初始化项目，由ModelHub XC社区提供模型
Model: jiosephlee/Intern-S1-mini-lm Source: Original Platform
This commit is contained in:
ModelHub XC
317
processing_interns1.py
Normal file
317
processing_interns1.py
Normal file
@@ -0,0 +1,317 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2025 HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
from typing import Optional, Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers.image_processing_utils import BatchFeature
|
||||
from transformers.image_utils import ImageInput, concatenate_list, make_flat_list_of_images
|
||||
from transformers.processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
|
||||
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
|
||||
from transformers.video_utils import VideoInput, make_batched_videos
|
||||
|
||||
|
||||
class InternS1ImagesKwargs(ImagesKwargs, total=False):
|
||||
crop_to_patches: Optional[bool]
|
||||
min_patches: Optional[int]
|
||||
max_patches: Optional[int]
|
||||
|
||||
|
||||
class InternS1ProcessorKwargs(ProcessingKwargs, total=False):
|
||||
images_kwargs: InternS1ImagesKwargs
|
||||
_defaults = {
|
||||
"text_kwargs": {
|
||||
"padding_side": "left",
|
||||
"return_mm_token_type_ids": False,
|
||||
},
|
||||
"images_kwargs": {
|
||||
"crop_to_patches": True,
|
||||
},
|
||||
"videos_kwargs": {},
|
||||
}
|
||||
|
||||
|
||||
class InternS1Processor(ProcessorMixin):
|
||||
r"""
|
||||
Constructs a InternS1 processor which wraps a [`AutoImageProcessor`] and
|
||||
[`PretrainedTokenizerFast`] tokenizer into a single processor that inherits both the image processor and
|
||||
tokenizer functionalities. See the [`~InternS1Processor.__call__`] and [`~InternS1Processor.decode`] for more information.
|
||||
Args:
|
||||
image_processor ([`AutoImageProcessor`], *optional*):
|
||||
The image processor is a required input.
|
||||
tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`], *optional*):
|
||||
The tokenizer is a required input.
|
||||
video_processor ([`AutoVideoProcessor`], *optional*):
|
||||
The video processor is a required input.
|
||||
image_seq_length (`int`, *optional*, defaults to 256):
|
||||
The number of image token to use per image patch. it should be set so that:
|
||||
image_seq_length = (config.image_size // config.patch_size) ** 2 * (config.scale_factor**2)
|
||||
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
|
||||
in a chat into a tokenizable string.
|
||||
"""
|
||||
|
||||
attributes = ["image_processor", "tokenizer", "video_processor"]
|
||||
image_processor_class = "AutoImageProcessor"
|
||||
video_processor_class = "AutoVideoProcessor"
|
||||
tokenizer_class = "AutoTokenizer"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
image_processor=None,
|
||||
tokenizer=None,
|
||||
video_processor=None,
|
||||
image_seq_length: int = 256,
|
||||
chat_template=None,
|
||||
**kwargs,
|
||||
):
|
||||
self.image_seq_length = image_seq_length
|
||||
self.start_image_token = tokenizer.start_image_token
|
||||
self.end_image_token = tokenizer.end_image_token
|
||||
self.start_image_token_id = tokenizer.start_image_token_id
|
||||
self.end_image_token_id = tokenizer.end_image_token_id
|
||||
self.image_token = tokenizer.context_image_token
|
||||
self.video_token = tokenizer.video_token
|
||||
self.image_token_id = tokenizer.context_image_token_id
|
||||
self.image_ids = [self.image_token_id, self.start_image_token_id, self.end_image_token_id]
|
||||
|
||||
super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template, **kwargs)
|
||||
|
||||
def _insert_media_placeholders(
|
||||
self,
|
||||
text: list[str],
|
||||
image_pixel_values,
|
||||
video_pixel_values,
|
||||
image_num_patches: list[int],
|
||||
video_num_patches: list[int],
|
||||
image_num_patches_indices: np.ndarray,
|
||||
video_num_patches_indices: np.ndarray,
|
||||
video_patch_indices: np.ndarray,
|
||||
):
|
||||
"""
|
||||
Processes interleaved text with <image> and <video> placeholders, replacing them with appropriate
|
||||
image and video tokens while keeping track of the patches used.
|
||||
"""
|
||||
image_index = 0
|
||||
video_index = 0
|
||||
processed_text = []
|
||||
image_video_patches = []
|
||||
replace_strings = []
|
||||
# Support interleaved image and video in prompts:
|
||||
# Processed patches of images and videos are inserted in `image_video_patches` in the order they appear in the prompts
|
||||
for prompt in text:
|
||||
new_prompt = prompt
|
||||
while self.image_token in new_prompt or self.video_token in new_prompt:
|
||||
if self.image_token in new_prompt and (
|
||||
self.video_token not in new_prompt
|
||||
or new_prompt.index(self.image_token) < new_prompt.index(self.video_token)
|
||||
):
|
||||
# Get the slice of patches corresponding to the current image
|
||||
start_index = image_num_patches_indices[image_index - 1] if image_index > 0 else 0
|
||||
end_index = image_num_patches_indices[image_index]
|
||||
image_video_patches.append(image_pixel_values[start_index:end_index])
|
||||
# Replace the corresponding image placeholder with the correct number of image tokens
|
||||
new_prompt = new_prompt.replace(self.image_token, "<placeholder>", 1)
|
||||
replace_strings.append(
|
||||
f"{self.start_image_token}{self.image_token * self.image_seq_length * image_num_patches[image_index]}{self.end_image_token}"
|
||||
)
|
||||
image_index += 1
|
||||
else:
|
||||
# Get the slice of patches corresponding to the current video
|
||||
# Here we need to account for both the multiple video frames and the potential multiple patches per frame
|
||||
# As of now, InternS1 only supports one patch per frame, but we keep the code flexible for future updates
|
||||
current_patch_index = video_patch_indices[video_index - 1] if video_index > 0 else 0
|
||||
end_patch_index = video_patch_indices[video_index]
|
||||
start_index = video_num_patches_indices[current_patch_index] if video_index > 0 else 0
|
||||
end_index = video_num_patches_indices[end_patch_index - 1]
|
||||
image_video_patches.append(video_pixel_values[start_index:end_index])
|
||||
# Get the number of patches per frame and replace the video placeholder with the correct number of image tokens
|
||||
num_patches = list(video_num_patches[current_patch_index:end_patch_index])
|
||||
video_prompt = "\n".join(
|
||||
f"Frame{i + 1}: {self.start_image_token}{self.image_token * self.image_seq_length * num_patches[i]}{self.end_image_token}"
|
||||
for i in range(len(num_patches))
|
||||
)
|
||||
replace_strings.append(video_prompt)
|
||||
new_prompt = new_prompt.replace(self.video_token, "<placeholder>", 1)
|
||||
video_index += 1
|
||||
while "<placeholder>" in new_prompt:
|
||||
replace_str = replace_strings.pop(0)
|
||||
new_prompt = new_prompt.replace("<placeholder>", replace_str, 1)
|
||||
processed_text.append(new_prompt)
|
||||
|
||||
return processed_text, image_video_patches, image_index, video_index
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
images: Optional[ImageInput] = None,
|
||||
text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None,
|
||||
audio=None,
|
||||
videos: Optional[VideoInput] = None,
|
||||
**kwargs: Unpack[InternS1ProcessorKwargs],
|
||||
) -> BatchFeature:
|
||||
"""
|
||||
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
|
||||
and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] to encode the text if `text`
|
||||
is not `None`, otherwise encode default OCR queries which depends on the `format`, `box`, `color`, `multi_page` and
|
||||
`crop_to_patches` arguments. To prepare the vision inputs, this method forwards the `images` and `kwrags` arguments to
|
||||
GotOcr2ImageProcessor's [`~GotOcr2ImageProcessor.__call__`] if `images` is not `None`.
|
||||
|
||||
Args:
|
||||
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
|
||||
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
||||
tensor. Both channels-first and channels-last formats are supported.
|
||||
text (`str`, `list[str]`, `list[list[str]]`):
|
||||
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
||||
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
||||
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
||||
videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
|
||||
The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
|
||||
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
||||
If set, will return tensors of a particular framework. Acceptable values are:
|
||||
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
||||
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
||||
- `'np'`: Return NumPy `np.ndarray` objects.
|
||||
- `'jax'`: Return JAX `jnp.ndarray` objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
||||
|
||||
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
|
||||
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
|
||||
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
|
||||
`None`).
|
||||
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
|
||||
"""
|
||||
if text is None:
|
||||
raise ValueError("You have to specify text.")
|
||||
|
||||
output_kwargs = self._merge_kwargs(
|
||||
InternS1ProcessorKwargs,
|
||||
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
if not isinstance(text, (list, tuple)):
|
||||
text = [text]
|
||||
|
||||
# Process images and videos separately, as videos don't support crop_to_patches
|
||||
image_num_patches = []
|
||||
video_num_patches = []
|
||||
image_videos_inputs = {}
|
||||
image_pixel_values = None
|
||||
video_pixel_values = None
|
||||
image_num_patches_indices = np.array([0])
|
||||
video_patch_indices = np.array([0])
|
||||
video_num_patches_indices = np.array([0])
|
||||
if images is not None:
|
||||
images = make_flat_list_of_images(images)
|
||||
image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
|
||||
image_num_patches = image_inputs.pop("num_patches")
|
||||
image_pixel_values = image_inputs.pop("pixel_values")
|
||||
image_num_patches_indices = np.cumsum(image_num_patches)
|
||||
if videos is not None:
|
||||
videos = make_batched_videos(videos)
|
||||
video_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
|
||||
video_pixel_values = video_inputs.pop("pixel_values_videos")
|
||||
|
||||
# Obtain per frame information first and then flatten to (BS * T, ...)
|
||||
num_frames_per_video = [len(video) for video in video_pixel_values]
|
||||
video_num_patches = [1 for frames in num_frames_per_video for _ in range(frames)]
|
||||
video_patch_indices = np.cumsum(num_frames_per_video)
|
||||
video_num_patches_indices = np.cumsum(video_num_patches)
|
||||
video_pixel_values = video_pixel_values.flatten(0, 1)
|
||||
|
||||
if images is not None or videos is not None:
|
||||
text, image_video_patches, image_index, video_index = self._insert_media_placeholders(
|
||||
text,
|
||||
image_pixel_values,
|
||||
video_pixel_values,
|
||||
image_num_patches,
|
||||
video_num_patches,
|
||||
image_num_patches_indices,
|
||||
video_num_patches_indices,
|
||||
video_patch_indices,
|
||||
)
|
||||
if images is not None and image_index != len(images):
|
||||
raise ValueError("Number of image placeholders in the prompt does not match the number of images.")
|
||||
if videos is not None and video_index != len(videos):
|
||||
raise ValueError("Number of video placeholders in the prompt does not match the number of videos.")
|
||||
|
||||
# Concatenate the interleaved image and video patches (function agnostic to the patches type (list, numpy array, torch tensor))
|
||||
image_videos_inputs = {"pixel_values": concatenate_list(image_video_patches)}
|
||||
|
||||
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
|
||||
return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
|
||||
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
|
||||
self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
|
||||
|
||||
if return_mm_token_type_ids:
|
||||
array_ids = np.array(text_inputs["input_ids"])
|
||||
mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
|
||||
mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1
|
||||
text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
|
||||
|
||||
return BatchFeature(data={**text_inputs, **image_videos_inputs}, tensor_type=return_tensors)
|
||||
|
||||
def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
|
||||
"""
|
||||
Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
|
||||
|
||||
Args:
|
||||
image_sizes (`list[list[int]]`, *optional*):
|
||||
The input sizes formatted as (height, width) per each image.
|
||||
|
||||
Returns:
|
||||
`MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
|
||||
input modalities, along with other useful data.
|
||||
"""
|
||||
|
||||
vision_data = {}
|
||||
if image_sizes is not None:
|
||||
images_kwargs = InternS1ProcessorKwargs._defaults.get("images_kwargs", {})
|
||||
images_kwargs.update(kwargs)
|
||||
|
||||
num_image_patches = [
|
||||
self.image_processor.get_number_of_image_tokens(*image_size, images_kwargs)
|
||||
for image_size in image_sizes
|
||||
]
|
||||
# Add 2 for BOI and EOI tokens
|
||||
num_image_tokens = [2 + (self.image_seq_length * num_patches) for num_patches in num_image_patches]
|
||||
vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
|
||||
|
||||
return MultiModalData(**vision_data)
|
||||
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
||||
refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
||||
the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.decode(*args, **kwargs)
|
||||
|
||||
@property
|
||||
def model_input_names(self):
|
||||
tokenizer_input_names = self.tokenizer.model_input_names
|
||||
image_processor_input_names = self.image_processor.model_input_names
|
||||
return list(tokenizer_input_names) + list(image_processor_input_names)
|
||||
|
||||
|
||||
__all__ = ["InternS1Processor"]
|
||||
Reference in New Issue
Block a user