Type cast before normalize yolos

src/transformers/models/yolos/feature_extraction_yolos.py

@@ -385,14 +385,14 @@ def get_size(image_size, size, max_size=None):
         return rescaled_image, target
 
     # Copied from transformers.models.detr.feature_extraction_detr.DetrFeatureExtractor._normalize
-    def _normalize(self, image, mean, std, target=None):
+    def _normalize(self, image, mean, std, target=None, rescale=False):
         """
         Normalize the image with a certain mean and std.
 
         If given, also normalize the target bounding boxes based on the size of the image.
         """
 
-        image = self.normalize(image, mean=mean, std=std)
+        image = self.normalize(image, mean=mean, std=std, rescale=rescale)
         if target is None:
             return image, None
 
@@ -456,9 +456,15 @@ def __call__(
             padding (`bool`, *optional*, defaults to `True`):
                 Whether or not to pad images up to the largest image in a batch.
 
-            return_tensors (`str` or [`~utils.TensorType`], *optional*):
-                If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
-                objects.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `None`):
+                If set, will return a tensor of a particular framework.
+
+                Acceptable values are:
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+                - None: Return list of `np.ndarray` objects.
 
         Returns:
             [`BatchFeature`]: A [`BatchFeature`] with the following fields:
@@ -566,17 +572,22 @@ def __call__(
                 for idx, image in enumerate(images):
                     images[idx] = self._resize(image=image, target=None, size=self.size, max_size=self.max_size)[0]
 
+        # if do_normalize=False, the casting to a numpy array won't happen, so we need to do it here
+        make_channel_first = True if isinstance(images[0], Image.Image) else images[0].shape[-1] in (1, 3)
+        images = [self.to_numpy_array(image, rescale=False, channel_first=make_channel_first) for image in images]
+
         if self.do_normalize:
             if annotations is not None:
                 for idx, (image, target) in enumerate(zip(images, annotations)):
                     image, target = self._normalize(
-                        image=image, mean=self.image_mean, std=self.image_std, target=target
+                        image=image, mean=self.image_mean, std=self.image_std, target=target, rescale=True
                     )
                     images[idx] = image
                     annotations[idx] = target
             else:
                 images = [
-                    self._normalize(image=image, mean=self.image_mean, std=self.image_std)[0] for image in images
+                    self._normalize(image=image, mean=self.image_mean, std=self.image_std, rescale=True)[0]
+                    for image in images
                 ]
 
         if padding:

tests/models/yolos/test_feature_extraction_yolos.py

@@ -20,6 +20,7 @@
 
 import numpy as np
 
+from parameterized import parameterized
 from transformers.testing_utils import require_torch, require_vision, slow
 from transformers.utils import is_torch_available, is_vision_available
 
@@ -334,3 +335,42 @@ def test_call_pytorch_with_coco_panoptic_annotations(self):
         # verify size
         expected_size = torch.tensor([800, 1066])
         assert torch.allclose(encoding["labels"][0]["size"], expected_size)
+
+    @parameterized.expand(
+        [
+            ("do_resize_True_do_normalize_True_padding_True", True, True, True),
+            ("do_resize_True_do_normalize_True_padding_False", True, True, False),
+            ("do_resize_True_do_normalize_False_padding_True", True, False, True),
+            ("do_resize_True_do_normalize_False_padding_False", True, False, False),
+            ("do_resize_False_do_normalize_True_padding_True", False, True, True),
+            ("do_resize_False_do_normalize_True_padding_False", False, True, False),
+            ("do_resize_False_do_normalize_False_padding_True", False, False, True),
+            ("do_resize_False_do_normalize_False_padding_False", False, False, False),
+        ]
+    )
+    def test_call_flags(self, _, do_resize, do_normalize, padding):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        feature_extractor.do_resize = do_resize
+        feature_extractor.do_normalize = do_normalize
+        # create random PIL images
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
+
+        expected_shapes = [(3, *x.size[::-1]) for x in image_inputs]
+        if do_resize:
+            expected_shapes = [
+                (
+                    self.feature_extract_tester.num_channels,
+                    *self.feature_extract_tester.get_expected_values([img], batched=False),
+                )
+                for img in image_inputs
+            ]
+        if padding:
+            channel, height, width = [max(x) for x in zip(*expected_shapes)]
+            expected_shapes = [(channel, height, width)] * self.feature_extract_tester.batch_size
+
+        pixel_values = feature_extractor(image_inputs, padding=padding, return_tensors=None)["pixel_values"]
+        self.assertEqual(len(pixel_values), self.feature_extract_tester.batch_size)
+        for idx, image in enumerate(pixel_values):
+            self.assertEqual(image.shape, expected_shapes[idx])
+            self.assertIsInstance(image, np.ndarray)