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README.md
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......@@ -72,146 +72,6 @@ pip install bitsandbytes-0.43.0-py3-none-any.whl
pip install -r requirements.txt # requirements.txt
```
## 数据集
- 建立data文件夹放置数据:
- `cd MobileVLM && mkdir -p data/pretrain_data data/finetune_data data/benchmark_data` # work_dir为MobileVLM
- 预训练数据
- `cd ${work_dir}/data/pretrain_data`
- download the ShareGPT4V-PT from [here](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/share-captioner_coco_lcs_sam_1246k_1107.json), which is provided by ShareGPT4V team.
- 多任务训练数据
- `cd ${work_dir}/data/finetune_data`
- download the annotation of our MobileVLM_V2_FT_Mix2M data from huggingface [here](https://huggingface.co/datasets/mtgv/MobileVLM_V2_FT_Mix2M), and download the images from constituting datasets:
[Text-VQA](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip),
[IConQA](https://drive.google.com/file/d/1Xqdt1zMcMZU5N_u1SAIjk-UAclriynGx/edit), [SQA](https://drive.google.com/drive/folders/1w8imCXWYn2LxajmGeGH_g5DaL2rabHev), [SBU](https://huggingface.co/datasets/sbu_captions), follow [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md) to download images from:
[LAION-CC-SBU-558K](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/images.zip), [COCO](http://images.cocodataset.org/zips/train2017.zip), [WebData](https://drive.google.com/drive/folders/1tCUQ-sq6vdshZVkF0ZeF3K4eztkXJgax?usp=sharing), [SAM](https://drive.google.com/file/d/1dKumdOKSXtV7lIXdrG7jsIK_z2vZv2gs/view?usp=drive_link), [GQA](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip), [OCR-VQA](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing), [TextVQA](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip), [VisualGnome](https://cs.stanford.edu/people/rak248/VG_100K_2) ([Part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [Part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip))
- benchmark测试数据
- We evaluate models on a diverse set of 6 benchmarks, *i.e.* GQA, MMBench, MME, POPE, SQA, TextVQA. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs. You should follow these instructions to manage the datasets.
- <details>
<summary> Data Download Instructions </summary>
- download some useful [data/scripts](https://github.com/Meituan-AutoML/MobileVLM/releases/download/v0.1/benchmark_data.zip) pre-collected by us.
- `unzip benchmark_data.zip && cd benchmark_data`
- `bmk_dir=${work_dir}/data/benchmark_data`
- gqa
- download its image data following the official instructions [here](https://cs.stanford.edu/people/dorarad/gqa/download.html)
- `cd ${bmk_dir}/gqa && ln -s /path/to/gqa/images images`
- mme
- download the data following the official instructions [here](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation).
- `cd ${bmk_dir}/mme && ln -s /path/to/MME/MME_Benchmark_release_version images`
- pope
- download coco from POPE following the official instructions [here](https://github.com/AoiDragon/POPE/tree/e3e39262c85a6a83f26cf5094022a782cb0df58d/output/coco).
- `cd ${bmk_dir}/pope && ln -s /path/to/pope/coco coco && ln -s /path/to/coco/val2014 val2014`
- sqa
- download images from the `data/scienceqa` folder of the ScienceQA [repo](https://github.com/lupantech/ScienceQA).
- `cd ${bmk_dir}/sqa && ln -s /path/to/sqa/images images`
- textvqa
- download images following the instructions [here](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip).
- `cd ${bmk_dir}/textvqa && ln -s /path/to/textvqa/train_images train_images`
- mmbench
- no action is needed.
</details>
更多资料可参考源项目的[`README_origin`](./README_origin.md)，由于本项目使用数据集过多，此处不方便提供迷你数据集，读者请根据是否需要自主微调决定是否进行全量下载。
完整数据目录结构如下：
```
data
├── benchmark_data
│   ├── gqa
│   │   ├── convert_gqa_for_eval.py
│   │   ├── eval.py
│   │   ├── images -> /path/to/your/gqa/images
│   │   ├── llava_gqa_testdev_balanced.jsonl
│   │   └── testdev_balanced_questions.json
│   ├── mmbench
│   │   ├── convert_mmbench_for_submission.py
│   │   ├── eval.py
│   │   └── mmbench_dev_en_20231003.tsv
│   ├── mme
│   │   ├── calculation.py
│   │   ├── convert_answer_to_mme.py
│   │   ├── images -> /path/to/your/MME/MME_Benchmark_release_version
│   │   └── llava_mme.jsonl
│   ├── pope
│   │   ├── coco -> /path/to/your/pope/coco
│   │   ├── eval.py
│   │   ├── llava_pope_test.jsonl
│   │   └── val2014 -> /path/to/your/coco/val2014
│   ├── sqa
│   │   ├── eval.py
│   │   ├── images -> /path/to/your/scienceqa/images
│   │   ├── llava_test_CQM-A.json
│   │   ├── pid_splits.json
│   │   └── problems.json
│   └── textvqa
│   ├── eval.py
│   ├── llava_textvqa_val_v051_ocr.jsonl
│   ├── TextVQA_0.5.1_val.json
│   └── train_images -> /path/to/your/textvqa/train_images
├── finetune_data
│ ├── llava_v1_5_mix665k.json
│ ├── MobileVLM_V2_FT_Mix2M.json
│ ├── coco
│ │ ├── train2017
│ │ └── val2017
│ ├── gqa
│ │ └── images
│ ├── iconqa_data
│ │ └── iconqa
│ │    └── train
│ │       ├── choose_img
│ │       ├── choose_txt
│ │       └── fill_in_blank
│ ├── ocr_vqa
│ │ └── images
│ ├── sam
│ │ └── images
│ ├── SBU
│ │ └── images
│ ├── ScienceQA
│ │ └── train
│ ├── share_textvqa
│ │ └── images
│ ├── textvqa
│ │ └── train_images
│ ├── vg
│ │ ├── VG_100K
│ │ └── VG_100K_2
│ ├── web-celebrity
│ │ └── images
│ ├── web-landmark
│ │ └── images
│ └── wikiart
│ └── images
└── pretrain_data
├── share-captioner_coco_lcs_sam_1246k_1107.json
├── blip_laion_cc_sbu_558k.json
├── images
├── coco
│   └── train2017
├── llava
│   └── llava_pretrain
└── sam
   └── images
```
更多资料可参考源项目的[`README_origin`](./README_origin.md)。
## 训练
finetune需要下载预训练权重`mtgv/MobileVLM_V2-1.7B`：https://huggingface.co/mtgv/MobileVLM_V2-1.7B
同时还需要下载图像-文本clip权重`openai/clip-vit-large-patch14-336`：https://huggingface.co/openai/clip-vit-large-patch14-336
### 单机多卡
```
bash run.sh mobilevlm_v2_1.7b pretrain mtgv/MobileVLM_V2-1.7B openai/clip-vit-large-patch14-336 # 或sh pretrain.sh
# 当前bnb库仅支持fp16微调，后期逐渐开放其它微调精度。
# 微调所需深度学习库参见前文环境配置，读者自行下载完整数据集后方可使用。
```
## 推理
```
......
README_pretrain.md 0 → 100644
View file @ fd871096
## 数据集
- 建立data文件夹放置数据:
- `cd MobileVLM && mkdir -p data/pretrain_data data/finetune_data data/benchmark_data` # work_dir为MobileVLM
- 预训练数据
- `cd ${work_dir}/data/pretrain_data`
- download the ShareGPT4V-PT from [here](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/share-captioner_coco_lcs_sam_1246k_1107.json), which is provided by ShareGPT4V team.
- 多任务训练数据
- `cd ${work_dir}/data/finetune_data`
- download the annotation of our MobileVLM_V2_FT_Mix2M data from huggingface [here](https://huggingface.co/datasets/mtgv/MobileVLM_V2_FT_Mix2M), and download the images from constituting datasets:
[Text-VQA](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip),
[IConQA](https://drive.google.com/file/d/1Xqdt1zMcMZU5N_u1SAIjk-UAclriynGx/edit), [SQA](https://drive.google.com/drive/folders/1w8imCXWYn2LxajmGeGH_g5DaL2rabHev), [SBU](https://huggingface.co/datasets/sbu_captions), follow [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md) to download images from:
[LAION-CC-SBU-558K](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/images.zip), [COCO](http://images.cocodataset.org/zips/train2017.zip), [WebData](https://drive.google.com/drive/folders/1tCUQ-sq6vdshZVkF0ZeF3K4eztkXJgax?usp=sharing), [SAM](https://drive.google.com/file/d/1dKumdOKSXtV7lIXdrG7jsIK_z2vZv2gs/view?usp=drive_link), [GQA](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip), [OCR-VQA](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing), [TextVQA](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip), [VisualGnome](https://cs.stanford.edu/people/rak248/VG_100K_2) ([Part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [Part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip))
- benchmark测试数据
- We evaluate models on a diverse set of 6 benchmarks, *i.e.* GQA, MMBench, MME, POPE, SQA, TextVQA. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs. You should follow these instructions to manage the datasets.
- <details>
<summary> Data Download Instructions </summary>
- download some useful [data/scripts](https://github.com/Meituan-AutoML/MobileVLM/releases/download/v0.1/benchmark_data.zip) pre-collected by us.
- `unzip benchmark_data.zip && cd benchmark_data`
- `bmk_dir=${work_dir}/data/benchmark_data`
- gqa
- download its image data following the official instructions [here](https://cs.stanford.edu/people/dorarad/gqa/download.html)
- `cd ${bmk_dir}/gqa && ln -s /path/to/gqa/images images`
- mme
- download the data following the official instructions [here](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation).
- `cd ${bmk_dir}/mme && ln -s /path/to/MME/MME_Benchmark_release_version images`
- pope
- download coco from POPE following the official instructions [here](https://github.com/AoiDragon/POPE/tree/e3e39262c85a6a83f26cf5094022a782cb0df58d/output/coco).
- `cd ${bmk_dir}/pope && ln -s /path/to/pope/coco coco && ln -s /path/to/coco/val2014 val2014`
- sqa
- download images from the `data/scienceqa` folder of the ScienceQA [repo](https://github.com/lupantech/ScienceQA).
- `cd ${bmk_dir}/sqa && ln -s /path/to/sqa/images images`
- textvqa
- download images following the instructions [here](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip).
- `cd ${bmk_dir}/textvqa && ln -s /path/to/textvqa/train_images train_images`
- mmbench
- no action is needed.
</details>
更多资料可参考源项目的[`README_origin`](./README_origin.md)，由于本项目使用数据集过多，此处不方便提供迷你数据集，读者请根据是否需要自主微调决定是否进行全量下载。
完整数据目录结构如下：
```
data
├── benchmark_data
│   ├── gqa
│   │   ├── convert_gqa_for_eval.py
│   │   ├── eval.py
│   │   ├── images -> /path/to/your/gqa/images
│   │   ├── llava_gqa_testdev_balanced.jsonl
│   │   └── testdev_balanced_questions.json
│   ├── mmbench
│   │   ├── convert_mmbench_for_submission.py
│   │   ├── eval.py
│   │   └── mmbench_dev_en_20231003.tsv
│   ├── mme
│   │   ├── calculation.py
│   │   ├── convert_answer_to_mme.py
│   │   ├── images -> /path/to/your/MME/MME_Benchmark_release_version
│   │   └── llava_mme.jsonl
│   ├── pope
│   │   ├── coco -> /path/to/your/pope/coco
│   │   ├── eval.py
│   │   ├── llava_pope_test.jsonl
│   │   └── val2014 -> /path/to/your/coco/val2014
│   ├── sqa
│   │   ├── eval.py
│   │   ├── images -> /path/to/your/scienceqa/images
│   │   ├── llava_test_CQM-A.json
│   │   ├── pid_splits.json
│   │   └── problems.json
│   └── textvqa
│   ├── eval.py
│   ├── llava_textvqa_val_v051_ocr.jsonl
│   ├── TextVQA_0.5.1_val.json
│   └── train_images -> /path/to/your/textvqa/train_images
├── finetune_data
│ ├── llava_v1_5_mix665k.json
│ ├── MobileVLM_V2_FT_Mix2M.json
│ ├── coco
│ │ ├── train2017
│ │ └── val2017
│ ├── gqa
│ │ └── images
│ ├── iconqa_data
│ │ └── iconqa
│ │    └── train
│ │       ├── choose_img
│ │       ├── choose_txt
│ │       └── fill_in_blank
│ ├── ocr_vqa
│ │ └── images
│ ├── sam
│ │ └── images
│ ├── SBU
│ │ └── images
│ ├── ScienceQA
│ │ └── train
│ ├── share_textvqa
│ │ └── images
│ ├── textvqa
│ │ └── train_images
│ ├── vg
│ │ ├── VG_100K
│ │ └── VG_100K_2
│ ├── web-celebrity
│ │ └── images
│ ├── web-landmark
│ │ └── images
│ └── wikiart
│ └── images
└── pretrain_data
├── share-captioner_coco_lcs_sam_1246k_1107.json
├── blip_laion_cc_sbu_558k.json
├── images
├── coco
│   └── train2017
├── llava
│   └── llava_pretrain
└── sam
   └── images
```
更多资料可参考源项目的[`README_origin`](./README_origin.md)。
## 训练
finetune需要下载预训练权重`mtgv/MobileVLM_V2-1.7B`：https://huggingface.co/mtgv/MobileVLM_V2-1.7B
同时还需要下载图像-文本clip权重`openai/clip-vit-large-patch14-336`：https://huggingface.co/openai/clip-vit-large-patch14-336
### 单机多卡
```
bash run.sh mobilevlm_v2_1.7b pretrain mtgv/MobileVLM_V2-1.7B openai/clip-vit-large-patch14-336 # 或sh pretrain.sh
# 当前bnb库仅支持fp16微调，后期逐渐开放其它微调精度。
# 微调所需深度学习库参见前文环境配置，读者自行下载完整数据集后方可使用。
```
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