{"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/lustre/share/cuda-11.1\nNVCC: Build cuda_11.1.TC455_06.29069683_0\nGCC: gcc (GCC) 7.3.0\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_Post',\n        deform_points=9,\n        embed_dim=112,\n        depths=[4, 4, 21, 4],\n        num_heads=[7, 14, 28, 56],\n        deform_padding=True,\n        dilation_rates=[1],\n        kernel_size=3,\n        mlp_ratio=4.0,\n        drop_path_rate=0.4,\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_post_b_v3_ls_2n/ckpt_epoch_299.pth'\n        )),\n    neck=dict(\n        type='FPN',\n        in_channels=[112, 224, 448, 896],\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=33, layer_decay_rate=1.0, depths=[4, 4, 21, 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_post_b_v3_ls_2n/ckpt_epoch_299.pth'\nwork_dir = './work_dirs/mask_rcnn_dcnv3_post_base_fpn_1x_coco_rmab'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 2033885427, "exp_name": "mask_rcnn_dcnv3_post_base_fpn_1x_coco_rmab.py", "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 19105, "data_time": 1.35513, "loss_rpn_cls": 0.65048, "loss_rpn_bbox": 0.12279, "loss_cls": 3.01104, "acc": 47.74536, "loss_bbox": 0.02023, "loss_mask": 0.97667, "loss": 4.7812, "time": 2.0052}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 19245, "data_time": 0.03163, "loss_rpn_cls": 0.38085, "loss_rpn_bbox": 0.11392, "loss_cls": 0.53215, "acc": 95.47705, "loss_bbox": 0.12839, "loss_mask": 0.76751, "loss": 1.92282, "time": 0.62536}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 19296, "data_time": 0.02901, "loss_rpn_cls": 0.26222, "loss_rpn_bbox": 0.102, "loss_cls": 0.39188, "acc": 94.77417, "loss_bbox": 0.1705, "loss_mask": 0.70867, "loss": 1.63527, "time": 0.61323}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 19555, "data_time": 0.02785, "loss_rpn_cls": 0.21763, "loss_rpn_bbox": 0.10328, "loss_cls": 0.40603, "acc": 93.92407, "loss_bbox": 0.20476, "loss_mask": 0.69253, "loss": 1.62423, "time": 0.61461}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 19555, "data_time": 0.0325, "loss_rpn_cls": 0.15868, "loss_rpn_bbox": 0.09492, "loss_cls": 0.40848, "acc": 93.74951, "loss_bbox": 0.21928, "loss_mask": 0.67765, "loss": 1.55901, "time": 0.60987}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 19707, "data_time": 0.03662, "loss_rpn_cls": 0.13895, "loss_rpn_bbox": 0.1015, "loss_cls": 0.42607, "acc": 93.0957, "loss_bbox": 0.24315, "loss_mask": 0.65635, "loss": 1.56602, "time": 0.61539}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 19707, "data_time": 0.02641, "loss_rpn_cls": 0.12261, "loss_rpn_bbox": 0.09884, "loss_cls": 0.44029, "acc": 92.41772, "loss_bbox": 0.27316, "loss_mask": 0.62301, "loss": 1.55791, "time": 0.60856}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 19707, "data_time": 0.02683, "loss_rpn_cls": 0.10683, "loss_rpn_bbox": 0.09352, "loss_cls": 0.46286, "acc": 91.39966, "loss_bbox": 0.31757, "loss_mask": 0.58209, "loss": 1.56286, "time": 0.61332}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 19707, "data_time": 0.0281, "loss_rpn_cls": 0.09109, "loss_rpn_bbox": 0.09103, "loss_cls": 0.45899, "acc": 90.79321, "loss_bbox": 0.34463, "loss_mask": 0.5431, "loss": 1.52883, "time": 0.61196}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 19707, "data_time": 0.02878, "loss_rpn_cls": 0.08523, "loss_rpn_bbox": 0.08557, "loss_cls": 0.44814, "acc": 90.53906, "loss_bbox": 0.35045, "loss_mask": 0.50791, "loss": 1.4773, "time": 0.60583}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 19728, "data_time": 0.02601, "loss_rpn_cls": 0.08554, "loss_rpn_bbox": 0.08459, "loss_cls": 0.41814, "acc": 90.40063, "loss_bbox": 0.35477, "loss_mask": 0.46657, "loss": 1.4096, "time": 0.61155}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 19728, "data_time": 0.0254, "loss_rpn_cls": 0.07769, "loss_rpn_bbox": 0.08521, "loss_cls": 0.41381, "acc": 90.00854, "loss_bbox": 0.36576, "loss_mask": 0.45406, "loss": 1.39653, "time": 0.60825}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 19754, "data_time": 0.03011, "loss_rpn_cls": 0.07154, "loss_rpn_bbox": 0.08267, "loss_cls": 0.40646, "acc": 89.76709, "loss_bbox": 0.37292, "loss_mask": 0.43273, "loss": 1.36633, "time": 0.60883}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 19755, "data_time": 0.02379, "loss_rpn_cls": 0.07062, "loss_rpn_bbox": 0.08253, "loss_cls": 0.39813, "acc": 89.52075, "loss_bbox": 0.37964, "loss_mask": 0.43037, "loss": 1.36129, "time": 0.61534}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 19784, "data_time": 0.02951, "loss_rpn_cls": 0.06952, "loss_rpn_bbox": 0.07953, "loss_cls": 0.36585, "acc": 90.07861, "loss_bbox": 0.35301, "loss_mask": 0.41739, "loss": 1.28531, "time": 0.6178}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 19833, "data_time": 0.02624, "loss_rpn_cls": 0.06795, "loss_rpn_bbox": 0.07821, "loss_cls": 0.37561, "acc": 89.55811, "loss_bbox": 0.36938, "loss_mask": 0.40748, "loss": 1.29863, "time": 0.60565}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 19833, "data_time": 0.02915, "loss_rpn_cls": 0.06998, "loss_rpn_bbox": 0.07937, "loss_cls": 0.36822, "acc": 89.51294, "loss_bbox": 0.37454, "loss_mask": 0.39825, "loss": 1.29035, "time": 0.61997}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 19833, "data_time": 0.02711, "loss_rpn_cls": 0.07049, "loss_rpn_bbox": 0.07735, "loss_cls": 0.34797, "acc": 89.87402, "loss_bbox": 0.36282, "loss_mask": 0.39012, "loss": 1.24875, "time": 0.60876}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 19836, "data_time": 0.02712, "loss_rpn_cls": 0.0654, "loss_rpn_bbox": 0.07617, "loss_cls": 0.35578, "acc": 89.53687, "loss_bbox": 0.36692, "loss_mask": 0.3834, "loss": 1.24767, "time": 0.62353}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 19836, "data_time": 0.02871, "loss_rpn_cls": 0.06126, "loss_rpn_bbox": 0.07247, "loss_cls": 0.34469, "acc": 89.79517, "loss_bbox": 0.36113, "loss_mask": 0.38073, "loss": 1.22028, "time": 0.609}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 19836, "data_time": 0.02792, "loss_rpn_cls": 0.05853, "loss_rpn_bbox": 0.06981, "loss_cls": 0.33935, "acc": 89.78882, "loss_bbox": 0.36351, "loss_mask": 0.36938, "loss": 1.20058, "time": 0.61184}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0001, "memory": 19836, "data_time": 0.0261, "loss_rpn_cls": 0.06415, "loss_rpn_bbox": 0.0775, "loss_cls": 0.35078, "acc": 89.27588, "loss_bbox": 0.37661, "loss_mask": 0.37341, "loss": 1.24245, "time": 0.61468}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0001, "memory": 19836, "data_time": 0.02904, "loss_rpn_cls": 0.06095, "loss_rpn_bbox": 0.0762, "loss_cls": 0.32711, "acc": 89.9021, "loss_bbox": 0.35706, "loss_mask": 0.36378, "loss": 1.1851, "time": 0.61734}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0001, "memory": 19839, "data_time": 0.03493, "loss_rpn_cls": 0.05879, "loss_rpn_bbox": 0.07108, "loss_cls": 0.32509, "acc": 89.88135, "loss_bbox": 0.34845, "loss_mask": 0.3466, "loss": 1.15002, "time": 0.61803}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0001, "memory": 19839, "data_time": 0.03293, "loss_rpn_cls": 0.05498, "loss_rpn_bbox": 0.07047, "loss_cls": 0.32684, "acc": 89.9375, "loss_bbox": 0.35762, "loss_mask": 0.35029, "loss": 1.1602, "time": 0.61387}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0001, "memory": 19839, "data_time": 0.02888, "loss_rpn_cls": 0.05678, "loss_rpn_bbox": 0.07337, "loss_cls": 0.33319, "acc": 89.65698, "loss_bbox": 0.35979, "loss_mask": 0.34607, "loss": 1.1692, "time": 0.61366}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0001, "memory": 19839, "data_time": 0.02991, "loss_rpn_cls": 0.06012, "loss_rpn_bbox": 0.07178, "loss_cls": 0.32029, "acc": 90.00952, "loss_bbox": 0.34929, "loss_mask": 0.35197, "loss": 1.15346, "time": 0.60409}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0001, "memory": 19839, "data_time": 0.02755, "loss_rpn_cls": 0.05605, "loss_rpn_bbox": 0.06861, "loss_cls": 0.31565, "acc": 90.25024, "loss_bbox": 0.33795, "loss_mask": 0.34545, "loss": 1.12371, "time": 0.60059}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0001, "memory": 19839, "data_time": 0.02946, "loss_rpn_cls": 0.05864, "loss_rpn_bbox": 0.07092, "loss_cls": 0.31578, "acc": 89.96533, "loss_bbox": 0.34407, "loss_mask": 0.3462, "loss": 1.13561, "time": 0.61525}
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