Moving normalize, erase to functional_tensor.py

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

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

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

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 F._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

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