Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

40
41 # Builds a weighted random sampler to handle imbalanced classes
42 sampler = make_balanced_sampler(y_train_tensor)
43
44 # Uses sampler in the training set to get a balanced data loader
45 train_loader = DataLoader(
46 dataset=train_dataset, batch_size=16, sampler=sampler)
47 val_loader = DataLoader(dataset=val_dataset, batch_size=16)
Patches
There are different ways of breaking up an image into patches. The most
straightforward one is simply rearranging the pixels, so let’s start with that one.
Rearranging
Tensorflow has a utility function called tf.image.extract_patches() that does the
job, and we’re implementing a simplified version of this function in PyTorch with
tensor.unfold() (using only a kernel size and a stride, but no padding or anything
else):
# Adapted from https://discuss.pytorch.org/t/tf-extract-image-
# patches-in-pytorch/43837
def extract_image_patches(x, kernel_size, stride=1):
# Extract patches
patches = x.unfold(2, kernel_size, stride)
patches = patches.unfold(3, kernel_size, stride)
patches = patches.permute(0, 2, 3, 1, 4, 5).contiguous()
return patches.view(n, patches.shape[1], patches.shape[2], -1)
It works as if we were applying a convolution to the image. Each patch is actually a
receptive field (the region the filter is moving over to convolve), but, instead of
convolving the region, we’re just taking it as it is. The kernel size is the patch size,
and the number of patches depends on the stride—the smaller the stride, the more
patches. If the stride matches the kernel size, we’re effectively breaking up the
image into non-overlapping patches, so let’s do that:
Vision Transformer | 849