{"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+2abe369", "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, 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(\n metric='bbox', save_best='auto', gpu_collect=True, interval=1)\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 = 'pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/keep_all.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_1x_coco_lr3e-2_wd5e-5_keep-all'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "faster_rcnn_r50_fpn_1x_coco.py", "hook_msgs": {}} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00297, "memory": 3998, "data_time": 0.10392, "loss_rpn_cls": 0.2137, "loss_rpn_bbox": 0.09646, "loss_cls": 2.19234, "acc": 73.8418, "loss_bbox": 0.2822, "loss": 2.78469, "time": 0.26046} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00596, "memory": 3998, "data_time": 0.03009, "loss_rpn_cls": 0.10835, "loss_rpn_bbox": 0.08065, "loss_cls": 0.64421, "acc": 89.6665, "loss_bbox": 0.37002, "loss": 1.20324, "time": 0.18648} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00896, "memory": 3998, "data_time": 0.03157, "loss_rpn_cls": 0.09679, "loss_rpn_bbox": 0.08327, "loss_cls": 0.59211, "acc": 89.45386, "loss_bbox": 0.38134, "loss": 1.15352, "time": 0.18485} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.01196, "memory": 3998, "data_time": 0.03796, "loss_rpn_cls": 0.09853, "loss_rpn_bbox": 0.08149, "loss_cls": 0.55016, "acc": 90.10327, "loss_bbox": 0.34418, "loss": 1.07436, "time": 0.18404} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.01496, "memory": 3998, "data_time": 0.03196, "loss_rpn_cls": 0.08635, "loss_rpn_bbox": 0.08319, "loss_cls": 0.59937, "acc": 89.19043, "loss_bbox": 0.36987, "loss": 1.13879, "time": 0.18315} {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.01795, "memory": 3998, "data_time": 0.03069, "loss_rpn_cls": 0.08803, "loss_rpn_bbox": 0.08144, "loss_cls": 0.55063, "acc": 89.82446, "loss_bbox": 0.33629, "loss": 1.05639, "time": 0.17945} {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.02095, "memory": 3998, "data_time": 0.02589, "loss_rpn_cls": 0.08116, "loss_rpn_bbox": 0.07936, "loss_cls": 0.5311, "acc": 89.79541, "loss_bbox": 0.31849, "loss": 1.0101, "time": 0.18109} {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.02395, "memory": 3998, "data_time": 0.02892, "loss_rpn_cls": 0.08242, "loss_rpn_bbox": 0.07458, "loss_cls": 0.4969, "acc": 90.01587, "loss_bbox": 0.30086, "loss": 0.95476, "time": 0.18549} {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.02694, "memory": 3998, "data_time": 0.03153, "loss_rpn_cls": 0.09163, "loss_rpn_bbox": 0.08097, "loss_cls": 0.52075, "acc": 89.39746, "loss_bbox": 0.30647, "loss": 0.99981, "time": 0.17927} {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.02994, "memory": 3998, "data_time": 0.03156, "loss_rpn_cls": 0.08358, "loss_rpn_bbox": 0.07801, "loss_cls": 0.47703, "acc": 90.073, "loss_bbox": 0.29443, "loss": 0.93305, "time": 0.17793} {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.03, "memory": 3998, "data_time": 0.03241, "loss_rpn_cls": 0.09026, "loss_rpn_bbox": 0.0791, "loss_cls": 0.45928, "acc": 90.396, "loss_bbox": 0.27503, "loss": 0.90366, "time": 0.17319} {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.03, "memory": 3998, "data_time": 0.03584, "loss_rpn_cls": 0.07649, "loss_rpn_bbox": 0.0797, "loss_cls": 0.43311, "acc": 90.4729, "loss_bbox": 0.2788, "loss": 0.86809, "time": 0.17657} {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.03, "memory": 3998, "data_time": 0.03203, "loss_rpn_cls": 0.08402, "loss_rpn_bbox": 0.07924, "loss_cls": 0.4295, "acc": 90.53613, "loss_bbox": 0.26967, "loss": 0.86244, "time": 0.17515} {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.03, "memory": 3998, "data_time": 0.03179, "loss_rpn_cls": 0.07563, "loss_rpn_bbox": 0.07698, "loss_cls": 0.43955, "acc": 89.98267, "loss_bbox": 0.28701, "loss": 0.87918, "time": 0.17526} {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.03, "memory": 4058, "data_time": 0.03633, "loss_rpn_cls": 0.0741, "loss_rpn_bbox": 0.07891, "loss_cls": 0.42224, "acc": 90.06152, "loss_bbox": 0.28535, "loss": 0.8606, "time": 0.18377} {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.03, "memory": 4058, "data_time": 0.03356, "loss_rpn_cls": 0.07561, "loss_rpn_bbox": 0.07624, "loss_cls": 0.40212, "acc": 90.49146, "loss_bbox": 0.27619, "loss": 0.83016, "time": 0.17844} {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.03, "memory": 4058, "data_time": 0.03549, "loss_rpn_cls": 0.07778, "loss_rpn_bbox": 0.08092, "loss_cls": 0.42948, "acc": 89.5625, "loss_bbox": 0.30079, "loss": 0.88897, "time": 0.18787} {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.03, "memory": 4058, "data_time": 0.03434, "loss_rpn_cls": 0.06596, "loss_rpn_bbox": 0.07295, "loss_cls": 0.39961, "acc": 90.32715, "loss_bbox": 0.27964, "loss": 0.81816, "time": 0.17698} {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.03, "memory": 4058, "data_time": 0.03039, "loss_rpn_cls": 0.07625, "loss_rpn_bbox": 0.076, "loss_cls": 0.39714, "acc": 90.28589, "loss_bbox": 0.28262, "loss": 0.83201, "time": 0.17341} {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.03, "memory": 4087, "data_time": 0.03227, "loss_rpn_cls": 0.07333, "loss_rpn_bbox": 0.07655, "loss_cls": 0.4015, "acc": 90.24927, "loss_bbox": 0.27911, "loss": 0.83049, "time": 0.18629} {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.03, "memory": 4087, "data_time": 0.03441, "loss_rpn_cls": 0.07139, "loss_rpn_bbox": 0.07593, "loss_cls": 0.39462, "acc": 90.37231, "loss_bbox": 0.27838, "loss": 0.82032, "time": 0.17526} {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.03, "memory": 4087, "data_time": 0.03546, "loss_rpn_cls": 0.07523, "loss_rpn_bbox": 0.07939, "loss_cls": 0.40186, "acc": 90.07251, "loss_bbox": 0.28545, "loss": 0.84193, "time": 0.18218} {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.03, "memory": 4087, "data_time": 0.03872, "loss_rpn_cls": 0.07352, "loss_rpn_bbox": 0.0757, "loss_cls": 0.38326, "acc": 90.38306, "loss_bbox": 0.27516, "loss": 0.80765, "time": 0.17853} {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.03, "memory": 4087, "data_time": 0.03401, "loss_rpn_cls": 0.07554, "loss_rpn_bbox": 0.08483, "loss_cls": 0.40145, "acc": 90.02905, "loss_bbox": 0.28799, "loss": 0.84981, "time": 0.18184} {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.03, "memory": 4087, "data_time": 0.03125, "loss_rpn_cls": 0.07121, "loss_rpn_bbox": 0.07101, "loss_cls": 0.37011, "acc": 90.6543, "loss_bbox": 0.27707, "loss": 0.7894, "time": 0.18515} {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.03, "memory": 4087, "data_time": 0.03643, "loss_rpn_cls": 0.07239, "loss_rpn_bbox": 0.07102, "loss_cls": 0.38704, "acc": 90.46826, "loss_bbox": 0.27233, "loss": 0.80279, "time": 0.17515} {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.03, "memory": 4087, "data_time": 0.02879, "loss_rpn_cls": 0.0681, "loss_rpn_bbox": 0.07127, "loss_cls": 0.37361, 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