updated transforms.ToPILImage, see #105

test/test_transforms.py

@@ -169,6 +169,12 @@ class Tester(unittest.TestCase):
         l, = img.split()
         assert np.allclose(l, img_data[:, :, 0])
 
+    def test_ndarray16_to_pil_image(self):
+        trans = transforms.ToPILImage()
+        img_data = np.random.randint(0, 65535, [4, 4, 1], np.uint16)
+        img = trans(img_data)
+        assert img.mode == 'I;16'
+        assert np.allclose(img, img_data[:, :, 0])
 
 if __name__ == '__main__':
     unittest.main()

torchvision/transforms.py

@@ -55,21 +55,32 @@ class ToTensor(object):
 
 
 class ToPILImage(object):
-    """Converts a torch.*Tensor of range [0, 1] and shape C x H x W
-    or numpy ndarray of dtype=uint8, range[0, 255] and shape H x W x C
-    to a PIL.Image of range [0, 255]
+    """Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape
+    H x W x C to a PIL.Image while preserving value range.
     """
 
     def __call__(self, pic):
         npimg = pic
         mode = None
-        if not isinstance(npimg, np.ndarray):
-            npimg = pic.mul(255).byte().numpy()
-            npimg = np.transpose(npimg, (1, 2, 0))
+        if isinstance(pic, torch.FloatTensor):
+            pic = pic.mul(255).byte()
+        if torch.is_tensor(pic):
+            npimg = np.transpose(pic.numpy(), (1, 2, 0))
+        assert isinstance(npimg, np.ndarray), 'pic should be Tensor or ndarray'
 
         if npimg.shape[2] == 1:
             npimg = npimg[:, :, 0]
-            mode = "L"
+
+            if npimg.dtype == np.uint8:
+                mode = 'L'
+            if npimg.dtype == np.uint16:
+                mode = 'I;16'
+            elif npimg.dtype == np.float32:
+                mode = 'F'
+        else:
+            if npimg.dtype == np.uint8:
+                mode = 'RGB'
+        assert mode is not None, '{} is not supported'.format(npimg.dtype)
 
         return Image.fromarray(npimg, mode=mode)