def __getitem__(self, idx):
item = self.data_source[idx]
output = {
'label': item.label,
'domain': item.domain,
'impath': item.impath
}
imgs = [] # List[List[tr_images]]
if isinstance(item.impath, (list, tuple)):
for impath in item.impath:
img_i0 = read_image(impath)
imgs.append(self.transform_item(img_i0))
else:
img0 = read_image(item.impath)
imgs.append(self.transform_item(img0))
for i in range(len(imgs[0])):
temp = []
if i != 1:
idx = f"img{i}"
else:
idx = "img"
for j in range(len(imgs)):
temp.append(imgs[j][i])
output[idx] = torch.cat(temp, 0)
return output
def __getitem__(self, idx):
item = self.data_source[idx]
output = {
'label': item.label,
'domain': item.domain,
'impath': item.impath
}
img0 = read_image(item.impath)
if self.transform is not None:
if isinstance(self.transform, (list, tuple)):
for i, tfm in enumerate(self.transform):
img = self._transform_image(tfm, img0)
keyname = 'img'
if (i + 1) > 1:
keyname += str(i + 1)
output[keyname] = img
else:
img = self._transform_image(self.transform, img0)
output['img'] = img
img0 = self.to_tensor(img0)
output['img0'] = img0
return output