Making RandomErasing Out of place by default and improvising tests for the same (#1060)

* test improved

* Update test_transforms.py

* behaviour changes RandomErasing

* test fixes

* linter fix

test/test_transforms.py

@@ -1345,20 +1345,33 @@ class Tester(unittest.TestCase):
     def test_random_erasing(self):
         """Unit tests for random erasing transform"""
 
-        img = torch.rand([3, 224, 224])
+        img = torch.rand([3, 60, 60])
 
         # Test Set 1: Erasing with int value
-        img_re = transforms.RandomErasing(value=0)(img)
-        assert img_re.size(0) == 3
-
-        # Test Set 2: Erasing with random value
+        img_re = transforms.RandomErasing(value=0.2)
+        i, j, h, w, v = img_re.get_params(img, scale=img_re.scale, ratio=img_re.ratio, value=img_re.value)
+        img_output = F.erase(img, i, j, h, w, v)
+        assert img_output.size(0) == 3
+
+        # Test Set 2: Check if the unerased region is preserved
+        orig_unerased = img.clone()
+        orig_unerased[:, i:i + h, j:j + w] = 0
+        output_unerased = img_output.clone()
+        output_unerased[:, i:i + h, j:j + w] = 0
+        assert torch.equal(orig_unerased, output_unerased)
+
+        # Test Set 3: Erasing with random value
         img_re = transforms.RandomErasing(value='random')(img)
         assert img_re.size(0) == 3
 
-        # Test Set 3: Erasing with tuple value
+        # Test Set 4: Erasing with tuple value
         img_re = transforms.RandomErasing(value=(0.2, 0.2, 0.2))(img)
         assert img_re.size(0) == 3
 
+        # Test Set 5: Testing the inplace behaviour
+        img_re = transforms.RandomErasing(value=(0.2), inplace=True)(img)
+        assert torch.equal(img_re, img)
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/transforms/functional.py

@@ -806,7 +806,7 @@ def to_grayscale(img, num_output_channels=1):
     return img
 
 
-def erase(img, i, j, h, w, v):
+def erase(img, i, j, h, w, v, inplace=False):
     """ Erase the input Tensor Image with given value.
 
     Args:
@@ -816,6 +816,7 @@ def erase(img, i, j, h, w, v):
         h (int): Height of the erased region.
         w (int): Width of the erased region.
         v: Erasing value.
+        inplace(bool,optional): For in-place operations. By default is set False.
 
     Returns:
         Tensor Image: Erased image.
@@ -823,5 +824,8 @@ def erase(img, i, j, h, w, v):
     if not isinstance(img, torch.Tensor):
         raise TypeError('img should be Tensor Image. Got {}'.format(type(img)))
 
+    if not inplace:
+        img = img.clone()
+
     img[:, i:i + h, j:j + w] = v
     return img

torchvision/transforms/transforms.py

@@ -1196,6 +1196,8 @@ class RandomErasing(object):
             erase all pixels. If a tuple of length 3, it is used to erase
             R, G, B channels respectively.
             If a str of 'random', erasing each pixel with random values.
+         inplace: boolean to make this transform inplace.Default set to False.
+
     Returns:
         Erased Image.
     # Examples:
@@ -1207,7 +1209,7 @@ class RandomErasing(object):
         >>> ])
     """
 
-    def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 1. / 0.3), value=0):
+    def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 1. / 0.3), value=0, inplace=False):
         assert isinstance(value, (numbers.Number, str, tuple, list))
         if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
             warnings.warn("range should be of kind (min, max)")
@@ -1218,6 +1220,7 @@ class RandomErasing(object):
         self.scale = scale
         self.ratio = ratio
         self.value = value
+        self.inplace = inplace
 
     @staticmethod
     def get_params(img, scale, ratio, value=0):
@@ -1261,5 +1264,5 @@ class RandomErasing(object):
         """
         if random.uniform(0, 1) < self.p:
             x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=self.value)
-            return F.erase(img, x, y, h, w, v)
+            return F.erase(img, x, y, h, w, v, self.inplace)
         return img