HaoxingChen commited on
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.gitattributes CHANGED
@@ -32,3 +32,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ simclr_256_e800/20221227_183052.log.json filter=lfs diff=lfs merge=lfs -text
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+ simclr_256_e800/train_20221227_183052.log filter=lfs diff=lfs merge=lfs -text
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simclr_256_e800/simclr_macla_resnet50_256_in1k.py ADDED
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+ model = dict(
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+ type='SimCLR',
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+ backbone=dict(
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+ type='ResNet',
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+ depth=50,
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+ in_channels=3,
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+ out_indices=[4],
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+ norm_cfg=dict(type='SyncBN'),
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+ zero_init_residual=True),
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+ neck=dict(
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+ type='NonLinearNeck',
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+ in_channels=2048,
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+ hid_channels=2048,
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+ out_channels=128,
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+ num_layers=2,
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+ with_avg_pool=True),
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+ head=dict(type='MaclaHead', temperature=0.1))
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+ data_source = 'ImageNet'
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+ dataset_type = 'MultiViewDataset'
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+ img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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+ train_pipeline = [
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+ dict(type='RandomResizedCrop', size=224),
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+ dict(type='RandomHorizontalFlip'),
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+ dict(
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+ type='RandomAppliedTrans',
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+ transforms=[
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+ dict(
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+ type='ColorJitter',
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+ brightness=0.8,
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+ contrast=0.8,
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+ saturation=0.8,
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+ hue=0.2)
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+ ],
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+ p=0.8),
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+ dict(type='RandomGrayscale', p=0.2),
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+ dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=0.5),
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+ dict(type='ToTensor'),
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+ dict(
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+ type='Normalize',
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+ mean=[0.485, 0.456, 0.406],
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+ std=[0.229, 0.224, 0.225])
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+ ]
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+ prefetch = False
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+ data = dict(
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+ samples_per_gpu=64,
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+ workers_per_gpu=4,
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+ train=dict(
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+ type='MultiViewDataset',
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+ data_source=dict(
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+ type='ImageNet',
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+ data_prefix='./data/train',
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+ ann_file='./data/train.txt'),
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+ num_views=[2],
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+ pipelines=[[{
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+ 'type': 'RandomResizedCrop',
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+ 'size': 224
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+ }, {
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+ 'type': 'RandomHorizontalFlip'
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+ }, {
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+ 'type':
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+ 'RandomAppliedTrans',
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+ 'transforms': [{
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+ 'type': 'ColorJitter',
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+ 'brightness': 0.8,
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+ 'contrast': 0.8,
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+ 'saturation': 0.8,
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+ 'hue': 0.2
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+ }],
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+ 'p':
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+ 0.8
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+ }, {
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+ 'type': 'RandomGrayscale',
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+ 'p': 0.2
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+ }, {
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+ 'type': 'GaussianBlur',
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+ 'sigma_min': 0.1,
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+ 'sigma_max': 2.0,
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+ 'p': 0.5
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+ }, {
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+ 'type': 'ToTensor'
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+ }, {
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+ 'type': 'Normalize',
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+ 'mean': [0.485, 0.456, 0.406],
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+ 'std': [0.229, 0.224, 0.225]
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+ }]],
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+ prefetch=False))
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+ optimizer = dict(
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+ type='LARS',
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+ lr=0.3,
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+ weight_decay=1e-06,
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+ momentum=0.9,
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+ paramwise_options=dict({
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+ '(bn|gn)(\d+)?.(weight|bias)':
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+ dict(weight_decay=0.0, lars_exclude=True),
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+ 'bias':
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+ dict(weight_decay=0.0, lars_exclude=True)
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+ }))
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+ optimizer_config = dict()
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+ lr_config = dict(
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+ policy='CosineAnnealing',
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+ min_lr=0.0,
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+ warmup='linear',
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+ warmup_iters=10,
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+ warmup_ratio=0.0001,
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+ warmup_by_epoch=True)
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+ runner = dict(type='EpochBasedRunner', max_epochs=800)
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+ checkpoint_config = dict(interval=10, max_keep_ckpts=3)
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+ log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
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+ dist_params = dict(backend='nccl')
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+ cudnn_benchmark = True
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+ log_level = 'INFO'
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+ load_from = None
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+ resume_from = None
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+ workflow = [('train', 1)]
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+ persistent_workers = True
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+ opencv_num_threads = 0
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+ mp_start_method = 'fork'
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+ work_dir = 'trained/pretrain/simclr_256_e800/'
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+ auto_resume = False
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+ gpu_ids = range(0, 4)
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