{"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: NVIDIA A100 80GB PCIe\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\n  - CuDNN 8.2.1\n    - Built with 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+d0a7efc", "config": "model = dict(\n    type='MaskRCNN',\n    backbone=dict(\n        type='MobileNetV2',\n        init_cfg=dict(\n            type='Pretrained', checkpoint='open-mmlab://mmdet/mobilenet_v2')),\n    neck=dict(\n        type='FPN',\n        in_channels=[24, 32, 96, 1280],\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=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        mask_head=dict(\n            type='FCNMaskHead',\n            num_convs=4,\n            in_channels=256,\n            conv_out_channels=256,\n            num_classes=80,\n            loss_mask=dict(\n                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\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, with_mask=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', 'gt_masks'])\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, with_mask=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(\n                type='Collect',\n                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\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(metric=['bbox', 'segm'], 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=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\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 = 'work_dirs/selfsup_mask-rcnn_mbv2_mstrain-soft-teacher_1x_coco_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_mbv2_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "mask_rcnn_mbv2_fpn_1x_coco.py", "hook_msgs": {}}
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{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00596, "memory": 5354, "data_time": 0.02668, "loss_rpn_cls": 0.26622, "loss_rpn_bbox": 0.08936, "loss_cls": 0.37714, "acc": 94.66235, "loss_bbox": 0.16927, "loss_mask": 0.68583, "loss": 1.58783, "time": 0.20933}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00896, "memory": 5601, "data_time": 0.02695, "loss_rpn_cls": 0.18185, "loss_rpn_bbox": 0.09353, "loss_cls": 0.46841, "acc": 92.18677, "loss_bbox": 0.27301, "loss_mask": 0.63669, "loss": 1.65349, "time": 0.20858}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.01196, "memory": 5736, "data_time": 0.03179, "loss_rpn_cls": 0.13034, "loss_rpn_bbox": 0.08803, "loss_cls": 0.52773, "acc": 90.88696, "loss_bbox": 0.33013, "loss_mask": 0.59243, "loss": 1.66866, "time": 0.21269}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.01496, "memory": 5736, "data_time": 0.02723, "loss_rpn_cls": 0.10927, "loss_rpn_bbox": 0.09081, "loss_cls": 0.57392, "acc": 89.95996, "loss_bbox": 0.35622, "loss_mask": 0.55915, "loss": 1.68937, "time": 0.21845}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.01795, "memory": 5813, "data_time": 0.02559, "loss_rpn_cls": 0.1081, "loss_rpn_bbox": 0.08629, "loss_cls": 0.54995, "acc": 90.21875, "loss_bbox": 0.33962, "loss_mask": 0.52954, "loss": 1.6135, "time": 0.21229}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.02095, "memory": 5813, "data_time": 0.02268, "loss_rpn_cls": 0.09514, "loss_rpn_bbox": 0.08436, "loss_cls": 0.54384, "acc": 90.10522, "loss_bbox": 0.32052, "loss_mask": 0.50345, "loss": 1.54731, "time": 0.21268}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.02395, "memory": 5813, "data_time": 0.02505, "loss_rpn_cls": 0.0956, "loss_rpn_bbox": 0.0789, "loss_cls": 0.49697, "acc": 90.66992, "loss_bbox": 0.28665, "loss_mask": 0.4706, "loss": 1.42873, "time": 0.2121}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.02694, "memory": 5871, "data_time": 0.02689, "loss_rpn_cls": 0.09954, "loss_rpn_bbox": 0.08311, "loss_cls": 0.53441, "acc": 89.67114, "loss_bbox": 0.30213, "loss_mask": 0.46539, "loss": 1.48459, "time": 0.20927}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.02994, "memory": 5871, "data_time": 0.0267, "loss_rpn_cls": 0.09588, "loss_rpn_bbox": 0.08092, "loss_cls": 0.48419, "acc": 90.49902, "loss_bbox": 0.27654, "loss_mask": 0.45844, "loss": 1.39597, "time": 0.20481}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.03, "memory": 5871, "data_time": 0.02771, "loss_rpn_cls": 0.09751, "loss_rpn_bbox": 0.08129, "loss_cls": 0.46933, "acc": 90.79077, "loss_bbox": 0.26478, "loss_mask": 0.44954, "loss": 1.36245, "time": 0.19879}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.03, "memory": 5871, "data_time": 0.02868, "loss_rpn_cls": 0.09592, "loss_rpn_bbox": 0.08012, "loss_cls": 0.44862, "acc": 90.66089, "loss_bbox": 0.27675, "loss_mask": 0.43126, "loss": 1.33268, "time": 0.20171}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.03, "memory": 5871, "data_time": 0.02704, "loss_rpn_cls": 0.09204, "loss_rpn_bbox": 0.0802, "loss_cls": 0.45184, "acc": 90.62134, "loss_bbox": 0.26778, "loss_mask": 0.4343, "loss": 1.32616, "time": 0.19995}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.03, "memory": 5871, "data_time": 0.02755, "loss_rpn_cls": 0.08982, "loss_rpn_bbox": 0.07901, "loss_cls": 0.45869, "acc": 90.38794, "loss_bbox": 0.27294, "loss_mask": 0.41816, "loss": 1.31862, "time": 0.20262}
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{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.03, "memory": 5872, "data_time": 0.02784, "loss_rpn_cls": 0.0895, "loss_rpn_bbox": 0.07869, "loss_cls": 0.43202, "acc": 90.54492, "loss_bbox": 0.26444, "loss_mask": 0.40728, "loss": 1.27194, "time": 0.2095}
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{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.03, "memory": 5900, "data_time": 0.02869, "loss_rpn_cls": 0.07979, "loss_rpn_bbox": 0.07438, "loss_cls": 0.43196, "acc": 90.33667, "loss_bbox": 0.27347, "loss_mask": 0.40398, "loss": 1.26358, "time": 0.2039}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.03, "memory": 5900, "data_time": 0.02566, "loss_rpn_cls": 0.08586, "loss_rpn_bbox": 0.07693, "loss_cls": 0.42214, "acc": 90.44849, "loss_bbox": 0.27404, "loss_mask": 0.40516, "loss": 1.26413, "time": 0.20247}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.03, "memory": 5900, "data_time": 0.02761, "loss_rpn_cls": 0.08563, "loss_rpn_bbox": 0.078, "loss_cls": 0.43256, "acc": 90.35474, "loss_bbox": 0.27049, "loss_mask": 0.40199, "loss": 1.26867, "time": 0.20814}
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{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.03, "memory": 5900, "data_time": 0.02991, "loss_rpn_cls": 0.08677, "loss_rpn_bbox": 0.08126, "loss_cls": 0.42809, "acc": 90.12866, "loss_bbox": 0.28115, "loss_mask": 0.40141, "loss": 1.27869, "time": 0.21095}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.03, "memory": 5900, "data_time": 0.0319, "loss_rpn_cls": 0.08306, "loss_rpn_bbox": 0.07852, "loss_cls": 0.41318, "acc": 90.39502, "loss_bbox": 0.26957, "loss_mask": 0.39507, "loss": 1.23939, "time": 0.20486}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.03, "memory": 5900, "data_time": 0.02797, "loss_rpn_cls": 0.08758, "loss_rpn_bbox": 0.08457, "loss_cls": 0.43137, "acc": 89.93188, "loss_bbox": 0.28433, "loss_mask": 0.40097, "loss": 1.28882, "time": 0.2104}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.03, "memory": 5900, "data_time": 0.0249, "loss_rpn_cls": 0.07984, "loss_rpn_bbox": 0.07174, "loss_cls": 0.40681, "acc": 90.63916, "loss_bbox": 0.2671, "loss_mask": 0.38583, "loss": 1.21132, "time": 0.21273}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.03, "memory": 5900, "data_time": 0.02945, "loss_rpn_cls": 0.0797, "loss_rpn_bbox": 0.0727, "loss_cls": 0.40547, "acc": 90.69165, "loss_bbox": 0.26133, "loss_mask": 0.38742, "loss": 1.20662, "time": 0.203}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.03, "memory": 5900, "data_time": 0.02371, "loss_rpn_cls": 0.08173, "loss_rpn_bbox": 0.07268, "loss_cls": 0.40131, "acc": 90.5, "loss_bbox": 0.26731, "loss_mask": 0.37874, "loss": 1.20178, "time": 0.20561}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.03, "memory": 5900, "data_time": 0.02881, "loss_rpn_cls": 0.0847, "loss_rpn_bbox": 0.07818, "loss_cls": 0.40576, "acc": 90.30396, "loss_bbox": 0.26738, "loss_mask": 0.38467, "loss": 1.22069, "time": 0.20767}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.03, "memory": 5900, "data_time": 0.02461, "loss_rpn_cls": 0.07809, "loss_rpn_bbox": 0.07208, "loss_cls": 0.3953, "acc": 90.56689, "loss_bbox": 0.26364, "loss_mask": 0.38285, "loss": 1.19197, "time": 0.20352}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.03, "memory": 5900, "data_time": 0.03051, "loss_rpn_cls": 0.07518, "loss_rpn_bbox": 0.07139, "loss_cls": 0.39153, "acc": 90.45215, "loss_bbox": 0.27124, "loss_mask": 0.38224, "loss": 1.19158, "time": 0.20221}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.03, "memory": 5900, "data_time": 0.02477, "loss_rpn_cls": 0.07704, "loss_rpn_bbox": 0.07282, "loss_cls": 0.38356, "acc": 90.89819, "loss_bbox": 0.26058, "loss_mask": 0.37826, "loss": 1.17225, "time": 0.20127}
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