| model = dict( | |
| type='SimCLR', | |
| backbone=dict( | |
| type='ResNet', | |
| depth=50, | |
| in_channels=3, | |
| out_indices=[4], | |
| norm_cfg=dict(type='SyncBN'), | |
| zero_init_residual=True), | |
| neck=dict( | |
| type='NonLinearNeck', | |
| in_channels=2048, | |
| hid_channels=2048, | |
| out_channels=128, | |
| num_layers=2, | |
| with_avg_pool=True), | |
| head=dict(type='MaclaHead', temperature=0.1)) | |
| data_source = 'ImageNet' | |
| dataset_type = 'MultiViewDataset' | |
| img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| train_pipeline = [ | |
| dict(type='RandomResizedCrop', size=224), | |
| dict(type='RandomHorizontalFlip'), | |
| dict( | |
| type='RandomAppliedTrans', | |
| transforms=[ | |
| dict( | |
| type='ColorJitter', | |
| brightness=0.8, | |
| contrast=0.8, | |
| saturation=0.8, | |
| hue=0.2) | |
| ], | |
| p=0.8), | |
| dict(type='RandomGrayscale', p=0.2), | |
| dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=0.5), | |
| dict(type='ToTensor'), | |
| dict( | |
| type='Normalize', | |
| mean=[0.485, 0.456, 0.406], | |
| std=[0.229, 0.224, 0.225]) | |
| ] | |
| prefetch = False | |
| data = dict( | |
| samples_per_gpu=64, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='MultiViewDataset', | |
| data_source=dict( | |
| type='ImageNet', | |
| data_prefix='./data/train', | |
| ann_file='./data/train.txt'), | |
| num_views=[2], | |
| pipelines=[[{ | |
| 'type': 'RandomResizedCrop', | |
| 'size': 224 | |
| }, { | |
| 'type': 'RandomHorizontalFlip' | |
| }, { | |
| 'type': | |
| 'RandomAppliedTrans', | |
| 'transforms': [{ | |
| 'type': 'ColorJitter', | |
| 'brightness': 0.8, | |
| 'contrast': 0.8, | |
| 'saturation': 0.8, | |
| 'hue': 0.2 | |
| }], | |
| 'p': | |
| 0.8 | |
| }, { | |
| 'type': 'RandomGrayscale', | |
| 'p': 0.2 | |
| }, { | |
| 'type': 'GaussianBlur', | |
| 'sigma_min': 0.1, | |
| 'sigma_max': 2.0, | |
| 'p': 0.5 | |
| }, { | |
| 'type': 'ToTensor' | |
| }, { | |
| 'type': 'Normalize', | |
| 'mean': [0.485, 0.456, 0.406], | |
| 'std': [0.229, 0.224, 0.225] | |
| }]], | |
| prefetch=False)) | |
| optimizer = dict( | |
| type='LARS', | |
| lr=0.3, | |
| weight_decay=1e-06, | |
| momentum=0.9, | |
| paramwise_options=dict({ | |
| '(bn|gn)(\d+)?.(weight|bias)': | |
| dict(weight_decay=0.0, lars_exclude=True), | |
| 'bias': | |
| dict(weight_decay=0.0, lars_exclude=True) | |
| })) | |
| optimizer_config = dict() | |
| lr_config = dict( | |
| policy='CosineAnnealing', | |
| min_lr=0.0, | |
| warmup='linear', | |
| warmup_iters=10, | |
| warmup_ratio=0.0001, | |
| warmup_by_epoch=True) | |
| runner = dict(type='EpochBasedRunner', max_epochs=800) | |
| checkpoint_config = dict(interval=10, max_keep_ckpts=3) | |
| log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) | |
| dist_params = dict(backend='nccl') | |
| cudnn_benchmark = True | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| persistent_workers = True | |
| opencv_num_threads = 0 | |
| mp_start_method = 'fork' | |
| work_dir = 'trained/pretrain/simclr_256_e800/' | |
| auto_resume = False | |
| gpu_ids = range(0, 4) | |