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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+finetune/finetune_detr_150e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0/20221105_015109.log filter=lfs diff=lfs merge=lfs -text
+finetune/finetune_detr_150e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0/20221105_015109.log.json filter=lfs diff=lfs merge=lfs -text

finetune/finetune_cbnetv2_swin-L_bs128_1x_coco_lr8e-4_wd2.5e-3/best_bbox_mAP_epoch_12.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a954f5e1532994ca286c85639742adb4701af91279315ebf9f0ea4c279665d40
+size 5404262999

finetune/finetune_cbnetv2_swin-L_bs128_1x_coco_lr8e-4_wd2.5e-3/cbnetv2_swin-L_8x2_1x_coco.py

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+model = dict(
+    type='CBNetHybridTaskCascade',
+    pretrained=
+    'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',
+    backbone=dict(
+        type='CBSwinTransformer',
+        embed_dim=192,
+        depths=[2, 2, 18, 2],
+        num_heads=[6, 12, 24, 48],
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.2,
+        ape=False,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        use_checkpoint=False),
+    neck=dict(
+        type='CBFPN',
+        in_channels=[192, 384, 768, 1536],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(
+            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
+    roi_head=dict(
+        type='HybridTaskCascadeRoIHead',
+        interleaved=True,
+        mask_info_flow=True,
+        num_stages=3,
+        stage_loss_weights=[1, 0.5, 0.25],
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0.0, 0.0, 0.0, 0.0],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0.0, 0.0, 0.0, 0.0],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0.0, 0.0, 0.0, 0.0],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_head=[
+            dict(
+                type='HTCMaskHead',
+                with_conv_res=False,
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=80,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
+            dict(
+                type='HTCMaskHead',
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=80,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
+            dict(
+                type='HTCMaskHead',
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=80,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
+        ]),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.5,
+                    min_pos_iou=0.5,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.6,
+                    min_pos_iou=0.6,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.7,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False)
+        ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.001,
+            nms=dict(type='soft_nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
+    dict(
+        type='Resize',
+        img_scale=[(1600, 400), (1600, 1400)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='SegRescale', scale_factor=0.125),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1600, 1400),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='LoadAnnotations',
+                with_bbox=True,
+                with_mask=True,
+                with_seg=True),
+            dict(
+                type='Resize',
+                img_scale=[(1600, 400), (1600, 1400)],
+                multiscale_mode='range',
+                keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='SegRescale', scale_factor=0.125),
+            dict(type='DefaultFormatBundle'),
+            dict(
+                type='Collect',
+                keys=[
+                    'img', 'gt_bboxes', 'gt_labels', 'gt_masks',
+                    'gt_semantic_seg'
+                ])
+        ],
+        seg_prefix='data/coco/stuffthingmaps/train2017/'),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1600, 1400),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1600, 1400),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(metric=['bbox', 'segm'], save_best='auto', gpu_collect=True)
+optimizer = dict(
+    type='AdamW',
+    lr=0.0008,
+    betas=(0.9, 0.999),
+    weight_decay=0.0025,
+    paramwise_cfg=dict(
+        custom_keys=dict(
+            absolute_pos_embed=dict(decay_mult=0.0),
+            relative_position_bias_table=dict(decay_mult=0.0),
+            norm=dict(decay_mult=0.0))))
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'work_dirs/selfsup_cbv2_swin-L_1x_coco/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = dict(
+    imports=[
+        'mmselfsup.datasets.pipelines',
+        'selfsup.core.hook.momentum_update_hook',
+        'selfsup.datasets.pipelines.selfsup_pipelines',
+        'selfsup.datasets.pipelines.rand_aug',
+        'selfsup.datasets.single_view_coco',
+        'selfsup.datasets.multi_view_coco',
+        'selfsup.models.losses.contrastive_loss',
+        'selfsup.models.dense_heads.fcos_head',
+        'selfsup.models.dense_heads.retina_head',
+        'selfsup.models.dense_heads.detr_head',
+        'selfsup.models.dense_heads.deformable_detr_head',
+        'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head',
+        'selfsup.models.roi_heads.standard_roi_head',
+        'selfsup.models.roi_heads.htc_roi_head',
+        'selfsup.models.roi_heads.cbv2_roi_head',
+        'selfsup.models.necks.cb_fpn', 'selfsup.models.backbones.cbv2',
+        'selfsup.models.backbones.swinv1',
+        'selfsup.models.detectors.selfsup_detector',
+        'selfsup.models.detectors.selfsup_fcos',
+        'selfsup.models.detectors.selfsup_detr',
+        'selfsup.models.detectors.selfsup_deformable_detr',
+        'selfsup.models.detectors.selfsup_retinanet',
+        'selfsup.models.detectors.selfsup_mask_rcnn',
+        'selfsup.models.detectors.selfsup_htc',
+        'selfsup.models.detectors.selfsup_cbv2',
+        'selfsup.models.detectors.cbv2',
+        'selfsup.core.bbox.assigners.hungarian_assigner',
+        'selfsup.core.bbox.assigners.pseudo_hungarian_assigner',
+        'selfsup.core.bbox.match_costs.match_cost'
+    ],
+    allow_failed_imports=False)
+fp16 = dict(loss_scale='dynamic')
+work_dir = 'work_dirs/finetune_cbnetv2_swin-L_bs128_1x_coco_lr8e-4_wd2.5e-3'
+auto_resume = False
+gpu_ids = range(0, 64)

finetune/finetune_detr_100e_coco_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0_lr-mul-0.1/best_bbox_mAP_epoch_100.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e2fa28c9e94cc5fd48d4986d4fe04381f7f0ad0433200ed50a28d6368399577
+size 499563034

finetune/finetune_detr_100e_coco_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0_lr-mul-0.1/detr_r50_8x2_100e_coco.py

@@ -0,0 +1,280 @@
+model = dict(
+    type='DETR',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        frozen_stages=-1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    bbox_head=dict(
+        type='DETRHead',
+        num_classes=80,
+        in_channels=2048,
+        transformer=dict(
+            type='Transformer',
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1)
+                    ],
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        dropout=0.1),
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')))),
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            bg_cls_weight=0.1,
+            use_sigmoid=False,
+            loss_weight=1.0,
+            class_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='HungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=1.0),
+            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
+            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
+    test_cfg=dict(max_per_img=100))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[{
+            'type':
+            'Resize',
+            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                          (736, 1333), (768, 1333), (800, 1333)],
+            'multiscale_mode':
+            'value',
+            'keep_ratio':
+            True
+        }],
+                  [{
+                      'type': 'Resize',
+                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],
+                      'multiscale_mode': 'value',
+                      'keep_ratio': True
+                  }, {
+                      'type': 'RandomCrop',
+                      'crop_type': 'absolute_range',
+                      'crop_size': (384, 600),
+                      'allow_negative_crop': True
+                  }, {
+                      'type':
+                      'Resize',
+                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),
+                                    (576, 1333), (608, 1333), (640, 1333),
+                                    (672, 1333), (704, 1333), (736, 1333),
+                                    (768, 1333), (800, 1333)],
+                      'multiscale_mode':
+                      'value',
+                      'override':
+                      True,
+                      'keep_ratio':
+                      True
+                  }]]),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=1),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='AutoAugment',
+                policies=[[{
+                    'type':
+                    'Resize',
+                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),
+                                  (576, 1333), (608, 1333), (640, 1333),
+                                  (672, 1333), (704, 1333), (736, 1333),
+                                  (768, 1333), (800, 1333)],
+                    'multiscale_mode':
+                    'value',
+                    'keep_ratio':
+                    True
+                }],
+                          [{
+                              'type': 'Resize',
+                              'img_scale': [(400, 1333), (500, 1333),
+                                            (600, 1333)],
+                              'multiscale_mode': 'value',
+                              'keep_ratio': True
+                          }, {
+                              'type': 'RandomCrop',
+                              'crop_type': 'absolute_range',
+                              'crop_size': (384, 600),
+                              'allow_negative_crop': True
+                          }, {
+                              'type':
+                              'Resize',
+                              'img_scale': [(480, 1333), (512, 1333),
+                                            (544, 1333), (576, 1333),
+                                            (608, 1333), (640, 1333),
+                                            (672, 1333), (704, 1333),
+                                            (736, 1333), (768, 1333),
+                                            (800, 1333)],
+                              'multiscale_mode':
+                              'value',
+                              'override':
+                              True,
+                              'keep_ratio':
+                              True
+                          }]]),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=1),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=1),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=1),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(
+    interval=1, metric='bbox', save_best='auto', gpu_collect=True)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_detr_clusters-as-classes_add-contrastive-temp0.5-weight1.0/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+optimizer = dict(
+    type='AdamW',
+    lr=0.0002,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys=dict(backbone=dict(lr_mult=0.1, decay_mult=1.0))))
+optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
+lr_config = dict(policy='step', step=[80])
+runner = dict(type='EpochBasedRunner', max_epochs=100)
+work_dir = 'work_dirs/finetune_detr_100e_coco_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0_lr-mul-0.1'
+auto_resume = False
+gpu_ids = range(0, 16)

finetune/finetune_detr_100e_voc0712/best_mAP_epoch_93.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab010ac6a87fe81e8b02dc5f4535671d19b9b5f2768a63f05cadd832071c0010
+size 497551580

finetune/finetune_detr_100e_voc0712/detr_mstrain_100e_voc0712.py

@@ -0,0 +1,218 @@
+model = dict(
+    type='DETR',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        frozen_stages=1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    bbox_head=dict(
+        type='DETRHead',
+        num_classes=20,
+        in_channels=2048,
+        transformer=dict(
+            type='Transformer',
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1)
+                    ],
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        dropout=0.1),
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')))),
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            bg_cls_weight=0.1,
+            use_sigmoid=False,
+            loss_weight=1.0,
+            class_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='HungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=1.0),
+            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
+            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
+    test_cfg=dict(max_per_img=100))
+dataset_type = 'VOCDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                   (1333, 736), (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='VOCDataset',
+        ann_file=[
+            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
+            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'
+        ],
+        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                type='Resize',
+                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                           (1333, 736), (1333, 768), (1333, 800)],
+                multiscale_mode='value',
+                keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='VOCDataset',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        img_prefix='data/VOCdevkit/VOC2007/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='VOCDataset',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        img_prefix='data/VOCdevkit/VOC2007/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(interval=1, metric='mAP', save_best='auto')
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_detr_clusters-as-classes_add-contrastive-temp0.5-weight1.0/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys=dict(backbone=dict(lr_mult=0.1, decay_mult=1.0))))
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='step', step=[70])
+runner = dict(type='EpochBasedRunner', max_epochs=100)
+work_dir = 'work_dirs/finetune_detr_100e_voc0712'
+auto_resume = False
+gpu_ids = range(0, 8)

finetune/finetune_detr_150e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0/20221105_015109.log

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finetune/finetune_detr_150e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0/20221105_015109.log.json

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+size 12772012

finetune/finetune_detr_150e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0/best_bbox_mAP_epoch_139.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c6a57209f79e6b91db40c7c91c28a52ec3ecaa34ebf1045b514399d64fe32c78
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finetune/finetune_detr_150e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0/detr_r50_8x2_150e_coco.py

@@ -0,0 +1,280 @@
+model = dict(
+    type='DETR',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        frozen_stages=-1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    bbox_head=dict(
+        type='DETRHead',
+        num_classes=80,
+        in_channels=2048,
+        transformer=dict(
+            type='Transformer',
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1)
+                    ],
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        dropout=0.1),
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')))),
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            bg_cls_weight=0.1,
+            use_sigmoid=False,
+            loss_weight=1.0,
+            class_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='HungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=1.0),
+            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
+            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
+    test_cfg=dict(max_per_img=100))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[{
+            'type':
+            'Resize',
+            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                          (736, 1333), (768, 1333), (800, 1333)],
+            'multiscale_mode':
+            'value',
+            'keep_ratio':
+            True
+        }],
+                  [{
+                      'type': 'Resize',
+                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],
+                      'multiscale_mode': 'value',
+                      'keep_ratio': True
+                  }, {
+                      'type': 'RandomCrop',
+                      'crop_type': 'absolute_range',
+                      'crop_size': (384, 600),
+                      'allow_negative_crop': True
+                  }, {
+                      'type':
+                      'Resize',
+                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),
+                                    (576, 1333), (608, 1333), (640, 1333),
+                                    (672, 1333), (704, 1333), (736, 1333),
+                                    (768, 1333), (800, 1333)],
+                      'multiscale_mode':
+                      'value',
+                      'override':
+                      True,
+                      'keep_ratio':
+                      True
+                  }]]),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=1),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='AutoAugment',
+                policies=[[{
+                    'type':
+                    'Resize',
+                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),
+                                  (576, 1333), (608, 1333), (640, 1333),
+                                  (672, 1333), (704, 1333), (736, 1333),
+                                  (768, 1333), (800, 1333)],
+                    'multiscale_mode':
+                    'value',
+                    'keep_ratio':
+                    True
+                }],
+                          [{
+                              'type': 'Resize',
+                              'img_scale': [(400, 1333), (500, 1333),
+                                            (600, 1333)],
+                              'multiscale_mode': 'value',
+                              'keep_ratio': True
+                          }, {
+                              'type': 'RandomCrop',
+                              'crop_type': 'absolute_range',
+                              'crop_size': (384, 600),
+                              'allow_negative_crop': True
+                          }, {
+                              'type':
+                              'Resize',
+                              'img_scale': [(480, 1333), (512, 1333),
+                                            (544, 1333), (576, 1333),
+                                            (608, 1333), (640, 1333),
+                                            (672, 1333), (704, 1333),
+                                            (736, 1333), (768, 1333),
+                                            (800, 1333)],
+                              'multiscale_mode':
+                              'value',
+                              'override':
+                              True,
+                              'keep_ratio':
+                              True
+                          }]]),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=1),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=1),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=1),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(
+    interval=1, metric='bbox', save_best='auto', gpu_collect=True)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'work_dirs/selfsup_detr_clusters-as-classes_add-contrastive-temp0.5-weight1.0/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys=dict(backbone=dict(lr_mult=0.1, decay_mult=1.0))))
+optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
+lr_config = dict(policy='step', step=[100])
+runner = dict(type='EpochBasedRunner', max_epochs=150)
+work_dir = 'work_dirs/finetune_detr_150e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0'
+auto_resume = False
+gpu_ids = range(0, 8)

finetune/finetune_detr_150e_coco_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0_swav_lr-mul-0.4/best_bbox_mAP_epoch_143.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a50ef6c9f605b6c0f9606834e2db57061eb470d15ecc59ebff46e2eb5be4aaf7
+size 499563098

finetune/finetune_detr_150e_coco_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0_swav_lr-mul-0.4/detr_r50_8x2_150e_coco.py

@@ -0,0 +1,280 @@
+model = dict(
+    type='DETR',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        frozen_stages=-1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    bbox_head=dict(
+        type='DETRHead',
+        num_classes=80,
+        in_channels=2048,
+        transformer=dict(
+            type='Transformer',
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1)
+                    ],
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        dropout=0.1),
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')))),
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            bg_cls_weight=0.1,
+            use_sigmoid=False,
+            loss_weight=1.0,
+            class_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='HungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=1.0),
+            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
+            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
+    test_cfg=dict(max_per_img=100))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[{
+            'type':
+            'Resize',
+            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                          (736, 1333), (768, 1333), (800, 1333)],
+            'multiscale_mode':
+            'value',
+            'keep_ratio':
+            True
+        }],
+                  [{
+                      'type': 'Resize',
+                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],
+                      'multiscale_mode': 'value',
+                      'keep_ratio': True
+                  }, {
+                      'type': 'RandomCrop',
+                      'crop_type': 'absolute_range',
+                      'crop_size': (384, 600),
+                      'allow_negative_crop': True
+                  }, {
+                      'type':
+                      'Resize',
+                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),
+                                    (576, 1333), (608, 1333), (640, 1333),
+                                    (672, 1333), (704, 1333), (736, 1333),
+                                    (768, 1333), (800, 1333)],
+                      'multiscale_mode':
+                      'value',
+                      'override':
+                      True,
+                      'keep_ratio':
+                      True
+                  }]]),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=1),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='AutoAugment',
+                policies=[[{
+                    'type':
+                    'Resize',
+                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),
+                                  (576, 1333), (608, 1333), (640, 1333),
+                                  (672, 1333), (704, 1333), (736, 1333),
+                                  (768, 1333), (800, 1333)],
+                    'multiscale_mode':
+                    'value',
+                    'keep_ratio':
+                    True
+                }],
+                          [{
+                              'type': 'Resize',
+                              'img_scale': [(400, 1333), (500, 1333),
+                                            (600, 1333)],
+                              'multiscale_mode': 'value',
+                              'keep_ratio': True
+                          }, {
+                              'type': 'RandomCrop',
+                              'crop_type': 'absolute_range',
+                              'crop_size': (384, 600),
+                              'allow_negative_crop': True
+                          }, {
+                              'type':
+                              'Resize',
+                              'img_scale': [(480, 1333), (512, 1333),
+                                            (544, 1333), (576, 1333),
+                                            (608, 1333), (640, 1333),
+                                            (672, 1333), (704, 1333),
+                                            (736, 1333), (768, 1333),
+                                            (800, 1333)],
+                              'multiscale_mode':
+                              'value',
+                              'override':
+                              True,
+                              'keep_ratio':
+                              True
+                          }]]),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=1),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=1),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=1),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(
+    interval=1, metric='bbox', save_best='auto', gpu_collect=True)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_detr_clusters-as-classes_add-contrastive-temp0.5-weight1.0_swav/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+optimizer = dict(
+    type='AdamW',
+    lr=0.0002,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys=dict(backbone=dict(lr_mult=0.4, decay_mult=1.0))))
+optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
+lr_config = dict(policy='step', step=[100])
+runner = dict(type='EpochBasedRunner', max_epochs=150)
+work_dir = 'work_dirs/finetune_detr_150e_coco_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0_swav_lr-mul-0.4'
+auto_resume = False
+gpu_ids = range(0, 16)

finetune/finetune_detr_50e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0/best_bbox_mAP_epoch_50.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad26dc9d44ca07dccdacbf55e779be7f3fa85a4f3f47e0193c39e7426b5774b8
+size 499563098

finetune/finetune_detr_50e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0/detr_r50_8x2_50e_coco.py

@@ -0,0 +1,281 @@
+model = dict(
+    type='DETR',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        frozen_stages=-1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    bbox_head=dict(
+        type='DETRHead',
+        num_classes=80,
+        in_channels=2048,
+        transformer=dict(
+            type='Transformer',
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1)
+                    ],
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        dropout=0.1),
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')))),
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            bg_cls_weight=0.1,
+            use_sigmoid=False,
+            loss_weight=1.0,
+            class_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='HungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=1.0),
+            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
+            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
+    test_cfg=dict(max_per_img=100))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[{
+            'type':
+            'Resize',
+            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                          (736, 1333), (768, 1333), (800, 1333)],
+            'multiscale_mode':
+            'value',
+            'keep_ratio':
+            True
+        }],
+                  [{
+                      'type': 'Resize',
+                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],
+                      'multiscale_mode': 'value',
+                      'keep_ratio': True
+                  }, {
+                      'type': 'RandomCrop',
+                      'crop_type': 'absolute_range',
+                      'crop_size': (384, 600),
+                      'allow_negative_crop': True
+                  }, {
+                      'type':
+                      'Resize',
+                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),
+                                    (576, 1333), (608, 1333), (640, 1333),
+                                    (672, 1333), (704, 1333), (736, 1333),
+                                    (768, 1333), (800, 1333)],
+                      'multiscale_mode':
+                      'value',
+                      'override':
+                      True,
+                      'keep_ratio':
+                      True
+                  }]]),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=1),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='AutoAugment',
+                policies=[[{
+                    'type':
+                    'Resize',
+                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),
+                                  (576, 1333), (608, 1333), (640, 1333),
+                                  (672, 1333), (704, 1333), (736, 1333),
+                                  (768, 1333), (800, 1333)],
+                    'multiscale_mode':
+                    'value',
+                    'keep_ratio':
+                    True
+                }],
+                          [{
+                              'type': 'Resize',
+                              'img_scale': [(400, 1333), (500, 1333),
+                                            (600, 1333)],
+                              'multiscale_mode': 'value',
+                              'keep_ratio': True
+                          }, {
+                              'type': 'RandomCrop',
+                              'crop_type': 'absolute_range',
+                              'crop_size': (384, 600),
+                              'allow_negative_crop': True
+                          }, {
+                              'type':
+                              'Resize',
+                              'img_scale': [(480, 1333), (512, 1333),
+                                            (544, 1333), (576, 1333),
+                                            (608, 1333), (640, 1333),
+                                            (672, 1333), (704, 1333),
+                                            (736, 1333), (768, 1333),
+                                            (800, 1333)],
+                              'multiscale_mode':
+                              'value',
+                              'override':
+                              True,
+                              'keep_ratio':
+                              True
+                          }]]),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=1),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=1),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=1),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(
+    interval=1, metric='bbox', save_best='auto', gpu_collect=True)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'work_dirs/selfsup_detr_clusters-as-classes_add-contrastive-temp0.5-weight1.0/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys=dict(
+            backbone=dict(lr_mult=0.1, decay_mult=1.0), lr_mult=0.1)))
+optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
+lr_config = dict(policy='step', step=[40])
+runner = dict(type='EpochBasedRunner', max_epochs=50)
+work_dir = 'work_dirs/finetune_detr_50e_coco_lr-mult-0.1_selfsup-clusters-as-classes_add-contrastive-temp0.5-weight1.0'
+auto_resume = False
+gpu_ids = range(0, 8)

finetune/finetune_faster-rcnn_12k_coco/20221022_163550.log.json

@@ -0,0 +1,242 @@
+{"env_info": "sys.platform: linux\nPython: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: A100-SXM-80GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0\nPyTorch: 1.10.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - LAPACK is enabled (usually provided by MKL)\n  - NNPACK is enabled\n  - CPU capability usage: AVX512\n  - CUDA Runtime 11.3\n  - NVCC architecture flags: -gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n  - CuDNN 8.2\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.11.1+cu113\nOpenCV: 4.6.0\nMMCV: 1.6.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.2+a7ef785", "config": "model = dict(\n    type='MaskRCNN',\n    backbone=dict(\n        type='ResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        norm_cfg=dict(type='SyncBN', requires_grad=True),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n    neck=dict(\n        type='FPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        num_outs=5,\n        norm_cfg=dict(type='SyncBN', requires_grad=True)),\n    rpn_head=dict(\n        type='RPNHead',\n        in_channels=256,\n        feat_channels=256,\n        anchor_generator=dict(\n            type='AnchorGenerator',\n            scales=[8],\n            ratios=[0.5, 1.0, 2.0],\n            strides=[4, 8, 16, 32, 64]),\n        bbox_coder=dict(\n            type='DeltaXYWHBBoxCoder',\n            target_means=[0.0, 0.0, 0.0, 0.0],\n            target_stds=[1.0, 1.0, 1.0, 1.0]),\n        loss_cls=dict(\n            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n    roi_head=dict(\n        type='StandardRoIHead',\n        bbox_roi_extractor=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        bbox_head=dict(\n            type='Shared4Conv1FCBBoxHead',\n            in_channels=256,\n            fc_out_channels=1024,\n            roi_feat_size=7,\n            num_classes=80,\n            bbox_coder=dict(\n                type='DeltaXYWHBBoxCoder',\n                target_means=[0.0, 0.0, 0.0, 0.0],\n                target_stds=[0.1, 0.1, 0.2, 0.2]),\n            reg_class_agnostic=False,\n            loss_cls=dict(\n                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n        mask_roi_extractor=None,\n        mask_head=None),\n    train_cfg=dict(\n        rpn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.7,\n                neg_iou_thr=0.3,\n                min_pos_iou=0.3,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=256,\n                pos_fraction=0.5,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=False),\n            allowed_border=-1,\n            pos_weight=-1,\n            debug=False),\n        rpn_proposal=dict(\n            nms_pre=2000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.5,\n                neg_iou_thr=0.5,\n                min_pos_iou=0.5,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=512,\n                pos_fraction=0.25,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=True),\n            mask_size=28,\n            pos_weight=-1,\n            debug=False)),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=1000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            score_thr=0.05,\n            nms=dict(type='nms', iou_threshold=0.5),\n            max_per_img=100,\n            mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True),\n    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='Pad', size_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1333, 800),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=32),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=2,\n    train=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_train2017.json',\n        img_prefix='data/coco/train2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True),\n            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n            dict(type='RandomFlip', flip_ratio=0.5),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=32),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n        ]),\n    val=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_val2017.json',\n        img_prefix='data/coco/val2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=32),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]),\n    test=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_val2017.json',\n        img_prefix='data/coco/val2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=32),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nevaluation = dict(\n    interval=12000, metric='bbox', save_best='auto', gpu_collect=True)\noptimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.001,\n    step=[9000, 11000],\n    by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=12000)\ncheckpoint_config = dict(interval=12000)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n    dict(type='NumClassCheckHook'),\n    dict(\n        type='MMDetWandbHook',\n        init_kwargs=dict(project='I2B', group='semi-coco'),\n        interval=50,\n        num_eval_images=0,\n        log_checkpoint=False)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth'\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\ncustom_imports = None\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\nwork_dir = 'work_dirs/finetune_faster-rcnn_12k_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "faster_rcnn_fpn_12k_semi-coco.py", "hook_msgs": {}}
+{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00297, "memory": 4029, "data_time": 0.00871, "loss_rpn_cls": 0.5004, "loss_rpn_bbox": 0.10321, "loss_cls": 1.45083, "acc": 83.72046, "loss_bbox": 0.04998, "loss": 2.10442, "time": 0.13065}
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+{"mode": "train", "epoch": 1, "iter": 11750, "lr": 0.0003, "memory": 4086, "data_time": 0.00688, "loss_rpn_cls": 0.0435, "loss_rpn_bbox": 0.06226, "loss_cls": 0.25516, "acc": 91.76685, "loss_bbox": 0.25191, "loss": 0.61283, "time": 0.12718}
+{"mode": "train", "epoch": 1, "iter": 11800, "lr": 0.0003, "memory": 4086, "data_time": 0.00688, "loss_rpn_cls": 0.04439, "loss_rpn_bbox": 0.05969, "loss_cls": 0.24676, "acc": 92.08594, "loss_bbox": 0.24233, "loss": 0.59316, "time": 0.12543}
+{"mode": "train", "epoch": 1, "iter": 11850, "lr": 0.0003, "memory": 4086, "data_time": 0.00696, "loss_rpn_cls": 0.04321, "loss_rpn_bbox": 0.05657, "loss_cls": 0.25807, "acc": 91.79932, "loss_bbox": 0.24744, "loss": 0.60529, "time": 0.1244}
+{"mode": "train", "epoch": 1, "iter": 11900, "lr": 0.0003, "memory": 4086, "data_time": 0.00679, "loss_rpn_cls": 0.04002, "loss_rpn_bbox": 0.05604, "loss_cls": 0.25288, "acc": 91.80713, "loss_bbox": 0.24632, "loss": 0.59526, "time": 0.1242}
+{"mode": "train", "epoch": 1, "iter": 11950, "lr": 0.0003, "memory": 4086, "data_time": 0.00669, "loss_rpn_cls": 0.03918, "loss_rpn_bbox": 0.05533, "loss_cls": 0.25328, "acc": 91.90625, "loss_bbox": 0.24626, "loss": 0.59404, "time": 0.1232}
+{"mode": "train", "epoch": 1, "iter": 12000, "lr": 0.0003, "memory": 4086, "data_time": 0.00689, "loss_rpn_cls": 0.04113, "loss_rpn_bbox": 0.05834, "loss_cls": 0.25107, "acc": 92.00781, "loss_bbox": 0.24318, "loss": 0.59372, "time": 0.13826}
+{"mode": "val", "epoch": 1, "iter": 625, "lr": 0.0003, "bbox_mAP": 0.306, "bbox_mAP_50": 0.49, "bbox_mAP_75": 0.327, "bbox_mAP_s": 0.169, "bbox_mAP_m": 0.336, "bbox_mAP_l": 0.395, "bbox_mAP_copypaste": "0.306 0.490 0.327 0.169 0.336 0.395"}

finetune/finetune_faster-rcnn_12k_coco/best_bbox_mAP_iter_12000.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b56f08c8cf0b7a3ccc07eced45e58595da0249a3d5070ffd61943db4a20acd1
+size 344045801

finetune/finetune_faster-rcnn_12k_coco/faster_rcnn_fpn_12k_semi-coco.py

@@ -0,0 +1,248 @@
+model = dict(
+    type='MaskRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5,
+        norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+        mask_roi_extractor=None,
+        mask_head=None),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(
+    interval=12000, metric='bbox', save_best='auto', gpu_collect=True)
+optimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[9000, 11000],
+    by_epoch=False)
+runner = dict(type='IterBasedRunner', max_iters=12000)
+checkpoint_config = dict(interval=12000)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='semi-coco'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+work_dir = 'work_dirs/finetune_faster-rcnn_12k_coco'
+auto_resume = False
+gpu_ids = range(0, 8)

finetune/finetune_faster-rcnn_1x_coco_lr3e-2_wd5e-5/best_bbox_mAP_epoch_12.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5696a2ed63a4ab5bb500bfbd76e57bffbb11a892a27d518c8a0ef35fe6b181ad
+size 344045929

finetune/finetune_faster-rcnn_1x_coco_lr3e-2_wd5e-5/faster_rcnn_r50_fpn_1x_coco.py

@@ -0,0 +1,249 @@
+model = dict(
+    type='MaskRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5,
+        norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+        mask_roi_extractor=None,
+        mask_head=None),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(
+                type='Collect',
+                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(
+    metric='bbox', save_best='auto', gpu_collect=True, interval=1)
+optimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/keep_all.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+work_dir = 'work_dirs/finetune_faster-rcnn_1x_coco_lr3e-2_wd5e-5_keep-all'
+auto_resume = False
+gpu_ids = range(0, 8)

finetune/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5/20221003_230350.log

@@ -0,0 +1,1257 @@
+2022-10-03 23:03:50,973 - mmdet - INFO - Environment info:
+------------------------------------------------------------
+sys.platform: linux
+Python: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]
+CUDA available: True
+GPU 0,1,2,3,4,5,6,7: A100-SXM-80GB
+CUDA_HOME: /usr/local/cuda
+NVCC: Cuda compilation tools, release 11.3, V11.3.109
+GCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0
+PyTorch: 1.10.0
+PyTorch compiling details: PyTorch built with:
+  - GCC 7.3
+  - C++ Version: 201402
+  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
+  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
+  - OpenMP 201511 (a.k.a. OpenMP 4.5)
+  - LAPACK is enabled (usually provided by MKL)
+  - NNPACK is enabled
+  - CPU capability usage: AVX512
+  - CUDA Runtime 11.3
+  - NVCC architecture flags: -gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
+  - CuDNN 8.2
+  - Magma 2.5.2
+  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, 
+
+TorchVision: 0.11.1+cu113
+OpenCV: 4.6.0
+MMCV: 1.6.1
+MMCV Compiler: GCC 9.3
+MMCV CUDA Compiler: 11.3
+MMDetection: 2.25.2+87c120c
+------------------------------------------------------------
+
+2022-10-03 23:03:52,207 - mmdet - INFO - Distributed training: True
+2022-10-03 23:03:53,323 - mmdet - INFO - Config:
+model = dict(
+    type='FCOS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5,
+        relu_before_extra_convs=True,
+        norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    bbox_head=dict(
+        type='FCOSHead',
+        num_classes=20,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+dataset_type = 'VOCDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                   (1333, 736), (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='VOCDataset',
+        ann_file=[
+            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
+            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'
+        ],
+        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                type='Resize',
+                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                           (1333, 736), (1333, 768), (1333, 800)],
+                multiscale_mode='value',
+                keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='VOCDataset',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        img_prefix='data/VOCdevkit/VOC2007/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='VOCDataset',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        img_prefix='data/VOCdevkit/VOC2007/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(interval=12000, metric='mAP', save_best='auto')
+optimizer = dict(
+    type='SGD',
+    lr=0.015,
+    momentum=0.9,
+    weight_decay=5e-05,
+    paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0))
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[9000, 11000],
+    by_epoch=False)
+runner = dict(type='IterBasedRunner', max_iters=12000)
+checkpoint_config = dict(interval=12000)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_fcos_mstrain-soft-teacher_sampler-2048_temp0.5/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+work_dir = 'work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5'
+auto_resume = False
+gpu_ids = range(0, 8)
+
+2022-10-03 23:03:53,326 - mmdet - INFO - Set random seed to 42, deterministic: False
+2022-10-03 23:03:53,597 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
+2022-10-03 23:04:07,756 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
+2022-10-03 23:04:07,780 - mmdet - INFO - initialize FCOSHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'conv_cls', 'std': 0.01, 'bias_prob': 0.01}}
+Name of parameter - Initialization information
+
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+
+backbone.bn1.weight - torch.Size([64]): 
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+
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+
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+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.1.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.2.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.3.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.4.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.bn1.weight - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.bn1.bias - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.bn2.weight - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.bn2.bias - torch.Size([256]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.bn3.weight - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer3.5.bn3.bias - torch.Size([1024]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.bn1.weight - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.bn1.bias - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.bn2.weight - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.bn2.bias - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.bn3.weight - torch.Size([2048]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.bn3.bias - torch.Size([2048]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.downsample.1.weight - torch.Size([2048]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.0.downsample.1.bias - torch.Size([2048]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.bn1.weight - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.bn1.bias - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.bn2.weight - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.bn2.bias - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.bn3.weight - torch.Size([2048]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.1.bn3.bias - torch.Size([2048]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.bn1.weight - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.bn1.bias - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.bn2.weight - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.bn2.bias - torch.Size([512]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.bn3.weight - torch.Size([2048]): 
+PretrainedInit: load from torchvision://resnet50 
+
+backbone.layer4.2.bn3.bias - torch.Size([2048]): 
+PretrainedInit: load from torchvision://resnet50 
+
+neck.lateral_convs.0.conv.weight - torch.Size([256, 512, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.lateral_convs.0.bn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.lateral_convs.0.bn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.lateral_convs.1.conv.weight - torch.Size([256, 1024, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.lateral_convs.1.bn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.lateral_convs.1.bn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.lateral_convs.2.conv.weight - torch.Size([256, 2048, 1, 1]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.lateral_convs.2.bn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.lateral_convs.2.bn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.0.bn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.0.bn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.1.bn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.1.bn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.2.bn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.2.bn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.3.bn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.3.bn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.4.conv.weight - torch.Size([256, 256, 3, 3]): 
+XavierInit: gain=1, distribution=uniform, bias=0 
+
+neck.fpn_convs.4.bn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+neck.fpn_convs.4.bn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.cls_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.cls_convs.0.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.cls_convs.0.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.cls_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.cls_convs.1.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.cls_convs.1.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.cls_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.cls_convs.2.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.cls_convs.2.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.cls_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.cls_convs.3.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.cls_convs.3.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.reg_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.reg_convs.0.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.reg_convs.0.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.reg_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.reg_convs.1.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.reg_convs.1.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.reg_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.reg_convs.2.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.reg_convs.2.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.reg_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.reg_convs.3.gn.weight - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.reg_convs.3.gn.bias - torch.Size([256]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.conv_cls.weight - torch.Size([20, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=-4.59511985013459 
+
+bbox_head.conv_cls.bias - torch.Size([20]): 
+NormalInit: mean=0, std=0.01, bias=-4.59511985013459 
+
+bbox_head.conv_reg.weight - torch.Size([4, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.conv_reg.bias - torch.Size([4]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.conv_centerness.weight - torch.Size([1, 256, 3, 3]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.conv_centerness.bias - torch.Size([1]): 
+NormalInit: mean=0, std=0.01, bias=0 
+
+bbox_head.scales.0.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.scales.1.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.scales.2.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.scales.3.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of FCOS  
+
+bbox_head.scales.4.scale - torch.Size([]): 
+The value is the same before and after calling `init_weights` of FCOS  
+2022-10-03 23:04:09,685 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
+2022-10-03 23:04:10,655 - mmdet - INFO - load checkpoint from local path: pretrain/selfsup_fcos_mstrain-soft-teacher_sampler-2048_temp0.5/final_model.pth
+2022-10-03 23:04:10,809 - mmdet - WARNING - The model and loaded state dict do not match exactly
+
+unexpected key in source state_dict: neck.lateral_convs.0.conv.bias, neck.lateral_convs.1.conv.bias, neck.lateral_convs.2.conv.bias, neck.fpn_convs.0.conv.bias, neck.fpn_convs.1.conv.bias, neck.fpn_convs.2.conv.bias, neck.fpn_convs.3.conv.bias, neck.fpn_convs.4.conv.bias
+
+missing keys in source state_dict: neck.lateral_convs.0.bn.weight, neck.lateral_convs.0.bn.bias, neck.lateral_convs.0.bn.running_mean, neck.lateral_convs.0.bn.running_var, neck.lateral_convs.1.bn.weight, neck.lateral_convs.1.bn.bias, neck.lateral_convs.1.bn.running_mean, neck.lateral_convs.1.bn.running_var, neck.lateral_convs.2.bn.weight, neck.lateral_convs.2.bn.bias, neck.lateral_convs.2.bn.running_mean, neck.lateral_convs.2.bn.running_var, neck.fpn_convs.0.bn.weight, neck.fpn_convs.0.bn.bias, neck.fpn_convs.0.bn.running_mean, neck.fpn_convs.0.bn.running_var, neck.fpn_convs.1.bn.weight, neck.fpn_convs.1.bn.bias, neck.fpn_convs.1.bn.running_mean, neck.fpn_convs.1.bn.running_var, neck.fpn_convs.2.bn.weight, neck.fpn_convs.2.bn.bias, neck.fpn_convs.2.bn.running_mean, neck.fpn_convs.2.bn.running_var, neck.fpn_convs.3.bn.weight, neck.fpn_convs.3.bn.bias, neck.fpn_convs.3.bn.running_mean, neck.fpn_convs.3.bn.running_var, neck.fpn_convs.4.bn.weight, neck.fpn_convs.4.bn.bias, neck.fpn_convs.4.bn.running_mean, neck.fpn_convs.4.bn.running_var, bbox_head.conv_cls.weight, bbox_head.conv_cls.bias, bbox_head.conv_reg.weight, bbox_head.conv_reg.bias, bbox_head.conv_centerness.weight, bbox_head.conv_centerness.bias
+
+2022-10-03 23:04:10,814 - mmdet - INFO - Start running, host: tiger@n136-144-086, work_dir: /home/tiger/code/mmdet/work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5
+2022-10-03 23:04:10,815 - mmdet - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(NORMAL      ) CheckpointHook                     
+(NORMAL      ) MMDetWandbHook                     
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(NORMAL      ) NumClassCheckHook                  
+(NORMAL      ) MMDetWandbHook                     
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) StepLrUpdaterHook                  
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+ -------------------- 
+after_train_iter:
+(ABOVE_NORMAL) OptimizerHook                      
+(NORMAL      ) CheckpointHook                     
+(NORMAL      ) MMDetWandbHook                     
+(LOW         ) IterTimerHook                      
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) CheckpointHook                     
+(NORMAL      ) MMDetWandbHook                     
+(LOW         ) DistEvalHook                       
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) NumClassCheckHook                  
+(NORMAL      ) MMDetWandbHook                     
+(LOW         ) IterTimerHook                      
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+before_val_iter:
+(LOW         ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(LOW         ) IterTimerHook                      
+ -------------------- 
+after_val_epoch:
+(NORMAL      ) MMDetWandbHook                     
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+after_run:
+(NORMAL      ) MMDetWandbHook                     
+(VERY_LOW    ) TextLoggerHook                     
+ -------------------- 
+2022-10-03 23:04:10,815 - mmdet - INFO - workflow: [('train', 1)], max: 12000 iters
+2022-10-03 23:04:10,815 - mmdet - INFO - Checkpoints will be saved to /home/tiger/code/mmdet/work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5 by HardDiskBackend.
+2022-10-03 23:04:17,705 - mmdet - INFO - Iter [50/12000]	lr: 1.484e-03, eta: 0:22:43, time: 0.114, data_time: 0.007, memory: 3374, loss_cls: 1.0537, loss_bbox: 4.8491, loss_centerness: 0.6754, loss: 6.5782, grad_norm: 12.3657
+2022-10-03 23:04:23,217 - mmdet - INFO - Iter [100/12000]	lr: 2.982e-03, eta: 0:22:14, time: 0.110, data_time: 0.006, memory: 3374, loss_cls: 0.6791, loss_bbox: 0.8601, loss_centerness: 0.6573, loss: 2.1965, grad_norm: 4.5347
+2022-10-03 23:04:28,829 - mmdet - INFO - Iter [150/12000]	lr: 4.481e-03, eta: 0:22:09, time: 0.112, data_time: 0.006, memory: 3374, loss_cls: 0.5333, loss_bbox: 0.7146, loss_centerness: 0.6428, loss: 1.8907, grad_norm: 5.2160
+2022-10-03 23:04:34,160 - mmdet - INFO - Iter [200/12000]	lr: 5.979e-03, eta: 0:21:47, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.4749, loss_bbox: 0.7338, loss_centerness: 0.6323, loss: 1.8410, grad_norm: 7.3390
+2022-10-03 23:04:39,394 - mmdet - INFO - Iter [250/12000]	lr: 7.478e-03, eta: 0:21:27, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.4591, loss_bbox: 0.6353, loss_centerness: 0.6251, loss: 1.7196, grad_norm: 6.4957
+2022-10-03 23:04:44,999 - mmdet - INFO - Iter [300/12000]	lr: 8.976e-03, eta: 0:21:26, time: 0.112, data_time: 0.007, memory: 3374, loss_cls: 0.4273, loss_bbox: 0.6177, loss_centerness: 0.6211, loss: 1.6661, grad_norm: 6.4898
+2022-10-03 23:04:50,570 - mmdet - INFO - Iter [350/12000]	lr: 1.047e-02, eta: 0:21:23, time: 0.111, data_time: 0.006, memory: 3374, loss_cls: 0.4455, loss_bbox: 0.6623, loss_centerness: 0.6199, loss: 1.7277, grad_norm: 6.3315
+2022-10-03 23:04:56,146 - mmdet - INFO - Iter [400/12000]	lr: 1.197e-02, eta: 0:21:20, time: 0.112, data_time: 0.006, memory: 3374, loss_cls: 0.4226, loss_bbox: 0.5643, loss_centerness: 0.6222, loss: 1.6091, grad_norm: 4.8027
+2022-10-03 23:05:01,689 - mmdet - INFO - Iter [450/12000]	lr: 1.347e-02, eta: 0:21:15, time: 0.111, data_time: 0.006, memory: 3374, loss_cls: 0.4131, loss_bbox: 0.6746, loss_centerness: 0.6191, loss: 1.7067, grad_norm: 6.1644
+2022-10-03 23:05:08,483 - mmdet - INFO - Iter [500/12000]	lr: 1.497e-02, eta: 0:21:39, time: 0.136, data_time: 0.006, memory: 3374, loss_cls: 0.3854, loss_bbox: 0.6034, loss_centerness: 0.6164, loss: 1.6052, grad_norm: 5.1157
+2022-10-03 23:05:13,624 - mmdet - INFO - Iter [550/12000]	lr: 1.500e-02, eta: 0:21:22, time: 0.103, data_time: 0.006, memory: 3374, loss_cls: 0.3810, loss_bbox: 0.6026, loss_centerness: 0.6148, loss: 1.5985, grad_norm: 5.2547
+2022-10-03 23:05:18,837 - mmdet - INFO - Iter [600/12000]	lr: 1.500e-02, eta: 0:21:09, time: 0.104, data_time: 0.006, memory: 3374, loss_cls: 0.3759, loss_bbox: 0.6033, loss_centerness: 0.6159, loss: 1.5950, grad_norm: 5.4132
+2022-10-03 23:05:24,127 - mmdet - INFO - Iter [650/12000]	lr: 1.500e-02, eta: 0:20:59, time: 0.106, data_time: 0.006, memory: 3374, loss_cls: 0.3536, loss_bbox: 0.5552, loss_centerness: 0.6144, loss: 1.5232, grad_norm: 4.6439
+2022-10-03 23:05:29,329 - mmdet - INFO - Iter [700/12000]	lr: 1.500e-02, eta: 0:20:48, time: 0.104, data_time: 0.006, memory: 3374, loss_cls: 0.3611, loss_bbox: 0.5496, loss_centerness: 0.6123, loss: 1.5230, grad_norm: 4.9982
+2022-10-03 23:05:34,597 - mmdet - INFO - Iter [750/12000]	lr: 1.500e-02, eta: 0:20:38, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.3628, loss_bbox: 0.6455, loss_centerness: 0.6124, loss: 1.6208, grad_norm: 5.6424
+2022-10-03 23:05:39,916 - mmdet - INFO - Iter [800/12000]	lr: 1.500e-02, eta: 0:20:30, time: 0.106, data_time: 0.006, memory: 3374, loss_cls: 0.3525, loss_bbox: 0.5528, loss_centerness: 0.6113, loss: 1.5166, grad_norm: 4.6742
+2022-10-03 23:05:44,962 - mmdet - INFO - Iter [850/12000]	lr: 1.500e-02, eta: 0:20:19, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.3506, loss_bbox: 0.5777, loss_centerness: 0.6112, loss: 1.5394, grad_norm: 5.0777
+2022-10-03 23:05:50,213 - mmdet - INFO - Iter [900/12000]	lr: 1.500e-02, eta: 0:20:11, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.3315, loss_bbox: 0.6549, loss_centerness: 0.6092, loss: 1.5956, grad_norm: 5.1478
+2022-10-03 23:05:55,528 - mmdet - INFO - Iter [950/12000]	lr: 1.500e-02, eta: 0:20:04, time: 0.106, data_time: 0.006, memory: 3374, loss_cls: 0.3390, loss_bbox: 0.5597, loss_centerness: 0.6095, loss: 1.5081, grad_norm: 4.3107
+2022-10-03 23:06:00,561 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:06:00,562 - mmdet - INFO - Iter [1000/12000]	lr: 1.500e-02, eta: 0:19:54, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.3277, loss_bbox: 0.4881, loss_centerness: 0.6082, loss: 1.4240, grad_norm: 4.3779
+2022-10-03 23:06:05,627 - mmdet - INFO - Iter [1050/12000]	lr: 1.500e-02, eta: 0:19:44, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.3301, loss_bbox: 0.5140, loss_centerness: 0.6098, loss: 1.4539, grad_norm: 4.5959
+2022-10-03 23:06:10,997 - mmdet - INFO - Iter [1100/12000]	lr: 1.500e-02, eta: 0:19:39, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.3150, loss_bbox: 0.5068, loss_centerness: 0.6070, loss: 1.4287, grad_norm: 4.3244
+2022-10-03 23:06:16,223 - mmdet - INFO - Iter [1150/12000]	lr: 1.500e-02, eta: 0:19:31, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.3163, loss_bbox: 0.5167, loss_centerness: 0.6070, loss: 1.4400, grad_norm: 4.7621
+2022-10-03 23:06:21,607 - mmdet - INFO - Iter [1200/12000]	lr: 1.500e-02, eta: 0:19:26, time: 0.108, data_time: 0.006, memory: 3374, loss_cls: 0.3054, loss_bbox: 0.4738, loss_centerness: 0.6063, loss: 1.3855, grad_norm: 3.7848
+2022-10-03 23:06:26,663 - mmdet - INFO - Iter [1250/12000]	lr: 1.500e-02, eta: 0:19:18, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.2982, loss_bbox: 0.5563, loss_centerness: 0.6080, loss: 1.4626, grad_norm: 5.4164
+2022-10-03 23:06:32,004 - mmdet - INFO - Iter [1300/12000]	lr: 1.500e-02, eta: 0:19:12, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.2959, loss_bbox: 0.4749, loss_centerness: 0.6046, loss: 1.3755, grad_norm: 4.0394
+2022-10-03 23:06:37,278 - mmdet - INFO - Iter [1350/12000]	lr: 1.500e-02, eta: 0:19:06, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.3065, loss_bbox: 0.4300, loss_centerness: 0.6061, loss: 1.3425, grad_norm: 3.6915
+2022-10-03 23:06:42,277 - mmdet - INFO - Iter [1400/12000]	lr: 1.500e-02, eta: 0:18:57, time: 0.100, data_time: 0.006, memory: 3374, loss_cls: 0.3188, loss_bbox: 0.4513, loss_centerness: 0.6068, loss: 1.3769, grad_norm: 3.7263
+2022-10-03 23:06:47,648 - mmdet - INFO - Iter [1450/12000]	lr: 1.500e-02, eta: 0:18:52, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.3040, loss_bbox: 0.4776, loss_centerness: 0.6043, loss: 1.3859, grad_norm: 4.1767
+2022-10-03 23:06:53,019 - mmdet - INFO - Iter [1500/12000]	lr: 1.500e-02, eta: 0:18:47, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.2978, loss_bbox: 0.4800, loss_centerness: 0.6047, loss: 1.3824, grad_norm: 4.1199
+2022-10-03 23:06:58,344 - mmdet - INFO - Iter [1550/12000]	lr: 1.500e-02, eta: 0:18:41, time: 0.106, data_time: 0.006, memory: 3374, loss_cls: 0.2975, loss_bbox: 0.4485, loss_centerness: 0.6025, loss: 1.3485, grad_norm: 3.9451
+2022-10-03 23:07:03,761 - mmdet - INFO - Iter [1600/12000]	lr: 1.500e-02, eta: 0:18:36, time: 0.108, data_time: 0.006, memory: 3374, loss_cls: 0.2885, loss_bbox: 0.4370, loss_centerness: 0.6048, loss: 1.3304, grad_norm: 3.3962
+2022-10-03 23:07:09,400 - mmdet - INFO - Iter [1650/12000]	lr: 1.500e-02, eta: 0:18:32, time: 0.113, data_time: 0.006, memory: 3374, loss_cls: 0.2843, loss_bbox: 0.4135, loss_centerness: 0.6059, loss: 1.3038, grad_norm: 3.2543
+2022-10-03 23:07:15,080 - mmdet - INFO - Iter [1700/12000]	lr: 1.500e-02, eta: 0:18:29, time: 0.114, data_time: 0.006, memory: 3374, loss_cls: 0.2856, loss_bbox: 0.4120, loss_centerness: 0.6029, loss: 1.3005, grad_norm: 3.1262
+2022-10-03 23:07:20,292 - mmdet - INFO - Iter [1750/12000]	lr: 1.500e-02, eta: 0:18:22, time: 0.104, data_time: 0.006, memory: 3374, loss_cls: 0.2861, loss_bbox: 0.4194, loss_centerness: 0.6016, loss: 1.3071, grad_norm: 3.4000
+2022-10-03 23:07:25,374 - mmdet - INFO - Iter [1800/12000]	lr: 1.500e-02, eta: 0:18:15, time: 0.102, data_time: 0.006, memory: 3374, loss_cls: 0.2892, loss_bbox: 0.3962, loss_centerness: 0.6040, loss: 1.2893, grad_norm: 2.8839
+2022-10-03 23:07:30,704 - mmdet - INFO - Iter [1850/12000]	lr: 1.500e-02, eta: 0:18:10, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.2739, loss_bbox: 0.3708, loss_centerness: 0.6029, loss: 1.2476, grad_norm: 2.6212
+2022-10-03 23:07:35,850 - mmdet - INFO - Iter [1900/12000]	lr: 1.500e-02, eta: 0:18:03, time: 0.103, data_time: 0.006, memory: 3374, loss_cls: 0.2914, loss_bbox: 0.4107, loss_centerness: 0.6050, loss: 1.3071, grad_norm: 3.1236
+2022-10-03 23:07:40,977 - mmdet - INFO - Iter [1950/12000]	lr: 1.500e-02, eta: 0:17:56, time: 0.103, data_time: 0.006, memory: 3374, loss_cls: 0.2795, loss_bbox: 0.4096, loss_centerness: 0.6022, loss: 1.2914, grad_norm: 3.0628
+2022-10-03 23:07:46,032 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:07:46,033 - mmdet - INFO - Iter [2000/12000]	lr: 1.500e-02, eta: 0:17:50, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.2805, loss_bbox: 0.3808, loss_centerness: 0.6005, loss: 1.2618, grad_norm: 2.8152
+2022-10-03 23:07:51,065 - mmdet - INFO - Iter [2050/12000]	lr: 1.500e-02, eta: 0:17:43, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.2772, loss_bbox: 0.3860, loss_centerness: 0.5991, loss: 1.2624, grad_norm: 2.9319
+2022-10-03 23:07:56,283 - mmdet - INFO - Iter [2100/12000]	lr: 1.500e-02, eta: 0:17:37, time: 0.104, data_time: 0.006, memory: 3374, loss_cls: 0.2703, loss_bbox: 0.3860, loss_centerness: 0.6015, loss: 1.2578, grad_norm: 2.6650
+2022-10-03 23:08:01,375 - mmdet - INFO - Iter [2150/12000]	lr: 1.500e-02, eta: 0:17:30, time: 0.102, data_time: 0.006, memory: 3374, loss_cls: 0.2569, loss_bbox: 0.3632, loss_centerness: 0.5993, loss: 1.2194, grad_norm: 2.6891
+2022-10-03 23:08:06,399 - mmdet - INFO - Iter [2200/12000]	lr: 1.500e-02, eta: 0:17:24, time: 0.100, data_time: 0.006, memory: 3374, loss_cls: 0.2682, loss_bbox: 0.3759, loss_centerness: 0.6009, loss: 1.2450, grad_norm: 2.7334
+2022-10-03 23:08:11,513 - mmdet - INFO - Iter [2250/12000]	lr: 1.500e-02, eta: 0:17:17, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2589, loss_bbox: 0.3624, loss_centerness: 0.5985, loss: 1.2198, grad_norm: 2.7641
+2022-10-03 23:08:16,930 - mmdet - INFO - Iter [2300/12000]	lr: 1.500e-02, eta: 0:17:12, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2619, loss_bbox: 0.3805, loss_centerness: 0.5989, loss: 1.2414, grad_norm: 2.8289
+2022-10-03 23:08:22,089 - mmdet - INFO - Iter [2350/12000]	lr: 1.500e-02, eta: 0:17:06, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2718, loss_bbox: 0.3752, loss_centerness: 0.6001, loss: 1.2471, grad_norm: 2.7427
+2022-10-03 23:08:27,145 - mmdet - INFO - Iter [2400/12000]	lr: 1.500e-02, eta: 0:17:00, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2640, loss_bbox: 0.3638, loss_centerness: 0.5997, loss: 1.2274, grad_norm: 2.5183
+2022-10-03 23:08:32,180 - mmdet - INFO - Iter [2450/12000]	lr: 1.500e-02, eta: 0:16:54, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2464, loss_bbox: 0.3551, loss_centerness: 0.6002, loss: 1.2016, grad_norm: 2.4714
+2022-10-03 23:08:37,449 - mmdet - INFO - Iter [2500/12000]	lr: 1.500e-02, eta: 0:16:48, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2611, loss_bbox: 0.3776, loss_centerness: 0.6007, loss: 1.2394, grad_norm: 2.7616
+2022-10-03 23:08:42,790 - mmdet - INFO - Iter [2550/12000]	lr: 1.500e-02, eta: 0:16:43, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.2558, loss_bbox: 0.3706, loss_centerness: 0.6010, loss: 1.2273, grad_norm: 2.6622
+2022-10-03 23:08:48,168 - mmdet - INFO - Iter [2600/12000]	lr: 1.500e-02, eta: 0:16:38, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2528, loss_bbox: 0.3718, loss_centerness: 0.6008, loss: 1.2255, grad_norm: 2.6662
+2022-10-03 23:08:53,369 - mmdet - INFO - Iter [2650/12000]	lr: 1.500e-02, eta: 0:16:32, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.2571, loss_bbox: 0.3540, loss_centerness: 0.5993, loss: 1.2104, grad_norm: 2.6098
+2022-10-03 23:08:58,471 - mmdet - INFO - Iter [2700/12000]	lr: 1.500e-02, eta: 0:16:26, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2488, loss_bbox: 0.3670, loss_centerness: 0.5980, loss: 1.2138, grad_norm: 2.7511
+2022-10-03 23:09:03,858 - mmdet - INFO - Iter [2750/12000]	lr: 1.500e-02, eta: 0:16:21, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2875, loss_bbox: 0.3567, loss_centerness: 0.5974, loss: 1.2417, grad_norm: 2.8518
+2022-10-03 23:09:09,385 - mmdet - INFO - Iter [2800/12000]	lr: 1.500e-02, eta: 0:16:17, time: 0.111, data_time: 0.006, memory: 3375, loss_cls: 0.2600, loss_bbox: 0.3723, loss_centerness: 0.5989, loss: 1.2311, grad_norm: 2.7242
+2022-10-03 23:09:14,773 - mmdet - INFO - Iter [2850/12000]	lr: 1.500e-02, eta: 0:16:12, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2638, loss_bbox: 0.3606, loss_centerness: 0.6012, loss: 1.2255, grad_norm: 2.5991
+2022-10-03 23:09:19,960 - mmdet - INFO - Iter [2900/12000]	lr: 1.500e-02, eta: 0:16:06, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.2658, loss_bbox: 0.3596, loss_centerness: 0.5990, loss: 1.2244, grad_norm: 2.6344
+2022-10-03 23:09:25,447 - mmdet - INFO - Iter [2950/12000]	lr: 1.500e-02, eta: 0:16:01, time: 0.110, data_time: 0.006, memory: 3375, loss_cls: 0.2623, loss_bbox: 0.3663, loss_centerness: 0.5994, loss: 1.2280, grad_norm: 2.5495
+2022-10-03 23:09:30,697 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:09:30,697 - mmdet - INFO - Iter [3000/12000]	lr: 1.500e-02, eta: 0:15:56, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2467, loss_bbox: 0.3484, loss_centerness: 0.5985, loss: 1.1936, grad_norm: 2.3818
+2022-10-03 23:09:35,762 - mmdet - INFO - Iter [3050/12000]	lr: 1.500e-02, eta: 0:15:50, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2489, loss_bbox: 0.3615, loss_centerness: 0.5986, loss: 1.2090, grad_norm: 2.6018
+2022-10-03 23:09:40,840 - mmdet - INFO - Iter [3100/12000]	lr: 1.500e-02, eta: 0:15:44, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2385, loss_bbox: 0.3538, loss_centerness: 0.5981, loss: 1.1904, grad_norm: 2.6130
+2022-10-03 23:09:46,209 - mmdet - INFO - Iter [3150/12000]	lr: 1.500e-02, eta: 0:15:38, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.2298, loss_bbox: 0.3453, loss_centerness: 0.5973, loss: 1.1723, grad_norm: 2.5113
+2022-10-03 23:09:51,589 - mmdet - INFO - Iter [3200/12000]	lr: 1.500e-02, eta: 0:15:33, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2480, loss_bbox: 0.3494, loss_centerness: 0.5984, loss: 1.1958, grad_norm: 2.5547
+2022-10-03 23:09:56,892 - mmdet - INFO - Iter [3250/12000]	lr: 1.500e-02, eta: 0:15:28, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.2448, loss_bbox: 0.3371, loss_centerness: 0.5989, loss: 1.1808, grad_norm: 2.4596
+2022-10-03 23:10:01,982 - mmdet - INFO - Iter [3300/12000]	lr: 1.500e-02, eta: 0:15:22, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2546, loss_bbox: 0.3607, loss_centerness: 0.5988, loss: 1.2141, grad_norm: 2.7104
+2022-10-03 23:10:07,046 - mmdet - INFO - Iter [3350/12000]	lr: 1.500e-02, eta: 0:15:16, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2385, loss_bbox: 0.3381, loss_centerness: 0.5972, loss: 1.1738, grad_norm: 2.4207
+2022-10-03 23:10:12,276 - mmdet - INFO - Iter [3400/12000]	lr: 1.500e-02, eta: 0:15:11, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2361, loss_bbox: 0.3271, loss_centerness: 0.5959, loss: 1.1592, grad_norm: 2.3731
+2022-10-03 23:10:17,293 - mmdet - INFO - Iter [3450/12000]	lr: 1.500e-02, eta: 0:15:05, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2472, loss_bbox: 0.3672, loss_centerness: 0.5965, loss: 1.2109, grad_norm: 2.8299
+2022-10-03 23:10:22,449 - mmdet - INFO - Iter [3500/12000]	lr: 1.500e-02, eta: 0:14:59, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2285, loss_bbox: 0.3372, loss_centerness: 0.5958, loss: 1.1614, grad_norm: 2.3995
+2022-10-03 23:10:27,682 - mmdet - INFO - Iter [3550/12000]	lr: 1.500e-02, eta: 0:14:54, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2323, loss_bbox: 0.3379, loss_centerness: 0.5953, loss: 1.1656, grad_norm: 2.7294
+2022-10-03 23:10:32,728 - mmdet - INFO - Iter [3600/12000]	lr: 1.500e-02, eta: 0:14:48, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2352, loss_bbox: 0.3351, loss_centerness: 0.5969, loss: 1.1672, grad_norm: 2.6776
+2022-10-03 23:10:37,762 - mmdet - INFO - Iter [3650/12000]	lr: 1.500e-02, eta: 0:14:42, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2406, loss_bbox: 0.3351, loss_centerness: 0.5969, loss: 1.1726, grad_norm: 2.5880
+2022-10-03 23:10:42,994 - mmdet - INFO - Iter [3700/12000]	lr: 1.500e-02, eta: 0:14:37, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2356, loss_bbox: 0.3268, loss_centerness: 0.5963, loss: 1.1587, grad_norm: 2.3722
+2022-10-03 23:10:48,338 - mmdet - INFO - Iter [3750/12000]	lr: 1.500e-02, eta: 0:14:31, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.2314, loss_bbox: 0.3354, loss_centerness: 0.5952, loss: 1.1620, grad_norm: 2.5687
+2022-10-03 23:10:53,688 - mmdet - INFO - Iter [3800/12000]	lr: 1.500e-02, eta: 0:14:26, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.2388, loss_bbox: 0.3303, loss_centerness: 0.5958, loss: 1.1649, grad_norm: 2.5392
+2022-10-03 23:10:58,875 - mmdet - INFO - Iter [3850/12000]	lr: 1.500e-02, eta: 0:14:21, time: 0.104, data_time: 0.007, memory: 3375, loss_cls: 0.2336, loss_bbox: 0.3207, loss_centerness: 0.5965, loss: 1.1509, grad_norm: 2.3348
+2022-10-03 23:11:04,134 - mmdet - INFO - Iter [3900/12000]	lr: 1.500e-02, eta: 0:14:15, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2293, loss_bbox: 0.3234, loss_centerness: 0.5946, loss: 1.1473, grad_norm: 2.3656
+2022-10-03 23:11:09,316 - mmdet - INFO - Iter [3950/12000]	lr: 1.500e-02, eta: 0:14:10, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.2271, loss_bbox: 0.3254, loss_centerness: 0.5963, loss: 1.1489, grad_norm: 2.3502
+2022-10-03 23:11:14,373 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:11:14,373 - mmdet - INFO - Iter [4000/12000]	lr: 1.500e-02, eta: 0:14:04, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2390, loss_bbox: 0.3419, loss_centerness: 0.5963, loss: 1.1772, grad_norm: 2.6043
+2022-10-03 23:11:19,472 - mmdet - INFO - Iter [4050/12000]	lr: 1.500e-02, eta: 0:13:59, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2309, loss_bbox: 0.3362, loss_centerness: 0.5982, loss: 1.1653, grad_norm: 2.5254
+2022-10-03 23:11:24,712 - mmdet - INFO - Iter [4100/12000]	lr: 1.500e-02, eta: 0:13:53, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2314, loss_bbox: 0.3494, loss_centerness: 0.5960, loss: 1.1768, grad_norm: 2.6908
+2022-10-03 23:11:30,137 - mmdet - INFO - Iter [4150/12000]	lr: 1.500e-02, eta: 0:13:48, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2227, loss_bbox: 0.3214, loss_centerness: 0.5937, loss: 1.1378, grad_norm: 2.5844
+2022-10-03 23:11:35,406 - mmdet - INFO - Iter [4200/12000]	lr: 1.500e-02, eta: 0:13:43, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2212, loss_bbox: 0.3142, loss_centerness: 0.5922, loss: 1.1276, grad_norm: 2.5120
+2022-10-03 23:11:40,647 - mmdet - INFO - Iter [4250/12000]	lr: 1.500e-02, eta: 0:13:38, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2243, loss_bbox: 0.3261, loss_centerness: 0.5936, loss: 1.1441, grad_norm: 2.5729
+2022-10-03 23:11:45,717 - mmdet - INFO - Iter [4300/12000]	lr: 1.500e-02, eta: 0:13:32, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2274, loss_bbox: 0.3149, loss_centerness: 0.5937, loss: 1.1361, grad_norm: 2.5403
+2022-10-03 23:11:50,857 - mmdet - INFO - Iter [4350/12000]	lr: 1.500e-02, eta: 0:13:26, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2301, loss_bbox: 0.3325, loss_centerness: 0.5947, loss: 1.1573, grad_norm: 2.6587
+2022-10-03 23:11:55,876 - mmdet - INFO - Iter [4400/12000]	lr: 1.500e-02, eta: 0:13:21, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2274, loss_bbox: 0.3267, loss_centerness: 0.5945, loss: 1.1486, grad_norm: 2.4360
+2022-10-03 23:12:00,878 - mmdet - INFO - Iter [4450/12000]	lr: 1.500e-02, eta: 0:13:15, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2180, loss_bbox: 0.3155, loss_centerness: 0.5942, loss: 1.1278, grad_norm: 2.4964
+2022-10-03 23:12:05,906 - mmdet - INFO - Iter [4500/12000]	lr: 1.500e-02, eta: 0:13:09, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2253, loss_bbox: 0.3083, loss_centerness: 0.5952, loss: 1.1288, grad_norm: 2.3991
+2022-10-03 23:12:10,944 - mmdet - INFO - Iter [4550/12000]	lr: 1.500e-02, eta: 0:13:04, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2151, loss_bbox: 0.3198, loss_centerness: 0.5954, loss: 1.1303, grad_norm: 2.4637
+2022-10-03 23:12:16,026 - mmdet - INFO - Iter [4600/12000]	lr: 1.500e-02, eta: 0:12:58, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2297, loss_bbox: 0.3213, loss_centerness: 0.5944, loss: 1.1455, grad_norm: 2.5831
+2022-10-03 23:12:21,092 - mmdet - INFO - Iter [4650/12000]	lr: 1.500e-02, eta: 0:12:53, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2188, loss_bbox: 0.3081, loss_centerness: 0.5925, loss: 1.1193, grad_norm: 2.2491
+2022-10-03 23:12:26,399 - mmdet - INFO - Iter [4700/12000]	lr: 1.500e-02, eta: 0:12:47, time: 0.106, data_time: 0.007, memory: 3375, loss_cls: 0.2252, loss_bbox: 0.3300, loss_centerness: 0.5961, loss: 1.1514, grad_norm: 2.5577
+2022-10-03 23:12:31,477 - mmdet - INFO - Iter [4750/12000]	lr: 1.500e-02, eta: 0:12:42, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2234, loss_bbox: 0.3177, loss_centerness: 0.5950, loss: 1.1361, grad_norm: 2.4445
+2022-10-03 23:12:36,463 - mmdet - INFO - Iter [4800/12000]	lr: 1.500e-02, eta: 0:12:36, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2094, loss_bbox: 0.3308, loss_centerness: 0.5942, loss: 1.1344, grad_norm: 2.5108
+2022-10-03 23:12:41,538 - mmdet - INFO - Iter [4850/12000]	lr: 1.500e-02, eta: 0:12:31, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2180, loss_bbox: 0.2990, loss_centerness: 0.5935, loss: 1.1104, grad_norm: 2.3976
+2022-10-03 23:12:46,590 - mmdet - INFO - Iter [4900/12000]	lr: 1.500e-02, eta: 0:12:25, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.2187, loss_bbox: 0.3108, loss_centerness: 0.5938, loss: 1.1232, grad_norm: 2.4357
+2022-10-03 23:12:51,755 - mmdet - INFO - Iter [4950/12000]	lr: 1.500e-02, eta: 0:12:20, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2167, loss_bbox: 0.3118, loss_centerness: 0.5939, loss: 1.1224, grad_norm: 2.4076
+2022-10-03 23:12:56,821 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:12:56,821 - mmdet - INFO - Iter [5000/12000]	lr: 1.500e-02, eta: 0:12:14, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2298, loss_bbox: 0.3248, loss_centerness: 0.5956, loss: 1.1501, grad_norm: 2.5838
+2022-10-03 23:13:01,852 - mmdet - INFO - Iter [5050/12000]	lr: 1.500e-02, eta: 0:12:09, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2233, loss_bbox: 0.3420, loss_centerness: 0.5964, loss: 1.1616, grad_norm: 2.4916
+2022-10-03 23:13:06,953 - mmdet - INFO - Iter [5100/12000]	lr: 1.500e-02, eta: 0:12:03, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2165, loss_bbox: 0.3110, loss_centerness: 0.5922, loss: 1.1196, grad_norm: 2.4828
+2022-10-03 23:13:11,976 - mmdet - INFO - Iter [5150/12000]	lr: 1.500e-02, eta: 0:11:58, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2346, loss_bbox: 0.3290, loss_centerness: 0.5967, loss: 1.1603, grad_norm: 2.5321
+2022-10-03 23:13:16,971 - mmdet - INFO - Iter [5200/12000]	lr: 1.500e-02, eta: 0:11:52, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2181, loss_bbox: 0.3112, loss_centerness: 0.5931, loss: 1.1224, grad_norm: 2.4412
+2022-10-03 23:13:22,234 - mmdet - INFO - Iter [5250/12000]	lr: 1.500e-02, eta: 0:11:47, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2033, loss_bbox: 0.2971, loss_centerness: 0.5904, loss: 1.0909, grad_norm: 2.3256
+2022-10-03 23:13:27,473 - mmdet - INFO - Iter [5300/12000]	lr: 1.500e-02, eta: 0:11:42, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2147, loss_bbox: 0.3131, loss_centerness: 0.5909, loss: 1.1187, grad_norm: 2.7216
+2022-10-03 23:13:32,537 - mmdet - INFO - Iter [5350/12000]	lr: 1.500e-02, eta: 0:11:36, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2090, loss_bbox: 0.3241, loss_centerness: 0.5906, loss: 1.1237, grad_norm: 2.6115
+2022-10-03 23:13:37,672 - mmdet - INFO - Iter [5400/12000]	lr: 1.500e-02, eta: 0:11:31, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1970, loss_bbox: 0.2998, loss_centerness: 0.5933, loss: 1.0901, grad_norm: 2.3276
+2022-10-03 23:13:42,654 - mmdet - INFO - Iter [5450/12000]	lr: 1.500e-02, eta: 0:11:25, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2139, loss_bbox: 0.3126, loss_centerness: 0.5936, loss: 1.1201, grad_norm: 2.6115
+2022-10-03 23:13:47,792 - mmdet - INFO - Iter [5500/12000]	lr: 1.500e-02, eta: 0:11:20, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2196, loss_bbox: 0.2999, loss_centerness: 0.5937, loss: 1.1133, grad_norm: 2.3717
+2022-10-03 23:13:52,833 - mmdet - INFO - Iter [5550/12000]	lr: 1.500e-02, eta: 0:11:14, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2144, loss_bbox: 0.3060, loss_centerness: 0.5949, loss: 1.1154, grad_norm: 2.4405
+2022-10-03 23:13:57,884 - mmdet - INFO - Iter [5600/12000]	lr: 1.500e-02, eta: 0:11:09, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.2133, loss_bbox: 0.3024, loss_centerness: 0.5918, loss: 1.1075, grad_norm: 2.5034
+2022-10-03 23:14:03,262 - mmdet - INFO - Iter [5650/12000]	lr: 1.500e-02, eta: 0:11:04, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1986, loss_bbox: 0.3024, loss_centerness: 0.5913, loss: 1.0922, grad_norm: 2.3927
+2022-10-03 23:14:08,662 - mmdet - INFO - Iter [5700/12000]	lr: 1.500e-02, eta: 0:10:59, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2162, loss_bbox: 0.3056, loss_centerness: 0.5944, loss: 1.1162, grad_norm: 2.5149
+2022-10-03 23:14:13,780 - mmdet - INFO - Iter [5750/12000]	lr: 1.500e-02, eta: 0:10:54, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2090, loss_bbox: 0.3121, loss_centerness: 0.5934, loss: 1.1145, grad_norm: 2.3781
+2022-10-03 23:14:18,881 - mmdet - INFO - Iter [5800/12000]	lr: 1.500e-02, eta: 0:10:48, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2121, loss_bbox: 0.3099, loss_centerness: 0.5922, loss: 1.1142, grad_norm: 2.6576
+2022-10-03 23:14:24,445 - mmdet - INFO - Iter [5850/12000]	lr: 1.500e-02, eta: 0:10:43, time: 0.111, data_time: 0.006, memory: 3375, loss_cls: 0.2230, loss_bbox: 0.3037, loss_centerness: 0.5928, loss: 1.1195, grad_norm: 2.5658
+2022-10-03 23:14:29,669 - mmdet - INFO - Iter [5900/12000]	lr: 1.500e-02, eta: 0:10:38, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2123, loss_bbox: 0.3129, loss_centerness: 0.5938, loss: 1.1190, grad_norm: 2.6781
+2022-10-03 23:14:34,710 - mmdet - INFO - Iter [5950/12000]	lr: 1.500e-02, eta: 0:10:33, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2086, loss_bbox: 0.3025, loss_centerness: 0.5915, loss: 1.1027, grad_norm: 2.4293
+2022-10-03 23:14:39,785 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:14:39,785 - mmdet - INFO - Iter [6000/12000]	lr: 1.500e-02, eta: 0:10:27, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2063, loss_bbox: 0.3019, loss_centerness: 0.5926, loss: 1.1008, grad_norm: 2.4104
+2022-10-03 23:14:44,827 - mmdet - INFO - Iter [6050/12000]	lr: 1.500e-02, eta: 0:10:22, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2097, loss_bbox: 0.3115, loss_centerness: 0.5933, loss: 1.1145, grad_norm: 2.4511
+2022-10-03 23:14:49,852 - mmdet - INFO - Iter [6100/12000]	lr: 1.500e-02, eta: 0:10:16, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2069, loss_bbox: 0.2894, loss_centerness: 0.5897, loss: 1.0860, grad_norm: 2.3739
+2022-10-03 23:14:54,937 - mmdet - INFO - Iter [6150/12000]	lr: 1.500e-02, eta: 0:10:11, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2026, loss_bbox: 0.3039, loss_centerness: 0.5934, loss: 1.0999, grad_norm: 2.4252
+2022-10-03 23:15:00,058 - mmdet - INFO - Iter [6200/12000]	lr: 1.500e-02, eta: 0:10:06, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1982, loss_bbox: 0.2990, loss_centerness: 0.5923, loss: 1.0895, grad_norm: 2.4156
+2022-10-03 23:15:05,099 - mmdet - INFO - Iter [6250/12000]	lr: 1.500e-02, eta: 0:10:00, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1977, loss_bbox: 0.3022, loss_centerness: 0.5897, loss: 1.0897, grad_norm: 2.5230
+2022-10-03 23:15:10,411 - mmdet - INFO - Iter [6300/12000]	lr: 1.500e-02, eta: 0:09:55, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.2001, loss_bbox: 0.2945, loss_centerness: 0.5892, loss: 1.0838, grad_norm: 2.5836
+2022-10-03 23:15:15,557 - mmdet - INFO - Iter [6350/12000]	lr: 1.500e-02, eta: 0:09:50, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1997, loss_bbox: 0.2807, loss_centerness: 0.5900, loss: 1.0703, grad_norm: 2.3159
+2022-10-03 23:15:21,104 - mmdet - INFO - Iter [6400/12000]	lr: 1.500e-02, eta: 0:09:45, time: 0.111, data_time: 0.006, memory: 3375, loss_cls: 0.1946, loss_bbox: 0.2813, loss_centerness: 0.5894, loss: 1.0652, grad_norm: 2.4935
+2022-10-03 23:15:26,515 - mmdet - INFO - Iter [6450/12000]	lr: 1.500e-02, eta: 0:09:40, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1993, loss_bbox: 0.2951, loss_centerness: 0.5912, loss: 1.0856, grad_norm: 2.3950
+2022-10-03 23:15:31,748 - mmdet - INFO - Iter [6500/12000]	lr: 1.500e-02, eta: 0:09:35, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2053, loss_bbox: 0.3171, loss_centerness: 0.5932, loss: 1.1157, grad_norm: 2.6241
+2022-10-03 23:15:36,910 - mmdet - INFO - Iter [6550/12000]	lr: 1.500e-02, eta: 0:09:29, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1946, loss_bbox: 0.2933, loss_centerness: 0.5913, loss: 1.0792, grad_norm: 2.3415
+2022-10-03 23:15:41,932 - mmdet - INFO - Iter [6600/12000]	lr: 1.500e-02, eta: 0:09:24, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1969, loss_bbox: 0.2923, loss_centerness: 0.5900, loss: 1.0792, grad_norm: 2.4594
+2022-10-03 23:15:47,006 - mmdet - INFO - Iter [6650/12000]	lr: 1.500e-02, eta: 0:09:19, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1952, loss_bbox: 0.2876, loss_centerness: 0.5895, loss: 1.0723, grad_norm: 2.4592
+2022-10-03 23:15:52,065 - mmdet - INFO - Iter [6700/12000]	lr: 1.500e-02, eta: 0:09:13, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2037, loss_bbox: 0.2893, loss_centerness: 0.5903, loss: 1.0833, grad_norm: 2.5001
+2022-10-03 23:15:57,122 - mmdet - INFO - Iter [6750/12000]	lr: 1.500e-02, eta: 0:09:08, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2060, loss_bbox: 0.3081, loss_centerness: 0.5938, loss: 1.1079, grad_norm: 2.4866
+2022-10-03 23:16:02,117 - mmdet - INFO - Iter [6800/12000]	lr: 1.500e-02, eta: 0:09:03, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1931, loss_bbox: 0.2915, loss_centerness: 0.5892, loss: 1.0738, grad_norm: 2.4755
+2022-10-03 23:16:07,139 - mmdet - INFO - Iter [6850/12000]	lr: 1.500e-02, eta: 0:08:57, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1999, loss_bbox: 0.3040, loss_centerness: 0.5905, loss: 1.0945, grad_norm: 2.5293
+2022-10-03 23:16:12,233 - mmdet - INFO - Iter [6900/12000]	lr: 1.500e-02, eta: 0:08:52, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1905, loss_bbox: 0.3017, loss_centerness: 0.5918, loss: 1.0839, grad_norm: 2.4225
+2022-10-03 23:16:17,543 - mmdet - INFO - Iter [6950/12000]	lr: 1.500e-02, eta: 0:08:47, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1933, loss_bbox: 0.2995, loss_centerness: 0.5909, loss: 1.0837, grad_norm: 2.4788
+2022-10-03 23:16:22,605 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:16:22,605 - mmdet - INFO - Iter [7000/12000]	lr: 1.500e-02, eta: 0:08:41, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2045, loss_bbox: 0.2978, loss_centerness: 0.5918, loss: 1.0941, grad_norm: 2.5168
+2022-10-03 23:16:27,624 - mmdet - INFO - Iter [7050/12000]	lr: 1.500e-02, eta: 0:08:36, time: 0.100, data_time: 0.007, memory: 3375, loss_cls: 0.1988, loss_bbox: 0.2890, loss_centerness: 0.5895, loss: 1.0774, grad_norm: 2.4041
+2022-10-03 23:16:32,749 - mmdet - INFO - Iter [7100/12000]	lr: 1.500e-02, eta: 0:08:31, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.1999, loss_bbox: 0.2849, loss_centerness: 0.5911, loss: 1.0759, grad_norm: 2.4509
+2022-10-03 23:16:37,770 - mmdet - INFO - Iter [7150/12000]	lr: 1.500e-02, eta: 0:08:25, time: 0.100, data_time: 0.007, memory: 3375, loss_cls: 0.2015, loss_bbox: 0.2874, loss_centerness: 0.5904, loss: 1.0793, grad_norm: 2.3829
+2022-10-03 23:16:42,849 - mmdet - INFO - Iter [7200/12000]	lr: 1.500e-02, eta: 0:08:20, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.2032, loss_bbox: 0.3085, loss_centerness: 0.5915, loss: 1.1032, grad_norm: 2.7239
+2022-10-03 23:16:47,768 - mmdet - INFO - Iter [7250/12000]	lr: 1.500e-02, eta: 0:08:15, time: 0.098, data_time: 0.007, memory: 3375, loss_cls: 0.1963, loss_bbox: 0.2936, loss_centerness: 0.5903, loss: 1.0802, grad_norm: 2.4589
+2022-10-03 23:16:53,108 - mmdet - INFO - Iter [7300/12000]	lr: 1.500e-02, eta: 0:08:10, time: 0.107, data_time: 0.007, memory: 3375, loss_cls: 0.1790, loss_bbox: 0.2900, loss_centerness: 0.5881, loss: 1.0571, grad_norm: 2.4370
+2022-10-03 23:16:58,574 - mmdet - INFO - Iter [7350/12000]	lr: 1.500e-02, eta: 0:08:04, time: 0.109, data_time: 0.006, memory: 3375, loss_cls: 0.1870, loss_bbox: 0.2812, loss_centerness: 0.5882, loss: 1.0564, grad_norm: 2.4754
+2022-10-03 23:17:03,591 - mmdet - INFO - Iter [7400/12000]	lr: 1.500e-02, eta: 0:07:59, time: 0.100, data_time: 0.007, memory: 3375, loss_cls: 0.1883, loss_bbox: 0.2878, loss_centerness: 0.5876, loss: 1.0637, grad_norm: 2.4153
+2022-10-03 23:17:08,694 - mmdet - INFO - Iter [7450/12000]	lr: 1.500e-02, eta: 0:07:54, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.1916, loss_bbox: 0.2811, loss_centerness: 0.5907, loss: 1.0635, grad_norm: 2.3504
+2022-10-03 23:17:13,736 - mmdet - INFO - Iter [7500/12000]	lr: 1.500e-02, eta: 0:07:49, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.1960, loss_bbox: 0.2863, loss_centerness: 0.5908, loss: 1.0732, grad_norm: 2.4362
+2022-10-03 23:17:19,114 - mmdet - INFO - Iter [7550/12000]	lr: 1.500e-02, eta: 0:07:43, time: 0.107, data_time: 0.007, memory: 3375, loss_cls: 0.1868, loss_bbox: 0.2858, loss_centerness: 0.5872, loss: 1.0598, grad_norm: 2.5911
+2022-10-03 23:17:24,208 - mmdet - INFO - Iter [7600/12000]	lr: 1.500e-02, eta: 0:07:38, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1832, loss_bbox: 0.2850, loss_centerness: 0.5891, loss: 1.0573, grad_norm: 2.4779
+2022-10-03 23:17:29,438 - mmdet - INFO - Iter [7650/12000]	lr: 1.500e-02, eta: 0:07:33, time: 0.105, data_time: 0.007, memory: 3375, loss_cls: 0.1894, loss_bbox: 0.2913, loss_centerness: 0.5899, loss: 1.0707, grad_norm: 2.5428
+2022-10-03 23:17:34,437 - mmdet - INFO - Iter [7700/12000]	lr: 1.500e-02, eta: 0:07:28, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1969, loss_bbox: 0.2840, loss_centerness: 0.5881, loss: 1.0691, grad_norm: 2.6974
+2022-10-03 23:17:39,440 - mmdet - INFO - Iter [7750/12000]	lr: 1.500e-02, eta: 0:07:22, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1931, loss_bbox: 0.3048, loss_centerness: 0.5913, loss: 1.0892, grad_norm: 2.6059
+2022-10-03 23:17:44,815 - mmdet - INFO - Iter [7800/12000]	lr: 1.500e-02, eta: 0:07:17, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1955, loss_bbox: 0.2941, loss_centerness: 0.5893, loss: 1.0789, grad_norm: 2.6701
+2022-10-03 23:17:50,063 - mmdet - INFO - Iter [7850/12000]	lr: 1.500e-02, eta: 0:07:12, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1865, loss_bbox: 0.2720, loss_centerness: 0.5877, loss: 1.0462, grad_norm: 2.3621
+2022-10-03 23:17:54,985 - mmdet - INFO - Iter [7900/12000]	lr: 1.500e-02, eta: 0:07:07, time: 0.098, data_time: 0.006, memory: 3375, loss_cls: 0.1968, loss_bbox: 0.2950, loss_centerness: 0.5900, loss: 1.0818, grad_norm: 2.6020
+2022-10-03 23:18:00,052 - mmdet - INFO - Iter [7950/12000]	lr: 1.500e-02, eta: 0:07:01, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1923, loss_bbox: 0.2947, loss_centerness: 0.5895, loss: 1.0765, grad_norm: 2.6275
+2022-10-03 23:18:05,119 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:18:05,119 - mmdet - INFO - Iter [8000/12000]	lr: 1.500e-02, eta: 0:06:56, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1962, loss_bbox: 0.2897, loss_centerness: 0.5913, loss: 1.0771, grad_norm: 2.4957
+2022-10-03 23:18:10,151 - mmdet - INFO - Iter [8050/12000]	lr: 1.500e-02, eta: 0:06:51, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1973, loss_bbox: 0.2891, loss_centerness: 0.5909, loss: 1.0773, grad_norm: 2.5776
+2022-10-03 23:18:15,373 - mmdet - INFO - Iter [8100/12000]	lr: 1.500e-02, eta: 0:06:46, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1929, loss_bbox: 0.2841, loss_centerness: 0.5903, loss: 1.0673, grad_norm: 2.5122
+2022-10-03 23:18:20,522 - mmdet - INFO - Iter [8150/12000]	lr: 1.500e-02, eta: 0:06:40, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1960, loss_bbox: 0.2843, loss_centerness: 0.5869, loss: 1.0673, grad_norm: 2.6011
+2022-10-03 23:18:25,561 - mmdet - INFO - Iter [8200/12000]	lr: 1.500e-02, eta: 0:06:35, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1907, loss_bbox: 0.2878, loss_centerness: 0.5906, loss: 1.0691, grad_norm: 2.4059
+2022-10-03 23:18:30,632 - mmdet - INFO - Iter [8250/12000]	lr: 1.500e-02, eta: 0:06:30, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1973, loss_bbox: 0.2953, loss_centerness: 0.5907, loss: 1.0833, grad_norm: 2.3690
+2022-10-03 23:18:35,602 - mmdet - INFO - Iter [8300/12000]	lr: 1.500e-02, eta: 0:06:24, time: 0.099, data_time: 0.006, memory: 3375, loss_cls: 0.1849, loss_bbox: 0.2971, loss_centerness: 0.5895, loss: 1.0716, grad_norm: 2.5134
+2022-10-03 23:18:40,600 - mmdet - INFO - Iter [8350/12000]	lr: 1.500e-02, eta: 0:06:19, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1811, loss_bbox: 0.2672, loss_centerness: 0.5855, loss: 1.0339, grad_norm: 2.4261
+2022-10-03 23:18:45,619 - mmdet - INFO - Iter [8400/12000]	lr: 1.500e-02, eta: 0:06:14, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1758, loss_bbox: 0.2833, loss_centerness: 0.5882, loss: 1.0473, grad_norm: 2.6022
+2022-10-03 23:18:50,700 - mmdet - INFO - Iter [8450/12000]	lr: 1.500e-02, eta: 0:06:09, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1811, loss_bbox: 0.2762, loss_centerness: 0.5893, loss: 1.0466, grad_norm: 2.3019
+2022-10-03 23:18:55,706 - mmdet - INFO - Iter [8500/12000]	lr: 1.500e-02, eta: 0:06:03, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1827, loss_bbox: 0.3011, loss_centerness: 0.5893, loss: 1.0731, grad_norm: 2.5493
+2022-10-03 23:19:00,820 - mmdet - INFO - Iter [8550/12000]	lr: 1.500e-02, eta: 0:05:58, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1774, loss_bbox: 0.2718, loss_centerness: 0.5866, loss: 1.0358, grad_norm: 2.5059
+2022-10-03 23:19:06,073 - mmdet - INFO - Iter [8600/12000]	lr: 1.500e-02, eta: 0:05:53, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1807, loss_bbox: 0.2822, loss_centerness: 0.5873, loss: 1.0503, grad_norm: 2.5063
+2022-10-03 23:19:11,427 - mmdet - INFO - Iter [8650/12000]	lr: 1.500e-02, eta: 0:05:48, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1833, loss_bbox: 0.2826, loss_centerness: 0.5887, loss: 1.0546, grad_norm: 2.5176
+2022-10-03 23:19:16,627 - mmdet - INFO - Iter [8700/12000]	lr: 1.500e-02, eta: 0:05:43, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1896, loss_bbox: 0.2769, loss_centerness: 0.5886, loss: 1.0551, grad_norm: 2.4687
+2022-10-03 23:19:21,661 - mmdet - INFO - Iter [8750/12000]	lr: 1.500e-02, eta: 0:05:37, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1801, loss_bbox: 0.2793, loss_centerness: 0.5897, loss: 1.0492, grad_norm: 2.3412
+2022-10-03 23:19:26,707 - mmdet - INFO - Iter [8800/12000]	lr: 1.500e-02, eta: 0:05:32, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1750, loss_bbox: 0.2684, loss_centerness: 0.5876, loss: 1.0310, grad_norm: 2.4336
+2022-10-03 23:19:31,728 - mmdet - INFO - Iter [8850/12000]	lr: 1.500e-02, eta: 0:05:27, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1809, loss_bbox: 0.2817, loss_centerness: 0.5880, loss: 1.0506, grad_norm: 2.6375
+2022-10-03 23:19:36,771 - mmdet - INFO - Iter [8900/12000]	lr: 1.500e-02, eta: 0:05:22, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1835, loss_bbox: 0.2802, loss_centerness: 0.5887, loss: 1.0524, grad_norm: 2.5153
+2022-10-03 23:19:41,994 - mmdet - INFO - Iter [8950/12000]	lr: 1.500e-02, eta: 0:05:16, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1850, loss_bbox: 0.2791, loss_centerness: 0.5898, loss: 1.0540, grad_norm: 2.4977
+2022-10-03 23:19:47,205 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:19:47,206 - mmdet - INFO - Iter [9000/12000]	lr: 1.500e-02, eta: 0:05:11, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1873, loss_bbox: 0.2851, loss_centerness: 0.5910, loss: 1.0634, grad_norm: 2.3387
+2022-10-03 23:19:52,498 - mmdet - INFO - Iter [9050/12000]	lr: 1.500e-03, eta: 0:05:06, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1636, loss_bbox: 0.2461, loss_centerness: 0.5873, loss: 0.9969, grad_norm: 1.9895
+2022-10-03 23:19:57,639 - mmdet - INFO - Iter [9100/12000]	lr: 1.500e-03, eta: 0:05:01, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1572, loss_bbox: 0.2387, loss_centerness: 0.5853, loss: 0.9812, grad_norm: 1.8355
+2022-10-03 23:20:02,708 - mmdet - INFO - Iter [9150/12000]	lr: 1.500e-03, eta: 0:04:56, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1616, loss_bbox: 0.2397, loss_centerness: 0.5860, loss: 0.9873, grad_norm: 1.8689
+2022-10-03 23:20:08,033 - mmdet - INFO - Iter [9200/12000]	lr: 1.500e-03, eta: 0:04:50, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1540, loss_bbox: 0.2367, loss_centerness: 0.5874, loss: 0.9782, grad_norm: 1.8470
+2022-10-03 23:20:13,407 - mmdet - INFO - Iter [9250/12000]	lr: 1.500e-03, eta: 0:04:45, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1511, loss_bbox: 0.2405, loss_centerness: 0.5854, loss: 0.9771, grad_norm: 1.8256
+2022-10-03 23:20:18,697 - mmdet - INFO - Iter [9300/12000]	lr: 1.500e-03, eta: 0:04:40, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1552, loss_bbox: 0.2260, loss_centerness: 0.5852, loss: 0.9664, grad_norm: 1.7953
+2022-10-03 23:20:23,716 - mmdet - INFO - Iter [9350/12000]	lr: 1.500e-03, eta: 0:04:35, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1468, loss_bbox: 0.2259, loss_centerness: 0.5828, loss: 0.9555, grad_norm: 1.8571
+2022-10-03 23:20:28,841 - mmdet - INFO - Iter [9400/12000]	lr: 1.500e-03, eta: 0:04:30, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.1413, loss_bbox: 0.2216, loss_centerness: 0.5825, loss: 0.9454, grad_norm: 1.7513
+2022-10-03 23:20:33,859 - mmdet - INFO - Iter [9450/12000]	lr: 1.500e-03, eta: 0:04:24, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1407, loss_bbox: 0.2201, loss_centerness: 0.5808, loss: 0.9416, grad_norm: 1.7782
+2022-10-03 23:20:39,102 - mmdet - INFO - Iter [9500/12000]	lr: 1.500e-03, eta: 0:04:19, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1407, loss_bbox: 0.2198, loss_centerness: 0.5811, loss: 0.9416, grad_norm: 1.7229
+2022-10-03 23:20:44,395 - mmdet - INFO - Iter [9550/12000]	lr: 1.500e-03, eta: 0:04:14, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1402, loss_bbox: 0.2166, loss_centerness: 0.5823, loss: 0.9391, grad_norm: 1.7978
+2022-10-03 23:20:49,562 - mmdet - INFO - Iter [9600/12000]	lr: 1.500e-03, eta: 0:04:09, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1398, loss_bbox: 0.2233, loss_centerness: 0.5835, loss: 0.9466, grad_norm: 1.7395
+2022-10-03 23:20:54,590 - mmdet - INFO - Iter [9650/12000]	lr: 1.500e-03, eta: 0:04:04, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1310, loss_bbox: 0.2175, loss_centerness: 0.5841, loss: 0.9325, grad_norm: 1.7419
+2022-10-03 23:20:59,841 - mmdet - INFO - Iter [9700/12000]	lr: 1.500e-03, eta: 0:03:58, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1369, loss_bbox: 0.2151, loss_centerness: 0.5828, loss: 0.9348, grad_norm: 1.7701
+2022-10-03 23:21:05,135 - mmdet - INFO - Iter [9750/12000]	lr: 1.500e-03, eta: 0:03:53, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1395, loss_bbox: 0.2123, loss_centerness: 0.5825, loss: 0.9343, grad_norm: 1.8823
+2022-10-03 23:21:10,242 - mmdet - INFO - Iter [9800/12000]	lr: 1.500e-03, eta: 0:03:48, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1400, loss_bbox: 0.2160, loss_centerness: 0.5829, loss: 0.9390, grad_norm: 1.7530
+2022-10-03 23:21:15,244 - mmdet - INFO - Iter [9850/12000]	lr: 1.500e-03, eta: 0:03:43, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1402, loss_bbox: 0.2162, loss_centerness: 0.5829, loss: 0.9394, grad_norm: 1.8387
+2022-10-03 23:21:20,277 - mmdet - INFO - Iter [9900/12000]	lr: 1.500e-03, eta: 0:03:38, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1377, loss_bbox: 0.2241, loss_centerness: 0.5840, loss: 0.9459, grad_norm: 1.7914
+2022-10-03 23:21:25,462 - mmdet - INFO - Iter [9950/12000]	lr: 1.500e-03, eta: 0:03:32, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1374, loss_bbox: 0.2235, loss_centerness: 0.5838, loss: 0.9447, grad_norm: 1.7652
+2022-10-03 23:21:31,026 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:21:31,026 - mmdet - INFO - Iter [10000/12000]	lr: 1.500e-03, eta: 0:03:27, time: 0.111, data_time: 0.006, memory: 3375, loss_cls: 0.1387, loss_bbox: 0.2153, loss_centerness: 0.5808, loss: 0.9347, grad_norm: 1.8058
+2022-10-03 23:21:36,386 - mmdet - INFO - Iter [10050/12000]	lr: 1.500e-03, eta: 0:03:22, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1394, loss_bbox: 0.2170, loss_centerness: 0.5833, loss: 0.9398, grad_norm: 1.8790
+2022-10-03 23:21:41,757 - mmdet - INFO - Iter [10100/12000]	lr: 1.500e-03, eta: 0:03:17, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1397, loss_bbox: 0.2223, loss_centerness: 0.5848, loss: 0.9468, grad_norm: 1.8400
+2022-10-03 23:21:47,164 - mmdet - INFO - Iter [10150/12000]	lr: 1.500e-03, eta: 0:03:12, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1342, loss_bbox: 0.2153, loss_centerness: 0.5826, loss: 0.9321, grad_norm: 1.7860
+2022-10-03 23:21:52,515 - mmdet - INFO - Iter [10200/12000]	lr: 1.500e-03, eta: 0:03:07, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1382, loss_bbox: 0.2165, loss_centerness: 0.5828, loss: 0.9375, grad_norm: 1.8351
+2022-10-03 23:21:57,603 - mmdet - INFO - Iter [10250/12000]	lr: 1.500e-03, eta: 0:03:01, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.1360, loss_bbox: 0.2164, loss_centerness: 0.5822, loss: 0.9347, grad_norm: 1.8078
+2022-10-03 23:22:02,711 - mmdet - INFO - Iter [10300/12000]	lr: 1.500e-03, eta: 0:02:56, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1381, loss_bbox: 0.2194, loss_centerness: 0.5833, loss: 0.9407, grad_norm: 1.8150
+2022-10-03 23:22:07,799 - mmdet - INFO - Iter [10350/12000]	lr: 1.500e-03, eta: 0:02:51, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1409, loss_bbox: 0.2201, loss_centerness: 0.5846, loss: 0.9456, grad_norm: 1.8660
+2022-10-03 23:22:12,820 - mmdet - INFO - Iter [10400/12000]	lr: 1.500e-03, eta: 0:02:46, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1360, loss_bbox: 0.2176, loss_centerness: 0.5821, loss: 0.9357, grad_norm: 1.8105
+2022-10-03 23:22:17,815 - mmdet - INFO - Iter [10450/12000]	lr: 1.500e-03, eta: 0:02:41, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1321, loss_bbox: 0.2089, loss_centerness: 0.5828, loss: 0.9239, grad_norm: 1.7748
+2022-10-03 23:22:22,853 - mmdet - INFO - Iter [10500/12000]	lr: 1.500e-03, eta: 0:02:35, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.1363, loss_bbox: 0.2266, loss_centerness: 0.5853, loss: 0.9483, grad_norm: 1.7853
+2022-10-03 23:22:27,885 - mmdet - INFO - Iter [10550/12000]	lr: 1.500e-03, eta: 0:02:30, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.1343, loss_bbox: 0.2127, loss_centerness: 0.5820, loss: 0.9290, grad_norm: 1.8488
+2022-10-03 23:22:32,971 - mmdet - INFO - Iter [10600/12000]	lr: 1.500e-03, eta: 0:02:25, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1314, loss_bbox: 0.2115, loss_centerness: 0.5817, loss: 0.9246, grad_norm: 1.8739
+2022-10-03 23:22:38,025 - mmdet - INFO - Iter [10650/12000]	lr: 1.500e-03, eta: 0:02:20, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1326, loss_bbox: 0.2134, loss_centerness: 0.5831, loss: 0.9291, grad_norm: 1.8480
+2022-10-03 23:22:43,120 - mmdet - INFO - Iter [10700/12000]	lr: 1.500e-03, eta: 0:02:14, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1348, loss_bbox: 0.2162, loss_centerness: 0.5823, loss: 0.9333, grad_norm: 1.8150
+2022-10-03 23:22:48,378 - mmdet - INFO - Iter [10750/12000]	lr: 1.500e-03, eta: 0:02:09, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1357, loss_bbox: 0.2097, loss_centerness: 0.5817, loss: 0.9272, grad_norm: 1.8364
+2022-10-03 23:22:53,464 - mmdet - INFO - Iter [10800/12000]	lr: 1.500e-03, eta: 0:02:04, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1358, loss_bbox: 0.2174, loss_centerness: 0.5816, loss: 0.9347, grad_norm: 1.8194
+2022-10-03 23:22:58,782 - mmdet - INFO - Iter [10850/12000]	lr: 1.500e-03, eta: 0:01:59, time: 0.106, data_time: 0.007, memory: 3375, loss_cls: 0.1340, loss_bbox: 0.2063, loss_centerness: 0.5809, loss: 0.9212, grad_norm: 1.8542
+2022-10-03 23:23:04,036 - mmdet - INFO - Iter [10900/12000]	lr: 1.500e-03, eta: 0:01:54, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1306, loss_bbox: 0.2119, loss_centerness: 0.5821, loss: 0.9246, grad_norm: 1.8321
+2022-10-03 23:23:09,311 - mmdet - INFO - Iter [10950/12000]	lr: 1.500e-03, eta: 0:01:49, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1319, loss_bbox: 0.2146, loss_centerness: 0.5822, loss: 0.9287, grad_norm: 1.8227
+2022-10-03 23:23:14,275 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:23:14,275 - mmdet - INFO - Iter [11000/12000]	lr: 1.500e-03, eta: 0:01:43, time: 0.099, data_time: 0.006, memory: 3375, loss_cls: 0.1372, loss_bbox: 0.2184, loss_centerness: 0.5836, loss: 0.9391, grad_norm: 1.8240
+2022-10-03 23:23:19,434 - mmdet - INFO - Iter [11050/12000]	lr: 1.500e-04, eta: 0:01:38, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1325, loss_bbox: 0.2064, loss_centerness: 0.5815, loss: 0.9204, grad_norm: 1.8704
+2022-10-03 23:23:24,889 - mmdet - INFO - Iter [11100/12000]	lr: 1.500e-04, eta: 0:01:33, time: 0.109, data_time: 0.006, memory: 3375, loss_cls: 0.1328, loss_bbox: 0.2112, loss_centerness: 0.5817, loss: 0.9256, grad_norm: 1.7883
+2022-10-03 23:23:30,285 - mmdet - INFO - Iter [11150/12000]	lr: 1.500e-04, eta: 0:01:28, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1279, loss_bbox: 0.2029, loss_centerness: 0.5819, loss: 0.9127, grad_norm: 1.7456
+2022-10-03 23:23:35,379 - mmdet - INFO - Iter [11200/12000]	lr: 1.500e-04, eta: 0:01:23, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1327, loss_bbox: 0.2078, loss_centerness: 0.5804, loss: 0.9210, grad_norm: 1.7849
+2022-10-03 23:23:40,596 - mmdet - INFO - Iter [11250/12000]	lr: 1.500e-04, eta: 0:01:17, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1286, loss_bbox: 0.2092, loss_centerness: 0.5804, loss: 0.9182, grad_norm: 1.7203
+2022-10-03 23:23:45,639 - mmdet - INFO - Iter [11300/12000]	lr: 1.500e-04, eta: 0:01:12, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1283, loss_bbox: 0.2036, loss_centerness: 0.5813, loss: 0.9133, grad_norm: 1.7688
+2022-10-03 23:23:50,736 - mmdet - INFO - Iter [11350/12000]	lr: 1.500e-04, eta: 0:01:07, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1354, loss_bbox: 0.2096, loss_centerness: 0.5816, loss: 0.9266, grad_norm: 1.7905
+2022-10-03 23:23:55,741 - mmdet - INFO - Iter [11400/12000]	lr: 1.500e-04, eta: 0:01:02, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1284, loss_bbox: 0.2049, loss_centerness: 0.5813, loss: 0.9146, grad_norm: 1.6940
+2022-10-03 23:24:00,882 - mmdet - INFO - Iter [11450/12000]	lr: 1.500e-04, eta: 0:00:57, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1310, loss_bbox: 0.2129, loss_centerness: 0.5837, loss: 0.9276, grad_norm: 1.7064
+2022-10-03 23:24:06,044 - mmdet - INFO - Iter [11500/12000]	lr: 1.500e-04, eta: 0:00:51, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1273, loss_bbox: 0.2013, loss_centerness: 0.5800, loss: 0.9086, grad_norm: 1.7567
+2022-10-03 23:24:11,063 - mmdet - INFO - Iter [11550/12000]	lr: 1.500e-04, eta: 0:00:46, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1315, loss_bbox: 0.2136, loss_centerness: 0.5837, loss: 0.9288, grad_norm: 1.7617
+2022-10-03 23:24:16,060 - mmdet - INFO - Iter [11600/12000]	lr: 1.500e-04, eta: 0:00:41, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1280, loss_bbox: 0.2022, loss_centerness: 0.5803, loss: 0.9105, grad_norm: 1.7358
+2022-10-03 23:24:21,325 - mmdet - INFO - Iter [11650/12000]	lr: 1.500e-04, eta: 0:00:36, time: 0.105, data_time: 0.007, memory: 3375, loss_cls: 0.1357, loss_bbox: 0.2126, loss_centerness: 0.5832, loss: 0.9316, grad_norm: 1.8105
+2022-10-03 23:24:26,491 - mmdet - INFO - Iter [11700/12000]	lr: 1.500e-04, eta: 0:00:31, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1233, loss_bbox: 0.2019, loss_centerness: 0.5799, loss: 0.9051, grad_norm: 1.6989
+2022-10-03 23:24:31,573 - mmdet - INFO - Iter [11750/12000]	lr: 1.500e-04, eta: 0:00:25, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1286, loss_bbox: 0.2183, loss_centerness: 0.5824, loss: 0.9292, grad_norm: 1.6568
+2022-10-03 23:24:36,544 - mmdet - INFO - Iter [11800/12000]	lr: 1.500e-04, eta: 0:00:20, time: 0.099, data_time: 0.007, memory: 3375, loss_cls: 0.1289, loss_bbox: 0.2059, loss_centerness: 0.5816, loss: 0.9164, grad_norm: 1.7113
+2022-10-03 23:24:41,573 - mmdet - INFO - Iter [11850/12000]	lr: 1.500e-04, eta: 0:00:15, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.1272, loss_bbox: 0.2092, loss_centerness: 0.5814, loss: 0.9177, grad_norm: 1.7427
+2022-10-03 23:24:46,727 - mmdet - INFO - Iter [11900/12000]	lr: 1.500e-04, eta: 0:00:10, time: 0.103, data_time: 0.007, memory: 3375, loss_cls: 0.1298, loss_bbox: 0.2030, loss_centerness: 0.5815, loss: 0.9143, grad_norm: 1.7562
+2022-10-03 23:24:51,989 - mmdet - INFO - Iter [11950/12000]	lr: 1.500e-04, eta: 0:00:05, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1301, loss_bbox: 0.2108, loss_centerness: 0.5819, loss: 0.9228, grad_norm: 1.7248
+2022-10-03 23:24:57,103 - mmdet - INFO - Saving checkpoint at 12000 iterations
+2022-10-03 23:24:57,589 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:24:57,589 - mmdet - INFO - Iter [12000/12000]	lr: 1.500e-04, eta: 0:00:00, time: 0.112, data_time: 0.006, memory: 3375, loss_cls: 0.1293, loss_bbox: 0.2050, loss_centerness: 0.5805, loss: 0.9149, grad_norm: 1.7602
+2022-10-03 23:25:20,639 - mmdet - INFO - 
++-------------+------+-------+--------+-------+
+| class       | gts  | dets  | recall | ap    |
++-------------+------+-------+--------+-------+
+| aeroplane   | 285  | 6095  | 0.993  | 0.874 |
+| bicycle     | 337  | 9704  | 0.988  | 0.819 |
+| bird        | 459  | 9515  | 0.980  | 0.833 |
+| boat        | 263  | 12281 | 0.962  | 0.736 |
+| bottle      | 469  | 17289 | 0.932  | 0.705 |
+| bus         | 213  | 7837  | 0.981  | 0.851 |
+| car         | 1201 | 23829 | 0.990  | 0.883 |
+| cat         | 358  | 6674  | 0.992  | 0.885 |
+| chair       | 756  | 31606 | 0.963  | 0.661 |
+| cow         | 244  | 6770  | 1.000  | 0.861 |
+| diningtable | 206  | 17352 | 0.942  | 0.701 |
+| dog         | 489  | 8899  | 1.000  | 0.880 |
+| horse       | 348  | 8452  | 0.989  | 0.822 |
+| motorbike   | 325  | 9942  | 0.972  | 0.826 |
+| person      | 4528 | 57741 | 0.978  | 0.854 |
+| pottedplant | 480  | 19546 | 0.931  | 0.586 |
+| sheep       | 242  | 6822  | 0.983  | 0.837 |
+| sofa        | 239  | 11389 | 0.975  | 0.716 |
+| train       | 282  | 7815  | 0.986  | 0.861 |
+| tvmonitor   | 308  | 10825 | 0.974  | 0.841 |
++-------------+------+-------+--------+-------+
+| mAP         |      |       |        | 0.802 |
++-------------+------+-------+--------+-------+
+2022-10-03 23:25:21,153 - mmdet - INFO - Now best checkpoint is saved as best_mAP_iter_12000.pth.
+2022-10-03 23:25:21,154 - mmdet - INFO - Best mAP is 0.8015 at 12000 iter.
+2022-10-03 23:25:21,154 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
+2022-10-03 23:25:21,154 - mmdet - INFO - Iter(val) [619]	mAP: 0.8015, AP50: 0.8020

finetune/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5/20221003_230350.log.json

@@ -0,0 +1,242 @@
+{"env_info": "sys.platform: linux\nPython: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: A100-SXM-80GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0\nPyTorch: 1.10.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - LAPACK is enabled (usually provided by MKL)\n  - NNPACK is enabled\n  - CPU capability usage: AVX512\n  - CUDA Runtime 11.3\n  - NVCC architecture flags: -gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n  - CuDNN 8.2\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.11.1+cu113\nOpenCV: 4.6.0\nMMCV: 1.6.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.2+87c120c", "config": "model = dict(\n    type='FCOS',\n    backbone=dict(\n        type='ResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        norm_cfg=dict(type='SyncBN', requires_grad=True),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n    neck=dict(\n        type='FPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        start_level=1,\n        add_extra_convs='on_output',\n        num_outs=5,\n        relu_before_extra_convs=True,\n        norm_cfg=dict(type='SyncBN', requires_grad=True)),\n    bbox_head=dict(\n        type='FCOSHead',\n        num_classes=20,\n        in_channels=256,\n        stacked_convs=4,\n        feat_channels=256,\n        strides=[8, 16, 32, 64, 128],\n        loss_cls=dict(\n            type='FocalLoss',\n            use_sigmoid=True,\n            gamma=2.0,\n            alpha=0.25,\n            loss_weight=1.0),\n        loss_bbox=dict(type='IoULoss', loss_weight=1.0),\n        loss_centerness=dict(\n            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),\n    train_cfg=dict(\n        assigner=dict(\n            type='MaxIoUAssigner',\n            pos_iou_thr=0.5,\n            neg_iou_thr=0.4,\n            min_pos_iou=0,\n            ignore_iof_thr=-1),\n        allowed_border=-1,\n        pos_weight=-1,\n        debug=False),\n    test_cfg=dict(\n        nms_pre=1000,\n        min_bbox_size=0,\n        score_thr=0.05,\n        nms=dict(type='nms', iou_threshold=0.5),\n        max_per_img=100))\ndataset_type = 'VOCDataset'\ndata_root = 'data/VOCdevkit/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True),\n    dict(\n        type='Resize',\n        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),\n                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),\n                   (1333, 736), (1333, 768), (1333, 800)],\n        multiscale_mode='value',\n        keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='Pad', size_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1333, 800),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=32),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=2,\n    train=dict(\n        type='VOCDataset',\n        ann_file=[\n            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',\n            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'\n        ],\n        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True),\n            dict(\n                type='Resize',\n                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),\n                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),\n                           (1333, 736), (1333, 768), (1333, 800)],\n                multiscale_mode='value',\n                keep_ratio=True),\n            dict(type='RandomFlip', flip_ratio=0.5),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=32),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n        ]),\n    val=dict(\n        type='VOCDataset',\n        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',\n        img_prefix='data/VOCdevkit/VOC2007/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=32),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]),\n    test=dict(\n        type='VOCDataset',\n        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',\n        img_prefix='data/VOCdevkit/VOC2007/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=32),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nevaluation = dict(interval=12000, metric='mAP', save_best='auto')\noptimizer = dict(\n    type='SGD',\n    lr=0.015,\n    momentum=0.9,\n    weight_decay=5e-05,\n    paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0))\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.001,\n    step=[9000, 11000],\n    by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=12000)\ncheckpoint_config = dict(interval=12000)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n    dict(type='NumClassCheckHook'),\n    dict(\n        type='MMDetWandbHook',\n        init_kwargs=dict(project='I2B', group='finetune'),\n        interval=50,\n        num_eval_images=0,\n        log_checkpoint=False)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'pretrain/selfsup_fcos_mstrain-soft-teacher_sampler-2048_temp0.5/final_model.pth'\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\ncustom_imports = None\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\nwork_dir = 'work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "fcos_mstrain_12k_voc0712.py", "hook_msgs": {}}
+{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00148, "memory": 3374, "data_time": 0.00727, "loss_cls": 1.05367, "loss_bbox": 4.84913, "loss_centerness": 0.67539, "loss": 6.57819, "grad_norm": 12.3657, "time": 0.11406}
+{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00298, "memory": 3374, "data_time": 0.00608, "loss_cls": 0.67905, "loss_bbox": 0.86012, "loss_centerness": 0.65732, "loss": 2.19649, "grad_norm": 4.53465, "time": 0.11025}
+{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00448, "memory": 3374, "data_time": 0.00605, "loss_cls": 0.53331, "loss_bbox": 0.7146, "loss_centerness": 0.64282, "loss": 1.89073, "grad_norm": 5.21603, "time": 0.11219}
+{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00598, "memory": 3374, "data_time": 0.00584, "loss_cls": 0.47495, "loss_bbox": 0.73378, "loss_centerness": 0.63228, "loss": 1.841, "grad_norm": 7.339, "time": 0.10665}
+{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00748, "memory": 3374, "data_time": 0.00619, "loss_cls": 0.45914, "loss_bbox": 0.63532, "loss_centerness": 0.6251, "loss": 1.71957, "grad_norm": 6.49574, "time": 0.10468}
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finetune/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5/best_mAP_iter_12000.pth

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+size 256817773

finetune/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5/fcos_mstrain_12k_voc0712.py

@@ -0,0 +1,212 @@
+model = dict(
+    type='FCOS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5,
+        relu_before_extra_convs=True,
+        norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    bbox_head=dict(
+        type='FCOSHead',
+        num_classes=20,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+dataset_type = 'VOCDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                   (1333, 736), (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='VOCDataset',
+        ann_file=[
+            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
+            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'
+        ],
+        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                type='Resize',
+                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                           (1333, 736), (1333, 768), (1333, 800)],
+                multiscale_mode='value',
+                keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='VOCDataset',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        img_prefix='data/VOCdevkit/VOC2007/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='VOCDataset',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        img_prefix='data/VOCdevkit/VOC2007/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(interval=12000, metric='mAP', save_best='auto')
+optimizer = dict(
+    type='SGD',
+    lr=0.015,
+    momentum=0.9,
+    weight_decay=5e-05,
+    paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0))
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[9000, 11000],
+    by_epoch=False)
+runner = dict(type='IterBasedRunner', max_iters=12000)
+checkpoint_config = dict(interval=12000)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_fcos_mstrain-soft-teacher_sampler-2048_temp0.5/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+work_dir = 'work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5'
+auto_resume = False
+gpu_ids = range(0, 8)

finetune/finetune_fcos_1x_coco_lr1.5e-2_wd5e.5/best_bbox_mAP_epoch_12.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a23cb08dcfde3078a0a804e45beff69bb475ef7ebb6268249ceec76b21052a53
+size 257924589

finetune/finetune_fcos_1x_coco_lr1.5e-2_wd5e.5/fcos_r50_fpn_1x_coco.py

@@ -0,0 +1,197 @@
+model = dict(
+    type='FCOS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5,
+        relu_before_extra_convs=True,
+        norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    bbox_head=dict(
+        type='FCOSHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(
+    interval=1, metric='bbox', save_best='auto', gpu_collect=True)
+optimizer = dict(
+    type='SGD',
+    lr=0.015,
+    momentum=0.9,
+    weight_decay=5e-05,
+    paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0))
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_fcos_mstrain-soft-teacher_sampler-2048_temp0.5/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+work_dir = 'work_dirs/finetune_fcos_1x_coco_lr1.5e-2_wd5e.5'
+auto_resume = False
+gpu_ids = range(0, 8)

finetune/finetune_mask-rcnn_12k_voc0712_lr3e-2_wd5e-5/20221003_234250.log.json

@@ -0,0 +1,242 @@
+{"env_info": "sys.platform: linux\nPython: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: A100-SXM-80GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0\nPyTorch: 1.10.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - LAPACK is enabled (usually provided by MKL)\n  - NNPACK is enabled\n  - CPU capability usage: AVX512\n  - CUDA Runtime 11.3\n  - NVCC architecture flags: -gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n  - CuDNN 8.2\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.11.1+cu113\nOpenCV: 4.6.0\nMMCV: 1.6.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.2+87c120c", "config": "model = dict(\n    type='MaskRCNN',\n    backbone=dict(\n        type='ResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        norm_cfg=dict(type='SyncBN', requires_grad=True),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n    neck=dict(\n        type='FPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        num_outs=5,\n        norm_cfg=dict(type='SyncBN', requires_grad=True)),\n    rpn_head=dict(\n        type='RPNHead',\n        in_channels=256,\n        feat_channels=256,\n        anchor_generator=dict(\n            type='AnchorGenerator',\n            scales=[8],\n            ratios=[0.5, 1.0, 2.0],\n            strides=[4, 8, 16, 32, 64]),\n        bbox_coder=dict(\n            type='DeltaXYWHBBoxCoder',\n            target_means=[0.0, 0.0, 0.0, 0.0],\n            target_stds=[1.0, 1.0, 1.0, 1.0]),\n        loss_cls=dict(\n            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n    roi_head=dict(\n        type='StandardRoIHead',\n        bbox_roi_extractor=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        bbox_head=dict(\n            type='Shared4Conv1FCBBoxHead',\n            in_channels=256,\n            fc_out_channels=1024,\n            roi_feat_size=7,\n            num_classes=20,\n            bbox_coder=dict(\n                type='DeltaXYWHBBoxCoder',\n                target_means=[0.0, 0.0, 0.0, 0.0],\n                target_stds=[0.1, 0.1, 0.2, 0.2]),\n            reg_class_agnostic=False,\n            loss_cls=dict(\n                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n        mask_roi_extractor=None,\n        mask_head=None),\n    train_cfg=dict(\n        rpn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.7,\n                neg_iou_thr=0.3,\n                min_pos_iou=0.3,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=256,\n                pos_fraction=0.5,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=False),\n            allowed_border=-1,\n            pos_weight=-1,\n            debug=False),\n        rpn_proposal=dict(\n            nms_pre=2000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.5,\n                neg_iou_thr=0.5,\n                min_pos_iou=0.5,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=512,\n                pos_fraction=0.25,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=True),\n            mask_size=28,\n            pos_weight=-1,\n            debug=False)),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=1000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            score_thr=0.05,\n            nms=dict(type='nms', iou_threshold=0.5),\n            max_per_img=100,\n            mask_thr_binary=0.5)))\ndataset_type = 'VOCDataset'\ndata_root = 'data/VOCdevkit/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True),\n    dict(\n        type='Resize',\n        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),\n                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),\n                   (1333, 736), (1333, 768), (1333, 800)],\n        multiscale_mode='value',\n        keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='Pad', size_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1333, 800),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=32),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=2,\n    train=dict(\n        type='VOCDataset',\n        ann_file=[\n            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',\n            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'\n        ],\n        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True),\n            dict(\n                type='Resize',\n                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),\n                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),\n                           (1333, 736), (1333, 768), (1333, 800)],\n                multiscale_mode='value',\n                keep_ratio=True),\n            dict(type='RandomFlip', flip_ratio=0.5),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=32),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n        ]),\n    val=dict(\n        type='VOCDataset',\n        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',\n        img_prefix='data/VOCdevkit/VOC2007/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=32),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]),\n    test=dict(\n        type='VOCDataset',\n        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',\n        img_prefix='data/VOCdevkit/VOC2007/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=32),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nevaluation = dict(interval=12000, metric='mAP', save_best='auto')\noptimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.001,\n    step=[9000, 11000],\n    by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=12000)\ncheckpoint_config = dict(interval=12000)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n    dict(type='NumClassCheckHook'),\n    dict(\n        type='MMDetWandbHook',\n        init_kwargs=dict(project='I2B', group='finetune'),\n        interval=50,\n        num_eval_images=0,\n        log_checkpoint=False)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth'\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\ncustom_imports = None\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\nwork_dir = 'work_dirs/finetune_mask-rcnn__12k_voc0712_lr3e-2_wd5e-5'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "mask_rcnn_mstrain_12k_voc0712.py", "hook_msgs": {}}
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+{"mode": "val", "epoch": 1, "iter": 619, "lr": 0.0003, "mAP": 0.82919, "AP50": 0.829}

finetune/finetune_mask-rcnn_12k_voc0712_lr3e-2_wd5e-5/best_mAP_iter_12000.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:092caa083e55583761d57ebe9b32117fbba98666429dbe5eee77220d37dacb08
+size 341585449

finetune/finetune_mask-rcnn_12k_voc0712_lr3e-2_wd5e-5/mask_rcnn_mstrain_12k_voc0712.py

@@ -0,0 +1,262 @@
+model = dict(
+    type='MaskRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5,
+        norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=20,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+        mask_roi_extractor=None,
+        mask_head=None),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
+dataset_type = 'VOCDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                   (1333, 736), (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='VOCDataset',
+        ann_file=[
+            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
+            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'
+        ],
+        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                type='Resize',
+                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                           (1333, 736), (1333, 768), (1333, 800)],
+                multiscale_mode='value',
+                keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ]),
+    val=dict(
+        type='VOCDataset',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        img_prefix='data/VOCdevkit/VOC2007/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='VOCDataset',
+        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        img_prefix='data/VOCdevkit/VOC2007/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(interval=12000, metric='mAP', save_best='auto')
+optimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[9000, 11000],
+    by_epoch=False)
+runner = dict(type='IterBasedRunner', max_iters=12000)
+checkpoint_config = dict(interval=12000)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+work_dir = 'work_dirs/finetune_mask-rcnn__12k_voc0712_lr3e-2_wd5e-5'
+auto_resume = False
+gpu_ids = range(0, 8)

finetune/finetune_mask-rcnn_1x_coco_lr3e-2_wd5e-5/best_bbox_mAP_epoch_12.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f36ebf2356dfc2f612293f95ea49046127d0a7e5680f1dc0d1bf0172fb4cf3ba
+size 365201337

finetune/finetune_mask-rcnn_1x_coco_lr3e-2_wd5e-5/mask_rcnn_r50_fpn_1x_coco.py

@@ -0,0 +1,259 @@
+model = dict(
+    type='MaskRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5,
+        norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0.0, 0.0, 0.0, 0.0],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=80,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(
+                type='Collect',
+                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(metric=['bbox', 'segm'], save_best='auto')
+optimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='MMDetWandbHook',
+        init_kwargs=dict(project='I2B', group='finetune'),
+        interval=50,
+        num_eval_images=0,
+        log_checkpoint=False)
+]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'work_dirs/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth'
+resume_from = None
+workflow = [('train', 1)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+custom_imports = None
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+work_dir = 'work_dirs/finetune_mask-rcnn_1x_coco_lr3e-2_wd5e-5'
+auto_resume = False
+gpu_ids = range(0, 8)