{"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: GeForce GTX 1080 Ti\nCUDA_HOME: /cluster/apps/cuda/10.1\nNVCC: Cuda compilation tools, release 10.1, V10.1.243\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.7.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.1\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_37,code=compute_37\n  - CuDNN 7.6.3\n  - Magma 2.5.2\n  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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: 0.8.0\nOpenCV: 4.5.5\nMMCV: 1.4.3\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 10.1\nMMRotate: 0.1.0+unknown", "config": "dataset_type = 'DOTADataset'\ndata_root = '/cluster/home/it_stu198/main/datasets/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        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/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        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/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=\n        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/test/images/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/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='OrientedRCNN',\n    backbone=dict(\n        type='ResNet',\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(type='Pretrained', checkpoint='torchvision://resnet50')),\n    neck=dict(\n        type='FPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        num_outs=5),\n    rpn_head=dict(\n        type='OrientedRPNHead',\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='MidpointOffsetCoder',\n            angle_range='le90',\n            target_means=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n            target_stds=[1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),\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='OrientedStandardRoIHead',\n        bbox_roi_extractor=dict(\n            type='RotatedSingleRoIExtractor',\n            roi_layer=dict(\n                type='RoIAlignRotated',\n                out_size=7,\n                sample_num=2,\n                clockwise=True),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        bbox_head=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=(0.1, 0.1, 0.2, 0.2, 0.1)),\n            reg_class_agnostic=True,\n            loss_cls=dict(\n                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, 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=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=0.8),\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=False,\n                iou_calculator=dict(type='RBboxOverlaps2D'),\n                ignore_iof_thr=-1),\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    test_cfg=dict(\n        rpn=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.8),\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)))\nwork_dir = './work_dirs/oriented_rcnn_r50_fpn_1x_dota_le90'\nauto_resume = False\ngpu_ids = range(0, 1)\n", "seed": 518, "exp_name": "oriented_rcnn_r50_fpn_1x_dota_le90.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00199, "memory": 6491, "data_time": 0.40748, "loss_rpn_cls": 0.43801, "loss_rpn_bbox": 0.23817, "loss_cls": 0.44348, "acc": 93.15625, "loss_bbox": 0.13724, "loss": 1.2569, "grad_norm": 7.63117, "time": 0.89915}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00233, "memory": 6491, "data_time": 0.00621, "loss_rpn_cls": 0.28355, "loss_rpn_bbox": 0.25821, "loss_cls": 0.30633, "acc": 94.80469, "loss_bbox": 0.15392, "loss": 1.00201, "grad_norm": 4.94452, "time": 0.47648}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00266, "memory": 6491, "data_time": 0.00623, "loss_rpn_cls": 0.25866, "loss_rpn_bbox": 0.26247, "loss_cls": 0.33965, "acc": 93.06445, "loss_bbox": 0.262, "loss": 1.12277, "grad_norm": 4.23314, "time": 0.46739}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00299, "memory": 6491, "data_time": 0.00621, "loss_rpn_cls": 0.20053, "loss_rpn_bbox": 0.23421, "loss_cls": 0.28088, "acc": 93.67969, "loss_bbox": 0.28892, "loss": 1.00454, "grad_norm": 4.21942, "time": 0.46321}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00333, "memory": 6491, "data_time": 0.00624, "loss_rpn_cls": 0.2011, "loss_rpn_bbox": 0.20723, "loss_cls": 0.24754, "acc": 94.86523, "loss_bbox": 0.17371, "loss": 0.82958, "grad_norm": 4.88007, "time": 0.47003}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00366, "memory": 6491, "data_time": 0.00651, "loss_rpn_cls": 0.16044, "loss_rpn_bbox": 0.20906, "loss_cls": 0.26865, "acc": 94.58008, "loss_bbox": 0.22688, "loss": 0.86503, "grad_norm": 3.87034, "time": 0.4676}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00399, "memory": 7316, "data_time": 0.00627, "loss_rpn_cls": 0.16691, "loss_rpn_bbox": 0.24547, "loss_cls": 0.30572, "acc": 92.60742, "loss_bbox": 0.29961, "loss": 1.01772, "grad_norm": 4.06446, "time": 0.4667}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00433, "memory": 7316, "data_time": 0.00617, "loss_rpn_cls": 0.16573, "loss_rpn_bbox": 0.22502, "loss_cls": 0.28573, "acc": 93.25586, "loss_bbox": 0.33383, "loss": 1.01031, "grad_norm": 4.85239, "time": 0.45723}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00466, "memory": 7316, "data_time": 0.00621, "loss_rpn_cls": 0.16747, "loss_rpn_bbox": 0.24181, "loss_cls": 0.37529, "acc": 91.23633, "loss_bbox": 0.34875, "loss": 1.13333, "grad_norm": 5.38347, "time": 0.46568}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00499, "memory": 7316, "data_time": 0.00626, "loss_rpn_cls": 0.14948, "loss_rpn_bbox": 0.22792, "loss_cls": 0.28432, "acc": 92.52734, "loss_bbox": 0.32806, "loss": 0.98977, "grad_norm": 5.05669, "time": 0.45099}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.005, "memory": 7316, "data_time": 0.00616, "loss_rpn_cls": 0.12774, "loss_rpn_bbox": 0.19439, "loss_cls": 0.29555, "acc": 92.57617, "loss_bbox": 0.35447, "loss": 0.97215, "grad_norm": 4.52512, "time": 0.46275}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.005, "memory": 7316, "data_time": 0.00626, "loss_rpn_cls": 0.1373, "loss_rpn_bbox": 0.24077, "loss_cls": 0.32094, "acc": 91.59766, "loss_bbox": 0.35821, "loss": 1.05721, "grad_norm": 4.32715, "time": 0.46546}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.005, "memory": 7316, "data_time": 0.0062, "loss_rpn_cls": 0.11423, "loss_rpn_bbox": 0.18625, "loss_cls": 0.29577, "acc": 92.15234, "loss_bbox": 0.30788, "loss": 0.90413, "grad_norm": 4.3822, "time": 0.46315}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.005, "memory": 7316, "data_time": 0.00628, "loss_rpn_cls": 0.12208, "loss_rpn_bbox": 0.22803, "loss_cls": 0.34583, "acc": 89.77734, "loss_bbox": 0.41833, "loss": 1.11428, "grad_norm": 4.96842, "time": 0.45752}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.005, "memory": 7316, "data_time": 0.00636, "loss_rpn_cls": 0.09754, "loss_rpn_bbox": 0.17569, "loss_cls": 0.29554, "acc": 92.14648, "loss_bbox": 0.32239, "loss": 0.89117, "grad_norm": 4.2855, "time": 0.47973}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.005, "memory": 7316, "data_time": 0.00632, "loss_rpn_cls": 0.11718, "loss_rpn_bbox": 0.18053, "loss_cls": 0.2863, "acc": 92.22852, "loss_bbox": 0.33173, "loss": 0.91574, "grad_norm": 4.21259, "time": 0.46498}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.005, "memory": 7316, "data_time": 0.00607, "loss_rpn_cls": 0.12167, "loss_rpn_bbox": 0.19139, "loss_cls": 0.29841, "acc": 91.73828, "loss_bbox": 0.31645, "loss": 0.92792, "grad_norm": 4.32213, "time": 0.46278}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.005, "memory": 7316, "data_time": 0.00614, "loss_rpn_cls": 0.1112, "loss_rpn_bbox": 0.19746, "loss_cls": 0.26554, "acc": 91.86523, "loss_bbox": 0.31369, "loss": 0.88789, "grad_norm": 4.41497, "time": 0.46331}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.005, "memory": 7316, "data_time": 0.00613, "loss_rpn_cls": 0.10888, "loss_rpn_bbox": 0.17628, "loss_cls": 0.30504, "acc": 91.17969, "loss_bbox": 0.30752, "loss": 0.89772, "grad_norm": 4.1732, "time": 0.45236}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.005, "memory": 7316, "data_time": 0.00615, "loss_rpn_cls": 0.10687, "loss_rpn_bbox": 0.17148, "loss_cls": 0.30136, "acc": 91.58203, "loss_bbox": 0.2973, "loss": 0.87701, "grad_norm": 4.13704, "time": 0.46334}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.005, "memory": 7316, "data_time": 0.00615, "loss_rpn_cls": 0.10419, "loss_rpn_bbox": 0.19639, "loss_cls": 0.32914, "acc": 90.44727, "loss_bbox": 0.36032, "loss": 0.99004, "grad_norm": 4.44738, "time": 0.46439}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.005, "memory": 7316, "data_time": 0.00609, "loss_rpn_cls": 0.10589, "loss_rpn_bbox": 0.18752, "loss_cls": 0.30691, "acc": 90.42383, "loss_bbox": 0.37269, "loss": 0.97301, "grad_norm": 4.56624, "time": 0.45766}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.005, "memory": 7316, "data_time": 0.00616, "loss_rpn_cls": 0.10966, "loss_rpn_bbox": 0.17196, "loss_cls": 0.29207, "acc": 91.29492, "loss_bbox": 0.34895, "loss": 0.92265, "grad_norm": 4.34482, "time": 0.46802}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.005, "memory": 7316, "data_time": 0.00617, "loss_rpn_cls": 0.08227, "loss_rpn_bbox": 0.15938, "loss_cls": 0.24401, "acc": 92.1543, "loss_bbox": 0.28132, "loss": 0.76699, "grad_norm": 3.61968, "time": 0.463}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.005, "memory": 7316, "data_time": 0.00621, "loss_rpn_cls": 0.10446, "loss_rpn_bbox": 0.21927, "loss_cls": 0.3302, "acc": 89.16211, "loss_bbox": 0.37535, "loss": 1.02928, "grad_norm": 4.67546, "time": 0.47234}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.005, "memory": 7316, "data_time": 0.00628, "loss_rpn_cls": 0.08226, "loss_rpn_bbox": 0.20649, "loss_cls": 0.27318, "acc": 91.54297, "loss_bbox": 0.28942, "loss": 0.85136, "grad_norm": 3.86565, "time": 0.46594}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.005, "memory": 7316, "data_time": 0.0063, "loss_rpn_cls": 0.10462, "loss_rpn_bbox": 0.16374, "loss_cls": 0.30516, "acc": 90.97852, "loss_bbox": 0.32821, "loss": 0.90174, "grad_norm": 4.53448, "time": 0.4652}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.005, "memory": 7316, "data_time": 0.00623, "loss_rpn_cls": 0.09119, "loss_rpn_bbox": 0.15593, "loss_cls": 0.2741, "acc": 91.59961, "loss_bbox": 0.28243, "loss": 0.80365, "grad_norm": 4.26215, "time": 0.45399}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.005, "memory": 7316, "data_time": 0.00627, "loss_rpn_cls": 0.11194, "loss_rpn_bbox": 0.17305, "loss_cls": 0.30632, "acc": 90.13672, "loss_bbox": 0.34103, "loss": 0.93234, "grad_norm": 4.40166, "time": 0.46276}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.005, "memory": 8396, "data_time": 0.00631, "loss_rpn_cls": 0.07523, "loss_rpn_bbox": 0.15044, "loss_cls": 0.29418, "acc": 91.62891, "loss_bbox": 0.28612, "loss": 0.80597, "grad_norm": 4.00773, "time": 0.47452}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.005, "memory": 8396, "data_time": 0.00621, "loss_rpn_cls": 0.09696, "loss_rpn_bbox": 0.19566, "loss_cls": 0.2916, "acc": 90.7168, "loss_bbox": 0.33512, "loss": 0.91933, "grad_norm": 4.25688, "time": 0.47171}
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