mmselfsup/configs/benchmarks/classification/_base_/datasets/cifar10.py

50 lines
1.3 KiB
Python

# dataset settings
data_source = 'CIFAR10'
dataset_type = 'SingleViewDataset'
img_norm_cfg = dict(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.201])
train_pipeline = [
dict(type='RandomCrop', size=32, padding=4),
dict(type='RandomHorizontalFlip'),
]
test_pipeline = []
# prefetch
prefetch = False
if not prefetch:
train_pipeline.extend(
[dict(type='ToTensor'),
dict(type='Normalize', **img_norm_cfg)])
test_pipeline.extend(
[dict(type='ToTensor'),
dict(type='Normalize', **img_norm_cfg)])
# dataset summary
data = dict(
samples_per_gpu=128,
workers_per_gpu=2,
train=dict(
type=dataset_type,
data_source=dict(
type=data_source,
data_prefix='data/cifar10',
),
pipeline=train_pipeline,
prefetch=prefetch),
val=dict(
type=dataset_type,
data_source=dict(
type=data_source,
data_prefix='data/cifar10',
),
pipeline=test_pipeline,
prefetch=prefetch),
test=dict(
type=dataset_type,
data_source=dict(
type=data_source,
data_prefix='data/cifar10',
),
pipeline=test_pipeline,
prefetch=prefetch))
evaluation = dict(interval=10, topk=(1, 5))