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# dataset settings | |
dataset_type = 'CIFAR100' | |
data_preprocessor = dict( | |
num_classes=100, | |
# RGB format normalization parameters | |
mean=[129.304, 124.070, 112.434], | |
std=[68.170, 65.392, 70.418], | |
# loaded images are already RGB format | |
to_rgb=False) | |
train_pipeline = [ | |
dict(type='RandomCrop', crop_size=32, padding=4), | |
dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
dict(type='PackClsInputs'), | |
] | |
test_pipeline = [ | |
dict(type='PackClsInputs'), | |
] | |
train_dataloader = dict( | |
batch_size=16, | |
num_workers=2, | |
dataset=dict( | |
type=dataset_type, | |
data_prefix='data/cifar100', | |
test_mode=False, | |
pipeline=train_pipeline), | |
sampler=dict(type='DefaultSampler', shuffle=True), | |
) | |
val_dataloader = dict( | |
batch_size=16, | |
num_workers=2, | |
dataset=dict( | |
type=dataset_type, | |
data_prefix='data/cifar100/', | |
test_mode=True, | |
pipeline=test_pipeline), | |
sampler=dict(type='DefaultSampler', shuffle=False), | |
) | |
val_evaluator = dict(type='Accuracy', topk=(1, )) | |
test_dataloader = val_dataloader | |
test_evaluator = val_evaluator | |