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dataset.py
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dataset.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
from matplotlib import pyplot as plt
import numpy as np
import random
import os
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import utils
class MNIST(Dataset):
data_dir = 'MNIST'
c, h, w = 1, 28, 28
n_class = 10
def __init__(self, phase, flip_rate=0.5):
if phase == 'train':
self.data = open(os.path.join(self.data_dir, 'mnist_train.csv'), 'r').read().split('\n')[:-1]
elif phase == 'val':
self.data = open(os.path.join(self.data_dir, 'mnist_test.csv'), 'r').read().split('\n')[:-1]
self.data = [d.split(',') for d in self.data]
self.flip_rate = flip_rate
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
cls = int(self.data[idx][0])
label = np.zeros(self.n_class)
label[cls] = 1
image = np.array(self.data[idx][1:]).reshape(self.h, self.w).astype(np.float32) / 255.
if random.random() < self.flip_rate:
image = np.fliplr(image)
image = image.reshape(self.c, self.h, self.w)
label = torch.from_numpy(label.copy()).float()
image = torch.from_numpy(image.copy()).float()
return {
'X': image,
'Y': label
}
class FashionMNIST(Dataset):
data_dir = 'fashionMNIST'
c, h, w = 1, 28, 28
n_class = 10
def __init__(self, phase, flip_rate=0.5):
if phase == 'train':
self.data = open(os.path.join(self.data_dir, 'fashion-mnist_train.csv'), 'r').read().split('\n')[1:-1]
elif phase == 'val':
self.data = open(os.path.join(self.data_dir, 'fashion-mnist_test.csv'), 'r').read().split('\n')[1:-1]
self.data = [d.split(',') for d in self.data]
self.flip_rate = flip_rate
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
cls = int(self.data[idx][0])
label = np.zeros(self.n_class)
label[cls] = 1
image = np.array(self.data[idx][1:]).reshape(self.h, self.w).astype(np.float32) / 255.
if random.random() < self.flip_rate:
image = np.fliplr(image)
image = image.reshape(self.c, self.h, self.w)
label = torch.from_numpy(label.copy()).float()
image = torch.from_numpy(image.copy()).float()
return {
'X': image,
'Y': label
}
class MNISTplusFashion(Dataset):
data_dirM = 'MNIST'
data_dirF = 'fashionMNIST'
c, h, w = 1, 28, 28
n_class = 20
def __init__(self, phase, flip_rate=0.5):
if phase == 'train':
self.dataM = open(os.path.join(self.data_dirM, 'mnist_train.csv'), 'r').read().split('\n')[:-1]
self.dataF = open(os.path.join(self.data_dirF, 'fashion-mnist_train.csv'), 'r').read().split('\n')[1:-1]
elif phase == 'val':
self.dataM = open(os.path.join(self.data_dirM, 'mnist_test.csv'), 'r').read().split('\n')[:-1]
self.dataF = open(os.path.join(self.data_dirF, 'fashion-mnist_test.csv'), 'r').read().split('\n')[1:-1]
self.dataM = [d.split(',') for d in self.dataM]
self.dataF = [d.split(',') for d in self.dataF]
for idx in range(len(self.dataF)):
self.dataF[idx][0] = int(self.dataF[idx][0]) + 10
self.data = self.dataM + self.dataF
self.flip_rate = flip_rate
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
cls = int(self.data[idx][0])
label = np.zeros(self.n_class)
label[cls] = 1
image = np.array(self.data[idx][1:]).reshape(self.h, self.w).astype(np.float32) / 255.
if random.random() < self.flip_rate:
image = np.fliplr(image)
image = image.reshape(self.c, self.h, self.w)
label = torch.from_numpy(label.copy()).float()
image = torch.from_numpy(image.copy()).float()
return {
'X': image,
'Y': label
}
def show_batch(batch):
img_batch = batch['X']
img_batch[:,0,...].mul_(1)
grid = utils.make_grid(img_batch)
plt.imshow(grid.numpy().transpose((1, 2, 0)))
plt.title('Batch from dataloader')
if __name__ == "__main__":
train_data = MNISTplusFashion(phase='train')
# test a batch
batch_size = 4
for i in range(batch_size):
sample = train_data[i]
print("sample %d," % i, sample['X'].shape, sample['Y'].shape)
# test dataloader
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=False, num_workers=4)
for idx, batch in enumerate(train_loader):
print("{} batch, x.size() {}, y.size() {}".format(idx, batch['X'].size(), batch['Y'].size()))
assert len(batch['X'].size()) == 4
assert batch['X'].size()[0] == batch_size
assert batch['Y'].size()[0] == batch_size
if idx == 3:
plt.figure()
show_batch(batch)
plt.axis('off')
plt.ioff()
plt.show()
break
print("Pass Test")