Deep-Learning-with-PyTorch

168 CHAPTER 7 Telling birds from airplanes: Learning from images
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Figure 7.3 The 99th image from the
CIFAR-10 dataset: an automobile
7.1.3 Dataset transforms
That’s all very nice, but we’ll likely need a way to convert the PIL image to a PyTorch
tensor before we can do anything with it. That’s where torchvision.transforms
comes in. This module defines a set of composable, function-like objects that can be
passed as an argument to a torchvision dataset such as datasets.CIFAR10(…), and
that perform transformations on the data after it is loaded but before it is returned by
__getitem__. We can see the list of available objects as follows:
# In[8]:
from torchvision import transforms
dir(transforms)
# Out[8]:
['CenterCrop',
'ColorJitter',
...
'Normalize',
'Pad',
'RandomAffine',
...
'RandomResizedCrop',
'RandomRotation',
'RandomSizedCrop',
...
'TenCrop',
'ToPILImage',
'ToTensor',
...
]