Speed up equalize transform: use bincount instead of histc

test/test_functional_tensor.py

@@ -977,6 +977,18 @@ class CUDATester(Tester):
     def setUp(self):
         self.device = "cuda"
 
+    def test_scale_channel(self):
+        """Make sure that _scale_channel gives the same results on CPU and GPU as
+        histc or bincount are used depending on the device.
+        """
+        # TODO: when # https://github.com/pytorch/pytorch/issues/53194 is fixed,
+        # only use bincount and remove that test.
+        size = (1_000,)
+        img_chan = torch.randint(0, 256, size=size).to('cpu')
+        scaled_cpu = F_t._scale_channel(img_chan)
+        scaled_cuda = F_t._scale_channel(img_chan.to('cuda'))
+        self.assertTrue(scaled_cpu.equal(scaled_cuda.to('cpu')))
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/transforms/functional_tensor.py

@@ -902,7 +902,14 @@ def autocontrast(img: Tensor) -> Tensor:
 
 
 def _scale_channel(img_chan):
-    hist = torch.histc(img_chan.to(torch.float32), bins=256, min=0, max=255)
+    # TODO: we should expect bincount to always be faster than histc, but this
+    # isn't always the case. Once
+    # https://github.com/pytorch/pytorch/issues/53194 is fixed, remove the if
+    # block and only use bincount.
+    if img_chan.is_cuda:
+        hist = torch.histc(img_chan.to(torch.float32), bins=256, min=0, max=255)
+    else:
+        hist = torch.bincount(img_chan.view(-1), minlength=256)
 
     nonzero_hist = hist[hist != 0]
     step = nonzero_hist[:-1].sum() // 255