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Raise unused kwargs image processor (#29063)

Browse Source 
* draft processor arg capture

* add missing vivit model

* add new common test for image preprocess signature

* fix quality

* fix up

* add back missing validations

* quality

* move info level to warning for unused kwargs
This commit is contained in:
Pablo Montalvo 2024-02-20 16:20:20 +01:00 committed by GitHub
parent b8b16475d4
commit 1c81132e80
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49 changed files with 954 additions and 2 deletions
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8
src/transformers/image_utils.py
View File

@ -759,3 +759,11 @@ def validate_annotations(
"(batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with "
"the latter being a list of annotations in the COCO format."
)
def validate_kwargs(valid_processor_keys: List[str], captured_kwargs: List[str]):
unused_keys = set(captured_kwargs).difference(set(valid_processor_keys))
if unused_keys:
unused_key_str = ", ".join(unused_keys)
# TODO raise a warning here instead of simply logging?
logger.warning(f"Unused or unrecognized kwargs: {unused_key_str}.")

24
src/transformers/models/beit/image_processing_beit.py
View File

@ -32,6 +32,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
@ -130,6 +131,24 @@ class BeitImageProcessor(BaseImageProcessor):
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.do_reduce_labels = do_reduce_labels
self._valid_processor_keys = [
"images",
"segmentation_maps",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_reduce_labels",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@ -337,6 +356,9 @@ class BeitImageProcessor(BaseImageProcessor):
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
segmentation_maps (`ImageInput`, *optional*)
Segmentation maps to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
@ -396,6 +418,8 @@ class BeitImageProcessor(BaseImageProcessor):
image_std = image_std if image_std is not None else self.image_std
do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
images = make_list_of_images(images)
if segmentation_maps is not None:

20
src/transformers/models/bit/image_processing_bit.py
View File

@ -36,6 +36,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -121,6 +122,23 @@ 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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_convert_rgb",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
def resize(
@ -256,6 +274,8 @@ class BitImageProcessor(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
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
images = make_list_of_images(images)
if not valid_images(images):

18
src/transformers/models/blip/image_processing_blip.py
View File

@ -31,6 +31,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -106,6 +107,21 @@ class BlipImageProcessor(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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_convert_rgb",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC
def resize(
@ -234,6 +250,8 @@ class BlipImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

21
src/transformers/models/bridgetower/image_processing_bridgetower.py
View File

@ -32,6 +32,7 @@ from ...image_utils import (
is_scaled_image,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -204,6 +205,24 @@ class BridgeTowerImageProcessor(BaseImageProcessor):
self.do_pad = do_pad
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"size_divisor",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor.resize
def resize(
@ -465,6 +484,8 @@ class BridgeTowerImageProcessor(BaseImageProcessor):
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not is_batched(images):
images = [images]

20
src/transformers/models/chinese_clip/image_processing_chinese_clip.py
View File

@ -36,6 +36,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -121,6 +122,23 @@ class ChineseCLIPImageProcessor(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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_convert_rgb",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -247,6 +265,8 @@ class ChineseCLIPImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

20
src/transformers/models/clip/image_processing_clip.py
View File

@ -36,6 +36,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -121,6 +122,23 @@ 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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_convert_rgb",
"return_tensors",
"data_format",
"input_data_format",
]
# for backwards compatibility of KOSMOS-2
if "use_square_size" in kwargs:
@ -259,6 +277,8 @@ class CLIPImageProcessor(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
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
images = make_list_of_images(images)
if not valid_images(images):

22
src/transformers/models/conditional_detr/image_processing_conditional_detr.py
View File

@ -49,6 +49,7 @@ from ...image_utils import (
to_numpy_array,
valid_images,
validate_annotations,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -845,6 +846,26 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.do_pad = do_pad
self._valid_processor_keys = [
"images",
"annotations",
"return_segmentation_masks",
"masks_path",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"do_convert_annotations",
"image_mean",
"image_std",
"do_pad",
"format",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->ConditionalDetr
@ -1299,6 +1320,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
# Here, the pad() method pads to the maximum of (width, height). It does not need to be validated.

18
src/transformers/models/convnext/image_processing_convnext.py
View File

@ -36,6 +36,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -113,6 +114,21 @@ class ConvNextImageProcessor(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._valid_processor_keys = [
"images",
"do_resize",
"size",
"crop_pct",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -260,6 +276,8 @@ class ConvNextImageProcessor(BaseImageProcessor):
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
images = make_list_of_images(images)
if not valid_images(images):

22
src/transformers/models/deformable_detr/image_processing_deformable_detr.py
View File

@ -49,6 +49,7 @@ from ...image_utils import (
to_numpy_array,
valid_images,
validate_annotations,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -843,6 +844,26 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.do_pad = do_pad
self._valid_processor_keys = [
"images",
"annotations",
"return_segmentation_masks",
"masks_path",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"do_convert_annotations",
"image_mean",
"image_std",
"do_pad",
"format",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->DeformableDetr
@ -1297,6 +1318,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
# Here, the pad() method pads to the maximum of (width, height). It does not need to be validated.

19
src/transformers/models/deit/image_processing_deit.py
View File

@ -31,6 +31,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -109,6 +110,22 @@ class DeiTImageProcessor(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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC
def resize(
@ -240,6 +257,8 @@ class DeiTImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

22
src/transformers/models/detr/image_processing_detr.py
View File

@ -48,6 +48,7 @@ from ...image_utils import (
to_numpy_array,
valid_images,
validate_annotations,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -828,6 +829,26 @@ class DetrImageProcessor(BaseImageProcessor):
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.do_pad = do_pad
self._valid_processor_keys = [
"images",
"annotations",
"return_segmentation_masks",
"masks_path",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"do_convert_annotations",
"image_mean",
"image_std",
"do_pad",
"format",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@ -1269,6 +1290,7 @@ class DetrImageProcessor(BaseImageProcessor):
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
# Here, the pad() method pads to the maximum of (width, height). It does not need to be validated.

21
src/transformers/models/donut/image_processing_donut.py
View File

@ -37,6 +37,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -123,6 +124,24 @@ class DonutImageProcessor(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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_thumbnail",
"do_align_long_axis",
"do_pad",
"random_padding",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def align_long_axis(
self,
@ -388,6 +407,8 @@ class DonutImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

21
src/transformers/models/dpt/image_processing_dpt.py
View File

@ -35,6 +35,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -164,6 +165,24 @@ class DPTImageProcessor(BaseImageProcessor):
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
self.do_pad = do_pad
self.size_divisor = size_divisor
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"keep_aspect_ratio",
"ensure_multiple_of",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"size_divisor",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -350,6 +369,8 @@ class DPTImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

19
src/transformers/models/efficientformer/image_processing_efficientformer.py
View File

@ -35,6 +35,7 @@ from ...image_utils import (
is_scaled_image,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -112,6 +113,22 @@ class EfficientFormerImageProcessor(BaseImageProcessor):
self.rescale_factor = rescale_factor
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -238,6 +255,8 @@ class EfficientFormerImageProcessor(BaseImageProcessor):
size = size if size is not None else self.size
size_dict = get_size_dict(size)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not is_batched(images):
images = [images]

21
src/transformers/models/efficientnet/image_processing_efficientnet.py
View File

@ -31,6 +31,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -118,6 +119,24 @@ class EfficientNetImageProcessor(BaseImageProcessor):
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.include_top = include_top
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"rescale_offset",
"do_normalize",
"image_mean",
"image_std",
"include_top",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.NEAREST
def resize(
@ -297,6 +316,8 @@ class EfficientNetImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

38
src/transformers/models/flava/image_processing_flava.py
View File

@ -34,6 +34,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -301,6 +302,41 @@ class FlavaImageProcessor(BaseImageProcessor):
self.codebook_image_mean = codebook_image_mean
self.codebook_image_mean = codebook_image_mean if codebook_image_mean is not None else FLAVA_CODEBOOK_MEAN
self.codebook_image_std = codebook_image_std if codebook_image_std is not None else FLAVA_CODEBOOK_STD
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_image_mask",
"input_size_patches",
"total_mask_patches",
"mask_group_min_patches",
"mask_group_max_patches",
"mask_group_min_aspect_ratio",
"mask_group_max_aspect_ratio",
"return_codebook_pixels",
"codebook_do_resize",
"codebook_size",
"codebook_resample",
"codebook_do_center_crop",
"codebook_crop_size",
"codebook_do_rescale",
"codebook_rescale_factor",
"codebook_do_map_pixels",
"codebook_do_normalize",
"codebook_image_mean",
"codebook_image_std",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@ -636,6 +672,8 @@ class FlavaImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

18
src/transformers/models/fuyu/image_processing_fuyu.py
View File

@ -261,6 +261,24 @@ class FuyuImageProcessor(BaseImageProcessor):
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.patch_size = patch_size if patch_size is not None else {"height": 30, "width": 30}
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_pad",
"padding_value",
"padding_mode",
"do_normalize",
"image_mean",
"image_std",
"do_rescale",
"rescale_factor",
"patch_size",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,

13
src/transformers/models/glpn/image_processing_glpn.py
View File

@ -30,6 +30,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -71,6 +72,16 @@ class GLPNImageProcessor(BaseImageProcessor):
self.size_divisor = size_divisor
self.resample = resample
super().__init__(**kwargs)
self._valid_processor_keys = [
"images",
"do_resize",
"size_divisor",
"resample",
"do_rescale",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -176,6 +187,8 @@ class GLPNImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

15
src/transformers/models/imagegpt/image_processing_imagegpt.py
View File

@ -29,6 +29,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -102,6 +103,18 @@ class ImageGPTImageProcessor(BaseImageProcessor):
self.resample = resample
self.do_normalize = do_normalize
self.do_color_quantize = do_color_quantize
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_normalize",
"do_color_quantize",
"clusters",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
def resize(
@ -238,6 +251,8 @@ class ImageGPTImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

15
src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py
View File

@ -28,6 +28,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
@ -137,6 +138,18 @@ class LayoutLMv2ImageProcessor(BaseImageProcessor):
self.apply_ocr = apply_ocr
self.ocr_lang = ocr_lang
self.tesseract_config = tesseract_config
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"apply_ocr",
"ocr_lang",
"tesseract_config",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
def resize(
@ -244,6 +257,8 @@ class LayoutLMv2ImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

20
src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py
View File

@ -31,6 +31,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
@ -164,6 +165,23 @@ class LayoutLMv3ImageProcessor(BaseImageProcessor):
self.apply_ocr = apply_ocr
self.ocr_lang = ocr_lang
self.tesseract_config = tesseract_config
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"apply_ocr",
"ocr_lang",
"tesseract_config",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
def resize(
@ -298,6 +316,8 @@ class LayoutLMv3ImageProcessor(BaseImageProcessor):
tesseract_config = tesseract_config if tesseract_config is not None else self.tesseract_config
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

19
src/transformers/models/levit/image_processing_levit.py
View File

@ -35,6 +35,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -115,6 +116,22 @@ class LevitImageProcessor(BaseImageProcessor):
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -254,6 +271,8 @@ class LevitImageProcessor(BaseImageProcessor):
crop_size = get_size_dict(crop_size, param_name="crop_size")
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

22
src/transformers/models/mask2former/image_processing_mask2former.py
View File

@ -39,6 +39,7 @@ from ...image_utils import (
is_scaled_image,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -439,6 +440,25 @@ class Mask2FormerImageProcessor(BaseImageProcessor):
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.ignore_index = ignore_index
self.reduce_labels = reduce_labels
self._valid_processor_keys = [
"images",
"segmentation_maps",
"instance_id_to_semantic_id",
"do_resize",
"size",
"size_divisor",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"ignore_index",
"reduce_labels",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@ -708,6 +728,8 @@ class Mask2FormerImageProcessor(BaseImageProcessor):
ignore_index = ignore_index if ignore_index is not None else self.ignore_index
reduce_labels = reduce_labels if reduce_labels is not None else self.reduce_labels
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

22
src/transformers/models/maskformer/image_processing_maskformer.py
View File

@ -39,6 +39,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -448,6 +449,25 @@ class MaskFormerImageProcessor(BaseImageProcessor):
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.ignore_index = ignore_index
self.do_reduce_labels = do_reduce_labels
self._valid_processor_keys = [
"images",
"segmentation_maps",
"instance_id_to_semantic_id",
"do_resize",
"size",
"size_divisor",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"ignore_index",
"do_reduce_labels",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@ -730,6 +750,8 @@ class MaskFormerImageProcessor(BaseImageProcessor):
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,

19
src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py
View File

@ -35,6 +35,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -113,6 +114,22 @@ 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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
def resize(
@ -245,6 +262,8 @@ class MobileNetV1ImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

19
src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py
View File

@ -35,6 +35,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
@ -117,6 +118,22 @@ 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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize
def resize(
@ -249,6 +266,8 @@ class MobileNetV2ImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

19
src/transformers/models/mobilevit/image_processing_mobilevit.py
View File

@ -29,6 +29,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
@ -104,6 +105,21 @@ 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._valid_processor_keys = [
"images",
"segmentation_maps",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_center_crop",
"crop_size",
"do_flip_channel_order",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize with PILImageResampling.BICUBIC->PILImageResampling.BILINEAR
def resize(
@ -366,6 +382,9 @@ class MobileViTImageProcessor(BaseImageProcessor):
crop_size = get_size_dict(crop_size, param_name="crop_size")
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if segmentation_maps is not None:
segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)

21
src/transformers/models/nougat/image_processing_nougat.py
View File

@ -38,6 +38,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -125,6 +126,24 @@ class NougatImageProcessor(BaseImageProcessor):
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self._valid_processor_keys = [
"images",
"do_crop_margin",
"do_resize",
"size",
"resample",
"do_thumbnail",
"do_align_long_axis",
"do_pad",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def python_find_non_zero(self, image: np.array):
"""This is a reimplementation of a findNonZero function equivalent to cv2."""
@ -442,6 +461,8 @@ class NougatImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

23
src/transformers/models/oneformer/image_processing_oneformer.py
View File

@ -42,6 +42,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -467,6 +468,25 @@ class OneFormerImageProcessor(BaseImageProcessor):
self.repo_path = repo_path
self.metadata = prepare_metadata(load_metadata(repo_path, class_info_file))
self.num_text = num_text
self._valid_processor_keys = [
"images",
"task_inputs",
"segmentation_maps",
"instance_id_to_semantic_id",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"ignore_index",
"do_reduce_labels",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -714,6 +734,9 @@ class OneFormerImageProcessor(BaseImageProcessor):
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,

17
src/transformers/models/owlv2/image_processing_owlv2.py
View File

@ -37,6 +37,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -232,6 +233,20 @@ class Owlv2ImageProcessor(BaseImageProcessor):
self.do_normalize = do_normalize
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._valid_processor_keys = [
"images",
"do_pad",
"do_resize",
"size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def pad(
self,
@ -401,6 +416,8 @@ class Owlv2ImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

18
src/transformers/models/owlvit/image_processing_owlvit.py
View File

@ -38,6 +38,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_torch_available, logging
@ -166,6 +167,22 @@ class OwlViTImageProcessor(BaseImageProcessor):
self.do_normalize = do_normalize
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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -356,6 +373,7 @@ class OwlViTImageProcessor(BaseImageProcessor):
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
validate_preprocess_arguments(
do_rescale=do_rescale,

19
src/transformers/models/perceiver/image_processing_perceiver.py
View File

@ -32,6 +32,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -113,6 +114,22 @@ class PerceiverImageProcessor(BaseImageProcessor):
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self._valid_processor_keys = [
"images",
"do_center_crop",
"crop_size",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def center_crop(
self,
@ -286,6 +303,8 @@ class PerceiverImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

20
src/transformers/models/poolformer/image_processing_poolformer.py
View File

@ -35,6 +35,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -132,6 +133,23 @@ class PoolFormerImageProcessor(BaseImageProcessor):
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"crop_pct",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -293,6 +311,8 @@ class PoolFormerImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

17
src/transformers/models/pvt/image_processing_pvt.py
View File

@ -31,6 +31,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -95,6 +96,20 @@ class PvtImageProcessor(BaseImageProcessor):
self.rescale_factor = rescale_factor
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
def resize(
@ -218,6 +233,8 @@ class PvtImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

23
src/transformers/models/sam/image_processing_sam.py
View File

@ -34,6 +34,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -160,6 +161,26 @@ class SamImageProcessor(BaseImageProcessor):
self.pad_size = pad_size
self.mask_pad_size = mask_pad_size
self.do_convert_rgb = do_convert_rgb
self._valid_processor_keys = [
"images",
"segmentation_maps",
"do_resize",
"size",
"mask_size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"pad_size",
"mask_pad_size",
"do_convert_rgb",
"return_tensors",
"data_format",
"input_data_format",
]
def pad_image(
self,
@ -491,6 +512,8 @@ class SamImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

20
src/transformers/models/segformer/image_processing_segformer.py
View File

@ -32,6 +32,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
@ -118,6 +119,22 @@ class SegformerImageProcessor(BaseImageProcessor):
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.do_reduce_labels = do_reduce_labels
self._valid_processor_keys = [
"images",
"segmentation_maps",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_reduce_labels",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@ -380,6 +397,9 @@ class SegformerImageProcessor(BaseImageProcessor):
image_std = image_std if image_std is not None else self.image_std
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if segmentation_maps is not None:
segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)

17
src/transformers/models/siglip/image_processing_siglip.py
View File

@ -32,6 +32,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -101,6 +102,20 @@ class SiglipImageProcessor(BaseImageProcessor):
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def preprocess(
self,
@ -174,6 +189,8 @@ class SiglipImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

13
src/transformers/models/swin2sr/image_processing_swin2sr.py
View File

@ -28,6 +28,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -65,6 +66,16 @@ class Swin2SRImageProcessor(BaseImageProcessor):
self.rescale_factor = rescale_factor
self.do_pad = do_pad
self.pad_size = pad_size
self._valid_processor_keys = [
"images",
"do_rescale",
"rescale_factor",
"do_pad",
"pad_size",
"return_tensors",
"data_format",
"input_data_format",
]
def pad(
self,
@ -161,6 +172,8 @@ class Swin2SRImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

22
src/transformers/models/tvlt/image_processing_tvlt.py
View File

@ -34,6 +34,7 @@ from ...image_utils import (
is_valid_image,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -151,6 +152,25 @@ class TvltImageProcessor(BaseImageProcessor):
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self._valid_processor_keys = [
"videos",
"do_resize",
"size",
"patch_size",
"num_frames",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"is_mixed",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -357,6 +377,8 @@ class TvltImageProcessor(BaseImageProcessor):
patch_size = patch_size if patch_size is not None else self.patch_size
num_frames = num_frames if patch_size is not None else self.num_frames
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(videos):
raise ValueError(
"Invalid image or video type. Must be of type PIL.Image.Image, numpy.ndarray, "

24
src/transformers/models/tvp/image_processing_tvp.py
View File

@ -36,6 +36,7 @@ from ...image_utils import (
is_valid_image,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -172,6 +173,27 @@ class TvpImageProcessor(BaseImageProcessor):
self.do_flip_channel_order = do_flip_channel_order
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._valid_processor_keys = [
"videos",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_pad",
"pad_size",
"constant_values",
"pad_mode",
"do_normalize",
"do_flip_channel_order",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -437,6 +459,8 @@ class TvpImageProcessor(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")
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(videos):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

19
src/transformers/models/videomae/image_processing_videomae.py
View File

@ -35,6 +35,7 @@ from ...image_utils import (
is_valid_image,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -130,6 +131,22 @@ class VideoMAEImageProcessor(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._valid_processor_keys = [
"videos",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -311,6 +328,8 @@ class VideoMAEImageProcessor(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")
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(videos):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

19
src/transformers/models/vilt/image_processing_vilt.py
View File

@ -32,6 +32,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -191,6 +192,22 @@ class ViltImageProcessor(BaseImageProcessor):
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.do_pad = do_pad
self._valid_processor_keys = [
"images",
"do_resize",
"size",
"size_divisor",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
@ -416,6 +433,8 @@ class ViltImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

17
src/transformers/models/vit/image_processing_vit.py
View File

@ -31,6 +31,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -95,6 +96,20 @@ class ViTImageProcessor(BaseImageProcessor):
self.rescale_factor = rescale_factor
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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -217,6 +232,8 @@ class ViTImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

20
src/transformers/models/vit_hybrid/image_processing_vit_hybrid.py
View File

@ -36,6 +36,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
@ -121,6 +122,23 @@ 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._valid_processor_keys = [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_convert_rgb",
"return_tensors",
"data_format",
"input_data_format",
]
# Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
def resize(
@ -258,6 +276,8 @@ class ViTHybridImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

19
src/transformers/models/vitmatte/image_processing_vitmatte.py
View File

@ -31,6 +31,7 @@ from ...image_utils import (
make_list_of_images,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import TensorType, logging
@ -87,6 +88,20 @@ class VitMatteImageProcessor(BaseImageProcessor):
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.size_divisibility = size_divisibility
self._valid_processor_keys = [
"images",
"trimaps",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"size_divisibility",
"return_tensors",
"data_format",
"input_data_format",
]
def pad_image(
self,
@ -198,14 +213,14 @@ class VitMatteImageProcessor(BaseImageProcessor):
images = make_list_of_images(images)
trimaps = make_list_of_images(trimaps, expected_ndims=2)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(trimaps):
raise ValueError(
"Invalid trimap type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

20
src/transformers/models/vivit/image_processing_vivit.py
View File

@ -38,6 +38,7 @@ from ...image_utils import (
is_valid_image,
to_numpy_array,
valid_images,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import logging
@ -137,6 +138,23 @@ class VivitImageProcessor(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._valid_processor_keys = [
"videos",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"offset",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format",
]
def resize(
self,
@ -368,6 +386,8 @@ class VivitImageProcessor(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")
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
if not valid_images(videos):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "

22
src/transformers/models/yolos/image_processing_yolos.py
View File

@ -47,6 +47,7 @@ from ...image_utils import (
to_numpy_array,
valid_images,
validate_annotations,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
@ -750,6 +751,26 @@ class YolosImageProcessor(BaseImageProcessor):
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.do_pad = do_pad
self._valid_processor_keys = [
"images",
"annotations",
"return_segmentation_masks",
"masks_path",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_convert_annotations",
"do_pad",
"format",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->Yolos
@ -1185,6 +1206,7 @@ class YolosImageProcessor(BaseImageProcessor):
)
do_pad = self.do_pad if do_pad is None else do_pad
format = self.format if format is None else format
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
images = make_list_of_images(images)

11
tests/test_image_processing_common.py
View File

@ -13,6 +13,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import json
import os
import pathlib
@ -289,6 +290,16 @@ class ImageProcessingTestMixin:
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
)
def test_image_processor_preprocess_arguments(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
if hasattr(image_processor, "_valid_processor_keys") and hasattr(image_processor, "preprocess"):
preprocess_parameter_names = inspect.getfullargspec(image_processor.preprocess).args
preprocess_parameter_names.remove("self")
preprocess_parameter_names.sort()
valid_processor_keys = image_processor._valid_processor_keys
valid_processor_keys.sort()
self.assertEqual(preprocess_parameter_names, valid_processor_keys)
class AnnotationFormatTestMixin:
# this mixin adds a test to assert that usages of the