Make image processors more general (#27690)

* Make image processors more general

* Add backwards compatibility for KOSMOS-2

* Remove use_square_size everywhere

* Remove script

src/transformers/models/bit/image_processing_bit.py

@@ -84,10 +84,6 @@ class BitImageProcessor(BaseImageProcessor):
             Can be overridden by the `image_std` parameter in the `preprocess` method.
         do_convert_rgb (`bool`, *optional*, defaults to `True`):
             Whether to convert the image to RGB.
-        use_square_size (`bool`, *optional*, defaults to `False`):
-            The value to be passed to `get_size_dict` as `default_to_square` when computing the image size. If the
-            `size` argument in `get_size_dict` is an `int`, it determines whether to default to a square image or not.
-            Note that this attribute is not used in computing `crop_size` via calling `get_size_dict`.
     """
 
     model_input_names = ["pixel_values"]
@@ -105,12 +101,11 @@ class BitImageProcessor(BaseImageProcessor):
         image_mean: Optional[Union[float, List[float]]] = None,
         image_std: Optional[Union[float, List[float]]] = None,
         do_convert_rgb: bool = True,
-        use_square_size: bool = False,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
         size = size if size is not None else {"shortest_edge": 224}
-        size = get_size_dict(size, default_to_square=use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
         crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
 
@@ -125,7 +120,6 @@ class BitImageProcessor(BaseImageProcessor):
         self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
         self.do_convert_rgb = do_convert_rgb
-        self.use_square_size = use_square_size
 
     # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
     def resize(
@@ -153,13 +147,19 @@ class BitImageProcessor(BaseImageProcessor):
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        default_to_square = True
-        if "shortest_edge" not in size:
+        if "shortest_edge" in size:
-            raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
+            size = size["shortest_edge"]
+            default_to_square = False
+        elif "height" in size and "width" in size:
+            size = (size["height"], size["width"])
+        else:
+            raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
         output_size = get_resize_output_image_size(
             image,
-            size=size["shortest_edge"],
+            size=size,
-            default_to_square=self.use_square_size,
+            default_to_square=default_to_square,
             input_data_format=input_data_format,
         )
         return resize(
@@ -243,7 +243,7 @@ class BitImageProcessor(BaseImageProcessor):
         """
         do_resize = do_resize if do_resize is not None else self.do_resize
         size = size if size is not None else self.size
-        size = get_size_dict(size, param_name="size", default_to_square=self.use_square_size)
+        size = get_size_dict(size, param_name="size", default_to_square=False)
         resample = resample if resample is not None else self.resample
         do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
         crop_size = crop_size if crop_size is not None else self.crop_size

src/transformers/models/clip/image_processing_clip.py

@@ -84,10 +84,6 @@ class CLIPImageProcessor(BaseImageProcessor):
             Can be overridden by the `image_std` parameter in the `preprocess` method.
         do_convert_rgb (`bool`, *optional*, defaults to `True`):
             Whether to convert the image to RGB.
-        use_square_size (`bool`, *optional*, defaults to `False`):
-            The value to be passed to `get_size_dict` as `default_to_square` when computing the image size. If the
-            `size` argument in `get_size_dict` is an `int`, it determines whether to default to a square image or not.
-            Note that this attribute is not used in computing `crop_size` via calling `get_size_dict`.
     """
 
     model_input_names = ["pixel_values"]
@@ -105,12 +101,11 @@ class CLIPImageProcessor(BaseImageProcessor):
         image_mean: Optional[Union[float, List[float]]] = None,
         image_std: Optional[Union[float, List[float]]] = None,
         do_convert_rgb: bool = True,
-        use_square_size: bool = False,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
         size = size if size is not None else {"shortest_edge": 224}
-        size = get_size_dict(size, default_to_square=use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
         crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
 
@@ -125,7 +120,10 @@ class CLIPImageProcessor(BaseImageProcessor):
         self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
         self.do_convert_rgb = do_convert_rgb
-        self.use_square_size = use_square_size
+
+        # for backwards compatibility of KOSMOS-2
+        if "use_square_size" in kwargs:
+            self.size = {"height": size["shortest_edge"], "width": size["shortest_edge"]}
 
     def resize(
         self,
@@ -152,13 +150,19 @@ class CLIPImageProcessor(BaseImageProcessor):
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        default_to_square = True
-        if "shortest_edge" not in size:
+        if "shortest_edge" in size:
-            raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
+            size = size["shortest_edge"]
+            default_to_square = False
+        elif "height" in size and "width" in size:
+            size = (size["height"], size["width"])
+        else:
+            raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
         output_size = get_resize_output_image_size(
             image,
-            size=size["shortest_edge"],
+            size=size,
-            default_to_square=self.use_square_size,
+            default_to_square=default_to_square,
             input_data_format=input_data_format,
         )
         return resize(
@@ -242,7 +246,7 @@ class CLIPImageProcessor(BaseImageProcessor):
         """
         do_resize = do_resize if do_resize is not None else self.do_resize
         size = size if size is not None else self.size
-        size = get_size_dict(size, param_name="size", default_to_square=self.use_square_size)
+        size = get_size_dict(size, param_name="size", default_to_square=False)
         resample = resample if resample is not None else self.resample
         do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
         crop_size = crop_size if crop_size is not None else self.crop_size

src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py

@@ -79,10 +79,6 @@ class MobileNetV1ImageProcessor(BaseImageProcessor):
         image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
             Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
             number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
-        use_square_size (`bool`, *optional*, defaults to `False`):
-            The value to be passed to `get_size_dict` as `default_to_square` when computing the image size. If the
-            `size` argument in `get_size_dict` is an `int`, it determines whether to default to a square image or not.
-            Note that this attribute is not used in computing `crop_size` via calling `get_size_dict`.
     """
 
     model_input_names = ["pixel_values"]
@@ -99,12 +95,11 @@ class MobileNetV1ImageProcessor(BaseImageProcessor):
         do_normalize: bool = True,
         image_mean: Optional[Union[float, List[float]]] = None,
         image_std: Optional[Union[float, List[float]]] = None,
-        use_square_size: bool = False,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
         size = size if size is not None else {"shortest_edge": 256}
-        size = get_size_dict(size, default_to_square=use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
         crop_size = get_size_dict(crop_size)
         self.do_resize = do_resize
@@ -117,7 +112,6 @@ class MobileNetV1ImageProcessor(BaseImageProcessor):
         self.do_normalize = do_normalize
         self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
-        self.use_square_size = use_square_size
 
     # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
     def resize(
@@ -145,13 +139,19 @@ class MobileNetV1ImageProcessor(BaseImageProcessor):
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        default_to_square = True
-        if "shortest_edge" not in size:
+        if "shortest_edge" in size:
-            raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
+            size = size["shortest_edge"]
+            default_to_square = False
+        elif "height" in size and "width" in size:
+            size = (size["height"], size["width"])
+        else:
+            raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
         output_size = get_resize_output_image_size(
             image,
-            size=size["shortest_edge"],
+            size=size,
-            default_to_square=self.use_square_size,
+            default_to_square=default_to_square,
             input_data_format=input_data_format,
         )
         return resize(
@@ -231,7 +231,7 @@ class MobileNetV1ImageProcessor(BaseImageProcessor):
         """
         do_resize = do_resize if do_resize is not None else self.do_resize
         size = size if size is not None else self.size
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         resample = resample if resample is not None else self.resample
         do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
         crop_size = crop_size if crop_size is not None else self.crop_size

src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py

@@ -83,10 +83,6 @@ class MobileNetV2ImageProcessor(BaseImageProcessor):
         image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
             Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
             number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
-        use_square_size (`bool`, *optional*, defaults to `False`):
-            The value to be passed to `get_size_dict` as `default_to_square` when computing the image size. If the
-            `size` argument in `get_size_dict` is an `int`, it determines whether to default to a square image or not.
-            Note that this attribute is not used in computing `crop_size` via calling `get_size_dict`.
     """
 
     model_input_names = ["pixel_values"]
@@ -103,12 +99,11 @@ class MobileNetV2ImageProcessor(BaseImageProcessor):
         do_normalize: bool = True,
         image_mean: Optional[Union[float, List[float]]] = None,
         image_std: Optional[Union[float, List[float]]] = None,
-        use_square_size: bool = False,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
         size = size if size is not None else {"shortest_edge": 256}
-        size = get_size_dict(size, default_to_square=use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
         crop_size = get_size_dict(crop_size, param_name="crop_size")
         self.do_resize = do_resize
@@ -121,7 +116,6 @@ class MobileNetV2ImageProcessor(BaseImageProcessor):
         self.do_normalize = do_normalize
         self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
-        self.use_square_size = use_square_size
 
     # Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize
     def resize(
@@ -149,13 +143,19 @@ class MobileNetV2ImageProcessor(BaseImageProcessor):
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        default_to_square = True
-        if "shortest_edge" not in size:
+        if "shortest_edge" in size:
-            raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
+            size = size["shortest_edge"]
+            default_to_square = False
+        elif "height" in size and "width" in size:
+            size = (size["height"], size["width"])
+        else:
+            raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
         output_size = get_resize_output_image_size(
             image,
-            size=size["shortest_edge"],
+            size=size,
-            default_to_square=self.use_square_size,
+            default_to_square=default_to_square,
             input_data_format=input_data_format,
         )
         return resize(
@@ -235,7 +235,7 @@ class MobileNetV2ImageProcessor(BaseImageProcessor):
         """
         do_resize = do_resize if do_resize is not None else self.do_resize
         size = size if size is not None else self.size
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         resample = resample if resample is not None else self.resample
         do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
         crop_size = crop_size if crop_size is not None else self.crop_size

src/transformers/models/mobilevit/image_processing_mobilevit.py

@@ -78,10 +78,6 @@ class MobileViTImageProcessor(BaseImageProcessor):
         do_flip_channel_order (`bool`, *optional*, defaults to `True`):
             Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order`
             parameter in the `preprocess` method.
-        use_square_size (`bool`, *optional*, defaults to `False`):
-            The value to be passed to `get_size_dict` as `default_to_square` when computing the image size. If the
-            `size` argument in `get_size_dict` is an `int`, it determines whether to default to a square image or not.
-            Note that this attribute is not used in computing `crop_size` via calling `get_size_dict`.
     """
 
     model_input_names = ["pixel_values"]
@@ -96,12 +92,11 @@ class MobileViTImageProcessor(BaseImageProcessor):
         do_center_crop: bool = True,
         crop_size: Dict[str, int] = None,
         do_flip_channel_order: bool = True,
-        use_square_size: bool = False,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
         size = size if size is not None else {"shortest_edge": 224}
-        size = get_size_dict(size, default_to_square=use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256}
         crop_size = get_size_dict(crop_size, param_name="crop_size")
 
@@ -113,7 +108,6 @@ class MobileViTImageProcessor(BaseImageProcessor):
         self.do_center_crop = do_center_crop
         self.crop_size = crop_size
         self.do_flip_channel_order = do_flip_channel_order
-        self.use_square_size = use_square_size
 
     # Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize with PILImageResampling.BICUBIC->PILImageResampling.BILINEAR
     def resize(
@@ -141,13 +135,19 @@ class MobileViTImageProcessor(BaseImageProcessor):
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        default_to_square = True
-        if "shortest_edge" not in size:
+        if "shortest_edge" in size:
-            raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
+            size = size["shortest_edge"]
+            default_to_square = False
+        elif "height" in size and "width" in size:
+            size = (size["height"], size["width"])
+        else:
+            raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
         output_size = get_resize_output_image_size(
             image,
-            size=size["shortest_edge"],
+            size=size,
-            default_to_square=self.use_square_size,
+            default_to_square=default_to_square,
             input_data_format=input_data_format,
         )
         return resize(
@@ -246,7 +246,7 @@ class MobileViTImageProcessor(BaseImageProcessor):
         )
 
         size = size if size is not None else self.size
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         crop_size = crop_size if crop_size is not None else self.crop_size
         crop_size = get_size_dict(crop_size, param_name="crop_size")
 

src/transformers/models/vit_hybrid/image_processing_vit_hybrid.py

@@ -84,10 +84,6 @@ class ViTHybridImageProcessor(BaseImageProcessor):
             Can be overridden by the `image_std` parameter in the `preprocess` method.
         do_convert_rgb (`bool`, *optional*, defaults to `True`):
             Whether to convert the image to RGB.
-        use_square_size (`bool`, *optional*, defaults to `False`):
-            The value to be passed to `get_size_dict` as `default_to_square` when computing the image size. If the
-            `size` argument in `get_size_dict` is an `int`, it determines whether to default to a square image or not.
-            Note that this attribute is not used in computing `crop_size` via calling `get_size_dict`.
     """
 
     model_input_names = ["pixel_values"]
@@ -105,12 +101,11 @@ class ViTHybridImageProcessor(BaseImageProcessor):
         image_mean: Optional[Union[float, List[float]]] = None,
         image_std: Optional[Union[float, List[float]]] = None,
         do_convert_rgb: bool = True,
-        use_square_size: bool = False,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
         size = size if size is not None else {"shortest_edge": 224}
-        size = get_size_dict(size, default_to_square=use_square_size)
+        size = get_size_dict(size, default_to_square=False)
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
         crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
 
@@ -125,7 +120,6 @@ class ViTHybridImageProcessor(BaseImageProcessor):
         self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
         self.do_convert_rgb = do_convert_rgb
-        self.use_square_size = use_square_size
 
     # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
     def resize(
@@ -153,13 +147,19 @@ class ViTHybridImageProcessor(BaseImageProcessor):
             input_data_format (`ChannelDimension` or `str`, *optional*):
                 The channel dimension format of the input image. If not provided, it will be inferred.
         """
-        size = get_size_dict(size, default_to_square=self.use_square_size)
+        default_to_square = True
-        if "shortest_edge" not in size:
+        if "shortest_edge" in size:
-            raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
+            size = size["shortest_edge"]
+            default_to_square = False
+        elif "height" in size and "width" in size:
+            size = (size["height"], size["width"])
+        else:
+            raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
         output_size = get_resize_output_image_size(
             image,
-            size=size["shortest_edge"],
+            size=size,
-            default_to_square=self.use_square_size,
+            default_to_square=default_to_square,
             input_data_format=input_data_format,
         )
         return resize(
@@ -243,7 +243,7 @@ class ViTHybridImageProcessor(BaseImageProcessor):
         """
         do_resize = do_resize if do_resize is not None else self.do_resize
         size = size if size is not None else self.size
-        size = get_size_dict(size, param_name="size", default_to_square=self.use_square_size)
+        size = get_size_dict(size, param_name="size", default_to_square=False)
         resample = resample if resample is not None else self.resample
         do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
         crop_size = crop_size if crop_size is not None else self.crop_size

tests/models/kosmos2/test_processor_kosmos2.py

@@ -55,7 +55,7 @@ class Kosmos2ProcessorTest(unittest.TestCase):
     def setUp(self):
         self.tmpdirname = tempfile.mkdtemp()
 
-        image_processor = CLIPImageProcessor(use_square_size=True)
+        image_processor = CLIPImageProcessor()
 
         # We have a SentencePiece fixture for testing
         slow_tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB)