model = dict( type='SelfSupDetector', backbone=dict( type='SelfSupRetinaNet', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=4, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs='on_input', num_outs=5), bbox_head=dict( type='SelfSupRetinaHead', num_classes=256, in_channels=256, stacked_convs=4, feat_channels=256, init_cfg=dict( type='Normal', layer='Conv2d', std=0.01, override=None), loss_cls=dict( type='ContrastiveLoss', loss_weight=1.0, temperature=0.5), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), train_cfg=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1, gpu_assign_thr=-1), sampler=dict( type='RandomSampler', num=2048, pos_fraction=1.0, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=-1, pos_weight=1, debug=False))) train_dataset_type = 'MultiViewCocoDataset' test_dataset_type = 'CocoDataset' data_root = 'data/coco/' classes = ['selective_search'] img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) load_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=False), dict(type='SelectTopKProposals', topk=80) ] train_pipeline1 = [ dict( type='Resize', img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)), dict(type='Pad', size_divisor=32), dict(type='RandFlip', flip_ratio=0.5), dict( type='OneOf', transforms=[ dict(type='Identity'), dict(type='AutoContrast'), dict(type='RandEqualize'), dict(type='RandSolarize'), dict(type='RandColor'), dict(type='RandContrast'), dict(type='RandBrightness'), dict(type='RandSharpness'), dict(type='RandPosterize') ]), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] train_pipeline2 = [ dict( type='Resize', img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], multiscale_mode='value', keep_ratio=True), dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)), dict(type='Pad', size_divisor=32), dict(type='RandFlip', flip_ratio=0.5), dict( type='OneOf', transforms=[ dict(type='Identity'), dict(type='AutoContrast'), dict(type='RandEqualize'), dict(type='RandSolarize'), dict(type='RandColor'), dict(type='RandContrast'), dict(type='RandBrightness'), dict(type='RandSharpness'), dict(type='RandPosterize') ]), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type='MultiViewCocoDataset', dataset=dict( type='CocoDataset', classes=['selective_search'], ann_file= 'data/coco/filtered_proposals/train2017_ratio3size0008@0.5.json', img_prefix='data/coco/train2017/', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=False), dict(type='SelectTopKProposals', topk=80) ]), num_views=2, pipelines=[[{ 'type': 'Resize', 'img_scale': [(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'FilterAnnotations', 'min_gt_bbox_wh': (0.01, 0.01) }, { 'type': 'Pad', 'size_divisor': 32 }, { 'type': 'RandFlip', 'flip_ratio': 0.5 }, { 'type': 'OneOf', 'transforms': [{ 'type': 'Identity' }, { 'type': 'AutoContrast' }, { 'type': 'RandEqualize' }, { 'type': 'RandSolarize' }, { 'type': 'RandColor' }, { 'type': 'RandContrast' }, { 'type': 'RandBrightness' }, { 'type': 'RandSharpness' }, { 'type': 'RandPosterize' }] }, { 'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True }, { 'type': 'DefaultFormatBundle' }, { 'type': 'Collect', 'keys': ['img', 'gt_bboxes', 'gt_labels'] }], [{ 'type': 'Resize', 'img_scale': [(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), (1333, 800)], 'multiscale_mode': 'value', 'keep_ratio': True }, { 'type': 'FilterAnnotations', 'min_gt_bbox_wh': (0.01, 0.01) }, { 'type': 'Pad', 'size_divisor': 32 }, { 'type': 'RandFlip', 'flip_ratio': 0.5 }, { 'type': 'OneOf', 'transforms': [{ 'type': 'Identity' }, { 'type': 'AutoContrast' }, { 'type': 'RandEqualize' }, { 'type': 'RandSolarize' }, { 'type': 'RandColor' }, { 'type': 'RandContrast' }, { 'type': 'RandBrightness' }, { 'type': 'RandSharpness' }, { 'type': 'RandPosterize' }] }, { 'type': 'Normalize', 'mean': [123.675, 116.28, 103.53], 'std': [58.395, 57.12, 57.375], 'to_rgb': True }, { 'type': 'DefaultFormatBundle' }, { 'type': 'Collect', 'keys': ['img', 'gt_bboxes', 'gt_labels'] }]]), val=dict( type='CocoDataset', classes=['selective_search'], ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='CocoDataset', classes=['selective_search'], ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) evaluation = dict(interval=65535) optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[8, 11]) runner = dict(type='EpochBasedRunner', max_epochs=12) checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [ dict(type='MomentumUpdateHook'), dict( type='MMDetWandbHook', init_kwargs=dict(project='I2B', group='pretrain'), interval=50, num_eval_images=0, log_checkpoint=False) ] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] opencv_num_threads = 0 mp_start_method = 'fork' auto_scale_lr = dict(enable=False, base_batch_size=16) custom_imports = dict( imports=[ 'mmselfsup.datasets.pipelines', 'selfsup.core.hook.momentum_update_hook', 'selfsup.datasets.pipelines.selfsup_pipelines', 'selfsup.datasets.pipelines.rand_aug', 'selfsup.datasets.single_view_coco', 'selfsup.datasets.multi_view_coco', 'selfsup.models.losses.contrastive_loss', 'selfsup.models.dense_heads.fcos_head', 'selfsup.models.dense_heads.retina_head', 'selfsup.models.dense_heads.detr_head', 'selfsup.models.dense_heads.deformable_detr_head', 'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head', 'selfsup.models.roi_heads.standard_roi_head', 'selfsup.models.detectors.selfsup_detector', 'selfsup.models.detectors.selfsup_fcos', 'selfsup.models.detectors.selfsup_detr', 'selfsup.models.detectors.selfsup_deformable_detr', 'selfsup.models.detectors.selfsup_retinanet', 'selfsup.models.detectors.selfsup_mask_rcnn', 'selfsup.core.bbox.assigners.hungarian_assigner', 'selfsup.core.bbox.match_costs.match_cost' ], allow_failed_imports=False) work_dir = 'work_dirs/selfsup_retinanet_mstrain-soft-teacher_sampler-2048_temp0.5' auto_resume = False gpu_ids = range(0, 8)