{"env_info": "sys.platform: linux\nPython: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 4090 D\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 12.1, V12.1.105\nGCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nPyTorch: 2.1.2\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201703\n  - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)\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: AVX512\n  - CUDA Runtime 12.1\n  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-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;-gencode;arch=compute_90,code=sm_90\n  - CuDNN 8.9.2\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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, USE_ROCM=OFF, \n\nTorchVision: 0.16.2\nOpenCV: 4.10.0\nMMCV: 1.7.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 12.1\nMMDetection: 2.28.2+08a7dbe", "config": "model = dict(\n    type='MaskRCNN',\n    pretrained='torchvision://resnet50',\n    backbone=dict(\n        type='recnext_a5',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        norm_cfg=dict(type='BN', requires_grad=True),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint='pretrain/recnext_a5_distill_300e.pth')),\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'])\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=1e-06,\n    step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\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)]\nwork_dir = './work_dirs/mask_rcnn_recnext_a5_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_recnext_a5_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 14344, "data_time": 0.10369, "loss_rpn_cls": 0.60353, "loss_rpn_bbox": 0.26244, "loss_cls": 1.44799, "acc": 72.85522, "loss_bbox": 0.01883, "loss_mask": 3.552, "loss": 5.8848, "time": 1.11171}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 14344, "data_time": 0.024, "loss_rpn_cls": 0.28749, "loss_rpn_bbox": 0.16879, "loss_cls": 0.35925, "acc": 96.07642, "loss_bbox": 0.11831, "loss_mask": 1.10414, "loss": 2.03799, "time": 0.97199}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 14344, "data_time": 0.02517, "loss_rpn_cls": 0.22509, "loss_rpn_bbox": 0.13566, "loss_cls": 0.37611, "acc": 94.42993, "loss_bbox": 0.18692, "loss_mask": 0.76055, "loss": 1.68432, "time": 1.01462}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 14508, "data_time": 0.02473, "loss_rpn_cls": 0.18542, "loss_rpn_bbox": 0.12595, "loss_cls": 0.39238, "acc": 93.77319, "loss_bbox": 0.21254, "loss_mask": 0.71007, "loss": 1.62636, "time": 1.13937}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 14508, "data_time": 0.02524, "loss_rpn_cls": 0.15713, "loss_rpn_bbox": 0.11548, "loss_cls": 0.38835, "acc": 93.58252, "loss_bbox": 0.22499, "loss_mask": 0.68077, "loss": 1.56672, "time": 1.04745}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 14591, "data_time": 0.03817, "loss_rpn_cls": 0.12744, "loss_rpn_bbox": 0.10401, "loss_cls": 0.42227, "acc": 92.73633, "loss_bbox": 0.26194, "loss_mask": 0.63866, "loss": 1.55433, "time": 1.09976}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 14591, "data_time": 0.02723, "loss_rpn_cls": 0.10893, "loss_rpn_bbox": 0.09833, "loss_cls": 0.42483, "acc": 92.20312, "loss_bbox": 0.28474, "loss_mask": 0.60685, "loss": 1.52368, "time": 1.11617}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 14707, "data_time": 0.03155, "loss_rpn_cls": 0.10476, "loss_rpn_bbox": 0.09747, "loss_cls": 0.45757, "acc": 91.10425, "loss_bbox": 0.32284, "loss_mask": 0.57544, "loss": 1.55807, "time": 1.13403}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 14713, "data_time": 0.02703, "loss_rpn_cls": 0.09327, "loss_rpn_bbox": 0.09211, "loss_cls": 0.45002, "acc": 90.87183, "loss_bbox": 0.33498, "loss_mask": 0.54559, "loss": 1.51598, "time": 1.12701}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 14713, "data_time": 0.02249, "loss_rpn_cls": 0.08718, "loss_rpn_bbox": 0.08796, "loss_cls": 0.45783, "acc": 90.37817, "loss_bbox": 0.3527, "loss_mask": 0.51665, "loss": 1.50231, "time": 1.12487}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 14774, "data_time": 0.02432, "loss_rpn_cls": 0.08508, "loss_rpn_bbox": 0.08269, "loss_cls": 0.44825, "acc": 90.11938, "loss_bbox": 0.36463, "loss_mask": 0.4763, "loss": 1.45695, "time": 1.14674}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 14798, "data_time": 0.02133, "loss_rpn_cls": 0.08233, "loss_rpn_bbox": 0.08341, "loss_cls": 0.43833, "acc": 89.70654, "loss_bbox": 0.37123, "loss_mask": 0.45579, "loss": 1.4311, "time": 1.16239}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 14833, "data_time": 0.03248, "loss_rpn_cls": 0.07789, "loss_rpn_bbox": 0.08587, "loss_cls": 0.42948, "acc": 89.52344, "loss_bbox": 0.37982, "loss_mask": 0.43621, "loss": 1.40927, "time": 1.1755}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 14833, "data_time": 0.02562, "loss_rpn_cls": 0.07727, "loss_rpn_bbox": 0.08433, "loss_cls": 0.41657, "acc": 89.33667, "loss_bbox": 0.37887, "loss_mask": 0.42451, "loss": 1.38155, "time": 1.17694}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 14833, "data_time": 0.02419, "loss_rpn_cls": 0.0743, "loss_rpn_bbox": 0.08202, "loss_cls": 0.4048, "acc": 89.51709, "loss_bbox": 0.37079, "loss_mask": 0.41253, "loss": 1.34444, "time": 1.17313}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 14833, "data_time": 0.03156, "loss_rpn_cls": 0.06987, "loss_rpn_bbox": 0.08259, "loss_cls": 0.40984, "acc": 89.16211, "loss_bbox": 0.37955, "loss_mask": 0.40877, "loss": 1.35062, "time": 1.1685}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 14841, "data_time": 0.02948, "loss_rpn_cls": 0.0692, "loss_rpn_bbox": 0.07851, "loss_cls": 0.376, "acc": 89.77148, "loss_bbox": 0.36317, "loss_mask": 0.39903, "loss": 1.28591, "time": 1.17115}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 14841, "data_time": 0.02676, "loss_rpn_cls": 0.06761, "loss_rpn_bbox": 0.07758, "loss_cls": 0.3834, "acc": 89.52026, "loss_bbox": 0.3703, "loss_mask": 0.40104, "loss": 1.29992, "time": 1.17225}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 14859, "data_time": 0.02646, "loss_rpn_cls": 0.06895, "loss_rpn_bbox": 0.07656, "loss_cls": 0.37806, "acc": 89.63208, "loss_bbox": 0.3578, "loss_mask": 0.38542, "loss": 1.2668, "time": 1.17289}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 14859, "data_time": 0.03077, "loss_rpn_cls": 0.06713, "loss_rpn_bbox": 0.07529, "loss_cls": 0.36139, "acc": 89.92041, "loss_bbox": 0.35159, "loss_mask": 0.37745, "loss": 1.23286, "time": 1.20096}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 14859, "data_time": 0.02703, "loss_rpn_cls": 0.06422, "loss_rpn_bbox": 0.07412, "loss_cls": 0.36371, "acc": 89.73828, "loss_bbox": 0.35492, "loss_mask": 0.376, "loss": 1.23297, "time": 1.20557}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 14859, "data_time": 0.03117, "loss_rpn_cls": 0.05943, "loss_rpn_bbox": 0.07237, "loss_cls": 0.35027, "acc": 90.04028, "loss_bbox": 0.34551, "loss_mask": 0.369, "loss": 1.19658, "time": 1.18805}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 14859, "data_time": 0.03187, "loss_rpn_cls": 0.0624, "loss_rpn_bbox": 0.07848, "loss_cls": 0.36322, "acc": 89.48291, "loss_bbox": 0.3663, "loss_mask": 0.36889, "loss": 1.23931, "time": 1.2083}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 14859, "data_time": 0.03314, "loss_rpn_cls": 0.06219, "loss_rpn_bbox": 0.07881, "loss_cls": 0.35159, "acc": 89.62598, "loss_bbox": 0.36276, "loss_mask": 0.36484, "loss": 1.22019, "time": 1.20937}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 14859, "data_time": 0.0302, "loss_rpn_cls": 0.05705, "loss_rpn_bbox": 0.06962, "loss_cls": 0.34375, "acc": 90.13525, "loss_bbox": 0.34114, "loss_mask": 0.3583, "loss": 1.16986, "time": 1.25758}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 14859, "data_time": 0.03183, "loss_rpn_cls": 0.05724, "loss_rpn_bbox": 0.07198, "loss_cls": 0.33952, "acc": 90.12427, "loss_bbox": 0.3394, "loss_mask": 0.35507, "loss": 1.16321, "time": 1.18003}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 14871, "data_time": 0.02768, "loss_rpn_cls": 0.06592, "loss_rpn_bbox": 0.07711, "loss_cls": 0.34333, "acc": 89.87183, "loss_bbox": 0.34785, "loss_mask": 0.34825, "loss": 1.18246, "time": 1.16862}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 14871, "data_time": 0.02704, "loss_rpn_cls": 0.0605, "loss_rpn_bbox": 0.0708, "loss_cls": 0.32462, "acc": 90.47095, "loss_bbox": 0.32796, "loss_mask": 0.34151, "loss": 1.12539, "time": 1.16575}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 14871, "data_time": 0.02581, "loss_rpn_cls": 0.0574, "loss_rpn_bbox": 0.07234, "loss_cls": 0.33345, "acc": 89.95386, "loss_bbox": 0.3474, "loss_mask": 0.33668, "loss": 1.14726, "time": 1.17827}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 14871, "data_time": 0.03661, "loss_rpn_cls": 0.06301, "loss_rpn_bbox": 0.07828, "loss_cls": 0.3237, "acc": 90.26123, "loss_bbox": 0.33893, "loss_mask": 0.34414, "loss": 1.14806, "time": 1.21706}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.0002, "memory": 14871, "data_time": 0.03794, "loss_rpn_cls": 0.05385, "loss_rpn_bbox": 0.0705, "loss_cls": 0.32351, "acc": 90.25, "loss_bbox": 0.33682, "loss_mask": 0.34048, "loss": 1.12517, "time": 1.19905}
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