repvit_m2_3_coco.json

{"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+ab02657", "config": "model = dict(\n    type='MaskRCNN',\n    pretrained='torchvision://resnet50',\n    backbone=dict(\n        type='repvit_m6',\n        depth=50,\n        num_stages=4,\n        out_indices=[6, 14, 50, 54],\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_m6_distill_450e.pth')),\n    neck=dict(\n        type='FPN',\n        in_channels=[80, 160, 320, 640],\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_m6_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repvit_m6_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 12069, "data_time": 0.15192, "loss_rpn_cls": 0.53878, "loss_rpn_bbox": 0.1359, "loss_cls": 1.98109, "acc": 67.09521, "loss_bbox": 0.05794, "loss_mask": 1.75779, "loss": 4.4715, "time": 0.80169}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 12284, "data_time": 0.05602, "loss_rpn_cls": 0.25366, "loss_rpn_bbox": 0.10515, "loss_cls": 0.40303, "acc": 94.82104, "loss_bbox": 0.17368, "loss_mask": 0.76105, "loss": 1.69657, "time": 0.56041}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 12284, "data_time": 0.05561, "loss_rpn_cls": 0.20733, "loss_rpn_bbox": 0.10429, "loss_cls": 0.38342, "acc": 94.09229, "loss_bbox": 0.20155, "loss_mask": 0.70071, "loss": 1.5973, "time": 0.5492}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 12284, "data_time": 0.04855, "loss_rpn_cls": 0.15439, "loss_rpn_bbox": 0.10531, "loss_cls": 0.43235, "acc": 92.91333, "loss_bbox": 0.24937, "loss_mask": 0.66155, "loss": 1.60297, "time": 0.54933}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 12284, "data_time": 0.05652, "loss_rpn_cls": 0.12144, "loss_rpn_bbox": 0.10281, "loss_cls": 0.46298, "acc": 91.89087, "loss_bbox": 0.29727, "loss_mask": 0.61037, "loss": 1.59487, "time": 0.53272}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 12370, "data_time": 0.07537, "loss_rpn_cls": 0.09661, "loss_rpn_bbox": 0.09357, "loss_cls": 0.49623, "acc": 90.67896, "loss_bbox": 0.34825, "loss_mask": 0.56895, "loss": 1.60361, "time": 0.53117}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 12370, "data_time": 0.05168, "loss_rpn_cls": 0.0893, "loss_rpn_bbox": 0.09051, "loss_cls": 0.47839, "acc": 90.70996, "loss_bbox": 0.34057, "loss_mask": 0.52023, "loss": 1.519, "time": 0.52195}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 12439, "data_time": 0.05805, "loss_rpn_cls": 0.08896, "loss_rpn_bbox": 0.09085, "loss_cls": 0.50361, "acc": 89.75708, "loss_bbox": 0.36831, "loss_mask": 0.49023, "loss": 1.54196, "time": 0.58059}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 12504, "data_time": 0.05437, "loss_rpn_cls": 0.08192, "loss_rpn_bbox": 0.08782, "loss_cls": 0.47399, "acc": 89.76367, "loss_bbox": 0.36414, "loss_mask": 0.45511, "loss": 1.46298, "time": 0.51593}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 12504, "data_time": 0.0408, "loss_rpn_cls": 0.07744, "loss_rpn_bbox": 0.08541, "loss_cls": 0.46854, "acc": 89.48267, "loss_bbox": 0.37875, "loss_mask": 0.44363, "loss": 1.45377, "time": 0.51048}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 12504, "data_time": 0.04945, "loss_rpn_cls": 0.0767, "loss_rpn_bbox": 0.08127, "loss_cls": 0.45755, "acc": 89.48804, "loss_bbox": 0.37599, "loss_mask": 0.42181, "loss": 1.41332, "time": 0.53289}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 12504, "data_time": 0.04376, "loss_rpn_cls": 0.075, "loss_rpn_bbox": 0.08242, "loss_cls": 0.43191, "acc": 89.55786, "loss_bbox": 0.36654, "loss_mask": 0.415, "loss": 1.37086, "time": 0.52822}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 12555, "data_time": 0.06405, "loss_rpn_cls": 0.07143, "loss_rpn_bbox": 0.08436, "loss_cls": 0.42985, "acc": 89.20337, "loss_bbox": 0.37571, "loss_mask": 0.39525, "loss": 1.3566, "time": 0.51781}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 12555, "data_time": 0.05297, "loss_rpn_cls": 0.07205, "loss_rpn_bbox": 0.0818, "loss_cls": 0.41066, "acc": 89.12451, "loss_bbox": 0.37493, "loss_mask": 0.38492, "loss": 1.32436, "time": 0.52597}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 12555, "data_time": 0.04922, "loss_rpn_cls": 0.06745, "loss_rpn_bbox": 0.08021, "loss_cls": 0.39479, "acc": 89.45068, "loss_bbox": 0.36178, "loss_mask": 0.37443, "loss": 1.27867, "time": 0.74636}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 12560, "data_time": 0.05238, "loss_rpn_cls": 0.06793, "loss_rpn_bbox": 0.08042, "loss_cls": 0.4028, "acc": 89.32471, "loss_bbox": 0.36602, "loss_mask": 0.37544, "loss": 1.29262, "time": 0.51142}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 12560, "data_time": 0.05793, "loss_rpn_cls": 0.06263, "loss_rpn_bbox": 0.07643, "loss_cls": 0.37681, "acc": 89.62769, "loss_bbox": 0.35866, "loss_mask": 0.36386, "loss": 1.23839, "time": 0.51532}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 12560, "data_time": 0.05623, "loss_rpn_cls": 0.06503, "loss_rpn_bbox": 0.07611, "loss_cls": 0.384, "acc": 89.45581, "loss_bbox": 0.36046, "loss_mask": 0.36492, "loss": 1.25052, "time": 0.50286}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 12560, "data_time": 0.05035, "loss_rpn_cls": 0.06276, "loss_rpn_bbox": 0.07511, "loss_cls": 0.379, "acc": 89.53418, "loss_bbox": 0.34573, "loss_mask": 0.35563, "loss": 1.21822, "time": 0.50216}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 12584, "data_time": 0.05453, "loss_rpn_cls": 0.06276, "loss_rpn_bbox": 0.07392, "loss_cls": 0.36088, "acc": 89.81543, "loss_bbox": 0.34008, "loss_mask": 0.34621, "loss": 1.18384, "time": 0.5166}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 12584, "data_time": 0.05202, "loss_rpn_cls": 0.0607, "loss_rpn_bbox": 0.07211, "loss_cls": 0.37023, "acc": 89.6377, "loss_bbox": 0.34647, "loss_mask": 0.34986, "loss": 1.19937, "time": 0.51722}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 12584, "data_time": 0.05682, "loss_rpn_cls": 0.05718, "loss_rpn_bbox": 0.07081, "loss_cls": 0.35775, "acc": 89.77832, "loss_bbox": 0.33718, "loss_mask": 0.34515, "loss": 1.16808, "time": 0.49537}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 12584, "data_time": 0.05745, "loss_rpn_cls": 0.06233, "loss_rpn_bbox": 0.07742, "loss_cls": 0.36444, "acc": 89.48682, "loss_bbox": 0.34774, "loss_mask": 0.34408, "loss": 1.19602, "time": 0.51968}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 12584, "data_time": 0.06079, "loss_rpn_cls": 0.06205, "loss_rpn_bbox": 0.07795, "loss_cls": 0.35728, "acc": 89.59668, "loss_bbox": 0.34681, "loss_mask": 0.34105, "loss": 1.18513, "time": 0.51552}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 12584, "data_time": 0.0475, "loss_rpn_cls": 0.05452, "loss_rpn_bbox": 0.06787, "loss_cls": 0.34568, "acc": 90.1084, "loss_bbox": 0.32642, "loss_mask": 0.33481, "loss": 1.12931, "time": 0.50004}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 12584, "data_time": 0.05889, "loss_rpn_cls": 0.05209, "loss_rpn_bbox": 0.06997, "loss_cls": 0.34646, "acc": 89.96094, "loss_bbox": 0.33459, "loss_mask": 0.33246, "loss": 1.13557, "time": 0.51249}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 12584, "data_time": 0.05307, "loss_rpn_cls": 0.05956, "loss_rpn_bbox": 0.07591, "loss_cls": 0.3452, "acc": 89.89941, "loss_bbox": 0.33452, "loss_mask": 0.33143, "loss": 1.14662, "time": 0.50452}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 12584, "data_time": 0.05699, "loss_rpn_cls": 0.05874, "loss_rpn_bbox": 0.06973, "loss_cls": 0.32136, "acc": 90.70142, "loss_bbox": 0.31458, "loss_mask": 0.32396, "loss": 1.08836, "time": 0.50144}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 12584, "data_time": 0.04879, "loss_rpn_cls": 0.0546, "loss_rpn_bbox": 0.07045, "loss_cls": 0.34428, "acc": 89.78955, "loss_bbox": 0.33855, "loss_mask": 0.31884, "loss": 1.12673, "time": 0.49621}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 12584, "data_time": 0.0627, "loss_rpn_cls": 0.06106, "loss_rpn_bbox": 0.076, "loss_cls": 0.33244, "acc": 90.11157, "loss_bbox": 0.33344, "loss_mask": 0.32693, "loss": 1.12988, "time": 0.50055}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.0002, "memory": 12584, "data_time": 0.06349, "loss_rpn_cls": 0.05226, "loss_rpn_bbox": 0.06964, "loss_cls": 0.32641, "acc": 90.33374, "loss_bbox": 0.32423, "loss_mask": 0.32559, "loss": 1.09813, "time": 0.50837}
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