{"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: NVIDIA A100 80GB PCIe\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\n - CuDNN 8.2.1\n - Built with 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: 1.6.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.2+d779fe3", "config": "model = dict(\n type='SelfSupDetector',\n backbone=dict(\n type='SelfSupRetinaNet',\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'\n )),\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_input',\n num_outs=5),\n bbox_head=dict(\n type='SelfSupRetinaHead',\n num_classes=256,\n in_channels=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_cls=dict(\n type='ContrastiveLoss', loss_weight=1.0, temperature=0.5),\n loss_bbox=dict(type='L1Loss', 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 dict(type='SelectTopKProposals', topk=80)\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 dict(type='SelectTopKProposals', topk=80)\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=0.01, 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.assigners.pseudo_hungarian_assigner',\n 'selfsup.core.bbox.match_costs.match_cost'\n ],\n allow_failed_imports=False)\nwork_dir = 'work_dirs/selfsup_retinanet_1x_coco_swav_moco-setting'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "retinanet.py"} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00099, "memory": 42603, "data_time": 0.06726, "loss_cls": 8.96841, "losses_bboxes": 0.65733, "loss": 9.62574, "time": 0.88862} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00199, "memory": 42603, "data_time": 0.00784, "loss_cls": 8.85488, "losses_bboxes": 0.63793, "loss": 9.49281, "time": 0.54488} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00299, "memory": 42603, "data_time": 0.00711, "loss_cls": 8.84636, "losses_bboxes": 0.61266, "loss": 9.45902, "time": 0.52006} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00399, "memory": 42603, "data_time": 0.00885, "loss_cls": 8.84104, "losses_bboxes": 0.60519, "loss": 9.44623, "time": 0.51411} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00499, "memory": 42603, "data_time": 0.00976, "loss_cls": 8.84144, "losses_bboxes": 0.60169, "loss": 9.44313, "time": 0.53094} {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00598, "memory": 42603, "data_time": 0.00772, "loss_cls": 8.83741, "losses_bboxes": 0.60047, "loss": 9.43788, "time": 0.50652} {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00698, "memory": 42603, "data_time": 0.00895, "loss_cls": 8.83198, "losses_bboxes": 0.59991, "loss": 9.43189, "time": 0.53934} {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00798, "memory": 42603, "data_time": 0.00817, "loss_cls": 8.83177, "losses_bboxes": 0.59899, "loss": 9.43076, "time": 0.5232} {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00898, "memory": 42685, "data_time": 0.00759, "loss_cls": 8.82968, "losses_bboxes": 0.59773, "loss": 9.42741, "time": 0.52492} {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00998, "memory": 42685, "data_time": 0.00766, "loss_cls": 8.82822, "losses_bboxes": 0.5905, "loss": 9.41872, "time": 0.52176} {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.01, "memory": 42685, "data_time": 0.00843, "loss_cls": 8.8234, "losses_bboxes": 0.58001, "loss": 9.40342, "time": 0.52511} {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.01, "memory": 42685, "data_time": 0.00764, "loss_cls": 8.81616, "losses_bboxes": 0.57319, "loss": 9.38935, "time": 0.52295} {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.01, "memory": 42685, "data_time": 0.01023, "loss_cls": 8.8023, "losses_bboxes": 0.56841, "loss": 9.37071, "time": 0.52314} {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.01, "memory": 42685, "data_time": 0.00809, "loss_cls": 8.78768, "losses_bboxes": 0.56573, "loss": 9.35341, "time": 0.52548} {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.01, "memory": 42685, "data_time": 0.00789, "loss_cls": 8.76938, "losses_bboxes": 0.55977, "loss": 9.32915, "time": 0.52137} {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.01, "memory": 42685, "data_time": 0.00788, "loss_cls": 8.74502, "losses_bboxes": 0.55438, "loss": 9.29939, "time": 0.51503} {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.01, "memory": 42685, "data_time": 0.00811, "loss_cls": 8.71936, "losses_bboxes": 0.54991, "loss": 9.26927, "time": 0.52352} {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.01, "memory": 42685, "data_time": 0.00737, "loss_cls": 8.68725, "losses_bboxes": 0.54627, "loss": 9.23352, "time": 0.52368} {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.01, "memory": 42685, "data_time": 0.00831, "loss_cls": 8.6498, "losses_bboxes": 0.54319, "loss": 9.19299, "time": 0.50883} {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.01, "memory": 42685, "data_time": 0.00888, "loss_cls": 8.60652, "losses_bboxes": 0.53906, "loss": 9.14558, "time": 0.52061} {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.01, "memory": 42685, "data_time": 0.00914, "loss_cls": 8.57433, "losses_bboxes": 0.53689, "loss": 9.11122, "time": 0.54813} {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.01, "memory": 42685, "data_time": 0.00857, "loss_cls": 8.53758, "losses_bboxes": 0.53567, "loss": 9.07325, "time": 0.51194} {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.01, "memory": 42685, "data_time": 0.00791, "loss_cls": 8.49497, "losses_bboxes": 0.53272, "loss": 9.02768, "time": 0.51798} {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.01, "memory": 42685, "data_time": 0.00766, "loss_cls": 8.46025, "losses_bboxes": 0.52979, "loss": 8.99004, "time": 0.51574} {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.01, "memory": 42685, "data_time": 0.00823, "loss_cls": 8.43909, "losses_bboxes": 0.53009, "loss": 8.96918, "time": 0.50967} {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.01, "memory": 42685, "data_time": 0.00787, "loss_cls": 8.40757, "losses_bboxes": 0.52799, "loss": 8.93556, "time": 0.52195} {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.01, "memory": 42685, "data_time": 0.00754, "loss_cls": 8.38146, "losses_bboxes": 0.52585, "loss": 8.90731, "time": 0.51651} {"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.01, "memory": 42685, "data_time": 0.00735, "loss_cls": 8.36169, "losses_bboxes": 0.52373, "loss": 8.88542, "time": 0.51951} {"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.01, "memory": 42685, "data_time": 0.00797, "loss_cls": 8.33674, "losses_bboxes": 0.52428, "loss": 8.86102, "time": 0.51145} {"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.01, "memory": 42685, "data_time": 0.00744, "loss_cls": 8.32399, "losses_bboxes": 0.52234, "loss": 8.84633, "time": 0.52489} {"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.01, "memory": 42685, "data_time": 0.00875, "loss_cls": 8.31338, "losses_bboxes": 0.52177, "loss": 8.83515, "time": 0.51469} {"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.01, "memory": 42685, "data_time": 0.00832, "loss_cls": 8.29884, "losses_bboxes": 0.5217, "loss": 8.82054, "time": 0.51754} {"mode": "train", "epoch": 1, "iter": 1650, "lr": 0.01, "memory": 42685, "data_time": 0.00761, "loss_cls": 8.29305, 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