# 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
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
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)