{"env_info": "sys.platform: linux\nPython: 3.9.19 (main, May  6 2024, 19:43:03) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/petrelfs/share/cuda-11.7/\nNVCC: Cuda compilation tools, release 11.7, V11.7.99\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.12.0+cu113\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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: AVX2\n  - CUDA Runtime 11.3\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-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 8.3.2  (built against CUDA 11.5)\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/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-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.13.0+cu113\nOpenCV: 4.9.0\nMMCV: 1.7.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.7\nMMSegmentation: 0.27.0+6d3ca17", "seed": 1473895943, "exp_name": "upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py", "mmseg_version": "0.27.0+6d3ca17", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n    type='EncoderDecoder',\n    pretrained=None,\n    backbone=dict(\n        type='PIIPTwoBranch',\n        n_points=4,\n        deform_num_heads=16,\n        cffn_ratio=0.25,\n        deform_ratio=0.5,\n        with_cffn=True,\n        interact_attn_type='deform',\n        interaction_drop_path_rate=0.4,\n        interaction_proj=False,\n        norm_layer='none',\n        branch1=dict(\n            img_size=512,\n            pretrain_img_size=224,\n            patch_size=16,\n            pretrain_patch_size=14,\n            depth=48,\n            embed_dim=3200,\n            num_heads=25,\n            mlp_ratio=4,\n            qkv_bias=False,\n            init_values=0.1,\n            with_cp=True,\n            use_flash_attn=True,\n            qk_normalization=True,\n            layerscale_force_fp32=False,\n            with_fpn=False,\n            drop_path_rate=0.4,\n            pretrained='pretrained/intern_vit_6b_224px.pth',\n            norm_layer_type='RMSNorm',\n            mlp_type='fused_mlp'),\n        branch2=dict(\n            img_size=512,\n            pretrain_img_size=224,\n            patch_size=16,\n            pretrain_patch_size=14,\n            depth=32,\n            embed_dim=1280,\n            num_heads=16,\n            mlp_ratio=4,\n            qkv_bias=True,\n            init_values=1.0,\n            with_cp=True,\n            use_flash_attn=True,\n            qk_normalization=False,\n            layerscale_force_fp32=False,\n            with_fpn=False,\n            drop_path_rate=0.4,\n            pretrained='pretrained/mae_pretrain_vit_huge.pth',\n            norm_layer_type='LayerNorm',\n            mlp_type='fused_mlp')),\n    decode_head=dict(\n        type='UPerHead',\n        in_channels=[3200, 3200, 3200, 3200],\n        in_index=[0, 1, 2, 3],\n        pool_scales=(1, 2, 3, 6),\n        channels=1536,\n        dropout_ratio=0.1,\n        num_classes=150,\n        norm_cfg=dict(type='SyncBN', requires_grad=True),\n        align_corners=False,\n        loss_decode=dict(\n            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n    auxiliary_head=dict(\n        type='FCNHead',\n        in_channels=3200,\n        in_index=2,\n        channels=1536,\n        num_convs=1,\n        concat_input=False,\n        dropout_ratio=0.1,\n        num_classes=150,\n        norm_cfg=dict(type='SyncBN', requires_grad=True),\n        align_corners=False,\n        loss_decode=dict(\n            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),\n    train_cfg=dict(),\n    test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)))\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (512, 512)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', reduce_zero_label=True),\n    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n    dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n    dict(type='RandomFlip', prob=0.5),\n    dict(type='PhotoMetricDistortion'),\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=(512, 512), pad_val=0, seg_pad_val=255),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(2048, 512),\n        flip=False,\n        transforms=[\n            dict(\n                type='SETR_Resize',\n                keep_ratio=True,\n                crop_size=(512, 512),\n                setr_multi_scale=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='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=4,\n    train=dict(\n        type='ADE20KDataset',\n        data_root='data/ade/ADEChallengeData2016',\n        img_dir='images/training',\n        ann_dir='annotations/training',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', reduce_zero_label=True),\n            dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n            dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n            dict(type='RandomFlip', prob=0.5),\n            dict(type='PhotoMetricDistortion'),\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=(512, 512), pad_val=0, seg_pad_val=255),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n        ]),\n    val=dict(\n        type='ADE20KDataset',\n        data_root='data/ade/ADEChallengeData2016',\n        img_dir='images/validation',\n        ann_dir='annotations/validation',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(2048, 512),\n                flip=False,\n                transforms=[\n                    dict(\n                        type='SETR_Resize',\n                        keep_ratio=True,\n                        crop_size=(512, 512),\n                        setr_multi_scale=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='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]),\n    test=dict(\n        type='ADE20KDataset',\n        data_root='data/ade/ADEChallengeData2016',\n        img_dir='images/validation',\n        ann_dir='annotations/validation',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(2048, 512),\n                flip=False,\n                transforms=[\n                    dict(\n                        type='SETR_Resize',\n                        keep_ratio=True,\n                        crop_size=(512, 512),\n                        setr_multi_scale=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='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nlog_config = dict(\n    interval=50,\n    hooks=[\n        dict(type='TextLoggerHook', by_epoch=False),\n        dict(type='TensorboardLoggerHook')\n    ])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\noptimizer = dict(\n    type='AdamW',\n    lr=4e-05,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    constructor='CustomLayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(num_layers=48, layer_decay_rate=0.95, skip_stride=1.5))\noptimizer_config = dict()\nlr_config = dict(\n    policy='poly',\n    warmup='linear',\n    warmup_iters=1500,\n    warmup_ratio=1e-06,\n    power=1.0,\n    min_lr=0.0,\n    by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=80000)\ncheckpoint_config = dict(\n    by_epoch=False, interval=2000, deepspeed=True, max_keep_ckpts=1)\nevaluation = dict(interval=1000, metric='mIoU', pre_eval=True, save_best=None)\ndeepspeed = True\ndeepspeed_config = 'zero_configs/adam_zero1_bf16.json'\npretrained = None\ncustom_hooks = [dict(type='ToBFloat16Hook', priority=49)]\nwork_dir = './work_dirs/upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5'\ngpu_ids = range(0, 8)\nauto_resume = True\ndevice = 'cuda'\nseed = 1473895943\n", "CLASSES": ["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", "awning", "streetlight", "booth", "television receiver", "airplane", "dirt track", "apparel", "pole", "land", "bannister", "escalator", "ottoman", "bottle", "buffet", "poster", "stage", "van", "ship", "fountain", "conveyer belt", "canopy", "washer", "plaything", "swimming pool", "stool", "barrel", "basket", "waterfall", "tent", "bag", "minibike", "cradle", "oven", "ball", "food", "step", "tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket", "sculpture", "hood", "sconce", "vase", "traffic light", "tray", "ashcan", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass", "clock", "flag"], "PALETTE": [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], [102, 255, 0], [92, 0, 255]]}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 72263, "data_time": 0.01176, "decode.loss_ce": 4.10691, "decode.acc_seg": 0.4028, "aux.loss_ce": 1.62909, "aux.acc_seg": 0.65814, "loss": 5.736, "time": 2.26368}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 72263, "data_time": 0.00986, "decode.loss_ce": 3.91203, "decode.acc_seg": 5.62351, "aux.loss_ce": 1.57378, "aux.acc_seg": 3.24367, "loss": 5.48582, "time": 1.9772}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0, "memory": 72263, "data_time": 0.00956, "decode.loss_ce": 3.60576, "decode.acc_seg": 23.99753, "aux.loss_ce": 1.52822, "aux.acc_seg": 14.01864, "loss": 5.13398, "time": 1.97635}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 1e-05, "memory": 72263, "data_time": 0.00969, "decode.loss_ce": 2.96531, "decode.acc_seg": 35.94202, "aux.loss_ce": 1.37621, "aux.acc_seg": 29.36989, "loss": 4.34153, "time": 1.97755}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 1e-05, "memory": 72263, "data_time": 0.00998, "decode.loss_ce": 2.33411, "decode.acc_seg": 45.26722, "aux.loss_ce": 1.08234, "aux.acc_seg": 39.16111, "loss": 3.41645, "time": 1.97827}
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{"mode": "train", "epoch": 1, "iter": 350, "lr": 1e-05, "memory": 72263, "data_time": 0.01024, "decode.loss_ce": 1.72802, "decode.acc_seg": 57.6607, "aux.loss_ce": 0.78909, "aux.acc_seg": 53.10962, "loss": 2.51711, "time": 1.97931}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 1e-05, "memory": 72263, "data_time": 0.00946, "decode.loss_ce": 1.51751, "decode.acc_seg": 61.78156, "aux.loss_ce": 0.67995, "aux.acc_seg": 59.39772, "loss": 2.19746, "time": 1.97943}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 1e-05, "memory": 72263, "data_time": 0.0097, "decode.loss_ce": 1.34799, "decode.acc_seg": 64.24139, "aux.loss_ce": 0.6031, "aux.acc_seg": 62.42167, "loss": 1.95109, "time": 1.98119}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 1e-05, "memory": 72263, "data_time": 0.0096, "decode.loss_ce": 1.33775, "decode.acc_seg": 64.1809, "aux.loss_ce": 0.59204, "aux.acc_seg": 62.43566, "loss": 1.92979, "time": 1.9808}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 1e-05, "memory": 72263, "data_time": 0.01014, "decode.loss_ce": 1.19679, "decode.acc_seg": 66.49904, "aux.loss_ce": 0.52061, "aux.acc_seg": 65.35734, "loss": 1.7174, "time": 1.98192}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 2e-05, "memory": 72263, "data_time": 0.00988, "decode.loss_ce": 1.17511, "decode.acc_seg": 66.78066, "aux.loss_ce": 0.51029, "aux.acc_seg": 65.88674, "loss": 1.68541, "time": 1.98041}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 2e-05, "memory": 72263, "data_time": 0.00955, "decode.loss_ce": 1.05719, "decode.acc_seg": 69.22411, "aux.loss_ce": 0.45236, "aux.acc_seg": 68.90921, "loss": 1.50955, "time": 1.98087}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 2e-05, "memory": 72263, "data_time": 0.00963, "decode.loss_ce": 1.04235, "decode.acc_seg": 69.86512, "aux.loss_ce": 0.44533, "aux.acc_seg": 69.37588, "loss": 1.48768, "time": 1.98164}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 2e-05, "memory": 72263, "data_time": 0.00959, "decode.loss_ce": 1.0033, "decode.acc_seg": 69.23196, "aux.loss_ce": 0.43152, "aux.acc_seg": 68.91732, "loss": 1.43482, "time": 1.98106}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 2e-05, "memory": 72263, "data_time": 0.00942, "decode.loss_ce": 0.92818, "decode.acc_seg": 72.03273, "aux.loss_ce": 0.39694, "aux.acc_seg": 71.46815, "loss": 1.32511, "time": 1.98317}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 2e-05, "memory": 72263, "data_time": 0.00964, "decode.loss_ce": 0.89809, "decode.acc_seg": 72.14546, "aux.loss_ce": 0.37977, "aux.acc_seg": 71.63919, "loss": 1.27786, "time": 1.98187}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 2e-05, "memory": 72263, "data_time": 0.00977, "decode.loss_ce": 0.89417, "decode.acc_seg": 72.82892, "aux.loss_ce": 0.37314, "aux.acc_seg": 72.65768, "loss": 1.26731, "time": 1.98095}
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