def make_pipeline(imageset_path, output_dir):
"""returns an augmentation pipeline for a given image set"""
p = Pipeline(imageset_path, output_dir)
p.random_distortion(probability=0.7,
grid_width=4,
grid_height=4,
magnitude=8)
p.flip_left_right(probability=0.5)
p.flip_top_bottom(probability=0.5)
p.zoom(probability=0.3, min_factor=1.1, max_factor=1.4)
p.rotate(probability=0.5, max_left_rotation=10, max_right_rotation=10)
return p
class Dataset(data.Dataset):
def __init__(self, train=True, root='../data'):
if train:
self.dataset = MNIST(root=root, train=True)
else:
self.dataset = MNIST(root=root, train=False)
self.transform = transforms.Compose( [transforms.ToTensor()] )
self.p = Pipeline()
self.p.random_distortion(probability=0.5, grid_width=7, grid_height=7, magnitude=1)
def __len__(self,):
return len(self.dataset)
def __getitem__(self, i):
img = self.dataset[i][0]
lab = self.dataset[i][1]
# img = self._sample([img], self.p)[0]
img = self.transform(img)
# img = 2 * (img - 0.5)
lab = torch.eye(10)[lab]
return img, lab
def _sample(self, image, p):
for op in p.operations:
r = round(random.uniform(0, 1), 1)
if r <= op.probability:
image = op.perform_operation(image)
return image