{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar  2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: GeForce RTX 3090\nCUDA_HOME: /home/wangao/cuda-11.2\nNVCC: Cuda compilation tools, release 11.2, V11.2.152\nGCC: gcc (GCC) 5.4.0\nPyTorch: 2.0.1+cu117\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 11.7\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\n  - CuDNN 8.5\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, 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=2.0.1, 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: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 5.4\nMMCV CUDA Compiler: 11.2\nMMDetection: 2.28.2+c54117a", "config": "model = dict(\n    type='MaskRCNN',\n    pretrained='torchvision://resnet50',\n    backbone=dict(\n        type='repvit_m4',\n        depth=50,\n        num_stages=4,\n        out_indices=[4, 10, 36, 42],\n        frozen_stages=1,\n        norm_cfg=dict(type='BN', requires_grad=True),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint='pretrain/repvit_m4_distill_300e.pth')),\n    neck=dict(\n        type='FPN',\n        in_channels=[64, 128, 256, 512],\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)))\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(type='AdamW', lr=0.0002, weight_decay=0.05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=1e-06,\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_repvit_m4_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repvit_m4_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 7980, "data_time": 0.14548, "loss_rpn_cls": 0.53314, "loss_rpn_bbox": 0.14748, "loss_cls": 1.91489, "acc": 68.13208, "loss_bbox": 0.072, "loss_mask": 1.59976, "loss": 4.26727, "time": 0.68673}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 8162, "data_time": 0.06063, "loss_rpn_cls": 0.25145, "loss_rpn_bbox": 0.11155, "loss_cls": 0.43818, "acc": 94.75, "loss_bbox": 0.17652, "loss_mask": 0.77439, "loss": 1.75208, "time": 0.47045}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 8162, "data_time": 0.06035, "loss_rpn_cls": 0.20735, "loss_rpn_bbox": 0.10837, "loss_cls": 0.39381, "acc": 93.92725, "loss_bbox": 0.20736, "loss_mask": 0.70913, "loss": 1.62602, "time": 0.46687}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 8162, "data_time": 0.05478, "loss_rpn_cls": 0.15562, "loss_rpn_bbox": 0.10826, "loss_cls": 0.43233, "acc": 92.84741, "loss_bbox": 0.25263, "loss_mask": 0.67926, "loss": 1.62809, "time": 0.45768}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 8162, "data_time": 0.0551, "loss_rpn_cls": 0.12255, "loss_rpn_bbox": 0.10449, "loss_cls": 0.4498, "acc": 91.91943, "loss_bbox": 0.29686, "loss_mask": 0.63444, "loss": 1.60814, "time": 0.43649}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 8212, "data_time": 0.07644, "loss_rpn_cls": 0.09739, "loss_rpn_bbox": 0.09526, "loss_cls": 0.49012, "acc": 90.57593, "loss_bbox": 0.35082, "loss_mask": 0.58461, "loss": 1.6182, "time": 0.45779}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 8212, "data_time": 0.05278, "loss_rpn_cls": 0.0899, "loss_rpn_bbox": 0.09086, "loss_cls": 0.46009, "acc": 90.81299, "loss_bbox": 0.3403, "loss_mask": 0.52724, "loss": 1.50839, "time": 0.43938}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 8351, "data_time": 0.05907, "loss_rpn_cls": 0.09468, "loss_rpn_bbox": 0.0926, "loss_cls": 0.4741, "acc": 90.06348, "loss_bbox": 0.35981, "loss_mask": 0.4983, "loss": 1.51949, "time": 0.4306}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 8351, "data_time": 0.05434, "loss_rpn_cls": 0.08586, "loss_rpn_bbox": 0.08794, "loss_cls": 0.45477, "acc": 89.84888, "loss_bbox": 0.36565, "loss_mask": 0.45791, "loss": 1.45213, "time": 0.42242}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 8351, "data_time": 0.04756, "loss_rpn_cls": 0.07821, "loss_rpn_bbox": 0.08602, "loss_cls": 0.45179, "acc": 89.7334, "loss_bbox": 0.37553, "loss_mask": 0.451, "loss": 1.44255, "time": 0.41952}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 8398, "data_time": 0.05398, "loss_rpn_cls": 0.08005, "loss_rpn_bbox": 0.08015, "loss_cls": 0.44438, "acc": 89.64673, "loss_bbox": 0.37342, "loss_mask": 0.41911, "loss": 1.39711, "time": 0.43381}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 8398, "data_time": 0.04911, "loss_rpn_cls": 0.07676, "loss_rpn_bbox": 0.08142, "loss_cls": 0.42188, "acc": 89.59058, "loss_bbox": 0.36926, "loss_mask": 0.40923, "loss": 1.35854, "time": 0.43607}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 8398, "data_time": 0.07102, "loss_rpn_cls": 0.07519, "loss_rpn_bbox": 0.08478, "loss_cls": 0.41821, "acc": 89.52515, "loss_bbox": 0.37333, "loss_mask": 0.39809, "loss": 1.34961, "time": 0.43863}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 8398, "data_time": 0.05426, "loss_rpn_cls": 0.07631, "loss_rpn_bbox": 0.08253, "loss_cls": 0.40514, "acc": 89.38013, "loss_bbox": 0.37422, "loss_mask": 0.39287, "loss": 1.33107, "time": 0.43034}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 8398, "data_time": 0.04916, "loss_rpn_cls": 0.07243, "loss_rpn_bbox": 0.08025, "loss_cls": 0.3955, "acc": 89.45923, "loss_bbox": 0.36461, "loss_mask": 0.38045, "loss": 1.29324, "time": 0.43622}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 8398, "data_time": 0.05913, "loss_rpn_cls": 0.06914, "loss_rpn_bbox": 0.08124, "loss_cls": 0.40151, "acc": 89.427, "loss_bbox": 0.36257, "loss_mask": 0.37968, "loss": 1.29413, "time": 0.54043}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 8398, "data_time": 0.06153, "loss_rpn_cls": 0.06676, "loss_rpn_bbox": 0.07747, "loss_cls": 0.3726, "acc": 89.71411, "loss_bbox": 0.35481, "loss_mask": 0.36996, "loss": 1.2416, "time": 0.4233}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 8404, "data_time": 0.05656, "loss_rpn_cls": 0.06777, "loss_rpn_bbox": 0.07761, "loss_cls": 0.38445, "acc": 89.5686, "loss_bbox": 0.35571, "loss_mask": 0.37542, "loss": 1.26096, "time": 0.41979}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 8415, "data_time": 0.05396, "loss_rpn_cls": 0.06608, "loss_rpn_bbox": 0.0761, "loss_cls": 0.3778, "acc": 89.68164, "loss_bbox": 0.34412, "loss_mask": 0.36283, "loss": 1.22693, "time": 0.41275}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 8415, "data_time": 0.06083, "loss_rpn_cls": 0.06699, "loss_rpn_bbox": 0.0749, "loss_cls": 0.36141, "acc": 89.95605, "loss_bbox": 0.34437, "loss_mask": 0.35494, "loss": 1.20261, "time": 0.42294}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 8415, "data_time": 0.04988, "loss_rpn_cls": 0.06339, "loss_rpn_bbox": 0.07299, "loss_cls": 0.36277, "acc": 89.83081, "loss_bbox": 0.34577, "loss_mask": 0.35495, "loss": 1.19986, "time": 0.42698}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 8415, "data_time": 0.05954, "loss_rpn_cls": 0.05847, "loss_rpn_bbox": 0.07187, "loss_cls": 0.36026, "acc": 89.68848, "loss_bbox": 0.349, "loss_mask": 0.35461, "loss": 1.19421, "time": 0.41342}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 8415, "data_time": 0.05858, "loss_rpn_cls": 0.06511, "loss_rpn_bbox": 0.07882, "loss_cls": 0.36549, "acc": 89.56396, "loss_bbox": 0.35267, "loss_mask": 0.35172, "loss": 1.21381, "time": 0.41625}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 8415, "data_time": 0.06615, "loss_rpn_cls": 0.06678, "loss_rpn_bbox": 0.07934, "loss_cls": 0.36381, "acc": 89.5354, "loss_bbox": 0.35422, "loss_mask": 0.35489, "loss": 1.21904, "time": 0.42158}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 8415, "data_time": 0.05749, "loss_rpn_cls": 0.05694, "loss_rpn_bbox": 0.0691, "loss_cls": 0.35693, "acc": 89.9856, "loss_bbox": 0.33243, "loss_mask": 0.34468, "loss": 1.16008, "time": 0.40236}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 8415, "data_time": 0.05652, "loss_rpn_cls": 0.05882, "loss_rpn_bbox": 0.07072, "loss_cls": 0.3518, "acc": 89.92432, "loss_bbox": 0.3382, "loss_mask": 0.34099, "loss": 1.16052, "time": 0.47858}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 8415, "data_time": 0.0554, "loss_rpn_cls": 0.06632, "loss_rpn_bbox": 0.0772, "loss_cls": 0.35289, "acc": 89.81982, "loss_bbox": 0.33598, "loss_mask": 0.33655, "loss": 1.16894, "time": 0.41736}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 8415, "data_time": 0.05635, "loss_rpn_cls": 0.06142, "loss_rpn_bbox": 0.07092, "loss_cls": 0.32754, "acc": 90.54077, "loss_bbox": 0.32229, "loss_mask": 0.32667, "loss": 1.10883, "time": 0.41364}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 8415, "data_time": 0.04895, "loss_rpn_cls": 0.05758, "loss_rpn_bbox": 0.07208, "loss_cls": 0.34706, "acc": 89.74316, "loss_bbox": 0.34157, "loss_mask": 0.32836, "loss": 1.14665, "time": 0.46464}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 8415, "data_time": 0.06182, "loss_rpn_cls": 0.0651, "loss_rpn_bbox": 0.079, "loss_cls": 0.32956, "acc": 90.37305, "loss_bbox": 0.32879, "loss_mask": 0.33533, "loss": 1.13778, "time": 0.42048}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.0002, "memory": 8415, "data_time": 0.06247, "loss_rpn_cls": 0.05696, "loss_rpn_bbox": 0.07231, "loss_cls": 0.33033, "acc": 90.24268, "loss_bbox": 0.32858, "loss_mask": 0.3375, "loss": 1.12568, "time": 0.41424}
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