repnext_m5_coco.json

{"env_info": "sys.platform: linux\nPython: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 4090 D\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 12.1, V12.1.105\nGCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nPyTorch: 2.1.2\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201703\n  - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)\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 12.1\n  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-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 8.9.2\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -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 -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -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=2.1.2, 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, USE_ROCM=OFF, \n\nTorchVision: 0.16.2\nOpenCV: 4.9.0\nMMCV: 1.7.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 12.1\nMMDetection: 2.28.2+", "config": "model = dict(\n    type='MaskRCNN',\n    pretrained='torchvision://resnet50',\n    backbone=dict(\n        type='repnext_m5',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\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=\n            'pretrain/repnext_m5_distill_300e.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=0.1)\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_repnext_m5_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repnext_m5_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 11560, "data_time": 0.09871, "loss_rpn_cls": 0.68795, "loss_rpn_bbox": 0.30074, "loss_cls": 2.27524, "acc": 67.58618, "loss_bbox": 0.02147, "loss_mask": 3.85379, "loss": 7.13919, "time": 0.99103}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 11778, "data_time": 0.02256, "loss_rpn_cls": 0.27543, "loss_rpn_bbox": 0.17005, "loss_cls": 0.34248, "acc": 96.52466, "loss_bbox": 0.09651, "loss_mask": 1.0975, "loss": 1.98197, "time": 0.86426}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 11778, "data_time": 0.02404, "loss_rpn_cls": 0.22358, "loss_rpn_bbox": 0.13985, "loss_cls": 0.34888, "acc": 94.81006, "loss_bbox": 0.16842, "loss_mask": 0.76978, "loss": 1.65051, "time": 0.90863}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 11778, "data_time": 0.0241, "loss_rpn_cls": 0.18577, "loss_rpn_bbox": 0.12893, "loss_cls": 0.3651, "acc": 94.16821, "loss_bbox": 0.19695, "loss_mask": 0.71756, "loss": 1.59431, "time": 0.93936}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 11778, "data_time": 0.02337, "loss_rpn_cls": 0.14911, "loss_rpn_bbox": 0.11879, "loss_cls": 0.38062, "acc": 93.64331, "loss_bbox": 0.22355, "loss_mask": 0.67086, "loss": 1.54293, "time": 0.92963}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 11778, "data_time": 0.03416, "loss_rpn_cls": 0.11708, "loss_rpn_bbox": 0.10581, "loss_cls": 0.42117, "acc": 92.51025, "loss_bbox": 0.26664, "loss_mask": 0.63886, "loss": 1.54957, "time": 0.96832}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 11808, "data_time": 0.02692, "loss_rpn_cls": 0.09847, "loss_rpn_bbox": 0.098, "loss_cls": 0.42512, "acc": 92.08545, "loss_bbox": 0.29035, "loss_mask": 0.60551, "loss": 1.51745, "time": 0.97184}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 11961, "data_time": 0.02536, "loss_rpn_cls": 0.09772, "loss_rpn_bbox": 0.0971, "loss_cls": 0.46117, "acc": 91.08276, "loss_bbox": 0.32629, "loss_mask": 0.57867, "loss": 1.56095, "time": 1.00233}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 11961, "data_time": 0.02553, "loss_rpn_cls": 0.08822, "loss_rpn_bbox": 0.09144, "loss_cls": 0.46458, "acc": 90.47607, "loss_bbox": 0.34629, "loss_mask": 0.5275, "loss": 1.51804, "time": 0.99345}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 12002, "data_time": 0.02072, "loss_rpn_cls": 0.08126, "loss_rpn_bbox": 0.08768, "loss_cls": 0.47498, "acc": 89.98022, "loss_bbox": 0.36547, "loss_mask": 0.4976, "loss": 1.507, "time": 1.09418}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 12002, "data_time": 0.0248, "loss_rpn_cls": 0.07943, "loss_rpn_bbox": 0.082, "loss_cls": 0.46702, "acc": 89.81226, "loss_bbox": 0.37211, "loss_mask": 0.45845, "loss": 1.459, "time": 1.05195}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 12022, "data_time": 0.02172, "loss_rpn_cls": 0.07899, "loss_rpn_bbox": 0.08275, "loss_cls": 0.45411, "acc": 89.55005, "loss_bbox": 0.37444, "loss_mask": 0.44354, "loss": 1.43383, "time": 1.01161}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 12028, "data_time": 0.0324, "loss_rpn_cls": 0.07665, "loss_rpn_bbox": 0.08518, "loss_cls": 0.44102, "acc": 89.38232, "loss_bbox": 0.37656, "loss_mask": 0.42955, "loss": 1.40896, "time": 1.01723}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 12032, "data_time": 0.02522, "loss_rpn_cls": 0.07526, "loss_rpn_bbox": 0.08311, "loss_cls": 0.4365, "acc": 89.08521, "loss_bbox": 0.38413, "loss_mask": 0.41539, "loss": 1.39439, "time": 1.04558}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 12032, "data_time": 0.02146, "loss_rpn_cls": 0.07021, "loss_rpn_bbox": 0.08069, "loss_cls": 0.42337, "acc": 89.36743, "loss_bbox": 0.36955, "loss_mask": 0.40561, "loss": 1.34942, "time": 1.016}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 12032, "data_time": 0.02808, "loss_rpn_cls": 0.06896, "loss_rpn_bbox": 0.08082, "loss_cls": 0.42341, "acc": 89.16284, "loss_bbox": 0.37926, "loss_mask": 0.39866, "loss": 1.3511, "time": 1.02403}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 12035, "data_time": 0.02935, "loss_rpn_cls": 0.0655, "loss_rpn_bbox": 0.07748, "loss_cls": 0.39118, "acc": 89.65332, "loss_bbox": 0.35824, "loss_mask": 0.38973, "loss": 1.28213, "time": 1.03213}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 12035, "data_time": 0.02654, "loss_rpn_cls": 0.06451, "loss_rpn_bbox": 0.07552, "loss_cls": 0.39572, "acc": 89.41504, "loss_bbox": 0.36782, "loss_mask": 0.39199, "loss": 1.29555, "time": 1.05002}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 12047, "data_time": 0.02659, "loss_rpn_cls": 0.06481, "loss_rpn_bbox": 0.07478, "loss_cls": 0.3891, "acc": 89.71851, "loss_bbox": 0.34973, "loss_mask": 0.3756, "loss": 1.25402, "time": 1.02953}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 12073, "data_time": 0.02723, "loss_rpn_cls": 0.06649, "loss_rpn_bbox": 0.07388, "loss_cls": 0.38227, "acc": 89.7251, "loss_bbox": 0.35065, "loss_mask": 0.37086, "loss": 1.24414, "time": 1.04105}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 12073, "data_time": 0.0258, "loss_rpn_cls": 0.06199, "loss_rpn_bbox": 0.07274, "loss_cls": 0.38148, "acc": 89.55029, "loss_bbox": 0.35182, "loss_mask": 0.36764, "loss": 1.23567, "time": 1.03233}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 12073, "data_time": 0.02934, "loss_rpn_cls": 0.0568, "loss_rpn_bbox": 0.0696, "loss_cls": 0.36536, "acc": 89.89575, "loss_bbox": 0.34336, "loss_mask": 0.36641, "loss": 1.20154, "time": 1.02465}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 12073, "data_time": 0.02821, "loss_rpn_cls": 0.06085, "loss_rpn_bbox": 0.07631, "loss_cls": 0.38342, "acc": 89.36523, "loss_bbox": 0.36454, "loss_mask": 0.36405, "loss": 1.24918, "time": 1.0329}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 12073, "data_time": 0.03067, "loss_rpn_cls": 0.06243, "loss_rpn_bbox": 0.07698, "loss_cls": 0.36998, "acc": 89.51172, "loss_bbox": 0.361, "loss_mask": 0.36104, "loss": 1.23144, "time": 1.04988}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 12073, "data_time": 0.02938, "loss_rpn_cls": 0.05586, "loss_rpn_bbox": 0.06738, "loss_cls": 0.35734, "acc": 90.10083, "loss_bbox": 0.33472, "loss_mask": 0.35407, "loss": 1.16938, "time": 1.02776}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 12073, "data_time": 0.02933, "loss_rpn_cls": 0.05609, "loss_rpn_bbox": 0.06887, "loss_cls": 0.35871, "acc": 89.97827, "loss_bbox": 0.34021, "loss_mask": 0.34841, "loss": 1.17229, "time": 1.10365}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 12073, "data_time": 0.02785, "loss_rpn_cls": 0.06197, "loss_rpn_bbox": 0.07471, "loss_cls": 0.35879, "acc": 89.73169, "loss_bbox": 0.34276, "loss_mask": 0.34451, "loss": 1.18274, "time": 1.05962}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 12073, "data_time": 0.02639, "loss_rpn_cls": 0.05841, "loss_rpn_bbox": 0.06783, "loss_cls": 0.33766, "acc": 90.54395, "loss_bbox": 0.32376, "loss_mask": 0.3383, "loss": 1.12596, "time": 1.06087}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 12073, "data_time": 0.02468, "loss_rpn_cls": 0.05673, "loss_rpn_bbox": 0.06926, "loss_cls": 0.3499, "acc": 89.8894, "loss_bbox": 0.3454, "loss_mask": 0.33197, "loss": 1.15325, "time": 1.02587}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 12073, "data_time": 0.03249, "loss_rpn_cls": 0.064, "loss_rpn_bbox": 0.07564, "loss_cls": 0.34103, "acc": 90.16797, "loss_bbox": 0.33576, "loss_mask": 0.33833, "loss": 1.15476, "time": 1.02064}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.0002, "memory": 12073, "data_time": 0.03427, "loss_rpn_cls": 0.05355, "loss_rpn_bbox": 0.06785, "loss_cls": 0.33922, "acc": 90.12842, "loss_bbox": 0.33557, "loss_mask": 0.33841, "loss": 1.13461, "time": 1.01319}
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