Deprecated F_t.center_crop, F_t.five_crop, F_t.ten_crop (#2568)

- Updated docs
- Put warning in the code
- Updated tests

Author: vfdev-5

test/test_functional_tensor.py

@@ -64,22 +64,24 @@ def test_hflip(self):
 
     def test_crop(self):
         script_crop = torch.jit.script(F_t.crop)
-        img_tensor = torch.randint(0, 255, (3, 16, 16), dtype=torch.uint8)
-        img_tensor_clone = img_tensor.clone()
-        top = random.randint(0, 15)
-        left = random.randint(0, 15)
-        height = random.randint(1, 16 - top)
-        width = random.randint(1, 16 - left)
-        img_cropped = F_t.crop(img_tensor, top, left, height, width)
-        img_PIL = transforms.ToPILImage()(img_tensor)
-        img_PIL_cropped = F.crop(img_PIL, top, left, height, width)
-        img_cropped_GT = transforms.ToTensor()(img_PIL_cropped)
-        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
-        self.assertTrue(torch.equal(img_cropped, (img_cropped_GT * 255).to(torch.uint8)),
-                        "functional_tensor crop not working")
-        # scriptable function test
-        cropped_img_script = script_crop(img_tensor, top, left, height, width)
-        self.assertTrue(torch.equal(img_cropped, cropped_img_script))
+
+        img_tensor, pil_img = self._create_data(16, 18)
+
+        test_configs = [
+            (1, 2, 4, 5),   # crop inside top-left corner
+            (2, 12, 3, 4),  # crop inside top-right corner
+            (8, 3, 5, 6),   # crop inside bottom-left corner
+            (8, 11, 4, 3),  # crop inside bottom-right corner
+        ]
+
+        for top, left, height, width in test_configs:
+            pil_img_cropped = F.crop(pil_img, top, left, height, width)
+
+            img_tensor_cropped = F.crop(img_tensor, top, left, height, width)
+            self.compareTensorToPIL(img_tensor_cropped, pil_img_cropped)
+
+            img_tensor_cropped = script_crop(img_tensor, top, left, height, width)
+            self.compareTensorToPIL(img_tensor_cropped, pil_img_cropped)
 
     def test_hsv2rgb(self):
         shape = (3, 100, 150)
@@ -198,71 +200,47 @@ def test_rgb_to_grayscale(self):
         self.assertTrue(torch.equal(grayscale_script, grayscale_tensor))
 
     def test_center_crop(self):
-        script_center_crop = torch.jit.script(F_t.center_crop)
-        img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
-        img_tensor_clone = img_tensor.clone()
-        cropped_tensor = F_t.center_crop(img_tensor, [10, 10])
-        cropped_pil_image = F.center_crop(transforms.ToPILImage()(img_tensor), [10, 10])
-        cropped_pil_tensor = (transforms.ToTensor()(cropped_pil_image) * 255).to(torch.uint8)
-        self.assertTrue(torch.equal(cropped_tensor, cropped_pil_tensor))
-        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
-        # scriptable function test
-        cropped_script = script_center_crop(img_tensor, [10, 10])
-        self.assertTrue(torch.equal(cropped_script, cropped_tensor))
+        script_center_crop = torch.jit.script(F.center_crop)
+
+        img_tensor, pil_img = self._create_data(32, 34)
+
+        cropped_pil_image = F.center_crop(pil_img, [10, 11])
+
+        cropped_tensor = F.center_crop(img_tensor, [10, 11])
+        self.compareTensorToPIL(cropped_tensor, cropped_pil_image)
+
+        cropped_tensor = script_center_crop(img_tensor, [10, 11])
+        self.compareTensorToPIL(cropped_tensor, cropped_pil_image)
 
     def test_five_crop(self):
-        script_five_crop = torch.jit.script(F_t.five_crop)
-        img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
-        img_tensor_clone = img_tensor.clone()
-        cropped_tensor = F_t.five_crop(img_tensor, [10, 10])
-        cropped_pil_image = F.five_crop(transforms.ToPILImage()(img_tensor), [10, 10])
-        self.assertTrue(torch.equal(cropped_tensor[0],
-                                    (transforms.ToTensor()(cropped_pil_image[0]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[1],
-                                    (transforms.ToTensor()(cropped_pil_image[2]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[2],
-                                    (transforms.ToTensor()(cropped_pil_image[1]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[3],
-                                    (transforms.ToTensor()(cropped_pil_image[3]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[4],
-                                    (transforms.ToTensor()(cropped_pil_image[4]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
-        # scriptable function test
-        cropped_script = script_five_crop(img_tensor, [10, 10])
-        for cropped_script_img, cropped_tensor_img in zip(cropped_script, cropped_tensor):
-            self.assertTrue(torch.equal(cropped_script_img, cropped_tensor_img))
+        script_five_crop = torch.jit.script(F.five_crop)
+
+        img_tensor, pil_img = self._create_data(32, 34)
+
+        cropped_pil_images = F.five_crop(pil_img, [10, 11])
+
+        cropped_tensors = F.five_crop(img_tensor, [10, 11])
+        for i in range(5):
+            self.compareTensorToPIL(cropped_tensors[i], cropped_pil_images[i])
+
+        cropped_tensors = script_five_crop(img_tensor, [10, 11])
+        for i in range(5):
+            self.compareTensorToPIL(cropped_tensors[i], cropped_pil_images[i])
 
     def test_ten_crop(self):
-        script_ten_crop = torch.jit.script(F_t.ten_crop)
-        img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
-        img_tensor_clone = img_tensor.clone()
-        cropped_tensor = F_t.ten_crop(img_tensor, [10, 10])
-        cropped_pil_image = F.ten_crop(transforms.ToPILImage()(img_tensor), [10, 10])
-        self.assertTrue(torch.equal(cropped_tensor[0],
-                                    (transforms.ToTensor()(cropped_pil_image[0]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[1],
-                                    (transforms.ToTensor()(cropped_pil_image[2]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[2],
-                                    (transforms.ToTensor()(cropped_pil_image[1]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[3],
-                                    (transforms.ToTensor()(cropped_pil_image[3]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[4],
-                                    (transforms.ToTensor()(cropped_pil_image[4]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[5],
-                                    (transforms.ToTensor()(cropped_pil_image[5]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[6],
-                                    (transforms.ToTensor()(cropped_pil_image[7]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[7],
-                                    (transforms.ToTensor()(cropped_pil_image[6]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[8],
-                                    (transforms.ToTensor()(cropped_pil_image[8]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(cropped_tensor[9],
-                                    (transforms.ToTensor()(cropped_pil_image[9]) * 255).to(torch.uint8)))
-        self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
-        # scriptable function test
-        cropped_script = script_ten_crop(img_tensor, [10, 10])
-        for cropped_script_img, cropped_tensor_img in zip(cropped_script, cropped_tensor):
-            self.assertTrue(torch.equal(cropped_script_img, cropped_tensor_img))
+        script_ten_crop = torch.jit.script(F.ten_crop)
+
+        img_tensor, pil_img = self._create_data(32, 34)
+
+        cropped_pil_images = F.ten_crop(pil_img, [10, 11])
+
+        cropped_tensors = F.ten_crop(img_tensor, [10, 11])
+        for i in range(10):
+            self.compareTensorToPIL(cropped_tensors[i], cropped_pil_images[i])
+
+        cropped_tensors = script_ten_crop(img_tensor, [10, 11])
+        for i in range(10):
+            self.compareTensorToPIL(cropped_tensors[i], cropped_pil_images[i])
 
     def test_pad(self):
         script_fn = torch.jit.script(F_t.pad)

torchvision/transforms/functional_tensor.py

@@ -240,7 +240,12 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
 
 
 def center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
-    """Crop the Image Tensor and resize it to desired size.
+    """DEPRECATED. Crop the Image Tensor and resize it to desired size.
+
+    .. warning::
+
+        This method is deprecated and will be removed in future releases.
+        Please, use ``F.center_crop`` instead.
 
     Args:
         img (Tensor): Image to be cropped.
@@ -250,6 +255,11 @@ def center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
     Returns:
             Tensor: Cropped image.
     """
+    warnings.warn(
+        "This method is deprecated and will be removed in future releases. "
+        "Please, use ``F.center_crop`` instead."
+    )
+
     if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
@@ -268,8 +278,15 @@ def center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
 
 
 def five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]:
-    """Crop the given Image Tensor into four corners and the central crop.
+    """DEPRECATED. Crop the given Image Tensor into four corners and the central crop.
+
+    .. warning::
+
+        This method is deprecated and will be removed in future releases.
+        Please, use ``F.five_crop`` instead.
+
     .. Note::
+
         This transform returns a List of Tensors and there may be a
         mismatch in the number of inputs and targets your ``Dataset`` returns.
 
@@ -283,6 +300,11 @@ def five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]:
        List: List (tl, tr, bl, br, center)
                 Corresponding top left, top right, bottom left, bottom right and center crop.
     """
+    warnings.warn(
+        "This method is deprecated and will be removed in future releases. "
+        "Please, use ``F.five_crop`` instead."
+    )
+
     if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
@@ -304,10 +326,16 @@ def five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]:
 
 
 def ten_crop(img: Tensor, size: BroadcastingList2[int], vertical_flip: bool = False) -> List[Tensor]:
-    """Crop the given Image Tensor into four corners and the central crop plus the
+    """DEPRECATED. Crop the given Image Tensor into four corners and the central crop plus the
         flipped version of these (horizontal flipping is used by default).
 
+    .. warning::
+
+        This method is deprecated and will be removed in future releases.
+        Please, use ``F.ten_crop`` instead.
+
     .. Note::
+
         This transform returns a List of images and there may be a
         mismatch in the number of inputs and targets your ``Dataset`` returns.
 
@@ -323,6 +351,11 @@ def ten_crop(img: Tensor, size: BroadcastingList2[int], vertical_flip: bool = Fa
                 Corresponding top left, top right, bottom left, bottom right and center crop
                 and same for the flipped image's tensor.
     """
+    warnings.warn(
+        "This method is deprecated and will be removed in future releases. "
+        "Please, use ``F.ten_crop`` instead."
+    )
+
     if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')