{"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='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='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_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)\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_retinanet_mstrain-soft-teacher_sampler-2048_temp0.5'\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": 42609, "data_time": 0.06926, "loss_cls": 8.97273, "losses_bboxes": 0.65762, "loss": 9.63035, "time": 0.59231} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00199, "memory": 42609, "data_time": 0.0086, "loss_cls": 8.85715, "losses_bboxes": 0.63799, "loss": 9.49514, "time": 0.58946} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.00299, "memory": 42609, "data_time": 0.00823, "loss_cls": 8.84848, "losses_bboxes": 0.61667, "loss": 9.46515, "time": 0.50931} {"mode": "train", "epoch": 1, "iter": 200, "lr": 0.00399, "memory": 42609, "data_time": 0.00976, "loss_cls": 8.84205, "losses_bboxes": 0.6111, "loss": 9.45316, "time": 0.50276} {"mode": "train", "epoch": 1, "iter": 250, "lr": 0.00499, "memory": 42609, "data_time": 0.01036, "loss_cls": 8.8422, "losses_bboxes": 0.60556, "loss": 9.44776, "time": 0.51458} {"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00598, "memory": 42609, "data_time": 0.0081, "loss_cls": 8.83823, "losses_bboxes": 0.60301, "loss": 9.44125, "time": 0.49814} {"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00698, "memory": 42609, "data_time": 0.00965, "loss_cls": 8.8329, "losses_bboxes": 0.60114, "loss": 9.43405, "time": 0.5183} {"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00798, "memory": 42609, "data_time": 0.00874, "loss_cls": 8.83258, "losses_bboxes": 0.59934, "loss": 9.43192, "time": 0.51311} {"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00898, "memory": 42688, "data_time": 0.00795, "loss_cls": 8.83078, "losses_bboxes": 0.59791, "loss": 9.42869, "time": 0.51482} {"mode": "train", "epoch": 1, "iter": 500, "lr": 0.00998, "memory": 42688, "data_time": 0.00861, "loss_cls": 8.82995, "losses_bboxes": 0.594, "loss": 9.42395, "time": 0.51271} {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.01, "memory": 42688, "data_time": 0.00955, "loss_cls": 8.82839, "losses_bboxes": 0.59099, "loss": 9.41937, "time": 0.51497} {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.01, "memory": 42688, "data_time": 0.00837, "loss_cls": 8.83288, "losses_bboxes": 0.58528, "loss": 9.41816, "time": 0.51343} {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.01, "memory": 42688, "data_time": 0.01066, "loss_cls": 8.83183, "losses_bboxes": 0.57935, "loss": 9.41118, "time": 0.51513} {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.01, "memory": 42688, "data_time": 0.00869, "loss_cls": 8.8309, "losses_bboxes": 0.57576, "loss": 9.40666, "time": 0.51839} {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.01, "memory": 42688, "data_time": 0.00824, "loss_cls": 8.83078, "losses_bboxes": 0.57075, "loss": 9.40153, "time": 0.51151} {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.01, "memory": 42688, "data_time": 0.00839, "loss_cls": 8.82914, "losses_bboxes": 0.56596, "loss": 9.3951, "time": 0.50767} {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.01, "memory": 42688, "data_time": 0.00868, "loss_cls": 8.82924, "losses_bboxes": 0.56396, "loss": 9.39321, "time": 0.51375} {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.01, "memory": 42688, "data_time": 0.00786, "loss_cls": 8.82946, "losses_bboxes": 0.56063, "loss": 9.39009, "time": 0.51264} {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.01, "memory": 42688, "data_time": 0.00879, "loss_cls": 8.82751, "losses_bboxes": 0.55851, "loss": 9.38602, "time": 0.4984} {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.01, "memory": 42688, "data_time": 0.00938, "loss_cls": 8.82674, "losses_bboxes": 0.55497, "loss": 9.38171, "time": 0.51237} {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.01, "memory": 42688, "data_time": 0.00959, "loss_cls": 8.83295, "losses_bboxes": 0.55338, "loss": 9.38633, "time": 0.51342} {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.01, "memory": 42688, "data_time": 0.00924, "loss_cls": 8.82802, "losses_bboxes": 0.55066, "loss": 9.37868, "time": 0.50192} {"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.01, "memory": 42688, "data_time": 0.00842, "loss_cls": 8.82725, "losses_bboxes": 0.5493, "loss": 9.37655, "time": 0.50798} {"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.01, "memory": 42688, "data_time": 0.00814, "loss_cls": 8.82676, "losses_bboxes": 0.54767, "loss": 9.37443, "time": 0.50544} {"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.01, "memory": 42688, "data_time": 0.00888, "loss_cls": 8.82965, "losses_bboxes": 0.54704, "loss": 9.37669, "time": 0.49843} {"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.01, "memory": 42688, "data_time": 0.00822, "loss_cls": 8.82157, "losses_bboxes": 0.54538, "loss": 9.36696, "time": 0.51398} {"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.01, "memory": 42688, "data_time": 0.00831, "loss_cls": 8.81004, "losses_bboxes": 0.5432, "loss": 9.35324, "time": 0.50936} {"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.01, "memory": 42688, "data_time": 0.00808, "loss_cls": 8.79406, "losses_bboxes": 0.54135, "loss": 9.33542, "time": 0.51147} {"mode": "train", "epoch": 1, "iter": 1450, "lr": 0.01, "memory": 42688, "data_time": 0.00873, "loss_cls": 8.77002, "losses_bboxes": 0.54267, "loss": 9.31269, "time": 0.50296} {"mode": "train", "epoch": 1, "iter": 1500, "lr": 0.01, "memory": 42688, "data_time": 0.00808, "loss_cls": 8.75563, "losses_bboxes": 0.53948, "loss": 9.29511, "time": 0.51383} {"mode": "train", "epoch": 1, "iter": 1550, "lr": 0.01, "memory": 42688, "data_time": 0.00921, "loss_cls": 8.72896, "losses_bboxes": 0.53923, "loss": 9.26819, "time": 0.50575} {"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.01, "memory": 42688, "data_time": 0.0088, "loss_cls": 8.69854, "losses_bboxes": 0.53869, "loss": 9.23724, "time": 0.51133} {"mode": "train", "epoch": 1, "iter": 1650, "lr": 0.01, "memory": 42688, "data_time": 0.00809, "loss_cls": 8.66643, "losses_bboxes": 0.53607, "loss": 9.2025, "time": 0.51307} 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