474 lines
21 KiB
Python
474 lines
21 KiB
Python
# Adopted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py.
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# Below is the original copyright:
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# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Image processor class for VideoLLaMA3."""
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import math
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from typing import Dict, List, Optional, Union
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import numpy as np
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import torch
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from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
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from transformers.image_utils import ImageInput
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from transformers.image_transforms import (
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convert_to_rgb,
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resize,
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to_channel_dimension_format,
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)
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from transformers.image_utils import (
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OPENAI_CLIP_MEAN,
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OPENAI_CLIP_STD,
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ChannelDimension,
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ImageInput,
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PILImageResampling,
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VideoInput,
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get_image_size,
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infer_channel_dimension_format,
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is_scaled_image,
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is_valid_image,
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make_list_of_images,
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to_numpy_array,
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)
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from transformers.utils import TensorType, is_vision_available, logging
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logger = logging.get_logger(__name__)
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if is_vision_available():
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from PIL import Image
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def is_valid_video(video) -> bool:
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if isinstance(video, (list, tuple)):
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return all(is_valid_image(frame) for frame in video)
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elif isinstance(video, np.ndarray):
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return video.ndim == 4
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elif isinstance(video, torch.Tensor):
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return video.ndim == 4
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return False
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def make_batched_images(images) -> List[List[ImageInput]]:
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"""
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Accepts images in list or nested list format, and makes a list of images for preprocessing.
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Args:
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images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`):
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The input image.
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Returns:
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list: A list of images.
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"""
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if isinstance(images, (list, tuple)):
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# list of images/videos
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if not all(is_valid_video(image) or is_valid_image(image) for image in images):
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raise ValueError(f"Could not make batched images from {images}")
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return images
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elif is_valid_video(images) or is_valid_image(images):
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# single image/video
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return [images]
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raise ValueError(f"Could not make batched images from {images}")
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def simple_batched_resize(
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images, factor: int = 28, min_tokens: int = 4 * 4, max_tokens: int = 16384, input_data_format: str = None
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):
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min_pixels = min_tokens * factor * factor
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max_pixels = max_tokens * factor * factor
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num_images = 0
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for image in images:
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if is_valid_video(image):
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num_images += len(image)
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else:
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num_images += 1
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image_sizes = []
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for image in images:
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if is_valid_video(image):
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image = image[0]
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if isinstance(image, Image.Image):
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height, width = image.size
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else:
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height, width = get_image_size(image, channel_dim=input_data_format)
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image_sizes.append([height, width])
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tmp_image_sizes = []
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for height, width in image_sizes:
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h_bar = round(height / factor) * factor
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w_bar = round(width / factor) * factor
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if h_bar * w_bar > (max_pixels // num_images):
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beta = math.sqrt((height * width) / (max_pixels // num_images))
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h_bar = math.floor(height / beta / factor) * factor
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w_bar = math.floor(width / beta / factor) * factor
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# per image min_pixels
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if h_bar * w_bar < min_pixels:
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beta = math.sqrt(min_pixels / (height * width))
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h_bar = math.ceil(height * beta / factor) * factor
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w_bar = math.ceil(width * beta / factor) * factor
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tmp_image_sizes.append((h_bar, w_bar))
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image_sizes = tmp_image_sizes
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return image_sizes
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def batched_resize(
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images, factors: List[int], min_tokens: int = 4 * 4, max_tokens: int = 16384, input_data_format: str = None
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):
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image_sizes = []
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for image in images:
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if is_valid_video(image):
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num_frame = len(image)
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image = image[0]
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else:
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num_frame = 1
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if isinstance(image, Image.Image):
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height, width = image.size
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else:
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height, width = get_image_size(image, channel_dim=input_data_format)
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image_sizes.append([num_frame, height, width])
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# global max_pixels
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smart_scale_factors = 1.0
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total_tokens = 0
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for (num_frame, height, width), factor in zip(image_sizes, factors):
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total_tokens += num_frame * math.ceil(height / factor) * math.ceil(width / factor)
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# TODO: add min_pixels
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if total_tokens > max_tokens:
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beta = math.sqrt(total_tokens / max_tokens)
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tmp_image_sizes = []
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for (_, height, width), factor in zip(image_sizes, factors):
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h_bar = math.floor(height / beta / factor) * factor
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w_bar = math.floor(width / beta / factor) * factor
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tmp_image_sizes.append((h_bar, w_bar))
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image_sizes = tmp_image_sizes
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else:
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tmp_image_sizes = []
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for (_, height, width), factor in zip(image_sizes, factors):
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height = round(height / factor) * factor
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width = round(width / factor) * factor
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tmp_image_sizes.append((height, width))
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image_sizes = tmp_image_sizes
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return image_sizes
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class Videollama3ImageProcessor(BaseImageProcessor):
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r"""
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Constructs a VideoLLaMA3 image processor that dynamically resizes images based on the original images.
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Args:
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do_resize (`bool`, *optional*, defaults to `True`):
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Whether to resize the image's (height, width) dimensions.
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resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
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Resampling filter to use when resizing the image.
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do_rescale (`bool`, *optional*, defaults to `True`):
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Whether to rescale the image by the specified scale `rescale_factor`.
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rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
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Scale factor to use if rescaling the image.
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do_normalize (`bool`, *optional*, defaults to `True`):
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Whether to normalize the image.
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image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
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Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
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image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
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Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
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do_convert_rgb (`bool`, *optional*, defaults to `True`):
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Whether to convert the image to RGB.
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min_pixels (`int`, *optional*, defaults to `56 * 56`):
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The min pixels of the image to resize the image.
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max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
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The max pixels of the image to resize the image.
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patch_size (`int`, *optional*, defaults to 14):
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The spacial patch size of the vision encoder.
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"""
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model_input_names = ["pixel_values", "grid_sizes", "merge_sizes"]
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def __init__(
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self,
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do_resize: bool = True,
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resample: PILImageResampling = PILImageResampling.BICUBIC,
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do_rescale: bool = True,
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rescale_factor: Union[int, float] = 1 / 255,
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do_normalize: bool = True,
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image_mean: Optional[Union[float, List[float]]] = None,
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image_std: Optional[Union[float, List[float]]] = None,
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do_convert_rgb: bool = True,
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min_tokens: int = 4 * 4,
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max_tokens: int = 16384,
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patch_size: int = 14,
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**kwargs,
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) -> None:
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super().__init__(**kwargs)
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self.do_resize = do_resize
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self.resample = resample
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self.do_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.do_normalize = do_normalize
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self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
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self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
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self.min_tokens = min_tokens
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self.max_tokens = max_tokens
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self.patch_size = patch_size
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self.do_convert_rgb = do_convert_rgb
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def _preprocess(
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self,
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images: Union[ImageInput, VideoInput],
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target_size: List[int],
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merge_size: int = 1,
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do_resize: bool = None,
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resample: PILImageResampling = None,
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do_rescale: bool = None,
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rescale_factor: float = None,
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do_normalize: bool = None,
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image_mean: Optional[Union[float, List[float]]] = None,
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image_std: Optional[Union[float, List[float]]] = None,
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do_convert_rgb: bool = None,
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data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
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input_data_format: Optional[Union[str, ChannelDimension]] = None,
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):
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"""
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Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
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Args:
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images (`ImageInput`):
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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`.
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target_size (`List[int]`):
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The target size to resize the image to. Should be a list of two integers: [target_height, target_width].
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merge_size (`int`, *optional*, defaults to `1`):
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The merge size after the vision encoder.
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do_resize (`bool`, *optional*, defaults to `self.do_resize`):
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Whether to resize the image.
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resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
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Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
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do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
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Whether to rescale the image.
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rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
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Scale factor to use if rescaling the image.
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do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
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Whether to normalize the image.
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image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
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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.
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image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
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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.
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do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
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Whether to convert the image to RGB.
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data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
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The channel dimension format for the output image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- Unset: Use the channel dimension format of the input image.
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input_data_format (`ChannelDimension` or `str`, *optional*):
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The channel dimension format for the input image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
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"""
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images = make_list_of_images(images)
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if do_convert_rgb:
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images = [convert_to_rgb(image) for image in images]
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# All transformations expect numpy arrays.
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images = [to_numpy_array(image) for image in images]
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if is_scaled_image(images[0]) and do_rescale:
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logger.warning_once(
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"It looks like you are trying to rescale already rescaled images. If the input"
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" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
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)
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if input_data_format is None:
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# We assume that all images have the same channel dimension format.
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input_data_format = infer_channel_dimension_format(images[0])
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height, width = get_image_size(images[0], channel_dim=input_data_format)
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resized_height, resized_width = height, width
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processed_images = []
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for image in images:
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if do_resize:
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resized_height, resized_width = target_size
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image = resize(
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image, size=(resized_height, resized_width), resample=resample, input_data_format=input_data_format
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)
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if do_rescale:
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image = self.rescale(image, scale=rescale_factor, input_data_format=input_data_format)
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if do_normalize:
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image = self.normalize(
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image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
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)
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image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
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processed_images.append(image)
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patches = np.array(processed_images)
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if data_format == ChannelDimension.LAST:
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patches = patches.transpose(0, 3, 1, 2)
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t = patches.shape[0]
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channel = patches.shape[1]
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grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size
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patches = patches.reshape(
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t,
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channel,
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grid_h // merge_size,
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merge_size,
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self.patch_size,
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grid_w // merge_size,
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merge_size,
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self.patch_size,
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)
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patches = patches.transpose(0, 2, 5, 3, 6, 1, 4, 7)
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flatten_patches = patches.reshape(
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t * grid_h * grid_w, channel * self.patch_size * self.patch_size
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)
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return flatten_patches, (t, grid_h, grid_w)
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def preprocess(
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self,
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images: ImageInput,
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do_resize: bool = None,
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resample: PILImageResampling = None,
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do_rescale: bool = None,
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rescale_factor: float = None,
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do_normalize: bool = None,
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image_mean: Optional[Union[float, List[float]]] = None,
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image_std: Optional[Union[float, List[float]]] = None,
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do_convert_rgb: bool = None,
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merge_size: Optional[Union[int, List[int]]] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
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input_data_format: Optional[Union[str, ChannelDimension]] = None,
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):
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"""
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Args:
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images (`ImageInput`):
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Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
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passing in images with pixel values between 0 and 1, set `do_rescale=False`.
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do_resize (`bool`, *optional*, defaults to `self.do_resize`):
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Whether to resize the image.
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resample (`int`, *optional*, defaults to `self.resample`):
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Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
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has an effect if `do_resize` is set to `True`.
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do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
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Whether to rescale the image.
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rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
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Rescale factor to rescale the image by if `do_rescale` is set to `True`.
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do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
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Whether to normalize the image.
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image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
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Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
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image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
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Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
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`True`.
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do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
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Whether to convert the image to RGB.
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return_tensors (`str` or `TensorType`, *optional*):
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The type of tensors to return. Can be one of:
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- Unset: Return a list of `np.ndarray`.
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- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
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- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
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- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
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- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
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data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
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The channel dimension format for the output image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- Unset: Use the channel dimension format of the input image.
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input_data_format (`ChannelDimension` or `str`, *optional*):
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The channel dimension format for the input image. If unset, the channel dimension format is inferred
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from the input image. Can be one of:
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
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- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
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"""
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do_resize = do_resize if do_resize is not None else self.do_resize
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resample = resample if resample is not None else self.resample
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do_rescale = do_rescale if do_rescale is not None else self.do_rescale
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rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
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do_normalize = do_normalize if do_normalize is not None else self.do_normalize
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image_mean = image_mean if image_mean is not None else self.image_mean
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image_std = image_std if image_std is not None else self.image_std
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merge_size = merge_size if merge_size is not None else self.merge_size
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do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
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images = make_batched_images(images)
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if isinstance(merge_size, (list, tuple)):
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assert len(merge_size) == len(images), "Merge size must be the same length as images."
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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)
|