{"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_a3',\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_a3_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_a3_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_recnext_a3_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 6231, "data_time": 0.10068, "loss_rpn_cls": 0.57436, "loss_rpn_bbox": 0.26812, "loss_cls": 2.14913, "acc": 64.50098, "loss_bbox": 0.02537, "loss_mask": 3.90148, "loss": 6.91846, "time": 0.87335}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 6431, "data_time": 0.02251, "loss_rpn_cls": 0.31697, "loss_rpn_bbox": 0.17772, "loss_cls": 0.4022, "acc": 95.72266, "loss_bbox": 0.13469, "loss_mask": 1.15202, "loss": 2.18359, "time": 0.75131}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 6431, "data_time": 0.02511, "loss_rpn_cls": 0.22344, "loss_rpn_bbox": 0.14206, "loss_cls": 0.38461, "acc": 94.2937, "loss_bbox": 0.19069, "loss_mask": 0.77815, "loss": 1.71895, "time": 0.78435}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 6431, "data_time": 0.02683, "loss_rpn_cls": 0.18245, "loss_rpn_bbox": 0.1301, "loss_cls": 0.39794, "acc": 93.66699, "loss_bbox": 0.21652, "loss_mask": 0.71531, "loss": 1.64232, "time": 0.80877}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 6431, "data_time": 0.02197, "loss_rpn_cls": 0.15352, "loss_rpn_bbox": 0.11943, "loss_cls": 0.40628, "acc": 93.29053, "loss_bbox": 0.23534, "loss_mask": 0.68189, "loss": 1.59646, "time": 0.79428}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 6431, "data_time": 0.0309, "loss_rpn_cls": 0.12594, "loss_rpn_bbox": 0.10685, "loss_cls": 0.42256, "acc": 92.70459, "loss_bbox": 0.26074, "loss_mask": 0.65135, "loss": 1.56744, "time": 0.82147}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 6463, "data_time": 0.02355, "loss_rpn_cls": 0.11054, "loss_rpn_bbox": 0.10013, "loss_cls": 0.43538, "acc": 92.04004, "loss_bbox": 0.29059, "loss_mask": 0.61631, "loss": 1.55295, "time": 0.82655}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 6544, "data_time": 0.02759, "loss_rpn_cls": 0.11136, "loss_rpn_bbox": 0.09942, "loss_cls": 0.46423, "acc": 91.08154, "loss_bbox": 0.32187, "loss_mask": 0.57896, "loss": 1.57583, "time": 0.86943}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 6575, "data_time": 0.02373, "loss_rpn_cls": 0.09864, "loss_rpn_bbox": 0.09359, "loss_cls": 0.45037, "acc": 90.90601, "loss_bbox": 0.33065, "loss_mask": 0.54725, "loss": 1.52051, "time": 0.8383}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 6575, "data_time": 0.02003, "loss_rpn_cls": 0.0927, "loss_rpn_bbox": 0.08966, "loss_cls": 0.45864, "acc": 90.57153, "loss_bbox": 0.34392, "loss_mask": 0.51832, "loss": 1.50324, "time": 0.86487}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 6589, "data_time": 0.02106, "loss_rpn_cls": 0.09105, "loss_rpn_bbox": 0.08418, "loss_cls": 0.44723, "acc": 90.36279, "loss_bbox": 0.34811, "loss_mask": 0.48591, "loss": 1.45647, "time": 0.85878}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 6598, "data_time": 0.02081, "loss_rpn_cls": 0.0887, "loss_rpn_bbox": 0.08527, "loss_cls": 0.44427, "acc": 90.07056, "loss_bbox": 0.351, "loss_mask": 0.47004, "loss": 1.43928, "time": 0.88779}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 6662, "data_time": 0.02716, "loss_rpn_cls": 0.08578, "loss_rpn_bbox": 0.08771, "loss_cls": 0.44242, "acc": 89.81421, "loss_bbox": 0.36359, "loss_mask": 0.44966, "loss": 1.42916, "time": 0.89045}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 6662, "data_time": 0.02179, "loss_rpn_cls": 0.08503, "loss_rpn_bbox": 0.08553, "loss_cls": 0.42594, "acc": 89.59009, "loss_bbox": 0.36341, "loss_mask": 0.43725, "loss": 1.39715, "time": 0.90221}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 6662, "data_time": 0.02055, "loss_rpn_cls": 0.08278, "loss_rpn_bbox": 0.08367, "loss_cls": 0.41435, "acc": 89.7793, "loss_bbox": 0.35664, "loss_mask": 0.42321, "loss": 1.36064, "time": 0.915}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 6667, "data_time": 0.02692, "loss_rpn_cls": 0.07605, "loss_rpn_bbox": 0.0839, "loss_cls": 0.41687, "acc": 89.5105, "loss_bbox": 0.36982, "loss_mask": 0.42569, "loss": 1.37233, "time": 0.90798}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 6667, "data_time": 0.02597, "loss_rpn_cls": 0.07614, "loss_rpn_bbox": 0.08119, "loss_cls": 0.38477, "acc": 90.04492, "loss_bbox": 0.3481, "loss_mask": 0.41135, "loss": 1.30154, "time": 0.95241}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 6667, "data_time": 0.02412, "loss_rpn_cls": 0.07507, "loss_rpn_bbox": 0.07911, "loss_cls": 0.3983, "acc": 89.69604, "loss_bbox": 0.36063, "loss_mask": 0.40974, "loss": 1.32285, "time": 0.90991}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 6667, "data_time": 0.02284, "loss_rpn_cls": 0.07428, "loss_rpn_bbox": 0.07859, "loss_cls": 0.39547, "acc": 89.81934, "loss_bbox": 0.35312, "loss_mask": 0.39783, "loss": 1.2993, "time": 0.89793}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 6678, "data_time": 0.0263, "loss_rpn_cls": 0.07431, "loss_rpn_bbox": 0.0774, "loss_cls": 0.37928, "acc": 90.06714, "loss_bbox": 0.34339, "loss_mask": 0.39666, "loss": 1.27104, "time": 0.91631}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 6678, "data_time": 0.02431, "loss_rpn_cls": 0.07203, "loss_rpn_bbox": 0.07623, "loss_cls": 0.38152, "acc": 89.88989, "loss_bbox": 0.34764, "loss_mask": 0.39088, "loss": 1.2683, "time": 0.90883}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 6678, "data_time": 0.02734, "loss_rpn_cls": 0.06434, "loss_rpn_bbox": 0.07409, "loss_cls": 0.37352, "acc": 89.96118, "loss_bbox": 0.34296, "loss_mask": 0.38434, "loss": 1.23925, "time": 0.89056}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 6678, "data_time": 0.02839, "loss_rpn_cls": 0.07102, "loss_rpn_bbox": 0.08097, "loss_cls": 0.38186, "acc": 89.50391, "loss_bbox": 0.35996, "loss_mask": 0.38178, "loss": 1.27559, "time": 0.90794}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 6678, "data_time": 0.03015, "loss_rpn_cls": 0.072, "loss_rpn_bbox": 0.08146, "loss_cls": 0.37047, "acc": 89.60864, "loss_bbox": 0.35837, "loss_mask": 0.38219, "loss": 1.26449, "time": 1.04513}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 6678, "data_time": 0.02602, "loss_rpn_cls": 0.06329, "loss_rpn_bbox": 0.07157, "loss_cls": 0.35666, "acc": 90.16846, "loss_bbox": 0.3353, "loss_mask": 0.37606, "loss": 1.20288, "time": 0.90037}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 6678, "data_time": 0.02856, "loss_rpn_cls": 0.06456, "loss_rpn_bbox": 0.07345, "loss_cls": 0.35843, "acc": 90.03955, "loss_bbox": 0.33871, "loss_mask": 0.36861, "loss": 1.20376, "time": 0.95081}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 6678, "data_time": 0.02583, "loss_rpn_cls": 0.07141, "loss_rpn_bbox": 0.07917, "loss_cls": 0.36621, "acc": 89.7417, "loss_bbox": 0.34587, "loss_mask": 0.36199, "loss": 1.22465, "time": 0.9232}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 6678, "data_time": 0.0261, "loss_rpn_cls": 0.06702, "loss_rpn_bbox": 0.07317, "loss_cls": 0.33977, "acc": 90.6167, "loss_bbox": 0.32253, "loss_mask": 0.35869, "loss": 1.16119, "time": 0.87048}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0002, "memory": 6678, "data_time": 0.02306, "loss_rpn_cls": 0.06533, "loss_rpn_bbox": 0.07527, "loss_cls": 0.35196, "acc": 90.00317, "loss_bbox": 0.34535, "loss_mask": 0.35373, "loss": 1.19165, "time": 0.89974}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0002, "memory": 6678, "data_time": 0.03286, "loss_rpn_cls": 0.0707, "loss_rpn_bbox": 0.08104, "loss_cls": 0.33674, "acc": 90.30176, "loss_bbox": 0.33672, "loss_mask": 0.35772, "loss": 1.18292, "time": 0.87958}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.0002, "memory": 6678, "data_time": 0.03456, "loss_rpn_cls": 0.06159, "loss_rpn_bbox": 0.07306, "loss_cls": 0.33925, "acc": 90.29126, "loss_bbox": 0.33453, "loss_mask": 0.35802, "loss": 1.16646, "time": 0.86665}
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