# 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.
"""
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)