def process(self):
folders = sorted(glob(osp.join(self.raw_dir, 'FaceTalk_*')))
if len(folders) == 0:
extract_zip(self.raw_paths[0], self.raw_dir, log=False)
folders = sorted(glob(osp.join(self.raw_dir, 'FaceTalk_*')))
train_data_list, test_data_list = [], []
for folder in folders:
for i, category in enumerate(self.categories):
files = sorted(glob(osp.join(folder, category, '*.ply')))
for j, f in enumerate(files):
data = read_ply(f)
data.y = torch.tensor([i], dtype=torch.long)
if self.pre_filter is not None and\
not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
if (j % 100) < 90:
train_data_list.append(data)
else:
test_data_list.append(data)
torch.save(self.collate(train_data_list), self.processed_paths[0])
torch.save(self.collate(test_data_list), self.processed_paths[1])
def process(self):
extract_zip(self.raw_paths[0], self.raw_dir, log=False)
path = osp.join(self.raw_dir, 'MPI-FAUST', 'training', 'registrations')
path = osp.join(path, 'tr_reg_{0:03d}.ply')
data_list = []
for i in range(100):
data = read_ply(path.format(i))
data.y = torch.tensor([i % 10], dtype=torch.long)
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
torch.save(self.collate(data_list[:80]), self.processed_paths[0])
torch.save(self.collate(data_list[80:]), self.processed_paths[1])
shutil.rmtree(osp.join(self.raw_dir, 'MPI-FAUST'))