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How to use CutMix and MixUp

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CutMix and MixUp are popular augmentation strategies that can improve classification accuracy.

These transforms are slightly different from the rest of the Torchvision transforms, because they expect batches of samples as input, not individual images. In this example we’ll explain how to use them: after the DataLoader, or as part of a collation function.

import torch
from torchvision.datasets import FakeData
from torchvision.transforms import v2


NUM_CLASSES = 100

Pre-processing pipeline

We’ll use a simple but typical image classification pipeline:

preproc = v2.Compose([
    v2.PILToTensor(),
    v2.RandomResizedCrop(size=(224, 224), antialias=True),
    v2.RandomHorizontalFlip(p=0.5),
    v2.ToDtype(torch.float32, scale=True),  # to float32 in [0, 1]
    v2.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),  # typically from ImageNet
])

dataset = FakeData(size=1000, num_classes=NUM_CLASSES, transform=preproc)

img, label = dataset[0]
print(f"{type(img) = }, {img.dtype = }, {img.shape = }, {label = }")
type(img) = <class 'torch.Tensor'>, img.dtype = torch.float32, img.shape = torch.Size([3, 224, 224]), label = 67

One important thing to note is that neither CutMix nor MixUp are part of this pre-processing pipeline. We’ll add them a bit later once we define the DataLoader. Just as a refresher, this is what the DataLoader and training loop would look like if we weren’t using CutMix or MixUp:

from torch.utils.data import DataLoader

dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

for images, labels in dataloader:
    print(f"{images.shape = }, {labels.shape = }")
    print(labels.dtype)
    # <rest of the training loop here>
    break
images.shape = torch.Size([4, 3, 224, 224]), labels.shape = torch.Size([4])
torch.int64

Where to use MixUp and CutMix

After the DataLoader

Now let’s add CutMix and MixUp. The simplest way to do this right after the DataLoader: the Dataloader has already batched the images and labels for us, and this is exactly what these transforms expect as input:

dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

cutmix = v2.CutMix(num_classes=NUM_CLASSES)
mixup = v2.MixUp(num_classes=NUM_CLASSES)
cutmix_or_mixup = v2.RandomChoice([cutmix, mixup])

for images, labels in dataloader:
    print(f"Before CutMix/MixUp: {images.shape = }, {labels.shape = }")
    images, labels = cutmix_or_mixup(images, labels)
    print(f"After CutMix/MixUp: {images.shape = }, {labels.shape = }")

    # <rest of the training loop here>
    break
Before CutMix/MixUp: images.shape = torch.Size([4, 3, 224, 224]), labels.shape = torch.Size([4])
After CutMix/MixUp: images.shape = torch.Size([4, 3, 224, 224]), labels.shape = torch.Size([4, 100])

Note how the labels were also transformed: we went from a batched label of shape (batch_size,) to a tensor of shape (batch_size, num_classes). The transformed labels can still be passed as-is to a loss function like torch.nn.functional.cross_entropy().

As part of the collation function

Passing the transforms after the DataLoader is the simplest way to use CutMix and MixUp, but one disadvantage is that it does not take advantage of the DataLoader multi-processing. For that, we can pass those transforms as part of the collation function (refer to the PyTorch docs to learn more about collation).

from torch.utils.data import default_collate


def collate_fn(batch):
    return cutmix_or_mixup(*default_collate(batch))


dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=2, collate_fn=collate_fn)

for images, labels in dataloader:
    print(f"{images.shape = }, {labels.shape = }")
    # No need to call cutmix_or_mixup, it's already been called as part of the DataLoader!
    # <rest of the training loop here>
    break
images.shape = torch.Size([4, 3, 224, 224]), labels.shape = torch.Size([4, 100])

Non-standard input format

So far we’ve used a typical sample structure where we pass (images, labels) as inputs. MixUp and CutMix will magically work by default with most common sample structures: tuples where the second parameter is a tensor label, or dict with a “label[s]” key. Look at the documentation of the labels_getter parameter for more details.

If your samples have a different structure, you can still use CutMix and MixUp by passing a callable to the labels_getter parameter. For example:

batch = {
    "imgs": torch.rand(4, 3, 224, 224),
    "target": {
        "classes": torch.randint(0, NUM_CLASSES, size=(4,)),
        "some_other_key": "this is going to be passed-through"
    }
}


def labels_getter(batch):
    return batch["target"]["classes"]


out = v2.CutMix(num_classes=NUM_CLASSES, labels_getter=labels_getter)(batch)
print(f"{out['imgs'].shape = }, {out['target']['classes'].shape = }")
out['imgs'].shape = torch.Size([4, 3, 224, 224]), out['target']['classes'].shape = torch.Size([4, 100])

Total running time of the script: (0 minutes 0.162 seconds)

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