初始化项目，由ModelHub XC社区提供模型
Model: AI-ModelScope/R-4B Source: Original Platform
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ModelHub XC
259
processing_r.py
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259
processing_r.py
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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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import math
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from collections.abc import Iterable
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from typing import Union
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import numpy as np
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.image_processing_utils import select_best_resolution
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from transformers.image_utils import ImageInput, get_image_size, to_numpy_array
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from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, MultiModalData
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from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class RProcessorKwargs(ProcessingKwargs, total=False):
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# see processing_utils.ProcessingKwargs documentation for usage.
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_defaults = {
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"text_kwargs": {
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"padding": False,
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},
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"image_kwargs": {},
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}
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class RProcessor(ProcessorMixin):
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attributes = ["image_processor", "tokenizer"]
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valid_kwargs = [
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"chat_template",
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"num_image_tokens",
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"image_processor_type",
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"vision_feature_select_strategy",
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"image_token",
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"vision_aspect_ratio",
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]
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image_processor_class = "AutoImageProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(
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self,
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image_processor=None,
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tokenizer=None,
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num_image_tokens=None,
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vision_feature_select_strategy=None,
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chat_template=None,
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image_token="<image>",
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vision_aspect_ratio= "anyres",
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**kwargs,
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):
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self.num_image_tokens = num_image_tokens
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self.vision_feature_select_strategy = vision_feature_select_strategy
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self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
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self.image_token_id = (
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tokenizer.image_token_id
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if getattr(tokenizer, "image_token_id", None)
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else tokenizer.convert_tokens_to_ids(self.image_token)
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)
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self.vision_aspect_ratio = vision_aspect_ratio
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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def __call__(
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self,
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images: ImageInput = None,
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text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
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audio=None,
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**kwargs: Unpack[RProcessorKwargs],
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) -> BatchFeature:
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output_kwargs = self._merge_kwargs(
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RProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if isinstance(text, str):
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text = [text]
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elif not isinstance(text, list) and not isinstance(text[0], str):
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raise ValueError("Invalid input text. Please provide a string, or a list of strings")
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image_inputs = {}
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if images is not None:
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image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
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batch_num_images = iter(image_inputs["batch_num_images"])
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image_sizes = iter(image_inputs["image_sizes"])
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height, width = get_image_size(
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to_numpy_array(image_inputs["pixel_values"][0][0]),
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channel_dim=output_kwargs["images_kwargs"].get("data_format"),
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)
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text, num_image_tokens = self._expand_image_tokens(
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text, image_sizes, height, width, self.image_token, batch_num_images
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)
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
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self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
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return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
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def _expand_image_tokens(
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self,
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text: list[TextInput],
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image_sizes: Iterable[Union[list[int], int]],
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height: int,
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width: int,
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special_token: str,
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batch_num_images: Iterable[int],
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):
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prompt_strings = []
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max_num_vision_tokens = 0
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for sample in text:
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if special_token in sample:
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is_multi_image = next(batch_num_images) != 1
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else:
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is_multi_image = False
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while special_token in sample:
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if is_multi_image:
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num_image_tokens = self.num_image_tokens + 1 # one for image_newline
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else:
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original_size = next(image_sizes)
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if not isinstance(original_size, (list, tuple)):
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# cast to list to avoid numerical precision errors when calculating unpadding
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original_size = original_size.tolist()
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orig_height, orig_width = original_size
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num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
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max_num_vision_tokens = max(max_num_vision_tokens, num_image_tokens)
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if self.vision_feature_select_strategy == "default":
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num_image_tokens -= 1
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sample = sample.replace(special_token, "<placeholder>" * num_image_tokens, 1)
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prompt_strings.append(sample)
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text = [sample.replace("<placeholder>", special_token) for sample in prompt_strings]
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return text, max_num_vision_tokens
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def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
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image_grid_pinpoints = self.image_processor.image_grid_pinpoints
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height_best_resolution, width_best_resolution = select_best_resolution(
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[orig_height, orig_width], image_grid_pinpoints
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)
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scale_height, scale_width = height_best_resolution // height, width_best_resolution // width
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patches_height = patches_width = int(math.sqrt(self.num_image_tokens))
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unpadded_features, newline_features = self._get_unpadded_features(
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orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
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)
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# The base patch covers the entire image (no CLS for SigLIP)
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base_features = self.num_image_tokens
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num_image_tokens = unpadded_features + newline_features + base_features
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return num_image_tokens
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# Adapted from transformers.models.llava_next.processing_llava_next.LlavaNextProcessor._get_unpadded_features
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def _get_unpadded_features(self, height, width, patches_height, patches_width, scale_height, scale_width):
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current_height = patches_height * scale_height
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current_width = patches_width * scale_width
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original_aspect_ratio = width / height
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current_aspect_ratio = current_width / current_height
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if original_aspect_ratio > current_aspect_ratio:
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new_height = int(round(height * (current_width / width), 7))
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padding = (current_height - new_height) // 2
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current_height -= padding * 2
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else:
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new_width = int(round(width * (current_height / height), 7))
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padding = (current_width - new_width) // 2
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current_width -= padding * 2
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unpadded_features = current_height * current_width
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newline_features = current_height
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return (unpadded_features, newline_features)
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def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
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"""
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Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
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Args:
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image_sizes (list[list[str]], *optional*):
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The input sizes formatted as (height, width) per each image.
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video_sizes (list[list[str]], *optional*):
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The input sizes formatted as (num_frames, height, width) per each video.
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audio_lengths (list[int], *optional*):
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The input length formatted as per each audio.
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Returns:
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dict[str, list[int]]: A dictionary mapping each modality ("image", "video", "audio")
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to a list containing the number of placeholder tokens required. If the model doesn't accept
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a certain modality or no input sizes are provided, the dict value is set to an empty list.
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"""
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vision_data = {}
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if image_sizes is not None:
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images_kwargs = RProcessorKwargs._defaults.get("images_kwargs", {})
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images_kwargs.update(kwargs)
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size = images_kwargs.get("size", None) or self.image_processor.size
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size = (
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(size["shortest_edge"], size["shortest_edge"])
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if "shortest_edge" in size
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else (min(size["height"], size["width"]), min(size["height"], size["width"]))
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)
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processed_height, processed_width = size
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batch_num_image_tokens = []
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num_image_patches = [1] * len(image_sizes) # llava-ov doesn't batch pixels as Idefics, thus `1` patch`
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for image_size in image_sizes:
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orig_height, orig_width = image_size
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num_image_tokens = self._get_number_of_features(
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orig_height, orig_width, processed_height, processed_width
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)
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if self.vision_feature_select_strategy == "default":
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num_image_tokens -= 1
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batch_num_image_tokens.append(num_image_tokens)
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vision_data.update({"num_image_tokens": batch_num_image_tokens, "num_image_patches": num_image_patches})
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return MultiModalData(**vision_data)
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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
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the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@property
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# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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__all__ = ["RProcessor"]
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