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

# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing,
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"""
Imagenet dataset implementations for the image classification field in computer vision.
More info for the dataset can be found `here <http://www.image-net.org/>`__.
"""

import os
import random


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 import clean_path
from sparseml.utils.datasets import (
    IMAGENET_RGB_MEANS,
    IMAGENET_RGB_STDS,
    default_dataset_path,
)


__all__ = ["ImageNetDataset"]


[docs]@DatasetRegistry.register( key=["imagenet"], attributes={ "num_classes": 1000, "transform_means": IMAGENET_RGB_MEANS, "transform_stds": IMAGENET_RGB_STDS, }, ) class ImageNetDataset(ImageFolder, FFCVImageNetDataset): """ Wrapper for the ImageNet dataset to apply standard transforms. :param root: The root folder to find the dataset at :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 image_size: the size of the image to output from the dataset """ def __init__( self, root: str = default_dataset_path("imagenet"), train: bool = True, rand_trans: bool = False, image_size: int = 224, ): if torchvision_import_error is not None: raise torchvision_import_error root = clean_path(root) non_rand_resize_scale = 256.0 / 224.0 # standard used init_trans = ( [ transforms.RandomResizedCrop(image_size), transforms.RandomHorizontalFlip(), ] if rand_trans else [ transforms.Resize(round(non_rand_resize_scale * image_size)), transforms.CenterCrop(image_size), ] ) trans = [ *init_trans, transforms.ToTensor(), transforms.Normalize(mean=IMAGENET_RGB_MEANS, std=IMAGENET_RGB_STDS), ] root = os.path.join( os.path.abspath(os.path.expanduser(root)), "train" if train else "val" ) super().__init__(root, transform=transforms.Compose(trans)) self.image_size = image_size self.rand_trans = rand_trans if train: # make sure we don't preserve the folder structure class order random.shuffle(self.samples)