File size: 17,625 Bytes
a04bbbc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 | from typing import Optional, Union
import numpy as np
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_transforms import (
convert_to_rgb,
resize,
to_channel_dimension_format,
)
from transformers.image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
make_flat_list_of_images,
to_numpy_array,
valid_images,
)
from transformers.utils import TensorType, logging
logger = logging.get_logger(__name__)
class VectorLLMImageProcessor(BaseImageProcessor):
r"""
Constructs a Qwen2-VL image processor that dynamically resizes images based on the original images.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions.
size (`dict[str, int]`, *optional*, defaults to `{"shortest_edge": 56 * 56, "longest_edge": 28 * 28 * 1280}`):
Size of the image after resizing. `shortest_edge` and `longest_edge` keys must be present.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use when resizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image.
image_mean (`float` or `list[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
image_std (`float` or `list[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB.
min_pixels (`int`, *optional*, defaults to `56 * 56`):
The min pixels of the image to resize the image.
max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
The max pixels of the image to resize the image.
patch_size (`int`, *optional*, defaults to 14):
The spatial patch size of the vision encoder.
temporal_patch_size (`int`, *optional*, defaults to 2):
The temporal patch size of the vision encoder.
merge_size (`int`, *optional*, defaults to 2):
The merge size of the vision encoder to llm encoder.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
do_convert_rgb: bool = True,
resized_size: int = 128,
patch_size: int = 16,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.resized_size = resized_size
self.do_resize = do_resize
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
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.patch_size = patch_size
self.do_convert_rgb = do_convert_rgb
def _preprocess(
self,
images: Union[ImageInput],
do_resize: Optional[bool] = 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,
patch_size: Optional[int] = None,
resized_size: Optional[int] = None,
do_convert_rgb: Optional[bool] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
Args:
images (`ImageInput`):
Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
vision_info (`list[Dict]`, *optional*):
Optional list of dictionaries containing additional information about vision inputs.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. `shortest_edge` and `longest_edge` keys must be present.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Scale factor to use if rescaling the image.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
patch_size (`int`, *optional*, defaults to `self.patch_size`):
The spatial patch size of the vision encoder.
temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`):
The temporal patch size of the vision encoder.
merge_size (`int`, *optional*, defaults to `self.merge_size`):
The merge size of the vision encoder to llm encoder.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
images = make_flat_list_of_images(images)
if do_convert_rgb:
images = [convert_to_rgb(image) for image in images]
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
height, width = get_image_size(images[0], channel_dim=input_data_format)
resized_height, resized_width = height, width
processed_images = []
for image in images:
if do_resize:
resized_height, resized_width = resized_size, resized_size
image = resize(
image, size=(resized_height, resized_width), resample=resample, input_data_format=input_data_format
)
if do_rescale:
image = self.rescale(image, scale=rescale_factor, input_data_format=input_data_format)
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 processed_images
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = 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,
patch_size: Optional[int] = None,
resized_size: Optional[int] = None,
do_convert_rgb: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Args:
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`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
min_pixels (`int`, *optional*, defaults to `self.min_pixels`):
The min pixels of the image to resize the image.
max_pixels (`int`, *optional*, defaults to `self.max_pixels`):
The max pixels of the image to resize the image.
patch_size (`int`, *optional*, defaults to `self.patch_size`):
The spatial patch size of the vision encoder.
temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`):
The temporal patch size of the vision encoder.
merge_size (`int`, *optional*, defaults to `self.merge_size`):
The merge size of the vision encoder to llm encoder.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
resized_size = resized_size if resized_size is not None else self.resized_size
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
patch_size = patch_size if patch_size is not None else self.patch_size
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
if images is not None:
images = self.fetch_images(images)
images = make_flat_list_of_images(images)
if images is not None and not valid_images(images):
raise ValueError("Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor")
data = {}
if images is not None:
pixel_values = []
for image in images:
patches = self._preprocess(
image,
do_resize=do_resize,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
patch_size=patch_size,
resized_size=resized_size,
data_format=data_format,
do_convert_rgb=do_convert_rgb,
input_data_format=input_data_format,
)
pixel_values.extend(patches)
pixel_values = np.array(pixel_values)
data.update({"pixel_values": pixel_values})
return BatchFeature(data=data, tensor_type=return_tensors)
def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
"""
A utility that returns number of image patches for a given image size.
Args:
height (`int`):
Height of the input image.
width (`int`):
Width of the input image.
images_kwargs (`dict`, *optional*)
Any kwargs to override defaults of the image processor.
Returns:
`int`: Number of image patches per image.
"""
resized_size = images_kwargs["resized_size"] if "resized_size" in images_kwargs else self.resized_size
patch_size = images_kwargs["patch_size"] if "patch_size" in images_kwargs else self.patch_size
return (resized_size // patch_size) ** 2
__all__ = ["VectorLLMImageProcessor"] |