{"env_info": "sys.platform: linux\nPython: 3.8.16 | packaged by conda-forge | (default, Feb  1 2023, 16:01:55) [GCC 11.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100 80GB PCIe\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.8, V11.8.89\nGCC: gcc (Ubuntu 11.1.0-1ubuntu1~20.04) 11.1.0\nPyTorch: 2.0.0+cu118\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201703\n  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\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 11.8\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;-gencode;arch=compute_90,code=sm_90\n  - CuDNN 8.7\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -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 -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.0+cu118\nOpenCV: 4.7.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 11.1\nMMCV CUDA Compiler: 11.8\nMMDetection: 2.28.2+", "config": "model = dict(\n    type='MaskRCNN',\n    neck=dict(\n        type='FPN',\n        in_channels=[96, 192, 384, 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)),\n    pretrained='pretrained/transnext_base/transnext_base.pth',\n    backbone=dict(type='transnext_base', pretrain_size=224, img_size=800))\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(\n    type='AdamW',\n    lr=0.0001,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    paramwise_cfg=dict(\n        custom_keys=dict(\n            query_embedding=dict(decay_mult=0.0),\n            relative_pos_bias_local=dict(decay_mult=0.0),\n            cpb=dict(decay_mult=0.0),\n            temperature=dict(decay_mult=0.0),\n            norm=dict(decay_mult=0.0))))\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)\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)]\nfp16 = dict(loss_scale=512.0)\nwork_dir = './work_dirs/mask_rcnn_transnext_base_800_fpn_1x_coco'\ngpu_ids = range(0, 8)\n", "seed": null, "exp_name": "mask_rcnn_transnext_base_800_fpn_1x_coco.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 1e-05, "memory": 71064, "data_time": 0.79631, "loss_rpn_cls": 0.68149, "loss_rpn_bbox": 0.10879, "loss_cls": 3.18976, "acc": 49.48975, "loss_bbox": 0.02148, "loss_mask": 0.8776, "loss": 4.87913, "time": 5.43637}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 2e-05, "memory": 71534, "data_time": 0.04292, "loss_rpn_cls": 0.44196, "loss_rpn_bbox": 0.10567, "loss_cls": 0.59596, "acc": 95.8335, "loss_bbox": 0.1263, "loss_mask": 0.76042, "loss": 2.03031, "time": 3.90087}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 3e-05, "memory": 71534, "data_time": 0.04019, "loss_rpn_cls": 0.27364, "loss_rpn_bbox": 0.10237, "loss_cls": 0.42705, "acc": 94.57983, "loss_bbox": 0.18018, "loss_mask": 0.70397, "loss": 1.6872, "time": 3.8826}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 4e-05, "memory": 71534, "data_time": 0.0381, "loss_rpn_cls": 0.22889, "loss_rpn_bbox": 0.10438, "loss_cls": 0.43273, "acc": 93.75049, "loss_bbox": 0.21148, "loss_mask": 0.68943, "loss": 1.6669, "time": 3.89218}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 5e-05, "memory": 71534, "data_time": 0.03916, "loss_rpn_cls": 0.18674, "loss_rpn_bbox": 0.10417, "loss_cls": 0.40791, "acc": 93.62524, "loss_bbox": 0.21854, "loss_mask": 0.66862, "loss": 1.58598, "time": 3.86255}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 6e-05, "memory": 71534, "data_time": 0.04616, "loss_rpn_cls": 0.138, "loss_rpn_bbox": 0.09552, "loss_cls": 0.42273, "acc": 92.62988, "loss_bbox": 0.26146, "loss_mask": 0.63763, "loss": 1.55534, "time": 3.87753}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 7e-05, "memory": 71591, "data_time": 0.04052, "loss_rpn_cls": 0.1101, "loss_rpn_bbox": 0.09235, "loss_cls": 0.43863, "acc": 92.03101, "loss_bbox": 0.29485, "loss_mask": 0.61133, "loss": 1.54726, "time": 3.88404}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 8e-05, "memory": 71657, "data_time": 0.04429, "loss_rpn_cls": 0.10476, "loss_rpn_bbox": 0.09308, "loss_cls": 0.46474, "acc": 90.96582, "loss_bbox": 0.33716, "loss_mask": 0.57727, "loss": 1.57699, "time": 3.89008}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 71657, "data_time": 0.0376, "loss_rpn_cls": 0.08999, "loss_rpn_bbox": 0.08926, "loss_cls": 0.43871, "acc": 90.69897, "loss_bbox": 0.35051, "loss_mask": 0.53317, "loss": 1.50165, "time": 3.89198}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 71679, "data_time": 0.03434, "loss_rpn_cls": 0.08297, "loss_rpn_bbox": 0.08608, "loss_cls": 0.44004, "acc": 89.96289, "loss_bbox": 0.37037, "loss_mask": 0.50203, "loss": 1.48148, "time": 3.85551}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 71679, "data_time": 0.03608, "loss_rpn_cls": 0.07793, "loss_rpn_bbox": 0.0804, "loss_cls": 0.41698, "acc": 89.84302, "loss_bbox": 0.37525, "loss_mask": 0.46153, "loss": 1.4121, "time": 3.85079}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 71682, "data_time": 0.03193, "loss_rpn_cls": 0.07605, "loss_rpn_bbox": 0.08224, "loss_cls": 0.39823, "acc": 89.4917, "loss_bbox": 0.37888, "loss_mask": 0.44008, "loss": 1.37548, "time": 3.88066}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 71684, "data_time": 0.03921, "loss_rpn_cls": 0.07109, "loss_rpn_bbox": 0.08618, "loss_cls": 0.38616, "acc": 89.36816, "loss_bbox": 0.3859, "loss_mask": 0.42998, "loss": 1.3593, "time": 3.85962}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 71708, "data_time": 0.03411, "loss_rpn_cls": 0.06969, "loss_rpn_bbox": 0.08087, "loss_cls": 0.37445, "acc": 89.13208, "loss_bbox": 0.39426, "loss_mask": 0.41967, "loss": 1.33894, "time": 3.89346}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 71708, "data_time": 0.03138, "loss_rpn_cls": 0.06938, "loss_rpn_bbox": 0.08129, "loss_cls": 0.36715, "acc": 89.26538, "loss_bbox": 0.38273, "loss_mask": 0.40649, "loss": 1.30703, "time": 3.87161}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 71708, "data_time": 0.03631, "loss_rpn_cls": 0.06464, "loss_rpn_bbox": 0.08007, "loss_cls": 0.36922, "acc": 89.01147, "loss_bbox": 0.38635, "loss_mask": 0.39865, "loss": 1.29893, "time": 3.87162}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 71708, "data_time": 0.03621, "loss_rpn_cls": 0.0637, "loss_rpn_bbox": 0.07712, "loss_cls": 0.336, "acc": 89.77954, "loss_bbox": 0.36986, "loss_mask": 0.38545, "loss": 1.23213, "time": 3.83599}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 71708, "data_time": 0.03389, "loss_rpn_cls": 0.06079, "loss_rpn_bbox": 0.07639, "loss_cls": 0.34397, "acc": 89.37915, "loss_bbox": 0.37804, "loss_mask": 0.38646, "loss": 1.24564, "time": 3.8649}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0001, "memory": 71708, "data_time": 0.03459, "loss_rpn_cls": 0.06181, "loss_rpn_bbox": 0.07414, "loss_cls": 0.34146, "acc": 89.59912, "loss_bbox": 0.36882, "loss_mask": 0.37201, "loss": 1.21823, "time": 3.8483}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0001, "memory": 71708, "data_time": 0.03699, "loss_rpn_cls": 0.05897, "loss_rpn_bbox": 0.07348, "loss_cls": 0.33049, "acc": 89.75366, "loss_bbox": 0.36068, "loss_mask": 0.36459, "loss": 1.18821, "time": 3.86981}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0001, "memory": 71708, "data_time": 0.03644, "loss_rpn_cls": 0.05799, "loss_rpn_bbox": 0.07309, "loss_cls": 0.32978, "acc": 89.77563, "loss_bbox": 0.35931, "loss_mask": 0.36278, "loss": 1.18296, "time": 3.86411}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0001, "memory": 71708, "data_time": 0.03763, "loss_rpn_cls": 0.05306, "loss_rpn_bbox": 0.06987, "loss_cls": 0.31928, "acc": 89.97754, "loss_bbox": 0.35345, "loss_mask": 0.35476, "loss": 1.15041, "time": 3.8598}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0001, "memory": 71708, "data_time": 0.04014, "loss_rpn_cls": 0.05619, "loss_rpn_bbox": 0.07649, "loss_cls": 0.32965, "acc": 89.50586, "loss_bbox": 0.37408, "loss_mask": 0.35325, "loss": 1.18966, "time": 3.86181}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0001, "memory": 71708, "data_time": 0.04401, "loss_rpn_cls": 0.05686, "loss_rpn_bbox": 0.07665, "loss_cls": 0.31586, "acc": 89.75977, "loss_bbox": 0.36439, "loss_mask": 0.35166, "loss": 1.16542, "time": 3.85025}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0001, "memory": 71711, "data_time": 0.03932, "loss_rpn_cls": 0.05056, "loss_rpn_bbox": 0.06747, "loss_cls": 0.30835, "acc": 90.20337, "loss_bbox": 0.34338, "loss_mask": 0.34399, "loss": 1.11375, "time": 3.92322}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0001, "memory": 71711, "data_time": 0.03916, "loss_rpn_cls": 0.0495, "loss_rpn_bbox": 0.0694, "loss_cls": 0.30529, "acc": 90.29321, "loss_bbox": 0.33912, "loss_mask": 0.33829, "loss": 1.1016, "time": 3.99694}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0001, "memory": 71711, "data_time": 0.03638, "loss_rpn_cls": 0.05634, "loss_rpn_bbox": 0.07422, "loss_cls": 0.31681, "acc": 89.73242, "loss_bbox": 0.34795, "loss_mask": 0.33478, "loss": 1.13009, "time": 3.9145}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0001, "memory": 71717, "data_time": 0.0362, "loss_rpn_cls": 0.05354, "loss_rpn_bbox": 0.0684, "loss_cls": 0.28888, "acc": 90.78784, "loss_bbox": 0.32632, "loss_mask": 0.32145, "loss": 1.0586, "time": 3.8669}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.0001, "memory": 71717, "data_time": 0.03506, "loss_rpn_cls": 0.05263, "loss_rpn_bbox": 0.06943, "loss_cls": 0.30418, "acc": 90.12524, "loss_bbox": 0.34528, "loss_mask": 0.32201, "loss": 1.09353, "time": 3.87256}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.0001, "memory": 71717, "data_time": 0.0411, "loss_rpn_cls": 0.05552, "loss_rpn_bbox": 0.07497, "loss_cls": 0.30017, "acc": 90.22095, "loss_bbox": 0.34367, "loss_mask": 0.32487, "loss": 1.0992, "time": 3.91334}
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