{"env_info": "sys.platform: linux\nPython: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: A100-SXM-80GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0\nPyTorch: 1.10.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\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 11.3\n  - NVCC architecture flags: -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 8.2\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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 -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-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=1.10.0, 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: 0.11.1+cu113\nOpenCV: 4.6.0\nMMCV: 1.6.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.2+146d174", "config": "model = dict(\n    type='SelfSupDetector',\n    backbone=dict(\n        type='SelfSupFCOS',\n        backbone=dict(\n            type='ResNet',\n            depth=50,\n            num_stages=4,\n            out_indices=(0, 1, 2, 3),\n            frozen_stages=4,\n            norm_cfg=dict(type='BN', requires_grad=False),\n            norm_eval=True,\n            style='pytorch',\n            init_cfg=dict(\n                type='Pretrained', checkpoint='torchvision://resnet50')),\n        neck=dict(\n            type='FPN',\n            in_channels=[256, 512, 1024, 2048],\n            out_channels=256,\n            start_level=1,\n            add_extra_convs='on_output',\n            num_outs=5,\n            relu_before_extra_convs=True),\n        bbox_head=dict(\n            type='SelfSupFCOSHead',\n            in_channels=256,\n            num_classes=256,\n            stacked_convs=4,\n            feat_channels=256,\n            init_cfg=dict(\n                type='Normal', layer='Conv2d', std=0.01, override=None),\n            loss_centerness=dict(\n                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n            loss_cls=dict(\n                type='ContrastiveLoss', loss_weight=1.0, temperature=0.5),\n            loss_bbox=dict(type='IoULoss', loss_weight=1.0)),\n        train_cfg=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.5,\n                neg_iou_thr=0.4,\n                min_pos_iou=0,\n                ignore_iof_thr=-1,\n                gpu_assign_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=2048,\n                pos_fraction=1.0,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=False),\n            allowed_border=-1,\n            pos_weight=1,\n            debug=False)))\ntrain_dataset_type = 'MultiViewCocoDataset'\ntest_dataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nclasses = ['selective_search']\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\nload_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True, with_mask=False)\n]\ntrain_pipeline1 = [\n    dict(\n        type='Resize',\n        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n                   (1333, 768), (1333, 800)],\n        multiscale_mode='value',\n        keep_ratio=True),\n    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),\n    dict(type='Pad', size_divisor=32),\n    dict(type='RandFlip', flip_ratio=0.5),\n    dict(\n        type='OneOf',\n        transforms=[\n            dict(type='Identity'),\n            dict(type='AutoContrast'),\n            dict(type='RandEqualize'),\n            dict(type='RandSolarize'),\n            dict(type='RandColor'),\n            dict(type='RandContrast'),\n            dict(type='RandBrightness'),\n            dict(type='RandSharpness'),\n            dict(type='RandPosterize')\n        ]),\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='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntrain_pipeline2 = [\n    dict(\n        type='Resize',\n        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n                   (1333, 768), (1333, 800)],\n        multiscale_mode='value',\n        keep_ratio=True),\n    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),\n    dict(type='Pad', size_divisor=32),\n    dict(type='RandFlip', flip_ratio=0.5),\n    dict(\n        type='OneOf',\n        transforms=[\n            dict(type='Identity'),\n            dict(type='AutoContrast'),\n            dict(type='RandEqualize'),\n            dict(type='RandSolarize'),\n            dict(type='RandColor'),\n            dict(type='RandContrast'),\n            dict(type='RandBrightness'),\n            dict(type='RandSharpness'),\n            dict(type='RandPosterize')\n        ]),\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='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=(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='MultiViewCocoDataset',\n        dataset=dict(\n            type='CocoDataset',\n            classes=['selective_search'],\n            ann_file=\n            'data/coco/filtered_proposals/train2017_ratio3size0008@0.5.json',\n            img_prefix='data/coco/train2017/',\n            pipeline=[\n                dict(type='LoadImageFromFile'),\n                dict(type='LoadAnnotations', with_bbox=True, with_mask=False)\n            ]),\n        num_views=2,\n        pipelines=[[{\n            'type':\n            'Resize',\n            'img_scale': [(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n                          (1333, 768), (1333, 800)],\n            'multiscale_mode':\n            'value',\n            'keep_ratio':\n            True\n        }, {\n            'type': 'FilterAnnotations',\n            'min_gt_bbox_wh': (0.01, 0.01)\n        }, {\n            'type': 'Pad',\n            'size_divisor': 32\n        }, {\n            'type': 'RandFlip',\n            'flip_ratio': 0.5\n        }, {\n            'type':\n            'OneOf',\n            'transforms': [{\n                'type': 'Identity'\n            }, {\n                'type': 'AutoContrast'\n            }, {\n                'type': 'RandEqualize'\n            }, {\n                'type': 'RandSolarize'\n            }, {\n                'type': 'RandColor'\n            }, {\n                'type': 'RandContrast'\n            }, {\n                'type': 'RandBrightness'\n            }, {\n                'type': 'RandSharpness'\n            }, {\n                'type': 'RandPosterize'\n            }]\n        }, {\n            'type': 'Normalize',\n            'mean': [123.675, 116.28, 103.53],\n            'std': [58.395, 57.12, 57.375],\n            'to_rgb': True\n        }, {\n            'type': 'DefaultFormatBundle'\n        }, {\n            'type': 'Collect',\n            'keys': ['img', 'gt_bboxes', 'gt_labels']\n        }],\n                   [{\n                       'type':\n                       'Resize',\n                       'img_scale': [(1333, 640), (1333, 672), (1333, 704),\n                                     (1333, 736), (1333, 768), (1333, 800)],\n                       'multiscale_mode':\n                       'value',\n                       'keep_ratio':\n                       True\n                   }, {\n                       'type': 'FilterAnnotations',\n                       'min_gt_bbox_wh': (0.01, 0.01)\n                   }, {\n                       'type': 'Pad',\n                       'size_divisor': 32\n                   }, {\n                       'type': 'RandFlip',\n                       'flip_ratio': 0.5\n                   }, {\n                       'type':\n                       'OneOf',\n                       'transforms': [{\n                           'type': 'Identity'\n                       }, {\n                           'type': 'AutoContrast'\n                       }, {\n                           'type': 'RandEqualize'\n                       }, {\n                           'type': 'RandSolarize'\n                       }, {\n                           'type': 'RandColor'\n                       }, {\n                           'type': 'RandContrast'\n                       }, {\n                           'type': 'RandBrightness'\n                       }, {\n                           'type': 'RandSharpness'\n                       }, {\n                           'type': 'RandPosterize'\n                       }]\n                   }, {\n                       'type': 'Normalize',\n                       'mean': [123.675, 116.28, 103.53],\n                       'std': [58.395, 57.12, 57.375],\n                       'to_rgb': True\n                   }, {\n                       'type': 'DefaultFormatBundle'\n                   }, {\n                       'type': 'Collect',\n                       'keys': ['img', 'gt_bboxes', 'gt_labels']\n                   }]]),\n    val=dict(\n        type='CocoDataset',\n        classes=['selective_search'],\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        classes=['selective_search'],\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(interval=65535)\noptimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)\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 = [\n    dict(type='MomentumUpdateHook'),\n    dict(\n        type='MMDetWandbHook',\n        init_kwargs=dict(project='I2B', group='pretrain'),\n        interval=50,\n        num_eval_images=0,\n        log_checkpoint=False)\n]\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)\ncustom_imports = dict(\n    imports=[\n        'mmselfsup.datasets.pipelines',\n        'selfsup.core.hook.momentum_update_hook',\n        'selfsup.datasets.pipelines.selfsup_pipelines',\n        'selfsup.datasets.pipelines.rand_aug',\n        'selfsup.datasets.single_view_coco',\n        'selfsup.datasets.multi_view_coco',\n        'selfsup.models.losses.contrastive_loss',\n        'selfsup.models.dense_heads.fcos_head',\n        'selfsup.models.dense_heads.retina_head',\n        'selfsup.models.dense_heads.detr_head',\n        'selfsup.models.dense_heads.deformable_detr_head',\n        'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head',\n        'selfsup.models.roi_heads.standard_roi_head',\n        'selfsup.models.detectors.selfsup_detector',\n        'selfsup.models.detectors.selfsup_fcos',\n        'selfsup.models.detectors.selfsup_detr',\n        'selfsup.models.detectors.selfsup_deformable_detr',\n        'selfsup.models.detectors.selfsup_retinanet',\n        'selfsup.models.detectors.selfsup_mask_rcnn',\n        'selfsup.core.bbox.assigners.hungarian_assigner',\n        'selfsup.core.bbox.match_costs.match_cost'\n    ],\n    allow_failed_imports=False)\nwork_dir = 'work_dirs/selfsup_fcos_mstrain-soft-teacher_sampler-2048_temp0.5'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "fcos.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00099, "memory": 19438, "data_time": 0.06894, "loss_cls": 8.42321, "loss_bbox": 2.74746, "loss_centerness": 0.67154, "loss": 11.8422, "time": 0.30715}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00199, "memory": 19438, "data_time": 0.00793, "loss_cls": 8.28484, "loss_bbox": 0.85374, "loss_centerness": 0.65869, "loss": 9.79727, "time": 0.23864}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00299, "memory": 19438, "data_time": 0.00815, "loss_cls": 8.1258, "loss_bbox": 0.82038, "loss_centerness": 0.6567, "loss": 9.60288, "time": 0.23049}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00399, "memory": 19717, "data_time": 0.00797, "loss_cls": 8.02235, "loss_bbox": 0.81716, "loss_centerness": 0.65531, "loss": 9.49482, "time": 0.23221}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00499, "memory": 20405, "data_time": 0.00826, "loss_cls": 7.91049, "loss_bbox": 0.81208, "loss_centerness": 0.65499, "loss": 9.37755, "time": 0.28025}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00598, "memory": 20405, "data_time": 0.00785, "loss_cls": 7.74966, "loss_bbox": 0.78761, "loss_centerness": 0.65513, "loss": 9.19241, "time": 0.24063}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00698, "memory": 20405, "data_time": 0.00787, "loss_cls": 7.59095, "loss_bbox": 0.80655, "loss_centerness": 0.65408, "loss": 9.05158, "time": 0.23666}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00798, "memory": 20405, "data_time": 0.00796, "loss_cls": 7.48609, "loss_bbox": 0.80848, "loss_centerness": 0.65457, "loss": 8.94914, "time": 0.23109}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00898, "memory": 20405, "data_time": 0.00807, "loss_cls": 7.41893, "loss_bbox": 0.79518, "loss_centerness": 0.65803, "loss": 8.87213, "time": 0.2324}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00998, "memory": 21303, "data_time": 0.00809, "loss_cls": 7.3734, "loss_bbox": 0.7755, "loss_centerness": 0.65424, "loss": 8.80314, "time": 0.22987}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.01, "memory": 21303, "data_time": 0.00828, "loss_cls": 7.33552, "loss_bbox": 0.78431, "loss_centerness": 0.65532, "loss": 8.77515, "time": 0.23081}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.01, "memory": 21303, "data_time": 0.00856, "loss_cls": 7.28684, "loss_bbox": 0.77646, "loss_centerness": 0.65478, "loss": 8.71808, "time": 0.23219}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.01, "memory": 21303, "data_time": 0.00818, "loss_cls": 7.26796, "loss_bbox": 0.77892, "loss_centerness": 0.65403, "loss": 8.70091, "time": 0.2292}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.01, "memory": 21303, "data_time": 0.00812, "loss_cls": 7.25219, "loss_bbox": 0.77542, "loss_centerness": 0.65432, "loss": 8.68193, "time": 0.23057}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.01, "memory": 21303, "data_time": 0.00875, "loss_cls": 7.24547, "loss_bbox": 0.77654, "loss_centerness": 0.65619, "loss": 8.6782, "time": 0.23415}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.01, "memory": 21303, "data_time": 0.00861, "loss_cls": 7.23444, "loss_bbox": 0.77718, "loss_centerness": 0.65493, "loss": 8.66655, "time": 0.23092}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.01, "memory": 21345, "data_time": 0.00811, "loss_cls": 7.22326, "loss_bbox": 0.76973, "loss_centerness": 0.65337, "loss": 8.64636, "time": 0.23152}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.01, "memory": 21345, "data_time": 0.00805, "loss_cls": 7.2054, "loss_bbox": 0.76969, "loss_centerness": 0.65401, "loss": 8.62911, "time": 0.23105}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.01, "memory": 21345, "data_time": 0.00802, "loss_cls": 7.20599, "loss_bbox": 0.76691, "loss_centerness": 0.6551, "loss": 8.628, "time": 0.22689}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.01, "memory": 21345, "data_time": 0.00814, "loss_cls": 7.19122, "loss_bbox": 0.76976, "loss_centerness": 0.65437, "loss": 8.61535, "time": 0.22956}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.01, "memory": 21345, "data_time": 0.00801, "loss_cls": 7.1846, "loss_bbox": 0.78064, "loss_centerness": 0.65158, "loss": 8.61682, "time": 0.22957}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.01, "memory": 21345, "data_time": 0.0081, "loss_cls": 7.19016, "loss_bbox": 0.77791, "loss_centerness": 0.65481, "loss": 8.62288, "time": 0.23169}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.01, "memory": 21345, "data_time": 0.00846, "loss_cls": 7.18961, "loss_bbox": 0.76711, "loss_centerness": 0.65345, "loss": 8.61018, "time": 0.23497}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.01, "memory": 21345, "data_time": 0.00822, "loss_cls": 7.17892, "loss_bbox": 0.7778, "loss_centerness": 0.6542, "loss": 8.61092, "time": 0.2309}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.01, "memory": 21345, "data_time": 0.00825, "loss_cls": 7.18014, "loss_bbox": 0.76689, "loss_centerness": 0.65323, "loss": 8.60027, "time": 0.22858}
{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.01, "memory": 21345, "data_time": 0.00826, "loss_cls": 7.18237, "loss_bbox": 0.77055, "loss_centerness": 0.65363, "loss": 8.60655, "time": 0.23181}
{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.01, "memory": 22169, "data_time": 0.00806, "loss_cls": 7.16545, "loss_bbox": 0.76773, "loss_centerness": 0.65402, "loss": 8.58719, "time": 0.22813}
{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.01, "memory": 22169, "data_time": 0.00806, "loss_cls": 7.1752, "loss_bbox": 0.76528, "loss_centerness": 0.65324, "loss": 8.59373, "time": 0.23226}
{"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.01, "memory": 22169, "data_time": 0.00797, "loss_cls": 7.16868, "loss_bbox": 0.77491, "loss_centerness": 0.65309, "loss": 8.59668, "time": 0.23056}
{"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.01, "memory": 22169, "data_time": 0.00859, "loss_cls": 7.16566, "loss_bbox": 0.76019, "loss_centerness": 0.65269, "loss": 8.57854, "time": 0.23269}
{"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.01, "memory": 22169, "data_time": 0.00838, "loss_cls": 7.16093, "loss_bbox": 0.76819, "loss_centerness": 0.65401, "loss": 8.58313, "time": 0.23089}
{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.01, "memory": 22169, "data_time": 0.00808, "loss_cls": 7.17183, "loss_bbox": 0.7585, "loss_centerness": 0.65362, "loss": 8.58395, "time": 0.22996}
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