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# ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# for Denoising Diffusion GAN. To view a copy of this license, see the LICENSE file.
# ---------------------------------------------------------------
import numpy as np
from PIL import Image
import torchvision.datasets as dset
import torchvision.transforms as transforms
class StackedMNIST(dset.MNIST):
def __init__(self, root, train=True, transform=None, target_transform=None,
download=False):
super(StackedMNIST, self).__init__(root=root, train=train, transform=transform,
target_transform=target_transform, download=download)
index1 = np.hstack([np.random.permutation(len(self.data)), np.random.permutation(len(self.data))])
index2 = np.hstack([np.random.permutation(len(self.data)), np.random.permutation(len(self.data))])
index3 = np.hstack([np.random.permutation(len(self.data)), np.random.permutation(len(self.data))])
self.num_images = 2 * len(self.data)
self.index = []
for i in range(self.num_images):
self.index.append((index1[i], index2[i], index3[i]))
def __len__(self):
return self.num_images
def __getitem__(self, index):
img = np.zeros((28, 28, 3), dtype=np.uint8)
target = 0
for i in range(3):
img_, target_ = self.data[self.index[index][i]], int(self.targets[self.index[index][i]])
img[:, :, i] = img_
target += target_ * 10 ** (2 - i)
img = Image.fromarray(img, mode="RGB")
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 _data_transforms_stacked_mnist():
"""Get data transforms for cifar10."""
train_transform = transforms.Compose([
transforms.Pad(padding=2),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
])
valid_transform = transforms.Compose([
transforms.Pad(padding=2),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
])
return train_transform, valid_transform
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