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# Adopted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py.
# Below is the original copyright:
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for VideoLLaMA3."""
import math
from typing import Dict, List, Optional, Union
import numpy as np
import torch
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_utils import ImageInput
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,
VideoInput,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
is_valid_image,
make_list_of_images,
to_numpy_array,
)
from transformers.utils import TensorType, is_vision_available, logging
logger = logging.get_logger(__name__)
if is_vision_available():
from PIL import Image
def is_valid_video(video) -> bool:
if isinstance(video, (list, tuple)):
return all(is_valid_image(frame) for frame in video)
elif isinstance(video, np.ndarray):
return video.ndim == 4
elif isinstance(video, torch.Tensor):
return video.ndim == 4
return False
def make_batched_images(images) -> List[List[ImageInput]]:
"""
Accepts images in list or nested list format, and makes a list of images for preprocessing.
Args:
images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`):
The input image.
Returns:
list: A list of images.
"""
if isinstance(images, (list, tuple)):
# list of images/videos
if not all(is_valid_video(image) or is_valid_image(image) for image in images):
raise ValueError(f"Could not make batched images from {images}")
return images
elif is_valid_video(images) or is_valid_image(images):
# single image/video
return [images]
raise ValueError(f"Could not make batched images from {images}")
def simple_batched_resize(
images, factor: int = 28, min_tokens: int = 4 * 4, max_tokens: int = 16384, input_data_format: str = None
):
min_pixels = min_tokens * factor * factor
max_pixels = max_tokens * factor * factor
num_images = 0
for image in images:
if is_valid_video(image):
num_images += len(image)
else:
num_images += 1
image_sizes = []
for image in images:
if is_valid_video(image):
image = image[0]
if isinstance(image, Image.Image):
height, width = image.size
else:
height, width = get_image_size(image, channel_dim=input_data_format)
image_sizes.append([height, width])
tmp_image_sizes = []
for height, width in image_sizes:
h_bar = round(height / factor) * factor
w_bar = round(width / factor) * factor
if h_bar * w_bar > (max_pixels // num_images):
beta = math.sqrt((height * width) / (max_pixels // num_images))
h_bar = math.floor(height / beta / factor) * factor
w_bar = math.floor(width / beta / factor) * factor
# per image min_pixels
if 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
tmp_image_sizes.append((h_bar, w_bar))
image_sizes = tmp_image_sizes
return image_sizes
def batched_resize(
images, factors: List[int], min_tokens: int = 4 * 4, max_tokens: int = 16384, input_data_format: str = None
):
image_sizes = []
for image in images:
if is_valid_video(image):
num_frame = len(image)
image = image[0]
else:
num_frame = 1
if isinstance(image, Image.Image):
height, width = image.size
else:
height, width = get_image_size(image, channel_dim=input_data_format)
image_sizes.append([num_frame, height, width])
# global max_pixels
smart_scale_factors = 1.0
total_tokens = 0
for (num_frame, height, width), factor in zip(image_sizes, factors):
total_tokens += num_frame * math.ceil(height / factor) * math.ceil(width / factor)
# TODO: add min_pixels
if total_tokens > max_tokens:
beta = math.sqrt(total_tokens / max_tokens)
tmp_image_sizes = []
for (_, height, width), factor in zip(image_sizes, factors):
h_bar = math.floor(height / beta / factor) * factor
w_bar = math.floor(width / beta / factor) * factor
tmp_image_sizes.append((h_bar, w_bar))
image_sizes = tmp_image_sizes
else:
tmp_image_sizes = []
for (_, height, width), factor in zip(image_sizes, factors):
height = round(height / factor) * factor
width = round(width / factor) * factor
tmp_image_sizes.append((height, width))
image_sizes = tmp_image_sizes
return image_sizes
class Videollama3ImageProcessor(BaseImageProcessor):
r"""
Constructs a DAMOVL 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.
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 spacial patch size of the vision encoder.
"""
model_input_names = ["pixel_values", "grid_sizes", "merge_sizes"]
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,
min_tokens: int = 4 * 4,
max_tokens: int = 16384,
patch_size: int = 14,
**kwargs,
) -> None:
super().__init__(**kwargs)
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.min_tokens = min_tokens
self.max_tokens = max_tokens
self.patch_size = patch_size
self.do_convert_rgb = do_convert_rgb
def _preprocess(
self,
images: Union[ImageInput, VideoInput],
target_size: List[int],
merge_size: int = 1,
do_resize: bool = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: 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`.
target_size (`List[int]`):
The target size to resize the image to. Should be a list of two integers: [target_height, target_width].
merge_size (`int`, *optional*, defaults to `1`):
The merge size after the vision encoder.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
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.
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_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 is_scaled_image(images[0]) and do_rescale:
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 = target_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)
patches = np.array(processed_images)
if data_format == ChannelDimension.LAST:
patches = patches.transpose(0, 3, 1, 2)
t = patches.shape[0]
channel = patches.shape[1]
grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size
patches = patches.reshape(
t,
channel,
grid_h // merge_size,
merge_size,
self.patch_size,
grid_w // merge_size,
merge_size,
self.patch_size,
)
patches = patches.transpose(0, 2, 5, 3, 6, 1, 4, 7)
flatten_patches = patches.reshape(
t * grid_h * grid_w, channel * self.patch_size * self.patch_size
)
return flatten_patches, (t, grid_h, grid_w)
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: bool = None,
merge_size: Optional[Union[int, List[int]]] = 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.
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`.
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.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.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
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
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
images = make_batched_images(images)
if isinstance(merge_size, (list, tuple)):
assert len(merge_size) == len(images), "Merge size must be the same length as images."
merge_sizes = merge_size
else:
merge_sizes = [merge_size for _ in images]
if all(merge_size == merge_sizes[0] for merge_size in merge_sizes):
target_sizes = simple_batched_resize(
images,
factor=self.patch_size * merge_sizes[0],
min_tokens=self.min_tokens,
max_tokens=self.max_tokens,
input_data_format=input_data_format,
)
else:
target_sizes = batched_resize(
images,
factors=[self.patch_size * merge_size for merge_size in merge_sizes],
min_tokens=self.min_tokens,
max_tokens=self.max_tokens,
input_data_format=input_data_format,
)
pixel_values, grid_sizes = [], []
for image, merge_size, target_size in zip(images, merge_sizes, target_sizes):
patches, grid_size = self._preprocess(
image,
target_size=target_size,
merge_size=merge_size,
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,
data_format=data_format,
do_convert_rgb=do_convert_rgb,
input_data_format=input_data_format,
)
pixel_values.append(patches)
grid_sizes.append(grid_size)
pixel_values = np.concatenate(pixel_values, axis=0)
grid_sizes = np.array(grid_sizes)
merge_sizes = np.array(merge_sizes)
data = {
"pixel_values": pixel_values,
"grid_sizes": grid_sizes,
"merge_sizes": merge_sizes,
}
return BatchFeature(data=data, tensor_type=return_tensors)
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