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423bf77 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | # Copyright 2026 SARMAE Authors and The HuggingFace Inc. team.
"""Image processor for SARMAE models (self-contained for trust_remote_code)."""
from typing import Optional, Union
import numpy as np
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from transformers.image_transforms import resize, to_channel_dimension_format
from transformers.image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
infer_channel_dimension_format,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from transformers.utils import TensorType, filter_out_non_signature_kwargs, logging
logger = logging.get_logger(__name__)
def _repeat_grayscale_channels(image: np.ndarray, target_channels: int, input_data_format: ChannelDimension) -> np.ndarray:
if input_data_format == ChannelDimension.FIRST:
num_channels = image.shape[0]
if num_channels == target_channels:
return image
if num_channels == 1:
return np.repeat(image, target_channels, axis=0)
return image[:target_channels]
num_channels = image.shape[-1]
if num_channels == target_channels:
return image
if num_channels == 1:
return np.repeat(image, target_channels, axis=-1)
return image[..., :target_channels]
def _prepare_image_batch(images: ImageInput) -> list:
if isinstance(images, np.ndarray):
images = [images]
elif not isinstance(images, (list, tuple)):
images = [images]
prepared = []
for image in images:
array = to_numpy_array(image)
if array.ndim == 2:
array = np.expand_dims(array, axis=-1)
prepared.append(array)
return prepared
class SarmaeImageProcessor(BaseImageProcessor):
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Optional[dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: float = 1 / 255.0,
do_normalize: bool = True,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
do_convert_rgb: bool = False,
repeat_grayscale_channels: bool = True,
**kwargs,
):
super().__init__(**kwargs)
size = size if size is not None else {"height": 224, "width": 224}
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_convert_rgb = do_convert_rgb
self.repeat_grayscale_channels = repeat_grayscale_channels
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Optional[dict[str, int]] = None,
resample: Optional[PILImageResampling] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
do_convert_rgb: Optional[bool] = None,
repeat_grayscale_channels: Optional[bool] = None,
):
do_resize = do_resize if do_resize is not None else self.do_resize
size = get_size_dict(size if size is not None else self.size, default_to_square=True)
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
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
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
repeat_grayscale_channels = (
repeat_grayscale_channels if repeat_grayscale_channels is not None else self.repeat_grayscale_channels
)
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Normalization requires `image_mean` and `image_std` with one value per channel.")
images = _prepare_image_batch(images)
if not valid_images(images):
raise ValueError("Invalid image type. Must be PIL, numpy, or torch tensor.")
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
processed_images = []
for image in images:
image = to_numpy_array(image)
if do_convert_rgb:
image = self._convert_image_to_rgb(image)
if input_data_format is None:
try:
input_data_format = infer_channel_dimension_format(image)
except ValueError:
input_data_format = ChannelDimension.LAST
if repeat_grayscale_channels:
image = _repeat_grayscale_channels(image, target_channels=3, input_data_format=input_data_format)
if do_resize:
image = resize(
image,
size=(size["height"], size["width"]),
resample=resample,
input_data_format=input_data_format,
)
if do_rescale:
image = image * rescale_factor
if do_normalize:
image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
processed_images.append(image)
return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
__all__ = ["SarmaeImageProcessor"]
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