def process(self):
from ase.io import read
extract_tar(self.raw_paths[0], self.raw_dir, log=False)
materials = read(osp.join(self.raw_dir, 'structures.xyz'), index=':')
bandgaps = np.loadtxt(osp.join(self.raw_dir, 'bandgaps.csv'))
data_list = []
for material, bandgap in zip(materials, bandgaps):
pos = torch.from_numpy(material.get_positions()).to(torch.float)
z = torch.from_numpy(material.get_atomic_numbers()).to(torch.int64)
y = torch.tensor([float(bandgap)])
data_list.append(Data(z=z, pos=pos, y=y))
train_data = data_list[:10000]
test_data = data_list[10000:]
if self.pre_filter is not None:
train_data = [d for d in train_data if self.pre_filter(d)]
test_data = [d for d in test_data if self.pre_filter(d)]
if self.pre_transform is not None:
train_data = [self.pre_transform(d) for d in train_data]
test_data = [self.pre_transform(d) for d in test_data]
torch.save(self.collate(train_data), self.processed_paths[0])
torch.save(self.collate(test_data), self.processed_paths[1])
def download(self):
url = self.processed_url if rdkit is None else self.raw_url
file_path = download_url(url, self.raw_dir)
print('------------')
print(self.raw_dir)
print('--------------')
extract_tar(file_path, self.raw_dir)
os.unlink(file_path)
def download(self):
for name in self.available_datasets[self.name]:
path = download_url(f'{self.url}/{name}', self.raw_dir)
if name.endswith('.tar.gz'):
extract_tar(path, self.raw_dir)
elif name.endswith('.gz'):
extract_gz(path, self.raw_dir)
os.unlink(path)
def download(self):
for name in self.available_datasets[self.name]:
path = download_url('{}/{}'.format(self.url, name), self.raw_dir)
print(path)
if name.endswith('.tar.gz'):
extract_tar(path, self.raw_dir)
elif name.endswith('.gz'):
extract_gz(path, self.raw_dir)
os.unlink(path)
def download(self):
folder = osp.join(self.root, self.name)
for file in self.available_sets_df[self.name]:
path = download_url('{}/{}'.format(self.url, file), folder)
if file.endswith('.tar.gz'):
extract_tar(path, folder)
elif file.endswith('.gz'):
extract_gz(path, folder)
os.unlink(path)
shutil.rmtree(self.raw_dir)
os.rename(folder, self.raw_dir)
print('Raw\n{}'.format(self.raw_dir))
def download(self):
path = download_url(self.image_url, self.raw_dir)
extract_tar(path, self.raw_dir, mode='r')
os.unlink(path)
image_path = osp.join(self.raw_dir, 'TrainVal', 'VOCdevkit', 'VOC2011')
os.rename(image_path, osp.join(self.raw_dir, 'images'))
shutil.rmtree(osp.join(self.raw_dir, 'TrainVal'))
path = download_url(self.annotation_url, self.raw_dir)
extract_tar(path, self.raw_dir)
os.unlink(path)
path = download_url(self.split_url, self.raw_dir)
os.rename(path, osp.join(self.raw_dir, 'splits.npz'))
def download(self):
# Download and extract the archive.
filename = self.url.rpartition('/')[2]
path = osp.join(self.raw_dir, filename)
if osp.exists(path): # pragma: no cover
print(f"Using existing file {filename}")
else:
# - We need to use the system 'wget' because urllib does not work
# with Dropbox.
print(f'Downloading {self.url}')
os.system(f"wget {self.url} -O {path} -q")
# - Extract the downloaded archive.
extract_tar(path, self.raw_dir)
# Delete the archive.
os.unlink(path)
def download(self):
path = download_url(self.url, self.root)
extract_tar(path, self.root)
os.unlink(path)
shutil.rmtree(self.raw_dir)
os.rename(osp.join(self.root, 'nell_data'), self.raw_dir)
def download(self):
path = download_url(self.url, self.raw_dir)
extract_tar(path, self.raw_dir)
os.unlink(path)
def download(self):
archive = download_url(self.raw_url, self.raw_dir)
extract_tar(archive, self.raw_dir)
os.remove(archive)
def download(self):
path = download_url(self.url.format(self.name), self.root)
extract_tar(path, self.raw_dir)
os.unlink(path)
def download(self):
file_path = download_url(self.url, self.raw_dir)
extract_tar(file_path, self.raw_dir, mode='r')
os.unlink(file_path)