Fix bug to RandomErasing (#1095)

* Fix bug to Random Erasing

1. Avoid forever loop for getting parameters of erase.
2. replace' img_b' by 'img_c', because it indicates the channel.
3. replace v = torch.rand([img_c, h, w]) by v = torch.empty([img_c, h, w], dtype=torch.float32).normal_(). Normally distributed achieves better performance.

* add test

* Update test_transforms.py

* Update transforms.py

* Update test_transforms.py

* Update transforms.py

* Update functional.py

test/test_transforms.py

@@ -1379,6 +1379,11 @@ class Tester(unittest.TestCase):
         img_re = transforms.RandomErasing(value=(0.2), inplace=True)(img)
         assert torch.equal(img_re, img)
 
+        # Test Set 6: Checking when no erased region is selected
+        img = torch.rand([3, 300, 1])
+        img_re = transforms.RandomErasing(ratio=(0.1, 0.2), value='random')(img)
+        assert torch.equal(img_re, img)
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/transforms/functional.py

@@ -828,7 +828,7 @@ def erase(img, i, j, h, w, v, inplace=False):
         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.
+        inplace(bool, optional): For in-place operations. By default is set False.
 
     Returns:
         Tensor Image: Erased image.

torchvision/transforms/transforms.py

@@ -1210,7 +1210,7 @@ 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.
+         inplace: boolean to make this transform inplace. Default set to False.
 
     Returns:
         Erased Image.
@@ -1223,7 +1223,7 @@ class RandomErasing(object):
         >>> ])
     """
 
-    def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 1. / 0.3), value=0, inplace=False):
+    def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), 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)")
@@ -1250,10 +1250,10 @@ class RandomErasing(object):
         Returns:
             tuple: params (i, j, h, w, v) to be passed to ``erase`` for random erasing.
         """
-        img_b, img_h, img_w = img.shape
+        img_c, img_h, img_w = img.shape
         area = img_h * img_w
 
-        while True:
+        for attempt in range(10):
             erase_area = random.uniform(scale[0], scale[1]) * area
             aspect_ratio = random.uniform(ratio[0], ratio[1])
 
@@ -1266,11 +1266,14 @@ class RandomErasing(object):
                 if isinstance(value, numbers.Number):
                     v = value
                 elif isinstance(value, torch._six.string_classes):
-                    v = torch.rand(img_b, h, w)
+                    v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
                 elif isinstance(value, (list, tuple)):
                     v = torch.tensor(value, dtype=torch.float32).view(-1, 1, 1).expand(-1, h, w)
                 return i, j, h, w, v
 
+        # Return original image
+        return 0, 0, img_h, img_w, img
+
     def __call__(self, img):
         """
         Args: