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dataset.py
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import torch
from torchvision import datasets, transforms
def mnist(root, batch_size=64, num_workers=0):
train_set = datasets.MNIST(root=root + 'mnist/train',
train=True,
download=True,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_set = datasets.MNIST(root=root + 'mnist/test',
train=False,
download=True,
transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers)
return train_loader, test_loader
def cifar10(root, batch_size=64):
train_set = datasets.CIFAR10(root=root + 'cifar/train',
train=True,
download=True,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=False, num_workers=0)
test_set = datasets.CIFAR10(root=root + 'cifar/test',
train=False,
download=True,
transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0)
return train_loader, test_loader
def cifar100(root, batch_size=64, num_workers=0):
train_set = datasets.CIFAR100(root=root + 'cifar100/train',
train=True,
download=True,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_set = datasets.CIFAR100(root=root + 'cifar100/test',
train=False,
download=True,
transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers)
return train_loader, test_loader
def svhn(root, batch_size=64, num_workers=0):
train_set = datasets.SVHN(root=root + 'svhn/train',
split='train',
download=True,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_set = datasets.SVHN(root=root + 'svhn/test',
split='test',
download=True,
transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers)
return train_loader, test_loader
class CIFAR_AUGMENTED:
def __init__(self, root):
self.cifar = datasets.CIFAR10
self.directory = root + 'cifar/train'
self.tensor = transforms.ToTensor()
# 1. original image
def original(self):
train_set_original = self.cifar(root=self.directory, train=True, download=True, transform=self.tensor)
return train_set_original
# 2. horizontal flip and jitter
def horizontal_flip_jitter(self):
flip = transforms.RandomHorizontalFlip(p=0.5)
jitter = transforms.ColorJitter(brightness=(.7, 1.3), contrast=(.7, 1.3), saturation=(.7, 1.3))
transform = transforms.Compose([self.tensor, flip, jitter])
train_set_flip_jitter = self.cifar(root=self.directory, train=True, download=False, transform=transform)
return train_set_flip_jitter
# 3. random crop and affine
def random_crop_affine(self):
crop = transforms.RandomResizedCrop(size=(32, 32), scale=(.7, 1.3))
affine = transforms.RandomAffine(degrees=(-10, 10), translate=(0.0, 0.1), scale=(.9, 1.1))
transform = transforms.Compose([self.tensor, crop, affine])
train_set_crop_affine = self.cifar(root=self.directory, train=True, download=False, transform=transform)
return train_set_crop_affine
# 4. gaussian blur
def gaussian_blur(self):
blur = transforms.GaussianBlur(kernel_size=(3, 3), sigma=(0.01, 1.0))
transform = transforms.Compose([self.tensor, blur])
train_set_blur = self.cifar(root=self.directory, train=True, download=False, transform=transform)
return train_set_blur
def cifar10_augmented(root, batch_size=64, num_workers=0):
train_set = []
augment = CIFAR_AUGMENTED(root=root)
train_set.append(augment.original())
train_set.append(augment.horizontal_flip_jitter())
train_set.append(augment.random_crop_affine())
train_set.append(augment.gaussian_blur())
train_set = torch.utils.data.ConcatDataset(train_set)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_set = datasets.CIFAR10(root=root + 'cifar/test', train=False, download=True, transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers)
return train_loader, test_loader
class CIFAR100_AUGMENTED:
def __init__(self, root):
self.cifar = datasets.CIFAR100
self.directory = root + 'cifar100/train'
self.tensor = transforms.ToTensor()
# 1. original image
def original(self):
train_set_original = self.cifar(root=self.directory, train=True, download=True, transform=self.tensor)
return train_set_original
# 2. horizontal flip and jitter
def horizontal_flip_jitter(self):
flip = transforms.RandomHorizontalFlip(p=0.25)
crop = transforms.RandomResizedCrop(size=(32, 32), scale=(.5, 1.))
jitter = transforms.ColorJitter(brightness=(.7, 1.3), contrast=(.7, 1.3), saturation=(.7, 1.3))
transform = transforms.Compose([self.tensor, flip, crop, jitter])
train_set_flip_jitter = self.cifar(root=self.directory, train=True, download=False, transform=transform)
return train_set_flip_jitter
# 3. affine transformation
def affine_transformation_blur(self):
affine = transforms.RandomAffine(degrees=(-90, 90), translate=(0.0, .25), scale=(.9, 1.1))
blur = transforms.GaussianBlur(kernel_size=(3, 3), sigma=(0.5, 1.5))
transform = transforms.Compose([self.tensor, affine, blur])
train_set_blur = self.cifar(root=self.directory, train=True, download=False, transform=transform)
return train_set_blur
def cifar100_augmented(root, batch_size=64, num_workers=0):
train_set = []
augment = CIFAR100_AUGMENTED(root=root)
train_set.append(augment.original())
train_set.append(augment.horizontal_flip_jitter())
train_set.append(augment.affine_transformation_blur())
train_set = torch.utils.data.ConcatDataset(train_set)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_set = datasets.CIFAR100(root=root + 'cifar100/test',
train=False,
download=True,
transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers)
return train_loader, test_loader
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
def imshow(img, name, n):
img = np.transpose(img.numpy(), (1, 2, 0))
plt.subplot(1, 2, n)
plt.title(name)
plt.imshow(img)
aug = CIFAR100_AUGMENTED(root='./data/')
train = torch.utils.data.DataLoader(aug.original(), batch_size=128, shuffle=False, num_workers=0)
x_train1, _ = next(iter(train))
train = torch.utils.data.DataLoader(aug.horizontal_flip_jitter(), batch_size=128, shuffle=False, num_workers=0)
x_train2, _ = next(iter(train))
for i1, i2 in zip(x_train1, x_train2):
plt.figure('figure')
imshow(i1, name='original image', n=1)
imshow(i2, name='augmented image', n=2)
plt.show()