{"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_a4',\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_a4_distill_300e.pth')),\n    neck=dict(\n        type='FPN',\n        in_channels=[64, 128, 256, 512],\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_a4_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_recnext_a4_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 9297, "data_time": 0.09813, "loss_rpn_cls": 0.65215, "loss_rpn_bbox": 0.25808, "loss_cls": 2.45463, "acc": 65.70312, "loss_bbox": 0.0243, "loss_mask": 4.86689, "loss": 8.25606, "time": 1.00475}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 9420, "data_time": 0.02382, "loss_rpn_cls": 0.28644, "loss_rpn_bbox": 0.19736, "loss_cls": 0.35406, "acc": 96.43018, "loss_bbox": 0.09636, "loss_mask": 1.42995, "loss": 2.36417, "time": 0.86065}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 9420, "data_time": 0.02574, "loss_rpn_cls": 0.21543, "loss_rpn_bbox": 0.15029, "loss_cls": 0.39283, "acc": 94.30591, "loss_bbox": 0.19243, "loss_mask": 0.8119, "loss": 1.76288, "time": 0.89845}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 9420, "data_time": 0.02499, "loss_rpn_cls": 0.18394, "loss_rpn_bbox": 0.13178, "loss_cls": 0.40352, "acc": 93.69702, "loss_bbox": 0.21816, "loss_mask": 0.72043, "loss": 1.65783, "time": 0.92684}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 9420, "data_time": 0.02657, "loss_rpn_cls": 0.15821, "loss_rpn_bbox": 0.12062, "loss_cls": 0.4172, "acc": 93.29614, "loss_bbox": 0.23726, "loss_mask": 0.68585, "loss": 1.61914, "time": 1.0092}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 9420, "data_time": 0.03745, "loss_rpn_cls": 0.12871, "loss_rpn_bbox": 0.10732, "loss_cls": 0.44762, "acc": 92.48267, "loss_bbox": 0.27387, "loss_mask": 0.65328, "loss": 1.61081, "time": 0.96303}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 9480, "data_time": 0.02904, "loss_rpn_cls": 0.11235, "loss_rpn_bbox": 0.10025, "loss_cls": 0.44684, "acc": 92.15112, "loss_bbox": 0.28817, "loss_mask": 0.61736, "loss": 1.56497, "time": 0.98829}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 9644, "data_time": 0.03013, "loss_rpn_cls": 0.11158, "loss_rpn_bbox": 0.09833, "loss_cls": 0.47891, "acc": 91.09326, "loss_bbox": 0.32456, "loss_mask": 0.58126, "loss": 1.59463, "time": 1.01776}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 9649, "data_time": 0.02596, "loss_rpn_cls": 0.09777, "loss_rpn_bbox": 0.09422, "loss_cls": 0.4732, "acc": 90.85986, "loss_bbox": 0.33483, "loss_mask": 0.54587, "loss": 1.54589, "time": 0.99764}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 9649, "data_time": 0.02281, "loss_rpn_cls": 0.09231, "loss_rpn_bbox": 0.08992, "loss_cls": 0.47648, "acc": 90.37964, "loss_bbox": 0.35556, "loss_mask": 0.5158, "loss": 1.53007, "time": 1.00458}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 9719, "data_time": 0.02461, "loss_rpn_cls": 0.0902, "loss_rpn_bbox": 0.08411, "loss_cls": 0.46026, "acc": 90.34131, "loss_bbox": 0.35574, "loss_mask": 0.48189, "loss": 1.47219, "time": 1.01175}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 9737, "data_time": 0.02218, "loss_rpn_cls": 0.0895, "loss_rpn_bbox": 0.08562, "loss_cls": 0.44884, "acc": 90.09473, "loss_bbox": 0.3577, "loss_mask": 0.46746, "loss": 1.44912, "time": 1.01447}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 9750, "data_time": 0.03371, "loss_rpn_cls": 0.08596, "loss_rpn_bbox": 0.08863, "loss_cls": 0.44817, "acc": 89.71826, "loss_bbox": 0.3677, "loss_mask": 0.44766, "loss": 1.43812, "time": 1.01112}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 9767, "data_time": 0.02616, "loss_rpn_cls": 0.08333, "loss_rpn_bbox": 0.08546, "loss_cls": 0.43984, "acc": 89.41455, "loss_bbox": 0.37745, "loss_mask": 0.43895, "loss": 1.42502, "time": 1.05126}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 9767, "data_time": 0.02332, "loss_rpn_cls": 0.08145, "loss_rpn_bbox": 0.0843, "loss_cls": 0.42428, "acc": 89.65698, "loss_bbox": 0.362, "loss_mask": 0.42823, "loss": 1.38025, "time": 1.03855}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 9767, "data_time": 0.02963, "loss_rpn_cls": 0.07571, "loss_rpn_bbox": 0.08348, "loss_cls": 0.42229, "acc": 89.44604, "loss_bbox": 0.37335, "loss_mask": 0.42576, "loss": 1.38058, "time": 1.04916}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 9767, "data_time": 0.02866, "loss_rpn_cls": 0.07357, "loss_rpn_bbox": 0.07981, "loss_cls": 0.39154, "acc": 89.91357, "loss_bbox": 0.35579, "loss_mask": 0.40833, "loss": 1.30904, "time": 1.03885}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 9767, "data_time": 0.02943, "loss_rpn_cls": 0.07204, "loss_rpn_bbox": 0.07846, "loss_cls": 0.40779, "acc": 89.5564, "loss_bbox": 0.36926, "loss_mask": 0.41275, "loss": 1.34031, "time": 1.03775}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 9767, "data_time": 0.02592, "loss_rpn_cls": 0.07187, "loss_rpn_bbox": 0.07877, "loss_cls": 0.40491, "acc": 89.66064, "loss_bbox": 0.36138, "loss_mask": 0.39487, "loss": 1.3118, "time": 1.04579}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 9767, "data_time": 0.02946, "loss_rpn_cls": 0.07187, "loss_rpn_bbox": 0.07668, "loss_cls": 0.38209, "acc": 89.92041, "loss_bbox": 0.35221, "loss_mask": 0.38828, "loss": 1.27112, "time": 1.03497}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 9767, "data_time": 0.02623, "loss_rpn_cls": 0.06855, "loss_rpn_bbox": 0.07553, "loss_cls": 0.38998, "acc": 89.64209, "loss_bbox": 0.35536, "loss_mask": 0.3877, "loss": 1.27713, "time": 1.06419}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 9767, "data_time": 0.03156, "loss_rpn_cls": 0.06326, "loss_rpn_bbox": 0.07328, "loss_cls": 0.37385, "acc": 89.92944, "loss_bbox": 0.34882, "loss_mask": 0.38257, "loss": 1.24178, "time": 1.04621}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 9767, "data_time": 0.02814, "loss_rpn_cls": 0.06791, "loss_rpn_bbox": 0.08021, "loss_cls": 0.38448, "acc": 89.35864, "loss_bbox": 0.36585, "loss_mask": 0.3771, "loss": 1.27555, "time": 1.04349}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 9767, "data_time": 0.03255, "loss_rpn_cls": 0.06848, "loss_rpn_bbox": 0.07966, "loss_cls": 0.37294, "acc": 89.52686, "loss_bbox": 0.36513, "loss_mask": 0.37478, "loss": 1.261, "time": 1.0578}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 9767, "data_time": 0.02956, "loss_rpn_cls": 0.06121, "loss_rpn_bbox": 0.07083, "loss_cls": 0.35998, "acc": 90.19336, "loss_bbox": 0.33812, "loss_mask": 0.3659, "loss": 1.19604, "time": 1.03864}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 9767, "data_time": 0.03004, "loss_rpn_cls": 0.06133, "loss_rpn_bbox": 0.07305, "loss_cls": 0.35659, "acc": 90.12598, "loss_bbox": 0.34375, "loss_mask": 0.36492, "loss": 1.19964, "time": 1.04038}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 9767, "data_time": 0.02639, "loss_rpn_cls": 0.0696, "loss_rpn_bbox": 0.07836, "loss_cls": 0.36456, "acc": 89.69019, "loss_bbox": 0.35144, "loss_mask": 0.36057, "loss": 1.22452, "time": 1.05228}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 9767, "data_time": 0.02716, "loss_rpn_cls": 0.06457, "loss_rpn_bbox": 0.07194, "loss_cls": 0.34275, "acc": 90.41992, "loss_bbox": 0.32968, "loss_mask": 0.35098, "loss": 1.15993, "time": 1.07771}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 9767, "data_time": 0.02503, "loss_rpn_cls": 0.06205, "loss_rpn_bbox": 0.07399, "loss_cls": 0.35808, "acc": 89.68457, "loss_bbox": 0.35266, "loss_mask": 0.34783, "loss": 1.19461, "time": 1.06274}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 9767, "data_time": 0.0332, "loss_rpn_cls": 0.0686, "loss_rpn_bbox": 0.08001, "loss_cls": 0.34095, "acc": 90.11523, "loss_bbox": 0.34352, "loss_mask": 0.35385, "loss": 1.18692, "time": 1.03729}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.0002, "memory": 9767, "data_time": 0.0309, "loss_rpn_cls": 0.05792, "loss_rpn_bbox": 0.07215, "loss_cls": 0.34498, "acc": 90.04199, "loss_bbox": 0.34295, "loss_mask": 0.35196, "loss": 1.16996, "time": 1.19984}
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