[Refactor]: Grascale return uint8 type

mmcv/transforms/processing.py

@@ -692,6 +692,7 @@ class RandomGrayscale(BaseTransform):
                 normalized_weights = (
                     np.array(self.channel_weights) / sum(self.channel_weights))
                 img = (normalized_weights * img).sum(axis=2)
+                img = img.astype('uint8')
                 if self.keep_channels:
                     img = img[:, :, None]
                     results['img'] = np.dstack(
@@ -699,6 +700,7 @@ class RandomGrayscale(BaseTransform):
                 else:
                     results['img'] = img
                 return results
+        img = img.astype('uint8')
         results['img'] = img
         return results
 

tests/test_transforms/test_transforms_processing.py

@@ -474,7 +474,7 @@ class TestRandomGrayscale:
 
     @classmethod
     def setup_class(cls):
-        cls.img = np.random.rand(10, 10, 3).astype(np.float32)
+        cls.img = (np.random.rand(10, 10, 3) * 255).astype(np.uint8)
 
     def test_repr(self):
         # test repr
@@ -504,9 +504,9 @@ class TestRandomGrayscale:
         random_gray_scale_module = TRANSFORMS.build(transform)
         results['img'] = copy.deepcopy(self.img)
         img = random_gray_scale_module(results)['img']
-        computed_gray = (
-            self.img[:, :, 0] * 0.299 + self.img[:, :, 1] * 0.587 +
-            self.img[:, :, 2] * 0.114)
+        computed_gray = (self.img[:, :, 0] * 0.299 +
+                         self.img[:, :, 1] * 0.587 +
+                         self.img[:, :, 2] * 0.114).astype(np.uint8)
         for i in range(img.shape[2]):
             assert_array_almost_equal(img[:, :, i], computed_gray, decimal=4)
         assert img.shape == (10, 10, 3)