mirror of https://github.com/hpcaitech/ColossalAI
47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
import os
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from pathlib import Path
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BATCH_SIZE = 128
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IMG_SIZE = 224
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DIM = 768
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NUM_CLASSES = 10
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NUM_ATTN_HEADS = 12
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# resnet 18
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model = dict(type='VanillaResNet',
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block_type='ResNetBasicBlock',
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layers=[2, 2, 2, 2],
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num_cls=10)
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train_data = dict(dataset=dict(type='CIFAR10Dataset',
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root=Path(os.environ['DATA']),
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download=True,
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transform_pipeline=[
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dict(type='Resize',
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size=(IMG_SIZE, IMG_SIZE)),
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dict(type='ToTensor'),
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dict(type='Normalize',
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mean=(0.5, 0.5, 0.5),
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std=(0.5, 0.5, 0.5))
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]),
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dataloader=dict(batch_size=BATCH_SIZE,
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pin_memory=True,
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num_workers=4,
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drop_last=True))
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optimizer = dict(type='Adam', lr=0.001)
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loss = dict(type='CrossEntropyLoss')
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parallel = dict(
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pipeline=dict(size=4),
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tensor=dict(size=1, mode=None)
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)
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engine = dict(
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schedule=dict(
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num_microbatches=4
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)
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)
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num_epochs = 10
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