Accept batched tensor of images as input to image processor (#21144)
* Accept a batched tensor of images as input * Add to all image processors * Update oneformer
This commit is contained in:
parent
6f3faf3863
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d18a1cba24
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@ -91,6 +91,45 @@ def is_batched(img):
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return False
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def make_list_of_images(images, expected_ndims: int = 3) -> List[ImageInput]:
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"""
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Ensure that the input is a list of images. If the input is a single image, it is converted to a list of length 1.
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If the input is a batch of images, it is converted to a list of images.
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Args:
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images (`ImageInput`):
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Image of images to turn into a list of images.
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expected_ndims (`int`, *optional*, defaults to 3):
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Expected number of dimensions for a single input image. If the input image has a different number of
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dimensions, an error is raised.
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"""
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if is_batched(images):
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return images
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# Either the input is a single image, in which case we create a list of length 1
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if isinstance(images, PIL.Image.Image):
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# PIL images are never batched
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return [images]
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if is_valid_image(images):
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if images.ndim == expected_ndims + 1:
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# Batch of images
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images = [image for image in images]
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elif images.ndim == expected_ndims:
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# Single image
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images = [images]
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else:
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raise ValueError(
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f"Invalid image shape. Expected either {expected_ndims + 1} or {expected_ndims} dimensions, but got"
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f" {images.ndim} dimensions."
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)
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return images
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raise ValueError(
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"Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or "
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f"jax.ndarray, but got {type(images)}."
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)
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def to_numpy_array(img) -> np.ndarray:
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if not is_valid_image(img):
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raise ValueError(f"Invalid image type: {type(img)}")
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@ -30,7 +30,7 @@ from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -438,9 +438,9 @@ class BeitImageProcessor(BaseImageProcessor):
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image_std = image_std if image_std is not None else self.image_std
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do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
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if not is_batched(images):
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images = [images]
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segmentation_maps = [segmentation_maps] if segmentation_maps is not None else None
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images = make_list_of_images(images)
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if segmentation_maps is not None:
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segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
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if not valid_images(images):
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raise ValueError(
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@ -30,7 +30,14 @@ from ...image_transforms import (
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resize,
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to_channel_dimension_format,
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)
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from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images
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from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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from ...utils import logging
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from ...utils.import_utils import is_vision_available
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@ -286,8 +293,7 @@ class BitImageProcessor(BaseImageProcessor):
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image_std = image_std if image_std is not None else self.image_std
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do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -29,7 +29,7 @@ from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -247,8 +247,7 @@ class BlipImageProcessor(BaseImageProcessor):
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size = size if size is not None else self.size
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size = get_size_dict(size, default_to_square=False)
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -30,7 +30,14 @@ from ...image_transforms import (
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resize,
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to_channel_dimension_format,
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)
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from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images
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from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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from ...utils import logging
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from ...utils.import_utils import is_vision_available
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@ -284,8 +291,7 @@ class ChineseCLIPImageProcessor(BaseImageProcessor):
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image_std = image_std if image_std is not None else self.image_std
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do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -30,7 +30,14 @@ from ...image_transforms import (
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resize,
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to_channel_dimension_format,
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)
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from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images
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from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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from ...utils import logging
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from ...utils.import_utils import is_vision_available
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@ -286,8 +293,7 @@ class CLIPImageProcessor(BaseImageProcessor):
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image_std = image_std if image_std is not None else self.image_std
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do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -44,7 +44,7 @@ from transformers.image_utils import (
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PILImageResampling,
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get_image_size,
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infer_channel_dimension_format,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_coco_detection_annotations,
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valid_coco_panoptic_annotations,
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@ -1172,9 +1172,9 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
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if do_normalize is not None and (image_mean is None or image_std is None):
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raise ValueError("Image mean and std must be specified if do_normalize is True.")
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if not is_batched(images):
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images = [images]
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annotations = [annotations] if annotations is not None else None
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images = make_list_of_images(images)
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if annotations is not None and isinstance(annotations[0], dict):
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annotations = [annotations]
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if annotations is not None and len(images) != len(annotations):
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raise ValueError(
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@ -36,7 +36,7 @@ from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -272,8 +272,7 @@ class ConvNextImageProcessor(BaseImageProcessor):
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size = size if size is not None else self.size
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size = get_size_dict(size, default_to_square=False)
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -44,7 +44,7 @@ from transformers.image_utils import (
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PILImageResampling,
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get_image_size,
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infer_channel_dimension_format,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_coco_detection_annotations,
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valid_coco_panoptic_annotations,
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@ -1170,9 +1170,9 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
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if do_normalize is not None and (image_mean is None or image_std is None):
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raise ValueError("Image mean and std must be specified if do_normalize is True.")
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if not is_batched(images):
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images = [images]
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annotations = [annotations] if annotations is not None else None
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images = make_list_of_images(images)
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if annotations is not None and isinstance(annotations[0], dict):
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annotations = [annotations]
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if annotations is not None and len(images) != len(annotations):
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raise ValueError(
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@ -29,7 +29,7 @@ from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -276,8 +276,7 @@ class DeiTImageProcessor(BaseImageProcessor):
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crop_size = crop_size if crop_size is not None else self.crop_size
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crop_size = get_size_dict(crop_size, param_name="crop_size")
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -43,7 +43,7 @@ from transformers.image_utils import (
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PILImageResampling,
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get_image_size,
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infer_channel_dimension_format,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_coco_detection_annotations,
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valid_coco_panoptic_annotations,
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@ -1138,9 +1138,9 @@ class DetrImageProcessor(BaseImageProcessor):
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if do_normalize is not None and (image_mean is None or image_std is None):
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raise ValueError("Image mean and std must be specified if do_normalize is True.")
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if not is_batched(images):
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images = [images]
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annotations = [annotations] if annotations is not None else None
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images = make_list_of_images(images)
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if annotations is not None and isinstance(annotations[0], dict):
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annotations = [annotations]
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if annotations is not None and len(images) != len(annotations):
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raise ValueError(
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@ -34,7 +34,7 @@ from ...image_utils import (
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ImageInput,
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PILImageResampling,
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get_image_size,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -396,8 +396,7 @@ class DonutImageProcessor(BaseImageProcessor):
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image_mean = image_mean if image_mean is not None else self.image_mean
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image_std = image_std if image_std is not None else self.image_std
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -31,9 +31,9 @@ from ...image_utils import (
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ImageInput,
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PILImageResampling,
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get_image_size,
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is_batched,
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is_torch_available,
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is_torch_tensor,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -308,8 +308,7 @@ class DPTImageProcessor(BaseImageProcessor):
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image_mean = image_mean if image_mean is not None else self.image_mean
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image_std = image_std if image_std is not None else self.image_std
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -26,7 +26,14 @@ from transformers.utils.generic import TensorType
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from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
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from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
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from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images
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from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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from ...utils import logging
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@ -647,8 +654,7 @@ class FlavaImageProcessor(BaseImageProcessor):
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codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else self.codebook_image_mean
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codebook_image_std = codebook_image_std if codebook_image_std is not None else self.codebook_image_std
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -24,7 +24,7 @@ from transformers.utils.generic import TensorType
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from ...image_processing_utils import BaseImageProcessor, BatchFeature
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from ...image_transforms import rescale, resize, to_channel_dimension_format
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from ...image_utils import ChannelDimension, get_image_size, is_batched, to_numpy_array, valid_images
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from ...image_utils import ChannelDimension, get_image_size, make_list_of_images, to_numpy_array, valid_images
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from ...utils import logging
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@ -166,8 +166,7 @@ class GLPNImageProcessor(BaseImageProcessor):
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if do_resize and size_divisor is None:
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raise ValueError("size_divisor is required for resizing")
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError("Invalid image(s)")
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@ -23,7 +23,14 @@ from transformers.utils.generic import TensorType
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from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
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from ...image_transforms import rescale, resize, to_channel_dimension_format
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from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images
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from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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from ...utils import logging
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@ -196,8 +203,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
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do_color_quantize = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
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clusters = clusters if clusters is not None else self.clusters
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -28,7 +28,7 @@ from ...image_utils import (
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ImageInput,
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PILImageResampling,
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infer_channel_dimension_format,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -230,8 +230,7 @@ class LayoutLMv2ImageProcessor(BaseImageProcessor):
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ocr_lang = ocr_lang if ocr_lang is not None else self.ocr_lang
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tesseract_config = tesseract_config if tesseract_config is not None else self.tesseract_config
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -30,7 +30,7 @@ from ...image_utils import (
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ImageInput,
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PILImageResampling,
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infer_channel_dimension_format,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -320,8 +320,7 @@ class LayoutLMv3ImageProcessor(BaseImageProcessor):
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ocr_lang = ocr_lang if ocr_lang is not None else self.ocr_lang
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tesseract_config = tesseract_config if tesseract_config is not None else self.tesseract_config
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -35,7 +35,7 @@ from ...image_utils import (
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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is_batched,
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make_list_of_images,
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to_numpy_array,
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valid_images,
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)
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@ -303,8 +303,7 @@ class LevitImageProcessor(BaseImageProcessor):
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crop_size = crop_size if crop_size is not None else self.crop_size
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crop_size = get_size_dict(crop_size, param_name="crop_size")
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if not is_batched(images):
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images = [images]
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images = make_list_of_images(images)
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if not valid_images(images):
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raise ValueError(
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@ -37,7 +37,7 @@ from transformers.image_utils import (
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PILImageResampling,
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get_image_size,
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infer_channel_dimension_format,
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is_batched,
|
||||
make_list_of_images,
|
||||
valid_images,
|
||||
)
|
||||
from transformers.utils import (
|
||||
|
@ -717,9 +717,9 @@ class MaskFormerImageProcessor(BaseImageProcessor):
|
|||
"torch.Tensor, tf.Tensor or jax.ndarray."
|
||||
)
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
segmentation_maps = [segmentation_maps] if segmentation_maps is not None else None
|
||||
images = make_list_of_images(images)
|
||||
if segmentation_maps is not None:
|
||||
segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
|
||||
|
||||
if segmentation_maps is not None and len(images) != len(segmentation_maps):
|
||||
raise ValueError("Images and segmentation maps must have the same length.")
|
||||
|
|
|
@ -35,7 +35,7 @@ from ...image_utils import (
|
|||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
|
@ -288,8 +288,7 @@ class MobileNetV1ImageProcessor(BaseImageProcessor):
|
|||
image_mean = image_mean if image_mean is not None else self.image_mean
|
||||
image_std = image_std if image_std is not None else self.image_std
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -36,7 +36,7 @@ from ...image_utils import (
|
|||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
|
@ -295,8 +295,7 @@ class MobileNetV2ImageProcessor(BaseImageProcessor):
|
|||
image_mean = image_mean if image_mean is not None else self.image_mean
|
||||
image_std = image_std if image_std is not None else self.image_std
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -28,7 +28,7 @@ from ...image_utils import (
|
|||
ImageInput,
|
||||
PILImageResampling,
|
||||
infer_channel_dimension_format,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
|
@ -284,8 +284,7 @@ class MobileViTImageProcessor(BaseImageProcessor):
|
|||
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")
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -38,7 +38,7 @@ from transformers.image_utils import (
|
|||
PILImageResampling,
|
||||
get_image_size,
|
||||
infer_channel_dimension_format,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
valid_images,
|
||||
)
|
||||
from transformers.utils import (
|
||||
|
@ -676,9 +676,9 @@ class OneFormerImageProcessor(BaseImageProcessor):
|
|||
"torch.Tensor, tf.Tensor or jax.ndarray."
|
||||
)
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
segmentation_maps = [segmentation_maps] if segmentation_maps is not None else None
|
||||
images = make_list_of_images(images)
|
||||
if segmentation_maps is not None:
|
||||
segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
|
||||
|
||||
if segmentation_maps is not None and len(images) != len(segmentation_maps):
|
||||
raise ValueError("Images and segmentation maps must have the same length.")
|
||||
|
|
|
@ -29,7 +29,13 @@ from transformers.image_transforms import (
|
|||
to_channel_dimension_format,
|
||||
to_numpy_array,
|
||||
)
|
||||
from transformers.image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, valid_images
|
||||
from transformers.image_utils import (
|
||||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
make_list_of_images,
|
||||
valid_images,
|
||||
)
|
||||
from transformers.utils import TensorType, is_torch_available, logging
|
||||
|
||||
|
||||
|
@ -300,8 +306,7 @@ class OwlViTImageProcessor(BaseImageProcessor):
|
|||
if do_normalize is not None and (image_mean is None or image_std is None):
|
||||
raise ValueError("Image mean and std must be specified if do_normalize is True.")
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -30,7 +30,7 @@ from ...image_utils import (
|
|||
ImageInput,
|
||||
PILImageResampling,
|
||||
get_image_size,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
|
@ -289,8 +289,7 @@ class PerceiverImageProcessor(BaseImageProcessor):
|
|||
image_mean = image_mean if image_mean is not None else self.image_mean
|
||||
image_std = image_std if image_std is not None else self.image_std
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -36,7 +36,7 @@ from ...image_utils import (
|
|||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
|
@ -339,8 +339,7 @@ class PoolFormerImageProcessor(BaseImageProcessor):
|
|||
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")
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -30,7 +30,7 @@ from ...image_utils import (
|
|||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
|
@ -385,9 +385,9 @@ class SegformerImageProcessor(BaseImageProcessor):
|
|||
image_mean = image_mean if image_mean is not None else self.image_mean
|
||||
image_std = image_std if image_std is not None else self.image_std
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
segmentation_maps = [segmentation_maps] if segmentation_maps is not None else None
|
||||
images = make_list_of_images(images)
|
||||
if segmentation_maps is not None:
|
||||
segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -22,7 +22,7 @@ from transformers.utils.generic import TensorType
|
|||
|
||||
from ...image_processing_utils import BaseImageProcessor, BatchFeature
|
||||
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
|
||||
from ...image_utils import ChannelDimension, ImageInput, is_batched, to_numpy_array, valid_images
|
||||
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
|
||||
from ...utils import logging
|
||||
|
||||
|
||||
|
@ -148,8 +148,7 @@ class Swin2SRImageProcessor(BaseImageProcessor):
|
|||
do_pad = do_pad if do_pad is not None else self.do_pad
|
||||
pad_size = pad_size if pad_size is not None else self.pad_size
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -32,7 +32,7 @@ from ...image_utils import (
|
|||
PILImageResampling,
|
||||
get_image_size,
|
||||
infer_channel_dimension_format,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
|
@ -441,8 +441,7 @@ class ViltImageProcessor(BaseImageProcessor):
|
|||
size = size if size is not None else self.size
|
||||
size = get_size_dict(size, default_to_square=False)
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -28,7 +28,7 @@ from ...image_utils import (
|
|||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
|
@ -243,8 +243,7 @@ class ViTImageProcessor(BaseImageProcessor):
|
|||
size = size if size is not None else self.size
|
||||
size_dict = get_size_dict(size)
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -30,7 +30,14 @@ from ...image_transforms import (
|
|||
resize,
|
||||
to_channel_dimension_format,
|
||||
)
|
||||
from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images
|
||||
from ...image_utils import (
|
||||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
from ...utils import logging
|
||||
from ...utils.import_utils import is_vision_available
|
||||
|
||||
|
@ -286,8 +293,7 @@ class ViTHybridImageProcessor(BaseImageProcessor):
|
|||
image_std = image_std if image_std is not None else self.image_std
|
||||
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
images = make_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
|
|
|
@ -42,7 +42,7 @@ from transformers.image_utils import (
|
|||
PILImageResampling,
|
||||
get_image_size,
|
||||
infer_channel_dimension_format,
|
||||
is_batched,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_coco_detection_annotations,
|
||||
valid_coco_panoptic_annotations,
|
||||
|
@ -1038,9 +1038,9 @@ class YolosImageProcessor(BaseImageProcessor):
|
|||
if do_normalize is not None and (image_mean is None or image_std is None):
|
||||
raise ValueError("Image mean and std must be specified if do_normalize is True.")
|
||||
|
||||
if not is_batched(images):
|
||||
images = [images]
|
||||
annotations = [annotations] if annotations is not None else None
|
||||
images = make_list_of_images(images)
|
||||
if annotations is not None and isinstance(annotations[0], dict):
|
||||
annotations = [annotations]
|
||||
|
||||
if annotations is not None and len(images) != len(annotations):
|
||||
raise ValueError(
|
||||
|
|
|
@ -20,7 +20,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from transformers import is_torch_available, is_vision_available
|
||||
from transformers.image_utils import ChannelDimension, get_channel_dimension_axis
|
||||
from transformers.image_utils import ChannelDimension, get_channel_dimension_axis, make_list_of_images
|
||||
from transformers.testing_utils import require_torch, require_vision
|
||||
|
||||
|
||||
|
@ -102,6 +102,58 @@ class ImageFeatureExtractionTester(unittest.TestCase):
|
|||
self.assertEqual(array5.shape, (3, 16, 32))
|
||||
self.assertTrue(np.array_equal(array5, array1))
|
||||
|
||||
def test_make_list_of_images_numpy(self):
|
||||
# Test a single image is converted to a list of 1 image
|
||||
images = np.random.randint(0, 256, (16, 32, 3))
|
||||
images_list = make_list_of_images(images)
|
||||
self.assertEqual(len(images_list), 1)
|
||||
self.assertTrue(np.array_equal(images_list[0], images))
|
||||
self.assertIsInstance(images_list, list)
|
||||
|
||||
# Test a batch of images is converted to a list of images
|
||||
images = np.random.randint(0, 256, (4, 16, 32, 3))
|
||||
images_list = make_list_of_images(images)
|
||||
self.assertEqual(len(images_list), 4)
|
||||
self.assertTrue(np.array_equal(images_list[0], images[0]))
|
||||
self.assertIsInstance(images_list, list)
|
||||
|
||||
# Test a list of images is not modified
|
||||
images = [np.random.randint(0, 256, (16, 32, 3)) for _ in range(4)]
|
||||
images_list = make_list_of_images(images)
|
||||
self.assertEqual(len(images_list), 4)
|
||||
self.assertTrue(np.array_equal(images_list[0], images[0]))
|
||||
self.assertIsInstance(images_list, list)
|
||||
|
||||
# Test batched masks with no channel dimension are converted to a list of masks
|
||||
masks = np.random.randint(0, 2, (4, 16, 32))
|
||||
masks_list = make_list_of_images(masks, expected_ndims=2)
|
||||
self.assertEqual(len(masks_list), 4)
|
||||
self.assertTrue(np.array_equal(masks_list[0], masks[0]))
|
||||
self.assertIsInstance(masks_list, list)
|
||||
|
||||
@require_torch
|
||||
def test_make_list_of_images_torch(self):
|
||||
# Test a single image is converted to a list of 1 image
|
||||
images = torch.randint(0, 256, (16, 32, 3))
|
||||
images_list = make_list_of_images(images)
|
||||
self.assertEqual(len(images_list), 1)
|
||||
self.assertTrue(np.array_equal(images_list[0], images))
|
||||
self.assertIsInstance(images_list, list)
|
||||
|
||||
# Test a batch of images is converted to a list of images
|
||||
images = torch.randint(0, 256, (4, 16, 32, 3))
|
||||
images_list = make_list_of_images(images)
|
||||
self.assertEqual(len(images_list), 4)
|
||||
self.assertTrue(np.array_equal(images_list[0], images[0]))
|
||||
self.assertIsInstance(images_list, list)
|
||||
|
||||
# Test a list of images is left unchanged
|
||||
images = [torch.randint(0, 256, (16, 32, 3)) for _ in range(4)]
|
||||
images_list = make_list_of_images(images)
|
||||
self.assertEqual(len(images_list), 4)
|
||||
self.assertTrue(np.array_equal(images_list[0], images[0]))
|
||||
self.assertIsInstance(images_list, list)
|
||||
|
||||
@require_torch
|
||||
def test_conversion_torch_to_array(self):
|
||||
feature_extractor = ImageFeatureExtractionMixin()
|
||||
|
|
Loading…
Reference in New Issue