{"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+d30b7c1", "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=448,\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            init_scale=1.0,\n            with_fpn=False,\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/deit_3_large_224_21k.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            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        branch2=dict(\n            real_size=672,\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            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_base_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        branch3=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=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=(1120, 672), 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=(1120, 672),\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=(1120, 672), 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=(1120, 672),\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=(1120, 672),\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_deit_sbl_1120_672_448_fpn_1x_coco_bs16'\nauto_resume = True\ngpu_ids = range(0, 8)\n", "seed": 2084664687, "exp_name": "mask_rcnn_deit_sbl_1120_672_448_fpn_1x_coco_bs16.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 9039, "data_time": 0.08821, "loss_rpn_cls": 0.62997, "loss_rpn_bbox": 0.10539, "loss_cls": 1.79868, "acc": 64.05957, "loss_bbox": 0.04687, "loss_mask": 1.32663, "loss": 3.90754, "time": 0.71521}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 9144, "data_time": 0.01975, "loss_rpn_cls": 0.31834, "loss_rpn_bbox": 0.08693, "loss_cls": 0.40673, "acc": 95.02954, "loss_bbox": 0.16298, "loss_mask": 0.75744, "loss": 1.73242, "time": 0.56354}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 9213, "data_time": 0.01727, "loss_rpn_cls": 0.26805, "loss_rpn_bbox": 0.08337, "loss_cls": 0.39145, "acc": 94.34033, "loss_bbox": 0.18944, "loss_mask": 0.69608, "loss": 1.62839, "time": 0.56291}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 9337, "data_time": 0.02222, "loss_rpn_cls": 0.26531, "loss_rpn_bbox": 0.08876, "loss_cls": 0.40895, "acc": 93.729, "loss_bbox": 0.21719, "loss_mask": 0.67889, "loss": 1.6591, "time": 0.56629}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 9385, "data_time": 0.02407, "loss_rpn_cls": 0.219, "loss_rpn_bbox": 0.08466, "loss_cls": 0.40748, "acc": 93.30957, "loss_bbox": 0.23835, "loss_mask": 0.65756, "loss": 1.60705, "time": 0.69703}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 9385, "data_time": 0.01742, "loss_rpn_cls": 0.17973, "loss_rpn_bbox": 0.08353, "loss_cls": 0.40648, "acc": 93.13062, "loss_bbox": 0.24497, "loss_mask": 0.63249, "loss": 1.54721, "time": 0.567}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 9403, "data_time": 0.02058, "loss_rpn_cls": 0.15419, "loss_rpn_bbox": 0.08537, "loss_cls": 0.43931, "acc": 92.29004, "loss_bbox": 0.28084, "loss_mask": 0.60373, "loss": 1.56345, "time": 0.57192}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 9428, "data_time": 0.0218, "loss_rpn_cls": 0.12335, "loss_rpn_bbox": 0.07782, "loss_cls": 0.44653, "acc": 91.82812, "loss_bbox": 0.30607, "loss_mask": 0.56648, "loss": 1.52024, "time": 0.57499}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 9428, "data_time": 0.01783, "loss_rpn_cls": 0.12377, "loss_rpn_bbox": 0.08094, "loss_cls": 0.44879, "acc": 91.2605, "loss_bbox": 0.32232, "loss_mask": 0.55348, "loss": 1.5293, "time": 0.57534}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 9428, "data_time": 0.01893, "loss_rpn_cls": 0.11122, "loss_rpn_bbox": 0.07709, "loss_cls": 0.43863, "acc": 91.04053, "loss_bbox": 0.32755, "loss_mask": 0.53383, "loss": 1.48832, "time": 0.58012}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 9428, "data_time": 0.01883, "loss_rpn_cls": 0.10425, "loss_rpn_bbox": 0.07546, "loss_cls": 0.40721, "acc": 90.86597, "loss_bbox": 0.33235, "loss_mask": 0.50299, "loss": 1.42226, "time": 0.57625}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 9428, "data_time": 0.01877, "loss_rpn_cls": 0.09997, "loss_rpn_bbox": 0.07474, "loss_cls": 0.40153, "acc": 91.01025, "loss_bbox": 0.32271, "loss_mask": 0.49693, "loss": 1.39588, "time": 0.5802}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 9456, "data_time": 0.02031, "loss_rpn_cls": 0.1084, "loss_rpn_bbox": 0.08001, "loss_cls": 0.40638, "acc": 90.41455, "loss_bbox": 0.34635, "loss_mask": 0.49246, "loss": 1.43361, "time": 0.57895}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 9456, "data_time": 0.02384, "loss_rpn_cls": 0.09781, "loss_rpn_bbox": 0.0771, "loss_cls": 0.38903, "acc": 90.34668, "loss_bbox": 0.34448, "loss_mask": 0.47843, "loss": 1.38684, "time": 0.6295}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 9456, "data_time": 0.02103, "loss_rpn_cls": 0.0964, "loss_rpn_bbox": 0.07446, "loss_cls": 0.38262, "acc": 90.31763, "loss_bbox": 0.35294, "loss_mask": 0.47283, "loss": 1.37926, "time": 0.58075}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 9458, "data_time": 0.02087, "loss_rpn_cls": 0.09948, "loss_rpn_bbox": 0.07774, "loss_cls": 0.39552, "acc": 89.81494, "loss_bbox": 0.36736, "loss_mask": 0.46311, "loss": 1.40321, "time": 0.5825}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 9458, "data_time": 0.01978, "loss_rpn_cls": 0.09295, "loss_rpn_bbox": 0.07485, "loss_cls": 0.3745, "acc": 90.12866, "loss_bbox": 0.35767, "loss_mask": 0.46156, "loss": 1.36153, "time": 0.57994}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 9458, "data_time": 0.02076, "loss_rpn_cls": 0.08614, "loss_rpn_bbox": 0.06978, "loss_cls": 0.36316, "acc": 90.0896, "loss_bbox": 0.36209, "loss_mask": 0.44052, "loss": 1.32168, "time": 0.58077}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 9458, "data_time": 0.01981, "loss_rpn_cls": 0.08439, "loss_rpn_bbox": 0.07141, "loss_cls": 0.35392, "acc": 90.24097, "loss_bbox": 0.35326, "loss_mask": 0.43915, "loss": 1.30213, "time": 0.58024}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 9458, "data_time": 0.02215, "loss_rpn_cls": 0.0886, "loss_rpn_bbox": 0.07462, "loss_cls": 0.35798, "acc": 89.94141, "loss_bbox": 0.36805, "loss_mask": 0.43666, "loss": 1.3259, "time": 0.5899}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 9458, "data_time": 0.02176, "loss_rpn_cls": 0.08498, "loss_rpn_bbox": 0.07576, "loss_cls": 0.35327, "acc": 90.16992, "loss_bbox": 0.35905, "loss_mask": 0.43422, "loss": 1.30728, "time": 0.5825}
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