Clean up to_tensor.

torchvision/transforms/functional.py

@@ -88,18 +88,9 @@ def get_image_num_channels(img: Tensor) -> int:
     return F_pil.get_image_num_channels(img)
 
 
-@torch.jit.unused
-def _is_numpy(img: Any) -> bool:
-    return isinstance(img, np.ndarray)
-
-
-@torch.jit.unused
-def _is_numpy_image(img: Any) -> bool:
-    return img.ndim in {2, 3}
-
-
 def to_tensor(pic):
-    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
+    """Convert a ``PIL Image`` or ``numpy.ndarray`` to a float tensor and normalize
+    the values to 0-1 scale if the input is of type `uint8`.
     This function does not support torchscript.
 
     See :class:`~torchvision.transforms.ToTensor` for more details.
@@ -110,46 +101,25 @@ def to_tensor(pic):
     Returns:
         Tensor: Converted image.
     """
-    if not(F_pil._is_pil_image(pic) or _is_numpy(pic)):
-        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
-
-    if _is_numpy(pic) and not _is_numpy_image(pic):
-        raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim))
-
     default_float_dtype = torch.get_default_dtype()
-
     if isinstance(pic, np.ndarray):
-        # handle numpy array
         if pic.ndim == 2:
             pic = pic[:, :, None]
+        elif pic.ndim != 3:
+            raise ValueError('pic should be 2/3 dimensional. Got {} dimensions.'.format(pic.ndim))
+        img = torch.from_numpy(pic.transpose((2, 0, 1)))
+    elif F_pil._is_pil_image(pic):
+        img = pil_to_tensor(pic)
+    else:
+        raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))
 
-        img = torch.from_numpy(pic.transpose((2, 0, 1))).contiguous()
-        # backward compatibility
-        if isinstance(img, torch.ByteTensor):
-            return img.to(dtype=default_float_dtype).div(255)
-        else:
-            return img
-
-    if accimage is not None and isinstance(pic, accimage.Image):
-        nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
-        pic.copyto(nppic)
-        return torch.from_numpy(nppic).to(dtype=default_float_dtype)
+    normalize = isinstance(img, torch.ByteTensor) and \
+                (accimage is None or isinstance(pic, accimage.Image))
+    img = img.to(dtype=default_float_dtype)
+    if normalize:
+        img.div_(255)
 
-    # handle PIL Image
-    mode_to_nptype = {'I': np.int32, 'I;16': np.int16, 'F': np.float32}
-    img = torch.from_numpy(
-        np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True)
-    )
-
-    if pic.mode == '1':
-        img = 255 * img
-    img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
-    # put it from HWC to CHW format
-    img = img.permute((2, 0, 1)).contiguous()
-    if isinstance(img, torch.ByteTensor):
-        return img.to(dtype=default_float_dtype).div(255)
-    else:
-        return img
+    return img
 
 
 def pil_to_tensor(pic):