Change AffineTransformation back to LinearTransformation (#843)

* Update test_transforms.py

* Update transforms.py

test/test_transforms.py

@@ -953,28 +953,6 @@ class Tester(unittest.TestCase):
         color_jitter.__repr__()
 
     def test_linear_transformation(self):
-        x = torch.randn(250, 10, 10, 3)
-        flat_x = x.view(x.size(0), x.size(1) * x.size(2) * x.size(3))
-        # compute principal components
-        sigma = torch.mm(flat_x.t(), flat_x) / flat_x.size(0)
-        u, s, _ = np.linalg.svd(sigma.numpy())
-        zca_epsilon = 1e-10  # avoid division by 0
-        d = torch.Tensor(np.diag(1. / np.sqrt(s + zca_epsilon)))
-        u = torch.Tensor(u)
-        principal_components = torch.mm(torch.mm(u, d), u.t())
-        # initialize whitening matrix
-        whitening = transforms.LinearTransformation(principal_components)
-        # pass first vector
-        xwhite = whitening(x[0].view(10, 10, 3))
-        # estimate covariance
-        xwhite = xwhite.view(1, 300).numpy()
-        cov = np.dot(xwhite, xwhite.T) / x.size(0)
-        assert np.allclose(cov, np.identity(1), rtol=1e-3)
-
-        # Checking if LinearTransformation can be printed as string
-        whitening.__repr__()
-
-    def test_affine_transformation(self):
         num_samples = 1000
         x = torch.randn(num_samples, 3, 10, 10)
         flat_x = x.view(x.size(0), x.size(1) * x.size(2) * x.size(3))
@@ -987,7 +965,7 @@ class Tester(unittest.TestCase):
         principal_components = torch.mm(torch.mm(u, d), u.t())
         mean_vector = (torch.sum(flat_x, dim=0) / flat_x.size(0))
         # initialize whitening matrix
-        whitening = transforms.AffineTransformation(principal_components, mean_vector)
+        whitening = transforms.LinearTransformation(principal_components, mean_vector)
         # estimate covariance and mean using weak law of large number
         num_features = flat_x.size(1)
         cov = 0.0
@@ -1001,7 +979,7 @@ class Tester(unittest.TestCase):
         assert np.allclose(cov / num_samples, np.identity(1), rtol=2e-3), "cov not close to 1"
         assert np.allclose(mean / num_samples, 0, rtol=1e-3), "mean not close to 0"
 
-        # Checking if AffineTransformation can be printed as string
+        # Checking if LinearTransformation can be printed as string
         whitening.__repr__()
 
     def test_rotate(self):

torchvision/transforms/transforms.py

@@ -27,7 +27,7 @@ else:
 __all__ = ["Compose", "ToTensor", "ToPILImage", "Normalize", "Resize", "Scale", "CenterCrop", "Pad",
            "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop", "RandomHorizontalFlip",
            "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop", "LinearTransformation",
-           "AffineTransformation", "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale"]
+           "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale"]
 
 _pil_interpolation_to_str = {
     Image.NEAREST: 'PIL.Image.NEAREST',
@@ -710,7 +710,7 @@ class TenCrop(object):
         return self.__class__.__name__ + '(size={0}, vertical_flip={1})'.format(self.size, self.vertical_flip)
 
 
-class AffineTransformation(object):
+class LinearTransformation(object):
     """Transform a tensor image with a square transformation matrix and a mean_vector computed
     offline.
     Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and
@@ -763,17 +763,6 @@ class AffineTransformation(object):
         return format_string
 
 
-class LinearTransformation(AffineTransformation):
-    """
-    Note: This transform is deprecated in favor of AffineTransformation.
-    """
-
-    def __init__(self, transformation_matrix):
-        warnings.warn("The use of the transforms.LinearTransformation transform is deprecated, " +
-                      "please use transforms.AffineTransformation instead.")
-        super(LinearTransformation, self).__init__(transformation_matrix, torch.zeros_like(transformation_matrix[0]))
-
-
 class ColorJitter(object):
     """Randomly change the brightness, contrast and saturation of an image.