{"env_info": "sys.platform: linux\nPython: 3.9.19 (main, May  6 2024, 19:43:03) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/petrelfs/share/cuda-11.7/\nNVCC: Cuda compilation tools, release 11.7, V11.7.99\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.12.0+cu113\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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: AVX2\n  - CUDA Runtime 11.3\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 8.3.2  (built against CUDA 11.5)\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/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 -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.13.0+cu113\nOpenCV: 4.9.0\nMMCV: 1.7.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.7\nMMDetection: 2.25.3+c8d02d2", "config": "model = dict(\n    type='MaskRCNN',\n    backbone=dict(\n        type='PIIPThreeBranch',\n        n_points=4,\n        deform_num_heads=16,\n        cffn_ratio=0.25,\n        deform_ratio=0.5,\n        with_cffn=True,\n        interact_attn_type='deform',\n        interaction_drop_path_rate=0.4,\n        branch1=dict(\n            real_size=672,\n            pretrain_img_size=224,\n            patch_size=16,\n            pretrain_patch_size=16,\n            depth=24,\n            embed_dim=1024,\n            num_heads=16,\n            mlp_ratio=4,\n            qkv_bias=True,\n            drop_path_rate=0.4,\n            use_abs_pos_emb=False,\n            use_rel_pos_bias=True,\n            init_values=1e-06,\n            interaction_indexes=[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9],\n                                 [10, 11], [12, 13], [14, 15], [16, 17],\n                                 [18, 19], [20, 21], [22, 23]],\n            pretrained='./pretrained/beitv2_large_patch16_224_pt1k_ft21k.pth',\n            window_attn=[\n                True, True, True, True, True, True, True, True, True, True,\n                True, True, True, True, True, True, True, True, True, True,\n                True, True, True, True\n            ],\n            window_size=[\n                28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n                28, 28, 28, 28, 28, 28, 28, 28\n            ],\n            img_norm_cfg=dict(\n                mean=[127.5, 127.5, 127.5],\n                std=[127.5, 127.5, 127.5],\n                to_rgb=True)),\n        branch2=dict(\n            real_size=1120,\n            pretrain_img_size=224,\n            patch_size=16,\n            pretrain_patch_size=16,\n            depth=12,\n            embed_dim=768,\n            num_heads=12,\n            mlp_ratio=4,\n            qkv_bias=True,\n            drop_path_rate=0.15,\n            use_abs_pos_emb=False,\n            use_rel_pos_bias=True,\n            init_values=1e-06,\n            interaction_indexes=[[0, 0], [1, 1], [2, 2], [3, 3], [4, 4],\n                                 [5, 5], [6, 6], [7, 7], [8, 8], [9, 9],\n                                 [10, 10], [11, 11]],\n            pretrained='./pretrained/beitv2_base_patch16_224_pt1k_ft21k.pth',\n            window_attn=[\n                True, True, True, True, True, True, True, True, True, True,\n                True, True\n            ],\n            window_size=[28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28],\n            img_norm_cfg=dict(\n                mean=[127.5, 127.5, 127.5],\n                std=[127.5, 127.5, 127.5],\n                to_rgb=True)),\n        branch3=dict(\n            real_size=1568,\n            pretrain_img_size=224,\n            patch_size=16,\n            pretrain_patch_size=16,\n            depth=12,\n            embed_dim=384,\n            num_heads=6,\n            mlp_ratio=4,\n            qkv_bias=True,\n            drop_path_rate=0.05,\n            init_scale=1.0,\n            with_fpn=False,\n            interaction_indexes=[[0, 0], [1, 1], [2, 2], [3, 3], [4, 4],\n                                 [5, 5], [6, 6], [7, 7], [8, 8], [9, 9],\n                                 [10, 10], [11, 11]],\n            pretrained='./pretrained/deit_3_small_224_21k.pth',\n            window_attn=[\n                True, True, True, True, True, True, True, True, True, True,\n                True, True\n            ],\n            window_size=[28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28],\n            use_flash_attn=True,\n            img_norm_cfg=dict(\n                mean=[127.5, 127.5, 127.5],\n                std=[127.5, 127.5, 127.5],\n                to_rgb=True))),\n    neck=dict(\n        type='FPN',\n        in_channels=[1024, 1024, 1024, 1024],\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=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], 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=(1568, 941), keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(\n        type='Normalize',\n        mean=[127.5, 127.5, 127.5],\n        std=[127.5, 127.5, 127.5],\n        to_rgb=True),\n    dict(type='Pad', size_divisor=224),\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=(1568, 941),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\n            dict(\n                type='Normalize',\n                mean=[127.5, 127.5, 127.5],\n                std=[127.5, 127.5, 127.5],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=224),\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=(1568, 941), keep_ratio=True),\n            dict(type='RandomFlip', flip_ratio=0.5),\n            dict(\n                type='Normalize',\n                mean=[127.5, 127.5, 127.5],\n                std=[127.5, 127.5, 127.5],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=224),\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=(1568, 941),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[127.5, 127.5, 127.5],\n                        std=[127.5, 127.5, 127.5],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=224),\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=(1568, 941),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\n                    dict(\n                        type='Normalize',\n                        mean=[127.5, 127.5, 127.5],\n                        std=[127.5, 127.5, 127.5],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=224),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nevaluation = dict(metric=['bbox', 'segm'], interval=1, save_best=None)\noptimizer = dict(\n    type='AdamW',\n    lr=0.0001,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    constructor='CustomLayerDecayOptimizerConstructorMMDet',\n    paramwise_cfg=dict(\n        num_layers=24, layer_decay_rate=0.85, skip_stride=[2, 2]))\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, deepspeed=True, max_keep_ckpts=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='ToBFloat16HookMMDet', priority=49)]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\ndeepspeed = True\ndeepspeed_config = 'zero_configs/adam_zero1_bf16.json'\ncustom_imports = dict(\n    imports=['mmdet.mmcv_custom'], allow_failed_imports=False)\nwork_dir = './work_dirs/mask_rcnn_beit_sbl_1568_1120_672_fpn_1x_coco_bs16'\nauto_resume = True\ngpu_ids = range(0, 8)\n", "seed": 1094034324, "exp_name": "mask_rcnn_beit_sbl_1568_1120_672_fpn_1x_coco_bs16.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 26116, "data_time": 0.13635, "loss_rpn_cls": 0.58828, "loss_rpn_bbox": 0.12894, "loss_cls": 2.26224, "acc": 60.02515, "loss_bbox": 0.02389, "loss_mask": 1.09759, "loss": 4.10093, "time": 1.11134}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 26220, "data_time": 0.05257, "loss_rpn_cls": 0.30483, "loss_rpn_bbox": 0.10464, "loss_cls": 0.37757, "acc": 95.85498, "loss_bbox": 0.12531, "loss_mask": 0.77835, "loss": 1.69069, "time": 0.9737}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 26220, "data_time": 0.05554, "loss_rpn_cls": 0.26839, "loss_rpn_bbox": 0.10867, "loss_cls": 0.36449, "acc": 94.99023, "loss_bbox": 0.16071, "loss_mask": 0.70212, "loss": 1.60439, "time": 0.97113}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 26220, "data_time": 0.07153, "loss_rpn_cls": 0.25254, "loss_rpn_bbox": 0.10346, "loss_cls": 0.33536, "acc": 95.1814, "loss_bbox": 0.15448, "loss_mask": 0.682, "loss": 1.52783, "time": 0.95679}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 26220, "data_time": 0.05402, "loss_rpn_cls": 0.22588, "loss_rpn_bbox": 0.10101, "loss_cls": 0.32176, "acc": 95.27026, "loss_bbox": 0.15511, "loss_mask": 0.67319, "loss": 1.47695, "time": 1.15811}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 26220, "data_time": 0.04301, "loss_rpn_cls": 0.18935, "loss_rpn_bbox": 0.09563, "loss_cls": 0.3582, "acc": 94.54834, "loss_bbox": 0.18805, "loss_mask": 0.66462, "loss": 1.49585, "time": 0.94447}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 26234, "data_time": 0.06109, "loss_rpn_cls": 0.16862, "loss_rpn_bbox": 0.10077, "loss_cls": 0.39171, "acc": 94.0249, "loss_bbox": 0.20585, "loss_mask": 0.65301, "loss": 1.51996, "time": 0.95956}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 26311, "data_time": 0.05758, "loss_rpn_cls": 0.15919, "loss_rpn_bbox": 0.10313, "loss_cls": 0.45151, "acc": 92.9375, "loss_bbox": 0.24462, "loss_mask": 0.6344, "loss": 1.59286, "time": 0.98304}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 26311, "data_time": 0.05784, "loss_rpn_cls": 0.1416, "loss_rpn_bbox": 0.1017, "loss_cls": 0.45426, "acc": 92.77148, "loss_bbox": 0.25392, "loss_mask": 0.60957, "loss": 1.56105, "time": 0.98788}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 26359, "data_time": 0.06009, "loss_rpn_cls": 0.12816, "loss_rpn_bbox": 0.09754, "loss_cls": 0.43374, "acc": 92.74219, "loss_bbox": 0.25266, "loss_mask": 0.58253, "loss": 1.49464, "time": 0.97012}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 26428, "data_time": 0.06358, "loss_rpn_cls": 0.11314, "loss_rpn_bbox": 0.09482, "loss_cls": 0.44778, "acc": 92.26685, "loss_bbox": 0.27014, "loss_mask": 0.55479, "loss": 1.48067, "time": 0.98067}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 26428, "data_time": 0.04316, "loss_rpn_cls": 0.10997, "loss_rpn_bbox": 0.09503, "loss_cls": 0.43838, "acc": 92.24292, "loss_bbox": 0.27293, "loss_mask": 0.53272, "loss": 1.44902, "time": 0.96507}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 26428, "data_time": 0.06541, "loss_rpn_cls": 0.10806, "loss_rpn_bbox": 0.09366, "loss_cls": 0.43157, "acc": 91.85107, "loss_bbox": 0.28427, "loss_mask": 0.52291, "loss": 1.44046, "time": 0.96568}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 26428, "data_time": 0.06465, "loss_rpn_cls": 0.10211, "loss_rpn_bbox": 0.09598, "loss_cls": 0.43592, "acc": 91.39844, "loss_bbox": 0.3003, "loss_mask": 0.51105, "loss": 1.44536, "time": 1.03531}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 26428, "data_time": 0.05523, "loss_rpn_cls": 0.10192, "loss_rpn_bbox": 0.09503, "loss_cls": 0.42643, "acc": 91.71143, "loss_bbox": 0.2884, "loss_mask": 0.50272, "loss": 1.41449, "time": 0.97831}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 26428, "data_time": 0.05117, "loss_rpn_cls": 0.10067, "loss_rpn_bbox": 0.08979, "loss_cls": 0.428, "acc": 91.19019, "loss_bbox": 0.30845, "loss_mask": 0.48881, "loss": 1.41571, "time": 0.98297}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 26440, "data_time": 0.05776, "loss_rpn_cls": 0.09512, "loss_rpn_bbox": 0.08957, "loss_cls": 0.41182, "acc": 91.53467, "loss_bbox": 0.29214, "loss_mask": 0.48318, "loss": 1.37182, "time": 0.96202}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 26449, "data_time": 0.06167, "loss_rpn_cls": 0.09762, "loss_rpn_bbox": 0.0949, "loss_cls": 0.4133, "acc": 91.06885, "loss_bbox": 0.30937, "loss_mask": 0.47305, "loss": 1.38824, "time": 0.98611}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 26449, "data_time": 0.05051, "loss_rpn_cls": 0.09027, "loss_rpn_bbox": 0.08881, "loss_cls": 0.39612, "acc": 91.41943, "loss_bbox": 0.29655, "loss_mask": 0.46665, "loss": 1.33839, "time": 0.96884}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 26449, "data_time": 0.05497, "loss_rpn_cls": 0.08701, "loss_rpn_bbox": 0.08855, "loss_cls": 0.40836, "acc": 90.74048, "loss_bbox": 0.32335, "loss_mask": 0.46769, "loss": 1.37496, "time": 0.98275}
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