[Image Transformers] to_pil fix float edge cases (#20406)

* Correct type checking

* up

src/transformers/image_transforms.py

@@ -145,7 +145,7 @@ def to_pil_image(
     image = np.squeeze(image, axis=-1) if image.shape[-1] == 1 else image
 
     # PIL.Image can only store uint8 values, so we rescale the image to be between 0 and 255 if needed.
-    do_rescale = isinstance(image.flat[0], float) if do_rescale is None else do_rescale
+    do_rescale = isinstance(image.flat[0], (float, np.float32, np.float64)) if do_rescale is None else do_rescale
     if do_rescale:
         image = rescale(image, 255)
     image = image.astype(np.uint8)

tests/test_image_transforms.py

@@ -61,6 +61,8 @@ class ImageTransformsTester(unittest.TestCase):
         [
             ("numpy_float_channels_first", (3, 4, 5), np.float32),
             ("numpy_float_channels_last", (4, 5, 3), np.float32),
+            ("numpy_float_channels_first", (3, 4, 5), np.float64),
+            ("numpy_float_channels_last", (4, 5, 3), np.float64),
             ("numpy_int_channels_first", (3, 4, 5), np.int32),
             ("numpy_uint_channels_first", (3, 4, 5), np.uint8),
         ]
@@ -72,6 +74,27 @@ class ImageTransformsTester(unittest.TestCase):
         self.assertIsInstance(pil_image, PIL.Image.Image)
         self.assertEqual(pil_image.size, (5, 4))
 
+        # make sure image is correctly rescaled
+        self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0)
+
+    @parameterized.expand(
+        [
+            ("numpy_float_channels_first", (3, 4, 5), np.float32),
+            ("numpy_float_channels_first", (3, 4, 5), np.float64),
+            ("numpy_float_channels_last", (4, 5, 3), np.float32),
+            ("numpy_float_channels_last", (4, 5, 3), np.float64),
+        ]
+    )
+    @require_vision
+    def test_to_pil_image_from_float(self, name, image_shape, dtype):
+        image = np.random.rand(*image_shape).astype(dtype)
+        pil_image = to_pil_image(image)
+        self.assertIsInstance(pil_image, PIL.Image.Image)
+        self.assertEqual(pil_image.size, (5, 4))
+
+        # make sure image is correctly rescaled
+        self.assertTrue(np.abs(np.asarray(pil_image)).sum() > 0)
+
     @require_tf
     def test_to_pil_image_from_tensorflow(self):
         # channels_first