{"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: Quadro RTX 8000\nCUDA_HOME: /data/home/hanbo/cuda-12.2/\nNVCC: Cuda compilation tools, release 12.2, V12.2.91\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nPyTorch: 1.12.1+cu116\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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 11.6\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\n  - CuDNN 8.3.2  (built against CUDA 11.5)\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.13.1+cu116\nOpenCV: 4.10.0\nMMCV: 1.7.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.6\nMMDetection: 2.28.2+6932ef1", "config": "norm_cfg = dict(type='BN', requires_grad=False)\nmodel = dict(\n    type='FasterRCNNVMRN',\n    backbone=dict(\n        type='mmdet.ResNet',\n        depth=101,\n        num_stages=3,\n        strides=(1, 2, 2),\n        dilations=(1, 1, 1),\n        out_indices=(2, ),\n        frozen_stages=1,\n        norm_cfg=dict(type='BN', requires_grad=False),\n        norm_eval=True,\n        style='caffe',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint='open-mmlab://detectron2/resnet101_caffe')),\n    rpn_head=dict(\n        type='mmdet.RPNHead',\n        in_channels=1024,\n        feat_channels=1024,\n        anchor_generator=dict(\n            type='AnchorGenerator',\n            scales=[8, 16, 32],\n            ratios=[0.33, 0.5, 1.0, 2.0, 3.0],\n            strides=[16]),\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='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n        loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0)),\n    roi_head=dict(\n        type='mmdet.StandardRoIHead',\n        shared_head=dict(\n            type='mmdet.ResLayer',\n            depth=50,\n            stage=3,\n            stride=1,\n            style='caffe',\n            norm_cfg=dict(type='BN', requires_grad=False),\n            norm_eval=True),\n        bbox_roi_extractor=dict(\n            type='mmdet.SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n            out_channels=1024,\n            featmap_strides=[16]),\n        bbox_head=dict(\n            type='mmdet.BBoxHead',\n            with_avg_pool=True,\n            roi_feat_size=7,\n            in_channels=2048,\n            num_classes=31,\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='mmdet.CrossEntropyLoss',\n                use_sigmoid=False,\n                loss_weight=1.0),\n            loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0))),\n    vmrn_head=dict(\n        type='invigorate.PairedRoIHead',\n        shared_head=dict(\n            type='invigorate.PairedResLayer',\n            depth=50,\n            stage=3,\n            stride=1,\n            style='caffe',\n            norm_eval=False,\n            share_weights=False),\n        paired_roi_extractor=dict(\n            type='invigorate.VMRNPairedRoIExtractor',\n            roi_layer=dict(type='RoIPool', output_size=7),\n            out_channels=1024,\n            featmap_strides=[16]),\n        relation_head=dict(\n            type='invigorate.BBoxPairHead',\n            with_avg_pool=True,\n            roi_feat_size=7,\n            in_channels=2048,\n            num_relations=2,\n            loss_cls=dict(\n                type='mmdet.CrossEntropyLoss',\n                use_sigmoid=False,\n                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=12000,\n            max_per_img=2000,\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=False,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=256,\n                pos_fraction=0.25,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=True),\n            pos_weight=-1,\n            debug=False),\n        vmrn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.7,\n                neg_iou_thr=0.5,\n                min_pos_iou=0.7,\n                match_low_quality=False,\n                ignore_iof_thr=-1),\n            relation_sampler=dict(\n                type='RandomRelationSampler',\n                num=32,\n                pos_fraction=0.5,\n                cls_ratio_ub=-1,\n                add_gt_as_proposals=True,\n                num_relation_cls=2),\n            pos_weight=-1,\n            online_data=True,\n            online_start_iteration=0)),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=6000,\n            max_per_img=300,\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=0.3),\n            max_per_img=100),\n        vmrn=dict(\n            bbox_score_thr=0.5, verbose_relation=False, average_scores=False)))\ndataset_type = 'VMRDDataset'\ndata_root = 'data/vmrd/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[1.0, 1.0, 1.0], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile', to_float32=True),\n    dict(\n        type='LoadAnnotationsCustom',\n        keys=['gt_bboxes', 'gt_labels', 'gt_relmats']),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(type='PhotoMetricDistortion'),\n    dict(type='Expand', mean=[123.675, 116.28, 103.53]),\n    dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[1.0, 1.0, 1.0],\n        to_rgb=True),\n    dict(type='Pad', size_divisor=32),\n    dict(\n        type='DefaultFormatBundleCustom',\n        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relmats']),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relmats'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1000, 600),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[1.0, 1.0, 1.0],\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=8,\n    workers_per_gpu=8,\n    train=dict(\n        type='RepeatDataset',\n        times=3,\n        dataset=dict(\n            type='VMRDDataset',\n            ann_file='data/vmrd/ImageSets/Main/trainval.txt',\n            img_prefix='data/vmrd/',\n            pipeline=[\n                dict(type='LoadImageFromFile', to_float32=True),\n                dict(\n                    type='LoadAnnotationsCustom',\n                    keys=['gt_bboxes', 'gt_labels', 'gt_relmats']),\n                dict(type='RandomFlip', flip_ratio=0.5),\n                dict(type='PhotoMetricDistortion'),\n                dict(type='Expand', mean=[123.675, 116.28, 103.53]),\n                dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),\n                dict(\n                    type='Normalize',\n                    mean=[123.675, 116.28, 103.53],\n                    std=[1.0, 1.0, 1.0],\n                    to_rgb=True),\n                dict(type='Pad', size_divisor=32),\n                dict(\n                    type='DefaultFormatBundleCustom',\n                    keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relmats']),\n                dict(\n                    type='Collect',\n                    keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relmats'])\n            ])),\n    val=dict(\n        type='VMRDDataset',\n        ann_file='data/vmrd/ImageSets/Main/test.txt',\n        img_prefix='data/vmrd/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1000, 600),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[1.0, 1.0, 1.0],\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='VMRDDataset',\n        ann_file='data/vmrd/ImageSets/Main/test.txt',\n        img_prefix='data/vmrd/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1000, 600),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[1.0, 1.0, 1.0],\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(interval=1, metric=['mAP', 'ImgAcc'])\noptimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=dict(max_norm=100, norm_type=2))\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.001,\n    step=[8])\nrunner = dict(type='EpochBasedRunner', max_epochs=20)\ncheckpoint_config = dict(interval=1, max_keep_ckpts=3)\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)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\nmmdet = None\nmmdet_root = '/data/home/hanbo/projects/alpha_vision/mmdetection/mmdet'\nwork_dir = './work_dirs/faster_rcnn_vmrn_r101_caffe_c4_1x_vmrd4683'\ngpu_ids = range(0, 2)\n", "seed": null, "exp_name": "faster_rcnn_vmrn_r101_caffe_c4_1x_vmrd4683.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00049, "memory": 19816, "data_time": 0.86397, "loss_rpn_cls": 0.42811, "loss_rpn_bbox": 0.15241, "loss_cls": 1.36906, "acc": 80.0249, "loss_bbox": 0.46152, "loss_relation": 1.02198, "acc_relation": 46.26378, "loss": 3.43308, "grad_norm": 25.67716, "time": 2.71411}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00099, "memory": 19943, "data_time": 0.04204, "loss_rpn_cls": 0.19397, "loss_rpn_bbox": 0.10636, "loss_cls": 0.64595, "acc": 84.95264, "loss_bbox": 0.57931, "loss_relation": 0.84192, "acc_relation": 62.00512, "loss": 2.3675, "grad_norm": 6.72324, "time": 1.74242}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00149, "memory": 19943, "data_time": 0.04089, "loss_rpn_cls": 0.14814, "loss_rpn_bbox": 0.10013, "loss_cls": 0.78422, "acc": 81.8291, "loss_bbox": 0.72549, "loss_relation": 0.7434, "acc_relation": 67.84091, "loss": 2.50139, "grad_norm": 7.10385, "time": 1.77862}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00199, "memory": 19943, "data_time": 0.03936, "loss_rpn_cls": 0.12526, "loss_rpn_bbox": 0.09883, "loss_cls": 0.78724, "acc": 80.92529, "loss_bbox": 0.76436, "loss_relation": 0.68536, "acc_relation": 70.11, "loss": 2.46105, "grad_norm": 8.70631, "time": 1.81037}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00249, "memory": 19943, "data_time": 0.04175, "loss_rpn_cls": 0.10463, "loss_rpn_bbox": 0.08649, "loss_cls": 0.80422, "acc": 80.10596, "loss_bbox": 0.74585, "loss_relation": 0.61807, "acc_relation": 72.76901, "loss": 2.35926, "grad_norm": 12.96585, "time": 1.81884}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00299, "memory": 20072, "data_time": 0.0397, "loss_rpn_cls": 0.08845, "loss_rpn_bbox": 0.08065, "loss_cls": 0.66023, "acc": 82.10303, "loss_bbox": 0.73476, "loss_relation": 0.60514, "acc_relation": 74.4425, "loss": 2.16923, "grad_norm": 9.15149, "time": 1.80639}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00349, "memory": 20072, "data_time": 0.04366, "loss_rpn_cls": 0.08454, "loss_rpn_bbox": 0.07989, "loss_cls": 0.67668, "acc": 81.18311, "loss_bbox": 0.72554, "loss_relation": 0.57508, "acc_relation": 75.41911, "loss": 2.14173, "grad_norm": 12.38501, "time": 1.81071}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00399, "memory": 20072, "data_time": 0.04166, "loss_rpn_cls": 0.07018, "loss_rpn_bbox": 0.06928, "loss_cls": 0.61128, "acc": 82.19336, "loss_bbox": 0.70136, "loss_relation": 0.54446, "acc_relation": 76.9408, "loss": 1.99656, "grad_norm": 10.1953, "time": 1.82753}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00449, "memory": 20072, "data_time": 0.0384, "loss_rpn_cls": 0.074, "loss_rpn_bbox": 0.07256, "loss_cls": 0.65129, "acc": 81.4209, "loss_bbox": 0.72915, "loss_relation": 0.52851, "acc_relation": 77.27549, "loss": 2.0555, "grad_norm": 9.91634, "time": 1.84124}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00499, "memory": 20072, "data_time": 0.04358, "loss_rpn_cls": 0.06759, "loss_rpn_bbox": 0.06474, "loss_cls": 0.5888, "acc": 82.55127, "loss_bbox": 0.68977, "loss_relation": 0.56967, "acc_relation": 75.49488, "loss": 1.98056, "grad_norm": 10.65094, "time": 1.83209}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.005, "memory": 20099, "data_time": 0.04108, "loss_rpn_cls": 0.07262, "loss_rpn_bbox": 0.06712, "loss_cls": 0.56022, "acc": 82.82812, "loss_bbox": 0.72813, "loss_relation": 0.51032, "acc_relation": 77.82808, "loss": 1.93841, "grad_norm": 8.69613, "time": 1.84572}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.005, "memory": 20099, "data_time": 0.04263, "loss_rpn_cls": 0.06377, "loss_rpn_bbox": 0.06049, "loss_cls": 0.52559, "acc": 83.72949, "loss_bbox": 0.68094, "loss_relation": 0.52868, "acc_relation": 78.50043, "loss": 1.85947, "grad_norm": 8.81978, "time": 1.83956}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.005, "memory": 20099, "data_time": 0.03972, "loss_rpn_cls": 0.06327, "loss_rpn_bbox": 0.06328, "loss_cls": 0.49372, "acc": 84.36182, "loss_bbox": 0.66478, "loss_relation": 0.50243, "acc_relation": 79.10771, "loss": 1.78749, "grad_norm": 8.47357, "time": 1.85449}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.005, "memory": 20099, "data_time": 0.04296, "loss_rpn_cls": 0.0559, "loss_rpn_bbox": 0.05394, "loss_cls": 0.45866, "acc": 85.38428, "loss_bbox": 0.63113, "loss_relation": 0.45798, "acc_relation": 81.03983, "loss": 1.65761, "grad_norm": 8.18049, "time": 1.85709}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.005, "memory": 20099, "data_time": 0.04215, "loss_rpn_cls": 0.05484, "loss_rpn_bbox": 0.05714, "loss_cls": 0.45388, "acc": 85.5083, "loss_bbox": 0.63237, "loss_relation": 0.4717, "acc_relation": 80.53383, "loss": 1.66992, "grad_norm": 8.7493, "time": 1.86659}
{"mode": "val", "epoch": 1, "iter": 225, "lr": 0.005, "AP50": 0.655, "mAP": 0.6555, "Image Acc": 0.11778}
{"mode": "train", "epoch": 2, "iter": 50, "lr": 0.005, "memory": 20099, "data_time": 0.89532, "loss_rpn_cls": 0.04878, "loss_rpn_bbox": 0.05253, "loss_cls": 0.40459, "acc": 86.85254, "loss_bbox": 0.59806, "loss_relation": 0.3922, "acc_relation": 84.46628, "loss": 1.49617, "grad_norm": 8.09951, "time": 2.71361}
{"mode": "train", "epoch": 2, "iter": 100, "lr": 0.005, "memory": 20099, "data_time": 0.04138, "loss_rpn_cls": 0.04959, "loss_rpn_bbox": 0.05412, "loss_cls": 0.41183, "acc": 86.70117, "loss_bbox": 0.58804, "loss_relation": 0.40346, "acc_relation": 83.35669, "loss": 1.50703, "grad_norm": 8.58166, "time": 1.84953}
{"mode": "train", "epoch": 2, "iter": 150, "lr": 0.005, "memory": 20099, "data_time": 0.04141, "loss_rpn_cls": 0.04531, "loss_rpn_bbox": 0.0513, "loss_cls": 0.38297, "acc": 87.47656, "loss_bbox": 0.56551, "loss_relation": 0.38122, "acc_relation": 83.63775, "loss": 1.42631, "grad_norm": 7.8896, "time": 1.85698}
{"mode": "train", "epoch": 2, "iter": 200, "lr": 0.005, "memory": 20099, "data_time": 0.04025, "loss_rpn_cls": 0.04569, "loss_rpn_bbox": 0.05389, "loss_cls": 0.38402, "acc": 87.60742, "loss_bbox": 0.58526, "loss_relation": 0.38906, "acc_relation": 84.30311, "loss": 1.45792, "grad_norm": 8.27376, "time": 1.85545}
{"mode": "train", "epoch": 2, "iter": 250, "lr": 0.005, "memory": 20099, "data_time": 0.04275, "loss_rpn_cls": 0.04429, "loss_rpn_bbox": 0.04956, "loss_cls": 0.36846, "acc": 88.08936, "loss_bbox": 0.56531, "loss_relation": 0.41319, "acc_relation": 82.61278, "loss": 1.4408, "grad_norm": 7.52661, "time": 1.84696}
{"mode": "train", "epoch": 2, "iter": 300, "lr": 0.005, "memory": 20099, "data_time": 0.0431, "loss_rpn_cls": 0.04347, "loss_rpn_bbox": 0.05122, "loss_cls": 0.36029, "acc": 88.04639, "loss_bbox": 0.56291, "loss_relation": 0.37398, "acc_relation": 85.06223, "loss": 1.39188, "grad_norm": 7.78123, "time": 1.85485}
{"mode": "train", "epoch": 2, "iter": 350, "lr": 0.005, "memory": 20099, "data_time": 0.04264, "loss_rpn_cls": 0.04303, "loss_rpn_bbox": 0.04993, "loss_cls": 0.32687, "acc": 89.11426, "loss_bbox": 0.54927, "loss_relation": 0.38037, "acc_relation": 84.38262, "loss": 1.34948, "grad_norm": 7.50544, "time": 1.86691}
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