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added the rfs transforms for cifarfs #303

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added the rfs transforms for cifarfs #303

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a9e9b9c
added the rfs transforms for cifarfs (and fc100)
brando90 Feb 5, 2022
6b01dea
Update cifarfs_benchmark.py
brando90 Feb 5, 2022
cbafcd0
forgot from torchvision.transforms import Compose
brando90 Feb 5, 2022
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56 changes: 52 additions & 4 deletions learn2learn/vision/benchmarks/cifarfs_benchmark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,28 +5,76 @@

from learn2learn.data.transforms import NWays, KShots, LoadData, RemapLabels, ConsecutiveLabels

from torchvision.transforms import Compose

def cifarfs_tasksets(
train_ways=5,
train_samples=10,
test_ways=5,
test_samples=10,
root='~/data',
data_augmentation=None,
device=None,
**kwargs,
):
"""Tasksets for CIFAR-FS benchmarks."""
data_transform = tv.transforms.ToTensor()
if data_augmentation is None:
train_data_transforms = tv.transforms.ToTensor()
test_data_transforms = tv.transforms.ToTensor()
elif data_augmentation == 'normalize':
train_data_transforms = Compose([
lambda x: x / 255.0,
])
test_data_transforms = train_data_transforms
elif data_augmentation == 'rfs2020':
"""
# original
if augment:
transform = transforms.Compose([
lambda x: Image.fromarray(x),
transforms.RandomCrop(32, padding=4),
transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
transforms.RandomHorizontalFlip(),
lambda x: np.asarray(x),
transforms.ToTensor(),
normalize_cifar100
])
else:
transform = transforms.Compose([
lambda x: Image.fromarray(x),
transforms.ToTensor(),
normalize_cifar100
])
return transform
"""
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mean = [0.5071, 0.4867, 0.4408]
std = [0.2675, 0.2565, 0.2761]
normalize = tv.transforms.Normalize(mean=mean, std=std)
train_data_transforms = Compose([
ToPILImage(),
RandomCrop(32, padding=4),
ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
RandomHorizontalFlip(),
ToTensor(),
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normalize,
])
test_data_transforms = Compose([
ToTensor(),
normalize,
])
else:
raise('Invalid data_augmentation argument.')

train_dataset = l2l.vision.datasets.CIFARFS(root=root,
transform=data_transform,
transform=train_data_transforms,
mode='train',
download=True)
valid_dataset = l2l.vision.datasets.CIFARFS(root=root,
transform=data_transform,
transform=train_data_transforms,
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Should be test_data_transforms, right?

mode='validation',
download=True)
test_dataset = l2l.vision.datasets.CIFARFS(root=root,
transform=data_transform,
transform=test_data_transforms,
mode='test',
download=True)
if device is not None:
Expand Down