{"env_info": "sys.platform: linux\nPython: 3.8.16 | packaged by conda-forge | (default, Feb  1 2023, 16:01:55) [GCC 11.3.0]\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 11.8, V11.8.89\nGCC: gcc (Ubuntu 11.1.0-1ubuntu1~20.04) 11.1.0\nPyTorch: 2.0.0+cu118\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201703\n  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\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 11.8\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;-gencode;arch=compute_90,code=sm_90\n  - CuDNN 8.7\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.0+cu118\nOpenCV: 4.7.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 11.1\nMMCV CUDA Compiler: 11.8\nMMDetection: 2.28.2+", "config": "model = dict(\n    type='MaskRCNN',\n    neck=dict(\n        type='FPN',\n        in_channels=[72, 144, 288, 576],\n        out_channels=256,\n        num_outs=5),\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='Shared2FCBBoxHead',\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)),\n    pretrained='pretrained/transnext_small/transnext_small.pth',\n    backbone=dict(type='transnext_small', pretrain_size=224, img_size=800))\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'])\noptimizer = dict(\n    type='AdamW',\n    lr=0.0001,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    paramwise_cfg=dict(\n        custom_keys=dict(\n            query_embedding=dict(decay_mult=0.0),\n            relative_pos_bias_local=dict(decay_mult=0.0),\n            cpb=dict(decay_mult=0.0),\n            temperature=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=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 = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/mask_rcnn_transnext_small_800_fpn_1x_coco'\ngpu_ids = range(0, 8)\n", "seed": null, "exp_name": "mask_rcnn_transnext_small_800_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 56523, "data_time": 0.72815, "loss_rpn_cls": 0.69084, "loss_rpn_bbox": 0.11321, "loss_cls": 2.94708, "acc": 52.64307, "loss_bbox": 0.0131, "loss_mask": 0.89711, "loss": 4.66134, "time": 4.09191}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 57100, "data_time": 0.03496, "loss_rpn_cls": 0.45678, "loss_rpn_bbox": 0.10777, "loss_cls": 0.57379, "acc": 96.35742, "loss_bbox": 0.10551, "loss_mask": 0.76938, "loss": 2.01324, "time": 3.29063}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 57100, "data_time": 0.0366, "loss_rpn_cls": 0.25459, "loss_rpn_bbox": 0.10418, "loss_cls": 0.46845, "acc": 94.30103, "loss_bbox": 0.19195, "loss_mask": 0.71101, "loss": 1.73018, "time": 3.29363}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 57100, "data_time": 0.03487, "loss_rpn_cls": 0.19379, "loss_rpn_bbox": 0.10441, "loss_cls": 0.46352, "acc": 93.14575, "loss_bbox": 0.23914, "loss_mask": 0.68925, "loss": 1.69011, "time": 3.2983}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 57100, "data_time": 0.03871, "loss_rpn_cls": 0.15562, "loss_rpn_bbox": 0.10275, "loss_cls": 0.43518, "acc": 92.8894, "loss_bbox": 0.24933, "loss_mask": 0.66881, "loss": 1.61169, "time": 3.27786}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 57100, "data_time": 0.04142, "loss_rpn_cls": 0.11986, "loss_rpn_bbox": 0.09479, "loss_cls": 0.4643, "acc": 91.78052, "loss_bbox": 0.30297, "loss_mask": 0.63537, "loss": 1.61728, "time": 3.28425}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 57164, "data_time": 0.03639, "loss_rpn_cls": 0.10416, "loss_rpn_bbox": 0.09036, "loss_cls": 0.46155, "acc": 91.5, "loss_bbox": 0.31863, "loss_mask": 0.59755, "loss": 1.57225, "time": 3.30127}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 57351, "data_time": 0.03954, "loss_rpn_cls": 0.10451, "loss_rpn_bbox": 0.09217, "loss_cls": 0.47204, "acc": 90.54688, "loss_bbox": 0.35108, "loss_mask": 0.55981, "loss": 1.57961, "time": 3.30408}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 57351, "data_time": 0.0359, "loss_rpn_cls": 0.09136, "loss_rpn_bbox": 0.08965, "loss_cls": 0.43626, "acc": 90.53589, "loss_bbox": 0.35207, "loss_mask": 0.52322, "loss": 1.49256, "time": 3.30255}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 57351, "data_time": 0.03127, "loss_rpn_cls": 0.08786, "loss_rpn_bbox": 0.08634, "loss_cls": 0.43439, "acc": 90.05176, "loss_bbox": 0.3663, "loss_mask": 0.50268, "loss": 1.47756, "time": 3.27598}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 57351, "data_time": 0.03395, "loss_rpn_cls": 0.08202, "loss_rpn_bbox": 0.08139, "loss_cls": 0.4179, "acc": 89.93848, "loss_bbox": 0.36745, "loss_mask": 0.46548, "loss": 1.41424, "time": 3.26791}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 57370, "data_time": 0.03215, "loss_rpn_cls": 0.08182, "loss_rpn_bbox": 0.08315, "loss_cls": 0.40614, "acc": 89.51074, "loss_bbox": 0.37154, "loss_mask": 0.44972, "loss": 1.39236, "time": 3.30414}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 57370, "data_time": 0.03753, "loss_rpn_cls": 0.07799, "loss_rpn_bbox": 0.08578, "loss_cls": 0.39464, "acc": 89.36401, "loss_bbox": 0.37531, "loss_mask": 0.43752, "loss": 1.37124, "time": 3.28157}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 57393, "data_time": 0.03553, "loss_rpn_cls": 0.07348, "loss_rpn_bbox": 0.08318, "loss_cls": 0.38161, "acc": 89.22778, "loss_bbox": 0.38687, "loss_mask": 0.42641, "loss": 1.35155, "time": 3.30684}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 57393, "data_time": 0.03228, "loss_rpn_cls": 0.07143, "loss_rpn_bbox": 0.08167, "loss_cls": 0.37159, "acc": 89.34351, "loss_bbox": 0.37774, "loss_mask": 0.41226, "loss": 1.31469, "time": 3.2822}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 57393, "data_time": 0.03599, "loss_rpn_cls": 0.06683, "loss_rpn_bbox": 0.08085, "loss_cls": 0.36849, "acc": 89.29761, "loss_bbox": 0.37931, "loss_mask": 0.40851, "loss": 1.30399, "time": 3.28453}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 57393, "data_time": 0.03705, "loss_rpn_cls": 0.06667, "loss_rpn_bbox": 0.07796, "loss_cls": 0.34924, "acc": 89.52612, "loss_bbox": 0.37319, "loss_mask": 0.39266, "loss": 1.25971, "time": 3.25998}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 57393, "data_time": 0.03533, "loss_rpn_cls": 0.06387, "loss_rpn_bbox": 0.07754, "loss_cls": 0.35576, "acc": 89.3291, "loss_bbox": 0.37828, "loss_mask": 0.39758, "loss": 1.27303, "time": 3.28326}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 57393, "data_time": 0.0335, "loss_rpn_cls": 0.06579, "loss_rpn_bbox": 0.07582, "loss_cls": 0.34828, "acc": 89.68994, "loss_bbox": 0.36607, "loss_mask": 0.38129, "loss": 1.23725, "time": 3.27375}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 57393, "data_time": 0.03701, "loss_rpn_cls": 0.0616, "loss_rpn_bbox": 0.0755, "loss_cls": 0.33978, "acc": 89.65503, "loss_bbox": 0.36555, "loss_mask": 0.36881, "loss": 1.21124, "time": 3.29296}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 57393, "data_time": 0.03517, "loss_rpn_cls": 0.06138, "loss_rpn_bbox": 0.07367, "loss_cls": 0.33266, "acc": 89.8147, "loss_bbox": 0.3635, "loss_mask": 0.36845, "loss": 1.19966, "time": 3.33712}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0001, "memory": 57393, "data_time": 0.03657, "loss_rpn_cls": 0.0569, "loss_rpn_bbox": 0.07173, "loss_cls": 0.33009, "acc": 89.88135, "loss_bbox": 0.35372, "loss_mask": 0.36559, "loss": 1.17803, "time": 3.27886}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0001, "memory": 57393, "data_time": 0.03588, "loss_rpn_cls": 0.05861, "loss_rpn_bbox": 0.07732, "loss_cls": 0.33532, "acc": 89.52441, "loss_bbox": 0.37109, "loss_mask": 0.3647, "loss": 1.20704, "time": 3.28714}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0001, "memory": 57399, "data_time": 0.04069, "loss_rpn_cls": 0.05987, "loss_rpn_bbox": 0.07825, "loss_cls": 0.32537, "acc": 89.62427, "loss_bbox": 0.36354, "loss_mask": 0.35815, "loss": 1.18517, "time": 3.27707}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0001, "memory": 57403, "data_time": 0.0366, "loss_rpn_cls": 0.0554, "loss_rpn_bbox": 0.06931, "loss_cls": 0.315, "acc": 90.28906, "loss_bbox": 0.34411, "loss_mask": 0.35116, "loss": 1.13498, "time": 3.28683}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0001, "memory": 57403, "data_time": 0.03688, "loss_rpn_cls": 0.0551, "loss_rpn_bbox": 0.07119, "loss_cls": 0.31712, "acc": 90.07983, "loss_bbox": 0.34582, "loss_mask": 0.3453, "loss": 1.13454, "time": 3.2824}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0001, "memory": 57403, "data_time": 0.03508, "loss_rpn_cls": 0.05934, "loss_rpn_bbox": 0.07615, "loss_cls": 0.31755, "acc": 89.82446, "loss_bbox": 0.35012, "loss_mask": 0.34257, "loss": 1.14574, "time": 3.28931}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0001, "memory": 57403, "data_time": 0.03622, "loss_rpn_cls": 0.0566, "loss_rpn_bbox": 0.06988, "loss_cls": 0.29547, "acc": 90.64307, "loss_bbox": 0.33023, "loss_mask": 0.32756, "loss": 1.07974, "time": 3.25475}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0001, "memory": 57403, "data_time": 0.03383, "loss_rpn_cls": 0.0536, "loss_rpn_bbox": 0.07058, "loss_cls": 0.3129, "acc": 89.95728, "loss_bbox": 0.35377, "loss_mask": 0.33, "loss": 1.12085, "time": 3.25317}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0001, "memory": 57403, "data_time": 0.04041, "loss_rpn_cls": 0.05836, "loss_rpn_bbox": 0.07617, "loss_cls": 0.30691, "acc": 90.13184, "loss_bbox": 0.34788, "loss_mask": 0.33453, "loss": 1.12386, "time": 3.46862}
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