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Make image processors more general (#27690) 

* Make image processors more general

* Add backwards compatibility for KOSMOS-2

* Remove use_square_size everywhere

* Remove script
This commit is contained in:
NielsRogge 2023-12-05 10:45:39 +01:00 committed by GitHub
parent 96f9caa10b
commit df40edfb00
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 83 additions and 79 deletions
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26
src/transformers/models/bit/image_processing_bit.py
View File

@ -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)
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
default_to_square = True
if "shortest_edge" in size:
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"],
default_to_square=self.use_square_size,
size=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

30
src/transformers/models/clip/image_processing_clip.py
View File

@ -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)
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
default_to_square = True
if "shortest_edge" in size:
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"],
default_to_square=self.use_square_size,
size=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

26
src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py
View File

@ -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)
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
default_to_square = True
if "shortest_edge" in size:
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"],
default_to_square=self.use_square_size,
size=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

26
src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py
View File

@ -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)
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
default_to_square = True
if "shortest_edge" in size:
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"],
default_to_square=self.use_square_size,
size=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

26
src/transformers/models/mobilevit/image_processing_mobilevit.py
View File

@ -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)
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
default_to_square = True
if "shortest_edge" in size:
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"],
default_to_square=self.use_square_size,
size=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")

26
src/transformers/models/vit_hybrid/image_processing_vit_hybrid.py
View File

@ -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)
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
default_to_square = True
if "shortest_edge" in size:
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"],
default_to_square=self.use_square_size,
size=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

2
tests/models/kosmos2/test_processor_kosmos2.py
View File

@ -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)