46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
# dataset settings
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data_source = 'ImageNet'
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dataset_type = 'RotationPredDataset'
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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),
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dict(type='RandomHorizontalFlip'),
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]
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test_pipeline = [
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dict(type='Resize', size=256),
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dict(type='CenterCrop', size=224),
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]
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# prefetch
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prefetch = False
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if not prefetch:
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train_pipeline.extend(
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[dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg)])
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test_pipeline.extend(
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[dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg)])
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# dataset summary
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data = dict(
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samples_per_gpu=16, # (16*4) x 8 = 512
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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data_source=dict(
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type=data_source,
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data_prefix='data/imagenet/train',
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ann_file='data/imagenet/meta/train.txt',
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),
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pipeline=train_pipeline,
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prefetch=prefetch),
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val=dict(
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type=dataset_type,
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data_source=dict(
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type=data_source,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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),
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pipeline=test_pipeline,
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prefetch=prefetch))
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