def download(self):
"""
Download, and unzip in the correct location.
Returns:
"""
import urllib
import zipfile
if self.check_exists():
return
# download files
try:
os.makedirs(self.root)
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, filename)
ext = os.path.splitext(file_path)[1]
with open(file_path, 'wb') as f:
f.write(data.read())
if ext == '.zip':
with zipfile.ZipFile(file_path) as zip_f:
zip_f.extractall(self.root)
os.unlink(file_path)
print('Done!')
def download(self):
"""Download the EMNIST data if it doesn't exist in processed_folder already."""
from six.moves import urllib
import gzip
import shutil
import zipfile
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
print('Downloading ' + self.url)
data = urllib.request.urlopen(self.url)
filename = self.url.rpartition('/')[2]
raw_folder = os.path.join(self.root, self.raw_folder)
file_path = os.path.join(raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
print('Extracting zip archive')
with zipfile.ZipFile(file_path) as zip_f:
zip_f.extractall(raw_folder)
os.unlink(file_path)
gzip_folder = os.path.join(raw_folder, 'gzip')
for gzip_file in os.listdir(gzip_folder):
if gzip_file.endswith('.gz'):
print('Extracting ' + gzip_file)
with open(os.path.join(raw_folder, gzip_file.replace('.gz', '')), 'wb') as out_f, \
gzip.GzipFile(os.path.join(gzip_folder, gzip_file)) as zip_f:
out_f.write(zip_f.read())
shutil.rmtree(gzip_folder)
# process and save as torch files
for split in self.splits:
print('Processing ' + split)
training_set = (
read_image_file(os.path.join(raw_folder, 'emnist-{}-train-images-idx3-ubyte'.format(split))),
read_label_file(os.path.join(raw_folder, 'emnist-{}-train-labels-idx1-ubyte'.format(split)))
)
test_set = (
read_image_file(os.path.join(raw_folder, 'emnist-{}-test-images-idx3-ubyte'.format(split))),
read_label_file(os.path.join(raw_folder, 'emnist-{}-test-labels-idx1-ubyte'.format(split)))
)
with open(os.path.join(self.root, self.processed_folder, self._training_file(split)), 'wb') as f:
torch.save(training_set, f)
with open(os.path.join(self.root, self.processed_folder, self._test_file(split)), 'wb') as f:
torch.save(test_set, f)
print('Done!')
def save_dataset(self):
""""""
import gzip
if self._check_exists(self.processed_folder):
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 download(self):
"""Download the Moving 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 = torch.from_numpy(
np.load(
os.path.join(self.root, self.raw_folder,
'mnist_test_seq.npy')).swapaxes(0,
1)[:-self.split])
test_set = torch.from_numpy(
np.load(
os.path.join(self.root, self.raw_folder,
'mnist_test_seq.npy')).swapaxes(0,
1)[-self.split:])
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 _download_files(self):
for key, url in self.urls.items():
filename = url.rpartition('/')[2]
file_path = join(self.root, self.raw_folder, filename)
if not os.path.exists(file_path) and\
not os.path.exists(file_path.replace('.zip', '')):
print('Downloading ' + url)
data = urllib.request.urlopen(url)
with open(file_path, 'wb') as f:
f.write(data.read())
def download(self):
"""Download the MusicNet data if it doesn't exist in ``raw_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))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
filename = self.url.rpartition('/')[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
if not os.path.exists(file_path):
print('Downloading ' + self.url)
data = urllib.request.urlopen(self.url)
with open(file_path, 'wb') as f:
# stream the download to disk (it might not fit in memory!)
while True:
chunk = data.read(16*1024)
if not chunk:
break
f.write(chunk)
if not all(map(lambda f: os.path.exists(os.path.join(self.root, f)), self.extracted_folders)):
print('Extracting ' + filename)
if call(["tar", "-xf", file_path, '-C', self.root, '--strip', '1']) != 0:
raise OSError("Failed tarball extraction")
# process and save as torch files
print('Processing...')
self.process_data(self.test_data)
trees = self.process_labels(self.test_labels)
with open(os.path.join(self.root, self.test_labels, self.test_tree), 'wb') as f:
pickle.dump(trees, f)
self.process_data(self.train_data)
trees = self.process_labels(self.train_labels)
with open(os.path.join(self.root, self.train_labels, self.train_tree), 'wb') as f:
pickle.dump(trees, f)
print('Download Complete')
def download(self):
"""Download the REDE data if it doesn't exist in `processed_folder` already."""
from six.moves import urllib
if self._check_exists():
return
# Make directories.
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
try:
os.makedirs(os.path.join(self.root, self.processed_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
# Download dataset.
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())
# Process and save as torch files.
print('Processing...')
full_set = (
read_file(
os.path.join(self.root, self.raw_folder,
'1848-62-111-images.pkl')),
read_file(
os.path.join(self.root, self.raw_folder,
'1848-5-parameters.pkl'))
# read_file(os.path.join(self.root, self.raw_folder, '1848-frequencies_modes.pkl'))
)
with open(
os.path.join(self.root, self.processed_folder, self.full_file),
'wb') as f:
torch.save(full_set, f)
print('Done!')
def download(self):
from six.moves import urllib
import zipfile
if self._check_exists():
return
try:
os.makedirs(os.path.join(self.root, self.splits_folder))
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 k, url in self.vinyals_split_sizes.items():
print('== Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[-1]
file_path = os.path.join(self.root, self.splits_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
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())
file_processed = os.path.join(self.root, self.processed_folder)
print("== Unzip from " + file_path + " to " + file_processed)
zip_ref = zipfile.ZipFile(file_path, 'r')
zip_ref.extractall(file_processed)
zip_ref.close()
print("Download finished.")
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
if not os.path.exists(
os.path.join(self.root, self.raw_folder,
'notMNIST_small.mat')):
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())
# process and save as torch files
print('Processing...')
import scipy.io as sio
data = sio.loadmat(
os.path.join(self.root, self.raw_folder, 'notMNIST_small.mat'))
images = torch.ByteTensor(data['images']).permute(
2, 0, 1) # The data is stored as HxWxN, need to permute!
labels = torch.LongTensor(data['labels'])
data_set = (
images,
labels,
)
with open(
os.path.join(self.root, self.processed_folder,
self.training_file), 'wb') as f:
torch.save(data_set, f)
print('Done!')
def download_url(url, folder, log=True):
if log:
print('Downloading', url)
makedirs(folder)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
path = osp.join(folder, filename)
with open(path, 'wb') as f:
f.write(data.read())
return path
def download(self):
"""Download the CamVid data if it doesn't exist in processed_folder already."""
self.raw_folder.mkdir(exist_ok=True, parents=True)
self.processed_folder.mkdir(exist_ok=True, parents=True)
print(f'Downloading {self.urls["raw"]}')
data = urllib.request.urlopen(self.urls["raw"])
with tempfile.NamedTemporaryFile('w') as tmp:
tmp.write(data.read())
with zipfile.ZipFile(tmp.name) as zip_f:
zip_f.extractall(self.raw_folder)
print(f'Downloading {self.urls["labels"]}')
data = urllib.request.urlopen(self.urls["labels"])
with tempfile.NamedTemporaryFile('wb') as tmp:
tmp.write(data.read())
with zipfile.ZipFile(tmp.name) as zip_f:
zip_f.extractall(self.raw_folder / 'LabeledApproved_full')
print(f'Downloading {self.urls["classes"]}')
data = urllib.request.urlopen(self.urls["classes"])
with open(self.processed_folder / 'label_colors.txt', 'wb') as class_list:
class_list.write(data.read())
# process and save as torch files
print('Processing...')
self.class_to_idx, colours = self.read_label_file(self.processed_folder / 'label_colors.txt')
with h5py.File(self.training_file, 'w') as f_train, h5py.File(self.test_file, 'w') as f_test:
self.process_raw_image_files(self.raw_folder / '701_StillsRaw_full', f_train, f_test)
self.process_label_image_files(self.raw_folder / 'LabeledApproved_full', colours, f_train, f_test)
print('Done!')
def download(self):
"""Download the rar files data if it doesn't exist in processed_folder already."""
from six.moves import urllib
import rarfile
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('/')[-1]
file_path = os.path.join(self.root, self.raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
with rarfile.RarFile(file_path) as rar_f:
rar_f.extractall(self.raw_folder)
#os.unlink(file_path)
# process and save as torch files
print('Processing...')
train_set = read_rar_file(
rarfile.RarFile(
os.path.join(self.root, self.raw_folder,
'experimental_dataset_2013.rar')))
test_set = read_rar_file(
rarfile.RarFile(
os.path.join(self.root, self.raw_folder,
'icdar2013_benchmarking_dataset.rar')))
with open(
os.path.join(self.root, self.processed_folder,
self.training_file), 'wb') as f:
torch.save(train_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 load_l8(path, use_hr, use_mr, use_lr):
bands_selected = []
if use_hr:
bands_selected = bands_selected + L8_BANDS_HR
if use_mr:
bands_selected = bands_selected + L8_BANDS_MR
if use_lr:
bands_selected = bands_selected + L8_BANDS_LR
bands_selected = sorted(bands_selected)
with rasterio.open(path) as data:
l8 = data.read(bands_selected)
l8 = l8.astype(np.float32)
l8 = np.clip(l8, 0, 1)
l8 = normalize_L8(l8)
return l8
def load_s2(path, use_hr, use_mr, use_lr):
bands_selected = []
if use_hr:
bands_selected = bands_selected + S2_BANDS_HR
if use_mr:
bands_selected = bands_selected + S2_BANDS_MR
if use_lr:
bands_selected = bands_selected + S2_BANDS_LR
bands_selected = sorted(bands_selected)
with rasterio.open(path) as data:
s2 = data.read(bands_selected)
s2 = s2.astype(np.float32)
s2 = np.clip(s2, 0, 10000)
s2 = normalize_S2(s2)
return s2
def load_s1(path, imgTransform):
with rasterio.open(path) as data:
band1 = data.read(1)
band2 = data.read(2)
band1 = band1.astype(np.float32)
band1 = np.nan_to_num(band1)
band1 = np.clip(band1, -25, 0)
if not imgTransform:
band1 /= 25
band1 += 1
band2 = band2.astype(np.float32)
band2 = np.nan_to_num(band2)
band2 = np.clip(band2, -25, 0)
if not imgTransform:
band2 /= 25
band2 += 1
# band3 = abs(band2 - band1)
# band3 /= 25
s1 = np.stack((band1, band2)) # , band3))
return s1
def download_data(url,save_path):
'''
数据下载工具，当原始数据不存在时会进行下载
:param url:
:param save_path:
:return:
'''
print('downloading data from {}'.format(url))
if not os.path.exists(save_path):
os.makedirs(save_path)
data=urllib.request.urlopen(url)
filename='DD.zip'
with open(os.path.join(save_path,filename),'wb') as f:
f.write(data.read())
return True
def download(self):
from six.moves import urllib
import zipfile
print('\n-- Loading PhotoTour dataset: {}'.format(self.name))
if self._check_exists():
print('Found cached data {}'.format(self.data_file))
return
# download files
try:
os.makedirs(self.root)
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
if not self._check_downloaded():
url = self.urls[self.name]
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.root, filename)
print('Downloading {}\nDownloading {}\n\nIt might take while. '
'Please grab yourself a coffee and relax.\n'.format(
url, file_path))
with open(file_path, 'wb') as f:
f.write(data.read())
print('Extracting data {}\n'.format(self.data_down))
with zipfile.ZipFile(file_path, 'r') as z:
z.extractall(self.data_dir)
os.unlink(file_path)
# process and save as torch files
print('Caching data {}'.format(self.data_file))
data_set = (read_image_file(self.data_dir, self.image_ext, self.size,
self.lens[self.name]),
read_info_file(self.data_dir, self.info_file),
read_matches_files(self.data_dir, self.matches_files))
with open(self.data_file, 'wb') as f:
torch.save(data_set, f)
def __init__(self, root, json, vocab, transform=None):
"""Set the path for images, captions and vocabulary wrapper.
Args:
root: image directory.
json: coco annotation file path.
vocab: vocabulary wrapper.
transform: image transformer.
"""
self.root = root
with open(json, "r") as data:
dict_data = ast.literal_eval(data.read())
self.coco = dict_data
self.ids = list(self.coco.keys())
self.vocab = vocab
self.transform = transform
def data_download(self):
"""Download the VCTK data if it doesn't exist in processed_folder already."""
from six.moves import urllib
import tarfile
raw_abs_dir = os.path.join(self.root, self.raw_folder)
splits = ['train', 'valid', 'test']
processed_abs_dirs = [os.path.join(self.root, self.processed_folder, \
split) for split in splits]
dset_abs_path = os.path.join(self.root, self.raw_folder,
self.dset_path)
if self._check_exists():
return raw_abs_dir, dset_abs_path, processed_abs_dirs, splits
# download files
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
try:
for processed_abs_dir in processed_abs_dirs:
os.makedirs(processed_abs_dir)
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
url = self.url
print('Downloading ' + url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
if not os.path.isfile(file_path):
data = urllib.request.urlopen(url)
with open(file_path, 'wb') as f:
f.write(data.read())
if not os.path.exists(dset_abs_path):
with tarfile.open(file_path) as zip_f:
zip_f.extractall(raw_abs_dir)
else:
print("Using existing raw folder")
if not self.dev_mode:
os.unlink(file_path)
return raw_abs_dir, dset_abs_path, processed_abs_dirs, splits
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 maybe_download(root):
from six.moves import urllib
import zipfile
processed_path = os.path.join(root, 'processed')
splits_dirs = {
'meta_train': os.path.join(processed_path, 'images_background'),
'meta_test': os.path.join(processed_path, 'images_evaluation')
}
if check_exists(splits_dirs):
return splits_dirs
# download files
data_urls = [
'https://github.com/brendenlake/omniglot/raw/master/python/images_background.zip',
'https://github.com/brendenlake/omniglot/raw/master/python/images_evaluation.zip'
]
raw_folder = 'raw'
processed_folder = 'processed'
try:
os.makedirs(os.path.join(root, raw_folder))
os.makedirs(os.path.join(root, processed_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
for url in data_urls:
print('== Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(root, raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
file_processed = os.path.join(root, processed_folder)
print("== Unzip from " + file_path + " to " + file_processed)
zip_ref = zipfile.ZipFile(file_path, 'r')
zip_ref.extractall(file_processed)
zip_ref.close()
print("Download finished.")
return splits_dirs
def load_lc(path, no_savanna=False, igbp=True):
# load labels
with rasterio.open(path) as data:
lc = data.read(1)
# convert IGBP to dfc2020 classes
if igbp:
lc = np.take(DFC2020_CLASSES, lc)
else:
lc = lc.astype(np.int64)
# adjust class scheme to ignore class savanna
if no_savanna:
lc[lc == 3] = 0
lc[lc > 3] -= 1
# convert to zero-based labels and set ignore mask
lc -= 1
lc[lc == -1] = 255
return lc
def download(self):
from six.moves import urllib
if self._check_exists():
return
# download files
try:
os.makedirs(self.root)
except OSError as e:
if e.errno == os.errno.EEXIST:
pass
else:
raise
print('Downloading ' + self.url)
data = urllib.request.urlopen(self.url)
file_path = os.path.join(self.root, self.mat_file)
with open(file_path, 'wb') as f:
f.write(data.read())
print('Done!')
def download(self):
from six.moves import urllib
import numpy as np
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
print('Downloading ' + self.url)
data = urllib.request.urlopen(self.url)
filename = os.path.basename(self.url)
file_path = os.path.join(self.root, self.raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
# Process and save as torch files
print('Processing...')
with open(file_path, 'rb') as f:
raw_dataset = np.load(f)
training_set = (255 * torch.ByteTensor(raw_dataset['imgs']),
torch.ByteTensor(raw_dataset['latents_classes']))
with open(
os.path.join(self.root, self.processed_folder,
self.training_file), 'wb') as f:
torch.save(training_set, f)
print('Done!')
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))
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())
def __download(self):
"""
Downloads the KMNIST dataset from the web if dataset
hasn't already been downloaded.
"""
from six.moves import urllib
if self.__check_exists():
return
print("Downloading KMNIST dataset")
urls = [
'http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-images-idx3-ubyte.gz',
'http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-labels-idx1-ubyte.gz',
'http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-images-idx3-ubyte.gz',
'http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-labels-idx1-ubyte.gz',
]
# download files
try:
os.makedirs(self.__path)
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
for url in urls:
print('Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.__path, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
print('Done!')
def download(self):
"""Download the FGVC-Aircraft data if it doesn't exist already."""
from six.moves import urllib
import tarfile
if self._check_exists():
return
# prepare to download data to PARENT_DIR/fgvc-aircraft-2013.tar.gz
print('Downloading %s ... (may take a few minutes)' % self.url)
parent_dir = os.path.abspath(os.path.join(self.root, os.pardir))
tar_name = self.url.rpartition('/')[-1]
tar_path = os.path.join(parent_dir, tar_name)
data = urllib.request.urlopen(self.url)
# download .tar.gz file
with open(tar_path, 'wb') as f:
f.write(data.read())
# extract .tar.gz to PARENT_DIR/fgvc-aircraft-2013b
data_folder = tar_path.strip('.tar.gz')
print('Extracting %s to %s ... (may take a few minutes)' %
(tar_path, data_folder))
tar = tarfile.open(tar_path)
tar.extractall(parent_dir)
# if necessary, rename data folder to self.root
if not os.path.samefile(data_folder, self.root):
print('Renaming %s to %s ...' % (data_folder, self.root))
os.rename(data_folder, self.root)
# delete .tar.gz file
print('Deleting %s ...' % tar_path)
os.remove(tar_path)
print('Done!')
def download(self):
"""Download CODH char shapes data if it doesn't exist in
processed_folder already."""
from six.moves import urllib
import zipfile
# download files
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
for book_id in self.book_ids:
url = self.download_url_format.format(book_id, book_id)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
if self._check_integrity(file_path, self.zips_md5[filename]):
print('File already downloaded and verified: ' + filename)
continue
print('Downloading ' + url)
with open(file_path, 'wb') as f:
f.write(data.read())
print('Extracting data: ' + filename)
with zipfile.ZipFile(file_path, 'r') as zip_ref:
target_dir = file_path.replace('.zip', '')
zip_ref.extractall(target_dir)
# remove download zip file
os.unlink(file_path)
def download(self):
"""Download the MNIST data if it doesn't exist in processed_folder already."""
from six.moves import urllib
import gzip
print("download: trying to download")
if self._check_exists():
print("download: already exists so exiting")
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...')
train_label, train_non_few_shot_ids, train_few_shot_ids = read_label_file(
os.path.join(self.root, self.raw_folder,
'train-labels-idx1-ubyte'), self.few_shot_class)
train_img = read_image_file(os.path.join(self.root, self.raw_folder,
'train-images-idx3-ubyte'),
non_few_shot_ids=train_non_few_shot_ids)
training_set = (train_img, train_label)
test_label, test_non_few_shot_ids, test_few_shot_ids = read_label_file(
os.path.join(self.root, self.raw_folder, 't10k-labels-idx1-ubyte'),
self.few_shot_class)
test_img = read_image_file(os.path.join(self.root, self.raw_folder,
't10k-images-idx3-ubyte'),
few_shot_ids=test_few_shot_ids)
if self.test_emnist:
print("Download: Entering Emnist test")
from emnist import extract_test_samples
images, labels = extract_test_samples('letters')
print(images.shape)
print(labels.shape)
#randomly grab a letter
import random
rand_letter_idx = random.randint(0, 25)
#idx for selected letter clas
test_sample_ids = np.where(labels < 10)[0]
np.random.seed(10)
np.random.shuffle(test_sample_ids)
print('test_sample_ids_len', len(test_sample_ids))
#grab labels and images from that class
labels = labels[test_sample_ids]
images = images[test_sample_ids]
print("After selecting one class")
print(images.shape)
print(labels.shape)
#assert(self.few_shot_class not in labels)
if self.max_test_sample:
test_set = {
torch.ByteTensor(list(images[:self.max_test_sample])).view(
-1, 28, 28),
torch.LongTensor(list(labels[:self.max_test_sample]))
}
else:
test_set = {
torch.ByteTensor(list(images)).view(-1, 28, 28),
torch.LongTensor(list(labels))
}
else:
# test_label, test_non_few_shot_ids, test_few_shot_ids= read_label_file(os.path.join(self.root, self.raw_folder, 't10k-labels-idx1-ubyte'), self.few_shot_class)
# test_img = read_image_file(os.path.join(self.root, self.raw_folder, 't10k-images-idx3-ubyte'), few_shot_ids=test_few_shot_ids)
if (self.max_test_sample):
print('testing max test sample')
test_set = (test_img[:self.max_test_sample],
test_label[:self.max_test_sample])
else:
test_set = (test_img, test_label)
print('confirming test size')
#print(len(test_set[0]), len(test_set[1]))
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 download(self):
"""Download the MNISTM data."""
# import essential packages
from six.moves import urllib
import gzip
import pickle
from torchvision import datasets
# check if dataset already exists
if self._check_exists():
return
# make data dirs
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
# download pkl files
logging.info("Downloading " + self.url)
filename = self.url.rpartition("/")[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
if not os.path.exists(file_path.replace(".gz", "")):
data = urllib.request.urlopen(self.url)
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
logging.info("Processing...")
# load MNIST-M images from pkl file
with open(file_path.replace(".gz", ""), "rb") as f:
mnist_m_data = pickle.load(f, encoding="bytes")
mnist_m_train_data = torch.ByteTensor(mnist_m_data[b"train"])
mnist_m_test_data = torch.ByteTensor(mnist_m_data[b"test"])
# get MNIST labels
mnist_train_labels = datasets.MNIST(
root=self.mnist_root, train=True, download=True
).targets
mnist_test_labels = datasets.MNIST(
root=self.mnist_root, train=False, download=True
).targets
# save MNIST-M dataset
training_set = (mnist_m_train_data, mnist_train_labels)
test_set = (mnist_m_test_data, mnist_test_labels)
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)
logging.info("[DONE]")
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...')
train_label, train_remove_mask = read_label_file(
os.path.join(self.root, self.raw_folder,
'train-labels-idx1-ubyte'),
remove_label=self.get_rid_of)
train_data = read_image_file(os.path.join(self.root, self.raw_folder,
'train-images-idx3-ubyte'),
remove_mask=train_remove_mask)
training_set = (train_data, train_label)
#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_label, test_remove_mask = read_label_file(
os.path.join(self.root, self.raw_folder, 't10k-labels-idx1-ubyte'),
remove_label=self.get_rid_of)
test_data = read_image_file(os.path.join(self.root, self.raw_folder,
't10k-images-idx3-ubyte'),
remove_mask=test_remove_mask)
test_set = (test_data, test_label)
#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 download(self):
"""Download the EMNIST data if it doesn't exist in processed_folder already."""
from six.moves import urllib
import gzip
import shutil
import zipfile
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
print('Downloading ' + self.url)
data = urllib.request.urlopen(self.url)
filename = self.url.rpartition('/')[2]
raw_folder = os.path.join(self.root, self.raw_folder)
file_path = os.path.join(raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
print('Extracting zip archive')
with zipfile.ZipFile(file_path) as zip_f:
zip_f.extractall(raw_folder)
os.unlink(file_path)
gzip_folder = os.path.join(raw_folder, 'gzip')
for gzip_file in os.listdir(gzip_folder):
if gzip_file.endswith('.gz'):
print('Extracting ' + gzip_file)
with open(os.path.join(raw_folder, gzip_file.replace('.gz', '')), 'wb') as out_f, \
gzip.GzipFile(os.path.join(gzip_folder, gzip_file)) as zip_f:
out_f.write(zip_f.read())
shutil.rmtree(gzip_folder)
# process and save as torch files
for split in self.splits:
print('Processing ' + split)
training_set = (
read_image_file(
os.path.join(
raw_folder,
'emnist-{}-train-images-idx3-ubyte'.format(split))),
read_label_file(
os.path.join(
raw_folder,
'emnist-{}-train-labels-idx1-ubyte'.format(split))))
test_set = (
read_image_file(
os.path.join(
raw_folder,
'emnist-{}-test-images-idx3-ubyte'.format(split))),
read_label_file(
os.path.join(
raw_folder,
'emnist-{}-test-labels-idx1-ubyte'.format(split))))
with open(
os.path.join(self.root, self.processed_folder,
self._training_file(split)), 'wb') as f:
torch.save(training_set, f)
with open(
os.path.join(self.root, self.processed_folder,
self._test_file(split)), 'wb') as f:
torch.save(test_set, f)
print('Done!')
def download(self):
"""Download the MNIST data."""
# import essential packages
from six.moves import urllib
import gzip
import pickle
from torchvision import datasets
# check if dataset already exists
if self._check_exists():
return
# make data dirs
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
# download pkl files
print('Downloading ' + self.url)
filename = self.url.rpartition('/')[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
if not os.path.exists(file_path.replace('.gz', '')):
data = urllib.request.urlopen(self.url)
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...')
# load MNIST-M images from pkl file
with open(file_path.replace('.gz', ''), "rb") as f:
mnist_m_data = pickle.load(f, encoding='bytes')
mnist_m_train_data = torch.ByteTensor(mnist_m_data[b'train'])
mnist_m_test_data = torch.ByteTensor(mnist_m_data[b'test'])
# get MNIST labels
mnist_train_labels = datasets.MNIST(root=self.mnist_root,
train=True,
download=True).train_labels
mnist_test_labels = datasets.MNIST(root=self.mnist_root,
train=False,
download=True).test_labels
# save MNIST-M dataset
training_set = (mnist_m_train_data, mnist_train_labels)
test_set = (mnist_m_test_data, mnist_test_labels)
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 download(self):
if not os.path.isdir(self.root):
os.makedirs(self.root)
log_path = os.path.join(self.root, "train.csv")
if not os.path.isfile(log_path):
print("Download log...", flush=True)
data = urllib.request.urlopen(self.url_train_log)
with open(log_path, 'wb') as f:
f.write(data.read())
keys = [
'', 'ID', 'x_crit', 'y_crit', 'source_ID', 'z_source', 'z_lens',
'mag_source', 'ein_area', 'n_crit', 'r_source', 'crit_area',
'n_pix_source', 'source_flux', 'n_pix_lens', 'lens_flux',
'n_source_im', 'mag_eff', 'sb_contrast', 'color_diff', 'n_gal_3',
'n_gal_5', 'n_gal_10', 'halo_mass', 'star_mass', 'mag_lens',
'n_sources'
]
assert len(keys) == 27
with open(log_path, 'rt') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
data = [x for x in reader if len(x) == 27 and not 'ID' in x]
data = [{k: float(x) if x else math.nan
for k, x in zip(keys, xs)} for xs in data]
self.data = {x['ID']: x for x in data}
gz_path = os.path.join(self.root, "datapack2.0train.tar.gz")
if not os.path.isfile(gz_path):
print("Download...", flush=True)
data = urllib.request.urlopen(self.url_train)
with open(gz_path, 'wb') as f:
f.write(data.read())
tar_path = os.path.join(self.root, "datapack2.0train.tar")
if not os.path.isfile(tar_path):
print("Decompress...", flush=True)
import gzip
import shutil
with gzip.open(gz_path, 'rb') as f_in:
with open(tar_path, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
dir_path = os.path.join(self.root, "datapack2.0train")
if not os.path.isdir(dir_path):
print("Extract...", flush=True)
import tarfile
tar = tarfile.open(tar_path)
tar.extractall(dir_path)
tar.close()
# print("Open tar...", flush=True)
# import tarfile
# self.tar = tarfile.open(tar_path)
self.files = list(
zip(*(sorted(
glob.glob(
os.path.join(dir_path, "Public/{}/*.fits".format(band))))
for band in ("EUC_VIS", "EUC_J", "EUC_Y", "EUC_H"))))
assert all(
len({x.split('-')[-1]
for x in fs}) == 1 for fs in self.files)