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fashion.py
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fashion.py
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from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import os
import os.path
import errno
import torch
import codecs
class MNIST_fashion(data.Dataset):
"""`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset.
Args:
root (string): Root directory of dataset where ``processed/training.pt``
and ``processed/test.pt`` exist.
train (bool, optional): If True, creates dataset from ``training.pt``,
otherwise from ``test.pt``.
download (bool, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
"""
urls = [
'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz',
'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz',
'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz',
'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz',
]
raw_folder = 'raw'
processed_folder = 'processed'
training_file = 'training.pt'
test_file = 'test.pt'
def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.train = train # training set or test set
if download:
self.download()
if not self._check_exists():
raise RuntimeError('Dataset not found.' +
' You can use download=True to download it')
if self.train:
self.train_data, self.train_labels = torch.load(
os.path.join(self.root, self.processed_folder, self.training_file))
else:
self.test_data, self.test_labels = torch.load(
os.path.join(self.root, self.processed_folder, self.test_file))
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[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):
if self.train:
return len(self.train_data)
else:
return len(self.test_data)
def _check_exists(self):
return os.path.exists(os.path.join(self.root, self.processed_folder, self.training_file)) and \
os.path.exists(os.path.join(self.root, self.processed_folder, self.test_file))
def download(self):
"""Download the MNIST data if it doesn't exist in processed_folder already."""
from six.moves import urllib
import gzip
if self._check_exists():
return
# download files
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
os.makedirs(os.path.join(self.root, self.processed_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
for url in self.urls:
print('Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
with open(file_path.replace('.gz', ''), 'wb') as out_f, \
gzip.GzipFile(file_path) as zip_f:
out_f.write(zip_f.read())
os.unlink(file_path)
# process and save as torch files
print('Processing...')
training_set = (
read_image_file(os.path.join(self.root, self.raw_folder, 'train-images-idx3-ubyte')),
read_label_file(os.path.join(self.root, self.raw_folder, 'train-labels-idx1-ubyte'))
)
test_set = (
read_image_file(os.path.join(self.root, self.raw_folder, 't10k-images-idx3-ubyte')),
read_label_file(os.path.join(self.root, self.raw_folder, 't10k-labels-idx1-ubyte'))
)
with open(os.path.join(self.root, self.processed_folder, self.training_file), 'wb') as f:
torch.save(training_set, f)
with open(os.path.join(self.root, self.processed_folder, self.test_file), 'wb') as f:
torch.save(test_set, f)
print('Done!')
def get_int(b):
return int(codecs.encode(b, 'hex'), 16)
def parse_byte(b):
if isinstance(b, str):
return ord(b)
return b
def read_label_file(path):
with open(path, 'rb') as f:
data = f.read()
assert get_int(data[:4]) == 2049
length = get_int(data[4:8])
labels = [parse_byte(b) for b in data[8:]]
assert len(labels) == length
return torch.LongTensor(labels)
def read_image_file(path):
with open(path, 'rb') as f:
data = f.read()
assert get_int(data[:4]) == 2051
length = get_int(data[4:8])
num_rows = get_int(data[8:12])
num_cols = get_int(data[12:16])
images = []
idx = 16
for l in range(length):
img = []
images.append(img)
for r in range(num_rows):
row = []
img.append(row)
for c in range(num_cols):
row.append(parse_byte(data[idx]))
idx += 1
assert len(images) == length
return torch.ByteTensor(images).view(-1, 28, 28)