Source code for sparseml.pytorch.datasets.classification.imagenette

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# Licensed under the Apache License, Version 2.0 (the "License");
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""
Imagenette and Imagewoof dataset implementations for the image classification field in
computer vision.
More info for the dataset can be found `here <https://github.com/fastai/imagenette>`__.
"""

import random
from typing import Union


try:
    from torchvision import transforms
    from torchvision.datasets import ImageFolder

    torchvision_import_error = None
except Exception as torchvision_error:
    transforms = None
    ImageFolder = object  # default for constructor
    torchvision_import_error = torchvision_error

from sparseml.pytorch.datasets.image_classification.ffcv_dataset import (
    FFCVImageNetDataset,
)
from sparseml.pytorch.datasets.registry import DatasetRegistry
from sparseml.utils.datasets import (
    IMAGENET_RGB_MEANS,
    IMAGENET_RGB_STDS,
    ImagenetteDownloader,
    ImagenetteSize,
    ImagewoofDownloader,
    default_dataset_path,
)


__all__ = ["ImagenetteSize", "ImagenetteDataset", "ImagewoofDataset"]


[docs]@DatasetRegistry.register( key=["imagenette"], attributes={ "num_classes": 10, "transform_means": IMAGENET_RGB_MEANS, "transform_stds": IMAGENET_RGB_STDS, }, ) class ImagenetteDataset(ImagenetteDownloader, ImageFolder, FFCVImageNetDataset): """ Wrapper for the imagenette (10 class) dataset that fastai created. Handles downloading and applying standard transforms. :param root: The root folder to find the dataset at, if not found will download here if download=True :param train: True if this is for the training distribution, False for the validation :param rand_trans: True to apply RandomCrop and RandomHorizontalFlip to the data, False otherwise :param dataset_size: The size of the dataset to use and download: See ImagenetteSize for options :param image_size: The image size to output from the dataset :param download: True to download the dataset, False otherwise """ def __init__( self, root: str = default_dataset_path("imagenette"), train: bool = True, rand_trans: bool = False, dataset_size: ImagenetteSize = ImagenetteSize.s160, image_size: Union[int, None] = None, download: bool = True, ): if torchvision_import_error is not None: raise torchvision_import_error ImagenetteDownloader.__init__(self, root, dataset_size, download) if image_size is None: image_size = 160 if dataset_size == ImagenetteSize.s160 else 224 if rand_trans: trans = [ transforms.RandomResizedCrop(image_size), transforms.RandomHorizontalFlip(), ] else: resize_scale = 256.0 / 224.0 # standard used trans = [ transforms.Resize(round(resize_scale * image_size)), transforms.CenterCrop(image_size), ] trans.extend( [ transforms.ToTensor(), transforms.Normalize(mean=IMAGENET_RGB_MEANS, std=IMAGENET_RGB_STDS), ] ) ImageFolder.__init__(self, self.split_root(train), transforms.Compose(trans)) self.image_size = image_size self.rand_trans = rand_trans # make sure we don't preserve the folder structure class order random.shuffle(self.samples)
[docs]@DatasetRegistry.register( key=["imagewoof"], attributes={ "num_classes": 10, "transform_means": IMAGENET_RGB_MEANS, "transform_stds": IMAGENET_RGB_STDS, }, ) class ImagewoofDataset(ImagewoofDownloader, ImageFolder, FFCVImageNetDataset): """ Wrapper for the imagewoof (10 class) dataset that fastai created. Handles downloading and applying standard transforms. More info for the dataset can be found `here <https://github.com/fastai/imagenette>` :param root: The root folder to find the dataset at, if not found will download here if download=True :param train: True if this is for the training distribution, False for the validation :param rand_trans: True to apply RandomCrop and RandomHorizontalFlip to the data, False otherwise :param dataset_size: The size of the dataset to use and download: See :py:func `~ImagewoofSize` for options :param image_size: The image size to output from the dataset :param download: True to download the dataset, False otherwise """ def __init__( self, root: str = default_dataset_path("imagewoof"), train: bool = True, rand_trans: bool = False, dataset_size: ImagenetteSize = ImagenetteSize.s160, image_size: Union[int, None] = None, download: bool = True, ): if torchvision_import_error is not None: raise torchvision_import_error ImagewoofDownloader.__init__(self, root, dataset_size, download) if image_size is None: image_size = 160 if dataset_size == ImagenetteSize.s160 else 224 if rand_trans: trans = [ transforms.RandomResizedCrop(image_size), transforms.RandomHorizontalFlip(), ] else: resize_scale = 256.0 / 224.0 # standard used trans = [ transforms.Resize(round(resize_scale * image_size)), transforms.CenterCrop(image_size), ] trans.extend( [ transforms.ToTensor(), transforms.Normalize(mean=IMAGENET_RGB_MEANS, std=IMAGENET_RGB_STDS), ] ) ImageFolder.__init__(self, self.split_root(train), transforms.Compose(trans)) self.image_size = image_size self.rand_trans = rand_trans # make sure we don't preserve the folder structure class order random.shuffle(self.samples)