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from typing import Dict, List, Optional, Union
import numpy as np
from transformers.image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
make_flat_list_of_images,
make_list_of_images,
load_image,
valid_images,
to_numpy_array,
)
from transformers.image_processing_utils import BatchFeature, BaseImageProcessor
from transformers.image_transforms import (
convert_to_rgb,
resize,
to_channel_dimension_format,
)
from transformers.utils import TensorType, logging
from transformers.models.auto import AutoImageProcessor
logger = logging.get_logger(__name__)
def smart_resize(
height: int, width: int, factor: int = 32, min_pixels: int = 16 * 16 * 4 * 64, max_pixels: int = 16 * 16 * 4 * 2048
):
if height < factor or width < factor:
raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}")
h_bar = round(height / factor) * factor
w_bar = round(width / factor) * factor
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = math.floor(height / beta / factor) * factor
w_bar = math.floor(width / beta / factor) * factor
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = math.ceil(height * beta / factor) * factor
w_bar = math.ceil(width * beta / factor) * factor
assert h_bar % factor == 0, f"h_bar:{h_bar} must be divisible by factor:{factor}"
assert w_bar % factor == 0, f"w_bar:{w_bar} must be divisible by factor:{factor}"
return h_bar, w_bar
def resize_small_images(image, min_size=32):
# `image` is a numpy array here (see to_numpy_array in _preprocess);
# get_image_size returns (height, width).
height, width = get_image_size(image)
factor = 1.
if height < min_size:
factor = min_size / height
if width < min_size:
factor = max(factor, min_size / width)
if factor > 1.:
new_height = round(height * factor + 0.5)
new_width = round(width * factor + 0.5)
# transformers' numpy resize takes size=(height, width); matches the
# resize() call in _preprocess and avoids PIL's (width, height) ordering.
image = resize(image, size=(new_height, new_width))
return image
class AXVEImageProcessor(BaseImageProcessor):
model_input_names = ["pixel_values_images", "image_grid_hw"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
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,
min_pixels: int = 16 * 16 * 4 * 64,
max_pixels: int = 16777216,
patch_size: int = 16,
merge_size: int = 2,
**kwargs,
) -> None:
super().__init__(**kwargs)
if size is not None:
print("size will be ignored. use min_pixels and max_pixels instead.")
if min_pixels is None or max_pixels is None:
raise ValueError("min_pixels and max_pixels must be provided")
self.min_pixels = min_pixels
self.max_pixels = max_pixels
self.size = 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 IMAGENET_STANDARD_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
self.patch_size = patch_size
self.merge_size = merge_size
self.do_convert_rgb = do_convert_rgb
def get_size_of_processed_image(self, image):
factor = self.patch_size * self.merge_size
image = resize_small_images(image, min_size=factor)
input_data_format = infer_channel_dimension_format(image)
height, width = get_image_size(image, channel_dim=input_data_format)
resized_height, resized_width = smart_resize(
height,
width,
factor=factor,
min_pixels=self.min_pixels,
max_pixels=self.max_pixels,
)
return resized_height, resized_width
def _preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Optional[dict[str, int]] = None,
resample: 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,
do_convert_rgb: Optional[bool] = None,
patch_size: Optional[int] = None,
merge_size: Optional[int] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
images = make_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])
patches_per_turn = []
hw_grids_per_turn = []
for image in images:
if do_resize:
image = resize_small_images(image, min_size=patch_size * merge_size)
height, width = get_image_size(image, channel_dim=input_data_format)
resized_height, resized_width = smart_resize(
height,
width,
factor=patch_size * merge_size,
min_pixels=self.min_pixels,
max_pixels=self.max_pixels,
)
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)
channel = image.shape[0]
grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size
patches = image.reshape(
1,
channel,
grid_h // self.merge_size,
self.merge_size,
self.patch_size,
grid_w // self.merge_size,
self.merge_size,
self.patch_size,
)
patches = patches.transpose(0, 2, 5, 3, 6, 1, 4, 7)
patches = patches.reshape(-1, channel, patch_size, patch_size)
patches = patches.reshape(
grid_h * grid_w, channel * self.patch_size * self.patch_size
)
hw_grid = (grid_h, grid_w)
assert patches.shape[0] == hw_grid[0] * hw_grid[1], f"patches.shape[0]: {patches.shape[0]} != hw_grid[0] * hw_grid[1]: {hw_grid[0] * hw_grid[1]}"
patches_per_turn.append(patches)
hw_grids_per_turn.append(hw_grid)
return np.concatenate(patches_per_turn, axis=0), hw_grids_per_turn
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Optional[dict[str, int]] = None,
min_pixels: Optional[int] = None,
max_pixels: Optional[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,
do_convert_rgb: Optional[bool] = None,
patch_size: Optional[int] = None,
merge_size: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
min_pixels = min_pixels if min_pixels is not None else self.min_pixels
max_pixels = max_pixels if max_pixels is not None else self.max_pixels
if size is not None:
raise ValueError("size is not supported. use min_pixels and max_pixels instead")
do_resize = do_resize if do_resize is not None else self.do_resize
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
merge_size = merge_size if merge_size is not None else self.merge_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, grid_hws = [], []
for image in images:
patches, grid_hw = self._preprocess(
image,
do_resize=do_resize,
size=size,
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,
merge_size=merge_size,
data_format=data_format,
do_convert_rgb=do_convert_rgb,
input_data_format=input_data_format,
)
pixel_values.extend(patches)
grid_hws += grid_hw
pixel_values = np.array(pixel_values)
grid_hws = np.array(grid_hws)
data.update({"pixel_values_images": pixel_values, "image_grid_hw": grid_hws})
return BatchFeature(data=data, tensor_type=return_tensors)
def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
images_kwargs = images_kwargs or {}
min_pixels = images_kwargs.get("min_pixels", self.min_pixels)
max_pixels = images_kwargs.get("max_pixels", self.max_pixels)
patch_size = images_kwargs.get("patch_size", self.patch_size)
merge_size = images_kwargs.get("merge_size", self.merge_size)
factor = patch_size * merge_size
resized_height, resized_width = smart_resize(
height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
)
grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
return grid_h * grid_w
def fetch_images(self, image_url_or_urls: Union[str, list[str], list[list[str]]]):
if isinstance(image_url_or_urls, list):
return [self.fetch_images(x) for x in image_url_or_urls]
elif isinstance(image_url_or_urls, str):
return load_image(image_url_or_urls)
elif valid_images(image_url_or_urls):
return image_url_or_urls
else:
raise TypeError(f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}") |