From 9664ec87f84e3db2974486016b9fe4a2c39f9e0d Mon Sep 17 00:00:00 2001
From: surgan12 <cs17btech11038@iith.ac.in>
Date: Tue, 15 Oct 2019 19:36:58 +0530
Subject: [PATCH 1/8] vflip and hflip tensor

---
 torchvision/transforms/functional_tensor.py | 32 +++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 torchvision/transforms/functional_tensor.py

diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
new file mode 100644
index 00000000000..6ce84e063da
--- /dev/null
+++ b/torchvision/transforms/functional_tensor.py
@@ -0,0 +1,32 @@
+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.
+
+    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(1)
+
+
+def hflip(img_tensor):
+    """Horizontally flip the given the Image Tensor.
+
+    Args:
+        img_tensor (Tensor): Image Tensor to be flipped.
+
+    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(2)

From 4a3946d4f8b7523892654fa5c781616a10b1fad4 Mon Sep 17 00:00:00 2001
From: surgan12 <cs17btech11038@iith.ac.in>
Date: Tue, 15 Oct 2019 19:38:14 +0530
Subject: [PATCH 2/8] vflip and hflip tensor

---
 test/test_functional_tensor.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)
 create mode 100644 test/test_functional_tensor.py

diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
new file mode 100644
index 00000000000..e68eed0e9a1
--- /dev/null
+++ b/test/test_functional_tensor.py
@@ -0,0 +1,24 @@
+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)
+
+		assert vflipped_img.shape == img_tensor.shape
+		assert 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)
+
+		assert hflipped_img.shape == img_tensor.shape
+		assert torch.equal(img_tensor, hflipped_img_again)
+
+if __name__ == '__main__':
+    unittest.main()

From 444a69f84df7d08bba07d87b19b15e684fee0aae Mon Sep 17 00:00:00 2001
From: surgan12 <cs17btech11038@iith.ac.in>
Date: Tue, 15 Oct 2019 20:08:53 +0530
Subject: [PATCH 3/8] changes made

---
 test/test_functional_tensor.py              | 29 ++++++++++-----------
 torchvision/transforms/functional_tensor.py | 17 ++++++------
 2 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
index e68eed0e9a1..02ce19a6462 100644
--- a/test/test_functional_tensor.py
+++ b/test/test_functional_tensor.py
@@ -2,23 +2,22 @@
 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)
 
-		assert vflipped_img.shape == img_tensor.shape
-		assert torch.equal(img_tensor, vflipped_img_again)
+class Tester(unittest.TestCase):
 
-	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)
+    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))
 
-		assert hflipped_img.shape == img_tensor.shape
-		assert torch.equal(img_tensor, hflipped_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()
+    unittest.main()
\ No newline at end of file
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index 6ce84e063da..abb802fc9bf 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -1,32 +1,33 @@
 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.
+        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(1)
+        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.
+        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(2)
+        raise TypeError('tensor is not a torch image.')
+
+    return img_tensor.flip(-1)
\ No newline at end of file

From 75d1396ad8aee08654724c052acb2a666a9a60a8 Mon Sep 17 00:00:00 2001
From: surgan12 <cs17btech11038@iith.ac.in>
Date: Tue, 15 Oct 2019 20:16:51 +0530
Subject: [PATCH 4/8] lint

---
 test/test_functional_tensor.py              | 5 +++--
 torchvision/transforms/functional_tensor.py | 2 +-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
index 02ce19a6462..4bdab11f6be 100644
--- a/test/test_functional_tensor.py
+++ b/test/test_functional_tensor.py
@@ -6,18 +6,19 @@
 class Tester(unittest.TestCase):
 
     def test_vflip(self):
-        img_tensor = torch.randn(3,16,16)
+        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)
+        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()
\ No newline at end of file
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index abb802fc9bf..bc12327f39b 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -30,4 +30,4 @@ def hflip(img_tensor):
     if not F._is_tensor_image(img_tensor):
         raise TypeError('tensor is not a torch image.')
 
-    return img_tensor.flip(-1)
\ No newline at end of file
+    return img_tensor.flip(-1)

From 8aaa8254f5047a36b6cd73bc8efe9ae68fc102e1 Mon Sep 17 00:00:00 2001
From: surgan12 <cs17btech11038@iith.ac.in>
Date: Tue, 15 Oct 2019 21:18:31 +0530
Subject: [PATCH 5/8] lint

---
 test/test_functional_tensor.py              | 3 ++-
 torchvision/transforms/functional_tensor.py | 4 ++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
index 4bdab11f6be..b39e53dcdee 100644
--- a/test/test_functional_tensor.py
+++ b/test/test_functional_tensor.py
@@ -21,4 +21,5 @@ def test_hflip(self):
 
 
 if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    unittest.main()
+    
\ No newline at end of file
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index bc12327f39b..ec530dc2f3a 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -6,7 +6,7 @@ def vflip(img_tensor):
     """Vertically flip the given the Image Tensor.
 
     Args:
-        img_tensor (Tensor): Image Tensor to be flipped in the form CXHXW.
+        img_tensor (Tensor): Image Tensor to be flipped in the form [C, H, W].
 
     Returns:
         Tensor:  Vertically flipped image Tensor.
@@ -21,7 +21,7 @@ def hflip(img_tensor):
     """Horizontally flip the given the Image Tensor.
 
     Args:
-        img_tensor (Tensor): Image Tensor to be flipped in the form CXHXW.
+        img_tensor (Tensor): Image Tensor to be flipped in the form [C, H, W].
 
     Returns:
         Tensor:  Horizontally flipped image Tensor.

From e52e7700868fa0662599f6d7d25bb04ee3301172 Mon Sep 17 00:00:00 2001
From: surgan12 <cs17btech11038@iith.ac.in>
Date: Tue, 15 Oct 2019 21:39:26 +0530
Subject: [PATCH 6/8] lint failing

---
 test/test_functional_tensor.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
index b39e53dcdee..d944a923387 100644
--- a/test/test_functional_tensor.py
+++ b/test/test_functional_tensor.py
@@ -22,4 +22,3 @@ def test_hflip(self):
 
 if __name__ == '__main__':
     unittest.main()
-    
\ No newline at end of file

From c5b084f2db7dd0139629a47054cbe68368f27aeb Mon Sep 17 00:00:00 2001
From: surgan12 <cs17btech11038@iith.ac.in>
Date: Wed, 16 Oct 2019 17:07:49 +0530
Subject: [PATCH 7/8] moving to tensor file

---
 torchvision/transforms/functional.py        | 55 ---------------------
 torchvision/transforms/functional_tensor.py | 55 +++++++++++++++++++++
 torchvision/transforms/transforms.py        |  5 +-
 3 files changed, 58 insertions(+), 57 deletions(-)

diff --git a/torchvision/transforms/functional.py b/torchvision/transforms/functional.py
index 6f43d5d263f..a2332bc7e4d 100644
--- a/torchvision/transforms/functional.py
+++ b/torchvision/transforms/functional.py
@@ -188,36 +188,6 @@ def to_pil_image(pic, mode=None):
     return Image.fromarray(npimg, mode=mode)
 
 
-def normalize(tensor, mean, std, inplace=False):
-    """Normalize a tensor image with mean and standard deviation.
-
-    .. note::
-        This transform acts out of place by default, i.e., it does not mutates the input tensor.
-
-    See :class:`~torchvision.transforms.Normalize` for more details.
-
-    Args:
-        tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
-        mean (sequence): Sequence of means for each channel.
-        std (sequence): Sequence of standard deviations for each channel.
-        inplace(bool,optional): Bool to make this operation inplace.
-
-    Returns:
-        Tensor: Normalized Tensor image.
-    """
-    if not _is_tensor_image(tensor):
-        raise TypeError('tensor is not a torch image.')
-
-    if not inplace:
-        tensor = tensor.clone()
-
-    dtype = tensor.dtype
-    mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
-    std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
-    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
-    return tensor
-
-
 def resize(img, size, interpolation=Image.BILINEAR):
     r"""Resize the input PIL Image to the given size.
 
@@ -830,28 +800,3 @@ def to_grayscale(img, num_output_channels=1):
         raise ValueError('num_output_channels should be either 1 or 3')
 
     return img
-
-
-def erase(img, i, j, h, w, v, inplace=False):
-    """ Erase the input Tensor Image with given value.
-
-    Args:
-        img (Tensor Image): Tensor image of size (C, H, W) to be erased
-        i (int): i in (i,j) i.e coordinates of the upper left corner.
-        j (int): j in (i,j) i.e coordinates of the upper left corner.
-        h (int): Height of the erased region.
-        w (int): Width of the erased region.
-        v: Erasing value.
-        inplace(bool, optional): For in-place operations. By default is set False.
-
-    Returns:
-        Tensor Image: Erased image.
-    """
-    if not isinstance(img, torch.Tensor):
-        raise TypeError('img should be Tensor Image. Got {}'.format(type(img)))
-
-    if not inplace:
-        img = img.clone()
-
-    img[:, i:i + h, j:j + w] = v
-    return img
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index ec530dc2f3a..0e9b022c3ed 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -31,3 +31,58 @@ def hflip(img_tensor):
         raise TypeError('tensor is not a torch image.')
 
     return img_tensor.flip(-1)
+
+
+def erase(img, i, j, h, w, v, inplace=False):
+    """ Erase the input Tensor Image with given value.
+
+    Args:
+        img (Tensor Image): Tensor image of size (C, H, W) to be erased
+        i (int): i in (i,j) i.e coordinates of the upper left corner.
+        j (int): j in (i,j) i.e coordinates of the upper left corner.
+        h (int): Height of the erased region.
+        w (int): Width of the erased region.
+        v: Erasing value.
+        inplace(bool, optional): For in-place operations. By default is set False.
+
+    Returns:
+        Tensor Image: Erased image.
+    """
+    if not isinstance(img, torch.Tensor):
+        raise TypeError('img should be Tensor Image. Got {}'.format(type(img)))
+
+    if not inplace:
+        img = img.clone()
+
+    img[:, i:i + h, j:j + w] = v
+    return img
+
+
+def normalize(tensor, mean, std, inplace=False):
+    """Normalize a tensor image with mean and standard deviation.
+
+    .. note::
+        This transform acts out of place by default, i.e., it does not mutates the input tensor.
+
+    See :class:`~torchvision.transforms.Normalize` for more details.
+
+    Args:
+        tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
+        mean (sequence): Sequence of means for each channel.
+        std (sequence): Sequence of standard deviations for each channel.
+        inplace(bool,optional): Bool to make this operation inplace.
+
+    Returns:
+        Tensor: Normalized Tensor image.
+    """
+    if not _is_tensor_image(tensor):
+        raise TypeError('tensor is not a torch image.')
+
+    if not inplace:
+        tensor = tensor.clone()
+
+    dtype = tensor.dtype
+    mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)
+    std = torch.as_tensor(std, dtype=dtype, device=tensor.device)
+    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
+    return tensor
diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index 3ec84aae84c..f7340e18572 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -15,6 +15,7 @@
 import warnings
 
 from . import functional as F
+from . import functional_tensor as F_t
 
 if sys.version_info < (3, 3):
     Sequence = collections.Sequence
@@ -172,7 +173,7 @@ def __call__(self, tensor):
         Returns:
             Tensor: Normalized Tensor image.
         """
-        return F.normalize(tensor, self.mean, self.std, self.inplace)
+        return F_t.normalize(tensor, self.mean, self.std, self.inplace)
 
     def __repr__(self):
         return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
@@ -1297,5 +1298,5 @@ def __call__(self, img):
         """
         if random.uniform(0, 1) < self.p:
             x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=self.value)
-            return F.erase(img, x, y, h, w, v, self.inplace)
+            return F_t.erase(img, x, y, h, w, v, self.inplace)
         return img

From 6097d19d9b80d9048ef17aee2595edf2b11cdfbc Mon Sep 17 00:00:00 2001
From: surgan12 <cs17btech11038@iith.ac.in>
Date: Wed, 16 Oct 2019 17:56:44 +0530
Subject: [PATCH 8/8] updates

---
 references/segmentation/transforms.py       | 4 ++--
 test/test_transforms.py                     | 5 +++--
 torchvision/transforms/functional_tensor.py | 2 +-
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/references/segmentation/transforms.py b/references/segmentation/transforms.py
index bce4bfbe639..d555d1ad751 100644
--- a/references/segmentation/transforms.py
+++ b/references/segmentation/transforms.py
@@ -5,7 +5,7 @@
 import torch
 from torchvision import transforms as T
 from torchvision.transforms import functional as F
-
+import torchvision.transforms.functional_tensor as F_t
 
 def pad_if_smaller(img, size, fill=0):
     min_size = min(img.size)
@@ -88,5 +88,5 @@ def __init__(self, mean, std):
         self.std = std
 
     def __call__(self, image, target):
-        image = F.normalize(image, mean=self.mean, std=self.std)
+        image = F_t.normalize(image, mean=self.mean, std=self.std)
         return image, target
diff --git a/test/test_transforms.py b/test/test_transforms.py
index 7e8320d6d6c..19689b7b7d6 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -3,6 +3,7 @@
 import torch
 import torchvision.transforms as transforms
 import torchvision.transforms.functional as F
+import torchvision.transforms.functional_tensor as F_t
 from torch._utils_internal import get_file_path_2
 import unittest
 import math
@@ -838,7 +839,7 @@ def test_normalize_different_dtype(self):
                 mean = torch.tensor([1, 2, 3], dtype=dtype2)
                 std = torch.tensor([1, 2, 1], dtype=dtype2)
                 # checks that it doesn't crash
-                transforms.functional.normalize(img, mean, std)
+                transforms.functional_tensor.normalize(img, mean, std)
 
     def test_adjust_brightness(self):
         x_shape = [2, 2, 3]
@@ -1363,7 +1364,7 @@ def test_random_erasing(self):
         # Test Set 1: Erasing with int value
         img_re = transforms.RandomErasing(value=0.2)
         i, j, h, w, v = img_re.get_params(img, scale=img_re.scale, ratio=img_re.ratio, value=img_re.value)
-        img_output = F.erase(img, i, j, h, w, v)
+        img_output = F_t.erase(img, i, j, h, w, v)
         assert img_output.size(0) == 3
 
         # Test Set 2: Check if the unerased region is preserved
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py
index 0e9b022c3ed..b1724b081b9 100644
--- a/torchvision/transforms/functional_tensor.py
+++ b/torchvision/transforms/functional_tensor.py
@@ -75,7 +75,7 @@ def normalize(tensor, mean, std, inplace=False):
     Returns:
         Tensor: Normalized Tensor image.
     """
-    if not _is_tensor_image(tensor):
+    if not F._is_tensor_image(tensor):
         raise TypeError('tensor is not a torch image.')
 
     if not inplace: