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dataloader.py
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import os
import os.path
import errno
import codecs
import numpy as np
import random
import torch
import torch.utils.data as data
from PIL import Image
def color_grayscale_arr(arr, color):
"""Converts grayscale image to red/green/blue image"""
assert arr.ndim == 2
dtype = arr.dtype
h, w = arr.shape
arr = np.reshape(arr, [h, w, 1])
if color == 0:
arr = np.concatenate([arr,
np.zeros((h, w, 2), dtype=dtype)], axis=2)
elif color == 1:
arr = np.concatenate([np.zeros((h, w, 1), dtype=dtype), arr,
np.zeros((h, w, 1), dtype=dtype)], axis=2)
elif color == 2:
arr = np.concatenate([np.zeros((h, w, 2), dtype=dtype), arr], axis=2)
return arr
# Dataset for colored MNIST
class ColorMNIST(data.Dataset):
urls = [
'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/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, val=False, transform=None, target_transform=None, download=False):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.train = train
self.val = val
self.dataset = 'mnist'
random.seed(100)
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))
if self.val:
idx_all = np.arange(len(self.train_labels))
idx_select = []
for i in range(10):
idx = (self.train_labels == i)
idx_select.extend(random.sample(idx_all[idx].tolist(), 2))
self.val_data = self.train_data[idx_select]
self.val_labels = self.train_labels[idx_select]
self.val_colored_data = self.prepare_rgb_data(self.val_data)
else:
self.train_colored_data = []
for idx, image in enumerate(self.train_data):
colored_arr = color_grayscale_arr(np.array(image), color=0)
self.train_colored_data.append(colored_arr)
else:
self.test_data, self.test_labels = torch.load(
os.path.join(self.root, self.processed_folder, self.test_file))
self.test_colored_data = self.prepare_rgb_data(self.test_data)
def __getitem__(self, index):
if self.train:
if self.val:
img, target = self.val_colored_data[index], self.val_labels[index]
else:
img, target = self.train_colored_data[index], self.train_labels[index]
else:
img, target = self.test_colored_data[index], self.test_labels[index]
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, index
def __len__(self):
if self.train:
if self.val:
return len(self.val_colored_data)
else:
return len(self.train_colored_data)
else:
return len(self.test_colored_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):
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 __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = 'train' if self.train is True else 'test'
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
def prepare_rgb_data(self, data):
num_total = len(data)
idx_all = np.arange(num_total)
random.shuffle(idx_all)
num_group = int(np.round(num_total / 3))
r_group = idx_all[:num_group]
g_group = idx_all[(num_group + 1):(2 * num_group + 1)]
# b_group = idx_all[(2 * num_group + 2):]
color_data = []
for idx, image in enumerate(data):
if idx in r_group:
colored_arr = color_grayscale_arr(np.array(image), color=0)
elif idx in g_group:
colored_arr = color_grayscale_arr(np.array(image), color=1)
else:
colored_arr = color_grayscale_arr(np.array(image), color=2)
color_data.append(colored_arr)
return color_data
def get_int(b):
return int(codecs.encode(b, 'hex'), 16)
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])
parsed = np.frombuffer(data, dtype=np.uint8, offset=8)
return torch.from_numpy(parsed.copy()).view(length).long()
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 = []
parsed = np.frombuffer(data, dtype=np.uint8, offset=16)
return torch.from_numpy(parsed.copy()).view(length, num_rows, num_cols)