{"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: 1.6.0\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.1+7241c62", "config": "model = dict(\n    type='SelfSupDetector',\n    backbone=dict(\n        type='SelfSupMaskRCNN',\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='SyncBN', requires_grad=False),\n            norm_eval=True,\n            style='pytorch',\n            init_cfg=dict(\n                type='Pretrained',\n                checkpoint=\n                'pretrain/image-level/swav/swav_resnet50_64xb64-mcrop-2-6-coslr-800e_in1k-224-96_official.pth.tar'\n            )),\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='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='SelfSupStandardRoIHead',\n            bbox_roi_extractor=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            bbox_head=dict(\n                type='SelfSupShared4Conv1FCBBoxHead',\n                in_channels=256,\n                num_classes=256,\n                roi_feat_size=7,\n                reg_class_agnostic=False,\n                loss_bbox=dict(type='L1Loss', loss_weight=1.0),\n                loss_cls=dict(\n                    type='ContrastiveLoss', loss_weight=1.0, temperature=0.5)),\n            mask_roi_extractor=None,\n            mask_head=None),\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=4096,\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),\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                    gt_max_assign_all=False),\n                sampler=dict(\n                    type='RandomSampler',\n                    num=4096,\n                    pos_fraction=1,\n                    neg_pos_ub=0,\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))))\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, gpu_collect=True)\noptimizer = dict(type='SGD', lr=0.02, 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.assigners.pseudo_hungarian_assigner',\n        'selfsup.core.bbox.match_costs.match_cost'\n    ],\n    allow_failed_imports=False)\nwork_dir = 'work_dirs/selfsup_mask-rcnn_1x_coco_swav'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "mask_rcnn.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00198, "memory": 2666, "data_time": 0.07098, "loss_rpn_cls": 0.3061, "loss_rpn_bbox": 0.02891, "loss_cls": 6.22497, "loss_bbox": 2.55497, "loss": 9.11494, "time": 0.45312}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00398, "memory": 2666, "data_time": 0.00776, "loss_rpn_cls": 0.11387, "loss_rpn_bbox": 0.02826, "loss_cls": 6.29118, "loss_bbox": 3.24782, "loss": 9.68113, "time": 0.35431}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00597, "memory": 2817, "data_time": 0.00829, "loss_rpn_cls": 0.11035, "loss_rpn_bbox": 0.02816, "loss_cls": 6.45938, "loss_bbox": 3.21972, "loss": 9.81761, "time": 0.37794}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00797, "memory": 3257, "data_time": 0.00838, "loss_rpn_cls": 0.10023, "loss_rpn_bbox": 0.02638, "loss_cls": 6.41873, "loss_bbox": 3.29365, "loss": 9.83898, "time": 0.34722}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00997, "memory": 3257, "data_time": 0.00876, "loss_rpn_cls": 0.09649, "loss_rpn_bbox": 0.0268, "loss_cls": 6.34907, "loss_bbox": 3.25409, "loss": 9.72645, "time": 0.35318}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.01197, "memory": 3257, "data_time": 0.00833, "loss_rpn_cls": 0.08837, "loss_rpn_bbox": 0.02488, "loss_cls": 6.2057, "loss_bbox": 3.253, "loss": 9.57194, "time": 0.34282}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.01397, "memory": 3257, "data_time": 0.00922, "loss_rpn_cls": 0.08996, "loss_rpn_bbox": 0.02709, "loss_cls": 6.23285, "loss_bbox": 3.16005, "loss": 9.50995, "time": 0.362}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.01596, "memory": 3257, "data_time": 0.00848, "loss_rpn_cls": 0.08614, "loss_rpn_bbox": 0.02676, "loss_cls": 6.17195, "loss_bbox": 3.15173, "loss": 9.43659, "time": 0.35171}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.01796, "memory": 3257, "data_time": 0.00856, "loss_rpn_cls": 0.08126, "loss_rpn_bbox": 0.02619, "loss_cls": 6.14625, "loss_bbox": 3.20473, "loss": 9.45844, "time": 0.34566}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.01996, "memory": 3257, "data_time": 0.00865, "loss_rpn_cls": 0.07975, "loss_rpn_bbox": 0.02541, "loss_cls": 6.10459, "loss_bbox": 3.26056, "loss": 9.47032, "time": 0.34747}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.02, "memory": 3257, "data_time": 0.00868, "loss_rpn_cls": 0.07838, "loss_rpn_bbox": 0.02618, "loss_cls": 6.11188, "loss_bbox": 3.25525, "loss": 9.47169, "time": 0.35828}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.02, "memory": 3257, "data_time": 0.00811, "loss_rpn_cls": 0.07645, "loss_rpn_bbox": 0.02547, "loss_cls": 6.09489, "loss_bbox": 3.26318, "loss": 9.45999, "time": 0.3431}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.02, "memory": 3257, "data_time": 0.00912, "loss_rpn_cls": 0.07571, "loss_rpn_bbox": 0.02616, "loss_cls": 6.10897, "loss_bbox": 3.24682, "loss": 9.45765, "time": 0.35958}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.02, "memory": 3304, "data_time": 0.00856, "loss_rpn_cls": 0.07322, "loss_rpn_bbox": 0.02635, "loss_cls": 6.10015, "loss_bbox": 3.25717, "loss": 9.4569, "time": 0.35797}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.02, "memory": 3304, "data_time": 0.00845, "loss_rpn_cls": 0.07158, "loss_rpn_bbox": 0.0257, "loss_cls": 6.08952, "loss_bbox": 3.24328, "loss": 9.43007, "time": 0.36015}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.02, "memory": 3304, "data_time": 0.00863, "loss_rpn_cls": 0.07039, "loss_rpn_bbox": 0.02546, "loss_cls": 6.06176, "loss_bbox": 3.23525, "loss": 9.39287, "time": 0.34889}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.02, "memory": 3304, "data_time": 0.00856, "loss_rpn_cls": 0.07042, "loss_rpn_bbox": 0.02584, "loss_cls": 6.05429, "loss_bbox": 3.20717, "loss": 9.35773, "time": 0.36064}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.02, "memory": 4126, "data_time": 0.00819, "loss_rpn_cls": 0.06889, "loss_rpn_bbox": 0.02563, "loss_cls": 6.03515, "loss_bbox": 3.20304, "loss": 9.33271, "time": 0.39294}
{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.02, "memory": 4126, "data_time": 0.00879, "loss_rpn_cls": 0.06707, "loss_rpn_bbox": 0.02441, "loss_cls": 5.97678, "loss_bbox": 3.21013, "loss": 9.27839, "time": 0.34419}
{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.02, "memory": 4126, "data_time": 0.00876, "loss_rpn_cls": 0.06768, "loss_rpn_bbox": 0.02528, "loss_cls": 6.00962, "loss_bbox": 3.19679, "loss": 9.29937, "time": 0.35908}
{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.02, "memory": 4126, "data_time": 0.00897, "loss_rpn_cls": 0.0667, "loss_rpn_bbox": 0.02503, "loss_cls": 5.97649, "loss_bbox": 3.19309, "loss": 9.2613, "time": 0.3483}
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