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

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merged 1 commit into from Aug 11, 2020
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Deprecated F_t.center_crop, F_t.five_crop, F_t.ten_crop #2568

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132 changes: 55 additions & 77 deletions test/test_functional_tensor.py
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Expand Up @@ -64,22 +64,24 @@ def test_hflip(self):

def test_crop(self):
script_crop = torch.jit.script(F_t.crop)
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Do we want to change this to be F.crop ?

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Sounds good, I can change it in #2569

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)
Expand Down Expand Up @@ -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)
Expand Down
39 changes: 36 additions & 3 deletions torchvision/transforms/functional_tensor.py
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Expand Up @@ -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.
Expand All @@ -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.')

Expand All @@ -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.

Expand All @@ -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.')

Expand All @@ -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.

Expand All @@ -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.')

Expand Down