77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
_base_ = '../../base.py'
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# model settings
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model = dict(
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type='MOCO',
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pretrained=None,
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queue_len=65536,
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feat_dim=128,
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momentum=0.999,
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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], # 0: conv-1, x: stage-x
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norm_cfg=dict(type='BN')),
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neck=dict(
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type='NonLinearNeckSimCLR', # SimCLR non-linear neck
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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='ContrastiveHead', temperature=0.2))
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# dataset settings
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data_source_cfg = dict(
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type='ImageNet',
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memcached=True,
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mclient_path='/mnt/lustre/share/memcached_client')
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data_train_list = 'data/imagenet/meta/train.txt'
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data_train_root = 'data/imagenet/train'
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dataset_type = 'ContrastiveDataset'
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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, scale=(0.2, 1.)),
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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=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.4)
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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(
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type='RandomAppliedTrans',
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transforms=[
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dict(
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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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kernel_size=23)
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],
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p=0.5),
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dict(type='RandomHorizontalFlip'),
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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]
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data = dict(
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imgs_per_gpu=32, # total 32*8=256
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workers_per_gpu=4,
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drop_last=True,
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train=dict(
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type=dataset_type,
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data_source=dict(
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list_file=data_train_list, root=data_train_root,
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**data_source_cfg),
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pipeline=train_pipeline))
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# optimizer
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optimizer = dict(type='SGD', lr=0.03, weight_decay=0.0001, momentum=0.9)
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# learning policy
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lr_config = dict(policy='CosineAnealing', min_lr=0.)
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checkpoint_config = dict(interval=10)
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# runtime settings
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total_epochs = 200
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