{"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+d0a7efc", "config": "model = dict(\n    type='MaskRCNN',\n    backbone=dict(\n        type='SwinTransformer',\n        embed_dims=96,\n        depths=[2, 2, 18, 2],\n        num_heads=[3, 6, 12, 24],\n        window_size=7,\n        mlp_ratio=4,\n        qkv_bias=True,\n        qk_scale=None,\n        drop_rate=0.0,\n        attn_drop_rate=0.0,\n        drop_path_rate=0.2,\n        patch_norm=True,\n        out_indices=(0, 1, 2, 3),\n        with_cp=False,\n        convert_weights=True,\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint=\n            'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth'\n        )),\n    neck=dict(\n        type='FPN',\n        in_channels=[96, 192, 384, 768],\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='RandomFlip', flip_ratio=0.5),\n    dict(\n        type='AutoAugment',\n        policies=[[{\n            'type':\n            'Resize',\n            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),\n                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),\n                          (736, 1333), (768, 1333), (800, 1333)],\n            'multiscale_mode':\n            'value',\n            'keep_ratio':\n            True\n        }],\n                  [{\n                      'type': 'Resize',\n                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],\n                      'multiscale_mode': 'value',\n                      'keep_ratio': True\n                  }, {\n                      'type': 'RandomCrop',\n                      'crop_type': 'absolute_range',\n                      'crop_size': (384, 600),\n                      'allow_negative_crop': True\n                  }, {\n                      'type':\n                      'Resize',\n                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n                                    (576, 1333), (608, 1333), (640, 1333),\n                                    (672, 1333), (704, 1333), (736, 1333),\n                                    (768, 1333), (800, 1333)],\n                      'multiscale_mode':\n                      'value',\n                      'override':\n                      True,\n                      'keep_ratio':\n                      True\n                  }]]),\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='RandomFlip', flip_ratio=0.5),\n            dict(\n                type='AutoAugment',\n                policies=[[{\n                    'type':\n                    'Resize',\n                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n                                  (576, 1333), (608, 1333), (640, 1333),\n                                  (672, 1333), (704, 1333), (736, 1333),\n                                  (768, 1333), (800, 1333)],\n                    'multiscale_mode':\n                    'value',\n                    'keep_ratio':\n                    True\n                }],\n                          [{\n                              'type': 'Resize',\n                              'img_scale': [(400, 1333), (500, 1333),\n                                            (600, 1333)],\n                              'multiscale_mode': 'value',\n                              'keep_ratio': True\n                          }, {\n                              'type': 'RandomCrop',\n                              'crop_type': 'absolute_range',\n                              'crop_size': (384, 600),\n                              'allow_negative_crop': True\n                          }, {\n                              'type':\n                              'Resize',\n                              'img_scale': [(480, 1333), (512, 1333),\n                                            (544, 1333), (576, 1333),\n                                            (608, 1333), (640, 1333),\n                                            (672, 1333), (704, 1333),\n                                            (736, 1333), (768, 1333),\n                                            (800, 1333)],\n                              'multiscale_mode':\n                              'value',\n                              'override':\n                              True,\n                              'keep_ratio':\n                              True\n                          }]]),\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(\n    type='AdamW',\n    lr=0.0001,\n    betas=(0.9, 0.999),\n    weight_decay=0.01,\n    paramwise_cfg=dict(\n        custom_keys=dict(\n            absolute_pos_embed=dict(decay_mult=0.0),\n            relative_position_bias_table=dict(decay_mult=0.0),\n            norm=dict(decay_mult=0.0))))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=1000,\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_swin-s_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)\npretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth'\nwork_dir = 'work_dirs/finetune_mask-rcnn_swin-s_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "mask_rcnn_swin-s-p4-w7_fpn_ms-crop_1x_coco.py", "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 10758, "data_time": 0.08102, "loss_rpn_cls": 0.69853, "loss_rpn_bbox": 0.11889, "loss_cls": 4.28337, "acc": 18.35791, "loss_bbox": 0.01634, "loss_mask": 0.84288, "loss": 5.96002, "time": 0.41371}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 1e-05, "memory": 11330, "data_time": 0.01738, "loss_rpn_cls": 0.6587, "loss_rpn_bbox": 0.10768, "loss_cls": 3.06854, "acc": 92.14233, "loss_bbox": 0.02714, "loss_mask": 0.74945, "loss": 4.61151, "time": 0.31379}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 1e-05, "memory": 11832, "data_time": 0.01766, "loss_rpn_cls": 0.54893, "loss_rpn_bbox": 0.10022, "loss_cls": 0.39285, "acc": 97.03418, "loss_bbox": 0.07356, "loss_mask": 0.70358, "loss": 1.81914, "time": 0.31644}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 2e-05, "memory": 11832, "data_time": 0.02113, "loss_rpn_cls": 0.34065, "loss_rpn_bbox": 0.09094, "loss_cls": 0.33231, "acc": 95.30615, "loss_bbox": 0.14931, "loss_mask": 0.68566, "loss": 1.59887, "time": 0.33205}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 2e-05, "memory": 11832, "data_time": 0.01919, "loss_rpn_cls": 0.22342, "loss_rpn_bbox": 0.08921, "loss_cls": 0.35694, "acc": 94.11182, "loss_bbox": 0.19881, "loss_mask": 0.64418, "loss": 1.51256, "time": 0.31582}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 3e-05, "memory": 11832, "data_time": 0.01644, "loss_rpn_cls": 0.16836, "loss_rpn_bbox": 0.07991, "loss_cls": 0.38361, "acc": 93.37598, "loss_bbox": 0.24007, "loss_mask": 0.61343, "loss": 1.48538, "time": 0.31738}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 3e-05, "memory": 11832, "data_time": 0.01459, "loss_rpn_cls": 0.14383, "loss_rpn_bbox": 0.07636, "loss_cls": 0.42958, "acc": 92.23779, "loss_bbox": 0.28624, "loss_mask": 0.57905, "loss": 1.51507, "time": 0.32473}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 4e-05, "memory": 11832, "data_time": 0.02009, "loss_rpn_cls": 0.12342, "loss_rpn_bbox": 0.07288, "loss_cls": 0.43441, "acc": 91.83325, "loss_bbox": 0.28659, "loss_mask": 0.53491, "loss": 1.45221, "time": 0.33228}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 4e-05, "memory": 11931, "data_time": 0.015, "loss_rpn_cls": 0.11998, "loss_rpn_bbox": 0.08024, "loss_cls": 0.49788, "acc": 90.55298, "loss_bbox": 0.32092, "loss_mask": 0.51982, "loss": 1.53885, "time": 0.32379}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 5e-05, "memory": 12840, "data_time": 0.01627, "loss_rpn_cls": 0.11029, "loss_rpn_bbox": 0.07112, "loss_cls": 0.46547, "acc": 90.71436, "loss_bbox": 0.2944, "loss_mask": 0.48233, "loss": 1.42361, "time": 0.32424}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 5e-05, "memory": 12840, "data_time": 0.01946, "loss_rpn_cls": 0.09903, "loss_rpn_bbox": 0.06913, "loss_cls": 0.44349, "acc": 90.59082, "loss_bbox": 0.29807, "loss_mask": 0.47601, "loss": 1.38572, "time": 0.31268}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 6e-05, "memory": 12840, "data_time": 0.0176, "loss_rpn_cls": 0.0961, "loss_rpn_bbox": 0.06815, "loss_cls": 0.40618, "acc": 90.88599, "loss_bbox": 0.28719, "loss_mask": 0.44961, "loss": 1.30722, "time": 0.3137}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 6e-05, "memory": 12840, "data_time": 0.01578, "loss_rpn_cls": 0.09641, "loss_rpn_bbox": 0.06909, "loss_cls": 0.41768, "acc": 90.54837, "loss_bbox": 0.2866, "loss_mask": 0.43637, "loss": 1.30616, "time": 0.31488}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 7e-05, "memory": 12840, "data_time": 0.01729, "loss_rpn_cls": 0.09104, "loss_rpn_bbox": 0.0716, "loss_cls": 0.41021, "acc": 90.02657, "loss_bbox": 0.30877, "loss_mask": 0.41955, "loss": 1.30116, "time": 0.33274}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 7e-05, "memory": 12840, "data_time": 0.01802, "loss_rpn_cls": 0.08765, "loss_rpn_bbox": 0.07322, "loss_cls": 0.39249, "acc": 90.06217, "loss_bbox": 0.30439, "loss_mask": 0.42234, "loss": 1.28009, "time": 0.3292}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 8e-05, "memory": 12840, "data_time": 0.01734, "loss_rpn_cls": 0.09068, "loss_rpn_bbox": 0.0694, "loss_cls": 0.37877, "acc": 90.334, "loss_bbox": 0.30177, "loss_mask": 0.39649, "loss": 1.23711, "time": 0.32491}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 8e-05, "memory": 12879, "data_time": 0.01833, "loss_rpn_cls": 0.08901, "loss_rpn_bbox": 0.07384, "loss_cls": 0.37889, "acc": 90.00605, "loss_bbox": 0.31062, "loss_mask": 0.40391, "loss": 1.25627, "time": 0.32915}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 9e-05, "memory": 12879, "data_time": 0.0207, "loss_rpn_cls": 0.07635, "loss_rpn_bbox": 0.06058, "loss_cls": 0.35143, "acc": 90.64603, "loss_bbox": 0.29074, "loss_mask": 0.38475, "loss": 1.16385, "time": 0.31993}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 9e-05, "memory": 12879, "data_time": 0.01723, "loss_rpn_cls": 0.08497, "loss_rpn_bbox": 0.06852, "loss_cls": 0.35657, "acc": 90.49609, "loss_bbox": 0.29891, "loss_mask": 0.38582, "loss": 1.19479, "time": 0.31984}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 12879, "data_time": 0.01775, "loss_rpn_cls": 0.07842, "loss_rpn_bbox": 0.06421, "loss_cls": 0.34699, "acc": 90.45684, "loss_bbox": 0.29401, "loss_mask": 0.37744, "loss": 1.16108, "time": 0.31989}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 12879, "data_time": 0.01865, "loss_rpn_cls": 0.07458, "loss_rpn_bbox": 0.06302, "loss_cls": 0.34047, "acc": 90.80151, "loss_bbox": 0.2853, "loss_mask": 0.36924, "loss": 1.13262, "time": 0.31341}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0001, "memory": 14243, "data_time": 0.01838, "loss_rpn_cls": 0.0799, "loss_rpn_bbox": 0.06784, "loss_cls": 0.34616, "acc": 90.46805, "loss_bbox": 0.301, "loss_mask": 0.37605, "loss": 1.17096, "time": 0.32139}
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