{"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=80,\n        depths=[4, 4, 21, 4],\n        num_heads=[5, 10, 20, 40],\n        deform_padding=True,\n        dilation_rates=[1],\n        kernel_size=3,\n        mlp_ratio=4.0,\n        drop_path_rate=0.3,\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_s_v3_ls_2n/ckpt_epoch_299.pth'\n        )),\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'], 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_s_v3_ls_2n/ckpt_epoch_299.pth'\nwork_dir = './work_dirs/mask_rcnn_dcnv3_post_small_fpn_1x_coco_rmab'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 1779808554, "exp_name": "mask_rcnn_dcnv3_post_small_fpn_1x_coco_rmab.py", "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 13765, "data_time": 1.2286, "loss_rpn_cls": 0.66625, "loss_rpn_bbox": 0.1187, "loss_cls": 2.95323, "acc": 51.48438, "loss_bbox": 0.01531, "loss_mask": 0.99556, "loss": 4.74906, "time": 1.76631}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 13765, "data_time": 0.03431, "loss_rpn_cls": 0.40036, "loss_rpn_bbox": 0.10837, "loss_cls": 0.51208, "acc": 96.23291, "loss_bbox": 0.10665, "loss_mask": 0.76943, "loss": 1.8969, "time": 0.55965}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 14072, "data_time": 0.02955, "loss_rpn_cls": 0.26535, "loss_rpn_bbox": 0.0955, "loss_cls": 0.36756, "acc": 95.05127, "loss_bbox": 0.1631, "loss_mask": 0.70808, "loss": 1.59959, "time": 0.54244}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 14260, "data_time": 0.02431, "loss_rpn_cls": 0.21655, "loss_rpn_bbox": 0.10198, "loss_cls": 0.4101, "acc": 93.78345, "loss_bbox": 0.21233, "loss_mask": 0.69293, "loss": 1.63389, "time": 0.55703}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 14260, "data_time": 0.03035, "loss_rpn_cls": 0.16274, "loss_rpn_bbox": 0.09698, "loss_cls": 0.42427, "acc": 93.2854, "loss_bbox": 0.23743, "loss_mask": 0.67113, "loss": 1.59255, "time": 0.54427}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 14308, "data_time": 0.02884, "loss_rpn_cls": 0.12922, "loss_rpn_bbox": 0.09689, "loss_cls": 0.43392, "acc": 92.73975, "loss_bbox": 0.26007, "loss_mask": 0.64615, "loss": 1.56625, "time": 0.5478}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 14308, "data_time": 0.02641, "loss_rpn_cls": 0.11174, "loss_rpn_bbox": 0.08985, "loss_cls": 0.46207, "acc": 91.61108, "loss_bbox": 0.31003, "loss_mask": 0.60191, "loss": 1.57561, "time": 0.55199}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 14449, "data_time": 0.02296, "loss_rpn_cls": 0.09727, "loss_rpn_bbox": 0.08878, "loss_cls": 0.47208, "acc": 90.98193, "loss_bbox": 0.33704, "loss_mask": 0.57431, "loss": 1.56948, "time": 0.55115}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 14449, "data_time": 0.02591, "loss_rpn_cls": 0.08682, "loss_rpn_bbox": 0.08236, "loss_cls": 0.45711, "acc": 90.6228, "loss_bbox": 0.35434, "loss_mask": 0.54264, "loss": 1.52328, "time": 0.54949}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 14449, "data_time": 0.0254, "loss_rpn_cls": 0.08572, "loss_rpn_bbox": 0.0886, "loss_cls": 0.47824, "acc": 89.56836, "loss_bbox": 0.38922, "loss_mask": 0.50636, "loss": 1.54815, "time": 0.55113}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 14449, "data_time": 0.03028, "loss_rpn_cls": 0.08419, "loss_rpn_bbox": 0.08725, "loss_cls": 0.44715, "acc": 89.16968, "loss_bbox": 0.39458, "loss_mask": 0.47544, "loss": 1.48861, "time": 0.55181}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 14449, "data_time": 0.03298, "loss_rpn_cls": 0.07331, "loss_rpn_bbox": 0.07856, "loss_cls": 0.40585, "acc": 89.96411, "loss_bbox": 0.36541, "loss_mask": 0.46264, "loss": 1.38577, "time": 0.54562}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 14492, "data_time": 0.03191, "loss_rpn_cls": 0.07442, "loss_rpn_bbox": 0.07954, "loss_cls": 0.39555, "acc": 89.76465, "loss_bbox": 0.36199, "loss_mask": 0.43699, "loss": 1.34849, "time": 0.55009}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 14492, "data_time": 0.0311, "loss_rpn_cls": 0.07213, "loss_rpn_bbox": 0.0812, "loss_cls": 0.38815, "acc": 89.64673, "loss_bbox": 0.37172, "loss_mask": 0.43612, "loss": 1.34932, "time": 0.53891}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 14492, "data_time": 0.02679, "loss_rpn_cls": 0.0692, "loss_rpn_bbox": 0.07603, "loss_cls": 0.36504, "acc": 89.84692, "loss_bbox": 0.36608, "loss_mask": 0.42078, "loss": 1.29713, "time": 0.5501}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 14492, "data_time": 0.03388, "loss_rpn_cls": 0.06433, "loss_rpn_bbox": 0.07388, "loss_cls": 0.36987, "acc": 89.57202, "loss_bbox": 0.37093, "loss_mask": 0.40881, "loss": 1.28781, "time": 0.56288}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 14492, "data_time": 0.02878, "loss_rpn_cls": 0.07305, "loss_rpn_bbox": 0.08122, "loss_cls": 0.37241, "acc": 89.23267, "loss_bbox": 0.3797, "loss_mask": 0.40362, "loss": 1.31, "time": 0.55044}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 14509, "data_time": 0.03733, "loss_rpn_cls": 0.06516, "loss_rpn_bbox": 0.07946, "loss_cls": 0.34846, "acc": 89.77661, "loss_bbox": 0.36585, "loss_mask": 0.38641, "loss": 1.24534, "time": 0.56317}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 14509, "data_time": 0.0308, "loss_rpn_cls": 0.06253, "loss_rpn_bbox": 0.07463, "loss_cls": 0.33923, "acc": 89.93848, "loss_bbox": 0.35789, "loss_mask": 0.38318, "loss": 1.21746, "time": 0.54736}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 14509, "data_time": 0.02884, "loss_rpn_cls": 0.06341, "loss_rpn_bbox": 0.07466, "loss_cls": 0.34107, "acc": 90.06738, "loss_bbox": 0.34981, "loss_mask": 0.37438, "loss": 1.20332, "time": 0.54901}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 14509, "data_time": 0.02223, "loss_rpn_cls": 0.06758, "loss_rpn_bbox": 0.07688, "loss_cls": 0.35636, "acc": 89.38525, "loss_bbox": 0.37092, "loss_mask": 0.36878, "loss": 1.24051, "time": 0.55473}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0001, "memory": 14509, "data_time": 0.02675, "loss_rpn_cls": 0.05913, "loss_rpn_bbox": 0.0736, "loss_cls": 0.33876, "acc": 89.89136, "loss_bbox": 0.34913, "loss_mask": 0.36513, "loss": 1.18575, "time": 0.54757}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0001, "memory": 14509, "data_time": 0.02842, "loss_rpn_cls": 0.06222, "loss_rpn_bbox": 0.07448, "loss_cls": 0.33739, "acc": 89.77612, "loss_bbox": 0.35376, "loss_mask": 0.36592, "loss": 1.19378, "time": 0.54772}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0001, "memory": 14510, "data_time": 0.02883, "loss_rpn_cls": 0.06468, "loss_rpn_bbox": 0.07644, "loss_cls": 0.32649, "acc": 90.05103, "loss_bbox": 0.34784, "loss_mask": 0.36376, "loss": 1.1792, "time": 0.55174}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0001, "memory": 14510, "data_time": 0.02408, "loss_rpn_cls": 0.0599, "loss_rpn_bbox": 0.06964, "loss_cls": 0.31642, "acc": 90.43921, "loss_bbox": 0.33438, "loss_mask": 0.35253, "loss": 1.13288, "time": 0.54543}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0001, "memory": 14510, "data_time": 0.02429, "loss_rpn_cls": 0.06205, "loss_rpn_bbox": 0.07421, "loss_cls": 0.32244, "acc": 90.1792, "loss_bbox": 0.34043, "loss_mask": 0.35145, "loss": 1.15058, "time": 0.55589}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0001, "memory": 14510, "data_time": 0.03558, "loss_rpn_cls": 0.06712, "loss_rpn_bbox": 0.07294, "loss_cls": 0.31658, "acc": 90.10962, "loss_bbox": 0.34517, "loss_mask": 0.34339, "loss": 1.14519, "time": 0.55042}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0001, "memory": 14510, "data_time": 0.02756, "loss_rpn_cls": 0.05998, "loss_rpn_bbox": 0.07216, "loss_cls": 0.31807, "acc": 90.30347, "loss_bbox": 0.33589, "loss_mask": 0.3375, "loss": 1.12362, "time": 0.547}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0001, "memory": 14510, "data_time": 0.02916, "loss_rpn_cls": 0.05465, "loss_rpn_bbox": 0.06888, "loss_cls": 0.31947, "acc": 90.14771, "loss_bbox": 0.34923, "loss_mask": 0.34552, "loss": 1.13775, "time": 0.55312}
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