{"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='RandomFlip', flip_ratio=0.5),\n    dict(\n        type='AutoAugment',\n        policies=[[{\n            'type':\n            'Resize',\n            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),\n                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),\n                          (736, 1333), (768, 1333), (800, 1333)],\n            'multiscale_mode':\n            'value',\n            'keep_ratio':\n            True\n        }],\n                  [{\n                      'type': 'Resize',\n                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],\n                      'multiscale_mode': 'value',\n                      'keep_ratio': True\n                  }, {\n                      'type': 'RandomCrop',\n                      'crop_type': 'absolute_range',\n                      'crop_size': (384, 600),\n                      'allow_negative_crop': True\n                  }, {\n                      'type':\n                      'Resize',\n                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n                                    (576, 1333), (608, 1333), (640, 1333),\n                                    (672, 1333), (704, 1333), (736, 1333),\n                                    (768, 1333), (800, 1333)],\n                      'multiscale_mode':\n                      'value',\n                      'override':\n                      True,\n                      'keep_ratio':\n                      True\n                  }]]),\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='RandomFlip', flip_ratio=0.5),\n            dict(\n                type='AutoAugment',\n                policies=[[{\n                    'type':\n                    'Resize',\n                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n                                  (576, 1333), (608, 1333), (640, 1333),\n                                  (672, 1333), (704, 1333), (736, 1333),\n                                  (768, 1333), (800, 1333)],\n                    'multiscale_mode':\n                    'value',\n                    'keep_ratio':\n                    True\n                }],\n                          [{\n                              'type': 'Resize',\n                              'img_scale': [(400, 1333), (500, 1333),\n                                            (600, 1333)],\n                              'multiscale_mode': 'value',\n                              'keep_ratio': True\n                          }, {\n                              'type': 'RandomCrop',\n                              'crop_type': 'absolute_range',\n                              'crop_size': (384, 600),\n                              'allow_negative_crop': True\n                          }, {\n                              'type':\n                              'Resize',\n                              'img_scale': [(480, 1333), (512, 1333),\n                                            (544, 1333), (576, 1333),\n                                            (608, 1333), (640, 1333),\n                                            (672, 1333), (704, 1333),\n                                            (736, 1333), (768, 1333),\n                                            (800, 1333)],\n                              'multiscale_mode':\n                              'value',\n                              'override':\n                              True,\n                              'keep_ratio':\n                              True\n                          }]]),\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=[27, 33])\nrunner = dict(type='EpochBasedRunner', max_epochs=36)\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_3x_coco_rmab'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 128799259, "exp_name": "mask_rcnn_dcnv3_pre_tiny_fpn_3x_coco_rmab.py", "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 10591, "data_time": 0.9392, "loss_rpn_cls": 0.64602, "loss_rpn_bbox": 0.08376, "loss_cls": 2.72473, "acc": 63.76758, "loss_bbox": 0.06594, "loss_mask": 0.85476, "loss": 4.37522, "time": 1.5341}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 13084, "data_time": 0.01854, "loss_rpn_cls": 0.40291, "loss_rpn_bbox": 0.0803, "loss_cls": 0.42894, "acc": 95.03345, "loss_bbox": 0.16691, "loss_mask": 0.72666, "loss": 1.80572, "time": 0.50712}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 13084, "data_time": 0.01807, "loss_rpn_cls": 0.26115, "loss_rpn_bbox": 0.07329, "loss_cls": 0.39431, "acc": 94.52466, "loss_bbox": 0.19115, "loss_mask": 0.69313, "loss": 1.61303, "time": 0.47749}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 13084, "data_time": 0.02017, "loss_rpn_cls": 0.25464, "loss_rpn_bbox": 0.07952, "loss_cls": 0.39857, "acc": 94.21387, "loss_bbox": 0.20223, "loss_mask": 0.6789, "loss": 1.61386, "time": 0.4822}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 13084, "data_time": 0.01946, "loss_rpn_cls": 0.24016, "loss_rpn_bbox": 0.07668, "loss_cls": 0.38561, "acc": 94.36133, "loss_bbox": 0.19544, "loss_mask": 0.66986, "loss": 1.56775, "time": 0.47449}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 13084, "data_time": 0.01786, "loss_rpn_cls": 0.22175, "loss_rpn_bbox": 0.07995, "loss_cls": 0.39609, "acc": 94.10938, "loss_bbox": 0.20794, "loss_mask": 0.66143, "loss": 1.56716, "time": 0.47677}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 13084, "data_time": 0.01815, "loss_rpn_cls": 0.19776, "loss_rpn_bbox": 0.08096, "loss_cls": 0.40596, "acc": 94.04858, "loss_bbox": 0.21132, "loss_mask": 0.65423, "loss": 1.55024, "time": 0.48322}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 13084, "data_time": 0.01727, "loss_rpn_cls": 0.18371, "loss_rpn_bbox": 0.08197, "loss_cls": 0.42041, "acc": 93.7085, "loss_bbox": 0.22262, "loss_mask": 0.64274, "loss": 1.55145, "time": 0.48332}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 13084, "data_time": 0.01877, "loss_rpn_cls": 0.15229, "loss_rpn_bbox": 0.07509, "loss_cls": 0.44655, "acc": 93.20264, "loss_bbox": 0.24715, "loss_mask": 0.62226, "loss": 1.54334, "time": 0.47288}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 13084, "data_time": 0.01729, "loss_rpn_cls": 0.145, "loss_rpn_bbox": 0.0785, "loss_cls": 0.46412, "acc": 92.69092, "loss_bbox": 0.26775, "loss_mask": 0.59301, "loss": 1.54837, "time": 0.47769}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 13084, "data_time": 0.01742, "loss_rpn_cls": 0.12899, "loss_rpn_bbox": 0.0754, "loss_cls": 0.48955, "acc": 91.76001, "loss_bbox": 0.30499, "loss_mask": 0.55804, "loss": 1.55696, "time": 0.48199}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 13191, "data_time": 0.0187, "loss_rpn_cls": 0.11085, "loss_rpn_bbox": 0.06998, "loss_cls": 0.51653, "acc": 90.81641, "loss_bbox": 0.34124, "loss_mask": 0.52277, "loss": 1.56137, "time": 0.48375}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 13191, "data_time": 0.01795, "loss_rpn_cls": 0.10255, "loss_rpn_bbox": 0.07242, "loss_cls": 0.50695, "acc": 90.37622, "loss_bbox": 0.35288, "loss_mask": 0.49122, "loss": 1.52601, "time": 0.48601}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 13191, "data_time": 0.01784, "loss_rpn_cls": 0.09321, "loss_rpn_bbox": 0.06971, "loss_cls": 0.48864, "acc": 90.14624, "loss_bbox": 0.35876, "loss_mask": 0.47231, "loss": 1.48262, "time": 0.48788}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 13191, "data_time": 0.01755, "loss_rpn_cls": 0.09243, "loss_rpn_bbox": 0.07187, "loss_cls": 0.49115, "acc": 89.83032, "loss_bbox": 0.37025, "loss_mask": 0.45299, "loss": 1.47869, "time": 0.48734}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 13191, "data_time": 0.01846, "loss_rpn_cls": 0.08969, "loss_rpn_bbox": 0.06434, "loss_cls": 0.46356, "acc": 89.86475, "loss_bbox": 0.35674, "loss_mask": 0.43496, "loss": 1.40928, "time": 0.48144}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 13191, "data_time": 0.02021, "loss_rpn_cls": 0.08284, "loss_rpn_bbox": 0.06182, "loss_cls": 0.44337, "acc": 90.03223, "loss_bbox": 0.34996, "loss_mask": 0.4191, "loss": 1.35709, "time": 0.4739}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 13191, "data_time": 0.01931, "loss_rpn_cls": 0.08229, "loss_rpn_bbox": 0.06573, "loss_cls": 0.45425, "acc": 89.44165, "loss_bbox": 0.36609, "loss_mask": 0.41215, "loss": 1.38051, "time": 0.48048}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 13553, "data_time": 0.02124, "loss_rpn_cls": 0.08204, "loss_rpn_bbox": 0.06322, "loss_cls": 0.40977, "acc": 90.15063, "loss_bbox": 0.33687, "loss_mask": 0.40523, "loss": 1.29713, "time": 0.48507}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 13553, "data_time": 0.02003, "loss_rpn_cls": 0.08619, "loss_rpn_bbox": 0.06568, "loss_cls": 0.40749, "acc": 90.0835, "loss_bbox": 0.33887, "loss_mask": 0.40391, "loss": 1.30213, "time": 0.48496}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 13553, "data_time": 0.01875, "loss_rpn_cls": 0.07603, "loss_rpn_bbox": 0.06549, "loss_cls": 0.42565, "acc": 89.33252, "loss_bbox": 0.36547, "loss_mask": 0.39395, "loss": 1.3266, "time": 0.48838}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0001, "memory": 13553, "data_time": 0.01674, "loss_rpn_cls": 0.07477, "loss_rpn_bbox": 0.06108, "loss_cls": 0.39743, "acc": 90.03442, "loss_bbox": 0.34076, "loss_mask": 0.38672, "loss": 1.26076, "time": 0.4765}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0001, "memory": 14531, "data_time": 0.01762, "loss_rpn_cls": 0.07189, "loss_rpn_bbox": 0.06119, "loss_cls": 0.39252, "acc": 90.06763, "loss_bbox": 0.33648, "loss_mask": 0.38407, "loss": 1.24614, "time": 0.48308}
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