Making large AI models cheaper, faster and more accessible
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
train_data = dict(
dataset=dict(
type='CIFAR10Dataset',
root='/path/to/data',
download=True,
transform_pipeline=[
dict(type='RandomResizedCrop', size=224),
dict(type='RandomHorizontalFlip'),
dict(type='ToTensor'),
dict(type='Normalize', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
]
),
dataloader=dict(
batch_size=64,
pin_memory=True,
num_workers=4,
sampler=dict(
type='DataParallelSampler',
shuffle=True,
)
)
)