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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.data import Cutout
import torch
import torchvision.transforms as transforms
import argparse
import os
from typing import TYPE_CHECKING
from torchvision.datasets import VisionDataset # TODO: python 3.10
import PIL.Image as Image
from typing import Union
if TYPE_CHECKING:
import torch.utils.data
class ImageSet(Dataset):
name: str = 'imageset'
data_type: str = 'image'
num_classes = 1000
data_shape = [3, 224, 224]
@classmethod
def add_argument(cls, group: argparse._ArgumentGroup):
super().add_argument(group)
group.add_argument('--transform', choices=[None, 'bit', 'pytorch'])
group.add_argument('--auto_augment', action='store_true', help='use auto augment')
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]] = {'mean': [0.0], 'std': [1.0], },
default_model: str = 'resnet18_comp',
transform: str = None, auto_augment: bool = False,
cutout: bool = False, cutout_length: int = None, **kwargs):
self.norm_par: dict[str, list[float]] = norm_par
self.transform = transform
self.auto_augment = auto_augment
self.cutout = cutout
self.cutout_length = cutout_length
super().__init__(default_model=default_model, **kwargs)
self.param_list['imageset'] = ['data_shape', 'norm_par', 'transform', 'auto_augment', 'cutout']
if cutout:
self.param_list['imageset'].append('cutout_length')
def get_transform(self, mode: str) -> Union[transforms.Compose, transforms.ToTensor]:
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.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.ToTensor()
return transform
def get_dataloader(self, mode: str = None, dataset: Dataset = None, batch_size: int = None, shuffle: bool = None,
num_workers: int = None, pin_memory=True, drop_last=False, **kwargs) -> torch.utils.data.DataLoader:
if batch_size is None:
batch_size = self.test_batch_size if mode == 'test' else self.batch_size
if shuffle is None:
shuffle = True if mode == 'train' else False
num_workers = num_workers if num_workers is not None else self.num_workers
if dataset is None:
dataset = self.get_dataset(mode, **kwargs)
if env['num_gpus'] == 0:
pin_memory = False
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle,
num_workers=num_workers, pin_memory=pin_memory, drop_last=drop_last)
@staticmethod
def get_data(data: tuple[torch.Tensor, torch.Tensor], **kwargs) -> tuple[torch.Tensor, torch.Tensor]:
return data[0].to(env['device'], non_blocking=True), data[1].to(env['device'], dtype=torch.long, non_blocking=True)
def get_class_to_idx(self, **kwargs) -> dict[str, int]:
if hasattr(self, 'class_to_idx'):
return getattr(self, 'class_to_idx')
return {str(i): i for i in range(self.num_classes)}
def make_folder(self, img_type: str = '.png', **kwargs):
mode_list: list[str] = ['train', 'valid'] if self.valid_set else ['train']
class_to_idx = self.get_class_to_idx(**kwargs)
idx_to_class = {v: k for k, v in class_to_idx.items()}
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 = idx_to_class[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
def get_transform_bit(mode: str, data_shape: list[int]) -> transforms.Compose:
hyperrule = data_shape[-2] * data_shape[-1] < 96 * 96
precrop, crop = (160, 128) if hyperrule else (512, 480)
if mode == 'train':
transform = transforms.Compose([
transforms.Resize((precrop, precrop)),
transforms.RandomCrop((crop, crop)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
else:
transform = transforms.Compose([
transforms.Resize((crop, crop)),
transforms.ToTensor()])
return transform
def get_transform_imagenet(mode: str, use_tuple: bool = False, auto_augment: bool = False) -> transforms.Compose:
if mode == 'train':
transform_list = [
transforms.RandomResizedCrop((224, 224) if use_tuple else 224),
transforms.RandomHorizontalFlip(),
# transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
]
if auto_augment:
transform_list.append(transforms.AutoAugment(transforms.AutoAugmentPolicy.IMAGENET))
transform_list.append(transforms.ToTensor())
transform = transforms.Compose(transform_list)
else:
transform = transforms.Compose([
transforms.Resize((256, 256) if use_tuple else 256),
transforms.CenterCrop((224, 224) if use_tuple else 224),
transforms.ToTensor()])
return transform
def get_transform_cifar(mode: str, auto_augment: bool = False,
cutout: bool = False, cutout_length: int = None,
data_shape: list[int] = [3, 32, 32]) -> transforms.Compose:
if mode != 'train':
return transforms.ToTensor()
cutout_length = data_shape[-1] // 2 if cutout_length is None else cutout_length
transform_list = [
transforms.RandomCrop(data_shape[-2:], padding=data_shape[-1] // 8),
transforms.RandomHorizontalFlip(),
]
if auto_augment:
transform_list.append(transforms.AutoAugment(transforms.AutoAugmentPolicy.CIFAR10))
transform_list.append(transforms.ToTensor())
if cutout:
transform_list.append(Cutout(cutout_length))
return transforms.Compose(transform_list)