{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0: Tesla PG503-216\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.2.r11.2/compiler.29618528_0\nGCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)\nPyTorch: 1.8.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.1\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_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_37,code=compute_37\n  - CuDNN 8.0.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/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 -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.0, 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=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.9.0\nOpenCV: 4.5.5\nMMCV: 1.4.5\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMRotate: 0.1.0+", "config": "dataset_type = 'DOTADataset'\ndata_root = '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/'\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),\n    dict(type='RResize', img_scale=(1024, 1024)),\n    dict(\n        type='RRandomFlip',\n        flip_ratio=[0.25, 0.25, 0.25],\n        direction=['horizontal', 'vertical', 'diagonal'],\n        version='le90'),\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'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1024, 1024),\n        flip=False,\n        transforms=[\n            dict(type='RResize'),\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'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=2,\n    train=dict(\n        type='DOTADataset',\n        ann_file=\n        '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True),\n            dict(type='RResize', img_scale=(1024, 1024)),\n            dict(\n                type='RRandomFlip',\n                flip_ratio=[0.25, 0.25, 0.25],\n                direction=['horizontal', 'vertical', 'diagonal'],\n                version='le90'),\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'])\n        ],\n        version='le90'),\n    val=dict(\n        type='DOTADataset',\n        ann_file=\n        '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/trainval/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1024, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='RResize'),\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'])\n                ])\n        ],\n        version='le90'),\n    test=dict(\n        type='DOTADataset',\n        ann_file='/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/test/images/',\n        img_prefix='/mnt/lustre/zhouyue.vendor/split_1024_dota1_0/test/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1024, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='RResize'),\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'])\n                ])\n        ],\n        version='le90'))\nevaluation = dict(interval=12, metric='mAP')\noptimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.3333333333333333,\n    step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=12)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nangle_version = 'le90'\nmodel = dict(\n    type='ReDet',\n    backbone=dict(\n        type='ReResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        style='pytorch',\n        pretrained='work_dirs/pretrain/re_resnet50_c8_batch256-25b16846.pth'),\n    neck=dict(\n        type='ReFPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        num_outs=5),\n    rpn_head=dict(\n        type='RotatedRPNHead',\n        in_channels=256,\n        feat_channels=256,\n        version='le90',\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(\n            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),\n    roi_head=dict(\n        type='RoITransRoIHead',\n        version='le90',\n        num_stages=2,\n        stage_loss_weights=[1, 1],\n        bbox_roi_extractor=[\n            dict(\n                type='SingleRoIExtractor',\n                roi_layer=dict(\n                    type='RoIAlign', output_size=7, sampling_ratio=0),\n                out_channels=256,\n                featmap_strides=[4, 8, 16, 32]),\n            dict(\n                type='RotatedSingleRoIExtractor',\n                roi_layer=dict(\n                    type='RiRoIAlignRotated',\n                    out_size=7,\n                    num_samples=2,\n                    num_orientations=8,\n                    clockwise=True),\n                out_channels=256,\n                featmap_strides=[4, 8, 16, 32])\n        ],\n        bbox_head=[\n            dict(\n                type='RotatedShared2FCBBoxHead',\n                in_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=15,\n                bbox_coder=dict(\n                    type='DeltaXYWHAHBBoxCoder',\n                    angle_range='le90',\n                    norm_factor=2,\n                    edge_swap=True,\n                    target_means=[0.0, 0.0, 0.0, 0.0, 0.0],\n                    target_stds=[0.1, 0.1, 0.2, 0.2, 1]),\n                reg_class_agnostic=True,\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,\n                               loss_weight=1.0)),\n            dict(\n                type='RotatedShared2FCBBoxHead',\n                in_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=15,\n                bbox_coder=dict(\n                    type='DeltaXYWHAOBBoxCoder',\n                    angle_range='le90',\n                    norm_factor=None,\n                    edge_swap=True,\n                    proj_xy=True,\n                    target_means=[0.0, 0.0, 0.0, 0.0, 0.0],\n                    target_stds=[0.05, 0.05, 0.1, 0.1, 0.5]),\n                reg_class_agnostic=False,\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))\n        ]),\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=0,\n            pos_weight=-1,\n            debug=False),\n        rpn_proposal=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=[\n            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=False,\n                    ignore_iof_thr=-1,\n                    iou_calculator=dict(type='BboxOverlaps2D')),\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                pos_weight=-1,\n                debug=False),\n            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=False,\n                    ignore_iof_thr=-1,\n                    iou_calculator=dict(type='RBboxOverlaps2D')),\n                sampler=dict(\n                    type='RRandomSampler',\n                    num=512,\n                    pos_fraction=0.25,\n                    neg_pos_ub=-1,\n                    add_gt_as_proposals=True),\n                pos_weight=-1,\n                debug=False)\n        ]),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            nms_pre=2000,\n            min_bbox_size=0,\n            score_thr=0.05,\n            nms=dict(iou_thr=0.1),\n            max_per_img=2000)))\nfp16 = dict(loss_scale='dynamic')\nwork_dir = 'work_dirs/redet_re50_refpn_fp16_1x_dota_le90'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 1592643219, "exp_name": "redet_re50_refpn_fp16_1x_dota_le90.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00199, "memory": 6359, "data_time": 2.97918, "loss_rpn_cls": 0.49897, "loss_rpn_bbox": 0.0816, "s0.loss_cls": 0.79192, "s0.acc": 87.08789, "s0.loss_bbox": 0.17453, "s1.loss_cls": 0.633, "s1.acc": 88.71875, "s1.loss_bbox": 0.10325, "loss": 2.28327, "grad_norm": NaN, "time": 3.56165}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00233, "memory": 6359, "data_time": 0.00852, "loss_rpn_cls": 0.35291, "loss_rpn_bbox": 0.10516, "s0.loss_cls": 0.45335, "s0.acc": 90.90039, "s0.loss_bbox": 0.3025, "s1.loss_cls": 0.31195, "s1.acc": 94.11328, "s1.loss_bbox": 0.14149, "loss": 1.66736, "grad_norm": 5.89955, "time": 0.57726}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00266, "memory": 6359, "data_time": 0.00735, "loss_rpn_cls": 0.28091, "loss_rpn_bbox": 0.0755, "s0.loss_cls": 0.37009, "s0.acc": 92.01562, "s0.loss_bbox": 0.25431, "s1.loss_cls": 0.2479, "s1.acc": 95.06836, "s1.loss_bbox": 0.13703, "loss": 1.36573, "grad_norm": 5.32893, "time": 0.5716}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00299, "memory": 6359, "data_time": 0.01034, "loss_rpn_cls": 0.18703, "loss_rpn_bbox": 0.04969, "s0.loss_cls": 0.37033, "s0.acc": 92.29297, "s0.loss_bbox": 0.22865, "s1.loss_cls": 0.23659, "s1.acc": 95.27539, "s1.loss_bbox": 0.18261, "loss": 1.2549, "grad_norm": 4.61307, "time": 0.57511}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00333, "memory": 6495, "data_time": 0.01564, "loss_rpn_cls": 0.19195, "loss_rpn_bbox": 0.06369, "s0.loss_cls": 0.40839, "s0.acc": 90.14258, "s0.loss_bbox": 0.28849, "s1.loss_cls": 0.27032, "s1.acc": 93.55759, "s1.loss_bbox": 0.2627, "loss": 1.48555, "grad_norm": 5.43333, "time": 0.59522}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00366, "memory": 6495, "data_time": 0.00968, "loss_rpn_cls": 0.19737, "loss_rpn_bbox": 0.07306, "s0.loss_cls": 0.38088, "s0.acc": 90.03711, "s0.loss_bbox": 0.26814, "s1.loss_cls": 0.27218, "s1.acc": 93.37033, "s1.loss_bbox": 0.27567, "loss": 1.46729, "grad_norm": 5.58807, "time": 0.56844}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00399, "memory": 6495, "data_time": 0.01095, "loss_rpn_cls": 0.18344, "loss_rpn_bbox": 0.07373, "s0.loss_cls": 0.41123, "s0.acc": 89.58008, "s0.loss_bbox": 0.29337, "s1.loss_cls": 0.24497, "s1.acc": 94.02556, "s1.loss_bbox": 0.25402, "loss": 1.46075, "grad_norm": 6.17622, "time": 0.58634}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00433, "memory": 6495, "data_time": 0.01513, "loss_rpn_cls": 0.14481, "loss_rpn_bbox": 0.06282, "s0.loss_cls": 0.3785, "s0.acc": 90.54883, "s0.loss_bbox": 0.27486, "s1.loss_cls": 0.22427, "s1.acc": 94.5293, "s1.loss_bbox": 0.23533, "loss": 1.32058, "grad_norm": 5.30472, "time": 0.59638}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00466, "memory": 6495, "data_time": 0.00873, "loss_rpn_cls": 0.13506, "loss_rpn_bbox": 0.06176, "s0.loss_cls": 0.34925, "s0.acc": 90.83594, "s0.loss_bbox": 0.24425, "s1.loss_cls": 0.23085, "s1.acc": 94.06352, "s1.loss_bbox": 0.26552, "loss": 1.28669, "grad_norm": 5.18008, "time": 0.57602}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00499, "memory": 6495, "data_time": 0.00793, "loss_rpn_cls": 0.13955, "loss_rpn_bbox": 0.09074, "s0.loss_cls": 0.37986, "s0.acc": 90.01172, "s0.loss_bbox": 0.22891, "s1.loss_cls": 0.30023, "s1.acc": 92.83541, "s1.loss_bbox": 0.29593, "loss": 1.43522, "grad_norm": 6.10188, "time": 0.57874}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.005, "memory": 6495, "data_time": 0.00739, "loss_rpn_cls": 0.11758, "loss_rpn_bbox": 0.07018, "s0.loss_cls": 0.38716, "s0.acc": 90.15039, "s0.loss_bbox": 0.22514, "s1.loss_cls": 0.25959, "s1.acc": 93.26615, "s1.loss_bbox": 0.28538, "loss": 1.34504, "grad_norm": 5.02652, "time": 0.60157}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.005, "memory": 6495, "data_time": 0.00663, "loss_rpn_cls": 0.1235, "loss_rpn_bbox": 0.08148, "s0.loss_cls": 0.37784, "s0.acc": 89.3125, "s0.loss_bbox": 0.31042, "s1.loss_cls": 0.22831, "s1.acc": 93.65246, "s1.loss_bbox": 0.27606, "loss": 1.39761, "grad_norm": 5.32568, "time": 0.57015}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.005, "memory": 6495, "data_time": 0.0081, "loss_rpn_cls": 0.09813, "loss_rpn_bbox": 0.05399, "s0.loss_cls": 0.39729, "s0.acc": 89.59375, "s0.loss_bbox": 0.29073, "s1.loss_cls": 0.24085, "s1.acc": 94.1952, "s1.loss_bbox": 0.27693, "loss": 1.35792, "grad_norm": 5.27261, "time": 0.57828}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.005, "memory": 6649, "data_time": 0.01209, "loss_rpn_cls": 0.10382, "loss_rpn_bbox": 0.0498, "s0.loss_cls": 0.32309, "s0.acc": 91.13672, "s0.loss_bbox": 0.2324, "s1.loss_cls": 0.19308, "s1.acc": 94.88534, "s1.loss_bbox": 0.25717, "loss": 1.15935, "grad_norm": 4.84434, "time": 0.58883}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.005, "memory": 6649, "data_time": 0.00842, "loss_rpn_cls": 0.09263, "loss_rpn_bbox": 0.08428, "s0.loss_cls": 0.36909, "s0.acc": 88.88867, "s0.loss_bbox": 0.27835, "s1.loss_cls": 0.23005, "s1.acc": 93.26462, "s1.loss_bbox": 0.34492, "loss": 1.39932, "grad_norm": 4.86716, "time": 0.56983}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.005, "memory": 6649, "data_time": 0.0083, "loss_rpn_cls": 0.10988, "loss_rpn_bbox": 0.08168, "s0.loss_cls": 0.34692, "s0.acc": 89.44336, "s0.loss_bbox": 0.26121, "s1.loss_cls": 0.21339, "s1.acc": 93.95936, "s1.loss_bbox": 0.34365, "loss": 1.35674, "grad_norm": 4.96108, "time": 0.57595}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.005, "memory": 6649, "data_time": 0.00759, "loss_rpn_cls": 0.11151, "loss_rpn_bbox": 0.08318, "s0.loss_cls": 0.34743, "s0.acc": 89.65625, "s0.loss_bbox": 0.25447, "s1.loss_cls": 0.20768, "s1.acc": 94.02957, "s1.loss_bbox": 0.2936, "loss": 1.29787, "grad_norm": 4.93981, "time": 0.57871}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.005, "memory": 6649, "data_time": 0.01007, "loss_rpn_cls": 0.08271, "loss_rpn_bbox": 0.06379, "s0.loss_cls": 0.34172, "s0.acc": 89.375, "s0.loss_bbox": 0.23151, "s1.loss_cls": 0.21155, "s1.acc": 93.51365, "s1.loss_bbox": 0.31105, "loss": 1.24232, "grad_norm": 4.60031, "time": 0.56773}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.005, "memory": 6649, "data_time": 0.0122, "loss_rpn_cls": 0.06505, "loss_rpn_bbox": 0.05993, "s0.loss_cls": 0.32721, "s0.acc": 89.64062, "s0.loss_bbox": 0.255, "s1.loss_cls": 0.19172, "s1.acc": 94.3453, "s1.loss_bbox": 0.302, "loss": 1.20091, "grad_norm": 4.55859, "time": 0.58844}
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