{"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: 4.7.0\nMMCV: 1.6.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.27.0+aacc584", "config": "model = dict(\n type='SelfSupDetector',\n backbone=dict(\n type='SelfSupMaskRCNN',\n backbone=dict(\n type='SwinTransformer',\n embed_dims=128,\n depths=[2, 2, 18, 2],\n num_heads=[4, 8, 16, 32],\n window_size=7,\n mlp_ratio=4,\n qkv_bias=True,\n qk_scale=None,\n drop_rate=0.0,\n attn_drop_rate=0.0,\n drop_path_rate=0.2,\n patch_norm=True,\n out_indices=(0, 1, 2, 3),\n with_cp=False,\n frozen_stages=4,\n convert_weights=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/simmim/simmim_800e_official.pth')),\n neck=dict(\n type='FPN',\n in_channels=[128, 256, 512, 1024],\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=4,\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, metric='bbox')\noptimizer = dict(\n type='AdamW',\n lr=6e-05,\n betas=(0.9, 0.999),\n weight_decay=0.05,\n paramwise_cfg=dict(\n custom_keys=dict(\n absolute_pos_embed=dict(decay_mult=0.0),\n relative_position_bias_table=dict(decay_mult=0.0),\n norm=dict(decay_mult=0.0))))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=1000,\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=True, base_batch_size=32)\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)\npretrained = 'pretrain/simmim/simmim_800e_official.pth'\nfind_unused_parameters = True\nwork_dir = 'work_dirs/selfsup_mask-rcnn_swin-b_simmim-800e'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "selfsup_mask-rcnn_swin-b_simmim.py"} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 5810, "data_time": 0.07143, "loss_rpn_cls": 0.63511, "loss_rpn_bbox": 0.0318, "loss_cls": 7.24074, "loss_bbox": 2.03951, "loss": 9.94716, "time": 1.05672} {"mode": "train", "epoch": 1, "iter": 100, "lr": 1e-05, "memory": 5810, "data_time": 0.01632, "loss_rpn_cls": 0.44935, "loss_rpn_bbox": 0.02938, "loss_cls": 6.60862, "loss_bbox": 2.77705, "loss": 9.86441, "time": 0.89172} {"mode": "train", "epoch": 1, "iter": 150, "lr": 1e-05, "memory": 5810, "data_time": 0.01634, "loss_rpn_cls": 0.21519, "loss_rpn_bbox": 0.02772, "loss_cls": 6.77949, "loss_bbox": 2.98665, "loss": 10.00905, "time": 0.89186} {"mode": "train", "epoch": 1, "iter": 200, "lr": 1e-05, "memory": 6168, "data_time": 0.01602, "loss_rpn_cls": 0.12562, "loss_rpn_bbox": 0.02718, "loss_cls": 6.91243, "loss_bbox": 3.13221, "loss": 10.19745, "time": 0.8962} {"mode": "train", "epoch": 1, "iter": 250, "lr": 1e-05, "memory": 7093, "data_time": 0.01609, "loss_rpn_cls": 0.11058, "loss_rpn_bbox": 0.02661, "loss_cls": 6.97581, "loss_bbox": 3.24896, "loss": 10.36197, "time": 0.88858} {"mode": "train", "epoch": 1, "iter": 300, "lr": 2e-05, "memory": 7262, "data_time": 0.01599, "loss_rpn_cls": 0.10726, "loss_rpn_bbox": 0.02651, "loss_cls": 6.92061, "loss_bbox": 3.26818, "loss": 10.32255, "time": 0.88732} {"mode": "train", "epoch": 1, "iter": 350, "lr": 2e-05, "memory": 8777, "data_time": 0.01636, "loss_rpn_cls": 0.1049, "loss_rpn_bbox": 0.02662, "loss_cls": 6.88785, "loss_bbox": 3.27543, "loss": 10.29479, "time": 0.91416} {"mode": "train", "epoch": 1, "iter": 400, "lr": 2e-05, "memory": 8777, "data_time": 0.0162, "loss_rpn_cls": 0.10084, "loss_rpn_bbox": 0.02609, "loss_cls": 6.83776, "loss_bbox": 3.29093, "loss": 10.25562, "time": 0.8981} {"mode": "train", "epoch": 1, "iter": 450, "lr": 3e-05, "memory": 8777, "data_time": 0.01617, "loss_rpn_cls": 0.10063, "loss_rpn_bbox": 0.02669, "loss_cls": 6.81591, "loss_bbox": 3.27044, "loss": 10.21367, "time": 0.90385} {"mode": "train", "epoch": 1, "iter": 500, "lr": 3e-05, "memory": 8777, "data_time": 0.01619, "loss_rpn_cls": 0.09704, "loss_rpn_bbox": 0.02727, "loss_cls": 6.80318, "loss_bbox": 3.2721, "loss": 10.19959, "time": 0.90527} {"mode": "train", "epoch": 1, "iter": 550, "lr": 3e-05, "memory": 8777, "data_time": 0.0165, "loss_rpn_cls": 0.09268, "loss_rpn_bbox": 0.02629, "loss_cls": 6.7585, "loss_bbox": 3.26017, "loss": 10.13764, "time": 0.89448} {"mode": "train", "epoch": 1, "iter": 600, "lr": 4e-05, "memory": 8777, "data_time": 0.01653, "loss_rpn_cls": 0.08885, "loss_rpn_bbox": 0.02632, "loss_cls": 6.74412, "loss_bbox": 3.27837, "loss": 10.13766, "time": 0.90011} {"mode": "train", "epoch": 1, "iter": 650, "lr": 4e-05, "memory": 8777, "data_time": 0.01592, "loss_rpn_cls": 0.08589, "loss_rpn_bbox": 0.02615, "loss_cls": 6.74233, "loss_bbox": 3.28585, "loss": 10.14023, "time": 0.88812} {"mode": "train", "epoch": 1, "iter": 700, "lr": 4e-05, "memory": 9290, "data_time": 0.01657, "loss_rpn_cls": 0.08466, "loss_rpn_bbox": 0.02642, "loss_cls": 6.72318, 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