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Model: AI-ModelScope/R-4B Source: Original Platform
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---
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base_model:
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- Qwen/Qwen3-4B
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language:
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- en
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license: apache-2.0
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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# R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning
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[[📚 Arxiv Paper](https://arxiv.org/pdf/2508.21113)] [[🤗 Hugging Face](https://huggingface.co/YannQi/R-4B)] [[🤖️ ModelScope](https://huggingface.co/YannQi/R-4B)] [[💻 Code](https://github.com/yannqi/R-4B)]
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<div align="center">
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<img src="asset/logo_R_4B.png" alt="logo" width="38" />
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</div>
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<div align="center">
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<img src="asset/R-4B.png" width="100%" alt="R-4B Performance">
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</div>
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## ⭐️ Introduction
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In this repo, we present **R-4B**, a multimodal large language model designed for general-purpose auto-thinking, autonomously switching between step-by-step thinking and direct response generation based on task complexity. This capability enables R-4B to deliver high-quality responses while significantly improving inference efficiency and reducing computational costs.
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The development of R-4B follows a two-stage training paradigm:
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(1) Bi-mode Annealing, which establishes both thinking and non-thinking capabilities for VQA; and
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(2) Bi-mode Policy Optimization (BPO), which enables the model to adaptively switch between thinking and non-thinking modes based on input demands.
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## 🚀 Key Features
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- 🧠 **Think Smart, Act Fast: Adaptive & Controllable Thinking!**
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Our model provides three-mode control over the response process.
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- **Auto-thinking Mode:** Unleash **auto-thinking** that works across general topics, from simple Q&A to complex scientific analysis. It saves time and computation by thinking only when it matters.
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- **Support Manual Control:** Explicitly command the model to use its `thinking` or `non-thinking` capabilities, enabling you to make your choices for every job.
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- 🏆 **Strong Performance, Open for Everyone!**
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Our model is now **fully open-source**. It achieves **state-of-the-art performance** among models of comparable size.
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## 📢 News
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- **[2025.08.20]** 🚀 **vLLM Support is Here!** Our R-4B model is now fully compatible with [vLLM](https://github.com/vllm-project/vllm) for high-performance inference.
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- **[2025.08.18]** 🏆 **Top Rank Achieved!** We are thrilled to announce that R-4B is now ranked #1 among all open-source models on the [OpenCompass Multi-modal Reasoning Leaderboard](https://rank.opencompass.org.cn/leaderboard-multimodal-reasoning/?m=REALTIME)!
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- **[2025.08.11]** 🥇 **Rank #1!** R-4B ranks first under 20B parameters on the [OpenCompass Multi-modal Academic Leaderboard](https://rank.opencompass.org.cn/leaderboard-multimodal/?m=REALTIME)!
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- **[2025.08.05]** 🎉 **R-4B is Released!** Our model is now publicly available. You can download it from [Hugging Face](https://huggingface.co/YannQi/R-4B).
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## 🔥 Quickstart
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Below, we provide simple examples to show how to use R-4B with 🤗 Transformers.
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### Using 🤗 Transformers to Chat
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> [!NOTE]
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> Users can dynamically control the model's response by selecting one of three modes (`auto-thinking`, `thinking`, or `non-thinking`) with `thinking_mode`. `thinking_mode=auto` for `auto-thinking` mode; `thinking_mode=long` for `thinking` mode; `thinking_mode=short` for `non-thinking` mode.
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> Default is `auto-thinking`.
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```python
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import requests
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from PIL import Image
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import torch
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from transformers import AutoModel, AutoProcessor
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model_path = "YannQi/R-4B"
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# Load model
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model = AutoModel.from_pretrained(
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model_path,
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torch_dtype=torch.float32,
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trust_remote_code=True,
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).to("cuda")
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# Load processor
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processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
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# Define conversation messages
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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]
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# Apply chat template
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text = processor.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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thinking_mode="auto"
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)
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# Load image
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image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(image_url, stream=True).raw)
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# Process inputs
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inputs = processor(
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images=image,
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text=text,
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return_tensors="pt"
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).to("cuda")
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# Generate output
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generated_ids = model.generate(**inputs, max_new_tokens=16384)
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output_ids = generated_ids[0][len(inputs.input_ids[0]):]
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# Decode output
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output_text = processor.decode(
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output_ids,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False
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)
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# Print result
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print("Auto-Thinking Output:", output_text)
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```
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</details>
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### Using vLLM for fast R-4B deployment and inference.
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- We recommend using vLLM for fast R-4B deployment and inference.
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#### Install
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The code of R-4B requires the newest vllm now. Please install from local source:
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```bash
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git clone https://github.com/vllm-project/vllm.git
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cd vllm
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VLLM_USE_PRECOMPILED=1 uv pip install --editable .
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```
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##### Online Serving
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> [!TIP]
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> The `thinking_mode` switch is also available in APIs created by [vLLM](https://github.com/vllm-project/vllm).
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> Default is `auto-thinking`.
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- Serve
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```bash
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vllm serve \
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yannqi/R-4B \
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--served-model-name r4b \
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--tensor-parallel-size 8 \
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--gpu-memory-utilization 0.8 \
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--host 0.0.0.0 \
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--port 8000 \
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--trust-remote-code
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```
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- Openai Chat Completion Client
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```python
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import base64
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from PIL import Image
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from openai import OpenAI
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# Set OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8000/v1"
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client = OpenAI(
|
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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# image url
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image_messages = [
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{
|
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"role": "user",
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"content": [
|
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{
|
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"type": "image_url",
|
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"image_url": {
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"url": "http://images.cocodataset.org/val2017/000000039769.jpg"
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},
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},
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{"type": "text", "text": "Describe this image."},
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],
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},
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]
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chat_response = client.chat.completions.create(
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model="r4b",
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messages=image_messages,
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max_tokens=16384,
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extra_body={
|
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"chat_template_kwargs": {"thinking_mode": "auto"},
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},
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)
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print("Chat response:", chat_response)
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```
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## 📈 Experimental Results
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<div align="center">
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<img src="asset/performance.png" width="100%" alt="R-4B Performance">
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</div>
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1. R-4B establishes itself with powerful, state-of-the-art perceptual abilities that are competitive with larger models.
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2. In evaluation sets that require complex logical reasoning and mathematical problem-solving, such as WeMath, MathVerse, and LogicVista, R-4B displays a strong performance curve. This highlights its advanced adaptive thinking capacity for logical deduction and solving complex quantitative problems.
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## ✒️ Citation
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```
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@misc{yang2025r4bincentivizinggeneralpurposeautothinking,
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title={R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning},
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author={Qi Yang and Bolin Ni and Shiming Xiang and Han Hu and Houwen Peng and Jie Jiang},
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year={2025},
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eprint={2508.21113},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2508.21113},
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}
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```
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## Acknowledgements
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R-4B is developed based on the codebases of the following projects: [LLaVA-Next](https://github.com/LLaVA-VL/LLaVA-NeXT), [SigLIP2](https://huggingface.co/google/siglip2-so400m-patch14-384), [Qwen3](https://github.com/QwenLM/Qwen3), [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL), [VLMEvalKit](https://github.com/open-compass/VLMEvalKit). We sincerely thank these projects for their outstanding work.
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asset/logo_R_4B.png
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After Width: | Height: | Size: 475 KiB |
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asset/performance.png
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After Width: | Height: | Size: 338 KiB |
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{% for message in messages %}{{'<|im_start|>' + message['role'] + '
|
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'}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>
|
||||
' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>
|
||||
' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>' + '
|
||||
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
|
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<think>' }}{% endif %}{%- if add_generation_prompt %}{%- if thinking_mode is defined and thinking_mode == 'short' %}{{- '
|
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|
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</think>
|
||||
|
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' }}{%- endif %}{%- if thinking_mode is defined and thinking_mode == 'long' %}{{- '
|
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' }}{%- endif %}{%- endif %}
|
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config.json
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config.json
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{
|
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"auto_map": {
|
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"AutoConfig": "configuration_r.RConfig",
|
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"AutoModel": "modeling_r.RForConditionalGeneration",
|
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"AutoModelForCausalLM": "modeling_r.RForConditionalGeneration"
|
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|
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"architectures": [
|
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|
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|
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|
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|
||||
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|
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|
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|
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|
||||
"_name_or_path": "Qwen/Qwen3-4B",
|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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"max_position_embeddings": 40960,
|
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"max_window_layers": 36,
|
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"model_type": "qwen3",
|
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
|
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
|
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"rope_scaling": null,
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"rope_theta": 1000000,
|
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"sliding_window": null,
|
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"tie_word_embeddings": true,
|
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"torch_dtype": "float32",
|
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"use_cache": true,
|
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"use_sliding_window": false,
|
||||
"vocab_size": 152000
|
||||
},
|
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"tie_word_embeddings": true,
|
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"torch_dtype": "float32",
|
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"transformers_version": "4.52.0",
|
||||
"use_image_newline_parameter": true,
|
||||
"video_token_index": 151670,
|
||||
"vision_aspect_ratio": "anyres",
|
||||
"vision_config": {
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_r.RConfig"
|
||||
},
|
||||
"attention_dropout": 0.0,
|
||||
"hidden_act": "gelu_pytorch_tanh",
|
||||
"hidden_size": 1152,
|
||||
"image_size": 384,
|
||||
"intermediate_size": 4304,
|
||||
"layer_norm_eps": 1e-06,
|
||||
"model_type": "siglip_vision_model",
|
||||
"num_attention_heads": 16,
|
||||
"num_channels": 3,
|
||||
"num_hidden_layers": 26,
|
||||
"patch_size": 14,
|
||||
"torch_dtype": "float32",
|
||||
"vision_use_head": false
|
||||
},
|
||||
"vision_feature_layer": -1,
|
||||
"vision_feature_select_strategy": "full"
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "visual-question-answering", "allow_remote": true}
|
||||
101
configuration_r.py
Normal file
101
configuration_r.py
Normal file
@@ -0,0 +1,101 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 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 transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import (
|
||||
logging,
|
||||
)
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class RConfig(PretrainedConfig):
|
||||
model_type = "R"
|
||||
attribute_map = {
|
||||
"image_token_id": "image_token_index",
|
||||
}
|
||||
|
||||
|
||||
# sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vision_config=None,
|
||||
text_config=None,
|
||||
image_token_index=151646,
|
||||
projector_hidden_act="gelu",
|
||||
vision_feature_select_strategy="full",
|
||||
vision_feature_layer=-1,
|
||||
vision_aspect_ratio= "anyres",
|
||||
image_grid_pinpoints=None,
|
||||
tie_word_embeddings=False,
|
||||
multimodal_projector_bias=True,
|
||||
max_position_embeddings=32768,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
from transformers.models.auto import CONFIG_MAPPING, AutoConfig # for vllm
|
||||
self.image_token_index = image_token_index
|
||||
self.projector_hidden_act = projector_hidden_act
|
||||
self.multimodal_projector_bias = multimodal_projector_bias
|
||||
|
||||
if vision_feature_select_strategy not in ["default", "full"]:
|
||||
raise ValueError(
|
||||
"vision_feature_select_strategy should be one of 'default', 'full'."
|
||||
f"Got: {vision_feature_select_strategy}"
|
||||
)
|
||||
|
||||
self.vision_feature_select_strategy = vision_feature_select_strategy
|
||||
self.vision_feature_layer = vision_feature_layer
|
||||
self.vision_aspect_ratio = vision_aspect_ratio
|
||||
|
||||
image_grid_pinpoints = (
|
||||
image_grid_pinpoints
|
||||
if image_grid_pinpoints is not None
|
||||
else [[384, 768], [768, 384], [768, 768], [1152, 384], [384, 1152]]
|
||||
)
|
||||
self.image_grid_pinpoints = image_grid_pinpoints
|
||||
|
||||
if isinstance(vision_config, dict):
|
||||
vision_config["model_type"] = (
|
||||
vision_config["model_type"] if "model_type" in vision_config else "siglip_vision_model"
|
||||
)
|
||||
vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
|
||||
elif vision_config is None:
|
||||
vision_config = CONFIG_MAPPING["siglip_vision_model"](
|
||||
hidden_size=1152,
|
||||
intermediate_size=4304,
|
||||
patch_size=14,
|
||||
image_size=384,
|
||||
num_hidden_layers=26,
|
||||
num_attention_heads=14,
|
||||
vision_use_head=False,
|
||||
)
|
||||
|
||||
self.vision_config = vision_config
|
||||
|
||||
if isinstance(text_config, dict):
|
||||
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "qwen2"
|
||||
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
|
||||
elif text_config is None:
|
||||
text_config = CONFIG_MAPPING["qwen2"]()
|
||||
|
||||
self.text_config = text_config
|
||||
|
||||
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
|
||||
|
||||
|
||||
__all__ = ["RConfig"]
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"transformers_version": "4.54.1"
|
||||
}
|
||||
499
image_processing_r.py
Normal file
499
image_processing_r.py
Normal file
@@ -0,0 +1,499 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The 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 collections.abc import Iterable
|
||||
from typing import Optional, Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers.image_processing_utils import (
|
||||
BaseImageProcessor,
|
||||
BatchFeature,
|
||||
get_patch_output_size,
|
||||
get_size_dict,
|
||||
select_best_resolution,
|
||||
)
|
||||
from transformers.image_transforms import (
|
||||
PaddingMode,
|
||||
convert_to_rgb,
|
||||
pad,
|
||||
resize,
|
||||
to_channel_dimension_format,
|
||||
)
|
||||
from transformers.image_utils import (
|
||||
OPENAI_CLIP_MEAN,
|
||||
OPENAI_CLIP_STD,
|
||||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
get_image_size,
|
||||
infer_channel_dimension_format,
|
||||
is_scaled_image,
|
||||
make_flat_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
validate_preprocess_arguments,
|
||||
)
|
||||
from transformers.utils import TensorType, is_vision_available, logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
|
||||
# Copied from transformers.models.llava_next.image_processing_llava_next.divide_to_patches
|
||||
def divide_to_patches(image: np.array, patch_size: int, input_data_format) -> list[np.array]:
|
||||
"""
|
||||
Divides an image into patches of a specified size.
|
||||
|
||||
Args:
|
||||
image (`np.array`):
|
||||
The input image.
|
||||
patch_size (`int`):
|
||||
The size of each patch.
|
||||
input_data_format (`ChannelDimension` or `str`):
|
||||
The channel dimension format of the input image.
|
||||
|
||||
Returns:
|
||||
list: A list of np.array representing the patches.
|
||||
"""
|
||||
patches = []
|
||||
height, width = get_image_size(image, channel_dim=input_data_format)
|
||||
for i in range(0, height, patch_size):
|
||||
for j in range(0, width, patch_size):
|
||||
if input_data_format == ChannelDimension.LAST:
|
||||
patch = image[i : i + patch_size, j : j + patch_size]
|
||||
else:
|
||||
patch = image[:, i : i + patch_size, j : j + patch_size]
|
||||
patches.append(patch)
|
||||
|
||||
return patches
|
||||
|
||||
|
||||
# Copied from transformers.models.llava_next.image_processing_llava_next.expand_to_square
|
||||
def expand_to_square(image: np.array, background_color, input_data_format) -> np.array:
|
||||
"""
|
||||
Expands an image to a square by adding a background color.
|
||||
"""
|
||||
|
||||
height, width = get_image_size(image, channel_dim=input_data_format)
|
||||
if width == height:
|
||||
return image
|
||||
elif width > height:
|
||||
result = np.ones((width, width, image.shape[2]), dtype=image.dtype) * background_color
|
||||
result[(width - height) // 2 : (width - height) // 2 + height, :] = image
|
||||
return result
|
||||
else:
|
||||
result = np.ones((height, height, image.shape[2]), dtype=image.dtype) * background_color
|
||||
result[:, (height - width) // 2 : (height - width) // 2 + width] = image
|
||||
return result
|
||||
|
||||
|
||||
class RImageProcessor(BaseImageProcessor):
|
||||
model_input_names = ["pixel_values_videos"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
do_resize: bool = True,
|
||||
size: Optional[dict[str, int]] = None,
|
||||
image_grid_pinpoints: Optional[list] = None,
|
||||
resample: PILImageResampling = PILImageResampling.BICUBIC,
|
||||
do_rescale: bool = True,
|
||||
rescale_factor: Union[int, float] = 1 / 255,
|
||||
do_normalize: bool = True,
|
||||
image_mean: Optional[Union[float, list[float]]] = None,
|
||||
image_std: Optional[Union[float, list[float]]] = None,
|
||||
do_pad: Optional[bool] = True,
|
||||
do_convert_rgb: bool = True,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__(**kwargs)
|
||||
size = size if size is not None else {"height": 384, "width": 384}
|
||||
size = get_size_dict(size, default_to_square=False)
|
||||
image_grid_pinpoints = (
|
||||
image_grid_pinpoints
|
||||
if image_grid_pinpoints is not None
|
||||
else [[384, 768], [768, 384], [768, 768], [1152, 384], [384, 1152]]
|
||||
)
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.image_grid_pinpoints = image_grid_pinpoints
|
||||
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.do_pad = do_pad
|
||||
self.do_convert_rgb = do_convert_rgb
|
||||
|
||||
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor.pad
|
||||
def pad(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
padding: Union[int, tuple[int, int], Iterable[tuple[int, int]]],
|
||||
mode: PaddingMode = PaddingMode.CONSTANT,
|
||||
constant_values: Union[float, Iterable[float]] = 0.0,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
) -> np.ndarray:
|
||||
|
||||
# call the general `pad` if padding on `height/width`, otherwise it's the `num_patched` dim
|
||||
if isinstance(padding, int) or len(padding) != 4:
|
||||
return pad(image, padding, mode, constant_values, data_format, input_data_format)
|
||||
|
||||
if input_data_format is None:
|
||||
input_data_format = infer_channel_dimension_format(image)
|
||||
if mode == PaddingMode.CONSTANT:
|
||||
image = np.pad(image, padding, mode="constant", constant_values=constant_values)
|
||||
elif mode == PaddingMode.REFLECT:
|
||||
image = np.pad(image, padding, mode="reflect")
|
||||
elif mode == PaddingMode.REPLICATE:
|
||||
image = np.pad(image, padding, mode="edge")
|
||||
elif mode == PaddingMode.SYMMETRIC:
|
||||
image = np.pad(image, padding, mode="symmetric")
|
||||
else:
|
||||
raise ValueError(f"Invalid padding mode: {mode}")
|
||||
image = (
|
||||
to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image
|
||||
)
|
||||
return image
|
||||
|
||||
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._resize_for_patching
|
||||
def _resize_for_patching(
|
||||
self, image: np.array, target_resolution: tuple, resample, input_data_format: ChannelDimension
|
||||
) -> np.array:
|
||||
new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
|
||||
|
||||
# Resize the image
|
||||
resized_image = resize(image, (new_height, new_width), resample=resample, input_data_format=input_data_format)
|
||||
|
||||
return resized_image
|
||||
|
||||
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._get_padding_size
|
||||
def _get_padding_size(self, original_resolution: tuple, target_resolution: tuple):
|
||||
original_height, original_width = original_resolution
|
||||
target_height, target_width = target_resolution
|
||||
paste_x, r_x = divmod(target_width - original_width, 2)
|
||||
paste_y, r_y = divmod(target_height - original_height, 2)
|
||||
return (paste_y, paste_y + r_y), (paste_x, paste_x + r_x)
|
||||
|
||||
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._pad_for_patching
|
||||
def _pad_for_patching(
|
||||
self, image: np.array, target_resolution: tuple, input_data_format: ChannelDimension
|
||||
) -> np.array:
|
||||
"""
|
||||
Pad an image to a target resolution while maintaining aspect ratio.
|
||||
"""
|
||||
new_resolution = get_patch_output_size(image, target_resolution, input_data_format)
|
||||
padding = self._get_padding_size(new_resolution, target_resolution)
|
||||
|
||||
padded_image = self.pad(image, padding=padding)
|
||||
|
||||
return padded_image
|
||||
|
||||
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor.get_image_patches
|
||||
def get_image_patches(
|
||||
self,
|
||||
image: np.array,
|
||||
grid_pinpoints,
|
||||
size: tuple,
|
||||
patch_size: int,
|
||||
resample: PILImageResampling,
|
||||
data_format: ChannelDimension,
|
||||
input_data_format: ChannelDimension,
|
||||
) -> list[np.array]:
|
||||
if not isinstance(grid_pinpoints, list):
|
||||
raise TypeError("grid_pinpoints must be a list of possible resolutions.")
|
||||
|
||||
possible_resolutions = grid_pinpoints
|
||||
|
||||
image_size = get_image_size(image, channel_dim=input_data_format)
|
||||
best_resolution = select_best_resolution(image_size, possible_resolutions)
|
||||
resized_image = self._resize_for_patching(
|
||||
image, best_resolution, resample=resample, input_data_format=input_data_format
|
||||
)
|
||||
padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=input_data_format)
|
||||
|
||||
patches = divide_to_patches(padded_image, patch_size=patch_size, input_data_format=input_data_format)
|
||||
|
||||
# make sure that all patches are in the input data format
|
||||
patches = [
|
||||
to_channel_dimension_format(patch, channel_dim=data_format, input_channel_dim=input_data_format)
|
||||
for patch in patches
|
||||
]
|
||||
|
||||
resized_original_image = resize(
|
||||
image,
|
||||
size=size,
|
||||
resample=resample,
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
|
||||
image_patches = [resized_original_image] + patches
|
||||
|
||||
return image_patches
|
||||
|
||||
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._pad_for_batching
|
||||
def _pad_for_batching(
|
||||
self,
|
||||
pixel_values: list[np.ndarray],
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
):
|
||||
max_patch = max(len(x) for x in pixel_values)
|
||||
pixel_values = [
|
||||
self.pad(
|
||||
image,
|
||||
padding=((0, max_patch - image.shape[0]), (0, 0), (0, 0), (0, 0)),
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
for image in pixel_values
|
||||
]
|
||||
|
||||
return pixel_values
|
||||
|
||||
# Copied from transformers.models.llava.image_processing_llava.LlavaImageProcessor.pad_to_square
|
||||
def pad_to_square(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
background_color: Union[int, tuple[int, int, int]] = 0,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
) -> np.array:
|
||||
height, width = get_image_size(image, input_data_format)
|
||||
num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1]
|
||||
|
||||
if height == width:
|
||||
image = (
|
||||
to_channel_dimension_format(image, data_format, input_data_format)
|
||||
if data_format is not None
|
||||
else image
|
||||
)
|
||||
return image
|
||||
|
||||
max_dim = max(height, width)
|
||||
|
||||
# Ensure background_color is the correct shape
|
||||
if isinstance(background_color, int):
|
||||
background_color = [background_color]
|
||||
elif len(background_color) != num_channels:
|
||||
raise ValueError(
|
||||
f"background_color must have no more than {num_channels} elements to match the number of channels"
|
||||
)
|
||||
|
||||
if input_data_format == ChannelDimension.FIRST:
|
||||
result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
|
||||
for i, color in enumerate(background_color):
|
||||
result[i, :, :] = color
|
||||
if width > height:
|
||||
start = (max_dim - height) // 2
|
||||
result[:, start : start + height, :] = image
|
||||
else:
|
||||
start = (max_dim - width) // 2
|
||||
result[:, :, start : start + width] = image
|
||||
else:
|
||||
result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype)
|
||||
for i, color in enumerate(background_color):
|
||||
result[:, :, i] = color
|
||||
if width > height:
|
||||
start = (max_dim - height) // 2
|
||||
result[start : start + height, :, :] = image
|
||||
else:
|
||||
start = (max_dim - width) // 2
|
||||
result[:, start : start + width, :] = image
|
||||
|
||||
image = (
|
||||
to_channel_dimension_format(result, data_format, input_data_format) if data_format is not None else result
|
||||
)
|
||||
return image
|
||||
|
||||
def _preprocess(
|
||||
self,
|
||||
images: ImageInput,
|
||||
do_resize: Optional[bool] = None,
|
||||
size: Optional[dict[str, int]] = None,
|
||||
resample: PILImageResampling = None,
|
||||
do_rescale: Optional[bool] = None,
|
||||
rescale_factor: Optional[float] = None,
|
||||
do_normalize: Optional[bool] = None,
|
||||
image_mean: Optional[Union[float, list[float]]] = None,
|
||||
image_std: Optional[Union[float, list[float]]] = None,
|
||||
do_convert_rgb: Optional[bool] = None,
|
||||
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
) -> Image.Image:
|
||||
if do_resize:
|
||||
images = [
|
||||
resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
if do_rescale:
|
||||
images = [
|
||||
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
if do_normalize:
|
||||
images = [
|
||||
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
images = [
|
||||
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
|
||||
]
|
||||
|
||||
return images
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
images: ImageInput,
|
||||
do_resize: Optional[bool] = None,
|
||||
size: Optional[dict[str, int]] = None,
|
||||
image_grid_pinpoints: Optional[list] = None,
|
||||
resample: PILImageResampling = None,
|
||||
do_rescale: Optional[bool] = None,
|
||||
rescale_factor: Optional[float] = None,
|
||||
do_normalize: Optional[bool] = None,
|
||||
image_mean: Optional[Union[float, list[float]]] = None,
|
||||
image_std: Optional[Union[float, list[float]]] = None,
|
||||
do_pad: Optional[bool] = None,
|
||||
do_convert_rgb: Optional[bool] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
):
|
||||
do_resize = do_resize if do_resize is not None else self.do_resize
|
||||
size = size if size is not None else self.size
|
||||
size = get_size_dict(size, default_to_square=False)
|
||||
image_grid_pinpoints = image_grid_pinpoints if image_grid_pinpoints is not None else self.image_grid_pinpoints
|
||||
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
|
||||
do_pad = do_pad if do_pad is not None else self.do_pad
|
||||
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
|
||||
|
||||
if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)):
|
||||
# if the first element is a list, we assume that all elements are lists
|
||||
batch_num_images = [len(x) for x in images]
|
||||
elif isinstance(images, (tuple, list)):
|
||||
# treat this as a single-image case for backward compatibility
|
||||
batch_num_images = [1] * len(images)
|
||||
else:
|
||||
batch_num_images = [1]
|
||||
# only single image patching is supported
|
||||
need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
|
||||
|
||||
images = make_flat_list_of_images(images)
|
||||
|
||||
if not valid_images(images):
|
||||
raise ValueError(
|
||||
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
|
||||
"torch.Tensor, tf.Tensor or jax.ndarray."
|
||||
)
|
||||
|
||||
validate_preprocess_arguments(
|
||||
do_rescale=do_rescale,
|
||||
rescale_factor=rescale_factor,
|
||||
do_normalize=do_normalize,
|
||||
image_mean=image_mean,
|
||||
image_std=image_std,
|
||||
do_resize=do_resize,
|
||||
size=size,
|
||||
resample=resample,
|
||||
)
|
||||
|
||||
if do_convert_rgb:
|
||||
images = [convert_to_rgb(image) for image in images]
|
||||
|
||||
# All transformations expect numpy arrays.
|
||||
images = [to_numpy_array(image) for image in images]
|
||||
|
||||
if do_rescale and is_scaled_image(images[0]):
|
||||
logger.warning_once(
|
||||
"It looks like you are trying to rescale already rescaled images. If the input"
|
||||
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
|
||||
)
|
||||
|
||||
if input_data_format is None:
|
||||
# We assume that all images have the same channel dimension format.
|
||||
input_data_format = infer_channel_dimension_format(images[0])
|
||||
|
||||
size_tuple = (
|
||||
(size["height"], size["width"])
|
||||
if "height" in size and "width" in size
|
||||
else (size["shortest_edge"], size["shortest_edge"])
|
||||
)
|
||||
|
||||
new_images = []
|
||||
image_sizes = [get_image_size(image, channel_dim=input_data_format) for image in images]
|
||||
for i, image in enumerate(images):
|
||||
if need_patching[i]:
|
||||
# convert image into a list of patches
|
||||
# we intentionally use the same data format as the input data format
|
||||
image_patches = self.get_image_patches(
|
||||
image,
|
||||
image_grid_pinpoints,
|
||||
size=size_tuple,
|
||||
patch_size=size_tuple[0],
|
||||
resample=resample,
|
||||
data_format=input_data_format,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
else:
|
||||
padded_image = self.pad_to_square(
|
||||
image=image,
|
||||
background_color=tuple(int(x * 255) for x in self.image_mean),
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
image_patches = [padded_image]
|
||||
|
||||
# preprocess patches
|
||||
pixel_values = self._preprocess(
|
||||
image_patches,
|
||||
do_resize=do_resize,
|
||||
size=size_tuple,
|
||||
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,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
pixel_values = np.array(pixel_values)
|
||||
new_images.append(pixel_values)
|
||||
|
||||
if do_pad:
|
||||
processed_images = self._pad_for_batching(new_images)
|
||||
|
||||
return BatchFeature(
|
||||
data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images},
|
||||
tensor_type=return_tensors,
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["RImageProcessor"]
|
||||
324
image_processing_r_fast.py
Normal file
324
image_processing_r_fast.py
Normal file
@@ -0,0 +1,324 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 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 torch
|
||||
|
||||
from transformers.image_processing_utils import BatchFeature, get_patch_output_size, select_best_resolution
|
||||
from transformers.image_processing_utils_fast import (
|
||||
BaseImageProcessorFast,
|
||||
DefaultFastImageProcessorKwargs,
|
||||
divide_to_patches,
|
||||
group_images_by_shape,
|
||||
reorder_images,
|
||||
)
|
||||
from transformers.image_utils import (
|
||||
OPENAI_CLIP_MEAN,
|
||||
OPENAI_CLIP_STD,
|
||||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
SizeDict,
|
||||
get_image_size,
|
||||
make_flat_list_of_images,
|
||||
)
|
||||
from transformers.processing_utils import Unpack
|
||||
from transformers.utils import TensorType, auto_docstring, is_torchvision_v2_available
|
||||
|
||||
|
||||
if is_torchvision_v2_available():
|
||||
from torchvision.transforms.v2 import functional as F
|
||||
else:
|
||||
from torchvision.transforms import functional as F
|
||||
|
||||
|
||||
class RFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
|
||||
image_grid_pinpoints: Optional[list[list[int]]]
|
||||
do_pad: Optional[bool]
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class RImageProcessorFast(BaseImageProcessorFast):
|
||||
resample = PILImageResampling.BICUBIC
|
||||
image_mean = OPENAI_CLIP_MEAN
|
||||
image_std = OPENAI_CLIP_STD
|
||||
size = {"height": 384, "width": 384}
|
||||
default_to_square = False
|
||||
crop_size = None
|
||||
do_resize = True
|
||||
do_center_crop = None
|
||||
do_rescale = True
|
||||
do_normalize = True
|
||||
do_convert_rgb = True
|
||||
do_pad = True
|
||||
image_grid_pinpoints = [[384,768],[768,384],[768,768],[1152,384],[384,1152]],
|
||||
valid_kwargs = RFastImageProcessorKwargs
|
||||
model_input_names = ["pixel_values_videos"]
|
||||
|
||||
def __init__(self, **kwargs: Unpack[RFastImageProcessorKwargs]):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
@auto_docstring
|
||||
def preprocess(
|
||||
self, images: ImageInput, **kwargs: Unpack[RFastImageProcessorKwargs]
|
||||
) -> BatchFeature:
|
||||
if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)):
|
||||
# if the first element is a list, we assume that all elements are lists
|
||||
batch_num_images = [len(x) for x in images]
|
||||
elif isinstance(images, (tuple, list)):
|
||||
# treat this as a single-image case for backward compatibility
|
||||
batch_num_images = [1] * len(images)
|
||||
else:
|
||||
batch_num_images = [1]
|
||||
kwargs["batch_num_images"] = batch_num_images
|
||||
return super().preprocess(images, **kwargs)
|
||||
|
||||
def _prepare_images_structure(
|
||||
self,
|
||||
images: ImageInput,
|
||||
) -> ImageInput:
|
||||
return make_flat_list_of_images(images)
|
||||
|
||||
def _resize_for_patching(
|
||||
self,
|
||||
image: "torch.Tensor",
|
||||
target_resolution: tuple,
|
||||
interpolation: "F.InterpolationMode",
|
||||
input_data_format: ChannelDimension,
|
||||
) -> "torch.Tensor":
|
||||
|
||||
new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
|
||||
|
||||
# Resize the image
|
||||
resized_image = F.resize(image, (new_height, new_width), interpolation=interpolation)
|
||||
|
||||
return resized_image
|
||||
|
||||
def _get_padding_size(self, original_resolution: tuple, target_resolution: tuple):
|
||||
original_height, original_width = original_resolution
|
||||
target_height, target_width = target_resolution
|
||||
paste_x, r_x = divmod(target_width - original_width, 2)
|
||||
paste_y, r_y = divmod(target_height - original_height, 2)
|
||||
return [paste_x, paste_y, paste_x + r_x, paste_y + r_y]
|
||||
|
||||
def _pad_for_patching(
|
||||
self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension
|
||||
) -> "torch.Tensor":
|
||||
"""
|
||||
Pad an image to a target resolution while maintaining aspect ratio.
|
||||
"""
|
||||
new_resolution = get_patch_output_size(image, target_resolution, input_data_format)
|
||||
padding = self._get_padding_size(new_resolution, target_resolution)
|
||||
|
||||
padded_image = F.pad(image, padding=padding)
|
||||
|
||||
return padded_image
|
||||
|
||||
def _get_image_patches(
|
||||
self,
|
||||
image: "torch.Tensor",
|
||||
grid_pinpoints,
|
||||
size: tuple,
|
||||
patch_size: int,
|
||||
interpolation: "F.InterpolationMode",
|
||||
) -> list["torch.Tensor"]:
|
||||
"""
|
||||
Process an image with variable resolutions by dividing it into patches.
|
||||
|
||||
Args:
|
||||
image ("torch.Tensor"):
|
||||
The input image to be processed.
|
||||
grid_pinpoints (List):
|
||||
A string representation of a list of possible resolutions.
|
||||
size (`tuple`):
|
||||
Size to resize the original image to.
|
||||
patch_size (`int`):
|
||||
Size of the patches to divide the image into.
|
||||
interpolation (`"InterpolationMode"`):
|
||||
Resampling filter to use if resizing the image.
|
||||
|
||||
Returns:
|
||||
list["torch.Tensor"]: A list of NumPy arrays containing the processed image patches.
|
||||
"""
|
||||
if not isinstance(grid_pinpoints, list):
|
||||
raise TypeError("grid_pinpoints must be a list of possible resolutions.")
|
||||
|
||||
possible_resolutions = grid_pinpoints
|
||||
|
||||
image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
|
||||
best_resolution = select_best_resolution(image_size, possible_resolutions)
|
||||
resized_image = self._resize_for_patching(
|
||||
image, best_resolution, interpolation=interpolation, input_data_format=ChannelDimension.FIRST
|
||||
)
|
||||
padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=ChannelDimension.FIRST)
|
||||
patches = divide_to_patches(padded_image, patch_size=patch_size)
|
||||
resized_original_image = F.resize(image, size=size, interpolation=interpolation)
|
||||
|
||||
image_patches = [resized_original_image] + patches
|
||||
|
||||
return image_patches
|
||||
|
||||
def _pad_for_batching(
|
||||
self,
|
||||
pixel_values: list["torch.Tensor"],
|
||||
) -> list["torch.Tensor"]:
|
||||
"""
|
||||
Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
|
||||
|
||||
Args:
|
||||
pixel_values (`list[torch.Tensor]`):
|
||||
An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
|
||||
|
||||
Returns:
|
||||
list[`torch.Tensor`]: The padded images.
|
||||
"""
|
||||
max_patch = max(len(x) for x in pixel_values)
|
||||
pixel_values = [
|
||||
torch.nn.functional.pad(image, pad=[0, 0, 0, 0, 0, 0, 0, max_patch - image.shape[0]])
|
||||
for image in pixel_values
|
||||
]
|
||||
|
||||
return pixel_values
|
||||
|
||||
def _preprocess(
|
||||
self,
|
||||
images: list["torch.Tensor"],
|
||||
do_resize: bool,
|
||||
size: SizeDict,
|
||||
image_grid_pinpoints: list[list[int]],
|
||||
interpolation: Optional["F.InterpolationMode"],
|
||||
do_center_crop: bool,
|
||||
crop_size: SizeDict,
|
||||
do_rescale: bool,
|
||||
rescale_factor: float,
|
||||
do_normalize: bool,
|
||||
image_mean: Optional[Union[float, list[float]]],
|
||||
image_std: Optional[Union[float, list[float]]],
|
||||
do_pad: bool,
|
||||
batch_num_images: list[int],
|
||||
return_tensors: Optional[Union[str, TensorType]],
|
||||
) -> BatchFeature:
|
||||
processed_images = []
|
||||
image_sizes = []
|
||||
|
||||
# only single image patching is supported
|
||||
need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
|
||||
|
||||
# Determine the size tuple
|
||||
if size and size.height and size.width:
|
||||
size_tuple = (size.height, size.width)
|
||||
else:
|
||||
size_tuple = (size.shortest_edge, size.shortest_edge)
|
||||
|
||||
# Determine the patch size
|
||||
if crop_size and crop_size.height:
|
||||
patch_size = crop_size.height
|
||||
elif size and size.height:
|
||||
patch_size = size.height
|
||||
else:
|
||||
patch_size = size.shortest_edge
|
||||
|
||||
for i, image in enumerate(images):
|
||||
if need_patching[i]:
|
||||
image_patches = self._get_image_patches(
|
||||
image,
|
||||
image_grid_pinpoints,
|
||||
size=size_tuple,
|
||||
patch_size=patch_size,
|
||||
interpolation=interpolation,
|
||||
)
|
||||
else:
|
||||
padded_image = self.pad_to_square(
|
||||
images=image, background_color=tuple(int(x * 255) for x in self.image_mean)
|
||||
)
|
||||
image_patches = [padded_image]
|
||||
|
||||
# Group images by size for batched processing
|
||||
processed_image_patches_grouped = {}
|
||||
grouped_image_patches, grouped_image_patches_index = group_images_by_shape(image_patches)
|
||||
for shape, stacked_image_patches in grouped_image_patches.items():
|
||||
if do_resize:
|
||||
stacked_image_patches = self.resize(
|
||||
image=stacked_image_patches,
|
||||
size=size,
|
||||
interpolation=interpolation,
|
||||
)
|
||||
if do_center_crop:
|
||||
stacked_image_patches = self.center_crop(stacked_image_patches, crop_size)
|
||||
# Fused rescale and normalize
|
||||
stacked_image_patches = self.rescale_and_normalize(
|
||||
stacked_image_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std
|
||||
)
|
||||
processed_image_patches_grouped[shape] = stacked_image_patches
|
||||
processed_image_patches = reorder_images(processed_image_patches_grouped, grouped_image_patches_index)
|
||||
processed_image_patches = (
|
||||
torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches
|
||||
)
|
||||
processed_images.append(processed_image_patches)
|
||||
image_sizes.append(get_image_size(image, ChannelDimension.FIRST))
|
||||
|
||||
if do_pad:
|
||||
processed_images = self._pad_for_batching(processed_images)
|
||||
processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
|
||||
return BatchFeature(
|
||||
data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images},
|
||||
tensor_type=return_tensors,
|
||||
)
|
||||
|
||||
# Copied from transformers.models.llava.image_processing_llava_fast.LlavaImageProcessorFast.pad_to_square
|
||||
def pad_to_square(
|
||||
self,
|
||||
images: "torch.Tensor",
|
||||
background_color: Union[int, tuple[int, int, int]] = 0,
|
||||
) -> "torch.Tensor":
|
||||
"""
|
||||
Pads an image to a square based on the longest edge.
|
||||
|
||||
Args:
|
||||
images (`np.ndarray`):
|
||||
The images to pad.
|
||||
background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
|
||||
The color to use for the padding. Can be an integer for single channel or a
|
||||
tuple of integers representing for multi-channel images. If passed as integer
|
||||
in mutli-channel mode, it will default to `0` in subsequent channels.
|
||||
Returns:
|
||||
`torch.Tensor`: The padded images.
|
||||
"""
|
||||
height, width = get_image_size(images, ChannelDimension.FIRST)
|
||||
|
||||
if height == width:
|
||||
return images
|
||||
|
||||
num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0]
|
||||
if isinstance(background_color, int):
|
||||
background_color = [background_color] + [0] * (num_channels - 1)
|
||||
elif len(background_color) != num_channels:
|
||||
raise ValueError(
|
||||
f"background_color must have no more than {num_channels} elements to match the number of channels"
|
||||
)
|
||||
|
||||
max_dim = max(height, width)
|
||||
paste_x_left = (max_dim - width) // 2
|
||||
paste_y_left = (max_dim - height) // 2
|
||||
paste_x_right = max_dim - width - paste_x_left
|
||||
paste_y_right = max_dim - height - paste_y_left
|
||||
padded_images = F.pad(
|
||||
images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color
|
||||
)
|
||||
|
||||
return padded_images
|
||||
|
||||
|
||||
__all__ = ["RImageProcessorFast"]
|
||||
BIN
merges.txt
(Stored with Git LFS)
Normal file
BIN
merges.txt
(Stored with Git LFS)
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Binary file not shown.
3
model-00001-of-00003.safetensors
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3
model-00001-of-00003.safetensors
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"model.vision_tower.vision_model.encoder.layers.8.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.8.self_attn.out_proj.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.8.self_attn.out_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.8.self_attn.q_proj.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.8.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.8.self_attn.v_proj.bias": "model-00002-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.8.self_attn.v_proj.weight": "model-00002-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.layer_norm1.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.layer_norm1.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.layer_norm2.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.layer_norm2.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.mlp.fc1.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.mlp.fc1.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.mlp.fc2.bias": "model-00002-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.mlp.fc2.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.weight": "model-00002-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.post_layernorm.bias": "model-00001-of-00003.safetensors",
|
||||
"model.vision_tower.vision_model.post_layernorm.weight": "model-00001-of-00003.safetensors"
|
||||
}
|
||||
}
|
||||
688
modeling_r.py
Normal file
688
modeling_r.py
Normal file
@@ -0,0 +1,688 @@
|
||||
# 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.
|
||||
|
||||
import math
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from transformers.activations import GELUActivation
|
||||
|
||||
from transformers.generation import GenerationMixin
|
||||
from transformers.image_processing_utils import select_best_resolution
|
||||
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast, ModelOutput
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.models.auto import AutoModel
|
||||
from transformers.processing_utils import Unpack
|
||||
from transformers.utils import (
|
||||
can_return_tuple,
|
||||
is_torchdynamo_compiling,
|
||||
logging,
|
||||
)
|
||||
from .configuration_r import RConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class RModelOutputWithPast(BaseModelOutputWithPast):
|
||||
|
||||
|
||||
image_hidden_states: Optional[torch.FloatTensor] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class RCausalLMOutputWithPast(ModelOutput):
|
||||
|
||||
loss: Optional[torch.FloatTensor] = None
|
||||
logits: Optional[torch.FloatTensor] = None
|
||||
past_key_values: Optional[list[torch.FloatTensor]] = None
|
||||
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
||||
attentions: Optional[tuple[torch.FloatTensor]] = None
|
||||
image_hidden_states: Optional[torch.FloatTensor] = None
|
||||
|
||||
|
||||
class RPooler(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
|
||||
mode = config.spatial_pool_mode
|
||||
stride = config.spatial_pool_stride
|
||||
out_channels = getattr(config, "spatial_pool_out_channels", config.vision_config.hidden_size)
|
||||
self.image_size = (config.vision_config.image_size // config.vision_config.patch_size) ** 2
|
||||
|
||||
if mode == "average":
|
||||
self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
|
||||
elif mode == "max":
|
||||
self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
|
||||
elif mode == "conv":
|
||||
self.pool = nn.Conv2d(
|
||||
in_channels=config.vision_config.hidden_size,
|
||||
out_channels=out_channels,
|
||||
kernel_size=stride,
|
||||
stride=stride,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unknown pooling mode: {mode}. Has to be one of [`average`, `max`, `conv`]")
|
||||
|
||||
def forward(self, image_features):
|
||||
ori_width = int(math.sqrt(image_features.shape[1] * self.image_size // self.image_size))
|
||||
ori_height = int(ori_width * self.image_size // self.image_size)
|
||||
|
||||
batch_size, _, dim = image_features.shape
|
||||
image_features_spatial = image_features.view(batch_size, ori_height, ori_height, dim).permute(0, 3, 1, 2)
|
||||
image_features_spatial_pool = self.pool(image_features_spatial)
|
||||
|
||||
return image_features_spatial_pool.flatten(2).transpose(1, 2).contiguous()
|
||||
|
||||
|
||||
def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
|
||||
if not isinstance(grid_pinpoints, list):
|
||||
raise TypeError("grid_pinpoints should be a list of tuples or lists")
|
||||
|
||||
# ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
|
||||
if not isinstance(image_size, (list, tuple)):
|
||||
if not isinstance(image_size, (torch.Tensor, np.ndarray)):
|
||||
raise TypeError(
|
||||
f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor"
|
||||
)
|
||||
image_size = image_size.tolist()
|
||||
|
||||
height, width = select_best_resolution(image_size, grid_pinpoints)
|
||||
return height // patch_size, width // patch_size
|
||||
|
||||
|
||||
def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int):
|
||||
if not isinstance(grid_pinpoints, list):
|
||||
raise TypeError("grid_pinpoints should be a list of tuples or lists")
|
||||
|
||||
# ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
|
||||
if not isinstance(image_size, (list, tuple)):
|
||||
if not isinstance(image_size, (torch.Tensor, np.ndarray)):
|
||||
raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}")
|
||||
image_size = image_size.tolist()
|
||||
|
||||
best_resolution = select_best_resolution(image_size, grid_pinpoints)
|
||||
height, width = best_resolution
|
||||
num_patches = 0
|
||||
# consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1
|
||||
for i in range(0, height, patch_size):
|
||||
for j in range(0, width, patch_size):
|
||||
num_patches += 1
|
||||
# add the base patch
|
||||
num_patches += 1
|
||||
return num_patches
|
||||
|
||||
|
||||
def unpad_image(tensor, original_size):
|
||||
if not isinstance(original_size, (list, tuple)):
|
||||
if not isinstance(original_size, (torch.Tensor, np.ndarray)):
|
||||
raise TypeError(
|
||||
f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor"
|
||||
)
|
||||
original_size = original_size.tolist()
|
||||
original_height, original_width = original_size
|
||||
current_height, current_width = tensor.shape[1:]
|
||||
|
||||
original_aspect_ratio = original_width / original_height
|
||||
current_aspect_ratio = current_width / current_height
|
||||
|
||||
if original_aspect_ratio > current_aspect_ratio:
|
||||
scale_factor = current_width / original_width
|
||||
new_height = int(round(original_height * scale_factor, 7))
|
||||
padding = (current_height - new_height) // 2
|
||||
unpadded_tensor = tensor[:, padding : current_height - padding, :]
|
||||
else:
|
||||
scale_factor = current_height / original_height
|
||||
new_width = int(round(original_width * scale_factor, 7))
|
||||
padding = (current_width - new_width) // 2
|
||||
unpadded_tensor = tensor[:, :, padding : current_width - padding]
|
||||
|
||||
return unpadded_tensor
|
||||
|
||||
|
||||
class RPreTrainedModel(PreTrainedModel):
|
||||
config_class = RConfig
|
||||
base_model_prefix = ""
|
||||
supports_gradient_checkpointing = True
|
||||
# _no_split_modules = ["LlamaDecoderLayer"]
|
||||
_no_split_modules = ["SiglipEncoderLayer", "Qwen3DecoderLayer", ]
|
||||
_skip_keys_device_placement = "past_key_values"
|
||||
_supports_cache_class = True
|
||||
_supports_flash_attn_2 = True
|
||||
_supports_sdpa = True
|
||||
_supports_quantized_cache = True
|
||||
_supports_static_cache = True
|
||||
_supports_flex_attn = True
|
||||
_supports_attention_backend = True
|
||||
|
||||
def _init_weights(self, module):
|
||||
std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)
|
||||
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, RModel):
|
||||
embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
|
||||
module.image_newline.data.normal_(mean=0.0, std=embed_std)
|
||||
|
||||
|
||||
class RMultiModalProjector(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
print("Using MultiModalProjector_withLayerNorm")
|
||||
|
||||
self.pre_norm = torch.nn.LayerNorm(config.vision_config.hidden_size, eps=1e-06)
|
||||
self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
|
||||
self.act = GELUActivation()
|
||||
self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
|
||||
|
||||
|
||||
def forward(self, image_feature: torch.Tensor) -> torch.Tensor:
|
||||
image_feature = self.pre_norm(image_feature)
|
||||
hidden_states = self.linear_1(image_feature)
|
||||
hidden_states = self.act(hidden_states)
|
||||
hidden_states = self.linear_2(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
class RModel(RPreTrainedModel):
|
||||
_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.vision_tower = AutoModel.from_config(config.vision_config)
|
||||
self.multi_modal_projector = RMultiModalProjector(config)
|
||||
embed_std = 1 / math.sqrt(config.text_config.hidden_size)
|
||||
self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std)
|
||||
|
||||
self.vocab_size = config.text_config.vocab_size
|
||||
self.language_model = AutoModel.from_config(config.text_config)
|
||||
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.language_model.get_input_embeddings()
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.language_model.set_input_embeddings(value)
|
||||
|
||||
def pack_image_features(self, image_features, image_sizes, image_newline=None, vision_aspect_ratio="anyres"):
|
||||
new_image_features = []
|
||||
feature_lens = []
|
||||
for image_idx, image_feature in enumerate(image_features):
|
||||
if image_feature.shape[0] > 1:
|
||||
base_image_feature = image_feature[0]
|
||||
image_feature = image_feature[1:]
|
||||
height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
|
||||
if height * width != base_image_feature.shape[0]:
|
||||
raise ValueError("The number of patches is not consistent with the image size.")
|
||||
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
|
||||
image_sizes[image_idx],
|
||||
self.config.image_grid_pinpoints,
|
||||
self.config.vision_config.image_size,
|
||||
)
|
||||
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
|
||||
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
|
||||
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
|
||||
image_feature = unpad_image(image_feature, image_sizes[image_idx])
|
||||
try:
|
||||
max_num_patches = int(vision_aspect_ratio.strip("anyres_max_"))
|
||||
channels, curr_height, curr_width = image_feature.shape
|
||||
ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2))
|
||||
if ratio > 1.1:
|
||||
image_feature = image_feature[None]
|
||||
image_feature = nn.functional.interpolate(
|
||||
image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear"
|
||||
)[0]
|
||||
except:
|
||||
pass
|
||||
if image_newline is not None:
|
||||
image_feature = torch.cat(
|
||||
(
|
||||
image_feature,
|
||||
image_newline[:, None, None]
|
||||
.expand(*image_feature.shape[:-1], 1)
|
||||
.to(image_feature.device, image_feature.dtype),
|
||||
),
|
||||
dim=-1,
|
||||
)
|
||||
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
|
||||
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
|
||||
else:
|
||||
image_feature = image_feature[0]
|
||||
if image_newline is not None:
|
||||
image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
|
||||
image_feature = image_feature.flatten(0, 1)
|
||||
new_image_features.append(image_feature)
|
||||
feature_lens.append(image_feature.size(0))
|
||||
feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device)
|
||||
return new_image_features, feature_lens
|
||||
|
||||
def get_image_features(
|
||||
self,
|
||||
pixel_values: torch.FloatTensor,
|
||||
image_sizes: torch.Tensor,
|
||||
vision_feature_layer: Optional[Union[int, list[int]]] = None,
|
||||
vision_feature_select_strategy: Optional[str] = None,
|
||||
vision_aspect_ratio: Optional[str] = None,
|
||||
batch_num_images: Optional[torch.LongTensor] = None,
|
||||
):
|
||||
vision_feature_layer = (
|
||||
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
|
||||
)
|
||||
vision_feature_select_strategy = (
|
||||
vision_feature_select_strategy
|
||||
if vision_feature_select_strategy is not None
|
||||
else self.config.vision_feature_select_strategy
|
||||
)
|
||||
vision_aspect_ratio = (
|
||||
vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
|
||||
)
|
||||
|
||||
if batch_num_images is None:
|
||||
# treat this as a single-image case for backward compatibility
|
||||
need_patching = [True] * len(image_sizes)
|
||||
else:
|
||||
need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
|
||||
image_num_patches = [
|
||||
image_size_to_num_patches(
|
||||
image_size=imsize,
|
||||
grid_pinpoints=self.config.image_grid_pinpoints,
|
||||
patch_size=self.config.vision_config.image_size,
|
||||
)
|
||||
if should_patch
|
||||
else 1
|
||||
for imsize, should_patch in zip(image_sizes, need_patching)
|
||||
]
|
||||
|
||||
if isinstance(pixel_values, torch.Tensor):
|
||||
if pixel_values.dim() == 5:
|
||||
# stacked if input is (batch_size, num_patches, num_channels, height, width)
|
||||
_pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
|
||||
pixel_values = torch.cat(_pixel_values_list, dim=0)
|
||||
elif pixel_values.dim() != 4:
|
||||
# otherwise has to be stacked from list of (num_patches, num_channels, height, width)
|
||||
raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
|
||||
elif isinstance(pixel_values, list):
|
||||
# list of [(batch_size, num_patches, num_channels, height, width)]
|
||||
assert len(pixel_values) == len(image_num_patches), (
|
||||
f"pixel_values is a list of {len(pixel_values)} tensors, but image_num_patches is of length {len(image_num_patches)}"
|
||||
)
|
||||
_pixel_values_list = [pix_val.squeeze(0)[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
|
||||
|
||||
pixel_values = torch.cat(_pixel_values_list, dim=0)
|
||||
|
||||
image_features = self.vision_tower(pixel_values, output_hidden_states=True)
|
||||
# If we have one vision feature layer, return the corresponding hidden states,
|
||||
# otherwise, select the hidden states of each feature layer and concatenate them
|
||||
if isinstance(vision_feature_layer, int):
|
||||
selected_image_feature = image_features.hidden_states[vision_feature_layer]
|
||||
else:
|
||||
hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
|
||||
selected_image_feature = torch.cat(hs_pool, dim=-1)
|
||||
|
||||
if vision_feature_select_strategy == "default":
|
||||
selected_image_feature = selected_image_feature[:, 1:]
|
||||
elif vision_feature_select_strategy == "full":
|
||||
selected_image_feature = selected_image_feature
|
||||
image_features = self.multi_modal_projector(selected_image_feature)
|
||||
|
||||
image_features = torch.split(image_features, image_num_patches, dim=0)
|
||||
|
||||
image_features, feature_lens = self.pack_image_features(
|
||||
image_features,
|
||||
image_sizes,
|
||||
image_newline=self.image_newline,
|
||||
vision_aspect_ratio=vision_aspect_ratio,
|
||||
)
|
||||
|
||||
return image_features
|
||||
|
||||
@can_return_tuple
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
pixel_values: torch.FloatTensor = None,
|
||||
image_sizes: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
vision_feature_layer: Optional[Union[int, list[int]]] = None,
|
||||
vision_feature_select_strategy: Optional[str] = None,
|
||||
vision_aspect_ratio: Optional[str] = None,
|
||||
batch_num_images: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> Union[tuple, RModelOutputWithPast]:
|
||||
|
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
vision_feature_layer = (
|
||||
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
|
||||
)
|
||||
vision_feature_select_strategy = (
|
||||
vision_feature_select_strategy
|
||||
if vision_feature_select_strategy is not None
|
||||
else self.config.vision_feature_select_strategy
|
||||
)
|
||||
vision_aspect_ratio = (
|
||||
vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
|
||||
)
|
||||
|
||||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||||
|
||||
if pixel_values is not None and inputs_embeds is not None:
|
||||
raise ValueError(
|
||||
"You cannot specify both `pixel_values` and `inputs_embeds` at the same time, "
|
||||
"and must specify either one"
|
||||
)
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.get_input_embeddings()(input_ids)
|
||||
|
||||
# Images are processed with Anyres
|
||||
|
||||
if pixel_values is not None:
|
||||
image_features = self.get_image_features(
|
||||
pixel_values,
|
||||
image_sizes,
|
||||
vision_feature_layer=vision_feature_layer,
|
||||
vision_feature_select_strategy=vision_feature_select_strategy,
|
||||
batch_num_images=batch_num_images,
|
||||
)
|
||||
image_features = torch.cat(image_features, dim=0)
|
||||
|
||||
special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1)
|
||||
special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
|
||||
if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
|
||||
n_image_tokens = (input_ids == self.config.image_token_id).sum()
|
||||
n_image_features = image_features.shape[0]
|
||||
raise ValueError(
|
||||
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
|
||||
)
|
||||
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
||||
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
|
||||
|
||||
outputs = self.language_model(
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=True,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
return RModelOutputWithPast(
|
||||
last_hidden_state=outputs.last_hidden_state,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
image_hidden_states=image_features if pixel_values is not None else None,
|
||||
)
|
||||
|
||||
def apply_pooling(self, image_features):
|
||||
height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
|
||||
batch_frames, seq_len, dim = image_features.shape
|
||||
image_features = image_features.view(batch_frames, height, width, -1)
|
||||
image_features = image_features.permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
height, width = image_features.shape[2:]
|
||||
scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)]
|
||||
image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear")
|
||||
|
||||
image_features = image_features.permute(0, 2, 3, 1)
|
||||
image_features = image_features.view(batch_frames, -1, dim)
|
||||
return image_features
|
||||
|
||||
class RForConditionalGeneration(RPreTrainedModel, GenerationMixin):
|
||||
_checkpoint_conversion_mapping = {
|
||||
"^language_model.model": "model.language_model",
|
||||
"^vision_tower": "model.vision_tower",
|
||||
"^multi_modal_projector": "model.multi_modal_projector",
|
||||
"^image_newline": "model.image_newline",
|
||||
"^language_model.lm_head": "lm_head",
|
||||
}
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
|
||||
def __init__(self, config: RConfig):
|
||||
super().__init__(config)
|
||||
self.model = RModel(config)
|
||||
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.get_input_embeddings()
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.set_input_embeddings(value)
|
||||
|
||||
def get_output_embeddings(self) -> nn.Module:
|
||||
return self.lm_head
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.lm_head = new_embeddings
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model
|
||||
|
||||
def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
|
||||
return self.model.pack_image_features(
|
||||
image_features=image_features,
|
||||
image_sizes=image_sizes,
|
||||
vision_feature_select_strategy=vision_feature_select_strategy,
|
||||
image_newline=image_newline,
|
||||
)
|
||||
|
||||
def get_image_features(
|
||||
self,
|
||||
pixel_values: torch.FloatTensor,
|
||||
image_sizes: torch.Tensor,
|
||||
vision_feature_layer: Optional[Union[int, list[int]]] = None,
|
||||
vision_feature_select_strategy: Optional[str] = None,
|
||||
):
|
||||
return self.model.get_image_features(
|
||||
pixel_values=pixel_values,
|
||||
image_sizes=image_sizes,
|
||||
vision_feature_layer=vision_feature_layer,
|
||||
vision_feature_select_strategy=vision_feature_select_strategy,
|
||||
)
|
||||
|
||||
# Make modules available throught conditional class for BC
|
||||
@property
|
||||
def language_model(self):
|
||||
return self.model.language_model
|
||||
|
||||
@property
|
||||
def vision_tower(self):
|
||||
return self.model.vision_tower
|
||||
|
||||
@property
|
||||
def multi_modal_projector(self):
|
||||
return self.model.multi_modal_projector
|
||||
|
||||
@can_return_tuple
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
pixel_values: torch.FloatTensor = None,
|
||||
image_sizes: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
vision_feature_layer: Optional[Union[int, list[int]]] = None,
|
||||
vision_feature_select_strategy: Optional[str] = None,
|
||||
vision_aspect_ratio: Optional[str] = None,
|
||||
batch_num_images: Optional[torch.LongTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||||
**kwargs,
|
||||
) -> Union[tuple, RCausalLMOutputWithPast]:
|
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
vision_feature_layer = (
|
||||
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
|
||||
)
|
||||
vision_feature_select_strategy = (
|
||||
vision_feature_select_strategy
|
||||
if vision_feature_select_strategy is not None
|
||||
else self.config.vision_feature_select_strategy
|
||||
)
|
||||
vision_aspect_ratio = (
|
||||
vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
|
||||
)
|
||||
|
||||
|
||||
|
||||
outputs = self.model(
|
||||
input_ids=input_ids,
|
||||
pixel_values=pixel_values,
|
||||
image_sizes=image_sizes,
|
||||
vision_aspect_ratio=vision_aspect_ratio,
|
||||
vision_feature_layer=vision_feature_layer,
|
||||
vision_feature_select_strategy=vision_feature_select_strategy,
|
||||
batch_num_images=batch_num_images,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=True,
|
||||
cache_position=cache_position,
|
||||
logits_to_keep=logits_to_keep,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
||||
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||||
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
loss = self.loss_function(
|
||||
logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
|
||||
)
|
||||
|
||||
return RCausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
image_hidden_states=outputs.image_hidden_states,
|
||||
)
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
self,
|
||||
input_ids,
|
||||
past_key_values=None,
|
||||
inputs_embeds=None,
|
||||
pixel_values=None,
|
||||
image_sizes=None,
|
||||
attention_mask=None,
|
||||
cache_position=None,
|
||||
logits_to_keep=None,
|
||||
**kwargs,
|
||||
):
|
||||
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
|
||||
|
||||
model_inputs = super().prepare_inputs_for_generation(
|
||||
input_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
attention_mask=attention_mask,
|
||||
cache_position=cache_position,
|
||||
logits_to_keep=logits_to_keep,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
if cache_position[0] == 0:
|
||||
# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
|
||||
# Otherwise we need pixel values to be passed to model
|
||||
model_inputs["pixel_values"] = pixel_values
|
||||
model_inputs["image_sizes"] = image_sizes
|
||||
|
||||
return model_inputs
|
||||
|
||||
@staticmethod
|
||||
def _prepare_4d_causal_attention_mask_with_cache_position(
|
||||
attention_mask: torch.Tensor,
|
||||
sequence_length: int,
|
||||
target_length: int,
|
||||
dtype: torch.dtype,
|
||||
cache_position: torch.Tensor,
|
||||
batch_size: int,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
if attention_mask is not None and attention_mask.dim() == 4:
|
||||
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
||||
causal_mask = attention_mask
|
||||
else:
|
||||
min_dtype = torch.finfo(dtype).min
|
||||
causal_mask = torch.full(
|
||||
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
||||
)
|
||||
if sequence_length != 1:
|
||||
causal_mask = torch.triu(causal_mask, diagonal=1)
|
||||
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
|
||||
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
||||
if attention_mask is not None:
|
||||
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
||||
mask_length = attention_mask.shape[-1]
|
||||
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
||||
causal_mask.device
|
||||
)
|
||||
padding_mask = padding_mask == 0
|
||||
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
||||
padding_mask, min_dtype
|
||||
)
|
||||
|
||||
return causal_mask
|
||||
|
||||
|
||||
__all__ = ["RModel", "RForConditionalGeneration", "RPreTrainedModel"]
|
||||
51
preprocessor_config.json
Normal file
51
preprocessor_config.json
Normal file
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"do_convert_rgb": null,
|
||||
"do_normalize": true,
|
||||
"do_pad": true,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"image_grid_pinpoints": [
|
||||
[
|
||||
384,
|
||||
768
|
||||
],
|
||||
[
|
||||
768,
|
||||
384
|
||||
],
|
||||
[
|
||||
768,
|
||||
768
|
||||
],
|
||||
[
|
||||
1152,
|
||||
384
|
||||
],
|
||||
[
|
||||
384,
|
||||
1152
|
||||
]
|
||||
],
|
||||
"image_mean": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"image_processor_type": "RImageProcessor",
|
||||
"image_std": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"processor_class": "RProcessor",
|
||||
"auto_map": {
|
||||
"AutoProcessor": "processing_r.RProcessor",
|
||||
"AutoImageProcessor": "image_processing_r.RImageProcessor"
|
||||
},
|
||||
"resample": 2,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"height": 384,
|
||||
"width": 384
|
||||
}
|
||||
}
|
||||
259
processing_r.py
Normal file
259
processing_r.py
Normal file
@@ -0,0 +1,259 @@
|
||||
# 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.
|
||||
|
||||
|
||||
import math
|
||||
from collections.abc import Iterable
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers.feature_extraction_utils import BatchFeature
|
||||
from transformers.image_processing_utils import select_best_resolution
|
||||
from transformers.image_utils import ImageInput, get_image_size, to_numpy_array
|
||||
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, MultiModalData
|
||||
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class RProcessorKwargs(ProcessingKwargs, total=False):
|
||||
# see processing_utils.ProcessingKwargs documentation for usage.
|
||||
_defaults = {
|
||||
"text_kwargs": {
|
||||
"padding": False,
|
||||
|
||||
},
|
||||
"image_kwargs": {},
|
||||
}
|
||||
|
||||
|
||||
class RProcessor(ProcessorMixin):
|
||||
attributes = ["image_processor", "tokenizer"]
|
||||
valid_kwargs = [
|
||||
"chat_template",
|
||||
"num_image_tokens",
|
||||
"image_processor_type",
|
||||
"vision_feature_select_strategy",
|
||||
"image_token",
|
||||
"vision_aspect_ratio",
|
||||
]
|
||||
image_processor_class = "AutoImageProcessor"
|
||||
tokenizer_class = "AutoTokenizer"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
image_processor=None,
|
||||
tokenizer=None,
|
||||
num_image_tokens=None,
|
||||
vision_feature_select_strategy=None,
|
||||
chat_template=None,
|
||||
image_token="<image>",
|
||||
vision_aspect_ratio= "anyres",
|
||||
**kwargs,
|
||||
):
|
||||
self.num_image_tokens = num_image_tokens
|
||||
self.vision_feature_select_strategy = vision_feature_select_strategy
|
||||
self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
|
||||
self.image_token_id = (
|
||||
tokenizer.image_token_id
|
||||
if getattr(tokenizer, "image_token_id", None)
|
||||
else tokenizer.convert_tokens_to_ids(self.image_token)
|
||||
)
|
||||
self.vision_aspect_ratio = vision_aspect_ratio
|
||||
super().__init__(image_processor, tokenizer, chat_template=chat_template)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
images: ImageInput = None,
|
||||
text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
|
||||
audio=None,
|
||||
**kwargs: Unpack[RProcessorKwargs],
|
||||
) -> BatchFeature:
|
||||
output_kwargs = self._merge_kwargs(
|
||||
RProcessorKwargs,
|
||||
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
if isinstance(text, str):
|
||||
text = [text]
|
||||
elif not isinstance(text, list) and not isinstance(text[0], str):
|
||||
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
|
||||
|
||||
image_inputs = {}
|
||||
|
||||
if images is not None:
|
||||
image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
|
||||
|
||||
batch_num_images = iter(image_inputs["batch_num_images"])
|
||||
image_sizes = iter(image_inputs["image_sizes"])
|
||||
height, width = get_image_size(
|
||||
to_numpy_array(image_inputs["pixel_values"][0][0]),
|
||||
channel_dim=output_kwargs["images_kwargs"].get("data_format"),
|
||||
)
|
||||
text, num_image_tokens = self._expand_image_tokens(
|
||||
text, image_sizes, height, width, self.image_token, batch_num_images
|
||||
)
|
||||
|
||||
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
|
||||
|
||||
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
|
||||
self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
|
||||
|
||||
|
||||
return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
|
||||
|
||||
def _expand_image_tokens(
|
||||
self,
|
||||
text: list[TextInput],
|
||||
image_sizes: Iterable[Union[list[int], int]],
|
||||
height: int,
|
||||
width: int,
|
||||
special_token: str,
|
||||
batch_num_images: Iterable[int],
|
||||
):
|
||||
|
||||
prompt_strings = []
|
||||
max_num_vision_tokens = 0
|
||||
for sample in text:
|
||||
if special_token in sample:
|
||||
is_multi_image = next(batch_num_images) != 1
|
||||
else:
|
||||
is_multi_image = False
|
||||
while special_token in sample:
|
||||
if is_multi_image:
|
||||
num_image_tokens = self.num_image_tokens + 1 # one for image_newline
|
||||
else:
|
||||
original_size = next(image_sizes)
|
||||
if not isinstance(original_size, (list, tuple)):
|
||||
# cast to list to avoid numerical precision errors when calculating unpadding
|
||||
original_size = original_size.tolist()
|
||||
orig_height, orig_width = original_size
|
||||
num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
|
||||
max_num_vision_tokens = max(max_num_vision_tokens, num_image_tokens)
|
||||
if self.vision_feature_select_strategy == "default":
|
||||
num_image_tokens -= 1
|
||||
sample = sample.replace(special_token, "<placeholder>" * num_image_tokens, 1)
|
||||
prompt_strings.append(sample)
|
||||
text = [sample.replace("<placeholder>", special_token) for sample in prompt_strings]
|
||||
return text, max_num_vision_tokens
|
||||
|
||||
def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
|
||||
image_grid_pinpoints = self.image_processor.image_grid_pinpoints
|
||||
|
||||
height_best_resolution, width_best_resolution = select_best_resolution(
|
||||
[orig_height, orig_width], image_grid_pinpoints
|
||||
)
|
||||
scale_height, scale_width = height_best_resolution // height, width_best_resolution // width
|
||||
|
||||
patches_height = patches_width = int(math.sqrt(self.num_image_tokens))
|
||||
unpadded_features, newline_features = self._get_unpadded_features(
|
||||
orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
|
||||
)
|
||||
|
||||
# The base patch covers the entire image (no CLS for SigLIP)
|
||||
base_features = self.num_image_tokens
|
||||
num_image_tokens = unpadded_features + newline_features + base_features
|
||||
return num_image_tokens
|
||||
|
||||
# Adapted from transformers.models.llava_next.processing_llava_next.LlavaNextProcessor._get_unpadded_features
|
||||
def _get_unpadded_features(self, height, width, patches_height, patches_width, scale_height, scale_width):
|
||||
current_height = patches_height * scale_height
|
||||
current_width = patches_width * scale_width
|
||||
|
||||
original_aspect_ratio = width / height
|
||||
current_aspect_ratio = current_width / current_height
|
||||
if original_aspect_ratio > current_aspect_ratio:
|
||||
new_height = int(round(height * (current_width / width), 7))
|
||||
padding = (current_height - new_height) // 2
|
||||
current_height -= padding * 2
|
||||
else:
|
||||
new_width = int(round(width * (current_height / height), 7))
|
||||
padding = (current_width - new_width) // 2
|
||||
current_width -= padding * 2
|
||||
|
||||
unpadded_features = current_height * current_width
|
||||
newline_features = current_height
|
||||
|
||||
return (unpadded_features, newline_features)
|
||||
|
||||
|
||||
def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
|
||||
"""
|
||||
Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
|
||||
Args:
|
||||
image_sizes (list[list[str]], *optional*):
|
||||
The input sizes formatted as (height, width) per each image.
|
||||
video_sizes (list[list[str]], *optional*):
|
||||
The input sizes formatted as (num_frames, height, width) per each video.
|
||||
audio_lengths (list[int], *optional*):
|
||||
The input length formatted as per each audio.
|
||||
Returns:
|
||||
dict[str, list[int]]: A dictionary mapping each modality ("image", "video", "audio")
|
||||
to a list containing the number of placeholder tokens required. If the model doesn't accept
|
||||
a certain modality or no input sizes are provided, the dict value is set to an empty list.
|
||||
"""
|
||||
vision_data = {}
|
||||
if image_sizes is not None:
|
||||
images_kwargs = RProcessorKwargs._defaults.get("images_kwargs", {})
|
||||
images_kwargs.update(kwargs)
|
||||
|
||||
size = images_kwargs.get("size", None) or self.image_processor.size
|
||||
size = (
|
||||
(size["shortest_edge"], size["shortest_edge"])
|
||||
if "shortest_edge" in size
|
||||
else (min(size["height"], size["width"]), min(size["height"], size["width"]))
|
||||
)
|
||||
processed_height, processed_width = size
|
||||
|
||||
batch_num_image_tokens = []
|
||||
num_image_patches = [1] * len(image_sizes) # llava-ov doesn't batch pixels as Idefics, thus `1` patch`
|
||||
for image_size in image_sizes:
|
||||
orig_height, orig_width = image_size
|
||||
num_image_tokens = self._get_number_of_features(
|
||||
orig_height, orig_width, processed_height, processed_width
|
||||
)
|
||||
if self.vision_feature_select_strategy == "default":
|
||||
num_image_tokens -= 1
|
||||
batch_num_image_tokens.append(num_image_tokens)
|
||||
vision_data.update({"num_image_tokens": batch_num_image_tokens, "num_image_patches": num_image_patches})
|
||||
|
||||
return MultiModalData(**vision_data)
|
||||
|
||||
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
||||
refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
||||
the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.decode(*args, **kwargs)
|
||||
|
||||
@property
|
||||
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
|
||||
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(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
||||
|
||||
|
||||
__all__ = ["RProcessor"]
|
||||
12
processor_config.json
Normal file
12
processor_config.json
Normal file
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"image_token": "<image>",
|
||||
"num_image_tokens": 729,
|
||||
"processor_class": "RProcessor",
|
||||
"auto_map": {
|
||||
"AutoProcessor": "processing_r.RProcessor",
|
||||
"AutoImageProcessor": "image_processing_r.RImageProcessor"
|
||||
},
|
||||
"video_token": "<video>",
|
||||
"vision_aspect_ratio": "anyres",
|
||||
"vision_feature_select_strategy": "full"
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c6a4e9901c580f8acc48cdbd2618c3b0ec673dcb91d44b555171844c707f28d2
|
||||
size 11423022
|
||||
256
tokenizer_config.json
Normal file
256
tokenizer_config.json
Normal file
@@ -0,0 +1,256 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151669": {
|
||||
"content": "<image>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151670": {
|
||||
"content": "<video>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"processor_class": "processing_r.RProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
26
video_preprocessor_config.json
Normal file
26
video_preprocessor_config.json
Normal file
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"do_convert_rgb": true,
|
||||
"do_normalize": true,
|
||||
"do_pad": true,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"image_mean": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"video_processor_type": "LlavaOnevisionVideoProcessor",
|
||||
"image_std": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"processor_class": "LlavaOnevisionProcessor",
|
||||
"resample": 3,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"height": 384,
|
||||
"width": 384
|
||||
}
|
||||
}
|
||||
|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user