def __getitem__(self, index):
img, target = ImageFolder.__getitem__(self, index)
#img = self.__image_transformer(img)
origin = img
img = transforms.ToPILImage()(img)
s = float(img.size[0]) / self.slice
a = s / 2
tiles = [None] * self.slice**2
for n in range(self.slice**2):
i = n // self.slice
j = n % self.slice
c = [a * i * 2 + a, a * j * 2 + a]
c = np.array([c[1] - a, c[0] - a, c[1] + a + 1,
c[0] + a + 1]).astype(int)
tile = img.crop(c.tolist())
tile = self.__augment_tile(tile)
# Normalize the patches indipendently to avoid low level features shortcut
#m, s = tile.view(3,-1).mean(dim=1).numpy(), tile.view(3,-1).std(dim=1).numpy()
#s[s==0]=1
#norm = transforms.Normalize(mean=m.tolist(),std=s.tolist())
#tile = norm(tile)
tiles[n] = tile
order = np.random.permutation(self.slice**2)
data = [tiles[order[t]] for t in range(self.slice**2)]
data = torch.stack(data, 0)
tiles = torch.stack(tiles)
return origin, data, order, tiles
class Faces(Dataset):
def __init__(self,
root,
loader=default_loader,
transform=None,
target_transform=None):
super(Faces, self).__init__()
self.folder = ImageFolder(root,
transform=transform,
target_transform=target_transform,
loader=loader)
def __getitem__(self, index):
return self.folder.__getitem__(index)
def __len__(self):
return self.folder.__len__()
