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DATASETS.md

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+Release the dataset after publication...

DATASET_LICENSE

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LICENSE

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+MIT License
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+SOFTWARE.

README.md

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----
-title: LanguageBind
-emoji: ⚡
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-pinned: false
----
+
 
 
 <p align="center">
     <img src="assets/logo.png" width="250" />
 <p>
-<h2 align="center"> LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment </h2>
-
-<h5 align="center"> If you like our project, please give us a star ✨ on Github for latest update.  </h2>
+<h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2>
+<h5 align="center"> If you like our project, please give us a star ✨ on GitHub for latest update.  </h2>
 
-[//]: # (<p align="center">)
+<p align="center">
+📖 <a href="https://arxiv.org/pdf/2310.01852.pdf">Paper</a>
+    &nbsp｜&nbsp
+🤗<a href="https://huggingface.co/spaces/lb203/LanguageBind">Demo</a>
+    &nbsp&nbsp|&nbsp&nbsp
+🤖 <a href="https://github.com/PKU-YuanGroup/LanguageBind#usage">API</a>
+    &nbsp&nbsp|&nbsp&nbsp
+📄<a href="TRAIN_AND_VALIDATE.md">Instruction</a>
+    &nbsp｜
+💥<a href="DATASETS.md">Datasets</a>
+</p>
 
-[//]: # (        📖 <a href="https://arxiv.org/abs/2305.11172">Paper</a>&nbsp&nbsp｜ &nbsp<a href="datasets.md">Datasets</a>)
+## 😮 Highlights
 
-[//]: # (</p>)
-<br>
+### 💡 High performance, but NO intermediate modality required
+LanguageBind is a **language-centric** multimodal pretraining approach, **taking the language as the bind across different modalities** because the language modality is well-explored and contains rich semantics. 
+* The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities. 
 
-LanguageBind is a language-centric multimodal pretraining approach, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. As a result, **all modalities are mapped to a shared feature space**, implementing multimodal semantic alignment. While LanguageBind ensures that we can extend VL modalities to N modalities, we also need a high-quality dataset with alignment data pairs centered on language. We thus propose **VIDAL-10M with 10 Million data with Video, Infrared, Depth, Audio and their corresponding Language.** In our VIDAL-10M, all videos are from short video platforms with **complete semantics** rather than truncated segments from long videos, and all the video, depth, infrared, and audio modalities are aligned to their textual descriptions
+### ⚡️ A multimodal, fully aligned and voluminous dataset
+We propose **VIDAL-10M**, **10 Million data** with **V**ideo, **I**nfrared, **D**epth, **A**udio and their corresponding **L**anguage, which greatly expands the data beyond visual modalities.
+* The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language.
 
-We have **open-sourced the VIDAL-10M dataset**, which greatly expands the data beyond visual modalities. The following figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities.
+### 🔥 Multi-view enhanced description for training
+We make multi-view enhancements to language. We produce multi-view description that combines **meta-data**, **spatial**, and **temporal** to greatly enhance the semantic information of the language. In addition we further **enhance the language with ChatGPT** to create a good semantic space for each modality aligned language.
 
 <p align="center">
 <img src="assets/languagebind.jpg" width=100%>
 </p>
-
-<br>
-
-
-# News
-* **2023.10.02:** Released the code. Training & validating scripts and checkpoints.
-<br></br>
-# Online Demo
-Coming soon...
-
-# Models and Results
-## Model Zoo
-We list the parameters and pretrained checkpoints of LanguageBind below. Note that LanguageBind can be disassembled into different branches to handle different tasks.
-The cache comes from OpenCLIP, which we downloaded from HuggingFace. Note that the original cache for pretrained weights is the Image-Language weights, just a few more HF profiles.
-We additionally trained Video-Language with the LanguageBind method, which is stronger than on CLIP4Clip framework.
-<table border="1" width="100%">
-    <tr align="center">
-        <th>Model</th><th>Ckpt</th><th>Params</th><th>Modality Hidden size</th><th>Modality Layers</th><th>Language Hidden size</th><th>Language Layers</th>
-    </tr>
-    <tr align="center">
-        <td>Video-Language</td><td>TODO</td><td>330M</td><td>1024</td><td>24</td><td>768</td><td>12</td>
-    </tr>
-    </tr>
-    <tr align="center">
-        <td>Audio-Language</td><td><a href="https://pan.baidu.com/s/1PFN8aGlnzsOkGjVk6Mzlfg?pwd=sisz">BaiDu</a></td><td>330M</td><td>1024</td><td>24</td><td>768</td><td>12</td>
-    </tr>
-    </tr>
-    <tr align="center">
-        <td>Depth-Language</td><td><a href="https://pan.baidu.com/s/1YWlaxqTRhpGvXqCyBbmhyg?pwd=olom">BaiDu</a></td><td>330M</td><td>1024</td><td>24</td><td>768</td><td>12</td>
-    </tr>
-    </tr>
-    <tr align="center">
-        <td>Thermal(Infrared)-Language</td><td><a href="https://pan.baidu.com/s/1luUyyKxhadKKc1nk1wizWg?pwd=raf5">BaiDu</a></td><td>330M</td><td>1024</td><td>24</td><td>768</td><td>12</td>
-    </tr>
-    </tr>
-    <tr align="center">
-        <td>Image-Language</td><td><a href="https://pan.baidu.com/s/1VBE4OjecMTeIzU08axfFHA?pwd=7j0m">BaiDu</a></td><td>330M</td><td>1024</td><td>24</td><td>768</td><td>12</td>
-    </tr>
-    </tr>
-    <tr align="center">
-        <td>Cache for pretrained weight</td><td><a href="https://pan.baidu.com/s/1Tytx5MDSo96rwUmQZVY1Ww?pwd=c7r0">BaiDu</a></td><td>330M</td><td>1024</td><td>24</td><td>768</td><td>12</td>
-    </tr>
-
-</table>
-<br>
-
-## Results
-Zero-shot Video-Text Retrieval Performance on MSR-VTT and MSVD datasets. We focus on reporting the parameters of the vision
-encoder. Our experiments are based on 3 million video-text pairs of VIDAL-10M, and we train on the CLIP4Clip framework.. 
 <p align="center">
-<img src="assets/res1.jpg" width=100%>
+<img src="assets/iclr_dataset_sample.jpg" width=99%>
 </p>
-Infrared-Language, Depth-Language, and Audio-Language zero-shot classification. We report the top-1 classification accuracy for all datasets.
+
+
+## 📰 News
+**[2023.10.10]**  🎉 We updated the weights of audio to exceed ImageBind by 16.2% on the ESC-50 dataset. Sample data can be found in [assets](assets), and [emergency zero-shot usage](#emergency-zero-shot) is described.<br>
+**[2023.10.07]**  The checkpoints are available on 🤗 [Huggingface Model](https://huggingface.co/lb203). <br>
+**[2023.10.04]**  Code and demo are available now! Welcome to **watch** 👀 this repository for the latest updates.
+
+## 🤗 Demo
+
+* **Local demo.** Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind.
+```bash
+python gradio_app.py
+```
+
+* **Online demo.** We provide the [online demo](https://huggingface.co/spaces/lb203/LanguageBind) in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image.
 <p align="center">
-<img src="assets/res2.jpg" width=100%>
+<img src="assets/demo.png" width=100%>
 </p>
 
 
-<br></br>
 
-# Requirements and Installation
+## 🚀 Main Results
+
+### ✨ Video-Language
+We focus on reporting the parameters of the vision encoder. Our experiments are based on 3 million video-text pairs of VIDAL-10M, and we train on the CLIP4Clip framework.
+<p align="center">
+<img src="assets/res1.jpg" width=80%>
+</p>
+
+### ✨ Multiple Modalities
+Infrared-Language, Depth-Language, and Audio-Language zero-shot classification. We report text-to-audio R@1 for the Clotho dataset and top-1 accuracy for the rest of the datasets.
+<p align="center">
+<img src="assets/res2.jpg" width=70%>
+</p>
+
+## 🛠️ Requirements and Installation
 * Python >= 3.8
 * Pytorch >= 1.13.0
 * CUDA Version >= 10.2 (recommend 11.6)
@@ -100,35 +84,203 @@ cd LanguageBind
 pip install -r requirements.txt
 ```
 
-<br></br>
+## 🤖 API
+**We open source all modalities preprocessing code.** If you want to load the model (e.g. ```lb203/LanguageBind_Thermal```) from the model hub on Huggingface or on local, you can use the following code snippets.
+
+### Inference for Multi-modal Binding 
+We have provided some sample datasets in [assets](assets) to quickly see how languagebind works.
+```python
+import torch
+from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer
+
+if __name__ == '__main__':
+    device = 'cuda:0'
+    device = torch.device(device)
+    clip_type = ('thermal', 'image', 'video', 'depth', 'audio')
+    model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir')
+    model = model.to(device)
+    model.eval()
+    pretrained_ckpt = f'lb203/LanguageBind_Image'
+    tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir')
+    modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type}
+
+    image = ['assets/image/0.jpg', 'assets/image/1.jpg']
+    audio = ['assets/audio/0.wav', 'assets/audio/1.wav']
+    video = ['assets/video/0.mp4', 'assets/video/1.mp4']
+    depth = ['assets/depth/0.png', 'assets/depth/1.png']
+    thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg']
+    language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.']
+
+    inputs = {
+        'image': to_device(modality_transform['image'](image), device),
+        'video': to_device(modality_transform['video'](video), device),
+        'audio': to_device(modality_transform['audio'](audio), device),
+        'depth': to_device(modality_transform['depth'](depth), device),
+        'thermal': to_device(modality_transform['thermal'](thermal), device),
+    }
+    inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length',
+                                             truncation=True, return_tensors='pt'), device)
+    with torch.no_grad():
+        embeddings = model(inputs)
+    print("Video x Text: \n",
+          torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+    print("Image x Text: \n",
+          torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+    print("Depth x Text: \n",
+          torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+    print("Audio x Text: \n",
+          torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+    print("Thermal x Text: \n",
+          torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+```
+Then returns the following result.
+```bash
+Video x Text: 
+ [[9.9999845e-01 1.5308899e-06]
+ [3.6420031e-06 9.9999630e-01]]
+Image x Text: 
+ [[1.0000000e+00 4.0599781e-09]
+ [1.2165208e-08 1.0000000e+00]]
+Depth x Text: 
+ [[9.9952829e-01 4.7178473e-04]
+ [1.6411507e-01 8.3588487e-01]]
+Audio x Text: 
+ [[0.61346906 0.38653097]
+ [0.00996918 0.99003077]]
+Thermal x Text: 
+ [[0.9744922  0.02550781]
+ [0.3656127  0.6343873 ]]
+```
+### Emergency zero-shot
+Since languagebind binds each modality together, we also found the **emergency zero-shot**. It's very simple to use.
+```python
+print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy())
+```
+Then, you will get:
+```
+Video x Audio: 
+ [[1.0000000e+00 0.0000000e+00]
+ [7.2774713e-22 1.0000000e+00]]
+ ```
+
+### Different branches for X-Language task
+**Additionally, LanguageBind can be disassembled into different branches to handle different tasks.**
+#### Thermal
+```python
+import torch
+from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor
+
+pretrained_ckpt = 'lb203/LanguageBind_Thermal'
+model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+thermal_process = LanguageBindThermalProcessor(model.config, tokenizer)
+
+model.eval()
+data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt')
+with torch.no_grad():
+    out = model(**data)
+
+print(out.text_embeds @ out.image_embeds.T)
+```
+
+#### Depth
+```python
+import torch
+from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor
 
-# VIDAL-10M
-Release the dataset after publication...
+pretrained_ckpt = 'lb203/LanguageBind_Depth'
+model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+depth_process = LanguageBindDepthProcessor(model.config, tokenizer)
 
-<br></br>
+model.eval()
+data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt')
+with torch.no_grad():
+    out = model(**data)
 
-# Training & Inference
-Release run scripts, details coming soon...
+print(out.text_embeds @ out.image_embeds.T)
+```
 
-<br></br>
+#### Video
+```python
+import torch
+from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor
 
-# Downstream datasets
-Coming soon...
+pretrained_ckpt = 'lb203/LanguageBind_Video'
+model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+video_process = LanguageBindVideoProcessor(model.config, tokenizer)
 
-<br></br>
+model.eval()
+data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt')
+with torch.no_grad():
+    out = model(**data)
 
-# Acknowledgement
-* [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework.
+print(out.text_embeds @ out.image_embeds.T)
+```
 
-<br></br>
+#### Audio
+```python
+import torch
+from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor
 
+pretrained_ckpt = 'lb203/LanguageBind_Audio'
+model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+audio_process = LanguageBindAudioProcessor(model.config, tokenizer)
 
-# Citation
+model.eval()
+data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt')
+with torch.no_grad():
+    out = model(**data)
 
-If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :)
+print(out.text_embeds @ out.image_embeds.T)
+```
 
-<br></br>
+#### Image
+```python
+import torch
+from languagebind import LanguageBindImage,  LanguageBindImageTokenizer,  LanguageBindImageProcessor
 
-```BibTeX
+pretrained_ckpt = 'lb203/LanguageBind_Image'
+model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./languagebind/cache_dir')
+image_process = LanguageBindImageProcessor(model.config, tokenizer)
 
+model.eval()
+data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt')
+with torch.no_grad():
+    out = model(**data)
+
+print(out.text_embeds @ out.image_embeds.T)
+```
+
+## 💥 VIDAL-10M
+The datasets is in [DATASETS.md](DATASETS.md).
+
+## 🗝️ Training & Validating
+The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md).
+
+## 👍 Acknowledgement
+* [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework.
+* [CLIP4Clip](https://github.com/ArrowLuo/CLIP4Clip) An open source Video-Text retrieval framework.
+* [sRGB-TIR](https://github.com/rpmsnu/sRGB-TIR) An open source framework to generate infrared (thermal) images.
+* [GLPN](https://github.com/vinvino02/GLPDepth) An open source framework to generate depth images.
+
+## 🔒 License
+* The majority of this project is released under the MIT license as found in the [LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/LICENSE) file.
+* The dataset of this project is released under the CC-BY-NC 4.0 license as found in the [DATASET_LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/DATASET_LICENSE) file. 
+
+## ✏️ Citation
+If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
+
+```BibTeX
+@misc{zhu2023languagebind,
+      title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment}, 
+      author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan},
+      year={2023},
+      eprint={2310.01852},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
 ```

TRAIN_AND_VALIDATE.md

@@ -0,0 +1,207 @@
+We provide the **off-the-shelf** scripts in the [scripts folder](scripts).
+
+## Training LanguageBind 
+
+For example, to **train** LanguageBind on **Depth-Language** with 16 GPUs (2 nodes x 8 GPUs).
+* First download the [cache of pretrained weight](https://github.com/PKU-YuanGroup/LanguageBind#-model-zoo) and specify ```CACHE_DIR```.
+* The second step is to develop a path to ```TRAIN_DATA``` according to the [dataset preparation](https://github.com/PKU-YuanGroup/LanguageBind#-vidal-10m).
+* Then you can run
+
+```bash
+CACHE_DIR="path/to/pretrained/weight"
+TRAIN_DATA="path/to/data"
+cd /path/to/LanguageBind
+TORCH_DISTRIBUTED_DEBUG=DETAIL HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 torchrun --nnodes=1 --nproc_per_node 8 \
+    -m main  \
+    --train-data ${TRAIN_DATA} \
+    --train-num-samples 3020000 \
+    --clip-type "dl" --max-depth 10 \
+    --do_train \
+    --lock-text --lock-image --text-type "polish_mplug" \
+    --init-temp 0.07 --learn-temp \
+    --model "ViT-L-14" --cache-dir ${CACHE_DIR} \
+    --convert_to_lora --lora_r 2 \
+    --lr 5e-4 --coef-lr 1e-3 \
+    --beta1 0.9 --beta2 0.98 --wd 0.2 --eps 1e-6 \
+    --num-frames 1 --force-patch-dropout 0.5 \
+    --epochs 1 --batch-size 128 --accum-freq 1 --warmup 200 \
+    --precision "amp" --workers 10 --video-decode-backend "imgs" \
+    --save-frequency 1 --log-every-n-steps 20 --report-to "tensorboard" --resume "latest" \
+    --do_eval \
+    --val_d_cls_data "NYUV2"
+```
+
+
+## Validating LanguageBind 
+
+For example, to **validate** LanguageBind on **Depth-Language** with 1 GPUs.
+* First specify ```RESUME```.
+* The second step is to prepare the [downstream dataset](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/TRAIN_AND_VALIDATE.md#downstream-datasets).
+* Then you can run
+
+```bash
+CACHE_DIR="path/to/pretrained/weight"
+RESUME="thermal_language.pt"
+TRAIN_DATA="path/to/data"
+cd /path/to/LanguageBind
+TORCH_DISTRIBUTED_DEBUG=DETAIL HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 torchrun --nproc_per_node 1 \
+    -m main  \
+    --train-data ${TRAIN_DATA} \
+    --train-num-samples 3020000 \
+    --clip-type "dl" --max-depth 10 \
+    --lock-text --lock-image --text-type "polish_mplug" \
+    --init-temp 0.07 --learn-temp \
+    --model "ViT-L-14" --cache-dir ${CACHE_DIR} \
+    --convert_to_lora --lora_r 2 \
+    --lr 5e-4 --coef-lr 1e-3 \
+    --beta1 0.9 --beta2 0.98 --wd 0.2 --eps 1e-6 \
+    --num-frames 1 --force-patch-dropout 0.5 \
+    --epochs 1 --batch-size 128 --accum-freq 1 --warmup 200 \
+    --precision "amp" --workers 10 --video-decode-backend "imgs" \
+    --save-frequency 1 --log-every-n-steps 20 --report-to "tensorboard" --resume ${RESUME} \
+    --do_eval \
+    --val_d_cls_data "NYUV2"
+```
+
+## Downstream datasets
+
+### Depth
+NYU V2 dataset is downloaded from [this repo](https://github.com/TUI-NICR/nicr-scene-analysis-datasets/tree/main/nicr_scene_analysis_datasets/datasets/nyuv2) and we reformat them to conform to the standard ImageNet format. Change the ```data_root``` [here](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/data/build_datasets.py#L148).
+
+### Video
+Video datasets are downloaded from [this repo](https://github.com/jpthu17/HBI) and we show the folder structure. Change the ```data_root``` [here](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/data/build_datasets.py#L74).
+
+### Audio
+Audio datasets are downloaded from [this repo](https://github.com/OFA-Sys/ONE-PEACE/blob/main/datasets.md#audio) and we reformat them to conform to the standard ImageNet format. Change the ```data_root``` [here](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/data/build_datasets.py#L127).
+
+### Infrared (Thermal)
+We download LLVIP from [official website](https://bupt-ai-cz.github.io/LLVIP/), and FLIR from [here](https://www.flir.com/oem/adas/adas-dataset-form/). We reformat them to conform to the standard ImageNet format. Change the ```data_root``` [here](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/data/build_datasets.py#L160). We also provide the processed data as follows.
+
+<div align="center">
+<table border="1" width="100%">
+    <tr align="center">
+        <th>Datasets</th><th>Baidu Yun</th><th>Google Cloud</th><th>Peking University Yun</th>
+    </tr>
+    <tr align="center">
+        <td>LLVIP</td><td><a href="https://pan.baidu.com/s/15HPVr016F7eO9005NDRJTg?pwd=46fh">Link</a></td><td><a href="https://drive.google.com/file/d/1RfKNR8q6dHiAHB4OlYecnkUSx-ghLuEO/view?usp=drive_link">Link</a></td><td><a href="https://disk.pku.edu.cn:443/link/30D592EA37AC7C411264801A74994376">Link</a></td>
+    </tr>
+    <tr align="center">
+        <td>FLIR V1</td><td><a href="https://pan.baidu.com/s/1ZDSo5VPxJ4SA7wS_rNk0uQ?pwd=l491">Link</a></td><td><a href="https://drive.google.com/file/d/1CezCLJ4GUfPMFimitPfK40OV2j2Kr8t8/view?usp=drive_link">Link</a></td><td><a href="https://disk.pku.edu.cn:443/link/AD89D6ADE2CAC2407B00650870CBBDEC">Link</a></td>
+    </tr>
+    <tr align="center">
+        <td>FLIR V2</td><td><a href="https://pan.baidu.com/s/16xdr2aQkHo3zJ4KbaTmO3Q?pwd=tj9f">Link</a></td><td><a href="https://drive.google.com/file/d/1Z2ThG5QH-9biFI2-Z8k2fBKSA6Nrees6/view?usp=drive_link">Link</a></td><td><a href="https://disk.pku.edu.cn:443/link/E06C010970B0ED51926700D2F7A21EA8">Link</a></td>
+    </tr>
+</table>
+</div>
+
+### Folder structure
+```bash
+downstream_datasets
+├── Audio
+│   ├── esc50
+│   │   └── test
+│   │       ├── airplane
+│   │       ├── breathing
+│   │       ├── brushing_teeth
+│   │       ├── can_opening
+│   │       ├── car_horn
+│   │       ├── cat
+│   │       ├── chainsaw
+│   │       ├── chirping_birds
+│   │       ├── church_bells
+│   │       ├── clapping
+│   │       ├── clock_alarm
+│   │       ├── clock_tick
+│   │       ├── coughing
+│   │       ├── cow
+│   │       ├── crackling_fire
+│   │       ├── crickets
+│   │       ├── crow
+│   │       ├── crying_baby
+│   │       ├── dog
+│   │       ├── door_wood_creaks
+│   │       ├── door_wood_knock
+│   │       ├── drinking_sipping
+│   │       ├── engine
+│   │       ├── fireworks
+│   │       ├── footsteps
+│   │       ├── frog
+│   │       ├── glass_breaking
+│   │       ├── hand_saw
+│   │       ├── helicopter
+│   │       ├── hen
+│   │       ├── insects
+│   │       ├── keyboard_typing
+│   │       ├── laughing
+│   │       ├── mouse_click
+│   │       ├── pig
+│   │       ├── pouring_water
+│   │       ├── rain
+│   │       ├── rooster
+│   │       ├── sea_waves
+│   │       ├── sheep
+│   │       ├── siren
+│   │       ├── sneezing
+│   │       ├── snoring
+│   │       ├── thunderstorm
+│   │       ├── toilet_flush
+│   │       ├── train
+│   │       ├── vacuum_cleaner
+│   │       ├── washing_machine
+│   │       ├── water_drops
+│   │       └── wind
+├── Depth
+│   ├── nyuv2
+│   │   ├── data
+│   │   │   └── val
+│   │   │       ├── bathroom
+│   │   │       ├── bedroom
+│   │   │       ├── bookstore
+│   │   │       ├── classroom
+│   │   │       ├── dining_room
+│   │   │       ├── home_office
+│   │   │       ├── kitchen
+│   │   │       ├── living_room
+│   │   │       ├── office
+│   │   │       └── others
+├── Thermal
+│   ├── flirv1
+│   │   └── val
+│   │       ├── bicycle
+│   │       ├── car
+│   │       ├── dog
+│   │       └── person
+│   ├── flirv2
+│   │   └── val
+│   │       ├── bike
+│   │       ├── bus
+│   │       ├── car
+│   │       ├── hydrant
+│   │       ├── light
+│   │       ├── motor
+│   │       ├── other\ vehicle
+│   │       ├── person
+│   │       ├── sign
+│   │       ├── skateboard
+│   │       ├── stroller
+│   │       └── truck
+│   ├── llvip
+│   │   ├── train
+│   │   │   ├── background
+│   │   │   └── person
+│   │   └── val
+│   │       ├── background
+│   │       └── person
+└── VideoTextRetrieval
+    ├── vtRetdata
+    │   ├── ActivityNet
+    │   │   └── Videos
+    │   │       └── Activity_Videos
+    │   ├── Didemo
+    │   │   └── videos
+    │   ├── MSRVTT
+    │   │   └── MSRVTT_Videos
+    │   └── MSVD
+    │       └── MSVD_Videos
+```
+

app.py

@@ -1,3 +1,4 @@
+import sys
 
 import gradio as gr
 import argparse
@@ -5,195 +6,7 @@ import numpy as np
 import torch
 from torch import nn
 
-from data.process_image import load_and_transform_image, get_image_transform
-from main import SET_GLOBAL_VALUE
-from model.build_model import create_vat_model
-from data.process_audio import load_and_transform_audio, get_audio_transform
-from data.process_video import load_and_transform_video, get_video_transform
-from data.process_depth import load_and_transform_depth, get_depth_transform
-from data.process_thermal import load_and_transform_thermal, get_thermal_transform
-from data.process_text import load_and_transform_text
-from open_clip import get_tokenizer
-from open_clip.factory import HF_HUB_PREFIX
-
-import os
-
-os.system("wget https://huggingface.co/lb203/LanguageBind/resolve/main/vl.pt")
-os.system("wget https://huggingface.co/lb203/LanguageBind/resolve/main/al.pt")
-os.system("wget https://huggingface.co/lb203/LanguageBind/resolve/main/il.pt")
-os.system("wget https://huggingface.co/lb203/LanguageBind/resolve/main/dl.pt")
-os.system("wget https://huggingface.co/lb203/LanguageBind/resolve/main/tl.pt")
-
-
-class LanguageBind(nn.Module):
-    def __init__(self, args):
-        super(LanguageBind, self).__init__()
-        temp_clip_type = args.clip_type
-        self.modality_encoder = {}
-        self.modality_proj = {}
-        self.modality_scale = {}
-        for c in temp_clip_type:
-            args.clip_type = c
-            if c == 'il':
-                args.convert_to_lora = False
-                model = create_vat_model(args)
-                args.convert_to_lora = True
-            elif c == 'vl':
-                args.lora_r = 64
-                args.add_time_attn = True
-                model = create_vat_model(args)
-                args.add_time_attn = False
-                args.lora_r = 2
-            elif c == 'al':
-                args.lora_r = 8
-                model = create_vat_model(args)
-                args.lora_r = 2
-            else:
-                model = create_vat_model(args)
-            
-            state_dict = torch.load(f'{c}.pt', map_location='cpu')
-            if state_dict.get('state_dict', None) is not None:
-                state_dict = state_dict['state_dict']
-            if next(iter(state_dict.items()))[0].startswith('module'):
-                state_dict = {k[7:]: v for k, v in state_dict.items()}
-            msg = model.load_state_dict(state_dict, strict=False)
-            print(f'load {c}, {msg}')
-            
-            if c == 'vl':
-                self.modality_encoder['video'] = model.vision_model
-                self.modality_proj['video'] = model.visual_projection
-                self.modality_scale['video'] = model.logit_scale
-            elif c == 'al':
-                self.modality_encoder['audio'] = model.vision_model
-                self.modality_proj['audio'] = model.visual_projection
-                self.modality_scale['audio'] = model.logit_scale
-            elif c == 'dl':
-                self.modality_encoder['depth'] = model.vision_model
-                self.modality_proj['depth'] = model.visual_projection
-                self.modality_scale['depth'] = model.logit_scale
-            elif c == 'tl':
-                self.modality_encoder['thermal'] = model.vision_model
-                self.modality_proj['thermal'] = model.visual_projection
-                self.modality_scale['thermal'] = model.logit_scale
-            elif c == 'il':
-                self.modality_encoder['image'] = model.vision_model
-                self.modality_proj['image'] = model.visual_projection
-                self.modality_scale['image'] = model.logit_scale
-            else:
-                raise NameError(f'No clip_type of {c}')
-        self.modality_encoder['language'] = model.text_model
-        self.modality_proj['language'] = model.text_projection
-
-        self.modality_encoder = nn.ModuleDict(self.modality_encoder)
-        self.modality_proj = nn.ModuleDict(self.modality_proj)
-
-    def forward(self, inputs):
-        outputs = {}
-        for key, value in inputs.items():
-            value = self.modality_encoder[key](**value)[1]
-            value = self.modality_proj[key](value)
-            value = value / value.norm(p=2, dim=-1, keepdim=True)
-            # if key != 'language':
-            #     value = value * self.modality_scale[key].exp()
-            outputs[key] = value
-        return outputs
-
-
-
-
-MODEL_DICT = {"ViT-L-14": "laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K",
-              "ViT-H-14": "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"}
-CHECKPOINT_DICT = {"ViT-L-14": "models--laion--CLIP-ViT-L-14-DataComp.XL-s13B-b90K/snapshots/84c9828e63dc9a9351d1fe637c346d4c1c4db341/pytorch_model.bin",
-                   "ViT-H-14": "models--laion--CLIP-ViT-H-14-laion2B-s32B-b79K/snapshots/94a64189c3535c1cb44acfcccd7b0908c1c8eb23/pytorch_model.bin"}
-parser = argparse.ArgumentParser()
-args = parser.parse_args()
-args.pretrained = False
-args.model = MODEL_DICT["ViT-L-14"]
-args.cache_dir = ''
-args.video_decode_backend = 'decord'
-args.device = 'cpu'
-# args.device = 'cuda:0'
-device = torch.device(args.device)
-args.precision = None
-args.init_temp = 0
-args.force_patch_dropout = 0.0
-args.add_time_attn = False
-args.convert_to_lora = True
-args.lora_r = 2
-args.lora_alpha = 16
-args.lora_dropout = 0.0  # 0.1?
-args.num_frames = 8
-args.clip_type = 'vl'
-args.num_mel_bins = 1008
-args.target_length = 112
-args.audio_sample_rate = 16000
-args.audio_mean = 4.5689974
-args.audio_std = -4.2677393
-args.max_depth = 10
-args.image_size = 224
-args.rank = 0
-SET_GLOBAL_VALUE('PATCH_DROPOUT', args.force_patch_dropout)
-SET_GLOBAL_VALUE('NUM_FRAMES', args.num_frames)
-args.clip_type = ['il', 'vl', 'al', 'dl', 'tl']
-model = LanguageBind(args).to(device)
-model.eval()
-
-modality_transform = {
-    'language': get_tokenizer(HF_HUB_PREFIX + args.model, cache_dir=args.cache_dir),
-    'video': get_video_transform(args),
-    'audio': get_audio_transform(args),
-    'depth': get_depth_transform(args),
-    'thermal': get_thermal_transform(args),
-    'image': get_image_transform(args),
-}
-
-
-def stack_dict(x, device):
-    out_dict = {}
-    keys = list(x[0].keys())
-    for key in keys:
-        out_dict[key] = torch.stack([i[key] for i in x]).to(device)
-    return out_dict
-
-def image_to_language(image, language):
-    inputs = {}
-    inputs['image'] = stack_dict([load_and_transform_image(image, modality_transform['image'])], device)
-    inputs['language'] = stack_dict([load_and_transform_text(language, modality_transform['language'])], device)
-    with torch.no_grad():
-        embeddings = model(inputs)
-    return (embeddings['image'] @ embeddings['language'].T).item()
-
-def video_to_language(video, language):
-    inputs = {}
-    inputs['video'] = stack_dict([load_and_transform_video(video, modality_transform['video'])], device)
-    inputs['language'] = stack_dict([load_and_transform_text(language, modality_transform['language'])], device)
-    with torch.no_grad():
-        embeddings = model(inputs)
-    return (embeddings['video'] @ embeddings['language'].T).item()
-
-def audio_to_language(audio, language):
-    inputs = {}
-    inputs['audio'] = stack_dict([load_and_transform_audio(audio, modality_transform['audio'])], device)
-    inputs['language'] = stack_dict([load_and_transform_text(language, modality_transform['language'])], device)
-    with torch.no_grad():
-        embeddings = model(inputs)
-    return (embeddings['audio'] @ embeddings['language'].T).item()
-
-def depth_to_language(depth, language):
-    inputs = {}
-    inputs['depth'] = stack_dict([load_and_transform_depth(depth.name, modality_transform['depth'])], device)
-    inputs['language'] = stack_dict([load_and_transform_text(language, modality_transform['language'])], device)
-    with torch.no_grad():
-        embeddings = model(inputs)
-    return (embeddings['depth'] @ embeddings['language'].T).item()
-
-def thermal_to_language(thermal, language):
-    inputs = {}
-    inputs['thermal'] = stack_dict([load_and_transform_thermal(thermal, modality_transform['thermal'])], device)
-    inputs['language'] = stack_dict([load_and_transform_text(language, modality_transform['language'])], device)
-    with torch.no_grad():
-        embeddings = model(inputs)
-    return (embeddings['thermal'] @ embeddings['language'].T).item()
+from languagebind import LanguageBind, transform_dict, LanguageBindImageTokenizer, to_device
 
 code_highlight_css = (
 """
@@ -293,40 +106,102 @@ pre {
 }
 """
 
-with gr.Blocks(title="LanguageBind🚀", css=css) as demo:
-    gr.Markdown(title_markdown)
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(type="filepath", height=224, width=224, label='Image Input')
-            language_i = gr.Textbox(lines=2, label='Text Input')
-            out_i = gr.Textbox(label='Similarity of Image to Text')
-            b_i = gr.Button("Calculate similarity of Image to Text")
-        with gr.Column():
-            video = gr.Video(type="filepath", height=224, width=224, label='Video Input')
-            language_v = gr.Textbox(lines=2, label='Text Input')
-            out_v = gr.Textbox(label='Similarity of Video to Text')
-            b_v = gr.Button("Calculate similarity of Video to Text")
-        with gr.Column():
-            audio = gr.Audio(type="filepath", label='Audio Input')
-            language_a = gr.Textbox(lines=2, label='Text Input')
-            out_a = gr.Textbox(label='Similarity of Audio to Text')
-            b_a = gr.Button("Calculate similarity of Audio to Text")
-    with gr.Row():
-        with gr.Column():
-            depth = gr.File(height=224, width=224, label='Depth Input, need a .png file, 16 bit, with values ranging from 0-10000 (representing 0-10 metres, but 1000 times)')
-            language_d = gr.Textbox(lines=2, label='Text Input')
-            out_d = gr.Textbox(label='Similarity of Depth to Text')
-            b_d = gr.Button("Calculate similarity of Depth to Text")
-        with gr.Column():
-            thermal = gr.Image(type="filepath", height=224, width=224, label='Thermal Input, you should first convert to RGB')
-            language_t = gr.Textbox(lines=2, label='Text Input')
-            out_t = gr.Textbox(label='Similarity of Thermal to Text')
-            b_t = gr.Button("Calculate similarity of Thermal to Text")
 
-    b_i.click(image_to_language, inputs=[image, language_i], outputs=out_i)
-    b_a.click(audio_to_language, inputs=[audio, language_a], outputs=out_a)
-    b_v.click(video_to_language, inputs=[video, language_v], outputs=out_v)
-    b_d.click(depth_to_language, inputs=[depth, language_d], outputs=out_d)
-    b_t.click(thermal_to_language, inputs=[thermal, language_t], outputs=out_t)
+def image_to_language(image, language):
+    inputs = {}
+    inputs['image'] = to_device(modality_transform['image'](image), device)
+    inputs['language'] = to_device(modality_transform['language'](language, max_length=77, padding='max_length',
+                                                                  truncation=True, return_tensors='pt'), device)
+    with torch.no_grad():
+        embeddings = model(inputs)
+    return (embeddings['image'] @ embeddings['language'].T).item()
+
+
+def video_to_language(video, language):
+    inputs = {}
+    inputs['video'] = to_device(modality_transform['video'](video), device)
+    inputs['language'] = to_device(modality_transform['language'](language, max_length=77, padding='max_length',
+                                                                  truncation=True, return_tensors='pt'), device)
+    with torch.no_grad():
+        embeddings = model(inputs)
+    return (embeddings['video'] @ embeddings['language'].T).item()
+
+
+def audio_to_language(audio, language):
+    inputs = {}
+    inputs['audio'] = to_device(modality_transform['audio'](audio), device)
+    inputs['language'] = to_device(modality_transform['language'](language, max_length=77, padding='max_length',
+                                                                  truncation=True, return_tensors='pt'), device)
+    with torch.no_grad():
+        embeddings = model(inputs)
+    return (embeddings['audio'] @ embeddings['language'].T).item()
+
+
+def depth_to_language(depth, language):
+    inputs = {}
+    inputs['depth'] = to_device(modality_transform['depth'](depth.name), device)
+    inputs['language'] = to_device(modality_transform['language'](language, max_length=77, padding='max_length',
+                                                                  truncation=True, return_tensors='pt'), device)
+    with torch.no_grad():
+        embeddings = model(inputs)
+    return (embeddings['depth'] @ embeddings['language'].T).item()
+
+
+def thermal_to_language(thermal, language):
+    inputs = {}
+    inputs['thermal'] = to_device(modality_transform['thermal'](thermal), device)
+    inputs['language'] = to_device(modality_transform['language'](language, max_length=77, padding='max_length',
+                                                                  truncation=True, return_tensors='pt'), device)
+    with torch.no_grad():
+        embeddings = model(inputs)
+    return (embeddings['thermal'] @ embeddings['language'].T).item()
 
-demo.launch()
+if __name__ == '__main__':
+    device = 'cpu'
+    device = torch.device(device)
+    clip_type = ('thermal', 'image', 'video', 'depth', 'audio')
+    model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir', use_temp=False)
+    model = model.to(device)
+    model.eval()
+    pretrained_ckpt = f'lb203/LanguageBind_Image'
+    tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir')
+    modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type}
+    modality_transform['language'] = tokenizer
+
+    with gr.Blocks(title="LanguageBind🚀", css=css) as demo:
+        gr.Markdown(title_markdown)
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="filepath", height=224, width=224, label='Image Input')
+                language_i = gr.Textbox(lines=2, label='Text Input')
+                out_i = gr.Textbox(label='Similarity of Image to Text')
+                b_i = gr.Button("Calculate similarity of Image to Text")
+            with gr.Column():
+                video = gr.Video(type="filepath", height=224, width=224, label='Video Input')
+                language_v = gr.Textbox(lines=2, label='Text Input')
+                out_v = gr.Textbox(label='Similarity of Video to Text')
+                b_v = gr.Button("Calculate similarity of Video to Text")
+            with gr.Column():
+                audio = gr.Audio(type="filepath", label='Audio Input')
+                language_a = gr.Textbox(lines=2, label='Text Input')
+                out_a = gr.Textbox(label='Similarity of Audio to Text')
+                b_a = gr.Button("Calculate similarity of Audio to Text")
+        with gr.Row():
+            with gr.Column():
+                depth = gr.File(height=224, width=224, label='Depth Input, need a .png file, 16 bit, with values ranging from 0-10000 (representing 0-10 metres, but 1000 times)')
+                language_d = gr.Textbox(lines=2, label='Text Input')
+                out_d = gr.Textbox(label='Similarity of Depth to Text')
+                b_d = gr.Button("Calculate similarity of Depth to Text")
+            with gr.Column():
+                thermal = gr.Image(type="filepath", height=224, width=224, label='Thermal Input, you should first convert to RGB')
+                language_t = gr.Textbox(lines=2, label='Text Input')
+                out_t = gr.Textbox(label='Similarity of Thermal to Text')
+                b_t = gr.Button("Calculate similarity of Thermal to Text")
+
+        b_i.click(image_to_language, inputs=[image, language_i], outputs=out_i)
+        b_a.click(audio_to_language, inputs=[audio, language_a], outputs=out_a)
+        b_v.click(video_to_language, inputs=[video, language_v], outputs=out_v)
+        b_d.click(depth_to_language, inputs=[depth, language_d], outputs=out_d)
+        b_t.click(thermal_to_language, inputs=[thermal, language_t], outputs=out_t)
+
+    demo.launch()

data/process_audio.py

@@ -56,10 +56,10 @@ class AudioTransform:
             if len(ranges[2]) == 0:  # if the audio is too short, we just use the first chunk
                 ranges[2] = [0]
             # randomly choose index for each part
-            idx_front = np.random.choice(ranges[0])
-            idx_middle = np.random.choice(ranges[1])
-            idx_back = np.random.choice(ranges[2])
-            idx_front = ranges[0][0]
+            # idx_front = np.random.choice(ranges[0])
+            # idx_middle = np.random.choice(ranges[1])
+            # idx_back = np.random.choice(ranges[2])
+            idx_front = ranges[0][0]  # fixed
             idx_middle = ranges[1][0]
             idx_back = ranges[2][0]
             # select mel

inference.py

@@ -0,0 +1,48 @@
+import torch
+from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer
+
+if __name__ == '__main__':
+    device = 'cuda:0'
+    device = torch.device(device)
+    clip_type = ('thermal', 'image', 'video', 'depth', 'audio')
+    model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir')
+    model = model.to(device)
+    model.eval()
+    pretrained_ckpt = f'lb203/LanguageBind_Image'
+    tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir')
+    modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type}
+
+    image = ['assets/image/0.jpg', 'assets/image/1.jpg']
+    audio = ['assets/audio/0.wav', 'assets/audio/1.wav']
+    video = ['assets/video/0.mp4', 'assets/video/1.mp4']
+    depth = ['assets/depth/0.png', 'assets/depth/1.png']
+    thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg']
+    language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.']
+
+    inputs = {
+        'image': to_device(modality_transform['image'](image), device),
+        'video': to_device(modality_transform['video'](video), device),
+        'audio': to_device(modality_transform['audio'](audio), device),
+        'depth': to_device(modality_transform['depth'](depth), device),
+        'thermal': to_device(modality_transform['thermal'](thermal), device),
+    }
+    inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length',
+                                             truncation=True, return_tensors='pt'), device)
+
+    with torch.no_grad():
+        embeddings = model(inputs)
+
+    print("Video x Text: \n",
+          torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+    print("Image x Text: \n",
+          torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+    print("Depth x Text: \n",
+          torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+    print("Audio x Text: \n",
+          torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+    print("Thermal x Text: \n",
+          torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
+
+    print("Video x Audio: \n",
+          torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy())
+

languagebind/__init__.py

@@ -0,0 +1,89 @@
+import torch
+from torch import nn
+from transformers import AutoConfig
+
+from .image.configuration_image import LanguageBindImageConfig
+from .image.modeling_image import LanguageBindImage
+from .image.tokenization_image import LanguageBindImageTokenizer
+from .image.processing_image import LanguageBindImageProcessor
+
+from .video.configuration_video import LanguageBindVideoConfig
+from .video.modeling_video import LanguageBindVideo
+from .video.tokenization_video import LanguageBindVideoTokenizer
+from .video.processing_video import LanguageBindVideoProcessor
+
+from .depth.configuration_depth import LanguageBindDepthConfig
+from .depth.modeling_depth import LanguageBindDepth
+from .depth.tokenization_depth import LanguageBindDepthTokenizer
+from .depth.processing_depth import LanguageBindDepthProcessor
+
+from .audio.configuration_audio import LanguageBindAudioConfig
+from .audio.modeling_audio import LanguageBindAudio
+from .audio.tokenization_audio import LanguageBindAudioTokenizer
+from .audio.processing_audio import LanguageBindAudioProcessor
+
+from .thermal.configuration_thermal import LanguageBindThermalConfig
+from .thermal.modeling_thermal import LanguageBindThermal
+from .thermal.tokenization_thermal import LanguageBindThermalTokenizer
+from .thermal.processing_thermal import LanguageBindThermalProcessor
+
+
+
+config_dict = {
+    'thermal': LanguageBindThermalConfig,
+    'image': LanguageBindImageConfig,
+    'video': LanguageBindVideoConfig,
+    'depth': LanguageBindDepthConfig,
+    'audio': LanguageBindAudioConfig
+}
+model_dict = {
+    'thermal': LanguageBindThermal,
+    'image': LanguageBindImage,
+    'video': LanguageBindVideo,
+    'depth': LanguageBindDepth,
+    'audio': LanguageBindAudio
+}
+transform_dict = {
+    'video': LanguageBindVideoProcessor,
+    'audio': LanguageBindAudioProcessor,
+    'depth': LanguageBindDepthProcessor,
+    'thermal': LanguageBindThermalProcessor,
+    'image': LanguageBindImageProcessor,
+}
+
+class LanguageBind(nn.Module):
+    def __init__(self, clip_type=('thermal', 'image', 'video', 'depth', 'audio'), cache_dir='cache_dir', use_temp=True):
+        super(LanguageBind, self).__init__()
+        self.use_temp = use_temp
+        self.modality_encoder = {}
+        self.modality_proj = {}
+        self.modality_scale = {}
+        self.modality_config = {}
+        for c in clip_type:
+            pretrained_ckpt = f'lb203/LanguageBind_{c}'
+            model = model_dict[c].from_pretrained(pretrained_ckpt, cache_dir=cache_dir)
+            self.modality_encoder[c] = model.vision_model
+            self.modality_proj[c] = model.visual_projection
+            self.modality_scale[c] = model.logit_scale
+            self.modality_config[c] = model.config
+        self.modality_encoder['language'] = model.text_model
+        self.modality_proj['language'] = model.text_projection
+
+        self.modality_encoder = nn.ModuleDict(self.modality_encoder)
+        self.modality_proj = nn.ModuleDict(self.modality_proj)
+
+    def forward(self, inputs):
+        outputs = {}
+        for key, value in inputs.items():
+            value = self.modality_encoder[key](**value)[1]
+            value = self.modality_proj[key](value)
+            value = value / value.norm(p=2, dim=-1, keepdim=True)
+            if self.use_temp:
+                if key != 'language':
+                    value = value * self.modality_scale[key].exp()
+            outputs[key] = value
+        return outputs
+
+def to_device(x, device):
+    out_dict = {k: v.to(device) for k, v in x.items()}
+    return out_dict