{"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=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=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            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=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_deit_sbl_1568_1120_672_fpn_1x_coco_bs16'\nauto_resume = True\ngpu_ids = range(0, 8)\n", "seed": 931828675, "exp_name": "mask_rcnn_deit_sbl_1568_1120_672_fpn_1x_coco_bs16.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 20573, "data_time": 0.13076, "loss_rpn_cls": 0.59922, "loss_rpn_bbox": 0.12915, "loss_cls": 1.83905, "acc": 65.4624, "loss_bbox": 0.01301, "loss_mask": 1.24139, "loss": 3.82182, "time": 1.06732}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 20731, "data_time": 0.1214, "loss_rpn_cls": 0.30124, "loss_rpn_bbox": 0.10395, "loss_cls": 0.33363, "acc": 96.36328, "loss_bbox": 0.10102, "loss_mask": 0.75303, "loss": 1.59286, "time": 0.96575}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 20731, "data_time": 0.04354, "loss_rpn_cls": 0.24526, "loss_rpn_bbox": 0.10254, "loss_cls": 0.35174, "acc": 94.98242, "loss_bbox": 0.1624, "loss_mask": 0.69585, "loss": 1.55779, "time": 0.89262}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 20752, "data_time": 0.04891, "loss_rpn_cls": 0.22455, "loss_rpn_bbox": 0.09821, "loss_cls": 0.30554, "acc": 95.36621, "loss_bbox": 0.1492, "loss_mask": 0.68401, "loss": 1.46151, "time": 0.89461}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 20752, "data_time": 0.04847, "loss_rpn_cls": 0.19944, "loss_rpn_bbox": 0.09853, "loss_cls": 0.35486, "acc": 94.38135, "loss_bbox": 0.19303, "loss_mask": 0.66696, "loss": 1.51284, "time": 1.03081}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 20814, "data_time": 0.0438, "loss_rpn_cls": 0.16118, "loss_rpn_bbox": 0.10058, "loss_cls": 0.41117, "acc": 93.32178, "loss_bbox": 0.23386, "loss_mask": 0.64618, "loss": 1.55297, "time": 0.91012}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 20894, "data_time": 0.0606, "loss_rpn_cls": 0.14126, "loss_rpn_bbox": 0.09801, "loss_cls": 0.42926, "acc": 92.9563, "loss_bbox": 0.25109, "loss_mask": 0.62296, "loss": 1.54258, "time": 0.88943}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 20894, "data_time": 0.05363, "loss_rpn_cls": 0.11498, "loss_rpn_bbox": 0.09413, "loss_cls": 0.44048, "acc": 92.21118, "loss_bbox": 0.28271, "loss_mask": 0.59401, "loss": 1.52631, "time": 0.89674}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 20894, "data_time": 0.04226, "loss_rpn_cls": 0.11114, "loss_rpn_bbox": 0.09764, "loss_cls": 0.45966, "acc": 91.82202, "loss_bbox": 0.2972, "loss_mask": 0.5639, "loss": 1.52954, "time": 0.90767}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 20895, "data_time": 0.04718, "loss_rpn_cls": 0.10496, "loss_rpn_bbox": 0.09612, "loss_cls": 0.43825, "acc": 91.52734, "loss_bbox": 0.30165, "loss_mask": 0.53693, "loss": 1.47791, "time": 0.89779}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 20895, "data_time": 0.04791, "loss_rpn_cls": 0.09884, "loss_rpn_bbox": 0.09629, "loss_cls": 0.44312, "acc": 90.86475, "loss_bbox": 0.32754, "loss_mask": 0.51922, "loss": 1.48501, "time": 0.91677}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 20895, "data_time": 0.0474, "loss_rpn_cls": 0.09283, "loss_rpn_bbox": 0.0912, "loss_cls": 0.39607, "acc": 91.53882, "loss_bbox": 0.29775, "loss_mask": 0.4976, "loss": 1.37545, "time": 0.88961}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 20895, "data_time": 0.05339, "loss_rpn_cls": 0.0875, "loss_rpn_bbox": 0.0921, "loss_cls": 0.41485, "acc": 90.68872, "loss_bbox": 0.33007, "loss_mask": 0.49408, "loss": 1.41861, "time": 0.90333}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 20895, "data_time": 0.04607, "loss_rpn_cls": 0.08332, "loss_rpn_bbox": 0.08723, "loss_cls": 0.39023, "acc": 91.14526, "loss_bbox": 0.31313, "loss_mask": 0.47236, "loss": 1.34627, "time": 0.94193}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 20895, "data_time": 0.0567, "loss_rpn_cls": 0.08253, "loss_rpn_bbox": 0.0904, "loss_cls": 0.39737, "acc": 90.38086, "loss_bbox": 0.33862, "loss_mask": 0.4657, "loss": 1.37462, "time": 0.90581}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 20895, "data_time": 0.04322, "loss_rpn_cls": 0.0775, "loss_rpn_bbox": 0.08391, "loss_cls": 0.38207, "acc": 90.67236, "loss_bbox": 0.33149, "loss_mask": 0.45244, "loss": 1.32741, "time": 0.90506}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 20895, "data_time": 0.04037, "loss_rpn_cls": 0.0684, "loss_rpn_bbox": 0.07929, "loss_cls": 0.36099, "acc": 90.7666, "loss_bbox": 0.33112, "loss_mask": 0.44415, "loss": 1.28394, "time": 0.88843}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 20895, "data_time": 0.05352, "loss_rpn_cls": 0.07717, "loss_rpn_bbox": 0.08979, "loss_cls": 0.37172, "acc": 90.26196, "loss_bbox": 0.34401, "loss_mask": 0.45071, "loss": 1.3334, "time": 0.9087}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 20895, "data_time": 0.03857, "loss_rpn_cls": 0.07115, "loss_rpn_bbox": 0.08547, "loss_cls": 0.3575, "acc": 90.57129, "loss_bbox": 0.32986, "loss_mask": 0.44038, "loss": 1.28436, "time": 0.899}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 20912, "data_time": 0.05624, "loss_rpn_cls": 0.0683, "loss_rpn_bbox": 0.08515, "loss_cls": 0.36443, "acc": 90.13892, "loss_bbox": 0.35471, "loss_mask": 0.43022, "loss": 1.30281, "time": 0.89254}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 20914, "data_time": 0.03934, "loss_rpn_cls": 0.07162, "loss_rpn_bbox": 0.08433, "loss_cls": 0.34627, "acc": 90.63184, "loss_bbox": 0.335, "loss_mask": 0.421, "loss": 1.25823, "time": 0.90442}
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