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imageset.py
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imageset.py
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#!/usr/bin/env python3
from trojanzoo.datasets import Dataset
from trojanvision.environ import env
from trojanvision.utils.transform import (get_transform_bit,
get_transform_imagenet,
get_transform_cifar,
RandomMixup,
RandomCutmix)
import torch
import torchvision.transforms as transforms
from torch.utils.data.dataloader import default_collate
import argparse
import os
from typing import TYPE_CHECKING
from typing import Iterable
from torchvision.datasets import VisionDataset # TODO: python 3.10
import PIL.Image as Image
if TYPE_CHECKING:
import torch.utils.data
class ImageSet(Dataset):
r"""
| The basic class representing an image dataset.
| It inherits :class:`trojanzoo.datasets.Dataset`.
Note:
This is the implementation of dataset.
For users, please use :func:`create` instead, which is more user-friendly.
Args:
norm_par (dict[str, list[float]]):
Data normalization parameters of ``'mean'`` and ``'std'``
(e.g., ``{'mean': [0.5, 0.4, 0.6], 'std': [0.2, 0.3, 0.1]}``).
Defaults to ``None``.
normalize (bool): Whether to use :any:`torchvision.transforms.Normalize`
in dataset transform. Otherwise, use it as model preprocess layer.
transform (str): The dataset transform type.
* ``None |'none'`` (:any:`torchvision.transforms.PILToTensor`
and :any:`torchvision.transforms.ConvertImageDtype`)
* ``'bit'`` (transform used in BiT network)
* ``'pytorch'`` (pytorch transform used in ImageNet training).
Defaults to ``None``.
Note:
See :meth:`get_transform()` to get more details.
auto_augment (bool): Whether to use
:any:`torchvision.transforms.AutoAugment`.
Defaults to ``False``.
mixup (bool): Whether to use
:class:`trojanvision.utils.transforms.RandomMixup`.
Defaults to ``False``.
mixup_alpha (float): :attr:`alpha` passed to
:class:`trojanvision.utils.transforms.RandomMixup`.
Defaults to ``0.0``.
cutmix (bool): Whether to use
:class:`trojanvision.utils.transforms.RandomCutmix`.
Defaults to ``False``.
cutmix_alpha (float): :attr:`alpha` passed to
:class:`trojanvision.utils.transforms.RandomCutmix`.
Defaults to ``0.0``.
cutout (bool): Whether to use
:class:`trojanvision.utils.transforms.Cutout`.
Defaults to ``False``.
cutout_length (int): Cutout length. Defaults to ``None``.
**kwargs: keyword argument passed to
:class:`trojanzoo.datasets.Dataset`.
Attributes:
data_type (str): Defaults to ``'image'``.
num_classes (int): Defaults to ``1000``.
data_shape (list[int]): The shape of image data ``[C, H, W]``.
Defaults to ``[3, 224, 224]``.
"""
name: str = 'imageset'
data_type: str = 'image'
num_classes = 1000
data_shape = [3, 224, 224]
@classmethod
def add_argument(cls, group: argparse._ArgumentGroup) -> argparse._ArgumentGroup:
r"""Add image dataset arguments to argument parser group.
View source to see specific arguments.
Note:
This is the implementation of adding arguments.
The concrete dataset class may override this method to add more arguments.
For users, please use :func:`add_argument()` instead, which is more user-friendly.
See Also:
:meth:`trojanzoo.datasets.Dataset.add_argument()`
"""
super().add_argument(group)
group.add_argument(
'--dataset_normalize', dest='normalize', action='store_true',
help='use transforms.Normalize in dataset transform. '
'(It\'s used in model as the first layer by default.)')
group.add_argument('--transform', choices=[None, 'none', 'bit', 'pytorch'])
group.add_argument('--auto_augment', action='store_true',
help='use auto augment')
group.add_argument('--mixup', action='store_true', help='use mixup')
group.add_argument('--mixup_alpha', type=float, help='mixup alpha (default: 0.0)')
group.add_argument('--cutmix', action='store_true', help='use cutmix')
group.add_argument('--cutmix_alpha', type=float, help='cutmix alpha (default: 0.0)')
group.add_argument('--cutout', action='store_true', help='use cutout')
group.add_argument('--cutout_length', type=int, help='cutout length')
return group
def __init__(self, norm_par: dict[str, list[float]] = None,
normalize: bool = False, transform: str = None,
auto_augment: bool = False,
mixup: bool = False, mixup_alpha: float = 0.0,
cutmix: bool = False, cutmix_alpha: float = 0.0,
cutout: bool = False, cutout_length: int = None,
**kwargs):
self.norm_par: dict[str, list[float]] = norm_par
self.normalize = normalize
self.transform = transform
self.auto_augment = auto_augment
self.mixup = mixup
self.mixup_alpha = mixup_alpha
self.cutmix = cutmix
self.cutmix_alpha = cutmix_alpha
self.cutout = cutout
self.cutout_length = cutout_length
mixup_transforms = []
if mixup:
mixup_transforms.append(RandomMixup(self.num_classes, p=1.0, alpha=mixup_alpha))
if cutmix:
mixup_transforms.append(RandomCutmix(self.num_classes, p=1.0, alpha=cutmix_alpha))
if len(mixup_transforms):
mixupcutmix = mixup_transforms[0] if len(mixup_transforms) == 1 \
else transforms.RandomChoice(mixup_transforms)
def collate_fn(batch: Iterable[torch.Tensor]) -> Iterable[torch.Tensor]:
return mixupcutmix(*default_collate(batch)) # noqa: E731
self.collate_fn = collate_fn
super().__init__(**kwargs)
self.param_list['imageset'] = ['data_shape', 'norm_par',
'normalize', 'transform',
'auto_augment']
if cutout:
self.param_list['imageset'].append('cutout_length')
if mixup:
self.param_list['imageset'].append('mixup_alpha')
if cutmix:
self.param_list['imageset'].append('cutmix_alpha')
def get_transform(self, mode: str, normalize: bool = None
) -> transforms.Compose:
r"""Get dataset transform based on :attr:`self.transform`.
* ``None |'none'`` (:any:`torchvision.transforms.PILToTensor`
and :any:`torchvision.transforms.ConvertImageDtype`)
* ``'bit'`` (transform used in BiT network)
* ``'pytorch'`` (pytorch transform used in ImageNet training).
Args:
mode (str): The dataset mode (e.g., ``'train' | 'valid'``).
normalize (bool | None):
Whether to use :any:`torchvision.transforms.Normalize`
in dataset transform. Defaults to ``self.normalize``.
Returns:
torchvision.transforms.Compose: The transform sequence.
"""
normalize = normalize if normalize is not None else self.normalize
if self.transform == 'bit':
return get_transform_bit(mode, self.data_shape)
elif self.data_shape == [3, 224, 224]:
transform = get_transform_imagenet(
mode, use_tuple=self.transform != 'pytorch',
auto_augment=self.auto_augment)
elif self.transform != 'none' and self.data_shape in ([3, 16, 16], [3, 32, 32]):
transform = get_transform_cifar(
mode, auto_augment=self.auto_augment,
cutout=self.cutout, cutout_length=self.cutout_length,
data_shape=self.data_shape)
else:
transform = transforms.Compose([transforms.PILToTensor(),
transforms.ConvertImageDtype(torch.float)])
if normalize and self.norm_par is not None:
transform.transforms.append(transforms.Normalize(
mean=self.norm_par['mean'], std=self.norm_par['std']))
return transform
@staticmethod
def get_data(data: tuple[torch.Tensor, torch.Tensor],
**kwargs) -> tuple[torch.Tensor, torch.Tensor]:
r"""Process image data.
Defaults to put input and label on ``env['device']`` with ``non_blocking``
and transform label to ``torch.LongTensor``.
Args:
data (tuple[torch.Tensor, torch.Tensor]): Tuple of batched input and label.
**kwargs: Any keyword argument (unused).
Returns:
(tuple[torch.Tensor, torch.Tensor]):
Tuple of batched input and label on ``env['device']``.
Label is transformed to ``torch.LongTensor``.
"""
return (data[0].to(env['device'], non_blocking=True),
data[1].to(env['device'], dtype=torch.long, non_blocking=True))
def make_folder(self, img_type: str = '.png', **kwargs):
r"""Save the dataset to ``self.folder_path``
as :class:`trojanvision.datasets.ImageFolder` format.
``'{self.folder_path}/{self.name}/{mode}/{class_name}/{img_idx}.png'``
Args:
img_type (str): The image types to save. Defaults to ``'.png'``.
"""
mode_list: list[str] = [
'train', 'valid'] if self.valid_set else ['train']
class_names = getattr(self, 'class_names',
[str(i) for i in range(self.num_classes)])
for mode in mode_list:
dataset: VisionDataset = self.get_org_dataset(mode, transform=None)
class_counters = [0] * self.num_classes
for image, target_class in list(dataset):
image: Image.Image
target_class: int
class_name = class_names[target_class]
_dir = os.path.join(
self.folder_path, self.name, mode, class_name)
if not os.path.exists(_dir):
os.makedirs(_dir)
image.save(os.path.join(
_dir, f'{class_counters[target_class]}{img_type}'))
class_counters[target_class] += 1