Make crop scriptable (#1379)

* Make crop torchscriptable

Relevant #1375

* Invert x and y axis

* fix lint

* Add crop test

* revert deletion of space in functional

* add import random

* add dimension in doc

* add import

* fix flake8

* change to self.assert*

* convert to uint8

* assertTrue

* lint

test/test_functional_tensor.py

+import torch
+import torchvision.transforms as transforms
 import torchvision.transforms.functional_tensor as F_t
+import torchvision.transforms.functional as F
 import unittest
-import torch
+import random
 
 
 class Tester(unittest.TestCase):
@@ -19,6 +22,20 @@ class Tester(unittest.TestCase):
         self.assertEqual(hflipped_img.shape, img_tensor.shape)
         self.assertTrue(torch.equal(img_tensor, hflipped_img_again))
 
+    def test_crop(self):
+        img_tensor = torch.randint(0, 255, (3, 16, 16), dtype=torch.uint8)
+        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_cropped, (img_cropped_GT * 255).to(torch.uint8)),
+                        "functional_tensor crop not working")
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/transforms/functional_tensor.py

@@ -31,3 +31,20 @@ def hflip(img_tensor):
         raise TypeError('tensor is not a torch image.')
 
     return img_tensor.flip(-1)
+
+
+def crop(img, top, left, height, width):
+    """Crop the given Image Tensor.
+    Args:
+        img (Tensor): Image to be cropped in the form [C, H, W]. (0,0) denotes the top left corner of the image.
+        top (int): Vertical component of the top left corner of the crop box.
+        left (int): Horizontal component of the top left corner of the crop box.
+        height (int): Height of the crop box.
+        width (int): Width of the crop box.
+    Returns:
+        Tensor: Cropped image.
+    """
+    if not F._is_tensor_image(img):
+        raise TypeError('tensor is not a torch image.')
+
+    return img[..., top:top + height, left:left + width]