| Crop a random part of the image, resize it, and normalize it with the image mean and standard deviation: | |
| from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor | |
| normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std) | |
| size = ( | |
| image_processor.size["shortest_edge"] | |
| if "shortest_edge" in image_processor.size | |
| else (image_processor.size["height"], image_processor.size["width"]) | |
| ) | |
| _transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize]) | |
| Then create a preprocessing function to apply the transforms and return the pixel_values - the inputs to the model - of the image: | |
| def transforms(examples): | |
| examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]] | |
| del examples["image"] | |
| return examples | |
| To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.with_transform] method. |