{"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+da84357", "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=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=[14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14],\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=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=[14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14],\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=1568,\n            pretrain_img_size=224,\n            patch_size=16,\n            pretrain_patch_size=16,\n            depth=12,\n            embed_dim=192,\n            num_heads=3,\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_tiny_patch16_224-a1311bcf.pth',\n            window_attn=[\n                True, True, True, True, True, True, True, True, True, True,\n                True, True\n            ],\n            window_size=[14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14],\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=[768, 768, 768, 768],\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=12, layer_decay_rate=0.85, skip_stride=[1, 1]))\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_tsb_1568_1120_672_fpn_1x_coco_bs16'\nauto_resume = True\ngpu_ids = range(0, 8)\n", "seed": 1088313239, "exp_name": "mask_rcnn_deit_tsb_1568_1120_672_fpn_1x_coco_bs16.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 11004, "data_time": 0.12725, "loss_rpn_cls": 0.58601, "loss_rpn_bbox": 0.12453, "loss_cls": 2.46133, "acc": 56.55444, "loss_bbox": 0.04809, "loss_mask": 1.14697, "loss": 4.36693, "time": 0.81299}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 11445, "data_time": 0.06639, "loss_rpn_cls": 0.29331, "loss_rpn_bbox": 0.10885, "loss_cls": 0.40696, "acc": 95.48218, "loss_bbox": 0.14093, "loss_mask": 0.75614, "loss": 1.70619, "time": 0.6875}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 11445, "data_time": 0.06273, "loss_rpn_cls": 0.22905, "loss_rpn_bbox": 0.09921, "loss_cls": 0.33689, "acc": 95.22144, "loss_bbox": 0.15375, "loss_mask": 0.69526, "loss": 1.51416, "time": 0.66282}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 11445, "data_time": 0.05649, "loss_rpn_cls": 0.20374, "loss_rpn_bbox": 0.09911, "loss_cls": 0.30438, "acc": 95.44678, "loss_bbox": 0.14686, "loss_mask": 0.68019, "loss": 1.43427, "time": 0.65095}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 11445, "data_time": 0.06058, "loss_rpn_cls": 0.18362, "loss_rpn_bbox": 0.09869, "loss_cls": 0.35591, "acc": 94.65771, "loss_bbox": 0.17432, "loss_mask": 0.66576, "loss": 1.4783, "time": 0.68284}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 11559, "data_time": 0.06413, "loss_rpn_cls": 0.15289, "loss_rpn_bbox": 0.10159, "loss_cls": 0.41727, "acc": 93.62866, "loss_bbox": 0.22477, "loss_mask": 0.64465, "loss": 1.54118, "time": 0.65365}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 11559, "data_time": 0.06408, "loss_rpn_cls": 0.13482, "loss_rpn_bbox": 0.09875, "loss_cls": 0.4474, "acc": 92.91772, "loss_bbox": 0.2523, "loss_mask": 0.62886, "loss": 1.56213, "time": 0.67846}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 11559, "data_time": 0.0567, "loss_rpn_cls": 0.11591, "loss_rpn_bbox": 0.09444, "loss_cls": 0.46411, "acc": 92.60767, "loss_bbox": 0.26493, "loss_mask": 0.60054, "loss": 1.53993, "time": 0.65427}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 11625, "data_time": 0.06907, "loss_rpn_cls": 0.12206, "loss_rpn_bbox": 0.10242, "loss_cls": 0.46242, "acc": 92.22827, "loss_bbox": 0.27323, "loss_mask": 0.56716, "loss": 1.5273, "time": 0.67313}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 11625, "data_time": 0.05988, "loss_rpn_cls": 0.10902, "loss_rpn_bbox": 0.09915, "loss_cls": 0.47796, "acc": 91.25952, "loss_bbox": 0.30574, "loss_mask": 0.53915, "loss": 1.53103, "time": 0.67555}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 11625, "data_time": 0.0618, "loss_rpn_cls": 0.09723, "loss_rpn_bbox": 0.09313, "loss_cls": 0.45365, "acc": 91.52051, "loss_bbox": 0.30088, "loss_mask": 0.50917, "loss": 1.45407, "time": 0.66298}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 11625, "data_time": 0.06566, "loss_rpn_cls": 0.09195, "loss_rpn_bbox": 0.09084, "loss_cls": 0.45334, "acc": 91.14795, "loss_bbox": 0.31078, "loss_mask": 0.50131, "loss": 1.44822, "time": 0.91295}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 11626, "data_time": 0.05446, "loss_rpn_cls": 0.08701, "loss_rpn_bbox": 0.08813, "loss_cls": 0.42739, "acc": 91.39014, "loss_bbox": 0.30301, "loss_mask": 0.496, "loss": 1.40154, "time": 0.66937}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 11626, "data_time": 0.05447, "loss_rpn_cls": 0.08672, "loss_rpn_bbox": 0.08858, "loss_cls": 0.43283, "acc": 90.80786, "loss_bbox": 0.3216, "loss_mask": 0.47675, "loss": 1.40647, "time": 0.68659}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 11626, "data_time": 0.06499, "loss_rpn_cls": 0.07967, "loss_rpn_bbox": 0.08671, "loss_cls": 0.42652, "acc": 90.46411, "loss_bbox": 0.33525, "loss_mask": 0.46697, "loss": 1.39513, "time": 0.67486}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 11626, "data_time": 0.06178, "loss_rpn_cls": 0.0775, "loss_rpn_bbox": 0.08765, "loss_cls": 0.41158, "acc": 90.63013, "loss_bbox": 0.33029, "loss_mask": 0.45184, "loss": 1.35885, "time": 0.66888}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 11626, "data_time": 0.06214, "loss_rpn_cls": 0.07714, "loss_rpn_bbox": 0.08944, "loss_cls": 0.41016, "acc": 90.35669, "loss_bbox": 0.34048, "loss_mask": 0.44567, "loss": 1.36289, "time": 0.67976}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 11626, "data_time": 0.05507, "loss_rpn_cls": 0.08343, "loss_rpn_bbox": 0.08737, "loss_cls": 0.41746, "acc": 90.0603, "loss_bbox": 0.34806, "loss_mask": 0.44104, "loss": 1.37736, "time": 0.67197}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 11626, "data_time": 0.06486, "loss_rpn_cls": 0.07755, "loss_rpn_bbox": 0.0885, "loss_cls": 0.40741, "acc": 90.2666, "loss_bbox": 0.33539, "loss_mask": 0.43479, "loss": 1.34363, "time": 0.67533}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 11626, "data_time": 0.06593, "loss_rpn_cls": 0.07832, "loss_rpn_bbox": 0.08977, "loss_cls": 0.38342, "acc": 90.42822, "loss_bbox": 0.3343, "loss_mask": 0.42674, "loss": 1.31255, "time": 0.68807}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 11626, "data_time": 0.0815, "loss_rpn_cls": 0.06906, "loss_rpn_bbox": 0.08904, "loss_cls": 0.40593, "acc": 89.47998, "loss_bbox": 0.36648, "loss_mask": 0.43036, "loss": 1.36087, "time": 0.68848}
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