Making Vflip and Hflip in Tensor format

test/test_functional_tensor.py

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+import torchvision.transforms.functional_tensor as F_t
+import unittest
+import torch
+
+
+class Tester(unittest.TestCase):
+
+    def test_vflip(self):
+        img_tensor = torch.randn(3, 16, 16)
+        vflipped_img = F_t.vflip(img_tensor)
+        vflipped_img_again = F_t.vflip(vflipped_img)
+        self.assertEqual(vflipped_img.shape, img_tensor.shape)
+        self.assertTrue(torch.equal(img_tensor, vflipped_img_again))
+
+    def test_hflip(self):
+        img_tensor = torch.randn(3, 16, 16)
+        hflipped_img = F_t.hflip(img_tensor)
+        hflipped_img_again = F_t.hflip(hflipped_img)
+        self.assertEqual(hflipped_img.shape, img_tensor.shape)
+        self.assertTrue(torch.equal(img_tensor, hflipped_img_again))
+
+
+if __name__ == '__main__':
+    unittest.main()

torchvision/transforms/functional_tensor.py

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+import torch
+import torchvision.transforms.functional as F
+
+
+def vflip(img_tensor):
+    """Vertically flip the given the Image Tensor.
+
+    Args:
+        img_tensor (Tensor): Image Tensor to be flipped in the form CXHXW.
+
+    Returns:
+        Tensor:  Vertically flipped image Tensor.
+    """
+    if not F._is_tensor_image(img_tensor):
+        raise TypeError('tensor is not a torch image.')
+
+    return img_tensor.flip(-2)
+
+
+def hflip(img_tensor):
+    """Horizontally flip the given the Image Tensor.
+
+    Args:
+        img_tensor (Tensor): Image Tensor to be flipped in the form CXHXW.
+
+    Returns:
+        Tensor:  Horizontally flipped image Tensor.
+    """
+
+    if not F._is_tensor_image(img_tensor):
+        raise TypeError('tensor is not a torch image.')
+
+    return img_tensor.flip(-1)