{"env_info": "sys.platform: linux\nPython: 3.7.0 (default, Oct  9 2018, 10:31:47) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/petrelfs/share/cuda-11.1\nNVCC: Build cuda_11.1.TC455_06.29069683_0\nGCC: gcc (GCC) 5.4.0\nPyTorch: 1.9.0+cu111\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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.1\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 8.0.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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 -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=1.9.0, 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: 0.10.0+cu111\nOpenCV: 4.6.0\nMMCV: 1.4.2\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMDetection: 2.22.0+", "config": "model = dict(\n    type='MaskRCNN',\n    backbone=dict(\n        type='DCNv3_5',\n        deform_points=9,\n        embed_dim=64,\n        depths=[4, 4, 18, 4],\n        num_heads=[4, 8, 16, 32],\n        deform_padding=True,\n        dilation_rates=[1],\n        kernel_size=3,\n        mlp_ratio=4.0,\n        drop_path_rate=0.2,\n        op_types=['D', 'D', 'D', 'D'],\n        out_indices=(0, 1, 2, 3),\n        dcnv3_core_type='MSDeformAttnGrid1_softmax',\n        layer_scale=1.0,\n        cpe_norm_layer='LN',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint=\n            '/mnt/petrelfs/wangwenhai/workspace/DCNv3-dev/classification/work_dirs_shared/dcnv3_5_t_v3/ckpt_epoch_299_rmab.pth'\n        )),\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'], classwise=True, save_best='auto')\noptimizer = dict(\n    type='AdamW',\n    lr=0.0001,\n    weight_decay=0.05,\n    constructor='CustomLayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(\n        num_layers=30, layer_decay_rate=1.0, depths=[4, 4, 18, 4]))\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, max_keep_ckpts=3, save_last=True)\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)]\npretrained = '/mnt/petrelfs/wangwenhai/workspace/DCNv3-dev/classification/work_dirs_shared/dcnv3_5_t_v3/ckpt_epoch_299_rmab.pth'\nwork_dir = './work_dirs/mask_rcnn_dcnv3_pre_tiny_fpn_1x_coco_rmab'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 638639090, "exp_name": "mask_rcnn_dcnv3_pre_tiny_fpn_1x_coco_rmab.py", "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 10540, "data_time": 0.93641, "loss_rpn_cls": 0.65495, "loss_rpn_bbox": 0.11141, "loss_cls": 1.93946, "acc": 85.01294, "loss_bbox": 0.03054, "loss_mask": 1.00698, "loss": 3.74333, "time": 1.61593}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 10750, "data_time": 0.0286, "loss_rpn_cls": 0.43298, "loss_rpn_bbox": 0.10155, "loss_cls": 0.42038, "acc": 95.81177, "loss_bbox": 0.12607, "loss_mask": 0.74338, "loss": 1.82435, "time": 0.54753}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 10789, "data_time": 0.03469, "loss_rpn_cls": 0.29131, "loss_rpn_bbox": 0.10013, "loss_cls": 0.40698, "acc": 94.45874, "loss_bbox": 0.18115, "loss_mask": 0.69895, "loss": 1.67851, "time": 0.54012}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 10989, "data_time": 0.02497, "loss_rpn_cls": 0.26242, "loss_rpn_bbox": 0.09171, "loss_cls": 0.38076, "acc": 94.6084, "loss_bbox": 0.17784, "loss_mask": 0.68564, "loss": 1.59838, "time": 0.52731}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 10989, "data_time": 0.02779, "loss_rpn_cls": 0.2553, "loss_rpn_bbox": 0.10013, "loss_cls": 0.38707, "acc": 94.302, "loss_bbox": 0.1881, "loss_mask": 0.66857, "loss": 1.59917, "time": 0.53236}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 10989, "data_time": 0.03004, "loss_rpn_cls": 0.20877, "loss_rpn_bbox": 0.09258, "loss_cls": 0.38081, "acc": 94.35059, "loss_bbox": 0.19066, "loss_mask": 0.65951, "loss": 1.53233, "time": 0.51993}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 11062, "data_time": 0.03064, "loss_rpn_cls": 0.17766, "loss_rpn_bbox": 0.09228, "loss_cls": 0.40604, "acc": 93.94775, "loss_bbox": 0.20944, "loss_mask": 0.64799, "loss": 1.53341, "time": 0.51796}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 11062, "data_time": 0.03051, "loss_rpn_cls": 0.15977, "loss_rpn_bbox": 0.09602, "loss_cls": 0.42656, "acc": 93.63794, "loss_bbox": 0.22213, "loss_mask": 0.63457, "loss": 1.53905, "time": 0.52761}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 11062, "data_time": 0.03154, "loss_rpn_cls": 0.13285, "loss_rpn_bbox": 0.08985, "loss_cls": 0.44471, "acc": 93.04004, "loss_bbox": 0.24904, "loss_mask": 0.61801, "loss": 1.53447, "time": 0.52867}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 11062, "data_time": 0.02458, "loss_rpn_cls": 0.11385, "loss_rpn_bbox": 0.08719, "loss_cls": 0.48285, "acc": 92.09668, "loss_bbox": 0.28718, "loss_mask": 0.58084, "loss": 1.55191, "time": 0.52503}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 11062, "data_time": 0.0301, "loss_rpn_cls": 0.10454, "loss_rpn_bbox": 0.09263, "loss_cls": 0.49876, "acc": 91.08472, "loss_bbox": 0.32915, "loss_mask": 0.53922, "loss": 1.56429, "time": 0.52069}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 11153, "data_time": 0.02537, "loss_rpn_cls": 0.0933, "loss_rpn_bbox": 0.08708, "loss_cls": 0.50804, "acc": 90.25635, "loss_bbox": 0.35308, "loss_mask": 0.50762, "loss": 1.54912, "time": 0.53584}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 11274, "data_time": 0.03036, "loss_rpn_cls": 0.09239, "loss_rpn_bbox": 0.08561, "loss_cls": 0.50573, "acc": 90.17407, "loss_bbox": 0.35041, "loss_mask": 0.47914, "loss": 1.51329, "time": 0.53437}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 11274, "data_time": 0.02563, "loss_rpn_cls": 0.08873, "loss_rpn_bbox": 0.08868, "loss_cls": 0.47805, "acc": 90.10205, "loss_bbox": 0.34831, "loss_mask": 0.45437, "loss": 1.45814, "time": 0.52395}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 11274, "data_time": 0.03277, "loss_rpn_cls": 0.08025, "loss_rpn_bbox": 0.07936, "loss_cls": 0.46262, "acc": 89.71875, "loss_bbox": 0.35523, "loss_mask": 0.43994, "loss": 1.41741, "time": 0.52965}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 11274, "data_time": 0.03353, "loss_rpn_cls": 0.08118, "loss_rpn_bbox": 0.08117, "loss_cls": 0.45406, "acc": 89.50659, "loss_bbox": 0.36003, "loss_mask": 0.42787, "loss": 1.40431, "time": 0.53535}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 11274, "data_time": 0.02945, "loss_rpn_cls": 0.07476, "loss_rpn_bbox": 0.07657, "loss_cls": 0.42192, "acc": 89.96704, "loss_bbox": 0.34019, "loss_mask": 0.40413, "loss": 1.31757, "time": 0.52619}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 11274, "data_time": 0.03581, "loss_rpn_cls": 0.076, "loss_rpn_bbox": 0.08068, "loss_cls": 0.44427, "acc": 89.38794, "loss_bbox": 0.35941, "loss_mask": 0.40472, "loss": 1.36508, "time": 0.52505}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 11274, "data_time": 0.02532, "loss_rpn_cls": 0.07225, "loss_rpn_bbox": 0.07655, "loss_cls": 0.42728, "acc": 89.52637, "loss_bbox": 0.35484, "loss_mask": 0.39915, "loss": 1.33006, "time": 0.52681}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 11274, "data_time": 0.02773, "loss_rpn_cls": 0.07148, "loss_rpn_bbox": 0.07538, "loss_cls": 0.41687, "acc": 89.66602, "loss_bbox": 0.34976, "loss_mask": 0.39844, "loss": 1.31192, "time": 0.52647}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 11274, "data_time": 0.02404, "loss_rpn_cls": 0.06467, "loss_rpn_bbox": 0.07395, "loss_cls": 0.40504, "acc": 89.62207, "loss_bbox": 0.3471, "loss_mask": 0.38495, "loss": 1.27571, "time": 0.52255}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0001, "memory": 11286, "data_time": 0.02528, "loss_rpn_cls": 0.07058, "loss_rpn_bbox": 0.07401, "loss_cls": 0.40876, "acc": 89.37549, "loss_bbox": 0.3522, "loss_mask": 0.38067, "loss": 1.28621, "time": 0.52437}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0001, "memory": 11286, "data_time": 0.02865, "loss_rpn_cls": 0.06627, "loss_rpn_bbox": 0.07249, "loss_cls": 0.3858, "acc": 90.00952, "loss_bbox": 0.33468, "loss_mask": 0.37558, "loss": 1.23482, "time": 0.52003}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0001, "memory": 11286, "data_time": 0.02971, "loss_rpn_cls": 0.06652, "loss_rpn_bbox": 0.07202, "loss_cls": 0.38407, "acc": 89.8252, "loss_bbox": 0.33489, "loss_mask": 0.37384, "loss": 1.23133, "time": 0.53075}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0001, "memory": 11286, "data_time": 0.02881, "loss_rpn_cls": 0.06407, "loss_rpn_bbox": 0.07359, "loss_cls": 0.39483, "acc": 89.4519, "loss_bbox": 0.34833, "loss_mask": 0.36918, "loss": 1.24999, "time": 0.52277}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0001, "memory": 11286, "data_time": 0.02791, "loss_rpn_cls": 0.06091, "loss_rpn_bbox": 0.06925, "loss_cls": 0.37551, "acc": 89.98633, "loss_bbox": 0.32943, "loss_mask": 0.36733, "loss": 1.20243, "time": 0.52061}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0001, "memory": 11286, "data_time": 0.03541, "loss_rpn_cls": 0.06987, "loss_rpn_bbox": 0.07702, "loss_cls": 0.37803, "acc": 89.64673, "loss_bbox": 0.34508, "loss_mask": 0.36139, "loss": 1.23139, "time": 0.52654}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0001, "memory": 11286, "data_time": 0.02791, "loss_rpn_cls": 0.06434, "loss_rpn_bbox": 0.0714, "loss_cls": 0.36215, "acc": 89.99634, "loss_bbox": 0.33086, "loss_mask": 0.35736, "loss": 1.1861, "time": 0.53319}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0001, "memory": 11286, "data_time": 0.0249, "loss_rpn_cls": 0.06084, "loss_rpn_bbox": 0.07046, "loss_cls": 0.37161, "acc": 89.67773, "loss_bbox": 0.33839, "loss_mask": 0.35261, "loss": 1.19389, "time": 0.52826}
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