Bugfix - same output for PIL and tensor when centercrop size is greater than imgsize

CONTRIBUTING.md

@@ -91,7 +91,7 @@ If you have modified the code by adding a new feature or a bug-fix, please add u
 test: 
 ```bash
 pytest test/<test-module.py> -vvv -k <test_myfunc>
-# e.g. pytest test/test_transforms.py -vvv -k test_crop
+# e.g. pytest test/test_transforms.py -vvv -k test_center_crop
 ```
 
 If you would like to run all tests:

test/test_transforms.py

@@ -1,3 +1,4 @@
+import itertools
 import os
 import torch
 import torchvision.transforms as transforms
@@ -29,7 +30,7 @@
 
 class Tester(unittest.TestCase):
 
-    def test_crop(self):
+    def test_center_crop(self):
         height = random.randint(10, 32) * 2
         width = random.randint(10, 32) * 2
         oheight = random.randint(5, (height - 2) / 2) * 2
@@ -70,6 +71,64 @@ def test_crop(self):
         self.assertGreater(sum2, sum1,
                            "height: {} width: {} oheight: {} owdith: {}".format(height, width, oheight, owidth))
 
+    def test_center_crop_2(self):
+        """ Tests when center crop size is larger than image size, along any dimension"""
+        even_image_size = (random.randint(10, 32) * 2, random.randint(10, 32) * 2)
+        odd_image_size = (even_image_size[0] + 1, even_image_size[1] + 1)
+
+        # Since height is independent of width, we can ignore images with odd height and even width and vice-versa.
+        input_image_sizes = [even_image_size, odd_image_size]
+
+        # Get different crop sizes
+        delta = random.choice((1, 3, 5))
+        crop_size_delta = [-2 * delta, -delta, 0, delta, 2 * delta]
+        crop_size_params = itertools.product(input_image_sizes, crop_size_delta, crop_size_delta)
+
+        for (input_image_size, delta_height, delta_width) in crop_size_params:
+            img = torch.ones(3, *input_image_size)
+            crop_size = (input_image_size[0] + delta_height, input_image_size[1] + delta_width)
+
+            # Test both transforms, one with PIL input and one with tensor
+            output_pil = transforms.Compose([
+                transforms.ToPILImage(),
+                transforms.CenterCrop(crop_size),
+                transforms.ToTensor()],
+            )(img)
+            self.assertEqual(output_pil.size()[1:3], crop_size,
+                             "image_size: {} crop_size: {}".format(input_image_size, crop_size))
+
+            output_tensor = transforms.CenterCrop(crop_size)(img)
+            self.assertEqual(output_tensor.size()[1:3], crop_size,
+                             "image_size: {} crop_size: {}".format(input_image_size, crop_size))
+
+            # Ensure output for PIL and Tensor are equal
+            self.assertEqual((output_tensor - output_pil).sum(), 0,
+                             "image_size: {} crop_size: {}".format(input_image_size, crop_size))
+
+            # Check if content in center of both image and cropped output is same.
+            center_size = (min(crop_size[0], input_image_size[0]), min(crop_size[1], input_image_size[1]))
+            crop_center_tl, input_center_tl = [0, 0], [0, 0]
+            for index in range(2):
+                if crop_size[index] > input_image_size[index]:
+                    crop_center_tl[index] = (crop_size[index] - input_image_size[index]) // 2
+                else:
+                    input_center_tl[index] = (input_image_size[index] - crop_size[index]) // 2
+
+            output_center = output_pil[
+                :,
+                crop_center_tl[0]:crop_center_tl[0] + center_size[0],
+                crop_center_tl[1]:crop_center_tl[1] + center_size[1]
+            ]
+
+            img_center = img[
+                :,
+                input_center_tl[0]:input_center_tl[0] + center_size[0],
+                input_center_tl[1]:input_center_tl[1] + center_size[1]
+            ]
+
+            self.assertEqual((output_center - img_center).sum(), 0,
+                             "image_size: {} crop_size: {}".format(input_image_size, crop_size))
+
     def test_five_crop(self):
         to_pil_image = transforms.ToPILImage()
         h = random.randint(5, 25)

torchvision/transforms/functional.py

@@ -451,7 +451,8 @@ def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:
 def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
     """Crops the given image at the center.
     If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
+    If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
 
     Args:
         img (PIL Image or Tensor): Image to be cropped.
@@ -469,6 +470,18 @@ def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
     image_width, image_height = _get_image_size(img)
     crop_height, crop_width = output_size
 
+    if crop_width > image_width or crop_height > image_height:
+        padding_ltrb = [
+            (crop_width - image_width) // 2 if crop_width > image_width else 0,
+            (crop_height - image_height) // 2 if crop_height > image_height else 0,
+            (crop_width - image_width + 1) // 2 if crop_width > image_width else 0,
+            (crop_height - image_height + 1) // 2 if crop_height > image_height else 0,
+        ]
+        img = pad(img, padding_ltrb, fill=0)  # PIL uses fill value 0
+        image_width, image_height = _get_image_size(img)
+        if crop_width == image_width and crop_height == image_height:
+            return img
+
     crop_top = int(round((image_height - crop_height) / 2.))
     crop_left = int(round((image_width - crop_width) / 2.))
     return crop(img, crop_top, crop_left, crop_height, crop_width)

torchvision/transforms/transforms.py

@@ -290,7 +290,8 @@ def __init__(self, *args, **kwargs):
 class CenterCrop(torch.nn.Module):
     """Crops the given image at the center.
     If the image is torch Tensor, it is expected
-    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
+    to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
+    If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
 
     Args:
         size (sequence or int): Desired output size of the crop. If size is an