Add param_name to size_dict logs & tidy (huggingface#20205)

src/transformers/image_processing_utils.py

@@ -440,11 +440,50 @@ def preprocess(self, images, **kwargs) -> BatchFeature:
         raise NotImplementedError("Each image processor must implement its own preprocess method")
 
 
+VALID_SIZE_DICT_KEYS = ({"height", "width"}, {"shortest_edge"}, {"shortest_edge", "longest_edge"})
+
+
+def is_valid_size_dict(size_dict):
+    if not isinstance(size_dict, dict):
+        return False
+
+    size_dict_keys = set(size_dict.keys())
+    for allowed_keys in VALID_SIZE_DICT_KEYS:
+        if size_dict_keys == allowed_keys:
+            return True
+    return False
+
+
+def convert_to_size_dict(
+    size, max_size: Optional[int] = None, default_to_square: bool = True, height_width_order: bool = True
+):
+    # By default, if size is an int we assume it represents a tuple of (size, size).
+    if isinstance(size, int) and default_to_square:
+        if max_size is not None:
+            raise ValueError("Cannot specify both size as an int, with default_to_square=True and max_size")
+        return {"height": size, "width": size}
+    # In other configs, if size is an int and default_to_square is False, size represents the length of
+    # the shortest edge after resizing.
+    elif isinstance(size, int) and not default_to_square:
+        size_dict = {"shortest_edge": size}
+        if max_size is not None:
+            size_dict["longest_edge"] = max_size
+        return size_dict
+    # Otherwise, if size is a tuple it's either (height, width) or (width, height)
+    elif isinstance(size, (tuple, list)) and height_width_order:
+        return {"height": size[0], "width": size[1]}
+    elif isinstance(size, (tuple, list)) and not height_width_order:
+        return {"height": size[1], "width": size[0]}
+
+    raise ValueError(f"Could not convert size input to size dict: {size}")
+
+
 def get_size_dict(
     size: Union[int, Iterable[int], Dict[str, int]] = None,
     max_size: Optional[int] = None,
     height_width_order: bool = True,
     default_to_square: bool = True,
+    param_name="size",
 ) -> dict:
     """
     Converts the old size parameter in the config into the new dict expected in the config. This is to ensure backwards
@@ -467,40 +506,19 @@ def get_size_dict(
         default_to_square (`bool`, *optional*, defaults to `True`):
             If `size` is an int, whether to default to a square image or not.
     """
-    # If a dict is passed, we check if it's a valid size dict and then return it.
-    if isinstance(size, dict):
-        size_keys = set(size.keys())
-        if (
-            size_keys != set(["height", "width"])
-            and size_keys != set(["shortest_edge"])
-            and size_keys != set(["shortest_edge", "longest_edge"])
-        ):
-            raise ValueError(
-                "The size dict must contain either the keys ('height', 'width') or ('shortest_edge')"
-                f"or ('shortest_edge', 'longest_edge') but got {size_keys}"
-            )
-        return size
-
-    # By default, if size is an int we assume it represents a tuple of (size, size).
-    elif isinstance(size, int) and default_to_square:
-        if max_size is not None:
-            raise ValueError("Cannot specify both size as an int, with default_to_square=True and max_size")
-        size_dict = {"height": size, "width": size}
-    # In other configs, if size is an int and default_to_square is False, size represents the length of the shortest edge after resizing.
-    elif isinstance(size, int) and not default_to_square:
-        if max_size is not None:
-            size_dict = {"shortest_edge": size, "longest_edge": max_size}
-        else:
-            size_dict = {"shortest_edge": size}
-    elif isinstance(size, (tuple, list)) and height_width_order:
-        size_dict = {"height": size[0], "width": size[1]}
-    elif isinstance(size, (tuple, list)) and not height_width_order:
-        size_dict = {"height": size[1], "width": size[0]}
+    if not isinstance(size, dict):
+        size_dict = convert_to_size_dict(size, max_size, default_to_square, height_width_order)
+        logger.info(
+            "{param_name} should be a dictionary on of the following set of keys: {VALID_SIZE_DICT_KEYS}, got {size}."
+            " Converted to {size_dict}.",
+        )
+    else:
+        size_dict = size
 
-    logger.info(
-        "The size parameter should be a dictionary with keys ('height', 'width'), ('shortest_edge', 'longest_edge')"
-        f" or ('shortest_edge',) got {size}. Setting as {size_dict}.",
-    )
+    if not is_valid_size_dict(size_dict):
+        raise ValueError(
+            f"{param_name} must have one of the following set of keys: {VALID_SIZE_DICT_KEYS}, got {size_dict.keys()}"
+        )
     return size_dict
 
 

src/transformers/models/beit/image_processing_beit.py

@@ -118,7 +118,7 @@ def __init__(
         size = size if size is not None else {"height": 256, "width": 256}
         size = get_size_dict(size)
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
         self.do_resize = do_resize
         self.size = size
         self.resample = resample
@@ -152,7 +152,7 @@ def resize(
             data_format (`str` or `ChannelDimension`, *optional*):
                 The channel dimension format of the image. If not provided, it will be the same as the input image.
         """
-        size = get_size_dict(size)
+        size = get_size_dict(size, default_to_square=True, param_name="size")
         if "height" not in size or "width" not in size:
             raise ValueError(f"The `size` argument must contain `height` and `width` keys. Got {size.keys()}")
         return resize(
@@ -178,7 +178,7 @@ def center_crop(
             data_format (`str` or `ChannelDimension`, *optional*):
                 The channel dimension format of the image. If not provided, it will be the same as the input image.
         """
-        size = get_size_dict(size)
+        size = get_size_dict(size, default_to_square=True, param_name="size")
         return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs)
 
     def rescale(
@@ -406,11 +406,11 @@ def preprocess(
         """
         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)
+        size = get_size_dict(size, default_to_square=True, param_name="size")
         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
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
         do_rescale = do_rescale if do_rescale is not None else self.do_rescale
         rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
         do_normalize = do_normalize if do_normalize is not None else self.do_normalize

src/transformers/models/clip/image_processing_clip.py

@@ -114,7 +114,7 @@ def __init__(
         size = size if size is not None else {"shortest_edge": 224}
         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)
+        crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
 
         self.do_resize = do_resize
         self.size = size
@@ -176,6 +176,8 @@ def center_crop(
                 The channel dimension format of the image. If not provided, it will be the same as the input image.
         """
         size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}")
         return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs)
 
     def rescale(
@@ -285,11 +287,11 @@ def preprocess(
         """
         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=False)
+        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
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True)
         do_rescale = do_rescale if do_rescale is not None else self.do_rescale
         rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
         do_normalize = do_normalize if do_normalize is not None else self.do_normalize

src/transformers/models/deit/image_processing_deit.py

@@ -97,7 +97,7 @@ def __init__(
         size = size if size is not None else {"height": 256, "width": 256}
         size = get_size_dict(size)
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
 
         self.do_resize = do_resize
         self.size = size
@@ -158,6 +158,8 @@ def center_crop(
                 The channel dimension format of the image. If not provided, it will be the same as the input image.
         """
         size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
         return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs)
 
     def rescale(
@@ -272,7 +274,7 @@ def preprocess(
         size = size if size is not None else self.size
         size = get_size_dict(size)
         crop_size = crop_size if crop_size is not None else self.crop_size
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
 
         if not is_batched(images):
             images = [images]

src/transformers/models/flava/image_processing_flava.py

@@ -253,12 +253,12 @@ def __init__(
         size = size if size is not None else {"height": 224, "width": 224}
         size = get_size_dict(size)
         crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
 
         codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112}
-        codebook_size = get_size_dict(codebook_size)
+        codebook_size = get_size_dict(codebook_size, param_name="codebook_size")
         codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}
-        codebook_crop_size = get_size_dict(codebook_crop_size)
+        codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size")
 
         self.do_resize = do_resize
         self.size = size
@@ -360,6 +360,8 @@ def center_crop(
                 The channel dimension format of the image. If not provided, it will be the same as the input image.
         """
         size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"The size dictionary must contain 'height' and 'width' keys. Got {size.keys()}")
         return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs)
 
     def rescale(
@@ -580,7 +582,7 @@ def preprocess(
         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
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
         do_rescale = do_rescale if do_rescale is not None else self.do_rescale
         rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
         do_normalize = do_normalize if do_normalize is not None else self.do_normalize
@@ -612,7 +614,7 @@ def preprocess(
         )
         codebook_do_resize = codebook_do_resize if codebook_do_resize is not None else self.codebook_do_resize
         codebook_size = codebook_size if codebook_size is not None else self.codebook_size
-        codebook_size = get_size_dict(codebook_size)
+        codebook_size = get_size_dict(codebook_size, param_name="codebook_size")
         codebook_resample = codebook_resample if codebook_resample is not None else self.codebook_resample
         codebook_do_rescale = codebook_do_rescale if codebook_do_rescale is not None else self.codebook_do_rescale
         codebook_rescale_factor = (
@@ -622,7 +624,7 @@ def preprocess(
             codebook_do_center_crop if codebook_do_center_crop is not None else self.codebook_do_center_crop
         )
         codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else self.codebook_crop_size
-        codebook_crop_size = get_size_dict(codebook_crop_size)
+        codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size")
         codebook_do_map_pixels = (
             codebook_do_map_pixels if codebook_do_map_pixels is not None else self.codebook_do_map_pixels
         )

src/transformers/models/levit/image_processing_levit.py

@@ -105,7 +105,7 @@ def __init__(
         size = size if size is not None else {"shortest_edge": 224}
         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)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
 
         self.do_resize = do_resize
         self.size = size
@@ -182,6 +182,8 @@ def center_crop(
                 The channel dimension format of the image. If not provided, it will be the same as the input image.
         """
         size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}")
         return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs)
 
     def rescale(
@@ -299,7 +301,7 @@ def preprocess(
         size = size if size is not None else self.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)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
 
         if not is_batched(images):
             images = [images]

src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py

@@ -109,7 +109,7 @@ def __init__(
         size = size if size is not None else {"shortest_edge": 256}
         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)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
         self.do_resize = do_resize
         self.size = size
         self.resample = resample
@@ -169,6 +169,8 @@ def center_crop(
                 The channel dimension format of the image. If not provided, it will be the same as the input image.
         """
         size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}")
         return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs)
 
     def rescale(
@@ -286,7 +288,7 @@ def preprocess(
         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
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
         do_rescale = do_rescale if do_rescale is not None else self.do_rescale
         rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
         do_normalize = do_normalize if do_normalize is not None else self.do_normalize

src/transformers/models/mobilevit/image_processing_mobilevit.py

@@ -123,7 +123,7 @@ def __init__(
         size = size if size is not None else {"shortest_edge": 224}
         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)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
 
         self.do_resize = do_resize
         self.size = size
@@ -182,6 +182,8 @@ def center_crop(
                 The channel dimension format of the image. If not provided, it will be the same as the input image.
         """
         size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
         return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs)
 
     def rescale(
@@ -280,7 +282,7 @@ def preprocess(
         size = size if size is not None else self.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)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
 
         if not is_batched(images):
             images = [images]

src/transformers/models/perceiver/image_processing_perceiver.py

@@ -99,7 +99,7 @@ def __init__(
     ) -> None:
         super().__init__(**kwargs)
         crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256}
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
         size = size if size is not None else {"height": 224, "width": 224}
         size = get_size_dict(size)
 
@@ -141,7 +141,7 @@ def center_crop(
         """
         size = self.size if size is None else size
         size = get_size_dict(size)
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
 
         height, width = get_image_size(image)
         min_dim = min(height, width)
@@ -278,7 +278,7 @@ def preprocess(
         """
         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
-        crop_size = get_size_dict(crop_size)
+        crop_size = get_size_dict(crop_size, param_name="crop_size")
         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)