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Subdata.py
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Subdata.py
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import torch
from torch.utils.data import Dataset
import torch.utils.data as data
from PIL import Image
import numpy as np
from torchvision.datasets import MNIST, CIFAR10
torch.manual_seed(0)
torch.cuda.manual_seed(0)
np.random.seed(0)
class MNIST_truncated(data.Dataset):
def __init__(self, root, dataidxs=None, transform=None, target_transform=None, download=False):
self.root = root
self.dataidxs = dataidxs
self.transform = transform
self.target_transform = target_transform
self.download = download
self.data, self.target = self.__build_truncated_dataset__()
def __build_truncated_dataset__(self):
data = self.root.data
target = self.root.targets
if self.dataidxs is not None:
data = data[self.dataidxs]
target = target[self.dataidxs]
return data, target
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.target[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img.numpy(), mode='L')
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return (img, target)
def __len__(self):
return len(self.data)
class CIFAR10_truncated(data.Dataset):
def __init__(self, root, dataidxs=None,transform=None, target_transform=None, download=False):
self.root = root
self.dataidxs = dataidxs
self.transform = transform
self.target_transform = target_transform
self.download = download
self.data, self.target = self.__build_truncated_dataset__()
def __build_truncated_dataset__(self):
data = self.root.data
target =np.array( self.root.targets)
# target = np.array(cifar_dataobj.targets)
if self.dataidxs is not None:
data = data[self.dataidxs]
target = target[self.dataidxs]
return data, target
def truncate_channel(self, index):
for i in range(index.shape[0]):
gs_index = index[i]
self.data[gs_index, :, :, 1] = 0.0
self.data[gs_index, :, :, 2] = 0.0
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.target[index]
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
return len(self.data)
# class SubDataset(Dataset):
# def __init__(self, original_dataset, sub_labels,aera=[0,1], target_transform=None):
# super().__init__()
# self.dataset = original_dataset
# self.sub_indeces = []
# for index in range(len(self.dataset)):
# if index<len(self.dataset)*aera[0] or index>len(self.dataset)*aera[1]:
# continue
# if hasattr(original_dataset, "train_labels"):
# if self.dataset.target_transform is None:
# label = self.dataset.train_labels[index]
# else:
# label = self.dataset.target_transform(self.dataset.train_labels[index])
# elif hasattr(self.dataset, "test_labels"):
# if self.dataset.target_transform is None:
# label = self.dataset.test_labels[index]
# else:
# label = self.dataset.target_transform(self.dataset.test_labels[index])
# else:
# label = self.dataset[index][1]
# if label in sub_labels:
# self.sub_indeces.append(index)
# self.target_transform = target_transform
# def __len__(self):
# return len(self.sub_indeces)
# def __getitem__(self, index):
# sample = self.dataset[self.sub_indeces[index]]
# if self.target_transform:
# target = self.target_transform(sample[1])
# sample = (sample[0], target)
# return sample