2023-12-25 19:54:38,493 INFO misc.py line 119 253097] Train: [77/100][502/510] Data 0.009 (0.098) Batch 1.077 (1.529) Remain 04:59:12 loss: 0.0894 Lr: 0.00083
[2023-12-25 19:54:39,754 INFO misc.py line 119 253097] Train: [77/100][503/510] Data 0.003 (0.098) Batch 1.260 (1.529) Remain 04:59:04 loss: 0.1316 Lr: 0.00083
[2023-12-25 19:54:41,001 INFO misc.py line 119 253097] Train: [77/100][504/510] Data 0.004 (0.098) Batch 1.243 (1.528) Remain 04:58:56 loss: 0.1719 Lr: 0.00083
[2023-12-25 19:54:42,117 INFO misc.py line 119 253097] Train: [77/100][505/510] Data 0.007 (0.098) Batch 1.115 (1.527) Remain 04:58:45 loss: 0.1202 Lr: 0.00083
[2023-12-25 19:54:43,346 INFO misc.py line 119 253097] Train: [77/100][506/510] Data 0.009 (0.097) Batch 1.228 (1.527) Remain 04:58:36 loss: 0.1791 Lr: 0.00083
[2023-12-25 19:54:44,475 INFO misc.py line 119 253097] Train: [77/100][507/510] Data 0.010 (0.097) Batch 1.135 (1.526) Remain 04:58:26 loss: 0.0910 Lr: 0.00083
[2023-12-25 19:54:45,611 INFO misc.py line 119 253097] Train: [77/100][508/510] Data 0.004 (0.097) Batch 1.131 (1.525) Remain 04:58:15 loss: 0.0736 Lr: 0.00083
[2023-12-25 19:54:46,611 INFO misc.py line 119 253097] Train: [77/100][509/510] Data 0.008 (0.097) Batch 1.002 (1.524) Remain 04:58:01 loss: 0.1057 Lr: 0.00083
[2023-12-25 19:54:47,727 INFO misc.py line 119 253097] Train: [77/100][510/510] Data 0.005 (0.097) Batch 1.118 (1.524) Remain 04:57:50 loss: 0.1052 Lr: 0.00083
[2023-12-25 19:54:47,728 INFO misc.py line 136 253097] Train result: loss: 0.1253 
[2023-12-25 19:54:47,728 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 19:55:21,409 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.8106 
[2023-12-25 19:55:21,753 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3043 
[2023-12-25 19:55:26,678 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3109 
[2023-12-25 19:55:27,202 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4343 
[2023-12-25 19:55:29,174 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9610 
[2023-12-25 19:55:29,608 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3199 
[2023-12-25 19:55:30,489 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1825 
[2023-12-25 19:55:31,041 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2652 
[2023-12-25 19:55:32,851 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9748 
[2023-12-25 19:55:34,969 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3525 
[2023-12-25 19:55:35,846 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3393 
[2023-12-25 19:55:36,271 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6896 
[2023-12-25 19:55:37,176 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3805 
[2023-12-25 19:55:40,117 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8979 
[2023-12-25 19:55:40,584 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3120 
[2023-12-25 19:55:41,207 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3924 
[2023-12-25 19:55:41,927 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3239 
[2023-12-25 19:55:43,532 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6790/0.7331/0.9026.
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9177/0.9472
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9829/0.9900
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8313/0.9777
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2766/0.2899
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6035/0.6211
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7032/0.7976
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8225/0.8994
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9064/0.9426
[2023-12-25 19:55:43,532 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.5989/0.6412
[2023-12-25 19:55:43,533 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7987/0.8827
[2023-12-25 19:55:43,533 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7797/0.8401
[2023-12-25 19:55:43,533 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6058/0.7015
[2023-12-25 19:55:43,533 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 19:55:43,534 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 19:55:43,534 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 19:55:55,558 INFO misc.py line 119 253097] Train: [78/100][1/510] Data 8.039 (8.039) Batch 8.999 (8.999) Remain 29:19:10 loss: 0.1114 Lr: 0.00083
[2023-12-25 19:55:56,783 INFO misc.py line 119 253097] Train: [78/100][2/510] Data 0.004 (0.004) Batch 1.226 (1.226) Remain 03:59:43 loss: 0.2342 Lr: 0.00083
[2023-12-25 19:55:59,703 INFO misc.py line 119 253097] Train: [78/100][3/510] Data 1.644 (1.644) Batch 2.919 (2.919) Remain 09:30:25 loss: 0.1239 Lr: 0.00083
[2023-12-25 19:56:00,977 INFO misc.py line 119 253097] Train: [78/100][4/510] Data 0.005 (0.005) Batch 1.269 (1.269) Remain 04:08:04 loss: 0.1389 Lr: 0.00083
[2023-12-25 19:56:02,098 INFO misc.py line 119 253097] Train: [78/100][5/510] Data 0.009 (0.007) Batch 1.126 (1.198) Remain 03:54:03 loss: 0.0955 Lr: 0.00083
[2023-12-25 19:56:03,275 INFO misc.py line 119 253097] Train: [78/100][6/510] Data 0.004 (0.006) Batch 1.173 (1.189) Remain 03:52:24 loss: 0.1259 Lr: 0.00083
[2023-12-25 19:56:04,377 INFO misc.py line 119 253097] Train: [78/100][7/510] Data 0.008 (0.006) Batch 1.100 (1.167) Remain 03:48:01 loss: 0.1068 Lr: 0.00083
[2023-12-25 19:56:05,636 INFO misc.py line 119 253097] Train: [78/100][8/510] Data 0.009 (0.007) Batch 1.265 (1.187) Remain 03:51:50 loss: 0.1549 Lr: 0.00083
[2023-12-25 19:56:06,778 INFO misc.py line 119 253097] Train: [78/100][9/510] Data 0.004 (0.006) Batch 1.137 (1.178) Remain 03:50:12 loss: 0.1510 Lr: 0.00083
[2023-12-25 19:56:07,941 INFO misc.py line 119 253097] Train: [78/100][10/510] Data 0.009 (0.007) Batch 1.166 (1.177) Remain 03:49:50 loss: 0.1293 Lr: 0.00083
[2023-12-25 19:56:08,876 INFO misc.py line 119 253097] Train: [78/100][11/510] Data 0.006 (0.007) Batch 0.935 (1.146) Remain 03:43:54 loss: 0.1060 Lr: 0.00083
[2023-12-25 19:56:10,014 INFO misc.py line 119 253097] Train: [78/100][12/510] Data 0.005 (0.006) Batch 1.141 (1.146) Remain 03:43:46 loss: 0.0847 Lr: 0.00083
[2023-12-25 19:56:11,244 INFO misc.py line 119 253097] Train: [78/100][13/510] Data 0.004 (0.006) Batch 1.225 (1.154) Remain 03:45:18 loss: 0.0746 Lr: 0.00083
[2023-12-25 19:56:12,478 INFO misc.py line 119 253097] Train: [78/100][14/510] Data 0.008 (0.006) Batch 1.233 (1.161) Remain 03:46:42 loss: 0.0880 Lr: 0.00083
[2023-12-25 19:56:13,626 INFO misc.py line 119 253097] Train: [78/100][15/510] Data 0.008 (0.007) Batch 1.151 (1.160) Remain 03:46:31 loss: 0.0483 Lr: 0.00083
[2023-12-25 19:56:14,886 INFO misc.py line 119 253097] Train: [78/100][16/510] Data 0.006 (0.006) Batch 1.263 (1.168) Remain 03:48:02 loss: 0.0697 Lr: 0.00083
[2023-12-25 19:56:16,071 INFO misc.py line 119 253097] Train: [78/100][17/510] Data 0.003 (0.006) Batch 1.184 (1.169) Remain 03:48:14 loss: 0.2334 Lr: 0.00083
[2023-12-25 19:56:22,197 INFO misc.py line 119 253097] Train: [78/100][18/510] Data 0.004 (0.006) Batch 6.126 (1.500) Remain 04:52:44 loss: 0.1346 Lr: 0.00083
[2023-12-25 19:56:23,172 INFO misc.py line 119 253097] Train: [78/100][19/510] Data 0.003 (0.006) Batch 0.974 (1.467) Remain 04:46:18 loss: 0.0974 Lr: 0.00083
[2023-12-25 19:56:24,389 INFO misc.py line 119 253097] Train: [78/100][20/510] Data 0.003 (0.006) Batch 1.209 (1.452) Remain 04:43:19 loss: 0.0685 Lr: 0.00083
[2023-12-25 19:56:25,447 INFO misc.py line 119 253097] Train: [78/100][21/510] Data 0.012 (0.006) Batch 1.066 (1.430) Remain 04:39:06 loss: 0.1349 Lr: 0.00083
[2023-12-25 19:56:26,564 INFO misc.py line 119 253097] Train: [78/100][22/510] Data 0.003 (0.006) Batch 1.117 (1.414) Remain 04:35:52 loss: 0.1654 Lr: 0.00083
[2023-12-25 19:56:27,754 INFO misc.py line 119 253097] Train: [78/100][23/510] Data 0.004 (0.006) Batch 1.189 (1.403) Remain 04:33:39 loss: 0.1310 Lr: 0.00083
[2023-12-25 19:56:28,872 INFO misc.py line 119 253097] Train: [78/100][24/510] Data 0.004 (0.006) Batch 1.118 (1.389) Remain 04:30:59 loss: 0.0832 Lr: 0.00083
[2023-12-25 19:56:30,147 INFO misc.py line 119 253097] Train: [78/100][25/510] Data 0.005 (0.006) Batch 1.273 (1.384) Remain 04:29:56 loss: 0.1709 Lr: 0.00083
[2023-12-25 19:56:31,203 INFO misc.py line 119 253097] Train: [78/100][26/510] Data 0.006 (0.006) Batch 1.055 (1.369) Remain 04:27:07 loss: 0.1637 Lr: 0.00083
[2023-12-25 19:56:32,487 INFO misc.py line 119 253097] Train: [78/100][27/510] Data 0.007 (0.006) Batch 1.288 (1.366) Remain 04:26:26 loss: 0.0889 Lr: 0.00083
[2023-12-25 19:56:33,714 INFO misc.py line 119 253097] Train: [78/100][28/510] Data 0.003 (0.006) Batch 1.226 (1.360) Remain 04:25:19 loss: 0.1261 Lr: 0.00083
[2023-12-25 19:56:34,809 INFO misc.py line 119 253097] Train: [78/100][29/510] Data 0.004 (0.006) Batch 1.091 (1.350) Remain 04:23:17 loss: 0.0508 Lr: 0.00083
[2023-12-25 19:56:35,894 INFO misc.py line 119 253097] Train: [78/100][30/510] Data 0.009 (0.006) Batch 1.047 (1.339) Remain 04:21:04 loss: 0.1072 Lr: 0.00083
[2023-12-25 19:56:37,279 INFO misc.py line 119 253097] Train: [78/100][31/510] Data 0.048 (0.007) Batch 1.424 (1.342) Remain 04:21:38 loss: 0.1119 Lr: 0.00083
[2023-12-25 19:56:38,504 INFO misc.py line 119 253097] Train: [78/100][32/510] Data 0.007 (0.007) Batch 1.226 (1.338) Remain 04:20:50 loss: 0.1179 Lr: 0.00082
[2023-12-25 19:56:39,723 INFO misc.py line 119 253097] Train: [78/100][33/510] Data 0.006 (0.007) Batch 1.217 (1.334) Remain 04:20:02 loss: 0.0862 Lr: 0.00082
[2023-12-25 19:56:40,887 INFO misc.py line 119 253097] Train: [78/100][34/510] Data 0.008 (0.007) Batch 1.154 (1.328) Remain 04:18:52 loss: 0.1145 Lr: 0.00082
[2023-12-25 19:56:42,072 INFO misc.py line 119 253097] Train: [78/100][35/510] Data 0.019 (0.008) Batch 1.199 (1.324) Remain 04:18:04 loss: 0.2365 Lr: 0.00082
[2023-12-25 19:56:48,428 INFO misc.py line 119 253097] Train: [78/100][36/510] Data 5.012 (0.159) Batch 6.357 (1.477) Remain 04:47:46 loss: 0.0683 Lr: 0.00082
[2023-12-25 19:56:49,504 INFO misc.py line 119 253097] Train: [78/100][37/510] Data 0.004 (0.155) Batch 1.075 (1.465) Remain 04:45:27 loss: 0.1711 Lr: 0.00082
[2023-12-25 19:56:50,678 INFO misc.py line 119 253097] Train: [78/100][38/510] Data 0.004 (0.150) Batch 1.170 (1.456) Remain 04:43:47 loss: 0.1415 Lr: 0.00082
[2023-12-25 19:56:51,934 INFO misc.py line 119 253097] Train: [78/100][39/510] Data 0.008 (0.146) Batch 1.260 (1.451) Remain 04:42:42 loss: 0.0618 Lr: 0.00082
[2023-12-25 19:56:53,204 INFO misc.py line 119 253097] Train: [78/100][40/510] Data 0.003 (0.143) Batch 1.270 (1.446) Remain 04:41:43 loss: 0.0766 Lr: 0.00082
[2023-12-25 19:56:54,421 INFO misc.py line 119 253097] Train: [78/100][41/510] Data 0.003 (0.139) Batch 1.217 (1.440) Remain 04:40:31 loss: 0.1161 Lr: 0.00082
[2023-12-25 19:56:55,614 INFO misc.py line 119 253097] Train: [78/100][42/510] Data 0.004 (0.135) Batch 1.193 (1.434) Remain 04:39:16 loss: 0.1315 Lr: 0.00082
[2023-12-25 19:56:56,787 INFO misc.py line 119 253097] Train: [78/100][43/510] Data 0.004 (0.132) Batch 1.174 (1.427) Remain 04:37:58 loss: 0.0668 Lr: 0.00082
[2023-12-25 19:56:58,006 INFO misc.py line 119 253097] Train: [78/100][44/510] Data 0.003 (0.129) Batch 1.215 (1.422) Remain 04:36:56 loss: 0.2117 Lr: 0.00082
[2023-12-25 19:56:59,096 INFO misc.py line 119 253097] Train: [78/100][45/510] Data 0.007 (0.126) Batch 1.093 (1.414) Remain 04:35:23 loss: 0.0966 Lr: 0.00082
[2023-12-25 19:57:00,398 INFO misc.py line 119 253097] Train: [78/100][46/510] Data 0.004 (0.123) Batch 1.299 (1.411) Remain 04:34:51 loss: 0.0943 Lr: 0.00082
[2023-12-25 19:57:01,707 INFO misc.py line 119 253097] Train: [78/100][47/510] Data 0.007 (0.121) Batch 1.310 (1.409) Remain 04:34:22 loss: 0.1473 Lr: 0.00082
[2023-12-25 19:57:02,794 INFO misc.py line 119 253097] Train: [78/100][48/510] Data 0.007 (0.118) Batch 1.090 (1.402) Remain 04:32:58 loss: 0.0799 Lr: 0.00082
[2023-12-25 19:57:03,741 INFO misc.py line 119 253097] Train: [78/100][49/510] Data 0.003 (0.116) Batch 0.948 (1.392) Remain 04:31:01 loss: 0.0778 Lr: 0.00082
[2023-12-25 19:57:04,996 INFO misc.py line 119 253097] Train: [78/100][50/510] Data 0.003 (0.113) Batch 1.249 (1.389) Remain 04:30:24 loss: 0.1450 Lr: 0.00082
[2023-12-25 19:57:05,918 INFO misc.py line 119 253097] Train: [78/100][51/510] Data 0.008 (0.111) Batch 0.928 (1.380) Remain 04:28:31 loss: 0.0798 Lr: 0.00082
[2023-12-25 19:57:06,960 INFO misc.py line 119 253097] Train: [78/100][52/510] Data 0.003 (0.109) Batch 1.042 (1.373) Remain 04:27:09 loss: 0.1293 Lr: 0.00082
[2023-12-25 19:57:08,177 INFO misc.py line 119 253097] Train: [78/100][53/510] Data 0.003 (0.107) Batch 1.217 (1.369) Remain 04:26:31 loss: 0.1491 Lr: 0.00082
[2023-12-25 19:57:13,613 INFO misc.py line 119 253097] Train: [78/100][54/510] Data 0.004 (0.105) Batch 5.426 (1.449) Remain 04:41:58 loss: 0.0889 Lr: 0.00082
[2023-12-25 19:57:14,915 INFO misc.py line 119 253097] Train: [78/100][55/510] Data 0.014 (0.103) Batch 1.311 (1.446) Remain 04:41:26 loss: 0.0876 Lr: 0.00082
[2023-12-25 19:57:15,979 INFO misc.py line 119 253097] Train: [78/100][56/510] Data 0.005 (0.101) Batch 1.061 (1.439) Remain 04:40:00 loss: 0.2387 Lr: 0.00082
[2023-12-25 19:57:17,163 INFO misc.py line 119 253097] Train: [78/100][57/510] Data 0.008 (0.099) Batch 1.187 (1.434) Remain 04:39:04 loss: 0.1467 Lr: 0.00082
[2023-12-25 19:57:18,138 INFO misc.py line 119 253097] Train: [78/100][58/510] Data 0.005 (0.098) Batch 0.976 (1.426) Remain 04:37:25 loss: 0.0776 Lr: 0.00082
[2023-12-25 19:57:19,222 INFO misc.py line 119 253097] Train: [78/100][59/510] Data 0.004 (0.096) Batch 1.084 (1.420) Remain 04:36:12 loss: 0.0994 Lr: 0.00082
[2023-12-25 19:57:25,210 INFO misc.py line 119 253097] Train: [78/100][60/510] Data 0.004 (0.094) Batch 5.986 (1.500) Remain 04:51:46 loss: 0.1001 Lr: 0.00082
[2023-12-25 19:57:26,340 INFO misc.py line 119 253097] Train: [78/100][61/510] Data 0.005 (0.093) Batch 1.132 (1.494) Remain 04:50:30 loss: 0.1215 Lr: 0.00082
[2023-12-25 19:57:27,651 INFO misc.py line 119 253097] Train: [78/100][62/510] Data 0.004 (0.091) Batch 1.309 (1.491) Remain 04:49:52 loss: 0.1398 Lr: 0.00082
[2023-12-25 19:57:28,896 INFO misc.py line 119 253097] Train: [78/100][63/510] Data 0.006 (0.090) Batch 1.247 (1.487) Remain 04:49:03 loss: 0.2590 Lr: 0.00082
[2023-12-25 19:57:29,963 INFO misc.py line 119 253097] Train: [78/100][64/510] Data 0.005 (0.089) Batch 1.067 (1.480) Remain 04:47:41 loss: 0.1502 Lr: 0.00082
[2023-12-25 19:57:35,817 INFO misc.py line 119 253097] Train: [78/100][65/510] Data 4.545 (0.160) Batch 5.854 (1.550) Remain 05:01:23 loss: 0.1170 Lr: 0.00082
[2023-12-25 19:57:36,849 INFO misc.py line 119 253097] Train: [78/100][66/510] Data 0.004 (0.158) Batch 1.031 (1.542) Remain 04:59:45 loss: 0.1330 Lr: 0.00082
[2023-12-25 19:57:38,179 INFO misc.py line 119 253097] Train: [78/100][67/510] Data 0.005 (0.156) Batch 1.327 (1.539) Remain 04:59:05 loss: 0.1709 Lr: 0.00082
[2023-12-25 19:57:39,395 INFO misc.py line 119 253097] Train: [78/100][68/510] Data 0.008 (0.153) Batch 1.220 (1.534) Remain 04:58:06 loss: 0.1023 Lr: 0.00082
[2023-12-25 19:57:40,694 INFO misc.py line 119 253097] Train: [78/100][69/510] Data 0.004 (0.151) Batch 1.293 (1.530) Remain 04:57:22 loss: 0.1085 Lr: 0.00082
[2023-12-25 19:57:41,702 INFO misc.py line 119 253097] Train: [78/100][70/510] Data 0.011 (0.149) Batch 1.009 (1.522) Remain 04:55:50 loss: 0.1309 Lr: 0.00082
[2023-12-25 19:57:42,900 INFO misc.py line 119 253097] Train: [78/100][71/510] Data 0.009 (0.147) Batch 1.202 (1.518) Remain 04:54:53 loss: 0.0739 Lr: 0.00082
[2023-12-25 19:57:44,167 INFO misc.py line 119 253097] Train: [78/100][72/510] Data 0.006 (0.145) Batch 1.267 (1.514) Remain 04:54:09 loss: 0.0948 Lr: 0.00082
[2023-12-25 19:57:45,144 INFO misc.py line 119 253097] Train: [78/100][73/510] Data 0.005 (0.143) Batch 0.976 (1.506) Remain 04:52:38 loss: 0.0986 Lr: 0.00082
[2023-12-25 19:57:46,372 INFO misc.py line 119 253097] Train: [78/100][74/510] Data 0.006 (0.141) Batch 1.231 (1.502) Remain 04:51:51 loss: 0.1155 Lr: 0.00082
[2023-12-25 19:57:47,485 INFO misc.py line 119 253097] Train: [78/100][75/510] Data 0.004 (0.139) Batch 1.113 (1.497) Remain 04:50:47 loss: 0.2200 Lr: 0.00082
[2023-12-25 19:57:52,288 INFO misc.py line 119 253097] Train: [78/100][76/510] Data 0.003 (0.137) Batch 4.801 (1.542) Remain 04:59:33 loss: 0.1129 Lr: 0.00082
[2023-12-25 19:57:54,229 INFO misc.py line 119 253097] Train: [78/100][77/510] Data 1.155 (0.151) Batch 1.940 (1.548) Remain 05:00:34 loss: 0.0612 Lr: 0.00082
[2023-12-25 19:57:55,333 INFO misc.py line 119 253097] Train: [78/100][78/510] Data 0.005 (0.149) Batch 1.092 (1.542) Remain 04:59:22 loss: 0.1297 Lr: 0.00082
[2023-12-25 19:57:56,485 INFO misc.py line 119 253097] Train: [78/100][79/510] Data 0.017 (0.147) Batch 1.161 (1.537) Remain 04:58:22 loss: 0.2169 Lr: 0.00082
[2023-12-25 19:57:57,668 INFO misc.py line 119 253097] Train: [78/100][80/510] Data 0.008 (0.145) Batch 1.184 (1.532) Remain 04:57:27 loss: 0.1423 Lr: 0.00082
[2023-12-25 19:57:58,871 INFO misc.py line 119 253097] Train: [78/100][81/510] Data 0.007 (0.144) Batch 1.206 (1.528) Remain 04:56:37 loss: 0.0707 Lr: 0.00082
[2023-12-25 19:58:00,062 INFO misc.py line 119 253097] Train: [78/100][82/510] Data 0.006 (0.142) Batch 1.184 (1.523) Remain 04:55:45 loss: 0.0953 Lr: 0.00082
[2023-12-25 19:58:00,904 INFO misc.py line 119 253097] Train: [78/100][83/510] Data 0.011 (0.140) Batch 0.849 (1.515) Remain 04:54:05 loss: 0.0718 Lr: 0.00082
[2023-12-25 19:58:02,172 INFO misc.py line 119 253097] Train: [78/100][84/510] Data 0.005 (0.139) Batch 1.264 (1.512) Remain 04:53:27 loss: 0.0882 Lr: 0.00082
[2023-12-25 19:58:03,338 INFO misc.py line 119 253097] Train: [78/100][85/510] Data 0.008 (0.137) Batch 1.166 (1.508) Remain 04:52:37 loss: 0.1072 Lr: 0.00082
[2023-12-25 19:58:04,540 INFO misc.py line 119 253097] Train: [78/100][86/510] Data 0.008 (0.135) Batch 1.206 (1.504) Remain 04:51:53 loss: 0.1011 Lr: 0.00082
[2023-12-25 19:58:05,538 INFO misc.py line 119 253097] Train: [78/100][87/510] Data 0.005 (0.134) Batch 0.994 (1.498) Remain 04:50:41 loss: 0.1080 Lr: 0.00082
[2023-12-25 19:58:06,758 INFO misc.py line 119 253097] Train: [78/100][88/510] Data 0.010 (0.132) Batch 1.223 (1.495) Remain 04:50:01 loss: 0.0871 Lr: 0.00082
[2023-12-25 19:58:07,630 INFO misc.py line 119 253097] Train: [78/100][89/510] Data 0.006 (0.131) Batch 0.872 (1.488) Remain 04:48:36 loss: 0.1514 Lr: 0.00082
[2023-12-25 19:58:08,778 INFO misc.py line 119 253097] Train: [78/100][90/510] Data 0.006 (0.129) Batch 1.146 (1.484) Remain 04:47:48 loss: 0.1012 Lr: 0.00082
[2023-12-25 19:58:09,945 INFO misc.py line 119 253097] Train: [78/100][91/510] Data 0.008 (0.128) Batch 1.167 (1.480) Remain 04:47:05 loss: 0.1026 Lr: 0.00082
[2023-12-25 19:58:11,002 INFO misc.py line 119 253097] Train: [78/100][92/510] Data 0.007 (0.127) Batch 1.056 (1.475) Remain 04:46:08 loss: 0.1104 Lr: 0.00082
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[2023-12-25 19:59:29,533 INFO misc.py line 119 253097] Train: [78/100][134/510] Data 0.182 (0.228) Batch 1.443 (1.602) Remain 05:09:33 loss: 0.0661 Lr: 0.00081
[2023-12-25 19:59:30,645 INFO misc.py line 119 253097] Train: [78/100][135/510] Data 0.004 (0.226) Batch 1.112 (1.598) Remain 05:08:49 loss: 0.0845 Lr: 0.00081
[2023-12-25 19:59:31,730 INFO misc.py line 119 253097] Train: [78/100][136/510] Data 0.004 (0.225) Batch 1.083 (1.594) Remain 05:08:02 loss: 0.1231 Lr: 0.00081
[2023-12-25 19:59:33,251 INFO misc.py line 119 253097] Train: [78/100][137/510] Data 0.006 (0.223) Batch 1.519 (1.594) Remain 05:07:54 loss: 0.1201 Lr: 0.00081
[2023-12-25 19:59:34,538 INFO misc.py line 119 253097] Train: [78/100][138/510] Data 0.007 (0.221) Batch 1.288 (1.591) Remain 05:07:27 loss: 0.1404 Lr: 0.00081
[2023-12-25 19:59:35,750 INFO misc.py line 119 253097] Train: [78/100][139/510] Data 0.005 (0.220) Batch 1.210 (1.589) Remain 05:06:52 loss: 0.1368 Lr: 0.00081
[2023-12-25 19:59:36,868 INFO misc.py line 119 253097] Train: [78/100][140/510] Data 0.008 (0.218) Batch 1.118 (1.585) Remain 05:06:11 loss: 0.1923 Lr: 0.00081
[2023-12-25 19:59:38,030 INFO misc.py line 119 253097] Train: [78/100][141/510] Data 0.008 (0.217) Batch 1.159 (1.582) Remain 05:05:34 loss: 0.1041 Lr: 0.00081
[2023-12-25 19:59:39,282 INFO misc.py line 119 253097] Train: [78/100][142/510] Data 0.012 (0.215) Batch 1.253 (1.580) Remain 05:05:05 loss: 0.0894 Lr: 0.00081
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[2023-12-25 19:59:41,392 INFO misc.py line 119 253097] Train: [78/100][144/510] Data 0.004 (0.212) Batch 1.075 (1.572) Remain 05:03:35 loss: 0.1239 Lr: 0.00081
[2023-12-25 19:59:42,428 INFO misc.py line 119 253097] Train: [78/100][145/510] Data 0.010 (0.211) Batch 1.038 (1.568) Remain 05:02:50 loss: 0.0678 Lr: 0.00081
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[2023-12-25 19:59:50,572 INFO misc.py line 119 253097] Train: [78/100][147/510] Data 0.010 (0.208) Batch 6.886 (1.603) Remain 05:09:30 loss: 0.1227 Lr: 0.00081
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[2023-12-25 20:02:08,230 INFO misc.py line 119 253097] Train: [78/100][238/510] Data 0.005 (0.164) Batch 6.155 (1.568) Remain 05:00:21 loss: 0.1880 Lr: 0.00080
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[2023-12-25 20:02:15,563 INFO misc.py line 119 253097] Train: [78/100][242/510] Data 2.563 (0.172) Batch 3.825 (1.573) Remain 05:01:06 loss: 0.1781 Lr: 0.00080
[2023-12-25 20:02:16,553 INFO misc.py line 119 253097] Train: [78/100][243/510] Data 0.005 (0.171) Batch 0.990 (1.570) Remain 05:00:36 loss: 0.1443 Lr: 0.00080
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[2023-12-25 20:02:20,934 INFO misc.py line 119 253097] Train: [78/100][247/510] Data 0.004 (0.169) Batch 1.079 (1.562) Remain 04:59:01 loss: 0.1635 Lr: 0.00080
[2023-12-25 20:02:22,171 INFO misc.py line 119 253097] Train: [78/100][248/510] Data 0.004 (0.168) Batch 1.232 (1.561) Remain 04:58:44 loss: 0.1183 Lr: 0.00080
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[2023-12-25 20:02:37,508 INFO misc.py line 119 253097] Train: [78/100][253/510] Data 0.011 (0.165) Batch 10.599 (1.591) Remain 05:04:22 loss: 0.1559 Lr: 0.00080
[2023-12-25 20:02:38,635 INFO misc.py line 119 253097] Train: [78/100][254/510] Data 0.004 (0.164) Batch 1.128 (1.589) Remain 05:03:59 loss: 0.2567 Lr: 0.00080
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[2023-12-25 20:02:40,953 INFO misc.py line 119 253097] Train: [78/100][256/510] Data 0.004 (0.163) Batch 1.242 (1.586) Remain 05:03:15 loss: 0.1115 Lr: 0.00080
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[2023-12-25 20:02:45,824 INFO misc.py line 119 253097] Train: [78/100][259/510] Data 0.006 (0.161) Batch 2.396 (1.586) Remain 05:03:17 loss: 0.1057 Lr: 0.00079
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[2023-12-25 20:06:14,057 INFO misc.py line 119 253097] Train: [78/100][406/510] Data 0.006 (0.111) Batch 1.136 (1.524) Remain 04:47:42 loss: 0.2195 Lr: 0.00078
[2023-12-25 20:06:15,170 INFO misc.py line 119 253097] Train: [78/100][407/510] Data 0.003 (0.111) Batch 1.113 (1.523) Remain 04:47:29 loss: 0.1945 Lr: 0.00078
[2023-12-25 20:06:16,435 INFO misc.py line 119 253097] Train: [78/100][408/510] Data 0.004 (0.111) Batch 1.264 (1.523) Remain 04:47:21 loss: 0.1081 Lr: 0.00078
[2023-12-25 20:06:17,413 INFO misc.py line 119 253097] Train: [78/100][409/510] Data 0.004 (0.110) Batch 0.977 (1.521) Remain 04:47:04 loss: 0.1210 Lr: 0.00078
[2023-12-25 20:06:18,422 INFO misc.py line 119 253097] Train: [78/100][410/510] Data 0.005 (0.110) Batch 1.008 (1.520) Remain 04:46:48 loss: 0.0548 Lr: 0.00077
[2023-12-25 20:06:19,586 INFO misc.py line 119 253097] Train: [78/100][411/510] Data 0.006 (0.110) Batch 1.166 (1.519) Remain 04:46:37 loss: 0.1375 Lr: 0.00077
[2023-12-25 20:06:20,769 INFO misc.py line 119 253097] Train: [78/100][412/510] Data 0.003 (0.110) Batch 1.178 (1.518) Remain 04:46:26 loss: 0.1423 Lr: 0.00077
[2023-12-25 20:06:22,059 INFO misc.py line 119 253097] Train: [78/100][413/510] Data 0.008 (0.109) Batch 1.291 (1.518) Remain 04:46:18 loss: 0.1155 Lr: 0.00077
[2023-12-25 20:06:23,086 INFO misc.py line 119 253097] Train: [78/100][414/510] Data 0.007 (0.109) Batch 1.031 (1.517) Remain 04:46:03 loss: 0.1005 Lr: 0.00077
[2023-12-25 20:06:24,283 INFO misc.py line 119 253097] Train: [78/100][415/510] Data 0.004 (0.109) Batch 1.197 (1.516) Remain 04:45:53 loss: 0.1061 Lr: 0.00077
[2023-12-25 20:06:26,161 INFO misc.py line 119 253097] Train: [78/100][416/510] Data 0.003 (0.109) Batch 1.877 (1.517) Remain 04:46:01 loss: 0.1072 Lr: 0.00077
[2023-12-25 20:06:26,986 INFO misc.py line 119 253097] Train: [78/100][417/510] Data 0.005 (0.108) Batch 0.823 (1.515) Remain 04:45:41 loss: 0.1238 Lr: 0.00077
[2023-12-25 20:06:28,169 INFO misc.py line 119 253097] Train: [78/100][418/510] Data 0.006 (0.108) Batch 1.177 (1.514) Remain 04:45:30 loss: 0.0714 Lr: 0.00077
[2023-12-25 20:06:29,513 INFO misc.py line 119 253097] Train: [78/100][419/510] Data 0.011 (0.108) Batch 1.349 (1.514) Remain 04:45:24 loss: 0.0947 Lr: 0.00077
[2023-12-25 20:06:30,517 INFO misc.py line 119 253097] Train: [78/100][420/510] Data 0.006 (0.108) Batch 1.005 (1.513) Remain 04:45:09 loss: 0.1059 Lr: 0.00077
[2023-12-25 20:06:33,492 INFO misc.py line 119 253097] Train: [78/100][421/510] Data 0.005 (0.107) Batch 2.976 (1.516) Remain 04:45:47 loss: 0.1192 Lr: 0.00077
[2023-12-25 20:06:35,920 INFO misc.py line 119 253097] Train: [78/100][422/510] Data 0.004 (0.107) Batch 2.427 (1.518) Remain 04:46:10 loss: 0.1594 Lr: 0.00077
[2023-12-25 20:06:37,065 INFO misc.py line 119 253097] Train: [78/100][423/510] Data 0.005 (0.107) Batch 1.145 (1.518) Remain 04:45:58 loss: 0.1685 Lr: 0.00077
[2023-12-25 20:06:38,140 INFO misc.py line 119 253097] Train: [78/100][424/510] Data 0.005 (0.107) Batch 1.076 (1.516) Remain 04:45:45 loss: 0.1386 Lr: 0.00077
[2023-12-25 20:06:41,632 INFO misc.py line 119 253097] Train: [78/100][425/510] Data 0.003 (0.106) Batch 3.492 (1.521) Remain 04:46:36 loss: 0.1099 Lr: 0.00077
[2023-12-25 20:06:42,673 INFO misc.py line 119 253097] Train: [78/100][426/510] Data 0.004 (0.106) Batch 1.041 (1.520) Remain 04:46:22 loss: 0.1385 Lr: 0.00077
[2023-12-25 20:06:43,762 INFO misc.py line 119 253097] Train: [78/100][427/510] Data 0.005 (0.106) Batch 1.083 (1.519) Remain 04:46:09 loss: 0.0667 Lr: 0.00077
[2023-12-25 20:06:44,787 INFO misc.py line 119 253097] Train: [78/100][428/510] Data 0.010 (0.106) Batch 1.033 (1.518) Remain 04:45:54 loss: 0.1820 Lr: 0.00077
[2023-12-25 20:06:45,806 INFO misc.py line 119 253097] Train: [78/100][429/510] Data 0.003 (0.105) Batch 1.016 (1.517) Remain 04:45:39 loss: 0.0906 Lr: 0.00077
[2023-12-25 20:06:46,908 INFO misc.py line 119 253097] Train: [78/100][430/510] Data 0.005 (0.105) Batch 1.098 (1.516) Remain 04:45:27 loss: 0.1423 Lr: 0.00077
[2023-12-25 20:06:48,083 INFO misc.py line 119 253097] Train: [78/100][431/510] Data 0.009 (0.105) Batch 1.178 (1.515) Remain 04:45:16 loss: 0.1296 Lr: 0.00077
[2023-12-25 20:06:49,059 INFO misc.py line 119 253097] Train: [78/100][432/510] Data 0.006 (0.105) Batch 0.978 (1.514) Remain 04:45:01 loss: 0.0876 Lr: 0.00077
[2023-12-25 20:06:50,161 INFO misc.py line 119 253097] Train: [78/100][433/510] Data 0.004 (0.105) Batch 1.102 (1.513) Remain 04:44:48 loss: 0.0738 Lr: 0.00077
[2023-12-25 20:06:51,224 INFO misc.py line 119 253097] Train: [78/100][434/510] Data 0.006 (0.104) Batch 1.064 (1.512) Remain 04:44:35 loss: 0.1031 Lr: 0.00077
[2023-12-25 20:06:52,472 INFO misc.py line 119 253097] Train: [78/100][435/510] Data 0.005 (0.104) Batch 1.244 (1.511) Remain 04:44:27 loss: 0.1740 Lr: 0.00077
[2023-12-25 20:06:53,547 INFO misc.py line 119 253097] Train: [78/100][436/510] Data 0.008 (0.104) Batch 1.079 (1.510) Remain 04:44:14 loss: 0.0786 Lr: 0.00077
[2023-12-25 20:06:54,490 INFO misc.py line 119 253097] Train: [78/100][437/510] Data 0.004 (0.104) Batch 0.943 (1.509) Remain 04:43:58 loss: 0.1516 Lr: 0.00077
[2023-12-25 20:06:55,501 INFO misc.py line 119 253097] Train: [78/100][438/510] Data 0.004 (0.103) Batch 1.010 (1.508) Remain 04:43:43 loss: 0.1322 Lr: 0.00077
[2023-12-25 20:07:08,273 INFO misc.py line 119 253097] Train: [78/100][439/510] Data 0.006 (0.103) Batch 12.774 (1.533) Remain 04:48:33 loss: 0.0901 Lr: 0.00077
[2023-12-25 20:07:09,377 INFO misc.py line 119 253097] Train: [78/100][440/510] Data 0.003 (0.103) Batch 1.104 (1.532) Remain 04:48:21 loss: 0.0631 Lr: 0.00077
[2023-12-25 20:07:10,665 INFO misc.py line 119 253097] Train: [78/100][441/510] Data 0.003 (0.103) Batch 1.282 (1.532) Remain 04:48:13 loss: 0.0766 Lr: 0.00077
[2023-12-25 20:07:11,819 INFO misc.py line 119 253097] Train: [78/100][442/510] Data 0.009 (0.102) Batch 1.153 (1.531) Remain 04:48:01 loss: 0.1016 Lr: 0.00077
[2023-12-25 20:07:12,854 INFO misc.py line 119 253097] Train: [78/100][443/510] Data 0.009 (0.102) Batch 1.041 (1.530) Remain 04:47:47 loss: 0.1020 Lr: 0.00077
[2023-12-25 20:07:13,913 INFO misc.py line 119 253097] Train: [78/100][444/510] Data 0.004 (0.102) Batch 1.059 (1.529) Remain 04:47:34 loss: 0.0815 Lr: 0.00077
[2023-12-25 20:07:15,183 INFO misc.py line 119 253097] Train: [78/100][445/510] Data 0.003 (0.102) Batch 1.269 (1.528) Remain 04:47:26 loss: 0.0868 Lr: 0.00077
[2023-12-25 20:07:16,197 INFO misc.py line 119 253097] Train: [78/100][446/510] Data 0.004 (0.102) Batch 1.011 (1.527) Remain 04:47:11 loss: 0.1994 Lr: 0.00077
[2023-12-25 20:07:17,202 INFO misc.py line 119 253097] Train: [78/100][447/510] Data 0.007 (0.101) Batch 1.005 (1.526) Remain 04:46:56 loss: 0.1107 Lr: 0.00077
[2023-12-25 20:07:18,344 INFO misc.py line 119 253097] Train: [78/100][448/510] Data 0.008 (0.101) Batch 1.144 (1.525) Remain 04:46:45 loss: 0.1291 Lr: 0.00077
[2023-12-25 20:07:19,603 INFO misc.py line 119 253097] Train: [78/100][449/510] Data 0.006 (0.101) Batch 1.257 (1.524) Remain 04:46:37 loss: 0.1036 Lr: 0.00077
[2023-12-25 20:07:20,888 INFO misc.py line 119 253097] Train: [78/100][450/510] Data 0.008 (0.101) Batch 1.286 (1.524) Remain 04:46:29 loss: 0.1584 Lr: 0.00077
[2023-12-25 20:07:22,190 INFO misc.py line 119 253097] Train: [78/100][451/510] Data 0.007 (0.101) Batch 1.304 (1.523) Remain 04:46:22 loss: 0.1342 Lr: 0.00077
[2023-12-25 20:07:23,264 INFO misc.py line 119 253097] Train: [78/100][452/510] Data 0.005 (0.100) Batch 1.070 (1.522) Remain 04:46:09 loss: 0.1638 Lr: 0.00077
[2023-12-25 20:07:24,180 INFO misc.py line 119 253097] Train: [78/100][453/510] Data 0.008 (0.100) Batch 0.921 (1.521) Remain 04:45:52 loss: 0.1049 Lr: 0.00077
[2023-12-25 20:07:25,105 INFO misc.py line 119 253097] Train: [78/100][454/510] Data 0.004 (0.100) Batch 0.924 (1.520) Remain 04:45:36 loss: 0.1662 Lr: 0.00077
[2023-12-25 20:07:26,353 INFO misc.py line 119 253097] Train: [78/100][455/510] Data 0.004 (0.100) Batch 1.249 (1.519) Remain 04:45:28 loss: 0.1014 Lr: 0.00077
[2023-12-25 20:07:33,712 INFO misc.py line 119 253097] Train: [78/100][456/510] Data 0.004 (0.099) Batch 7.359 (1.532) Remain 04:47:52 loss: 0.1114 Lr: 0.00077
[2023-12-25 20:07:34,847 INFO misc.py line 119 253097] Train: [78/100][457/510] Data 0.003 (0.099) Batch 1.135 (1.531) Remain 04:47:40 loss: 0.1102 Lr: 0.00077
[2023-12-25 20:07:35,988 INFO misc.py line 119 253097] Train: [78/100][458/510] Data 0.003 (0.099) Batch 1.140 (1.530) Remain 04:47:29 loss: 0.1620 Lr: 0.00077
[2023-12-25 20:07:37,088 INFO misc.py line 119 253097] Train: [78/100][459/510] Data 0.003 (0.099) Batch 1.100 (1.529) Remain 04:47:17 loss: 0.0977 Lr: 0.00077
[2023-12-25 20:07:38,144 INFO misc.py line 119 253097] Train: [78/100][460/510] Data 0.005 (0.099) Batch 1.055 (1.528) Remain 04:47:04 loss: 0.1150 Lr: 0.00077
[2023-12-25 20:07:39,209 INFO misc.py line 119 253097] Train: [78/100][461/510] Data 0.004 (0.098) Batch 1.066 (1.527) Remain 04:46:51 loss: 0.1100 Lr: 0.00077
[2023-12-25 20:07:41,439 INFO misc.py line 119 253097] Train: [78/100][462/510] Data 0.003 (0.098) Batch 2.229 (1.529) Remain 04:47:06 loss: 0.2613 Lr: 0.00077
[2023-12-25 20:07:42,647 INFO misc.py line 119 253097] Train: [78/100][463/510] Data 0.005 (0.098) Batch 1.209 (1.528) Remain 04:46:57 loss: 0.1923 Lr: 0.00077
[2023-12-25 20:07:43,782 INFO misc.py line 119 253097] Train: [78/100][464/510] Data 0.005 (0.098) Batch 1.136 (1.527) Remain 04:46:46 loss: 0.0704 Lr: 0.00077
[2023-12-25 20:07:44,960 INFO misc.py line 119 253097] Train: [78/100][465/510] Data 0.003 (0.098) Batch 1.178 (1.527) Remain 04:46:36 loss: 0.1944 Lr: 0.00077
[2023-12-25 20:07:46,111 INFO misc.py line 119 253097] Train: [78/100][466/510] Data 0.003 (0.097) Batch 1.151 (1.526) Remain 04:46:25 loss: 0.0598 Lr: 0.00077
[2023-12-25 20:07:47,314 INFO misc.py line 119 253097] Train: [78/100][467/510] Data 0.003 (0.097) Batch 1.199 (1.525) Remain 04:46:16 loss: 0.1594 Lr: 0.00077
[2023-12-25 20:07:55,639 INFO misc.py line 119 253097] Train: [78/100][468/510] Data 0.008 (0.097) Batch 8.327 (1.540) Remain 04:48:59 loss: 0.0692 Lr: 0.00077
[2023-12-25 20:07:56,766 INFO misc.py line 119 253097] Train: [78/100][469/510] Data 0.005 (0.097) Batch 1.123 (1.539) Remain 04:48:47 loss: 0.2407 Lr: 0.00077
[2023-12-25 20:07:57,739 INFO misc.py line 119 253097] Train: [78/100][470/510] Data 0.008 (0.097) Batch 0.978 (1.538) Remain 04:48:32 loss: 0.1230 Lr: 0.00077
[2023-12-25 20:07:58,909 INFO misc.py line 119 253097] Train: [78/100][471/510] Data 0.003 (0.096) Batch 1.169 (1.537) Remain 04:48:22 loss: 0.3205 Lr: 0.00077
[2023-12-25 20:07:59,930 INFO misc.py line 119 253097] Train: [78/100][472/510] Data 0.005 (0.096) Batch 1.022 (1.536) Remain 04:48:08 loss: 0.2222 Lr: 0.00077
[2023-12-25 20:08:01,317 INFO misc.py line 119 253097] Train: [78/100][473/510] Data 0.004 (0.096) Batch 1.383 (1.535) Remain 04:48:03 loss: 0.1555 Lr: 0.00077
[2023-12-25 20:08:02,378 INFO misc.py line 119 253097] Train: [78/100][474/510] Data 0.007 (0.096) Batch 1.061 (1.534) Remain 04:47:50 loss: 0.0856 Lr: 0.00077
[2023-12-25 20:08:03,482 INFO misc.py line 119 253097] Train: [78/100][475/510] Data 0.007 (0.096) Batch 1.102 (1.533) Remain 04:47:38 loss: 0.1304 Lr: 0.00077
[2023-12-25 20:08:04,687 INFO misc.py line 119 253097] Train: [78/100][476/510] Data 0.010 (0.095) Batch 1.210 (1.533) Remain 04:47:29 loss: 0.0913 Lr: 0.00077
[2023-12-25 20:08:05,858 INFO misc.py line 119 253097] Train: [78/100][477/510] Data 0.005 (0.095) Batch 1.167 (1.532) Remain 04:47:19 loss: 0.1767 Lr: 0.00077
[2023-12-25 20:08:07,139 INFO misc.py line 119 253097] Train: [78/100][478/510] Data 0.010 (0.095) Batch 1.242 (1.531) Remain 04:47:10 loss: 0.0686 Lr: 0.00077
[2023-12-25 20:08:08,407 INFO misc.py line 119 253097] Train: [78/100][479/510] Data 0.049 (0.095) Batch 1.311 (1.531) Remain 04:47:04 loss: 0.1072 Lr: 0.00077
[2023-12-25 20:08:09,757 INFO misc.py line 119 253097] Train: [78/100][480/510] Data 0.005 (0.095) Batch 1.335 (1.530) Remain 04:46:57 loss: 0.2271 Lr: 0.00077
[2023-12-25 20:08:10,886 INFO misc.py line 119 253097] Train: [78/100][481/510] Data 0.022 (0.095) Batch 1.143 (1.530) Remain 04:46:47 loss: 0.1168 Lr: 0.00077
[2023-12-25 20:08:11,830 INFO misc.py line 119 253097] Train: [78/100][482/510] Data 0.007 (0.094) Batch 0.945 (1.528) Remain 04:46:31 loss: 0.0647 Lr: 0.00077
[2023-12-25 20:08:13,154 INFO misc.py line 119 253097] Train: [78/100][483/510] Data 0.006 (0.094) Batch 1.324 (1.528) Remain 04:46:25 loss: 0.1807 Lr: 0.00077
[2023-12-25 20:08:24,193 INFO misc.py line 119 253097] Train: [78/100][484/510] Data 0.006 (0.094) Batch 11.041 (1.548) Remain 04:50:06 loss: 0.1461 Lr: 0.00077
[2023-12-25 20:08:25,214 INFO misc.py line 119 253097] Train: [78/100][485/510] Data 0.004 (0.094) Batch 1.021 (1.547) Remain 04:49:52 loss: 0.1179 Lr: 0.00077
[2023-12-25 20:08:26,349 INFO misc.py line 119 253097] Train: [78/100][486/510] Data 0.004 (0.094) Batch 1.135 (1.546) Remain 04:49:41 loss: 0.1945 Lr: 0.00077
[2023-12-25 20:08:27,363 INFO misc.py line 119 253097] Train: [78/100][487/510] Data 0.004 (0.094) Batch 1.014 (1.545) Remain 04:49:27 loss: 0.1467 Lr: 0.00076
[2023-12-25 20:08:28,438 INFO misc.py line 119 253097] Train: [78/100][488/510] Data 0.004 (0.093) Batch 1.075 (1.544) Remain 04:49:15 loss: 0.1106 Lr: 0.00076
[2023-12-25 20:08:29,635 INFO misc.py line 119 253097] Train: [78/100][489/510] Data 0.003 (0.093) Batch 1.197 (1.543) Remain 04:49:05 loss: 0.1143 Lr: 0.00076
[2023-12-25 20:08:30,889 INFO misc.py line 119 253097] Train: [78/100][490/510] Data 0.005 (0.093) Batch 1.254 (1.542) Remain 04:48:57 loss: 0.1084 Lr: 0.00076
[2023-12-25 20:08:32,182 INFO misc.py line 119 253097] Train: [78/100][491/510] Data 0.005 (0.093) Batch 1.289 (1.542) Remain 04:48:50 loss: 0.0923 Lr: 0.00076
[2023-12-25 20:08:33,380 INFO misc.py line 119 253097] Train: [78/100][492/510] Data 0.008 (0.093) Batch 1.200 (1.541) Remain 04:48:40 loss: 0.0571 Lr: 0.00076
[2023-12-25 20:08:34,550 INFO misc.py line 119 253097] Train: [78/100][493/510] Data 0.006 (0.092) Batch 1.172 (1.541) Remain 04:48:30 loss: 0.0813 Lr: 0.00076
[2023-12-25 20:08:35,717 INFO misc.py line 119 253097] Train: [78/100][494/510] Data 0.004 (0.092) Batch 1.162 (1.540) Remain 04:48:20 loss: 0.1409 Lr: 0.00076
[2023-12-25 20:08:36,749 INFO misc.py line 119 253097] Train: [78/100][495/510] Data 0.009 (0.092) Batch 1.023 (1.539) Remain 04:48:07 loss: 0.0920 Lr: 0.00076
[2023-12-25 20:08:37,769 INFO misc.py line 119 253097] Train: [78/100][496/510] Data 0.018 (0.092) Batch 1.033 (1.538) Remain 04:47:54 loss: 0.1210 Lr: 0.00076
[2023-12-25 20:08:39,062 INFO misc.py line 119 253097] Train: [78/100][497/510] Data 0.006 (0.092) Batch 1.289 (1.537) Remain 04:47:46 loss: 0.1496 Lr: 0.00076
[2023-12-25 20:08:40,401 INFO misc.py line 119 253097] Train: [78/100][498/510] Data 0.009 (0.092) Batch 1.339 (1.537) Remain 04:47:40 loss: 0.0765 Lr: 0.00076
[2023-12-25 20:08:41,502 INFO misc.py line 119 253097] Train: [78/100][499/510] Data 0.009 (0.091) Batch 1.104 (1.536) Remain 04:47:29 loss: 0.1291 Lr: 0.00076
[2023-12-25 20:08:42,539 INFO misc.py line 119 253097] Train: [78/100][500/510] Data 0.007 (0.091) Batch 1.034 (1.535) Remain 04:47:16 loss: 0.2131 Lr: 0.00076
[2023-12-25 20:08:43,603 INFO misc.py line 119 253097] Train: [78/100][501/510] Data 0.010 (0.091) Batch 1.057 (1.534) Remain 04:47:04 loss: 0.1430 Lr: 0.00076
[2023-12-25 20:08:44,625 INFO misc.py line 119 253097] Train: [78/100][502/510] Data 0.016 (0.091) Batch 1.034 (1.533) Remain 04:46:51 loss: 0.0668 Lr: 0.00076
[2023-12-25 20:08:45,884 INFO misc.py line 119 253097] Train: [78/100][503/510] Data 0.004 (0.091) Batch 1.259 (1.532) Remain 04:46:43 loss: 0.1678 Lr: 0.00076
[2023-12-25 20:08:46,960 INFO misc.py line 119 253097] Train: [78/100][504/510] Data 0.004 (0.091) Batch 1.075 (1.531) Remain 04:46:32 loss: 0.1102 Lr: 0.00076
[2023-12-25 20:08:48,191 INFO misc.py line 119 253097] Train: [78/100][505/510] Data 0.005 (0.090) Batch 1.222 (1.531) Remain 04:46:23 loss: 0.2041 Lr: 0.00076
[2023-12-25 20:08:49,420 INFO misc.py line 119 253097] Train: [78/100][506/510] Data 0.014 (0.090) Batch 1.234 (1.530) Remain 04:46:15 loss: 0.0632 Lr: 0.00076
[2023-12-25 20:08:50,675 INFO misc.py line 119 253097] Train: [78/100][507/510] Data 0.009 (0.090) Batch 1.260 (1.530) Remain 04:46:07 loss: 0.1026 Lr: 0.00076
[2023-12-25 20:08:51,873 INFO misc.py line 119 253097] Train: [78/100][508/510] Data 0.004 (0.090) Batch 1.193 (1.529) Remain 04:45:58 loss: 0.1402 Lr: 0.00076
[2023-12-25 20:08:53,076 INFO misc.py line 119 253097] Train: [78/100][509/510] Data 0.009 (0.090) Batch 1.210 (1.528) Remain 04:45:50 loss: 0.0983 Lr: 0.00076
[2023-12-25 20:08:57,719 INFO misc.py line 119 253097] Train: [78/100][510/510] Data 0.003 (0.090) Batch 4.643 (1.535) Remain 04:46:57 loss: 0.1057 Lr: 0.00076
[2023-12-25 20:08:57,720 INFO misc.py line 136 253097] Train result: loss: 0.1247 
[2023-12-25 20:08:57,720 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 20:09:30,081 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5999 
[2023-12-25 20:09:30,427 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3954 
[2023-12-25 20:09:35,864 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3720 
[2023-12-25 20:09:36,383 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3999 
[2023-12-25 20:09:38,356 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8735 
[2023-12-25 20:09:38,781 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3401 
[2023-12-25 20:09:39,663 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2116 
[2023-12-25 20:09:40,215 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3205 
[2023-12-25 20:09:42,022 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1091 
[2023-12-25 20:09:44,149 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3561 
[2023-12-25 20:09:45,003 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2820 
[2023-12-25 20:09:45,425 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0656 
[2023-12-25 20:09:46,325 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3706 
[2023-12-25 20:09:49,264 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9113 
[2023-12-25 20:09:49,730 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3146 
[2023-12-25 20:09:50,340 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3702 
[2023-12-25 20:09:51,039 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3903 
[2023-12-25 20:09:52,928 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6794/0.7331/0.8997.
[2023-12-25 20:09:52,929 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9178/0.9487
[2023-12-25 20:09:52,929 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9830/0.9902
[2023-12-25 20:09:52,929 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8276/0.9776
[2023-12-25 20:09:52,929 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 20:09:52,929 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3257/0.3449
[2023-12-25 20:09:52,929 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.5248/0.5344
[2023-12-25 20:09:52,930 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6670/0.7256
[2023-12-25 20:09:52,930 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8104/0.9040
[2023-12-25 20:09:52,930 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9124/0.9508
[2023-12-25 20:09:52,930 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6653/0.7233
[2023-12-25 20:09:52,930 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7823/0.8649
[2023-12-25 20:09:52,930 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8076/0.8395
[2023-12-25 20:09:52,930 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6087/0.7269
[2023-12-25 20:09:52,931 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 20:09:52,932 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 20:09:52,932 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 20:10:15,212 INFO misc.py line 119 253097] Train: [79/100][1/510] Data 3.234 (3.234) Batch 19.697 (19.697) Remain 61:23:04 loss: 0.1761 Lr: 0.00076
[2023-12-25 20:10:16,325 INFO misc.py line 119 253097] Train: [79/100][2/510] Data 0.005 (0.005) Batch 1.115 (1.115) Remain 03:28:30 loss: 0.1463 Lr: 0.00076
[2023-12-25 20:10:17,527 INFO misc.py line 119 253097] Train: [79/100][3/510] Data 0.004 (0.004) Batch 1.201 (1.201) Remain 03:44:30 loss: 0.0922 Lr: 0.00076
[2023-12-25 20:10:18,743 INFO misc.py line 119 253097] Train: [79/100][4/510] Data 0.005 (0.005) Batch 1.212 (1.212) Remain 03:46:31 loss: 0.1269 Lr: 0.00076
[2023-12-25 20:10:19,875 INFO misc.py line 119 253097] Train: [79/100][5/510] Data 0.009 (0.007) Batch 1.128 (1.170) Remain 03:38:41 loss: 0.1034 Lr: 0.00076
[2023-12-25 20:10:20,979 INFO misc.py line 119 253097] Train: [79/100][6/510] Data 0.012 (0.009) Batch 1.110 (1.150) Remain 03:34:55 loss: 0.1870 Lr: 0.00076
[2023-12-25 20:10:22,168 INFO misc.py line 119 253097] Train: [79/100][7/510] Data 0.006 (0.008) Batch 1.191 (1.160) Remain 03:36:50 loss: 0.2294 Lr: 0.00076
[2023-12-25 20:10:23,063 INFO misc.py line 119 253097] Train: [79/100][8/510] Data 0.005 (0.007) Batch 0.895 (1.107) Remain 03:26:54 loss: 0.1723 Lr: 0.00076
[2023-12-25 20:10:24,294 INFO misc.py line 119 253097] Train: [79/100][9/510] Data 0.005 (0.007) Batch 1.232 (1.128) Remain 03:30:46 loss: 0.1603 Lr: 0.00076
[2023-12-25 20:10:25,450 INFO misc.py line 119 253097] Train: [79/100][10/510] Data 0.004 (0.007) Batch 1.155 (1.132) Remain 03:31:28 loss: 0.0694 Lr: 0.00076
[2023-12-25 20:10:26,299 INFO misc.py line 119 253097] Train: [79/100][11/510] Data 0.006 (0.006) Batch 0.850 (1.097) Remain 03:24:52 loss: 0.1182 Lr: 0.00076
[2023-12-25 20:10:27,429 INFO misc.py line 119 253097] Train: [79/100][12/510] Data 0.003 (0.006) Batch 1.127 (1.100) Remain 03:25:29 loss: 0.0603 Lr: 0.00076
[2023-12-25 20:10:28,480 INFO misc.py line 119 253097] Train: [79/100][13/510] Data 0.006 (0.006) Batch 1.048 (1.095) Remain 03:24:30 loss: 0.1051 Lr: 0.00076
[2023-12-25 20:10:29,606 INFO misc.py line 119 253097] Train: [79/100][14/510] Data 0.008 (0.006) Batch 1.126 (1.098) Remain 03:25:01 loss: 0.1031 Lr: 0.00076
[2023-12-25 20:10:30,646 INFO misc.py line 119 253097] Train: [79/100][15/510] Data 0.009 (0.006) Batch 1.029 (1.092) Remain 03:23:56 loss: 0.1242 Lr: 0.00076
[2023-12-25 20:10:31,776 INFO misc.py line 119 253097] Train: [79/100][16/510] Data 0.019 (0.007) Batch 1.145 (1.096) Remain 03:24:40 loss: 0.1385 Lr: 0.00076
[2023-12-25 20:10:32,854 INFO misc.py line 119 253097] Train: [79/100][17/510] Data 0.006 (0.007) Batch 1.077 (1.095) Remain 03:24:24 loss: 0.1254 Lr: 0.00076
[2023-12-25 20:10:34,043 INFO misc.py line 119 253097] Train: [79/100][18/510] Data 0.006 (0.007) Batch 1.191 (1.101) Remain 03:25:34 loss: 0.0667 Lr: 0.00076
[2023-12-25 20:10:35,332 INFO misc.py line 119 253097] Train: [79/100][19/510] Data 0.003 (0.007) Batch 1.285 (1.113) Remain 03:27:42 loss: 0.1190 Lr: 0.00076
[2023-12-25 20:10:36,537 INFO misc.py line 119 253097] Train: [79/100][20/510] Data 0.008 (0.007) Batch 1.206 (1.118) Remain 03:28:43 loss: 0.1310 Lr: 0.00076
[2023-12-25 20:10:37,745 INFO misc.py line 119 253097] Train: [79/100][21/510] Data 0.006 (0.007) Batch 1.210 (1.123) Remain 03:29:39 loss: 0.1686 Lr: 0.00076
[2023-12-25 20:10:38,657 INFO misc.py line 119 253097] Train: [79/100][22/510] Data 0.004 (0.007) Batch 0.913 (1.112) Remain 03:27:33 loss: 0.1043 Lr: 0.00076
[2023-12-25 20:10:39,844 INFO misc.py line 119 253097] Train: [79/100][23/510] Data 0.004 (0.007) Batch 1.186 (1.116) Remain 03:28:14 loss: 0.1853 Lr: 0.00076
[2023-12-25 20:10:41,094 INFO misc.py line 119 253097] Train: [79/100][24/510] Data 0.004 (0.007) Batch 1.246 (1.122) Remain 03:29:22 loss: 0.0674 Lr: 0.00076
[2023-12-25 20:10:42,216 INFO misc.py line 119 253097] Train: [79/100][25/510] Data 0.008 (0.007) Batch 1.123 (1.122) Remain 03:29:21 loss: 0.1247 Lr: 0.00076
[2023-12-25 20:10:43,476 INFO misc.py line 119 253097] Train: [79/100][26/510] Data 0.008 (0.007) Batch 1.262 (1.128) Remain 03:30:28 loss: 0.1285 Lr: 0.00076
[2023-12-25 20:10:44,553 INFO misc.py line 119 253097] Train: [79/100][27/510] Data 0.007 (0.007) Batch 1.080 (1.126) Remain 03:30:04 loss: 0.1262 Lr: 0.00076
[2023-12-25 20:10:45,662 INFO misc.py line 119 253097] Train: [79/100][28/510] Data 0.004 (0.007) Batch 1.108 (1.125) Remain 03:29:55 loss: 0.2049 Lr: 0.00076
[2023-12-25 20:10:46,798 INFO misc.py line 119 253097] Train: [79/100][29/510] Data 0.004 (0.006) Batch 1.135 (1.126) Remain 03:29:58 loss: 0.1709 Lr: 0.00076
[2023-12-25 20:10:52,399 INFO misc.py line 119 253097] Train: [79/100][30/510] Data 0.006 (0.006) Batch 5.601 (1.292) Remain 04:00:52 loss: 0.1203 Lr: 0.00076
[2023-12-25 20:10:53,363 INFO misc.py line 119 253097] Train: [79/100][31/510] Data 0.006 (0.006) Batch 0.966 (1.280) Remain 03:58:40 loss: 0.0851 Lr: 0.00076
[2023-12-25 20:10:54,625 INFO misc.py line 119 253097] Train: [79/100][32/510] Data 0.003 (0.006) Batch 1.261 (1.279) Remain 03:58:32 loss: 0.1393 Lr: 0.00076
[2023-12-25 20:10:55,911 INFO misc.py line 119 253097] Train: [79/100][33/510] Data 0.005 (0.006) Batch 1.277 (1.279) Remain 03:58:30 loss: 0.0824 Lr: 0.00076
[2023-12-25 20:10:57,015 INFO misc.py line 119 253097] Train: [79/100][34/510] Data 0.013 (0.006) Batch 1.108 (1.274) Remain 03:57:26 loss: 0.0959 Lr: 0.00076
[2023-12-25 20:10:58,021 INFO misc.py line 119 253097] Train: [79/100][35/510] Data 0.009 (0.007) Batch 1.010 (1.265) Remain 03:55:53 loss: 0.1326 Lr: 0.00076
[2023-12-25 20:10:59,127 INFO misc.py line 119 253097] Train: [79/100][36/510] Data 0.006 (0.007) Batch 1.104 (1.261) Remain 03:54:57 loss: 0.1056 Lr: 0.00076
[2023-12-25 20:11:00,404 INFO misc.py line 119 253097] Train: [79/100][37/510] Data 0.007 (0.007) Batch 1.280 (1.261) Remain 03:55:02 loss: 0.1164 Lr: 0.00076
[2023-12-25 20:11:01,669 INFO misc.py line 119 253097] Train: [79/100][38/510] Data 0.004 (0.006) Batch 1.260 (1.261) Remain 03:55:01 loss: 0.1263 Lr: 0.00076
[2023-12-25 20:11:02,630 INFO misc.py line 119 253097] Train: [79/100][39/510] Data 0.010 (0.007) Batch 0.966 (1.253) Remain 03:53:28 loss: 0.0839 Lr: 0.00076
[2023-12-25 20:11:03,877 INFO misc.py line 119 253097] Train: [79/100][40/510] Data 0.005 (0.007) Batch 1.242 (1.253) Remain 03:53:23 loss: 0.2274 Lr: 0.00076
[2023-12-25 20:11:04,950 INFO misc.py line 119 253097] Train: [79/100][41/510] Data 0.009 (0.007) Batch 1.069 (1.248) Remain 03:52:28 loss: 0.0727 Lr: 0.00076
[2023-12-25 20:11:06,084 INFO misc.py line 119 253097] Train: [79/100][42/510] Data 0.013 (0.007) Batch 1.142 (1.245) Remain 03:51:57 loss: 0.2143 Lr: 0.00076
[2023-12-25 20:11:07,151 INFO misc.py line 119 253097] Train: [79/100][43/510] Data 0.005 (0.007) Batch 1.068 (1.241) Remain 03:51:06 loss: 0.2389 Lr: 0.00076
[2023-12-25 20:11:08,311 INFO misc.py line 119 253097] Train: [79/100][44/510] Data 0.004 (0.007) Batch 1.159 (1.239) Remain 03:50:42 loss: 0.0865 Lr: 0.00076
[2023-12-25 20:11:10,563 INFO misc.py line 119 253097] Train: [79/100][45/510] Data 0.005 (0.007) Batch 2.253 (1.263) Remain 03:55:11 loss: 0.1084 Lr: 0.00076
[2023-12-25 20:11:11,680 INFO misc.py line 119 253097] Train: [79/100][46/510] Data 0.005 (0.007) Batch 1.117 (1.259) Remain 03:54:32 loss: 0.1882 Lr: 0.00076
[2023-12-25 20:11:12,684 INFO misc.py line 119 253097] Train: [79/100][47/510] Data 0.004 (0.007) Batch 1.003 (1.254) Remain 03:53:25 loss: 0.1466 Lr: 0.00076
[2023-12-25 20:11:19,267 INFO misc.py line 119 253097] Train: [79/100][48/510] Data 0.005 (0.006) Batch 6.585 (1.372) Remain 04:15:28 loss: 0.1862 Lr: 0.00076
[2023-12-25 20:11:22,343 INFO misc.py line 119 253097] Train: [79/100][49/510] Data 1.899 (0.048) Batch 3.074 (1.409) Remain 04:22:20 loss: 0.0643 Lr: 0.00076
[2023-12-25 20:11:23,506 INFO misc.py line 119 253097] Train: [79/100][50/510] Data 0.006 (0.047) Batch 1.164 (1.404) Remain 04:21:20 loss: 0.2676 Lr: 0.00076
[2023-12-25 20:11:24,847 INFO misc.py line 119 253097] Train: [79/100][51/510] Data 0.005 (0.046) Batch 1.341 (1.403) Remain 04:21:04 loss: 0.1023 Lr: 0.00076
[2023-12-25 20:11:26,035 INFO misc.py line 119 253097] Train: [79/100][52/510] Data 0.004 (0.045) Batch 1.185 (1.398) Remain 04:20:13 loss: 0.1043 Lr: 0.00076
[2023-12-25 20:11:27,345 INFO misc.py line 119 253097] Train: [79/100][53/510] Data 0.007 (0.044) Batch 1.311 (1.396) Remain 04:19:52 loss: 0.0857 Lr: 0.00076
[2023-12-25 20:11:28,422 INFO misc.py line 119 253097] Train: [79/100][54/510] Data 0.006 (0.043) Batch 1.079 (1.390) Remain 04:18:41 loss: 0.0707 Lr: 0.00076
[2023-12-25 20:11:29,740 INFO misc.py line 119 253097] Train: [79/100][55/510] Data 0.004 (0.043) Batch 1.317 (1.389) Remain 04:18:24 loss: 0.1337 Lr: 0.00075
[2023-12-25 20:11:30,985 INFO misc.py line 119 253097] Train: [79/100][56/510] Data 0.007 (0.042) Batch 1.211 (1.385) Remain 04:17:46 loss: 0.1304 Lr: 0.00075
[2023-12-25 20:11:31,944 INFO misc.py line 119 253097] Train: [79/100][57/510] Data 0.039 (0.042) Batch 0.992 (1.378) Remain 04:16:23 loss: 0.0812 Lr: 0.00075
[2023-12-25 20:11:32,951 INFO misc.py line 119 253097] Train: [79/100][58/510] Data 0.005 (0.041) Batch 1.007 (1.371) Remain 04:15:06 loss: 0.0888 Lr: 0.00075
[2023-12-25 20:11:33,895 INFO misc.py line 119 253097] Train: [79/100][59/510] Data 0.005 (0.041) Batch 0.945 (1.364) Remain 04:13:40 loss: 0.1398 Lr: 0.00075
[2023-12-25 20:11:34,984 INFO misc.py line 119 253097] Train: [79/100][60/510] Data 0.005 (0.040) Batch 1.078 (1.359) Remain 04:12:43 loss: 0.0995 Lr: 0.00075
[2023-12-25 20:11:36,207 INFO misc.py line 119 253097] Train: [79/100][61/510] Data 0.015 (0.040) Batch 1.227 (1.356) Remain 04:12:16 loss: 0.0603 Lr: 0.00075
[2023-12-25 20:11:37,149 INFO misc.py line 119 253097] Train: [79/100][62/510] Data 0.012 (0.039) Batch 0.949 (1.350) Remain 04:10:57 loss: 0.1093 Lr: 0.00075
[2023-12-25 20:11:38,407 INFO misc.py line 119 253097] Train: [79/100][63/510] Data 0.005 (0.039) Batch 1.258 (1.348) Remain 04:10:39 loss: 0.1700 Lr: 0.00075
[2023-12-25 20:11:39,282 INFO misc.py line 119 253097] Train: [79/100][64/510] Data 0.004 (0.038) Batch 0.875 (1.340) Remain 04:09:11 loss: 0.1099 Lr: 0.00075
[2023-12-25 20:11:40,292 INFO misc.py line 119 253097] Train: [79/100][65/510] Data 0.004 (0.037) Batch 1.010 (1.335) Remain 04:08:11 loss: 0.1676 Lr: 0.00075
[2023-12-25 20:11:41,225 INFO misc.py line 119 253097] Train: [79/100][66/510] Data 0.005 (0.037) Batch 0.933 (1.329) Remain 04:06:58 loss: 0.1302 Lr: 0.00075
[2023-12-25 20:11:42,102 INFO misc.py line 119 253097] Train: [79/100][67/510] Data 0.004 (0.036) Batch 0.875 (1.321) Remain 04:05:38 loss: 0.1110 Lr: 0.00075
[2023-12-25 20:11:55,013 INFO misc.py line 119 253097] Train: [79/100][68/510] Data 0.006 (0.036) Batch 12.913 (1.500) Remain 04:38:45 loss: 0.0523 Lr: 0.00075
[2023-12-25 20:11:56,197 INFO misc.py line 119 253097] Train: [79/100][69/510] Data 0.004 (0.035) Batch 1.167 (1.495) Remain 04:37:48 loss: 0.1160 Lr: 0.00075
[2023-12-25 20:11:57,324 INFO misc.py line 119 253097] Train: [79/100][70/510] Data 0.022 (0.035) Batch 1.142 (1.489) Remain 04:36:47 loss: 0.1244 Lr: 0.00075
[2023-12-25 20:11:58,325 INFO misc.py line 119 253097] Train: [79/100][71/510] Data 0.005 (0.035) Batch 1.001 (1.482) Remain 04:35:26 loss: 0.1300 Lr: 0.00075
[2023-12-25 20:11:59,464 INFO misc.py line 119 253097] Train: [79/100][72/510] Data 0.005 (0.034) Batch 1.138 (1.477) Remain 04:34:29 loss: 0.1578 Lr: 0.00075
[2023-12-25 20:12:00,690 INFO misc.py line 119 253097] Train: [79/100][73/510] Data 0.006 (0.034) Batch 1.226 (1.474) Remain 04:33:47 loss: 0.2633 Lr: 0.00075
[2023-12-25 20:12:01,976 INFO misc.py line 119 253097] Train: [79/100][74/510] Data 0.007 (0.034) Batch 1.284 (1.471) Remain 04:33:16 loss: 0.1687 Lr: 0.00075
[2023-12-25 20:12:03,122 INFO misc.py line 119 253097] Train: [79/100][75/510] Data 0.009 (0.033) Batch 1.149 (1.467) Remain 04:32:25 loss: 0.1030 Lr: 0.00075
[2023-12-25 20:12:04,415 INFO misc.py line 119 253097] Train: [79/100][76/510] Data 0.004 (0.033) Batch 1.290 (1.464) Remain 04:31:56 loss: 0.1188 Lr: 0.00075
[2023-12-25 20:12:05,667 INFO misc.py line 119 253097] Train: [79/100][77/510] Data 0.008 (0.033) Batch 1.255 (1.461) Remain 04:31:23 loss: 0.1425 Lr: 0.00075
[2023-12-25 20:12:06,954 INFO misc.py line 119 253097] Train: [79/100][78/510] Data 0.005 (0.032) Batch 1.284 (1.459) Remain 04:30:55 loss: 0.1003 Lr: 0.00075
[2023-12-25 20:12:08,040 INFO misc.py line 119 253097] Train: [79/100][79/510] Data 0.009 (0.032) Batch 1.088 (1.454) Remain 04:30:00 loss: 0.0941 Lr: 0.00075
[2023-12-25 20:12:09,217 INFO misc.py line 119 253097] Train: [79/100][80/510] Data 0.007 (0.032) Batch 1.179 (1.451) Remain 04:29:18 loss: 0.1440 Lr: 0.00075
[2023-12-25 20:12:10,212 INFO misc.py line 119 253097] Train: [79/100][81/510] Data 0.004 (0.031) Batch 0.992 (1.445) Remain 04:28:11 loss: 0.1083 Lr: 0.00075
[2023-12-25 20:12:11,341 INFO misc.py line 119 253097] Train: [79/100][82/510] Data 0.008 (0.031) Batch 1.133 (1.441) Remain 04:27:26 loss: 0.0873 Lr: 0.00075
[2023-12-25 20:12:13,375 INFO misc.py line 119 253097] Train: [79/100][83/510] Data 1.183 (0.045) Batch 2.033 (1.448) Remain 04:28:47 loss: 0.1428 Lr: 0.00075
[2023-12-25 20:12:25,143 INFO misc.py line 119 253097] Train: [79/100][84/510] Data 10.516 (0.175) Batch 11.768 (1.576) Remain 04:52:24 loss: 0.1067 Lr: 0.00075
[2023-12-25 20:12:26,230 INFO misc.py line 119 253097] Train: [79/100][85/510] Data 0.004 (0.172) Batch 1.087 (1.570) Remain 04:51:16 loss: 0.1998 Lr: 0.00075
[2023-12-25 20:12:27,430 INFO misc.py line 119 253097] Train: [79/100][86/510] Data 0.004 (0.170) Batch 1.198 (1.565) Remain 04:50:25 loss: 0.1137 Lr: 0.00075
[2023-12-25 20:12:28,415 INFO misc.py line 119 253097] Train: [79/100][87/510] Data 0.006 (0.169) Batch 0.987 (1.558) Remain 04:49:07 loss: 0.1510 Lr: 0.00075
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[2023-12-25 20:13:45,679 INFO misc.py line 119 253097] Train: [79/100][137/510] Data 0.004 (0.134) Batch 1.216 (1.553) Remain 04:46:56 loss: 0.0989 Lr: 0.00074
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[2023-12-25 20:13:51,392 INFO misc.py line 119 253097] Train: [79/100][142/510] Data 0.004 (0.129) Batch 1.224 (1.539) Remain 04:44:04 loss: 0.1724 Lr: 0.00074
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[2023-12-25 20:19:12,038 INFO misc.py line 119 253097] Train: [79/100][351/510] Data 0.008 (0.058) Batch 1.148 (1.536) Remain 04:38:14 loss: 0.0901 Lr: 0.00072
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[2023-12-25 20:19:24,223 INFO misc.py line 119 253097] Train: [79/100][357/510] Data 0.005 (0.057) Batch 1.075 (1.544) Remain 04:39:36 loss: 0.1709 Lr: 0.00072
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[2023-12-25 20:22:00,796 INFO misc.py line 119 253097] Train: [79/100][451/510] Data 2.190 (0.067) Batch 3.106 (1.570) Remain 04:41:45 loss: 0.1644 Lr: 0.00070
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[2023-12-25 20:22:05,718 INFO misc.py line 119 253097] Train: [79/100][455/510] Data 0.008 (0.066) Batch 1.233 (1.567) Remain 04:41:06 loss: 0.1069 Lr: 0.00070
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[2023-12-25 20:22:21,319 INFO misc.py line 119 253097] Train: [79/100][463/510] Data 5.201 (0.077) Batch 6.473 (1.573) Remain 04:42:05 loss: 0.1127 Lr: 0.00070
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[2023-12-25 20:23:25,117 INFO misc.py line 119 253097] Train: [79/100][506/510] Data 0.011 (0.071) Batch 1.169 (1.566) Remain 04:39:35 loss: 0.2770 Lr: 0.00070
[2023-12-25 20:23:26,180 INFO misc.py line 119 253097] Train: [79/100][507/510] Data 0.004 (0.071) Batch 1.060 (1.565) Remain 04:39:23 loss: 0.0723 Lr: 0.00070
[2023-12-25 20:23:27,112 INFO misc.py line 119 253097] Train: [79/100][508/510] Data 0.007 (0.071) Batch 0.936 (1.564) Remain 04:39:08 loss: 0.1541 Lr: 0.00070
[2023-12-25 20:23:28,388 INFO misc.py line 119 253097] Train: [79/100][509/510] Data 0.004 (0.071) Batch 1.272 (1.563) Remain 04:39:00 loss: 0.1253 Lr: 0.00070
[2023-12-25 20:23:29,588 INFO misc.py line 119 253097] Train: [79/100][510/510] Data 0.009 (0.071) Batch 1.200 (1.562) Remain 04:38:51 loss: 0.0961 Lr: 0.00070
[2023-12-25 20:23:29,589 INFO misc.py line 136 253097] Train result: loss: 0.1268 
[2023-12-25 20:23:29,589 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 20:24:02,094 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7565 
[2023-12-25 20:24:02,453 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2875 
[2023-12-25 20:24:07,387 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3833 
[2023-12-25 20:24:07,909 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4520 
[2023-12-25 20:24:09,874 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9943 
[2023-12-25 20:24:10,299 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3555 
[2023-12-25 20:24:11,179 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.3093 
[2023-12-25 20:24:11,741 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2809 
[2023-12-25 20:24:13,552 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9882 
[2023-12-25 20:24:15,691 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.4072 
[2023-12-25 20:24:16,559 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3427 
[2023-12-25 20:24:16,988 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9604 
[2023-12-25 20:24:17,891 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4522 
[2023-12-25 20:24:20,841 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8770 
[2023-12-25 20:24:21,310 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3756 
[2023-12-25 20:24:21,933 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4980 
[2023-12-25 20:24:22,658 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4804 
[2023-12-25 20:24:24,198 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6777/0.7291/0.9002.
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9126/0.9492
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9825/0.9906
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8234/0.9770
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2355/0.2536
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6387/0.6653
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6378/0.6754
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8251/0.9041
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9192/0.9588
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6397/0.6932
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7895/0.8850
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7970/0.8334
[2023-12-25 20:24:24,199 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6092/0.6932
[2023-12-25 20:24:24,200 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 20:24:24,201 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 20:24:24,201 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 20:24:33,243 INFO misc.py line 119 253097] Train: [80/100][1/510] Data 6.436 (6.436) Batch 7.236 (7.236) Remain 21:31:31 loss: 0.2595 Lr: 0.00070
[2023-12-25 20:24:34,496 INFO misc.py line 119 253097] Train: [80/100][2/510] Data 0.005 (0.005) Batch 1.251 (1.251) Remain 03:43:15 loss: 0.2209 Lr: 0.00070
[2023-12-25 20:24:35,618 INFO misc.py line 119 253097] Train: [80/100][3/510] Data 0.006 (0.006) Batch 1.120 (1.120) Remain 03:19:56 loss: 0.1206 Lr: 0.00070
[2023-12-25 20:24:46,383 INFO misc.py line 119 253097] Train: [80/100][4/510] Data 9.645 (9.645) Batch 10.766 (10.766) Remain 32:01:03 loss: 0.0960 Lr: 0.00070
[2023-12-25 20:24:47,461 INFO misc.py line 119 253097] Train: [80/100][5/510] Data 0.006 (4.826) Batch 1.081 (5.924) Remain 17:36:52 loss: 0.1127 Lr: 0.00070
[2023-12-25 20:24:48,658 INFO misc.py line 119 253097] Train: [80/100][6/510] Data 0.002 (3.218) Batch 1.196 (4.348) Remain 12:55:39 loss: 0.0915 Lr: 0.00070
[2023-12-25 20:24:53,727 INFO misc.py line 119 253097] Train: [80/100][7/510] Data 3.878 (3.383) Batch 5.069 (4.528) Remain 13:27:44 loss: 0.0946 Lr: 0.00070
[2023-12-25 20:24:54,819 INFO misc.py line 119 253097] Train: [80/100][8/510] Data 0.004 (2.707) Batch 1.092 (3.841) Remain 11:25:06 loss: 0.2498 Lr: 0.00070
[2023-12-25 20:24:56,084 INFO misc.py line 119 253097] Train: [80/100][9/510] Data 0.005 (2.257) Batch 1.260 (3.411) Remain 10:08:19 loss: 0.1820 Lr: 0.00070
[2023-12-25 20:24:57,158 INFO misc.py line 119 253097] Train: [80/100][10/510] Data 0.008 (1.935) Batch 1.077 (3.077) Remain 09:08:47 loss: 0.0789 Lr: 0.00070
[2023-12-25 20:24:58,450 INFO misc.py line 119 253097] Train: [80/100][11/510] Data 0.006 (1.694) Batch 1.295 (2.855) Remain 08:29:00 loss: 0.1845 Lr: 0.00070
[2023-12-25 20:24:59,661 INFO misc.py line 119 253097] Train: [80/100][12/510] Data 0.004 (1.506) Batch 1.200 (2.671) Remain 07:56:10 loss: 0.0892 Lr: 0.00070
[2023-12-25 20:25:13,118 INFO misc.py line 119 253097] Train: [80/100][13/510] Data 12.428 (2.599) Batch 13.467 (3.750) Remain 11:08:37 loss: 0.0930 Lr: 0.00070
[2023-12-25 20:25:14,135 INFO misc.py line 119 253097] Train: [80/100][14/510] Data 0.004 (2.363) Batch 1.017 (3.502) Remain 10:24:15 loss: 0.0809 Lr: 0.00070
[2023-12-25 20:25:15,238 INFO misc.py line 119 253097] Train: [80/100][15/510] Data 0.005 (2.166) Batch 1.104 (3.302) Remain 09:48:34 loss: 0.0986 Lr: 0.00070
[2023-12-25 20:25:16,267 INFO misc.py line 119 253097] Train: [80/100][16/510] Data 0.004 (2.000) Batch 1.027 (3.127) Remain 09:17:19 loss: 0.1023 Lr: 0.00070
[2023-12-25 20:25:17,551 INFO misc.py line 119 253097] Train: [80/100][17/510] Data 0.007 (1.858) Batch 1.283 (2.995) Remain 08:53:48 loss: 0.1597 Lr: 0.00070
[2023-12-25 20:25:18,803 INFO misc.py line 119 253097] Train: [80/100][18/510] Data 0.007 (1.734) Batch 1.253 (2.879) Remain 08:33:03 loss: 0.1090 Lr: 0.00069
[2023-12-25 20:25:20,054 INFO misc.py line 119 253097] Train: [80/100][19/510] Data 0.005 (1.626) Batch 1.252 (2.777) Remain 08:14:53 loss: 0.1043 Lr: 0.00069
[2023-12-25 20:25:21,182 INFO misc.py line 119 253097] Train: [80/100][20/510] Data 0.004 (1.531) Batch 1.128 (2.680) Remain 07:57:33 loss: 0.1332 Lr: 0.00069
[2023-12-25 20:25:22,325 INFO misc.py line 119 253097] Train: [80/100][21/510] Data 0.004 (1.446) Batch 1.140 (2.595) Remain 07:42:16 loss: 0.0768 Lr: 0.00069
[2023-12-25 20:25:23,648 INFO misc.py line 119 253097] Train: [80/100][22/510] Data 0.008 (1.370) Batch 1.326 (2.528) Remain 07:30:20 loss: 0.1413 Lr: 0.00069
[2023-12-25 20:25:24,816 INFO misc.py line 119 253097] Train: [80/100][23/510] Data 0.005 (1.302) Batch 1.166 (2.460) Remain 07:18:09 loss: 0.0648 Lr: 0.00069
[2023-12-25 20:25:25,961 INFO misc.py line 119 253097] Train: [80/100][24/510] Data 0.007 (1.240) Batch 1.145 (2.397) Remain 07:06:57 loss: 0.0769 Lr: 0.00069
[2023-12-25 20:25:27,161 INFO misc.py line 119 253097] Train: [80/100][25/510] Data 0.009 (1.184) Batch 1.199 (2.343) Remain 06:57:13 loss: 0.1407 Lr: 0.00069
[2023-12-25 20:25:28,289 INFO misc.py line 119 253097] Train: [80/100][26/510] Data 0.008 (1.133) Batch 1.130 (2.290) Remain 06:47:47 loss: 0.2317 Lr: 0.00069
[2023-12-25 20:25:29,281 INFO misc.py line 119 253097] Train: [80/100][27/510] Data 0.007 (1.086) Batch 0.990 (2.236) Remain 06:38:06 loss: 0.1175 Lr: 0.00069
[2023-12-25 20:25:30,289 INFO misc.py line 119 253097] Train: [80/100][28/510] Data 0.009 (1.043) Batch 1.008 (2.187) Remain 06:29:20 loss: 0.1036 Lr: 0.00069
[2023-12-25 20:25:34,188 INFO misc.py line 119 253097] Train: [80/100][29/510] Data 0.007 (1.003) Batch 3.901 (2.253) Remain 06:41:02 loss: 0.1074 Lr: 0.00069
[2023-12-25 20:25:35,325 INFO misc.py line 119 253097] Train: [80/100][30/510] Data 0.005 (0.966) Batch 1.138 (2.212) Remain 06:33:38 loss: 0.1145 Lr: 0.00069
[2023-12-25 20:25:36,407 INFO misc.py line 119 253097] Train: [80/100][31/510] Data 0.004 (0.932) Batch 1.078 (2.171) Remain 06:26:24 loss: 0.1179 Lr: 0.00069
[2023-12-25 20:25:39,724 INFO misc.py line 119 253097] Train: [80/100][32/510] Data 2.119 (0.973) Batch 3.320 (2.211) Remain 06:33:25 loss: 0.0639 Lr: 0.00069
[2023-12-25 20:25:40,784 INFO misc.py line 119 253097] Train: [80/100][33/510] Data 0.005 (0.941) Batch 1.060 (2.172) Remain 06:26:33 loss: 0.1170 Lr: 0.00069
[2023-12-25 20:25:41,803 INFO misc.py line 119 253097] Train: [80/100][34/510] Data 0.004 (0.910) Batch 1.017 (2.135) Remain 06:19:53 loss: 0.0690 Lr: 0.00069
[2023-12-25 20:25:42,783 INFO misc.py line 119 253097] Train: [80/100][35/510] Data 0.007 (0.882) Batch 0.983 (2.099) Remain 06:13:27 loss: 0.0942 Lr: 0.00069
[2023-12-25 20:25:43,839 INFO misc.py line 119 253097] Train: [80/100][36/510] Data 0.003 (0.856) Batch 1.054 (2.067) Remain 06:07:47 loss: 0.0993 Lr: 0.00069
[2023-12-25 20:25:44,997 INFO misc.py line 119 253097] Train: [80/100][37/510] Data 0.005 (0.831) Batch 1.155 (2.041) Remain 06:02:58 loss: 0.0757 Lr: 0.00069
[2023-12-25 20:25:56,511 INFO misc.py line 119 253097] Train: [80/100][38/510] Data 10.513 (1.107) Batch 11.518 (2.311) Remain 06:51:06 loss: 0.0989 Lr: 0.00069
[2023-12-25 20:25:57,710 INFO misc.py line 119 253097] Train: [80/100][39/510] Data 0.003 (1.077) Batch 1.199 (2.280) Remain 06:45:34 loss: 0.1352 Lr: 0.00069
[2023-12-25 20:25:58,764 INFO misc.py line 119 253097] Train: [80/100][40/510] Data 0.004 (1.048) Batch 1.054 (2.247) Remain 06:39:38 loss: 0.1254 Lr: 0.00069
[2023-12-25 20:25:59,844 INFO misc.py line 119 253097] Train: [80/100][41/510] Data 0.003 (1.020) Batch 1.080 (2.217) Remain 06:34:08 loss: 0.0996 Lr: 0.00069
[2023-12-25 20:26:01,071 INFO misc.py line 119 253097] Train: [80/100][42/510] Data 0.003 (0.994) Batch 1.222 (2.191) Remain 06:29:34 loss: 0.1463 Lr: 0.00069
[2023-12-25 20:26:02,038 INFO misc.py line 119 253097] Train: [80/100][43/510] Data 0.009 (0.969) Batch 0.971 (2.161) Remain 06:24:06 loss: 0.0735 Lr: 0.00069
[2023-12-25 20:26:02,889 INFO misc.py line 119 253097] Train: [80/100][44/510] Data 0.005 (0.946) Batch 0.850 (2.129) Remain 06:18:23 loss: 0.1117 Lr: 0.00069
[2023-12-25 20:26:03,993 INFO misc.py line 119 253097] Train: [80/100][45/510] Data 0.005 (0.923) Batch 1.105 (2.104) Remain 06:14:01 loss: 0.0891 Lr: 0.00069
[2023-12-25 20:26:05,000 INFO misc.py line 119 253097] Train: [80/100][46/510] Data 0.005 (0.902) Batch 1.008 (2.079) Remain 06:09:27 loss: 0.0782 Lr: 0.00069
[2023-12-25 20:26:06,077 INFO misc.py line 119 253097] Train: [80/100][47/510] Data 0.004 (0.882) Batch 1.074 (2.056) Remain 06:05:22 loss: 0.0847 Lr: 0.00069
[2023-12-25 20:26:07,317 INFO misc.py line 119 253097] Train: [80/100][48/510] Data 0.007 (0.862) Batch 1.243 (2.038) Remain 06:02:07 loss: 0.0802 Lr: 0.00069
[2023-12-25 20:26:08,647 INFO misc.py line 119 253097] Train: [80/100][49/510] Data 0.003 (0.844) Batch 1.315 (2.022) Remain 05:59:17 loss: 0.1595 Lr: 0.00069
[2023-12-25 20:26:09,891 INFO misc.py line 119 253097] Train: [80/100][50/510] Data 0.019 (0.826) Batch 1.252 (2.006) Remain 05:56:21 loss: 0.1316 Lr: 0.00069
[2023-12-25 20:26:11,113 INFO misc.py line 119 253097] Train: [80/100][51/510] Data 0.011 (0.809) Batch 1.220 (1.989) Remain 05:53:24 loss: 0.0950 Lr: 0.00069
[2023-12-25 20:26:14,151 INFO misc.py line 119 253097] Train: [80/100][52/510] Data 0.012 (0.793) Batch 3.044 (2.011) Remain 05:57:12 loss: 0.1323 Lr: 0.00069
[2023-12-25 20:26:15,200 INFO misc.py line 119 253097] Train: [80/100][53/510] Data 0.006 (0.777) Batch 1.051 (1.992) Remain 05:53:45 loss: 0.0976 Lr: 0.00069
[2023-12-25 20:26:16,343 INFO misc.py line 119 253097] Train: [80/100][54/510] Data 0.004 (0.762) Batch 1.144 (1.975) Remain 05:50:46 loss: 0.1816 Lr: 0.00069
[2023-12-25 20:26:17,289 INFO misc.py line 119 253097] Train: [80/100][55/510] Data 0.003 (0.747) Batch 0.944 (1.955) Remain 05:47:13 loss: 0.1674 Lr: 0.00069
[2023-12-25 20:26:18,331 INFO misc.py line 119 253097] Train: [80/100][56/510] Data 0.006 (0.733) Batch 1.042 (1.938) Remain 05:44:07 loss: 0.1365 Lr: 0.00069
[2023-12-25 20:26:19,633 INFO misc.py line 119 253097] Train: [80/100][57/510] Data 0.005 (0.720) Batch 1.301 (1.926) Remain 05:42:00 loss: 0.0962 Lr: 0.00069
[2023-12-25 20:26:20,791 INFO misc.py line 119 253097] Train: [80/100][58/510] Data 0.006 (0.707) Batch 1.161 (1.912) Remain 05:39:30 loss: 0.0860 Lr: 0.00069
[2023-12-25 20:26:22,050 INFO misc.py line 119 253097] Train: [80/100][59/510] Data 0.003 (0.694) Batch 1.254 (1.901) Remain 05:37:22 loss: 0.0777 Lr: 0.00069
[2023-12-25 20:26:23,160 INFO misc.py line 119 253097] Train: [80/100][60/510] Data 0.008 (0.682) Batch 1.102 (1.887) Remain 05:34:51 loss: 0.0654 Lr: 0.00069
[2023-12-25 20:26:24,433 INFO misc.py line 119 253097] Train: [80/100][61/510] Data 0.017 (0.671) Batch 1.285 (1.876) Remain 05:32:59 loss: 0.1164 Lr: 0.00069
[2023-12-25 20:26:25,631 INFO misc.py line 119 253097] Train: [80/100][62/510] Data 0.004 (0.659) Batch 1.197 (1.865) Remain 05:30:55 loss: 0.0849 Lr: 0.00069
[2023-12-25 20:26:26,740 INFO misc.py line 119 253097] Train: [80/100][63/510] Data 0.005 (0.649) Batch 1.106 (1.852) Remain 05:28:38 loss: 0.1150 Lr: 0.00069
[2023-12-25 20:26:31,274 INFO misc.py line 119 253097] Train: [80/100][64/510] Data 0.007 (0.638) Batch 4.536 (1.896) Remain 05:36:25 loss: 0.1266 Lr: 0.00069
[2023-12-25 20:26:32,430 INFO misc.py line 119 253097] Train: [80/100][65/510] Data 0.006 (0.628) Batch 1.152 (1.884) Remain 05:34:15 loss: 0.0974 Lr: 0.00069
[2023-12-25 20:26:33,644 INFO misc.py line 119 253097] Train: [80/100][66/510] Data 0.010 (0.618) Batch 1.200 (1.873) Remain 05:32:18 loss: 0.1128 Lr: 0.00069
[2023-12-25 20:26:34,811 INFO misc.py line 119 253097] Train: [80/100][67/510] Data 0.024 (0.609) Batch 1.186 (1.862) Remain 05:30:21 loss: 0.0564 Lr: 0.00069
[2023-12-25 20:26:38,780 INFO misc.py line 119 253097] Train: [80/100][68/510] Data 0.004 (0.599) Batch 3.965 (1.895) Remain 05:36:04 loss: 0.0491 Lr: 0.00069
[2023-12-25 20:26:39,938 INFO misc.py line 119 253097] Train: [80/100][69/510] Data 0.008 (0.591) Batch 1.161 (1.884) Remain 05:34:04 loss: 0.0696 Lr: 0.00069
[2023-12-25 20:26:41,158 INFO misc.py line 119 253097] Train: [80/100][70/510] Data 0.004 (0.582) Batch 1.220 (1.874) Remain 05:32:16 loss: 0.1049 Lr: 0.00069
[2023-12-25 20:26:42,319 INFO misc.py line 119 253097] Train: [80/100][71/510] Data 0.005 (0.573) Batch 1.156 (1.863) Remain 05:30:22 loss: 0.1622 Lr: 0.00069
[2023-12-25 20:26:43,465 INFO misc.py line 119 253097] Train: [80/100][72/510] Data 0.010 (0.565) Batch 1.151 (1.853) Remain 05:28:31 loss: 0.1471 Lr: 0.00069
[2023-12-25 20:26:44,568 INFO misc.py line 119 253097] Train: [80/100][73/510] Data 0.004 (0.557) Batch 1.095 (1.842) Remain 05:26:34 loss: 0.0885 Lr: 0.00069
[2023-12-25 20:26:45,749 INFO misc.py line 119 253097] Train: [80/100][74/510] Data 0.012 (0.549) Batch 1.189 (1.833) Remain 05:24:54 loss: 0.0942 Lr: 0.00069
[2023-12-25 20:26:47,038 INFO misc.py line 119 253097] Train: [80/100][75/510] Data 0.004 (0.542) Batch 1.288 (1.825) Remain 05:23:32 loss: 0.1407 Lr: 0.00069
[2023-12-25 20:26:48,025 INFO misc.py line 119 253097] Train: [80/100][76/510] Data 0.006 (0.535) Batch 0.987 (1.814) Remain 05:21:28 loss: 0.1129 Lr: 0.00069
[2023-12-25 20:26:49,201 INFO misc.py line 119 253097] Train: [80/100][77/510] Data 0.006 (0.527) Batch 1.178 (1.805) Remain 05:19:54 loss: 0.0951 Lr: 0.00069
[2023-12-25 20:26:50,239 INFO misc.py line 119 253097] Train: [80/100][78/510] Data 0.005 (0.520) Batch 1.037 (1.795) Remain 05:18:04 loss: 0.1986 Lr: 0.00069
[2023-12-25 20:26:51,529 INFO misc.py line 119 253097] Train: [80/100][79/510] Data 0.005 (0.514) Batch 1.285 (1.788) Remain 05:16:51 loss: 0.1008 Lr: 0.00069
[2023-12-25 20:26:52,777 INFO misc.py line 119 253097] Train: [80/100][80/510] Data 0.010 (0.507) Batch 1.255 (1.781) Remain 05:15:35 loss: 0.1975 Lr: 0.00069
[2023-12-25 20:26:54,007 INFO misc.py line 119 253097] Train: [80/100][81/510] Data 0.003 (0.501) Batch 1.226 (1.774) Remain 05:14:18 loss: 0.0803 Lr: 0.00069
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[2023-12-25 20:28:29,335 INFO misc.py line 119 253097] Train: [80/100][136/510] Data 0.016 (0.395) Batch 6.395 (1.757) Remain 05:09:41 loss: 0.1677 Lr: 0.00068
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[2023-12-25 20:28:31,825 INFO misc.py line 119 253097] Train: [80/100][138/510] Data 0.009 (0.389) Batch 1.260 (1.750) Remain 05:08:17 loss: 0.1440 Lr: 0.00068
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[2023-12-25 20:28:35,237 INFO misc.py line 119 253097] Train: [80/100][141/510] Data 0.015 (0.381) Batch 1.068 (1.736) Remain 05:05:51 loss: 0.2184 Lr: 0.00068
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[2023-12-25 20:28:37,428 INFO misc.py line 119 253097] Train: [80/100][143/510] Data 0.004 (0.375) Batch 1.022 (1.727) Remain 05:04:11 loss: 0.1070 Lr: 0.00068
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[2023-12-25 20:30:56,394 INFO misc.py line 119 253097] Train: [80/100][234/510] Data 0.004 (0.236) Batch 1.050 (1.648) Remain 04:47:48 loss: 0.1746 Lr: 0.00067
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[2023-12-25 20:31:06,497 INFO misc.py line 119 253097] Train: [80/100][237/510] Data 6.949 (0.263) Batch 8.007 (1.670) Remain 04:51:34 loss: 0.1445 Lr: 0.00067
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[2023-12-25 20:31:08,779 INFO misc.py line 119 253097] Train: [80/100][239/510] Data 0.004 (0.260) Batch 1.200 (1.666) Remain 04:50:44 loss: 0.0587 Lr: 0.00067
[2023-12-25 20:31:09,964 INFO misc.py line 119 253097] Train: [80/100][240/510] Data 0.004 (0.259) Batch 1.187 (1.664) Remain 04:50:21 loss: 0.1043 Lr: 0.00067
[2023-12-25 20:31:11,008 INFO misc.py line 119 253097] Train: [80/100][241/510] Data 0.003 (0.258) Batch 1.043 (1.661) Remain 04:49:52 loss: 0.0951 Lr: 0.00067
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[2023-12-25 20:31:27,265 INFO misc.py line 119 253097] Train: [80/100][253/510] Data 0.002 (0.246) Batch 1.098 (1.647) Remain 04:46:58 loss: 0.0830 Lr: 0.00067
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[2023-12-25 20:31:34,038 INFO misc.py line 119 253097] Train: [80/100][259/510] Data 0.007 (0.241) Batch 0.954 (1.634) Remain 04:44:41 loss: 0.1093 Lr: 0.00067
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[2023-12-25 20:33:01,192 INFO misc.py line 119 253097] Train: [80/100][324/510] Data 0.004 (0.212) Batch 1.182 (1.575) Remain 04:32:37 loss: 0.1157 Lr: 0.00066
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[2023-12-25 20:33:06,847 INFO misc.py line 119 253097] Train: [80/100][329/510] Data 0.004 (0.209) Batch 1.147 (1.568) Remain 04:31:19 loss: 0.2105 Lr: 0.00066
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[2023-12-25 20:33:22,862 INFO misc.py line 119 253097] Train: [80/100][338/510] Data 0.006 (0.204) Batch 6.902 (1.574) Remain 04:32:04 loss: 0.0880 Lr: 0.00066
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[2023-12-25 20:33:28,710 INFO misc.py line 119 253097] Train: [80/100][343/510] Data 0.009 (0.201) Batch 1.123 (1.568) Remain 04:30:54 loss: 0.1264 Lr: 0.00066
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[2023-12-25 20:33:31,168 INFO misc.py line 119 253097] Train: [80/100][345/510] Data 0.004 (0.200) Batch 1.214 (1.566) Remain 04:30:31 loss: 0.0848 Lr: 0.00065
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[2023-12-25 20:33:34,356 INFO misc.py line 119 253097] Train: [80/100][348/510] Data 0.007 (0.198) Batch 0.989 (1.562) Remain 04:29:40 loss: 0.1293 Lr: 0.00065
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[2023-12-25 20:33:37,789 INFO misc.py line 119 253097] Train: [80/100][351/510] Data 0.004 (0.196) Batch 0.974 (1.558) Remain 04:28:58 loss: 0.0659 Lr: 0.00065
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[2023-12-25 20:33:58,338 INFO misc.py line 119 253097] Train: [80/100][360/510] Data 0.003 (0.191) Batch 1.306 (1.576) Remain 04:31:54 loss: 0.1415 Lr: 0.00065
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[2023-12-25 20:37:04,753 INFO misc.py line 119 253097] Train: [80/100][500/510] Data 0.005 (0.156) Batch 1.175 (1.507) Remain 04:16:29 loss: 0.1474 Lr: 0.00064
[2023-12-25 20:37:05,839 INFO misc.py line 119 253097] Train: [80/100][501/510] Data 0.008 (0.156) Batch 1.087 (1.506) Remain 04:16:19 loss: 0.0928 Lr: 0.00064
[2023-12-25 20:37:07,072 INFO misc.py line 119 253097] Train: [80/100][502/510] Data 0.008 (0.156) Batch 1.236 (1.506) Remain 04:16:12 loss: 0.1057 Lr: 0.00064
[2023-12-25 20:37:08,048 INFO misc.py line 119 253097] Train: [80/100][503/510] Data 0.004 (0.155) Batch 0.977 (1.505) Remain 04:16:00 loss: 0.1098 Lr: 0.00064
[2023-12-25 20:37:09,304 INFO misc.py line 119 253097] Train: [80/100][504/510] Data 0.003 (0.155) Batch 1.250 (1.504) Remain 04:15:53 loss: 0.1669 Lr: 0.00064
[2023-12-25 20:37:10,390 INFO misc.py line 119 253097] Train: [80/100][505/510] Data 0.009 (0.155) Batch 1.091 (1.504) Remain 04:15:43 loss: 0.0815 Lr: 0.00064
[2023-12-25 20:37:11,628 INFO misc.py line 119 253097] Train: [80/100][506/510] Data 0.004 (0.155) Batch 1.239 (1.503) Remain 04:15:36 loss: 0.0953 Lr: 0.00064
[2023-12-25 20:37:12,841 INFO misc.py line 119 253097] Train: [80/100][507/510] Data 0.003 (0.154) Batch 1.207 (1.502) Remain 04:15:29 loss: 0.0671 Lr: 0.00064
[2023-12-25 20:37:14,064 INFO misc.py line 119 253097] Train: [80/100][508/510] Data 0.009 (0.154) Batch 1.222 (1.502) Remain 04:15:22 loss: 0.1437 Lr: 0.00064
[2023-12-25 20:37:15,309 INFO misc.py line 119 253097] Train: [80/100][509/510] Data 0.010 (0.154) Batch 1.246 (1.501) Remain 04:15:15 loss: 0.1532 Lr: 0.00064
[2023-12-25 20:37:16,336 INFO misc.py line 119 253097] Train: [80/100][510/510] Data 0.009 (0.153) Batch 1.027 (1.500) Remain 04:15:04 loss: 0.1494 Lr: 0.00063
[2023-12-25 20:37:16,336 INFO misc.py line 136 253097] Train result: loss: 0.1195 
[2023-12-25 20:37:16,337 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 20:37:45,501 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5849 
[2023-12-25 20:37:45,845 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2909 
[2023-12-25 20:37:51,381 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3798 
[2023-12-25 20:37:51,896 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3961 
[2023-12-25 20:37:53,866 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7985 
[2023-12-25 20:37:54,290 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3695 
[2023-12-25 20:37:55,165 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1709 
[2023-12-25 20:37:55,721 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2866 
[2023-12-25 20:37:57,527 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9475 
[2023-12-25 20:37:59,657 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2236 
[2023-12-25 20:38:00,511 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2829 
[2023-12-25 20:38:00,932 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9120 
[2023-12-25 20:38:01,831 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3969 
[2023-12-25 20:38:04,774 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8034 
[2023-12-25 20:38:05,240 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3426 
[2023-12-25 20:38:05,850 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3600 
[2023-12-25 20:38:06,549 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3378 
[2023-12-25 20:38:07,911 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6901/0.7443/0.9038.
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9152/0.9490
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9828/0.9907
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8378/0.9720
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3079/0.3397
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6333/0.6536
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7040/0.7782
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8157/0.9079
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9156/0.9545
[2023-12-25 20:38:07,911 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6367/0.6635
[2023-12-25 20:38:07,912 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7842/0.8813
[2023-12-25 20:38:07,912 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8349/0.8856
[2023-12-25 20:38:07,912 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6037/0.7003
[2023-12-25 20:38:07,912 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 20:38:07,913 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 20:38:07,913 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 20:38:13,601 INFO misc.py line 119 253097] Train: [81/100][1/510] Data 2.625 (2.625) Batch 3.452 (3.452) Remain 09:46:44 loss: 0.1057 Lr: 0.00063
[2023-12-25 20:38:20,485 INFO misc.py line 119 253097] Train: [81/100][2/510] Data 5.655 (5.655) Batch 6.885 (6.885) Remain 19:30:12 loss: 0.0713 Lr: 0.00063
[2023-12-25 20:38:21,764 INFO misc.py line 119 253097] Train: [81/100][3/510] Data 0.004 (0.004) Batch 1.278 (1.278) Remain 03:37:15 loss: 0.1058 Lr: 0.00063
[2023-12-25 20:38:23,040 INFO misc.py line 119 253097] Train: [81/100][4/510] Data 0.005 (0.005) Batch 1.274 (1.274) Remain 03:36:30 loss: 0.1144 Lr: 0.00063
[2023-12-25 20:38:24,052 INFO misc.py line 119 253097] Train: [81/100][5/510] Data 0.007 (0.006) Batch 1.011 (1.143) Remain 03:14:09 loss: 0.3321 Lr: 0.00063
[2023-12-25 20:38:25,350 INFO misc.py line 119 253097] Train: [81/100][6/510] Data 0.008 (0.007) Batch 1.298 (1.194) Remain 03:22:55 loss: 0.1092 Lr: 0.00063
[2023-12-25 20:38:26,572 INFO misc.py line 119 253097] Train: [81/100][7/510] Data 0.008 (0.007) Batch 1.221 (1.201) Remain 03:24:02 loss: 0.0684 Lr: 0.00063
[2023-12-25 20:38:27,847 INFO misc.py line 119 253097] Train: [81/100][8/510] Data 0.008 (0.007) Batch 1.279 (1.217) Remain 03:26:40 loss: 0.0857 Lr: 0.00063
[2023-12-25 20:38:28,921 INFO misc.py line 119 253097] Train: [81/100][9/510] Data 0.005 (0.007) Batch 1.071 (1.192) Remain 03:22:31 loss: 0.1272 Lr: 0.00063
[2023-12-25 20:38:30,128 INFO misc.py line 119 253097] Train: [81/100][10/510] Data 0.008 (0.007) Batch 1.192 (1.192) Remain 03:22:29 loss: 0.2998 Lr: 0.00063
[2023-12-25 20:38:31,128 INFO misc.py line 119 253097] Train: [81/100][11/510] Data 0.024 (0.009) Batch 1.016 (1.170) Remain 03:18:44 loss: 0.2382 Lr: 0.00063
[2023-12-25 20:38:32,191 INFO misc.py line 119 253097] Train: [81/100][12/510] Data 0.007 (0.009) Batch 1.061 (1.158) Remain 03:16:39 loss: 0.0851 Lr: 0.00063
[2023-12-25 20:38:33,525 INFO misc.py line 119 253097] Train: [81/100][13/510] Data 0.008 (0.009) Batch 1.338 (1.176) Remain 03:19:41 loss: 0.0822 Lr: 0.00063
[2023-12-25 20:38:34,723 INFO misc.py line 119 253097] Train: [81/100][14/510] Data 0.004 (0.008) Batch 1.194 (1.178) Remain 03:19:56 loss: 0.1166 Lr: 0.00063
[2023-12-25 20:38:35,916 INFO misc.py line 119 253097] Train: [81/100][15/510] Data 0.009 (0.008) Batch 1.193 (1.179) Remain 03:20:08 loss: 0.0650 Lr: 0.00063
[2023-12-25 20:38:37,003 INFO misc.py line 119 253097] Train: [81/100][16/510] Data 0.008 (0.008) Batch 1.086 (1.172) Remain 03:18:54 loss: 0.2003 Lr: 0.00063
[2023-12-25 20:38:38,010 INFO misc.py line 119 253097] Train: [81/100][17/510] Data 0.009 (0.008) Batch 1.007 (1.160) Remain 03:16:54 loss: 0.0870 Lr: 0.00063
[2023-12-25 20:38:39,183 INFO misc.py line 119 253097] Train: [81/100][18/510] Data 0.008 (0.008) Batch 1.172 (1.161) Remain 03:17:01 loss: 0.1230 Lr: 0.00063
[2023-12-25 20:38:40,253 INFO misc.py line 119 253097] Train: [81/100][19/510] Data 0.009 (0.008) Batch 1.072 (1.155) Remain 03:16:03 loss: 0.2206 Lr: 0.00063
[2023-12-25 20:38:41,296 INFO misc.py line 119 253097] Train: [81/100][20/510] Data 0.007 (0.008) Batch 1.046 (1.149) Remain 03:14:56 loss: 0.1329 Lr: 0.00063
[2023-12-25 20:38:48,089 INFO misc.py line 119 253097] Train: [81/100][21/510] Data 1.128 (0.071) Batch 6.794 (1.463) Remain 04:08:07 loss: 0.0694 Lr: 0.00063
[2023-12-25 20:38:49,651 INFO misc.py line 119 253097] Train: [81/100][22/510] Data 0.003 (0.067) Batch 1.558 (1.468) Remain 04:08:57 loss: 0.0768 Lr: 0.00063
[2023-12-25 20:38:50,848 INFO misc.py line 119 253097] Train: [81/100][23/510] Data 0.007 (0.064) Batch 1.194 (1.454) Remain 04:06:36 loss: 0.1306 Lr: 0.00063
[2023-12-25 20:38:52,006 INFO misc.py line 119 253097] Train: [81/100][24/510] Data 0.012 (0.061) Batch 1.164 (1.440) Remain 04:04:14 loss: 0.1132 Lr: 0.00063
[2023-12-25 20:38:53,330 INFO misc.py line 119 253097] Train: [81/100][25/510] Data 0.005 (0.059) Batch 1.322 (1.435) Remain 04:03:18 loss: 0.1358 Lr: 0.00063
[2023-12-25 20:38:54,439 INFO misc.py line 119 253097] Train: [81/100][26/510] Data 0.007 (0.057) Batch 1.112 (1.421) Remain 04:00:54 loss: 0.1554 Lr: 0.00063
[2023-12-25 20:38:55,577 INFO misc.py line 119 253097] Train: [81/100][27/510] Data 0.003 (0.054) Batch 1.139 (1.409) Remain 03:58:53 loss: 0.1430 Lr: 0.00063
[2023-12-25 20:38:58,223 INFO misc.py line 119 253097] Train: [81/100][28/510] Data 0.003 (0.052) Batch 2.644 (1.458) Remain 04:07:14 loss: 0.1469 Lr: 0.00063
[2023-12-25 20:38:59,248 INFO misc.py line 119 253097] Train: [81/100][29/510] Data 0.005 (0.051) Batch 1.026 (1.442) Remain 04:04:23 loss: 0.1472 Lr: 0.00063
[2023-12-25 20:39:00,535 INFO misc.py line 119 253097] Train: [81/100][30/510] Data 0.004 (0.049) Batch 1.285 (1.436) Remain 04:03:22 loss: 0.0971 Lr: 0.00063
[2023-12-25 20:39:01,796 INFO misc.py line 119 253097] Train: [81/100][31/510] Data 0.007 (0.047) Batch 1.261 (1.430) Remain 04:02:18 loss: 0.0792 Lr: 0.00063
[2023-12-25 20:39:03,009 INFO misc.py line 119 253097] Train: [81/100][32/510] Data 0.006 (0.046) Batch 1.212 (1.422) Remain 04:01:00 loss: 0.1495 Lr: 0.00063
[2023-12-25 20:39:03,970 INFO misc.py line 119 253097] Train: [81/100][33/510] Data 0.007 (0.045) Batch 0.965 (1.407) Remain 03:58:24 loss: 0.1151 Lr: 0.00063
[2023-12-25 20:39:05,197 INFO misc.py line 119 253097] Train: [81/100][34/510] Data 0.004 (0.043) Batch 1.223 (1.401) Remain 03:57:22 loss: 0.1055 Lr: 0.00063
[2023-12-25 20:39:06,324 INFO misc.py line 119 253097] Train: [81/100][35/510] Data 0.007 (0.042) Batch 1.126 (1.392) Remain 03:55:53 loss: 0.1059 Lr: 0.00063
[2023-12-25 20:39:07,346 INFO misc.py line 119 253097] Train: [81/100][36/510] Data 0.008 (0.041) Batch 1.022 (1.381) Remain 03:53:58 loss: 0.0962 Lr: 0.00063
[2023-12-25 20:39:08,556 INFO misc.py line 119 253097] Train: [81/100][37/510] Data 0.008 (0.040) Batch 1.209 (1.376) Remain 03:53:05 loss: 0.1635 Lr: 0.00063
[2023-12-25 20:39:09,735 INFO misc.py line 119 253097] Train: [81/100][38/510] Data 0.009 (0.039) Batch 1.185 (1.371) Remain 03:52:08 loss: 0.0953 Lr: 0.00063
[2023-12-25 20:39:10,876 INFO misc.py line 119 253097] Train: [81/100][39/510] Data 0.003 (0.038) Batch 1.141 (1.364) Remain 03:51:02 loss: 0.0968 Lr: 0.00063
[2023-12-25 20:39:11,780 INFO misc.py line 119 253097] Train: [81/100][40/510] Data 0.006 (0.037) Batch 0.904 (1.352) Remain 03:48:54 loss: 0.1168 Lr: 0.00063
[2023-12-25 20:39:16,804 INFO misc.py line 119 253097] Train: [81/100][41/510] Data 0.004 (0.036) Batch 5.025 (1.448) Remain 04:05:14 loss: 0.1102 Lr: 0.00063
[2023-12-25 20:39:17,792 INFO misc.py line 119 253097] Train: [81/100][42/510] Data 0.004 (0.036) Batch 0.988 (1.437) Remain 04:03:13 loss: 0.1194 Lr: 0.00063
[2023-12-25 20:39:18,764 INFO misc.py line 119 253097] Train: [81/100][43/510] Data 0.003 (0.035) Batch 0.972 (1.425) Remain 04:01:14 loss: 0.0982 Lr: 0.00063
[2023-12-25 20:39:19,822 INFO misc.py line 119 253097] Train: [81/100][44/510] Data 0.003 (0.034) Batch 1.057 (1.416) Remain 03:59:41 loss: 0.1051 Lr: 0.00063
[2023-12-25 20:39:20,971 INFO misc.py line 119 253097] Train: [81/100][45/510] Data 0.004 (0.033) Batch 1.148 (1.410) Remain 03:58:35 loss: 0.1258 Lr: 0.00063
[2023-12-25 20:39:22,207 INFO misc.py line 119 253097] Train: [81/100][46/510] Data 0.004 (0.033) Batch 1.233 (1.406) Remain 03:57:52 loss: 0.1065 Lr: 0.00063
[2023-12-25 20:39:23,342 INFO misc.py line 119 253097] Train: [81/100][47/510] Data 0.007 (0.032) Batch 1.136 (1.399) Remain 03:56:48 loss: 0.1208 Lr: 0.00063
[2023-12-25 20:39:24,425 INFO misc.py line 119 253097] Train: [81/100][48/510] Data 0.007 (0.031) Batch 1.081 (1.392) Remain 03:55:35 loss: 0.0764 Lr: 0.00063
[2023-12-25 20:39:25,661 INFO misc.py line 119 253097] Train: [81/100][49/510] Data 0.010 (0.031) Batch 1.241 (1.389) Remain 03:55:00 loss: 0.0930 Lr: 0.00063
[2023-12-25 20:39:26,831 INFO misc.py line 119 253097] Train: [81/100][50/510] Data 0.004 (0.030) Batch 1.168 (1.384) Remain 03:54:11 loss: 0.1366 Lr: 0.00063
[2023-12-25 20:39:28,070 INFO misc.py line 119 253097] Train: [81/100][51/510] Data 0.005 (0.030) Batch 1.239 (1.381) Remain 03:53:39 loss: 0.1854 Lr: 0.00063
[2023-12-25 20:39:29,084 INFO misc.py line 119 253097] Train: [81/100][52/510] Data 0.007 (0.029) Batch 1.016 (1.374) Remain 03:52:22 loss: 0.1050 Lr: 0.00063
[2023-12-25 20:39:30,146 INFO misc.py line 119 253097] Train: [81/100][53/510] Data 0.004 (0.029) Batch 1.056 (1.368) Remain 03:51:16 loss: 0.2863 Lr: 0.00063
[2023-12-25 20:39:31,291 INFO misc.py line 119 253097] Train: [81/100][54/510] Data 0.010 (0.029) Batch 1.147 (1.363) Remain 03:50:31 loss: 0.1436 Lr: 0.00063
[2023-12-25 20:39:32,452 INFO misc.py line 119 253097] Train: [81/100][55/510] Data 0.008 (0.028) Batch 1.161 (1.359) Remain 03:49:50 loss: 0.0595 Lr: 0.00063
[2023-12-25 20:39:33,673 INFO misc.py line 119 253097] Train: [81/100][56/510] Data 0.008 (0.028) Batch 1.221 (1.357) Remain 03:49:22 loss: 0.0795 Lr: 0.00063
[2023-12-25 20:39:40,480 INFO misc.py line 119 253097] Train: [81/100][57/510] Data 0.008 (0.027) Batch 6.811 (1.458) Remain 04:06:25 loss: 0.0676 Lr: 0.00063
[2023-12-25 20:39:41,562 INFO misc.py line 119 253097] Train: [81/100][58/510] Data 0.005 (0.027) Batch 1.082 (1.451) Remain 04:05:14 loss: 0.1490 Lr: 0.00063
[2023-12-25 20:39:42,719 INFO misc.py line 119 253097] Train: [81/100][59/510] Data 0.004 (0.027) Batch 1.156 (1.446) Remain 04:04:19 loss: 0.0940 Lr: 0.00063
[2023-12-25 20:39:44,026 INFO misc.py line 119 253097] Train: [81/100][60/510] Data 0.005 (0.026) Batch 1.309 (1.443) Remain 04:03:54 loss: 0.0865 Lr: 0.00063
[2023-12-25 20:39:45,254 INFO misc.py line 119 253097] Train: [81/100][61/510] Data 0.004 (0.026) Batch 1.220 (1.439) Remain 04:03:13 loss: 0.0690 Lr: 0.00063
[2023-12-25 20:39:46,425 INFO misc.py line 119 253097] Train: [81/100][62/510] Data 0.013 (0.026) Batch 1.178 (1.435) Remain 04:02:27 loss: 0.1173 Lr: 0.00063
[2023-12-25 20:39:48,783 INFO misc.py line 119 253097] Train: [81/100][63/510] Data 0.004 (0.025) Batch 2.357 (1.450) Remain 04:05:01 loss: 0.1124 Lr: 0.00063
[2023-12-25 20:39:49,873 INFO misc.py line 119 253097] Train: [81/100][64/510] Data 0.006 (0.025) Batch 1.091 (1.444) Remain 04:04:00 loss: 0.1717 Lr: 0.00063
[2023-12-25 20:39:51,121 INFO misc.py line 119 253097] Train: [81/100][65/510] Data 0.004 (0.025) Batch 1.248 (1.441) Remain 04:03:27 loss: 0.1598 Lr: 0.00063
[2023-12-25 20:39:52,240 INFO misc.py line 119 253097] Train: [81/100][66/510] Data 0.004 (0.024) Batch 1.119 (1.436) Remain 04:02:33 loss: 0.2159 Lr: 0.00063
[2023-12-25 20:39:53,278 INFO misc.py line 119 253097] Train: [81/100][67/510] Data 0.004 (0.024) Batch 1.036 (1.430) Remain 04:01:29 loss: 0.0813 Lr: 0.00063
[2023-12-25 20:39:54,397 INFO misc.py line 119 253097] Train: [81/100][68/510] Data 0.006 (0.024) Batch 1.121 (1.425) Remain 04:00:39 loss: 0.1301 Lr: 0.00063
[2023-12-25 20:39:55,590 INFO misc.py line 119 253097] Train: [81/100][69/510] Data 0.004 (0.023) Batch 1.193 (1.422) Remain 04:00:02 loss: 0.1475 Lr: 0.00063
[2023-12-25 20:39:56,622 INFO misc.py line 119 253097] Train: [81/100][70/510] Data 0.004 (0.023) Batch 1.032 (1.416) Remain 03:59:02 loss: 0.1000 Lr: 0.00063
[2023-12-25 20:39:57,707 INFO misc.py line 119 253097] Train: [81/100][71/510] Data 0.003 (0.023) Batch 1.085 (1.411) Remain 03:58:11 loss: 0.0796 Lr: 0.00063
[2023-12-25 20:39:58,787 INFO misc.py line 119 253097] Train: [81/100][72/510] Data 0.003 (0.023) Batch 1.080 (1.406) Remain 03:57:21 loss: 0.2445 Lr: 0.00063
[2023-12-25 20:39:59,924 INFO misc.py line 119 253097] Train: [81/100][73/510] Data 0.004 (0.022) Batch 1.137 (1.402) Remain 03:56:41 loss: 0.1312 Lr: 0.00063
[2023-12-25 20:40:01,108 INFO misc.py line 119 253097] Train: [81/100][74/510] Data 0.003 (0.022) Batch 1.184 (1.399) Remain 03:56:08 loss: 0.1367 Lr: 0.00063
[2023-12-25 20:40:02,328 INFO misc.py line 119 253097] Train: [81/100][75/510] Data 0.004 (0.022) Batch 1.217 (1.397) Remain 03:55:41 loss: 0.0957 Lr: 0.00063
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[2023-12-25 20:41:28,064 INFO misc.py line 119 253097] Train: [81/100][132/510] Data 0.008 (0.073) Batch 1.071 (1.444) Remain 04:02:19 loss: 0.0998 Lr: 0.00062
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[2023-12-25 20:43:44,768 INFO misc.py line 119 253097] Train: [81/100][228/510] Data 0.476 (0.084) Batch 1.691 (1.436) Remain 03:58:35 loss: 0.0877 Lr: 0.00061
[2023-12-25 20:43:45,916 INFO misc.py line 119 253097] Train: [81/100][229/510] Data 0.005 (0.084) Batch 1.149 (1.434) Remain 03:58:21 loss: 0.1836 Lr: 0.00061
[2023-12-25 20:43:47,245 INFO misc.py line 119 253097] Train: [81/100][230/510] Data 0.004 (0.084) Batch 1.329 (1.434) Remain 03:58:15 loss: 0.0791 Lr: 0.00061
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[2023-12-25 20:43:53,373 INFO misc.py line 119 253097] Train: [81/100][234/510] Data 1.813 (0.090) Batch 2.897 (1.436) Remain 03:58:26 loss: 0.1096 Lr: 0.00061
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[2023-12-25 20:43:56,962 INFO misc.py line 119 253097] Train: [81/100][237/510] Data 0.003 (0.089) Batch 1.248 (1.432) Remain 03:57:51 loss: 0.1352 Lr: 0.00061
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[2023-12-25 20:44:24,277 INFO misc.py line 119 253097] Train: [81/100][257/510] Data 0.008 (0.082) Batch 0.956 (1.427) Remain 03:56:30 loss: 0.1010 Lr: 0.00060
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[2023-12-25 20:45:27,983 INFO misc.py line 119 253097] Train: [81/100][302/510] Data 0.003 (0.071) Batch 1.151 (1.425) Remain 03:55:09 loss: 0.2790 Lr: 0.00060
[2023-12-25 20:45:29,180 INFO misc.py line 119 253097] Train: [81/100][303/510] Data 0.007 (0.071) Batch 1.186 (1.425) Remain 03:54:59 loss: 0.1131 Lr: 0.00060
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[2023-12-25 20:45:35,460 INFO misc.py line 119 253097] Train: [81/100][309/510] Data 0.004 (0.069) Batch 1.144 (1.417) Remain 03:53:38 loss: 0.1169 Lr: 0.00060
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[2023-12-25 20:45:46,149 INFO misc.py line 119 253097] Train: [81/100][311/510] Data 8.645 (0.097) Batch 9.676 (1.443) Remain 03:57:47 loss: 0.0780 Lr: 0.00060
[2023-12-25 20:45:47,302 INFO misc.py line 119 253097] Train: [81/100][312/510] Data 0.005 (0.097) Batch 1.153 (1.442) Remain 03:57:37 loss: 0.1225 Lr: 0.00060
[2023-12-25 20:45:48,389 INFO misc.py line 119 253097] Train: [81/100][313/510] Data 0.004 (0.096) Batch 1.088 (1.441) Remain 03:57:24 loss: 0.1192 Lr: 0.00060
[2023-12-25 20:45:49,388 INFO misc.py line 119 253097] Train: [81/100][314/510] Data 0.003 (0.096) Batch 0.999 (1.439) Remain 03:57:09 loss: 0.0882 Lr: 0.00060
[2023-12-25 20:45:50,525 INFO misc.py line 119 253097] Train: [81/100][315/510] Data 0.003 (0.096) Batch 1.136 (1.438) Remain 03:56:57 loss: 0.0841 Lr: 0.00060
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[2023-12-25 20:45:52,974 INFO misc.py line 119 253097] Train: [81/100][317/510] Data 0.008 (0.095) Batch 1.207 (1.437) Remain 03:56:41 loss: 0.1805 Lr: 0.00060
[2023-12-25 20:45:54,113 INFO misc.py line 119 253097] Train: [81/100][318/510] Data 0.012 (0.095) Batch 1.149 (1.436) Remain 03:56:30 loss: 0.0837 Lr: 0.00060
[2023-12-25 20:45:55,117 INFO misc.py line 119 253097] Train: [81/100][319/510] Data 0.003 (0.095) Batch 0.995 (1.435) Remain 03:56:15 loss: 0.2054 Lr: 0.00060
[2023-12-25 20:45:56,095 INFO misc.py line 119 253097] Train: [81/100][320/510] Data 0.012 (0.094) Batch 0.985 (1.433) Remain 03:56:00 loss: 0.0585 Lr: 0.00060
[2023-12-25 20:45:57,250 INFO misc.py line 119 253097] Train: [81/100][321/510] Data 0.006 (0.094) Batch 1.156 (1.432) Remain 03:55:50 loss: 0.1441 Lr: 0.00060
[2023-12-25 20:45:58,593 INFO misc.py line 119 253097] Train: [81/100][322/510] Data 0.003 (0.094) Batch 1.341 (1.432) Remain 03:55:45 loss: 0.1025 Lr: 0.00060
[2023-12-25 20:45:59,627 INFO misc.py line 119 253097] Train: [81/100][323/510] Data 0.006 (0.094) Batch 1.032 (1.431) Remain 03:55:32 loss: 0.1341 Lr: 0.00060
[2023-12-25 20:46:00,880 INFO misc.py line 119 253097] Train: [81/100][324/510] Data 0.007 (0.093) Batch 1.253 (1.430) Remain 03:55:25 loss: 0.0898 Lr: 0.00060
[2023-12-25 20:46:02,806 INFO misc.py line 119 253097] Train: [81/100][325/510] Data 0.008 (0.093) Batch 1.930 (1.432) Remain 03:55:39 loss: 0.0961 Lr: 0.00060
[2023-12-25 20:46:03,926 INFO misc.py line 119 253097] Train: [81/100][326/510] Data 0.004 (0.093) Batch 1.118 (1.431) Remain 03:55:28 loss: 0.0939 Lr: 0.00060
[2023-12-25 20:46:05,114 INFO misc.py line 119 253097] Train: [81/100][327/510] Data 0.005 (0.092) Batch 1.188 (1.430) Remain 03:55:19 loss: 0.0526 Lr: 0.00060
[2023-12-25 20:46:06,123 INFO misc.py line 119 253097] Train: [81/100][328/510] Data 0.006 (0.092) Batch 1.010 (1.429) Remain 03:55:05 loss: 0.1151 Lr: 0.00060
[2023-12-25 20:46:13,714 INFO misc.py line 119 253097] Train: [81/100][329/510] Data 6.285 (0.111) Batch 7.591 (1.448) Remain 03:58:10 loss: 0.0782 Lr: 0.00060
[2023-12-25 20:46:14,719 INFO misc.py line 119 253097] Train: [81/100][330/510] Data 0.005 (0.111) Batch 1.005 (1.446) Remain 03:57:55 loss: 0.2053 Lr: 0.00060
[2023-12-25 20:46:15,976 INFO misc.py line 119 253097] Train: [81/100][331/510] Data 0.004 (0.111) Batch 1.258 (1.446) Remain 03:57:48 loss: 0.0784 Lr: 0.00060
[2023-12-25 20:46:17,087 INFO misc.py line 119 253097] Train: [81/100][332/510] Data 0.004 (0.110) Batch 1.112 (1.445) Remain 03:57:36 loss: 0.0836 Lr: 0.00060
[2023-12-25 20:46:18,036 INFO misc.py line 119 253097] Train: [81/100][333/510] Data 0.003 (0.110) Batch 0.948 (1.443) Remain 03:57:20 loss: 0.0887 Lr: 0.00060
[2023-12-25 20:46:19,108 INFO misc.py line 119 253097] Train: [81/100][334/510] Data 0.004 (0.110) Batch 1.073 (1.442) Remain 03:57:08 loss: 0.1108 Lr: 0.00060
[2023-12-25 20:46:25,960 INFO misc.py line 119 253097] Train: [81/100][335/510] Data 5.571 (0.126) Batch 6.851 (1.458) Remain 03:59:47 loss: 0.1129 Lr: 0.00060
[2023-12-25 20:46:27,117 INFO misc.py line 119 253097] Train: [81/100][336/510] Data 0.005 (0.126) Batch 1.158 (1.458) Remain 03:59:36 loss: 0.0846 Lr: 0.00060
[2023-12-25 20:46:28,187 INFO misc.py line 119 253097] Train: [81/100][337/510] Data 0.003 (0.125) Batch 1.070 (1.456) Remain 03:59:24 loss: 0.1109 Lr: 0.00060
[2023-12-25 20:46:29,345 INFO misc.py line 119 253097] Train: [81/100][338/510] Data 0.003 (0.125) Batch 1.157 (1.455) Remain 03:59:13 loss: 0.0925 Lr: 0.00060
[2023-12-25 20:46:30,512 INFO misc.py line 119 253097] Train: [81/100][339/510] Data 0.003 (0.125) Batch 1.167 (1.455) Remain 03:59:03 loss: 0.0882 Lr: 0.00059
[2023-12-25 20:46:31,689 INFO misc.py line 119 253097] Train: [81/100][340/510] Data 0.004 (0.124) Batch 1.176 (1.454) Remain 03:58:54 loss: 0.1048 Lr: 0.00059
[2023-12-25 20:46:32,760 INFO misc.py line 119 253097] Train: [81/100][341/510] Data 0.004 (0.124) Batch 1.072 (1.453) Remain 03:58:41 loss: 0.0921 Lr: 0.00059
[2023-12-25 20:46:33,772 INFO misc.py line 119 253097] Train: [81/100][342/510] Data 0.003 (0.124) Batch 1.012 (1.451) Remain 03:58:27 loss: 0.1312 Lr: 0.00059
[2023-12-25 20:46:34,891 INFO misc.py line 119 253097] Train: [81/100][343/510] Data 0.003 (0.123) Batch 1.119 (1.450) Remain 03:58:16 loss: 0.1404 Lr: 0.00059
[2023-12-25 20:46:36,229 INFO misc.py line 119 253097] Train: [81/100][344/510] Data 0.004 (0.123) Batch 1.337 (1.450) Remain 03:58:11 loss: 0.1192 Lr: 0.00059
[2023-12-25 20:46:40,691 INFO misc.py line 119 253097] Train: [81/100][345/510] Data 3.223 (0.132) Batch 4.461 (1.459) Remain 03:59:36 loss: 0.1158 Lr: 0.00059
[2023-12-25 20:46:41,703 INFO misc.py line 119 253097] Train: [81/100][346/510] Data 0.005 (0.132) Batch 1.012 (1.458) Remain 03:59:22 loss: 0.0893 Lr: 0.00059
[2023-12-25 20:46:42,755 INFO misc.py line 119 253097] Train: [81/100][347/510] Data 0.005 (0.131) Batch 1.054 (1.456) Remain 03:59:09 loss: 0.2844 Lr: 0.00059
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[2023-12-25 20:46:45,059 INFO misc.py line 119 253097] Train: [81/100][349/510] Data 0.004 (0.130) Batch 1.071 (1.455) Remain 03:58:49 loss: 0.0892 Lr: 0.00059
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[2023-12-25 20:46:48,744 INFO misc.py line 119 253097] Train: [81/100][351/510] Data 1.711 (0.135) Batch 2.710 (1.457) Remain 03:59:08 loss: 0.1318 Lr: 0.00059
[2023-12-25 20:46:49,825 INFO misc.py line 119 253097] Train: [81/100][352/510] Data 0.004 (0.134) Batch 1.077 (1.456) Remain 03:58:56 loss: 0.1070 Lr: 0.00059
[2023-12-25 20:46:50,670 INFO misc.py line 119 253097] Train: [81/100][353/510] Data 0.008 (0.134) Batch 0.850 (1.454) Remain 03:58:37 loss: 0.1749 Lr: 0.00059
[2023-12-25 20:46:51,757 INFO misc.py line 119 253097] Train: [81/100][354/510] Data 0.003 (0.133) Batch 1.087 (1.453) Remain 03:58:25 loss: 0.1651 Lr: 0.00059
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[2023-12-25 20:46:58,172 INFO misc.py line 119 253097] Train: [81/100][359/510] Data 0.009 (0.134) Batch 1.097 (1.451) Remain 03:57:55 loss: 0.0845 Lr: 0.00059
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[2023-12-25 20:47:45,317 INFO misc.py line 119 253097] Train: [81/100][385/510] Data 4.855 (0.151) Batch 5.993 (1.475) Remain 04:01:19 loss: 0.0896 Lr: 0.00059
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[2023-12-25 20:50:15,012 INFO misc.py line 119 253097] Train: [81/100][494/510] Data 0.003 (0.126) Batch 0.992 (1.453) Remain 03:54:59 loss: 0.1514 Lr: 0.00058
[2023-12-25 20:50:16,095 INFO misc.py line 119 253097] Train: [81/100][495/510] Data 0.003 (0.126) Batch 1.083 (1.452) Remain 03:54:50 loss: 0.0995 Lr: 0.00058
[2023-12-25 20:50:17,299 INFO misc.py line 119 253097] Train: [81/100][496/510] Data 0.004 (0.125) Batch 1.203 (1.451) Remain 03:54:44 loss: 0.0836 Lr: 0.00058
[2023-12-25 20:50:18,324 INFO misc.py line 119 253097] Train: [81/100][497/510] Data 0.004 (0.125) Batch 1.025 (1.451) Remain 03:54:34 loss: 0.0838 Lr: 0.00058
[2023-12-25 20:50:19,346 INFO misc.py line 119 253097] Train: [81/100][498/510] Data 0.004 (0.125) Batch 1.022 (1.450) Remain 03:54:24 loss: 0.0977 Lr: 0.00058
[2023-12-25 20:50:20,659 INFO misc.py line 119 253097] Train: [81/100][499/510] Data 0.003 (0.125) Batch 1.313 (1.449) Remain 03:54:20 loss: 0.0596 Lr: 0.00058
[2023-12-25 20:50:21,849 INFO misc.py line 119 253097] Train: [81/100][500/510] Data 0.005 (0.124) Batch 1.185 (1.449) Remain 03:54:13 loss: 0.1050 Lr: 0.00058
[2023-12-25 20:50:23,125 INFO misc.py line 119 253097] Train: [81/100][501/510] Data 0.010 (0.124) Batch 1.282 (1.449) Remain 03:54:09 loss: 0.1012 Lr: 0.00058
[2023-12-25 20:50:24,288 INFO misc.py line 119 253097] Train: [81/100][502/510] Data 0.003 (0.124) Batch 1.157 (1.448) Remain 03:54:02 loss: 0.0983 Lr: 0.00058
[2023-12-25 20:50:27,675 INFO misc.py line 119 253097] Train: [81/100][503/510] Data 0.009 (0.124) Batch 3.394 (1.452) Remain 03:54:38 loss: 0.1574 Lr: 0.00058
[2023-12-25 20:50:28,858 INFO misc.py line 119 253097] Train: [81/100][504/510] Data 0.002 (0.123) Batch 1.181 (1.451) Remain 03:54:31 loss: 0.1159 Lr: 0.00058
[2023-12-25 20:50:35,134 INFO misc.py line 119 253097] Train: [81/100][505/510] Data 0.005 (0.123) Batch 6.277 (1.461) Remain 03:56:03 loss: 0.0959 Lr: 0.00058
[2023-12-25 20:50:36,175 INFO misc.py line 119 253097] Train: [81/100][506/510] Data 0.005 (0.123) Batch 1.041 (1.460) Remain 03:55:53 loss: 0.1827 Lr: 0.00058
[2023-12-25 20:50:37,529 INFO misc.py line 119 253097] Train: [81/100][507/510] Data 0.004 (0.123) Batch 1.201 (1.460) Remain 03:55:47 loss: 0.1027 Lr: 0.00058
[2023-12-25 20:50:38,583 INFO misc.py line 119 253097] Train: [81/100][508/510] Data 0.157 (0.123) Batch 1.205 (1.459) Remain 03:55:41 loss: 0.0627 Lr: 0.00058
[2023-12-25 20:50:39,701 INFO misc.py line 119 253097] Train: [81/100][509/510] Data 0.006 (0.123) Batch 1.119 (1.458) Remain 03:55:33 loss: 0.0971 Lr: 0.00058
[2023-12-25 20:50:40,821 INFO misc.py line 119 253097] Train: [81/100][510/510] Data 0.005 (0.122) Batch 1.120 (1.458) Remain 03:55:25 loss: 0.1252 Lr: 0.00058
[2023-12-25 20:50:40,821 INFO misc.py line 136 253097] Train result: loss: 0.1196 
[2023-12-25 20:50:40,822 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 20:51:14,610 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7059 
[2023-12-25 20:51:14,954 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3436 
[2023-12-25 20:51:19,914 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4067 
[2023-12-25 20:51:20,438 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4374 
[2023-12-25 20:51:22,416 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9406 
[2023-12-25 20:51:22,839 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.4661 
[2023-12-25 20:51:23,717 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2920 
[2023-12-25 20:51:24,274 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2532 
[2023-12-25 20:51:26,086 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0939 
[2023-12-25 20:51:28,208 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.3709 
[2023-12-25 20:51:29,064 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3208 
[2023-12-25 20:51:29,485 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9072 
[2023-12-25 20:51:30,383 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3970 
[2023-12-25 20:51:33,332 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8574 
[2023-12-25 20:51:33,797 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3457 
[2023-12-25 20:51:34,404 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4912 
[2023-12-25 20:51:35,103 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3711 
[2023-12-25 20:51:36,485 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6750/0.7323/0.8998.
[2023-12-25 20:51:36,485 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9119/0.9491
[2023-12-25 20:51:36,485 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9834/0.9903
[2023-12-25 20:51:36,485 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8341/0.9758
[2023-12-25 20:51:36,485 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 20:51:36,485 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2384/0.2466
[2023-12-25 20:51:36,485 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6299/0.6537
[2023-12-25 20:51:36,485 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6262/0.6822
[2023-12-25 20:51:36,485 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8068/0.8815
[2023-12-25 20:51:36,486 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9266/0.9674
[2023-12-25 20:51:36,486 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6916/0.7329
[2023-12-25 20:51:36,486 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7850/0.8662
[2023-12-25 20:51:36,486 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7417/0.8517
[2023-12-25 20:51:36,486 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.5988/0.7219
[2023-12-25 20:51:36,486 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 20:51:36,488 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 20:51:36,488 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 20:51:45,006 INFO misc.py line 119 253097] Train: [82/100][1/510] Data 5.386 (5.386) Batch 6.479 (6.479) Remain 17:26:16 loss: 0.1127 Lr: 0.00058
[2023-12-25 20:51:46,142 INFO misc.py line 119 253097] Train: [82/100][2/510] Data 0.003 (0.003) Batch 1.136 (1.136) Remain 03:03:23 loss: 0.0842 Lr: 0.00057
[2023-12-25 20:51:47,074 INFO misc.py line 119 253097] Train: [82/100][3/510] Data 0.003 (0.003) Batch 0.932 (0.932) Remain 02:30:28 loss: 0.0823 Lr: 0.00057
[2023-12-25 20:51:48,342 INFO misc.py line 119 253097] Train: [82/100][4/510] Data 0.003 (0.003) Batch 1.267 (1.267) Remain 03:24:33 loss: 0.0856 Lr: 0.00057
[2023-12-25 20:51:56,755 INFO misc.py line 119 253097] Train: [82/100][5/510] Data 7.249 (3.626) Batch 8.414 (4.840) Remain 13:01:19 loss: 0.0920 Lr: 0.00057
[2023-12-25 20:51:57,879 INFO misc.py line 119 253097] Train: [82/100][6/510] Data 0.004 (2.419) Batch 1.124 (3.602) Remain 09:41:16 loss: 0.1161 Lr: 0.00057
[2023-12-25 20:52:02,590 INFO misc.py line 119 253097] Train: [82/100][7/510] Data 3.734 (2.748) Batch 4.711 (3.879) Remain 10:26:00 loss: 0.0871 Lr: 0.00057
[2023-12-25 20:52:03,716 INFO misc.py line 119 253097] Train: [82/100][8/510] Data 0.003 (2.199) Batch 1.121 (3.327) Remain 08:56:55 loss: 0.1434 Lr: 0.00057
[2023-12-25 20:52:04,865 INFO misc.py line 119 253097] Train: [82/100][9/510] Data 0.008 (1.834) Batch 1.149 (2.964) Remain 07:58:17 loss: 0.1108 Lr: 0.00057
[2023-12-25 20:52:05,933 INFO misc.py line 119 253097] Train: [82/100][10/510] Data 0.009 (1.573) Batch 1.071 (2.694) Remain 07:14:36 loss: 0.1301 Lr: 0.00057
[2023-12-25 20:52:06,993 INFO misc.py line 119 253097] Train: [82/100][11/510] Data 0.005 (1.377) Batch 1.061 (2.490) Remain 06:41:38 loss: 0.1678 Lr: 0.00057
[2023-12-25 20:52:08,181 INFO misc.py line 119 253097] Train: [82/100][12/510] Data 0.004 (1.224) Batch 1.185 (2.345) Remain 06:18:13 loss: 0.1096 Lr: 0.00057
[2023-12-25 20:52:09,347 INFO misc.py line 119 253097] Train: [82/100][13/510] Data 0.007 (1.103) Batch 1.164 (2.227) Remain 05:59:08 loss: 0.1054 Lr: 0.00057
[2023-12-25 20:52:10,570 INFO misc.py line 119 253097] Train: [82/100][14/510] Data 0.009 (1.003) Batch 1.227 (2.136) Remain 05:44:26 loss: 0.1741 Lr: 0.00057
[2023-12-25 20:52:11,813 INFO misc.py line 119 253097] Train: [82/100][15/510] Data 0.005 (0.920) Batch 1.244 (2.061) Remain 05:32:24 loss: 0.1249 Lr: 0.00057
[2023-12-25 20:52:12,960 INFO misc.py line 119 253097] Train: [82/100][16/510] Data 0.005 (0.850) Batch 1.144 (1.991) Remain 05:20:59 loss: 0.0925 Lr: 0.00057
[2023-12-25 20:52:13,926 INFO misc.py line 119 253097] Train: [82/100][17/510] Data 0.008 (0.790) Batch 0.969 (1.918) Remain 05:09:12 loss: 0.1105 Lr: 0.00057
[2023-12-25 20:52:15,029 INFO misc.py line 119 253097] Train: [82/100][18/510] Data 0.005 (0.737) Batch 1.105 (1.864) Remain 05:00:25 loss: 0.1670 Lr: 0.00057
[2023-12-25 20:52:16,053 INFO misc.py line 119 253097] Train: [82/100][19/510] Data 0.003 (0.691) Batch 1.023 (1.811) Remain 04:51:55 loss: 0.1734 Lr: 0.00057
[2023-12-25 20:52:17,221 INFO misc.py line 119 253097] Train: [82/100][20/510] Data 0.003 (0.651) Batch 1.167 (1.773) Remain 04:45:47 loss: 0.0859 Lr: 0.00057
[2023-12-25 20:52:21,859 INFO misc.py line 119 253097] Train: [82/100][21/510] Data 3.647 (0.817) Batch 4.639 (1.932) Remain 05:11:25 loss: 0.0870 Lr: 0.00057
[2023-12-25 20:52:22,994 INFO misc.py line 119 253097] Train: [82/100][22/510] Data 0.003 (0.774) Batch 1.135 (1.891) Remain 05:04:37 loss: 0.0813 Lr: 0.00057
[2023-12-25 20:52:24,208 INFO misc.py line 119 253097] Train: [82/100][23/510] Data 0.003 (0.736) Batch 1.214 (1.857) Remain 04:59:08 loss: 0.0946 Lr: 0.00057
[2023-12-25 20:52:25,293 INFO misc.py line 119 253097] Train: [82/100][24/510] Data 0.003 (0.701) Batch 1.081 (1.820) Remain 04:53:09 loss: 0.2313 Lr: 0.00057
[2023-12-25 20:52:26,383 INFO misc.py line 119 253097] Train: [82/100][25/510] Data 0.007 (0.669) Batch 1.088 (1.787) Remain 04:47:46 loss: 0.0834 Lr: 0.00057
[2023-12-25 20:52:28,843 INFO misc.py line 119 253097] Train: [82/100][26/510] Data 0.009 (0.641) Batch 2.466 (1.816) Remain 04:52:30 loss: 0.1147 Lr: 0.00057
[2023-12-25 20:52:29,796 INFO misc.py line 119 253097] Train: [82/100][27/510] Data 0.003 (0.614) Batch 0.952 (1.780) Remain 04:46:40 loss: 0.1063 Lr: 0.00057
[2023-12-25 20:52:31,114 INFO misc.py line 119 253097] Train: [82/100][28/510] Data 0.004 (0.590) Batch 1.310 (1.761) Remain 04:43:37 loss: 0.0977 Lr: 0.00057
[2023-12-25 20:52:32,047 INFO misc.py line 119 253097] Train: [82/100][29/510] Data 0.013 (0.568) Batch 0.941 (1.730) Remain 04:38:30 loss: 0.1874 Lr: 0.00057
[2023-12-25 20:52:33,275 INFO misc.py line 119 253097] Train: [82/100][30/510] Data 0.005 (0.547) Batch 1.229 (1.711) Remain 04:35:29 loss: 0.0966 Lr: 0.00057
[2023-12-25 20:52:34,508 INFO misc.py line 119 253097] Train: [82/100][31/510] Data 0.004 (0.527) Batch 1.233 (1.694) Remain 04:32:42 loss: 0.1284 Lr: 0.00057
[2023-12-25 20:52:35,666 INFO misc.py line 119 253097] Train: [82/100][32/510] Data 0.004 (0.509) Batch 1.154 (1.675) Remain 04:29:41 loss: 0.1117 Lr: 0.00057
[2023-12-25 20:52:36,788 INFO misc.py line 119 253097] Train: [82/100][33/510] Data 0.008 (0.493) Batch 1.125 (1.657) Remain 04:26:42 loss: 0.1631 Lr: 0.00057
[2023-12-25 20:52:38,062 INFO misc.py line 119 253097] Train: [82/100][34/510] Data 0.005 (0.477) Batch 1.273 (1.645) Remain 04:24:41 loss: 0.0951 Lr: 0.00057
[2023-12-25 20:52:39,184 INFO misc.py line 119 253097] Train: [82/100][35/510] Data 0.006 (0.462) Batch 1.124 (1.628) Remain 04:22:02 loss: 0.1916 Lr: 0.00057
[2023-12-25 20:52:40,198 INFO misc.py line 119 253097] Train: [82/100][36/510] Data 0.005 (0.448) Batch 1.013 (1.610) Remain 04:19:00 loss: 0.0844 Lr: 0.00057
[2023-12-25 20:52:41,207 INFO misc.py line 119 253097] Train: [82/100][37/510] Data 0.004 (0.435) Batch 1.011 (1.592) Remain 04:16:09 loss: 0.1615 Lr: 0.00057
[2023-12-25 20:52:42,395 INFO misc.py line 119 253097] Train: [82/100][38/510] Data 0.003 (0.423) Batch 1.188 (1.581) Remain 04:14:15 loss: 0.1870 Lr: 0.00057
[2023-12-25 20:52:43,493 INFO misc.py line 119 253097] Train: [82/100][39/510] Data 0.003 (0.411) Batch 1.098 (1.567) Remain 04:12:05 loss: 0.1995 Lr: 0.00057
[2023-12-25 20:52:44,499 INFO misc.py line 119 253097] Train: [82/100][40/510] Data 0.003 (0.400) Batch 1.005 (1.552) Remain 04:09:36 loss: 0.1214 Lr: 0.00057
[2023-12-25 20:52:45,593 INFO misc.py line 119 253097] Train: [82/100][41/510] Data 0.003 (0.390) Batch 1.091 (1.540) Remain 04:07:38 loss: 0.1136 Lr: 0.00057
[2023-12-25 20:52:46,697 INFO misc.py line 119 253097] Train: [82/100][42/510] Data 0.007 (0.380) Batch 1.109 (1.529) Remain 04:05:49 loss: 0.1068 Lr: 0.00057
[2023-12-25 20:52:47,857 INFO misc.py line 119 253097] Train: [82/100][43/510] Data 0.002 (0.370) Batch 1.155 (1.519) Remain 04:04:18 loss: 0.1219 Lr: 0.00057
[2023-12-25 20:52:48,882 INFO misc.py line 119 253097] Train: [82/100][44/510] Data 0.008 (0.362) Batch 1.025 (1.507) Remain 04:02:20 loss: 0.1676 Lr: 0.00057
[2023-12-25 20:52:50,070 INFO misc.py line 119 253097] Train: [82/100][45/510] Data 0.008 (0.353) Batch 1.190 (1.500) Remain 04:01:05 loss: 0.1397 Lr: 0.00057
[2023-12-25 20:52:53,426 INFO misc.py line 119 253097] Train: [82/100][46/510] Data 0.006 (0.345) Batch 3.357 (1.543) Remain 04:08:01 loss: 0.0954 Lr: 0.00057
[2023-12-25 20:52:54,722 INFO misc.py line 119 253097] Train: [82/100][47/510] Data 0.004 (0.337) Batch 1.293 (1.537) Remain 04:07:04 loss: 0.1037 Lr: 0.00057
[2023-12-25 20:52:55,708 INFO misc.py line 119 253097] Train: [82/100][48/510] Data 0.007 (0.330) Batch 0.989 (1.525) Remain 04:05:05 loss: 0.1359 Lr: 0.00057
[2023-12-25 20:52:56,707 INFO misc.py line 119 253097] Train: [82/100][49/510] Data 0.004 (0.323) Batch 1.000 (1.514) Remain 04:03:14 loss: 0.1335 Lr: 0.00057
[2023-12-25 20:52:57,719 INFO misc.py line 119 253097] Train: [82/100][50/510] Data 0.003 (0.316) Batch 1.011 (1.503) Remain 04:01:29 loss: 0.0894 Lr: 0.00057
[2023-12-25 20:52:58,950 INFO misc.py line 119 253097] Train: [82/100][51/510] Data 0.004 (0.310) Batch 1.232 (1.497) Remain 04:00:33 loss: 0.1212 Lr: 0.00057
[2023-12-25 20:53:00,198 INFO misc.py line 119 253097] Train: [82/100][52/510] Data 0.003 (0.303) Batch 1.248 (1.492) Remain 03:59:43 loss: 0.1698 Lr: 0.00057
[2023-12-25 20:53:07,082 INFO misc.py line 119 253097] Train: [82/100][53/510] Data 0.003 (0.297) Batch 6.883 (1.600) Remain 04:17:00 loss: 0.0845 Lr: 0.00057
[2023-12-25 20:53:08,106 INFO misc.py line 119 253097] Train: [82/100][54/510] Data 0.004 (0.292) Batch 1.023 (1.589) Remain 04:15:09 loss: 0.1074 Lr: 0.00057
[2023-12-25 20:53:09,156 INFO misc.py line 119 253097] Train: [82/100][55/510] Data 0.005 (0.286) Batch 1.051 (1.578) Remain 04:13:28 loss: 0.1835 Lr: 0.00057
[2023-12-25 20:53:10,404 INFO misc.py line 119 253097] Train: [82/100][56/510] Data 0.004 (0.281) Batch 1.244 (1.572) Remain 04:12:26 loss: 0.0983 Lr: 0.00057
[2023-12-25 20:53:11,463 INFO misc.py line 119 253097] Train: [82/100][57/510] Data 0.009 (0.276) Batch 1.064 (1.563) Remain 04:10:54 loss: 0.1876 Lr: 0.00057
[2023-12-25 20:53:12,503 INFO misc.py line 119 253097] Train: [82/100][58/510] Data 0.003 (0.271) Batch 1.037 (1.553) Remain 04:09:20 loss: 0.1156 Lr: 0.00057
[2023-12-25 20:53:13,427 INFO misc.py line 119 253097] Train: [82/100][59/510] Data 0.007 (0.266) Batch 0.926 (1.542) Remain 04:07:30 loss: 0.0861 Lr: 0.00057
[2023-12-25 20:53:14,582 INFO misc.py line 119 253097] Train: [82/100][60/510] Data 0.004 (0.261) Batch 1.156 (1.535) Remain 04:06:24 loss: 0.1319 Lr: 0.00057
[2023-12-25 20:53:15,545 INFO misc.py line 119 253097] Train: [82/100][61/510] Data 0.004 (0.257) Batch 0.960 (1.525) Remain 04:04:47 loss: 0.0931 Lr: 0.00057
[2023-12-25 20:53:16,627 INFO misc.py line 119 253097] Train: [82/100][62/510] Data 0.008 (0.253) Batch 1.085 (1.518) Remain 04:03:33 loss: 0.1554 Lr: 0.00057
[2023-12-25 20:53:17,731 INFO misc.py line 119 253097] Train: [82/100][63/510] Data 0.004 (0.249) Batch 1.104 (1.511) Remain 04:02:25 loss: 0.1197 Lr: 0.00057
[2023-12-25 20:53:18,965 INFO misc.py line 119 253097] Train: [82/100][64/510] Data 0.003 (0.245) Batch 1.231 (1.506) Remain 04:01:40 loss: 0.0954 Lr: 0.00057
[2023-12-25 20:53:20,115 INFO misc.py line 119 253097] Train: [82/100][65/510] Data 0.008 (0.241) Batch 1.149 (1.501) Remain 04:00:43 loss: 0.1042 Lr: 0.00057
[2023-12-25 20:53:21,044 INFO misc.py line 119 253097] Train: [82/100][66/510] Data 0.008 (0.237) Batch 0.933 (1.492) Remain 03:59:15 loss: 0.2319 Lr: 0.00057
[2023-12-25 20:53:22,143 INFO misc.py line 119 253097] Train: [82/100][67/510] Data 0.004 (0.233) Batch 1.099 (1.485) Remain 03:58:14 loss: 0.1480 Lr: 0.00057
[2023-12-25 20:53:23,293 INFO misc.py line 119 253097] Train: [82/100][68/510] Data 0.004 (0.230) Batch 1.150 (1.480) Remain 03:57:23 loss: 0.1267 Lr: 0.00057
[2023-12-25 20:53:24,420 INFO misc.py line 119 253097] Train: [82/100][69/510] Data 0.004 (0.226) Batch 1.127 (1.475) Remain 03:56:30 loss: 0.2380 Lr: 0.00057
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[2023-12-25 20:56:41,769 INFO misc.py line 119 253097] Train: [82/100][211/510] Data 6.407 (0.126) Batch 7.594 (1.417) Remain 03:43:49 loss: 0.0932 Lr: 0.00055
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[2023-12-25 20:57:09,944 INFO misc.py line 119 253097] Train: [82/100][231/510] Data 0.004 (0.115) Batch 2.726 (1.416) Remain 03:43:14 loss: 0.1299 Lr: 0.00055
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[2023-12-25 20:57:16,808 INFO misc.py line 119 253097] Train: [82/100][234/510] Data 3.225 (0.128) Batch 4.448 (1.427) Remain 03:44:57 loss: 0.1215 Lr: 0.00055
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[2023-12-25 21:00:18,258 INFO misc.py line 119 253097] Train: [82/100][344/510] Data 14.999 (0.156) Batch 15.920 (1.499) Remain 03:53:30 loss: 0.0310 Lr: 0.00054
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[2023-12-25 21:03:49,911 INFO misc.py line 119 253097] Train: [82/100][488/510] Data 0.006 (0.149) Batch 1.285 (1.490) Remain 03:48:34 loss: 0.1119 Lr: 0.00052
[2023-12-25 21:03:51,189 INFO misc.py line 119 253097] Train: [82/100][489/510] Data 0.006 (0.149) Batch 1.280 (1.490) Remain 03:48:28 loss: 0.1268 Lr: 0.00052
[2023-12-25 21:03:52,364 INFO misc.py line 119 253097] Train: [82/100][490/510] Data 0.003 (0.148) Batch 1.171 (1.489) Remain 03:48:21 loss: 0.2944 Lr: 0.00052
[2023-12-25 21:03:53,172 INFO misc.py line 119 253097] Train: [82/100][491/510] Data 0.008 (0.148) Batch 0.812 (1.488) Remain 03:48:07 loss: 0.0473 Lr: 0.00052
[2023-12-25 21:03:54,229 INFO misc.py line 119 253097] Train: [82/100][492/510] Data 0.003 (0.148) Batch 1.055 (1.487) Remain 03:47:57 loss: 0.2274 Lr: 0.00052
[2023-12-25 21:03:55,313 INFO misc.py line 119 253097] Train: [82/100][493/510] Data 0.005 (0.147) Batch 1.085 (1.486) Remain 03:47:48 loss: 0.1370 Lr: 0.00052
[2023-12-25 21:03:56,479 INFO misc.py line 119 253097] Train: [82/100][494/510] Data 0.005 (0.147) Batch 1.166 (1.486) Remain 03:47:41 loss: 0.1086 Lr: 0.00052
[2023-12-25 21:03:57,764 INFO misc.py line 119 253097] Train: [82/100][495/510] Data 0.004 (0.147) Batch 1.281 (1.485) Remain 03:47:35 loss: 0.1427 Lr: 0.00052
[2023-12-25 21:03:59,062 INFO misc.py line 119 253097] Train: [82/100][496/510] Data 0.008 (0.147) Batch 1.301 (1.485) Remain 03:47:30 loss: 0.0999 Lr: 0.00052
[2023-12-25 21:04:00,149 INFO misc.py line 119 253097] Train: [82/100][497/510] Data 0.005 (0.146) Batch 1.089 (1.484) Remain 03:47:22 loss: 0.0964 Lr: 0.00052
[2023-12-25 21:04:01,420 INFO misc.py line 119 253097] Train: [82/100][498/510] Data 0.003 (0.146) Batch 1.268 (1.484) Remain 03:47:16 loss: 0.0823 Lr: 0.00052
[2023-12-25 21:04:02,627 INFO misc.py line 119 253097] Train: [82/100][499/510] Data 0.007 (0.146) Batch 1.202 (1.483) Remain 03:47:09 loss: 0.1674 Lr: 0.00052
[2023-12-25 21:04:03,852 INFO misc.py line 119 253097] Train: [82/100][500/510] Data 0.011 (0.145) Batch 1.229 (1.482) Remain 03:47:03 loss: 0.0910 Lr: 0.00052
[2023-12-25 21:04:05,237 INFO misc.py line 119 253097] Train: [82/100][501/510] Data 0.008 (0.145) Batch 1.390 (1.482) Remain 03:47:00 loss: 0.1292 Lr: 0.00052
[2023-12-25 21:04:06,328 INFO misc.py line 119 253097] Train: [82/100][502/510] Data 0.003 (0.145) Batch 1.091 (1.481) Remain 03:46:51 loss: 0.0905 Lr: 0.00052
[2023-12-25 21:04:07,475 INFO misc.py line 119 253097] Train: [82/100][503/510] Data 0.003 (0.145) Batch 1.145 (1.481) Remain 03:46:44 loss: 0.0750 Lr: 0.00052
[2023-12-25 21:04:08,730 INFO misc.py line 119 253097] Train: [82/100][504/510] Data 0.004 (0.144) Batch 1.255 (1.480) Remain 03:46:38 loss: 0.0667 Lr: 0.00052
[2023-12-25 21:04:09,846 INFO misc.py line 119 253097] Train: [82/100][505/510] Data 0.003 (0.144) Batch 1.113 (1.480) Remain 03:46:30 loss: 0.1319 Lr: 0.00052
[2023-12-25 21:04:11,076 INFO misc.py line 119 253097] Train: [82/100][506/510] Data 0.007 (0.144) Batch 1.229 (1.479) Remain 03:46:24 loss: 0.0936 Lr: 0.00052
[2023-12-25 21:04:12,271 INFO misc.py line 119 253097] Train: [82/100][507/510] Data 0.007 (0.143) Batch 1.196 (1.479) Remain 03:46:17 loss: 0.1118 Lr: 0.00052
[2023-12-25 21:04:13,424 INFO misc.py line 119 253097] Train: [82/100][508/510] Data 0.006 (0.143) Batch 1.151 (1.478) Remain 03:46:10 loss: 0.1340 Lr: 0.00052
[2023-12-25 21:04:14,603 INFO misc.py line 119 253097] Train: [82/100][509/510] Data 0.007 (0.143) Batch 1.177 (1.477) Remain 03:46:03 loss: 0.0828 Lr: 0.00052
[2023-12-25 21:04:15,801 INFO misc.py line 119 253097] Train: [82/100][510/510] Data 0.009 (0.143) Batch 1.184 (1.477) Remain 03:45:56 loss: 0.1136 Lr: 0.00052
[2023-12-25 21:04:15,802 INFO misc.py line 136 253097] Train result: loss: 0.1217 
[2023-12-25 21:04:15,802 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 21:04:42,807 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6741 
[2023-12-25 21:04:43,165 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3147 
[2023-12-25 21:04:48,129 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3872 
[2023-12-25 21:04:48,645 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3885 
[2023-12-25 21:04:50,633 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9603 
[2023-12-25 21:04:51,058 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3283 
[2023-12-25 21:04:51,935 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1827 
[2023-12-25 21:04:52,488 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2153 
[2023-12-25 21:04:54,298 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.0276 
[2023-12-25 21:04:56,422 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1781 
[2023-12-25 21:04:57,276 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2705 
[2023-12-25 21:04:57,703 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7779 
[2023-12-25 21:04:58,601 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3482 
[2023-12-25 21:05:01,546 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9373 
[2023-12-25 21:05:02,012 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2434 
[2023-12-25 21:05:02,619 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4082 
[2023-12-25 21:05:03,317 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3862 
[2023-12-25 21:05:04,634 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6921/0.7450/0.9073.
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9192/0.9462
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9834/0.9908
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8445/0.9758
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0007/0.0041
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3044/0.3276
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6357/0.6560
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7167/0.7957
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8122/0.9001
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9193/0.9598
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6289/0.6603
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7928/0.8683
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8120/0.8492
[2023-12-25 21:05:04,635 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6268/0.7505
[2023-12-25 21:05:04,636 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 21:05:04,637 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 21:05:04,637 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 21:05:13,333 INFO misc.py line 119 253097] Train: [83/100][1/510] Data 5.816 (5.816) Batch 7.032 (7.032) Remain 17:55:50 loss: 0.0856 Lr: 0.00052
[2023-12-25 21:05:14,522 INFO misc.py line 119 253097] Train: [83/100][2/510] Data 0.003 (0.003) Batch 1.187 (1.187) Remain 03:01:34 loss: 0.1791 Lr: 0.00052
[2023-12-25 21:05:15,628 INFO misc.py line 119 253097] Train: [83/100][3/510] Data 0.006 (0.006) Batch 1.107 (1.107) Remain 02:49:17 loss: 0.1748 Lr: 0.00052
[2023-12-25 21:05:16,678 INFO misc.py line 119 253097] Train: [83/100][4/510] Data 0.005 (0.005) Batch 1.051 (1.051) Remain 02:40:40 loss: 0.1020 Lr: 0.00052
[2023-12-25 21:05:17,837 INFO misc.py line 119 253097] Train: [83/100][5/510] Data 0.004 (0.005) Batch 1.155 (1.103) Remain 02:48:40 loss: 0.1811 Lr: 0.00052
[2023-12-25 21:05:18,923 INFO misc.py line 119 253097] Train: [83/100][6/510] Data 0.008 (0.006) Batch 1.088 (1.098) Remain 02:47:52 loss: 0.1070 Lr: 0.00052
[2023-12-25 21:05:19,997 INFO misc.py line 119 253097] Train: [83/100][7/510] Data 0.005 (0.006) Batch 1.074 (1.092) Remain 02:46:56 loss: 0.0926 Lr: 0.00052
[2023-12-25 21:05:21,138 INFO misc.py line 119 253097] Train: [83/100][8/510] Data 0.005 (0.006) Batch 1.139 (1.101) Remain 02:48:21 loss: 0.0784 Lr: 0.00052
[2023-12-25 21:05:22,221 INFO misc.py line 119 253097] Train: [83/100][9/510] Data 0.008 (0.006) Batch 1.084 (1.098) Remain 02:47:53 loss: 0.1488 Lr: 0.00052
[2023-12-25 21:05:23,381 INFO misc.py line 119 253097] Train: [83/100][10/510] Data 0.007 (0.006) Batch 1.160 (1.107) Remain 02:49:12 loss: 0.1220 Lr: 0.00052
[2023-12-25 21:05:24,572 INFO misc.py line 119 253097] Train: [83/100][11/510] Data 0.007 (0.006) Batch 1.190 (1.117) Remain 02:50:46 loss: 0.0765 Lr: 0.00052
[2023-12-25 21:05:30,233 INFO misc.py line 119 253097] Train: [83/100][12/510] Data 0.009 (0.006) Batch 5.667 (1.623) Remain 04:07:59 loss: 0.1238 Lr: 0.00052
[2023-12-25 21:05:31,323 INFO misc.py line 119 253097] Train: [83/100][13/510] Data 0.003 (0.006) Batch 1.086 (1.569) Remain 03:59:44 loss: 0.1482 Lr: 0.00052
[2023-12-25 21:05:32,418 INFO misc.py line 119 253097] Train: [83/100][14/510] Data 0.007 (0.006) Batch 1.098 (1.526) Remain 03:53:10 loss: 0.1348 Lr: 0.00052
[2023-12-25 21:05:33,538 INFO misc.py line 119 253097] Train: [83/100][15/510] Data 0.004 (0.006) Batch 1.122 (1.493) Remain 03:48:00 loss: 0.0841 Lr: 0.00052
[2023-12-25 21:05:34,534 INFO misc.py line 119 253097] Train: [83/100][16/510] Data 0.002 (0.006) Batch 0.995 (1.454) Remain 03:42:07 loss: 0.0625 Lr: 0.00052
[2023-12-25 21:05:35,480 INFO misc.py line 119 253097] Train: [83/100][17/510] Data 0.005 (0.006) Batch 0.946 (1.418) Remain 03:36:33 loss: 0.1032 Lr: 0.00052
[2023-12-25 21:05:41,561 INFO misc.py line 119 253097] Train: [83/100][18/510] Data 0.004 (0.006) Batch 6.082 (1.729) Remain 04:24:00 loss: 0.1341 Lr: 0.00052
[2023-12-25 21:05:42,716 INFO misc.py line 119 253097] Train: [83/100][19/510] Data 0.004 (0.005) Batch 1.154 (1.693) Remain 04:18:30 loss: 0.0720 Lr: 0.00052
[2023-12-25 21:05:43,889 INFO misc.py line 119 253097] Train: [83/100][20/510] Data 0.003 (0.005) Batch 1.172 (1.662) Remain 04:13:47 loss: 0.0895 Lr: 0.00052
[2023-12-25 21:05:44,904 INFO misc.py line 119 253097] Train: [83/100][21/510] Data 0.004 (0.005) Batch 1.016 (1.627) Remain 04:08:17 loss: 0.1094 Lr: 0.00052
[2023-12-25 21:05:46,092 INFO misc.py line 119 253097] Train: [83/100][22/510] Data 0.003 (0.005) Batch 1.188 (1.603) Remain 04:04:44 loss: 0.1455 Lr: 0.00052
[2023-12-25 21:05:47,177 INFO misc.py line 119 253097] Train: [83/100][23/510] Data 0.003 (0.005) Batch 1.084 (1.577) Remain 04:00:44 loss: 0.0780 Lr: 0.00052
[2023-12-25 21:05:48,373 INFO misc.py line 119 253097] Train: [83/100][24/510] Data 0.004 (0.005) Batch 1.197 (1.559) Remain 03:57:57 loss: 0.1458 Lr: 0.00052
[2023-12-25 21:05:49,361 INFO misc.py line 119 253097] Train: [83/100][25/510] Data 0.003 (0.005) Batch 0.988 (1.533) Remain 03:53:58 loss: 0.0586 Lr: 0.00052
[2023-12-25 21:05:50,424 INFO misc.py line 119 253097] Train: [83/100][26/510] Data 0.003 (0.005) Batch 1.062 (1.513) Remain 03:50:49 loss: 0.0845 Lr: 0.00052
[2023-12-25 21:05:51,600 INFO misc.py line 119 253097] Train: [83/100][27/510] Data 0.003 (0.005) Batch 1.176 (1.499) Remain 03:48:39 loss: 0.2805 Lr: 0.00052
[2023-12-25 21:05:52,686 INFO misc.py line 119 253097] Train: [83/100][28/510] Data 0.003 (0.005) Batch 1.087 (1.482) Remain 03:46:06 loss: 0.1054 Lr: 0.00052
[2023-12-25 21:05:53,787 INFO misc.py line 119 253097] Train: [83/100][29/510] Data 0.003 (0.005) Batch 1.101 (1.468) Remain 03:43:51 loss: 0.1051 Lr: 0.00051
[2023-12-25 21:06:07,415 INFO misc.py line 119 253097] Train: [83/100][30/510] Data 12.327 (0.461) Batch 13.627 (1.918) Remain 04:52:30 loss: 0.0934 Lr: 0.00051
[2023-12-25 21:06:08,280 INFO misc.py line 119 253097] Train: [83/100][31/510] Data 0.004 (0.445) Batch 0.865 (1.880) Remain 04:46:44 loss: 0.0585 Lr: 0.00051
[2023-12-25 21:06:09,219 INFO misc.py line 119 253097] Train: [83/100][32/510] Data 0.003 (0.429) Batch 0.932 (1.848) Remain 04:41:43 loss: 0.1788 Lr: 0.00051
[2023-12-25 21:06:10,450 INFO misc.py line 119 253097] Train: [83/100][33/510] Data 0.011 (0.415) Batch 1.233 (1.827) Remain 04:38:33 loss: 0.0780 Lr: 0.00051
[2023-12-25 21:06:11,451 INFO misc.py line 119 253097] Train: [83/100][34/510] Data 0.009 (0.402) Batch 1.005 (1.801) Remain 04:34:29 loss: 0.0701 Lr: 0.00051
[2023-12-25 21:06:12,424 INFO misc.py line 119 253097] Train: [83/100][35/510] Data 0.005 (0.390) Batch 0.975 (1.775) Remain 04:30:31 loss: 0.1498 Lr: 0.00051
[2023-12-25 21:06:13,501 INFO misc.py line 119 253097] Train: [83/100][36/510] Data 0.003 (0.378) Batch 1.077 (1.754) Remain 04:27:16 loss: 0.0671 Lr: 0.00051
[2023-12-25 21:06:14,497 INFO misc.py line 119 253097] Train: [83/100][37/510] Data 0.003 (0.367) Batch 0.997 (1.731) Remain 04:23:50 loss: 0.0978 Lr: 0.00051
[2023-12-25 21:06:15,641 INFO misc.py line 119 253097] Train: [83/100][38/510] Data 0.003 (0.357) Batch 1.144 (1.715) Remain 04:21:15 loss: 0.1217 Lr: 0.00051
[2023-12-25 21:06:16,791 INFO misc.py line 119 253097] Train: [83/100][39/510] Data 0.003 (0.347) Batch 1.150 (1.699) Remain 04:18:50 loss: 0.2092 Lr: 0.00051
[2023-12-25 21:06:17,962 INFO misc.py line 119 253097] Train: [83/100][40/510] Data 0.003 (0.338) Batch 1.172 (1.685) Remain 04:16:38 loss: 0.1439 Lr: 0.00051
[2023-12-25 21:06:19,139 INFO misc.py line 119 253097] Train: [83/100][41/510] Data 0.003 (0.329) Batch 1.177 (1.671) Remain 04:14:34 loss: 0.0834 Lr: 0.00051
[2023-12-25 21:06:20,198 INFO misc.py line 119 253097] Train: [83/100][42/510] Data 0.004 (0.321) Batch 1.059 (1.656) Remain 04:12:09 loss: 0.0975 Lr: 0.00051
[2023-12-25 21:06:21,197 INFO misc.py line 119 253097] Train: [83/100][43/510] Data 0.004 (0.313) Batch 0.999 (1.639) Remain 04:09:37 loss: 0.0552 Lr: 0.00051
[2023-12-25 21:06:22,319 INFO misc.py line 119 253097] Train: [83/100][44/510] Data 0.003 (0.305) Batch 1.120 (1.627) Remain 04:07:40 loss: 0.1061 Lr: 0.00051
[2023-12-25 21:06:23,604 INFO misc.py line 119 253097] Train: [83/100][45/510] Data 0.004 (0.298) Batch 1.282 (1.618) Remain 04:06:24 loss: 0.0635 Lr: 0.00051
[2023-12-25 21:06:24,789 INFO misc.py line 119 253097] Train: [83/100][46/510] Data 0.008 (0.291) Batch 1.184 (1.608) Remain 04:04:50 loss: 0.0856 Lr: 0.00051
[2023-12-25 21:06:25,793 INFO misc.py line 119 253097] Train: [83/100][47/510] Data 0.008 (0.285) Batch 1.010 (1.595) Remain 04:02:44 loss: 0.1512 Lr: 0.00051
[2023-12-25 21:06:26,794 INFO misc.py line 119 253097] Train: [83/100][48/510] Data 0.003 (0.278) Batch 0.997 (1.581) Remain 04:00:41 loss: 0.0675 Lr: 0.00051
[2023-12-25 21:06:39,841 INFO misc.py line 119 253097] Train: [83/100][49/510] Data 11.723 (0.527) Batch 13.051 (1.831) Remain 04:38:36 loss: 0.1632 Lr: 0.00051
[2023-12-25 21:06:40,954 INFO misc.py line 119 253097] Train: [83/100][50/510] Data 0.003 (0.516) Batch 1.109 (1.815) Remain 04:36:14 loss: 0.1419 Lr: 0.00051
[2023-12-25 21:06:42,234 INFO misc.py line 119 253097] Train: [83/100][51/510] Data 0.006 (0.505) Batch 1.278 (1.804) Remain 04:34:30 loss: 0.0723 Lr: 0.00051
[2023-12-25 21:06:43,356 INFO misc.py line 119 253097] Train: [83/100][52/510] Data 0.009 (0.495) Batch 1.121 (1.790) Remain 04:32:21 loss: 0.2235 Lr: 0.00051
[2023-12-25 21:06:44,531 INFO misc.py line 119 253097] Train: [83/100][53/510] Data 0.009 (0.486) Batch 1.175 (1.778) Remain 04:30:27 loss: 0.1042 Lr: 0.00051
[2023-12-25 21:06:45,619 INFO misc.py line 119 253097] Train: [83/100][54/510] Data 0.009 (0.476) Batch 1.089 (1.764) Remain 04:28:22 loss: 0.1270 Lr: 0.00051
[2023-12-25 21:06:46,674 INFO misc.py line 119 253097] Train: [83/100][55/510] Data 0.008 (0.467) Batch 1.059 (1.751) Remain 04:26:17 loss: 0.1482 Lr: 0.00051
[2023-12-25 21:06:47,865 INFO misc.py line 119 253097] Train: [83/100][56/510] Data 0.004 (0.458) Batch 1.187 (1.740) Remain 04:24:38 loss: 0.3429 Lr: 0.00051
[2023-12-25 21:06:49,117 INFO misc.py line 119 253097] Train: [83/100][57/510] Data 0.007 (0.450) Batch 1.251 (1.731) Remain 04:23:13 loss: 0.0989 Lr: 0.00051
[2023-12-25 21:06:50,185 INFO misc.py line 119 253097] Train: [83/100][58/510] Data 0.007 (0.442) Batch 1.072 (1.719) Remain 04:21:22 loss: 0.0748 Lr: 0.00051
[2023-12-25 21:06:51,285 INFO misc.py line 119 253097] Train: [83/100][59/510] Data 0.005 (0.434) Batch 1.100 (1.708) Remain 04:19:40 loss: 0.0681 Lr: 0.00051
[2023-12-25 21:06:52,343 INFO misc.py line 119 253097] Train: [83/100][60/510] Data 0.004 (0.427) Batch 1.057 (1.697) Remain 04:17:54 loss: 0.1450 Lr: 0.00051
[2023-12-25 21:06:53,530 INFO misc.py line 119 253097] Train: [83/100][61/510] Data 0.005 (0.419) Batch 1.188 (1.688) Remain 04:16:32 loss: 0.1151 Lr: 0.00051
[2023-12-25 21:06:54,667 INFO misc.py line 119 253097] Train: [83/100][62/510] Data 0.003 (0.412) Batch 1.137 (1.679) Remain 04:15:05 loss: 0.1342 Lr: 0.00051
[2023-12-25 21:06:55,817 INFO misc.py line 119 253097] Train: [83/100][63/510] Data 0.004 (0.406) Batch 1.151 (1.670) Remain 04:13:43 loss: 0.1123 Lr: 0.00051
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[2023-12-25 21:09:42,820 INFO misc.py line 119 253097] Train: [83/100][176/510] Data 0.006 (0.190) Batch 1.152 (1.544) Remain 03:51:46 loss: 0.1115 Lr: 0.00050
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[2023-12-25 21:09:48,528 INFO misc.py line 119 253097] Train: [83/100][181/510] Data 0.003 (0.185) Batch 1.055 (1.533) Remain 03:49:56 loss: 0.2058 Lr: 0.00050
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[2023-12-25 21:09:50,984 INFO misc.py line 119 253097] Train: [83/100][183/510] Data 0.009 (0.183) Batch 1.255 (1.530) Remain 03:49:23 loss: 0.1098 Lr: 0.00050
[2023-12-25 21:09:52,197 INFO misc.py line 119 253097] Train: [83/100][184/510] Data 0.008 (0.182) Batch 1.211 (1.528) Remain 03:49:05 loss: 0.1104 Lr: 0.00050
[2023-12-25 21:09:53,268 INFO misc.py line 119 253097] Train: [83/100][185/510] Data 0.010 (0.181) Batch 1.072 (1.525) Remain 03:48:41 loss: 0.1375 Lr: 0.00050
[2023-12-25 21:09:54,310 INFO misc.py line 119 253097] Train: [83/100][186/510] Data 0.008 (0.180) Batch 1.044 (1.523) Remain 03:48:16 loss: 0.0664 Lr: 0.00050
[2023-12-25 21:09:55,440 INFO misc.py line 119 253097] Train: [83/100][187/510] Data 0.006 (0.179) Batch 1.134 (1.521) Remain 03:47:55 loss: 0.1321 Lr: 0.00050
[2023-12-25 21:09:56,632 INFO misc.py line 119 253097] Train: [83/100][188/510] Data 0.004 (0.178) Batch 1.192 (1.519) Remain 03:47:38 loss: 0.0956 Lr: 0.00050
[2023-12-25 21:09:57,681 INFO misc.py line 119 253097] Train: [83/100][189/510] Data 0.004 (0.177) Batch 1.049 (1.516) Remain 03:47:14 loss: 0.1106 Lr: 0.00050
[2023-12-25 21:10:07,294 INFO misc.py line 119 253097] Train: [83/100][190/510] Data 0.003 (0.176) Batch 9.612 (1.560) Remain 03:53:41 loss: 0.0801 Lr: 0.00050
[2023-12-25 21:10:08,566 INFO misc.py line 119 253097] Train: [83/100][191/510] Data 0.004 (0.176) Batch 1.270 (1.558) Remain 03:53:26 loss: 0.0837 Lr: 0.00050
[2023-12-25 21:10:09,762 INFO misc.py line 119 253097] Train: [83/100][192/510] Data 0.007 (0.175) Batch 1.195 (1.556) Remain 03:53:07 loss: 0.0705 Lr: 0.00050
[2023-12-25 21:10:10,917 INFO misc.py line 119 253097] Train: [83/100][193/510] Data 0.008 (0.174) Batch 1.156 (1.554) Remain 03:52:47 loss: 0.0901 Lr: 0.00050
[2023-12-25 21:10:11,939 INFO misc.py line 119 253097] Train: [83/100][194/510] Data 0.007 (0.173) Batch 1.025 (1.551) Remain 03:52:20 loss: 0.1257 Lr: 0.00050
[2023-12-25 21:10:13,008 INFO misc.py line 119 253097] Train: [83/100][195/510] Data 0.003 (0.172) Batch 1.064 (1.549) Remain 03:51:56 loss: 0.1016 Lr: 0.00050
[2023-12-25 21:10:14,235 INFO misc.py line 119 253097] Train: [83/100][196/510] Data 0.008 (0.171) Batch 1.232 (1.547) Remain 03:51:39 loss: 0.1097 Lr: 0.00050
[2023-12-25 21:10:15,490 INFO misc.py line 119 253097] Train: [83/100][197/510] Data 0.004 (0.170) Batch 1.254 (1.546) Remain 03:51:24 loss: 0.1276 Lr: 0.00050
[2023-12-25 21:10:16,647 INFO misc.py line 119 253097] Train: [83/100][198/510] Data 0.004 (0.169) Batch 1.157 (1.544) Remain 03:51:05 loss: 0.0570 Lr: 0.00050
[2023-12-25 21:10:17,699 INFO misc.py line 119 253097] Train: [83/100][199/510] Data 0.004 (0.169) Batch 1.052 (1.541) Remain 03:50:41 loss: 0.1916 Lr: 0.00050
[2023-12-25 21:10:18,870 INFO misc.py line 119 253097] Train: [83/100][200/510] Data 0.005 (0.168) Batch 1.167 (1.539) Remain 03:50:22 loss: 0.1380 Lr: 0.00050
[2023-12-25 21:10:19,994 INFO misc.py line 119 253097] Train: [83/100][201/510] Data 0.008 (0.167) Batch 1.124 (1.537) Remain 03:50:02 loss: 0.0784 Lr: 0.00050
[2023-12-25 21:10:21,082 INFO misc.py line 119 253097] Train: [83/100][202/510] Data 0.007 (0.166) Batch 1.085 (1.535) Remain 03:49:40 loss: 0.0585 Lr: 0.00050
[2023-12-25 21:10:24,562 INFO misc.py line 119 253097] Train: [83/100][203/510] Data 0.010 (0.165) Batch 3.486 (1.545) Remain 03:51:06 loss: 0.1205 Lr: 0.00050
[2023-12-25 21:10:25,718 INFO misc.py line 119 253097] Train: [83/100][204/510] Data 0.005 (0.165) Batch 1.154 (1.543) Remain 03:50:47 loss: 0.0832 Lr: 0.00050
[2023-12-25 21:10:26,832 INFO misc.py line 119 253097] Train: [83/100][205/510] Data 0.005 (0.164) Batch 1.114 (1.541) Remain 03:50:26 loss: 0.0926 Lr: 0.00050
[2023-12-25 21:10:27,760 INFO misc.py line 119 253097] Train: [83/100][206/510] Data 0.006 (0.163) Batch 0.930 (1.538) Remain 03:49:58 loss: 0.1088 Lr: 0.00050
[2023-12-25 21:10:28,757 INFO misc.py line 119 253097] Train: [83/100][207/510] Data 0.004 (0.162) Batch 0.998 (1.535) Remain 03:49:33 loss: 0.0836 Lr: 0.00050
[2023-12-25 21:10:29,896 INFO misc.py line 119 253097] Train: [83/100][208/510] Data 0.003 (0.161) Batch 1.137 (1.533) Remain 03:49:14 loss: 0.1052 Lr: 0.00050
[2023-12-25 21:10:30,905 INFO misc.py line 119 253097] Train: [83/100][209/510] Data 0.005 (0.161) Batch 1.010 (1.530) Remain 03:48:49 loss: 0.1196 Lr: 0.00050
[2023-12-25 21:10:35,645 INFO misc.py line 119 253097] Train: [83/100][210/510] Data 0.353 (0.162) Batch 4.739 (1.546) Remain 03:51:07 loss: 0.0813 Lr: 0.00050
[2023-12-25 21:10:36,746 INFO misc.py line 119 253097] Train: [83/100][211/510] Data 0.005 (0.161) Batch 1.097 (1.544) Remain 03:50:46 loss: 0.1242 Lr: 0.00050
[2023-12-25 21:10:38,900 INFO misc.py line 119 253097] Train: [83/100][212/510] Data 0.009 (0.160) Batch 2.158 (1.547) Remain 03:51:11 loss: 0.1163 Lr: 0.00050
[2023-12-25 21:10:40,065 INFO misc.py line 119 253097] Train: [83/100][213/510] Data 0.005 (0.159) Batch 1.166 (1.545) Remain 03:50:53 loss: 0.1446 Lr: 0.00050
[2023-12-25 21:10:41,275 INFO misc.py line 119 253097] Train: [83/100][214/510] Data 0.003 (0.159) Batch 1.204 (1.543) Remain 03:50:37 loss: 0.1160 Lr: 0.00050
[2023-12-25 21:10:42,511 INFO misc.py line 119 253097] Train: [83/100][215/510] Data 0.008 (0.158) Batch 1.235 (1.542) Remain 03:50:22 loss: 0.0742 Lr: 0.00049
[2023-12-25 21:10:43,545 INFO misc.py line 119 253097] Train: [83/100][216/510] Data 0.010 (0.157) Batch 1.040 (1.540) Remain 03:50:00 loss: 0.0806 Lr: 0.00049
[2023-12-25 21:10:44,631 INFO misc.py line 119 253097] Train: [83/100][217/510] Data 0.004 (0.157) Batch 1.086 (1.537) Remain 03:49:39 loss: 0.1033 Lr: 0.00049
[2023-12-25 21:10:45,661 INFO misc.py line 119 253097] Train: [83/100][218/510] Data 0.004 (0.156) Batch 1.027 (1.535) Remain 03:49:16 loss: 0.0872 Lr: 0.00049
[2023-12-25 21:10:46,765 INFO misc.py line 119 253097] Train: [83/100][219/510] Data 0.007 (0.155) Batch 1.108 (1.533) Remain 03:48:57 loss: 0.0934 Lr: 0.00049
[2023-12-25 21:10:47,723 INFO misc.py line 119 253097] Train: [83/100][220/510] Data 0.004 (0.154) Batch 0.958 (1.530) Remain 03:48:32 loss: 0.0618 Lr: 0.00049
[2023-12-25 21:10:52,413 INFO misc.py line 119 253097] Train: [83/100][221/510] Data 0.004 (0.154) Batch 4.690 (1.545) Remain 03:50:40 loss: 0.2906 Lr: 0.00049
[2023-12-25 21:10:53,653 INFO misc.py line 119 253097] Train: [83/100][222/510] Data 0.004 (0.153) Batch 1.240 (1.543) Remain 03:50:26 loss: 0.1169 Lr: 0.00049
[2023-12-25 21:10:54,961 INFO misc.py line 119 253097] Train: [83/100][223/510] Data 0.003 (0.152) Batch 1.306 (1.542) Remain 03:50:15 loss: 0.1150 Lr: 0.00049
[2023-12-25 21:10:56,248 INFO misc.py line 119 253097] Train: [83/100][224/510] Data 0.005 (0.152) Batch 1.288 (1.541) Remain 03:50:03 loss: 0.0959 Lr: 0.00049
[2023-12-25 21:10:57,451 INFO misc.py line 119 253097] Train: [83/100][225/510] Data 0.005 (0.151) Batch 1.201 (1.540) Remain 03:49:48 loss: 0.1025 Lr: 0.00049
[2023-12-25 21:10:58,590 INFO misc.py line 119 253097] Train: [83/100][226/510] Data 0.006 (0.150) Batch 1.138 (1.538) Remain 03:49:30 loss: 0.1242 Lr: 0.00049
[2023-12-25 21:10:59,787 INFO misc.py line 119 253097] Train: [83/100][227/510] Data 0.007 (0.150) Batch 1.196 (1.536) Remain 03:49:15 loss: 0.2312 Lr: 0.00049
[2023-12-25 21:11:01,044 INFO misc.py line 119 253097] Train: [83/100][228/510] Data 0.007 (0.149) Batch 1.260 (1.535) Remain 03:49:02 loss: 0.0889 Lr: 0.00049
[2023-12-25 21:11:02,270 INFO misc.py line 119 253097] Train: [83/100][229/510] Data 0.006 (0.149) Batch 1.228 (1.534) Remain 03:48:49 loss: 0.0757 Lr: 0.00049
[2023-12-25 21:11:03,358 INFO misc.py line 119 253097] Train: [83/100][230/510] Data 0.003 (0.148) Batch 1.083 (1.532) Remain 03:48:29 loss: 0.2178 Lr: 0.00049
[2023-12-25 21:11:05,263 INFO misc.py line 119 253097] Train: [83/100][231/510] Data 0.009 (0.147) Batch 1.909 (1.533) Remain 03:48:43 loss: 0.0586 Lr: 0.00049
[2023-12-25 21:11:06,305 INFO misc.py line 119 253097] Train: [83/100][232/510] Data 0.004 (0.147) Batch 1.042 (1.531) Remain 03:48:22 loss: 0.1231 Lr: 0.00049
[2023-12-25 21:11:07,435 INFO misc.py line 119 253097] Train: [83/100][233/510] Data 0.007 (0.146) Batch 1.131 (1.530) Remain 03:48:05 loss: 0.1821 Lr: 0.00049
[2023-12-25 21:11:08,678 INFO misc.py line 119 253097] Train: [83/100][234/510] Data 0.003 (0.145) Batch 1.231 (1.528) Remain 03:47:52 loss: 0.0950 Lr: 0.00049
[2023-12-25 21:11:09,621 INFO misc.py line 119 253097] Train: [83/100][235/510] Data 0.016 (0.145) Batch 0.956 (1.526) Remain 03:47:28 loss: 0.1335 Lr: 0.00049
[2023-12-25 21:11:10,787 INFO misc.py line 119 253097] Train: [83/100][236/510] Data 0.004 (0.144) Batch 1.165 (1.524) Remain 03:47:13 loss: 0.1250 Lr: 0.00049
[2023-12-25 21:11:11,808 INFO misc.py line 119 253097] Train: [83/100][237/510] Data 0.004 (0.144) Batch 1.019 (1.522) Remain 03:46:52 loss: 0.2661 Lr: 0.00049
[2023-12-25 21:11:13,020 INFO misc.py line 119 253097] Train: [83/100][238/510] Data 0.005 (0.143) Batch 1.213 (1.521) Remain 03:46:39 loss: 0.1316 Lr: 0.00049
[2023-12-25 21:11:14,194 INFO misc.py line 119 253097] Train: [83/100][239/510] Data 0.004 (0.143) Batch 1.175 (1.519) Remain 03:46:24 loss: 0.0993 Lr: 0.00049
[2023-12-25 21:11:15,440 INFO misc.py line 119 253097] Train: [83/100][240/510] Data 0.003 (0.142) Batch 1.240 (1.518) Remain 03:46:12 loss: 0.1651 Lr: 0.00049
[2023-12-25 21:11:16,522 INFO misc.py line 119 253097] Train: [83/100][241/510] Data 0.009 (0.141) Batch 1.087 (1.516) Remain 03:45:54 loss: 0.0666 Lr: 0.00049
[2023-12-25 21:11:17,512 INFO misc.py line 119 253097] Train: [83/100][242/510] Data 0.003 (0.141) Batch 0.990 (1.514) Remain 03:45:33 loss: 0.1325 Lr: 0.00049
[2023-12-25 21:11:23,856 INFO misc.py line 119 253097] Train: [83/100][243/510] Data 5.026 (0.161) Batch 6.342 (1.534) Remain 03:48:31 loss: 0.0692 Lr: 0.00049
[2023-12-25 21:11:24,918 INFO misc.py line 119 253097] Train: [83/100][244/510] Data 0.004 (0.160) Batch 1.059 (1.532) Remain 03:48:12 loss: 0.1812 Lr: 0.00049
[2023-12-25 21:11:26,185 INFO misc.py line 119 253097] Train: [83/100][245/510] Data 0.007 (0.160) Batch 1.268 (1.531) Remain 03:48:01 loss: 0.1311 Lr: 0.00049
[2023-12-25 21:11:27,341 INFO misc.py line 119 253097] Train: [83/100][246/510] Data 0.007 (0.159) Batch 1.156 (1.530) Remain 03:47:46 loss: 0.0938 Lr: 0.00049
[2023-12-25 21:11:28,401 INFO misc.py line 119 253097] Train: [83/100][247/510] Data 0.006 (0.159) Batch 1.058 (1.528) Remain 03:47:27 loss: 0.0838 Lr: 0.00049
[2023-12-25 21:11:29,633 INFO misc.py line 119 253097] Train: [83/100][248/510] Data 0.009 (0.158) Batch 1.233 (1.527) Remain 03:47:15 loss: 0.1058 Lr: 0.00049
[2023-12-25 21:11:30,733 INFO misc.py line 119 253097] Train: [83/100][249/510] Data 0.008 (0.157) Batch 1.104 (1.525) Remain 03:46:58 loss: 0.1215 Lr: 0.00049
[2023-12-25 21:11:31,794 INFO misc.py line 119 253097] Train: [83/100][250/510] Data 0.003 (0.157) Batch 1.056 (1.523) Remain 03:46:39 loss: 0.1237 Lr: 0.00049
[2023-12-25 21:11:32,905 INFO misc.py line 119 253097] Train: [83/100][251/510] Data 0.008 (0.156) Batch 1.116 (1.521) Remain 03:46:23 loss: 0.1388 Lr: 0.00049
[2023-12-25 21:11:36,925 INFO misc.py line 119 253097] Train: [83/100][252/510] Data 0.004 (0.156) Batch 4.019 (1.531) Remain 03:47:51 loss: 0.1208 Lr: 0.00049
[2023-12-25 21:11:37,924 INFO misc.py line 119 253097] Train: [83/100][253/510] Data 0.004 (0.155) Batch 0.998 (1.529) Remain 03:47:30 loss: 0.0864 Lr: 0.00049
[2023-12-25 21:11:39,049 INFO misc.py line 119 253097] Train: [83/100][254/510] Data 0.005 (0.154) Batch 1.126 (1.528) Remain 03:47:15 loss: 0.1093 Lr: 0.00049
[2023-12-25 21:11:40,161 INFO misc.py line 119 253097] Train: [83/100][255/510] Data 0.004 (0.154) Batch 1.112 (1.526) Remain 03:46:58 loss: 0.2796 Lr: 0.00049
[2023-12-25 21:11:41,221 INFO misc.py line 119 253097] Train: [83/100][256/510] Data 0.004 (0.153) Batch 1.061 (1.524) Remain 03:46:40 loss: 0.0478 Lr: 0.00049
[2023-12-25 21:11:42,234 INFO misc.py line 119 253097] Train: [83/100][257/510] Data 0.004 (0.153) Batch 1.012 (1.522) Remain 03:46:21 loss: 0.0952 Lr: 0.00049
[2023-12-25 21:11:43,315 INFO misc.py line 119 253097] Train: [83/100][258/510] Data 0.004 (0.152) Batch 1.081 (1.520) Remain 03:46:04 loss: 0.0685 Lr: 0.00049
[2023-12-25 21:11:44,484 INFO misc.py line 119 253097] Train: [83/100][259/510] Data 0.004 (0.151) Batch 1.169 (1.519) Remain 03:45:50 loss: 0.1529 Lr: 0.00049
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[2023-12-25 21:11:51,018 INFO misc.py line 119 253097] Train: [83/100][263/510] Data 0.004 (0.157) Batch 0.926 (1.521) Remain 03:46:00 loss: 0.1420 Lr: 0.00049
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[2023-12-25 21:11:58,786 INFO misc.py line 119 253097] Train: [83/100][265/510] Data 0.011 (0.156) Batch 6.694 (1.539) Remain 03:48:38 loss: 0.1004 Lr: 0.00049
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[2023-12-25 21:12:00,761 INFO misc.py line 119 253097] Train: [83/100][267/510] Data 0.003 (0.155) Batch 0.925 (1.535) Remain 03:47:57 loss: 0.0618 Lr: 0.00049
[2023-12-25 21:12:01,591 INFO misc.py line 119 253097] Train: [83/100][268/510] Data 0.003 (0.154) Batch 0.830 (1.532) Remain 03:47:32 loss: 0.1541 Lr: 0.00049
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[2023-12-25 21:12:03,909 INFO misc.py line 119 253097] Train: [83/100][270/510] Data 0.004 (0.153) Batch 1.204 (1.529) Remain 03:47:04 loss: 0.1721 Lr: 0.00049
[2023-12-25 21:12:04,959 INFO misc.py line 119 253097] Train: [83/100][271/510] Data 0.007 (0.153) Batch 1.053 (1.527) Remain 03:46:47 loss: 0.1055 Lr: 0.00049
[2023-12-25 21:12:06,072 INFO misc.py line 119 253097] Train: [83/100][272/510] Data 0.005 (0.152) Batch 1.111 (1.526) Remain 03:46:31 loss: 0.1027 Lr: 0.00049
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[2023-12-25 21:13:19,799 INFO misc.py line 119 253097] Train: [83/100][308/510] Data 0.003 (0.237) Batch 1.222 (1.587) Remain 03:54:43 loss: 0.0858 Lr: 0.00049
[2023-12-25 21:13:20,868 INFO misc.py line 119 253097] Train: [83/100][309/510] Data 0.003 (0.236) Batch 1.066 (1.586) Remain 03:54:27 loss: 0.1286 Lr: 0.00048
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[2023-12-25 21:13:26,179 INFO misc.py line 119 253097] Train: [83/100][314/510] Data 0.003 (0.232) Batch 1.029 (1.577) Remain 03:53:04 loss: 0.0968 Lr: 0.00048
[2023-12-25 21:13:27,145 INFO misc.py line 119 253097] Train: [83/100][315/510] Data 0.003 (0.231) Batch 0.966 (1.575) Remain 03:52:45 loss: 0.2244 Lr: 0.00048
[2023-12-25 21:13:28,218 INFO misc.py line 119 253097] Train: [83/100][316/510] Data 0.005 (0.231) Batch 1.073 (1.574) Remain 03:52:29 loss: 0.1792 Lr: 0.00048
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[2023-12-25 21:13:30,639 INFO misc.py line 119 253097] Train: [83/100][318/510] Data 0.003 (0.229) Batch 1.242 (1.571) Remain 03:52:06 loss: 0.1818 Lr: 0.00048
[2023-12-25 21:13:31,822 INFO misc.py line 119 253097] Train: [83/100][319/510] Data 0.008 (0.228) Batch 1.185 (1.570) Remain 03:51:53 loss: 0.1359 Lr: 0.00048
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[2023-12-25 21:13:34,111 INFO misc.py line 119 253097] Train: [83/100][321/510] Data 0.009 (0.227) Batch 1.159 (1.568) Remain 03:51:26 loss: 0.0944 Lr: 0.00048
[2023-12-25 21:13:35,268 INFO misc.py line 119 253097] Train: [83/100][322/510] Data 0.006 (0.226) Batch 1.156 (1.566) Remain 03:51:13 loss: 0.1326 Lr: 0.00048
[2023-12-25 21:13:36,235 INFO misc.py line 119 253097] Train: [83/100][323/510] Data 0.006 (0.226) Batch 0.970 (1.564) Remain 03:50:55 loss: 0.1044 Lr: 0.00048
[2023-12-25 21:13:37,516 INFO misc.py line 119 253097] Train: [83/100][324/510] Data 0.004 (0.225) Batch 1.280 (1.564) Remain 03:50:46 loss: 0.0770 Lr: 0.00048
[2023-12-25 21:13:38,705 INFO misc.py line 119 253097] Train: [83/100][325/510] Data 0.004 (0.224) Batch 1.186 (1.562) Remain 03:50:34 loss: 0.1028 Lr: 0.00048
[2023-12-25 21:13:39,905 INFO misc.py line 119 253097] Train: [83/100][326/510] Data 0.007 (0.224) Batch 1.199 (1.561) Remain 03:50:23 loss: 0.1336 Lr: 0.00048
[2023-12-25 21:13:41,187 INFO misc.py line 119 253097] Train: [83/100][327/510] Data 0.008 (0.223) Batch 1.281 (1.560) Remain 03:50:13 loss: 0.1237 Lr: 0.00048
[2023-12-25 21:13:42,412 INFO misc.py line 119 253097] Train: [83/100][328/510] Data 0.008 (0.222) Batch 1.227 (1.559) Remain 03:50:03 loss: 0.1030 Lr: 0.00048
[2023-12-25 21:13:43,596 INFO misc.py line 119 253097] Train: [83/100][329/510] Data 0.007 (0.222) Batch 1.183 (1.558) Remain 03:49:51 loss: 0.3337 Lr: 0.00048
[2023-12-25 21:13:44,566 INFO misc.py line 119 253097] Train: [83/100][330/510] Data 0.008 (0.221) Batch 0.974 (1.556) Remain 03:49:34 loss: 0.0887 Lr: 0.00048
[2023-12-25 21:13:45,554 INFO misc.py line 119 253097] Train: [83/100][331/510] Data 0.004 (0.220) Batch 0.988 (1.555) Remain 03:49:17 loss: 0.1142 Lr: 0.00048
[2023-12-25 21:13:47,143 INFO misc.py line 119 253097] Train: [83/100][332/510] Data 0.004 (0.220) Batch 1.585 (1.555) Remain 03:49:16 loss: 0.1058 Lr: 0.00048
[2023-12-25 21:13:48,297 INFO misc.py line 119 253097] Train: [83/100][333/510] Data 0.008 (0.219) Batch 1.158 (1.554) Remain 03:49:04 loss: 0.1062 Lr: 0.00048
[2023-12-25 21:13:49,315 INFO misc.py line 119 253097] Train: [83/100][334/510] Data 0.004 (0.218) Batch 1.018 (1.552) Remain 03:48:48 loss: 0.1391 Lr: 0.00048
[2023-12-25 21:13:50,569 INFO misc.py line 119 253097] Train: [83/100][335/510] Data 0.004 (0.218) Batch 1.255 (1.551) Remain 03:48:38 loss: 0.0876 Lr: 0.00048
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[2023-12-25 21:13:56,498 INFO misc.py line 119 253097] Train: [83/100][338/510] Data 2.674 (0.224) Batch 3.802 (1.555) Remain 03:49:07 loss: 0.1127 Lr: 0.00048
[2023-12-25 21:13:57,755 INFO misc.py line 119 253097] Train: [83/100][339/510] Data 0.003 (0.223) Batch 1.254 (1.554) Remain 03:48:58 loss: 0.0870 Lr: 0.00048
[2023-12-25 21:13:58,789 INFO misc.py line 119 253097] Train: [83/100][340/510] Data 0.007 (0.222) Batch 1.033 (1.552) Remain 03:48:43 loss: 0.1875 Lr: 0.00048
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[2023-12-25 21:14:01,126 INFO misc.py line 119 253097] Train: [83/100][342/510] Data 0.007 (0.221) Batch 1.212 (1.550) Remain 03:48:19 loss: 0.1651 Lr: 0.00048
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[2023-12-25 21:14:03,197 INFO misc.py line 119 253097] Train: [83/100][344/510] Data 0.003 (0.220) Batch 1.097 (1.547) Remain 03:47:50 loss: 0.1196 Lr: 0.00048
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[2023-12-25 21:15:31,831 INFO misc.py line 119 253097] Train: [83/100][416/510] Data 0.007 (0.185) Batch 1.061 (1.492) Remain 03:37:55 loss: 0.1129 Lr: 0.00047
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[2023-12-25 21:15:41,966 INFO misc.py line 119 253097] Train: [83/100][424/510] Data 0.004 (0.181) Batch 2.323 (1.488) Remain 03:37:06 loss: 0.1147 Lr: 0.00047
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[2023-12-25 21:15:51,219 INFO misc.py line 119 253097] Train: [83/100][432/510] Data 0.003 (0.178) Batch 0.957 (1.482) Remain 03:36:00 loss: 0.0812 Lr: 0.00047
[2023-12-25 21:15:52,239 INFO misc.py line 119 253097] Train: [83/100][433/510] Data 0.003 (0.178) Batch 1.020 (1.480) Remain 03:35:49 loss: 0.0823 Lr: 0.00047
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[2023-12-25 21:15:54,429 INFO misc.py line 119 253097] Train: [83/100][435/510] Data 0.003 (0.177) Batch 1.050 (1.479) Remain 03:35:31 loss: 0.0775 Lr: 0.00047
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[2023-12-25 21:17:05,502 INFO misc.py line 119 253097] Train: [83/100][482/510] Data 0.007 (0.173) Batch 1.155 (1.482) Remain 03:34:50 loss: 0.0879 Lr: 0.00047
[2023-12-25 21:17:06,484 INFO misc.py line 119 253097] Train: [83/100][483/510] Data 0.003 (0.172) Batch 0.981 (1.481) Remain 03:34:39 loss: 0.1444 Lr: 0.00047
[2023-12-25 21:17:07,602 INFO misc.py line 119 253097] Train: [83/100][484/510] Data 0.004 (0.172) Batch 1.119 (1.480) Remain 03:34:31 loss: 0.0862 Lr: 0.00047
[2023-12-25 21:17:08,712 INFO misc.py line 119 253097] Train: [83/100][485/510] Data 0.003 (0.172) Batch 1.110 (1.479) Remain 03:34:23 loss: 0.1140 Lr: 0.00047
[2023-12-25 21:17:09,959 INFO misc.py line 119 253097] Train: [83/100][486/510] Data 0.004 (0.171) Batch 1.247 (1.479) Remain 03:34:17 loss: 0.1257 Lr: 0.00047
[2023-12-25 21:17:11,004 INFO misc.py line 119 253097] Train: [83/100][487/510] Data 0.003 (0.171) Batch 1.040 (1.478) Remain 03:34:08 loss: 0.1745 Lr: 0.00047
[2023-12-25 21:17:12,334 INFO misc.py line 119 253097] Train: [83/100][488/510] Data 0.009 (0.170) Batch 1.335 (1.478) Remain 03:34:04 loss: 0.0948 Lr: 0.00047
[2023-12-25 21:17:13,483 INFO misc.py line 119 253097] Train: [83/100][489/510] Data 0.003 (0.170) Batch 1.145 (1.477) Remain 03:33:57 loss: 0.1940 Lr: 0.00047
[2023-12-25 21:17:14,779 INFO misc.py line 119 253097] Train: [83/100][490/510] Data 0.008 (0.170) Batch 1.295 (1.477) Remain 03:33:52 loss: 0.0878 Lr: 0.00047
[2023-12-25 21:17:15,818 INFO misc.py line 119 253097] Train: [83/100][491/510] Data 0.010 (0.169) Batch 1.045 (1.476) Remain 03:33:43 loss: 0.1908 Lr: 0.00047
[2023-12-25 21:17:16,984 INFO misc.py line 119 253097] Train: [83/100][492/510] Data 0.003 (0.169) Batch 1.161 (1.475) Remain 03:33:36 loss: 0.0717 Lr: 0.00047
[2023-12-25 21:17:18,269 INFO misc.py line 119 253097] Train: [83/100][493/510] Data 0.007 (0.169) Batch 1.289 (1.475) Remain 03:33:31 loss: 0.1550 Lr: 0.00047
[2023-12-25 21:17:20,044 INFO misc.py line 119 253097] Train: [83/100][494/510] Data 0.003 (0.168) Batch 1.774 (1.475) Remain 03:33:35 loss: 0.0741 Lr: 0.00047
[2023-12-25 21:17:21,255 INFO misc.py line 119 253097] Train: [83/100][495/510] Data 0.005 (0.168) Batch 1.211 (1.475) Remain 03:33:29 loss: 0.1463 Lr: 0.00047
[2023-12-25 21:17:22,420 INFO misc.py line 119 253097] Train: [83/100][496/510] Data 0.003 (0.168) Batch 1.165 (1.474) Remain 03:33:22 loss: 0.0730 Lr: 0.00047
[2023-12-25 21:17:23,687 INFO misc.py line 119 253097] Train: [83/100][497/510] Data 0.004 (0.167) Batch 1.266 (1.474) Remain 03:33:17 loss: 0.0875 Lr: 0.00047
[2023-12-25 21:17:24,809 INFO misc.py line 119 253097] Train: [83/100][498/510] Data 0.004 (0.167) Batch 1.118 (1.473) Remain 03:33:09 loss: 0.0789 Lr: 0.00047
[2023-12-25 21:17:25,995 INFO misc.py line 119 253097] Train: [83/100][499/510] Data 0.008 (0.167) Batch 1.190 (1.473) Remain 03:33:02 loss: 0.0894 Lr: 0.00046
[2023-12-25 21:17:28,215 INFO misc.py line 119 253097] Train: [83/100][500/510] Data 0.003 (0.166) Batch 2.220 (1.474) Remain 03:33:14 loss: 0.0809 Lr: 0.00046
[2023-12-25 21:17:29,296 INFO misc.py line 119 253097] Train: [83/100][501/510] Data 0.003 (0.166) Batch 1.081 (1.473) Remain 03:33:06 loss: 0.1949 Lr: 0.00046
[2023-12-25 21:17:30,245 INFO misc.py line 119 253097] Train: [83/100][502/510] Data 0.003 (0.166) Batch 0.949 (1.472) Remain 03:32:55 loss: 0.2084 Lr: 0.00046
[2023-12-25 21:17:31,273 INFO misc.py line 119 253097] Train: [83/100][503/510] Data 0.003 (0.166) Batch 1.024 (1.471) Remain 03:32:46 loss: 0.1176 Lr: 0.00046
[2023-12-25 21:17:34,578 INFO misc.py line 119 253097] Train: [83/100][504/510] Data 1.998 (0.169) Batch 3.310 (1.475) Remain 03:33:16 loss: 0.0468 Lr: 0.00046
[2023-12-25 21:17:35,596 INFO misc.py line 119 253097] Train: [83/100][505/510] Data 0.003 (0.169) Batch 1.017 (1.474) Remain 03:33:07 loss: 0.1350 Lr: 0.00046
[2023-12-25 21:17:37,778 INFO misc.py line 119 253097] Train: [83/100][506/510] Data 0.003 (0.169) Batch 2.183 (1.475) Remain 03:33:18 loss: 0.2118 Lr: 0.00046
[2023-12-25 21:17:38,884 INFO misc.py line 119 253097] Train: [83/100][507/510] Data 0.003 (0.168) Batch 1.105 (1.475) Remain 03:33:10 loss: 0.1045 Lr: 0.00046
[2023-12-25 21:17:40,140 INFO misc.py line 119 253097] Train: [83/100][508/510] Data 0.003 (0.168) Batch 1.256 (1.474) Remain 03:33:04 loss: 0.1204 Lr: 0.00046
[2023-12-25 21:17:41,259 INFO misc.py line 119 253097] Train: [83/100][509/510] Data 0.003 (0.168) Batch 1.120 (1.474) Remain 03:32:57 loss: 0.0921 Lr: 0.00046
[2023-12-25 21:17:42,229 INFO misc.py line 119 253097] Train: [83/100][510/510] Data 0.003 (0.167) Batch 0.969 (1.473) Remain 03:32:47 loss: 0.1130 Lr: 0.00046
[2023-12-25 21:17:42,229 INFO misc.py line 136 253097] Train result: loss: 0.1200 
[2023-12-25 21:17:42,230 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 21:18:10,709 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5367 
[2023-12-25 21:18:11,064 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3060 
[2023-12-25 21:18:16,017 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3843 
[2023-12-25 21:18:16,533 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4466 
[2023-12-25 21:18:18,508 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8352 
[2023-12-25 21:18:18,930 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3918 
[2023-12-25 21:18:19,811 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1128 
[2023-12-25 21:18:20,365 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.3342 
[2023-12-25 21:18:22,177 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6580 
[2023-12-25 21:18:24,297 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1245 
[2023-12-25 21:18:25,155 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2856 
[2023-12-25 21:18:25,576 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7283 
[2023-12-25 21:18:26,475 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6408 
[2023-12-25 21:18:29,419 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8242 
[2023-12-25 21:18:29,884 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3879 
[2023-12-25 21:18:30,495 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3518 
[2023-12-25 21:18:31,197 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3926 
[2023-12-25 21:18:32,535 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6956/0.7543/0.9053.
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9159/0.9467
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9818/0.9894
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8462/0.9715
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3371/0.3677
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6461/0.6802
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7273/0.8349
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8060/0.8955
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9117/0.9601
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6693/0.7323
[2023-12-25 21:18:32,535 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7827/0.8670
[2023-12-25 21:18:32,536 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8163/0.8464
[2023-12-25 21:18:32,536 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6025/0.7147
[2023-12-25 21:18:32,536 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 21:18:32,537 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 21:18:32,537 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 21:18:40,492 INFO misc.py line 119 253097] Train: [84/100][1/510] Data 4.097 (4.097) Batch 5.332 (5.332) Remain 12:50:24 loss: 0.1249 Lr: 0.00046
[2023-12-25 21:18:52,866 INFO misc.py line 119 253097] Train: [84/100][2/510] Data 0.003 (0.003) Batch 12.372 (12.372) Remain 29:47:21 loss: 0.2098 Lr: 0.00046
[2023-12-25 21:18:54,067 INFO misc.py line 119 253097] Train: [84/100][3/510] Data 0.005 (0.005) Batch 1.203 (1.203) Remain 02:53:46 loss: 0.1336 Lr: 0.00046
[2023-12-25 21:18:55,187 INFO misc.py line 119 253097] Train: [84/100][4/510] Data 0.003 (0.003) Batch 1.119 (1.119) Remain 02:41:34 loss: 0.0991 Lr: 0.00046
[2023-12-25 21:18:56,145 INFO misc.py line 119 253097] Train: [84/100][5/510] Data 0.004 (0.004) Batch 0.959 (1.039) Remain 02:30:02 loss: 0.1550 Lr: 0.00046
[2023-12-25 21:18:57,185 INFO misc.py line 119 253097] Train: [84/100][6/510] Data 0.004 (0.004) Batch 1.040 (1.039) Remain 02:30:03 loss: 0.1059 Lr: 0.00046
[2023-12-25 21:18:58,434 INFO misc.py line 119 253097] Train: [84/100][7/510] Data 0.004 (0.004) Batch 1.249 (1.092) Remain 02:37:37 loss: 0.1459 Lr: 0.00046
[2023-12-25 21:18:59,385 INFO misc.py line 119 253097] Train: [84/100][8/510] Data 0.004 (0.004) Batch 0.951 (1.064) Remain 02:33:33 loss: 0.0788 Lr: 0.00046
[2023-12-25 21:19:00,380 INFO misc.py line 119 253097] Train: [84/100][9/510] Data 0.004 (0.004) Batch 0.995 (1.052) Remain 02:31:53 loss: 0.0742 Lr: 0.00046
[2023-12-25 21:19:01,468 INFO misc.py line 119 253097] Train: [84/100][10/510] Data 0.003 (0.004) Batch 1.088 (1.057) Remain 02:32:36 loss: 0.2075 Lr: 0.00046
[2023-12-25 21:19:02,544 INFO misc.py line 119 253097] Train: [84/100][11/510] Data 0.003 (0.003) Batch 1.076 (1.060) Remain 02:32:55 loss: 0.1848 Lr: 0.00046
[2023-12-25 21:19:03,740 INFO misc.py line 119 253097] Train: [84/100][12/510] Data 0.003 (0.003) Batch 1.196 (1.075) Remain 02:35:05 loss: 0.0592 Lr: 0.00046
[2023-12-25 21:19:05,006 INFO misc.py line 119 253097] Train: [84/100][13/510] Data 0.003 (0.003) Batch 1.265 (1.094) Remain 02:37:49 loss: 0.1099 Lr: 0.00046
[2023-12-25 21:19:06,167 INFO misc.py line 119 253097] Train: [84/100][14/510] Data 0.005 (0.004) Batch 1.162 (1.100) Remain 02:38:41 loss: 0.1198 Lr: 0.00046
[2023-12-25 21:19:07,407 INFO misc.py line 119 253097] Train: [84/100][15/510] Data 0.003 (0.004) Batch 1.234 (1.111) Remain 02:40:17 loss: 0.0847 Lr: 0.00046
[2023-12-25 21:19:08,497 INFO misc.py line 119 253097] Train: [84/100][16/510] Data 0.009 (0.004) Batch 1.093 (1.110) Remain 02:40:03 loss: 0.0478 Lr: 0.00046
[2023-12-25 21:19:09,593 INFO misc.py line 119 253097] Train: [84/100][17/510] Data 0.006 (0.004) Batch 1.095 (1.109) Remain 02:39:54 loss: 0.1870 Lr: 0.00046
[2023-12-25 21:19:10,851 INFO misc.py line 119 253097] Train: [84/100][18/510] Data 0.007 (0.004) Batch 1.261 (1.119) Remain 02:41:20 loss: 0.1972 Lr: 0.00046
[2023-12-25 21:19:12,072 INFO misc.py line 119 253097] Train: [84/100][19/510] Data 0.004 (0.004) Batch 1.222 (1.125) Remain 02:42:15 loss: 0.1067 Lr: 0.00046
[2023-12-25 21:19:13,279 INFO misc.py line 119 253097] Train: [84/100][20/510] Data 0.003 (0.004) Batch 1.207 (1.130) Remain 02:42:55 loss: 0.0961 Lr: 0.00046
[2023-12-25 21:19:14,411 INFO misc.py line 119 253097] Train: [84/100][21/510] Data 0.003 (0.004) Batch 1.127 (1.130) Remain 02:42:52 loss: 0.1294 Lr: 0.00046
[2023-12-25 21:19:15,660 INFO misc.py line 119 253097] Train: [84/100][22/510] Data 0.009 (0.004) Batch 1.250 (1.136) Remain 02:43:45 loss: 0.1658 Lr: 0.00046
[2023-12-25 21:19:16,774 INFO misc.py line 119 253097] Train: [84/100][23/510] Data 0.253 (0.017) Batch 1.119 (1.135) Remain 02:43:37 loss: 0.1591 Lr: 0.00046
[2023-12-25 21:19:18,530 INFO misc.py line 119 253097] Train: [84/100][24/510] Data 0.524 (0.041) Batch 1.755 (1.165) Remain 02:47:51 loss: 0.1204 Lr: 0.00046
[2023-12-25 21:19:19,747 INFO misc.py line 119 253097] Train: [84/100][25/510] Data 0.005 (0.039) Batch 1.215 (1.167) Remain 02:48:09 loss: 0.0567 Lr: 0.00046
[2023-12-25 21:19:20,876 INFO misc.py line 119 253097] Train: [84/100][26/510] Data 0.006 (0.038) Batch 1.131 (1.166) Remain 02:47:55 loss: 0.1334 Lr: 0.00046
[2023-12-25 21:19:21,861 INFO misc.py line 119 253097] Train: [84/100][27/510] Data 0.004 (0.036) Batch 0.986 (1.158) Remain 02:46:49 loss: 0.0800 Lr: 0.00046
[2023-12-25 21:19:22,958 INFO misc.py line 119 253097] Train: [84/100][28/510] Data 0.003 (0.035) Batch 1.096 (1.156) Remain 02:46:26 loss: 0.0936 Lr: 0.00046
[2023-12-25 21:19:35,768 INFO misc.py line 119 253097] Train: [84/100][29/510] Data 11.743 (0.485) Batch 12.811 (1.604) Remain 03:50:59 loss: 0.0895 Lr: 0.00046
[2023-12-25 21:19:36,899 INFO misc.py line 119 253097] Train: [84/100][30/510] Data 0.004 (0.468) Batch 1.131 (1.586) Remain 03:48:26 loss: 0.1230 Lr: 0.00046
[2023-12-25 21:19:38,048 INFO misc.py line 119 253097] Train: [84/100][31/510] Data 0.003 (0.451) Batch 1.149 (1.571) Remain 03:46:09 loss: 0.1349 Lr: 0.00046
[2023-12-25 21:19:39,187 INFO misc.py line 119 253097] Train: [84/100][32/510] Data 0.003 (0.436) Batch 1.140 (1.556) Remain 03:43:59 loss: 0.0724 Lr: 0.00046
[2023-12-25 21:19:40,371 INFO misc.py line 119 253097] Train: [84/100][33/510] Data 0.003 (0.421) Batch 1.184 (1.543) Remain 03:42:10 loss: 0.0657 Lr: 0.00046
[2023-12-25 21:19:41,571 INFO misc.py line 119 253097] Train: [84/100][34/510] Data 0.003 (0.408) Batch 1.200 (1.532) Remain 03:40:33 loss: 0.1608 Lr: 0.00046
[2023-12-25 21:19:42,745 INFO misc.py line 119 253097] Train: [84/100][35/510] Data 0.003 (0.395) Batch 1.173 (1.521) Remain 03:38:55 loss: 0.1857 Lr: 0.00046
[2023-12-25 21:19:43,814 INFO misc.py line 119 253097] Train: [84/100][36/510] Data 0.003 (0.383) Batch 1.069 (1.507) Remain 03:36:55 loss: 0.0917 Lr: 0.00046
[2023-12-25 21:19:44,955 INFO misc.py line 119 253097] Train: [84/100][37/510] Data 0.003 (0.372) Batch 1.140 (1.497) Remain 03:35:20 loss: 0.1007 Lr: 0.00046
[2023-12-25 21:19:46,224 INFO misc.py line 119 253097] Train: [84/100][38/510] Data 0.004 (0.361) Batch 1.270 (1.490) Remain 03:34:23 loss: 0.0791 Lr: 0.00046
[2023-12-25 21:19:47,463 INFO misc.py line 119 253097] Train: [84/100][39/510] Data 0.003 (0.351) Batch 1.238 (1.483) Remain 03:33:21 loss: 0.0692 Lr: 0.00046
[2023-12-25 21:19:48,706 INFO misc.py line 119 253097] Train: [84/100][40/510] Data 0.004 (0.342) Batch 1.241 (1.477) Remain 03:32:23 loss: 0.1932 Lr: 0.00046
[2023-12-25 21:19:49,891 INFO misc.py line 119 253097] Train: [84/100][41/510] Data 0.006 (0.333) Batch 1.182 (1.469) Remain 03:31:15 loss: 0.2447 Lr: 0.00046
[2023-12-25 21:19:50,948 INFO misc.py line 119 253097] Train: [84/100][42/510] Data 0.008 (0.325) Batch 1.057 (1.458) Remain 03:29:42 loss: 0.0754 Lr: 0.00046
[2023-12-25 21:19:52,166 INFO misc.py line 119 253097] Train: [84/100][43/510] Data 0.009 (0.317) Batch 1.219 (1.452) Remain 03:28:49 loss: 0.1081 Lr: 0.00046
[2023-12-25 21:19:53,361 INFO misc.py line 119 253097] Train: [84/100][44/510] Data 0.008 (0.309) Batch 1.193 (1.446) Remain 03:27:53 loss: 0.1054 Lr: 0.00046
[2023-12-25 21:20:06,766 INFO misc.py line 119 253097] Train: [84/100][45/510] Data 0.009 (0.302) Batch 13.406 (1.731) Remain 04:08:48 loss: 0.1995 Lr: 0.00046
[2023-12-25 21:20:08,019 INFO misc.py line 119 253097] Train: [84/100][46/510] Data 0.008 (0.295) Batch 1.258 (1.720) Remain 04:07:11 loss: 0.0833 Lr: 0.00046
[2023-12-25 21:20:08,832 INFO misc.py line 119 253097] Train: [84/100][47/510] Data 0.003 (0.289) Batch 0.813 (1.699) Remain 04:04:12 loss: 0.1423 Lr: 0.00046
[2023-12-25 21:20:09,846 INFO misc.py line 119 253097] Train: [84/100][48/510] Data 0.003 (0.282) Batch 1.014 (1.684) Remain 04:01:59 loss: 0.0769 Lr: 0.00046
[2023-12-25 21:20:10,830 INFO misc.py line 119 253097] Train: [84/100][49/510] Data 0.004 (0.276) Batch 0.984 (1.669) Remain 03:59:46 loss: 0.1293 Lr: 0.00046
[2023-12-25 21:20:12,074 INFO misc.py line 119 253097] Train: [84/100][50/510] Data 0.003 (0.271) Batch 1.240 (1.660) Remain 03:58:26 loss: 0.1068 Lr: 0.00046
[2023-12-25 21:20:13,108 INFO misc.py line 119 253097] Train: [84/100][51/510] Data 0.008 (0.265) Batch 1.032 (1.647) Remain 03:56:31 loss: 0.0694 Lr: 0.00046
[2023-12-25 21:20:14,154 INFO misc.py line 119 253097] Train: [84/100][52/510] Data 0.009 (0.260) Batch 1.048 (1.634) Remain 03:54:44 loss: 0.1456 Lr: 0.00046
[2023-12-25 21:20:15,115 INFO misc.py line 119 253097] Train: [84/100][53/510] Data 0.007 (0.255) Batch 0.964 (1.621) Remain 03:52:47 loss: 0.1068 Lr: 0.00046
[2023-12-25 21:20:16,126 INFO misc.py line 119 253097] Train: [84/100][54/510] Data 0.003 (0.250) Batch 1.011 (1.609) Remain 03:51:03 loss: 0.0691 Lr: 0.00046
[2023-12-25 21:20:17,109 INFO misc.py line 119 253097] Train: [84/100][55/510] Data 0.003 (0.245) Batch 0.983 (1.597) Remain 03:49:17 loss: 0.0966 Lr: 0.00046
[2023-12-25 21:20:18,430 INFO misc.py line 119 253097] Train: [84/100][56/510] Data 0.003 (0.241) Batch 1.315 (1.592) Remain 03:48:30 loss: 0.1021 Lr: 0.00046
[2023-12-25 21:20:29,097 INFO misc.py line 119 253097] Train: [84/100][57/510] Data 0.010 (0.236) Batch 10.673 (1.760) Remain 04:12:37 loss: 0.0932 Lr: 0.00046
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[2023-12-25 21:21:05,773 INFO misc.py line 119 253097] Train: [84/100][90/510] Data 0.007 (0.149) Batch 1.222 (1.514) Remain 03:36:28 loss: 0.1649 Lr: 0.00045
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[2023-12-25 21:21:26,193 INFO misc.py line 119 253097] Train: [84/100][101/510] Data 8.154 (0.216) Batch 9.251 (1.552) Remain 03:41:41 loss: 0.1365 Lr: 0.00045
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[2023-12-25 21:21:34,360 INFO misc.py line 119 253097] Train: [84/100][108/510] Data 0.006 (0.202) Batch 1.094 (1.527) Remain 03:37:50 loss: 0.1214 Lr: 0.00045
[2023-12-25 21:21:35,361 INFO misc.py line 119 253097] Train: [84/100][109/510] Data 0.003 (0.200) Batch 0.999 (1.522) Remain 03:37:06 loss: 0.0617 Lr: 0.00045
[2023-12-25 21:21:36,507 INFO misc.py line 119 253097] Train: [84/100][110/510] Data 0.005 (0.198) Batch 1.142 (1.518) Remain 03:36:34 loss: 0.1741 Lr: 0.00045
[2023-12-25 21:21:37,429 INFO misc.py line 119 253097] Train: [84/100][111/510] Data 0.009 (0.196) Batch 0.927 (1.513) Remain 03:35:46 loss: 0.0875 Lr: 0.00045
[2023-12-25 21:21:38,527 INFO misc.py line 119 253097] Train: [84/100][112/510] Data 0.003 (0.195) Batch 1.097 (1.509) Remain 03:35:12 loss: 0.0764 Lr: 0.00045
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[2023-12-25 21:21:40,444 INFO misc.py line 119 253097] Train: [84/100][114/510] Data 0.004 (0.191) Batch 1.003 (1.499) Remain 03:33:44 loss: 0.0679 Lr: 0.00045
[2023-12-25 21:21:50,693 INFO misc.py line 119 253097] Train: [84/100][115/510] Data 8.990 (0.270) Batch 10.249 (1.577) Remain 03:44:51 loss: 0.0668 Lr: 0.00045
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[2023-12-25 21:21:55,316 INFO misc.py line 119 253097] Train: [84/100][119/510] Data 0.003 (0.261) Batch 1.180 (1.562) Remain 03:42:40 loss: 0.1006 Lr: 0.00045
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[2023-12-25 21:23:35,734 INFO misc.py line 119 253097] Train: [84/100][181/510] Data 0.008 (0.172) Batch 1.182 (1.582) Remain 03:43:52 loss: 0.1039 Lr: 0.00045
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[2023-12-25 21:23:37,906 INFO misc.py line 119 253097] Train: [84/100][183/510] Data 0.003 (0.170) Batch 1.185 (1.577) Remain 03:43:02 loss: 0.1250 Lr: 0.00045
[2023-12-25 21:23:39,068 INFO misc.py line 119 253097] Train: [84/100][184/510] Data 0.008 (0.169) Batch 1.160 (1.575) Remain 03:42:41 loss: 0.1085 Lr: 0.00044
[2023-12-25 21:23:40,232 INFO misc.py line 119 253097] Train: [84/100][185/510] Data 0.008 (0.168) Batch 1.165 (1.572) Remain 03:42:21 loss: 0.1042 Lr: 0.00044
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[2023-12-25 21:23:54,939 INFO misc.py line 119 253097] Train: [84/100][190/510] Data 0.007 (0.164) Batch 10.120 (1.609) Remain 03:47:23 loss: 0.1512 Lr: 0.00044
[2023-12-25 21:23:55,713 INFO misc.py line 119 253097] Train: [84/100][191/510] Data 0.003 (0.163) Batch 0.774 (1.604) Remain 03:46:44 loss: 0.0632 Lr: 0.00044
[2023-12-25 21:23:56,891 INFO misc.py line 119 253097] Train: [84/100][192/510] Data 0.004 (0.162) Batch 1.178 (1.602) Remain 03:46:23 loss: 0.1082 Lr: 0.00044
[2023-12-25 21:23:57,971 INFO misc.py line 119 253097] Train: [84/100][193/510] Data 0.003 (0.161) Batch 1.080 (1.599) Remain 03:45:58 loss: 0.0999 Lr: 0.00044
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[2023-12-25 21:24:00,303 INFO misc.py line 119 253097] Train: [84/100][195/510] Data 0.003 (0.159) Batch 1.243 (1.595) Remain 03:45:17 loss: 0.0988 Lr: 0.00044
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[2023-12-25 21:24:03,850 INFO misc.py line 119 253097] Train: [84/100][198/510] Data 0.008 (0.157) Batch 1.236 (1.589) Remain 03:44:18 loss: 0.0865 Lr: 0.00044
[2023-12-25 21:24:05,120 INFO misc.py line 119 253097] Train: [84/100][199/510] Data 0.004 (0.156) Batch 1.266 (1.587) Remain 03:44:03 loss: 0.0922 Lr: 0.00044
[2023-12-25 21:24:06,151 INFO misc.py line 119 253097] Train: [84/100][200/510] Data 0.007 (0.156) Batch 1.034 (1.584) Remain 03:43:37 loss: 0.0742 Lr: 0.00044
[2023-12-25 21:24:07,389 INFO misc.py line 119 253097] Train: [84/100][201/510] Data 0.004 (0.155) Batch 1.238 (1.582) Remain 03:43:21 loss: 0.1233 Lr: 0.00044
[2023-12-25 21:24:08,627 INFO misc.py line 119 253097] Train: [84/100][202/510] Data 0.004 (0.154) Batch 1.239 (1.581) Remain 03:43:05 loss: 0.1744 Lr: 0.00044
[2023-12-25 21:24:09,868 INFO misc.py line 119 253097] Train: [84/100][203/510] Data 0.003 (0.153) Batch 1.238 (1.579) Remain 03:42:49 loss: 0.0966 Lr: 0.00044
[2023-12-25 21:24:11,066 INFO misc.py line 119 253097] Train: [84/100][204/510] Data 0.006 (0.153) Batch 1.198 (1.577) Remain 03:42:31 loss: 0.0903 Lr: 0.00044
[2023-12-25 21:24:12,280 INFO misc.py line 119 253097] Train: [84/100][205/510] Data 0.006 (0.152) Batch 1.216 (1.575) Remain 03:42:14 loss: 0.1034 Lr: 0.00044
[2023-12-25 21:24:13,488 INFO misc.py line 119 253097] Train: [84/100][206/510] Data 0.004 (0.151) Batch 1.202 (1.573) Remain 03:41:57 loss: 0.0734 Lr: 0.00044
[2023-12-25 21:24:14,507 INFO misc.py line 119 253097] Train: [84/100][207/510] Data 0.009 (0.150) Batch 1.024 (1.571) Remain 03:41:33 loss: 0.1155 Lr: 0.00044
[2023-12-25 21:24:15,862 INFO misc.py line 119 253097] Train: [84/100][208/510] Data 0.004 (0.150) Batch 1.355 (1.570) Remain 03:41:22 loss: 0.0812 Lr: 0.00044
[2023-12-25 21:24:16,905 INFO misc.py line 119 253097] Train: [84/100][209/510] Data 0.004 (0.149) Batch 1.041 (1.567) Remain 03:40:59 loss: 0.2034 Lr: 0.00044
[2023-12-25 21:24:18,010 INFO misc.py line 119 253097] Train: [84/100][210/510] Data 0.007 (0.148) Batch 1.109 (1.565) Remain 03:40:39 loss: 0.0826 Lr: 0.00044
[2023-12-25 21:24:19,120 INFO misc.py line 119 253097] Train: [84/100][211/510] Data 0.004 (0.148) Batch 1.104 (1.563) Remain 03:40:19 loss: 0.0804 Lr: 0.00044
[2023-12-25 21:24:20,296 INFO misc.py line 119 253097] Train: [84/100][212/510] Data 0.009 (0.147) Batch 1.177 (1.561) Remain 03:40:01 loss: 0.0958 Lr: 0.00044
[2023-12-25 21:24:21,300 INFO misc.py line 119 253097] Train: [84/100][213/510] Data 0.008 (0.146) Batch 1.009 (1.558) Remain 03:39:38 loss: 0.1001 Lr: 0.00044
[2023-12-25 21:24:22,588 INFO misc.py line 119 253097] Train: [84/100][214/510] Data 0.003 (0.146) Batch 1.288 (1.557) Remain 03:39:25 loss: 0.1171 Lr: 0.00044
[2023-12-25 21:24:23,773 INFO misc.py line 119 253097] Train: [84/100][215/510] Data 0.003 (0.145) Batch 1.181 (1.555) Remain 03:39:09 loss: 0.0851 Lr: 0.00044
[2023-12-25 21:24:24,848 INFO misc.py line 119 253097] Train: [84/100][216/510] Data 0.008 (0.144) Batch 1.077 (1.553) Remain 03:38:48 loss: 0.0871 Lr: 0.00044
[2023-12-25 21:24:26,020 INFO misc.py line 119 253097] Train: [84/100][217/510] Data 0.006 (0.144) Batch 1.171 (1.551) Remain 03:38:31 loss: 0.1138 Lr: 0.00044
[2023-12-25 21:24:27,100 INFO misc.py line 119 253097] Train: [84/100][218/510] Data 0.007 (0.143) Batch 1.079 (1.549) Remain 03:38:11 loss: 0.0987 Lr: 0.00044
[2023-12-25 21:24:28,154 INFO misc.py line 119 253097] Train: [84/100][219/510] Data 0.008 (0.142) Batch 1.058 (1.547) Remain 03:37:51 loss: 0.1315 Lr: 0.00044
[2023-12-25 21:24:31,831 INFO misc.py line 119 253097] Train: [84/100][220/510] Data 0.003 (0.142) Batch 3.678 (1.557) Remain 03:39:12 loss: 0.1123 Lr: 0.00044
[2023-12-25 21:24:33,009 INFO misc.py line 119 253097] Train: [84/100][221/510] Data 0.003 (0.141) Batch 1.178 (1.555) Remain 03:38:56 loss: 0.1068 Lr: 0.00044
[2023-12-25 21:24:34,245 INFO misc.py line 119 253097] Train: [84/100][222/510] Data 0.003 (0.140) Batch 1.232 (1.553) Remain 03:38:42 loss: 0.1882 Lr: 0.00044
[2023-12-25 21:24:35,278 INFO misc.py line 119 253097] Train: [84/100][223/510] Data 0.007 (0.140) Batch 1.037 (1.551) Remain 03:38:20 loss: 0.2038 Lr: 0.00044
[2023-12-25 21:24:36,272 INFO misc.py line 119 253097] Train: [84/100][224/510] Data 0.003 (0.139) Batch 0.994 (1.548) Remain 03:37:58 loss: 0.1283 Lr: 0.00044
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[2023-12-25 21:24:39,600 INFO misc.py line 119 253097] Train: [84/100][226/510] Data 0.003 (0.138) Batch 2.075 (1.549) Remain 03:38:03 loss: 0.1040 Lr: 0.00044
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[2023-12-25 21:24:41,798 INFO misc.py line 119 253097] Train: [84/100][228/510] Data 0.003 (0.137) Batch 1.084 (1.545) Remain 03:37:26 loss: 0.1426 Lr: 0.00044
[2023-12-25 21:24:43,043 INFO misc.py line 119 253097] Train: [84/100][229/510] Data 0.005 (0.136) Batch 1.243 (1.544) Remain 03:37:13 loss: 0.0791 Lr: 0.00044
[2023-12-25 21:24:44,051 INFO misc.py line 119 253097] Train: [84/100][230/510] Data 0.008 (0.136) Batch 1.008 (1.542) Remain 03:36:52 loss: 0.1108 Lr: 0.00044
[2023-12-25 21:24:45,118 INFO misc.py line 119 253097] Train: [84/100][231/510] Data 0.007 (0.135) Batch 1.071 (1.540) Remain 03:36:33 loss: 0.1383 Lr: 0.00044
[2023-12-25 21:24:46,135 INFO misc.py line 119 253097] Train: [84/100][232/510] Data 0.003 (0.135) Batch 1.011 (1.537) Remain 03:36:12 loss: 0.1207 Lr: 0.00044
[2023-12-25 21:24:47,430 INFO misc.py line 119 253097] Train: [84/100][233/510] Data 0.008 (0.134) Batch 1.296 (1.536) Remain 03:36:02 loss: 0.1028 Lr: 0.00044
[2023-12-25 21:24:48,461 INFO misc.py line 119 253097] Train: [84/100][234/510] Data 0.008 (0.133) Batch 1.035 (1.534) Remain 03:35:42 loss: 0.0930 Lr: 0.00044
[2023-12-25 21:24:49,648 INFO misc.py line 119 253097] Train: [84/100][235/510] Data 0.003 (0.133) Batch 1.182 (1.533) Remain 03:35:27 loss: 0.0925 Lr: 0.00044
[2023-12-25 21:24:50,712 INFO misc.py line 119 253097] Train: [84/100][236/510] Data 0.009 (0.132) Batch 1.063 (1.531) Remain 03:35:09 loss: 0.0660 Lr: 0.00044
[2023-12-25 21:24:51,930 INFO misc.py line 119 253097] Train: [84/100][237/510] Data 0.010 (0.132) Batch 1.220 (1.529) Remain 03:34:56 loss: 0.1268 Lr: 0.00044
[2023-12-25 21:24:52,833 INFO misc.py line 119 253097] Train: [84/100][238/510] Data 0.008 (0.131) Batch 0.907 (1.527) Remain 03:34:32 loss: 0.0883 Lr: 0.00044
[2023-12-25 21:24:55,348 INFO misc.py line 119 253097] Train: [84/100][239/510] Data 1.315 (0.136) Batch 2.516 (1.531) Remain 03:35:06 loss: 0.1251 Lr: 0.00044
[2023-12-25 21:24:56,418 INFO misc.py line 119 253097] Train: [84/100][240/510] Data 0.003 (0.136) Batch 1.070 (1.529) Remain 03:34:48 loss: 0.0735 Lr: 0.00044
[2023-12-25 21:24:57,388 INFO misc.py line 119 253097] Train: [84/100][241/510] Data 0.003 (0.135) Batch 0.971 (1.527) Remain 03:34:27 loss: 0.0825 Lr: 0.00044
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[2023-12-25 21:24:59,546 INFO misc.py line 119 253097] Train: [84/100][243/510] Data 0.003 (0.134) Batch 0.994 (1.523) Remain 03:33:52 loss: 0.0675 Lr: 0.00044
[2023-12-25 21:25:00,709 INFO misc.py line 119 253097] Train: [84/100][244/510] Data 0.003 (0.134) Batch 1.163 (1.521) Remain 03:33:38 loss: 0.0936 Lr: 0.00044
[2023-12-25 21:25:01,871 INFO misc.py line 119 253097] Train: [84/100][245/510] Data 0.003 (0.133) Batch 1.161 (1.520) Remain 03:33:24 loss: 0.1082 Lr: 0.00044
[2023-12-25 21:25:03,943 INFO misc.py line 119 253097] Train: [84/100][246/510] Data 0.004 (0.132) Batch 2.072 (1.522) Remain 03:33:42 loss: 0.0860 Lr: 0.00044
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[2023-12-25 21:25:25,364 INFO misc.py line 119 253097] Train: [84/100][264/510] Data 0.005 (0.131) Batch 1.064 (1.499) Remain 03:30:02 loss: 0.2086 Lr: 0.00044
[2023-12-25 21:25:26,537 INFO misc.py line 119 253097] Train: [84/100][265/510] Data 0.004 (0.131) Batch 1.174 (1.498) Remain 03:29:50 loss: 0.0784 Lr: 0.00044
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[2023-12-25 21:26:06,628 INFO misc.py line 119 253097] Train: [84/100][299/510] Data 0.006 (0.119) Batch 1.152 (1.461) Remain 03:23:52 loss: 0.0736 Lr: 0.00043
[2023-12-25 21:26:16,517 INFO misc.py line 119 253097] Train: [84/100][300/510] Data 8.612 (0.148) Batch 9.889 (1.490) Remain 03:27:49 loss: 0.0759 Lr: 0.00043
[2023-12-25 21:26:17,734 INFO misc.py line 119 253097] Train: [84/100][301/510] Data 0.003 (0.147) Batch 1.217 (1.489) Remain 03:27:39 loss: 0.1346 Lr: 0.00043
[2023-12-25 21:26:18,954 INFO misc.py line 119 253097] Train: [84/100][302/510] Data 0.003 (0.147) Batch 1.216 (1.488) Remain 03:27:30 loss: 0.1411 Lr: 0.00043
[2023-12-25 21:26:19,969 INFO misc.py line 119 253097] Train: [84/100][303/510] Data 0.007 (0.146) Batch 1.018 (1.486) Remain 03:27:16 loss: 0.0944 Lr: 0.00043
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[2023-12-25 21:26:28,755 INFO misc.py line 119 253097] Train: [84/100][311/510] Data 0.008 (0.143) Batch 1.162 (1.476) Remain 03:25:39 loss: 0.1204 Lr: 0.00043
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[2023-12-25 21:26:31,181 INFO misc.py line 119 253097] Train: [84/100][313/510] Data 0.004 (0.142) Batch 1.258 (1.475) Remain 03:25:22 loss: 0.1031 Lr: 0.00043
[2023-12-25 21:26:32,406 INFO misc.py line 119 253097] Train: [84/100][314/510] Data 0.019 (0.141) Batch 1.237 (1.474) Remain 03:25:14 loss: 0.1205 Lr: 0.00043
[2023-12-25 21:26:33,640 INFO misc.py line 119 253097] Train: [84/100][315/510] Data 0.007 (0.141) Batch 1.231 (1.473) Remain 03:25:06 loss: 0.1045 Lr: 0.00043
[2023-12-25 21:26:34,526 INFO misc.py line 119 253097] Train: [84/100][316/510] Data 0.009 (0.141) Batch 0.892 (1.471) Remain 03:24:49 loss: 0.1003 Lr: 0.00043
[2023-12-25 21:26:35,606 INFO misc.py line 119 253097] Train: [84/100][317/510] Data 0.003 (0.140) Batch 1.080 (1.470) Remain 03:24:37 loss: 0.0891 Lr: 0.00043
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[2023-12-25 21:26:38,053 INFO misc.py line 119 253097] Train: [84/100][319/510] Data 0.003 (0.139) Batch 1.227 (1.468) Remain 03:24:21 loss: 0.1559 Lr: 0.00043
[2023-12-25 21:26:39,027 INFO misc.py line 119 253097] Train: [84/100][320/510] Data 0.006 (0.139) Batch 0.978 (1.467) Remain 03:24:07 loss: 0.1937 Lr: 0.00043
[2023-12-25 21:26:40,070 INFO misc.py line 119 253097] Train: [84/100][321/510] Data 0.003 (0.138) Batch 1.043 (1.465) Remain 03:23:54 loss: 0.1324 Lr: 0.00043
[2023-12-25 21:26:41,183 INFO misc.py line 119 253097] Train: [84/100][322/510] Data 0.003 (0.138) Batch 1.113 (1.464) Remain 03:23:44 loss: 0.0525 Lr: 0.00043
[2023-12-25 21:26:42,428 INFO misc.py line 119 253097] Train: [84/100][323/510] Data 0.003 (0.138) Batch 1.244 (1.464) Remain 03:23:36 loss: 0.1238 Lr: 0.00043
[2023-12-25 21:26:43,493 INFO misc.py line 119 253097] Train: [84/100][324/510] Data 0.003 (0.137) Batch 1.065 (1.462) Remain 03:23:25 loss: 0.2008 Lr: 0.00043
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[2023-12-25 21:26:48,165 INFO misc.py line 119 253097] Train: [84/100][328/510] Data 0.004 (0.135) Batch 1.204 (1.459) Remain 03:22:48 loss: 0.0553 Lr: 0.00043
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[2023-12-25 21:29:18,654 INFO misc.py line 119 253097] Train: [84/100][426/510] Data 6.222 (0.137) Batch 7.359 (1.477) Remain 03:22:52 loss: 0.1419 Lr: 0.00042
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[2023-12-25 21:30:37,598 INFO misc.py line 119 253097] Train: [84/100][476/510] Data 0.004 (0.128) Batch 1.000 (1.487) Remain 03:23:07 loss: 0.1034 Lr: 0.00042
[2023-12-25 21:30:38,862 INFO misc.py line 119 253097] Train: [84/100][477/510] Data 0.004 (0.128) Batch 1.261 (1.487) Remain 03:23:02 loss: 0.0870 Lr: 0.00042
[2023-12-25 21:30:39,881 INFO misc.py line 119 253097] Train: [84/100][478/510] Data 0.007 (0.127) Batch 1.018 (1.486) Remain 03:22:52 loss: 0.1088 Lr: 0.00042
[2023-12-25 21:30:41,744 INFO misc.py line 119 253097] Train: [84/100][479/510] Data 0.008 (0.127) Batch 1.868 (1.487) Remain 03:22:57 loss: 0.1229 Lr: 0.00042
[2023-12-25 21:30:42,772 INFO misc.py line 119 253097] Train: [84/100][480/510] Data 0.003 (0.127) Batch 1.029 (1.486) Remain 03:22:48 loss: 0.0917 Lr: 0.00042
[2023-12-25 21:30:43,997 INFO misc.py line 119 253097] Train: [84/100][481/510] Data 0.003 (0.127) Batch 1.224 (1.485) Remain 03:22:42 loss: 0.1398 Lr: 0.00042
[2023-12-25 21:30:45,237 INFO misc.py line 119 253097] Train: [84/100][482/510] Data 0.003 (0.126) Batch 1.237 (1.485) Remain 03:22:36 loss: 0.0903 Lr: 0.00042
[2023-12-25 21:30:46,138 INFO misc.py line 119 253097] Train: [84/100][483/510] Data 0.006 (0.126) Batch 0.902 (1.483) Remain 03:22:25 loss: 0.0896 Lr: 0.00042
[2023-12-25 21:30:47,307 INFO misc.py line 119 253097] Train: [84/100][484/510] Data 0.005 (0.126) Batch 1.171 (1.483) Remain 03:22:18 loss: 0.0817 Lr: 0.00041
[2023-12-25 21:30:48,347 INFO misc.py line 119 253097] Train: [84/100][485/510] Data 0.003 (0.126) Batch 1.039 (1.482) Remain 03:22:09 loss: 0.0785 Lr: 0.00041
[2023-12-25 21:30:53,945 INFO misc.py line 119 253097] Train: [84/100][486/510] Data 0.004 (0.125) Batch 5.598 (1.490) Remain 03:23:17 loss: 0.1735 Lr: 0.00041
[2023-12-25 21:30:55,141 INFO misc.py line 119 253097] Train: [84/100][487/510] Data 0.003 (0.125) Batch 1.196 (1.490) Remain 03:23:11 loss: 0.1063 Lr: 0.00041
[2023-12-25 21:30:56,370 INFO misc.py line 119 253097] Train: [84/100][488/510] Data 0.003 (0.125) Batch 1.228 (1.489) Remain 03:23:05 loss: 0.1322 Lr: 0.00041
[2023-12-25 21:30:57,518 INFO misc.py line 119 253097] Train: [84/100][489/510] Data 0.004 (0.125) Batch 1.146 (1.489) Remain 03:22:58 loss: 0.1730 Lr: 0.00041
[2023-12-25 21:30:58,663 INFO misc.py line 119 253097] Train: [84/100][490/510] Data 0.007 (0.124) Batch 1.148 (1.488) Remain 03:22:50 loss: 0.1024 Lr: 0.00041
[2023-12-25 21:31:00,010 INFO misc.py line 119 253097] Train: [84/100][491/510] Data 0.004 (0.124) Batch 1.346 (1.488) Remain 03:22:46 loss: 0.1009 Lr: 0.00041
[2023-12-25 21:31:01,212 INFO misc.py line 119 253097] Train: [84/100][492/510] Data 0.005 (0.124) Batch 1.190 (1.487) Remain 03:22:40 loss: 0.0757 Lr: 0.00041
[2023-12-25 21:31:02,130 INFO misc.py line 119 253097] Train: [84/100][493/510] Data 0.017 (0.124) Batch 0.931 (1.486) Remain 03:22:29 loss: 0.0731 Lr: 0.00041
[2023-12-25 21:31:03,266 INFO misc.py line 119 253097] Train: [84/100][494/510] Data 0.003 (0.123) Batch 1.136 (1.485) Remain 03:22:22 loss: 0.0519 Lr: 0.00041
[2023-12-25 21:31:04,442 INFO misc.py line 119 253097] Train: [84/100][495/510] Data 0.003 (0.123) Batch 1.175 (1.484) Remain 03:22:15 loss: 0.0983 Lr: 0.00041
[2023-12-25 21:31:05,434 INFO misc.py line 119 253097] Train: [84/100][496/510] Data 0.004 (0.123) Batch 0.993 (1.484) Remain 03:22:06 loss: 0.1115 Lr: 0.00041
[2023-12-25 21:31:06,743 INFO misc.py line 119 253097] Train: [84/100][497/510] Data 0.004 (0.123) Batch 1.304 (1.483) Remain 03:22:01 loss: 0.1025 Lr: 0.00041
[2023-12-25 21:31:07,924 INFO misc.py line 119 253097] Train: [84/100][498/510] Data 0.009 (0.122) Batch 1.183 (1.483) Remain 03:21:55 loss: 0.1259 Lr: 0.00041
[2023-12-25 21:31:09,176 INFO misc.py line 119 253097] Train: [84/100][499/510] Data 0.007 (0.122) Batch 1.251 (1.482) Remain 03:21:49 loss: 0.0907 Lr: 0.00041
[2023-12-25 21:31:10,306 INFO misc.py line 119 253097] Train: [84/100][500/510] Data 0.008 (0.122) Batch 1.129 (1.481) Remain 03:21:42 loss: 0.1140 Lr: 0.00041
[2023-12-25 21:31:11,317 INFO misc.py line 119 253097] Train: [84/100][501/510] Data 0.008 (0.122) Batch 1.012 (1.480) Remain 03:21:33 loss: 0.1100 Lr: 0.00041
[2023-12-25 21:31:12,354 INFO misc.py line 119 253097] Train: [84/100][502/510] Data 0.008 (0.122) Batch 1.037 (1.480) Remain 03:21:24 loss: 0.1305 Lr: 0.00041
[2023-12-25 21:31:13,469 INFO misc.py line 119 253097] Train: [84/100][503/510] Data 0.008 (0.121) Batch 1.117 (1.479) Remain 03:21:17 loss: 0.0958 Lr: 0.00041
[2023-12-25 21:31:14,555 INFO misc.py line 119 253097] Train: [84/100][504/510] Data 0.007 (0.121) Batch 1.084 (1.478) Remain 03:21:09 loss: 0.1610 Lr: 0.00041
[2023-12-25 21:31:15,834 INFO misc.py line 119 253097] Train: [84/100][505/510] Data 0.008 (0.121) Batch 1.273 (1.478) Remain 03:21:04 loss: 0.1174 Lr: 0.00041
[2023-12-25 21:31:16,902 INFO misc.py line 119 253097] Train: [84/100][506/510] Data 0.014 (0.121) Batch 1.074 (1.477) Remain 03:20:56 loss: 0.1050 Lr: 0.00041
[2023-12-25 21:31:18,027 INFO misc.py line 119 253097] Train: [84/100][507/510] Data 0.008 (0.120) Batch 1.130 (1.476) Remain 03:20:49 loss: 0.2767 Lr: 0.00041
[2023-12-25 21:31:19,162 INFO misc.py line 119 253097] Train: [84/100][508/510] Data 0.003 (0.120) Batch 1.134 (1.475) Remain 03:20:42 loss: 0.0741 Lr: 0.00041
[2023-12-25 21:31:20,219 INFO misc.py line 119 253097] Train: [84/100][509/510] Data 0.004 (0.120) Batch 1.057 (1.475) Remain 03:20:34 loss: 0.1139 Lr: 0.00041
[2023-12-25 21:31:21,426 INFO misc.py line 119 253097] Train: [84/100][510/510] Data 0.005 (0.120) Batch 1.203 (1.474) Remain 03:20:28 loss: 0.0713 Lr: 0.00041
[2023-12-25 21:31:21,427 INFO misc.py line 136 253097] Train result: loss: 0.1136 
[2023-12-25 21:31:21,427 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 21:31:50,006 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6380 
[2023-12-25 21:31:50,354 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2768 
[2023-12-25 21:31:55,299 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4279 
[2023-12-25 21:31:55,811 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3866 
[2023-12-25 21:31:57,780 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8321 
[2023-12-25 21:31:58,200 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3498 
[2023-12-25 21:31:59,076 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1973 
[2023-12-25 21:31:59,631 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2346 
[2023-12-25 21:32:01,437 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8144 
[2023-12-25 21:32:03,554 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1094 
[2023-12-25 21:32:04,412 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2969 
[2023-12-25 21:32:04,833 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.6342 
[2023-12-25 21:32:05,732 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5201 
[2023-12-25 21:32:08,670 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9168 
[2023-12-25 21:32:09,135 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2634 
[2023-12-25 21:32:09,748 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3914 
[2023-12-25 21:32:10,452 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3853 
[2023-12-25 21:32:11,899 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6921/0.7540/0.9063.
[2023-12-25 21:32:11,899 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9192/0.9488
[2023-12-25 21:32:11,899 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9827/0.9909
[2023-12-25 21:32:11,899 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8492/0.9682
[2023-12-25 21:32:11,899 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 21:32:11,899 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3034/0.3222
[2023-12-25 21:32:11,899 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6356/0.6604
[2023-12-25 21:32:11,900 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7374/0.8295
[2023-12-25 21:32:11,900 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8104/0.8977
[2023-12-25 21:32:11,900 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9209/0.9637
[2023-12-25 21:32:11,900 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6979/0.7520
[2023-12-25 21:32:11,900 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7887/0.8816
[2023-12-25 21:32:11,900 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7440/0.8597
[2023-12-25 21:32:11,900 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6084/0.7273
[2023-12-25 21:32:11,900 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 21:32:11,902 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 21:32:11,902 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 21:32:21,665 INFO misc.py line 119 253097] Train: [85/100][1/510] Data 6.246 (6.246) Batch 7.453 (7.453) Remain 16:53:31 loss: 0.1484 Lr: 0.00041
[2023-12-25 21:32:22,862 INFO misc.py line 119 253097] Train: [85/100][2/510] Data 0.003 (0.003) Batch 1.193 (1.193) Remain 02:42:15 loss: 0.0877 Lr: 0.00041
[2023-12-25 21:32:23,828 INFO misc.py line 119 253097] Train: [85/100][3/510] Data 0.007 (0.007) Batch 0.970 (0.970) Remain 02:11:50 loss: 0.0865 Lr: 0.00041
[2023-12-25 21:32:29,305 INFO misc.py line 119 253097] Train: [85/100][4/510] Data 0.003 (0.003) Batch 5.476 (5.476) Remain 12:24:23 loss: 0.1162 Lr: 0.00041
[2023-12-25 21:32:30,520 INFO misc.py line 119 253097] Train: [85/100][5/510] Data 0.004 (0.003) Batch 1.215 (3.346) Remain 07:34:43 loss: 0.1646 Lr: 0.00041
[2023-12-25 21:32:31,691 INFO misc.py line 119 253097] Train: [85/100][6/510] Data 0.004 (0.004) Batch 1.171 (2.621) Remain 05:56:08 loss: 0.0643 Lr: 0.00041
[2023-12-25 21:32:32,952 INFO misc.py line 119 253097] Train: [85/100][7/510] Data 0.004 (0.004) Batch 1.259 (2.280) Remain 05:09:50 loss: 0.1119 Lr: 0.00041
[2023-12-25 21:32:34,142 INFO misc.py line 119 253097] Train: [85/100][8/510] Data 0.006 (0.004) Batch 1.187 (2.061) Remain 04:40:05 loss: 0.2885 Lr: 0.00041
[2023-12-25 21:32:35,347 INFO misc.py line 119 253097] Train: [85/100][9/510] Data 0.010 (0.005) Batch 1.211 (1.920) Remain 04:20:48 loss: 0.0822 Lr: 0.00041
[2023-12-25 21:32:36,648 INFO misc.py line 119 253097] Train: [85/100][10/510] Data 0.004 (0.005) Batch 1.296 (1.831) Remain 04:08:40 loss: 0.0840 Lr: 0.00041
[2023-12-25 21:32:37,809 INFO misc.py line 119 253097] Train: [85/100][11/510] Data 0.008 (0.005) Batch 1.165 (1.748) Remain 03:57:20 loss: 0.0681 Lr: 0.00041
[2023-12-25 21:32:38,988 INFO misc.py line 119 253097] Train: [85/100][12/510] Data 0.003 (0.005) Batch 1.179 (1.684) Remain 03:48:44 loss: 0.1411 Lr: 0.00041
[2023-12-25 21:32:40,102 INFO misc.py line 119 253097] Train: [85/100][13/510] Data 0.003 (0.005) Batch 1.114 (1.627) Remain 03:40:57 loss: 0.1639 Lr: 0.00041
[2023-12-25 21:32:41,271 INFO misc.py line 119 253097] Train: [85/100][14/510] Data 0.003 (0.005) Batch 1.166 (1.585) Remain 03:35:14 loss: 0.1578 Lr: 0.00041
[2023-12-25 21:32:42,521 INFO misc.py line 119 253097] Train: [85/100][15/510] Data 0.006 (0.005) Batch 1.252 (1.558) Remain 03:31:26 loss: 0.0991 Lr: 0.00041
[2023-12-25 21:32:43,707 INFO misc.py line 119 253097] Train: [85/100][16/510] Data 0.005 (0.005) Batch 1.188 (1.529) Remain 03:27:33 loss: 0.0965 Lr: 0.00041
[2023-12-25 21:32:44,894 INFO misc.py line 119 253097] Train: [85/100][17/510] Data 0.003 (0.005) Batch 1.187 (1.505) Remain 03:24:12 loss: 0.1163 Lr: 0.00041
[2023-12-25 21:32:45,875 INFO misc.py line 119 253097] Train: [85/100][18/510] Data 0.004 (0.005) Batch 0.980 (1.470) Remain 03:19:26 loss: 0.0653 Lr: 0.00041
[2023-12-25 21:32:47,104 INFO misc.py line 119 253097] Train: [85/100][19/510] Data 0.003 (0.005) Batch 1.226 (1.455) Remain 03:17:21 loss: 0.0783 Lr: 0.00041
[2023-12-25 21:32:48,155 INFO misc.py line 119 253097] Train: [85/100][20/510] Data 0.007 (0.005) Batch 1.055 (1.431) Remain 03:14:08 loss: 0.1072 Lr: 0.00041
[2023-12-25 21:32:53,699 INFO misc.py line 119 253097] Train: [85/100][21/510] Data 0.003 (0.005) Batch 5.543 (1.659) Remain 03:45:06 loss: 0.0613 Lr: 0.00041
[2023-12-25 21:32:54,797 INFO misc.py line 119 253097] Train: [85/100][22/510] Data 0.004 (0.005) Batch 1.099 (1.630) Remain 03:41:04 loss: 0.0677 Lr: 0.00041
[2023-12-25 21:32:56,017 INFO misc.py line 119 253097] Train: [85/100][23/510] Data 0.003 (0.005) Batch 1.213 (1.609) Remain 03:38:13 loss: 0.1070 Lr: 0.00041
[2023-12-25 21:32:57,203 INFO misc.py line 119 253097] Train: [85/100][24/510] Data 0.010 (0.005) Batch 1.192 (1.589) Remain 03:35:30 loss: 0.1266 Lr: 0.00041
[2023-12-25 21:32:58,389 INFO misc.py line 119 253097] Train: [85/100][25/510] Data 0.003 (0.005) Batch 1.182 (1.571) Remain 03:32:58 loss: 0.0823 Lr: 0.00041
[2023-12-25 21:32:59,536 INFO misc.py line 119 253097] Train: [85/100][26/510] Data 0.007 (0.005) Batch 1.147 (1.552) Remain 03:30:26 loss: 0.0974 Lr: 0.00041
[2023-12-25 21:33:00,795 INFO misc.py line 119 253097] Train: [85/100][27/510] Data 0.007 (0.005) Batch 1.258 (1.540) Remain 03:28:45 loss: 0.0891 Lr: 0.00041
[2023-12-25 21:33:01,943 INFO misc.py line 119 253097] Train: [85/100][28/510] Data 0.012 (0.005) Batch 1.147 (1.524) Remain 03:26:36 loss: 0.0979 Lr: 0.00041
[2023-12-25 21:33:03,169 INFO misc.py line 119 253097] Train: [85/100][29/510] Data 0.009 (0.005) Batch 1.231 (1.513) Remain 03:25:02 loss: 0.1207 Lr: 0.00041
[2023-12-25 21:33:04,190 INFO misc.py line 119 253097] Train: [85/100][30/510] Data 0.004 (0.005) Batch 1.016 (1.495) Remain 03:22:31 loss: 0.0750 Lr: 0.00041
[2023-12-25 21:33:05,096 INFO misc.py line 119 253097] Train: [85/100][31/510] Data 0.008 (0.005) Batch 0.911 (1.474) Remain 03:19:40 loss: 0.1439 Lr: 0.00041
[2023-12-25 21:33:06,141 INFO misc.py line 119 253097] Train: [85/100][32/510] Data 0.003 (0.005) Batch 1.045 (1.459) Remain 03:17:39 loss: 0.2338 Lr: 0.00041
[2023-12-25 21:33:07,148 INFO misc.py line 119 253097] Train: [85/100][33/510] Data 0.003 (0.005) Batch 1.007 (1.444) Remain 03:15:35 loss: 0.0655 Lr: 0.00041
[2023-12-25 21:33:08,164 INFO misc.py line 119 253097] Train: [85/100][34/510] Data 0.004 (0.005) Batch 1.016 (1.430) Remain 03:13:41 loss: 0.1055 Lr: 0.00041
[2023-12-25 21:33:09,240 INFO misc.py line 119 253097] Train: [85/100][35/510] Data 0.003 (0.005) Batch 1.077 (1.419) Remain 03:12:10 loss: 0.1210 Lr: 0.00041
[2023-12-25 21:33:10,365 INFO misc.py line 119 253097] Train: [85/100][36/510] Data 0.003 (0.005) Batch 1.124 (1.410) Remain 03:10:56 loss: 0.1161 Lr: 0.00041
[2023-12-25 21:33:11,508 INFO misc.py line 119 253097] Train: [85/100][37/510] Data 0.005 (0.005) Batch 1.142 (1.402) Remain 03:09:51 loss: 0.0788 Lr: 0.00041
[2023-12-25 21:33:12,646 INFO misc.py line 119 253097] Train: [85/100][38/510] Data 0.004 (0.005) Batch 1.139 (1.395) Remain 03:08:48 loss: 0.0857 Lr: 0.00041
[2023-12-25 21:33:13,921 INFO misc.py line 119 253097] Train: [85/100][39/510] Data 0.004 (0.005) Batch 1.275 (1.391) Remain 03:08:19 loss: 0.0913 Lr: 0.00041
[2023-12-25 21:33:14,947 INFO misc.py line 119 253097] Train: [85/100][40/510] Data 0.004 (0.005) Batch 1.027 (1.382) Remain 03:06:58 loss: 0.1778 Lr: 0.00041
[2023-12-25 21:33:17,231 INFO misc.py line 119 253097] Train: [85/100][41/510] Data 0.004 (0.005) Batch 2.283 (1.405) Remain 03:10:09 loss: 0.0865 Lr: 0.00041
[2023-12-25 21:33:18,260 INFO misc.py line 119 253097] Train: [85/100][42/510] Data 0.004 (0.005) Batch 1.028 (1.396) Remain 03:08:49 loss: 0.0981 Lr: 0.00041
[2023-12-25 21:33:19,322 INFO misc.py line 119 253097] Train: [85/100][43/510] Data 0.005 (0.005) Batch 1.060 (1.387) Remain 03:07:40 loss: 0.0849 Lr: 0.00041
[2023-12-25 21:33:20,587 INFO misc.py line 119 253097] Train: [85/100][44/510] Data 0.007 (0.005) Batch 1.266 (1.384) Remain 03:07:15 loss: 0.0748 Lr: 0.00041
[2023-12-25 21:33:21,680 INFO misc.py line 119 253097] Train: [85/100][45/510] Data 0.006 (0.005) Batch 1.090 (1.377) Remain 03:06:16 loss: 0.1132 Lr: 0.00041
[2023-12-25 21:33:27,171 INFO misc.py line 119 253097] Train: [85/100][46/510] Data 0.009 (0.005) Batch 5.496 (1.473) Remain 03:19:12 loss: 0.1294 Lr: 0.00041
[2023-12-25 21:33:28,267 INFO misc.py line 119 253097] Train: [85/100][47/510] Data 0.003 (0.005) Batch 1.096 (1.465) Remain 03:18:01 loss: 0.1164 Lr: 0.00041
[2023-12-25 21:33:29,354 INFO misc.py line 119 253097] Train: [85/100][48/510] Data 0.004 (0.005) Batch 1.087 (1.456) Remain 03:16:52 loss: 0.0816 Lr: 0.00041
[2023-12-25 21:33:30,372 INFO misc.py line 119 253097] Train: [85/100][49/510] Data 0.004 (0.005) Batch 1.018 (1.447) Remain 03:15:33 loss: 0.0762 Lr: 0.00041
[2023-12-25 21:33:31,528 INFO misc.py line 119 253097] Train: [85/100][50/510] Data 0.004 (0.005) Batch 1.156 (1.440) Remain 03:14:41 loss: 0.2230 Lr: 0.00041
[2023-12-25 21:33:32,677 INFO misc.py line 119 253097] Train: [85/100][51/510] Data 0.004 (0.005) Batch 1.150 (1.434) Remain 03:13:51 loss: 0.0998 Lr: 0.00041
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[2023-12-25 21:37:23,648 INFO misc.py line 119 253097] Train: [85/100][214/510] Data 4.413 (0.056) Batch 5.572 (1.421) Remain 03:08:10 loss: 0.0861 Lr: 0.00039
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[2023-12-25 21:43:40,848 INFO misc.py line 119 253097] Train: [85/100][470/510] Data 0.004 (0.031) Batch 1.239 (1.450) Remain 03:05:48 loss: 0.1557 Lr: 0.00037
[2023-12-25 21:43:41,921 INFO misc.py line 119 253097] Train: [85/100][471/510] Data 0.005 (0.031) Batch 1.072 (1.449) Remain 03:05:40 loss: 0.0869 Lr: 0.00037
[2023-12-25 21:43:43,182 INFO misc.py line 119 253097] Train: [85/100][472/510] Data 0.006 (0.031) Batch 1.263 (1.449) Remain 03:05:36 loss: 0.0443 Lr: 0.00037
[2023-12-25 21:43:44,340 INFO misc.py line 119 253097] Train: [85/100][473/510] Data 0.004 (0.031) Batch 1.156 (1.448) Remain 03:05:29 loss: 0.0719 Lr: 0.00037
[2023-12-25 21:43:45,385 INFO misc.py line 119 253097] Train: [85/100][474/510] Data 0.006 (0.031) Batch 1.045 (1.447) Remain 03:05:21 loss: 0.2049 Lr: 0.00037
[2023-12-25 21:43:46,370 INFO misc.py line 119 253097] Train: [85/100][475/510] Data 0.006 (0.031) Batch 0.985 (1.446) Remain 03:05:12 loss: 0.0940 Lr: 0.00037
[2023-12-25 21:43:47,637 INFO misc.py line 119 253097] Train: [85/100][476/510] Data 0.007 (0.031) Batch 1.268 (1.446) Remain 03:05:08 loss: 0.1398 Lr: 0.00037
[2023-12-25 21:43:48,667 INFO misc.py line 119 253097] Train: [85/100][477/510] Data 0.005 (0.031) Batch 1.031 (1.445) Remain 03:05:00 loss: 0.0903 Lr: 0.00037
[2023-12-25 21:43:49,528 INFO misc.py line 119 253097] Train: [85/100][478/510] Data 0.004 (0.031) Batch 0.861 (1.444) Remain 03:04:49 loss: 0.1781 Lr: 0.00037
[2023-12-25 21:43:50,735 INFO misc.py line 119 253097] Train: [85/100][479/510] Data 0.003 (0.031) Batch 1.205 (1.443) Remain 03:04:44 loss: 0.1706 Lr: 0.00037
[2023-12-25 21:43:53,756 INFO misc.py line 119 253097] Train: [85/100][480/510] Data 0.006 (0.031) Batch 3.022 (1.446) Remain 03:05:08 loss: 0.0890 Lr: 0.00037
[2023-12-25 21:43:54,811 INFO misc.py line 119 253097] Train: [85/100][481/510] Data 0.005 (0.031) Batch 1.055 (1.446) Remain 03:05:00 loss: 0.0974 Lr: 0.00037
[2023-12-25 21:43:56,062 INFO misc.py line 119 253097] Train: [85/100][482/510] Data 0.005 (0.031) Batch 1.252 (1.445) Remain 03:04:55 loss: 0.0887 Lr: 0.00037
[2023-12-25 21:43:57,295 INFO misc.py line 119 253097] Train: [85/100][483/510] Data 0.004 (0.031) Batch 1.233 (1.445) Remain 03:04:51 loss: 0.0655 Lr: 0.00037
[2023-12-25 21:43:58,464 INFO misc.py line 119 253097] Train: [85/100][484/510] Data 0.004 (0.031) Batch 1.165 (1.444) Remain 03:04:45 loss: 0.1644 Lr: 0.00037
[2023-12-25 21:43:59,728 INFO misc.py line 119 253097] Train: [85/100][485/510] Data 0.008 (0.031) Batch 1.266 (1.444) Remain 03:04:40 loss: 0.0882 Lr: 0.00037
[2023-12-25 21:44:00,694 INFO misc.py line 119 253097] Train: [85/100][486/510] Data 0.007 (0.031) Batch 0.969 (1.443) Remain 03:04:31 loss: 0.1043 Lr: 0.00037
[2023-12-25 21:44:01,865 INFO misc.py line 119 253097] Train: [85/100][487/510] Data 0.004 (0.030) Batch 1.171 (1.442) Remain 03:04:26 loss: 0.0785 Lr: 0.00037
[2023-12-25 21:44:02,892 INFO misc.py line 119 253097] Train: [85/100][488/510] Data 0.004 (0.030) Batch 1.027 (1.441) Remain 03:04:18 loss: 0.1686 Lr: 0.00037
[2023-12-25 21:44:04,014 INFO misc.py line 119 253097] Train: [85/100][489/510] Data 0.004 (0.030) Batch 1.122 (1.441) Remain 03:04:11 loss: 0.0847 Lr: 0.00037
[2023-12-25 21:44:05,074 INFO misc.py line 119 253097] Train: [85/100][490/510] Data 0.004 (0.030) Batch 1.059 (1.440) Remain 03:04:04 loss: 0.0961 Lr: 0.00037
[2023-12-25 21:44:06,308 INFO misc.py line 119 253097] Train: [85/100][491/510] Data 0.005 (0.030) Batch 1.229 (1.439) Remain 03:03:59 loss: 0.0900 Lr: 0.00037
[2023-12-25 21:44:07,449 INFO misc.py line 119 253097] Train: [85/100][492/510] Data 0.009 (0.030) Batch 1.144 (1.439) Remain 03:03:53 loss: 0.1485 Lr: 0.00037
[2023-12-25 21:44:08,419 INFO misc.py line 119 253097] Train: [85/100][493/510] Data 0.008 (0.030) Batch 0.972 (1.438) Remain 03:03:44 loss: 0.0805 Lr: 0.00037
[2023-12-25 21:44:09,438 INFO misc.py line 119 253097] Train: [85/100][494/510] Data 0.004 (0.030) Batch 1.019 (1.437) Remain 03:03:36 loss: 0.0881 Lr: 0.00037
[2023-12-25 21:44:10,521 INFO misc.py line 119 253097] Train: [85/100][495/510] Data 0.005 (0.030) Batch 1.082 (1.436) Remain 03:03:29 loss: 0.0837 Lr: 0.00037
[2023-12-25 21:44:11,738 INFO misc.py line 119 253097] Train: [85/100][496/510] Data 0.006 (0.030) Batch 1.218 (1.436) Remain 03:03:24 loss: 0.0606 Lr: 0.00037
[2023-12-25 21:44:12,942 INFO misc.py line 119 253097] Train: [85/100][497/510] Data 0.005 (0.030) Batch 1.205 (1.435) Remain 03:03:19 loss: 0.0778 Lr: 0.00036
[2023-12-25 21:44:14,094 INFO misc.py line 119 253097] Train: [85/100][498/510] Data 0.004 (0.030) Batch 1.150 (1.435) Remain 03:03:14 loss: 0.0822 Lr: 0.00036
[2023-12-25 21:44:15,312 INFO misc.py line 119 253097] Train: [85/100][499/510] Data 0.006 (0.030) Batch 1.219 (1.434) Remain 03:03:09 loss: 0.0900 Lr: 0.00036
[2023-12-25 21:44:16,390 INFO misc.py line 119 253097] Train: [85/100][500/510] Data 0.005 (0.030) Batch 1.079 (1.434) Remain 03:03:02 loss: 0.0646 Lr: 0.00036
[2023-12-25 21:44:17,363 INFO misc.py line 119 253097] Train: [85/100][501/510] Data 0.003 (0.030) Batch 0.972 (1.433) Remain 03:02:53 loss: 0.1274 Lr: 0.00036
[2023-12-25 21:44:18,295 INFO misc.py line 119 253097] Train: [85/100][502/510] Data 0.004 (0.030) Batch 0.932 (1.432) Remain 03:02:44 loss: 0.0719 Lr: 0.00036
[2023-12-25 21:44:19,487 INFO misc.py line 119 253097] Train: [85/100][503/510] Data 0.004 (0.030) Batch 1.191 (1.431) Remain 03:02:39 loss: 0.0669 Lr: 0.00036
[2023-12-25 21:44:20,720 INFO misc.py line 119 253097] Train: [85/100][504/510] Data 0.006 (0.030) Batch 1.233 (1.431) Remain 03:02:35 loss: 0.0994 Lr: 0.00036
[2023-12-25 21:44:22,042 INFO misc.py line 119 253097] Train: [85/100][505/510] Data 0.004 (0.030) Batch 1.322 (1.431) Remain 03:02:32 loss: 0.1780 Lr: 0.00036
[2023-12-25 21:44:23,034 INFO misc.py line 119 253097] Train: [85/100][506/510] Data 0.004 (0.029) Batch 0.990 (1.430) Remain 03:02:23 loss: 0.0784 Lr: 0.00036
[2023-12-25 21:44:24,196 INFO misc.py line 119 253097] Train: [85/100][507/510] Data 0.006 (0.029) Batch 1.161 (1.429) Remain 03:02:18 loss: 0.0829 Lr: 0.00036
[2023-12-25 21:44:25,355 INFO misc.py line 119 253097] Train: [85/100][508/510] Data 0.005 (0.029) Batch 1.161 (1.429) Remain 03:02:12 loss: 0.0975 Lr: 0.00036
[2023-12-25 21:44:26,642 INFO misc.py line 119 253097] Train: [85/100][509/510] Data 0.004 (0.029) Batch 1.287 (1.428) Remain 03:02:09 loss: 0.0579 Lr: 0.00036
[2023-12-25 21:44:27,801 INFO misc.py line 119 253097] Train: [85/100][510/510] Data 0.005 (0.029) Batch 1.160 (1.428) Remain 03:02:03 loss: 0.1344 Lr: 0.00036
[2023-12-25 21:44:27,802 INFO misc.py line 136 253097] Train result: loss: 0.1139 
[2023-12-25 21:44:27,803 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 21:44:57,010 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.7038 
[2023-12-25 21:44:57,352 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3074 
[2023-12-25 21:45:02,287 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.2711 
[2023-12-25 21:45:02,800 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4153 
[2023-12-25 21:45:04,767 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 1.0077 
[2023-12-25 21:45:05,190 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3128 
[2023-12-25 21:45:06,067 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2140 
[2023-12-25 21:45:06,620 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2547 
[2023-12-25 21:45:08,424 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9655 
[2023-12-25 21:45:10,544 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1281 
[2023-12-25 21:45:11,400 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2563 
[2023-12-25 21:45:11,822 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8466 
[2023-12-25 21:45:12,723 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5000 
[2023-12-25 21:45:15,669 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8402 
[2023-12-25 21:45:16,135 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3169 
[2023-12-25 21:45:16,756 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3677 
[2023-12-25 21:45:17,464 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4470 
[2023-12-25 21:45:18,857 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.7007/0.7563/0.9068.
[2023-12-25 21:45:18,857 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9196/0.9458
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9831/0.9903
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8480/0.9747
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3293/0.3605
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6272/0.6459
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7496/0.8482
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8040/0.8877
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9129/0.9539
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.7110/0.7732
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7795/0.8464
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8248/0.8465
[2023-12-25 21:45:18,858 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6201/0.7588
[2023-12-25 21:45:18,859 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 21:45:18,861 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 21:45:18,861 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 21:45:26,939 INFO misc.py line 119 253097] Train: [86/100][1/510] Data 4.331 (4.331) Batch 5.592 (5.592) Remain 11:52:54 loss: 0.1150 Lr: 0.00036
[2023-12-25 21:45:39,852 INFO misc.py line 119 253097] Train: [86/100][2/510] Data 0.003 (0.003) Batch 12.913 (12.913) Remain 27:25:58 loss: 0.1226 Lr: 0.00036
[2023-12-25 21:45:40,978 INFO misc.py line 119 253097] Train: [86/100][3/510] Data 0.003 (0.003) Batch 1.125 (1.125) Remain 02:23:22 loss: 0.1444 Lr: 0.00036
[2023-12-25 21:45:42,154 INFO misc.py line 119 253097] Train: [86/100][4/510] Data 0.004 (0.004) Batch 1.177 (1.177) Remain 02:30:01 loss: 0.1475 Lr: 0.00036
[2023-12-25 21:45:43,243 INFO misc.py line 119 253097] Train: [86/100][5/510] Data 0.003 (0.004) Batch 1.088 (1.133) Remain 02:24:19 loss: 0.0960 Lr: 0.00036
[2023-12-25 21:45:44,482 INFO misc.py line 119 253097] Train: [86/100][6/510] Data 0.004 (0.004) Batch 1.240 (1.168) Remain 02:28:50 loss: 0.1269 Lr: 0.00036
[2023-12-25 21:45:45,617 INFO misc.py line 119 253097] Train: [86/100][7/510] Data 0.004 (0.004) Batch 1.135 (1.160) Remain 02:27:46 loss: 0.0973 Lr: 0.00036
[2023-12-25 21:45:47,046 INFO misc.py line 119 253097] Train: [86/100][8/510] Data 0.003 (0.004) Batch 1.428 (1.214) Remain 02:34:34 loss: 0.0950 Lr: 0.00036
[2023-12-25 21:45:48,022 INFO misc.py line 119 253097] Train: [86/100][9/510] Data 0.005 (0.004) Batch 0.977 (1.174) Remain 02:29:31 loss: 0.2067 Lr: 0.00036
[2023-12-25 21:45:49,166 INFO misc.py line 119 253097] Train: [86/100][10/510] Data 0.003 (0.004) Batch 1.142 (1.170) Remain 02:28:55 loss: 0.0824 Lr: 0.00036
[2023-12-25 21:45:50,228 INFO misc.py line 119 253097] Train: [86/100][11/510] Data 0.006 (0.004) Batch 1.064 (1.156) Remain 02:27:12 loss: 0.2040 Lr: 0.00036
[2023-12-25 21:45:51,275 INFO misc.py line 119 253097] Train: [86/100][12/510] Data 0.004 (0.004) Batch 1.047 (1.144) Remain 02:25:39 loss: 0.0732 Lr: 0.00036
[2023-12-25 21:45:52,561 INFO misc.py line 119 253097] Train: [86/100][13/510] Data 0.003 (0.004) Batch 1.281 (1.158) Remain 02:27:23 loss: 0.0801 Lr: 0.00036
[2023-12-25 21:45:53,580 INFO misc.py line 119 253097] Train: [86/100][14/510] Data 0.008 (0.004) Batch 1.021 (1.145) Remain 02:25:46 loss: 0.1169 Lr: 0.00036
[2023-12-25 21:45:54,716 INFO misc.py line 119 253097] Train: [86/100][15/510] Data 0.006 (0.005) Batch 1.136 (1.145) Remain 02:25:39 loss: 0.0759 Lr: 0.00036
[2023-12-25 21:45:58,532 INFO misc.py line 119 253097] Train: [86/100][16/510] Data 2.816 (0.221) Batch 3.818 (1.350) Remain 02:51:48 loss: 0.1833 Lr: 0.00036
[2023-12-25 21:45:59,761 INFO misc.py line 119 253097] Train: [86/100][17/510] Data 0.004 (0.205) Batch 1.230 (1.342) Remain 02:50:41 loss: 0.2504 Lr: 0.00036
[2023-12-25 21:46:00,897 INFO misc.py line 119 253097] Train: [86/100][18/510] Data 0.003 (0.192) Batch 1.136 (1.328) Remain 02:48:55 loss: 0.1394 Lr: 0.00036
[2023-12-25 21:46:02,017 INFO misc.py line 119 253097] Train: [86/100][19/510] Data 0.003 (0.180) Batch 1.119 (1.315) Remain 02:47:14 loss: 0.1473 Lr: 0.00036
[2023-12-25 21:46:02,950 INFO misc.py line 119 253097] Train: [86/100][20/510] Data 0.003 (0.170) Batch 0.933 (1.293) Remain 02:44:21 loss: 0.1237 Lr: 0.00036
[2023-12-25 21:46:06,560 INFO misc.py line 119 253097] Train: [86/100][21/510] Data 2.615 (0.305) Batch 3.610 (1.421) Remain 03:00:42 loss: 0.0907 Lr: 0.00036
[2023-12-25 21:46:07,742 INFO misc.py line 119 253097] Train: [86/100][22/510] Data 0.010 (0.290) Batch 1.183 (1.409) Remain 02:59:05 loss: 0.0698 Lr: 0.00036
[2023-12-25 21:46:08,979 INFO misc.py line 119 253097] Train: [86/100][23/510] Data 0.004 (0.276) Batch 1.236 (1.400) Remain 02:57:58 loss: 0.1938 Lr: 0.00036
[2023-12-25 21:46:10,248 INFO misc.py line 119 253097] Train: [86/100][24/510] Data 0.004 (0.263) Batch 1.265 (1.394) Remain 02:57:07 loss: 0.1582 Lr: 0.00036
[2023-12-25 21:46:11,282 INFO misc.py line 119 253097] Train: [86/100][25/510] Data 0.008 (0.251) Batch 1.036 (1.377) Remain 02:55:02 loss: 0.0962 Lr: 0.00036
[2023-12-25 21:46:12,392 INFO misc.py line 119 253097] Train: [86/100][26/510] Data 0.008 (0.241) Batch 1.109 (1.366) Remain 02:53:32 loss: 0.1542 Lr: 0.00036
[2023-12-25 21:46:13,509 INFO misc.py line 119 253097] Train: [86/100][27/510] Data 0.007 (0.231) Batch 1.118 (1.355) Remain 02:52:12 loss: 0.0938 Lr: 0.00036
[2023-12-25 21:46:18,066 INFO misc.py line 119 253097] Train: [86/100][28/510] Data 3.456 (0.360) Batch 4.560 (1.484) Remain 03:08:27 loss: 0.0740 Lr: 0.00036
[2023-12-25 21:46:19,255 INFO misc.py line 119 253097] Train: [86/100][29/510] Data 0.004 (0.346) Batch 1.188 (1.472) Remain 03:06:59 loss: 0.0896 Lr: 0.00036
[2023-12-25 21:46:20,448 INFO misc.py line 119 253097] Train: [86/100][30/510] Data 0.004 (0.334) Batch 1.187 (1.462) Remain 03:05:37 loss: 0.0460 Lr: 0.00036
[2023-12-25 21:46:21,577 INFO misc.py line 119 253097] Train: [86/100][31/510] Data 0.009 (0.322) Batch 1.131 (1.450) Remain 03:04:06 loss: 0.1232 Lr: 0.00036
[2023-12-25 21:46:22,784 INFO misc.py line 119 253097] Train: [86/100][32/510] Data 0.008 (0.311) Batch 1.210 (1.442) Remain 03:03:01 loss: 0.1503 Lr: 0.00036
[2023-12-25 21:46:24,068 INFO misc.py line 119 253097] Train: [86/100][33/510] Data 0.004 (0.301) Batch 1.280 (1.436) Remain 03:02:19 loss: 0.0944 Lr: 0.00036
[2023-12-25 21:46:25,297 INFO misc.py line 119 253097] Train: [86/100][34/510] Data 0.008 (0.291) Batch 1.233 (1.430) Remain 03:01:28 loss: 0.1103 Lr: 0.00036
[2023-12-25 21:46:33,355 INFO misc.py line 119 253097] Train: [86/100][35/510] Data 0.006 (0.282) Batch 8.058 (1.637) Remain 03:27:44 loss: 0.1377 Lr: 0.00036
[2023-12-25 21:46:34,511 INFO misc.py line 119 253097] Train: [86/100][36/510] Data 0.004 (0.274) Batch 1.156 (1.622) Remain 03:25:51 loss: 0.0813 Lr: 0.00036
[2023-12-25 21:46:35,578 INFO misc.py line 119 253097] Train: [86/100][37/510] Data 0.003 (0.266) Batch 1.067 (1.606) Remain 03:23:45 loss: 0.1243 Lr: 0.00036
[2023-12-25 21:46:36,536 INFO misc.py line 119 253097] Train: [86/100][38/510] Data 0.003 (0.259) Batch 0.958 (1.587) Remain 03:21:23 loss: 0.0761 Lr: 0.00036
[2023-12-25 21:46:37,777 INFO misc.py line 119 253097] Train: [86/100][39/510] Data 0.003 (0.251) Batch 1.237 (1.578) Remain 03:20:07 loss: 0.0994 Lr: 0.00036
[2023-12-25 21:46:38,770 INFO misc.py line 119 253097] Train: [86/100][40/510] Data 0.007 (0.245) Batch 0.997 (1.562) Remain 03:18:06 loss: 0.1167 Lr: 0.00036
[2023-12-25 21:46:39,897 INFO misc.py line 119 253097] Train: [86/100][41/510] Data 0.003 (0.238) Batch 1.121 (1.550) Remain 03:16:36 loss: 0.1997 Lr: 0.00036
[2023-12-25 21:46:40,651 INFO misc.py line 119 253097] Train: [86/100][42/510] Data 0.009 (0.233) Batch 0.759 (1.530) Remain 03:14:00 loss: 0.1106 Lr: 0.00036
[2023-12-25 21:46:41,778 INFO misc.py line 119 253097] Train: [86/100][43/510] Data 0.003 (0.227) Batch 1.127 (1.520) Remain 03:12:42 loss: 0.1294 Lr: 0.00036
[2023-12-25 21:46:42,997 INFO misc.py line 119 253097] Train: [86/100][44/510] Data 0.003 (0.221) Batch 1.219 (1.513) Remain 03:11:45 loss: 0.1188 Lr: 0.00036
[2023-12-25 21:46:44,136 INFO misc.py line 119 253097] Train: [86/100][45/510] Data 0.004 (0.216) Batch 1.139 (1.504) Remain 03:10:36 loss: 0.2520 Lr: 0.00036
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[2023-12-25 21:47:36,987 INFO misc.py line 119 253097] Train: [86/100][79/510] Data 0.003 (0.146) Batch 3.916 (1.526) Remain 03:12:36 loss: 0.2194 Lr: 0.00036
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[2023-12-25 21:47:44,848 INFO misc.py line 119 253097] Train: [86/100][86/510] Data 0.004 (0.134) Batch 1.212 (1.492) Remain 03:08:08 loss: 0.0611 Lr: 0.00036
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[2023-12-25 21:48:07,965 INFO misc.py line 119 253097] Train: [86/100][101/510] Data 0.008 (0.141) Batch 0.981 (1.500) Remain 03:08:42 loss: 0.0570 Lr: 0.00035
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[2023-12-25 21:48:31,738 INFO misc.py line 119 253097] Train: [86/100][113/510] Data 3.271 (0.156) Batch 4.530 (1.552) Remain 03:15:00 loss: 0.0810 Lr: 0.00035
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[2023-12-25 21:48:38,527 INFO misc.py line 119 253097] Train: [86/100][119/510] Data 0.003 (0.148) Batch 1.206 (1.531) Remain 03:12:06 loss: 0.1312 Lr: 0.00035
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[2023-12-25 21:48:49,606 INFO misc.py line 119 253097] Train: [86/100][126/510] Data 0.007 (0.140) Batch 4.238 (1.534) Remain 03:12:18 loss: 0.0877 Lr: 0.00035
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[2023-12-25 21:50:09,387 INFO misc.py line 119 253097] Train: [86/100][185/510] Data 0.007 (0.096) Batch 1.117 (1.475) Remain 03:03:29 loss: 0.2585 Lr: 0.00035
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[2023-12-25 21:50:23,113 INFO misc.py line 119 253097] Train: [86/100][197/510] Data 0.005 (0.090) Batch 1.065 (1.454) Remain 03:00:38 loss: 0.0883 Lr: 0.00035
[2023-12-25 21:50:24,277 INFO misc.py line 119 253097] Train: [86/100][198/510] Data 0.005 (0.090) Batch 1.150 (1.453) Remain 03:00:25 loss: 0.1425 Lr: 0.00035
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[2023-12-25 21:50:26,714 INFO misc.py line 119 253097] Train: [86/100][200/510] Data 0.007 (0.089) Batch 1.153 (1.450) Remain 03:00:05 loss: 0.1116 Lr: 0.00035
[2023-12-25 21:50:27,803 INFO misc.py line 119 253097] Train: [86/100][201/510] Data 0.006 (0.089) Batch 1.091 (1.449) Remain 02:59:50 loss: 0.0841 Lr: 0.00035
[2023-12-25 21:50:28,915 INFO misc.py line 119 253097] Train: [86/100][202/510] Data 0.004 (0.088) Batch 1.111 (1.447) Remain 02:59:36 loss: 0.0679 Lr: 0.00035
[2023-12-25 21:50:30,144 INFO misc.py line 119 253097] Train: [86/100][203/510] Data 0.004 (0.088) Batch 1.219 (1.446) Remain 02:59:26 loss: 0.0658 Lr: 0.00035
[2023-12-25 21:50:31,295 INFO misc.py line 119 253097] Train: [86/100][204/510] Data 0.016 (0.088) Batch 1.153 (1.444) Remain 02:59:14 loss: 0.1611 Lr: 0.00035
[2023-12-25 21:50:32,531 INFO misc.py line 119 253097] Train: [86/100][205/510] Data 0.013 (0.087) Batch 1.244 (1.443) Remain 02:59:05 loss: 0.0963 Lr: 0.00034
[2023-12-25 21:50:33,765 INFO misc.py line 119 253097] Train: [86/100][206/510] Data 0.004 (0.087) Batch 1.234 (1.442) Remain 02:58:56 loss: 0.0820 Lr: 0.00034
[2023-12-25 21:50:35,608 INFO misc.py line 119 253097] Train: [86/100][207/510] Data 0.005 (0.086) Batch 1.842 (1.444) Remain 02:59:09 loss: 0.1067 Lr: 0.00034
[2023-12-25 21:50:36,769 INFO misc.py line 119 253097] Train: [86/100][208/510] Data 0.007 (0.086) Batch 1.163 (1.443) Remain 02:58:57 loss: 0.1026 Lr: 0.00034
[2023-12-25 21:50:37,962 INFO misc.py line 119 253097] Train: [86/100][209/510] Data 0.003 (0.086) Batch 1.193 (1.442) Remain 02:58:47 loss: 0.0871 Lr: 0.00034
[2023-12-25 21:50:38,980 INFO misc.py line 119 253097] Train: [86/100][210/510] Data 0.004 (0.085) Batch 1.017 (1.440) Remain 02:58:30 loss: 0.1596 Lr: 0.00034
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[2023-12-25 21:50:41,129 INFO misc.py line 119 253097] Train: [86/100][212/510] Data 0.003 (0.084) Batch 1.090 (1.436) Remain 02:58:01 loss: 0.1734 Lr: 0.00034
[2023-12-25 21:50:42,349 INFO misc.py line 119 253097] Train: [86/100][213/510] Data 0.004 (0.084) Batch 1.219 (1.435) Remain 02:57:52 loss: 0.1039 Lr: 0.00034
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[2023-12-25 21:50:46,971 INFO misc.py line 119 253097] Train: [86/100][217/510] Data 0.006 (0.083) Batch 1.056 (1.430) Remain 02:57:08 loss: 0.1695 Lr: 0.00034
[2023-12-25 21:50:48,152 INFO misc.py line 119 253097] Train: [86/100][218/510] Data 0.007 (0.082) Batch 1.181 (1.429) Remain 02:56:58 loss: 0.0986 Lr: 0.00034
[2023-12-25 21:50:49,211 INFO misc.py line 119 253097] Train: [86/100][219/510] Data 0.006 (0.082) Batch 1.061 (1.427) Remain 02:56:44 loss: 0.1571 Lr: 0.00034
[2023-12-25 21:50:50,285 INFO misc.py line 119 253097] Train: [86/100][220/510] Data 0.005 (0.082) Batch 1.072 (1.425) Remain 02:56:30 loss: 0.1109 Lr: 0.00034
[2023-12-25 21:50:51,540 INFO misc.py line 119 253097] Train: [86/100][221/510] Data 0.007 (0.081) Batch 1.259 (1.425) Remain 02:56:23 loss: 0.0922 Lr: 0.00034
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[2023-12-25 21:50:54,767 INFO misc.py line 119 253097] Train: [86/100][224/510] Data 0.005 (0.080) Batch 0.982 (1.420) Remain 02:55:43 loss: 0.1250 Lr: 0.00034
[2023-12-25 21:50:55,992 INFO misc.py line 119 253097] Train: [86/100][225/510] Data 0.004 (0.080) Batch 1.226 (1.419) Remain 02:55:35 loss: 0.1152 Lr: 0.00034
[2023-12-25 21:50:57,118 INFO misc.py line 119 253097] Train: [86/100][226/510] Data 0.003 (0.079) Batch 1.126 (1.418) Remain 02:55:24 loss: 0.1312 Lr: 0.00034
[2023-12-25 21:50:58,323 INFO misc.py line 119 253097] Train: [86/100][227/510] Data 0.003 (0.079) Batch 1.203 (1.417) Remain 02:55:16 loss: 0.1845 Lr: 0.00034
[2023-12-25 21:50:59,530 INFO misc.py line 119 253097] Train: [86/100][228/510] Data 0.004 (0.079) Batch 1.208 (1.416) Remain 02:55:07 loss: 0.0939 Lr: 0.00034
[2023-12-25 21:51:00,739 INFO misc.py line 119 253097] Train: [86/100][229/510] Data 0.003 (0.078) Batch 1.209 (1.415) Remain 02:54:59 loss: 0.1014 Lr: 0.00034
[2023-12-25 21:51:01,956 INFO misc.py line 119 253097] Train: [86/100][230/510] Data 0.004 (0.078) Batch 1.214 (1.414) Remain 02:54:51 loss: 0.1323 Lr: 0.00034
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[2023-12-25 21:51:04,309 INFO misc.py line 119 253097] Train: [86/100][232/510] Data 0.005 (0.077) Batch 1.109 (1.412) Remain 02:54:33 loss: 0.1481 Lr: 0.00034
[2023-12-25 21:51:05,466 INFO misc.py line 119 253097] Train: [86/100][233/510] Data 0.004 (0.077) Batch 1.159 (1.411) Remain 02:54:24 loss: 0.2032 Lr: 0.00034
[2023-12-25 21:51:06,467 INFO misc.py line 119 253097] Train: [86/100][234/510] Data 0.003 (0.077) Batch 0.999 (1.409) Remain 02:54:09 loss: 0.0923 Lr: 0.00034
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[2023-12-25 21:54:46,510 INFO misc.py line 119 253097] Train: [86/100][397/510] Data 0.003 (0.048) Batch 3.409 (1.385) Remain 02:47:22 loss: 0.0841 Lr: 0.00033
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[2023-12-25 21:54:50,064 INFO misc.py line 119 253097] Train: [86/100][400/510] Data 0.004 (0.048) Batch 1.228 (1.383) Remain 02:47:07 loss: 0.1205 Lr: 0.00033
[2023-12-25 21:54:51,183 INFO misc.py line 119 253097] Train: [86/100][401/510] Data 0.009 (0.047) Batch 1.120 (1.382) Remain 02:47:01 loss: 0.1085 Lr: 0.00033
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[2023-12-25 21:55:08,045 INFO misc.py line 119 253097] Train: [86/100][403/510] Data 0.003 (0.047) Batch 15.637 (1.418) Remain 02:51:13 loss: 0.0892 Lr: 0.00033
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[2023-12-25 21:55:16,306 INFO misc.py line 119 253097] Train: [86/100][410/510] Data 0.005 (0.047) Batch 1.183 (1.414) Remain 02:50:34 loss: 0.0848 Lr: 0.00033
[2023-12-25 21:55:17,361 INFO misc.py line 119 253097] Train: [86/100][411/510] Data 0.006 (0.046) Batch 1.055 (1.413) Remain 02:50:26 loss: 0.0679 Lr: 0.00033
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[2023-12-25 21:55:19,950 INFO misc.py line 119 253097] Train: [86/100][413/510] Data 0.017 (0.046) Batch 1.287 (1.412) Remain 02:50:19 loss: 0.0949 Lr: 0.00033
[2023-12-25 21:55:21,288 INFO misc.py line 119 253097] Train: [86/100][414/510] Data 0.008 (0.046) Batch 1.329 (1.412) Remain 02:50:16 loss: 0.0850 Lr: 0.00033
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[2023-12-25 21:55:27,649 INFO misc.py line 119 253097] Train: [86/100][417/510] Data 0.004 (0.046) Batch 4.228 (1.417) Remain 02:50:49 loss: 0.1263 Lr: 0.00033
[2023-12-25 21:55:28,816 INFO misc.py line 119 253097] Train: [86/100][418/510] Data 0.005 (0.046) Batch 1.167 (1.416) Remain 02:50:43 loss: 0.0653 Lr: 0.00033
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[2023-12-25 21:55:49,831 INFO misc.py line 119 253097] Train: [86/100][434/510] Data 0.009 (0.046) Batch 1.053 (1.413) Remain 02:49:53 loss: 0.1838 Lr: 0.00032
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[2023-12-25 21:56:30,608 INFO misc.py line 119 253097] Train: [86/100][464/510] Data 0.004 (0.046) Batch 1.008 (1.409) Remain 02:48:46 loss: 0.1168 Lr: 0.00032
[2023-12-25 21:56:31,786 INFO misc.py line 119 253097] Train: [86/100][465/510] Data 0.004 (0.045) Batch 1.176 (1.409) Remain 02:48:41 loss: 0.1853 Lr: 0.00032
[2023-12-25 21:56:32,880 INFO misc.py line 119 253097] Train: [86/100][466/510] Data 0.006 (0.045) Batch 1.094 (1.408) Remain 02:48:35 loss: 0.0898 Lr: 0.00032
[2023-12-25 21:56:34,170 INFO misc.py line 119 253097] Train: [86/100][467/510] Data 0.005 (0.045) Batch 1.291 (1.408) Remain 02:48:31 loss: 0.0758 Lr: 0.00032
[2023-12-25 21:56:35,110 INFO misc.py line 119 253097] Train: [86/100][468/510] Data 0.005 (0.045) Batch 0.941 (1.407) Remain 02:48:23 loss: 0.1101 Lr: 0.00032
[2023-12-25 21:56:36,278 INFO misc.py line 119 253097] Train: [86/100][469/510] Data 0.004 (0.045) Batch 1.167 (1.406) Remain 02:48:18 loss: 0.1186 Lr: 0.00032
[2023-12-25 21:56:37,581 INFO misc.py line 119 253097] Train: [86/100][470/510] Data 0.005 (0.045) Batch 1.299 (1.406) Remain 02:48:15 loss: 0.0707 Lr: 0.00032
[2023-12-25 21:56:38,804 INFO misc.py line 119 253097] Train: [86/100][471/510] Data 0.008 (0.045) Batch 1.228 (1.406) Remain 02:48:10 loss: 0.3057 Lr: 0.00032
[2023-12-25 21:56:39,984 INFO misc.py line 119 253097] Train: [86/100][472/510] Data 0.004 (0.045) Batch 1.176 (1.405) Remain 02:48:05 loss: 0.1127 Lr: 0.00032
[2023-12-25 21:56:41,106 INFO misc.py line 119 253097] Train: [86/100][473/510] Data 0.008 (0.045) Batch 1.121 (1.405) Remain 02:48:00 loss: 0.0789 Lr: 0.00032
[2023-12-25 21:56:42,139 INFO misc.py line 119 253097] Train: [86/100][474/510] Data 0.008 (0.045) Batch 1.034 (1.404) Remain 02:47:53 loss: 0.0870 Lr: 0.00032
[2023-12-25 21:56:43,127 INFO misc.py line 119 253097] Train: [86/100][475/510] Data 0.007 (0.045) Batch 0.991 (1.403) Remain 02:47:45 loss: 0.0792 Lr: 0.00032
[2023-12-25 21:56:44,240 INFO misc.py line 119 253097] Train: [86/100][476/510] Data 0.004 (0.045) Batch 1.112 (1.402) Remain 02:47:39 loss: 0.1153 Lr: 0.00032
[2023-12-25 21:56:45,178 INFO misc.py line 119 253097] Train: [86/100][477/510] Data 0.005 (0.044) Batch 0.939 (1.401) Remain 02:47:31 loss: 0.0790 Lr: 0.00032
[2023-12-25 21:56:46,391 INFO misc.py line 119 253097] Train: [86/100][478/510] Data 0.003 (0.044) Batch 1.213 (1.401) Remain 02:47:27 loss: 0.1520 Lr: 0.00032
[2023-12-25 21:56:47,505 INFO misc.py line 119 253097] Train: [86/100][479/510] Data 0.004 (0.044) Batch 1.115 (1.400) Remain 02:47:21 loss: 0.1573 Lr: 0.00032
[2023-12-25 21:56:48,747 INFO misc.py line 119 253097] Train: [86/100][480/510] Data 0.003 (0.044) Batch 1.241 (1.400) Remain 02:47:17 loss: 0.0890 Lr: 0.00032
[2023-12-25 21:56:49,877 INFO misc.py line 119 253097] Train: [86/100][481/510] Data 0.004 (0.044) Batch 1.126 (1.399) Remain 02:47:12 loss: 0.1350 Lr: 0.00032
[2023-12-25 21:56:51,082 INFO misc.py line 119 253097] Train: [86/100][482/510] Data 0.008 (0.044) Batch 1.209 (1.399) Remain 02:47:07 loss: 0.0981 Lr: 0.00032
[2023-12-25 21:56:51,937 INFO misc.py line 119 253097] Train: [86/100][483/510] Data 0.004 (0.044) Batch 0.855 (1.398) Remain 02:46:58 loss: 0.1155 Lr: 0.00032
[2023-12-25 21:56:53,224 INFO misc.py line 119 253097] Train: [86/100][484/510] Data 0.004 (0.044) Batch 1.287 (1.398) Remain 02:46:55 loss: 0.0796 Lr: 0.00032
[2023-12-25 21:57:01,529 INFO misc.py line 119 253097] Train: [86/100][485/510] Data 7.198 (0.059) Batch 8.305 (1.412) Remain 02:48:36 loss: 0.0602 Lr: 0.00032
[2023-12-25 21:57:02,601 INFO misc.py line 119 253097] Train: [86/100][486/510] Data 0.004 (0.059) Batch 1.073 (1.411) Remain 02:48:30 loss: 0.1367 Lr: 0.00032
[2023-12-25 21:57:03,905 INFO misc.py line 119 253097] Train: [86/100][487/510] Data 0.004 (0.059) Batch 1.300 (1.411) Remain 02:48:26 loss: 0.0972 Lr: 0.00032
[2023-12-25 21:57:04,905 INFO misc.py line 119 253097] Train: [86/100][488/510] Data 0.007 (0.058) Batch 1.000 (1.410) Remain 02:48:19 loss: 0.0853 Lr: 0.00032
[2023-12-25 21:57:06,131 INFO misc.py line 119 253097] Train: [86/100][489/510] Data 0.007 (0.058) Batch 1.226 (1.410) Remain 02:48:15 loss: 0.1323 Lr: 0.00032
[2023-12-25 21:57:07,232 INFO misc.py line 119 253097] Train: [86/100][490/510] Data 0.007 (0.058) Batch 1.105 (1.409) Remain 02:48:09 loss: 0.1368 Lr: 0.00032
[2023-12-25 21:57:08,483 INFO misc.py line 119 253097] Train: [86/100][491/510] Data 0.003 (0.058) Batch 1.247 (1.409) Remain 02:48:05 loss: 0.1032 Lr: 0.00032
[2023-12-25 21:57:09,723 INFO misc.py line 119 253097] Train: [86/100][492/510] Data 0.007 (0.058) Batch 1.241 (1.408) Remain 02:48:01 loss: 0.1343 Lr: 0.00032
[2023-12-25 21:57:10,855 INFO misc.py line 119 253097] Train: [86/100][493/510] Data 0.007 (0.058) Batch 1.134 (1.408) Remain 02:47:56 loss: 0.1599 Lr: 0.00032
[2023-12-25 21:57:12,080 INFO misc.py line 119 253097] Train: [86/100][494/510] Data 0.005 (0.058) Batch 1.221 (1.408) Remain 02:47:52 loss: 0.1156 Lr: 0.00032
[2023-12-25 21:57:12,969 INFO misc.py line 119 253097] Train: [86/100][495/510] Data 0.009 (0.058) Batch 0.895 (1.406) Remain 02:47:43 loss: 0.0955 Lr: 0.00032
[2023-12-25 21:57:14,203 INFO misc.py line 119 253097] Train: [86/100][496/510] Data 0.003 (0.058) Batch 1.229 (1.406) Remain 02:47:39 loss: 0.1111 Lr: 0.00032
[2023-12-25 21:57:15,298 INFO misc.py line 119 253097] Train: [86/100][497/510] Data 0.007 (0.057) Batch 1.099 (1.406) Remain 02:47:33 loss: 0.0835 Lr: 0.00032
[2023-12-25 21:57:16,498 INFO misc.py line 119 253097] Train: [86/100][498/510] Data 0.004 (0.057) Batch 1.193 (1.405) Remain 02:47:29 loss: 0.1077 Lr: 0.00032
[2023-12-25 21:57:17,719 INFO misc.py line 119 253097] Train: [86/100][499/510] Data 0.012 (0.057) Batch 1.222 (1.405) Remain 02:47:25 loss: 0.1031 Lr: 0.00032
[2023-12-25 21:57:18,772 INFO misc.py line 119 253097] Train: [86/100][500/510] Data 0.009 (0.057) Batch 1.056 (1.404) Remain 02:47:18 loss: 0.0566 Lr: 0.00032
[2023-12-25 21:57:19,901 INFO misc.py line 119 253097] Train: [86/100][501/510] Data 0.007 (0.057) Batch 1.127 (1.403) Remain 02:47:13 loss: 0.1084 Lr: 0.00032
[2023-12-25 21:57:21,044 INFO misc.py line 119 253097] Train: [86/100][502/510] Data 0.009 (0.057) Batch 1.146 (1.403) Remain 02:47:08 loss: 0.0699 Lr: 0.00032
[2023-12-25 21:57:22,130 INFO misc.py line 119 253097] Train: [86/100][503/510] Data 0.007 (0.057) Batch 1.088 (1.402) Remain 02:47:02 loss: 0.0694 Lr: 0.00032
[2023-12-25 21:57:25,764 INFO misc.py line 119 253097] Train: [86/100][504/510] Data 0.003 (0.057) Batch 3.634 (1.407) Remain 02:47:32 loss: 0.2017 Lr: 0.00032
[2023-12-25 21:57:26,885 INFO misc.py line 119 253097] Train: [86/100][505/510] Data 0.003 (0.057) Batch 1.120 (1.406) Remain 02:47:27 loss: 0.0908 Lr: 0.00032
[2023-12-25 21:57:28,006 INFO misc.py line 119 253097] Train: [86/100][506/510] Data 0.005 (0.057) Batch 1.122 (1.406) Remain 02:47:21 loss: 0.2119 Lr: 0.00032
[2023-12-25 21:57:29,168 INFO misc.py line 119 253097] Train: [86/100][507/510] Data 0.004 (0.056) Batch 1.161 (1.405) Remain 02:47:16 loss: 0.0916 Lr: 0.00032
[2023-12-25 21:57:30,244 INFO misc.py line 119 253097] Train: [86/100][508/510] Data 0.005 (0.056) Batch 1.077 (1.404) Remain 02:47:10 loss: 0.1727 Lr: 0.00032
[2023-12-25 21:57:31,259 INFO misc.py line 119 253097] Train: [86/100][509/510] Data 0.004 (0.056) Batch 1.016 (1.404) Remain 02:47:03 loss: 0.1500 Lr: 0.00032
[2023-12-25 21:57:32,397 INFO misc.py line 119 253097] Train: [86/100][510/510] Data 0.004 (0.056) Batch 1.136 (1.403) Remain 02:46:58 loss: 0.0706 Lr: 0.00032
[2023-12-25 21:57:32,397 INFO misc.py line 136 253097] Train result: loss: 0.1163 
[2023-12-25 21:57:32,398 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 21:58:01,264 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6274 
[2023-12-25 21:58:01,610 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2738 
[2023-12-25 21:58:06,541 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.2878 
[2023-12-25 21:58:07,055 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4097 
[2023-12-25 21:58:09,023 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9187 
[2023-12-25 21:58:09,448 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2997 
[2023-12-25 21:58:10,324 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1893 
[2023-12-25 21:58:10,878 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2409 
[2023-12-25 21:58:12,686 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.7722 
[2023-12-25 21:58:14,803 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.0980 
[2023-12-25 21:58:15,661 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3165 
[2023-12-25 21:58:16,081 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8109 
[2023-12-25 21:58:16,983 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.6502 
[2023-12-25 21:58:19,925 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8198 
[2023-12-25 21:58:20,397 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2931 
[2023-12-25 21:58:21,008 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3458 
[2023-12-25 21:58:21,710 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3665 
[2023-12-25 21:58:22,940 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6995/0.7561/0.9055.
[2023-12-25 21:58:22,940 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9156/0.9466
[2023-12-25 21:58:22,940 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9834/0.9907
[2023-12-25 21:58:22,940 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8438/0.9707
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3611/0.4181
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6325/0.6530
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7268/0.8277
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8125/0.8974
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9110/0.9492
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6959/0.7431
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7831/0.8680
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8145/0.8436
[2023-12-25 21:58:22,941 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6138/0.7210
[2023-12-25 21:58:22,941 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 21:58:22,942 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 21:58:22,942 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 21:58:28,759 INFO misc.py line 119 253097] Train: [87/100][1/510] Data 2.056 (2.056) Batch 4.189 (4.189) Remain 08:18:27 loss: 0.0839 Lr: 0.00032
[2023-12-25 21:58:29,822 INFO misc.py line 119 253097] Train: [87/100][2/510] Data 0.004 (0.004) Batch 1.063 (1.063) Remain 02:06:27 loss: 0.1638 Lr: 0.00032
[2023-12-25 21:58:32,105 INFO misc.py line 119 253097] Train: [87/100][3/510] Data 1.264 (1.264) Batch 2.283 (2.283) Remain 04:31:33 loss: 0.0770 Lr: 0.00032
[2023-12-25 21:58:34,170 INFO misc.py line 119 253097] Train: [87/100][4/510] Data 0.268 (0.268) Batch 2.061 (2.061) Remain 04:05:09 loss: 0.2102 Lr: 0.00032
[2023-12-25 21:58:35,448 INFO misc.py line 119 253097] Train: [87/100][5/510] Data 0.008 (0.138) Batch 1.275 (1.668) Remain 03:18:22 loss: 0.1391 Lr: 0.00032
[2023-12-25 21:58:36,667 INFO misc.py line 119 253097] Train: [87/100][6/510] Data 0.010 (0.095) Batch 1.225 (1.521) Remain 03:00:47 loss: 0.1574 Lr: 0.00032
[2023-12-25 21:58:37,871 INFO misc.py line 119 253097] Train: [87/100][7/510] Data 0.003 (0.072) Batch 1.205 (1.442) Remain 02:51:24 loss: 0.1446 Lr: 0.00032
[2023-12-25 21:58:38,986 INFO misc.py line 119 253097] Train: [87/100][8/510] Data 0.003 (0.059) Batch 1.109 (1.375) Remain 02:43:28 loss: 0.2020 Lr: 0.00032
[2023-12-25 21:58:40,187 INFO misc.py line 119 253097] Train: [87/100][9/510] Data 0.009 (0.050) Batch 1.206 (1.347) Remain 02:40:05 loss: 0.0844 Lr: 0.00032
[2023-12-25 21:58:41,331 INFO misc.py line 119 253097] Train: [87/100][10/510] Data 0.004 (0.044) Batch 1.139 (1.317) Remain 02:36:33 loss: 0.0846 Lr: 0.00032
[2023-12-25 21:58:42,477 INFO misc.py line 119 253097] Train: [87/100][11/510] Data 0.008 (0.039) Batch 1.147 (1.296) Remain 02:34:00 loss: 0.2602 Lr: 0.00032
[2023-12-25 21:58:43,604 INFO misc.py line 119 253097] Train: [87/100][12/510] Data 0.007 (0.036) Batch 1.130 (1.278) Remain 02:31:47 loss: 0.0906 Lr: 0.00032
[2023-12-25 21:58:44,770 INFO misc.py line 119 253097] Train: [87/100][13/510] Data 0.004 (0.033) Batch 1.164 (1.266) Remain 02:30:25 loss: 0.0632 Lr: 0.00032
[2023-12-25 21:58:45,959 INFO misc.py line 119 253097] Train: [87/100][14/510] Data 0.006 (0.030) Batch 1.186 (1.259) Remain 02:29:31 loss: 0.0798 Lr: 0.00032
[2023-12-25 21:58:47,112 INFO misc.py line 119 253097] Train: [87/100][15/510] Data 0.008 (0.028) Batch 1.154 (1.250) Remain 02:28:28 loss: 0.0894 Lr: 0.00032
[2023-12-25 21:58:48,306 INFO misc.py line 119 253097] Train: [87/100][16/510] Data 0.007 (0.027) Batch 1.198 (1.246) Remain 02:27:58 loss: 0.0824 Lr: 0.00032
[2023-12-25 21:58:52,512 INFO misc.py line 119 253097] Train: [87/100][17/510] Data 0.003 (0.025) Batch 4.205 (1.458) Remain 02:53:02 loss: 0.1313 Lr: 0.00032
[2023-12-25 21:58:53,639 INFO misc.py line 119 253097] Train: [87/100][18/510] Data 0.004 (0.024) Batch 1.127 (1.436) Remain 02:50:24 loss: 0.0891 Lr: 0.00032
[2023-12-25 21:58:54,937 INFO misc.py line 119 253097] Train: [87/100][19/510] Data 0.004 (0.022) Batch 1.293 (1.427) Remain 02:49:19 loss: 0.1821 Lr: 0.00032
[2023-12-25 21:58:56,112 INFO misc.py line 119 253097] Train: [87/100][20/510] Data 0.008 (0.021) Batch 1.156 (1.411) Remain 02:47:24 loss: 0.1389 Lr: 0.00032
[2023-12-25 21:58:57,214 INFO misc.py line 119 253097] Train: [87/100][21/510] Data 0.028 (0.022) Batch 1.125 (1.395) Remain 02:45:30 loss: 0.1590 Lr: 0.00032
[2023-12-25 21:58:58,295 INFO misc.py line 119 253097] Train: [87/100][22/510] Data 0.005 (0.021) Batch 1.082 (1.378) Remain 02:43:31 loss: 0.1077 Lr: 0.00032
[2023-12-25 21:58:59,478 INFO misc.py line 119 253097] Train: [87/100][23/510] Data 0.004 (0.020) Batch 1.181 (1.369) Remain 02:42:19 loss: 0.1980 Lr: 0.00032
[2023-12-25 21:59:00,499 INFO misc.py line 119 253097] Train: [87/100][24/510] Data 0.007 (0.019) Batch 1.020 (1.352) Remain 02:40:20 loss: 0.1084 Lr: 0.00032
[2023-12-25 21:59:01,662 INFO misc.py line 119 253097] Train: [87/100][25/510] Data 0.007 (0.019) Batch 1.167 (1.343) Remain 02:39:18 loss: 0.1274 Lr: 0.00032
[2023-12-25 21:59:02,679 INFO misc.py line 119 253097] Train: [87/100][26/510] Data 0.006 (0.018) Batch 1.013 (1.329) Remain 02:37:35 loss: 0.1439 Lr: 0.00032
[2023-12-25 21:59:19,768 INFO misc.py line 119 253097] Train: [87/100][27/510] Data 0.008 (0.018) Batch 17.093 (1.986) Remain 03:55:26 loss: 0.0938 Lr: 0.00032
[2023-12-25 21:59:20,840 INFO misc.py line 119 253097] Train: [87/100][28/510] Data 0.004 (0.017) Batch 1.073 (1.949) Remain 03:51:04 loss: 0.1267 Lr: 0.00032
[2023-12-25 21:59:22,190 INFO misc.py line 119 253097] Train: [87/100][29/510] Data 0.004 (0.017) Batch 1.346 (1.926) Remain 03:48:17 loss: 0.0558 Lr: 0.00032
[2023-12-25 21:59:23,416 INFO misc.py line 119 253097] Train: [87/100][30/510] Data 0.006 (0.016) Batch 1.227 (1.900) Remain 03:45:11 loss: 0.1010 Lr: 0.00032
[2023-12-25 21:59:24,624 INFO misc.py line 119 253097] Train: [87/100][31/510] Data 0.006 (0.016) Batch 1.206 (1.876) Remain 03:42:13 loss: 0.0901 Lr: 0.00032
[2023-12-25 21:59:25,779 INFO misc.py line 119 253097] Train: [87/100][32/510] Data 0.008 (0.016) Batch 1.156 (1.851) Remain 03:39:14 loss: 0.0934 Lr: 0.00032
[2023-12-25 21:59:27,046 INFO misc.py line 119 253097] Train: [87/100][33/510] Data 0.008 (0.016) Batch 1.266 (1.831) Remain 03:36:54 loss: 0.0892 Lr: 0.00031
[2023-12-25 21:59:28,158 INFO misc.py line 119 253097] Train: [87/100][34/510] Data 0.007 (0.015) Batch 1.114 (1.808) Remain 03:34:08 loss: 0.1187 Lr: 0.00031
[2023-12-25 21:59:29,384 INFO misc.py line 119 253097] Train: [87/100][35/510] Data 0.007 (0.015) Batch 1.225 (1.790) Remain 03:31:56 loss: 0.1839 Lr: 0.00031
[2023-12-25 21:59:30,404 INFO misc.py line 119 253097] Train: [87/100][36/510] Data 0.008 (0.015) Batch 1.021 (1.767) Remain 03:29:09 loss: 0.0609 Lr: 0.00031
[2023-12-25 21:59:31,530 INFO misc.py line 119 253097] Train: [87/100][37/510] Data 0.007 (0.015) Batch 1.125 (1.748) Remain 03:26:53 loss: 0.0679 Lr: 0.00031
[2023-12-25 21:59:32,681 INFO misc.py line 119 253097] Train: [87/100][38/510] Data 0.008 (0.014) Batch 1.150 (1.731) Remain 03:24:50 loss: 0.0517 Lr: 0.00031
[2023-12-25 21:59:33,801 INFO misc.py line 119 253097] Train: [87/100][39/510] Data 0.008 (0.014) Batch 1.122 (1.714) Remain 03:22:48 loss: 0.1829 Lr: 0.00031
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[2023-12-25 22:02:32,218 INFO misc.py line 119 253097] Train: [87/100][169/510] Data 0.014 (0.108) Batch 1.069 (1.446) Remain 02:48:03 loss: 0.0603 Lr: 0.00030
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[2023-12-25 22:02:49,343 INFO misc.py line 119 253097] Train: [87/100][176/510] Data 0.004 (0.104) Batch 1.243 (1.487) Remain 02:52:34 loss: 0.1078 Lr: 0.00030
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[2023-12-25 22:03:02,424 INFO misc.py line 119 253097] Train: [87/100][181/510] Data 7.077 (0.141) Batch 8.376 (1.519) Remain 02:56:08 loss: 0.0563 Lr: 0.00030
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[2023-12-25 22:03:15,118 INFO misc.py line 119 253097] Train: [87/100][192/510] Data 0.008 (0.133) Batch 0.984 (1.497) Remain 02:53:24 loss: 0.1612 Lr: 0.00030
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[2023-12-25 22:03:23,510 INFO misc.py line 119 253097] Train: [87/100][196/510] Data 0.016 (0.131) Batch 4.817 (1.510) Remain 02:54:44 loss: 0.0508 Lr: 0.00030
[2023-12-25 22:03:24,360 INFO misc.py line 119 253097] Train: [87/100][197/510] Data 0.004 (0.130) Batch 0.850 (1.506) Remain 02:54:19 loss: 0.1490 Lr: 0.00030
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[2023-12-25 22:03:27,708 INFO misc.py line 119 253097] Train: [87/100][200/510] Data 0.008 (0.128) Batch 1.236 (1.501) Remain 02:53:33 loss: 0.1227 Lr: 0.00030
[2023-12-25 22:03:28,831 INFO misc.py line 119 253097] Train: [87/100][201/510] Data 0.008 (0.128) Batch 1.127 (1.499) Remain 02:53:18 loss: 0.1127 Lr: 0.00030
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[2023-12-25 22:03:31,935 INFO misc.py line 119 253097] Train: [87/100][204/510] Data 0.003 (0.126) Batch 1.163 (1.492) Remain 02:52:26 loss: 0.1306 Lr: 0.00030
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[2023-12-25 22:03:34,018 INFO misc.py line 119 253097] Train: [87/100][206/510] Data 0.007 (0.125) Batch 1.022 (1.487) Remain 02:51:52 loss: 0.0769 Lr: 0.00030
[2023-12-25 22:03:35,149 INFO misc.py line 119 253097] Train: [87/100][207/510] Data 0.008 (0.124) Batch 1.130 (1.485) Remain 02:51:38 loss: 0.1384 Lr: 0.00030
[2023-12-25 22:03:36,328 INFO misc.py line 119 253097] Train: [87/100][208/510] Data 0.009 (0.123) Batch 1.155 (1.484) Remain 02:51:26 loss: 0.1020 Lr: 0.00030
[2023-12-25 22:03:37,586 INFO misc.py line 119 253097] Train: [87/100][209/510] Data 0.032 (0.123) Batch 1.282 (1.483) Remain 02:51:17 loss: 0.1039 Lr: 0.00030
[2023-12-25 22:03:38,609 INFO misc.py line 119 253097] Train: [87/100][210/510] Data 0.008 (0.122) Batch 1.026 (1.481) Remain 02:51:01 loss: 0.1534 Lr: 0.00030
[2023-12-25 22:03:39,887 INFO misc.py line 119 253097] Train: [87/100][211/510] Data 0.006 (0.122) Batch 1.276 (1.480) Remain 02:50:52 loss: 0.1634 Lr: 0.00030
[2023-12-25 22:03:40,966 INFO misc.py line 119 253097] Train: [87/100][212/510] Data 0.007 (0.121) Batch 1.078 (1.478) Remain 02:50:38 loss: 0.1386 Lr: 0.00030
[2023-12-25 22:03:42,106 INFO misc.py line 119 253097] Train: [87/100][213/510] Data 0.008 (0.121) Batch 1.140 (1.476) Remain 02:50:25 loss: 0.1092 Lr: 0.00030
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[2023-12-25 22:03:45,442 INFO misc.py line 119 253097] Train: [87/100][216/510] Data 0.004 (0.119) Batch 1.209 (1.471) Remain 02:49:45 loss: 0.1855 Lr: 0.00030
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[2023-12-25 22:03:51,559 INFO misc.py line 119 253097] Train: [87/100][220/510] Data 0.003 (0.117) Batch 2.960 (1.472) Remain 02:49:47 loss: 0.1010 Lr: 0.00030
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[2023-12-25 22:03:53,726 INFO misc.py line 119 253097] Train: [87/100][222/510] Data 0.007 (0.116) Batch 1.167 (1.469) Remain 02:49:19 loss: 0.1140 Lr: 0.00030
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[2023-12-25 22:04:28,968 INFO misc.py line 119 253097] Train: [87/100][246/510] Data 0.004 (0.113) Batch 1.242 (1.469) Remain 02:48:44 loss: 0.0932 Lr: 0.00030
[2023-12-25 22:04:30,137 INFO misc.py line 119 253097] Train: [87/100][247/510] Data 0.006 (0.113) Batch 1.170 (1.467) Remain 02:48:34 loss: 0.1024 Lr: 0.00030
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[2023-12-25 22:08:15,148 INFO misc.py line 119 253097] Train: [87/100][412/510] Data 5.376 (0.094) Batch 6.479 (1.426) Remain 02:39:50 loss: 0.0737 Lr: 0.00028
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[2023-12-25 22:09:20,491 INFO misc.py line 119 253097] Train: [87/100][458/510] Data 0.007 (0.112) Batch 1.039 (1.425) Remain 02:38:42 loss: 0.0941 Lr: 0.00028
[2023-12-25 22:09:21,722 INFO misc.py line 119 253097] Train: [87/100][459/510] Data 0.003 (0.111) Batch 1.231 (1.425) Remain 02:38:37 loss: 0.1347 Lr: 0.00028
[2023-12-25 22:09:22,865 INFO misc.py line 119 253097] Train: [87/100][460/510] Data 0.003 (0.111) Batch 1.139 (1.424) Remain 02:38:32 loss: 0.1414 Lr: 0.00028
[2023-12-25 22:09:24,072 INFO misc.py line 119 253097] Train: [87/100][461/510] Data 0.007 (0.111) Batch 1.211 (1.424) Remain 02:38:27 loss: 0.1978 Lr: 0.00028
[2023-12-25 22:09:25,110 INFO misc.py line 119 253097] Train: [87/100][462/510] Data 0.003 (0.111) Batch 1.034 (1.423) Remain 02:38:20 loss: 0.0999 Lr: 0.00028
[2023-12-25 22:09:26,156 INFO misc.py line 119 253097] Train: [87/100][463/510] Data 0.007 (0.110) Batch 1.046 (1.422) Remain 02:38:13 loss: 0.1565 Lr: 0.00028
[2023-12-25 22:09:27,210 INFO misc.py line 119 253097] Train: [87/100][464/510] Data 0.007 (0.110) Batch 1.053 (1.421) Remain 02:38:06 loss: 0.0502 Lr: 0.00028
[2023-12-25 22:09:28,210 INFO misc.py line 119 253097] Train: [87/100][465/510] Data 0.008 (0.110) Batch 1.002 (1.420) Remain 02:37:59 loss: 0.2071 Lr: 0.00028
[2023-12-25 22:09:29,214 INFO misc.py line 119 253097] Train: [87/100][466/510] Data 0.006 (0.110) Batch 1.004 (1.419) Remain 02:37:51 loss: 0.1102 Lr: 0.00028
[2023-12-25 22:09:30,314 INFO misc.py line 119 253097] Train: [87/100][467/510] Data 0.006 (0.109) Batch 1.103 (1.419) Remain 02:37:46 loss: 0.1251 Lr: 0.00028
[2023-12-25 22:09:31,333 INFO misc.py line 119 253097] Train: [87/100][468/510] Data 0.004 (0.109) Batch 1.019 (1.418) Remain 02:37:38 loss: 0.1013 Lr: 0.00028
[2023-12-25 22:09:32,352 INFO misc.py line 119 253097] Train: [87/100][469/510] Data 0.003 (0.109) Batch 1.018 (1.417) Remain 02:37:31 loss: 0.1134 Lr: 0.00028
[2023-12-25 22:09:33,646 INFO misc.py line 119 253097] Train: [87/100][470/510] Data 0.005 (0.109) Batch 1.291 (1.417) Remain 02:37:28 loss: 0.1608 Lr: 0.00028
[2023-12-25 22:09:34,584 INFO misc.py line 119 253097] Train: [87/100][471/510] Data 0.006 (0.109) Batch 0.942 (1.416) Remain 02:37:20 loss: 0.0692 Lr: 0.00028
[2023-12-25 22:09:35,682 INFO misc.py line 119 253097] Train: [87/100][472/510] Data 0.002 (0.108) Batch 1.098 (1.415) Remain 02:37:14 loss: 0.1473 Lr: 0.00028
[2023-12-25 22:09:36,936 INFO misc.py line 119 253097] Train: [87/100][473/510] Data 0.003 (0.108) Batch 1.249 (1.415) Remain 02:37:10 loss: 0.1199 Lr: 0.00028
[2023-12-25 22:09:38,036 INFO misc.py line 119 253097] Train: [87/100][474/510] Data 0.008 (0.108) Batch 1.099 (1.414) Remain 02:37:04 loss: 0.1218 Lr: 0.00028
[2023-12-25 22:09:39,067 INFO misc.py line 119 253097] Train: [87/100][475/510] Data 0.008 (0.108) Batch 1.033 (1.413) Remain 02:36:57 loss: 0.1029 Lr: 0.00028
[2023-12-25 22:09:39,975 INFO misc.py line 119 253097] Train: [87/100][476/510] Data 0.006 (0.107) Batch 0.911 (1.412) Remain 02:36:49 loss: 0.1092 Lr: 0.00028
[2023-12-25 22:09:41,022 INFO misc.py line 119 253097] Train: [87/100][477/510] Data 0.004 (0.107) Batch 1.047 (1.411) Remain 02:36:42 loss: 0.0900 Lr: 0.00028
[2023-12-25 22:09:44,480 INFO misc.py line 119 253097] Train: [87/100][478/510] Data 0.004 (0.107) Batch 3.458 (1.416) Remain 02:37:10 loss: 0.1183 Lr: 0.00028
[2023-12-25 22:09:45,538 INFO misc.py line 119 253097] Train: [87/100][479/510] Data 0.004 (0.107) Batch 1.058 (1.415) Remain 02:37:03 loss: 0.1177 Lr: 0.00028
[2023-12-25 22:09:46,675 INFO misc.py line 119 253097] Train: [87/100][480/510] Data 0.003 (0.107) Batch 1.137 (1.414) Remain 02:36:58 loss: 0.0718 Lr: 0.00028
[2023-12-25 22:09:48,199 INFO misc.py line 119 253097] Train: [87/100][481/510] Data 0.004 (0.106) Batch 1.519 (1.414) Remain 02:36:58 loss: 0.0944 Lr: 0.00028
[2023-12-25 22:09:49,220 INFO misc.py line 119 253097] Train: [87/100][482/510] Data 0.009 (0.106) Batch 1.026 (1.414) Remain 02:36:51 loss: 0.1003 Lr: 0.00028
[2023-12-25 22:09:50,535 INFO misc.py line 119 253097] Train: [87/100][483/510] Data 0.004 (0.106) Batch 1.316 (1.413) Remain 02:36:48 loss: 0.0835 Lr: 0.00028
[2023-12-25 22:09:56,911 INFO misc.py line 119 253097] Train: [87/100][484/510] Data 0.003 (0.106) Batch 6.375 (1.424) Remain 02:37:56 loss: 0.1296 Lr: 0.00028
[2023-12-25 22:09:58,142 INFO misc.py line 119 253097] Train: [87/100][485/510] Data 0.003 (0.106) Batch 1.228 (1.423) Remain 02:37:52 loss: 0.0995 Lr: 0.00028
[2023-12-25 22:09:59,042 INFO misc.py line 119 253097] Train: [87/100][486/510] Data 0.006 (0.105) Batch 0.903 (1.422) Remain 02:37:43 loss: 0.0963 Lr: 0.00028
[2023-12-25 22:10:00,108 INFO misc.py line 119 253097] Train: [87/100][487/510] Data 0.003 (0.105) Batch 1.065 (1.421) Remain 02:37:37 loss: 0.0921 Lr: 0.00028
[2023-12-25 22:10:01,345 INFO misc.py line 119 253097] Train: [87/100][488/510] Data 0.004 (0.105) Batch 1.234 (1.421) Remain 02:37:33 loss: 0.1335 Lr: 0.00028
[2023-12-25 22:10:02,540 INFO misc.py line 119 253097] Train: [87/100][489/510] Data 0.007 (0.105) Batch 1.199 (1.421) Remain 02:37:28 loss: 0.2410 Lr: 0.00028
[2023-12-25 22:10:03,573 INFO misc.py line 119 253097] Train: [87/100][490/510] Data 0.004 (0.105) Batch 1.030 (1.420) Remain 02:37:21 loss: 0.2374 Lr: 0.00028
[2023-12-25 22:10:04,783 INFO misc.py line 119 253097] Train: [87/100][491/510] Data 0.008 (0.104) Batch 1.213 (1.419) Remain 02:37:17 loss: 0.2268 Lr: 0.00028
[2023-12-25 22:10:06,035 INFO misc.py line 119 253097] Train: [87/100][492/510] Data 0.005 (0.104) Batch 1.253 (1.419) Remain 02:37:14 loss: 0.0771 Lr: 0.00028
[2023-12-25 22:10:07,002 INFO misc.py line 119 253097] Train: [87/100][493/510] Data 0.003 (0.104) Batch 0.967 (1.418) Remain 02:37:06 loss: 0.1085 Lr: 0.00028
[2023-12-25 22:10:08,174 INFO misc.py line 119 253097] Train: [87/100][494/510] Data 0.002 (0.104) Batch 1.173 (1.418) Remain 02:37:01 loss: 0.1117 Lr: 0.00028
[2023-12-25 22:10:09,334 INFO misc.py line 119 253097] Train: [87/100][495/510] Data 0.003 (0.103) Batch 1.159 (1.417) Remain 02:36:56 loss: 0.0617 Lr: 0.00028
[2023-12-25 22:10:10,415 INFO misc.py line 119 253097] Train: [87/100][496/510] Data 0.004 (0.103) Batch 1.080 (1.416) Remain 02:36:50 loss: 0.0818 Lr: 0.00028
[2023-12-25 22:10:11,591 INFO misc.py line 119 253097] Train: [87/100][497/510] Data 0.004 (0.103) Batch 1.177 (1.416) Remain 02:36:46 loss: 0.0565 Lr: 0.00028
[2023-12-25 22:10:12,712 INFO misc.py line 119 253097] Train: [87/100][498/510] Data 0.004 (0.103) Batch 1.118 (1.415) Remain 02:36:40 loss: 0.0666 Lr: 0.00028
[2023-12-25 22:10:13,522 INFO misc.py line 119 253097] Train: [87/100][499/510] Data 0.007 (0.103) Batch 0.813 (1.414) Remain 02:36:31 loss: 0.0804 Lr: 0.00028
[2023-12-25 22:10:14,667 INFO misc.py line 119 253097] Train: [87/100][500/510] Data 0.003 (0.102) Batch 1.143 (1.414) Remain 02:36:26 loss: 0.1713 Lr: 0.00028
[2023-12-25 22:10:15,714 INFO misc.py line 119 253097] Train: [87/100][501/510] Data 0.005 (0.102) Batch 1.047 (1.413) Remain 02:36:20 loss: 0.0924 Lr: 0.00028
[2023-12-25 22:10:16,890 INFO misc.py line 119 253097] Train: [87/100][502/510] Data 0.004 (0.102) Batch 1.177 (1.412) Remain 02:36:15 loss: 0.1116 Lr: 0.00028
[2023-12-25 22:10:27,872 INFO misc.py line 119 253097] Train: [87/100][503/510] Data 0.003 (0.102) Batch 10.982 (1.432) Remain 02:38:21 loss: 0.1183 Lr: 0.00028
[2023-12-25 22:10:28,962 INFO misc.py line 119 253097] Train: [87/100][504/510] Data 0.004 (0.102) Batch 1.089 (1.431) Remain 02:38:15 loss: 0.0812 Lr: 0.00028
[2023-12-25 22:10:29,830 INFO misc.py line 119 253097] Train: [87/100][505/510] Data 0.003 (0.102) Batch 0.869 (1.430) Remain 02:38:06 loss: 0.0891 Lr: 0.00028
[2023-12-25 22:10:30,977 INFO misc.py line 119 253097] Train: [87/100][506/510] Data 0.003 (0.101) Batch 1.147 (1.429) Remain 02:38:01 loss: 0.0718 Lr: 0.00028
[2023-12-25 22:10:32,056 INFO misc.py line 119 253097] Train: [87/100][507/510] Data 0.003 (0.101) Batch 1.079 (1.428) Remain 02:37:55 loss: 0.1771 Lr: 0.00028
[2023-12-25 22:10:33,042 INFO misc.py line 119 253097] Train: [87/100][508/510] Data 0.003 (0.101) Batch 0.986 (1.428) Remain 02:37:47 loss: 0.1203 Lr: 0.00028
[2023-12-25 22:10:34,200 INFO misc.py line 119 253097] Train: [87/100][509/510] Data 0.003 (0.101) Batch 1.159 (1.427) Remain 02:37:42 loss: 0.1405 Lr: 0.00028
[2023-12-25 22:10:35,164 INFO misc.py line 119 253097] Train: [87/100][510/510] Data 0.003 (0.101) Batch 0.964 (1.426) Remain 02:37:35 loss: 0.0756 Lr: 0.00027
[2023-12-25 22:10:35,165 INFO misc.py line 136 253097] Train result: loss: 0.1169 
[2023-12-25 22:10:35,165 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 22:11:04,299 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5208 
[2023-12-25 22:11:04,644 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3055 
[2023-12-25 22:11:11,712 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3878 
[2023-12-25 22:11:12,229 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3919 
[2023-12-25 22:11:14,196 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8397 
[2023-12-25 22:11:14,617 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3353 
[2023-12-25 22:11:15,496 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1418 
[2023-12-25 22:11:16,048 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2541 
[2023-12-25 22:11:17,854 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1058 
[2023-12-25 22:11:19,974 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1058 
[2023-12-25 22:11:20,829 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3059 
[2023-12-25 22:11:21,251 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7376 
[2023-12-25 22:11:22,150 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4653 
[2023-12-25 22:11:25,095 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8733 
[2023-12-25 22:11:25,563 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3006 
[2023-12-25 22:11:26,173 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3542 
[2023-12-25 22:11:26,872 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3683 
[2023-12-25 22:11:28,188 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6956/0.7521/0.9067.
[2023-12-25 22:11:28,188 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9177/0.9474
[2023-12-25 22:11:28,188 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9829/0.9893
[2023-12-25 22:11:28,188 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8472/0.9721
[2023-12-25 22:11:28,188 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3284/0.3557
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6307/0.6507
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7200/0.8158
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8081/0.8919
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9189/0.9619
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6723/0.7195
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7870/0.8702
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8110/0.8595
[2023-12-25 22:11:28,189 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6190/0.7427
[2023-12-25 22:11:28,189 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 22:11:28,191 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 22:11:28,191 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 22:11:34,614 INFO misc.py line 119 253097] Train: [88/100][1/510] Data 1.913 (1.913) Batch 4.151 (4.151) Remain 07:38:34 loss: 0.0915 Lr: 0.00027
[2023-12-25 22:11:49,458 INFO misc.py line 119 253097] Train: [88/100][2/510] Data 2.167 (2.167) Batch 14.844 (14.844) Remain 27:19:45 loss: 0.0646 Lr: 0.00027
[2023-12-25 22:11:50,788 INFO misc.py line 119 253097] Train: [88/100][3/510] Data 0.006 (0.006) Batch 1.328 (1.328) Remain 02:26:41 loss: 0.1214 Lr: 0.00027
[2023-12-25 22:11:51,777 INFO misc.py line 119 253097] Train: [88/100][4/510] Data 0.007 (0.007) Batch 0.992 (0.992) Remain 01:49:31 loss: 0.1582 Lr: 0.00027
[2023-12-25 22:11:52,698 INFO misc.py line 119 253097] Train: [88/100][5/510] Data 0.003 (0.005) Batch 0.921 (0.957) Remain 01:45:37 loss: 0.0847 Lr: 0.00027
[2023-12-25 22:11:53,941 INFO misc.py line 119 253097] Train: [88/100][6/510] Data 0.003 (0.004) Batch 1.242 (1.052) Remain 01:56:07 loss: 0.1186 Lr: 0.00027
[2023-12-25 22:11:55,075 INFO misc.py line 119 253097] Train: [88/100][7/510] Data 0.004 (0.004) Batch 1.134 (1.072) Remain 01:58:21 loss: 0.1320 Lr: 0.00027
[2023-12-25 22:11:56,063 INFO misc.py line 119 253097] Train: [88/100][8/510] Data 0.004 (0.004) Batch 0.988 (1.055) Remain 01:56:29 loss: 0.0756 Lr: 0.00027
[2023-12-25 22:11:57,355 INFO misc.py line 119 253097] Train: [88/100][9/510] Data 0.003 (0.004) Batch 1.287 (1.094) Remain 02:00:43 loss: 0.2075 Lr: 0.00027
[2023-12-25 22:11:58,444 INFO misc.py line 119 253097] Train: [88/100][10/510] Data 0.009 (0.005) Batch 1.095 (1.094) Remain 02:00:42 loss: 0.0912 Lr: 0.00027
[2023-12-25 22:11:59,531 INFO misc.py line 119 253097] Train: [88/100][11/510] Data 0.004 (0.005) Batch 1.085 (1.093) Remain 02:00:34 loss: 0.1345 Lr: 0.00027
[2023-12-25 22:12:00,466 INFO misc.py line 119 253097] Train: [88/100][12/510] Data 0.006 (0.005) Batch 0.937 (1.076) Remain 01:58:38 loss: 0.1293 Lr: 0.00027
[2023-12-25 22:12:01,680 INFO misc.py line 119 253097] Train: [88/100][13/510] Data 0.004 (0.005) Batch 1.210 (1.089) Remain 02:00:06 loss: 0.1131 Lr: 0.00027
[2023-12-25 22:12:02,777 INFO misc.py line 119 253097] Train: [88/100][14/510] Data 0.007 (0.005) Batch 1.100 (1.090) Remain 02:00:12 loss: 0.0673 Lr: 0.00027
[2023-12-25 22:12:06,629 INFO misc.py line 119 253097] Train: [88/100][15/510] Data 3.022 (0.256) Batch 3.852 (1.320) Remain 02:25:33 loss: 0.0689 Lr: 0.00027
[2023-12-25 22:12:07,880 INFO misc.py line 119 253097] Train: [88/100][16/510] Data 0.004 (0.237) Batch 1.248 (1.315) Remain 02:24:55 loss: 0.1935 Lr: 0.00027
[2023-12-25 22:12:09,940 INFO misc.py line 119 253097] Train: [88/100][17/510] Data 1.178 (0.304) Batch 2.063 (1.368) Remain 02:30:47 loss: 0.0864 Lr: 0.00027
[2023-12-25 22:12:10,959 INFO misc.py line 119 253097] Train: [88/100][18/510] Data 0.004 (0.284) Batch 1.014 (1.345) Remain 02:28:10 loss: 0.0812 Lr: 0.00027
[2023-12-25 22:12:12,040 INFO misc.py line 119 253097] Train: [88/100][19/510] Data 0.009 (0.267) Batch 1.081 (1.328) Remain 02:26:20 loss: 0.0724 Lr: 0.00027
[2023-12-25 22:12:13,115 INFO misc.py line 119 253097] Train: [88/100][20/510] Data 0.008 (0.252) Batch 1.073 (1.313) Remain 02:24:39 loss: 0.0806 Lr: 0.00027
[2023-12-25 22:12:14,333 INFO misc.py line 119 253097] Train: [88/100][21/510] Data 0.010 (0.238) Batch 1.225 (1.308) Remain 02:24:05 loss: 0.0913 Lr: 0.00027
[2023-12-25 22:12:15,250 INFO misc.py line 119 253097] Train: [88/100][22/510] Data 0.004 (0.226) Batch 0.917 (1.288) Remain 02:21:48 loss: 0.1729 Lr: 0.00027
[2023-12-25 22:12:16,412 INFO misc.py line 119 253097] Train: [88/100][23/510] Data 0.003 (0.215) Batch 1.162 (1.281) Remain 02:21:05 loss: 0.1712 Lr: 0.00027
[2023-12-25 22:12:18,710 INFO misc.py line 119 253097] Train: [88/100][24/510] Data 0.004 (0.205) Batch 2.298 (1.330) Remain 02:26:24 loss: 0.1052 Lr: 0.00027
[2023-12-25 22:12:19,893 INFO misc.py line 119 253097] Train: [88/100][25/510] Data 0.003 (0.196) Batch 1.183 (1.323) Remain 02:25:38 loss: 0.0830 Lr: 0.00027
[2023-12-25 22:12:20,919 INFO misc.py line 119 253097] Train: [88/100][26/510] Data 0.004 (0.187) Batch 1.025 (1.310) Remain 02:24:11 loss: 0.0680 Lr: 0.00027
[2023-12-25 22:12:21,991 INFO misc.py line 119 253097] Train: [88/100][27/510] Data 0.004 (0.180) Batch 1.073 (1.300) Remain 02:23:05 loss: 0.0639 Lr: 0.00027
[2023-12-25 22:12:23,085 INFO misc.py line 119 253097] Train: [88/100][28/510] Data 0.005 (0.173) Batch 1.093 (1.292) Remain 02:22:09 loss: 0.0981 Lr: 0.00027
[2023-12-25 22:12:24,096 INFO misc.py line 119 253097] Train: [88/100][29/510] Data 0.005 (0.166) Batch 1.011 (1.281) Remain 02:20:56 loss: 0.1761 Lr: 0.00027
[2023-12-25 22:12:25,211 INFO misc.py line 119 253097] Train: [88/100][30/510] Data 0.005 (0.160) Batch 1.117 (1.275) Remain 02:20:15 loss: 0.0825 Lr: 0.00027
[2023-12-25 22:12:26,446 INFO misc.py line 119 253097] Train: [88/100][31/510] Data 0.004 (0.155) Batch 1.235 (1.274) Remain 02:20:04 loss: 0.0642 Lr: 0.00027
[2023-12-25 22:12:27,307 INFO misc.py line 119 253097] Train: [88/100][32/510] Data 0.003 (0.149) Batch 0.860 (1.259) Remain 02:18:29 loss: 0.0782 Lr: 0.00027
[2023-12-25 22:12:32,745 INFO misc.py line 119 253097] Train: [88/100][33/510] Data 0.003 (0.145) Batch 5.439 (1.399) Remain 02:33:47 loss: 0.1945 Lr: 0.00027
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[2023-12-25 22:18:42,509 INFO misc.py line 119 253097] Train: [88/100][299/510] Data 0.003 (0.037) Batch 1.113 (1.391) Remain 02:26:46 loss: 0.0881 Lr: 0.00025
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[2023-12-25 22:22:29,423 INFO misc.py line 119 253097] Train: [88/100][452/510] Data 0.003 (0.048) Batch 1.193 (1.422) Remain 02:26:27 loss: 0.0896 Lr: 0.00024
[2023-12-25 22:22:30,577 INFO misc.py line 119 253097] Train: [88/100][453/510] Data 0.007 (0.048) Batch 1.153 (1.422) Remain 02:26:22 loss: 0.0850 Lr: 0.00024
[2023-12-25 22:22:31,743 INFO misc.py line 119 253097] Train: [88/100][454/510] Data 0.007 (0.048) Batch 1.168 (1.421) Remain 02:26:17 loss: 0.0761 Lr: 0.00024
[2023-12-25 22:22:32,764 INFO misc.py line 119 253097] Train: [88/100][455/510] Data 0.005 (0.048) Batch 1.021 (1.420) Remain 02:26:10 loss: 0.2009 Lr: 0.00024
[2023-12-25 22:22:33,844 INFO misc.py line 119 253097] Train: [88/100][456/510] Data 0.005 (0.048) Batch 1.081 (1.420) Remain 02:26:04 loss: 0.1299 Lr: 0.00024
[2023-12-25 22:22:35,113 INFO misc.py line 119 253097] Train: [88/100][457/510] Data 0.003 (0.047) Batch 1.267 (1.419) Remain 02:26:00 loss: 0.0800 Lr: 0.00024
[2023-12-25 22:22:36,362 INFO misc.py line 119 253097] Train: [88/100][458/510] Data 0.005 (0.047) Batch 1.249 (1.419) Remain 02:25:57 loss: 0.1003 Lr: 0.00024
[2023-12-25 22:22:37,540 INFO misc.py line 119 253097] Train: [88/100][459/510] Data 0.006 (0.047) Batch 1.181 (1.418) Remain 02:25:52 loss: 0.1052 Lr: 0.00024
[2023-12-25 22:22:38,448 INFO misc.py line 119 253097] Train: [88/100][460/510] Data 0.003 (0.047) Batch 0.908 (1.417) Remain 02:25:44 loss: 0.1078 Lr: 0.00024
[2023-12-25 22:22:39,465 INFO misc.py line 119 253097] Train: [88/100][461/510] Data 0.003 (0.047) Batch 1.016 (1.416) Remain 02:25:37 loss: 0.1454 Lr: 0.00024
[2023-12-25 22:22:40,805 INFO misc.py line 119 253097] Train: [88/100][462/510] Data 0.003 (0.047) Batch 1.339 (1.416) Remain 02:25:34 loss: 0.0668 Lr: 0.00024
[2023-12-25 22:22:42,058 INFO misc.py line 119 253097] Train: [88/100][463/510] Data 0.005 (0.047) Batch 1.251 (1.416) Remain 02:25:31 loss: 0.1060 Lr: 0.00024
[2023-12-25 22:22:43,284 INFO misc.py line 119 253097] Train: [88/100][464/510] Data 0.006 (0.047) Batch 1.224 (1.415) Remain 02:25:27 loss: 0.2506 Lr: 0.00024
[2023-12-25 22:22:44,384 INFO misc.py line 119 253097] Train: [88/100][465/510] Data 0.007 (0.047) Batch 1.099 (1.415) Remain 02:25:21 loss: 0.1038 Lr: 0.00024
[2023-12-25 22:22:45,442 INFO misc.py line 119 253097] Train: [88/100][466/510] Data 0.009 (0.047) Batch 1.064 (1.414) Remain 02:25:15 loss: 0.1764 Lr: 0.00024
[2023-12-25 22:22:46,480 INFO misc.py line 119 253097] Train: [88/100][467/510] Data 0.003 (0.047) Batch 1.038 (1.413) Remain 02:25:09 loss: 0.0990 Lr: 0.00024
[2023-12-25 22:22:47,605 INFO misc.py line 119 253097] Train: [88/100][468/510] Data 0.003 (0.046) Batch 1.123 (1.413) Remain 02:25:03 loss: 0.0842 Lr: 0.00024
[2023-12-25 22:22:48,906 INFO misc.py line 119 253097] Train: [88/100][469/510] Data 0.006 (0.046) Batch 1.301 (1.412) Remain 02:25:00 loss: 0.0865 Lr: 0.00024
[2023-12-25 22:22:49,917 INFO misc.py line 119 253097] Train: [88/100][470/510] Data 0.006 (0.046) Batch 1.009 (1.411) Remain 02:24:54 loss: 0.0750 Lr: 0.00024
[2023-12-25 22:22:51,231 INFO misc.py line 119 253097] Train: [88/100][471/510] Data 0.007 (0.046) Batch 1.318 (1.411) Remain 02:24:51 loss: 0.0567 Lr: 0.00024
[2023-12-25 22:22:52,415 INFO misc.py line 119 253097] Train: [88/100][472/510] Data 0.004 (0.046) Batch 1.182 (1.411) Remain 02:24:47 loss: 0.0768 Lr: 0.00024
[2023-12-25 22:22:53,186 INFO misc.py line 119 253097] Train: [88/100][473/510] Data 0.006 (0.046) Batch 0.774 (1.409) Remain 02:24:37 loss: 0.2216 Lr: 0.00024
[2023-12-25 22:22:54,322 INFO misc.py line 119 253097] Train: [88/100][474/510] Data 0.003 (0.046) Batch 1.136 (1.409) Remain 02:24:32 loss: 0.1139 Lr: 0.00024
[2023-12-25 22:22:55,471 INFO misc.py line 119 253097] Train: [88/100][475/510] Data 0.003 (0.046) Batch 1.150 (1.408) Remain 02:24:27 loss: 0.1778 Lr: 0.00024
[2023-12-25 22:22:56,373 INFO misc.py line 119 253097] Train: [88/100][476/510] Data 0.003 (0.046) Batch 0.901 (1.407) Remain 02:24:19 loss: 0.0777 Lr: 0.00024
[2023-12-25 22:22:57,324 INFO misc.py line 119 253097] Train: [88/100][477/510] Data 0.004 (0.046) Batch 0.948 (1.406) Remain 02:24:12 loss: 0.1094 Lr: 0.00024
[2023-12-25 22:22:58,427 INFO misc.py line 119 253097] Train: [88/100][478/510] Data 0.006 (0.046) Batch 1.104 (1.406) Remain 02:24:06 loss: 0.1213 Lr: 0.00024
[2023-12-25 22:22:59,621 INFO misc.py line 119 253097] Train: [88/100][479/510] Data 0.006 (0.045) Batch 1.194 (1.405) Remain 02:24:02 loss: 0.1055 Lr: 0.00024
[2023-12-25 22:23:00,755 INFO misc.py line 119 253097] Train: [88/100][480/510] Data 0.005 (0.045) Batch 1.132 (1.405) Remain 02:23:57 loss: 0.0912 Lr: 0.00024
[2023-12-25 22:23:01,932 INFO misc.py line 119 253097] Train: [88/100][481/510] Data 0.007 (0.045) Batch 1.175 (1.404) Remain 02:23:53 loss: 0.2201 Lr: 0.00024
[2023-12-25 22:23:02,889 INFO misc.py line 119 253097] Train: [88/100][482/510] Data 0.009 (0.045) Batch 0.963 (1.403) Remain 02:23:46 loss: 0.1966 Lr: 0.00024
[2023-12-25 22:23:03,964 INFO misc.py line 119 253097] Train: [88/100][483/510] Data 0.003 (0.045) Batch 1.075 (1.402) Remain 02:23:40 loss: 0.0583 Lr: 0.00024
[2023-12-25 22:23:05,169 INFO misc.py line 119 253097] Train: [88/100][484/510] Data 0.003 (0.045) Batch 1.204 (1.402) Remain 02:23:36 loss: 0.0635 Lr: 0.00024
[2023-12-25 22:23:06,219 INFO misc.py line 119 253097] Train: [88/100][485/510] Data 0.004 (0.045) Batch 1.051 (1.401) Remain 02:23:31 loss: 0.1374 Lr: 0.00024
[2023-12-25 22:23:07,430 INFO misc.py line 119 253097] Train: [88/100][486/510] Data 0.003 (0.045) Batch 1.211 (1.401) Remain 02:23:27 loss: 0.1513 Lr: 0.00024
[2023-12-25 22:23:12,861 INFO misc.py line 119 253097] Train: [88/100][487/510] Data 0.003 (0.045) Batch 5.431 (1.409) Remain 02:24:16 loss: 0.0823 Lr: 0.00024
[2023-12-25 22:23:13,947 INFO misc.py line 119 253097] Train: [88/100][488/510] Data 0.004 (0.045) Batch 1.085 (1.409) Remain 02:24:11 loss: 0.1367 Lr: 0.00024
[2023-12-25 22:23:15,109 INFO misc.py line 119 253097] Train: [88/100][489/510] Data 0.003 (0.045) Batch 1.162 (1.408) Remain 02:24:06 loss: 0.0879 Lr: 0.00024
[2023-12-25 22:23:16,242 INFO misc.py line 119 253097] Train: [88/100][490/510] Data 0.003 (0.045) Batch 1.133 (1.408) Remain 02:24:02 loss: 0.1335 Lr: 0.00024
[2023-12-25 22:23:17,483 INFO misc.py line 119 253097] Train: [88/100][491/510] Data 0.003 (0.044) Batch 1.240 (1.407) Remain 02:23:58 loss: 0.1439 Lr: 0.00024
[2023-12-25 22:23:18,477 INFO misc.py line 119 253097] Train: [88/100][492/510] Data 0.002 (0.044) Batch 0.995 (1.406) Remain 02:23:52 loss: 0.0738 Lr: 0.00024
[2023-12-25 22:23:31,567 INFO misc.py line 119 253097] Train: [88/100][493/510] Data 0.003 (0.044) Batch 13.089 (1.430) Remain 02:26:16 loss: 0.1273 Lr: 0.00024
[2023-12-25 22:23:32,773 INFO misc.py line 119 253097] Train: [88/100][494/510] Data 0.004 (0.044) Batch 1.204 (1.430) Remain 02:26:12 loss: 0.1166 Lr: 0.00024
[2023-12-25 22:23:34,034 INFO misc.py line 119 253097] Train: [88/100][495/510] Data 0.006 (0.044) Batch 1.261 (1.429) Remain 02:26:09 loss: 0.1210 Lr: 0.00024
[2023-12-25 22:23:35,208 INFO misc.py line 119 253097] Train: [88/100][496/510] Data 0.008 (0.044) Batch 1.171 (1.429) Remain 02:26:04 loss: 0.1077 Lr: 0.00024
[2023-12-25 22:23:36,300 INFO misc.py line 119 253097] Train: [88/100][497/510] Data 0.010 (0.044) Batch 1.094 (1.428) Remain 02:25:58 loss: 0.0459 Lr: 0.00024
[2023-12-25 22:23:37,499 INFO misc.py line 119 253097] Train: [88/100][498/510] Data 0.007 (0.044) Batch 1.200 (1.428) Remain 02:25:54 loss: 0.1114 Lr: 0.00024
[2023-12-25 22:23:38,753 INFO misc.py line 119 253097] Train: [88/100][499/510] Data 0.007 (0.044) Batch 1.255 (1.427) Remain 02:25:51 loss: 0.0524 Lr: 0.00024
[2023-12-25 22:23:39,932 INFO misc.py line 119 253097] Train: [88/100][500/510] Data 0.005 (0.044) Batch 1.178 (1.427) Remain 02:25:46 loss: 0.0708 Lr: 0.00024
[2023-12-25 22:23:41,219 INFO misc.py line 119 253097] Train: [88/100][501/510] Data 0.005 (0.044) Batch 1.288 (1.427) Remain 02:25:43 loss: 0.1635 Lr: 0.00024
[2023-12-25 22:23:42,423 INFO misc.py line 119 253097] Train: [88/100][502/510] Data 0.004 (0.044) Batch 1.202 (1.426) Remain 02:25:39 loss: 0.1474 Lr: 0.00024
[2023-12-25 22:23:43,529 INFO misc.py line 119 253097] Train: [88/100][503/510] Data 0.006 (0.044) Batch 1.108 (1.425) Remain 02:25:33 loss: 0.1146 Lr: 0.00024
[2023-12-25 22:23:44,554 INFO misc.py line 119 253097] Train: [88/100][504/510] Data 0.004 (0.043) Batch 1.021 (1.425) Remain 02:25:27 loss: 0.0979 Lr: 0.00024
[2023-12-25 22:23:45,475 INFO misc.py line 119 253097] Train: [88/100][505/510] Data 0.007 (0.043) Batch 0.925 (1.424) Remain 02:25:20 loss: 0.1456 Lr: 0.00024
[2023-12-25 22:23:46,607 INFO misc.py line 119 253097] Train: [88/100][506/510] Data 0.004 (0.043) Batch 1.132 (1.423) Remain 02:25:15 loss: 0.1137 Lr: 0.00024
[2023-12-25 22:23:47,675 INFO misc.py line 119 253097] Train: [88/100][507/510] Data 0.003 (0.043) Batch 1.068 (1.422) Remain 02:25:09 loss: 0.0890 Lr: 0.00024
[2023-12-25 22:23:48,796 INFO misc.py line 119 253097] Train: [88/100][508/510] Data 0.003 (0.043) Batch 1.121 (1.422) Remain 02:25:04 loss: 0.0934 Lr: 0.00024
[2023-12-25 22:23:49,978 INFO misc.py line 119 253097] Train: [88/100][509/510] Data 0.003 (0.043) Batch 1.182 (1.421) Remain 02:24:59 loss: 0.1160 Lr: 0.00024
[2023-12-25 22:23:51,065 INFO misc.py line 119 253097] Train: [88/100][510/510] Data 0.003 (0.043) Batch 1.086 (1.421) Remain 02:24:54 loss: 0.1178 Lr: 0.00023
[2023-12-25 22:23:51,065 INFO misc.py line 136 253097] Train result: loss: 0.1125 
[2023-12-25 22:23:51,066 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 22:24:21,607 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6713 
[2023-12-25 22:24:21,949 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2756 
[2023-12-25 22:24:26,889 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.4785 
[2023-12-25 22:24:27,410 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4353 
[2023-12-25 22:24:29,379 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9146 
[2023-12-25 22:24:29,799 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3129 
[2023-12-25 22:24:30,675 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1332 
[2023-12-25 22:24:31,229 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2899 
[2023-12-25 22:24:33,036 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 1.1848 
[2023-12-25 22:24:35,154 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.0949 
[2023-12-25 22:24:36,007 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.2878 
[2023-12-25 22:24:36,432 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7985 
[2023-12-25 22:24:37,333 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4331 
[2023-12-25 22:24:40,280 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9380 
[2023-12-25 22:24:40,746 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3095 
[2023-12-25 22:24:41,353 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3811 
[2023-12-25 22:24:42,051 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3821 
[2023-12-25 22:24:43,210 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6889/0.7439/0.9045.
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9171/0.9470
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9835/0.9904
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8397/0.9747
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.2830/0.3046
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6336/0.6557
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7123/0.8018
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8119/0.9075
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9184/0.9609
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6582/0.6999
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7905/0.8733
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.7965/0.8392
[2023-12-25 22:24:43,210 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6109/0.7156
[2023-12-25 22:24:43,211 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 22:24:43,212 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 22:24:43,212 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 22:24:50,229 INFO misc.py line 119 253097] Train: [89/100][1/510] Data 1.387 (1.387) Batch 5.226 (5.226) Remain 08:53:00 loss: 0.1675 Lr: 0.00023
[2023-12-25 22:24:51,188 INFO misc.py line 119 253097] Train: [89/100][2/510] Data 0.003 (0.003) Batch 0.960 (0.960) Remain 01:37:52 loss: 0.1180 Lr: 0.00023
[2023-12-25 22:24:52,407 INFO misc.py line 119 253097] Train: [89/100][3/510] Data 0.003 (0.003) Batch 1.219 (1.219) Remain 02:04:14 loss: 0.0962 Lr: 0.00023
[2023-12-25 22:24:53,288 INFO misc.py line 119 253097] Train: [89/100][4/510] Data 0.003 (0.003) Batch 0.881 (0.881) Remain 01:29:47 loss: 0.1883 Lr: 0.00023
[2023-12-25 22:24:54,445 INFO misc.py line 119 253097] Train: [89/100][5/510] Data 0.003 (0.003) Batch 1.157 (1.019) Remain 01:43:49 loss: 0.1089 Lr: 0.00023
[2023-12-25 22:24:55,469 INFO misc.py line 119 253097] Train: [89/100][6/510] Data 0.003 (0.003) Batch 1.024 (1.020) Remain 01:43:59 loss: 0.0707 Lr: 0.00023
[2023-12-25 22:24:59,023 INFO misc.py line 119 253097] Train: [89/100][7/510] Data 0.004 (0.003) Batch 3.554 (1.654) Remain 02:48:29 loss: 0.0852 Lr: 0.00023
[2023-12-25 22:24:59,995 INFO misc.py line 119 253097] Train: [89/100][8/510] Data 0.004 (0.003) Batch 0.972 (1.517) Remain 02:34:33 loss: 0.0701 Lr: 0.00023
[2023-12-25 22:25:01,268 INFO misc.py line 119 253097] Train: [89/100][9/510] Data 0.004 (0.003) Batch 1.274 (1.477) Remain 02:30:24 loss: 0.1042 Lr: 0.00023
[2023-12-25 22:25:02,538 INFO misc.py line 119 253097] Train: [89/100][10/510] Data 0.003 (0.003) Batch 1.270 (1.447) Remain 02:27:22 loss: 0.0817 Lr: 0.00023
[2023-12-25 22:25:03,687 INFO misc.py line 119 253097] Train: [89/100][11/510] Data 0.004 (0.003) Batch 1.146 (1.409) Remain 02:23:30 loss: 0.0484 Lr: 0.00023
[2023-12-25 22:25:04,730 INFO misc.py line 119 253097] Train: [89/100][12/510] Data 0.007 (0.004) Batch 1.042 (1.369) Remain 02:19:19 loss: 0.1020 Lr: 0.00023
[2023-12-25 22:25:05,927 INFO misc.py line 119 253097] Train: [89/100][13/510] Data 0.008 (0.004) Batch 1.202 (1.352) Remain 02:17:36 loss: 0.1273 Lr: 0.00023
[2023-12-25 22:25:07,010 INFO misc.py line 119 253097] Train: [89/100][14/510] Data 0.003 (0.004) Batch 1.081 (1.327) Remain 02:15:04 loss: 0.1131 Lr: 0.00023
[2023-12-25 22:25:07,960 INFO misc.py line 119 253097] Train: [89/100][15/510] Data 0.005 (0.004) Batch 0.951 (1.296) Remain 02:11:51 loss: 0.1126 Lr: 0.00023
[2023-12-25 22:25:09,263 INFO misc.py line 119 253097] Train: [89/100][16/510] Data 0.005 (0.004) Batch 1.304 (1.297) Remain 02:11:54 loss: 0.0865 Lr: 0.00023
[2023-12-25 22:25:10,181 INFO misc.py line 119 253097] Train: [89/100][17/510] Data 0.003 (0.004) Batch 0.917 (1.269) Remain 02:09:07 loss: 0.0755 Lr: 0.00023
[2023-12-25 22:25:11,285 INFO misc.py line 119 253097] Train: [89/100][18/510] Data 0.005 (0.004) Batch 1.104 (1.258) Remain 02:07:58 loss: 0.1015 Lr: 0.00023
[2023-12-25 22:25:12,373 INFO misc.py line 119 253097] Train: [89/100][19/510] Data 0.005 (0.004) Batch 1.089 (1.248) Remain 02:06:52 loss: 0.0985 Lr: 0.00023
[2023-12-25 22:25:13,667 INFO misc.py line 119 253097] Train: [89/100][20/510] Data 0.004 (0.004) Batch 1.283 (1.250) Remain 02:07:04 loss: 0.0731 Lr: 0.00023
[2023-12-25 22:25:14,715 INFO misc.py line 119 253097] Train: [89/100][21/510] Data 0.014 (0.005) Batch 1.056 (1.239) Remain 02:05:57 loss: 0.0900 Lr: 0.00023
[2023-12-25 22:25:16,631 INFO misc.py line 119 253097] Train: [89/100][22/510] Data 0.653 (0.039) Batch 1.918 (1.275) Remain 02:09:34 loss: 0.1084 Lr: 0.00023
[2023-12-25 22:25:17,848 INFO misc.py line 119 253097] Train: [89/100][23/510] Data 0.004 (0.037) Batch 1.217 (1.272) Remain 02:09:15 loss: 0.1518 Lr: 0.00023
[2023-12-25 22:25:18,802 INFO misc.py line 119 253097] Train: [89/100][24/510] Data 0.005 (0.036) Batch 0.956 (1.257) Remain 02:07:41 loss: 0.0986 Lr: 0.00023
[2023-12-25 22:25:19,789 INFO misc.py line 119 253097] Train: [89/100][25/510] Data 0.003 (0.034) Batch 0.987 (1.245) Remain 02:06:25 loss: 0.1018 Lr: 0.00023
[2023-12-25 22:25:20,923 INFO misc.py line 119 253097] Train: [89/100][26/510] Data 0.005 (0.033) Batch 1.132 (1.240) Remain 02:05:54 loss: 0.0680 Lr: 0.00023
[2023-12-25 22:25:24,341 INFO misc.py line 119 253097] Train: [89/100][27/510] Data 0.006 (0.032) Batch 3.421 (1.331) Remain 02:15:07 loss: 0.1161 Lr: 0.00023
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[2023-12-25 22:31:50,434 INFO misc.py line 119 253097] Train: [89/100][297/510] Data 8.262 (0.059) Batch 9.368 (1.422) Remain 02:17:59 loss: 0.1451 Lr: 0.00021
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[2023-12-25 22:35:18,990 INFO misc.py line 119 253097] Train: [89/100][446/510] Data 0.006 (0.076) Batch 1.168 (1.414) Remain 02:13:45 loss: 0.0909 Lr: 0.00020
[2023-12-25 22:35:20,296 INFO misc.py line 119 253097] Train: [89/100][447/510] Data 0.004 (0.076) Batch 1.302 (1.414) Remain 02:13:42 loss: 0.0770 Lr: 0.00020
[2023-12-25 22:35:21,487 INFO misc.py line 119 253097] Train: [89/100][448/510] Data 0.008 (0.076) Batch 1.191 (1.414) Remain 02:13:38 loss: 0.0771 Lr: 0.00020
[2023-12-25 22:35:22,582 INFO misc.py line 119 253097] Train: [89/100][449/510] Data 0.008 (0.076) Batch 1.100 (1.413) Remain 02:13:32 loss: 0.0956 Lr: 0.00020
[2023-12-25 22:35:23,659 INFO misc.py line 119 253097] Train: [89/100][450/510] Data 0.003 (0.076) Batch 1.073 (1.412) Remain 02:13:27 loss: 0.0799 Lr: 0.00020
[2023-12-25 22:35:24,810 INFO misc.py line 119 253097] Train: [89/100][451/510] Data 0.008 (0.075) Batch 1.150 (1.412) Remain 02:13:22 loss: 0.1016 Lr: 0.00020
[2023-12-25 22:35:26,293 INFO misc.py line 119 253097] Train: [89/100][452/510] Data 0.704 (0.077) Batch 1.488 (1.412) Remain 02:13:21 loss: 0.0540 Lr: 0.00020
[2023-12-25 22:35:27,211 INFO misc.py line 119 253097] Train: [89/100][453/510] Data 0.003 (0.077) Batch 0.918 (1.411) Remain 02:13:14 loss: 0.1836 Lr: 0.00020
[2023-12-25 22:35:33,478 INFO misc.py line 119 253097] Train: [89/100][454/510] Data 0.003 (0.077) Batch 6.265 (1.421) Remain 02:14:13 loss: 0.1275 Lr: 0.00020
[2023-12-25 22:35:34,705 INFO misc.py line 119 253097] Train: [89/100][455/510] Data 0.005 (0.076) Batch 1.226 (1.421) Remain 02:14:10 loss: 0.1407 Lr: 0.00020
[2023-12-25 22:35:35,864 INFO misc.py line 119 253097] Train: [89/100][456/510] Data 0.006 (0.076) Batch 1.161 (1.420) Remain 02:14:05 loss: 0.0799 Lr: 0.00020
[2023-12-25 22:35:37,053 INFO misc.py line 119 253097] Train: [89/100][457/510] Data 0.003 (0.076) Batch 1.188 (1.420) Remain 02:14:01 loss: 0.0988 Lr: 0.00020
[2023-12-25 22:35:38,179 INFO misc.py line 119 253097] Train: [89/100][458/510] Data 0.004 (0.076) Batch 1.127 (1.419) Remain 02:13:55 loss: 0.1648 Lr: 0.00020
[2023-12-25 22:35:39,380 INFO misc.py line 119 253097] Train: [89/100][459/510] Data 0.004 (0.076) Batch 1.201 (1.419) Remain 02:13:51 loss: 0.1719 Lr: 0.00020
[2023-12-25 22:35:40,365 INFO misc.py line 119 253097] Train: [89/100][460/510] Data 0.003 (0.076) Batch 0.984 (1.418) Remain 02:13:45 loss: 0.1599 Lr: 0.00020
[2023-12-25 22:35:41,372 INFO misc.py line 119 253097] Train: [89/100][461/510] Data 0.004 (0.075) Batch 1.008 (1.417) Remain 02:13:38 loss: 0.1468 Lr: 0.00020
[2023-12-25 22:35:42,647 INFO misc.py line 119 253097] Train: [89/100][462/510] Data 0.003 (0.075) Batch 1.274 (1.417) Remain 02:13:35 loss: 0.1483 Lr: 0.00020
[2023-12-25 22:35:43,644 INFO misc.py line 119 253097] Train: [89/100][463/510] Data 0.004 (0.075) Batch 0.997 (1.416) Remain 02:13:28 loss: 0.1481 Lr: 0.00020
[2023-12-25 22:35:44,752 INFO misc.py line 119 253097] Train: [89/100][464/510] Data 0.004 (0.075) Batch 1.108 (1.415) Remain 02:13:23 loss: 0.0800 Lr: 0.00020
[2023-12-25 22:35:45,847 INFO misc.py line 119 253097] Train: [89/100][465/510] Data 0.004 (0.075) Batch 1.095 (1.414) Remain 02:13:18 loss: 0.0616 Lr: 0.00020
[2023-12-25 22:35:46,851 INFO misc.py line 119 253097] Train: [89/100][466/510] Data 0.004 (0.075) Batch 1.005 (1.413) Remain 02:13:11 loss: 0.1853 Lr: 0.00020
[2023-12-25 22:35:47,906 INFO misc.py line 119 253097] Train: [89/100][467/510] Data 0.003 (0.074) Batch 1.053 (1.413) Remain 02:13:06 loss: 0.0953 Lr: 0.00020
[2023-12-25 22:35:48,974 INFO misc.py line 119 253097] Train: [89/100][468/510] Data 0.005 (0.074) Batch 1.070 (1.412) Remain 02:13:00 loss: 0.1181 Lr: 0.00020
[2023-12-25 22:35:50,043 INFO misc.py line 119 253097] Train: [89/100][469/510] Data 0.003 (0.074) Batch 1.068 (1.411) Remain 02:12:54 loss: 0.0728 Lr: 0.00020
[2023-12-25 22:35:57,586 INFO misc.py line 119 253097] Train: [89/100][470/510] Data 5.720 (0.086) Batch 7.541 (1.424) Remain 02:14:07 loss: 0.0656 Lr: 0.00020
[2023-12-25 22:35:58,711 INFO misc.py line 119 253097] Train: [89/100][471/510] Data 0.007 (0.086) Batch 1.128 (1.424) Remain 02:14:02 loss: 0.1273 Lr: 0.00020
[2023-12-25 22:35:59,806 INFO misc.py line 119 253097] Train: [89/100][472/510] Data 0.003 (0.086) Batch 1.092 (1.423) Remain 02:13:57 loss: 0.0944 Lr: 0.00020
[2023-12-25 22:36:00,945 INFO misc.py line 119 253097] Train: [89/100][473/510] Data 0.006 (0.086) Batch 1.142 (1.422) Remain 02:13:52 loss: 0.0897 Lr: 0.00020
[2023-12-25 22:36:02,082 INFO misc.py line 119 253097] Train: [89/100][474/510] Data 0.003 (0.086) Batch 1.133 (1.422) Remain 02:13:47 loss: 0.1978 Lr: 0.00020
[2023-12-25 22:36:03,106 INFO misc.py line 119 253097] Train: [89/100][475/510] Data 0.008 (0.085) Batch 1.024 (1.421) Remain 02:13:41 loss: 0.1333 Lr: 0.00020
[2023-12-25 22:36:04,122 INFO misc.py line 119 253097] Train: [89/100][476/510] Data 0.008 (0.085) Batch 1.019 (1.420) Remain 02:13:35 loss: 0.1031 Lr: 0.00020
[2023-12-25 22:36:05,898 INFO misc.py line 119 253097] Train: [89/100][477/510] Data 0.568 (0.086) Batch 1.776 (1.421) Remain 02:13:37 loss: 0.0571 Lr: 0.00020
[2023-12-25 22:36:07,006 INFO misc.py line 119 253097] Train: [89/100][478/510] Data 0.004 (0.086) Batch 1.107 (1.420) Remain 02:13:32 loss: 0.1005 Lr: 0.00020
[2023-12-25 22:36:08,098 INFO misc.py line 119 253097] Train: [89/100][479/510] Data 0.005 (0.086) Batch 1.093 (1.420) Remain 02:13:27 loss: 0.1126 Lr: 0.00020
[2023-12-25 22:36:09,305 INFO misc.py line 119 253097] Train: [89/100][480/510] Data 0.004 (0.086) Batch 1.209 (1.419) Remain 02:13:23 loss: 0.1791 Lr: 0.00020
[2023-12-25 22:36:10,564 INFO misc.py line 119 253097] Train: [89/100][481/510] Data 0.003 (0.086) Batch 1.257 (1.419) Remain 02:13:20 loss: 0.1865 Lr: 0.00020
[2023-12-25 22:36:11,823 INFO misc.py line 119 253097] Train: [89/100][482/510] Data 0.004 (0.085) Batch 1.259 (1.418) Remain 02:13:16 loss: 0.0898 Lr: 0.00020
[2023-12-25 22:36:12,967 INFO misc.py line 119 253097] Train: [89/100][483/510] Data 0.005 (0.085) Batch 1.145 (1.418) Remain 02:13:12 loss: 0.0778 Lr: 0.00020
[2023-12-25 22:36:14,132 INFO misc.py line 119 253097] Train: [89/100][484/510] Data 0.003 (0.085) Batch 1.164 (1.417) Remain 02:13:07 loss: 0.1192 Lr: 0.00020
[2023-12-25 22:36:15,075 INFO misc.py line 119 253097] Train: [89/100][485/510] Data 0.004 (0.085) Batch 0.944 (1.416) Remain 02:13:00 loss: 0.0936 Lr: 0.00020
[2023-12-25 22:36:16,109 INFO misc.py line 119 253097] Train: [89/100][486/510] Data 0.004 (0.085) Batch 1.032 (1.416) Remain 02:12:55 loss: 0.0894 Lr: 0.00020
[2023-12-25 22:36:17,058 INFO misc.py line 119 253097] Train: [89/100][487/510] Data 0.005 (0.085) Batch 0.949 (1.415) Remain 02:12:48 loss: 0.1480 Lr: 0.00020
[2023-12-25 22:36:18,169 INFO misc.py line 119 253097] Train: [89/100][488/510] Data 0.005 (0.084) Batch 1.113 (1.414) Remain 02:12:43 loss: 0.1026 Lr: 0.00020
[2023-12-25 22:36:19,381 INFO misc.py line 119 253097] Train: [89/100][489/510] Data 0.003 (0.084) Batch 1.210 (1.414) Remain 02:12:39 loss: 0.0643 Lr: 0.00020
[2023-12-25 22:36:28,692 INFO misc.py line 119 253097] Train: [89/100][490/510] Data 0.005 (0.084) Batch 9.312 (1.430) Remain 02:14:09 loss: 0.0916 Lr: 0.00020
[2023-12-25 22:36:29,827 INFO misc.py line 119 253097] Train: [89/100][491/510] Data 0.004 (0.084) Batch 1.134 (1.429) Remain 02:14:04 loss: 0.1787 Lr: 0.00020
[2023-12-25 22:36:30,867 INFO misc.py line 119 253097] Train: [89/100][492/510] Data 0.005 (0.084) Batch 1.040 (1.428) Remain 02:13:58 loss: 0.0796 Lr: 0.00020
[2023-12-25 22:36:32,085 INFO misc.py line 119 253097] Train: [89/100][493/510] Data 0.005 (0.084) Batch 1.217 (1.428) Remain 02:13:54 loss: 0.1249 Lr: 0.00020
[2023-12-25 22:36:33,327 INFO misc.py line 119 253097] Train: [89/100][494/510] Data 0.007 (0.083) Batch 1.244 (1.428) Remain 02:13:51 loss: 0.1212 Lr: 0.00020
[2023-12-25 22:36:34,344 INFO misc.py line 119 253097] Train: [89/100][495/510] Data 0.005 (0.083) Batch 1.013 (1.427) Remain 02:13:45 loss: 0.0844 Lr: 0.00020
[2023-12-25 22:36:37,676 INFO misc.py line 119 253097] Train: [89/100][496/510] Data 0.008 (0.083) Batch 3.337 (1.431) Remain 02:14:05 loss: 0.0810 Lr: 0.00020
[2023-12-25 22:36:38,834 INFO misc.py line 119 253097] Train: [89/100][497/510] Data 0.004 (0.083) Batch 1.141 (1.430) Remain 02:14:00 loss: 0.1398 Lr: 0.00020
[2023-12-25 22:36:40,013 INFO misc.py line 119 253097] Train: [89/100][498/510] Data 0.022 (0.083) Batch 1.196 (1.430) Remain 02:13:56 loss: 0.2107 Lr: 0.00020
[2023-12-25 22:36:40,908 INFO misc.py line 119 253097] Train: [89/100][499/510] Data 0.003 (0.083) Batch 0.895 (1.428) Remain 02:13:49 loss: 0.1038 Lr: 0.00020
[2023-12-25 22:36:42,140 INFO misc.py line 119 253097] Train: [89/100][500/510] Data 0.003 (0.083) Batch 1.232 (1.428) Remain 02:13:45 loss: 0.2398 Lr: 0.00020
[2023-12-25 22:36:43,456 INFO misc.py line 119 253097] Train: [89/100][501/510] Data 0.003 (0.082) Batch 1.312 (1.428) Remain 02:13:42 loss: 0.2806 Lr: 0.00020
[2023-12-25 22:36:44,474 INFO misc.py line 119 253097] Train: [89/100][502/510] Data 0.008 (0.082) Batch 1.016 (1.427) Remain 02:13:36 loss: 0.1208 Lr: 0.00020
[2023-12-25 22:36:45,603 INFO misc.py line 119 253097] Train: [89/100][503/510] Data 0.010 (0.082) Batch 1.133 (1.426) Remain 02:13:32 loss: 0.0564 Lr: 0.00020
[2023-12-25 22:36:46,688 INFO misc.py line 119 253097] Train: [89/100][504/510] Data 0.005 (0.082) Batch 1.087 (1.426) Remain 02:13:26 loss: 0.1213 Lr: 0.00020
[2023-12-25 22:36:47,756 INFO misc.py line 119 253097] Train: [89/100][505/510] Data 0.003 (0.082) Batch 1.063 (1.425) Remain 02:13:21 loss: 0.0697 Lr: 0.00020
[2023-12-25 22:36:48,829 INFO misc.py line 119 253097] Train: [89/100][506/510] Data 0.007 (0.082) Batch 1.073 (1.424) Remain 02:13:15 loss: 0.0647 Lr: 0.00020
[2023-12-25 22:36:49,949 INFO misc.py line 119 253097] Train: [89/100][507/510] Data 0.008 (0.081) Batch 1.120 (1.424) Remain 02:13:11 loss: 0.1255 Lr: 0.00020
[2023-12-25 22:36:50,919 INFO misc.py line 119 253097] Train: [89/100][508/510] Data 0.009 (0.081) Batch 0.975 (1.423) Remain 02:13:04 loss: 0.1043 Lr: 0.00020
[2023-12-25 22:36:52,037 INFO misc.py line 119 253097] Train: [89/100][509/510] Data 0.004 (0.081) Batch 1.118 (1.422) Remain 02:12:59 loss: 0.0546 Lr: 0.00020
[2023-12-25 22:36:53,040 INFO misc.py line 119 253097] Train: [89/100][510/510] Data 0.004 (0.081) Batch 1.003 (1.421) Remain 02:12:53 loss: 0.1284 Lr: 0.00020
[2023-12-25 22:36:53,040 INFO misc.py line 136 253097] Train result: loss: 0.1144 
[2023-12-25 22:36:53,040 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 22:37:22,711 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4503 
[2023-12-25 22:37:23,061 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2766 
[2023-12-25 22:37:28,009 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.2799 
[2023-12-25 22:37:28,522 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4185 
[2023-12-25 22:37:30,493 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7962 
[2023-12-25 22:37:30,916 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2749 
[2023-12-25 22:37:31,793 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1566 
[2023-12-25 22:37:32,346 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2725 
[2023-12-25 22:37:34,155 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6664 
[2023-12-25 22:37:36,277 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1331 
[2023-12-25 22:37:37,134 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3143 
[2023-12-25 22:37:37,561 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8845 
[2023-12-25 22:37:38,460 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4788 
[2023-12-25 22:37:41,412 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.7722 
[2023-12-25 22:37:41,880 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2660 
[2023-12-25 22:37:42,488 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3612 
[2023-12-25 22:37:43,189 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3050 
[2023-12-25 22:37:44,344 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.7022/0.7617/0.9078.
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9191/0.9492
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9831/0.9906
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8524/0.9666
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3956/0.4706
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6446/0.6646
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7047/0.8059
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8127/0.9019
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9180/0.9617
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6490/0.6936
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7855/0.8742
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8457/0.8928
[2023-12-25 22:37:44,344 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6187/0.7310
[2023-12-25 22:37:44,345 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 22:37:44,347 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 22:37:44,347 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 22:37:52,260 INFO misc.py line 119 253097] Train: [90/100][1/510] Data 4.838 (4.838) Batch 6.174 (6.174) Remain 09:37:08 loss: 0.0942 Lr: 0.00020
[2023-12-25 22:37:54,167 INFO misc.py line 119 253097] Train: [90/100][2/510] Data 0.004 (0.004) Batch 1.904 (1.904) Remain 02:57:59 loss: 0.1811 Lr: 0.00020
[2023-12-25 22:37:55,385 INFO misc.py line 119 253097] Train: [90/100][3/510] Data 0.007 (0.007) Batch 1.221 (1.221) Remain 01:54:06 loss: 0.2456 Lr: 0.00020
[2023-12-25 22:37:56,459 INFO misc.py line 119 253097] Train: [90/100][4/510] Data 0.003 (0.003) Batch 1.068 (1.068) Remain 01:39:47 loss: 0.1038 Lr: 0.00020
[2023-12-25 22:37:57,636 INFO misc.py line 119 253097] Train: [90/100][5/510] Data 0.009 (0.006) Batch 1.180 (1.124) Remain 01:44:59 loss: 0.0714 Lr: 0.00020
[2023-12-25 22:37:58,869 INFO misc.py line 119 253097] Train: [90/100][6/510] Data 0.006 (0.006) Batch 1.235 (1.161) Remain 01:48:26 loss: 0.1023 Lr: 0.00020
[2023-12-25 22:38:00,040 INFO misc.py line 119 253097] Train: [90/100][7/510] Data 0.004 (0.006) Batch 1.168 (1.163) Remain 01:48:34 loss: 0.1027 Lr: 0.00020
[2023-12-25 22:38:01,150 INFO misc.py line 119 253097] Train: [90/100][8/510] Data 0.007 (0.006) Batch 1.111 (1.152) Remain 01:47:35 loss: 0.1603 Lr: 0.00020
[2023-12-25 22:38:02,432 INFO misc.py line 119 253097] Train: [90/100][9/510] Data 0.006 (0.006) Batch 1.282 (1.174) Remain 01:49:35 loss: 0.1065 Lr: 0.00020
[2023-12-25 22:38:03,632 INFO misc.py line 119 253097] Train: [90/100][10/510] Data 0.006 (0.006) Batch 1.198 (1.177) Remain 01:49:53 loss: 0.1089 Lr: 0.00020
[2023-12-25 22:38:04,776 INFO misc.py line 119 253097] Train: [90/100][11/510] Data 0.008 (0.006) Batch 1.148 (1.174) Remain 01:49:32 loss: 0.4355 Lr: 0.00020
[2023-12-25 22:38:07,014 INFO misc.py line 119 253097] Train: [90/100][12/510] Data 0.981 (0.115) Batch 2.238 (1.292) Remain 02:00:33 loss: 0.1498 Lr: 0.00020
[2023-12-25 22:38:07,888 INFO misc.py line 119 253097] Train: [90/100][13/510] Data 0.004 (0.103) Batch 0.871 (1.250) Remain 01:56:36 loss: 0.0876 Lr: 0.00020
[2023-12-25 22:38:09,043 INFO misc.py line 119 253097] Train: [90/100][14/510] Data 0.007 (0.095) Batch 1.154 (1.241) Remain 01:55:46 loss: 0.0921 Lr: 0.00020
[2023-12-25 22:38:09,975 INFO misc.py line 119 253097] Train: [90/100][15/510] Data 0.007 (0.087) Batch 0.936 (1.216) Remain 01:53:22 loss: 0.1322 Lr: 0.00020
[2023-12-25 22:38:10,942 INFO misc.py line 119 253097] Train: [90/100][16/510] Data 0.003 (0.081) Batch 0.967 (1.197) Remain 01:51:34 loss: 0.1267 Lr: 0.00020
[2023-12-25 22:38:13,752 INFO misc.py line 119 253097] Train: [90/100][17/510] Data 1.734 (0.199) Batch 2.811 (1.312) Remain 02:02:17 loss: 0.0835 Lr: 0.00020
[2023-12-25 22:38:14,894 INFO misc.py line 119 253097] Train: [90/100][18/510] Data 0.003 (0.186) Batch 1.141 (1.301) Remain 02:01:12 loss: 0.1004 Lr: 0.00020
[2023-12-25 22:38:15,982 INFO misc.py line 119 253097] Train: [90/100][19/510] Data 0.003 (0.174) Batch 1.088 (1.287) Remain 01:59:57 loss: 0.1023 Lr: 0.00020
[2023-12-25 22:38:17,232 INFO misc.py line 119 253097] Train: [90/100][20/510] Data 0.003 (0.164) Batch 1.250 (1.285) Remain 01:59:43 loss: 0.1389 Lr: 0.00020
[2023-12-25 22:38:18,484 INFO misc.py line 119 253097] Train: [90/100][21/510] Data 0.004 (0.155) Batch 1.249 (1.283) Remain 01:59:31 loss: 0.0830 Lr: 0.00020
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[2023-12-25 22:48:12,908 INFO misc.py line 119 253097] Train: [90/100][440/510] Data 0.007 (0.099) Batch 1.163 (1.413) Remain 02:01:45 loss: 0.0898 Lr: 0.00017
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[2023-12-25 22:48:15,209 INFO misc.py line 119 253097] Train: [90/100][442/510] Data 0.004 (0.099) Batch 1.117 (1.412) Remain 02:01:36 loss: 0.0786 Lr: 0.00017
[2023-12-25 22:48:16,304 INFO misc.py line 119 253097] Train: [90/100][443/510] Data 0.004 (0.098) Batch 1.096 (1.411) Remain 02:01:31 loss: 0.1045 Lr: 0.00017
[2023-12-25 22:48:17,320 INFO misc.py line 119 253097] Train: [90/100][444/510] Data 0.003 (0.098) Batch 1.016 (1.410) Remain 02:01:25 loss: 0.0737 Lr: 0.00017
[2023-12-25 22:48:19,933 INFO misc.py line 119 253097] Train: [90/100][445/510] Data 1.443 (0.101) Batch 2.614 (1.413) Remain 02:01:38 loss: 0.1040 Lr: 0.00017
[2023-12-25 22:48:20,960 INFO misc.py line 119 253097] Train: [90/100][446/510] Data 0.003 (0.101) Batch 1.025 (1.412) Remain 02:01:32 loss: 0.1126 Lr: 0.00017
[2023-12-25 22:48:22,293 INFO misc.py line 119 253097] Train: [90/100][447/510] Data 0.005 (0.101) Batch 1.331 (1.412) Remain 02:01:29 loss: 0.0753 Lr: 0.00017
[2023-12-25 22:48:23,412 INFO misc.py line 119 253097] Train: [90/100][448/510] Data 0.008 (0.101) Batch 1.122 (1.411) Remain 02:01:25 loss: 0.0861 Lr: 0.00017
[2023-12-25 22:48:24,511 INFO misc.py line 119 253097] Train: [90/100][449/510] Data 0.004 (0.100) Batch 1.095 (1.411) Remain 02:01:20 loss: 0.1472 Lr: 0.00017
[2023-12-25 22:48:25,703 INFO misc.py line 119 253097] Train: [90/100][450/510] Data 0.009 (0.100) Batch 1.198 (1.410) Remain 02:01:16 loss: 0.0990 Lr: 0.00017
[2023-12-25 22:48:29,492 INFO misc.py line 119 253097] Train: [90/100][451/510] Data 2.573 (0.106) Batch 3.785 (1.415) Remain 02:01:42 loss: 0.1041 Lr: 0.00017
[2023-12-25 22:48:30,570 INFO misc.py line 119 253097] Train: [90/100][452/510] Data 0.007 (0.105) Batch 1.083 (1.415) Remain 02:01:36 loss: 0.0917 Lr: 0.00017
[2023-12-25 22:48:31,659 INFO misc.py line 119 253097] Train: [90/100][453/510] Data 0.003 (0.105) Batch 1.087 (1.414) Remain 02:01:31 loss: 0.0720 Lr: 0.00017
[2023-12-25 22:48:32,788 INFO misc.py line 119 253097] Train: [90/100][454/510] Data 0.006 (0.105) Batch 1.130 (1.413) Remain 02:01:27 loss: 0.1688 Lr: 0.00017
[2023-12-25 22:48:34,010 INFO misc.py line 119 253097] Train: [90/100][455/510] Data 0.003 (0.105) Batch 1.222 (1.413) Remain 02:01:23 loss: 0.1391 Lr: 0.00017
[2023-12-25 22:48:35,214 INFO misc.py line 119 253097] Train: [90/100][456/510] Data 0.004 (0.105) Batch 1.204 (1.412) Remain 02:01:19 loss: 0.1059 Lr: 0.00017
[2023-12-25 22:48:36,292 INFO misc.py line 119 253097] Train: [90/100][457/510] Data 0.003 (0.104) Batch 1.075 (1.412) Remain 02:01:14 loss: 0.0503 Lr: 0.00017
[2023-12-25 22:48:37,367 INFO misc.py line 119 253097] Train: [90/100][458/510] Data 0.007 (0.104) Batch 1.074 (1.411) Remain 02:01:09 loss: 0.0995 Lr: 0.00017
[2023-12-25 22:48:38,491 INFO misc.py line 119 253097] Train: [90/100][459/510] Data 0.007 (0.104) Batch 1.129 (1.410) Remain 02:01:04 loss: 0.1385 Lr: 0.00017
[2023-12-25 22:48:39,562 INFO misc.py line 119 253097] Train: [90/100][460/510] Data 0.003 (0.104) Batch 1.070 (1.410) Remain 02:00:59 loss: 0.1051 Lr: 0.00017
[2023-12-25 22:48:40,770 INFO misc.py line 119 253097] Train: [90/100][461/510] Data 0.004 (0.103) Batch 1.208 (1.409) Remain 02:00:55 loss: 0.1492 Lr: 0.00017
[2023-12-25 22:48:41,715 INFO misc.py line 119 253097] Train: [90/100][462/510] Data 0.004 (0.103) Batch 0.945 (1.408) Remain 02:00:49 loss: 0.0862 Lr: 0.00017
[2023-12-25 22:48:42,864 INFO misc.py line 119 253097] Train: [90/100][463/510] Data 0.004 (0.103) Batch 1.149 (1.408) Remain 02:00:44 loss: 0.1925 Lr: 0.00017
[2023-12-25 22:48:43,994 INFO misc.py line 119 253097] Train: [90/100][464/510] Data 0.003 (0.103) Batch 1.131 (1.407) Remain 02:00:40 loss: 0.1501 Lr: 0.00017
[2023-12-25 22:48:44,912 INFO misc.py line 119 253097] Train: [90/100][465/510] Data 0.003 (0.103) Batch 0.916 (1.406) Remain 02:00:33 loss: 0.0899 Lr: 0.00017
[2023-12-25 22:48:45,778 INFO misc.py line 119 253097] Train: [90/100][466/510] Data 0.004 (0.102) Batch 0.867 (1.405) Remain 02:00:25 loss: 0.0542 Lr: 0.00017
[2023-12-25 22:48:46,892 INFO misc.py line 119 253097] Train: [90/100][467/510] Data 0.004 (0.102) Batch 1.112 (1.404) Remain 02:00:21 loss: 0.0715 Lr: 0.00017
[2023-12-25 22:48:47,916 INFO misc.py line 119 253097] Train: [90/100][468/510] Data 0.006 (0.102) Batch 1.022 (1.403) Remain 02:00:15 loss: 0.0831 Lr: 0.00017
[2023-12-25 22:48:49,140 INFO misc.py line 119 253097] Train: [90/100][469/510] Data 0.008 (0.102) Batch 1.223 (1.403) Remain 02:00:12 loss: 0.0821 Lr: 0.00017
[2023-12-25 22:48:50,149 INFO misc.py line 119 253097] Train: [90/100][470/510] Data 0.008 (0.102) Batch 1.009 (1.402) Remain 02:00:06 loss: 0.0869 Lr: 0.00017
[2023-12-25 22:48:51,287 INFO misc.py line 119 253097] Train: [90/100][471/510] Data 0.008 (0.101) Batch 1.139 (1.401) Remain 02:00:02 loss: 0.0647 Lr: 0.00017
[2023-12-25 22:48:52,527 INFO misc.py line 119 253097] Train: [90/100][472/510] Data 0.006 (0.101) Batch 1.239 (1.401) Remain 01:59:59 loss: 0.0756 Lr: 0.00017
[2023-12-25 22:48:53,511 INFO misc.py line 119 253097] Train: [90/100][473/510] Data 0.007 (0.101) Batch 0.988 (1.400) Remain 01:59:53 loss: 0.3153 Lr: 0.00017
[2023-12-25 22:48:54,690 INFO misc.py line 119 253097] Train: [90/100][474/510] Data 0.003 (0.101) Batch 1.179 (1.400) Remain 01:59:49 loss: 0.1027 Lr: 0.00017
[2023-12-25 22:48:55,666 INFO misc.py line 119 253097] Train: [90/100][475/510] Data 0.004 (0.101) Batch 0.975 (1.399) Remain 01:59:43 loss: 0.2799 Lr: 0.00017
[2023-12-25 22:48:56,750 INFO misc.py line 119 253097] Train: [90/100][476/510] Data 0.006 (0.100) Batch 1.085 (1.398) Remain 01:59:38 loss: 0.0843 Lr: 0.00017
[2023-12-25 22:48:57,894 INFO misc.py line 119 253097] Train: [90/100][477/510] Data 0.004 (0.100) Batch 1.143 (1.398) Remain 01:59:34 loss: 0.0732 Lr: 0.00017
[2023-12-25 22:48:59,109 INFO misc.py line 119 253097] Train: [90/100][478/510] Data 0.005 (0.100) Batch 1.215 (1.397) Remain 01:59:30 loss: 0.0947 Lr: 0.00017
[2023-12-25 22:49:00,434 INFO misc.py line 119 253097] Train: [90/100][479/510] Data 0.005 (0.100) Batch 1.322 (1.397) Remain 01:59:28 loss: 0.0582 Lr: 0.00017
[2023-12-25 22:49:01,555 INFO misc.py line 119 253097] Train: [90/100][480/510] Data 0.007 (0.100) Batch 1.123 (1.397) Remain 01:59:24 loss: 0.0952 Lr: 0.00017
[2023-12-25 22:49:02,876 INFO misc.py line 119 253097] Train: [90/100][481/510] Data 0.005 (0.099) Batch 1.321 (1.396) Remain 01:59:22 loss: 0.0821 Lr: 0.00017
[2023-12-25 22:49:04,140 INFO misc.py line 119 253097] Train: [90/100][482/510] Data 0.006 (0.099) Batch 1.259 (1.396) Remain 01:59:19 loss: 0.0620 Lr: 0.00017
[2023-12-25 22:49:05,248 INFO misc.py line 119 253097] Train: [90/100][483/510] Data 0.011 (0.099) Batch 1.113 (1.396) Remain 01:59:14 loss: 0.0842 Lr: 0.00017
[2023-12-25 22:49:06,434 INFO misc.py line 119 253097] Train: [90/100][484/510] Data 0.005 (0.099) Batch 1.187 (1.395) Remain 01:59:11 loss: 0.1913 Lr: 0.00017
[2023-12-25 22:49:25,305 INFO misc.py line 119 253097] Train: [90/100][485/510] Data 0.003 (0.099) Batch 18.871 (1.431) Remain 02:02:15 loss: 0.1197 Lr: 0.00017
[2023-12-25 22:49:26,613 INFO misc.py line 119 253097] Train: [90/100][486/510] Data 0.004 (0.098) Batch 1.305 (1.431) Remain 02:02:12 loss: 0.0940 Lr: 0.00017
[2023-12-25 22:49:27,891 INFO misc.py line 119 253097] Train: [90/100][487/510] Data 0.008 (0.098) Batch 1.278 (1.431) Remain 02:02:09 loss: 0.1916 Lr: 0.00017
[2023-12-25 22:49:29,171 INFO misc.py line 119 253097] Train: [90/100][488/510] Data 0.007 (0.098) Batch 1.265 (1.430) Remain 02:02:06 loss: 0.1123 Lr: 0.00017
[2023-12-25 22:49:30,245 INFO misc.py line 119 253097] Train: [90/100][489/510] Data 0.025 (0.098) Batch 1.090 (1.430) Remain 02:02:01 loss: 0.1716 Lr: 0.00017
[2023-12-25 22:49:31,179 INFO misc.py line 119 253097] Train: [90/100][490/510] Data 0.007 (0.098) Batch 0.938 (1.429) Remain 02:01:55 loss: 0.0778 Lr: 0.00017
[2023-12-25 22:49:32,204 INFO misc.py line 119 253097] Train: [90/100][491/510] Data 0.003 (0.097) Batch 1.023 (1.428) Remain 02:01:49 loss: 0.1102 Lr: 0.00017
[2023-12-25 22:49:33,311 INFO misc.py line 119 253097] Train: [90/100][492/510] Data 0.006 (0.097) Batch 1.108 (1.427) Remain 02:01:44 loss: 0.1433 Lr: 0.00017
[2023-12-25 22:49:34,537 INFO misc.py line 119 253097] Train: [90/100][493/510] Data 0.003 (0.097) Batch 1.225 (1.427) Remain 02:01:41 loss: 0.1034 Lr: 0.00017
[2023-12-25 22:49:35,677 INFO misc.py line 119 253097] Train: [90/100][494/510] Data 0.004 (0.097) Batch 1.141 (1.426) Remain 02:01:36 loss: 0.1394 Lr: 0.00017
[2023-12-25 22:49:36,842 INFO misc.py line 119 253097] Train: [90/100][495/510] Data 0.003 (0.097) Batch 1.164 (1.426) Remain 02:01:32 loss: 0.0759 Lr: 0.00017
[2023-12-25 22:49:38,123 INFO misc.py line 119 253097] Train: [90/100][496/510] Data 0.004 (0.097) Batch 1.281 (1.425) Remain 02:01:29 loss: 0.0816 Lr: 0.00017
[2023-12-25 22:49:39,241 INFO misc.py line 119 253097] Train: [90/100][497/510] Data 0.004 (0.096) Batch 1.118 (1.425) Remain 02:01:25 loss: 0.1047 Lr: 0.00017
[2023-12-25 22:49:40,404 INFO misc.py line 119 253097] Train: [90/100][498/510] Data 0.004 (0.096) Batch 1.160 (1.424) Remain 02:01:20 loss: 0.2197 Lr: 0.00016
[2023-12-25 22:49:41,505 INFO misc.py line 119 253097] Train: [90/100][499/510] Data 0.008 (0.096) Batch 1.104 (1.424) Remain 02:01:16 loss: 0.1829 Lr: 0.00016
[2023-12-25 22:49:42,698 INFO misc.py line 119 253097] Train: [90/100][500/510] Data 0.004 (0.096) Batch 1.188 (1.423) Remain 02:01:12 loss: 0.0852 Lr: 0.00016
[2023-12-25 22:49:43,646 INFO misc.py line 119 253097] Train: [90/100][501/510] Data 0.009 (0.096) Batch 0.954 (1.422) Remain 02:01:06 loss: 0.0696 Lr: 0.00016
[2023-12-25 22:49:44,824 INFO misc.py line 119 253097] Train: [90/100][502/510] Data 0.003 (0.095) Batch 1.175 (1.422) Remain 02:01:02 loss: 0.0727 Lr: 0.00016
[2023-12-25 22:49:50,251 INFO misc.py line 119 253097] Train: [90/100][503/510] Data 4.214 (0.104) Batch 5.430 (1.430) Remain 02:01:41 loss: 0.2230 Lr: 0.00016
[2023-12-25 22:49:51,508 INFO misc.py line 119 253097] Train: [90/100][504/510] Data 0.003 (0.103) Batch 1.251 (1.429) Remain 02:01:38 loss: 0.1280 Lr: 0.00016
[2023-12-25 22:49:52,563 INFO misc.py line 119 253097] Train: [90/100][505/510] Data 0.009 (0.103) Batch 1.055 (1.429) Remain 02:01:33 loss: 0.1955 Lr: 0.00016
[2023-12-25 22:49:53,500 INFO misc.py line 119 253097] Train: [90/100][506/510] Data 0.009 (0.103) Batch 0.943 (1.428) Remain 02:01:26 loss: 0.1266 Lr: 0.00016
[2023-12-25 22:49:54,671 INFO misc.py line 119 253097] Train: [90/100][507/510] Data 0.003 (0.103) Batch 1.172 (1.427) Remain 02:01:22 loss: 0.1315 Lr: 0.00016
[2023-12-25 22:49:55,791 INFO misc.py line 119 253097] Train: [90/100][508/510] Data 0.003 (0.103) Batch 1.120 (1.427) Remain 02:01:18 loss: 0.0509 Lr: 0.00016
[2023-12-25 22:49:56,911 INFO misc.py line 119 253097] Train: [90/100][509/510] Data 0.003 (0.103) Batch 1.119 (1.426) Remain 02:01:13 loss: 0.1098 Lr: 0.00016
[2023-12-25 22:50:03,895 INFO misc.py line 119 253097] Train: [90/100][510/510] Data 5.835 (0.114) Batch 6.985 (1.437) Remain 02:02:08 loss: 0.0831 Lr: 0.00016
[2023-12-25 22:50:03,896 INFO misc.py line 136 253097] Train result: loss: 0.1135 
[2023-12-25 22:50:03,896 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 22:50:32,158 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6245 
[2023-12-25 22:50:32,501 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3166 
[2023-12-25 22:50:37,460 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3067 
[2023-12-25 22:50:37,974 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4101 
[2023-12-25 22:50:39,945 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.7904 
[2023-12-25 22:50:40,369 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2574 
[2023-12-25 22:50:41,246 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0910 
[2023-12-25 22:50:41,799 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2752 
[2023-12-25 22:50:43,607 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8144 
[2023-12-25 22:50:45,726 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.2574 
[2023-12-25 22:50:46,582 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3154 
[2023-12-25 22:50:47,003 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7943 
[2023-12-25 22:50:47,903 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3928 
[2023-12-25 22:50:50,852 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8627 
[2023-12-25 22:50:51,320 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2486 
[2023-12-25 22:50:51,929 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3862 
[2023-12-25 22:50:52,629 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3174 
[2023-12-25 22:50:54,007 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6931/0.7509/0.9041.
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9171/0.9444
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9829/0.9899
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8440/0.9711
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3846/0.4365
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6111/0.6267
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6920/0.7726
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8086/0.9010
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9158/0.9596
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6539/0.7070
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7798/0.8698
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8085/0.8536
[2023-12-25 22:50:54,007 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6123/0.7291
[2023-12-25 22:50:54,008 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 22:50:54,010 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 22:50:54,010 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 22:51:00,340 INFO misc.py line 119 253097] Train: [91/100][1/510] Data 2.940 (2.940) Batch 3.809 (3.809) Remain 05:23:40 loss: 0.0656 Lr: 0.00016
[2023-12-25 22:51:03,963 INFO misc.py line 119 253097] Train: [91/100][2/510] Data 0.012 (0.012) Batch 3.631 (3.631) Remain 05:08:30 loss: 0.1221 Lr: 0.00016
[2023-12-25 22:51:07,518 INFO misc.py line 119 253097] Train: [91/100][3/510] Data 2.388 (2.388) Batch 3.555 (3.555) Remain 05:02:02 loss: 0.0908 Lr: 0.00016
[2023-12-25 22:51:08,531 INFO misc.py line 119 253097] Train: [91/100][4/510] Data 0.004 (0.004) Batch 1.013 (1.013) Remain 01:26:02 loss: 0.0946 Lr: 0.00016
[2023-12-25 22:51:09,718 INFO misc.py line 119 253097] Train: [91/100][5/510] Data 0.004 (0.004) Batch 1.187 (1.100) Remain 01:33:24 loss: 0.0973 Lr: 0.00016
[2023-12-25 22:51:10,984 INFO misc.py line 119 253097] Train: [91/100][6/510] Data 0.003 (0.004) Batch 1.266 (1.156) Remain 01:38:06 loss: 0.1390 Lr: 0.00016
[2023-12-25 22:51:14,623 INFO misc.py line 119 253097] Train: [91/100][7/510] Data 2.422 (0.608) Batch 3.638 (1.776) Remain 02:30:46 loss: 0.0846 Lr: 0.00016
[2023-12-25 22:51:15,918 INFO misc.py line 119 253097] Train: [91/100][8/510] Data 0.003 (0.487) Batch 1.287 (1.678) Remain 02:22:26 loss: 0.0939 Lr: 0.00016
[2023-12-25 22:51:17,171 INFO misc.py line 119 253097] Train: [91/100][9/510] Data 0.011 (0.408) Batch 1.261 (1.609) Remain 02:16:30 loss: 0.0695 Lr: 0.00016
[2023-12-25 22:51:18,321 INFO misc.py line 119 253097] Train: [91/100][10/510] Data 0.003 (0.350) Batch 1.143 (1.542) Remain 02:10:50 loss: 0.1477 Lr: 0.00016
[2023-12-25 22:51:19,323 INFO misc.py line 119 253097] Train: [91/100][11/510] Data 0.010 (0.307) Batch 1.007 (1.475) Remain 02:05:08 loss: 0.0929 Lr: 0.00016
[2023-12-25 22:51:20,606 INFO misc.py line 119 253097] Train: [91/100][12/510] Data 0.005 (0.274) Batch 1.284 (1.454) Remain 02:03:18 loss: 0.1206 Lr: 0.00016
[2023-12-25 22:51:21,463 INFO misc.py line 119 253097] Train: [91/100][13/510] Data 0.004 (0.247) Batch 0.855 (1.394) Remain 01:58:12 loss: 0.0685 Lr: 0.00016
[2023-12-25 22:51:22,478 INFO misc.py line 119 253097] Train: [91/100][14/510] Data 0.006 (0.225) Batch 1.013 (1.360) Remain 01:55:14 loss: 0.0758 Lr: 0.00016
[2023-12-25 22:51:23,709 INFO misc.py line 119 253097] Train: [91/100][15/510] Data 0.009 (0.207) Batch 1.236 (1.349) Remain 01:54:21 loss: 0.1114 Lr: 0.00016
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[2023-12-25 23:01:10,957 INFO misc.py line 119 253097] Train: [91/100][434/510] Data 0.003 (0.098) Batch 1.109 (1.400) Remain 01:48:52 loss: 0.1886 Lr: 0.00014
[2023-12-25 23:01:12,103 INFO misc.py line 119 253097] Train: [91/100][435/510] Data 0.003 (0.098) Batch 1.145 (1.400) Remain 01:48:48 loss: 0.0686 Lr: 0.00014
[2023-12-25 23:01:13,069 INFO misc.py line 119 253097] Train: [91/100][436/510] Data 0.003 (0.098) Batch 0.966 (1.399) Remain 01:48:42 loss: 0.0512 Lr: 0.00014
[2023-12-25 23:01:14,315 INFO misc.py line 119 253097] Train: [91/100][437/510] Data 0.004 (0.098) Batch 1.245 (1.398) Remain 01:48:39 loss: 0.0947 Lr: 0.00014
[2023-12-25 23:01:15,525 INFO misc.py line 119 253097] Train: [91/100][438/510] Data 0.004 (0.098) Batch 1.202 (1.398) Remain 01:48:36 loss: 0.2139 Lr: 0.00014
[2023-12-25 23:01:16,745 INFO misc.py line 119 253097] Train: [91/100][439/510] Data 0.011 (0.097) Batch 1.227 (1.397) Remain 01:48:32 loss: 0.1792 Lr: 0.00014
[2023-12-25 23:01:17,988 INFO misc.py line 119 253097] Train: [91/100][440/510] Data 0.005 (0.097) Batch 1.238 (1.397) Remain 01:48:29 loss: 0.0960 Lr: 0.00014
[2023-12-25 23:01:19,319 INFO misc.py line 119 253097] Train: [91/100][441/510] Data 0.009 (0.097) Batch 1.333 (1.397) Remain 01:48:27 loss: 0.0750 Lr: 0.00014
[2023-12-25 23:01:20,498 INFO misc.py line 119 253097] Train: [91/100][442/510] Data 0.006 (0.097) Batch 1.178 (1.396) Remain 01:48:23 loss: 0.1286 Lr: 0.00014
[2023-12-25 23:01:29,924 INFO misc.py line 119 253097] Train: [91/100][443/510] Data 0.008 (0.096) Batch 9.430 (1.415) Remain 01:49:47 loss: 0.0526 Lr: 0.00014
[2023-12-25 23:01:31,023 INFO misc.py line 119 253097] Train: [91/100][444/510] Data 0.004 (0.096) Batch 1.100 (1.414) Remain 01:49:42 loss: 0.1192 Lr: 0.00014
[2023-12-25 23:01:32,200 INFO misc.py line 119 253097] Train: [91/100][445/510] Data 0.003 (0.096) Batch 1.173 (1.413) Remain 01:49:38 loss: 0.2236 Lr: 0.00014
[2023-12-25 23:01:33,455 INFO misc.py line 119 253097] Train: [91/100][446/510] Data 0.008 (0.096) Batch 1.258 (1.413) Remain 01:49:35 loss: 0.0766 Lr: 0.00014
[2023-12-25 23:01:34,424 INFO misc.py line 119 253097] Train: [91/100][447/510] Data 0.004 (0.096) Batch 0.969 (1.412) Remain 01:49:29 loss: 0.0926 Lr: 0.00014
[2023-12-25 23:01:35,526 INFO misc.py line 119 253097] Train: [91/100][448/510] Data 0.004 (0.095) Batch 1.102 (1.411) Remain 01:49:25 loss: 0.1262 Lr: 0.00014
[2023-12-25 23:01:36,600 INFO misc.py line 119 253097] Train: [91/100][449/510] Data 0.004 (0.095) Batch 1.075 (1.410) Remain 01:49:20 loss: 0.1156 Lr: 0.00014
[2023-12-25 23:01:37,865 INFO misc.py line 119 253097] Train: [91/100][450/510] Data 0.003 (0.095) Batch 1.262 (1.410) Remain 01:49:17 loss: 0.0645 Lr: 0.00014
[2023-12-25 23:01:38,973 INFO misc.py line 119 253097] Train: [91/100][451/510] Data 0.007 (0.095) Batch 1.111 (1.409) Remain 01:49:12 loss: 0.1355 Lr: 0.00014
[2023-12-25 23:01:39,997 INFO misc.py line 119 253097] Train: [91/100][452/510] Data 0.003 (0.095) Batch 1.023 (1.409) Remain 01:49:07 loss: 0.1152 Lr: 0.00014
[2023-12-25 23:01:41,139 INFO misc.py line 119 253097] Train: [91/100][453/510] Data 0.005 (0.094) Batch 1.139 (1.408) Remain 01:49:03 loss: 0.0660 Lr: 0.00014
[2023-12-25 23:01:42,224 INFO misc.py line 119 253097] Train: [91/100][454/510] Data 0.007 (0.094) Batch 1.087 (1.407) Remain 01:48:58 loss: 0.2870 Lr: 0.00014
[2023-12-25 23:01:43,272 INFO misc.py line 119 253097] Train: [91/100][455/510] Data 0.005 (0.094) Batch 1.046 (1.407) Remain 01:48:53 loss: 0.1355 Lr: 0.00014
[2023-12-25 23:01:44,524 INFO misc.py line 119 253097] Train: [91/100][456/510] Data 0.008 (0.094) Batch 1.255 (1.406) Remain 01:48:50 loss: 0.1738 Lr: 0.00014
[2023-12-25 23:01:45,479 INFO misc.py line 119 253097] Train: [91/100][457/510] Data 0.005 (0.094) Batch 0.956 (1.405) Remain 01:48:44 loss: 0.2395 Lr: 0.00014
[2023-12-25 23:01:46,758 INFO misc.py line 119 253097] Train: [91/100][458/510] Data 0.005 (0.093) Batch 1.280 (1.405) Remain 01:48:41 loss: 0.2579 Lr: 0.00014
[2023-12-25 23:01:48,054 INFO misc.py line 119 253097] Train: [91/100][459/510] Data 0.004 (0.093) Batch 1.272 (1.405) Remain 01:48:38 loss: 0.1026 Lr: 0.00014
[2023-12-25 23:01:49,219 INFO misc.py line 119 253097] Train: [91/100][460/510] Data 0.028 (0.093) Batch 1.165 (1.404) Remain 01:48:35 loss: 0.1913 Lr: 0.00014
[2023-12-25 23:01:50,203 INFO misc.py line 119 253097] Train: [91/100][461/510] Data 0.029 (0.093) Batch 1.008 (1.403) Remain 01:48:29 loss: 0.0937 Lr: 0.00014
[2023-12-25 23:01:51,364 INFO misc.py line 119 253097] Train: [91/100][462/510] Data 0.004 (0.093) Batch 1.141 (1.403) Remain 01:48:25 loss: 0.0760 Lr: 0.00014
[2023-12-25 23:01:52,424 INFO misc.py line 119 253097] Train: [91/100][463/510] Data 0.024 (0.093) Batch 1.058 (1.402) Remain 01:48:20 loss: 0.0904 Lr: 0.00014
[2023-12-25 23:01:53,627 INFO misc.py line 119 253097] Train: [91/100][464/510] Data 0.025 (0.093) Batch 1.204 (1.401) Remain 01:48:17 loss: 0.1146 Lr: 0.00014
[2023-12-25 23:01:54,701 INFO misc.py line 119 253097] Train: [91/100][465/510] Data 0.024 (0.092) Batch 1.094 (1.401) Remain 01:48:12 loss: 0.1834 Lr: 0.00014
[2023-12-25 23:01:56,044 INFO misc.py line 119 253097] Train: [91/100][466/510] Data 0.005 (0.092) Batch 1.326 (1.401) Remain 01:48:10 loss: 0.0739 Lr: 0.00014
[2023-12-25 23:01:57,079 INFO misc.py line 119 253097] Train: [91/100][467/510] Data 0.022 (0.092) Batch 1.032 (1.400) Remain 01:48:05 loss: 0.0930 Lr: 0.00014
[2023-12-25 23:01:57,970 INFO misc.py line 119 253097] Train: [91/100][468/510] Data 0.025 (0.092) Batch 0.912 (1.399) Remain 01:47:59 loss: 0.1011 Lr: 0.00014
[2023-12-25 23:01:59,141 INFO misc.py line 119 253097] Train: [91/100][469/510] Data 0.004 (0.092) Batch 1.170 (1.398) Remain 01:47:55 loss: 0.1157 Lr: 0.00014
[2023-12-25 23:02:00,273 INFO misc.py line 119 253097] Train: [91/100][470/510] Data 0.005 (0.091) Batch 1.132 (1.398) Remain 01:47:51 loss: 0.0736 Lr: 0.00014
[2023-12-25 23:02:01,425 INFO misc.py line 119 253097] Train: [91/100][471/510] Data 0.003 (0.091) Batch 1.152 (1.397) Remain 01:47:47 loss: 0.2055 Lr: 0.00014
[2023-12-25 23:02:02,635 INFO misc.py line 119 253097] Train: [91/100][472/510] Data 0.004 (0.091) Batch 1.209 (1.397) Remain 01:47:44 loss: 0.1072 Lr: 0.00014
[2023-12-25 23:02:03,822 INFO misc.py line 119 253097] Train: [91/100][473/510] Data 0.007 (0.091) Batch 1.187 (1.396) Remain 01:47:41 loss: 0.1970 Lr: 0.00014
[2023-12-25 23:02:05,004 INFO misc.py line 119 253097] Train: [91/100][474/510] Data 0.004 (0.091) Batch 1.183 (1.396) Remain 01:47:37 loss: 0.0466 Lr: 0.00014
[2023-12-25 23:02:06,198 INFO misc.py line 119 253097] Train: [91/100][475/510] Data 0.005 (0.091) Batch 1.193 (1.396) Remain 01:47:34 loss: 0.1333 Lr: 0.00014
[2023-12-25 23:02:07,480 INFO misc.py line 119 253097] Train: [91/100][476/510] Data 0.005 (0.090) Batch 1.282 (1.395) Remain 01:47:31 loss: 0.0781 Lr: 0.00014
[2023-12-25 23:02:12,658 INFO misc.py line 119 253097] Train: [91/100][477/510] Data 0.005 (0.090) Batch 5.176 (1.403) Remain 01:48:07 loss: 0.0561 Lr: 0.00014
[2023-12-25 23:02:13,665 INFO misc.py line 119 253097] Train: [91/100][478/510] Data 0.007 (0.090) Batch 1.011 (1.402) Remain 01:48:01 loss: 0.1396 Lr: 0.00014
[2023-12-25 23:02:14,861 INFO misc.py line 119 253097] Train: [91/100][479/510] Data 0.003 (0.090) Batch 1.195 (1.402) Remain 01:47:58 loss: 0.0905 Lr: 0.00014
[2023-12-25 23:02:15,829 INFO misc.py line 119 253097] Train: [91/100][480/510] Data 0.003 (0.090) Batch 0.967 (1.401) Remain 01:47:52 loss: 0.1833 Lr: 0.00014
[2023-12-25 23:02:16,874 INFO misc.py line 119 253097] Train: [91/100][481/510] Data 0.004 (0.089) Batch 1.046 (1.400) Remain 01:47:48 loss: 0.1293 Lr: 0.00014
[2023-12-25 23:02:18,090 INFO misc.py line 119 253097] Train: [91/100][482/510] Data 0.003 (0.089) Batch 1.217 (1.400) Remain 01:47:44 loss: 0.1061 Lr: 0.00014
[2023-12-25 23:02:26,351 INFO misc.py line 119 253097] Train: [91/100][483/510] Data 6.946 (0.104) Batch 8.260 (1.414) Remain 01:48:49 loss: 0.0972 Lr: 0.00014
[2023-12-25 23:02:27,672 INFO misc.py line 119 253097] Train: [91/100][484/510] Data 0.003 (0.103) Batch 1.317 (1.414) Remain 01:48:47 loss: 0.0652 Lr: 0.00013
[2023-12-25 23:02:28,725 INFO misc.py line 119 253097] Train: [91/100][485/510] Data 0.007 (0.103) Batch 1.048 (1.413) Remain 01:48:42 loss: 0.0704 Lr: 0.00013
[2023-12-25 23:02:29,745 INFO misc.py line 119 253097] Train: [91/100][486/510] Data 0.013 (0.103) Batch 1.026 (1.412) Remain 01:48:37 loss: 0.1439 Lr: 0.00013
[2023-12-25 23:02:30,752 INFO misc.py line 119 253097] Train: [91/100][487/510] Data 0.007 (0.103) Batch 1.009 (1.412) Remain 01:48:31 loss: 0.0858 Lr: 0.00013
[2023-12-25 23:02:31,990 INFO misc.py line 119 253097] Train: [91/100][488/510] Data 0.004 (0.103) Batch 1.235 (1.411) Remain 01:48:28 loss: 0.3459 Lr: 0.00013
[2023-12-25 23:02:33,077 INFO misc.py line 119 253097] Train: [91/100][489/510] Data 0.008 (0.102) Batch 1.088 (1.411) Remain 01:48:24 loss: 0.0838 Lr: 0.00013
[2023-12-25 23:02:34,021 INFO misc.py line 119 253097] Train: [91/100][490/510] Data 0.007 (0.102) Batch 0.947 (1.410) Remain 01:48:18 loss: 0.0710 Lr: 0.00013
[2023-12-25 23:02:35,176 INFO misc.py line 119 253097] Train: [91/100][491/510] Data 0.003 (0.102) Batch 1.155 (1.409) Remain 01:48:14 loss: 0.1617 Lr: 0.00013
[2023-12-25 23:02:36,591 INFO misc.py line 119 253097] Train: [91/100][492/510] Data 0.003 (0.102) Batch 1.414 (1.409) Remain 01:48:13 loss: 0.1359 Lr: 0.00013
[2023-12-25 23:02:37,858 INFO misc.py line 119 253097] Train: [91/100][493/510] Data 0.005 (0.102) Batch 1.265 (1.409) Remain 01:48:10 loss: 0.2160 Lr: 0.00013
[2023-12-25 23:02:39,058 INFO misc.py line 119 253097] Train: [91/100][494/510] Data 0.007 (0.101) Batch 1.199 (1.408) Remain 01:48:07 loss: 0.1340 Lr: 0.00013
[2023-12-25 23:02:40,260 INFO misc.py line 119 253097] Train: [91/100][495/510] Data 0.008 (0.101) Batch 1.206 (1.408) Remain 01:48:03 loss: 0.0853 Lr: 0.00013
[2023-12-25 23:02:41,353 INFO misc.py line 119 253097] Train: [91/100][496/510] Data 0.003 (0.101) Batch 1.093 (1.407) Remain 01:47:59 loss: 0.1044 Lr: 0.00013
[2023-12-25 23:02:42,423 INFO misc.py line 119 253097] Train: [91/100][497/510] Data 0.003 (0.101) Batch 1.065 (1.407) Remain 01:47:54 loss: 0.1644 Lr: 0.00013
[2023-12-25 23:02:43,743 INFO misc.py line 119 253097] Train: [91/100][498/510] Data 0.008 (0.101) Batch 1.319 (1.407) Remain 01:47:52 loss: 0.0852 Lr: 0.00013
[2023-12-25 23:02:44,821 INFO misc.py line 119 253097] Train: [91/100][499/510] Data 0.009 (0.100) Batch 1.080 (1.406) Remain 01:47:48 loss: 0.1158 Lr: 0.00013
[2023-12-25 23:02:45,987 INFO misc.py line 119 253097] Train: [91/100][500/510] Data 0.007 (0.100) Batch 1.166 (1.405) Remain 01:47:44 loss: 0.0837 Lr: 0.00013
[2023-12-25 23:02:47,112 INFO misc.py line 119 253097] Train: [91/100][501/510] Data 0.058 (0.100) Batch 1.129 (1.405) Remain 01:47:40 loss: 0.1529 Lr: 0.00013
[2023-12-25 23:02:48,339 INFO misc.py line 119 253097] Train: [91/100][502/510] Data 0.003 (0.100) Batch 1.221 (1.404) Remain 01:47:37 loss: 0.1483 Lr: 0.00013
[2023-12-25 23:02:49,468 INFO misc.py line 119 253097] Train: [91/100][503/510] Data 0.009 (0.100) Batch 1.134 (1.404) Remain 01:47:33 loss: 0.0874 Lr: 0.00013
[2023-12-25 23:02:50,717 INFO misc.py line 119 253097] Train: [91/100][504/510] Data 0.003 (0.100) Batch 1.245 (1.404) Remain 01:47:30 loss: 0.0931 Lr: 0.00013
[2023-12-25 23:02:51,937 INFO misc.py line 119 253097] Train: [91/100][505/510] Data 0.007 (0.099) Batch 1.224 (1.403) Remain 01:47:27 loss: 0.0785 Lr: 0.00013
[2023-12-25 23:02:53,121 INFO misc.py line 119 253097] Train: [91/100][506/510] Data 0.003 (0.099) Batch 1.184 (1.403) Remain 01:47:24 loss: 0.0853 Lr: 0.00013
[2023-12-25 23:02:54,239 INFO misc.py line 119 253097] Train: [91/100][507/510] Data 0.002 (0.099) Batch 1.111 (1.402) Remain 01:47:20 loss: 0.2042 Lr: 0.00013
[2023-12-25 23:02:59,588 INFO misc.py line 119 253097] Train: [91/100][508/510] Data 4.228 (0.107) Batch 5.355 (1.410) Remain 01:47:54 loss: 0.0751 Lr: 0.00013
[2023-12-25 23:03:00,858 INFO misc.py line 119 253097] Train: [91/100][509/510] Data 0.003 (0.107) Batch 1.266 (1.410) Remain 01:47:52 loss: 0.1043 Lr: 0.00013
[2023-12-25 23:03:01,971 INFO misc.py line 119 253097] Train: [91/100][510/510] Data 0.007 (0.107) Batch 1.111 (1.409) Remain 01:47:48 loss: 0.1373 Lr: 0.00013
[2023-12-25 23:03:01,971 INFO misc.py line 136 253097] Train result: loss: 0.1128 
[2023-12-25 23:03:01,971 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 23:03:35,391 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.4955 
[2023-12-25 23:03:35,733 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3286 
[2023-12-25 23:03:40,669 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.5029 
[2023-12-25 23:03:41,185 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3711 
[2023-12-25 23:03:43,154 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8589 
[2023-12-25 23:03:43,578 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3415 
[2023-12-25 23:03:44,457 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1624 
[2023-12-25 23:03:45,008 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2739 
[2023-12-25 23:03:46,815 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.7407 
[2023-12-25 23:03:48,933 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1462 
[2023-12-25 23:03:49,789 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3090 
[2023-12-25 23:03:50,211 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 1.0560 
[2023-12-25 23:03:51,110 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4371 
[2023-12-25 23:03:54,058 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9130 
[2023-12-25 23:03:54,522 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2787 
[2023-12-25 23:03:55,132 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4179 
[2023-12-25 23:03:55,831 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3403 
[2023-12-25 23:03:57,431 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6896/0.7445/0.9042.
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9181/0.9457
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9833/0.9908
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8446/0.9731
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3559/0.3854
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6335/0.6522
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6556/0.7368
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8100/0.9002
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9181/0.9558
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6329/0.6710
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7798/0.8677
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8217/0.8648
[2023-12-25 23:03:57,432 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6113/0.7350
[2023-12-25 23:03:57,433 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 23:03:57,439 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 23:03:57,439 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 23:04:12,999 INFO misc.py line 119 253097] Train: [92/100][1/510] Data 2.441 (2.441) Batch 13.926 (13.926) Remain 17:45:05 loss: 0.1513 Lr: 0.00013
[2023-12-25 23:04:14,204 INFO misc.py line 119 253097] Train: [92/100][2/510] Data 0.004 (0.004) Batch 1.201 (1.201) Remain 01:31:50 loss: 0.2054 Lr: 0.00013
[2023-12-25 23:04:15,458 INFO misc.py line 119 253097] Train: [92/100][3/510] Data 0.007 (0.007) Batch 1.254 (1.254) Remain 01:35:52 loss: 0.1371 Lr: 0.00013
[2023-12-25 23:04:16,649 INFO misc.py line 119 253097] Train: [92/100][4/510] Data 0.006 (0.006) Batch 1.191 (1.191) Remain 01:31:03 loss: 0.0628 Lr: 0.00013
[2023-12-25 23:04:17,926 INFO misc.py line 119 253097] Train: [92/100][5/510] Data 0.006 (0.006) Batch 1.279 (1.235) Remain 01:34:23 loss: 0.0769 Lr: 0.00013
[2023-12-25 23:04:19,086 INFO misc.py line 119 253097] Train: [92/100][6/510] Data 0.004 (0.006) Batch 1.159 (1.210) Remain 01:32:25 loss: 0.1182 Lr: 0.00013
[2023-12-25 23:04:20,071 INFO misc.py line 119 253097] Train: [92/100][7/510] Data 0.006 (0.006) Batch 0.986 (1.154) Remain 01:28:08 loss: 0.1037 Lr: 0.00013
[2023-12-25 23:04:27,944 INFO misc.py line 119 253097] Train: [92/100][8/510] Data 0.004 (0.005) Batch 7.870 (2.497) Remain 03:10:41 loss: 0.0860 Lr: 0.00013
[2023-12-25 23:04:29,233 INFO misc.py line 119 253097] Train: [92/100][9/510] Data 0.008 (0.006) Batch 1.290 (2.296) Remain 02:55:17 loss: 0.1318 Lr: 0.00013
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[2023-12-25 23:14:52,476 INFO misc.py line 119 253097] Train: [92/100][428/510] Data 0.004 (0.108) Batch 8.714 (1.499) Remain 01:43:58 loss: 0.0974 Lr: 0.00011
[2023-12-25 23:14:53,484 INFO misc.py line 119 253097] Train: [92/100][429/510] Data 0.007 (0.107) Batch 1.012 (1.498) Remain 01:43:52 loss: 0.0841 Lr: 0.00011
[2023-12-25 23:14:54,402 INFO misc.py line 119 253097] Train: [92/100][430/510] Data 0.002 (0.107) Batch 0.918 (1.496) Remain 01:43:44 loss: 0.0740 Lr: 0.00011
[2023-12-25 23:14:55,682 INFO misc.py line 119 253097] Train: [92/100][431/510] Data 0.003 (0.107) Batch 1.280 (1.496) Remain 01:43:41 loss: 0.1346 Lr: 0.00011
[2023-12-25 23:14:56,859 INFO misc.py line 119 253097] Train: [92/100][432/510] Data 0.003 (0.107) Batch 1.177 (1.495) Remain 01:43:36 loss: 0.1210 Lr: 0.00011
[2023-12-25 23:14:58,193 INFO misc.py line 119 253097] Train: [92/100][433/510] Data 0.003 (0.107) Batch 1.330 (1.495) Remain 01:43:33 loss: 0.0875 Lr: 0.00011
[2023-12-25 23:14:59,228 INFO misc.py line 119 253097] Train: [92/100][434/510] Data 0.007 (0.106) Batch 1.032 (1.494) Remain 01:43:27 loss: 0.1333 Lr: 0.00011
[2023-12-25 23:15:00,395 INFO misc.py line 119 253097] Train: [92/100][435/510] Data 0.010 (0.106) Batch 1.170 (1.493) Remain 01:43:23 loss: 0.1638 Lr: 0.00011
[2023-12-25 23:15:01,473 INFO misc.py line 119 253097] Train: [92/100][436/510] Data 0.007 (0.106) Batch 1.077 (1.492) Remain 01:43:17 loss: 0.1104 Lr: 0.00011
[2023-12-25 23:15:02,610 INFO misc.py line 119 253097] Train: [92/100][437/510] Data 0.008 (0.106) Batch 1.134 (1.491) Remain 01:43:12 loss: 0.0724 Lr: 0.00011
[2023-12-25 23:15:03,659 INFO misc.py line 119 253097] Train: [92/100][438/510] Data 0.011 (0.105) Batch 1.055 (1.490) Remain 01:43:06 loss: 0.1189 Lr: 0.00011
[2023-12-25 23:15:04,789 INFO misc.py line 119 253097] Train: [92/100][439/510] Data 0.006 (0.105) Batch 1.129 (1.489) Remain 01:43:02 loss: 0.0902 Lr: 0.00011
[2023-12-25 23:15:05,986 INFO misc.py line 119 253097] Train: [92/100][440/510] Data 0.008 (0.105) Batch 1.191 (1.489) Remain 01:42:57 loss: 0.0879 Lr: 0.00011
[2023-12-25 23:15:07,248 INFO misc.py line 119 253097] Train: [92/100][441/510] Data 0.013 (0.105) Batch 1.263 (1.488) Remain 01:42:54 loss: 0.0793 Lr: 0.00011
[2023-12-25 23:15:08,553 INFO misc.py line 119 253097] Train: [92/100][442/510] Data 0.012 (0.105) Batch 1.310 (1.488) Remain 01:42:50 loss: 0.1036 Lr: 0.00011
[2023-12-25 23:15:09,810 INFO misc.py line 119 253097] Train: [92/100][443/510] Data 0.006 (0.104) Batch 1.251 (1.487) Remain 01:42:47 loss: 0.0555 Lr: 0.00011
[2023-12-25 23:15:10,982 INFO misc.py line 119 253097] Train: [92/100][444/510] Data 0.013 (0.104) Batch 1.180 (1.486) Remain 01:42:42 loss: 0.0927 Lr: 0.00011
[2023-12-25 23:15:12,082 INFO misc.py line 119 253097] Train: [92/100][445/510] Data 0.005 (0.104) Batch 1.096 (1.486) Remain 01:42:37 loss: 0.0866 Lr: 0.00011
[2023-12-25 23:15:13,255 INFO misc.py line 119 253097] Train: [92/100][446/510] Data 0.009 (0.104) Batch 1.177 (1.485) Remain 01:42:33 loss: 0.1875 Lr: 0.00011
[2023-12-25 23:15:14,355 INFO misc.py line 119 253097] Train: [92/100][447/510] Data 0.005 (0.103) Batch 1.101 (1.484) Remain 01:42:28 loss: 0.1004 Lr: 0.00011
[2023-12-25 23:15:15,524 INFO misc.py line 119 253097] Train: [92/100][448/510] Data 0.005 (0.103) Batch 1.170 (1.483) Remain 01:42:23 loss: 0.0978 Lr: 0.00011
[2023-12-25 23:15:16,770 INFO misc.py line 119 253097] Train: [92/100][449/510] Data 0.002 (0.103) Batch 1.246 (1.483) Remain 01:42:20 loss: 0.0837 Lr: 0.00011
[2023-12-25 23:15:17,803 INFO misc.py line 119 253097] Train: [92/100][450/510] Data 0.003 (0.103) Batch 1.011 (1.482) Remain 01:42:14 loss: 0.0854 Lr: 0.00011
[2023-12-25 23:15:18,919 INFO misc.py line 119 253097] Train: [92/100][451/510] Data 0.025 (0.103) Batch 1.133 (1.481) Remain 01:42:09 loss: 0.0759 Lr: 0.00011
[2023-12-25 23:15:20,016 INFO misc.py line 119 253097] Train: [92/100][452/510] Data 0.008 (0.102) Batch 1.098 (1.480) Remain 01:42:04 loss: 0.0969 Lr: 0.00011
[2023-12-25 23:15:21,341 INFO misc.py line 119 253097] Train: [92/100][453/510] Data 0.007 (0.102) Batch 1.328 (1.480) Remain 01:42:01 loss: 0.1097 Lr: 0.00011
[2023-12-25 23:15:22,456 INFO misc.py line 119 253097] Train: [92/100][454/510] Data 0.004 (0.102) Batch 1.113 (1.479) Remain 01:41:56 loss: 0.0777 Lr: 0.00011
[2023-12-25 23:15:23,285 INFO misc.py line 119 253097] Train: [92/100][455/510] Data 0.006 (0.102) Batch 0.832 (1.477) Remain 01:41:49 loss: 0.0893 Lr: 0.00011
[2023-12-25 23:15:24,494 INFO misc.py line 119 253097] Train: [92/100][456/510] Data 0.003 (0.102) Batch 1.207 (1.477) Remain 01:41:45 loss: 0.0781 Lr: 0.00011
[2023-12-25 23:15:25,693 INFO misc.py line 119 253097] Train: [92/100][457/510] Data 0.005 (0.101) Batch 1.198 (1.476) Remain 01:41:41 loss: 0.1514 Lr: 0.00011
[2023-12-25 23:15:26,842 INFO misc.py line 119 253097] Train: [92/100][458/510] Data 0.006 (0.101) Batch 1.121 (1.476) Remain 01:41:36 loss: 0.1555 Lr: 0.00011
[2023-12-25 23:15:27,862 INFO misc.py line 119 253097] Train: [92/100][459/510] Data 0.034 (0.101) Batch 1.050 (1.475) Remain 01:41:31 loss: 0.0890 Lr: 0.00011
[2023-12-25 23:15:28,704 INFO misc.py line 119 253097] Train: [92/100][460/510] Data 0.004 (0.101) Batch 0.843 (1.473) Remain 01:41:24 loss: 0.0614 Lr: 0.00011
[2023-12-25 23:15:32,571 INFO misc.py line 119 253097] Train: [92/100][461/510] Data 0.003 (0.101) Batch 3.868 (1.478) Remain 01:41:44 loss: 0.0577 Lr: 0.00011
[2023-12-25 23:15:33,631 INFO misc.py line 119 253097] Train: [92/100][462/510] Data 0.003 (0.100) Batch 1.060 (1.478) Remain 01:41:39 loss: 0.0646 Lr: 0.00011
[2023-12-25 23:15:34,812 INFO misc.py line 119 253097] Train: [92/100][463/510] Data 0.003 (0.100) Batch 1.176 (1.477) Remain 01:41:34 loss: 0.1260 Lr: 0.00011
[2023-12-25 23:15:36,082 INFO misc.py line 119 253097] Train: [92/100][464/510] Data 0.008 (0.100) Batch 1.274 (1.476) Remain 01:41:31 loss: 0.1468 Lr: 0.00011
[2023-12-25 23:15:37,061 INFO misc.py line 119 253097] Train: [92/100][465/510] Data 0.004 (0.100) Batch 0.980 (1.475) Remain 01:41:25 loss: 0.1059 Lr: 0.00011
[2023-12-25 23:15:38,191 INFO misc.py line 119 253097] Train: [92/100][466/510] Data 0.004 (0.099) Batch 1.130 (1.475) Remain 01:41:21 loss: 0.1681 Lr: 0.00011
[2023-12-25 23:15:39,424 INFO misc.py line 119 253097] Train: [92/100][467/510] Data 0.003 (0.099) Batch 1.233 (1.474) Remain 01:41:17 loss: 0.0739 Lr: 0.00011
[2023-12-25 23:15:40,338 INFO misc.py line 119 253097] Train: [92/100][468/510] Data 0.003 (0.099) Batch 0.914 (1.473) Remain 01:41:11 loss: 0.0875 Lr: 0.00011
[2023-12-25 23:15:41,543 INFO misc.py line 119 253097] Train: [92/100][469/510] Data 0.003 (0.099) Batch 1.204 (1.472) Remain 01:41:07 loss: 0.1838 Lr: 0.00011
[2023-12-25 23:15:42,693 INFO misc.py line 119 253097] Train: [92/100][470/510] Data 0.003 (0.099) Batch 1.150 (1.472) Remain 01:41:02 loss: 0.1176 Lr: 0.00011
[2023-12-25 23:15:43,932 INFO misc.py line 119 253097] Train: [92/100][471/510] Data 0.004 (0.098) Batch 1.239 (1.471) Remain 01:40:59 loss: 0.1020 Lr: 0.00011
[2023-12-25 23:15:45,130 INFO misc.py line 119 253097] Train: [92/100][472/510] Data 0.003 (0.098) Batch 1.196 (1.471) Remain 01:40:55 loss: 0.0648 Lr: 0.00011
[2023-12-25 23:15:46,349 INFO misc.py line 119 253097] Train: [92/100][473/510] Data 0.006 (0.098) Batch 1.217 (1.470) Remain 01:40:51 loss: 0.1220 Lr: 0.00011
[2023-12-25 23:15:47,362 INFO misc.py line 119 253097] Train: [92/100][474/510] Data 0.007 (0.098) Batch 1.013 (1.469) Remain 01:40:46 loss: 0.0935 Lr: 0.00011
[2023-12-25 23:15:48,422 INFO misc.py line 119 253097] Train: [92/100][475/510] Data 0.007 (0.098) Batch 1.060 (1.468) Remain 01:40:41 loss: 0.2308 Lr: 0.00011
[2023-12-25 23:15:49,464 INFO misc.py line 119 253097] Train: [92/100][476/510] Data 0.007 (0.097) Batch 1.046 (1.467) Remain 01:40:36 loss: 0.0802 Lr: 0.00011
[2023-12-25 23:15:50,910 INFO misc.py line 119 253097] Train: [92/100][477/510] Data 0.003 (0.097) Batch 1.446 (1.467) Remain 01:40:34 loss: 0.1615 Lr: 0.00011
[2023-12-25 23:15:51,977 INFO misc.py line 119 253097] Train: [92/100][478/510] Data 0.003 (0.097) Batch 1.065 (1.466) Remain 01:40:29 loss: 0.0938 Lr: 0.00011
[2023-12-25 23:15:53,116 INFO misc.py line 119 253097] Train: [92/100][479/510] Data 0.005 (0.097) Batch 1.141 (1.466) Remain 01:40:25 loss: 0.1801 Lr: 0.00011
[2023-12-25 23:15:54,108 INFO misc.py line 119 253097] Train: [92/100][480/510] Data 0.003 (0.097) Batch 0.991 (1.465) Remain 01:40:19 loss: 0.1152 Lr: 0.00011
[2023-12-25 23:16:04,807 INFO misc.py line 119 253097] Train: [92/100][481/510] Data 9.397 (0.116) Batch 10.700 (1.484) Remain 01:41:37 loss: 0.1437 Lr: 0.00011
[2023-12-25 23:16:05,671 INFO misc.py line 119 253097] Train: [92/100][482/510] Data 0.003 (0.116) Batch 0.864 (1.483) Remain 01:41:30 loss: 0.0767 Lr: 0.00011
[2023-12-25 23:16:06,869 INFO misc.py line 119 253097] Train: [92/100][483/510] Data 0.003 (0.116) Batch 1.198 (1.482) Remain 01:41:27 loss: 0.0626 Lr: 0.00011
[2023-12-25 23:16:07,713 INFO misc.py line 119 253097] Train: [92/100][484/510] Data 0.003 (0.115) Batch 0.844 (1.481) Remain 01:41:20 loss: 0.0809 Lr: 0.00011
[2023-12-25 23:16:08,550 INFO misc.py line 119 253097] Train: [92/100][485/510] Data 0.003 (0.115) Batch 0.833 (1.479) Remain 01:41:13 loss: 0.1590 Lr: 0.00011
[2023-12-25 23:16:09,683 INFO misc.py line 119 253097] Train: [92/100][486/510] Data 0.007 (0.115) Batch 1.133 (1.479) Remain 01:41:08 loss: 0.0548 Lr: 0.00011
[2023-12-25 23:16:10,826 INFO misc.py line 119 253097] Train: [92/100][487/510] Data 0.007 (0.115) Batch 1.144 (1.478) Remain 01:41:04 loss: 0.0960 Lr: 0.00011
[2023-12-25 23:16:11,914 INFO misc.py line 119 253097] Train: [92/100][488/510] Data 0.006 (0.115) Batch 1.086 (1.477) Remain 01:40:59 loss: 0.0605 Lr: 0.00011
[2023-12-25 23:16:13,071 INFO misc.py line 119 253097] Train: [92/100][489/510] Data 0.008 (0.114) Batch 1.161 (1.477) Remain 01:40:55 loss: 0.0984 Lr: 0.00011
[2023-12-25 23:16:14,176 INFO misc.py line 119 253097] Train: [92/100][490/510] Data 0.003 (0.114) Batch 1.105 (1.476) Remain 01:40:50 loss: 0.0976 Lr: 0.00011
[2023-12-25 23:16:15,142 INFO misc.py line 119 253097] Train: [92/100][491/510] Data 0.004 (0.114) Batch 0.967 (1.475) Remain 01:40:45 loss: 0.1237 Lr: 0.00011
[2023-12-25 23:16:16,204 INFO misc.py line 119 253097] Train: [92/100][492/510] Data 0.003 (0.114) Batch 1.062 (1.474) Remain 01:40:40 loss: 0.0993 Lr: 0.00011
[2023-12-25 23:16:17,225 INFO misc.py line 119 253097] Train: [92/100][493/510] Data 0.003 (0.113) Batch 1.020 (1.473) Remain 01:40:34 loss: 0.0910 Lr: 0.00011
[2023-12-25 23:16:18,195 INFO misc.py line 119 253097] Train: [92/100][494/510] Data 0.004 (0.113) Batch 0.971 (1.472) Remain 01:40:29 loss: 0.1553 Lr: 0.00011
[2023-12-25 23:16:19,488 INFO misc.py line 119 253097] Train: [92/100][495/510] Data 0.002 (0.113) Batch 1.276 (1.472) Remain 01:40:26 loss: 0.0790 Lr: 0.00011
[2023-12-25 23:16:20,680 INFO misc.py line 119 253097] Train: [92/100][496/510] Data 0.020 (0.113) Batch 1.209 (1.471) Remain 01:40:22 loss: 0.0911 Lr: 0.00011
[2023-12-25 23:16:21,901 INFO misc.py line 119 253097] Train: [92/100][497/510] Data 0.003 (0.113) Batch 1.216 (1.471) Remain 01:40:18 loss: 0.1443 Lr: 0.00011
[2023-12-25 23:16:23,186 INFO misc.py line 119 253097] Train: [92/100][498/510] Data 0.008 (0.112) Batch 1.286 (1.470) Remain 01:40:15 loss: 0.0735 Lr: 0.00011
[2023-12-25 23:16:24,172 INFO misc.py line 119 253097] Train: [92/100][499/510] Data 0.007 (0.112) Batch 0.990 (1.469) Remain 01:40:10 loss: 0.0693 Lr: 0.00011
[2023-12-25 23:16:25,280 INFO misc.py line 119 253097] Train: [92/100][500/510] Data 0.003 (0.112) Batch 1.108 (1.468) Remain 01:40:05 loss: 0.0861 Lr: 0.00011
[2023-12-25 23:16:26,385 INFO misc.py line 119 253097] Train: [92/100][501/510] Data 0.004 (0.112) Batch 1.105 (1.468) Remain 01:40:01 loss: 0.0844 Lr: 0.00011
[2023-12-25 23:16:27,520 INFO misc.py line 119 253097] Train: [92/100][502/510] Data 0.003 (0.111) Batch 1.134 (1.467) Remain 01:39:57 loss: 0.1471 Lr: 0.00011
[2023-12-25 23:16:28,703 INFO misc.py line 119 253097] Train: [92/100][503/510] Data 0.004 (0.111) Batch 1.184 (1.466) Remain 01:39:53 loss: 0.1509 Lr: 0.00011
[2023-12-25 23:16:29,821 INFO misc.py line 119 253097] Train: [92/100][504/510] Data 0.002 (0.111) Batch 1.117 (1.466) Remain 01:39:49 loss: 0.0863 Lr: 0.00011
[2023-12-25 23:16:30,883 INFO misc.py line 119 253097] Train: [92/100][505/510] Data 0.004 (0.111) Batch 1.062 (1.465) Remain 01:39:44 loss: 0.1170 Lr: 0.00011
[2023-12-25 23:16:32,117 INFO misc.py line 119 253097] Train: [92/100][506/510] Data 0.004 (0.111) Batch 1.231 (1.465) Remain 01:39:41 loss: 0.1276 Lr: 0.00011
[2023-12-25 23:16:33,349 INFO misc.py line 119 253097] Train: [92/100][507/510] Data 0.007 (0.110) Batch 1.235 (1.464) Remain 01:39:37 loss: 0.1576 Lr: 0.00011
[2023-12-25 23:16:34,427 INFO misc.py line 119 253097] Train: [92/100][508/510] Data 0.003 (0.110) Batch 1.074 (1.463) Remain 01:39:33 loss: 0.2207 Lr: 0.00011
[2023-12-25 23:16:35,619 INFO misc.py line 119 253097] Train: [92/100][509/510] Data 0.007 (0.110) Batch 1.192 (1.463) Remain 01:39:29 loss: 0.0600 Lr: 0.00011
[2023-12-25 23:16:36,709 INFO misc.py line 119 253097] Train: [92/100][510/510] Data 0.007 (0.110) Batch 1.091 (1.462) Remain 01:39:25 loss: 0.1490 Lr: 0.00011
[2023-12-25 23:16:36,709 INFO misc.py line 136 253097] Train result: loss: 0.1130 
[2023-12-25 23:16:36,709 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 23:17:12,755 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5988 
[2023-12-25 23:17:13,098 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2759 
[2023-12-25 23:17:18,047 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3217 
[2023-12-25 23:17:18,562 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3702 
[2023-12-25 23:17:20,534 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9065 
[2023-12-25 23:17:20,957 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3080 
[2023-12-25 23:17:21,833 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.2457 
[2023-12-25 23:17:22,386 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2789 
[2023-12-25 23:17:24,199 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8255 
[2023-12-25 23:17:26,323 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1747 
[2023-12-25 23:17:27,178 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3085 
[2023-12-25 23:17:27,600 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9293 
[2023-12-25 23:17:28,499 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4421 
[2023-12-25 23:17:31,444 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8909 
[2023-12-25 23:17:31,912 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2571 
[2023-12-25 23:17:32,521 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.4063 
[2023-12-25 23:17:33,220 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3294 
[2023-12-25 23:17:34,886 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6885/0.7428/0.9041.
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9165/0.9449
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9831/0.9899
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8418/0.9720
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3241/0.3592
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6503/0.6705
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6758/0.7558
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8012/0.8912
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9086/0.9513
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6253/0.6624
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7858/0.8676
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8230/0.8515
[2023-12-25 23:17:34,886 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6146/0.7396
[2023-12-25 23:17:34,887 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 23:17:34,888 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 23:17:34,888 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 23:17:51,635 INFO misc.py line 119 253097] Train: [93/100][1/510] Data 4.139 (4.139) Batch 15.267 (15.267) Remain 17:17:52 loss: 0.0551 Lr: 0.00011
[2023-12-25 23:17:52,894 INFO misc.py line 119 253097] Train: [93/100][2/510] Data 0.003 (0.003) Batch 1.254 (1.254) Remain 01:25:14 loss: 0.0775 Lr: 0.00011
[2023-12-25 23:17:54,069 INFO misc.py line 119 253097] Train: [93/100][3/510] Data 0.008 (0.008) Batch 1.176 (1.176) Remain 01:19:56 loss: 0.0981 Lr: 0.00011
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[2023-12-25 23:23:22,711 INFO misc.py line 119 253097] Train: [93/100][233/510] Data 8.324 (0.090) Batch 9.481 (1.429) Remain 01:31:36 loss: 0.1655 Lr: 0.00009
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[2023-12-25 23:23:40,951 INFO misc.py line 119 253097] Train: [93/100][249/510] Data 0.004 (0.084) Batch 1.214 (1.410) Remain 01:30:02 loss: 0.1525 Lr: 0.00009
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[2023-12-25 23:23:45,461 INFO misc.py line 119 253097] Train: [93/100][253/510] Data 0.004 (0.083) Batch 1.088 (1.406) Remain 01:29:39 loss: 0.1319 Lr: 0.00009
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[2023-12-25 23:23:49,149 INFO misc.py line 119 253097] Train: [93/100][256/510] Data 0.011 (0.082) Batch 1.275 (1.403) Remain 01:29:26 loss: 0.0540 Lr: 0.00009
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[2023-12-25 23:24:03,873 INFO misc.py line 119 253097] Train: [93/100][263/510] Data 0.007 (0.080) Batch 7.525 (1.422) Remain 01:30:29 loss: 0.1264 Lr: 0.00009
[2023-12-25 23:24:05,104 INFO misc.py line 119 253097] Train: [93/100][264/510] Data 0.004 (0.080) Batch 1.231 (1.422) Remain 01:30:24 loss: 0.0815 Lr: 0.00009
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[2023-12-25 23:24:09,741 INFO misc.py line 119 253097] Train: [93/100][266/510] Data 2.374 (0.088) Batch 3.427 (1.428) Remain 01:30:48 loss: 0.0842 Lr: 0.00009
[2023-12-25 23:24:10,822 INFO misc.py line 119 253097] Train: [93/100][267/510] Data 0.003 (0.088) Batch 1.080 (1.427) Remain 01:30:41 loss: 0.1215 Lr: 0.00009
[2023-12-25 23:24:12,098 INFO misc.py line 119 253097] Train: [93/100][268/510] Data 0.004 (0.088) Batch 1.274 (1.427) Remain 01:30:37 loss: 0.0700 Lr: 0.00009
[2023-12-25 23:24:13,337 INFO misc.py line 119 253097] Train: [93/100][269/510] Data 0.005 (0.087) Batch 1.236 (1.426) Remain 01:30:33 loss: 0.1040 Lr: 0.00009
[2023-12-25 23:24:14,255 INFO misc.py line 119 253097] Train: [93/100][270/510] Data 0.009 (0.087) Batch 0.923 (1.424) Remain 01:30:25 loss: 0.1220 Lr: 0.00009
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[2023-12-25 23:24:23,210 INFO misc.py line 119 253097] Train: [93/100][272/510] Data 6.715 (0.111) Batch 7.820 (1.447) Remain 01:31:48 loss: 0.1639 Lr: 0.00009
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[2023-12-25 23:24:25,342 INFO misc.py line 119 253097] Train: [93/100][274/510] Data 0.004 (0.111) Batch 1.117 (1.444) Remain 01:31:35 loss: 0.1001 Lr: 0.00009
[2023-12-25 23:24:26,343 INFO misc.py line 119 253097] Train: [93/100][275/510] Data 0.004 (0.110) Batch 1.000 (1.442) Remain 01:31:27 loss: 0.1783 Lr: 0.00009
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[2023-12-25 23:24:28,740 INFO misc.py line 119 253097] Train: [93/100][277/510] Data 0.003 (0.109) Batch 1.141 (1.440) Remain 01:31:17 loss: 0.0837 Lr: 0.00009
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[2023-12-25 23:24:30,973 INFO misc.py line 119 253097] Train: [93/100][279/510] Data 0.008 (0.109) Batch 1.171 (1.438) Remain 01:31:06 loss: 0.0789 Lr: 0.00009
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[2023-12-25 23:24:36,600 INFO misc.py line 119 253097] Train: [93/100][284/510] Data 0.007 (0.107) Batch 1.012 (1.433) Remain 01:30:37 loss: 0.0800 Lr: 0.00009
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[2023-12-25 23:26:45,323 INFO misc.py line 119 253097] Train: [93/100][375/510] Data 0.007 (0.100) Batch 1.164 (1.428) Remain 01:28:11 loss: 0.0910 Lr: 0.00009
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[2023-12-25 23:27:54,829 INFO misc.py line 119 253097] Train: [93/100][422/510] Data 0.005 (0.091) Batch 1.193 (1.434) Remain 01:27:24 loss: 0.0859 Lr: 0.00009
[2023-12-25 23:27:55,864 INFO misc.py line 119 253097] Train: [93/100][423/510] Data 0.003 (0.091) Batch 1.029 (1.433) Remain 01:27:19 loss: 0.1133 Lr: 0.00009
[2023-12-25 23:27:56,973 INFO misc.py line 119 253097] Train: [93/100][424/510] Data 0.010 (0.091) Batch 1.114 (1.432) Remain 01:27:15 loss: 0.0946 Lr: 0.00009
[2023-12-25 23:27:58,271 INFO misc.py line 119 253097] Train: [93/100][425/510] Data 0.004 (0.090) Batch 1.298 (1.432) Remain 01:27:13 loss: 0.0393 Lr: 0.00009
[2023-12-25 23:27:59,408 INFO misc.py line 119 253097] Train: [93/100][426/510] Data 0.003 (0.090) Batch 1.138 (1.431) Remain 01:27:09 loss: 0.0937 Lr: 0.00009
[2023-12-25 23:28:00,556 INFO misc.py line 119 253097] Train: [93/100][427/510] Data 0.003 (0.090) Batch 1.144 (1.430) Remain 01:27:05 loss: 0.1497 Lr: 0.00009
[2023-12-25 23:28:07,262 INFO misc.py line 119 253097] Train: [93/100][428/510] Data 0.008 (0.090) Batch 6.710 (1.443) Remain 01:27:49 loss: 0.0724 Lr: 0.00009
[2023-12-25 23:28:08,518 INFO misc.py line 119 253097] Train: [93/100][429/510] Data 0.004 (0.090) Batch 1.252 (1.442) Remain 01:27:46 loss: 0.1163 Lr: 0.00009
[2023-12-25 23:28:09,774 INFO misc.py line 119 253097] Train: [93/100][430/510] Data 0.008 (0.089) Batch 1.255 (1.442) Remain 01:27:43 loss: 0.1283 Lr: 0.00008
[2023-12-25 23:28:10,936 INFO misc.py line 119 253097] Train: [93/100][431/510] Data 0.008 (0.089) Batch 1.162 (1.441) Remain 01:27:39 loss: 0.0802 Lr: 0.00008
[2023-12-25 23:28:12,060 INFO misc.py line 119 253097] Train: [93/100][432/510] Data 0.009 (0.089) Batch 1.129 (1.441) Remain 01:27:35 loss: 0.1555 Lr: 0.00008
[2023-12-25 23:28:13,403 INFO misc.py line 119 253097] Train: [93/100][433/510] Data 0.003 (0.089) Batch 1.340 (1.440) Remain 01:27:32 loss: 0.1022 Lr: 0.00008
[2023-12-25 23:28:14,657 INFO misc.py line 119 253097] Train: [93/100][434/510] Data 0.006 (0.089) Batch 1.252 (1.440) Remain 01:27:29 loss: 0.1324 Lr: 0.00008
[2023-12-25 23:28:15,760 INFO misc.py line 119 253097] Train: [93/100][435/510] Data 0.008 (0.088) Batch 1.103 (1.439) Remain 01:27:25 loss: 0.0910 Lr: 0.00008
[2023-12-25 23:28:16,860 INFO misc.py line 119 253097] Train: [93/100][436/510] Data 0.007 (0.088) Batch 1.100 (1.438) Remain 01:27:21 loss: 0.0825 Lr: 0.00008
[2023-12-25 23:28:18,109 INFO misc.py line 119 253097] Train: [93/100][437/510] Data 0.008 (0.088) Batch 1.254 (1.438) Remain 01:27:18 loss: 0.0617 Lr: 0.00008
[2023-12-25 23:28:19,242 INFO misc.py line 119 253097] Train: [93/100][438/510] Data 0.003 (0.088) Batch 1.132 (1.437) Remain 01:27:14 loss: 0.0812 Lr: 0.00008
[2023-12-25 23:28:20,277 INFO misc.py line 119 253097] Train: [93/100][439/510] Data 0.004 (0.088) Batch 1.030 (1.436) Remain 01:27:09 loss: 0.0888 Lr: 0.00008
[2023-12-25 23:28:21,491 INFO misc.py line 119 253097] Train: [93/100][440/510] Data 0.009 (0.087) Batch 1.215 (1.436) Remain 01:27:06 loss: 0.0741 Lr: 0.00008
[2023-12-25 23:28:22,553 INFO misc.py line 119 253097] Train: [93/100][441/510] Data 0.007 (0.087) Batch 1.063 (1.435) Remain 01:27:01 loss: 0.1381 Lr: 0.00008
[2023-12-25 23:28:23,632 INFO misc.py line 119 253097] Train: [93/100][442/510] Data 0.007 (0.087) Batch 1.077 (1.434) Remain 01:26:57 loss: 0.1102 Lr: 0.00008
[2023-12-25 23:28:24,798 INFO misc.py line 119 253097] Train: [93/100][443/510] Data 0.008 (0.087) Batch 1.166 (1.433) Remain 01:26:53 loss: 0.0756 Lr: 0.00008
[2023-12-25 23:28:25,928 INFO misc.py line 119 253097] Train: [93/100][444/510] Data 0.009 (0.087) Batch 1.135 (1.433) Remain 01:26:49 loss: 0.2189 Lr: 0.00008
[2023-12-25 23:28:27,008 INFO misc.py line 119 253097] Train: [93/100][445/510] Data 0.003 (0.087) Batch 1.076 (1.432) Remain 01:26:45 loss: 0.1028 Lr: 0.00008
[2023-12-25 23:28:28,046 INFO misc.py line 119 253097] Train: [93/100][446/510] Data 0.007 (0.086) Batch 1.042 (1.431) Remain 01:26:40 loss: 0.0856 Lr: 0.00008
[2023-12-25 23:28:29,091 INFO misc.py line 119 253097] Train: [93/100][447/510] Data 0.004 (0.086) Batch 1.032 (1.430) Remain 01:26:35 loss: 0.0795 Lr: 0.00008
[2023-12-25 23:28:30,155 INFO misc.py line 119 253097] Train: [93/100][448/510] Data 0.017 (0.086) Batch 1.075 (1.429) Remain 01:26:31 loss: 0.0763 Lr: 0.00008
[2023-12-25 23:28:31,148 INFO misc.py line 119 253097] Train: [93/100][449/510] Data 0.005 (0.086) Batch 0.995 (1.428) Remain 01:26:26 loss: 0.0627 Lr: 0.00008
[2023-12-25 23:28:32,128 INFO misc.py line 119 253097] Train: [93/100][450/510] Data 0.004 (0.086) Batch 0.979 (1.427) Remain 01:26:21 loss: 0.0906 Lr: 0.00008
[2023-12-25 23:28:33,278 INFO misc.py line 119 253097] Train: [93/100][451/510] Data 0.004 (0.085) Batch 1.151 (1.427) Remain 01:26:17 loss: 0.1699 Lr: 0.00008
[2023-12-25 23:28:34,454 INFO misc.py line 119 253097] Train: [93/100][452/510] Data 0.004 (0.085) Batch 1.176 (1.426) Remain 01:26:14 loss: 0.0671 Lr: 0.00008
[2023-12-25 23:28:35,688 INFO misc.py line 119 253097] Train: [93/100][453/510] Data 0.003 (0.085) Batch 1.235 (1.426) Remain 01:26:11 loss: 0.1649 Lr: 0.00008
[2023-12-25 23:28:36,786 INFO misc.py line 119 253097] Train: [93/100][454/510] Data 0.003 (0.085) Batch 1.098 (1.425) Remain 01:26:07 loss: 0.1104 Lr: 0.00008
[2023-12-25 23:28:44,765 INFO misc.py line 119 253097] Train: [93/100][455/510] Data 0.004 (0.085) Batch 7.978 (1.440) Remain 01:26:58 loss: 0.2086 Lr: 0.00008
[2023-12-25 23:28:46,020 INFO misc.py line 119 253097] Train: [93/100][456/510] Data 0.005 (0.085) Batch 1.253 (1.439) Remain 01:26:55 loss: 0.1476 Lr: 0.00008
[2023-12-25 23:28:47,282 INFO misc.py line 119 253097] Train: [93/100][457/510] Data 0.006 (0.084) Batch 1.263 (1.439) Remain 01:26:52 loss: 0.1145 Lr: 0.00008
[2023-12-25 23:28:48,251 INFO misc.py line 119 253097] Train: [93/100][458/510] Data 0.005 (0.084) Batch 0.970 (1.438) Remain 01:26:47 loss: 0.1178 Lr: 0.00008
[2023-12-25 23:28:49,264 INFO misc.py line 119 253097] Train: [93/100][459/510] Data 0.005 (0.084) Batch 1.013 (1.437) Remain 01:26:42 loss: 0.1202 Lr: 0.00008
[2023-12-25 23:28:50,419 INFO misc.py line 119 253097] Train: [93/100][460/510] Data 0.004 (0.084) Batch 1.155 (1.436) Remain 01:26:39 loss: 0.0696 Lr: 0.00008
[2023-12-25 23:28:51,595 INFO misc.py line 119 253097] Train: [93/100][461/510] Data 0.004 (0.084) Batch 1.177 (1.436) Remain 01:26:35 loss: 0.1267 Lr: 0.00008
[2023-12-25 23:28:52,799 INFO misc.py line 119 253097] Train: [93/100][462/510] Data 0.003 (0.083) Batch 1.203 (1.435) Remain 01:26:32 loss: 0.0783 Lr: 0.00008
[2023-12-25 23:28:53,874 INFO misc.py line 119 253097] Train: [93/100][463/510] Data 0.005 (0.083) Batch 1.076 (1.434) Remain 01:26:28 loss: 0.0809 Lr: 0.00008
[2023-12-25 23:28:55,052 INFO misc.py line 119 253097] Train: [93/100][464/510] Data 0.004 (0.083) Batch 1.174 (1.434) Remain 01:26:24 loss: 0.1873 Lr: 0.00008
[2023-12-25 23:28:56,142 INFO misc.py line 119 253097] Train: [93/100][465/510] Data 0.009 (0.083) Batch 1.092 (1.433) Remain 01:26:20 loss: 0.0828 Lr: 0.00008
[2023-12-25 23:28:57,306 INFO misc.py line 119 253097] Train: [93/100][466/510] Data 0.005 (0.083) Batch 1.164 (1.432) Remain 01:26:16 loss: 0.1198 Lr: 0.00008
[2023-12-25 23:28:58,471 INFO misc.py line 119 253097] Train: [93/100][467/510] Data 0.006 (0.083) Batch 1.163 (1.432) Remain 01:26:13 loss: 0.0689 Lr: 0.00008
[2023-12-25 23:28:59,704 INFO misc.py line 119 253097] Train: [93/100][468/510] Data 0.007 (0.082) Batch 1.236 (1.431) Remain 01:26:10 loss: 0.1067 Lr: 0.00008
[2023-12-25 23:29:00,817 INFO misc.py line 119 253097] Train: [93/100][469/510] Data 0.004 (0.082) Batch 1.110 (1.431) Remain 01:26:06 loss: 0.0998 Lr: 0.00008
[2023-12-25 23:29:01,931 INFO misc.py line 119 253097] Train: [93/100][470/510] Data 0.007 (0.082) Batch 1.103 (1.430) Remain 01:26:02 loss: 0.1610 Lr: 0.00008
[2023-12-25 23:29:03,066 INFO misc.py line 119 253097] Train: [93/100][471/510] Data 0.018 (0.082) Batch 1.146 (1.429) Remain 01:25:58 loss: 0.1167 Lr: 0.00008
[2023-12-25 23:29:04,331 INFO misc.py line 119 253097] Train: [93/100][472/510] Data 0.007 (0.082) Batch 1.264 (1.429) Remain 01:25:56 loss: 0.1526 Lr: 0.00008
[2023-12-25 23:29:05,490 INFO misc.py line 119 253097] Train: [93/100][473/510] Data 0.008 (0.082) Batch 1.163 (1.429) Remain 01:25:52 loss: 0.1110 Lr: 0.00008
[2023-12-25 23:29:10,105 INFO misc.py line 119 253097] Train: [93/100][474/510] Data 0.004 (0.082) Batch 4.615 (1.435) Remain 01:26:15 loss: 0.0711 Lr: 0.00008
[2023-12-25 23:29:11,140 INFO misc.py line 119 253097] Train: [93/100][475/510] Data 0.005 (0.081) Batch 1.034 (1.434) Remain 01:26:11 loss: 0.2518 Lr: 0.00008
[2023-12-25 23:29:12,375 INFO misc.py line 119 253097] Train: [93/100][476/510] Data 0.005 (0.081) Batch 1.237 (1.434) Remain 01:26:08 loss: 0.1180 Lr: 0.00008
[2023-12-25 23:29:13,458 INFO misc.py line 119 253097] Train: [93/100][477/510] Data 0.003 (0.081) Batch 1.082 (1.433) Remain 01:26:04 loss: 0.1681 Lr: 0.00008
[2023-12-25 23:29:14,648 INFO misc.py line 119 253097] Train: [93/100][478/510] Data 0.004 (0.081) Batch 1.187 (1.433) Remain 01:26:00 loss: 0.1013 Lr: 0.00008
[2023-12-25 23:29:15,741 INFO misc.py line 119 253097] Train: [93/100][479/510] Data 0.007 (0.081) Batch 1.093 (1.432) Remain 01:25:56 loss: 0.1801 Lr: 0.00008
[2023-12-25 23:29:16,872 INFO misc.py line 119 253097] Train: [93/100][480/510] Data 0.007 (0.081) Batch 1.134 (1.431) Remain 01:25:53 loss: 0.0875 Lr: 0.00008
[2023-12-25 23:29:17,839 INFO misc.py line 119 253097] Train: [93/100][481/510] Data 0.005 (0.080) Batch 0.968 (1.430) Remain 01:25:48 loss: 0.0715 Lr: 0.00008
[2023-12-25 23:29:18,986 INFO misc.py line 119 253097] Train: [93/100][482/510] Data 0.003 (0.080) Batch 1.146 (1.430) Remain 01:25:44 loss: 0.0637 Lr: 0.00008
[2023-12-25 23:29:20,176 INFO misc.py line 119 253097] Train: [93/100][483/510] Data 0.005 (0.080) Batch 1.186 (1.429) Remain 01:25:41 loss: 0.0686 Lr: 0.00008
[2023-12-25 23:29:21,195 INFO misc.py line 119 253097] Train: [93/100][484/510] Data 0.008 (0.080) Batch 1.019 (1.429) Remain 01:25:37 loss: 0.0870 Lr: 0.00008
[2023-12-25 23:29:22,255 INFO misc.py line 119 253097] Train: [93/100][485/510] Data 0.008 (0.080) Batch 1.064 (1.428) Remain 01:25:32 loss: 0.0623 Lr: 0.00008
[2023-12-25 23:29:23,463 INFO misc.py line 119 253097] Train: [93/100][486/510] Data 0.004 (0.080) Batch 1.206 (1.427) Remain 01:25:29 loss: 0.0874 Lr: 0.00008
[2023-12-25 23:29:24,710 INFO misc.py line 119 253097] Train: [93/100][487/510] Data 0.005 (0.079) Batch 1.247 (1.427) Remain 01:25:27 loss: 0.0760 Lr: 0.00008
[2023-12-25 23:29:25,764 INFO misc.py line 119 253097] Train: [93/100][488/510] Data 0.007 (0.079) Batch 1.056 (1.426) Remain 01:25:22 loss: 0.1980 Lr: 0.00008
[2023-12-25 23:29:26,834 INFO misc.py line 119 253097] Train: [93/100][489/510] Data 0.005 (0.079) Batch 1.067 (1.425) Remain 01:25:18 loss: 0.0669 Lr: 0.00008
[2023-12-25 23:29:28,041 INFO misc.py line 119 253097] Train: [93/100][490/510] Data 0.008 (0.079) Batch 1.211 (1.425) Remain 01:25:15 loss: 0.1408 Lr: 0.00008
[2023-12-25 23:29:29,230 INFO misc.py line 119 253097] Train: [93/100][491/510] Data 0.004 (0.079) Batch 1.185 (1.425) Remain 01:25:12 loss: 0.1258 Lr: 0.00008
[2023-12-25 23:29:30,525 INFO misc.py line 119 253097] Train: [93/100][492/510] Data 0.007 (0.079) Batch 1.294 (1.424) Remain 01:25:10 loss: 0.0618 Lr: 0.00008
[2023-12-25 23:29:31,595 INFO misc.py line 119 253097] Train: [93/100][493/510] Data 0.008 (0.079) Batch 1.073 (1.424) Remain 01:25:06 loss: 0.0770 Lr: 0.00008
[2023-12-25 23:29:32,751 INFO misc.py line 119 253097] Train: [93/100][494/510] Data 0.004 (0.078) Batch 1.156 (1.423) Remain 01:25:02 loss: 0.1564 Lr: 0.00008
[2023-12-25 23:29:33,902 INFO misc.py line 119 253097] Train: [93/100][495/510] Data 0.005 (0.078) Batch 1.146 (1.422) Remain 01:24:59 loss: 0.0865 Lr: 0.00008
[2023-12-25 23:29:34,972 INFO misc.py line 119 253097] Train: [93/100][496/510] Data 0.010 (0.078) Batch 1.076 (1.422) Remain 01:24:55 loss: 0.0659 Lr: 0.00008
[2023-12-25 23:29:40,578 INFO misc.py line 119 253097] Train: [93/100][497/510] Data 0.004 (0.078) Batch 5.606 (1.430) Remain 01:25:24 loss: 0.1436 Lr: 0.00008
[2023-12-25 23:29:41,860 INFO misc.py line 119 253097] Train: [93/100][498/510] Data 0.004 (0.078) Batch 1.279 (1.430) Remain 01:25:21 loss: 0.0772 Lr: 0.00008
[2023-12-25 23:29:42,762 INFO misc.py line 119 253097] Train: [93/100][499/510] Data 0.006 (0.078) Batch 0.905 (1.429) Remain 01:25:16 loss: 0.1280 Lr: 0.00008
[2023-12-25 23:29:43,800 INFO misc.py line 119 253097] Train: [93/100][500/510] Data 0.003 (0.078) Batch 1.036 (1.428) Remain 01:25:12 loss: 0.1663 Lr: 0.00008
[2023-12-25 23:29:44,871 INFO misc.py line 119 253097] Train: [93/100][501/510] Data 0.005 (0.077) Batch 1.070 (1.427) Remain 01:25:08 loss: 0.1870 Lr: 0.00008
[2023-12-25 23:29:46,123 INFO misc.py line 119 253097] Train: [93/100][502/510] Data 0.006 (0.077) Batch 1.253 (1.427) Remain 01:25:05 loss: 0.0807 Lr: 0.00008
[2023-12-25 23:29:47,170 INFO misc.py line 119 253097] Train: [93/100][503/510] Data 0.005 (0.077) Batch 1.048 (1.426) Remain 01:25:01 loss: 0.1088 Lr: 0.00008
[2023-12-25 23:29:48,191 INFO misc.py line 119 253097] Train: [93/100][504/510] Data 0.003 (0.077) Batch 1.015 (1.425) Remain 01:24:57 loss: 0.1587 Lr: 0.00008
[2023-12-25 23:29:49,292 INFO misc.py line 119 253097] Train: [93/100][505/510] Data 0.008 (0.077) Batch 1.101 (1.425) Remain 01:24:53 loss: 0.1609 Lr: 0.00008
[2023-12-25 23:29:50,473 INFO misc.py line 119 253097] Train: [93/100][506/510] Data 0.009 (0.077) Batch 1.180 (1.424) Remain 01:24:50 loss: 0.0794 Lr: 0.00008
[2023-12-25 23:29:51,828 INFO misc.py line 119 253097] Train: [93/100][507/510] Data 0.009 (0.077) Batch 1.319 (1.424) Remain 01:24:48 loss: 0.1566 Lr: 0.00008
[2023-12-25 23:29:52,879 INFO misc.py line 119 253097] Train: [93/100][508/510] Data 0.046 (0.077) Batch 1.088 (1.423) Remain 01:24:44 loss: 0.0922 Lr: 0.00008
[2023-12-25 23:29:57,047 INFO misc.py line 119 253097] Train: [93/100][509/510] Data 0.008 (0.076) Batch 4.172 (1.429) Remain 01:25:02 loss: 0.1995 Lr: 0.00008
[2023-12-25 23:29:58,215 INFO misc.py line 119 253097] Train: [93/100][510/510] Data 0.003 (0.076) Batch 1.168 (1.428) Remain 01:24:59 loss: 0.0800 Lr: 0.00008
[2023-12-25 23:29:58,215 INFO misc.py line 136 253097] Train result: loss: 0.1094 
[2023-12-25 23:29:58,216 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 23:30:36,365 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6179 
[2023-12-25 23:30:36,708 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3198 
[2023-12-25 23:30:41,647 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3121 
[2023-12-25 23:30:42,165 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3675 
[2023-12-25 23:30:44,135 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8289 
[2023-12-25 23:30:44,558 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3623 
[2023-12-25 23:30:45,436 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.0917 
[2023-12-25 23:30:45,988 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2847 
[2023-12-25 23:30:47,796 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.7160 
[2023-12-25 23:30:49,920 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1143 
[2023-12-25 23:30:50,774 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3080 
[2023-12-25 23:30:51,196 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7205 
[2023-12-25 23:30:52,099 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5654 
[2023-12-25 23:30:55,043 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8207 
[2023-12-25 23:30:55,509 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2844 
[2023-12-25 23:30:56,120 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3772 
[2023-12-25 23:30:56,820 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.2970 
[2023-12-25 23:30:58,132 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6976/0.7525/0.9060.
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9166/0.9461
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9831/0.9904
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8441/0.9719
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3782/0.4186
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6294/0.6481
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7109/0.8011
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8014/0.8935
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9141/0.9554
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6613/0.6932
[2023-12-25 23:30:58,132 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7836/0.8669
[2023-12-25 23:30:58,133 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8280/0.8608
[2023-12-25 23:30:58,133 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6180/0.7362
[2023-12-25 23:30:58,133 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 23:30:58,134 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 23:30:58,134 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 23:31:03,638 INFO misc.py line 119 253097] Train: [94/100][1/510] Data 2.194 (2.194) Batch 3.867 (3.867) Remain 03:50:01 loss: 0.0755 Lr: 0.00008
[2023-12-25 23:31:10,474 INFO misc.py line 119 253097] Train: [94/100][2/510] Data 5.596 (5.596) Batch 6.837 (6.837) Remain 06:46:32 loss: 0.1033 Lr: 0.00008
[2023-12-25 23:31:11,683 INFO misc.py line 119 253097] Train: [94/100][3/510] Data 0.003 (0.003) Batch 1.209 (1.209) Remain 01:11:52 loss: 0.0849 Lr: 0.00008
[2023-12-25 23:31:12,802 INFO misc.py line 119 253097] Train: [94/100][4/510] Data 0.003 (0.003) Batch 1.119 (1.119) Remain 01:06:30 loss: 0.0704 Lr: 0.00008
[2023-12-25 23:31:14,016 INFO misc.py line 119 253097] Train: [94/100][5/510] Data 0.003 (0.003) Batch 1.214 (1.167) Remain 01:09:18 loss: 0.0651 Lr: 0.00008
[2023-12-25 23:31:15,117 INFO misc.py line 119 253097] Train: [94/100][6/510] Data 0.003 (0.003) Batch 1.099 (1.144) Remain 01:07:57 loss: 0.1084 Lr: 0.00008
[2023-12-25 23:31:16,469 INFO misc.py line 119 253097] Train: [94/100][7/510] Data 0.004 (0.003) Batch 1.349 (1.195) Remain 01:10:58 loss: 0.0949 Lr: 0.00008
[2023-12-25 23:31:18,966 INFO misc.py line 119 253097] Train: [94/100][8/510] Data 1.372 (0.277) Batch 2.503 (1.457) Remain 01:26:28 loss: 0.0579 Lr: 0.00008
[2023-12-25 23:31:20,247 INFO misc.py line 119 253097] Train: [94/100][9/510] Data 0.003 (0.231) Batch 1.275 (1.426) Remain 01:24:39 loss: 0.0859 Lr: 0.00008
[2023-12-25 23:31:21,495 INFO misc.py line 119 253097] Train: [94/100][10/510] Data 0.008 (0.199) Batch 1.251 (1.401) Remain 01:23:09 loss: 0.1344 Lr: 0.00008
[2023-12-25 23:31:22,754 INFO misc.py line 119 253097] Train: [94/100][11/510] Data 0.005 (0.175) Batch 1.255 (1.383) Remain 01:22:02 loss: 0.1203 Lr: 0.00008
[2023-12-25 23:31:24,060 INFO misc.py line 119 253097] Train: [94/100][12/510] Data 0.010 (0.157) Batch 1.311 (1.375) Remain 01:21:32 loss: 0.0746 Lr: 0.00008
[2023-12-25 23:31:25,372 INFO misc.py line 119 253097] Train: [94/100][13/510] Data 0.005 (0.142) Batch 1.309 (1.368) Remain 01:21:07 loss: 0.1370 Lr: 0.00008
[2023-12-25 23:31:26,407 INFO misc.py line 119 253097] Train: [94/100][14/510] Data 0.007 (0.129) Batch 1.023 (1.337) Remain 01:19:14 loss: 0.0737 Lr: 0.00008
[2023-12-25 23:31:27,429 INFO misc.py line 119 253097] Train: [94/100][15/510] Data 0.019 (0.120) Batch 1.031 (1.312) Remain 01:17:42 loss: 0.1267 Lr: 0.00008
[2023-12-25 23:31:28,563 INFO misc.py line 119 253097] Train: [94/100][16/510] Data 0.010 (0.112) Batch 1.141 (1.298) Remain 01:16:54 loss: 0.0942 Lr: 0.00008
[2023-12-25 23:31:29,796 INFO misc.py line 119 253097] Train: [94/100][17/510] Data 0.004 (0.104) Batch 1.229 (1.293) Remain 01:16:35 loss: 0.0763 Lr: 0.00008
[2023-12-25 23:31:31,000 INFO misc.py line 119 253097] Train: [94/100][18/510] Data 0.007 (0.098) Batch 1.207 (1.288) Remain 01:16:14 loss: 0.2006 Lr: 0.00008
[2023-12-25 23:31:32,155 INFO misc.py line 119 253097] Train: [94/100][19/510] Data 0.004 (0.092) Batch 1.151 (1.279) Remain 01:15:42 loss: 0.0709 Lr: 0.00008
[2023-12-25 23:31:33,468 INFO misc.py line 119 253097] Train: [94/100][20/510] Data 0.008 (0.087) Batch 1.313 (1.281) Remain 01:15:48 loss: 0.1659 Lr: 0.00008
[2023-12-25 23:31:34,629 INFO misc.py line 119 253097] Train: [94/100][21/510] Data 0.008 (0.082) Batch 1.166 (1.275) Remain 01:15:24 loss: 0.1161 Lr: 0.00008
[2023-12-25 23:31:40,227 INFO misc.py line 119 253097] Train: [94/100][22/510] Data 0.003 (0.078) Batch 5.597 (1.502) Remain 01:28:49 loss: 0.1103 Lr: 0.00008
[2023-12-25 23:31:41,274 INFO misc.py line 119 253097] Train: [94/100][23/510] Data 0.004 (0.075) Batch 1.049 (1.480) Remain 01:27:27 loss: 0.0595 Lr: 0.00008
[2023-12-25 23:31:45,765 INFO misc.py line 119 253097] Train: [94/100][24/510] Data 0.003 (0.071) Batch 4.489 (1.623) Remain 01:35:54 loss: 0.0540 Lr: 0.00008
[2023-12-25 23:31:46,972 INFO misc.py line 119 253097] Train: [94/100][25/510] Data 0.004 (0.068) Batch 1.207 (1.604) Remain 01:34:46 loss: 0.1330 Lr: 0.00008
[2023-12-25 23:31:48,175 INFO misc.py line 119 253097] Train: [94/100][26/510] Data 0.004 (0.065) Batch 1.204 (1.587) Remain 01:33:42 loss: 0.0860 Lr: 0.00008
[2023-12-25 23:31:49,482 INFO misc.py line 119 253097] Train: [94/100][27/510] Data 0.003 (0.063) Batch 1.301 (1.575) Remain 01:32:59 loss: 0.0762 Lr: 0.00008
[2023-12-25 23:31:50,592 INFO misc.py line 119 253097] Train: [94/100][28/510] Data 0.008 (0.060) Batch 1.115 (1.556) Remain 01:31:52 loss: 0.0804 Lr: 0.00008
[2023-12-25 23:31:51,702 INFO misc.py line 119 253097] Train: [94/100][29/510] Data 0.003 (0.058) Batch 1.109 (1.539) Remain 01:30:49 loss: 0.2418 Lr: 0.00008
[2023-12-25 23:31:52,760 INFO misc.py line 119 253097] Train: [94/100][30/510] Data 0.005 (0.056) Batch 1.057 (1.521) Remain 01:29:45 loss: 0.0679 Lr: 0.00008
[2023-12-25 23:31:53,921 INFO misc.py line 119 253097] Train: [94/100][31/510] Data 0.007 (0.055) Batch 1.164 (1.508) Remain 01:28:58 loss: 0.0892 Lr: 0.00008
[2023-12-25 23:31:55,150 INFO misc.py line 119 253097] Train: [94/100][32/510] Data 0.005 (0.053) Batch 1.226 (1.499) Remain 01:28:22 loss: 0.1192 Lr: 0.00008
[2023-12-25 23:31:56,386 INFO misc.py line 119 253097] Train: [94/100][33/510] Data 0.007 (0.051) Batch 1.238 (1.490) Remain 01:27:50 loss: 0.1345 Lr: 0.00008
[2023-12-25 23:31:57,498 INFO misc.py line 119 253097] Train: [94/100][34/510] Data 0.004 (0.050) Batch 1.111 (1.478) Remain 01:27:05 loss: 0.0576 Lr: 0.00008
[2023-12-25 23:31:58,584 INFO misc.py line 119 253097] Train: [94/100][35/510] Data 0.005 (0.048) Batch 1.085 (1.466) Remain 01:26:20 loss: 0.1602 Lr: 0.00008
[2023-12-25 23:31:59,709 INFO misc.py line 119 253097] Train: [94/100][36/510] Data 0.006 (0.047) Batch 1.127 (1.455) Remain 01:25:43 loss: 0.1064 Lr: 0.00008
[2023-12-25 23:32:00,897 INFO misc.py line 119 253097] Train: [94/100][37/510] Data 0.005 (0.046) Batch 1.188 (1.447) Remain 01:25:13 loss: 0.1327 Lr: 0.00008
[2023-12-25 23:32:02,051 INFO misc.py line 119 253097] Train: [94/100][38/510] Data 0.005 (0.045) Batch 1.151 (1.439) Remain 01:24:42 loss: 0.0889 Lr: 0.00008
[2023-12-25 23:32:06,774 INFO misc.py line 119 253097] Train: [94/100][39/510] Data 3.837 (0.150) Batch 4.726 (1.530) Remain 01:30:03 loss: 0.1179 Lr: 0.00008
[2023-12-25 23:32:07,589 INFO misc.py line 119 253097] Train: [94/100][40/510] Data 0.006 (0.146) Batch 0.816 (1.511) Remain 01:28:53 loss: 0.0828 Lr: 0.00008
[2023-12-25 23:32:08,646 INFO misc.py line 119 253097] Train: [94/100][41/510] Data 0.005 (0.142) Batch 1.057 (1.499) Remain 01:28:09 loss: 0.1276 Lr: 0.00008
[2023-12-25 23:32:09,804 INFO misc.py line 119 253097] Train: [94/100][42/510] Data 0.004 (0.139) Batch 1.158 (1.490) Remain 01:27:37 loss: 0.0961 Lr: 0.00008
[2023-12-25 23:32:10,916 INFO misc.py line 119 253097] Train: [94/100][43/510] Data 0.004 (0.135) Batch 1.112 (1.481) Remain 01:27:02 loss: 0.0878 Lr: 0.00008
[2023-12-25 23:32:12,031 INFO misc.py line 119 253097] Train: [94/100][44/510] Data 0.005 (0.132) Batch 1.114 (1.472) Remain 01:26:29 loss: 0.0849 Lr: 0.00008
[2023-12-25 23:32:13,101 INFO misc.py line 119 253097] Train: [94/100][45/510] Data 0.005 (0.129) Batch 1.068 (1.462) Remain 01:25:54 loss: 0.1524 Lr: 0.00008
[2023-12-25 23:32:14,185 INFO misc.py line 119 253097] Train: [94/100][46/510] Data 0.006 (0.126) Batch 1.084 (1.453) Remain 01:25:22 loss: 0.0831 Lr: 0.00008
[2023-12-25 23:32:15,358 INFO misc.py line 119 253097] Train: [94/100][47/510] Data 0.006 (0.124) Batch 1.175 (1.447) Remain 01:24:58 loss: 0.1395 Lr: 0.00008
[2023-12-25 23:32:16,392 INFO misc.py line 119 253097] Train: [94/100][48/510] Data 0.005 (0.121) Batch 1.031 (1.438) Remain 01:24:24 loss: 0.1644 Lr: 0.00008
[2023-12-25 23:32:17,480 INFO misc.py line 119 253097] Train: [94/100][49/510] Data 0.007 (0.119) Batch 1.091 (1.430) Remain 01:23:56 loss: 0.0866 Lr: 0.00008
[2023-12-25 23:32:18,618 INFO misc.py line 119 253097] Train: [94/100][50/510] Data 0.004 (0.116) Batch 1.136 (1.424) Remain 01:23:32 loss: 0.0645 Lr: 0.00008
[2023-12-25 23:32:19,762 INFO misc.py line 119 253097] Train: [94/100][51/510] Data 0.007 (0.114) Batch 1.141 (1.418) Remain 01:23:10 loss: 0.0519 Lr: 0.00008
[2023-12-25 23:32:21,013 INFO misc.py line 119 253097] Train: [94/100][52/510] Data 0.011 (0.112) Batch 1.256 (1.415) Remain 01:22:57 loss: 0.1253 Lr: 0.00008
[2023-12-25 23:32:22,110 INFO misc.py line 119 253097] Train: [94/100][53/510] Data 0.006 (0.110) Batch 1.095 (1.408) Remain 01:22:33 loss: 0.1263 Lr: 0.00008
[2023-12-25 23:32:23,112 INFO misc.py line 119 253097] Train: [94/100][54/510] Data 0.007 (0.108) Batch 1.004 (1.401) Remain 01:22:04 loss: 0.2011 Lr: 0.00008
[2023-12-25 23:32:24,406 INFO misc.py line 119 253097] Train: [94/100][55/510] Data 0.005 (0.106) Batch 1.295 (1.399) Remain 01:21:55 loss: 0.1028 Lr: 0.00008
[2023-12-25 23:32:25,331 INFO misc.py line 119 253097] Train: [94/100][56/510] Data 0.004 (0.104) Batch 0.925 (1.390) Remain 01:21:22 loss: 0.0487 Lr: 0.00008
[2023-12-25 23:32:29,477 INFO misc.py line 119 253097] Train: [94/100][57/510] Data 0.003 (0.102) Batch 4.146 (1.441) Remain 01:24:20 loss: 0.0997 Lr: 0.00008
[2023-12-25 23:32:30,424 INFO misc.py line 119 253097] Train: [94/100][58/510] Data 0.005 (0.100) Batch 0.947 (1.432) Remain 01:23:47 loss: 0.1709 Lr: 0.00008
[2023-12-25 23:32:31,417 INFO misc.py line 119 253097] Train: [94/100][59/510] Data 0.005 (0.098) Batch 0.993 (1.424) Remain 01:23:18 loss: 0.0627 Lr: 0.00008
[2023-12-25 23:32:35,564 INFO misc.py line 119 253097] Train: [94/100][60/510] Data 2.884 (0.147) Batch 4.147 (1.472) Remain 01:26:05 loss: 0.1567 Lr: 0.00008
[2023-12-25 23:32:36,743 INFO misc.py line 119 253097] Train: [94/100][61/510] Data 0.004 (0.145) Batch 1.178 (1.467) Remain 01:25:46 loss: 0.1192 Lr: 0.00008
[2023-12-25 23:32:37,942 INFO misc.py line 119 253097] Train: [94/100][62/510] Data 0.005 (0.142) Batch 1.197 (1.462) Remain 01:25:28 loss: 0.0945 Lr: 0.00008
[2023-12-25 23:32:38,919 INFO misc.py line 119 253097] Train: [94/100][63/510] Data 0.007 (0.140) Batch 0.979 (1.454) Remain 01:24:58 loss: 0.1330 Lr: 0.00008
[2023-12-25 23:32:40,168 INFO misc.py line 119 253097] Train: [94/100][64/510] Data 0.004 (0.138) Batch 1.249 (1.451) Remain 01:24:45 loss: 0.1191 Lr: 0.00008
[2023-12-25 23:32:41,326 INFO misc.py line 119 253097] Train: [94/100][65/510] Data 0.005 (0.136) Batch 1.155 (1.446) Remain 01:24:27 loss: 0.1046 Lr: 0.00008
[2023-12-25 23:32:42,469 INFO misc.py line 119 253097] Train: [94/100][66/510] Data 0.008 (0.134) Batch 1.144 (1.441) Remain 01:24:09 loss: 0.1219 Lr: 0.00008
[2023-12-25 23:32:43,640 INFO misc.py line 119 253097] Train: [94/100][67/510] Data 0.006 (0.132) Batch 1.171 (1.437) Remain 01:23:53 loss: 0.0788 Lr: 0.00008
[2023-12-25 23:32:44,724 INFO misc.py line 119 253097] Train: [94/100][68/510] Data 0.007 (0.130) Batch 1.083 (1.431) Remain 01:23:32 loss: 0.1130 Lr: 0.00008
[2023-12-25 23:32:45,796 INFO misc.py line 119 253097] Train: [94/100][69/510] Data 0.007 (0.128) Batch 1.072 (1.426) Remain 01:23:11 loss: 0.0977 Lr: 0.00008
[2023-12-25 23:32:46,908 INFO misc.py line 119 253097] Train: [94/100][70/510] Data 0.007 (0.126) Batch 1.116 (1.421) Remain 01:22:54 loss: 0.1299 Lr: 0.00008
[2023-12-25 23:32:47,932 INFO misc.py line 119 253097] Train: [94/100][71/510] Data 0.003 (0.124) Batch 1.020 (1.415) Remain 01:22:32 loss: 0.1644 Lr: 0.00008
[2023-12-25 23:32:48,948 INFO misc.py line 119 253097] Train: [94/100][72/510] Data 0.008 (0.123) Batch 1.019 (1.410) Remain 01:22:10 loss: 0.0961 Lr: 0.00008
[2023-12-25 23:32:50,167 INFO misc.py line 119 253097] Train: [94/100][73/510] Data 0.005 (0.121) Batch 1.220 (1.407) Remain 01:21:59 loss: 0.1593 Lr: 0.00008
[2023-12-25 23:32:51,412 INFO misc.py line 119 253097] Train: [94/100][74/510] Data 0.004 (0.119) Batch 1.239 (1.405) Remain 01:21:50 loss: 0.1831 Lr: 0.00008
[2023-12-25 23:32:52,638 INFO misc.py line 119 253097] Train: [94/100][75/510] Data 0.009 (0.118) Batch 1.231 (1.402) Remain 01:21:40 loss: 0.0948 Lr: 0.00008
[2023-12-25 23:32:53,794 INFO misc.py line 119 253097] Train: [94/100][76/510] Data 0.006 (0.116) Batch 1.153 (1.399) Remain 01:21:27 loss: 0.0869 Lr: 0.00008
[2023-12-25 23:33:00,151 INFO misc.py line 119 253097] Train: [94/100][77/510] Data 5.204 (0.185) Batch 6.359 (1.466) Remain 01:25:19 loss: 0.1018 Lr: 0.00008
[2023-12-25 23:33:01,300 INFO misc.py line 119 253097] Train: [94/100][78/510] Data 0.008 (0.183) Batch 1.151 (1.462) Remain 01:25:03 loss: 0.0901 Lr: 0.00008
[2023-12-25 23:33:06,579 INFO misc.py line 119 253097] Train: [94/100][79/510] Data 0.004 (0.180) Batch 5.279 (1.512) Remain 01:27:57 loss: 0.2054 Lr: 0.00008
[2023-12-25 23:33:07,829 INFO misc.py line 119 253097] Train: [94/100][80/510] Data 0.005 (0.178) Batch 1.250 (1.508) Remain 01:27:44 loss: 0.1106 Lr: 0.00008
[2023-12-25 23:33:08,920 INFO misc.py line 119 253097] Train: [94/100][81/510] Data 0.005 (0.176) Batch 1.092 (1.503) Remain 01:27:24 loss: 0.1549 Lr: 0.00008
[2023-12-25 23:33:10,206 INFO misc.py line 119 253097] Train: [94/100][82/510] Data 0.003 (0.174) Batch 1.286 (1.500) Remain 01:27:12 loss: 0.0942 Lr: 0.00008
[2023-12-25 23:33:11,318 INFO misc.py line 119 253097] Train: [94/100][83/510] Data 0.007 (0.172) Batch 1.111 (1.495) Remain 01:26:54 loss: 0.1055 Lr: 0.00008
[2023-12-25 23:33:12,534 INFO misc.py line 119 253097] Train: [94/100][84/510] Data 0.005 (0.169) Batch 1.217 (1.492) Remain 01:26:41 loss: 0.0748 Lr: 0.00008
[2023-12-25 23:33:13,560 INFO misc.py line 119 253097] Train: [94/100][85/510] Data 0.005 (0.167) Batch 1.023 (1.486) Remain 01:26:19 loss: 0.0754 Lr: 0.00008
[2023-12-25 23:33:14,633 INFO misc.py line 119 253097] Train: [94/100][86/510] Data 0.008 (0.166) Batch 1.076 (1.481) Remain 01:26:00 loss: 0.0568 Lr: 0.00008
[2023-12-25 23:33:15,734 INFO misc.py line 119 253097] Train: [94/100][87/510] Data 0.005 (0.164) Batch 1.100 (1.477) Remain 01:25:43 loss: 0.0686 Lr: 0.00008
[2023-12-25 23:33:16,819 INFO misc.py line 119 253097] Train: [94/100][88/510] Data 0.006 (0.162) Batch 1.086 (1.472) Remain 01:25:26 loss: 0.0810 Lr: 0.00008
[2023-12-25 23:33:17,986 INFO misc.py line 119 253097] Train: [94/100][89/510] Data 0.005 (0.160) Batch 1.166 (1.469) Remain 01:25:12 loss: 0.0917 Lr: 0.00008
[2023-12-25 23:33:19,099 INFO misc.py line 119 253097] Train: [94/100][90/510] Data 0.005 (0.158) Batch 1.113 (1.465) Remain 01:24:56 loss: 0.0968 Lr: 0.00008
[2023-12-25 23:33:20,020 INFO misc.py line 119 253097] Train: [94/100][91/510] Data 0.006 (0.156) Batch 0.922 (1.458) Remain 01:24:33 loss: 0.1204 Lr: 0.00008
[2023-12-25 23:33:21,084 INFO misc.py line 119 253097] Train: [94/100][92/510] Data 0.004 (0.155) Batch 1.064 (1.454) Remain 01:24:16 loss: 0.1054 Lr: 0.00008
[2023-12-25 23:33:22,238 INFO misc.py line 119 253097] Train: [94/100][93/510] Data 0.004 (0.153) Batch 1.153 (1.451) Remain 01:24:03 loss: 0.0941 Lr: 0.00008
[2023-12-25 23:33:24,928 INFO misc.py line 119 253097] Train: [94/100][94/510] Data 0.005 (0.151) Batch 2.690 (1.464) Remain 01:24:49 loss: 0.2465 Lr: 0.00008
[2023-12-25 23:33:26,043 INFO misc.py line 119 253097] Train: [94/100][95/510] Data 0.006 (0.150) Batch 1.115 (1.460) Remain 01:24:34 loss: 0.0755 Lr: 0.00008
[2023-12-25 23:33:27,146 INFO misc.py line 119 253097] Train: [94/100][96/510] Data 0.005 (0.148) Batch 1.104 (1.457) Remain 01:24:20 loss: 0.0871 Lr: 0.00008
[2023-12-25 23:33:28,272 INFO misc.py line 119 253097] Train: [94/100][97/510] Data 0.003 (0.147) Batch 1.125 (1.453) Remain 01:24:06 loss: 0.1029 Lr: 0.00008
[2023-12-25 23:33:29,406 INFO misc.py line 119 253097] Train: [94/100][98/510] Data 0.004 (0.145) Batch 1.133 (1.450) Remain 01:23:53 loss: 0.1176 Lr: 0.00008
[2023-12-25 23:33:30,538 INFO misc.py line 119 253097] Train: [94/100][99/510] Data 0.005 (0.144) Batch 1.133 (1.446) Remain 01:23:40 loss: 0.1138 Lr: 0.00008
[2023-12-25 23:33:31,596 INFO misc.py line 119 253097] Train: [94/100][100/510] Data 0.005 (0.142) Batch 1.058 (1.442) Remain 01:23:25 loss: 0.2645 Lr: 0.00008
[2023-12-25 23:33:32,591 INFO misc.py line 119 253097] Train: [94/100][101/510] Data 0.005 (0.141) Batch 0.996 (1.438) Remain 01:23:07 loss: 0.0477 Lr: 0.00008
[2023-12-25 23:33:33,890 INFO misc.py line 119 253097] Train: [94/100][102/510] Data 0.004 (0.140) Batch 1.294 (1.436) Remain 01:23:01 loss: 0.0563 Lr: 0.00008
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[2023-12-25 23:34:32,385 INFO misc.py line 119 253097] Train: [94/100][146/510] Data 0.007 (0.146) Batch 1.074 (1.403) Remain 01:20:05 loss: 0.1156 Lr: 0.00007
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[2023-12-25 23:34:39,155 INFO misc.py line 119 253097] Train: [94/100][152/510] Data 0.007 (0.140) Batch 1.227 (1.392) Remain 01:19:19 loss: 0.0766 Lr: 0.00007
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[2023-12-25 23:36:37,335 INFO misc.py line 119 253097] Train: [94/100][239/510] Data 0.004 (0.091) Batch 6.556 (1.380) Remain 01:16:36 loss: 0.0878 Lr: 0.00007
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[2023-12-25 23:36:41,971 INFO misc.py line 119 253097] Train: [94/100][243/510] Data 0.009 (0.089) Batch 1.194 (1.376) Remain 01:16:18 loss: 0.1703 Lr: 0.00007
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[2023-12-25 23:37:00,656 INFO misc.py line 119 253097] Train: [94/100][255/510] Data 3.760 (0.100) Batch 4.900 (1.385) Remain 01:16:30 loss: 0.1028 Lr: 0.00007
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[2023-12-25 23:37:02,926 INFO misc.py line 119 253097] Train: [94/100][257/510] Data 0.003 (0.099) Batch 1.164 (1.383) Remain 01:16:21 loss: 0.1077 Lr: 0.00007
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[2023-12-25 23:37:06,141 INFO misc.py line 119 253097] Train: [94/100][260/510] Data 0.005 (0.098) Batch 0.927 (1.379) Remain 01:16:05 loss: 0.1254 Lr: 0.00007
[2023-12-25 23:37:07,197 INFO misc.py line 119 253097] Train: [94/100][261/510] Data 0.004 (0.098) Batch 1.056 (1.378) Remain 01:15:59 loss: 0.1034 Lr: 0.00007
[2023-12-25 23:37:08,437 INFO misc.py line 119 253097] Train: [94/100][262/510] Data 0.003 (0.098) Batch 1.239 (1.377) Remain 01:15:56 loss: 0.1156 Lr: 0.00007
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[2023-12-25 23:37:10,681 INFO misc.py line 119 253097] Train: [94/100][264/510] Data 0.003 (0.097) Batch 1.083 (1.375) Remain 01:15:47 loss: 0.1155 Lr: 0.00007
[2023-12-25 23:37:13,185 INFO misc.py line 119 253097] Train: [94/100][265/510] Data 1.478 (0.102) Batch 2.505 (1.380) Remain 01:16:00 loss: 0.0963 Lr: 0.00007
[2023-12-25 23:37:14,390 INFO misc.py line 119 253097] Train: [94/100][266/510] Data 0.003 (0.102) Batch 1.204 (1.379) Remain 01:15:56 loss: 0.1205 Lr: 0.00007
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[2023-12-25 23:37:16,723 INFO misc.py line 119 253097] Train: [94/100][268/510] Data 0.005 (0.101) Batch 1.202 (1.377) Remain 01:15:48 loss: 0.0705 Lr: 0.00007
[2023-12-25 23:37:17,677 INFO misc.py line 119 253097] Train: [94/100][269/510] Data 0.007 (0.101) Batch 0.959 (1.376) Remain 01:15:41 loss: 0.0838 Lr: 0.00007
[2023-12-25 23:37:23,874 INFO misc.py line 119 253097] Train: [94/100][270/510] Data 4.905 (0.119) Batch 6.196 (1.394) Remain 01:16:40 loss: 0.0595 Lr: 0.00007
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[2023-12-25 23:37:30,731 INFO misc.py line 119 253097] Train: [94/100][276/510] Data 0.008 (0.116) Batch 1.220 (1.388) Remain 01:16:13 loss: 0.0864 Lr: 0.00007
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[2023-12-25 23:37:36,340 INFO misc.py line 119 253097] Train: [94/100][281/510] Data 0.005 (0.114) Batch 1.039 (1.384) Remain 01:15:50 loss: 0.1376 Lr: 0.00007
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[2023-12-25 23:37:38,347 INFO misc.py line 119 253097] Train: [94/100][283/510] Data 0.008 (0.113) Batch 0.942 (1.381) Remain 01:15:39 loss: 0.1055 Lr: 0.00007
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[2023-12-25 23:40:54,636 INFO misc.py line 119 253097] Train: [94/100][416/510] Data 0.002 (0.127) Batch 1.004 (1.411) Remain 01:14:11 loss: 0.0838 Lr: 0.00006
[2023-12-25 23:41:00,245 INFO misc.py line 119 253097] Train: [94/100][417/510] Data 0.009 (0.127) Batch 5.614 (1.422) Remain 01:14:42 loss: 0.1421 Lr: 0.00006
[2023-12-25 23:41:01,396 INFO misc.py line 119 253097] Train: [94/100][418/510] Data 0.004 (0.127) Batch 1.151 (1.421) Remain 01:14:38 loss: 0.0859 Lr: 0.00006
[2023-12-25 23:41:02,620 INFO misc.py line 119 253097] Train: [94/100][419/510] Data 0.003 (0.126) Batch 1.219 (1.421) Remain 01:14:36 loss: 0.0910 Lr: 0.00006
[2023-12-25 23:41:03,917 INFO misc.py line 119 253097] Train: [94/100][420/510] Data 0.008 (0.126) Batch 1.298 (1.420) Remain 01:14:33 loss: 0.0764 Lr: 0.00006
[2023-12-25 23:41:04,955 INFO misc.py line 119 253097] Train: [94/100][421/510] Data 0.006 (0.126) Batch 1.037 (1.419) Remain 01:14:29 loss: 0.1133 Lr: 0.00006
[2023-12-25 23:41:06,088 INFO misc.py line 119 253097] Train: [94/100][422/510] Data 0.007 (0.126) Batch 1.133 (1.419) Remain 01:14:25 loss: 0.1310 Lr: 0.00006
[2023-12-25 23:41:07,331 INFO misc.py line 119 253097] Train: [94/100][423/510] Data 0.007 (0.125) Batch 1.247 (1.418) Remain 01:14:23 loss: 0.1049 Lr: 0.00006
[2023-12-25 23:41:08,626 INFO misc.py line 119 253097] Train: [94/100][424/510] Data 0.003 (0.125) Batch 1.295 (1.418) Remain 01:14:20 loss: 0.1114 Lr: 0.00006
[2023-12-25 23:41:09,675 INFO misc.py line 119 253097] Train: [94/100][425/510] Data 0.003 (0.125) Batch 1.048 (1.417) Remain 01:14:16 loss: 0.1510 Lr: 0.00006
[2023-12-25 23:41:10,869 INFO misc.py line 119 253097] Train: [94/100][426/510] Data 0.003 (0.124) Batch 1.192 (1.417) Remain 01:14:13 loss: 0.0743 Lr: 0.00006
[2023-12-25 23:41:11,850 INFO misc.py line 119 253097] Train: [94/100][427/510] Data 0.006 (0.124) Batch 0.984 (1.415) Remain 01:14:08 loss: 0.1535 Lr: 0.00006
[2023-12-25 23:41:13,161 INFO misc.py line 119 253097] Train: [94/100][428/510] Data 0.003 (0.124) Batch 1.306 (1.415) Remain 01:14:06 loss: 0.3026 Lr: 0.00006
[2023-12-25 23:41:14,142 INFO misc.py line 119 253097] Train: [94/100][429/510] Data 0.007 (0.124) Batch 0.985 (1.414) Remain 01:14:02 loss: 0.1173 Lr: 0.00006
[2023-12-25 23:41:19,835 INFO misc.py line 119 253097] Train: [94/100][430/510] Data 0.003 (0.123) Batch 5.692 (1.424) Remain 01:14:32 loss: 0.0962 Lr: 0.00006
[2023-12-25 23:41:20,964 INFO misc.py line 119 253097] Train: [94/100][431/510] Data 0.004 (0.123) Batch 1.126 (1.424) Remain 01:14:28 loss: 0.1075 Lr: 0.00006
[2023-12-25 23:41:22,113 INFO misc.py line 119 253097] Train: [94/100][432/510] Data 0.007 (0.123) Batch 1.152 (1.423) Remain 01:14:25 loss: 0.1093 Lr: 0.00006
[2023-12-25 23:41:23,264 INFO misc.py line 119 253097] Train: [94/100][433/510] Data 0.003 (0.122) Batch 1.147 (1.422) Remain 01:14:21 loss: 0.1124 Lr: 0.00006
[2023-12-25 23:41:24,467 INFO misc.py line 119 253097] Train: [94/100][434/510] Data 0.007 (0.122) Batch 1.208 (1.422) Remain 01:14:18 loss: 0.0892 Lr: 0.00006
[2023-12-25 23:41:25,753 INFO misc.py line 119 253097] Train: [94/100][435/510] Data 0.003 (0.122) Batch 1.287 (1.421) Remain 01:14:16 loss: 0.0649 Lr: 0.00006
[2023-12-25 23:41:26,984 INFO misc.py line 119 253097] Train: [94/100][436/510] Data 0.003 (0.122) Batch 1.230 (1.421) Remain 01:14:13 loss: 0.0639 Lr: 0.00006
[2023-12-25 23:41:28,074 INFO misc.py line 119 253097] Train: [94/100][437/510] Data 0.004 (0.121) Batch 1.090 (1.420) Remain 01:14:09 loss: 0.0814 Lr: 0.00006
[2023-12-25 23:41:29,275 INFO misc.py line 119 253097] Train: [94/100][438/510] Data 0.004 (0.121) Batch 1.201 (1.420) Remain 01:14:06 loss: 0.0966 Lr: 0.00006
[2023-12-25 23:41:30,549 INFO misc.py line 119 253097] Train: [94/100][439/510] Data 0.004 (0.121) Batch 1.269 (1.419) Remain 01:14:04 loss: 0.0973 Lr: 0.00006
[2023-12-25 23:41:31,860 INFO misc.py line 119 253097] Train: [94/100][440/510] Data 0.009 (0.121) Batch 1.278 (1.419) Remain 01:14:01 loss: 0.2132 Lr: 0.00006
[2023-12-25 23:41:32,899 INFO misc.py line 119 253097] Train: [94/100][441/510] Data 0.041 (0.120) Batch 1.074 (1.418) Remain 01:13:57 loss: 0.1122 Lr: 0.00006
[2023-12-25 23:41:34,065 INFO misc.py line 119 253097] Train: [94/100][442/510] Data 0.006 (0.120) Batch 1.167 (1.418) Remain 01:13:54 loss: 0.1198 Lr: 0.00006
[2023-12-25 23:41:40,052 INFO misc.py line 119 253097] Train: [94/100][443/510] Data 0.006 (0.120) Batch 5.990 (1.428) Remain 01:14:25 loss: 0.0847 Lr: 0.00006
[2023-12-25 23:41:41,161 INFO misc.py line 119 253097] Train: [94/100][444/510] Data 0.003 (0.120) Batch 1.109 (1.427) Remain 01:14:22 loss: 0.1233 Lr: 0.00006
[2023-12-25 23:41:42,376 INFO misc.py line 119 253097] Train: [94/100][445/510] Data 0.004 (0.119) Batch 1.215 (1.427) Remain 01:14:19 loss: 0.0623 Lr: 0.00006
[2023-12-25 23:41:43,443 INFO misc.py line 119 253097] Train: [94/100][446/510] Data 0.003 (0.119) Batch 1.066 (1.426) Remain 01:14:15 loss: 0.2018 Lr: 0.00006
[2023-12-25 23:41:44,594 INFO misc.py line 119 253097] Train: [94/100][447/510] Data 0.009 (0.119) Batch 1.152 (1.425) Remain 01:14:11 loss: 0.0944 Lr: 0.00006
[2023-12-25 23:41:45,897 INFO misc.py line 119 253097] Train: [94/100][448/510] Data 0.004 (0.119) Batch 1.300 (1.425) Remain 01:14:09 loss: 0.0781 Lr: 0.00006
[2023-12-25 23:41:47,114 INFO misc.py line 119 253097] Train: [94/100][449/510] Data 0.007 (0.118) Batch 1.218 (1.425) Remain 01:14:06 loss: 0.0821 Lr: 0.00006
[2023-12-25 23:41:48,428 INFO misc.py line 119 253097] Train: [94/100][450/510] Data 0.005 (0.118) Batch 1.315 (1.424) Remain 01:14:04 loss: 0.0955 Lr: 0.00006
[2023-12-25 23:41:49,638 INFO misc.py line 119 253097] Train: [94/100][451/510] Data 0.005 (0.118) Batch 1.211 (1.424) Remain 01:14:01 loss: 0.0919 Lr: 0.00006
[2023-12-25 23:41:50,860 INFO misc.py line 119 253097] Train: [94/100][452/510] Data 0.004 (0.118) Batch 1.218 (1.424) Remain 01:13:58 loss: 0.0810 Lr: 0.00006
[2023-12-25 23:41:52,011 INFO misc.py line 119 253097] Train: [94/100][453/510] Data 0.008 (0.117) Batch 1.155 (1.423) Remain 01:13:55 loss: 0.1210 Lr: 0.00006
[2023-12-25 23:41:53,327 INFO misc.py line 119 253097] Train: [94/100][454/510] Data 0.004 (0.117) Batch 1.303 (1.423) Remain 01:13:53 loss: 0.1160 Lr: 0.00006
[2023-12-25 23:41:54,384 INFO misc.py line 119 253097] Train: [94/100][455/510] Data 0.017 (0.117) Batch 1.065 (1.422) Remain 01:13:49 loss: 0.1512 Lr: 0.00006
[2023-12-25 23:41:55,370 INFO misc.py line 119 253097] Train: [94/100][456/510] Data 0.008 (0.117) Batch 0.991 (1.421) Remain 01:13:44 loss: 0.0866 Lr: 0.00006
[2023-12-25 23:41:56,563 INFO misc.py line 119 253097] Train: [94/100][457/510] Data 0.004 (0.116) Batch 1.192 (1.420) Remain 01:13:41 loss: 0.0634 Lr: 0.00006
[2023-12-25 23:42:04,074 INFO misc.py line 119 253097] Train: [94/100][458/510] Data 0.006 (0.116) Batch 7.513 (1.434) Remain 01:14:22 loss: 0.0816 Lr: 0.00006
[2023-12-25 23:42:05,205 INFO misc.py line 119 253097] Train: [94/100][459/510] Data 0.004 (0.116) Batch 1.131 (1.433) Remain 01:14:18 loss: 0.1122 Lr: 0.00006
[2023-12-25 23:42:06,382 INFO misc.py line 119 253097] Train: [94/100][460/510] Data 0.003 (0.116) Batch 1.177 (1.433) Remain 01:14:15 loss: 0.1101 Lr: 0.00006
[2023-12-25 23:42:07,385 INFO misc.py line 119 253097] Train: [94/100][461/510] Data 0.003 (0.115) Batch 1.003 (1.432) Remain 01:14:11 loss: 0.0892 Lr: 0.00006
[2023-12-25 23:42:08,537 INFO misc.py line 119 253097] Train: [94/100][462/510] Data 0.003 (0.115) Batch 1.152 (1.431) Remain 01:14:07 loss: 0.0853 Lr: 0.00006
[2023-12-25 23:42:09,718 INFO misc.py line 119 253097] Train: [94/100][463/510] Data 0.004 (0.115) Batch 1.181 (1.431) Remain 01:14:04 loss: 0.1011 Lr: 0.00006
[2023-12-25 23:42:10,783 INFO misc.py line 119 253097] Train: [94/100][464/510] Data 0.004 (0.115) Batch 1.065 (1.430) Remain 01:14:00 loss: 0.1408 Lr: 0.00006
[2023-12-25 23:42:14,097 INFO misc.py line 119 253097] Train: [94/100][465/510] Data 0.003 (0.114) Batch 3.310 (1.434) Remain 01:14:11 loss: 0.1193 Lr: 0.00006
[2023-12-25 23:42:15,163 INFO misc.py line 119 253097] Train: [94/100][466/510] Data 0.008 (0.114) Batch 1.070 (1.433) Remain 01:14:08 loss: 0.1461 Lr: 0.00006
[2023-12-25 23:42:16,205 INFO misc.py line 119 253097] Train: [94/100][467/510] Data 0.004 (0.114) Batch 1.043 (1.432) Remain 01:14:03 loss: 0.0580 Lr: 0.00006
[2023-12-25 23:42:17,324 INFO misc.py line 119 253097] Train: [94/100][468/510] Data 0.002 (0.114) Batch 1.118 (1.431) Remain 01:14:00 loss: 0.1982 Lr: 0.00006
[2023-12-25 23:42:18,632 INFO misc.py line 119 253097] Train: [94/100][469/510] Data 0.003 (0.113) Batch 1.305 (1.431) Remain 01:13:58 loss: 0.0831 Lr: 0.00006
[2023-12-25 23:42:19,909 INFO misc.py line 119 253097] Train: [94/100][470/510] Data 0.007 (0.113) Batch 1.279 (1.431) Remain 01:13:55 loss: 0.0706 Lr: 0.00006
[2023-12-25 23:42:20,952 INFO misc.py line 119 253097] Train: [94/100][471/510] Data 0.004 (0.113) Batch 1.037 (1.430) Remain 01:13:51 loss: 0.1088 Lr: 0.00006
[2023-12-25 23:42:22,871 INFO misc.py line 119 253097] Train: [94/100][472/510] Data 0.010 (0.113) Batch 1.926 (1.431) Remain 01:13:53 loss: 0.0940 Lr: 0.00006
[2023-12-25 23:42:24,005 INFO misc.py line 119 253097] Train: [94/100][473/510] Data 0.004 (0.113) Batch 1.132 (1.430) Remain 01:13:50 loss: 0.0940 Lr: 0.00006
[2023-12-25 23:42:25,001 INFO misc.py line 119 253097] Train: [94/100][474/510] Data 0.005 (0.112) Batch 0.998 (1.430) Remain 01:13:45 loss: 0.0746 Lr: 0.00006
[2023-12-25 23:42:26,154 INFO misc.py line 119 253097] Train: [94/100][475/510] Data 0.003 (0.112) Batch 1.152 (1.429) Remain 01:13:42 loss: 0.0669 Lr: 0.00006
[2023-12-25 23:42:27,320 INFO misc.py line 119 253097] Train: [94/100][476/510] Data 0.003 (0.112) Batch 1.166 (1.428) Remain 01:13:39 loss: 0.0882 Lr: 0.00006
[2023-12-25 23:42:28,541 INFO misc.py line 119 253097] Train: [94/100][477/510] Data 0.003 (0.112) Batch 1.221 (1.428) Remain 01:13:36 loss: 0.0610 Lr: 0.00006
[2023-12-25 23:42:29,677 INFO misc.py line 119 253097] Train: [94/100][478/510] Data 0.003 (0.111) Batch 1.135 (1.427) Remain 01:13:33 loss: 0.1021 Lr: 0.00006
[2023-12-25 23:42:30,689 INFO misc.py line 119 253097] Train: [94/100][479/510] Data 0.003 (0.111) Batch 1.011 (1.426) Remain 01:13:29 loss: 0.1838 Lr: 0.00006
[2023-12-25 23:42:31,891 INFO misc.py line 119 253097] Train: [94/100][480/510] Data 0.005 (0.111) Batch 1.203 (1.426) Remain 01:13:26 loss: 0.0944 Lr: 0.00006
[2023-12-25 23:42:32,959 INFO misc.py line 119 253097] Train: [94/100][481/510] Data 0.003 (0.111) Batch 1.067 (1.425) Remain 01:13:22 loss: 0.0740 Lr: 0.00006
[2023-12-25 23:42:34,118 INFO misc.py line 119 253097] Train: [94/100][482/510] Data 0.004 (0.111) Batch 1.161 (1.425) Remain 01:13:19 loss: 0.0774 Lr: 0.00006
[2023-12-25 23:42:35,228 INFO misc.py line 119 253097] Train: [94/100][483/510] Data 0.003 (0.110) Batch 1.109 (1.424) Remain 01:13:16 loss: 0.1210 Lr: 0.00006
[2023-12-25 23:42:37,083 INFO misc.py line 119 253097] Train: [94/100][484/510] Data 0.003 (0.110) Batch 1.851 (1.425) Remain 01:13:17 loss: 0.0892 Lr: 0.00006
[2023-12-25 23:42:38,133 INFO misc.py line 119 253097] Train: [94/100][485/510] Data 0.007 (0.110) Batch 1.049 (1.424) Remain 01:13:13 loss: 0.0661 Lr: 0.00006
[2023-12-25 23:42:39,348 INFO misc.py line 119 253097] Train: [94/100][486/510] Data 0.009 (0.110) Batch 1.199 (1.424) Remain 01:13:10 loss: 0.1864 Lr: 0.00006
[2023-12-25 23:42:40,518 INFO misc.py line 119 253097] Train: [94/100][487/510] Data 0.024 (0.109) Batch 1.190 (1.423) Remain 01:13:07 loss: 0.0971 Lr: 0.00006
[2023-12-25 23:42:41,576 INFO misc.py line 119 253097] Train: [94/100][488/510] Data 0.004 (0.109) Batch 1.057 (1.422) Remain 01:13:03 loss: 0.0758 Lr: 0.00006
[2023-12-25 23:42:42,654 INFO misc.py line 119 253097] Train: [94/100][489/510] Data 0.006 (0.109) Batch 1.080 (1.422) Remain 01:13:00 loss: 0.0617 Lr: 0.00006
[2023-12-25 23:42:43,946 INFO misc.py line 119 253097] Train: [94/100][490/510] Data 0.004 (0.109) Batch 1.291 (1.421) Remain 01:12:58 loss: 0.0697 Lr: 0.00006
[2023-12-25 23:42:44,940 INFO misc.py line 119 253097] Train: [94/100][491/510] Data 0.005 (0.109) Batch 0.995 (1.421) Remain 01:12:54 loss: 0.1413 Lr: 0.00006
[2023-12-25 23:42:46,213 INFO misc.py line 119 253097] Train: [94/100][492/510] Data 0.004 (0.108) Batch 1.269 (1.420) Remain 01:12:51 loss: 0.0938 Lr: 0.00006
[2023-12-25 23:42:47,527 INFO misc.py line 119 253097] Train: [94/100][493/510] Data 0.008 (0.108) Batch 1.314 (1.420) Remain 01:12:49 loss: 0.0898 Lr: 0.00006
[2023-12-25 23:42:48,651 INFO misc.py line 119 253097] Train: [94/100][494/510] Data 0.008 (0.108) Batch 1.126 (1.419) Remain 01:12:46 loss: 0.1614 Lr: 0.00006
[2023-12-25 23:42:49,831 INFO misc.py line 119 253097] Train: [94/100][495/510] Data 0.006 (0.108) Batch 1.171 (1.419) Remain 01:12:43 loss: 0.0844 Lr: 0.00006
[2023-12-25 23:42:50,998 INFO misc.py line 119 253097] Train: [94/100][496/510] Data 0.015 (0.108) Batch 1.178 (1.418) Remain 01:12:40 loss: 0.2481 Lr: 0.00006
[2023-12-25 23:42:52,093 INFO misc.py line 119 253097] Train: [94/100][497/510] Data 0.003 (0.107) Batch 1.090 (1.418) Remain 01:12:36 loss: 0.1140 Lr: 0.00006
[2023-12-25 23:42:53,123 INFO misc.py line 119 253097] Train: [94/100][498/510] Data 0.008 (0.107) Batch 1.033 (1.417) Remain 01:12:33 loss: 0.1133 Lr: 0.00006
[2023-12-25 23:42:54,247 INFO misc.py line 119 253097] Train: [94/100][499/510] Data 0.004 (0.107) Batch 1.122 (1.416) Remain 01:12:29 loss: 0.0861 Lr: 0.00006
[2023-12-25 23:42:55,328 INFO misc.py line 119 253097] Train: [94/100][500/510] Data 0.007 (0.107) Batch 1.067 (1.416) Remain 01:12:26 loss: 0.1166 Lr: 0.00006
[2023-12-25 23:42:56,289 INFO misc.py line 119 253097] Train: [94/100][501/510] Data 0.021 (0.107) Batch 0.978 (1.415) Remain 01:12:22 loss: 0.0702 Lr: 0.00006
[2023-12-25 23:42:57,274 INFO misc.py line 119 253097] Train: [94/100][502/510] Data 0.003 (0.106) Batch 0.985 (1.414) Remain 01:12:18 loss: 0.0795 Lr: 0.00006
[2023-12-25 23:42:58,454 INFO misc.py line 119 253097] Train: [94/100][503/510] Data 0.004 (0.106) Batch 1.179 (1.414) Remain 01:12:15 loss: 0.1041 Lr: 0.00006
[2023-12-25 23:42:59,548 INFO misc.py line 119 253097] Train: [94/100][504/510] Data 0.004 (0.106) Batch 1.094 (1.413) Remain 01:12:11 loss: 0.0795 Lr: 0.00006
[2023-12-25 23:43:00,713 INFO misc.py line 119 253097] Train: [94/100][505/510] Data 0.004 (0.106) Batch 1.166 (1.412) Remain 01:12:09 loss: 0.1576 Lr: 0.00006
[2023-12-25 23:43:01,780 INFO misc.py line 119 253097] Train: [94/100][506/510] Data 0.004 (0.106) Batch 1.066 (1.412) Remain 01:12:05 loss: 0.1116 Lr: 0.00006
[2023-12-25 23:43:03,002 INFO misc.py line 119 253097] Train: [94/100][507/510] Data 0.004 (0.105) Batch 1.220 (1.411) Remain 01:12:02 loss: 0.1033 Lr: 0.00006
[2023-12-25 23:43:04,109 INFO misc.py line 119 253097] Train: [94/100][508/510] Data 0.005 (0.105) Batch 1.110 (1.411) Remain 01:11:59 loss: 0.1267 Lr: 0.00006
[2023-12-25 23:43:05,240 INFO misc.py line 119 253097] Train: [94/100][509/510] Data 0.003 (0.105) Batch 1.129 (1.410) Remain 01:11:56 loss: 0.0552 Lr: 0.00006
[2023-12-25 23:43:06,519 INFO misc.py line 119 253097] Train: [94/100][510/510] Data 0.006 (0.105) Batch 1.277 (1.410) Remain 01:11:54 loss: 0.1057 Lr: 0.00006
[2023-12-25 23:43:06,520 INFO misc.py line 136 253097] Train result: loss: 0.1103 
[2023-12-25 23:43:06,520 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 23:43:39,482 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6213 
[2023-12-25 23:43:39,832 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2890 
[2023-12-25 23:43:48,412 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3711 
[2023-12-25 23:43:48,934 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3862 
[2023-12-25 23:43:50,907 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9008 
[2023-12-25 23:43:51,332 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.2934 
[2023-12-25 23:43:52,210 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1597 
[2023-12-25 23:43:52,771 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2638 
[2023-12-25 23:43:54,580 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8210 
[2023-12-25 23:43:56,711 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1208 
[2023-12-25 23:43:57,567 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3095 
[2023-12-25 23:43:57,991 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7300 
[2023-12-25 23:43:58,891 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4032 
[2023-12-25 23:44:01,840 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.9127 
[2023-12-25 23:44:02,306 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2689 
[2023-12-25 23:44:02,915 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3975 
[2023-12-25 23:44:03,615 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3329 
[2023-12-25 23:44:04,951 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6964/0.7502/0.9060.
[2023-12-25 23:44:04,951 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9177/0.9473
[2023-12-25 23:44:04,951 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9834/0.9906
[2023-12-25 23:44:04,951 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8417/0.9737
[2023-12-25 23:44:04,951 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 23:44:04,951 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3473/0.3826
[2023-12-25 23:44:04,952 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6399/0.6599
[2023-12-25 23:44:04,952 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6961/0.7737
[2023-12-25 23:44:04,952 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8088/0.8941
[2023-12-25 23:44:04,952 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9189/0.9610
[2023-12-25 23:44:04,952 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6662/0.7100
[2023-12-25 23:44:04,952 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7848/0.8768
[2023-12-25 23:44:04,952 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8269/0.8576
[2023-12-25 23:44:04,952 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6214/0.7254
[2023-12-25 23:44:04,952 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 23:44:04,954 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 23:44:04,954 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 23:44:13,068 INFO misc.py line 119 253097] Train: [95/100][1/510] Data 4.248 (4.248) Batch 6.577 (6.577) Remain 05:35:20 loss: 0.0576 Lr: 0.00006
[2023-12-25 23:44:15,700 INFO misc.py line 119 253097] Train: [95/100][2/510] Data 1.406 (1.406) Batch 2.632 (2.632) Remain 02:14:09 loss: 0.0926 Lr: 0.00006
[2023-12-25 23:44:16,713 INFO misc.py line 119 253097] Train: [95/100][3/510] Data 0.004 (0.004) Batch 1.013 (1.013) Remain 00:51:35 loss: 0.1569 Lr: 0.00006
[2023-12-25 23:44:17,925 INFO misc.py line 119 253097] Train: [95/100][4/510] Data 0.003 (0.003) Batch 1.212 (1.212) Remain 01:01:45 loss: 0.1800 Lr: 0.00006
[2023-12-25 23:44:19,083 INFO misc.py line 119 253097] Train: [95/100][5/510] Data 0.003 (0.003) Batch 1.158 (1.185) Remain 01:00:20 loss: 0.0752 Lr: 0.00006
[2023-12-25 23:44:20,303 INFO misc.py line 119 253097] Train: [95/100][6/510] Data 0.003 (0.003) Batch 1.220 (1.197) Remain 01:00:55 loss: 0.0847 Lr: 0.00006
[2023-12-25 23:44:22,651 INFO misc.py line 119 253097] Train: [95/100][7/510] Data 0.003 (0.003) Batch 2.348 (1.485) Remain 01:15:32 loss: 0.1317 Lr: 0.00006
[2023-12-25 23:44:23,791 INFO misc.py line 119 253097] Train: [95/100][8/510] Data 0.003 (0.003) Batch 1.140 (1.416) Remain 01:12:00 loss: 0.1232 Lr: 0.00006
[2023-12-25 23:44:27,570 INFO misc.py line 119 253097] Train: [95/100][9/510] Data 0.003 (0.003) Batch 3.780 (1.810) Remain 01:32:01 loss: 0.1288 Lr: 0.00006
[2023-12-25 23:44:28,639 INFO misc.py line 119 253097] Train: [95/100][10/510] Data 0.002 (0.003) Batch 1.068 (1.704) Remain 01:26:36 loss: 0.1793 Lr: 0.00006
[2023-12-25 23:44:30,263 INFO misc.py line 119 253097] Train: [95/100][11/510] Data 0.003 (0.003) Batch 1.619 (1.693) Remain 01:26:02 loss: 0.0865 Lr: 0.00006
[2023-12-25 23:44:31,449 INFO misc.py line 119 253097] Train: [95/100][12/510] Data 0.008 (0.003) Batch 1.187 (1.637) Remain 01:23:09 loss: 0.0851 Lr: 0.00006
[2023-12-25 23:44:32,576 INFO misc.py line 119 253097] Train: [95/100][13/510] Data 0.008 (0.004) Batch 1.128 (1.586) Remain 01:20:32 loss: 0.1122 Lr: 0.00006
[2023-12-25 23:44:33,657 INFO misc.py line 119 253097] Train: [95/100][14/510] Data 0.007 (0.004) Batch 1.084 (1.540) Remain 01:18:11 loss: 0.1626 Lr: 0.00006
[2023-12-25 23:44:34,744 INFO misc.py line 119 253097] Train: [95/100][15/510] Data 0.004 (0.004) Batch 1.085 (1.502) Remain 01:16:14 loss: 0.0895 Lr: 0.00006
[2023-12-25 23:44:35,987 INFO misc.py line 119 253097] Train: [95/100][16/510] Data 0.005 (0.004) Batch 1.242 (1.482) Remain 01:15:12 loss: 0.1011 Lr: 0.00006
[2023-12-25 23:44:37,062 INFO misc.py line 119 253097] Train: [95/100][17/510] Data 0.006 (0.004) Batch 1.077 (1.453) Remain 01:13:42 loss: 0.0691 Lr: 0.00006
[2023-12-25 23:44:38,194 INFO misc.py line 119 253097] Train: [95/100][18/510] Data 0.005 (0.004) Batch 1.130 (1.432) Remain 01:12:35 loss: 0.1561 Lr: 0.00006
[2023-12-25 23:44:39,294 INFO misc.py line 119 253097] Train: [95/100][19/510] Data 0.006 (0.004) Batch 1.101 (1.411) Remain 01:11:31 loss: 0.0968 Lr: 0.00006
[2023-12-25 23:44:40,359 INFO misc.py line 119 253097] Train: [95/100][20/510] Data 0.006 (0.004) Batch 1.068 (1.391) Remain 01:10:28 loss: 0.1153 Lr: 0.00006
[2023-12-25 23:44:41,615 INFO misc.py line 119 253097] Train: [95/100][21/510] Data 0.003 (0.004) Batch 1.254 (1.383) Remain 01:10:04 loss: 0.1034 Lr: 0.00006
[2023-12-25 23:44:42,677 INFO misc.py line 119 253097] Train: [95/100][22/510] Data 0.006 (0.004) Batch 1.057 (1.366) Remain 01:09:10 loss: 0.1179 Lr: 0.00006
[2023-12-25 23:44:43,936 INFO misc.py line 119 253097] Train: [95/100][23/510] Data 0.010 (0.005) Batch 1.261 (1.361) Remain 01:08:53 loss: 0.1420 Lr: 0.00006
[2023-12-25 23:44:49,719 INFO misc.py line 119 253097] Train: [95/100][24/510] Data 4.649 (0.226) Batch 5.788 (1.572) Remain 01:19:31 loss: 0.1308 Lr: 0.00006
[2023-12-25 23:44:50,781 INFO misc.py line 119 253097] Train: [95/100][25/510] Data 0.003 (0.216) Batch 1.062 (1.549) Remain 01:18:19 loss: 0.0923 Lr: 0.00006
[2023-12-25 23:44:51,908 INFO misc.py line 119 253097] Train: [95/100][26/510] Data 0.003 (0.207) Batch 1.127 (1.530) Remain 01:17:22 loss: 0.1106 Lr: 0.00006
[2023-12-25 23:44:53,204 INFO misc.py line 119 253097] Train: [95/100][27/510] Data 0.003 (0.198) Batch 1.292 (1.520) Remain 01:16:51 loss: 0.0892 Lr: 0.00006
[2023-12-25 23:44:54,164 INFO misc.py line 119 253097] Train: [95/100][28/510] Data 0.007 (0.190) Batch 0.962 (1.498) Remain 01:15:41 loss: 0.0849 Lr: 0.00006
[2023-12-25 23:44:55,295 INFO misc.py line 119 253097] Train: [95/100][29/510] Data 0.006 (0.183) Batch 1.133 (1.484) Remain 01:14:57 loss: 0.1017 Lr: 0.00006
[2023-12-25 23:44:56,473 INFO misc.py line 119 253097] Train: [95/100][30/510] Data 0.003 (0.177) Batch 1.178 (1.473) Remain 01:14:21 loss: 0.1236 Lr: 0.00006
[2023-12-25 23:44:57,551 INFO misc.py line 119 253097] Train: [95/100][31/510] Data 0.002 (0.170) Batch 1.078 (1.458) Remain 01:13:37 loss: 0.1842 Lr: 0.00006
[2023-12-25 23:44:58,714 INFO misc.py line 119 253097] Train: [95/100][32/510] Data 0.003 (0.165) Batch 1.163 (1.448) Remain 01:13:05 loss: 0.0535 Lr: 0.00006
[2023-12-25 23:44:59,782 INFO misc.py line 119 253097] Train: [95/100][33/510] Data 0.003 (0.159) Batch 1.068 (1.436) Remain 01:12:25 loss: 0.0515 Lr: 0.00006
[2023-12-25 23:45:00,730 INFO misc.py line 119 253097] Train: [95/100][34/510] Data 0.003 (0.154) Batch 0.949 (1.420) Remain 01:11:36 loss: 0.0615 Lr: 0.00006
[2023-12-25 23:45:01,954 INFO misc.py line 119 253097] Train: [95/100][35/510] Data 0.003 (0.150) Batch 1.224 (1.414) Remain 01:11:16 loss: 0.1813 Lr: 0.00006
[2023-12-25 23:45:02,964 INFO misc.py line 119 253097] Train: [95/100][36/510] Data 0.003 (0.145) Batch 1.009 (1.402) Remain 01:10:38 loss: 0.0980 Lr: 0.00006
[2023-12-25 23:45:09,629 INFO misc.py line 119 253097] Train: [95/100][37/510] Data 0.004 (0.141) Batch 6.665 (1.556) Remain 01:18:24 loss: 0.0864 Lr: 0.00006
[2023-12-25 23:45:10,550 INFO misc.py line 119 253097] Train: [95/100][38/510] Data 0.003 (0.137) Batch 0.922 (1.538) Remain 01:17:28 loss: 0.1653 Lr: 0.00006
[2023-12-25 23:45:11,577 INFO misc.py line 119 253097] Train: [95/100][39/510] Data 0.003 (0.133) Batch 1.026 (1.524) Remain 01:16:43 loss: 0.0817 Lr: 0.00006
[2023-12-25 23:45:12,643 INFO misc.py line 119 253097] Train: [95/100][40/510] Data 0.004 (0.130) Batch 1.067 (1.512) Remain 01:16:05 loss: 0.1032 Lr: 0.00006
[2023-12-25 23:45:13,558 INFO misc.py line 119 253097] Train: [95/100][41/510] Data 0.003 (0.126) Batch 0.915 (1.496) Remain 01:15:16 loss: 0.1100 Lr: 0.00006
[2023-12-25 23:45:14,692 INFO misc.py line 119 253097] Train: [95/100][42/510] Data 0.003 (0.123) Batch 1.133 (1.487) Remain 01:14:46 loss: 0.0647 Lr: 0.00006
[2023-12-25 23:45:15,969 INFO misc.py line 119 253097] Train: [95/100][43/510] Data 0.003 (0.120) Batch 1.274 (1.481) Remain 01:14:29 loss: 0.0914 Lr: 0.00006
[2023-12-25 23:45:16,878 INFO misc.py line 119 253097] Train: [95/100][44/510] Data 0.007 (0.118) Batch 0.912 (1.467) Remain 01:13:45 loss: 0.0791 Lr: 0.00006
[2023-12-25 23:45:18,031 INFO misc.py line 119 253097] Train: [95/100][45/510] Data 0.003 (0.115) Batch 1.153 (1.460) Remain 01:13:21 loss: 0.1072 Lr: 0.00006
[2023-12-25 23:45:19,344 INFO misc.py line 119 253097] Train: [95/100][46/510] Data 0.003 (0.112) Batch 1.309 (1.456) Remain 01:13:09 loss: 0.0985 Lr: 0.00006
[2023-12-25 23:45:20,651 INFO misc.py line 119 253097] Train: [95/100][47/510] Data 0.007 (0.110) Batch 1.306 (1.453) Remain 01:12:57 loss: 0.1531 Lr: 0.00006
[2023-12-25 23:45:21,725 INFO misc.py line 119 253097] Train: [95/100][48/510] Data 0.008 (0.108) Batch 1.074 (1.445) Remain 01:12:31 loss: 0.0955 Lr: 0.00006
[2023-12-25 23:45:23,049 INFO misc.py line 119 253097] Train: [95/100][49/510] Data 0.009 (0.105) Batch 1.328 (1.442) Remain 01:12:22 loss: 0.1053 Lr: 0.00006
[2023-12-25 23:45:24,244 INFO misc.py line 119 253097] Train: [95/100][50/510] Data 0.004 (0.103) Batch 1.193 (1.437) Remain 01:12:04 loss: 0.0917 Lr: 0.00006
[2023-12-25 23:45:25,130 INFO misc.py line 119 253097] Train: [95/100][51/510] Data 0.006 (0.101) Batch 0.888 (1.425) Remain 01:11:28 loss: 0.0853 Lr: 0.00006
[2023-12-25 23:45:26,370 INFO misc.py line 119 253097] Train: [95/100][52/510] Data 0.004 (0.099) Batch 1.240 (1.422) Remain 01:11:16 loss: 0.0663 Lr: 0.00006
[2023-12-25 23:45:27,581 INFO misc.py line 119 253097] Train: [95/100][53/510] Data 0.004 (0.097) Batch 1.211 (1.417) Remain 01:11:02 loss: 0.1533 Lr: 0.00006
[2023-12-25 23:45:28,776 INFO misc.py line 119 253097] Train: [95/100][54/510] Data 0.004 (0.095) Batch 1.195 (1.413) Remain 01:10:47 loss: 0.0810 Lr: 0.00006
[2023-12-25 23:45:29,786 INFO misc.py line 119 253097] Train: [95/100][55/510] Data 0.003 (0.094) Batch 1.010 (1.405) Remain 01:10:22 loss: 0.1811 Lr: 0.00006
[2023-12-25 23:45:30,923 INFO misc.py line 119 253097] Train: [95/100][56/510] Data 0.003 (0.092) Batch 1.136 (1.400) Remain 01:10:06 loss: 0.0609 Lr: 0.00006
[2023-12-25 23:45:32,000 INFO misc.py line 119 253097] Train: [95/100][57/510] Data 0.003 (0.090) Batch 1.076 (1.394) Remain 01:09:46 loss: 0.0714 Lr: 0.00006
[2023-12-25 23:45:35,049 INFO misc.py line 119 253097] Train: [95/100][58/510] Data 1.805 (0.122) Batch 3.050 (1.424) Remain 01:11:15 loss: 0.1041 Lr: 0.00006
[2023-12-25 23:45:36,001 INFO misc.py line 119 253097] Train: [95/100][59/510] Data 0.003 (0.119) Batch 0.952 (1.416) Remain 01:10:49 loss: 0.0746 Lr: 0.00006
[2023-12-25 23:45:37,261 INFO misc.py line 119 253097] Train: [95/100][60/510] Data 0.002 (0.117) Batch 1.260 (1.413) Remain 01:10:39 loss: 0.0763 Lr: 0.00006
[2023-12-25 23:45:38,317 INFO misc.py line 119 253097] Train: [95/100][61/510] Data 0.003 (0.115) Batch 1.054 (1.407) Remain 01:10:19 loss: 0.0720 Lr: 0.00006
[2023-12-25 23:45:39,532 INFO misc.py line 119 253097] Train: [95/100][62/510] Data 0.005 (0.114) Batch 1.215 (1.404) Remain 01:10:08 loss: 0.0879 Lr: 0.00006
[2023-12-25 23:45:40,679 INFO misc.py line 119 253097] Train: [95/100][63/510] Data 0.006 (0.112) Batch 1.150 (1.399) Remain 01:09:54 loss: 0.2288 Lr: 0.00006
[2023-12-25 23:45:41,659 INFO misc.py line 119 253097] Train: [95/100][64/510] Data 0.004 (0.110) Batch 0.980 (1.393) Remain 01:09:32 loss: 0.1520 Lr: 0.00006
[2023-12-25 23:45:42,734 INFO misc.py line 119 253097] Train: [95/100][65/510] Data 0.003 (0.108) Batch 1.074 (1.387) Remain 01:09:15 loss: 0.1582 Lr: 0.00006
[2023-12-25 23:45:47,017 INFO misc.py line 119 253097] Train: [95/100][66/510] Data 0.005 (0.107) Batch 4.284 (1.433) Remain 01:11:31 loss: 0.1291 Lr: 0.00006
[2023-12-25 23:45:48,083 INFO misc.py line 119 253097] Train: [95/100][67/510] Data 0.004 (0.105) Batch 1.066 (1.428) Remain 01:11:12 loss: 0.0895 Lr: 0.00006
[2023-12-25 23:45:49,103 INFO misc.py line 119 253097] Train: [95/100][68/510] Data 0.003 (0.103) Batch 1.010 (1.421) Remain 01:10:52 loss: 0.3349 Lr: 0.00006
[2023-12-25 23:45:50,341 INFO misc.py line 119 253097] Train: [95/100][69/510] Data 0.014 (0.102) Batch 1.233 (1.418) Remain 01:10:42 loss: 0.0764 Lr: 0.00006
[2023-12-25 23:45:53,528 INFO misc.py line 119 253097] Train: [95/100][70/510] Data 2.100 (0.132) Batch 3.201 (1.445) Remain 01:12:00 loss: 0.1148 Lr: 0.00006
[2023-12-25 23:45:54,707 INFO misc.py line 119 253097] Train: [95/100][71/510] Data 0.004 (0.130) Batch 1.180 (1.441) Remain 01:11:47 loss: 0.1419 Lr: 0.00006
[2023-12-25 23:45:55,738 INFO misc.py line 119 253097] Train: [95/100][72/510] Data 0.003 (0.128) Batch 1.030 (1.435) Remain 01:11:28 loss: 0.0807 Lr: 0.00006
[2023-12-25 23:45:56,815 INFO misc.py line 119 253097] Train: [95/100][73/510] Data 0.004 (0.126) Batch 1.078 (1.430) Remain 01:11:11 loss: 0.0498 Lr: 0.00006
[2023-12-25 23:45:57,943 INFO misc.py line 119 253097] Train: [95/100][74/510] Data 0.003 (0.125) Batch 1.128 (1.426) Remain 01:10:57 loss: 0.1946 Lr: 0.00006
[2023-12-25 23:45:59,130 INFO misc.py line 119 253097] Train: [95/100][75/510] Data 0.003 (0.123) Batch 1.186 (1.422) Remain 01:10:46 loss: 0.0788 Lr: 0.00006
[2023-12-25 23:46:00,245 INFO misc.py line 119 253097] Train: [95/100][76/510] Data 0.003 (0.121) Batch 1.115 (1.418) Remain 01:10:32 loss: 0.0970 Lr: 0.00006
[2023-12-25 23:46:03,733 INFO misc.py line 119 253097] Train: [95/100][77/510] Data 0.003 (0.120) Batch 3.489 (1.446) Remain 01:11:54 loss: 0.0909 Lr: 0.00006
[2023-12-25 23:46:05,015 INFO misc.py line 119 253097] Train: [95/100][78/510] Data 0.003 (0.118) Batch 1.278 (1.444) Remain 01:11:45 loss: 0.1178 Lr: 0.00006
[2023-12-25 23:46:05,944 INFO misc.py line 119 253097] Train: [95/100][79/510] Data 0.006 (0.117) Batch 0.933 (1.437) Remain 01:11:24 loss: 0.0789 Lr: 0.00006
[2023-12-25 23:46:07,092 INFO misc.py line 119 253097] Train: [95/100][80/510] Data 0.003 (0.115) Batch 1.148 (1.433) Remain 01:11:11 loss: 0.1022 Lr: 0.00006
[2023-12-25 23:46:08,145 INFO misc.py line 119 253097] Train: [95/100][81/510] Data 0.003 (0.114) Batch 1.053 (1.429) Remain 01:10:55 loss: 0.0702 Lr: 0.00006
[2023-12-25 23:46:15,959 INFO misc.py line 119 253097] Train: [95/100][82/510] Data 0.003 (0.112) Batch 7.814 (1.509) Remain 01:14:55 loss: 0.3026 Lr: 0.00006
[2023-12-25 23:46:17,033 INFO misc.py line 119 253097] Train: [95/100][83/510] Data 0.003 (0.111) Batch 1.074 (1.504) Remain 01:14:37 loss: 0.0743 Lr: 0.00006
[2023-12-25 23:46:18,135 INFO misc.py line 119 253097] Train: [95/100][84/510] Data 0.003 (0.110) Batch 1.102 (1.499) Remain 01:14:21 loss: 0.1263 Lr: 0.00006
[2023-12-25 23:46:19,083 INFO misc.py line 119 253097] Train: [95/100][85/510] Data 0.003 (0.108) Batch 0.948 (1.492) Remain 01:13:59 loss: 0.2042 Lr: 0.00006
[2023-12-25 23:46:20,305 INFO misc.py line 119 253097] Train: [95/100][86/510] Data 0.003 (0.107) Batch 1.222 (1.489) Remain 01:13:48 loss: 0.1171 Lr: 0.00006
[2023-12-25 23:46:21,261 INFO misc.py line 119 253097] Train: [95/100][87/510] Data 0.004 (0.106) Batch 0.956 (1.483) Remain 01:13:28 loss: 0.1822 Lr: 0.00006
[2023-12-25 23:46:22,348 INFO misc.py line 119 253097] Train: [95/100][88/510] Data 0.005 (0.105) Batch 1.087 (1.478) Remain 01:13:12 loss: 0.1202 Lr: 0.00006
[2023-12-25 23:46:23,481 INFO misc.py line 119 253097] Train: [95/100][89/510] Data 0.003 (0.103) Batch 1.132 (1.474) Remain 01:12:59 loss: 0.1417 Lr: 0.00006
[2023-12-25 23:46:24,421 INFO misc.py line 119 253097] Train: [95/100][90/510] Data 0.005 (0.102) Batch 0.942 (1.468) Remain 01:12:39 loss: 0.1604 Lr: 0.00006
[2023-12-25 23:46:25,644 INFO misc.py line 119 253097] Train: [95/100][91/510] Data 0.004 (0.101) Batch 1.223 (1.465) Remain 01:12:29 loss: 0.1033 Lr: 0.00006
[2023-12-25 23:46:27,254 INFO misc.py line 119 253097] Train: [95/100][92/510] Data 0.003 (0.100) Batch 1.606 (1.467) Remain 01:12:33 loss: 0.0674 Lr: 0.00006
[2023-12-25 23:46:28,273 INFO misc.py line 119 253097] Train: [95/100][93/510] Data 0.009 (0.099) Batch 1.020 (1.462) Remain 01:12:17 loss: 0.0891 Lr: 0.00006
[2023-12-25 23:46:29,430 INFO misc.py line 119 253097] Train: [95/100][94/510] Data 0.007 (0.098) Batch 1.144 (1.458) Remain 01:12:05 loss: 0.0672 Lr: 0.00006
[2023-12-25 23:46:30,567 INFO misc.py line 119 253097] Train: [95/100][95/510] Data 0.021 (0.097) Batch 1.149 (1.455) Remain 01:11:53 loss: 0.1196 Lr: 0.00006
[2023-12-25 23:46:31,569 INFO misc.py line 119 253097] Train: [95/100][96/510] Data 0.007 (0.096) Batch 1.006 (1.450) Remain 01:11:37 loss: 0.0764 Lr: 0.00006
[2023-12-25 23:46:32,745 INFO misc.py line 119 253097] Train: [95/100][97/510] Data 0.003 (0.095) Batch 1.173 (1.447) Remain 01:11:27 loss: 0.0713 Lr: 0.00006
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[2023-12-25 23:47:32,837 INFO misc.py line 119 253097] Train: [95/100][136/510] Data 0.004 (0.069) Batch 1.187 (1.474) Remain 01:11:51 loss: 0.0995 Lr: 0.00005
[2023-12-25 23:47:33,852 INFO misc.py line 119 253097] Train: [95/100][137/510] Data 0.023 (0.069) Batch 1.034 (1.471) Remain 01:11:40 loss: 0.0909 Lr: 0.00005
[2023-12-25 23:47:34,972 INFO misc.py line 119 253097] Train: [95/100][138/510] Data 0.004 (0.068) Batch 1.116 (1.469) Remain 01:11:31 loss: 0.1408 Lr: 0.00005
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[2023-12-25 23:47:37,268 INFO misc.py line 119 253097] Train: [95/100][140/510] Data 0.005 (0.068) Batch 1.236 (1.464) Remain 01:11:14 loss: 0.0678 Lr: 0.00005
[2023-12-25 23:47:40,426 INFO misc.py line 119 253097] Train: [95/100][141/510] Data 0.008 (0.067) Batch 3.160 (1.476) Remain 01:11:48 loss: 0.1173 Lr: 0.00005
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[2023-12-25 23:47:45,128 INFO misc.py line 119 253097] Train: [95/100][145/510] Data 0.005 (0.065) Batch 1.199 (1.468) Remain 01:11:18 loss: 0.0856 Lr: 0.00005
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[2023-12-25 23:47:48,505 INFO misc.py line 119 253097] Train: [95/100][148/510] Data 0.007 (0.064) Batch 1.157 (1.461) Remain 01:10:53 loss: 0.0990 Lr: 0.00005
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[2023-12-25 23:47:51,516 INFO misc.py line 119 253097] Train: [95/100][151/510] Data 0.003 (0.063) Batch 1.126 (1.451) Remain 01:10:22 loss: 0.0655 Lr: 0.00005
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[2023-12-25 23:48:21,457 INFO misc.py line 119 253097] Train: [95/100][173/510] Data 0.006 (0.082) Batch 1.280 (1.440) Remain 01:09:16 loss: 0.0683 Lr: 0.00005
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[2023-12-25 23:48:29,693 INFO misc.py line 119 253097] Train: [95/100][175/510] Data 6.155 (0.116) Batch 7.040 (1.471) Remain 01:10:43 loss: 0.1049 Lr: 0.00005
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[2023-12-25 23:54:03,565 INFO misc.py line 119 253097] Train: [95/100][411/510] Data 0.017 (0.084) Batch 1.106 (1.438) Remain 01:03:30 loss: 0.1384 Lr: 0.00005
[2023-12-25 23:54:04,669 INFO misc.py line 119 253097] Train: [95/100][412/510] Data 0.035 (0.084) Batch 1.131 (1.438) Remain 01:03:26 loss: 0.1417 Lr: 0.00005
[2023-12-25 23:54:05,795 INFO misc.py line 119 253097] Train: [95/100][413/510] Data 0.007 (0.084) Batch 1.128 (1.437) Remain 01:03:23 loss: 0.0953 Lr: 0.00005
[2023-12-25 23:54:07,008 INFO misc.py line 119 253097] Train: [95/100][414/510] Data 0.006 (0.084) Batch 1.215 (1.436) Remain 01:03:20 loss: 0.1001 Lr: 0.00005
[2023-12-25 23:54:08,228 INFO misc.py line 119 253097] Train: [95/100][415/510] Data 0.004 (0.084) Batch 1.216 (1.436) Remain 01:03:17 loss: 0.1193 Lr: 0.00005
[2023-12-25 23:54:09,270 INFO misc.py line 119 253097] Train: [95/100][416/510] Data 0.007 (0.083) Batch 1.041 (1.435) Remain 01:03:13 loss: 0.0746 Lr: 0.00005
[2023-12-25 23:54:10,389 INFO misc.py line 119 253097] Train: [95/100][417/510] Data 0.009 (0.083) Batch 1.121 (1.434) Remain 01:03:10 loss: 0.1106 Lr: 0.00005
[2023-12-25 23:54:11,545 INFO misc.py line 119 253097] Train: [95/100][418/510] Data 0.007 (0.083) Batch 1.155 (1.433) Remain 01:03:06 loss: 0.0536 Lr: 0.00005
[2023-12-25 23:54:12,553 INFO misc.py line 119 253097] Train: [95/100][419/510] Data 0.008 (0.083) Batch 1.012 (1.432) Remain 01:03:02 loss: 0.1702 Lr: 0.00005
[2023-12-25 23:54:13,807 INFO misc.py line 119 253097] Train: [95/100][420/510] Data 0.005 (0.083) Batch 1.255 (1.432) Remain 01:03:00 loss: 0.1145 Lr: 0.00005
[2023-12-25 23:54:14,988 INFO misc.py line 119 253097] Train: [95/100][421/510] Data 0.004 (0.082) Batch 1.181 (1.431) Remain 01:02:57 loss: 0.0480 Lr: 0.00004
[2023-12-25 23:54:16,240 INFO misc.py line 119 253097] Train: [95/100][422/510] Data 0.003 (0.082) Batch 1.252 (1.431) Remain 01:02:54 loss: 0.1350 Lr: 0.00004
[2023-12-25 23:54:17,334 INFO misc.py line 119 253097] Train: [95/100][423/510] Data 0.003 (0.082) Batch 1.092 (1.430) Remain 01:02:51 loss: 0.0736 Lr: 0.00004
[2023-12-25 23:54:18,244 INFO misc.py line 119 253097] Train: [95/100][424/510] Data 0.004 (0.082) Batch 0.910 (1.429) Remain 01:02:46 loss: 0.2420 Lr: 0.00004
[2023-12-25 23:54:19,276 INFO misc.py line 119 253097] Train: [95/100][425/510] Data 0.004 (0.082) Batch 1.033 (1.428) Remain 01:02:42 loss: 0.1142 Lr: 0.00004
[2023-12-25 23:54:20,429 INFO misc.py line 119 253097] Train: [95/100][426/510] Data 0.003 (0.082) Batch 1.153 (1.427) Remain 01:02:39 loss: 0.0583 Lr: 0.00004
[2023-12-25 23:54:21,405 INFO misc.py line 119 253097] Train: [95/100][427/510] Data 0.004 (0.081) Batch 0.977 (1.426) Remain 01:02:35 loss: 0.1314 Lr: 0.00004
[2023-12-25 23:54:22,488 INFO misc.py line 119 253097] Train: [95/100][428/510] Data 0.003 (0.081) Batch 1.083 (1.425) Remain 01:02:31 loss: 0.1145 Lr: 0.00004
[2023-12-25 23:54:23,545 INFO misc.py line 119 253097] Train: [95/100][429/510] Data 0.003 (0.081) Batch 1.056 (1.424) Remain 01:02:27 loss: 0.1482 Lr: 0.00004
[2023-12-25 23:54:24,516 INFO misc.py line 119 253097] Train: [95/100][430/510] Data 0.004 (0.081) Batch 0.971 (1.423) Remain 01:02:23 loss: 0.0810 Lr: 0.00004
[2023-12-25 23:54:25,771 INFO misc.py line 119 253097] Train: [95/100][431/510] Data 0.003 (0.081) Batch 1.256 (1.423) Remain 01:02:21 loss: 0.1087 Lr: 0.00004
[2023-12-25 23:54:26,903 INFO misc.py line 119 253097] Train: [95/100][432/510] Data 0.004 (0.080) Batch 1.128 (1.422) Remain 01:02:17 loss: 0.1854 Lr: 0.00004
[2023-12-25 23:54:27,785 INFO misc.py line 119 253097] Train: [95/100][433/510] Data 0.006 (0.080) Batch 0.886 (1.421) Remain 01:02:13 loss: 0.1097 Lr: 0.00004
[2023-12-25 23:54:28,894 INFO misc.py line 119 253097] Train: [95/100][434/510] Data 0.003 (0.080) Batch 1.109 (1.420) Remain 01:02:09 loss: 0.1315 Lr: 0.00004
[2023-12-25 23:54:29,978 INFO misc.py line 119 253097] Train: [95/100][435/510] Data 0.003 (0.080) Batch 1.084 (1.420) Remain 01:02:06 loss: 0.0890 Lr: 0.00004
[2023-12-25 23:54:30,986 INFO misc.py line 119 253097] Train: [95/100][436/510] Data 0.004 (0.080) Batch 1.006 (1.419) Remain 01:02:02 loss: 0.0975 Lr: 0.00004
[2023-12-25 23:54:33,675 INFO misc.py line 119 253097] Train: [95/100][437/510] Data 1.430 (0.083) Batch 2.691 (1.422) Remain 01:02:08 loss: 0.1667 Lr: 0.00004
[2023-12-25 23:54:34,688 INFO misc.py line 119 253097] Train: [95/100][438/510] Data 0.003 (0.083) Batch 1.013 (1.421) Remain 01:02:04 loss: 0.1052 Lr: 0.00004
[2023-12-25 23:54:35,412 INFO misc.py line 119 253097] Train: [95/100][439/510] Data 0.003 (0.083) Batch 0.724 (1.419) Remain 01:01:59 loss: 0.1531 Lr: 0.00004
[2023-12-25 23:54:36,508 INFO misc.py line 119 253097] Train: [95/100][440/510] Data 0.004 (0.082) Batch 1.096 (1.418) Remain 01:01:55 loss: 0.0661 Lr: 0.00004
[2023-12-25 23:54:37,610 INFO misc.py line 119 253097] Train: [95/100][441/510] Data 0.004 (0.082) Batch 1.098 (1.418) Remain 01:01:52 loss: 0.0588 Lr: 0.00004
[2023-12-25 23:54:38,802 INFO misc.py line 119 253097] Train: [95/100][442/510] Data 0.008 (0.082) Batch 1.192 (1.417) Remain 01:01:49 loss: 0.1223 Lr: 0.00004
[2023-12-25 23:54:39,916 INFO misc.py line 119 253097] Train: [95/100][443/510] Data 0.007 (0.082) Batch 1.115 (1.416) Remain 01:01:46 loss: 0.1219 Lr: 0.00004
[2023-12-25 23:54:41,010 INFO misc.py line 119 253097] Train: [95/100][444/510] Data 0.007 (0.082) Batch 1.095 (1.416) Remain 01:01:43 loss: 0.0813 Lr: 0.00004
[2023-12-25 23:54:42,123 INFO misc.py line 119 253097] Train: [95/100][445/510] Data 0.005 (0.081) Batch 1.111 (1.415) Remain 01:01:40 loss: 0.0978 Lr: 0.00004
[2023-12-25 23:54:43,504 INFO misc.py line 119 253097] Train: [95/100][446/510] Data 0.007 (0.081) Batch 1.385 (1.415) Remain 01:01:38 loss: 0.0983 Lr: 0.00004
[2023-12-25 23:54:44,651 INFO misc.py line 119 253097] Train: [95/100][447/510] Data 0.004 (0.081) Batch 1.145 (1.414) Remain 01:01:35 loss: 0.1061 Lr: 0.00004
[2023-12-25 23:54:45,782 INFO misc.py line 119 253097] Train: [95/100][448/510] Data 0.006 (0.081) Batch 1.134 (1.414) Remain 01:01:32 loss: 0.0928 Lr: 0.00004
[2023-12-25 23:54:46,892 INFO misc.py line 119 253097] Train: [95/100][449/510] Data 0.003 (0.081) Batch 1.105 (1.413) Remain 01:01:29 loss: 0.0871 Lr: 0.00004
[2023-12-25 23:54:47,915 INFO misc.py line 119 253097] Train: [95/100][450/510] Data 0.008 (0.081) Batch 1.026 (1.412) Remain 01:01:25 loss: 0.0842 Lr: 0.00004
[2023-12-25 23:54:49,132 INFO misc.py line 119 253097] Train: [95/100][451/510] Data 0.004 (0.080) Batch 1.219 (1.412) Remain 01:01:22 loss: 0.1533 Lr: 0.00004
[2023-12-25 23:54:50,419 INFO misc.py line 119 253097] Train: [95/100][452/510] Data 0.002 (0.080) Batch 1.287 (1.411) Remain 01:01:20 loss: 0.0996 Lr: 0.00004
[2023-12-25 23:54:51,476 INFO misc.py line 119 253097] Train: [95/100][453/510] Data 0.003 (0.080) Batch 1.052 (1.411) Remain 01:01:17 loss: 0.1104 Lr: 0.00004
[2023-12-25 23:54:52,562 INFO misc.py line 119 253097] Train: [95/100][454/510] Data 0.007 (0.080) Batch 1.084 (1.410) Remain 01:01:14 loss: 0.2480 Lr: 0.00004
[2023-12-25 23:54:53,710 INFO misc.py line 119 253097] Train: [95/100][455/510] Data 0.009 (0.080) Batch 1.150 (1.409) Remain 01:01:11 loss: 0.1919 Lr: 0.00004
[2023-12-25 23:54:54,756 INFO misc.py line 119 253097] Train: [95/100][456/510] Data 0.007 (0.080) Batch 1.047 (1.408) Remain 01:01:07 loss: 0.0602 Lr: 0.00004
[2023-12-25 23:54:55,709 INFO misc.py line 119 253097] Train: [95/100][457/510] Data 0.006 (0.079) Batch 0.956 (1.407) Remain 01:01:03 loss: 0.1034 Lr: 0.00004
[2023-12-25 23:54:56,954 INFO misc.py line 119 253097] Train: [95/100][458/510] Data 0.003 (0.079) Batch 1.243 (1.407) Remain 01:01:01 loss: 0.1284 Lr: 0.00004
[2023-12-25 23:54:57,831 INFO misc.py line 119 253097] Train: [95/100][459/510] Data 0.005 (0.079) Batch 0.879 (1.406) Remain 01:00:56 loss: 0.0766 Lr: 0.00004
[2023-12-25 23:54:59,077 INFO misc.py line 119 253097] Train: [95/100][460/510] Data 0.004 (0.079) Batch 1.241 (1.406) Remain 01:00:54 loss: 0.0802 Lr: 0.00004
[2023-12-25 23:54:59,960 INFO misc.py line 119 253097] Train: [95/100][461/510] Data 0.008 (0.079) Batch 0.888 (1.404) Remain 01:00:50 loss: 0.0833 Lr: 0.00004
[2023-12-25 23:55:01,003 INFO misc.py line 119 253097] Train: [95/100][462/510] Data 0.003 (0.079) Batch 1.044 (1.404) Remain 01:00:46 loss: 0.1222 Lr: 0.00004
[2023-12-25 23:55:02,200 INFO misc.py line 119 253097] Train: [95/100][463/510] Data 0.003 (0.078) Batch 1.197 (1.403) Remain 01:00:44 loss: 0.1454 Lr: 0.00004
[2023-12-25 23:55:03,385 INFO misc.py line 119 253097] Train: [95/100][464/510] Data 0.003 (0.078) Batch 1.183 (1.403) Remain 01:00:41 loss: 0.0766 Lr: 0.00004
[2023-12-25 23:55:04,650 INFO misc.py line 119 253097] Train: [95/100][465/510] Data 0.006 (0.078) Batch 1.263 (1.402) Remain 01:00:39 loss: 0.0668 Lr: 0.00004
[2023-12-25 23:55:05,680 INFO misc.py line 119 253097] Train: [95/100][466/510] Data 0.007 (0.078) Batch 1.029 (1.402) Remain 01:00:35 loss: 0.1185 Lr: 0.00004
[2023-12-25 23:55:06,968 INFO misc.py line 119 253097] Train: [95/100][467/510] Data 0.008 (0.078) Batch 1.289 (1.401) Remain 01:00:33 loss: 0.0773 Lr: 0.00004
[2023-12-25 23:55:16,357 INFO misc.py line 119 253097] Train: [95/100][468/510] Data 0.007 (0.078) Batch 9.392 (1.419) Remain 01:01:16 loss: 0.0846 Lr: 0.00004
[2023-12-25 23:55:17,560 INFO misc.py line 119 253097] Train: [95/100][469/510] Data 0.004 (0.078) Batch 1.203 (1.418) Remain 01:01:14 loss: 0.0738 Lr: 0.00004
[2023-12-25 23:55:18,697 INFO misc.py line 119 253097] Train: [95/100][470/510] Data 0.003 (0.077) Batch 1.136 (1.418) Remain 01:01:11 loss: 0.0667 Lr: 0.00004
[2023-12-25 23:55:19,884 INFO misc.py line 119 253097] Train: [95/100][471/510] Data 0.004 (0.077) Batch 1.188 (1.417) Remain 01:01:08 loss: 0.0889 Lr: 0.00004
[2023-12-25 23:55:21,141 INFO misc.py line 119 253097] Train: [95/100][472/510] Data 0.002 (0.077) Batch 1.255 (1.417) Remain 01:01:06 loss: 0.0865 Lr: 0.00004
[2023-12-25 23:55:22,307 INFO misc.py line 119 253097] Train: [95/100][473/510] Data 0.004 (0.077) Batch 1.167 (1.416) Remain 01:01:03 loss: 0.0719 Lr: 0.00004
[2023-12-25 23:55:33,659 INFO misc.py line 119 253097] Train: [95/100][474/510] Data 0.003 (0.077) Batch 11.352 (1.437) Remain 01:01:56 loss: 0.1238 Lr: 0.00004
[2023-12-25 23:55:34,597 INFO misc.py line 119 253097] Train: [95/100][475/510] Data 0.003 (0.077) Batch 0.938 (1.436) Remain 01:01:52 loss: 0.1331 Lr: 0.00004
[2023-12-25 23:55:35,726 INFO misc.py line 119 253097] Train: [95/100][476/510] Data 0.003 (0.076) Batch 1.129 (1.436) Remain 01:01:49 loss: 0.0860 Lr: 0.00004
[2023-12-25 23:55:36,868 INFO misc.py line 119 253097] Train: [95/100][477/510] Data 0.004 (0.076) Batch 1.131 (1.435) Remain 01:01:46 loss: 0.1231 Lr: 0.00004
[2023-12-25 23:55:38,051 INFO misc.py line 119 253097] Train: [95/100][478/510] Data 0.014 (0.076) Batch 1.190 (1.434) Remain 01:01:43 loss: 0.1023 Lr: 0.00004
[2023-12-25 23:55:39,042 INFO misc.py line 119 253097] Train: [95/100][479/510] Data 0.006 (0.076) Batch 0.994 (1.433) Remain 01:01:39 loss: 0.1185 Lr: 0.00004
[2023-12-25 23:55:40,245 INFO misc.py line 119 253097] Train: [95/100][480/510] Data 0.004 (0.076) Batch 1.203 (1.433) Remain 01:01:37 loss: 0.1379 Lr: 0.00004
[2023-12-25 23:55:41,426 INFO misc.py line 119 253097] Train: [95/100][481/510] Data 0.003 (0.076) Batch 1.144 (1.432) Remain 01:01:34 loss: 0.1147 Lr: 0.00004
[2023-12-25 23:55:42,684 INFO misc.py line 119 253097] Train: [95/100][482/510] Data 0.040 (0.076) Batch 1.293 (1.432) Remain 01:01:31 loss: 0.1062 Lr: 0.00004
[2023-12-25 23:55:43,819 INFO misc.py line 119 253097] Train: [95/100][483/510] Data 0.006 (0.075) Batch 1.132 (1.431) Remain 01:01:28 loss: 0.0932 Lr: 0.00004
[2023-12-25 23:55:45,138 INFO misc.py line 119 253097] Train: [95/100][484/510] Data 0.008 (0.075) Batch 1.320 (1.431) Remain 01:01:26 loss: 0.1234 Lr: 0.00004
[2023-12-25 23:55:46,130 INFO misc.py line 119 253097] Train: [95/100][485/510] Data 0.008 (0.075) Batch 0.997 (1.430) Remain 01:01:23 loss: 0.1692 Lr: 0.00004
[2023-12-25 23:55:47,300 INFO misc.py line 119 253097] Train: [95/100][486/510] Data 0.003 (0.075) Batch 1.169 (1.430) Remain 01:01:20 loss: 0.1070 Lr: 0.00004
[2023-12-25 23:55:48,411 INFO misc.py line 119 253097] Train: [95/100][487/510] Data 0.003 (0.075) Batch 1.111 (1.429) Remain 01:01:17 loss: 0.0561 Lr: 0.00004
[2023-12-25 23:55:49,562 INFO misc.py line 119 253097] Train: [95/100][488/510] Data 0.004 (0.075) Batch 1.151 (1.429) Remain 01:01:14 loss: 0.2215 Lr: 0.00004
[2023-12-25 23:55:50,850 INFO misc.py line 119 253097] Train: [95/100][489/510] Data 0.004 (0.075) Batch 1.289 (1.428) Remain 01:01:12 loss: 0.1274 Lr: 0.00004
[2023-12-25 23:55:51,842 INFO misc.py line 119 253097] Train: [95/100][490/510] Data 0.003 (0.074) Batch 0.990 (1.427) Remain 01:01:08 loss: 0.1054 Lr: 0.00004
[2023-12-25 23:55:54,820 INFO misc.py line 119 253097] Train: [95/100][491/510] Data 0.005 (0.074) Batch 2.979 (1.431) Remain 01:01:15 loss: 0.1036 Lr: 0.00004
[2023-12-25 23:55:55,935 INFO misc.py line 119 253097] Train: [95/100][492/510] Data 0.005 (0.074) Batch 1.115 (1.430) Remain 01:01:11 loss: 0.2910 Lr: 0.00004
[2023-12-25 23:55:57,059 INFO misc.py line 119 253097] Train: [95/100][493/510] Data 0.003 (0.074) Batch 1.124 (1.429) Remain 01:01:08 loss: 0.0711 Lr: 0.00004
[2023-12-25 23:55:58,158 INFO misc.py line 119 253097] Train: [95/100][494/510] Data 0.004 (0.074) Batch 1.099 (1.429) Remain 01:01:05 loss: 0.0566 Lr: 0.00004
[2023-12-25 23:56:00,722 INFO misc.py line 119 253097] Train: [95/100][495/510] Data 0.003 (0.074) Batch 2.561 (1.431) Remain 01:01:10 loss: 0.0852 Lr: 0.00004
[2023-12-25 23:56:01,773 INFO misc.py line 119 253097] Train: [95/100][496/510] Data 0.007 (0.074) Batch 1.050 (1.430) Remain 01:01:06 loss: 0.2082 Lr: 0.00004
[2023-12-25 23:56:02,772 INFO misc.py line 119 253097] Train: [95/100][497/510] Data 0.008 (0.073) Batch 1.002 (1.429) Remain 01:01:03 loss: 0.1490 Lr: 0.00004
[2023-12-25 23:56:04,030 INFO misc.py line 119 253097] Train: [95/100][498/510] Data 0.004 (0.073) Batch 1.258 (1.429) Remain 01:01:00 loss: 0.0854 Lr: 0.00004
[2023-12-25 23:56:05,116 INFO misc.py line 119 253097] Train: [95/100][499/510] Data 0.004 (0.073) Batch 1.086 (1.428) Remain 01:00:57 loss: 0.2733 Lr: 0.00004
[2023-12-25 23:56:06,281 INFO misc.py line 119 253097] Train: [95/100][500/510] Data 0.003 (0.073) Batch 1.162 (1.428) Remain 01:00:54 loss: 0.0927 Lr: 0.00004
[2023-12-25 23:56:07,463 INFO misc.py line 119 253097] Train: [95/100][501/510] Data 0.006 (0.073) Batch 1.184 (1.427) Remain 01:00:52 loss: 0.0837 Lr: 0.00004
[2023-12-25 23:56:08,598 INFO misc.py line 119 253097] Train: [95/100][502/510] Data 0.004 (0.073) Batch 1.135 (1.427) Remain 01:00:49 loss: 0.1139 Lr: 0.00004
[2023-12-25 23:56:10,246 INFO misc.py line 119 253097] Train: [95/100][503/510] Data 0.004 (0.073) Batch 1.649 (1.427) Remain 01:00:49 loss: 0.1232 Lr: 0.00004
[2023-12-25 23:56:11,265 INFO misc.py line 119 253097] Train: [95/100][504/510] Data 0.003 (0.073) Batch 1.015 (1.426) Remain 01:00:45 loss: 0.0531 Lr: 0.00004
[2023-12-25 23:56:12,316 INFO misc.py line 119 253097] Train: [95/100][505/510] Data 0.007 (0.072) Batch 1.050 (1.425) Remain 01:00:42 loss: 0.1057 Lr: 0.00004
[2023-12-25 23:56:13,391 INFO misc.py line 119 253097] Train: [95/100][506/510] Data 0.007 (0.072) Batch 1.075 (1.425) Remain 01:00:38 loss: 0.1773 Lr: 0.00004
[2023-12-25 23:56:14,449 INFO misc.py line 119 253097] Train: [95/100][507/510] Data 0.007 (0.072) Batch 1.059 (1.424) Remain 01:00:35 loss: 0.1640 Lr: 0.00004
[2023-12-25 23:56:15,623 INFO misc.py line 119 253097] Train: [95/100][508/510] Data 0.007 (0.072) Batch 1.174 (1.424) Remain 01:00:32 loss: 0.2481 Lr: 0.00004
[2023-12-25 23:56:16,769 INFO misc.py line 119 253097] Train: [95/100][509/510] Data 0.007 (0.072) Batch 1.145 (1.423) Remain 01:00:30 loss: 0.0812 Lr: 0.00004
[2023-12-25 23:56:17,851 INFO misc.py line 119 253097] Train: [95/100][510/510] Data 0.007 (0.072) Batch 1.077 (1.422) Remain 01:00:26 loss: 0.0727 Lr: 0.00004
[2023-12-25 23:56:17,851 INFO misc.py line 136 253097] Train result: loss: 0.1109 
[2023-12-25 23:56:17,852 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-25 23:56:52,969 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5739 
[2023-12-25 23:56:53,316 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.2938 
[2023-12-25 23:56:58,253 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3748 
[2023-12-25 23:56:58,774 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4093 
[2023-12-25 23:57:00,762 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8522 
[2023-12-25 23:57:01,189 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3275 
[2023-12-25 23:57:02,066 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1870 
[2023-12-25 23:57:02,619 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2540 
[2023-12-25 23:57:04,439 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9165 
[2023-12-25 23:57:06,562 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1532 
[2023-12-25 23:57:07,419 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3281 
[2023-12-25 23:57:07,841 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.9155 
[2023-12-25 23:57:08,743 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4054 
[2023-12-25 23:57:11,689 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8498 
[2023-12-25 23:57:12,160 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2550 
[2023-12-25 23:57:12,780 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3814 
[2023-12-25 23:57:13,484 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.4438 
[2023-12-25 23:57:14,674 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6910/0.7464/0.9057.
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9188/0.9472
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9833/0.9903
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8435/0.9741
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3327/0.3578
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6338/0.6528
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6959/0.7724
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8032/0.8894
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9077/0.9483
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6446/0.6998
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7882/0.8784
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8125/0.8599
[2023-12-25 23:57:14,674 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6182/0.7326
[2023-12-25 23:57:14,675 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-25 23:57:14,676 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-25 23:57:14,676 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-25 23:57:21,654 INFO misc.py line 119 253097] Train: [96/100][1/510] Data 1.608 (1.608) Batch 5.149 (5.149) Remain 03:38:45 loss: 0.0838 Lr: 0.00004
[2023-12-25 23:57:23,791 INFO misc.py line 119 253097] Train: [96/100][2/510] Data 0.009 (0.009) Batch 2.142 (2.142) Remain 01:30:58 loss: 0.1661 Lr: 0.00004
[2023-12-25 23:57:26,704 INFO misc.py line 119 253097] Train: [96/100][3/510] Data 0.003 (0.003) Batch 2.913 (2.913) Remain 02:03:39 loss: 0.1091 Lr: 0.00004
[2023-12-25 23:57:27,869 INFO misc.py line 119 253097] Train: [96/100][4/510] Data 0.004 (0.004) Batch 1.165 (1.165) Remain 00:49:24 loss: 0.1082 Lr: 0.00004
[2023-12-25 23:57:29,086 INFO misc.py line 119 253097] Train: [96/100][5/510] Data 0.003 (0.003) Batch 1.213 (1.189) Remain 00:50:25 loss: 0.1050 Lr: 0.00004
[2023-12-25 23:57:30,077 INFO misc.py line 119 253097] Train: [96/100][6/510] Data 0.008 (0.005) Batch 0.995 (1.124) Remain 00:47:39 loss: 0.0912 Lr: 0.00004
[2023-12-25 23:57:31,212 INFO misc.py line 119 253097] Train: [96/100][7/510] Data 0.003 (0.004) Batch 1.135 (1.127) Remain 00:47:45 loss: 0.3290 Lr: 0.00004
[2023-12-25 23:57:32,340 INFO misc.py line 119 253097] Train: [96/100][8/510] Data 0.004 (0.004) Batch 1.125 (1.126) Remain 00:47:43 loss: 0.1117 Lr: 0.00004
[2023-12-25 23:57:33,453 INFO misc.py line 119 253097] Train: [96/100][9/510] Data 0.006 (0.005) Batch 1.116 (1.125) Remain 00:47:38 loss: 0.0762 Lr: 0.00004
[2023-12-25 23:57:34,701 INFO misc.py line 119 253097] Train: [96/100][10/510] Data 0.004 (0.004) Batch 1.248 (1.142) Remain 00:48:21 loss: 0.0509 Lr: 0.00004
[2023-12-25 23:57:35,895 INFO misc.py line 119 253097] Train: [96/100][11/510] Data 0.004 (0.004) Batch 1.191 (1.149) Remain 00:48:36 loss: 0.0744 Lr: 0.00004
[2023-12-25 23:57:36,996 INFO misc.py line 119 253097] Train: [96/100][12/510] Data 0.006 (0.005) Batch 1.104 (1.144) Remain 00:48:22 loss: 0.0718 Lr: 0.00004
[2023-12-25 23:57:37,962 INFO misc.py line 119 253097] Train: [96/100][13/510] Data 0.003 (0.004) Batch 0.966 (1.126) Remain 00:47:36 loss: 0.1469 Lr: 0.00004
[2023-12-25 23:57:39,208 INFO misc.py line 119 253097] Train: [96/100][14/510] Data 0.004 (0.004) Batch 1.245 (1.137) Remain 00:48:02 loss: 0.0643 Lr: 0.00004
[2023-12-25 23:57:40,434 INFO misc.py line 119 253097] Train: [96/100][15/510] Data 0.003 (0.004) Batch 1.223 (1.144) Remain 00:48:19 loss: 0.0595 Lr: 0.00004
[2023-12-25 23:57:41,444 INFO misc.py line 119 253097] Train: [96/100][16/510] Data 0.007 (0.004) Batch 1.009 (1.134) Remain 00:47:52 loss: 0.0848 Lr: 0.00004
[2023-12-25 23:57:42,661 INFO misc.py line 119 253097] Train: [96/100][17/510] Data 0.007 (0.005) Batch 1.216 (1.139) Remain 00:48:06 loss: 0.1031 Lr: 0.00004
[2023-12-25 23:57:43,820 INFO misc.py line 119 253097] Train: [96/100][18/510] Data 0.009 (0.005) Batch 1.161 (1.141) Remain 00:48:08 loss: 0.1602 Lr: 0.00004
[2023-12-25 23:57:44,962 INFO misc.py line 119 253097] Train: [96/100][19/510] Data 0.006 (0.005) Batch 1.141 (1.141) Remain 00:48:07 loss: 0.1548 Lr: 0.00004
[2023-12-25 23:57:45,916 INFO misc.py line 119 253097] Train: [96/100][20/510] Data 0.006 (0.005) Batch 0.957 (1.130) Remain 00:47:39 loss: 0.0642 Lr: 0.00004
[2023-12-25 23:57:46,780 INFO misc.py line 119 253097] Train: [96/100][21/510] Data 0.003 (0.005) Batch 0.864 (1.115) Remain 00:47:00 loss: 0.1872 Lr: 0.00004
[2023-12-25 23:57:47,932 INFO misc.py line 119 253097] Train: [96/100][22/510] Data 0.002 (0.005) Batch 1.152 (1.117) Remain 00:47:04 loss: 0.0975 Lr: 0.00004
[2023-12-25 23:57:48,921 INFO misc.py line 119 253097] Train: [96/100][23/510] Data 0.003 (0.005) Batch 0.988 (1.111) Remain 00:46:46 loss: 0.0646 Lr: 0.00004
[2023-12-25 23:57:50,092 INFO misc.py line 119 253097] Train: [96/100][24/510] Data 0.004 (0.005) Batch 1.171 (1.114) Remain 00:46:53 loss: 0.0913 Lr: 0.00004
[2023-12-25 23:57:54,640 INFO misc.py line 119 253097] Train: [96/100][25/510] Data 3.623 (0.169) Batch 4.549 (1.270) Remain 00:53:26 loss: 0.2562 Lr: 0.00004
[2023-12-25 23:57:55,829 INFO misc.py line 119 253097] Train: [96/100][26/510] Data 0.003 (0.162) Batch 1.184 (1.266) Remain 00:53:15 loss: 0.1048 Lr: 0.00004
[2023-12-25 23:57:56,914 INFO misc.py line 119 253097] Train: [96/100][27/510] Data 0.008 (0.156) Batch 1.087 (1.259) Remain 00:52:55 loss: 0.0972 Lr: 0.00004
[2023-12-25 23:57:58,229 INFO misc.py line 119 253097] Train: [96/100][28/510] Data 0.006 (0.150) Batch 1.319 (1.261) Remain 00:53:00 loss: 0.1129 Lr: 0.00004
[2023-12-25 23:57:59,274 INFO misc.py line 119 253097] Train: [96/100][29/510] Data 0.003 (0.144) Batch 1.040 (1.253) Remain 00:52:37 loss: 0.0989 Lr: 0.00004
[2023-12-25 23:58:05,038 INFO misc.py line 119 253097] Train: [96/100][30/510] Data 0.007 (0.139) Batch 5.768 (1.420) Remain 00:59:37 loss: 0.1345 Lr: 0.00004
[2023-12-25 23:58:06,164 INFO misc.py line 119 253097] Train: [96/100][31/510] Data 0.003 (0.134) Batch 1.126 (1.409) Remain 00:59:09 loss: 0.2081 Lr: 0.00004
[2023-12-25 23:58:07,339 INFO misc.py line 119 253097] Train: [96/100][32/510] Data 0.003 (0.130) Batch 1.175 (1.401) Remain 00:58:48 loss: 0.0714 Lr: 0.00004
[2023-12-25 23:58:08,536 INFO misc.py line 119 253097] Train: [96/100][33/510] Data 0.002 (0.125) Batch 1.197 (1.394) Remain 00:58:29 loss: 0.0723 Lr: 0.00004
[2023-12-25 23:58:09,885 INFO misc.py line 119 253097] Train: [96/100][34/510] Data 0.004 (0.121) Batch 1.347 (1.393) Remain 00:58:24 loss: 0.0382 Lr: 0.00004
[2023-12-25 23:58:11,158 INFO misc.py line 119 253097] Train: [96/100][35/510] Data 0.005 (0.118) Batch 1.274 (1.389) Remain 00:58:13 loss: 0.1152 Lr: 0.00004
[2023-12-25 23:58:12,317 INFO misc.py line 119 253097] Train: [96/100][36/510] Data 0.004 (0.114) Batch 1.159 (1.382) Remain 00:57:54 loss: 0.1072 Lr: 0.00004
[2023-12-25 23:58:13,516 INFO misc.py line 119 253097] Train: [96/100][37/510] Data 0.005 (0.111) Batch 1.199 (1.377) Remain 00:57:39 loss: 0.1290 Lr: 0.00004
[2023-12-25 23:58:14,714 INFO misc.py line 119 253097] Train: [96/100][38/510] Data 0.004 (0.108) Batch 1.197 (1.372) Remain 00:57:25 loss: 0.0751 Lr: 0.00004
[2023-12-25 23:58:15,714 INFO misc.py line 119 253097] Train: [96/100][39/510] Data 0.006 (0.105) Batch 0.999 (1.361) Remain 00:56:58 loss: 0.0759 Lr: 0.00004
[2023-12-25 23:58:16,811 INFO misc.py line 119 253097] Train: [96/100][40/510] Data 0.006 (0.103) Batch 1.096 (1.354) Remain 00:56:38 loss: 0.0963 Lr: 0.00004
[2023-12-25 23:58:18,091 INFO misc.py line 119 253097] Train: [96/100][41/510] Data 0.007 (0.100) Batch 1.279 (1.352) Remain 00:56:32 loss: 0.0837 Lr: 0.00004
[2023-12-25 23:58:19,372 INFO misc.py line 119 253097] Train: [96/100][42/510] Data 0.009 (0.098) Batch 1.282 (1.350) Remain 00:56:26 loss: 0.1571 Lr: 0.00004
[2023-12-25 23:58:20,419 INFO misc.py line 119 253097] Train: [96/100][43/510] Data 0.007 (0.095) Batch 1.050 (1.343) Remain 00:56:06 loss: 0.1108 Lr: 0.00004
[2023-12-25 23:58:21,617 INFO misc.py line 119 253097] Train: [96/100][44/510] Data 0.004 (0.093) Batch 1.196 (1.339) Remain 00:55:56 loss: 0.0700 Lr: 0.00004
[2023-12-25 23:58:22,730 INFO misc.py line 119 253097] Train: [96/100][45/510] Data 0.006 (0.091) Batch 1.113 (1.334) Remain 00:55:41 loss: 0.0768 Lr: 0.00004
[2023-12-25 23:58:23,697 INFO misc.py line 119 253097] Train: [96/100][46/510] Data 0.006 (0.089) Batch 0.970 (1.325) Remain 00:55:18 loss: 0.1232 Lr: 0.00004
[2023-12-25 23:58:24,997 INFO misc.py line 119 253097] Train: [96/100][47/510] Data 0.003 (0.087) Batch 1.292 (1.325) Remain 00:55:15 loss: 0.0939 Lr: 0.00004
[2023-12-25 23:58:26,237 INFO misc.py line 119 253097] Train: [96/100][48/510] Data 0.011 (0.085) Batch 1.244 (1.323) Remain 00:55:09 loss: 0.1409 Lr: 0.00004
[2023-12-25 23:58:27,359 INFO misc.py line 119 253097] Train: [96/100][49/510] Data 0.007 (0.084) Batch 1.126 (1.319) Remain 00:54:57 loss: 0.1400 Lr: 0.00004
[2023-12-25 23:58:28,342 INFO misc.py line 119 253097] Train: [96/100][50/510] Data 0.003 (0.082) Batch 0.984 (1.311) Remain 00:54:38 loss: 0.0656 Lr: 0.00004
[2023-12-25 23:58:29,627 INFO misc.py line 119 253097] Train: [96/100][51/510] Data 0.003 (0.080) Batch 1.280 (1.311) Remain 00:54:35 loss: 0.0826 Lr: 0.00004
[2023-12-25 23:58:32,911 INFO misc.py line 119 253097] Train: [96/100][52/510] Data 0.008 (0.079) Batch 3.290 (1.351) Remain 00:56:15 loss: 0.2195 Lr: 0.00004
[2023-12-25 23:58:34,157 INFO misc.py line 119 253097] Train: [96/100][53/510] Data 0.003 (0.077) Batch 1.239 (1.349) Remain 00:56:08 loss: 0.1553 Lr: 0.00004
[2023-12-25 23:58:35,194 INFO misc.py line 119 253097] Train: [96/100][54/510] Data 0.009 (0.076) Batch 1.038 (1.343) Remain 00:55:51 loss: 0.1116 Lr: 0.00004
[2023-12-25 23:58:36,242 INFO misc.py line 119 253097] Train: [96/100][55/510] Data 0.008 (0.075) Batch 1.049 (1.337) Remain 00:55:36 loss: 0.0901 Lr: 0.00004
[2023-12-25 23:58:37,415 INFO misc.py line 119 253097] Train: [96/100][56/510] Data 0.007 (0.073) Batch 1.173 (1.334) Remain 00:55:27 loss: 0.0777 Lr: 0.00004
[2023-12-25 23:58:38,647 INFO misc.py line 119 253097] Train: [96/100][57/510] Data 0.007 (0.072) Batch 1.231 (1.332) Remain 00:55:21 loss: 0.1034 Lr: 0.00004
[2023-12-25 23:58:39,899 INFO misc.py line 119 253097] Train: [96/100][58/510] Data 0.007 (0.071) Batch 1.251 (1.331) Remain 00:55:16 loss: 0.1053 Lr: 0.00004
[2023-12-25 23:58:45,896 INFO misc.py line 119 253097] Train: [96/100][59/510] Data 0.008 (0.070) Batch 6.000 (1.414) Remain 00:58:42 loss: 0.0855 Lr: 0.00004
[2023-12-25 23:58:47,087 INFO misc.py line 119 253097] Train: [96/100][60/510] Data 0.006 (0.069) Batch 1.193 (1.410) Remain 00:58:31 loss: 0.0736 Lr: 0.00004
[2023-12-25 23:58:48,041 INFO misc.py line 119 253097] Train: [96/100][61/510] Data 0.004 (0.068) Batch 0.953 (1.402) Remain 00:58:10 loss: 0.0721 Lr: 0.00004
[2023-12-25 23:58:49,164 INFO misc.py line 119 253097] Train: [96/100][62/510] Data 0.003 (0.067) Batch 1.124 (1.398) Remain 00:57:57 loss: 0.0914 Lr: 0.00004
[2023-12-25 23:58:50,311 INFO misc.py line 119 253097] Train: [96/100][63/510] Data 0.004 (0.066) Batch 1.147 (1.393) Remain 00:57:45 loss: 0.0831 Lr: 0.00004
[2023-12-25 23:58:51,492 INFO misc.py line 119 253097] Train: [96/100][64/510] Data 0.003 (0.065) Batch 1.181 (1.390) Remain 00:57:35 loss: 0.0798 Lr: 0.00004
[2023-12-25 23:58:52,825 INFO misc.py line 119 253097] Train: [96/100][65/510] Data 0.004 (0.064) Batch 1.333 (1.389) Remain 00:57:31 loss: 0.0947 Lr: 0.00004
[2023-12-25 23:58:53,911 INFO misc.py line 119 253097] Train: [96/100][66/510] Data 0.004 (0.063) Batch 1.084 (1.384) Remain 00:57:18 loss: 0.0885 Lr: 0.00004
[2023-12-25 23:58:55,108 INFO misc.py line 119 253097] Train: [96/100][67/510] Data 0.006 (0.062) Batch 1.199 (1.381) Remain 00:57:09 loss: 0.1732 Lr: 0.00004
[2023-12-25 23:58:56,151 INFO misc.py line 119 253097] Train: [96/100][68/510] Data 0.004 (0.061) Batch 1.044 (1.376) Remain 00:56:55 loss: 0.0642 Lr: 0.00004
[2023-12-25 23:58:57,369 INFO misc.py line 119 253097] Train: [96/100][69/510] Data 0.003 (0.060) Batch 1.213 (1.374) Remain 00:56:47 loss: 0.0979 Lr: 0.00004
[2023-12-25 23:58:58,443 INFO misc.py line 119 253097] Train: [96/100][70/510] Data 0.007 (0.059) Batch 1.076 (1.369) Remain 00:56:35 loss: 0.0943 Lr: 0.00004
[2023-12-25 23:58:59,480 INFO misc.py line 119 253097] Train: [96/100][71/510] Data 0.007 (0.058) Batch 1.035 (1.364) Remain 00:56:22 loss: 0.1061 Lr: 0.00004
[2023-12-25 23:59:10,678 INFO misc.py line 119 253097] Train: [96/100][72/510] Data 0.008 (0.058) Batch 11.199 (1.507) Remain 01:02:13 loss: 0.1298 Lr: 0.00004
[2023-12-25 23:59:11,742 INFO misc.py line 119 253097] Train: [96/100][73/510] Data 0.008 (0.057) Batch 1.064 (1.500) Remain 01:01:56 loss: 0.1424 Lr: 0.00004
[2023-12-25 23:59:13,020 INFO misc.py line 119 253097] Train: [96/100][74/510] Data 0.007 (0.056) Batch 1.278 (1.497) Remain 01:01:47 loss: 0.1230 Lr: 0.00004
[2023-12-25 23:59:14,221 INFO misc.py line 119 253097] Train: [96/100][75/510] Data 0.007 (0.056) Batch 1.202 (1.493) Remain 01:01:35 loss: 0.0552 Lr: 0.00004
[2023-12-25 23:59:15,393 INFO misc.py line 119 253097] Train: [96/100][76/510] Data 0.008 (0.055) Batch 1.176 (1.489) Remain 01:01:23 loss: 0.1024 Lr: 0.00004
[2023-12-25 23:59:16,460 INFO misc.py line 119 253097] Train: [96/100][77/510] Data 0.003 (0.054) Batch 1.036 (1.483) Remain 01:01:06 loss: 0.1452 Lr: 0.00004
[2023-12-25 23:59:17,318 INFO misc.py line 119 253097] Train: [96/100][78/510] Data 0.034 (0.054) Batch 0.889 (1.475) Remain 01:00:45 loss: 0.1672 Lr: 0.00004
[2023-12-25 23:59:18,341 INFO misc.py line 119 253097] Train: [96/100][79/510] Data 0.003 (0.053) Batch 1.023 (1.469) Remain 01:00:29 loss: 0.1731 Lr: 0.00004
[2023-12-25 23:59:19,397 INFO misc.py line 119 253097] Train: [96/100][80/510] Data 0.002 (0.053) Batch 1.056 (1.464) Remain 01:00:14 loss: 0.1226 Lr: 0.00004
[2023-12-25 23:59:20,431 INFO misc.py line 119 253097] Train: [96/100][81/510] Data 0.003 (0.052) Batch 1.034 (1.458) Remain 00:59:59 loss: 0.1634 Lr: 0.00004
[2023-12-25 23:59:21,675 INFO misc.py line 119 253097] Train: [96/100][82/510] Data 0.002 (0.051) Batch 1.240 (1.455) Remain 00:59:51 loss: 0.0872 Lr: 0.00004
[2023-12-25 23:59:22,857 INFO misc.py line 119 253097] Train: [96/100][83/510] Data 0.007 (0.051) Batch 1.167 (1.452) Remain 00:59:41 loss: 0.1167 Lr: 0.00004
[2023-12-25 23:59:24,084 INFO misc.py line 119 253097] Train: [96/100][84/510] Data 0.022 (0.050) Batch 1.242 (1.449) Remain 00:59:33 loss: 0.0663 Lr: 0.00004
[2023-12-25 23:59:25,143 INFO misc.py line 119 253097] Train: [96/100][85/510] Data 0.007 (0.050) Batch 1.059 (1.444) Remain 00:59:20 loss: 0.1167 Lr: 0.00004
[2023-12-25 23:59:26,294 INFO misc.py line 119 253097] Train: [96/100][86/510] Data 0.006 (0.049) Batch 1.154 (1.441) Remain 00:59:10 loss: 0.0935 Lr: 0.00004
[2023-12-25 23:59:27,285 INFO misc.py line 119 253097] Train: [96/100][87/510] Data 0.004 (0.049) Batch 0.991 (1.435) Remain 00:58:55 loss: 0.1006 Lr: 0.00004
[2023-12-25 23:59:28,457 INFO misc.py line 119 253097] Train: [96/100][88/510] Data 0.003 (0.048) Batch 1.172 (1.432) Remain 00:58:46 loss: 0.1451 Lr: 0.00004
[2023-12-25 23:59:29,760 INFO misc.py line 119 253097] Train: [96/100][89/510] Data 0.004 (0.048) Batch 1.301 (1.431) Remain 00:58:41 loss: 0.0609 Lr: 0.00004
[2023-12-25 23:59:30,839 INFO misc.py line 119 253097] Train: [96/100][90/510] Data 0.005 (0.047) Batch 1.077 (1.427) Remain 00:58:29 loss: 0.0768 Lr: 0.00004
[2023-12-25 23:59:31,993 INFO misc.py line 119 253097] Train: [96/100][91/510] Data 0.007 (0.047) Batch 1.154 (1.424) Remain 00:58:20 loss: 0.1047 Lr: 0.00004
[2023-12-25 23:59:39,565 INFO misc.py line 119 253097] Train: [96/100][92/510] Data 0.007 (0.046) Batch 7.560 (1.493) Remain 01:01:08 loss: 0.1587 Lr: 0.00004
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[2023-12-26 00:00:25,090 INFO misc.py line 119 253097] Train: [96/100][132/510] Data 0.003 (0.034) Batch 1.251 (1.383) Remain 00:55:43 loss: 0.0994 Lr: 0.00004
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[2023-12-26 00:00:31,585 INFO misc.py line 119 253097] Train: [96/100][138/510] Data 0.007 (0.032) Batch 0.950 (1.369) Remain 00:55:03 loss: 0.0602 Lr: 0.00004
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[2023-12-26 00:00:34,775 INFO misc.py line 119 253097] Train: [96/100][141/510] Data 0.003 (0.032) Batch 1.088 (1.363) Remain 00:54:43 loss: 0.1111 Lr: 0.00004
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[2023-12-26 00:00:39,276 INFO misc.py line 119 253097] Train: [96/100][145/510] Data 0.003 (0.031) Batch 1.238 (1.356) Remain 00:54:21 loss: 0.0849 Lr: 0.00004
[2023-12-26 00:00:40,540 INFO misc.py line 119 253097] Train: [96/100][146/510] Data 0.006 (0.031) Batch 1.264 (1.355) Remain 00:54:18 loss: 0.0810 Lr: 0.00004
[2023-12-26 00:00:41,726 INFO misc.py line 119 253097] Train: [96/100][147/510] Data 0.007 (0.031) Batch 1.186 (1.354) Remain 00:54:14 loss: 0.0895 Lr: 0.00004
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[2023-12-26 00:00:51,239 INFO misc.py line 119 253097] Train: [96/100][149/510] Data 7.019 (0.078) Batch 8.330 (1.401) Remain 00:56:03 loss: 0.0718 Lr: 0.00004
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[2023-12-26 00:02:10,378 INFO misc.py line 119 253097] Train: [96/100][209/510] Data 0.021 (0.058) Batch 4.788 (1.377) Remain 00:53:43 loss: 0.1626 Lr: 0.00004
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[2023-12-26 00:02:21,970 INFO misc.py line 119 253097] Train: [96/100][219/510] Data 0.007 (0.056) Batch 1.124 (1.367) Remain 00:53:06 loss: 0.0810 Lr: 0.00004
[2023-12-26 00:02:23,245 INFO misc.py line 119 253097] Train: [96/100][220/510] Data 0.007 (0.055) Batch 1.275 (1.367) Remain 00:53:04 loss: 0.0759 Lr: 0.00004
[2023-12-26 00:02:24,378 INFO misc.py line 119 253097] Train: [96/100][221/510] Data 0.007 (0.055) Batch 1.133 (1.365) Remain 00:53:00 loss: 0.1022 Lr: 0.00004
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[2023-12-26 00:02:26,648 INFO misc.py line 119 253097] Train: [96/100][223/510] Data 0.009 (0.055) Batch 1.181 (1.363) Remain 00:52:52 loss: 0.2121 Lr: 0.00004
[2023-12-26 00:02:27,831 INFO misc.py line 119 253097] Train: [96/100][224/510] Data 0.005 (0.055) Batch 1.129 (1.362) Remain 00:52:48 loss: 0.0607 Lr: 0.00004
[2023-12-26 00:02:29,064 INFO misc.py line 119 253097] Train: [96/100][225/510] Data 0.061 (0.055) Batch 1.278 (1.362) Remain 00:52:46 loss: 0.0781 Lr: 0.00004
[2023-12-26 00:02:30,208 INFO misc.py line 119 253097] Train: [96/100][226/510] Data 0.014 (0.054) Batch 1.135 (1.361) Remain 00:52:42 loss: 0.2032 Lr: 0.00004
[2023-12-26 00:02:31,286 INFO misc.py line 119 253097] Train: [96/100][227/510] Data 0.023 (0.054) Batch 1.080 (1.360) Remain 00:52:38 loss: 0.1114 Lr: 0.00004
[2023-12-26 00:02:32,378 INFO misc.py line 119 253097] Train: [96/100][228/510] Data 0.022 (0.054) Batch 1.078 (1.358) Remain 00:52:34 loss: 0.0781 Lr: 0.00004
[2023-12-26 00:02:33,445 INFO misc.py line 119 253097] Train: [96/100][229/510] Data 0.035 (0.054) Batch 1.093 (1.357) Remain 00:52:30 loss: 0.0987 Lr: 0.00004
[2023-12-26 00:02:34,400 INFO misc.py line 119 253097] Train: [96/100][230/510] Data 0.009 (0.054) Batch 0.960 (1.355) Remain 00:52:24 loss: 0.1372 Lr: 0.00003
[2023-12-26 00:02:38,261 INFO misc.py line 119 253097] Train: [96/100][231/510] Data 2.838 (0.066) Batch 3.845 (1.366) Remain 00:52:48 loss: 0.1198 Lr: 0.00003
[2023-12-26 00:02:39,402 INFO misc.py line 119 253097] Train: [96/100][232/510] Data 0.021 (0.066) Batch 1.127 (1.365) Remain 00:52:44 loss: 0.0949 Lr: 0.00003
[2023-12-26 00:02:40,678 INFO misc.py line 119 253097] Train: [96/100][233/510] Data 0.035 (0.066) Batch 1.290 (1.365) Remain 00:52:42 loss: 0.0855 Lr: 0.00003
[2023-12-26 00:02:41,850 INFO misc.py line 119 253097] Train: [96/100][234/510] Data 0.021 (0.066) Batch 1.166 (1.364) Remain 00:52:39 loss: 0.1116 Lr: 0.00003
[2023-12-26 00:02:42,712 INFO misc.py line 119 253097] Train: [96/100][235/510] Data 0.027 (0.065) Batch 0.884 (1.362) Remain 00:52:33 loss: 0.1644 Lr: 0.00003
[2023-12-26 00:02:44,013 INFO misc.py line 119 253097] Train: [96/100][236/510] Data 0.004 (0.065) Batch 1.277 (1.362) Remain 00:52:31 loss: 0.1115 Lr: 0.00003
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[2023-12-26 00:02:48,477 INFO misc.py line 119 253097] Train: [96/100][238/510] Data 0.004 (0.065) Batch 1.131 (1.369) Remain 00:52:45 loss: 0.0709 Lr: 0.00003
[2023-12-26 00:02:49,506 INFO misc.py line 119 253097] Train: [96/100][239/510] Data 0.003 (0.064) Batch 1.029 (1.368) Remain 00:52:40 loss: 0.2147 Lr: 0.00003
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[2023-12-26 00:02:51,833 INFO misc.py line 119 253097] Train: [96/100][241/510] Data 0.003 (0.064) Batch 1.129 (1.366) Remain 00:52:34 loss: 0.1386 Lr: 0.00003
[2023-12-26 00:02:52,821 INFO misc.py line 119 253097] Train: [96/100][242/510] Data 0.019 (0.064) Batch 1.002 (1.364) Remain 00:52:29 loss: 0.1217 Lr: 0.00003
[2023-12-26 00:02:58,093 INFO misc.py line 119 253097] Train: [96/100][243/510] Data 4.065 (0.080) Batch 5.273 (1.381) Remain 00:53:05 loss: 0.1112 Lr: 0.00003
[2023-12-26 00:02:59,420 INFO misc.py line 119 253097] Train: [96/100][244/510] Data 0.004 (0.080) Batch 1.303 (1.380) Remain 00:53:03 loss: 0.0944 Lr: 0.00003
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[2023-12-26 00:03:01,781 INFO misc.py line 119 253097] Train: [96/100][246/510] Data 0.014 (0.080) Batch 1.121 (1.379) Remain 00:52:56 loss: 0.1500 Lr: 0.00003
[2023-12-26 00:03:02,936 INFO misc.py line 119 253097] Train: [96/100][247/510] Data 0.031 (0.079) Batch 1.157 (1.378) Remain 00:52:53 loss: 0.1116 Lr: 0.00003
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[2023-12-26 00:03:06,899 INFO misc.py line 119 253097] Train: [96/100][250/510] Data 0.020 (0.081) Batch 1.069 (1.377) Remain 00:52:47 loss: 0.1160 Lr: 0.00003
[2023-12-26 00:03:07,856 INFO misc.py line 119 253097] Train: [96/100][251/510] Data 0.043 (0.081) Batch 0.996 (1.376) Remain 00:52:42 loss: 0.0634 Lr: 0.00003
[2023-12-26 00:03:08,898 INFO misc.py line 119 253097] Train: [96/100][252/510] Data 0.006 (0.081) Batch 1.038 (1.374) Remain 00:52:38 loss: 0.1735 Lr: 0.00003
[2023-12-26 00:03:10,077 INFO misc.py line 119 253097] Train: [96/100][253/510] Data 0.010 (0.081) Batch 1.183 (1.373) Remain 00:52:34 loss: 0.1163 Lr: 0.00003
[2023-12-26 00:03:11,027 INFO misc.py line 119 253097] Train: [96/100][254/510] Data 0.004 (0.080) Batch 0.951 (1.372) Remain 00:52:29 loss: 0.0801 Lr: 0.00003
[2023-12-26 00:03:12,221 INFO misc.py line 119 253097] Train: [96/100][255/510] Data 0.004 (0.080) Batch 1.192 (1.371) Remain 00:52:26 loss: 0.0836 Lr: 0.00003
[2023-12-26 00:03:13,521 INFO misc.py line 119 253097] Train: [96/100][256/510] Data 0.006 (0.080) Batch 1.282 (1.371) Remain 00:52:24 loss: 0.1069 Lr: 0.00003
[2023-12-26 00:03:14,417 INFO misc.py line 119 253097] Train: [96/100][257/510] Data 0.024 (0.080) Batch 0.910 (1.369) Remain 00:52:18 loss: 0.1237 Lr: 0.00003
[2023-12-26 00:03:15,358 INFO misc.py line 119 253097] Train: [96/100][258/510] Data 0.010 (0.079) Batch 0.947 (1.367) Remain 00:52:13 loss: 0.1776 Lr: 0.00003
[2023-12-26 00:03:16,405 INFO misc.py line 119 253097] Train: [96/100][259/510] Data 0.004 (0.079) Batch 1.048 (1.366) Remain 00:52:09 loss: 0.0952 Lr: 0.00003
[2023-12-26 00:03:21,238 INFO misc.py line 119 253097] Train: [96/100][260/510] Data 3.660 (0.093) Batch 4.822 (1.379) Remain 00:52:38 loss: 0.0776 Lr: 0.00003
[2023-12-26 00:03:22,399 INFO misc.py line 119 253097] Train: [96/100][261/510] Data 0.015 (0.093) Batch 1.171 (1.379) Remain 00:52:35 loss: 0.1635 Lr: 0.00003
[2023-12-26 00:03:23,565 INFO misc.py line 119 253097] Train: [96/100][262/510] Data 0.004 (0.092) Batch 1.166 (1.378) Remain 00:52:32 loss: 0.1014 Lr: 0.00003
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[2023-12-26 00:03:25,950 INFO misc.py line 119 253097] Train: [96/100][264/510] Data 0.018 (0.092) Batch 1.144 (1.376) Remain 00:52:26 loss: 0.1201 Lr: 0.00003
[2023-12-26 00:03:27,102 INFO misc.py line 119 253097] Train: [96/100][265/510] Data 0.018 (0.091) Batch 1.149 (1.375) Remain 00:52:23 loss: 0.1007 Lr: 0.00003
[2023-12-26 00:03:28,307 INFO misc.py line 119 253097] Train: [96/100][266/510] Data 0.021 (0.091) Batch 1.204 (1.375) Remain 00:52:20 loss: 0.1235 Lr: 0.00003
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[2023-12-26 00:03:30,725 INFO misc.py line 119 253097] Train: [96/100][268/510] Data 0.016 (0.091) Batch 1.153 (1.374) Remain 00:52:14 loss: 0.0973 Lr: 0.00003
[2023-12-26 00:03:31,757 INFO misc.py line 119 253097] Train: [96/100][269/510] Data 0.025 (0.090) Batch 1.051 (1.372) Remain 00:52:10 loss: 0.1329 Lr: 0.00003
[2023-12-26 00:03:32,996 INFO misc.py line 119 253097] Train: [96/100][270/510] Data 0.005 (0.090) Batch 1.238 (1.372) Remain 00:52:07 loss: 0.0900 Lr: 0.00003
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[2023-12-26 00:06:54,520 INFO misc.py line 119 253097] Train: [96/100][406/510] Data 0.007 (0.091) Batch 0.964 (1.409) Remain 00:50:20 loss: 0.0776 Lr: 0.00003
[2023-12-26 00:06:55,726 INFO misc.py line 119 253097] Train: [96/100][407/510] Data 0.007 (0.091) Batch 1.205 (1.408) Remain 00:50:18 loss: 0.1335 Lr: 0.00003
[2023-12-26 00:06:56,927 INFO misc.py line 119 253097] Train: [96/100][408/510] Data 0.007 (0.091) Batch 1.202 (1.408) Remain 00:50:15 loss: 0.1107 Lr: 0.00003
[2023-12-26 00:06:58,103 INFO misc.py line 119 253097] Train: [96/100][409/510] Data 0.006 (0.090) Batch 1.175 (1.407) Remain 00:50:13 loss: 0.2110 Lr: 0.00003
[2023-12-26 00:06:59,126 INFO misc.py line 119 253097] Train: [96/100][410/510] Data 0.006 (0.090) Batch 1.027 (1.406) Remain 00:50:09 loss: 0.1676 Lr: 0.00003
[2023-12-26 00:07:03,810 INFO misc.py line 119 253097] Train: [96/100][411/510] Data 3.603 (0.099) Batch 4.684 (1.414) Remain 00:50:25 loss: 0.0735 Lr: 0.00003
[2023-12-26 00:07:05,041 INFO misc.py line 119 253097] Train: [96/100][412/510] Data 0.003 (0.099) Batch 1.231 (1.414) Remain 00:50:23 loss: 0.1590 Lr: 0.00003
[2023-12-26 00:07:06,279 INFO misc.py line 119 253097] Train: [96/100][413/510] Data 0.003 (0.098) Batch 1.234 (1.414) Remain 00:50:20 loss: 0.0956 Lr: 0.00003
[2023-12-26 00:07:07,398 INFO misc.py line 119 253097] Train: [96/100][414/510] Data 0.006 (0.098) Batch 1.120 (1.413) Remain 00:50:17 loss: 0.2460 Lr: 0.00003
[2023-12-26 00:07:08,676 INFO misc.py line 119 253097] Train: [96/100][415/510] Data 0.006 (0.098) Batch 1.281 (1.413) Remain 00:50:15 loss: 0.1687 Lr: 0.00003
[2023-12-26 00:07:09,798 INFO misc.py line 119 253097] Train: [96/100][416/510] Data 0.003 (0.098) Batch 1.121 (1.412) Remain 00:50:12 loss: 0.1015 Lr: 0.00003
[2023-12-26 00:07:10,914 INFO misc.py line 119 253097] Train: [96/100][417/510] Data 0.004 (0.097) Batch 1.117 (1.411) Remain 00:50:09 loss: 0.2083 Lr: 0.00003
[2023-12-26 00:07:12,135 INFO misc.py line 119 253097] Train: [96/100][418/510] Data 0.003 (0.097) Batch 1.220 (1.411) Remain 00:50:07 loss: 0.1203 Lr: 0.00003
[2023-12-26 00:07:13,097 INFO misc.py line 119 253097] Train: [96/100][419/510] Data 0.004 (0.097) Batch 0.962 (1.410) Remain 00:50:03 loss: 0.0845 Lr: 0.00003
[2023-12-26 00:07:16,316 INFO misc.py line 119 253097] Train: [96/100][420/510] Data 1.988 (0.102) Batch 3.220 (1.414) Remain 00:50:11 loss: 0.0701 Lr: 0.00003
[2023-12-26 00:07:24,712 INFO misc.py line 119 253097] Train: [96/100][421/510] Data 0.004 (0.101) Batch 8.396 (1.431) Remain 00:50:45 loss: 0.1029 Lr: 0.00003
[2023-12-26 00:07:25,869 INFO misc.py line 119 253097] Train: [96/100][422/510] Data 0.003 (0.101) Batch 1.153 (1.430) Remain 00:50:42 loss: 0.0608 Lr: 0.00003
[2023-12-26 00:07:27,159 INFO misc.py line 119 253097] Train: [96/100][423/510] Data 0.006 (0.101) Batch 1.290 (1.430) Remain 00:50:40 loss: 0.1434 Lr: 0.00003
[2023-12-26 00:07:28,335 INFO misc.py line 119 253097] Train: [96/100][424/510] Data 0.006 (0.101) Batch 1.178 (1.429) Remain 00:50:38 loss: 0.0778 Lr: 0.00003
[2023-12-26 00:07:29,601 INFO misc.py line 119 253097] Train: [96/100][425/510] Data 0.004 (0.100) Batch 1.266 (1.429) Remain 00:50:35 loss: 0.0851 Lr: 0.00003
[2023-12-26 00:07:30,614 INFO misc.py line 119 253097] Train: [96/100][426/510] Data 0.003 (0.100) Batch 1.009 (1.428) Remain 00:50:32 loss: 0.0976 Lr: 0.00003
[2023-12-26 00:07:31,806 INFO misc.py line 119 253097] Train: [96/100][427/510] Data 0.007 (0.100) Batch 1.191 (1.427) Remain 00:50:29 loss: 0.0849 Lr: 0.00003
[2023-12-26 00:07:32,843 INFO misc.py line 119 253097] Train: [96/100][428/510] Data 0.007 (0.100) Batch 1.020 (1.426) Remain 00:50:26 loss: 0.2090 Lr: 0.00003
[2023-12-26 00:07:33,959 INFO misc.py line 119 253097] Train: [96/100][429/510] Data 0.024 (0.100) Batch 1.138 (1.425) Remain 00:50:23 loss: 0.0945 Lr: 0.00003
[2023-12-26 00:07:35,151 INFO misc.py line 119 253097] Train: [96/100][430/510] Data 0.003 (0.099) Batch 1.191 (1.425) Remain 00:50:20 loss: 0.1920 Lr: 0.00003
[2023-12-26 00:07:36,238 INFO misc.py line 119 253097] Train: [96/100][431/510] Data 0.003 (0.099) Batch 1.086 (1.424) Remain 00:50:17 loss: 0.0906 Lr: 0.00003
[2023-12-26 00:07:37,262 INFO misc.py line 119 253097] Train: [96/100][432/510] Data 0.005 (0.099) Batch 1.026 (1.423) Remain 00:50:14 loss: 0.0903 Lr: 0.00003
[2023-12-26 00:07:38,133 INFO misc.py line 119 253097] Train: [96/100][433/510] Data 0.004 (0.099) Batch 0.871 (1.422) Remain 00:50:10 loss: 0.1206 Lr: 0.00003
[2023-12-26 00:07:39,233 INFO misc.py line 119 253097] Train: [96/100][434/510] Data 0.004 (0.098) Batch 1.100 (1.421) Remain 00:50:07 loss: 0.1670 Lr: 0.00003
[2023-12-26 00:07:40,334 INFO misc.py line 119 253097] Train: [96/100][435/510] Data 0.004 (0.098) Batch 1.102 (1.420) Remain 00:50:04 loss: 0.1170 Lr: 0.00003
[2023-12-26 00:07:41,400 INFO misc.py line 119 253097] Train: [96/100][436/510] Data 0.003 (0.098) Batch 1.065 (1.420) Remain 00:50:01 loss: 0.1405 Lr: 0.00003
[2023-12-26 00:07:42,384 INFO misc.py line 119 253097] Train: [96/100][437/510] Data 0.004 (0.098) Batch 0.985 (1.419) Remain 00:49:57 loss: 0.0839 Lr: 0.00003
[2023-12-26 00:07:43,626 INFO misc.py line 119 253097] Train: [96/100][438/510] Data 0.003 (0.098) Batch 1.237 (1.418) Remain 00:49:55 loss: 0.1199 Lr: 0.00003
[2023-12-26 00:07:46,322 INFO misc.py line 119 253097] Train: [96/100][439/510] Data 0.007 (0.097) Batch 2.700 (1.421) Remain 00:50:00 loss: 0.1475 Lr: 0.00003
[2023-12-26 00:07:47,608 INFO misc.py line 119 253097] Train: [96/100][440/510] Data 0.003 (0.097) Batch 1.286 (1.421) Remain 00:49:57 loss: 0.1098 Lr: 0.00003
[2023-12-26 00:07:48,885 INFO misc.py line 119 253097] Train: [96/100][441/510] Data 0.004 (0.097) Batch 1.274 (1.420) Remain 00:49:55 loss: 0.0683 Lr: 0.00003
[2023-12-26 00:07:49,951 INFO misc.py line 119 253097] Train: [96/100][442/510] Data 0.006 (0.097) Batch 1.069 (1.420) Remain 00:49:52 loss: 0.0802 Lr: 0.00003
[2023-12-26 00:07:51,251 INFO misc.py line 119 253097] Train: [96/100][443/510] Data 0.003 (0.096) Batch 1.299 (1.419) Remain 00:49:50 loss: 0.1958 Lr: 0.00003
[2023-12-26 00:07:52,466 INFO misc.py line 119 253097] Train: [96/100][444/510] Data 0.003 (0.096) Batch 1.210 (1.419) Remain 00:49:48 loss: 0.0894 Lr: 0.00003
[2023-12-26 00:07:53,639 INFO misc.py line 119 253097] Train: [96/100][445/510] Data 0.008 (0.096) Batch 1.175 (1.418) Remain 00:49:45 loss: 0.1177 Lr: 0.00003
[2023-12-26 00:07:54,758 INFO misc.py line 119 253097] Train: [96/100][446/510] Data 0.006 (0.096) Batch 1.122 (1.418) Remain 00:49:42 loss: 0.1966 Lr: 0.00003
[2023-12-26 00:07:55,844 INFO misc.py line 119 253097] Train: [96/100][447/510] Data 0.004 (0.096) Batch 1.084 (1.417) Remain 00:49:39 loss: 0.1394 Lr: 0.00003
[2023-12-26 00:07:57,122 INFO misc.py line 119 253097] Train: [96/100][448/510] Data 0.006 (0.095) Batch 1.280 (1.417) Remain 00:49:37 loss: 0.0562 Lr: 0.00003
[2023-12-26 00:07:58,303 INFO misc.py line 119 253097] Train: [96/100][449/510] Data 0.004 (0.095) Batch 1.178 (1.416) Remain 00:49:35 loss: 0.0909 Lr: 0.00003
[2023-12-26 00:07:59,214 INFO misc.py line 119 253097] Train: [96/100][450/510] Data 0.008 (0.095) Batch 0.916 (1.415) Remain 00:49:31 loss: 0.0771 Lr: 0.00003
[2023-12-26 00:08:04,719 INFO misc.py line 119 253097] Train: [96/100][451/510] Data 0.003 (0.095) Batch 5.504 (1.424) Remain 00:49:49 loss: 0.0896 Lr: 0.00003
[2023-12-26 00:08:05,872 INFO misc.py line 119 253097] Train: [96/100][452/510] Data 0.004 (0.095) Batch 1.153 (1.424) Remain 00:49:46 loss: 0.1082 Lr: 0.00003
[2023-12-26 00:08:07,220 INFO misc.py line 119 253097] Train: [96/100][453/510] Data 0.003 (0.094) Batch 1.343 (1.423) Remain 00:49:44 loss: 0.1076 Lr: 0.00003
[2023-12-26 00:08:08,439 INFO misc.py line 119 253097] Train: [96/100][454/510] Data 0.008 (0.094) Batch 1.224 (1.423) Remain 00:49:42 loss: 0.0605 Lr: 0.00003
[2023-12-26 00:08:09,539 INFO misc.py line 119 253097] Train: [96/100][455/510] Data 0.004 (0.094) Batch 1.098 (1.422) Remain 00:49:39 loss: 0.1989 Lr: 0.00003
[2023-12-26 00:08:10,666 INFO misc.py line 119 253097] Train: [96/100][456/510] Data 0.006 (0.094) Batch 1.125 (1.422) Remain 00:49:36 loss: 0.0719 Lr: 0.00003
[2023-12-26 00:08:11,713 INFO misc.py line 119 253097] Train: [96/100][457/510] Data 0.008 (0.094) Batch 1.047 (1.421) Remain 00:49:33 loss: 0.2053 Lr: 0.00003
[2023-12-26 00:08:12,862 INFO misc.py line 119 253097] Train: [96/100][458/510] Data 0.007 (0.093) Batch 1.151 (1.420) Remain 00:49:30 loss: 0.1559 Lr: 0.00003
[2023-12-26 00:08:13,994 INFO misc.py line 119 253097] Train: [96/100][459/510] Data 0.007 (0.093) Batch 1.131 (1.419) Remain 00:49:28 loss: 0.0634 Lr: 0.00003
[2023-12-26 00:08:15,006 INFO misc.py line 119 253097] Train: [96/100][460/510] Data 0.007 (0.093) Batch 1.012 (1.419) Remain 00:49:24 loss: 0.0888 Lr: 0.00003
[2023-12-26 00:08:16,147 INFO misc.py line 119 253097] Train: [96/100][461/510] Data 0.007 (0.093) Batch 1.145 (1.418) Remain 00:49:22 loss: 0.1343 Lr: 0.00003
[2023-12-26 00:08:17,338 INFO misc.py line 119 253097] Train: [96/100][462/510] Data 0.004 (0.093) Batch 1.188 (1.417) Remain 00:49:19 loss: 0.0607 Lr: 0.00003
[2023-12-26 00:08:18,427 INFO misc.py line 119 253097] Train: [96/100][463/510] Data 0.006 (0.093) Batch 1.089 (1.417) Remain 00:49:16 loss: 0.1515 Lr: 0.00003
[2023-12-26 00:08:19,623 INFO misc.py line 119 253097] Train: [96/100][464/510] Data 0.005 (0.092) Batch 1.197 (1.416) Remain 00:49:14 loss: 0.0742 Lr: 0.00003
[2023-12-26 00:08:20,825 INFO misc.py line 119 253097] Train: [96/100][465/510] Data 0.005 (0.092) Batch 1.199 (1.416) Remain 00:49:12 loss: 0.1143 Lr: 0.00003
[2023-12-26 00:08:21,925 INFO misc.py line 119 253097] Train: [96/100][466/510] Data 0.007 (0.092) Batch 1.101 (1.415) Remain 00:49:09 loss: 0.0598 Lr: 0.00003
[2023-12-26 00:08:22,945 INFO misc.py line 119 253097] Train: [96/100][467/510] Data 0.006 (0.092) Batch 1.023 (1.414) Remain 00:49:06 loss: 0.0918 Lr: 0.00003
[2023-12-26 00:08:24,111 INFO misc.py line 119 253097] Train: [96/100][468/510] Data 0.003 (0.092) Batch 1.162 (1.414) Remain 00:49:03 loss: 0.1251 Lr: 0.00003
[2023-12-26 00:08:25,305 INFO misc.py line 119 253097] Train: [96/100][469/510] Data 0.006 (0.091) Batch 1.197 (1.413) Remain 00:49:01 loss: 0.0704 Lr: 0.00003
[2023-12-26 00:08:26,511 INFO misc.py line 119 253097] Train: [96/100][470/510] Data 0.003 (0.091) Batch 1.185 (1.413) Remain 00:48:58 loss: 0.1163 Lr: 0.00003
[2023-12-26 00:08:27,704 INFO misc.py line 119 253097] Train: [96/100][471/510] Data 0.024 (0.091) Batch 1.211 (1.412) Remain 00:48:56 loss: 0.1803 Lr: 0.00003
[2023-12-26 00:08:28,866 INFO misc.py line 119 253097] Train: [96/100][472/510] Data 0.007 (0.091) Batch 1.161 (1.412) Remain 00:48:53 loss: 0.1369 Lr: 0.00003
[2023-12-26 00:08:29,844 INFO misc.py line 119 253097] Train: [96/100][473/510] Data 0.008 (0.091) Batch 0.982 (1.411) Remain 00:48:50 loss: 0.1017 Lr: 0.00003
[2023-12-26 00:08:31,083 INFO misc.py line 119 253097] Train: [96/100][474/510] Data 0.003 (0.091) Batch 1.239 (1.411) Remain 00:48:48 loss: 0.1274 Lr: 0.00003
[2023-12-26 00:08:32,296 INFO misc.py line 119 253097] Train: [96/100][475/510] Data 0.004 (0.090) Batch 1.209 (1.410) Remain 00:48:46 loss: 0.1177 Lr: 0.00003
[2023-12-26 00:08:33,344 INFO misc.py line 119 253097] Train: [96/100][476/510] Data 0.007 (0.090) Batch 1.047 (1.409) Remain 00:48:43 loss: 0.1030 Lr: 0.00003
[2023-12-26 00:08:34,425 INFO misc.py line 119 253097] Train: [96/100][477/510] Data 0.010 (0.090) Batch 1.085 (1.409) Remain 00:48:40 loss: 0.1158 Lr: 0.00003
[2023-12-26 00:08:35,482 INFO misc.py line 119 253097] Train: [96/100][478/510] Data 0.004 (0.090) Batch 1.057 (1.408) Remain 00:48:37 loss: 0.1780 Lr: 0.00003
[2023-12-26 00:08:36,443 INFO misc.py line 119 253097] Train: [96/100][479/510] Data 0.004 (0.090) Batch 0.961 (1.407) Remain 00:48:33 loss: 0.0973 Lr: 0.00003
[2023-12-26 00:08:37,719 INFO misc.py line 119 253097] Train: [96/100][480/510] Data 0.004 (0.089) Batch 1.276 (1.407) Remain 00:48:31 loss: 0.1779 Lr: 0.00003
[2023-12-26 00:08:39,013 INFO misc.py line 119 253097] Train: [96/100][481/510] Data 0.004 (0.089) Batch 1.294 (1.407) Remain 00:48:30 loss: 0.0997 Lr: 0.00003
[2023-12-26 00:08:40,019 INFO misc.py line 119 253097] Train: [96/100][482/510] Data 0.004 (0.089) Batch 1.006 (1.406) Remain 00:48:26 loss: 0.1043 Lr: 0.00003
[2023-12-26 00:08:41,056 INFO misc.py line 119 253097] Train: [96/100][483/510] Data 0.005 (0.089) Batch 1.036 (1.405) Remain 00:48:23 loss: 0.1591 Lr: 0.00003
[2023-12-26 00:08:47,852 INFO misc.py line 119 253097] Train: [96/100][484/510] Data 5.744 (0.101) Batch 6.798 (1.416) Remain 00:48:45 loss: 0.0659 Lr: 0.00003
[2023-12-26 00:08:48,968 INFO misc.py line 119 253097] Train: [96/100][485/510] Data 0.002 (0.100) Batch 1.116 (1.415) Remain 00:48:42 loss: 0.0816 Lr: 0.00003
[2023-12-26 00:08:50,207 INFO misc.py line 119 253097] Train: [96/100][486/510] Data 0.003 (0.100) Batch 1.235 (1.415) Remain 00:48:40 loss: 0.0862 Lr: 0.00003
[2023-12-26 00:08:51,339 INFO misc.py line 119 253097] Train: [96/100][487/510] Data 0.006 (0.100) Batch 1.133 (1.415) Remain 00:48:38 loss: 0.0654 Lr: 0.00003
[2023-12-26 00:08:52,427 INFO misc.py line 119 253097] Train: [96/100][488/510] Data 0.006 (0.100) Batch 1.087 (1.414) Remain 00:48:35 loss: 0.0945 Lr: 0.00003
[2023-12-26 00:08:53,465 INFO misc.py line 119 253097] Train: [96/100][489/510] Data 0.007 (0.100) Batch 1.040 (1.413) Remain 00:48:32 loss: 0.1356 Lr: 0.00003
[2023-12-26 00:08:54,670 INFO misc.py line 119 253097] Train: [96/100][490/510] Data 0.005 (0.100) Batch 1.203 (1.413) Remain 00:48:30 loss: 0.1242 Lr: 0.00003
[2023-12-26 00:08:55,757 INFO misc.py line 119 253097] Train: [96/100][491/510] Data 0.008 (0.099) Batch 1.088 (1.412) Remain 00:48:27 loss: 0.0961 Lr: 0.00003
[2023-12-26 00:08:56,891 INFO misc.py line 119 253097] Train: [96/100][492/510] Data 0.006 (0.099) Batch 1.123 (1.411) Remain 00:48:24 loss: 0.1295 Lr: 0.00003
[2023-12-26 00:08:58,100 INFO misc.py line 119 253097] Train: [96/100][493/510] Data 0.018 (0.099) Batch 1.221 (1.411) Remain 00:48:22 loss: 0.1021 Lr: 0.00003
[2023-12-26 00:08:59,244 INFO misc.py line 119 253097] Train: [96/100][494/510] Data 0.005 (0.099) Batch 1.143 (1.410) Remain 00:48:19 loss: 0.0776 Lr: 0.00003
[2023-12-26 00:09:00,509 INFO misc.py line 119 253097] Train: [96/100][495/510] Data 0.006 (0.099) Batch 1.269 (1.410) Remain 00:48:17 loss: 0.0803 Lr: 0.00003
[2023-12-26 00:09:01,727 INFO misc.py line 119 253097] Train: [96/100][496/510] Data 0.002 (0.098) Batch 1.217 (1.410) Remain 00:48:15 loss: 0.1219 Lr: 0.00003
[2023-12-26 00:09:02,919 INFO misc.py line 119 253097] Train: [96/100][497/510] Data 0.003 (0.098) Batch 1.190 (1.409) Remain 00:48:13 loss: 0.1330 Lr: 0.00003
[2023-12-26 00:09:03,966 INFO misc.py line 119 253097] Train: [96/100][498/510] Data 0.005 (0.098) Batch 1.048 (1.409) Remain 00:48:10 loss: 0.0938 Lr: 0.00003
[2023-12-26 00:09:05,031 INFO misc.py line 119 253097] Train: [96/100][499/510] Data 0.004 (0.098) Batch 1.063 (1.408) Remain 00:48:07 loss: 0.2609 Lr: 0.00003
[2023-12-26 00:09:06,038 INFO misc.py line 119 253097] Train: [96/100][500/510] Data 0.006 (0.098) Batch 1.007 (1.407) Remain 00:48:04 loss: 0.1111 Lr: 0.00003
[2023-12-26 00:09:07,269 INFO misc.py line 119 253097] Train: [96/100][501/510] Data 0.006 (0.097) Batch 1.230 (1.407) Remain 00:48:02 loss: 0.1345 Lr: 0.00003
[2023-12-26 00:09:09,621 INFO misc.py line 119 253097] Train: [96/100][502/510] Data 0.007 (0.097) Batch 2.356 (1.409) Remain 00:48:04 loss: 0.1047 Lr: 0.00003
[2023-12-26 00:09:10,900 INFO misc.py line 119 253097] Train: [96/100][503/510] Data 0.003 (0.097) Batch 1.276 (1.408) Remain 00:48:02 loss: 0.1061 Lr: 0.00003
[2023-12-26 00:09:12,044 INFO misc.py line 119 253097] Train: [96/100][504/510] Data 0.005 (0.097) Batch 1.140 (1.408) Remain 00:48:00 loss: 0.0940 Lr: 0.00003
[2023-12-26 00:09:12,924 INFO misc.py line 119 253097] Train: [96/100][505/510] Data 0.009 (0.097) Batch 0.887 (1.407) Remain 00:47:56 loss: 0.1075 Lr: 0.00003
[2023-12-26 00:09:14,154 INFO misc.py line 119 253097] Train: [96/100][506/510] Data 0.002 (0.097) Batch 1.228 (1.406) Remain 00:47:54 loss: 0.1096 Lr: 0.00003
[2023-12-26 00:09:15,355 INFO misc.py line 119 253097] Train: [96/100][507/510] Data 0.005 (0.096) Batch 1.198 (1.406) Remain 00:47:52 loss: 0.1071 Lr: 0.00003
[2023-12-26 00:09:16,382 INFO misc.py line 119 253097] Train: [96/100][508/510] Data 0.007 (0.096) Batch 1.031 (1.405) Remain 00:47:49 loss: 0.1137 Lr: 0.00003
[2023-12-26 00:09:17,573 INFO misc.py line 119 253097] Train: [96/100][509/510] Data 0.003 (0.096) Batch 1.189 (1.405) Remain 00:47:47 loss: 0.0840 Lr: 0.00003
[2023-12-26 00:09:18,582 INFO misc.py line 119 253097] Train: [96/100][510/510] Data 0.005 (0.096) Batch 1.009 (1.404) Remain 00:47:44 loss: 0.1076 Lr: 0.00003
[2023-12-26 00:09:18,582 INFO misc.py line 136 253097] Train result: loss: 0.1122 
[2023-12-26 00:09:18,583 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-26 00:09:52,852 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5935 
[2023-12-26 00:09:53,204 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3331 
[2023-12-26 00:09:58,148 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.2959 
[2023-12-26 00:09:58,665 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3806 
[2023-12-26 00:10:00,651 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.9152 
[2023-12-26 00:10:01,075 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3716 
[2023-12-26 00:10:01,953 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1152 
[2023-12-26 00:10:02,504 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2464 
[2023-12-26 00:10:04,315 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8667 
[2023-12-26 00:10:06,442 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.0790 
[2023-12-26 00:10:07,296 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3315 
[2023-12-26 00:10:07,720 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7280 
[2023-12-26 00:10:08,622 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3360 
[2023-12-26 00:10:11,566 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8707 
[2023-12-26 00:10:12,041 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2420 
[2023-12-26 00:10:12,648 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3923 
[2023-12-26 00:10:13,347 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3858 
[2023-12-26 00:10:14,670 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6989/0.7543/0.9071.
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9185/0.9478
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9834/0.9903
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8471/0.9720
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3423/0.3726
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6333/0.6536
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7163/0.8059
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8109/0.8991
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9159/0.9574
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6908/0.7349
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7869/0.8744
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8201/0.8604
[2023-12-26 00:10:14,670 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6205/0.7374
[2023-12-26 00:10:14,671 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-26 00:10:14,672 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-26 00:10:14,672 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-26 00:10:22,979 INFO misc.py line 119 253097] Train: [97/100][1/510] Data 3.313 (3.313) Batch 6.584 (6.584) Remain 03:43:45 loss: 0.1213 Lr: 0.00003
[2023-12-26 00:10:26,336 INFO misc.py line 119 253097] Train: [97/100][2/510] Data 0.003 (0.003) Batch 3.357 (3.357) Remain 01:54:00 loss: 0.0666 Lr: 0.00003
[2023-12-26 00:10:42,475 INFO misc.py line 119 253097] Train: [97/100][3/510] Data 14.810 (14.810) Batch 16.140 (16.140) Remain 09:07:56 loss: 0.2283 Lr: 0.00003
[2023-12-26 00:10:43,426 INFO misc.py line 119 253097] Train: [97/100][4/510] Data 0.003 (0.003) Batch 0.951 (0.951) Remain 00:32:15 loss: 0.1768 Lr: 0.00003
[2023-12-26 00:10:44,399 INFO misc.py line 119 253097] Train: [97/100][5/510] Data 0.003 (0.003) Batch 0.973 (0.962) Remain 00:32:37 loss: 0.2200 Lr: 0.00003
[2023-12-26 00:10:45,405 INFO misc.py line 119 253097] Train: [97/100][6/510] Data 0.003 (0.003) Batch 1.005 (0.976) Remain 00:33:06 loss: 0.0815 Lr: 0.00003
[2023-12-26 00:10:46,594 INFO misc.py line 119 253097] Train: [97/100][7/510] Data 0.002 (0.003) Batch 1.189 (1.030) Remain 00:34:53 loss: 0.1492 Lr: 0.00003
[2023-12-26 00:10:47,650 INFO misc.py line 119 253097] Train: [97/100][8/510] Data 0.003 (0.003) Batch 1.055 (1.035) Remain 00:35:02 loss: 0.1066 Lr: 0.00003
[2023-12-26 00:10:48,923 INFO misc.py line 119 253097] Train: [97/100][9/510] Data 0.003 (0.003) Batch 1.272 (1.074) Remain 00:36:21 loss: 0.0854 Lr: 0.00003
[2023-12-26 00:10:50,169 INFO misc.py line 119 253097] Train: [97/100][10/510] Data 0.005 (0.003) Batch 1.245 (1.099) Remain 00:37:10 loss: 0.0797 Lr: 0.00003
[2023-12-26 00:10:51,488 INFO misc.py line 119 253097] Train: [97/100][11/510] Data 0.006 (0.003) Batch 1.312 (1.125) Remain 00:38:03 loss: 0.0754 Lr: 0.00003
[2023-12-26 00:10:52,651 INFO misc.py line 119 253097] Train: [97/100][12/510] Data 0.013 (0.004) Batch 1.173 (1.131) Remain 00:38:12 loss: 0.1175 Lr: 0.00003
[2023-12-26 00:10:53,981 INFO misc.py line 119 253097] Train: [97/100][13/510] Data 0.005 (0.005) Batch 1.325 (1.150) Remain 00:38:51 loss: 0.0742 Lr: 0.00003
[2023-12-26 00:10:55,088 INFO misc.py line 119 253097] Train: [97/100][14/510] Data 0.007 (0.005) Batch 1.108 (1.146) Remain 00:38:42 loss: 0.0843 Lr: 0.00003
[2023-12-26 00:10:56,255 INFO misc.py line 119 253097] Train: [97/100][15/510] Data 0.006 (0.005) Batch 1.166 (1.148) Remain 00:38:44 loss: 0.1196 Lr: 0.00003
[2023-12-26 00:10:57,453 INFO misc.py line 119 253097] Train: [97/100][16/510] Data 0.007 (0.005) Batch 1.201 (1.152) Remain 00:38:51 loss: 0.0884 Lr: 0.00003
[2023-12-26 00:10:58,433 INFO misc.py line 119 253097] Train: [97/100][17/510] Data 0.004 (0.005) Batch 0.979 (1.140) Remain 00:38:25 loss: 0.1420 Lr: 0.00003
[2023-12-26 00:10:59,751 INFO misc.py line 119 253097] Train: [97/100][18/510] Data 0.004 (0.005) Batch 1.316 (1.151) Remain 00:38:48 loss: 0.1231 Lr: 0.00003
[2023-12-26 00:11:00,856 INFO misc.py line 119 253097] Train: [97/100][19/510] Data 0.007 (0.005) Batch 1.104 (1.148) Remain 00:38:41 loss: 0.0782 Lr: 0.00003
[2023-12-26 00:11:01,946 INFO misc.py line 119 253097] Train: [97/100][20/510] Data 0.008 (0.005) Batch 1.089 (1.145) Remain 00:38:32 loss: 0.0595 Lr: 0.00003
[2023-12-26 00:11:03,234 INFO misc.py line 119 253097] Train: [97/100][21/510] Data 0.009 (0.005) Batch 1.291 (1.153) Remain 00:38:48 loss: 0.0986 Lr: 0.00003
[2023-12-26 00:11:04,198 INFO misc.py line 119 253097] Train: [97/100][22/510] Data 0.005 (0.005) Batch 0.965 (1.143) Remain 00:38:27 loss: 0.0813 Lr: 0.00003
[2023-12-26 00:11:06,685 INFO misc.py line 119 253097] Train: [97/100][23/510] Data 0.004 (0.005) Batch 2.487 (1.210) Remain 00:40:41 loss: 0.1051 Lr: 0.00003
[2023-12-26 00:11:07,949 INFO misc.py line 119 253097] Train: [97/100][24/510] Data 0.004 (0.005) Batch 1.265 (1.213) Remain 00:40:45 loss: 0.0958 Lr: 0.00003
[2023-12-26 00:11:09,241 INFO misc.py line 119 253097] Train: [97/100][25/510] Data 0.003 (0.005) Batch 1.287 (1.216) Remain 00:40:51 loss: 0.1128 Lr: 0.00003
[2023-12-26 00:11:10,402 INFO misc.py line 119 253097] Train: [97/100][26/510] Data 0.008 (0.005) Batch 1.164 (1.214) Remain 00:40:45 loss: 0.1512 Lr: 0.00003
[2023-12-26 00:11:11,465 INFO misc.py line 119 253097] Train: [97/100][27/510] Data 0.004 (0.005) Batch 1.061 (1.208) Remain 00:40:31 loss: 0.1035 Lr: 0.00003
[2023-12-26 00:11:12,666 INFO misc.py line 119 253097] Train: [97/100][28/510] Data 0.007 (0.005) Batch 1.204 (1.208) Remain 00:40:29 loss: 0.1069 Lr: 0.00003
[2023-12-26 00:11:13,870 INFO misc.py line 119 253097] Train: [97/100][29/510] Data 0.004 (0.005) Batch 1.205 (1.207) Remain 00:40:28 loss: 0.1092 Lr: 0.00003
[2023-12-26 00:11:14,890 INFO misc.py line 119 253097] Train: [97/100][30/510] Data 0.003 (0.005) Batch 1.015 (1.200) Remain 00:40:12 loss: 0.1114 Lr: 0.00003
[2023-12-26 00:11:15,912 INFO misc.py line 119 253097] Train: [97/100][31/510] Data 0.007 (0.005) Batch 1.027 (1.194) Remain 00:39:59 loss: 0.1608 Lr: 0.00003
[2023-12-26 00:11:23,475 INFO misc.py line 119 253097] Train: [97/100][32/510] Data 0.003 (0.005) Batch 7.562 (1.414) Remain 00:47:18 loss: 0.0592 Lr: 0.00003
[2023-12-26 00:11:24,489 INFO misc.py line 119 253097] Train: [97/100][33/510] Data 0.004 (0.005) Batch 1.014 (1.400) Remain 00:46:50 loss: 0.1165 Lr: 0.00003
[2023-12-26 00:11:25,679 INFO misc.py line 119 253097] Train: [97/100][34/510] Data 0.004 (0.005) Batch 1.190 (1.394) Remain 00:46:35 loss: 0.0687 Lr: 0.00003
[2023-12-26 00:11:26,724 INFO misc.py line 119 253097] Train: [97/100][35/510] Data 0.004 (0.005) Batch 1.045 (1.383) Remain 00:46:12 loss: 0.0733 Lr: 0.00003
[2023-12-26 00:11:27,608 INFO misc.py line 119 253097] Train: [97/100][36/510] Data 0.003 (0.005) Batch 0.884 (1.368) Remain 00:45:40 loss: 0.0969 Lr: 0.00003
[2023-12-26 00:11:28,637 INFO misc.py line 119 253097] Train: [97/100][37/510] Data 0.003 (0.005) Batch 1.028 (1.358) Remain 00:45:19 loss: 0.0827 Lr: 0.00003
[2023-12-26 00:11:29,754 INFO misc.py line 119 253097] Train: [97/100][38/510] Data 0.005 (0.005) Batch 1.114 (1.351) Remain 00:45:04 loss: 0.1318 Lr: 0.00003
[2023-12-26 00:11:30,939 INFO misc.py line 119 253097] Train: [97/100][39/510] Data 0.007 (0.005) Batch 1.181 (1.346) Remain 00:44:53 loss: 0.0926 Lr: 0.00003
[2023-12-26 00:11:32,092 INFO misc.py line 119 253097] Train: [97/100][40/510] Data 0.011 (0.005) Batch 1.161 (1.341) Remain 00:44:41 loss: 0.0472 Lr: 0.00003
[2023-12-26 00:11:33,370 INFO misc.py line 119 253097] Train: [97/100][41/510] Data 0.004 (0.005) Batch 1.273 (1.339) Remain 00:44:37 loss: 0.1193 Lr: 0.00003
[2023-12-26 00:11:34,520 INFO misc.py line 119 253097] Train: [97/100][42/510] Data 0.008 (0.005) Batch 1.156 (1.334) Remain 00:44:26 loss: 0.1674 Lr: 0.00003
[2023-12-26 00:11:35,751 INFO misc.py line 119 253097] Train: [97/100][43/510] Data 0.003 (0.005) Batch 1.229 (1.332) Remain 00:44:19 loss: 0.0899 Lr: 0.00003
[2023-12-26 00:11:36,810 INFO misc.py line 119 253097] Train: [97/100][44/510] Data 0.005 (0.005) Batch 1.055 (1.325) Remain 00:44:04 loss: 0.1747 Lr: 0.00003
[2023-12-26 00:11:37,974 INFO misc.py line 119 253097] Train: [97/100][45/510] Data 0.010 (0.005) Batch 1.167 (1.321) Remain 00:43:56 loss: 0.1130 Lr: 0.00003
[2023-12-26 00:11:38,938 INFO misc.py line 119 253097] Train: [97/100][46/510] Data 0.007 (0.005) Batch 0.966 (1.313) Remain 00:43:38 loss: 0.1775 Lr: 0.00003
[2023-12-26 00:11:40,086 INFO misc.py line 119 253097] Train: [97/100][47/510] Data 0.005 (0.005) Batch 1.148 (1.309) Remain 00:43:29 loss: 0.0997 Lr: 0.00003
[2023-12-26 00:11:41,151 INFO misc.py line 119 253097] Train: [97/100][48/510] Data 0.005 (0.005) Batch 1.065 (1.304) Remain 00:43:17 loss: 0.1117 Lr: 0.00003
[2023-12-26 00:11:47,841 INFO misc.py line 119 253097] Train: [97/100][49/510] Data 0.005 (0.005) Batch 6.692 (1.421) Remain 00:47:09 loss: 0.0874 Lr: 0.00003
[2023-12-26 00:11:49,066 INFO misc.py line 119 253097] Train: [97/100][50/510] Data 0.003 (0.005) Batch 1.220 (1.417) Remain 00:46:59 loss: 0.0698 Lr: 0.00003
[2023-12-26 00:11:50,215 INFO misc.py line 119 253097] Train: [97/100][51/510] Data 0.007 (0.005) Batch 1.149 (1.411) Remain 00:46:46 loss: 0.0698 Lr: 0.00003
[2023-12-26 00:11:51,262 INFO misc.py line 119 253097] Train: [97/100][52/510] Data 0.007 (0.005) Batch 1.049 (1.404) Remain 00:46:30 loss: 0.0942 Lr: 0.00003
[2023-12-26 00:11:52,117 INFO misc.py line 119 253097] Train: [97/100][53/510] Data 0.005 (0.005) Batch 0.857 (1.393) Remain 00:46:07 loss: 0.1832 Lr: 0.00003
[2023-12-26 00:11:53,210 INFO misc.py line 119 253097] Train: [97/100][54/510] Data 0.002 (0.005) Batch 1.093 (1.387) Remain 00:45:54 loss: 0.0792 Lr: 0.00003
[2023-12-26 00:11:55,042 INFO misc.py line 119 253097] Train: [97/100][55/510] Data 0.004 (0.005) Batch 1.828 (1.395) Remain 00:46:09 loss: 0.2141 Lr: 0.00003
[2023-12-26 00:11:56,157 INFO misc.py line 119 253097] Train: [97/100][56/510] Data 0.006 (0.005) Batch 1.117 (1.390) Remain 00:45:58 loss: 0.0742 Lr: 0.00003
[2023-12-26 00:11:57,007 INFO misc.py line 119 253097] Train: [97/100][57/510] Data 0.005 (0.005) Batch 0.851 (1.380) Remain 00:45:36 loss: 0.1368 Lr: 0.00003
[2023-12-26 00:11:58,275 INFO misc.py line 119 253097] Train: [97/100][58/510] Data 0.004 (0.005) Batch 1.265 (1.378) Remain 00:45:31 loss: 0.0916 Lr: 0.00003
[2023-12-26 00:11:59,292 INFO misc.py line 119 253097] Train: [97/100][59/510] Data 0.006 (0.005) Batch 1.020 (1.372) Remain 00:45:17 loss: 0.2595 Lr: 0.00003
[2023-12-26 00:12:00,423 INFO misc.py line 119 253097] Train: [97/100][60/510] Data 0.004 (0.005) Batch 1.128 (1.367) Remain 00:45:07 loss: 0.1053 Lr: 0.00003
[2023-12-26 00:12:01,510 INFO misc.py line 119 253097] Train: [97/100][61/510] Data 0.006 (0.005) Batch 1.089 (1.363) Remain 00:44:56 loss: 0.1245 Lr: 0.00003
[2023-12-26 00:12:02,532 INFO misc.py line 119 253097] Train: [97/100][62/510] Data 0.004 (0.005) Batch 1.018 (1.357) Remain 00:44:43 loss: 0.0889 Lr: 0.00003
[2023-12-26 00:12:03,704 INFO misc.py line 119 253097] Train: [97/100][63/510] Data 0.008 (0.005) Batch 1.177 (1.354) Remain 00:44:36 loss: 0.0940 Lr: 0.00003
[2023-12-26 00:12:04,771 INFO misc.py line 119 253097] Train: [97/100][64/510] Data 0.004 (0.005) Batch 1.064 (1.349) Remain 00:44:25 loss: 0.0776 Lr: 0.00003
[2023-12-26 00:12:05,749 INFO misc.py line 119 253097] Train: [97/100][65/510] Data 0.007 (0.005) Batch 0.982 (1.343) Remain 00:44:12 loss: 0.0658 Lr: 0.00003
[2023-12-26 00:12:06,735 INFO misc.py line 119 253097] Train: [97/100][66/510] Data 0.003 (0.005) Batch 0.985 (1.337) Remain 00:44:00 loss: 0.1634 Lr: 0.00003
[2023-12-26 00:12:07,800 INFO misc.py line 119 253097] Train: [97/100][67/510] Data 0.003 (0.005) Batch 1.066 (1.333) Remain 00:43:50 loss: 0.0601 Lr: 0.00003
[2023-12-26 00:12:08,881 INFO misc.py line 119 253097] Train: [97/100][68/510] Data 0.002 (0.005) Batch 1.081 (1.329) Remain 00:43:41 loss: 0.0829 Lr: 0.00003
[2023-12-26 00:12:10,104 INFO misc.py line 119 253097] Train: [97/100][69/510] Data 0.003 (0.005) Batch 1.223 (1.328) Remain 00:43:36 loss: 0.0849 Lr: 0.00003
[2023-12-26 00:12:11,165 INFO misc.py line 119 253097] Train: [97/100][70/510] Data 0.002 (0.005) Batch 1.056 (1.324) Remain 00:43:27 loss: 0.0772 Lr: 0.00003
[2023-12-26 00:12:19,497 INFO misc.py line 119 253097] Train: [97/100][71/510] Data 0.007 (0.005) Batch 8.336 (1.427) Remain 00:46:49 loss: 0.1039 Lr: 0.00003
[2023-12-26 00:12:20,489 INFO misc.py line 119 253097] Train: [97/100][72/510] Data 0.005 (0.005) Batch 0.988 (1.420) Remain 00:46:35 loss: 0.1024 Lr: 0.00003
[2023-12-26 00:12:21,656 INFO misc.py line 119 253097] Train: [97/100][73/510] Data 0.008 (0.005) Batch 1.167 (1.417) Remain 00:46:26 loss: 0.1045 Lr: 0.00003
[2023-12-26 00:12:22,672 INFO misc.py line 119 253097] Train: [97/100][74/510] Data 0.007 (0.005) Batch 1.016 (1.411) Remain 00:46:14 loss: 0.0619 Lr: 0.00003
[2023-12-26 00:12:23,713 INFO misc.py line 119 253097] Train: [97/100][75/510] Data 0.007 (0.005) Batch 1.040 (1.406) Remain 00:46:02 loss: 0.0944 Lr: 0.00003
[2023-12-26 00:12:24,883 INFO misc.py line 119 253097] Train: [97/100][76/510] Data 0.008 (0.005) Batch 1.174 (1.403) Remain 00:45:55 loss: 0.0596 Lr: 0.00003
[2023-12-26 00:12:26,241 INFO misc.py line 119 253097] Train: [97/100][77/510] Data 0.005 (0.005) Batch 1.355 (1.402) Remain 00:45:52 loss: 0.2051 Lr: 0.00003
[2023-12-26 00:12:27,453 INFO misc.py line 119 253097] Train: [97/100][78/510] Data 0.007 (0.005) Batch 1.214 (1.400) Remain 00:45:46 loss: 0.0638 Lr: 0.00003
[2023-12-26 00:12:28,448 INFO misc.py line 119 253097] Train: [97/100][79/510] Data 0.006 (0.005) Batch 0.998 (1.394) Remain 00:45:34 loss: 0.1305 Lr: 0.00003
[2023-12-26 00:12:29,713 INFO misc.py line 119 253097] Train: [97/100][80/510] Data 0.004 (0.005) Batch 1.261 (1.393) Remain 00:45:29 loss: 0.1049 Lr: 0.00003
[2023-12-26 00:12:30,934 INFO misc.py line 119 253097] Train: [97/100][81/510] Data 0.008 (0.005) Batch 1.222 (1.390) Remain 00:45:23 loss: 0.0749 Lr: 0.00003
[2023-12-26 00:12:32,044 INFO misc.py line 119 253097] Train: [97/100][82/510] Data 0.006 (0.005) Batch 1.108 (1.387) Remain 00:45:15 loss: 0.1705 Lr: 0.00003
[2023-12-26 00:12:33,249 INFO misc.py line 119 253097] Train: [97/100][83/510] Data 0.008 (0.005) Batch 1.204 (1.385) Remain 00:45:09 loss: 0.0595 Lr: 0.00003
[2023-12-26 00:12:38,838 INFO misc.py line 119 253097] Train: [97/100][84/510] Data 0.008 (0.005) Batch 5.595 (1.437) Remain 00:46:49 loss: 0.0989 Lr: 0.00003
[2023-12-26 00:12:40,148 INFO misc.py line 119 253097] Train: [97/100][85/510] Data 0.002 (0.005) Batch 1.306 (1.435) Remain 00:46:45 loss: 0.1583 Lr: 0.00003
[2023-12-26 00:12:41,061 INFO misc.py line 119 253097] Train: [97/100][86/510] Data 0.007 (0.005) Batch 0.917 (1.429) Remain 00:46:31 loss: 0.0805 Lr: 0.00003
[2023-12-26 00:12:42,283 INFO misc.py line 119 253097] Train: [97/100][87/510] Data 0.003 (0.005) Batch 1.218 (1.426) Remain 00:46:25 loss: 0.1211 Lr: 0.00003
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[2023-12-26 00:15:55,240 INFO misc.py line 119 253097] Train: [97/100][223/510] Data 5.865 (0.067) Batch 7.017 (1.422) Remain 00:43:03 loss: 0.1102 Lr: 0.00002
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[2023-12-26 00:16:09,843 INFO misc.py line 119 253097] Train: [97/100][236/510] Data 0.007 (0.063) Batch 0.936 (1.405) Remain 00:42:14 loss: 0.0695 Lr: 0.00002
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[2023-12-26 00:16:21,185 INFO misc.py line 119 253097] Train: [97/100][246/510] Data 0.003 (0.061) Batch 1.062 (1.394) Remain 00:41:40 loss: 0.0764 Lr: 0.00002
[2023-12-26 00:16:22,234 INFO misc.py line 119 253097] Train: [97/100][247/510] Data 0.003 (0.061) Batch 1.048 (1.392) Remain 00:41:36 loss: 0.2106 Lr: 0.00002
[2023-12-26 00:16:27,439 INFO misc.py line 119 253097] Train: [97/100][248/510] Data 3.993 (0.077) Batch 5.204 (1.408) Remain 00:42:03 loss: 0.0703 Lr: 0.00002
[2023-12-26 00:16:28,625 INFO misc.py line 119 253097] Train: [97/100][249/510] Data 0.005 (0.076) Batch 1.183 (1.407) Remain 00:42:00 loss: 0.1238 Lr: 0.00002
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[2023-12-26 00:18:37,247 INFO misc.py line 119 253097] Train: [97/100][333/510] Data 0.004 (0.075) Batch 14.287 (1.439) Remain 00:40:55 loss: 0.1359 Lr: 0.00002
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[2023-12-26 00:21:26,285 INFO misc.py line 119 253097] Train: [97/100][456/510] Data 0.027 (0.070) Batch 0.978 (1.421) Remain 00:37:31 loss: 0.1143 Lr: 0.00002
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[2023-12-26 00:22:41,437 INFO misc.py line 119 253097] Train: [97/100][506/510] Data 0.003 (0.065) Batch 1.161 (1.429) Remain 00:36:32 loss: 0.0722 Lr: 0.00002
[2023-12-26 00:22:42,502 INFO misc.py line 119 253097] Train: [97/100][507/510] Data 0.003 (0.064) Batch 1.065 (1.429) Remain 00:36:30 loss: 0.0751 Lr: 0.00002
[2023-12-26 00:22:43,368 INFO misc.py line 119 253097] Train: [97/100][508/510] Data 0.003 (0.064) Batch 0.865 (1.428) Remain 00:36:26 loss: 0.0642 Lr: 0.00002
[2023-12-26 00:22:44,412 INFO misc.py line 119 253097] Train: [97/100][509/510] Data 0.004 (0.064) Batch 1.039 (1.427) Remain 00:36:24 loss: 0.0822 Lr: 0.00002
[2023-12-26 00:22:45,383 INFO misc.py line 119 253097] Train: [97/100][510/510] Data 0.008 (0.064) Batch 0.977 (1.426) Remain 00:36:21 loss: 0.1221 Lr: 0.00002
[2023-12-26 00:22:45,384 INFO misc.py line 136 253097] Train result: loss: 0.1081 
[2023-12-26 00:22:45,384 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-26 00:23:20,913 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6111 
[2023-12-26 00:23:21,262 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3292 
[2023-12-26 00:23:26,211 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3219 
[2023-12-26 00:23:26,726 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3559 
[2023-12-26 00:23:28,709 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8625 
[2023-12-26 00:23:29,134 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3383 
[2023-12-26 00:23:30,015 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1818 
[2023-12-26 00:23:30,568 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2648 
[2023-12-26 00:23:32,383 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9241 
[2023-12-26 00:23:34,502 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1429 
[2023-12-26 00:23:35,357 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3160 
[2023-12-26 00:23:35,780 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7541 
[2023-12-26 00:23:36,680 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3639 
[2023-12-26 00:23:39,629 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8751 
[2023-12-26 00:23:40,099 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2502 
[2023-12-26 00:23:40,709 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3718 
[2023-12-26 00:23:41,407 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3188 
[2023-12-26 00:23:42,651 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6964/0.7519/0.9067.
[2023-12-26 00:23:42,651 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9174/0.9477
[2023-12-26 00:23:42,651 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9832/0.9904
[2023-12-26 00:23:42,651 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8463/0.9729
[2023-12-26 00:23:42,651 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-26 00:23:42,651 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3478/0.3833
[2023-12-26 00:23:42,652 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6357/0.6557
[2023-12-26 00:23:42,652 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7096/0.7987
[2023-12-26 00:23:42,652 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8127/0.8923
[2023-12-26 00:23:42,652 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9098/0.9554
[2023-12-26 00:23:42,652 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6649/0.7111
[2023-12-26 00:23:42,652 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7890/0.8748
[2023-12-26 00:23:42,652 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8200/0.8599
[2023-12-26 00:23:42,652 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6165/0.7324
[2023-12-26 00:23:42,652 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-26 00:23:42,654 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-26 00:23:42,654 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-26 00:23:48,611 INFO misc.py line 119 253097] Train: [98/100][1/510] Data 3.313 (3.313) Batch 4.327 (4.327) Remain 01:50:16 loss: 0.2515 Lr: 0.00002
[2023-12-26 00:23:49,586 INFO misc.py line 119 253097] Train: [98/100][2/510] Data 0.004 (0.004) Batch 0.975 (0.975) Remain 00:24:50 loss: 0.0863 Lr: 0.00002
[2023-12-26 00:23:50,707 INFO misc.py line 119 253097] Train: [98/100][3/510] Data 0.004 (0.004) Batch 1.118 (1.118) Remain 00:28:27 loss: 0.0647 Lr: 0.00002
[2023-12-26 00:23:51,986 INFO misc.py line 119 253097] Train: [98/100][4/510] Data 0.007 (0.007) Batch 1.278 (1.278) Remain 00:32:29 loss: 0.0675 Lr: 0.00002
[2023-12-26 00:23:53,031 INFO misc.py line 119 253097] Train: [98/100][5/510] Data 0.008 (0.007) Batch 1.046 (1.162) Remain 00:29:31 loss: 0.0756 Lr: 0.00002
[2023-12-26 00:23:54,054 INFO misc.py line 119 253097] Train: [98/100][6/510] Data 0.006 (0.007) Batch 1.026 (1.117) Remain 00:28:21 loss: 0.1212 Lr: 0.00002
[2023-12-26 00:23:55,901 INFO misc.py line 119 253097] Train: [98/100][7/510] Data 0.003 (0.006) Batch 1.847 (1.299) Remain 00:32:58 loss: 0.1423 Lr: 0.00002
[2023-12-26 00:24:06,321 INFO misc.py line 119 253097] Train: [98/100][8/510] Data 0.005 (0.006) Batch 10.420 (3.123) Remain 01:19:13 loss: 0.1040 Lr: 0.00002
[2023-12-26 00:24:07,535 INFO misc.py line 119 253097] Train: [98/100][9/510] Data 0.005 (0.006) Batch 1.215 (2.805) Remain 01:11:06 loss: 0.1307 Lr: 0.00002
[2023-12-26 00:24:08,704 INFO misc.py line 119 253097] Train: [98/100][10/510] Data 0.003 (0.005) Batch 1.169 (2.571) Remain 01:05:08 loss: 0.1184 Lr: 0.00002
[2023-12-26 00:24:09,924 INFO misc.py line 119 253097] Train: [98/100][11/510] Data 0.005 (0.005) Batch 1.220 (2.402) Remain 01:00:49 loss: 0.0883 Lr: 0.00002
[2023-12-26 00:24:17,000 INFO misc.py line 119 253097] Train: [98/100][12/510] Data 0.003 (0.005) Batch 7.076 (2.922) Remain 01:13:55 loss: 0.1462 Lr: 0.00002
[2023-12-26 00:24:18,195 INFO misc.py line 119 253097] Train: [98/100][13/510] Data 0.003 (0.005) Batch 1.189 (2.748) Remain 01:09:29 loss: 0.1515 Lr: 0.00002
[2023-12-26 00:24:19,195 INFO misc.py line 119 253097] Train: [98/100][14/510] Data 0.009 (0.005) Batch 1.002 (2.590) Remain 01:05:26 loss: 0.0813 Lr: 0.00002
[2023-12-26 00:24:20,153 INFO misc.py line 119 253097] Train: [98/100][15/510] Data 0.007 (0.005) Batch 0.962 (2.454) Remain 01:01:57 loss: 0.1489 Lr: 0.00002
[2023-12-26 00:24:21,278 INFO misc.py line 119 253097] Train: [98/100][16/510] Data 0.005 (0.005) Batch 1.126 (2.352) Remain 00:59:20 loss: 0.0875 Lr: 0.00002
[2023-12-26 00:24:22,520 INFO misc.py line 119 253097] Train: [98/100][17/510] Data 0.003 (0.005) Batch 1.238 (2.272) Remain 00:57:18 loss: 0.1483 Lr: 0.00002
[2023-12-26 00:24:23,755 INFO misc.py line 119 253097] Train: [98/100][18/510] Data 0.006 (0.005) Batch 1.234 (2.203) Remain 00:55:31 loss: 0.0877 Lr: 0.00002
[2023-12-26 00:24:24,824 INFO misc.py line 119 253097] Train: [98/100][19/510] Data 0.007 (0.005) Batch 1.070 (2.132) Remain 00:53:41 loss: 0.1099 Lr: 0.00002
[2023-12-26 00:24:25,987 INFO misc.py line 119 253097] Train: [98/100][20/510] Data 0.008 (0.005) Batch 1.166 (2.075) Remain 00:52:13 loss: 0.0719 Lr: 0.00002
[2023-12-26 00:24:27,011 INFO misc.py line 119 253097] Train: [98/100][21/510] Data 0.003 (0.005) Batch 1.020 (2.017) Remain 00:50:43 loss: 0.1271 Lr: 0.00002
[2023-12-26 00:24:28,330 INFO misc.py line 119 253097] Train: [98/100][22/510] Data 0.007 (0.005) Batch 1.322 (1.980) Remain 00:49:46 loss: 0.0781 Lr: 0.00002
[2023-12-26 00:24:29,398 INFO misc.py line 119 253097] Train: [98/100][23/510] Data 0.004 (0.005) Batch 1.068 (1.935) Remain 00:48:35 loss: 0.0948 Lr: 0.00002
[2023-12-26 00:24:30,605 INFO misc.py line 119 253097] Train: [98/100][24/510] Data 0.004 (0.005) Batch 1.207 (1.900) Remain 00:47:41 loss: 0.0949 Lr: 0.00002
[2023-12-26 00:24:31,634 INFO misc.py line 119 253097] Train: [98/100][25/510] Data 0.005 (0.005) Batch 1.025 (1.860) Remain 00:46:39 loss: 0.1891 Lr: 0.00002
[2023-12-26 00:24:32,623 INFO misc.py line 119 253097] Train: [98/100][26/510] Data 0.008 (0.005) Batch 0.994 (1.823) Remain 00:45:41 loss: 0.0799 Lr: 0.00002
[2023-12-26 00:24:33,713 INFO misc.py line 119 253097] Train: [98/100][27/510] Data 0.003 (0.005) Batch 1.090 (1.792) Remain 00:44:53 loss: 0.1062 Lr: 0.00002
[2023-12-26 00:24:34,863 INFO misc.py line 119 253097] Train: [98/100][28/510] Data 0.004 (0.005) Batch 1.150 (1.766) Remain 00:44:13 loss: 0.0809 Lr: 0.00002
[2023-12-26 00:24:36,107 INFO misc.py line 119 253097] Train: [98/100][29/510] Data 0.004 (0.005) Batch 1.244 (1.746) Remain 00:43:41 loss: 0.1216 Lr: 0.00002
[2023-12-26 00:24:37,331 INFO misc.py line 119 253097] Train: [98/100][30/510] Data 0.005 (0.005) Batch 1.222 (1.727) Remain 00:43:10 loss: 0.0900 Lr: 0.00002
[2023-12-26 00:24:38,480 INFO misc.py line 119 253097] Train: [98/100][31/510] Data 0.007 (0.005) Batch 1.150 (1.706) Remain 00:42:37 loss: 0.0909 Lr: 0.00002
[2023-12-26 00:24:39,670 INFO misc.py line 119 253097] Train: [98/100][32/510] Data 0.006 (0.005) Batch 1.189 (1.688) Remain 00:42:09 loss: 0.0885 Lr: 0.00002
[2023-12-26 00:24:40,538 INFO misc.py line 119 253097] Train: [98/100][33/510] Data 0.007 (0.005) Batch 0.870 (1.661) Remain 00:41:26 loss: 0.1487 Lr: 0.00002
[2023-12-26 00:24:41,633 INFO misc.py line 119 253097] Train: [98/100][34/510] Data 0.005 (0.005) Batch 1.096 (1.643) Remain 00:40:57 loss: 0.0659 Lr: 0.00002
[2023-12-26 00:24:42,710 INFO misc.py line 119 253097] Train: [98/100][35/510] Data 0.003 (0.005) Batch 1.077 (1.625) Remain 00:40:29 loss: 0.0756 Lr: 0.00002
[2023-12-26 00:24:43,817 INFO misc.py line 119 253097] Train: [98/100][36/510] Data 0.004 (0.005) Batch 1.107 (1.609) Remain 00:40:04 loss: 0.1762 Lr: 0.00002
[2023-12-26 00:24:45,038 INFO misc.py line 119 253097] Train: [98/100][37/510] Data 0.003 (0.005) Batch 1.217 (1.598) Remain 00:39:45 loss: 0.0491 Lr: 0.00002
[2023-12-26 00:24:46,310 INFO misc.py line 119 253097] Train: [98/100][38/510] Data 0.007 (0.005) Batch 1.276 (1.589) Remain 00:39:30 loss: 0.1172 Lr: 0.00001
[2023-12-26 00:24:47,346 INFO misc.py line 119 253097] Train: [98/100][39/510] Data 0.003 (0.005) Batch 1.033 (1.573) Remain 00:39:05 loss: 0.1115 Lr: 0.00001
[2023-12-26 00:24:48,384 INFO misc.py line 119 253097] Train: [98/100][40/510] Data 0.007 (0.005) Batch 1.038 (1.559) Remain 00:38:42 loss: 0.1465 Lr: 0.00001
[2023-12-26 00:24:49,515 INFO misc.py line 119 253097] Train: [98/100][41/510] Data 0.007 (0.005) Batch 1.132 (1.548) Remain 00:38:24 loss: 0.1022 Lr: 0.00001
[2023-12-26 00:24:50,554 INFO misc.py line 119 253097] Train: [98/100][42/510] Data 0.006 (0.005) Batch 1.037 (1.535) Remain 00:38:03 loss: 0.1429 Lr: 0.00001
[2023-12-26 00:24:51,492 INFO misc.py line 119 253097] Train: [98/100][43/510] Data 0.008 (0.005) Batch 0.942 (1.520) Remain 00:37:39 loss: 0.0693 Lr: 0.00001
[2023-12-26 00:24:52,619 INFO misc.py line 119 253097] Train: [98/100][44/510] Data 0.004 (0.005) Batch 1.125 (1.510) Remain 00:37:23 loss: 0.1403 Lr: 0.00001
[2023-12-26 00:24:53,889 INFO misc.py line 119 253097] Train: [98/100][45/510] Data 0.006 (0.005) Batch 1.270 (1.504) Remain 00:37:13 loss: 0.1191 Lr: 0.00001
[2023-12-26 00:24:55,100 INFO misc.py line 119 253097] Train: [98/100][46/510] Data 0.007 (0.005) Batch 1.211 (1.498) Remain 00:37:02 loss: 0.1209 Lr: 0.00001
[2023-12-26 00:24:56,129 INFO misc.py line 119 253097] Train: [98/100][47/510] Data 0.005 (0.005) Batch 1.027 (1.487) Remain 00:36:44 loss: 0.1585 Lr: 0.00001
[2023-12-26 00:24:57,763 INFO misc.py line 119 253097] Train: [98/100][48/510] Data 0.007 (0.005) Batch 1.635 (1.490) Remain 00:36:48 loss: 0.1121 Lr: 0.00001
[2023-12-26 00:24:58,826 INFO misc.py line 119 253097] Train: [98/100][49/510] Data 0.025 (0.006) Batch 1.064 (1.481) Remain 00:36:33 loss: 0.0958 Lr: 0.00001
[2023-12-26 00:24:59,957 INFO misc.py line 119 253097] Train: [98/100][50/510] Data 0.006 (0.006) Batch 1.130 (1.473) Remain 00:36:20 loss: 0.1122 Lr: 0.00001
[2023-12-26 00:25:01,054 INFO misc.py line 119 253097] Train: [98/100][51/510] Data 0.004 (0.006) Batch 1.096 (1.466) Remain 00:36:07 loss: 0.0941 Lr: 0.00001
[2023-12-26 00:25:02,281 INFO misc.py line 119 253097] Train: [98/100][52/510] Data 0.007 (0.006) Batch 1.228 (1.461) Remain 00:35:58 loss: 0.1731 Lr: 0.00001
[2023-12-26 00:25:03,350 INFO misc.py line 119 253097] Train: [98/100][53/510] Data 0.005 (0.006) Batch 1.067 (1.453) Remain 00:35:45 loss: 0.0728 Lr: 0.00001
[2023-12-26 00:25:04,263 INFO misc.py line 119 253097] Train: [98/100][54/510] Data 0.007 (0.006) Batch 0.916 (1.442) Remain 00:35:28 loss: 0.0949 Lr: 0.00001
[2023-12-26 00:25:05,347 INFO misc.py line 119 253097] Train: [98/100][55/510] Data 0.004 (0.006) Batch 1.084 (1.435) Remain 00:35:17 loss: 0.1038 Lr: 0.00001
[2023-12-26 00:25:06,469 INFO misc.py line 119 253097] Train: [98/100][56/510] Data 0.004 (0.006) Batch 1.123 (1.430) Remain 00:35:07 loss: 0.1688 Lr: 0.00001
[2023-12-26 00:25:07,714 INFO misc.py line 119 253097] Train: [98/100][57/510] Data 0.004 (0.006) Batch 1.245 (1.426) Remain 00:35:00 loss: 0.1172 Lr: 0.00001
[2023-12-26 00:25:08,872 INFO misc.py line 119 253097] Train: [98/100][58/510] Data 0.004 (0.006) Batch 1.157 (1.421) Remain 00:34:52 loss: 0.0974 Lr: 0.00001
[2023-12-26 00:25:11,642 INFO misc.py line 119 253097] Train: [98/100][59/510] Data 0.003 (0.006) Batch 2.770 (1.445) Remain 00:35:26 loss: 0.0945 Lr: 0.00001
[2023-12-26 00:25:12,819 INFO misc.py line 119 253097] Train: [98/100][60/510] Data 0.004 (0.006) Batch 1.177 (1.441) Remain 00:35:17 loss: 0.1101 Lr: 0.00001
[2023-12-26 00:25:14,140 INFO misc.py line 119 253097] Train: [98/100][61/510] Data 0.004 (0.005) Batch 1.304 (1.438) Remain 00:35:12 loss: 0.0938 Lr: 0.00001
[2023-12-26 00:25:21,031 INFO misc.py line 119 253097] Train: [98/100][62/510] Data 0.021 (0.006) Batch 6.908 (1.531) Remain 00:37:27 loss: 0.1356 Lr: 0.00001
[2023-12-26 00:25:22,166 INFO misc.py line 119 253097] Train: [98/100][63/510] Data 0.005 (0.006) Batch 1.134 (1.524) Remain 00:37:16 loss: 0.0946 Lr: 0.00001
[2023-12-26 00:25:23,136 INFO misc.py line 119 253097] Train: [98/100][64/510] Data 0.006 (0.006) Batch 0.970 (1.515) Remain 00:37:01 loss: 0.1085 Lr: 0.00001
[2023-12-26 00:25:24,087 INFO misc.py line 119 253097] Train: [98/100][65/510] Data 0.005 (0.006) Batch 0.951 (1.506) Remain 00:36:46 loss: 0.1107 Lr: 0.00001
[2023-12-26 00:25:25,088 INFO misc.py line 119 253097] Train: [98/100][66/510] Data 0.006 (0.006) Batch 1.002 (1.498) Remain 00:36:33 loss: 0.1302 Lr: 0.00001
[2023-12-26 00:25:26,186 INFO misc.py line 119 253097] Train: [98/100][67/510] Data 0.006 (0.006) Batch 1.098 (1.492) Remain 00:36:22 loss: 0.0701 Lr: 0.00001
[2023-12-26 00:25:27,253 INFO misc.py line 119 253097] Train: [98/100][68/510] Data 0.006 (0.006) Batch 1.067 (1.485) Remain 00:36:11 loss: 0.0802 Lr: 0.00001
[2023-12-26 00:25:28,349 INFO misc.py line 119 253097] Train: [98/100][69/510] Data 0.006 (0.006) Batch 1.097 (1.479) Remain 00:36:01 loss: 0.0890 Lr: 0.00001
[2023-12-26 00:25:29,641 INFO misc.py line 119 253097] Train: [98/100][70/510] Data 0.004 (0.006) Batch 1.287 (1.477) Remain 00:35:55 loss: 0.1208 Lr: 0.00001
[2023-12-26 00:25:30,733 INFO misc.py line 119 253097] Train: [98/100][71/510] Data 0.010 (0.006) Batch 1.097 (1.471) Remain 00:35:46 loss: 0.1059 Lr: 0.00001
[2023-12-26 00:25:31,812 INFO misc.py line 119 253097] Train: [98/100][72/510] Data 0.005 (0.006) Batch 1.080 (1.465) Remain 00:35:36 loss: 0.1464 Lr: 0.00001
[2023-12-26 00:25:32,907 INFO misc.py line 119 253097] Train: [98/100][73/510] Data 0.004 (0.006) Batch 1.094 (1.460) Remain 00:35:27 loss: 0.1841 Lr: 0.00001
[2023-12-26 00:25:38,889 INFO misc.py line 119 253097] Train: [98/100][74/510] Data 0.003 (0.006) Batch 5.981 (1.524) Remain 00:36:58 loss: 0.1851 Lr: 0.00001
[2023-12-26 00:25:40,181 INFO misc.py line 119 253097] Train: [98/100][75/510] Data 0.005 (0.006) Batch 1.288 (1.520) Remain 00:36:52 loss: 0.1066 Lr: 0.00001
[2023-12-26 00:25:41,252 INFO misc.py line 119 253097] Train: [98/100][76/510] Data 0.008 (0.006) Batch 1.076 (1.514) Remain 00:36:41 loss: 0.0961 Lr: 0.00001
[2023-12-26 00:25:42,197 INFO misc.py line 119 253097] Train: [98/100][77/510] Data 0.003 (0.006) Batch 0.945 (1.507) Remain 00:36:29 loss: 0.0674 Lr: 0.00001
[2023-12-26 00:25:43,439 INFO misc.py line 119 253097] Train: [98/100][78/510] Data 0.003 (0.006) Batch 1.241 (1.503) Remain 00:36:22 loss: 0.0995 Lr: 0.00001
[2023-12-26 00:25:44,533 INFO misc.py line 119 253097] Train: [98/100][79/510] Data 0.006 (0.006) Batch 1.093 (1.498) Remain 00:36:13 loss: 0.0903 Lr: 0.00001
[2023-12-26 00:25:45,763 INFO misc.py line 119 253097] Train: [98/100][80/510] Data 0.007 (0.006) Batch 1.227 (1.494) Remain 00:36:06 loss: 0.1065 Lr: 0.00001
[2023-12-26 00:25:46,921 INFO misc.py line 119 253097] Train: [98/100][81/510] Data 0.009 (0.006) Batch 1.159 (1.490) Remain 00:35:58 loss: 0.1189 Lr: 0.00001
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[2023-12-26 00:26:33,196 INFO misc.py line 119 253097] Train: [98/100][119/510] Data 0.004 (0.030) Batch 0.958 (1.401) Remain 00:32:56 loss: 0.0851 Lr: 0.00001
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[2023-12-26 00:26:44,942 INFO misc.py line 119 253097] Train: [98/100][126/510] Data 0.004 (0.028) Batch 1.027 (1.417) Remain 00:33:08 loss: 0.1663 Lr: 0.00001
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[2023-12-26 00:26:53,156 INFO misc.py line 119 253097] Train: [98/100][134/510] Data 0.010 (0.027) Batch 1.003 (1.393) Remain 00:32:24 loss: 0.2495 Lr: 0.00001
[2023-12-26 00:26:54,236 INFO misc.py line 119 253097] Train: [98/100][135/510] Data 0.009 (0.027) Batch 1.080 (1.390) Remain 00:32:19 loss: 0.0993 Lr: 0.00001
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[2023-12-26 00:26:56,344 INFO misc.py line 119 253097] Train: [98/100][137/510] Data 0.009 (0.027) Batch 1.086 (1.385) Remain 00:32:09 loss: 0.1026 Lr: 0.00001
[2023-12-26 00:27:03,854 INFO misc.py line 119 253097] Train: [98/100][138/510] Data 6.450 (0.074) Batch 7.516 (1.431) Remain 00:33:11 loss: 0.1139 Lr: 0.00001
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[2023-12-26 00:35:25,927 INFO misc.py line 119 253097] Train: [98/100][506/510] Data 0.005 (0.047) Batch 1.252 (1.382) Remain 00:23:35 loss: 0.1084 Lr: 0.00001
[2023-12-26 00:35:26,788 INFO misc.py line 119 253097] Train: [98/100][507/510] Data 0.014 (0.047) Batch 0.872 (1.381) Remain 00:23:32 loss: 0.0988 Lr: 0.00001
[2023-12-26 00:35:27,944 INFO misc.py line 119 253097] Train: [98/100][508/510] Data 0.003 (0.046) Batch 1.156 (1.381) Remain 00:23:31 loss: 0.0500 Lr: 0.00001
[2023-12-26 00:35:29,066 INFO misc.py line 119 253097] Train: [98/100][509/510] Data 0.003 (0.046) Batch 1.122 (1.380) Remain 00:23:29 loss: 0.1106 Lr: 0.00001
[2023-12-26 00:35:30,025 INFO misc.py line 119 253097] Train: [98/100][510/510] Data 0.003 (0.046) Batch 0.959 (1.379) Remain 00:23:26 loss: 0.0653 Lr: 0.00001
[2023-12-26 00:35:30,025 INFO misc.py line 136 253097] Train result: loss: 0.1076 
[2023-12-26 00:35:30,026 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-26 00:36:06,950 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.6301 
[2023-12-26 00:36:07,294 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3152 
[2023-12-26 00:36:12,232 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3873 
[2023-12-26 00:36:12,761 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3743 
[2023-12-26 00:36:14,733 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8970 
[2023-12-26 00:36:15,160 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3782 
[2023-12-26 00:36:16,042 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1562 
[2023-12-26 00:36:16,594 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2604 
[2023-12-26 00:36:18,404 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.9044 
[2023-12-26 00:36:20,526 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.1202 
[2023-12-26 00:36:21,383 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3012 
[2023-12-26 00:36:21,805 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7063 
[2023-12-26 00:36:22,705 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.3958 
[2023-12-26 00:36:25,647 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8669 
[2023-12-26 00:36:26,115 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2721 
[2023-12-26 00:36:26,725 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3730 
[2023-12-26 00:36:27,431 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3422 
[2023-12-26 00:36:28,805 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6908/0.7455/0.9054.
[2023-12-26 00:36:28,805 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9189/0.9482
[2023-12-26 00:36:28,805 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9835/0.9905
[2023-12-26 00:36:28,805 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8436/0.9739
[2023-12-26 00:36:28,805 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3225/0.3503
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6300/0.6481
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.6897/0.7851
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8070/0.8905
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9213/0.9629
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6352/0.6812
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7833/0.8757
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8275/0.8583
[2023-12-26 00:36:28,806 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6176/0.7272
[2023-12-26 00:36:28,806 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-26 00:36:28,808 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-26 00:36:28,808 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-26 00:36:49,011 INFO misc.py line 119 253097] Train: [99/100][1/510] Data 3.425 (3.425) Batch 18.755 (18.755) Remain 05:18:31 loss: 0.1097 Lr: 0.00001
[2023-12-26 00:36:50,070 INFO misc.py line 119 253097] Train: [99/100][2/510] Data 0.004 (0.004) Batch 1.059 (1.059) Remain 00:17:58 loss: 0.0874 Lr: 0.00001
[2023-12-26 00:36:51,235 INFO misc.py line 119 253097] Train: [99/100][3/510] Data 0.003 (0.003) Batch 1.165 (1.165) Remain 00:19:44 loss: 0.0570 Lr: 0.00001
[2023-12-26 00:36:52,284 INFO misc.py line 119 253097] Train: [99/100][4/510] Data 0.003 (0.003) Batch 1.049 (1.049) Remain 00:17:46 loss: 0.0833 Lr: 0.00001
[2023-12-26 00:36:53,408 INFO misc.py line 119 253097] Train: [99/100][5/510] Data 0.004 (0.003) Batch 1.123 (1.086) Remain 00:18:22 loss: 0.0736 Lr: 0.00001
[2023-12-26 00:36:54,544 INFO misc.py line 119 253097] Train: [99/100][6/510] Data 0.004 (0.004) Batch 1.137 (1.103) Remain 00:18:38 loss: 0.1062 Lr: 0.00001
[2023-12-26 00:36:55,568 INFO misc.py line 119 253097] Train: [99/100][7/510] Data 0.004 (0.004) Batch 1.023 (1.083) Remain 00:18:17 loss: 0.0714 Lr: 0.00001
[2023-12-26 00:36:56,495 INFO misc.py line 119 253097] Train: [99/100][8/510] Data 0.004 (0.004) Batch 0.928 (1.052) Remain 00:17:44 loss: 0.0685 Lr: 0.00001
[2023-12-26 00:36:57,749 INFO misc.py line 119 253097] Train: [99/100][9/510] Data 0.003 (0.004) Batch 1.254 (1.086) Remain 00:18:17 loss: 0.0614 Lr: 0.00001
[2023-12-26 00:36:58,745 INFO misc.py line 119 253097] Train: [99/100][10/510] Data 0.003 (0.003) Batch 0.995 (1.073) Remain 00:18:03 loss: 0.1904 Lr: 0.00001
[2023-12-26 00:36:59,764 INFO misc.py line 119 253097] Train: [99/100][11/510] Data 0.005 (0.004) Batch 1.020 (1.066) Remain 00:17:55 loss: 0.1002 Lr: 0.00001
[2023-12-26 00:37:01,034 INFO misc.py line 119 253097] Train: [99/100][12/510] Data 0.003 (0.004) Batch 1.266 (1.088) Remain 00:18:17 loss: 0.0992 Lr: 0.00001
[2023-12-26 00:37:02,040 INFO misc.py line 119 253097] Train: [99/100][13/510] Data 0.007 (0.004) Batch 1.007 (1.080) Remain 00:18:07 loss: 0.0689 Lr: 0.00001
[2023-12-26 00:37:03,029 INFO misc.py line 119 253097] Train: [99/100][14/510] Data 0.006 (0.004) Batch 0.990 (1.072) Remain 00:17:58 loss: 0.0838 Lr: 0.00001
[2023-12-26 00:37:04,108 INFO misc.py line 119 253097] Train: [99/100][15/510] Data 0.005 (0.004) Batch 1.080 (1.073) Remain 00:17:58 loss: 0.1236 Lr: 0.00001
[2023-12-26 00:37:05,271 INFO misc.py line 119 253097] Train: [99/100][16/510] Data 0.003 (0.004) Batch 1.160 (1.080) Remain 00:18:03 loss: 0.1561 Lr: 0.00001
[2023-12-26 00:37:06,365 INFO misc.py line 119 253097] Train: [99/100][17/510] Data 0.007 (0.004) Batch 1.096 (1.081) Remain 00:18:03 loss: 0.1000 Lr: 0.00001
[2023-12-26 00:37:07,691 INFO misc.py line 119 253097] Train: [99/100][18/510] Data 0.003 (0.004) Batch 1.323 (1.097) Remain 00:18:19 loss: 0.0882 Lr: 0.00001
[2023-12-26 00:37:08,983 INFO misc.py line 119 253097] Train: [99/100][19/510] Data 0.006 (0.004) Batch 1.296 (1.109) Remain 00:18:30 loss: 0.1381 Lr: 0.00001
[2023-12-26 00:37:10,208 INFO misc.py line 119 253097] Train: [99/100][20/510] Data 0.004 (0.004) Batch 1.222 (1.116) Remain 00:18:35 loss: 0.1097 Lr: 0.00001
[2023-12-26 00:37:11,423 INFO misc.py line 119 253097] Train: [99/100][21/510] Data 0.007 (0.004) Batch 1.215 (1.121) Remain 00:18:40 loss: 0.0789 Lr: 0.00001
[2023-12-26 00:37:18,862 INFO misc.py line 119 253097] Train: [99/100][22/510] Data 6.453 (0.344) Batch 7.441 (1.454) Remain 00:24:11 loss: 0.0982 Lr: 0.00001
[2023-12-26 00:37:19,918 INFO misc.py line 119 253097] Train: [99/100][23/510] Data 0.003 (0.327) Batch 1.054 (1.434) Remain 00:23:49 loss: 0.0860 Lr: 0.00001
[2023-12-26 00:37:21,211 INFO misc.py line 119 253097] Train: [99/100][24/510] Data 0.006 (0.312) Batch 1.295 (1.427) Remain 00:23:41 loss: 0.2647 Lr: 0.00001
[2023-12-26 00:37:22,294 INFO misc.py line 119 253097] Train: [99/100][25/510] Data 0.004 (0.298) Batch 1.081 (1.412) Remain 00:23:24 loss: 0.1605 Lr: 0.00001
[2023-12-26 00:37:23,410 INFO misc.py line 119 253097] Train: [99/100][26/510] Data 0.006 (0.285) Batch 1.113 (1.399) Remain 00:23:10 loss: 0.1166 Lr: 0.00001
[2023-12-26 00:37:24,675 INFO misc.py line 119 253097] Train: [99/100][27/510] Data 0.008 (0.273) Batch 1.269 (1.393) Remain 00:23:03 loss: 0.0930 Lr: 0.00001
[2023-12-26 00:37:25,808 INFO misc.py line 119 253097] Train: [99/100][28/510] Data 0.004 (0.263) Batch 1.133 (1.383) Remain 00:22:51 loss: 0.1146 Lr: 0.00001
[2023-12-26 00:37:26,981 INFO misc.py line 119 253097] Train: [99/100][29/510] Data 0.005 (0.253) Batch 1.171 (1.375) Remain 00:22:42 loss: 0.1711 Lr: 0.00001
[2023-12-26 00:37:28,120 INFO misc.py line 119 253097] Train: [99/100][30/510] Data 0.006 (0.244) Batch 1.139 (1.366) Remain 00:22:32 loss: 0.0980 Lr: 0.00001
[2023-12-26 00:37:29,243 INFO misc.py line 119 253097] Train: [99/100][31/510] Data 0.006 (0.235) Batch 1.125 (1.357) Remain 00:22:22 loss: 0.1589 Lr: 0.00001
[2023-12-26 00:37:30,374 INFO misc.py line 119 253097] Train: [99/100][32/510] Data 0.005 (0.227) Batch 1.129 (1.350) Remain 00:22:13 loss: 0.1775 Lr: 0.00001
[2023-12-26 00:37:31,547 INFO misc.py line 119 253097] Train: [99/100][33/510] Data 0.007 (0.220) Batch 1.146 (1.343) Remain 00:22:05 loss: 0.0745 Lr: 0.00001
[2023-12-26 00:37:32,574 INFO misc.py line 119 253097] Train: [99/100][34/510] Data 0.034 (0.214) Batch 1.038 (1.333) Remain 00:21:54 loss: 0.1057 Lr: 0.00001
[2023-12-26 00:37:33,625 INFO misc.py line 119 253097] Train: [99/100][35/510] Data 0.022 (0.208) Batch 1.070 (1.325) Remain 00:21:44 loss: 0.1026 Lr: 0.00001
[2023-12-26 00:37:34,814 INFO misc.py line 119 253097] Train: [99/100][36/510] Data 0.003 (0.202) Batch 1.185 (1.320) Remain 00:21:39 loss: 0.1292 Lr: 0.00001
[2023-12-26 00:37:35,967 INFO misc.py line 119 253097] Train: [99/100][37/510] Data 0.007 (0.196) Batch 1.152 (1.316) Remain 00:21:33 loss: 0.1164 Lr: 0.00001
[2023-12-26 00:37:37,119 INFO misc.py line 119 253097] Train: [99/100][38/510] Data 0.008 (0.190) Batch 1.153 (1.311) Remain 00:21:27 loss: 0.2284 Lr: 0.00001
[2023-12-26 00:37:38,034 INFO misc.py line 119 253097] Train: [99/100][39/510] Data 0.007 (0.185) Batch 0.917 (1.300) Remain 00:21:15 loss: 0.0664 Lr: 0.00001
[2023-12-26 00:37:39,275 INFO misc.py line 119 253097] Train: [99/100][40/510] Data 0.005 (0.181) Batch 1.236 (1.298) Remain 00:21:12 loss: 0.1424 Lr: 0.00001
[2023-12-26 00:37:40,490 INFO misc.py line 119 253097] Train: [99/100][41/510] Data 0.009 (0.176) Batch 1.220 (1.296) Remain 00:21:08 loss: 0.1381 Lr: 0.00001
[2023-12-26 00:37:41,394 INFO misc.py line 119 253097] Train: [99/100][42/510] Data 0.005 (0.172) Batch 0.903 (1.286) Remain 00:20:57 loss: 0.2139 Lr: 0.00001
[2023-12-26 00:37:42,574 INFO misc.py line 119 253097] Train: [99/100][43/510] Data 0.005 (0.167) Batch 1.180 (1.283) Remain 00:20:53 loss: 0.1139 Lr: 0.00001
[2023-12-26 00:37:43,659 INFO misc.py line 119 253097] Train: [99/100][44/510] Data 0.006 (0.164) Batch 1.087 (1.279) Remain 00:20:47 loss: 0.0943 Lr: 0.00001
[2023-12-26 00:37:44,853 INFO misc.py line 119 253097] Train: [99/100][45/510] Data 0.004 (0.160) Batch 1.194 (1.277) Remain 00:20:44 loss: 0.0812 Lr: 0.00001
[2023-12-26 00:37:45,961 INFO misc.py line 119 253097] Train: [99/100][46/510] Data 0.004 (0.156) Batch 1.109 (1.273) Remain 00:20:39 loss: 0.1317 Lr: 0.00001
[2023-12-26 00:37:47,250 INFO misc.py line 119 253097] Train: [99/100][47/510] Data 0.003 (0.153) Batch 1.289 (1.273) Remain 00:20:38 loss: 0.0890 Lr: 0.00001
[2023-12-26 00:37:48,535 INFO misc.py line 119 253097] Train: [99/100][48/510] Data 0.003 (0.149) Batch 1.281 (1.273) Remain 00:20:37 loss: 0.0661 Lr: 0.00001
[2023-12-26 00:37:49,590 INFO misc.py line 119 253097] Train: [99/100][49/510] Data 0.007 (0.146) Batch 1.059 (1.269) Remain 00:20:31 loss: 0.0629 Lr: 0.00001
[2023-12-26 00:37:50,729 INFO misc.py line 119 253097] Train: [99/100][50/510] Data 0.003 (0.143) Batch 1.134 (1.266) Remain 00:20:27 loss: 0.1135 Lr: 0.00001
[2023-12-26 00:37:51,721 INFO misc.py line 119 253097] Train: [99/100][51/510] Data 0.008 (0.140) Batch 0.996 (1.260) Remain 00:20:21 loss: 0.0739 Lr: 0.00001
[2023-12-26 00:37:52,990 INFO misc.py line 119 253097] Train: [99/100][52/510] Data 0.004 (0.138) Batch 1.270 (1.260) Remain 00:20:19 loss: 0.0985 Lr: 0.00001
[2023-12-26 00:37:54,102 INFO misc.py line 119 253097] Train: [99/100][53/510] Data 0.003 (0.135) Batch 1.109 (1.257) Remain 00:20:15 loss: 0.0779 Lr: 0.00001
[2023-12-26 00:37:55,335 INFO misc.py line 119 253097] Train: [99/100][54/510] Data 0.007 (0.132) Batch 1.232 (1.257) Remain 00:20:14 loss: 0.1159 Lr: 0.00001
[2023-12-26 00:37:56,336 INFO misc.py line 119 253097] Train: [99/100][55/510] Data 0.007 (0.130) Batch 1.002 (1.252) Remain 00:20:08 loss: 0.0834 Lr: 0.00001
[2023-12-26 00:37:57,445 INFO misc.py line 119 253097] Train: [99/100][56/510] Data 0.007 (0.128) Batch 1.110 (1.249) Remain 00:20:04 loss: 0.0821 Lr: 0.00001
[2023-12-26 00:37:58,707 INFO misc.py line 119 253097] Train: [99/100][57/510] Data 0.005 (0.125) Batch 1.263 (1.249) Remain 00:20:03 loss: 0.0679 Lr: 0.00001
[2023-12-26 00:37:59,899 INFO misc.py line 119 253097] Train: [99/100][58/510] Data 0.006 (0.123) Batch 1.192 (1.248) Remain 00:20:00 loss: 0.1200 Lr: 0.00001
[2023-12-26 00:38:01,076 INFO misc.py line 119 253097] Train: [99/100][59/510] Data 0.006 (0.121) Batch 1.175 (1.247) Remain 00:19:58 loss: 0.0718 Lr: 0.00001
[2023-12-26 00:38:02,252 INFO misc.py line 119 253097] Train: [99/100][60/510] Data 0.007 (0.119) Batch 1.178 (1.246) Remain 00:19:56 loss: 0.0998 Lr: 0.00001
[2023-12-26 00:38:03,458 INFO misc.py line 119 253097] Train: [99/100][61/510] Data 0.006 (0.117) Batch 1.205 (1.245) Remain 00:19:54 loss: 0.1302 Lr: 0.00001
[2023-12-26 00:38:04,654 INFO misc.py line 119 253097] Train: [99/100][62/510] Data 0.006 (0.115) Batch 1.196 (1.244) Remain 00:19:52 loss: 0.0980 Lr: 0.00001
[2023-12-26 00:38:05,963 INFO misc.py line 119 253097] Train: [99/100][63/510] Data 0.007 (0.113) Batch 1.312 (1.245) Remain 00:19:51 loss: 0.1352 Lr: 0.00001
[2023-12-26 00:38:07,187 INFO misc.py line 119 253097] Train: [99/100][64/510] Data 0.003 (0.112) Batch 1.224 (1.245) Remain 00:19:50 loss: 0.1238 Lr: 0.00001
[2023-12-26 00:38:08,109 INFO misc.py line 119 253097] Train: [99/100][65/510] Data 0.003 (0.110) Batch 0.922 (1.240) Remain 00:19:44 loss: 0.0804 Lr: 0.00001
[2023-12-26 00:38:09,350 INFO misc.py line 119 253097] Train: [99/100][66/510] Data 0.004 (0.108) Batch 1.240 (1.240) Remain 00:19:42 loss: 0.1291 Lr: 0.00001
[2023-12-26 00:38:10,419 INFO misc.py line 119 253097] Train: [99/100][67/510] Data 0.004 (0.107) Batch 1.068 (1.237) Remain 00:19:39 loss: 0.0839 Lr: 0.00001
[2023-12-26 00:38:11,619 INFO misc.py line 119 253097] Train: [99/100][68/510] Data 0.006 (0.105) Batch 1.202 (1.237) Remain 00:19:37 loss: 0.2111 Lr: 0.00001
[2023-12-26 00:38:12,687 INFO misc.py line 119 253097] Train: [99/100][69/510] Data 0.004 (0.103) Batch 1.065 (1.234) Remain 00:19:33 loss: 0.1607 Lr: 0.00001
[2023-12-26 00:38:13,786 INFO misc.py line 119 253097] Train: [99/100][70/510] Data 0.007 (0.102) Batch 1.103 (1.232) Remain 00:19:30 loss: 0.1125 Lr: 0.00001
[2023-12-26 00:38:14,943 INFO misc.py line 119 253097] Train: [99/100][71/510] Data 0.004 (0.101) Batch 1.155 (1.231) Remain 00:19:28 loss: 0.0691 Lr: 0.00001
[2023-12-26 00:38:16,018 INFO misc.py line 119 253097] Train: [99/100][72/510] Data 0.005 (0.099) Batch 1.077 (1.229) Remain 00:19:24 loss: 0.0801 Lr: 0.00001
[2023-12-26 00:38:17,165 INFO misc.py line 119 253097] Train: [99/100][73/510] Data 0.003 (0.098) Batch 1.143 (1.228) Remain 00:19:22 loss: 0.0887 Lr: 0.00001
[2023-12-26 00:38:18,327 INFO misc.py line 119 253097] Train: [99/100][74/510] Data 0.007 (0.097) Batch 1.166 (1.227) Remain 00:19:20 loss: 0.1224 Lr: 0.00001
[2023-12-26 00:38:24,168 INFO misc.py line 119 253097] Train: [99/100][75/510] Data 0.004 (0.095) Batch 5.840 (1.291) Remain 00:20:19 loss: 0.1026 Lr: 0.00001
[2023-12-26 00:38:25,256 INFO misc.py line 119 253097] Train: [99/100][76/510] Data 0.004 (0.094) Batch 1.089 (1.288) Remain 00:20:15 loss: 0.0913 Lr: 0.00001
[2023-12-26 00:38:26,315 INFO misc.py line 119 253097] Train: [99/100][77/510] Data 0.004 (0.093) Batch 1.059 (1.285) Remain 00:20:11 loss: 0.1040 Lr: 0.00001
[2023-12-26 00:38:27,619 INFO misc.py line 119 253097] Train: [99/100][78/510] Data 0.004 (0.092) Batch 1.304 (1.285) Remain 00:20:10 loss: 0.0595 Lr: 0.00001
[2023-12-26 00:38:28,576 INFO misc.py line 119 253097] Train: [99/100][79/510] Data 0.004 (0.090) Batch 0.958 (1.281) Remain 00:20:05 loss: 0.0944 Lr: 0.00001
[2023-12-26 00:38:29,641 INFO misc.py line 119 253097] Train: [99/100][80/510] Data 0.002 (0.089) Batch 1.065 (1.278) Remain 00:20:01 loss: 0.0777 Lr: 0.00001
[2023-12-26 00:38:30,648 INFO misc.py line 119 253097] Train: [99/100][81/510] Data 0.003 (0.088) Batch 1.007 (1.275) Remain 00:19:56 loss: 0.1302 Lr: 0.00001
[2023-12-26 00:38:31,957 INFO misc.py line 119 253097] Train: [99/100][82/510] Data 0.003 (0.087) Batch 1.306 (1.275) Remain 00:19:55 loss: 0.0739 Lr: 0.00001
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[2023-12-26 00:39:37,430 INFO misc.py line 119 253097] Train: [99/100][128/510] Data 0.003 (0.057) Batch 5.499 (1.330) Remain 00:19:45 loss: 0.2731 Lr: 0.00001
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[2023-12-26 00:39:42,232 INFO misc.py line 119 253097] Train: [99/100][130/510] Data 2.557 (0.076) Batch 3.781 (1.346) Remain 00:19:58 loss: 0.0770 Lr: 0.00001
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[2023-12-26 00:39:51,448 INFO misc.py line 119 253097] Train: [99/100][138/510] Data 0.006 (0.072) Batch 1.093 (1.335) Remain 00:19:37 loss: 0.0811 Lr: 0.00001
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[2023-12-26 00:39:53,614 INFO misc.py line 119 253097] Train: [99/100][140/510] Data 0.008 (0.071) Batch 1.014 (1.331) Remain 00:19:31 loss: 0.1932 Lr: 0.00001
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[2023-12-26 00:48:25,980 INFO misc.py line 119 253097] Train: [99/100][506/510] Data 0.003 (0.049) Batch 0.922 (1.381) Remain 00:11:49 loss: 0.1348 Lr: 0.00000
[2023-12-26 00:48:27,274 INFO misc.py line 119 253097] Train: [99/100][507/510] Data 0.003 (0.049) Batch 1.294 (1.381) Remain 00:11:48 loss: 0.0448 Lr: 0.00000
[2023-12-26 00:48:28,295 INFO misc.py line 119 253097] Train: [99/100][508/510] Data 0.003 (0.049) Batch 1.021 (1.380) Remain 00:11:46 loss: 0.0956 Lr: 0.00000
[2023-12-26 00:48:29,467 INFO misc.py line 119 253097] Train: [99/100][509/510] Data 0.003 (0.048) Batch 1.171 (1.380) Remain 00:11:45 loss: 0.1510 Lr: 0.00000
[2023-12-26 00:48:30,574 INFO misc.py line 119 253097] Train: [99/100][510/510] Data 0.003 (0.048) Batch 1.107 (1.379) Remain 00:11:43 loss: 0.1117 Lr: 0.00000
[2023-12-26 00:48:30,574 INFO misc.py line 136 253097] Train result: loss: 0.1106 
[2023-12-26 00:48:30,575 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-26 00:49:06,524 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5871 
[2023-12-26 00:49:06,872 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3041 
[2023-12-26 00:49:11,816 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3270 
[2023-12-26 00:49:12,329 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.4064 
[2023-12-26 00:49:14,306 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8238 
[2023-12-26 00:49:14,728 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3722 
[2023-12-26 00:49:15,603 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1352 
[2023-12-26 00:49:16,157 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2608 
[2023-12-26 00:49:17,967 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.6933 
[2023-12-26 00:49:20,087 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.0858 
[2023-12-26 00:49:20,945 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3106 
[2023-12-26 00:49:21,366 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.8341 
[2023-12-26 00:49:22,265 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.4031 
[2023-12-26 00:49:25,207 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8659 
[2023-12-26 00:49:25,675 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.2641 
[2023-12-26 00:49:26,283 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3679 
[2023-12-26 00:49:26,985 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3228 
[2023-12-26 00:49:28,164 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6993/0.7540/0.9071.
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9178/0.9466
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9828/0.9904
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8472/0.9745
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3944/0.4207
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6284/0.6470
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7220/0.8094
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8073/0.8911
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9169/0.9578
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6622/0.7105
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7829/0.8723
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8112/0.8508
[2023-12-26 00:49:28,164 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6173/0.7312
[2023-12-26 00:49:28,165 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-26 00:49:28,166 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-26 00:49:28,166 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-26 00:49:46,556 INFO misc.py line 119 253097] Train: [100/100][1/510] Data 7.812 (7.812) Batch 15.591 (15.591) Remain 02:12:15 loss: 0.0922 Lr: 0.00000
[2023-12-26 00:49:47,772 INFO misc.py line 119 253097] Train: [100/100][2/510] Data 0.005 (0.005) Batch 1.217 (1.217) Remain 00:10:18 loss: 0.2097 Lr: 0.00000
[2023-12-26 00:49:48,953 INFO misc.py line 119 253097] Train: [100/100][3/510] Data 0.003 (0.003) Batch 1.181 (1.181) Remain 00:09:58 loss: 0.1534 Lr: 0.00000
[2023-12-26 00:49:50,141 INFO misc.py line 119 253097] Train: [100/100][4/510] Data 0.003 (0.003) Batch 1.187 (1.187) Remain 00:10:00 loss: 0.0925 Lr: 0.00000
[2023-12-26 00:49:51,406 INFO misc.py line 119 253097] Train: [100/100][5/510] Data 0.004 (0.003) Batch 1.266 (1.226) Remain 00:10:19 loss: 0.1269 Lr: 0.00000
[2023-12-26 00:49:52,455 INFO misc.py line 119 253097] Train: [100/100][6/510] Data 0.003 (0.003) Batch 1.047 (1.167) Remain 00:09:48 loss: 0.0864 Lr: 0.00000
[2023-12-26 00:49:53,522 INFO misc.py line 119 253097] Train: [100/100][7/510] Data 0.004 (0.004) Batch 1.066 (1.142) Remain 00:09:34 loss: 0.1040 Lr: 0.00000
[2023-12-26 00:49:54,657 INFO misc.py line 119 253097] Train: [100/100][8/510] Data 0.005 (0.004) Batch 1.135 (1.140) Remain 00:09:32 loss: 0.0651 Lr: 0.00000
[2023-12-26 00:49:55,823 INFO misc.py line 119 253097] Train: [100/100][9/510] Data 0.006 (0.004) Batch 1.166 (1.145) Remain 00:09:33 loss: 0.0694 Lr: 0.00000
[2023-12-26 00:49:57,033 INFO misc.py line 119 253097] Train: [100/100][10/510] Data 0.005 (0.004) Batch 1.212 (1.154) Remain 00:09:37 loss: 0.0759 Lr: 0.00000
[2023-12-26 00:49:58,337 INFO misc.py line 119 253097] Train: [100/100][11/510] Data 0.004 (0.004) Batch 1.300 (1.172) Remain 00:09:45 loss: 0.0914 Lr: 0.00000
[2023-12-26 00:49:59,258 INFO misc.py line 119 253097] Train: [100/100][12/510] Data 0.008 (0.005) Batch 0.926 (1.145) Remain 00:09:30 loss: 0.1709 Lr: 0.00000
[2023-12-26 00:50:00,433 INFO misc.py line 119 253097] Train: [100/100][13/510] Data 0.003 (0.005) Batch 1.174 (1.148) Remain 00:09:30 loss: 0.0814 Lr: 0.00000
[2023-12-26 00:50:06,183 INFO misc.py line 119 253097] Train: [100/100][14/510] Data 4.505 (0.414) Batch 5.750 (1.566) Remain 00:12:56 loss: 0.0656 Lr: 0.00000
[2023-12-26 00:50:07,250 INFO misc.py line 119 253097] Train: [100/100][15/510] Data 0.003 (0.379) Batch 1.066 (1.525) Remain 00:12:34 loss: 0.0705 Lr: 0.00000
[2023-12-26 00:50:08,102 INFO misc.py line 119 253097] Train: [100/100][16/510] Data 0.004 (0.351) Batch 0.853 (1.473) Remain 00:12:07 loss: 0.0995 Lr: 0.00000
[2023-12-26 00:50:09,333 INFO misc.py line 119 253097] Train: [100/100][17/510] Data 0.003 (0.326) Batch 1.231 (1.456) Remain 00:11:57 loss: 0.0799 Lr: 0.00000
[2023-12-26 00:50:10,624 INFO misc.py line 119 253097] Train: [100/100][18/510] Data 0.005 (0.304) Batch 1.288 (1.444) Remain 00:11:50 loss: 0.1681 Lr: 0.00000
[2023-12-26 00:50:11,961 INFO misc.py line 119 253097] Train: [100/100][19/510] Data 0.006 (0.286) Batch 1.337 (1.438) Remain 00:11:45 loss: 0.1417 Lr: 0.00000
[2023-12-26 00:50:13,094 INFO misc.py line 119 253097] Train: [100/100][20/510] Data 0.006 (0.269) Batch 1.134 (1.420) Remain 00:11:35 loss: 0.0953 Lr: 0.00000
[2023-12-26 00:50:14,165 INFO misc.py line 119 253097] Train: [100/100][21/510] Data 0.005 (0.255) Batch 1.071 (1.401) Remain 00:11:24 loss: 0.1169 Lr: 0.00000
[2023-12-26 00:50:15,312 INFO misc.py line 119 253097] Train: [100/100][22/510] Data 0.005 (0.241) Batch 1.147 (1.387) Remain 00:11:16 loss: 0.0911 Lr: 0.00000
[2023-12-26 00:50:16,544 INFO misc.py line 119 253097] Train: [100/100][23/510] Data 0.007 (0.230) Batch 1.225 (1.379) Remain 00:11:11 loss: 0.0800 Lr: 0.00000
[2023-12-26 00:50:17,434 INFO misc.py line 119 253097] Train: [100/100][24/510] Data 0.013 (0.219) Batch 0.899 (1.356) Remain 00:10:59 loss: 0.1154 Lr: 0.00000
[2023-12-26 00:50:18,570 INFO misc.py line 119 253097] Train: [100/100][25/510] Data 0.003 (0.210) Batch 1.136 (1.346) Remain 00:10:52 loss: 0.0686 Lr: 0.00000
[2023-12-26 00:50:19,768 INFO misc.py line 119 253097] Train: [100/100][26/510] Data 0.003 (0.201) Batch 1.197 (1.340) Remain 00:10:48 loss: 0.1187 Lr: 0.00000
[2023-12-26 00:50:20,828 INFO misc.py line 119 253097] Train: [100/100][27/510] Data 0.004 (0.192) Batch 1.061 (1.328) Remain 00:10:41 loss: 0.0833 Lr: 0.00000
[2023-12-26 00:50:22,020 INFO misc.py line 119 253097] Train: [100/100][28/510] Data 0.004 (0.185) Batch 1.188 (1.323) Remain 00:10:37 loss: 0.0489 Lr: 0.00000
[2023-12-26 00:50:23,118 INFO misc.py line 119 253097] Train: [100/100][29/510] Data 0.007 (0.178) Batch 1.097 (1.314) Remain 00:10:31 loss: 0.0826 Lr: 0.00000
[2023-12-26 00:50:30,024 INFO misc.py line 119 253097] Train: [100/100][30/510] Data 0.008 (0.172) Batch 6.910 (1.521) Remain 00:12:10 loss: 0.0934 Lr: 0.00000
[2023-12-26 00:50:30,972 INFO misc.py line 119 253097] Train: [100/100][31/510] Data 0.004 (0.166) Batch 0.949 (1.501) Remain 00:11:58 loss: 0.1109 Lr: 0.00000
[2023-12-26 00:50:32,154 INFO misc.py line 119 253097] Train: [100/100][32/510] Data 0.002 (0.160) Batch 1.182 (1.490) Remain 00:11:52 loss: 0.0755 Lr: 0.00000
[2023-12-26 00:50:32,990 INFO misc.py line 119 253097] Train: [100/100][33/510] Data 0.002 (0.155) Batch 0.835 (1.468) Remain 00:11:40 loss: 0.1004 Lr: 0.00000
[2023-12-26 00:50:34,298 INFO misc.py line 119 253097] Train: [100/100][34/510] Data 0.003 (0.150) Batch 1.308 (1.463) Remain 00:11:36 loss: 0.0958 Lr: 0.00000
[2023-12-26 00:50:35,493 INFO misc.py line 119 253097] Train: [100/100][35/510] Data 0.003 (0.145) Batch 1.190 (1.454) Remain 00:11:30 loss: 0.1705 Lr: 0.00000
[2023-12-26 00:50:38,481 INFO misc.py line 119 253097] Train: [100/100][36/510] Data 0.006 (0.141) Batch 2.992 (1.501) Remain 00:11:51 loss: 0.0522 Lr: 0.00000
[2023-12-26 00:50:39,691 INFO misc.py line 119 253097] Train: [100/100][37/510] Data 0.002 (0.137) Batch 1.210 (1.492) Remain 00:11:45 loss: 0.1360 Lr: 0.00000
[2023-12-26 00:50:40,938 INFO misc.py line 119 253097] Train: [100/100][38/510] Data 0.002 (0.133) Batch 1.243 (1.485) Remain 00:11:40 loss: 0.0976 Lr: 0.00000
[2023-12-26 00:50:42,095 INFO misc.py line 119 253097] Train: [100/100][39/510] Data 0.007 (0.130) Batch 1.158 (1.476) Remain 00:11:35 loss: 0.0926 Lr: 0.00000
[2023-12-26 00:50:43,283 INFO misc.py line 119 253097] Train: [100/100][40/510] Data 0.006 (0.126) Batch 1.189 (1.468) Remain 00:11:30 loss: 0.0783 Lr: 0.00000
[2023-12-26 00:50:44,324 INFO misc.py line 119 253097] Train: [100/100][41/510] Data 0.007 (0.123) Batch 1.039 (1.457) Remain 00:11:23 loss: 0.0924 Lr: 0.00000
[2023-12-26 00:50:45,410 INFO misc.py line 119 253097] Train: [100/100][42/510] Data 0.007 (0.120) Batch 1.090 (1.448) Remain 00:11:17 loss: 0.0685 Lr: 0.00000
[2023-12-26 00:50:46,614 INFO misc.py line 119 253097] Train: [100/100][43/510] Data 0.003 (0.117) Batch 1.199 (1.441) Remain 00:11:13 loss: 0.0756 Lr: 0.00000
[2023-12-26 00:50:52,937 INFO misc.py line 119 253097] Train: [100/100][44/510] Data 5.112 (0.239) Batch 6.328 (1.561) Remain 00:12:07 loss: 0.0803 Lr: 0.00000
[2023-12-26 00:50:53,762 INFO misc.py line 119 253097] Train: [100/100][45/510] Data 0.003 (0.234) Batch 0.825 (1.543) Remain 00:11:57 loss: 0.1199 Lr: 0.00000
[2023-12-26 00:50:54,830 INFO misc.py line 119 253097] Train: [100/100][46/510] Data 0.002 (0.228) Batch 1.064 (1.532) Remain 00:11:50 loss: 0.0746 Lr: 0.00000
[2023-12-26 00:50:56,112 INFO misc.py line 119 253097] Train: [100/100][47/510] Data 0.007 (0.223) Batch 1.280 (1.526) Remain 00:11:46 loss: 0.0946 Lr: 0.00000
[2023-12-26 00:50:57,083 INFO misc.py line 119 253097] Train: [100/100][48/510] Data 0.009 (0.218) Batch 0.977 (1.514) Remain 00:11:39 loss: 0.0978 Lr: 0.00000
[2023-12-26 00:50:58,084 INFO misc.py line 119 253097] Train: [100/100][49/510] Data 0.003 (0.214) Batch 1.001 (1.503) Remain 00:11:32 loss: 0.1511 Lr: 0.00000
[2023-12-26 00:50:59,108 INFO misc.py line 119 253097] Train: [100/100][50/510] Data 0.003 (0.209) Batch 1.024 (1.493) Remain 00:11:26 loss: 0.1758 Lr: 0.00000
[2023-12-26 00:51:00,269 INFO misc.py line 119 253097] Train: [100/100][51/510] Data 0.003 (0.205) Batch 1.161 (1.486) Remain 00:11:21 loss: 0.1375 Lr: 0.00000
[2023-12-26 00:51:01,197 INFO misc.py line 119 253097] Train: [100/100][52/510] Data 0.003 (0.201) Batch 0.928 (1.474) Remain 00:11:15 loss: 0.1306 Lr: 0.00000
[2023-12-26 00:51:02,330 INFO misc.py line 119 253097] Train: [100/100][53/510] Data 0.002 (0.197) Batch 1.132 (1.468) Remain 00:11:10 loss: 0.0775 Lr: 0.00000
[2023-12-26 00:51:03,515 INFO misc.py line 119 253097] Train: [100/100][54/510] Data 0.003 (0.193) Batch 1.186 (1.462) Remain 00:11:06 loss: 0.1359 Lr: 0.00000
[2023-12-26 00:51:04,454 INFO misc.py line 119 253097] Train: [100/100][55/510] Data 0.003 (0.189) Batch 0.939 (1.452) Remain 00:11:00 loss: 0.0940 Lr: 0.00000
[2023-12-26 00:51:05,544 INFO misc.py line 119 253097] Train: [100/100][56/510] Data 0.003 (0.186) Batch 1.089 (1.445) Remain 00:10:56 loss: 0.1620 Lr: 0.00000
[2023-12-26 00:51:06,538 INFO misc.py line 119 253097] Train: [100/100][57/510] Data 0.004 (0.182) Batch 0.996 (1.437) Remain 00:10:50 loss: 0.1574 Lr: 0.00000
[2023-12-26 00:51:07,632 INFO misc.py line 119 253097] Train: [100/100][58/510] Data 0.003 (0.179) Batch 1.093 (1.431) Remain 00:10:46 loss: 0.1725 Lr: 0.00000
[2023-12-26 00:51:08,687 INFO misc.py line 119 253097] Train: [100/100][59/510] Data 0.003 (0.176) Batch 1.056 (1.424) Remain 00:10:42 loss: 0.1093 Lr: 0.00000
[2023-12-26 00:51:09,772 INFO misc.py line 119 253097] Train: [100/100][60/510] Data 0.002 (0.173) Batch 1.084 (1.418) Remain 00:10:38 loss: 0.0572 Lr: 0.00000
[2023-12-26 00:51:10,959 INFO misc.py line 119 253097] Train: [100/100][61/510] Data 0.003 (0.170) Batch 1.187 (1.414) Remain 00:10:34 loss: 0.0913 Lr: 0.00000
[2023-12-26 00:51:12,162 INFO misc.py line 119 253097] Train: [100/100][62/510] Data 0.003 (0.167) Batch 1.201 (1.410) Remain 00:10:31 loss: 0.1080 Lr: 0.00000
[2023-12-26 00:51:13,351 INFO misc.py line 119 253097] Train: [100/100][63/510] Data 0.005 (0.165) Batch 1.188 (1.407) Remain 00:10:28 loss: 0.0899 Lr: 0.00000
[2023-12-26 00:51:14,453 INFO misc.py line 119 253097] Train: [100/100][64/510] Data 0.007 (0.162) Batch 1.101 (1.402) Remain 00:10:25 loss: 0.1310 Lr: 0.00000
[2023-12-26 00:51:15,732 INFO misc.py line 119 253097] Train: [100/100][65/510] Data 0.006 (0.159) Batch 1.279 (1.400) Remain 00:10:22 loss: 0.1324 Lr: 0.00000
[2023-12-26 00:51:16,680 INFO misc.py line 119 253097] Train: [100/100][66/510] Data 0.007 (0.157) Batch 0.951 (1.392) Remain 00:10:18 loss: 0.0508 Lr: 0.00000
[2023-12-26 00:51:23,760 INFO misc.py line 119 253097] Train: [100/100][67/510] Data 5.914 (0.247) Batch 7.081 (1.481) Remain 00:10:56 loss: 0.0778 Lr: 0.00000
[2023-12-26 00:51:24,821 INFO misc.py line 119 253097] Train: [100/100][68/510] Data 0.003 (0.243) Batch 1.061 (1.475) Remain 00:10:51 loss: 0.0870 Lr: 0.00000
[2023-12-26 00:51:25,916 INFO misc.py line 119 253097] Train: [100/100][69/510] Data 0.003 (0.240) Batch 1.094 (1.469) Remain 00:10:47 loss: 0.0962 Lr: 0.00000
[2023-12-26 00:51:27,129 INFO misc.py line 119 253097] Train: [100/100][70/510] Data 0.004 (0.236) Batch 1.214 (1.465) Remain 00:10:44 loss: 0.2659 Lr: 0.00000
[2023-12-26 00:51:28,176 INFO misc.py line 119 253097] Train: [100/100][71/510] Data 0.003 (0.233) Batch 1.046 (1.459) Remain 00:10:40 loss: 0.1030 Lr: 0.00000
[2023-12-26 00:51:29,385 INFO misc.py line 119 253097] Train: [100/100][72/510] Data 0.004 (0.229) Batch 1.206 (1.455) Remain 00:10:37 loss: 0.1318 Lr: 0.00000
[2023-12-26 00:51:30,569 INFO misc.py line 119 253097] Train: [100/100][73/510] Data 0.007 (0.226) Batch 1.182 (1.452) Remain 00:10:34 loss: 0.1106 Lr: 0.00000
[2023-12-26 00:51:31,757 INFO misc.py line 119 253097] Train: [100/100][74/510] Data 0.009 (0.223) Batch 1.192 (1.448) Remain 00:10:31 loss: 0.1967 Lr: 0.00000
[2023-12-26 00:51:32,875 INFO misc.py line 119 253097] Train: [100/100][75/510] Data 0.006 (0.220) Batch 1.117 (1.443) Remain 00:10:27 loss: 0.0825 Lr: 0.00000
[2023-12-26 00:51:33,914 INFO misc.py line 119 253097] Train: [100/100][76/510] Data 0.006 (0.217) Batch 1.037 (1.438) Remain 00:10:23 loss: 0.0907 Lr: 0.00000
[2023-12-26 00:51:35,045 INFO misc.py line 119 253097] Train: [100/100][77/510] Data 0.008 (0.214) Batch 1.133 (1.434) Remain 00:10:20 loss: 0.1407 Lr: 0.00000
[2023-12-26 00:51:36,169 INFO misc.py line 119 253097] Train: [100/100][78/510] Data 0.006 (0.212) Batch 1.124 (1.429) Remain 00:10:17 loss: 0.1988 Lr: 0.00000
[2023-12-26 00:51:37,355 INFO misc.py line 119 253097] Train: [100/100][79/510] Data 0.007 (0.209) Batch 1.188 (1.426) Remain 00:10:14 loss: 0.1379 Lr: 0.00000
[2023-12-26 00:51:38,492 INFO misc.py line 119 253097] Train: [100/100][80/510] Data 0.005 (0.206) Batch 1.134 (1.423) Remain 00:10:11 loss: 0.0799 Lr: 0.00000
[2023-12-26 00:51:39,399 INFO misc.py line 119 253097] Train: [100/100][81/510] Data 0.008 (0.204) Batch 0.911 (1.416) Remain 00:10:07 loss: 0.1175 Lr: 0.00000
[2023-12-26 00:51:44,039 INFO misc.py line 119 253097] Train: [100/100][82/510] Data 3.657 (0.247) Batch 4.640 (1.457) Remain 00:10:23 loss: 0.2248 Lr: 0.00000
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[2023-12-26 00:52:34,515 INFO misc.py line 119 253097] Train: [100/100][119/510] Data 0.002 (0.170) Batch 1.021 (1.427) Remain 00:09:18 loss: 0.1044 Lr: 0.00000
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[2023-12-26 00:57:06,869 INFO misc.py line 119 253097] Train: [100/100][331/510] Data 0.008 (0.064) Batch 1.187 (1.335) Remain 00:03:58 loss: 0.0756 Lr: 0.00000
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[2023-12-26 01:01:09,296 INFO misc.py line 119 253097] Train: [100/100][507/510] Data 0.031 (0.058) Batch 1.113 (1.350) Remain 00:00:04 loss: 0.0760 Lr: 0.00000
[2023-12-26 01:01:10,409 INFO misc.py line 119 253097] Train: [100/100][508/510] Data 0.035 (0.057) Batch 1.105 (1.349) Remain 00:00:02 loss: 0.0952 Lr: 0.00000
[2023-12-26 01:01:11,752 INFO misc.py line 119 253097] Train: [100/100][509/510] Data 0.043 (0.057) Batch 1.351 (1.349) Remain 00:00:01 loss: 0.0942 Lr: 0.00000
[2023-12-26 01:01:12,975 INFO misc.py line 119 253097] Train: [100/100][510/510] Data 0.035 (0.057) Batch 1.235 (1.349) Remain 00:00:00 loss: 0.0899 Lr: 0.00000
[2023-12-26 01:01:12,976 INFO misc.py line 136 253097] Train result: loss: 0.1085 
[2023-12-26 01:01:12,976 INFO evaluator.py line 112 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2023-12-26 01:02:01,100 INFO evaluator.py line 159 253097] Interp. Test: [1/17] Loss 0.5082 
[2023-12-26 01:02:01,451 INFO evaluator.py line 159 253097] Interp. Test: [2/17] Loss 0.3175 
[2023-12-26 01:02:07,033 INFO evaluator.py line 159 253097] Interp. Test: [3/17] Loss 0.3221 
[2023-12-26 01:02:07,547 INFO evaluator.py line 159 253097] Interp. Test: [4/17] Loss 0.3611 
[2023-12-26 01:02:09,522 INFO evaluator.py line 159 253097] Interp. Test: [5/17] Loss 0.8957 
[2023-12-26 01:02:09,940 INFO evaluator.py line 159 253097] Interp. Test: [6/17] Loss 0.3658 
[2023-12-26 01:02:10,816 INFO evaluator.py line 159 253097] Interp. Test: [7/17] Loss 1.1428 
[2023-12-26 01:02:11,367 INFO evaluator.py line 159 253097] Interp. Test: [8/17] Loss 0.2398 
[2023-12-26 01:02:13,187 INFO evaluator.py line 159 253097] Interp. Test: [9/17] Loss 0.8304 
[2023-12-26 01:02:15,310 INFO evaluator.py line 159 253097] Interp. Test: [10/17] Loss 0.0823 
[2023-12-26 01:02:16,163 INFO evaluator.py line 159 253097] Interp. Test: [11/17] Loss 0.3072 
[2023-12-26 01:02:16,584 INFO evaluator.py line 159 253097] Interp. Test: [12/17] Loss 0.7400 
[2023-12-26 01:02:17,482 INFO evaluator.py line 159 253097] Interp. Test: [13/17] Loss 0.5593 
[2023-12-26 01:02:20,423 INFO evaluator.py line 159 253097] Interp. Test: [14/17] Loss 0.8398 
[2023-12-26 01:02:20,893 INFO evaluator.py line 159 253097] Interp. Test: [15/17] Loss 0.3585 
[2023-12-26 01:02:21,501 INFO evaluator.py line 159 253097] Interp. Test: [16/17] Loss 0.3918 
[2023-12-26 01:02:22,199 INFO evaluator.py line 159 253097] Interp. Test: [17/17] Loss 0.3374 
[2023-12-26 01:02:23,544 INFO evaluator.py line 174 253097] Val result: mIoU/mAcc/allAcc 0.6976/0.7532/0.9063.
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_0-ceiling Result: iou/accuracy 0.9185/0.9474
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_1-floor Result: iou/accuracy 0.9833/0.9904
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_2-wall Result: iou/accuracy 0.8460/0.9730
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_3-beam Result: iou/accuracy 0.0000/0.0000
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_4-column Result: iou/accuracy 0.3538/0.3867
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_5-window Result: iou/accuracy 0.6262/0.6449
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_6-door Result: iou/accuracy 0.7058/0.8002
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_7-table Result: iou/accuracy 0.8084/0.8936
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_8-chair Result: iou/accuracy 0.9188/0.9595
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_9-sofa Result: iou/accuracy 0.6640/0.7056
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_10-bookcase Result: iou/accuracy 0.7821/0.8721
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_11-board Result: iou/accuracy 0.8452/0.8878
[2023-12-26 01:02:23,544 INFO evaluator.py line 180 253097] Class_12-clutter Result: iou/accuracy 0.6174/0.7299
[2023-12-26 01:02:23,545 INFO evaluator.py line 194 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<
[2023-12-26 01:02:23,546 INFO misc.py line 165 253097] Currently Best mIoU: 0.7192
[2023-12-26 01:02:23,546 INFO misc.py line 174 253097] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-0-rpe/model/model_last.pth
[2023-12-26 01:02:24,877 INFO evaluator.py line 199 253097] Best mIoU: 0.7192
[2023-12-26 01:02:24,878 INFO misc.py line 259 253097] >>>>>>>>>>>>>>>> Start Precise Evaluation >>>>>>>>>>>>>>>>
[2023-12-26 01:02:25,182 INFO test.py line 41 253097] => Loading config ...
[2023-12-26 01:02:25,182 INFO test.py line 53 253097] => Building test dataset & dataloader ...
[2023-12-26 01:02:25,184 INFO s3dis.py line 55 253097] Totally 68 x 1 samples in Area_5 set.
[2023-12-26 01:02:25,185 INFO misc.py line 270 253097] => Testing on model_best ...
[2023-12-26 01:02:26,672 INFO test.py line 119 253097] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
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[2023-12-26 01:06:08,084 INFO test.py line 230 253097] Test: conferenceRoom_2 [1/17]-1922357 Batch 107.137 (107.137) Accuracy 0.9460 (0.7827) mIoU 0.7452 (0.7452)
[2023-12-26 01:06:08,230 INFO test.py line 196 253097] Test: 2/17-office_22, Batch: 0/192
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[2023-12-26 01:06:37,079 INFO test.py line 196 253097] Test: 2/17-office_22, Batch: 191/192
[2023-12-26 01:06:37,126 INFO test.py line 230 253097] Test: office_22 [2/17]-789364 Batch 29.041 (68.089) Accuracy 0.9699 (0.8549) mIoU 0.9214 (0.8257)
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[2023-12-26 01:07:09,641 INFO test.py line 230 253097] Test: office_7 [3/17]-821442 Batch 32.515 (56.231) Accuracy 0.9534 (0.8585) mIoU 0.8834 (0.8261)
[2023-12-26 01:07:09,797 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 0/190
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[2023-12-26 01:07:36,876 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 174/190
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[2023-12-26 01:07:37,240 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 176/190
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[2023-12-26 01:07:37,966 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 180/190
[2023-12-26 01:07:38,148 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 181/190
[2023-12-26 01:07:38,330 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 182/190
[2023-12-26 01:07:38,511 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 183/190
[2023-12-26 01:07:38,692 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 184/190
[2023-12-26 01:07:38,873 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 185/190
[2023-12-26 01:07:39,054 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 186/190
[2023-12-26 01:07:39,235 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 187/190
[2023-12-26 01:07:39,419 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 188/190
[2023-12-26 01:07:39,601 INFO test.py line 196 253097] Test: 4/17-office_4, Batch: 189/190
[2023-12-26 01:07:39,645 INFO test.py line 230 253097] Test: office_4 [4/17]-820397 Batch 30.003 (49.674) Accuracy 0.9523 (0.8563) mIoU 0.8599 (0.8245)
[2023-12-26 01:07:39,960 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 0/226
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[2023-12-26 01:08:36,105 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 174/226
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[2023-12-26 01:08:36,776 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 176/226
[2023-12-26 01:08:37,111 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 177/226
[2023-12-26 01:08:37,447 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 178/226
[2023-12-26 01:08:37,781 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 179/226
[2023-12-26 01:08:38,116 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 180/226
[2023-12-26 01:08:38,454 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 181/226
[2023-12-26 01:08:38,790 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 182/226
[2023-12-26 01:08:39,127 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 183/226
[2023-12-26 01:08:39,463 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 184/226
[2023-12-26 01:08:39,800 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 185/226
[2023-12-26 01:08:40,164 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 186/226
[2023-12-26 01:08:40,527 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 187/226
[2023-12-26 01:08:40,890 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 188/226
[2023-12-26 01:08:41,252 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 189/226
[2023-12-26 01:08:41,615 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 190/226
[2023-12-26 01:08:41,981 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 191/226
[2023-12-26 01:08:42,344 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 192/226
[2023-12-26 01:08:42,708 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 193/226
[2023-12-26 01:08:43,071 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 194/226
[2023-12-26 01:08:43,435 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 195/226
[2023-12-26 01:08:43,800 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 196/226
[2023-12-26 01:08:44,167 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 197/226
[2023-12-26 01:08:44,531 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 198/226
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[2023-12-26 01:08:46,348 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 203/226
[2023-12-26 01:08:46,711 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 204/226
[2023-12-26 01:08:47,076 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 205/226
[2023-12-26 01:08:47,468 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 206/226
[2023-12-26 01:08:47,864 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 207/226
[2023-12-26 01:08:48,255 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 208/226
[2023-12-26 01:08:48,646 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 209/226
[2023-12-26 01:08:49,043 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 210/226
[2023-12-26 01:08:49,439 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 211/226
[2023-12-26 01:08:49,832 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 212/226
[2023-12-26 01:08:50,224 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 213/226
[2023-12-26 01:08:50,616 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 214/226
[2023-12-26 01:08:51,007 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 215/226
[2023-12-26 01:08:51,399 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 216/226
[2023-12-26 01:08:51,790 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 217/226
[2023-12-26 01:08:52,182 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 218/226
[2023-12-26 01:08:52,574 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 219/226
[2023-12-26 01:08:52,966 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 220/226
[2023-12-26 01:08:53,357 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 221/226
[2023-12-26 01:08:53,750 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 222/226
[2023-12-26 01:08:54,142 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 223/226
[2023-12-26 01:08:54,534 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 224/226
[2023-12-26 01:08:54,926 INFO test.py line 196 253097] Test: 5/17-office_21, Batch: 225/226
[2023-12-26 01:08:55,034 INFO test.py line 230 253097] Test: office_21 [5/17]-2047655 Batch 75.389 (54.817) Accuracy 0.9206 (0.8410) mIoU 0.8405 (0.8087)
[2023-12-26 01:08:55,178 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 0/192
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[2023-12-26 01:08:55,947 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 6/192
[2023-12-26 01:08:56,074 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 7/192
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[2023-12-26 01:08:56,327 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 9/192
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[2023-12-26 01:08:56,708 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 12/192
[2023-12-26 01:08:56,834 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 13/192
[2023-12-26 01:08:56,961 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 14/192
[2023-12-26 01:08:57,088 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 15/192
[2023-12-26 01:08:57,215 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 16/192
[2023-12-26 01:08:57,342 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 17/192
[2023-12-26 01:08:57,468 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 18/192
[2023-12-26 01:08:57,595 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 19/192
[2023-12-26 01:08:57,721 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 20/192
[2023-12-26 01:08:57,848 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 21/192
[2023-12-26 01:08:57,987 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 22/192
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[2023-12-26 01:08:58,676 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 27/192
[2023-12-26 01:08:58,813 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 28/192
[2023-12-26 01:08:58,950 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 29/192
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[2023-12-26 01:08:59,226 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 31/192
[2023-12-26 01:08:59,363 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 32/192
[2023-12-26 01:08:59,500 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 33/192
[2023-12-26 01:08:59,638 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 34/192
[2023-12-26 01:08:59,776 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 35/192
[2023-12-26 01:08:59,913 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 36/192
[2023-12-26 01:09:00,051 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 37/192
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[2023-12-26 01:09:00,326 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 39/192
[2023-12-26 01:09:00,463 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 40/192
[2023-12-26 01:09:00,600 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 41/192
[2023-12-26 01:09:00,748 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 42/192
[2023-12-26 01:09:00,896 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 43/192
[2023-12-26 01:09:01,044 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 44/192
[2023-12-26 01:09:01,191 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 45/192
[2023-12-26 01:09:01,339 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 46/192
[2023-12-26 01:09:01,486 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 47/192
[2023-12-26 01:09:01,633 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 48/192
[2023-12-26 01:09:01,782 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 49/192
[2023-12-26 01:09:01,931 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 50/192
[2023-12-26 01:09:02,079 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 51/192
[2023-12-26 01:09:02,227 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 52/192
[2023-12-26 01:09:02,375 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 53/192
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[2023-12-26 01:09:02,819 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 56/192
[2023-12-26 01:09:02,968 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 57/192
[2023-12-26 01:09:03,116 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 58/192
[2023-12-26 01:09:03,263 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 59/192
[2023-12-26 01:09:03,410 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 60/192
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[2023-12-26 01:09:04,524 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 67/192
[2023-12-26 01:09:04,686 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 68/192
[2023-12-26 01:09:04,844 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 69/192
[2023-12-26 01:09:05,001 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 70/192
[2023-12-26 01:09:05,159 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 71/192
[2023-12-26 01:09:05,317 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 72/192
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[2023-12-26 01:09:05,791 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 75/192
[2023-12-26 01:09:05,948 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 76/192
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[2023-12-26 01:09:07,115 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 83/192
[2023-12-26 01:09:07,285 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 84/192
[2023-12-26 01:09:07,455 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 85/192
[2023-12-26 01:09:07,625 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 86/192
[2023-12-26 01:09:07,795 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 87/192
[2023-12-26 01:09:07,965 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 88/192
[2023-12-26 01:09:08,135 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 89/192
[2023-12-26 01:09:08,307 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 90/192
[2023-12-26 01:09:08,478 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 91/192
[2023-12-26 01:09:08,648 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 92/192
[2023-12-26 01:09:08,819 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 93/192
[2023-12-26 01:09:08,989 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 94/192
[2023-12-26 01:09:09,159 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 95/192
[2023-12-26 01:09:09,287 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 96/192
[2023-12-26 01:09:09,416 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 97/192
[2023-12-26 01:09:09,543 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 98/192
[2023-12-26 01:09:09,670 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 99/192
[2023-12-26 01:09:09,797 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 100/192
[2023-12-26 01:09:09,924 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 101/192
[2023-12-26 01:09:10,051 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 102/192
[2023-12-26 01:09:10,178 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 103/192
[2023-12-26 01:09:10,305 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 104/192
[2023-12-26 01:09:10,432 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 105/192
[2023-12-26 01:09:10,560 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 106/192
[2023-12-26 01:09:10,686 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 107/192
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[2023-12-26 01:09:10,940 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 109/192
[2023-12-26 01:09:11,067 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 110/192
[2023-12-26 01:09:11,195 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 111/192
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[2023-12-26 01:09:13,473 INFO test.py line 196 253097] Test: 6/17-office_10, Batch: 128/192
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[2023-12-26 01:09:23,317 INFO test.py line 230 253097] Test: office_10 [6/17]-752349 Batch 28.282 (50.395) Accuracy 0.9605 (0.8449) mIoU 0.8794 (0.8123)
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[2023-12-26 01:10:18,042 INFO test.py line 230 253097] Test: conferenceRoom_3 [7/17]-1507439 Batch 54.725 (51.013) Accuracy 0.8860 (0.8184) mIoU 0.6593 (0.7793)
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[2023-12-26 01:11:13,492 INFO test.py line 230 253097] Test: hallway_7 [8/17]-1256534 Batch 55.449 (51.568) Accuracy 0.9292 (0.8190) mIoU 0.6354 (0.7828)
[2023-12-26 01:11:13,669 INFO test.py line 196 253097] Test: 9/17-conferenceRoom_1, Batch: 0/180
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[2023-12-26 01:11:46,600 INFO test.py line 230 253097] Test: conferenceRoom_1 [9/17]-1047554 Batch 33.107 (49.516) Accuracy 0.8787 (0.8107) mIoU 0.6956 (0.7715)
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[2023-12-26 01:12:11,330 INFO test.py line 196 253097] Test: 10/17-hallway_14, Batch: 177/186
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[2023-12-26 01:12:11,650 INFO test.py line 196 253097] Test: 10/17-hallway_14, Batch: 179/186
[2023-12-26 01:12:11,809 INFO test.py line 196 253097] Test: 10/17-hallway_14, Batch: 180/186
[2023-12-26 01:12:11,968 INFO test.py line 196 253097] Test: 10/17-hallway_14, Batch: 181/186
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[2023-12-26 01:12:12,287 INFO test.py line 196 253097] Test: 10/17-hallway_14, Batch: 183/186
[2023-12-26 01:12:12,446 INFO test.py line 196 253097] Test: 10/17-hallway_14, Batch: 184/186
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[2023-12-26 01:12:12,645 INFO test.py line 230 253097] Test: hallway_14 [10/17]-743965 Batch 26.044 (47.169) Accuracy 0.9862 (0.8160) mIoU 0.8010 (0.7782)
[2023-12-26 01:12:12,787 INFO test.py line 196 253097] Test: 11/17-office_5, Batch: 0/208
[2023-12-26 01:12:12,913 INFO test.py line 196 253097] Test: 11/17-office_5, Batch: 1/208
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[2023-12-26 01:12:42,693 INFO test.py line 196 253097] Test: 11/17-office_5, Batch: 207/208
[2023-12-26 01:12:42,734 INFO test.py line 230 253097] Test: office_5 [11/17]-766453 Batch 30.088 (45.616) Accuracy 0.9602 (0.8186) mIoU 0.8783 (0.7814)
[2023-12-26 01:12:42,846 INFO test.py line 196 253097] Test: 12/17-office_20, Batch: 0/212
[2023-12-26 01:12:42,946 INFO test.py line 196 253097] Test: 12/17-office_20, Batch: 1/212
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[2023-12-26 01:13:07,139 INFO test.py line 230 253097] Test: office_20 [12/17]-513674 Batch 24.405 (43.849) Accuracy 0.9202 (0.8188) mIoU 0.7444 (0.7813)
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[2023-12-26 01:13:31,064 INFO test.py line 196 253097] Test: 13/17-WC_2, Batch: 180/184
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[2023-12-26 01:13:31,547 INFO test.py line 230 253097] Test: WC_2 [13/17]-640250 Batch 24.407 (42.353) Accuracy 0.9200 (0.8189) mIoU 0.5388 (0.7813)
[2023-12-26 01:13:31,757 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 0/196
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[2023-12-26 01:14:12,435 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 187/196
[2023-12-26 01:14:12,688 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 188/196
[2023-12-26 01:14:12,945 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 189/196
[2023-12-26 01:14:13,198 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 190/196
[2023-12-26 01:14:13,450 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 191/196
[2023-12-26 01:14:13,703 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 192/196
[2023-12-26 01:14:13,956 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 193/196
[2023-12-26 01:14:14,208 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 194/196
[2023-12-26 01:14:14,460 INFO test.py line 196 253097] Test: 14/17-office_14, Batch: 195/196
[2023-12-26 01:14:14,517 INFO test.py line 230 253097] Test: office_14 [14/17]-1192488 Batch 42.970 (42.397) Accuracy 0.8923 (0.8164) mIoU 0.8073 (0.7783)
[2023-12-26 01:14:14,621 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 0/222
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[2023-12-26 01:14:18,526 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 40/222
[2023-12-26 01:14:18,628 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 41/222
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[2023-12-26 01:14:19,036 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 45/222
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[2023-12-26 01:14:19,240 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 47/222
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[2023-12-26 01:14:21,160 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 65/222
[2023-12-26 01:14:21,267 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 66/222
[2023-12-26 01:14:21,374 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 67/222
[2023-12-26 01:14:21,481 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 68/222
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[2023-12-26 01:14:21,695 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 70/222
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[2023-12-26 01:14:22,366 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 76/222
[2023-12-26 01:14:22,480 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 77/222
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[2023-12-26 01:14:22,707 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 79/222
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[2023-12-26 01:14:23,048 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 82/222
[2023-12-26 01:14:23,162 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 83/222
[2023-12-26 01:14:23,275 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 84/222
[2023-12-26 01:14:23,392 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 85/222
[2023-12-26 01:14:23,506 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 86/222
[2023-12-26 01:14:23,620 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 87/222
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[2023-12-26 01:14:23,961 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 90/222
[2023-12-26 01:14:24,074 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 91/222
[2023-12-26 01:14:24,188 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 92/222
[2023-12-26 01:14:24,309 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 93/222
[2023-12-26 01:14:24,430 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 94/222
[2023-12-26 01:14:24,550 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 95/222
[2023-12-26 01:14:24,671 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 96/222
[2023-12-26 01:14:24,793 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 97/222
[2023-12-26 01:14:24,914 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 98/222
[2023-12-26 01:14:25,035 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 99/222
[2023-12-26 01:14:25,156 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 100/222
[2023-12-26 01:14:25,278 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 101/222
[2023-12-26 01:14:25,400 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 102/222
[2023-12-26 01:14:25,520 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 103/222
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[2023-12-26 01:14:26,005 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 107/222
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[2023-12-26 01:14:26,368 INFO test.py line 196 253097] Test: 15/17-hallway_8, Batch: 110/222
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[2023-12-26 01:14:38,380 INFO test.py line 230 253097] Test: hallway_8 [15/17]-526782 Batch 23.863 (41.162) Accuracy 0.9692 (0.8167) mIoU 0.6258 (0.7790)
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[2023-12-26 01:15:01,824 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 144/202
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[2023-12-26 01:15:09,266 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 186/202
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[2023-12-26 01:15:10,622 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 193/202
[2023-12-26 01:15:10,816 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 194/202
[2023-12-26 01:15:11,010 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 195/202
[2023-12-26 01:15:11,203 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 196/202
[2023-12-26 01:15:11,397 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 197/202
[2023-12-26 01:15:11,590 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 198/202
[2023-12-26 01:15:11,784 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 199/202
[2023-12-26 01:15:11,977 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 200/202
[2023-12-26 01:15:12,171 INFO test.py line 196 253097] Test: 16/17-office_23, Batch: 201/202
[2023-12-26 01:15:12,220 INFO test.py line 230 253097] Test: office_23 [16/17]-897251 Batch 33.839 (40.704) Accuracy 0.9555 (0.8199) mIoU 0.8861 (0.7822)
[2023-12-26 01:15:12,369 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 0/186
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[2023-12-26 01:15:16,327 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 29/186
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[2023-12-26 01:15:16,760 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 32/186
[2023-12-26 01:15:16,905 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 33/186
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[2023-12-26 01:15:18,984 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 47/186
[2023-12-26 01:15:19,139 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 48/186
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[2023-12-26 01:15:22,178 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 67/186
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[2023-12-26 01:15:22,843 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 71/186
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[2023-12-26 01:15:23,175 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 73/186
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[2023-12-26 01:15:23,672 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 76/186
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[2023-12-26 01:15:24,029 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 78/186
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[2023-12-26 01:15:25,096 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 84/186
[2023-12-26 01:15:25,274 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 85/186
[2023-12-26 01:15:25,452 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 86/186
[2023-12-26 01:15:25,630 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 87/186
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[2023-12-26 01:15:26,163 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 90/186
[2023-12-26 01:15:26,342 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 91/186
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[2023-12-26 01:15:27,057 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 96/186
[2023-12-26 01:15:27,191 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 97/186
[2023-12-26 01:15:27,325 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 98/186
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[2023-12-26 01:15:40,241 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 181/186
[2023-12-26 01:15:40,424 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 182/186
[2023-12-26 01:15:40,608 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 183/186
[2023-12-26 01:15:40,792 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 184/186
[2023-12-26 01:15:40,975 INFO test.py line 196 253097] Test: 17/17-office_32, Batch: 185/186
[2023-12-26 01:15:41,024 INFO test.py line 230 253097] Test: office_32 [17/17]-809428 Batch 28.804 (40.004) Accuracy 0.9545 (0.8237) mIoU 0.9200 (0.7862)
[2023-12-26 01:15:42,811 INFO test.py line 289 253097] Syncing ...
[2023-12-26 01:21:09,046 INFO test.py line 317 253097] Val result: mIoU/mAcc/allAcc 0.7358/0.7901/0.9167
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_0 - ceiling Result: iou/accuracy 0.9237/0.9564
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_1 - floor Result: iou/accuracy 0.9833/0.9923
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_2 - wall Result: iou/accuracy 0.8660/0.9768
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_3 - beam Result: iou/accuracy 0.0000/0.0000
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_4 - column Result: iou/accuracy 0.5576/0.6271
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_5 - window Result: iou/accuracy 0.6369/0.6556
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_6 - door Result: iou/accuracy 0.7712/0.8877
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_7 - table Result: iou/accuracy 0.8375/0.9031
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_8 - chair Result: iou/accuracy 0.9334/0.9679
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_9 - sofa Result: iou/accuracy 0.7905/0.8430
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_10 - bookcase Result: iou/accuracy 0.7942/0.8887
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_11 - board Result: iou/accuracy 0.8541/0.8701
[2023-12-26 01:21:09,046 INFO test.py line 323 253097] Class_12 - clutter Result: iou/accuracy 0.6166/0.7031
[2023-12-26 01:21:09,046 INFO test.py line 331 253097] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<