{"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+146d174", "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=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_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_1x_coco_lr3e-2_wd5e-5'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "mask_rcnn_r50_fpn_1x_coco.py", "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00297, "memory": 4288, "data_time": 0.10467, "loss_rpn_cls": 0.50193, "loss_rpn_bbox": 0.10348, "loss_cls": 1.46568, "acc": 85.86987, "loss_bbox": 0.03634, "loss_mask": 0.75628, "loss": 2.86371, "time": 0.31043}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00596, "memory": 4613, "data_time": 0.0295, "loss_rpn_cls": 0.22479, "loss_rpn_bbox": 0.08789, "loss_cls": 0.44189, "acc": 93.35889, "loss_bbox": 0.22591, "loss_mask": 0.67873, "loss": 1.65922, "time": 0.20459}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00896, "memory": 4772, "data_time": 0.03028, "loss_rpn_cls": 0.13614, "loss_rpn_bbox": 0.09111, "loss_cls": 0.54347, "acc": 90.64722, "loss_bbox": 0.34164, "loss_mask": 0.62638, "loss": 1.73874, "time": 0.21029}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.01196, "memory": 4867, "data_time": 0.03679, "loss_rpn_cls": 0.10805, "loss_rpn_bbox": 0.08761, "loss_cls": 0.54458, "acc": 90.35474, "loss_bbox": 0.34697, "loss_mask": 0.58682, "loss": 1.67403, "time": 0.20834}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.01496, "memory": 4903, "data_time": 0.03102, "loss_rpn_cls": 0.09362, "loss_rpn_bbox": 0.08858, "loss_cls": 0.58335, "acc": 89.36841, "loss_bbox": 0.37096, "loss_mask": 0.54406, "loss": 1.68057, "time": 0.20814}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.01795, "memory": 4903, "data_time": 0.02986, "loss_rpn_cls": 0.09416, "loss_rpn_bbox": 0.0864, "loss_cls": 0.54935, "acc": 89.83765, "loss_bbox": 0.32857, "loss_mask": 0.51574, "loss": 1.57423, "time": 0.20283}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.02095, "memory": 4904, "data_time": 0.02547, "loss_rpn_cls": 0.08432, "loss_rpn_bbox": 0.08423, "loss_cls": 0.52005, "acc": 89.95801, "loss_bbox": 0.31152, "loss_mask": 0.49045, "loss": 1.49058, "time": 0.20559}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.02395, "memory": 4912, "data_time": 0.0303, "loss_rpn_cls": 0.0895, "loss_rpn_bbox": 0.07889, "loss_cls": 0.48665, "acc": 90.35693, "loss_bbox": 0.28751, "loss_mask": 0.46101, "loss": 1.40355, "time": 0.20891}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.02694, "memory": 4965, "data_time": 0.03106, "loss_rpn_cls": 0.09023, "loss_rpn_bbox": 0.08313, "loss_cls": 0.51556, "acc": 89.48706, "loss_bbox": 0.3022, "loss_mask": 0.447, "loss": 1.43812, "time": 0.20403}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.02994, "memory": 4965, "data_time": 0.03055, "loss_rpn_cls": 0.08446, "loss_rpn_bbox": 0.08123, "loss_cls": 0.47397, "acc": 90.20483, "loss_bbox": 0.28709, "loss_mask": 0.43819, "loss": 1.36494, "time": 0.20186}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.03, "memory": 4965, "data_time": 0.03225, "loss_rpn_cls": 0.09258, "loss_rpn_bbox": 0.0812, "loss_cls": 0.46548, "acc": 90.52661, "loss_bbox": 0.27568, "loss_mask": 0.44467, "loss": 1.3596, "time": 0.19817}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.03, "memory": 4965, "data_time": 0.03461, "loss_rpn_cls": 0.08253, "loss_rpn_bbox": 0.08105, "loss_cls": 0.4371, "acc": 90.5459, "loss_bbox": 0.27641, "loss_mask": 0.41649, "loss": 1.29359, "time": 0.2018}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.03, "memory": 4965, "data_time": 0.03239, "loss_rpn_cls": 0.08089, "loss_rpn_bbox": 0.07928, "loss_cls": 0.43947, "acc": 90.38208, "loss_bbox": 0.27193, "loss_mask": 0.41458, "loss": 1.28614, "time": 0.19704}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.03, "memory": 4965, "data_time": 0.03223, "loss_rpn_cls": 0.07981, "loss_rpn_bbox": 0.07916, "loss_cls": 0.4345, "acc": 90.14722, "loss_bbox": 0.27938, "loss_mask": 0.39923, "loss": 1.27207, "time": 0.19664}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.03, "memory": 4965, "data_time": 0.03462, "loss_rpn_cls": 0.07573, "loss_rpn_bbox": 0.08084, "loss_cls": 0.42373, "acc": 90.11646, "loss_bbox": 0.28552, "loss_mask": 0.40462, "loss": 1.27043, "time": 0.20702}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.03, "memory": 4965, "data_time": 0.03217, "loss_rpn_cls": 0.07796, "loss_rpn_bbox": 0.07832, "loss_cls": 0.41273, "acc": 90.43408, "loss_bbox": 0.27511, "loss_mask": 0.39453, "loss": 1.23865, "time": 0.20483}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.03, "memory": 4995, "data_time": 0.03523, "loss_rpn_cls": 0.0817, "loss_rpn_bbox": 0.0816, "loss_cls": 0.43041, "acc": 89.81128, "loss_bbox": 0.29582, "loss_mask": 0.40007, "loss": 1.2896, "time": 0.21025}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.03, "memory": 4995, "data_time": 0.03688, "loss_rpn_cls": 0.06966, "loss_rpn_bbox": 0.07415, "loss_cls": 0.40491, "acc": 90.3501, "loss_bbox": 0.27844, "loss_mask": 0.38188, "loss": 1.20905, "time": 0.19578}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.03, "memory": 4995, "data_time": 0.03078, "loss_rpn_cls": 0.0785, "loss_rpn_bbox": 0.077, "loss_cls": 0.40899, "acc": 90.24609, "loss_bbox": 0.28396, "loss_mask": 0.38788, "loss": 1.23633, "time": 0.19552}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.03, "memory": 4995, "data_time": 0.03295, "loss_rpn_cls": 0.07784, "loss_rpn_bbox": 0.07754, "loss_cls": 0.40973, "acc": 90.26709, "loss_bbox": 0.27414, "loss_mask": 0.38955, "loss": 1.22881, "time": 0.20797}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.03, "memory": 4995, "data_time": 0.03556, "loss_rpn_cls": 0.07499, "loss_rpn_bbox": 0.07611, "loss_cls": 0.39972, "acc": 90.33179, "loss_bbox": 0.27534, "loss_mask": 0.37535, "loss": 1.20152, "time": 0.20014}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.03, "memory": 4995, "data_time": 0.03678, "loss_rpn_cls": 0.07812, "loss_rpn_bbox": 0.081, "loss_cls": 0.40613, "acc": 90.0061, "loss_bbox": 0.28578, "loss_mask": 0.38153, "loss": 1.23257, "time": 0.20659}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.03, "memory": 4995, "data_time": 0.03975, "loss_rpn_cls": 0.07732, "loss_rpn_bbox": 0.07716, "loss_cls": 0.39299, "acc": 90.3374, "loss_bbox": 0.27253, "loss_mask": 0.37895, "loss": 1.19895, "time": 0.20126}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.03, "memory": 4995, "data_time": 0.03424, "loss_rpn_cls": 0.07922, "loss_rpn_bbox": 0.08514, "loss_cls": 0.40775, "acc": 89.93213, "loss_bbox": 0.28759, "loss_mask": 0.38845, "loss": 1.24815, "time": 0.20544}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.03, "memory": 4995, "data_time": 0.03033, "loss_rpn_cls": 0.07242, "loss_rpn_bbox": 0.07206, "loss_cls": 0.37308, "acc": 90.83154, "loss_bbox": 0.26655, "loss_mask": 0.37369, "loss": 1.15779, "time": 0.20549}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.03, "memory": 4995, "data_time": 0.03748, "loss_rpn_cls": 0.07425, "loss_rpn_bbox": 0.07173, "loss_cls": 0.38396, "acc": 90.6814, "loss_bbox": 0.26362, "loss_mask": 0.36942, "loss": 1.16298, "time": 0.20028}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.03, "memory": 4995, "data_time": 0.02972, "loss_rpn_cls": 0.07198, "loss_rpn_bbox": 0.07318, "loss_cls": 0.38477, "acc": 90.30347, "loss_bbox": 0.27374, "loss_mask": 0.36216, "loss": 1.16583, "time": 0.2007}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.03, "memory": 4995, "data_time": 0.03497, "loss_rpn_cls": 0.07651, "loss_rpn_bbox": 0.07651, "loss_cls": 0.3797, "acc": 90.38818, "loss_bbox": 0.26918, "loss_mask": 0.36823, "loss": 1.17012, "time": 0.20023}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.03, "memory": 4995, "data_time": 0.03041, "loss_rpn_cls": 0.07155, "loss_rpn_bbox": 0.07105, "loss_cls": 0.37521, "acc": 90.46875, "loss_bbox": 0.27341, "loss_mask": 0.3684, "loss": 1.15963, "time": 0.2005}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.03, "memory": 4995, "data_time": 0.03783, "loss_rpn_cls": 0.06888, "loss_rpn_bbox": 0.07064, "loss_cls": 0.36517, "acc": 90.57593, "loss_bbox": 0.27049, "loss_mask": 0.36395, "loss": 1.13913, "time": 0.19616}
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