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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)
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