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dataset.py
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dataset.py
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import codecs
import errno
import numpy as np
import os
import random
import torch
from torch.nn.functional import interpolate
from torch.utils.data import Dataset
from torchvision import transforms
# https://github.com/proceduralia/tgan-pytorch/blob/master/dataset.py
class MovingMNIST(Dataset):
def __init__(self, dataset_path, n_frames=16, norm_mean=0, norm_std=1, rescale=None):
self.norm_mean = norm_mean
self.norm_std = norm_std
self.data = torch.from_numpy(np.float32(np.load(dataset_path)))
#Dataset will be of the form (L, T, C, H, W)
self.data = self.data.permute(1, 0, 2, 3).unsqueeze(2)
# rescale if specified
if rescale:
self.data = interpolate(self.data, scale_factor=(1, rescale, rescale))
self.n_frames = n_frames #This can't be greater than 20
#self.normalize = transforms.Normalize(self.norm_mean, self.norm_std)
self.normalize = lambda x: (x - 128)/128
self.denormalize = lambda x: x*128 + 128
def __len__(self):
return self.data.size(0)
def __getitem__(self, i):
T = self.data.size(1)
ot = np.random.randint(T - self.n_frames) if T > self.n_frames else 0
x = self.data[i, ot:(ot + self.n_frames)]
x = self.normalize(x)
return x
class MovingMNISTLR(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, download=False):
self.root = os.path.expanduser(root)
self.train = train
if download:
self.download()
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):
if self.train:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
img = np.array(img) / 255.
img = np.roll(img, random.randrange(28), 1)
left = random.choice([0, 1])
seq = [img]
for i in range(19):
img = np.roll(img, -1, 1) if left else np.roll(img, 1, 1)
seq.append(img)
seq = torch.from_numpy(np.array(seq)).view(-1, 1, 28, 28).float()
return seq
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 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).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).view(length, num_rows, num_cols)
if __name__ == "__main__":
dset = MovingMNIST("data/mnist_test_seq.npy")
zero = dset[0]
print(type(zero))
print(zero.size())