diff --git a/src/transformers/image_transforms.py b/src/transformers/image_transforms.py index f0ec21476..57bed1805 100644 --- a/src/transformers/image_transforms.py +++ b/src/transformers/image_transforms.py @@ -232,7 +232,7 @@ def resize( The image to resize. size (`Tuple[int, int]`): The size to use for resizing the image. - resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`): + resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. data_format (`ChannelDimension`, *optional*): The channel dimension format of the output image. If `None`, will use the inferred format from the input. diff --git a/src/transformers/image_utils.py b/src/transformers/image_utils.py index 13a82aebc..03aa15d72 100644 --- a/src/transformers/image_utils.py +++ b/src/transformers/image_utils.py @@ -372,7 +372,7 @@ class ImageFeatureExtractionMixin: If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). - resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`): + resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): The filter to user for resampling. default_to_square (`bool`, *optional*, defaults to `True`): How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a diff --git a/src/transformers/models/donut/feature_extraction_donut.py b/src/transformers/models/donut/feature_extraction_donut.py index f657fdbf9..4bbc74193 100644 --- a/src/transformers/models/donut/feature_extraction_donut.py +++ b/src/transformers/models/donut/feature_extraction_donut.py @@ -48,11 +48,10 @@ class DonutFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin) size (`Tuple(int)`, *optional*, defaults to [1920, 2560]): Resize the shorter edge of the input to the minimum value of the given size. Should be a tuple of (width, height). Only has an effect if `do_resize` is set to `True`. - resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`): - An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`, - `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`, - `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set - to `True`. + resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): + An optional resampling filter. This can be one of `PILImageResampling.NEAREST`, `PILImageResampling.BOX`, + `PILImageResampling.BILINEAR`, `PILImageResampling.HAMMING`, `PILImageResampling.BICUBIC` or + `PILImageResampling.LANCZOS`. Only has an effect if `do_resize` is set to `True`. do_thumbnail (`bool`, *optional*, defaults to `True`): Whether to thumbnail the input to the given `size`. do_align_long_axis (`bool`, *optional*, defaults to `False`): diff --git a/src/transformers/models/glpn/image_processing_glpn.py b/src/transformers/models/glpn/image_processing_glpn.py index 50fe8c67c..5d5cd8c19 100644 --- a/src/transformers/models/glpn/image_processing_glpn.py +++ b/src/transformers/models/glpn/image_processing_glpn.py @@ -42,7 +42,7 @@ class GLPNImageProcessor(BaseImageProcessor): size_divisor (`int`, *optional*, defaults to 32): When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest multiple of `size_divisor`. Can be overridden by `size_divisor` in `preprocess`. - resample (`PIL.Image` resampling filter, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`): + resample (`PIL.Image` resampling filter, *optional*, defaults to `PILImageResampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_rescale (`bool`, *optional*, defaults to `True`): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Can be @@ -80,7 +80,7 @@ class GLPNImageProcessor(BaseImageProcessor): The image is resized so its height and width are rounded down to the closest multiple of `size_divisor`. resample: - `PIL.Image` resampling filter to use when resizing the image e.g. `PIL.Image.Resampling.BILINEAR`. + `PIL.Image` resampling filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If `None`, the channel dimension format of the input image is used. Can be one of: @@ -142,8 +142,8 @@ class GLPNImageProcessor(BaseImageProcessor): When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest multiple of `size_divisor`. resample (`PIL.Image` resampling filter, *optional*, defaults to `self.resample`): - `PIL.Image` resampling filter to use if resizing the image e.g. `PIL.Image.Resampling.BILINEAR`. Only - has an effect if `do_resize` is set to `True`. + `PIL.Image` resampling filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has + an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). return_tensors (`str` or `TensorType`, *optional*): diff --git a/src/transformers/models/maskformer/feature_extraction_maskformer.py b/src/transformers/models/maskformer/feature_extraction_maskformer.py index 615f223df..2473a589c 100644 --- a/src/transformers/models/maskformer/feature_extraction_maskformer.py +++ b/src/transformers/models/maskformer/feature_extraction_maskformer.py @@ -209,11 +209,10 @@ class MaskFormerFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionM max_size (`int`, *optional*, defaults to 1333): The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is set to `True`. - resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`): - An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`, - `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`, - `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set - to `True`. + resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): + An optional resampling filter. This can be one of `PILImageResampling.NEAREST`, `PILImageResampling.BOX`, + `PILImageResampling.BILINEAR`, `PILImageResampling.HAMMING`, `PILImageResampling.BICUBIC` or + `PILImageResampling.LANCZOS`. Only has an effect if `do_resize` is set to `True`. size_divisibility (`int`, *optional*, defaults to 32): Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in Swin Transformer. diff --git a/src/transformers/models/owlvit/feature_extraction_owlvit.py b/src/transformers/models/owlvit/feature_extraction_owlvit.py index 108e89ce0..1590337cf 100644 --- a/src/transformers/models/owlvit/feature_extraction_owlvit.py +++ b/src/transformers/models/owlvit/feature_extraction_owlvit.py @@ -56,11 +56,10 @@ class OwlViTFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin The size to use for resizing the image. Only has an effect if `do_resize` is set to `True`. If `size` is a sequence like (h, w), output size will be matched to this. If `size` is an int, then image will be resized to (size, size). - resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BICUBIC`): - An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`, - `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`, - `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set - to `True`. + resample (`int`, *optional*, defaults to `PILImageResampling.BICUBIC`): + An optional resampling filter. This can be one of `PILImageResampling.NEAREST`, `PILImageResampling.BOX`, + `PILImageResampling.BILINEAR`, `PILImageResampling.HAMMING`, `PILImageResampling.BICUBIC` or + `PILImageResampling.LANCZOS`. Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `False`): Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. diff --git a/src/transformers/models/segformer/image_processing_segformer.py b/src/transformers/models/segformer/image_processing_segformer.py index 72d5c9f12..5f32338db 100644 --- a/src/transformers/models/segformer/image_processing_segformer.py +++ b/src/transformers/models/segformer/image_processing_segformer.py @@ -228,7 +228,7 @@ class SegformerImageProcessor(BaseImageProcessor): do_rescale: bool, do_normalize: bool, size: Optional[Dict[str, int]] = None, - resample: Optional[PILImageResampling] = None, + resample: PILImageResampling = None, rescale_factor: Optional[float] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, @@ -325,7 +325,7 @@ class SegformerImageProcessor(BaseImageProcessor): segmentation_maps: Optional[ImageInput] = None, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, - resample: Optional[PILImageResampling] = None, + resample: PILImageResampling = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, diff --git a/tests/models/flava/test_feature_extraction_flava.py b/tests/models/flava/test_feature_extraction_flava.py index fe0e6dca2..bb771de36 100644 --- a/tests/models/flava/test_feature_extraction_flava.py +++ b/tests/models/flava/test_feature_extraction_flava.py @@ -31,6 +31,7 @@ if is_vision_available(): import PIL from transformers import FlavaFeatureExtractor + from transformers.image_utils import PILImageResampling from transformers.models.flava.image_processing_flava import ( FLAVA_CODEBOOK_MEAN, FLAVA_CODEBOOK_STD, @@ -89,7 +90,7 @@ class FlavaFeatureExtractionTester(unittest.TestCase): self.min_resolution = min_resolution self.max_resolution = max_resolution self.size = size - self.resample = resample if resample is not None else PIL.Image.Resampling.BICUBIC + self.resample = resample if resample is not None else PILImageResampling.BICUBIC self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std @@ -105,7 +106,7 @@ class FlavaFeatureExtractionTester(unittest.TestCase): self.codebook_do_resize = codebook_do_resize self.codebook_size = codebook_size - self.codebook_resample = codebook_resample if codebook_resample is not None else PIL.Image.Resampling.LANCZOS + self.codebook_resample = codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS self.codebook_do_center_crop = codebook_do_center_crop self.codebook_crop_size = codebook_crop_size self.codebook_do_map_pixels = codebook_do_map_pixels