[NOMRG] WIP Use tensor transforms within PIL transforms

test/test_functional_tensor.py

@@ -418,7 +418,7 @@ def test_resize(self):
 
                         self.assertEqual(
                             resized_tensor.size()[1:], resized_pil_img.size[::-1],
-                            msg="{}, {}".format(size, interpolation)
+                            msg="{}, {}, {}".format(size, interpolation, max_size)
                         )
 
                         if interpolation not in [NEAREST, ]:

torchvision/transforms/functional.py

@@ -380,8 +380,9 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = Inte
         raise TypeError("Argument interpolation should be a InterpolationMode")
 
     if not isinstance(img, torch.Tensor):
-        pil_interpolation = pil_modes_mapping[interpolation]
-        return F_pil.resize(img, size=size, interpolation=pil_interpolation, max_size=max_size)
+        # pil_interpolation = pil_modes_mapping[interpolation]
+        # return F_pil.resize(img, size=size, interpolation=pil_interpolation)
+        return F_pil.resize(img, size=size, interpolation=interpolation, max_size=max_size)
 
     return F_t.resize(img, size=size, interpolation=interpolation.value, max_size=max_size)
 

torchvision/transforms/functional_pil.py

@@ -204,41 +204,13 @@ def crop(img: Image.Image, top: int, left: int, height: int, width: int) -> Imag
 
 
 @torch.jit.unused
-def resize(img, size, interpolation=Image.BILINEAR, max_size=None):
-    if not _is_pil_image(img):
-        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
-    if not (isinstance(size, int) or (isinstance(size, Sequence) and len(size) in (1, 2))):
-        raise TypeError('Got inappropriate size arg: {}'.format(size))
-
-    if isinstance(size, Sequence) and len(size) == 1:
-        size = size[0]
-    if isinstance(size, int):
-        w, h = img.size
-
-        short, long = (w, h) if w <= h else (h, w)
-        if short == size:
-            return img
-
-        new_short, new_long = size, int(size * long / short)
+def resize(img, size, interpolation=None, max_size=None):
+    from .functional_tensor import resize as ft_resize
+    from .functional import pil_to_tensor, to_pil_image
 
-        if max_size is not None:
-            if max_size <= size:
-                raise ValueError(
-                    f"max_size = {max_size} must be strictly greater than the requested "
-                    f"size for the smaller edge size = {size}"
-                )
-            if new_long > max_size:
-                new_short, new_long = int(max_size * new_short / new_long), max_size
-
-        new_w, new_h = (new_short, new_long) if w <= h else (new_long, new_short)
-        return img.resize((new_w, new_h), interpolation)
-    else:
-        if max_size is not None:
-            raise ValueError(
-                "max_size should only be passed if size specifies the length of the smaller edge, "
-                "i.e. size should be an int or a sequence of length 1 in torchscript mode."
-            )
-        return img.resize(size[::-1], interpolation)
+    t = pil_to_tensor(img)
+    resized_t = ft_resize(t, size, interpolation.value, max_size=max_size)
+    return to_pil_image(resized_t)
 
 
 @torch.jit.unused