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Fix type - update any PIL.Image.Resampling (#20172)

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amyeroberts 2022-11-11 16:55:59 +00:00 committed by GitHub
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8 changed files with 23 additions and 25 deletions
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2
src/transformers/image_transforms.py
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@ -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.

2
src/transformers/image_utils.py
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@ -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

9
src/transformers/models/donut/feature_extraction_donut.py
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@ -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`):

8
src/transformers/models/glpn/image_processing_glpn.py
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@ -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*):

9
src/transformers/models/maskformer/feature_extraction_maskformer.py
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@ -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.

9
src/transformers/models/owlvit/feature_extraction_owlvit.py
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@ -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.

4
src/transformers/models/segformer/image_processing_segformer.py
View file

@ -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,

5
tests/models/flava/test_feature_extraction_flava.py
View file

@ -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