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ImageBind/.gitignore

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+**__pycache__
+.vscode
+.idea/
+.python-version
+build/
+imagebind.egg-info
+.DS_Store
+venv/

ImageBind/CODE_OF_CONDUCT.md

@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <opensource-conduct@fb.com>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

ImageBind/CONTRIBUTING.md

@@ -0,0 +1,31 @@
+# Contributing to ImageBind
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to Omnivore, you agree that your contributions will be licensed
+under the [LICENSE](LICENSE) file in the root directory of this source tree.

ImageBind/LEGRAD_IMAGEBIND_USAGE.md

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+# LeGrad + ImageBind Notebook Usage
+
+This folder includes `legrad_imagebind.ipynb`, a notebook that demonstrates **LeGrad** explanations for the **ImageBind** model using `ImageBindLeWrapper` (`imagebind/legrad_wrapper.py`).
+
+## 1. Environment and installation
+
+From the `ImageBind` repo root:
+
+```bash
+pip install -e .
+```
+
+Install **LeGrad** and make sure it is on `PYTHONPATH`:
+
+```bash
+pip install -e /path/to/LeGrad
+```
+
+or set `PYTHONPATH` so that the `legrad` package is importable.
+
+You also need a working CUDA‑enabled PyTorch environment for GPU execution.
+
+## 2. Open the notebook
+
+From this folder:
+
+```bash
+cd xai/ImageBind
+jupyter lab legrad_imagebind.ipynb
+```
+
+## 3. What the notebook does
+
+The notebook walks through:
+
+1. **Importing ImageBind and LeGrad**  
+   - Adds the local `LeGrad` repo to `sys.path`.  
+   - Imports `imagebind_model`, `ModalityType`, and utility functions from `imagebind.data`.  
+2. **Loading text and image inputs**  
+   - Defines `text_list` (e.g. `"A dog."`, `"A car"`, `"A bird"`).  
+   - Loads and preprocesses images from `.assets/*.jpg` using `data.load_and_transform_vision_data`.  
+3. **Running the base ImageBind model**  
+   - Creates `model = imagebind_model.imagebind_huge(pretrained=True)`.  
+   - Moves it to CUDA and evaluates vanilla similarity scores `embeddings[VISION] @ embeddings[TEXT].T`.  
+4. **Wrapping with LeGrad**  
+   - Uses `ImageBindLeWrapper` from `imagebind/legrad_wrapper.py`.  
+   - Hooks transformer blocks and `nn.MultiheadAttention` to keep attention probabilities (`attention_maps`) in the autograd graph.  
+5. **Computing explanations**  
+   - Vision: spatial heatmaps over image patches (`compute_legrad_image(...)` / `compute_legrad_image_one_layer(...)`).  
+   - Text: per‑token relevance scores (`compute_legrad_text(...)` / `compute_legrad_text_one_layer(...)`).  
+6. **Visualizing results**  
+   - Converts heatmaps to numpy and overlays them on the input images; plots token relevance over the prompt.
+
+## 4. Minimal code sketch (inside the notebook)
+
+The core pattern is:
+
+```python
+import torch
+from imagebind import data
+from imagebind.models import imagebind_model
+from imagebind.models.imagebind_model import ModalityType
+from imagebind.legrad_wrapper import ImageBindLeWrapper
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model = imagebind_model.imagebind_huge(pretrained=True).to(device).eval()
+
+wrapper = ImageBindLeWrapper(model, layer_index=-2, trunk_key=ModalityType.VISION)
+
+text_list = ["A dog.", "A car", "A bird"]
+image_paths = [".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
+
+inputs = {
+    ModalityType.TEXT: data.load_and_transform_text(text_list, device),
+    ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
+}
+```
+
+You can then call the wrapper’s `compute_legrad_*` helpers in the notebook to obtain:
+
+- **Vision heatmaps** for each (image, text) pair.
+- **Text token relevance** for a chosen image or batch of images.
+

ImageBind/LICENSE

@@ -0,0 +1,437 @@
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+
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+       2. upon express reinstatement by the Licensor.
+
+     For the avoidance of doubt, this Section 6(b) does not affect any
+     right the Licensor may have to seek remedies for Your violations
+     of this Public License.
+
+  c. For the avoidance of doubt, the Licensor may also offer the
+     Licensed Material under separate terms or conditions or stop
+     distributing the Licensed Material at any time; however, doing so
+     will not terminate this Public License.
+
+  d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
+     License.
+
+
+Section 7 -- Other Terms and Conditions.
+
+  a. The Licensor shall not be bound by any additional or different
+     terms or conditions communicated by You unless expressly agreed.
+
+  b. Any arrangements, understandings, or agreements regarding the
+     Licensed Material not stated herein are separate from and
+     independent of the terms and conditions of this Public License.
+
+
+Section 8 -- Interpretation.
+
+  a. For the avoidance of doubt, this Public License does not, and
+     shall not be interpreted to, reduce, limit, restrict, or impose
+     conditions on any use of the Licensed Material that could lawfully
+     be made without permission under this Public License.
+
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+     failure to comply consented to unless expressly agreed to by the
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+=======================================================================
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+the avoidance of doubt, this paragraph does not form part of the
+public licenses.
+
+Creative Commons may be contacted at creativecommons.org.

ImageBind/README.md

@@ -0,0 +1,155 @@
+# ImageBind: One Embedding Space To Bind Them All
+
+**[FAIR, Meta AI](https://ai.facebook.com/research/)** 
+
+Rohit Girdhar*,
+Alaaeldin El-Nouby*,
+Zhuang Liu,
+Mannat Singh,
+Kalyan Vasudev Alwala,
+Armand Joulin,
+Ishan Misra*
+
+To appear at CVPR 2023 (*Highlighted paper*)
+
+[[`Paper`](https://facebookresearch.github.io/ImageBind/paper)] [[`Blog`](https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/)] [[`Demo`](https://imagebind.metademolab.com/)] [[`Supplementary Video`](https://dl.fbaipublicfiles.com/imagebind/imagebind_video.mp4)] [[`BibTex`](#citing-imagebind)]
+
+PyTorch implementation and pretrained models for ImageBind. For details, see the paper: **[ImageBind: One Embedding Space To Bind Them All](https://facebookresearch.github.io/ImageBind/paper)**.
+
+ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.
+
+
+
+![ImageBind](https://user-images.githubusercontent.com/8495451/236859695-ffa13364-3e39-4d99-a8da-fbfab17f9a6b.gif)
+
+## ImageBind model
+
+Emergent zero-shot classification performance.
+
+<table style="margin: auto">
+  <tr>
+    <th>Model</th>
+    <th><span style="color:blue">IN1k</span></th>
+    <th><span style="color:purple">K400</span></th>
+    <th><span style="color:green">NYU-D</span></th>
+    <th><span style="color:LightBlue">ESC</span></th>
+    <th><span style="color:orange">LLVIP</span></th>
+    <th><span style="color:purple">Ego4D</span></th>
+    <th>download</th>
+  </tr>
+  <tr>
+    <td>imagebind_huge</td>
+    <td align="right">77.7</td>
+    <td align="right">50.0</td>
+    <td align="right">54.0</td>
+    <td align="right">66.9</td>
+    <td align="right">63.4</td>
+    <td align="right">25.0</td>
+    <td><a href="https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth">checkpoint</a></td>
+  </tr>
+  
+</table>
+
+## Usage
+
+Install pytorch 2.0+ and other 3rd party dependencies.
+
+```shell
+conda create --name imagebind python=3.10 -y
+conda activate imagebind
+
+pip install .
+```
+
+For windows users, you might need to install `soundfile` for reading/writing audio files. (Thanks @congyue1977)
+
+```
+pip install soundfile
+```
+
+
+Extract and compare features across modalities (e.g. Image, Text and Audio).
+
+```python
+from imagebind import data
+import torch
+from imagebind.models import imagebind_model
+from imagebind.models.imagebind_model import ModalityType
+
+text_list=["A dog.", "A car", "A bird"]
+image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
+audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Instantiate model
+model = imagebind_model.imagebind_huge(pretrained=True)
+model.eval()
+model.to(device)
+
+# Load data
+inputs = {
+    ModalityType.TEXT: data.load_and_transform_text(text_list, device),
+    ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
+    ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, device),
+}
+
+with torch.no_grad():
+    embeddings = model(inputs)
+
+print(
+    "Vision x Text: ",
+    torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1),
+)
+print(
+    "Audio x Text: ",
+    torch.softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, dim=-1),
+)
+print(
+    "Vision x Audio: ",
+    torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=-1),
+)
+
+# Expected output:
+#
+# Vision x Text:
+# tensor([[9.9761e-01, 2.3694e-03, 1.8612e-05],
+#         [3.3836e-05, 9.9994e-01, 2.4118e-05],
+#         [4.7997e-05, 1.3496e-02, 9.8646e-01]])
+#
+# Audio x Text:
+# tensor([[1., 0., 0.],
+#         [0., 1., 0.],
+#         [0., 0., 1.]])
+#
+# Vision x Audio:
+# tensor([[0.8070, 0.1088, 0.0842],
+#         [0.1036, 0.7884, 0.1079],
+#         [0.0018, 0.0022, 0.9960]])
+
+```
+
+## Model card
+Please see the [model card](model_card.md) for details.
+
+## License
+
+ImageBind code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details.
+
+## Contributing
+
+See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
+
+## Citing ImageBind
+
+If you find this repository useful, please consider giving a star :star: and citation
+
+```
+@inproceedings{girdhar2023imagebind,
+  title={ImageBind: One Embedding Space To Bind Them All},
+  author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
+and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
+  booktitle={CVPR},
+  year={2023}
+}
+```

ImageBind/imagebind.egg-info/PKG-INFO

@@ -0,0 +1,183 @@
+Metadata-Version: 2.4
+Name: imagebind
+Version: 0.1.0
+Summary: A brief description of the package
+Home-page: https://github.com/facebookresearch/ImageBind
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: torch>=2.0.0
+Requires-Dist: torchvision
+Requires-Dist: torchaudio
+Requires-Dist: pytorchvideo @ git+https://github.com/facebookresearch/pytorchvideo.git@6cdc929315aab1b5674b6dcf73b16ec99147735f
+Requires-Dist: timm
+Requires-Dist: ftfy
+Requires-Dist: regex
+Requires-Dist: einops
+Requires-Dist: iopath
+Requires-Dist: numpy>=1.19
+Requires-Dist: types-regex
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: license-file
+Dynamic: requires-dist
+Dynamic: summary
+
+# ImageBind: One Embedding Space To Bind Them All
+
+**[FAIR, Meta AI](https://ai.facebook.com/research/)** 
+
+Rohit Girdhar*,
+Alaaeldin El-Nouby*,
+Zhuang Liu,
+Mannat Singh,
+Kalyan Vasudev Alwala,
+Armand Joulin,
+Ishan Misra*
+
+To appear at CVPR 2023 (*Highlighted paper*)
+
+[[`Paper`](https://facebookresearch.github.io/ImageBind/paper)] [[`Blog`](https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/)] [[`Demo`](https://imagebind.metademolab.com/)] [[`Supplementary Video`](https://dl.fbaipublicfiles.com/imagebind/imagebind_video.mp4)] [[`BibTex`](#citing-imagebind)]
+
+PyTorch implementation and pretrained models for ImageBind. For details, see the paper: **[ImageBind: One Embedding Space To Bind Them All](https://facebookresearch.github.io/ImageBind/paper)**.
+
+ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.
+
+
+
+![ImageBind](https://user-images.githubusercontent.com/8495451/236859695-ffa13364-3e39-4d99-a8da-fbfab17f9a6b.gif)
+
+## ImageBind model
+
+Emergent zero-shot classification performance.
+
+<table style="margin: auto">
+  <tr>
+    <th>Model</th>
+    <th><span style="color:blue">IN1k</span></th>
+    <th><span style="color:purple">K400</span></th>
+    <th><span style="color:green">NYU-D</span></th>
+    <th><span style="color:LightBlue">ESC</span></th>
+    <th><span style="color:orange">LLVIP</span></th>
+    <th><span style="color:purple">Ego4D</span></th>
+    <th>download</th>
+  </tr>
+  <tr>
+    <td>imagebind_huge</td>
+    <td align="right">77.7</td>
+    <td align="right">50.0</td>
+    <td align="right">54.0</td>
+    <td align="right">66.9</td>
+    <td align="right">63.4</td>
+    <td align="right">25.0</td>
+    <td><a href="https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth">checkpoint</a></td>
+  </tr>
+  
+</table>
+
+## Usage
+
+Install pytorch 2.0+ and other 3rd party dependencies.
+
+```shell
+conda create --name imagebind python=3.10 -y
+conda activate imagebind
+
+pip install .
+```
+
+For windows users, you might need to install `soundfile` for reading/writing audio files. (Thanks @congyue1977)
+
+```
+pip install soundfile
+```
+
+
+Extract and compare features across modalities (e.g. Image, Text and Audio).
+
+```python
+from imagebind import data
+import torch
+from imagebind.models import imagebind_model
+from imagebind.models.imagebind_model import ModalityType
+
+text_list=["A dog.", "A car", "A bird"]
+image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
+audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Instantiate model
+model = imagebind_model.imagebind_huge(pretrained=True)
+model.eval()
+model.to(device)
+
+# Load data
+inputs = {
+    ModalityType.TEXT: data.load_and_transform_text(text_list, device),
+    ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
+    ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, device),
+}
+
+with torch.no_grad():
+    embeddings = model(inputs)
+
+print(
+    "Vision x Text: ",
+    torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1),
+)
+print(
+    "Audio x Text: ",
+    torch.softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, dim=-1),
+)
+print(
+    "Vision x Audio: ",
+    torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=-1),
+)
+
+# Expected output:
+#
+# Vision x Text:
+# tensor([[9.9761e-01, 2.3694e-03, 1.8612e-05],
+#         [3.3836e-05, 9.9994e-01, 2.4118e-05],
+#         [4.7997e-05, 1.3496e-02, 9.8646e-01]])
+#
+# Audio x Text:
+# tensor([[1., 0., 0.],
+#         [0., 1., 0.],
+#         [0., 0., 1.]])
+#
+# Vision x Audio:
+# tensor([[0.8070, 0.1088, 0.0842],
+#         [0.1036, 0.7884, 0.1079],
+#         [0.0018, 0.0022, 0.9960]])
+
+```
+
+## Model card
+Please see the [model card](model_card.md) for details.
+
+## License
+
+ImageBind code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details.
+
+## Contributing
+
+See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
+
+## Citing ImageBind
+
+If you find this repository useful, please consider giving a star :star: and citation
+
+```
+@inproceedings{girdhar2023imagebind,
+  title={ImageBind: One Embedding Space To Bind Them All},
+  author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
+and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
+  booktitle={CVPR},
+  year={2023}
+}
+```

ImageBind/imagebind.egg-info/SOURCES.txt

@@ -0,0 +1,16 @@
+LICENSE
+README.md
+setup.py
+imagebind/__init__.py
+imagebind/data.py
+imagebind.egg-info/PKG-INFO
+imagebind.egg-info/SOURCES.txt
+imagebind.egg-info/dependency_links.txt
+imagebind.egg-info/requires.txt
+imagebind.egg-info/top_level.txt
+imagebind/bpe/bpe_simple_vocab_16e6.txt.gz
+imagebind/models/__init__.py
+imagebind/models/helpers.py
+imagebind/models/imagebind_model.py
+imagebind/models/multimodal_preprocessors.py
+imagebind/models/transformer.py

ImageBind/imagebind.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+https://download.pytorch.org/whl/cu113

ImageBind/imagebind.egg-info/requires.txt

@@ -0,0 +1,11 @@
+torch>=2.0.0
+torchvision
+torchaudio
+pytorchvideo @ git+https://github.com/facebookresearch/pytorchvideo.git@6cdc929315aab1b5674b6dcf73b16ec99147735f
+timm
+ftfy
+regex
+einops
+iopath
+numpy>=1.19
+types-regex

ImageBind/imagebind.egg-info/top_level.txt

@@ -0,0 +1 @@
+imagebind

ImageBind/imagebind/__init__.py

@@ -0,0 +1,8 @@
+from imagebind import data
+from imagebind.models import imagebind_model
+from imagebind.models.imagebind_model import ModalityType
+
+try:
+    from imagebind.legrad_wrapper import ImageBindLeWrapper
+except ImportError:  # pragma: no cover — optional LeGrad dependency
+    ImageBindLeWrapper = None  # type: ignore

ImageBind/imagebind/bpe/bpe_simple_vocab_16e6.txt.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
+size 1356917

ImageBind/imagebind/data.py

@@ -0,0 +1,357 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import math
+import pkg_resources
+
+import torch
+import torch.nn as nn
+import torchaudio
+from PIL import Image
+from pytorchvideo import transforms as pv_transforms
+from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler
+from pytorchvideo.data.encoded_video import EncodedVideo
+from torchvision import transforms
+
+from imagebind.models.multimodal_preprocessors import SimpleTokenizer
+
+DEFAULT_AUDIO_FRAME_SHIFT_MS = 10  # in milliseconds
+
+
+def return_bpe_path():
+    return pkg_resources.resource_filename(
+        "imagebind", "bpe/bpe_simple_vocab_16e6.txt.gz"
+    )
+
+
+def waveform2melspec(waveform, sample_rate, num_mel_bins, target_length):
+    # Based on https://github.com/YuanGongND/ast/blob/d7d8b4b8e06cdaeb6c843cdb38794c1c7692234c/src/dataloader.py#L102
+    waveform -= waveform.mean()
+    fbank = torchaudio.compliance.kaldi.fbank(
+        waveform,
+        htk_compat=True,
+        sample_frequency=sample_rate,
+        use_energy=False,
+        window_type="hanning",
+        num_mel_bins=num_mel_bins,
+        dither=0.0,
+        frame_length=25,
+        frame_shift=DEFAULT_AUDIO_FRAME_SHIFT_MS,
+    )
+    # Convert to [mel_bins, num_frames] shape
+    fbank = fbank.transpose(0, 1)
+    # Pad to target_length
+    n_frames = fbank.size(1)
+    p = target_length - n_frames
+    # if p is too large (say >20%), flash a warning
+    if abs(p) / n_frames > 0.2:
+        logging.warning(
+            "Large gap between audio n_frames(%d) and "
+            "target_length (%d). Is the audio_target_length "
+            "setting correct?",
+            n_frames,
+            target_length,
+        )
+    # cut and pad
+    if p > 0:
+        fbank = torch.nn.functional.pad(fbank, (0, p), mode="constant", value=0)
+    elif p < 0:
+        fbank = fbank[:, 0:target_length]
+    # Convert to [1, mel_bins, num_frames] shape, essentially like a 1
+    # channel image
+    fbank = fbank.unsqueeze(0)
+    return fbank
+
+
+def get_clip_timepoints(clip_sampler, duration):
+    # Read out all clips in this video
+    all_clips_timepoints = []
+    is_last_clip = False
+    end = 0.0
+    while not is_last_clip:
+        start, end, _, _, is_last_clip = clip_sampler(end, duration, annotation=None)
+        all_clips_timepoints.append((start, end))
+    return all_clips_timepoints
+
+
+def load_and_transform_vision_data(image_paths, device):
+    if image_paths is None:
+        return None
+
+    image_outputs = []
+
+    data_transform = transforms.Compose(
+        [
+            transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=(0.48145466, 0.4578275, 0.40821073),
+                std=(0.26862954, 0.26130258, 0.27577711),
+            ),
+        ]
+    )
+
+    for image_path in image_paths:
+        with open(image_path, "rb") as fopen:
+            image = Image.open(fopen).convert("RGB")
+
+        image = data_transform(image).to(device)
+        image_outputs.append(image)
+    return torch.stack(image_outputs, dim=0)
+
+
+def load_and_transform_text(text, device):
+    if text is None:
+        return None
+    tokenizer = SimpleTokenizer(bpe_path=return_bpe_path())
+    tokens = [tokenizer(t).unsqueeze(0).to(device) for t in text]
+    tokens = torch.cat(tokens, dim=0)
+    return tokens
+
+
+def load_and_transform_audio_data(
+    audio_paths,
+    device,
+    num_mel_bins=128,
+    target_length=204,
+    sample_rate=16000,
+    clip_duration=2,
+    clips_per_video=3,
+    mean=-4.268,
+    std=9.138,
+):
+    if audio_paths is None:
+        return None
+
+    audio_outputs = []
+    clip_sampler = ConstantClipsPerVideoSampler(
+        clip_duration=clip_duration, clips_per_video=clips_per_video
+    )
+
+    for audio_path in audio_paths:
+        waveform, sr = torchaudio.load(audio_path)
+        if sample_rate != sr:
+            waveform = torchaudio.functional.resample(
+                waveform, orig_freq=sr, new_freq=sample_rate
+            )
+        all_clips_timepoints = get_clip_timepoints(
+            clip_sampler, waveform.size(1) / sample_rate
+        )
+        all_clips = []
+        for clip_timepoints in all_clips_timepoints:
+            waveform_clip = waveform[
+                :,
+                int(clip_timepoints[0] * sample_rate) : int(
+                    clip_timepoints[1] * sample_rate
+                ),
+            ]
+            waveform_melspec = waveform2melspec(
+                waveform_clip, sample_rate, num_mel_bins, target_length
+            )
+            all_clips.append(waveform_melspec)
+
+        normalize = transforms.Normalize(mean=mean, std=std)
+        all_clips = [normalize(ac).to(device) for ac in all_clips]
+
+        all_clips = torch.stack(all_clips, dim=0)
+        audio_outputs.append(all_clips)
+
+    return torch.stack(audio_outputs, dim=0)
+
+
+def crop_boxes(boxes, x_offset, y_offset):
+    """
+    Perform crop on the bounding boxes given the offsets.
+    Args:
+        boxes (ndarray or None): bounding boxes to perform crop. The dimension
+            is `num boxes` x 4.
+        x_offset (int): cropping offset in the x axis.
+        y_offset (int): cropping offset in the y axis.
+    Returns:
+        cropped_boxes (ndarray or None): the cropped boxes with dimension of
+            `num boxes` x 4.
+    """
+    cropped_boxes = boxes.copy()
+    cropped_boxes[:, [0, 2]] = boxes[:, [0, 2]] - x_offset
+    cropped_boxes[:, [1, 3]] = boxes[:, [1, 3]] - y_offset
+
+    return cropped_boxes
+
+
+def uniform_crop(images, size, spatial_idx, boxes=None, scale_size=None):
+    """
+    Perform uniform spatial sampling on the images and corresponding boxes.
+    Args:
+        images (tensor): images to perform uniform crop. The dimension is
+            `num frames` x `channel` x `height` x `width`.
+        size (int): size of height and weight to crop the images.
+        spatial_idx (int): 0, 1, or 2 for left, center, and right crop if width
+            is larger than height. Or 0, 1, or 2 for top, center, and bottom
+            crop if height is larger than width.
+        boxes (ndarray or None): optional. Corresponding boxes to images.
+            Dimension is `num boxes` x 4.
+        scale_size (int): optinal. If not None, resize the images to scale_size before
+            performing any crop.
+    Returns:
+        cropped (tensor): images with dimension of
+            `num frames` x `channel` x `size` x `size`.
+        cropped_boxes (ndarray or None): the cropped boxes with dimension of
+            `num boxes` x 4.
+    """
+    assert spatial_idx in [0, 1, 2]
+    ndim = len(images.shape)
+    if ndim == 3:
+        images = images.unsqueeze(0)
+    height = images.shape[2]
+    width = images.shape[3]
+
+    if scale_size is not None:
+        if width <= height:
+            width, height = scale_size, int(height / width * scale_size)
+        else:
+            width, height = int(width / height * scale_size), scale_size
+        images = torch.nn.functional.interpolate(
+            images,
+            size=(height, width),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+    y_offset = int(math.ceil((height - size) / 2))
+    x_offset = int(math.ceil((width - size) / 2))
+
+    if height > width:
+        if spatial_idx == 0:
+            y_offset = 0
+        elif spatial_idx == 2:
+            y_offset = height - size
+    else:
+        if spatial_idx == 0:
+            x_offset = 0
+        elif spatial_idx == 2:
+            x_offset = width - size
+    cropped = images[:, :, y_offset : y_offset + size, x_offset : x_offset + size]
+    cropped_boxes = crop_boxes(boxes, x_offset, y_offset) if boxes is not None else None
+    if ndim == 3:
+        cropped = cropped.squeeze(0)
+    return cropped, cropped_boxes
+
+
+class SpatialCrop(nn.Module):
+    """
+    Convert the video into 3 smaller clips spatially. Must be used after the
+        temporal crops to get spatial crops, and should be used with
+        -2 in the spatial crop at the slowfast augmentation stage (so full
+        frames are passed in here). Will return a larger list with the
+        3x spatial crops as well.
+    """
+
+    def __init__(self, crop_size: int = 224, num_crops: int = 3):
+        super().__init__()
+        self.crop_size = crop_size
+        if num_crops == 3:
+            self.crops_to_ext = [0, 1, 2]
+            self.flipped_crops_to_ext = []
+        elif num_crops == 1:
+            self.crops_to_ext = [1]
+            self.flipped_crops_to_ext = []
+        else:
+            raise NotImplementedError("Nothing else supported yet")
+
+    def forward(self, videos):
+        """
+        Args:
+            videos: A list of C, T, H, W videos.
+        Returns:
+            videos: A list with 3x the number of elements. Each video converted
+                to C, T, H', W' by spatial cropping.
+        """
+        assert isinstance(videos, list), "Must be a list of videos after temporal crops"
+        assert all([video.ndim == 4 for video in videos]), "Must be (C,T,H,W)"
+        res = []
+        for video in videos:
+            for spatial_idx in self.crops_to_ext:
+                res.append(uniform_crop(video, self.crop_size, spatial_idx)[0])
+            if not self.flipped_crops_to_ext:
+                continue
+            flipped_video = transforms.functional.hflip(video)
+            for spatial_idx in self.flipped_crops_to_ext:
+                res.append(uniform_crop(flipped_video, self.crop_size, spatial_idx)[0])
+        return res
+
+
+class NormalizeVideo:
+    def __init__(self, mean, std, inplace=False):
+        self.mean = mean
+        self.std = std
+        self.inplace = inplace
+
+    def __call__(self, clip):
+        if not self.inplace:
+            clip = clip.clone()
+        mean = torch.as_tensor(self.mean, dtype=clip.dtype, device=clip.device)
+        std = torch.as_tensor(self.std, dtype=clip.dtype, device=clip.device)
+        clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])
+        return clip
+
+
+def load_and_transform_video_data(
+    video_paths,
+    device,
+    clip_duration=2,
+    clips_per_video=5,
+    sample_rate=16000,
+):
+    if video_paths is None:
+        return None
+
+    video_outputs = []
+    video_transform = transforms.Compose(
+        [
+            pv_transforms.ShortSideScale(224),
+            NormalizeVideo(
+                mean=(0.48145466, 0.4578275, 0.40821073),
+                std=(0.26862954, 0.26130258, 0.27577711),
+            ),
+        ]
+    )
+
+    clip_sampler = ConstantClipsPerVideoSampler(
+        clip_duration=clip_duration, clips_per_video=clips_per_video
+    )
+    frame_sampler = pv_transforms.UniformTemporalSubsample(num_samples=clip_duration)
+
+    for video_path in video_paths:
+        video = EncodedVideo.from_path(
+            video_path,
+            decoder="decord",
+            decode_audio=False,
+            **{"sample_rate": sample_rate},
+        )
+
+        all_clips_timepoints = get_clip_timepoints(clip_sampler, video.duration)
+
+        all_video = []
+        for clip_timepoints in all_clips_timepoints:
+            # Read the clip, get frames
+            clip = video.get_clip(clip_timepoints[0], clip_timepoints[1])
+            if clip is None:
+                raise ValueError("No clip found")
+            video_clip = frame_sampler(clip["video"])
+            video_clip = video_clip / 255.0  # since this is float, need 0-1
+
+            all_video.append(video_clip)
+
+        all_video = [video_transform(clip) for clip in all_video]
+        all_video = SpatialCrop(224, num_crops=3)(all_video)
+
+        all_video = torch.stack(all_video, dim=0)
+        video_outputs.append(all_video)
+
+    return torch.stack(video_outputs, dim=0).to(device)

ImageBind/imagebind/legrad_wrapper.py

@@ -0,0 +1,316 @@
+"""
+LeGrad for ImageBind — same spirit as ``legrad.wrapper.LeWrapper`` / ``legrad_api.ipynb``:
+hook residual blocks + PyTorch ``nn.MultiheadAttention``, then
+``grad(sum(text · vision))`` w.r.t. attention probabilities.
+
+Requires the ``legrad`` package (``pip install -e /path/to/LeGrad`` or PYTHONPATH).
+
+Vision: heatmap over image patches (CLS query → patch keys).
+Text: relevance vector over context positions (EOS query row → all keys).
+"""
+
+from __future__ import annotations
+
+import math
+import types
+from typing import List, Optional, Sequence
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from imagebind.models.imagebind_model import ImageBindModel, ModalityType
+
+
+def _import_legrad_utils():
+    try:
+        from legrad.utils import hooked_torch_multi_head_attention_forward, min_max
+
+        return hooked_torch_multi_head_attention_forward, min_max
+    except ImportError as e:  # pragma: no cover
+        raise ImportError(
+            "ImageBind LeGrad needs the `legrad` package. Install with "
+            "`pip install -e <path-to-LeGrad>` or add LeGrad to PYTHONPATH."
+        ) from e
+
+
+def hooked_imagebind_block_forward(self, x: torch.Tensor, attn_mask: torch.Tensor):
+    """Store features after attention and after MLP (ImageBind ``BlockWithMasking``)."""
+    if self.layer_scale_type is None:
+        x = x + self.drop_path(self.attn(self.norm_1(x), attn_mask))
+        self.feat_post_attn = x
+        x = x + self.drop_path(self.mlp(self.norm_2(x)))
+        self.feat_post_mlp = x
+    else:
+        x = (
+            x
+            + self.drop_path(self.attn(self.norm_1(x), attn_mask))
+            * self.layer_scale_gamma1
+        )
+        self.feat_post_attn = x
+        x = x + self.drop_path(self.mlp(self.norm_2(x))) * self.layer_scale_gamma2
+        self.feat_post_mlp = x
+    return x
+
+
+def _make_hooked_imagebind_mha_forward(hooked_torch_mha_forward):
+    def hooked_imagebind_mha_forward(self, x: torch.Tensor, attn_mask: torch.Tensor):
+        """Adapter: LeGrad hooked MHA expects ``(q,k,v,...)``; ImageBind calls ``(x, attn_mask)``."""
+        out, _ = hooked_torch_mha_forward(
+            self,
+            x,
+            x,
+            x,
+            key_padding_mask=None,
+            need_weights=True,
+            attn_mask=attn_mask,
+        )
+        return out
+
+    return hooked_imagebind_mha_forward
+
+
+class ImageBindLeWrapper(nn.Module):
+    """
+    Thin wrapper around ``ImageBindModel`` for LeGrad (vision and/or text branches).
+
+    Mirrors ``LeWrapper`` from ``legrad/wrapper.py``: copies public attributes/methods from
+    the base model, patches transformer blocks and attention with hooks, and provides
+    ``compute_legrad_*`` helpers similar to ``compute_legrad_coca`` / ``compute_legrad_clip``.
+    """
+
+    def __init__(
+        self,
+        model: ImageBindModel,
+        layer_index: int = -2,
+        trunk_key: str = ModalityType.VISION,
+    ):
+        super().__init__()
+        for attr in dir(model):
+            if not attr.startswith("__"):
+                setattr(self, attr, getattr(model, attr))
+
+        self._legrad_trunk_key = trunk_key
+        hooked_torch_mha_forward, self._min_max = _import_legrad_utils()
+        self._hooked_mha_fn = _make_hooked_imagebind_mha_forward(hooked_torch_mha_forward)
+        self._activate_hooks(layer_index=layer_index, trunk_key=trunk_key)
+
+    def _trunk(self, key: Optional[str] = None):
+        key = key or self._legrad_trunk_key
+        return self.modality_trunks[key]
+
+    def _activate_hooks(self, layer_index: int, trunk_key: str) -> None:
+        trunk = self._trunk(trunk_key)
+        n_blocks = len(trunk.blocks)
+        self.starting_depth = (
+            layer_index if layer_index >= 0 else n_blocks + layer_index
+        )
+        self.starting_depth = max(0, min(self.starting_depth, n_blocks - 1))
+
+        prefix = f"modality_trunks.{trunk_key}.blocks"
+        for name, param in self.named_parameters():
+            param.requires_grad = False
+            if name.startswith(prefix):
+                depth = int(name.split(f"{prefix}.")[-1].split(".")[0])
+                if depth >= self.starting_depth:
+                    param.requires_grad = True
+
+        for layer in range(self.starting_depth, n_blocks):
+            blk = trunk.blocks[layer]
+            blk.forward = types.MethodType(hooked_imagebind_block_forward, blk)
+            blk.attn.forward = types.MethodType(self._hooked_mha_fn, blk.attn)
+
+        print(
+            f"LeGrad (ImageBind): hooks on `{trunk_key}` blocks "
+            f"[{self.starting_depth}, {n_blocks - 1}] — gradients enabled from block "
+            f"{self.starting_depth} onward."
+        )
+
+    def _encode_vision_trunk(self, vision: torch.Tensor) -> torch.Tensor:
+        p = self.modality_preprocessors[ModalityType.VISION](vision=vision)
+        return self.modality_trunks[ModalityType.VISION](**p["trunk"])
+
+    def _encode_text_trunk(self, text: torch.Tensor) -> torch.Tensor:
+        p = self.modality_preprocessors[ModalityType.TEXT](text=text)
+        self._text_head_kwargs = dict(p.get("head", {}))
+        return self.modality_trunks[ModalityType.TEXT](**p["trunk"])
+
+    def _vision_embed_from_layer(self, layer_idx: int) -> torch.Tensor:
+        x_bld = (
+            self._trunk(ModalityType.VISION).blocks[layer_idx].feat_post_mlp.permute(
+                1, 0, 2
+            )
+        )
+        h = self.modality_heads[ModalityType.VISION](x_bld)
+        return self.modality_postprocessors[ModalityType.VISION](h)
+
+    def _text_embed_from_layer(self, layer_idx: int) -> torch.Tensor:
+        x_bld = (
+            self._trunk(ModalityType.TEXT).blocks[layer_idx].feat_post_mlp.permute(
+                1, 0, 2
+            )
+        )
+        h = self.modality_heads[ModalityType.TEXT](x_bld, **self._text_head_kwargs)
+        return self.modality_postprocessors[ModalityType.TEXT](h)
+
+    @staticmethod
+    def _cls_to_patch_relevance(
+        attn_grad: torch.Tensor, batch_size: int, num_heads: int
+    ) -> torch.Tensor:
+        """attn_grad: (B*H, L, L) -> (B, num_patches) CLS row, patch columns."""
+        L = attn_grad.shape[-1]
+        g = attn_grad.view(batch_size, num_heads, L, L).clamp(min=0.0)
+        g = g.mean(dim=1)
+        return g[:, 0, 1:]
+
+    @staticmethod
+    def _relevance_to_spatial_map(
+        relevance: torch.Tensor, patches_layout: Sequence[int], out_hw: tuple = (224, 224)
+    ) -> torch.Tensor:
+        """relevance: (num_patches,) → (1,1,H,W) upsampled."""
+        pl = tuple(patches_layout)
+        if len(pl) == 3:
+            t, h, w = pl
+            g = relevance.reshape(t, h, w).float()
+            g = g.mean(dim=0) if t > 1 else g[0]
+        elif len(pl) == 2:
+            g = relevance.reshape(pl[0], pl[1]).float()
+        else:
+            side = int(math.sqrt(relevance.numel()))
+            g = relevance.reshape(side, side).float()
+        m = g.unsqueeze(0).unsqueeze(0)
+        return F.interpolate(m, size=out_hw, mode="bilinear", align_corners=False)
+
+    def compute_legrad_imagebind(
+        self,
+        text_embedding: torch.Tensor,
+        vision: Optional[torch.Tensor] = None,
+        normalize: bool = True,
+    ) -> torch.Tensor:
+        """
+        Accumulate LeGrad maps over vision blocks ``[starting_depth, n_blocks)`` (CLIP-style).
+
+        ``text_embedding``: (B, D) same ordering as ``vision`` batch, L2-normalized like
+        ``model({TEXT: ...})`` outputs.
+        """
+        if vision is not None:
+            _ = self._encode_vision_trunk(vision)
+
+        trunk = self._trunk(ModalityType.VISION)
+        blocks: List = list(trunk.blocks)
+        layout = self.modality_preprocessors[ModalityType.VISION].patches_layout
+        num_heads = blocks[0].attn.num_heads
+        bsz = text_embedding.shape[0]
+
+        accum = 0.0
+        for layer in range(self.starting_depth, len(blocks)):
+            self.zero_grad(set_to_none=True)
+            vision_emb = self._vision_embed_from_layer(layer)
+            one_hot = (text_embedding * vision_emb).sum()
+            attn_map = blocks[layer].attn.attention_maps
+            grad = torch.autograd.grad(
+                one_hot, [attn_map], retain_graph=True, create_graph=True
+            )[0]
+            rel = self._cls_to_patch_relevance(grad, bsz, num_heads)
+            expl = self._relevance_to_spatial_map(rel[0], layout)
+            accum = accum + expl
+
+        if normalize:
+            accum = self._min_max(accum)
+        return accum
+
+    def compute_legrad_imagebind_one_layer(
+        self,
+        text_embedding: torch.Tensor,
+        vision: Optional[torch.Tensor] = None,
+        layer_idx: Optional[int] = None,
+        normalize: bool = True,
+    ) -> torch.Tensor:
+        """Single vision block (``legrad_api.compute_legrad_coca_one_layer`` style)."""
+        if vision is not None:
+            _ = self._encode_vision_trunk(vision)
+
+        trunk = self._trunk(ModalityType.VISION)
+        blocks = trunk.blocks
+        n_blocks = len(blocks)
+        if layer_idx is None:
+            layer_idx = n_blocks - 1
+        if layer_idx < self.starting_depth or layer_idx >= n_blocks:
+            raise ValueError(
+                f"layer_idx must be in [{self.starting_depth}, {n_blocks - 1}], got {layer_idx}"
+            )
+
+        layout = self.modality_preprocessors[ModalityType.VISION].patches_layout
+        num_heads = blocks[layer_idx].attn.num_heads
+        bsz = text_embedding.shape[0]
+
+        self.zero_grad(set_to_none=True)
+        vision_emb = self._vision_embed_from_layer(layer_idx)
+        one_hot = (text_embedding * vision_emb).sum()
+        attn_map = blocks[layer_idx].attn.attention_maps
+        grad = torch.autograd.grad(
+            one_hot, [attn_map], retain_graph=True, create_graph=True
+        )[0]
+        rel = self._cls_to_patch_relevance(grad, bsz, num_heads)
+        expl = self._relevance_to_spatial_map(rel[0], layout)
+        if normalize:
+            expl = (expl - expl.min()) / (expl.max() - expl.min() + 1e-8)
+        return expl
+
+    def compute_legrad_text_imagebind(
+        self,
+        vision_embedding: torch.Tensor,
+        text: torch.Tensor,
+        layer_idx: Optional[int] = None,
+        normalize: bool = True,
+    ) -> torch.Tensor:
+        """
+        Text-branch LeGrad: gradient of ``sum(vision · text)`` w.r.t. attention at one layer.
+
+        ``vision_embedding``: (B, D) detached reference (e.g. from ``model({VISION})``).
+        ``text``: token ids (B, L). Returns (B, L_ctx) relevance over token positions for EOS
+        query row (uses ``seq_len`` from the text preprocessor).
+        """
+        if self._legrad_trunk_key != ModalityType.TEXT:
+            raise RuntimeError(
+                "compute_legrad_text_imagebind requires wrapping with trunk_key=TEXT. "
+                "Instantiate ImageBindLeWrapper(model, layer_index=..., trunk_key=ModalityType.TEXT)."
+            )
+
+        _ = self._encode_text_trunk(text)
+
+        trunk = self._trunk(ModalityType.TEXT)
+        blocks = trunk.blocks
+        n_blocks = len(blocks)
+        if layer_idx is None:
+            layer_idx = n_blocks - 1
+        seq_len = self._text_head_kwargs["seq_len"]
+        num_heads = blocks[layer_idx].attn.num_heads
+        bsz = vision_embedding.shape[0]
+
+        self.zero_grad(set_to_none=True)
+        text_emb = self._text_embed_from_layer(layer_idx)
+        one_hot = (vision_embedding.detach() * text_emb).sum()
+        attn_map = blocks[layer_idx].attn.attention_maps
+        grad = torch.autograd.grad(
+            one_hot, [attn_map], retain_graph=True, create_graph=True
+        )[0]
+        # (B*H, L, L) → EOS query → key importances
+        L = grad.shape[-1]
+        g = grad.view(bsz, num_heads, L, L).clamp(min=0.0).mean(dim=1)
+        idx = torch.arange(bsz, device=g.device)
+        eos_rel = g[idx, seq_len, :]
+        if normalize:
+            eos_rel = self._min_max(eos_rel)
+        return eos_rel
+
+    def compute_legrad(
+        self,
+        text_embedding: torch.Tensor,
+        vision: Optional[torch.Tensor] = None,
+        trunk: str = "vision",
+    ) -> torch.Tensor:
+        """Dispatch: ``trunk=='vision'`` → ``compute_legrad_imagebind`` (multi-layer sum)."""
+        if trunk in ("vision", ModalityType.VISION):
+            return self.compute_legrad_imagebind(text_embedding, vision=vision)
+        raise ValueError(f"Unknown trunk {trunk!r}; use compute_legrad_* methods directly.")

ImageBind/imagebind/models/helpers.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import einops
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class Normalize(nn.Module):
+    def __init__(self, dim: int) -> None:
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        return torch.nn.functional.normalize(x, dim=self.dim, p=2)
+
+
+class LearnableLogitScaling(nn.Module):
+    def __init__(
+        self,
+        logit_scale_init: float = 1 / 0.07,
+        learnable: bool = True,
+        max_logit_scale: float = 100,
+    ) -> None:
+        super().__init__()
+        self.max_logit_scale = max_logit_scale
+        self.logit_scale_init = logit_scale_init
+        self.learnable = learnable
+        log_logit_scale = torch.ones([]) * np.log(self.logit_scale_init)
+        if learnable:
+            self.log_logit_scale = nn.Parameter(log_logit_scale)
+        else:
+            self.register_buffer("log_logit_scale", log_logit_scale)
+
+    def forward(self, x):
+        return torch.clip(self.log_logit_scale.exp(), max=self.max_logit_scale) * x
+
+    def extra_repr(self):
+        st = f"logit_scale_init={self.logit_scale_init},learnable={self.learnable}," \
+             f" max_logit_scale={self.max_logit_scale}"
+        return st
+
+
+class EinOpsRearrange(nn.Module):
+    def __init__(self, rearrange_expr: str, **kwargs) -> None:
+        super().__init__()
+        self.rearrange_expr = rearrange_expr
+        self.kwargs = kwargs
+
+    def forward(self, x):
+        assert isinstance(x, torch.Tensor)
+        return einops.rearrange(x, self.rearrange_expr, **self.kwargs)
+
+
+class VerboseNNModule(nn.Module):
+    """
+    Wrapper around nn.Module that prints registered buffers and parameter names.
+    """
+
+    @staticmethod
+    def get_readable_tensor_repr(name: str, tensor: torch.Tensor) -> str:
+        st = (
+            "("
+            + name
+            + "): "
+            + "tensor("
+            + str(tuple(tensor[1].shape))
+            + ", requires_grad="
+            + str(tensor[1].requires_grad)
+            + ")\n"
+        )
+        return st
+
+    def extra_repr(self) -> str:
+        named_modules = set()
+        for p in self.named_modules():
+            named_modules.update([p[0]])
+        named_modules = list(named_modules)
+
+        string_repr = ""
+        for p in self.named_parameters():
+            name = p[0].split(".")[0]
+            if name not in named_modules:
+                string_repr += self.get_readable_tensor_repr(name, p)
+
+        for p in self.named_buffers():
+            name = p[0].split(".")[0]
+            string_repr += self.get_readable_tensor_repr(name, p)
+
+        return string_repr
+
+
+def cast_if_src_dtype(
+    tensor: torch.Tensor, src_dtype: torch.dtype, tgt_dtype: torch.dtype
+):
+    updated = False
+    if tensor.dtype == src_dtype:
+        tensor = tensor.to(dtype=tgt_dtype)
+        updated = True
+    return tensor, updated
+
+
+class QuickGELU(nn.Module):
+    # From https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/clip/model.py#L166
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class SelectElement(nn.Module):
+    def __init__(self, index) -> None:
+        super().__init__()
+        self.index = index
+
+    def forward(self, x):
+        assert x.ndim >= 3
+        return x[:, self.index, ...]
+
+
+class SelectEOSAndProject(nn.Module):
+    """
+    Text Pooling used in OpenCLIP
+    """
+
+    def __init__(self, proj: nn.Module) -> None:
+        super().__init__()
+        self.proj = proj
+
+    def forward(self, x, seq_len):
+        assert x.ndim == 3
+        # x is of shape B x L x D
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        x = x[torch.arange(x.shape[0]), seq_len]
+        x = self.proj(x)
+        return x

ImageBind/imagebind/models/imagebind_model.py

@@ -0,0 +1,534 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import os
+from functools import partial
+from types import SimpleNamespace
+from typing import Optional, Sequence
+
+import torch
+import torch.nn as nn
+
+from imagebind.models.helpers import (EinOpsRearrange, LearnableLogitScaling, Normalize,
+                            SelectElement, SelectEOSAndProject)
+from imagebind.models.multimodal_preprocessors import (AudioPreprocessor,
+                                             IMUPreprocessor, PadIm2Video,
+                                             PatchEmbedGeneric,
+                                             RGBDTPreprocessor,
+                                             SpatioTemporalPosEmbeddingHelper,
+                                             TextPreprocessor,
+                                             ThermalPreprocessor)
+from imagebind.models.transformer import MultiheadAttention, SimpleTransformer
+
+ModalityType = SimpleNamespace(
+    VISION="vision",
+    TEXT="text",
+    AUDIO="audio",
+    THERMAL="thermal",
+    DEPTH="depth",
+    IMU="imu",
+)
+
+
+class ImageBindModel(nn.Module):
+    def __init__(
+        self,
+        video_frames=2,
+        kernel_size=(2, 14, 14),
+        audio_kernel_size=16,
+        audio_stride=10,
+        out_embed_dim=768,
+        vision_embed_dim=1024,
+        vision_num_blocks=24,
+        vision_num_heads=16,
+        audio_embed_dim=768,
+        audio_num_blocks=12,
+        audio_num_heads=12,
+        audio_num_mel_bins=128,
+        audio_target_len=204,
+        audio_drop_path=0.1,
+        text_embed_dim=768,
+        text_num_blocks=12,
+        text_num_heads=12,
+        depth_embed_dim=384,
+        depth_kernel_size=16,
+        depth_num_blocks=12,
+        depth_num_heads=8,
+        depth_drop_path=0.0,
+        thermal_embed_dim=768,
+        thermal_kernel_size=16,
+        thermal_num_blocks=12,
+        thermal_num_heads=12,
+        thermal_drop_path=0.0,
+        imu_embed_dim=512,
+        imu_kernel_size=8,
+        imu_num_blocks=6,
+        imu_num_heads=8,
+        imu_drop_path=0.7,
+    ):
+        super().__init__()
+
+        self.modality_preprocessors = self._create_modality_preprocessors(
+            video_frames,
+            vision_embed_dim,
+            kernel_size,
+            text_embed_dim,
+            audio_embed_dim,
+            audio_kernel_size,
+            audio_stride,
+            audio_num_mel_bins,
+            audio_target_len,
+            depth_embed_dim,
+            depth_kernel_size,
+            thermal_embed_dim,
+            thermal_kernel_size,
+            imu_embed_dim,
+        )
+
+        self.modality_trunks = self._create_modality_trunks(
+            vision_embed_dim,
+            vision_num_blocks,
+            vision_num_heads,
+            text_embed_dim,
+            text_num_blocks,
+            text_num_heads,
+            audio_embed_dim,
+            audio_num_blocks,
+            audio_num_heads,
+            audio_drop_path,
+            depth_embed_dim,
+            depth_num_blocks,
+            depth_num_heads,
+            depth_drop_path,
+            thermal_embed_dim,
+            thermal_num_blocks,
+            thermal_num_heads,
+            thermal_drop_path,
+            imu_embed_dim,
+            imu_num_blocks,
+            imu_num_heads,
+            imu_drop_path,
+        )
+
+        self.modality_heads = self._create_modality_heads(
+            out_embed_dim,
+            vision_embed_dim,
+            text_embed_dim,
+            audio_embed_dim,
+            depth_embed_dim,
+            thermal_embed_dim,
+            imu_embed_dim,
+        )
+
+        self.modality_postprocessors = self._create_modality_postprocessors(
+            out_embed_dim
+        )
+
+    def _create_modality_preprocessors(
+        self,
+        video_frames=2,
+        vision_embed_dim=1024,
+        kernel_size=(2, 14, 14),
+        text_embed_dim=768,
+        audio_embed_dim=768,
+        audio_kernel_size=16,
+        audio_stride=10,
+        audio_num_mel_bins=128,
+        audio_target_len=204,
+        depth_embed_dim=768,
+        depth_kernel_size=16,
+        thermal_embed_dim=768,
+        thermal_kernel_size=16,
+        imu_embed_dim=512,
+    ):
+        rgbt_stem = PatchEmbedGeneric(
+            proj_stem=[
+                PadIm2Video(pad_type="repeat", ntimes=2),
+                nn.Conv3d(
+                    in_channels=3,
+                    kernel_size=kernel_size,
+                    out_channels=vision_embed_dim,
+                    stride=kernel_size,
+                    bias=False,
+                ),
+            ]
+        )
+        rgbt_preprocessor = RGBDTPreprocessor(
+            img_size=[3, video_frames, 224, 224],
+            num_cls_tokens=1,
+            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+            rgbt_stem=rgbt_stem,
+            depth_stem=None,
+        )
+
+        text_preprocessor = TextPreprocessor(
+            context_length=77,
+            vocab_size=49408,
+            embed_dim=text_embed_dim,
+            causal_masking=True,
+        )
+
+        audio_stem = PatchEmbedGeneric(
+            proj_stem=[
+                nn.Conv2d(
+                    in_channels=1,
+                    kernel_size=audio_kernel_size,
+                    stride=audio_stride,
+                    out_channels=audio_embed_dim,
+                    bias=False,
+                ),
+            ],
+            norm_layer=nn.LayerNorm(normalized_shape=audio_embed_dim),
+        )
+        audio_preprocessor = AudioPreprocessor(
+            img_size=[1, audio_num_mel_bins, audio_target_len],
+            num_cls_tokens=1,
+            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+            audio_stem=audio_stem,
+        )
+
+        depth_stem = PatchEmbedGeneric(
+            [
+                nn.Conv2d(
+                    kernel_size=depth_kernel_size,
+                    in_channels=1,
+                    out_channels=depth_embed_dim,
+                    stride=depth_kernel_size,
+                    bias=False,
+                ),
+            ],
+            norm_layer=nn.LayerNorm(normalized_shape=depth_embed_dim),
+        )
+
+        depth_preprocessor = RGBDTPreprocessor(
+            img_size=[1, 224, 224],
+            num_cls_tokens=1,
+            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+            rgbt_stem=None,
+            depth_stem=depth_stem,
+        )
+
+        thermal_stem = PatchEmbedGeneric(
+            [
+                nn.Conv2d(
+                    kernel_size=thermal_kernel_size,
+                    in_channels=1,
+                    out_channels=thermal_embed_dim,
+                    stride=thermal_kernel_size,
+                    bias=False,
+                ),
+            ],
+            norm_layer=nn.LayerNorm(normalized_shape=thermal_embed_dim),
+        )
+        thermal_preprocessor = ThermalPreprocessor(
+            img_size=[1, 224, 224],
+            num_cls_tokens=1,
+            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+            thermal_stem=thermal_stem,
+        )
+
+        imu_stem = PatchEmbedGeneric(
+            [
+                nn.Linear(
+                    in_features=48,
+                    out_features=imu_embed_dim,
+                    bias=False,
+                ),
+            ],
+            norm_layer=nn.LayerNorm(normalized_shape=imu_embed_dim),
+        )
+
+        imu_preprocessor = IMUPreprocessor(
+            img_size=[6, 2000],
+            num_cls_tokens=1,
+            kernel_size=8,
+            embed_dim=imu_embed_dim,
+            pos_embed_fn=partial(SpatioTemporalPosEmbeddingHelper, learnable=True),
+            imu_stem=imu_stem,
+        )
+
+        modality_preprocessors = {
+            ModalityType.VISION: rgbt_preprocessor,
+            ModalityType.TEXT: text_preprocessor,
+            ModalityType.AUDIO: audio_preprocessor,
+            ModalityType.DEPTH: depth_preprocessor,
+            ModalityType.THERMAL: thermal_preprocessor,
+            ModalityType.IMU: imu_preprocessor,
+        }
+
+        return nn.ModuleDict(modality_preprocessors)
+
+    def _create_modality_trunks(
+        self,
+        vision_embed_dim=1024,
+        vision_num_blocks=24,
+        vision_num_heads=16,
+        text_embed_dim=768,
+        text_num_blocks=12,
+        text_num_heads=12,
+        audio_embed_dim=768,
+        audio_num_blocks=12,
+        audio_num_heads=12,
+        audio_drop_path=0.0,
+        depth_embed_dim=768,
+        depth_num_blocks=12,
+        depth_num_heads=12,
+        depth_drop_path=0.0,
+        thermal_embed_dim=768,
+        thermal_num_blocks=12,
+        thermal_num_heads=12,
+        thermal_drop_path=0.0,
+        imu_embed_dim=512,
+        imu_num_blocks=6,
+        imu_num_heads=8,
+        imu_drop_path=0.7,
+    ):
+        def instantiate_trunk(
+            embed_dim, num_blocks, num_heads, pre_transformer_ln, add_bias_kv, drop_path
+        ):
+            return SimpleTransformer(
+                embed_dim=embed_dim,
+                num_blocks=num_blocks,
+                ffn_dropout_rate=0.0,
+                drop_path_rate=drop_path,
+                attn_target=partial(
+                    MultiheadAttention,
+                    embed_dim=embed_dim,
+                    num_heads=num_heads,
+                    bias=True,
+                    add_bias_kv=add_bias_kv,
+                ),
+                pre_transformer_layer=nn.Sequential(
+                    nn.LayerNorm(embed_dim, eps=1e-6)
+                    if pre_transformer_ln
+                    else nn.Identity(),
+                    EinOpsRearrange("b l d -> l b d"),
+                ),
+                post_transformer_layer=EinOpsRearrange("l b d -> b l d"),
+            )
+
+        modality_trunks = {}
+        modality_trunks[ModalityType.VISION] = instantiate_trunk(
+            vision_embed_dim,
+            vision_num_blocks,
+            vision_num_heads,
+            pre_transformer_ln=True,
+            add_bias_kv=False,
+            drop_path=0.0,
+        )
+        modality_trunks[ModalityType.TEXT] = instantiate_trunk(
+            text_embed_dim,
+            text_num_blocks,
+            text_num_heads,
+            pre_transformer_ln=False,
+            add_bias_kv=False,
+            drop_path=0.0,
+        )
+        modality_trunks[ModalityType.AUDIO] = instantiate_trunk(
+            audio_embed_dim,
+            audio_num_blocks,
+            audio_num_heads,
+            pre_transformer_ln=False,
+            add_bias_kv=True,
+            drop_path=audio_drop_path,
+        )
+        modality_trunks[ModalityType.DEPTH] = instantiate_trunk(
+            depth_embed_dim,
+            depth_num_blocks,
+            depth_num_heads,
+            pre_transformer_ln=False,
+            add_bias_kv=True,
+            drop_path=depth_drop_path,
+        )
+        modality_trunks[ModalityType.THERMAL] = instantiate_trunk(
+            thermal_embed_dim,
+            thermal_num_blocks,
+            thermal_num_heads,
+            pre_transformer_ln=False,
+            add_bias_kv=True,
+            drop_path=thermal_drop_path,
+        )
+        modality_trunks[ModalityType.IMU] = instantiate_trunk(
+            imu_embed_dim,
+            imu_num_blocks,
+            imu_num_heads,
+            pre_transformer_ln=False,
+            add_bias_kv=True,
+            drop_path=imu_drop_path,
+        )
+
+        return nn.ModuleDict(modality_trunks)
+
+    def _create_modality_heads(
+        self,
+        out_embed_dim,
+        vision_embed_dim,
+        text_embed_dim,
+        audio_embed_dim,
+        depth_embed_dim,
+        thermal_embed_dim,
+        imu_embed_dim,
+    ):
+        modality_heads = {}
+
+        modality_heads[ModalityType.VISION] = nn.Sequential(
+            nn.LayerNorm(normalized_shape=vision_embed_dim, eps=1e-6),
+            SelectElement(index=0),
+            nn.Linear(vision_embed_dim, out_embed_dim, bias=False),
+        )
+
+        modality_heads[ModalityType.TEXT] = SelectEOSAndProject(
+            proj=nn.Sequential(
+                nn.LayerNorm(normalized_shape=text_embed_dim, eps=1e-6),
+                nn.Linear(text_embed_dim, out_embed_dim, bias=False),
+            )
+        )
+
+        modality_heads[ModalityType.AUDIO] = nn.Sequential(
+            nn.LayerNorm(normalized_shape=audio_embed_dim, eps=1e-6),
+            SelectElement(index=0),
+            nn.Linear(audio_embed_dim, out_embed_dim, bias=False),
+        )
+
+        modality_heads[ModalityType.DEPTH] = nn.Sequential(
+            nn.LayerNorm(normalized_shape=depth_embed_dim, eps=1e-6),
+            SelectElement(index=0),
+            nn.Linear(depth_embed_dim, out_embed_dim, bias=False),
+        )
+
+        modality_heads[ModalityType.THERMAL] = nn.Sequential(
+            nn.LayerNorm(normalized_shape=thermal_embed_dim, eps=1e-6),
+            SelectElement(index=0),
+            nn.Linear(thermal_embed_dim, out_embed_dim, bias=False),
+        )
+
+        modality_heads[ModalityType.IMU] = nn.Sequential(
+            nn.LayerNorm(normalized_shape=imu_embed_dim, eps=1e-6),
+            SelectElement(index=0),
+            nn.Dropout(p=0.5),
+            nn.Linear(imu_embed_dim, out_embed_dim, bias=False),
+        )
+
+        return nn.ModuleDict(modality_heads)
+
+    def _create_modality_postprocessors(self, out_embed_dim):
+        modality_postprocessors = {}
+
+        modality_postprocessors[ModalityType.VISION] = Normalize(dim=-1)
+        modality_postprocessors[ModalityType.TEXT] = nn.Sequential(
+            Normalize(dim=-1), LearnableLogitScaling(learnable=True)
+        )
+        modality_postprocessors[ModalityType.AUDIO] = nn.Sequential(
+            Normalize(dim=-1),
+            LearnableLogitScaling(logit_scale_init=20.0, learnable=False),
+        )
+        modality_postprocessors[ModalityType.DEPTH] = nn.Sequential(
+            Normalize(dim=-1),
+            LearnableLogitScaling(logit_scale_init=5.0, learnable=False),
+        )
+        modality_postprocessors[ModalityType.THERMAL] = nn.Sequential(
+            Normalize(dim=-1),
+            LearnableLogitScaling(logit_scale_init=10.0, learnable=False),
+        )
+        modality_postprocessors[ModalityType.IMU] = nn.Sequential(
+            Normalize(dim=-1),
+            LearnableLogitScaling(logit_scale_init=5.0, learnable=False),
+        )
+
+        return nn.ModuleDict(modality_postprocessors)
+
+    def set_modality_attention_capture(
+        self,
+        modality_key: str,
+        enabled: bool,
+        block_indices: Optional[Sequence[int]] = None,
+    ) -> None:
+        """
+        Toggle attention-weight capture for one modality trunk (see
+        ``SimpleTransformer.set_attention_capture`` in ``transformer.py``).
+
+        After a forward pass with capture enabled on the last block, read weights via
+        ``get_modality_attention_weights(modality_key)``.
+        """
+        self.modality_trunks[modality_key].set_attention_capture(
+            enabled, block_indices=block_indices
+        )
+
+    def get_modality_attention_weights(
+        self, modality_key: str, block_index: int = -1
+    ) -> Optional[torch.Tensor]:
+        """Return ``last_attn_weights`` from the given block's ``MultiheadAttention``, if any."""
+        blk = self.modality_trunks[modality_key].blocks[block_index]
+        attn = blk.attn
+        if isinstance(attn, MultiheadAttention):
+            return attn.last_attn_weights
+        return None
+
+    def forward(self, inputs):
+        outputs = {}
+        for modality_key, modality_value in inputs.items():
+            reduce_list = (
+                modality_value.ndim >= 5
+            )  # Audio and Video inputs consist of multiple clips
+            if reduce_list:
+                B, S = modality_value.shape[:2]
+                modality_value = modality_value.reshape(
+                    B * S, *modality_value.shape[2:]
+                )
+
+            if modality_value is not None:
+                modality_value = self.modality_preprocessors[modality_key](
+                    **{modality_key: modality_value}
+                )
+                trunk_inputs = modality_value["trunk"]
+                head_inputs = modality_value["head"]
+                modality_value = self.modality_trunks[modality_key](**trunk_inputs)
+                modality_value = self.modality_heads[modality_key](
+                    modality_value, **head_inputs
+                )
+                modality_value = self.modality_postprocessors[modality_key](
+                    modality_value
+                )
+
+                if reduce_list:
+                    modality_value = modality_value.reshape(B, S, -1)
+                    modality_value = modality_value.mean(dim=1)
+
+                outputs[modality_key] = modality_value
+
+        return outputs
+
+
+def imagebind_huge(pretrained=False):
+    model = ImageBindModel(
+        vision_embed_dim=1280,
+        vision_num_blocks=32,
+        vision_num_heads=16,
+        text_embed_dim=1024,
+        text_num_blocks=24,
+        text_num_heads=16,
+        out_embed_dim=1024,
+        audio_drop_path=0.1,
+        imu_drop_path=0.7,
+    )
+
+    if pretrained:
+        if not os.path.exists(".checkpoints/imagebind_huge.pth"):
+            print(
+                "Downloading imagebind weights to .checkpoints/imagebind_huge.pth ..."
+            )
+            os.makedirs(".checkpoints", exist_ok=True)
+            torch.hub.download_url_to_file(
+                "https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth",
+                ".checkpoints/imagebind_huge.pth",
+                progress=True,
+            )
+
+        model.load_state_dict(torch.load(".checkpoints/imagebind_huge.pth", weights_only=True))
+
+    return model

ImageBind/imagebind/models/multimodal_preprocessors.py

@@ -0,0 +1,685 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import gzip
+import html
+import io
+import math
+from functools import lru_cache
+from typing import Callable, List, Optional, Tuple
+
+import ftfy
+import numpy as np
+import regex as re
+import torch
+import torch.nn as nn
+from iopath.common.file_io import g_pathmgr
+from timm.layers import trunc_normal_
+
+from imagebind.models.helpers import VerboseNNModule, cast_if_src_dtype
+
+
+def get_sinusoid_encoding_table(n_position, d_hid):
+    """Sinusoid position encoding table"""
+
+    # TODO: make it with torch instead of numpy
+    def get_position_angle_vec(position):
+        return [
+            position / np.power(10000, 2 * (hid_j // 2) / d_hid)
+            for hid_j in range(d_hid)
+        ]
+
+    sinusoid_table = np.array(
+        [get_position_angle_vec(pos_i) for pos_i in range(n_position)]
+    )
+    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
+    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
+
+    return torch.FloatTensor(sinusoid_table).unsqueeze(0)
+
+
+def interpolate_pos_encoding_2d(target_spatial_size, pos_embed):
+    N = pos_embed.shape[1]
+    if N == target_spatial_size:
+        return pos_embed
+    dim = pos_embed.shape[-1]
+    # nn.functional.interpolate doesn't work with bfloat16 so we cast to float32
+    pos_embed, updated = cast_if_src_dtype(pos_embed, torch.bfloat16, torch.float32)
+    pos_embed = nn.functional.interpolate(
+        pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(
+            0, 3, 1, 2
+        ),
+        scale_factor=math.sqrt(target_spatial_size / N),
+        mode="bicubic",
+    )
+    if updated:
+        pos_embed, _ = cast_if_src_dtype(pos_embed, torch.float32, torch.bfloat16)
+    pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+    return pos_embed
+
+
+def interpolate_pos_encoding(
+    npatch_per_img,
+    pos_embed,
+    patches_layout,
+    input_shape=None,
+    first_patch_idx=1,
+):
+    assert first_patch_idx == 0 or first_patch_idx == 1, "there is 1 CLS token or none"
+    N = pos_embed.shape[1] - first_patch_idx  # since it's 1 if cls_token exists
+    if npatch_per_img == N:
+        return pos_embed
+
+    assert (
+        patches_layout[-1] == patches_layout[-2]
+    ), "Interpolation of pos embed not supported for non-square layouts"
+
+    class_emb = pos_embed[:, :first_patch_idx]
+    pos_embed = pos_embed[:, first_patch_idx:]
+
+    if input_shape is None or patches_layout[0] == 1:
+        # simple 2D pos embedding, no temporal component
+        pos_embed = interpolate_pos_encoding_2d(npatch_per_img, pos_embed)
+    elif patches_layout[0] > 1:
+        # pos embed has a temporal component
+        assert len(input_shape) == 4, "temporal interpolation not supported"
+        # we only support 2D interpolation in this case
+        num_frames = patches_layout[0]
+        num_spatial_tokens = patches_layout[1] * patches_layout[2]
+        pos_embed = pos_embed.view(1, num_frames, num_spatial_tokens, -1)
+        # interpolate embedding for zeroth frame
+        pos_embed = interpolate_pos_encoding_2d(
+            npatch_per_img, pos_embed[0, 0, ...].unsqueeze(0)
+        )
+    else:
+        raise ValueError("This type of interpolation isn't implemented")
+
+    return torch.cat((class_emb, pos_embed), dim=1)
+
+
+def _get_pos_embedding(
+    npatch_per_img,
+    pos_embed,
+    patches_layout,
+    input_shape,
+    first_patch_idx=1,
+):
+    pos_embed = interpolate_pos_encoding(
+        npatch_per_img,
+        pos_embed,
+        patches_layout,
+        input_shape=input_shape,
+        first_patch_idx=first_patch_idx,
+    )
+    return pos_embed
+
+
+class PatchEmbedGeneric(nn.Module):
+    """
+    PatchEmbed from Hydra
+    """
+
+    def __init__(self, proj_stem, norm_layer: Optional[nn.Module] = None):
+        super().__init__()
+
+        if len(proj_stem) > 1:
+            self.proj = nn.Sequential(*proj_stem)
+        else:
+            # Special case to be able to load pre-trained models that were
+            # trained with a standard stem
+            self.proj = proj_stem[0]
+        self.norm_layer = norm_layer
+
+    def get_patch_layout(self, img_size):
+        with torch.no_grad():
+            dummy_img = torch.zeros(
+                [
+                    1,
+                ]
+                + img_size
+            )
+            dummy_out = self.proj(dummy_img)
+        embed_dim = dummy_out.shape[1]
+        patches_layout = tuple(dummy_out.shape[2:])
+        num_patches = np.prod(patches_layout)
+        return patches_layout, num_patches, embed_dim
+
+    def forward(self, x):
+        x = self.proj(x)
+        # B C (T) H W -> B (T)HW C
+        x = x.flatten(2).transpose(1, 2)
+        if self.norm_layer is not None:
+            x = self.norm_layer(x)
+        return x
+
+
+class SpatioTemporalPosEmbeddingHelper(VerboseNNModule):
+    def __init__(
+        self,
+        patches_layout: List,
+        num_patches: int,
+        num_cls_tokens: int,
+        embed_dim: int,
+        learnable: bool,
+    ) -> None:
+        super().__init__()
+        self.num_cls_tokens = num_cls_tokens
+        self.patches_layout = patches_layout
+        self.num_patches = num_patches
+        self.num_tokens = num_cls_tokens + num_patches
+        self.learnable = learnable
+        if self.learnable:
+            self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim))
+            trunc_normal_(self.pos_embed, std=0.02)
+        else:
+            self.register_buffer(
+                "pos_embed", get_sinusoid_encoding_table(self.num_tokens, embed_dim)
+            )
+
+    def get_pos_embedding(self, vision_input, all_vision_tokens):
+        input_shape = vision_input.shape
+        pos_embed = _get_pos_embedding(
+            all_vision_tokens.size(1) - self.num_cls_tokens,
+            pos_embed=self.pos_embed,
+            patches_layout=self.patches_layout,
+            input_shape=input_shape,
+            first_patch_idx=self.num_cls_tokens,
+        )
+        return pos_embed
+
+
+class RGBDTPreprocessor(VerboseNNModule):
+    def __init__(
+        self,
+        rgbt_stem: PatchEmbedGeneric,
+        depth_stem: Optional[PatchEmbedGeneric],
+        img_size: Tuple = (3, 224, 224),
+        num_cls_tokens: int = 1,
+        pos_embed_fn: Optional[Callable] = None,
+        use_type_embed: bool = False,
+        init_param_style: str = "openclip",
+    ) -> None:
+        super().__init__()
+        stem = rgbt_stem if rgbt_stem is not None else depth_stem
+        (
+            self.patches_layout,
+            self.num_patches,
+            self.embed_dim,
+        ) = stem.get_patch_layout(img_size)
+        self.rgbt_stem = rgbt_stem
+        self.depth_stem = depth_stem
+        self.use_pos_embed = pos_embed_fn is not None
+        self.use_type_embed = use_type_embed
+        self.num_cls_tokens = num_cls_tokens
+
+        if self.use_pos_embed:
+            self.pos_embedding_helper = pos_embed_fn(
+                patches_layout=self.patches_layout,
+                num_cls_tokens=num_cls_tokens,
+                num_patches=self.num_patches,
+                embed_dim=self.embed_dim,
+            )
+        if self.num_cls_tokens > 0:
+            self.cls_token = nn.Parameter(
+                torch.zeros(1, self.num_cls_tokens, self.embed_dim)
+            )
+        if self.use_type_embed:
+            self.type_embed = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
+
+        self.init_parameters(init_param_style)
+
+    @torch.no_grad()
+    def init_parameters(self, init_param_style):
+        if init_param_style == "openclip":
+            # OpenCLIP style initialization
+            scale = self.embed_dim**-0.5
+            if self.use_pos_embed:
+                nn.init.normal_(self.pos_embedding_helper.pos_embed)
+                self.pos_embedding_helper.pos_embed *= scale
+
+            if self.num_cls_tokens > 0:
+                nn.init.normal_(self.cls_token)
+                self.cls_token *= scale
+        elif init_param_style == "vit":
+            self.cls_token.data.fill_(0)
+        else:
+            raise ValueError(f"Unknown init {init_param_style}")
+
+        if self.use_type_embed:
+            nn.init.normal_(self.type_embed)
+
+    def tokenize_input_and_cls_pos(self, input, stem, mask):
+        # tokens is of shape B x L x D
+        tokens = stem(input)
+        assert tokens.ndim == 3
+        assert tokens.shape[2] == self.embed_dim
+        B = tokens.shape[0]
+        if self.num_cls_tokens > 0:
+            class_tokens = self.cls_token.expand(
+                B, -1, -1
+            )  # stole class_tokens impl from Phil Wang, thanks
+            tokens = torch.cat((class_tokens, tokens), dim=1)
+        if self.use_pos_embed:
+            pos_embed = self.pos_embedding_helper.get_pos_embedding(input, tokens)
+            tokens = tokens + pos_embed
+        if self.use_type_embed:
+            tokens = tokens + self.type_embed.expand(B, -1, -1)
+        return tokens
+
+    def forward(self, vision=None, depth=None, patch_mask=None):
+        if patch_mask is not None:
+            raise NotImplementedError()
+
+        if vision is not None:
+            vision_tokens = self.tokenize_input_and_cls_pos(
+                vision, self.rgbt_stem, patch_mask
+            )
+
+        if depth is not None:
+            depth_tokens = self.tokenize_input_and_cls_pos(
+                depth, self.depth_stem, patch_mask
+            )
+
+        # aggregate tokens
+        if vision is not None and depth is not None:
+            final_tokens = vision_tokens + depth_tokens
+        else:
+            final_tokens = vision_tokens if vision is not None else depth_tokens
+        return_dict = {
+            "trunk": {
+                "tokens": final_tokens,
+            },
+            "head": {},
+        }
+        return return_dict
+
+
+class AudioPreprocessor(RGBDTPreprocessor):
+    def __init__(self, audio_stem: PatchEmbedGeneric, **kwargs) -> None:
+        super().__init__(rgbt_stem=audio_stem, depth_stem=None, **kwargs)
+
+    def forward(self, audio=None):
+        return super().forward(vision=audio)
+
+
+class ThermalPreprocessor(RGBDTPreprocessor):
+    def __init__(self, thermal_stem: PatchEmbedGeneric, **kwargs) -> None:
+        super().__init__(rgbt_stem=thermal_stem, depth_stem=None, **kwargs)
+
+    def forward(self, thermal=None):
+        return super().forward(vision=thermal)
+
+
+def build_causal_attention_mask(context_length):
+    # lazily create causal attention mask, with full attention between the vision tokens
+    # pytorch uses additive attention mask; fill with -inf
+    mask = torch.empty(context_length, context_length, requires_grad=False)
+    mask.fill_(float("-inf"))
+    mask.triu_(1)  # zero out the lower diagonal
+    return mask
+
+
+class TextPreprocessor(VerboseNNModule):
+    def __init__(
+        self,
+        vocab_size: int,
+        context_length: int,
+        embed_dim: int,
+        causal_masking: bool,
+        supply_seq_len_to_head: bool = True,
+        num_cls_tokens: int = 0,
+        init_param_style: str = "openclip",
+    ) -> None:
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.context_length = context_length
+        self.token_embedding = nn.Embedding(vocab_size, embed_dim)
+        self.pos_embed = nn.Parameter(
+            torch.empty(1, self.context_length + num_cls_tokens, embed_dim)
+        )
+        self.causal_masking = causal_masking
+        if self.causal_masking:
+            mask = build_causal_attention_mask(self.context_length)
+            # register the mask as a buffer so it can be moved to the right device
+            self.register_buffer("mask", mask)
+
+        self.supply_seq_len_to_head = supply_seq_len_to_head
+        self.num_cls_tokens = num_cls_tokens
+        self.embed_dim = embed_dim
+        if num_cls_tokens > 0:
+            assert self.causal_masking is False, "Masking + CLS token isn't implemented"
+            self.cls_token = nn.Parameter(
+                torch.zeros(1, self.num_cls_tokens, embed_dim)
+            )
+
+        self.init_parameters(init_param_style)
+
+    @torch.no_grad()
+    def init_parameters(self, init_param_style="openclip"):
+        # OpenCLIP style initialization
+        nn.init.normal_(self.token_embedding.weight, std=0.02)
+        nn.init.normal_(self.pos_embed, std=0.01)
+
+        if init_param_style == "openclip":
+            # OpenCLIP style initialization
+            scale = self.embed_dim**-0.5
+            if self.num_cls_tokens > 0:
+                nn.init.normal_(self.cls_token)
+                self.cls_token *= scale
+        elif init_param_style == "vit":
+            self.cls_token.data.fill_(0)
+        else:
+            raise ValueError(f"Unknown init {init_param_style}")
+
+    def forward(self, text):
+        # text tokens are of shape B x L x D
+        text_tokens = self.token_embedding(text)
+        # concat CLS tokens if any
+        if self.num_cls_tokens > 0:
+            B = text_tokens.shape[0]
+            class_tokens = self.cls_token.expand(
+                B, -1, -1
+            )  # stole class_tokens impl from Phil Wang, thanks
+            text_tokens = torch.cat((class_tokens, text_tokens), dim=1)
+        text_tokens = text_tokens + self.pos_embed
+        return_dict = {
+            "trunk": {
+                "tokens": text_tokens,
+            },
+            "head": {},
+        }
+        # Compute sequence length after adding CLS tokens
+        if self.supply_seq_len_to_head:
+            text_lengths = text.argmax(dim=-1)
+            return_dict["head"] = {
+                "seq_len": text_lengths,
+            }
+        if self.causal_masking:
+            return_dict["trunk"].update({"attn_mask": self.mask})
+        return return_dict
+
+
+class Im2Video(nn.Module):
+    """Convert an image into a trivial video."""
+
+    def __init__(self, time_dim=2):
+        super().__init__()
+        self.time_dim = time_dim
+
+    def forward(self, x):
+        if x.ndim == 4:
+            # B, C, H, W -> B, C, T, H, W
+            return x.unsqueeze(self.time_dim)
+        elif x.ndim == 5:
+            return x
+        else:
+            raise ValueError(f"Dimension incorrect {x.shape}")
+
+
+class PadIm2Video(Im2Video):
+    def __init__(self, ntimes, pad_type, time_dim=2):
+        super().__init__(time_dim=time_dim)
+        assert ntimes > 0
+        assert pad_type in ["zero", "repeat"]
+        self.ntimes = ntimes
+        self.pad_type = pad_type
+
+    def forward(self, x):
+        x = super().forward(x)
+        if x.shape[self.time_dim] == 1:
+            if self.pad_type == "repeat":
+                new_shape = [1] * len(x.shape)
+                new_shape[self.time_dim] = self.ntimes
+                x = x.repeat(new_shape)
+            elif self.pad_type == "zero":
+                padarg = [0, 0] * len(x.shape)
+                padarg[2 * self.time_dim + 1] = self.ntimes - x.shape[self.time_dim]
+                x = nn.functional.pad(x, padarg)
+        return x
+
+
+# Modified from github.com/openai/CLIP
+@lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+class SimpleTokenizer(object):
+    def __init__(self, bpe_path: str, context_length=77):
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+
+        with g_pathmgr.open(bpe_path, "rb") as fh:
+            bpe_bytes = io.BytesIO(fh.read())
+            merges: List[str] = gzip.open(bpe_bytes).read().decode("utf-8").split("\n")
+        merges = merges[1 : 49152 - 256 - 2 + 1]
+        merges: List[Tuple[str, ...]] = [tuple(merge.split()) for merge in merges]
+        vocab = list(bytes_to_unicode().values())
+        vocab = vocab + [v + "</w>" for v in vocab]
+        for merge in merges:
+            vocab.append("".join(merge))
+        vocab.extend(["<|startoftext|>", "<|endoftext|>"])
+        self.encoder = dict(zip(vocab, range(len(vocab))))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {
+            "<|startoftext|>": "<|startoftext|>",
+            "<|endoftext|>": "<|endoftext|>",
+        }
+        self.pat = re.compile(
+            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
+            re.IGNORECASE,
+        )
+        self.context_length = context_length
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token[:-1]) + (token[-1] + "</w>",)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token + "</w>"
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except:
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        bpe_tokens = []
+        text = whitespace_clean(basic_clean(text)).lower()
+        for token in re.findall(self.pat, text):
+            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
+            bpe_tokens.extend(
+                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
+            )
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = "".join([self.decoder[token] for token in tokens])
+        text = (
+            bytearray([self.byte_decoder[c] for c in text])
+            .decode("utf-8", errors="replace")
+            .replace("</w>", " ")
+        )
+        return text
+
+    def __call__(self, texts, context_length=None):
+        if not context_length:
+            context_length = self.context_length
+
+        if isinstance(texts, str):
+            texts = [texts]
+
+        sot_token = self.encoder["<|startoftext|>"]
+        eot_token = self.encoder["<|endoftext|>"]
+        all_tokens = [[sot_token] + self.encode(text) for text in texts]
+        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
+
+        for i, tokens in enumerate(all_tokens):
+            tokens = tokens[:context_length - 1] + [eot_token]
+            result[i, : len(tokens)] = torch.tensor(tokens)
+
+        if len(result) == 1:
+            return result[0]
+        return result
+
+
+class IMUPreprocessor(VerboseNNModule):
+    def __init__(
+        self,
+        kernel_size: int,
+        imu_stem: PatchEmbedGeneric,
+        embed_dim: int,
+        img_size: Tuple = (6, 2000),
+        num_cls_tokens: int = 1,
+        pos_embed_fn: Optional[Callable] = None,
+        init_param_style: str = "openclip",
+    ) -> None:
+        super().__init__()
+        self.imu_stem = imu_stem
+        self.embed_dim = embed_dim
+        self.use_pos_embed = pos_embed_fn is not None
+        self.num_cls_tokens = num_cls_tokens
+        self.kernel_size = kernel_size
+        self.pos_embed = nn.Parameter(
+            torch.empty(1, (img_size[1] // kernel_size) + num_cls_tokens, embed_dim)
+        )
+
+        if self.num_cls_tokens > 0:
+            self.cls_token = nn.Parameter(
+                torch.zeros(1, self.num_cls_tokens, self.embed_dim)
+            )
+
+        self.init_parameters(init_param_style)
+
+    @torch.no_grad()
+    def init_parameters(self, init_param_style):
+        nn.init.normal_(self.pos_embed, std=0.01)
+
+        if init_param_style == "openclip":
+            # OpenCLIP style initialization
+            scale = self.embed_dim**-0.5
+
+            if self.num_cls_tokens > 0:
+                nn.init.normal_(self.cls_token)
+                self.cls_token *= scale
+        elif init_param_style == "vit":
+            self.cls_token.data.fill_(0)
+        else:
+            raise ValueError(f"Unknown init {init_param_style}")
+
+    def tokenize_input_and_cls_pos(self, input, stem):
+        # tokens is of shape B x L x D
+        tokens = stem.norm_layer(stem.proj(input))
+        assert tokens.ndim == 3
+        assert tokens.shape[2] == self.embed_dim
+        B = tokens.shape[0]
+        if self.num_cls_tokens > 0:
+            class_tokens = self.cls_token.expand(
+                B, -1, -1
+            )  # stole class_tokens impl from Phil Wang, thanks
+            tokens = torch.cat((class_tokens, tokens), dim=1)
+        if self.use_pos_embed:
+            tokens = tokens + self.pos_embed
+        return tokens
+
+    def forward(self, imu):
+        # Patchify
+        imu = imu.unfold(
+            -1,
+            self.kernel_size,
+            self.kernel_size,
+        ).permute(0, 2, 1, 3)
+        imu = imu.reshape(imu.size(0), imu.size(1), -1)
+
+        imu_tokens = self.tokenize_input_and_cls_pos(
+            imu,
+            self.imu_stem,
+        )
+
+        return_dict = {
+            "trunk": {
+                "tokens": imu_tokens,
+            },
+            "head": {},
+        }
+        return return_dict

ImageBind/imagebind/models/transformer.py

@@ -0,0 +1,336 @@
+#!/usr/bin/env python3
+# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Code modified from
+# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py ;
+# https://github.com/facebookresearch/deit/blob/main/models.py
+# and https://github.com/facebookresearch/vissl/blob/main/vissl/models/trunks/vision_transformer.py
+
+
+from functools import partial
+from inspect import signature
+from typing import Callable, List, Optional, Sequence
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+from timm.layers import DropPath, trunc_normal_
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version,
+        # can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = (
+            qkv[0],
+            qkv[1],
+            qkv[2],
+        )  # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        drop=0.0,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class MultiheadAttention(nn.MultiheadAttention):
+    """
+    Same as ``nn.MultiheadAttention`` for ImageBind trunks, with an optional capture path
+    so attention weights can be read for visualization without duplicating the forward pass.
+
+    Call ``set_attention_capture(True)`` before ``forward``; averaged weights are stored in
+    ``last_attn_weights`` (shape ``(B, L, L)``) when capture is enabled.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._capture_attention = False
+        self.last_attn_weights: Optional[torch.Tensor] = None
+
+    def set_attention_capture(self, enabled: bool) -> None:
+        self._capture_attention = bool(enabled)
+        if not enabled:
+            self.last_attn_weights = None
+
+    def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):
+        if not self._capture_attention:
+            return super().forward(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
+        kwargs = dict(
+            key_padding_mask=None,
+            need_weights=True,
+            attn_mask=attn_mask,
+            average_attn_weights=True,
+        )
+        if "is_causal" in signature(super().forward).parameters:
+            kwargs["is_causal"] = False
+        out, attn_weights = super().forward(x, x, x, **kwargs)
+        self.last_attn_weights = attn_weights
+        return out
+
+
+class ViTAttention(Attention):
+    def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):
+        assert attn_mask is None
+        return super().forward(x)
+
+
+class BlockWithMasking(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        attn_target: Callable,
+        mlp_ratio: int = 4,
+        act_layer: Callable = nn.GELU,
+        norm_layer: Callable = nn.LayerNorm,
+        ffn_dropout_rate: float = 0.0,
+        drop_path: float = 0.0,
+        layer_scale_type: Optional[str] = None,
+        layer_scale_init_value: float = 1e-4,
+    ):
+        super().__init__()
+
+        assert not isinstance(
+            attn_target, nn.Module
+        ), "attn_target should be a Callable. Otherwise attn_target is shared across blocks!"
+        self.attn = attn_target()
+        if drop_path > 0.0:
+            self.drop_path = DropPath(drop_path)
+        else:
+            self.drop_path = nn.Identity()
+        self.norm_1 = norm_layer(dim)
+        mlp_hidden_dim = int(mlp_ratio * dim)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=ffn_dropout_rate,
+        )
+        self.norm_2 = norm_layer(dim)
+        self.layer_scale_type = layer_scale_type
+        if self.layer_scale_type is not None:
+            assert self.layer_scale_type in [
+                "per_channel",
+                "scalar",
+            ], f"Found Layer scale type {self.layer_scale_type}"
+            if self.layer_scale_type == "per_channel":
+                # one gamma value per channel
+                gamma_shape = [1, 1, dim]
+            elif self.layer_scale_type == "scalar":
+                # single gamma value for all channels
+                gamma_shape = [1, 1, 1]
+            # two gammas: for each part of the fwd in the encoder
+            self.layer_scale_gamma1 = nn.Parameter(
+                torch.ones(size=gamma_shape) * layer_scale_init_value,
+                requires_grad=True,
+            )
+            self.layer_scale_gamma2 = nn.Parameter(
+                torch.ones(size=gamma_shape) * layer_scale_init_value,
+                requires_grad=True,
+            )
+
+    def forward(self, x: torch.Tensor, attn_mask: torch.Tensor):
+        if self.layer_scale_type is None:
+            x = x + self.drop_path(self.attn(self.norm_1(x), attn_mask))
+            x = x + self.drop_path(self.mlp(self.norm_2(x)))
+        else:
+            x = (
+                x
+                + self.drop_path(self.attn(self.norm_1(x), attn_mask))
+                * self.layer_scale_gamma1
+            )
+            x = x + self.drop_path(self.mlp(self.norm_2(x))) * self.layer_scale_gamma2
+        return x
+
+
+_LAYER_NORM = partial(nn.LayerNorm, eps=1e-6)
+
+
+class SimpleTransformer(nn.Module):
+    def __init__(
+        self,
+        attn_target: Callable,
+        embed_dim: int,
+        num_blocks: int,
+        block: Callable = BlockWithMasking,
+        pre_transformer_layer: Optional[Callable] = None,
+        post_transformer_layer: Optional[Callable] = None,
+        drop_path_rate: float = 0.0,
+        drop_path_type: str = "progressive",
+        norm_layer: Callable = _LAYER_NORM,
+        mlp_ratio: int = 4,
+        ffn_dropout_rate: float = 0.0,
+        layer_scale_type: Optional[str] = None,  # from cait; possible values are None, "per_channel", "scalar"
+        layer_scale_init_value: float = 1e-4,  # from cait; float
+        weight_init_style: str = "jax",  # possible values jax or pytorch
+    ):
+        """
+        Simple Transformer with the following features
+        1. Supports masked attention
+        2. Supports DropPath
+        3. Supports LayerScale
+        4. Supports Dropout in Attention and FFN
+        5. Makes few assumptions about the input except that it is a Tensor
+        """
+        super().__init__()
+        self.pre_transformer_layer = pre_transformer_layer
+        if drop_path_type == "progressive":
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, num_blocks)]
+        elif drop_path_type == "uniform":
+            dpr = [drop_path_rate for i in range(num_blocks)]
+        else:
+            raise ValueError(f"Unknown drop_path_type: {drop_path_type}")
+
+        self.blocks = nn.Sequential(
+            *[
+                block(
+                    dim=embed_dim,
+                    attn_target=attn_target,
+                    mlp_ratio=mlp_ratio,
+                    ffn_dropout_rate=ffn_dropout_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    layer_scale_type=layer_scale_type,
+                    layer_scale_init_value=layer_scale_init_value,
+                )
+                for i in range(num_blocks)
+            ]
+        )
+        self.post_transformer_layer = post_transformer_layer
+        self.weight_init_style = weight_init_style
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            if self.weight_init_style == "jax":
+                # Based on MAE and official Jax ViT implementation
+                torch.nn.init.xavier_uniform_(m.weight)
+            elif self.weight_init_style == "pytorch":
+                # PyTorch ViT uses trunc_normal_
+                trunc_normal_(m.weight, std=0.02)
+
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, (nn.LayerNorm)):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def set_attention_capture(
+        self,
+        enabled: bool,
+        block_indices: Optional[Sequence[int]] = None,
+    ) -> None:
+        """
+        Enable or disable attention weight capture on ``MultiheadAttention`` blocks.
+
+        When ``enabled`` is True and ``block_indices`` is None, only the **last** block
+        records attention (typical for CLS–patch maps). Pass an explicit index sequence
+        (e.g. ``range(num_blocks)``) to capture specific layers.
+        """
+        n = len(self.blocks)
+        for i, blk in enumerate(self.blocks):
+            if not isinstance(blk.attn, MultiheadAttention):
+                continue
+            if not enabled:
+                blk.attn.set_attention_capture(False)
+                continue
+            if block_indices is None:
+                capture = i == n - 1
+            else:
+                capture = i in block_indices
+            blk.attn.set_attention_capture(capture)
+
+    def forward(
+        self,
+        tokens: torch.Tensor,
+        attn_mask: torch.Tensor = None,
+        use_checkpoint: bool = False,
+        checkpoint_every_n: int = 1,
+        checkpoint_blk_ids: Optional[List[int]] = None,
+    ):
+        """
+        Inputs
+        - tokens: data of shape N x L x D (or L x N x D depending on the attention implementation)
+        - attn: mask of shape L x L
+
+        Output
+        - x: data of shape N x L x D (or L x N x D depending on the attention implementation)
+        """
+        if self.pre_transformer_layer:
+            tokens = self.pre_transformer_layer(tokens)
+        if use_checkpoint and checkpoint_blk_ids is None:
+            checkpoint_blk_ids = [
+                blk_id
+                for blk_id in range(len(self.blocks))
+                if blk_id % checkpoint_every_n == 0
+            ]
+        if checkpoint_blk_ids:
+            checkpoint_blk_ids = set(checkpoint_blk_ids)
+        for blk_id, blk in enumerate(self.blocks):
+            if use_checkpoint and blk_id in checkpoint_blk_ids:
+                tokens = checkpoint.checkpoint(
+                    blk, tokens, attn_mask, use_reentrant=False
+                )
+            else:
+                tokens = blk(tokens, attn_mask=attn_mask)
+        if self.post_transformer_layer:
+            tokens = self.post_transformer_layer(tokens)
+        return tokens

ImageBind/legrad_imagebind.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6912a420f874b43e531664f02d0db42a31637a9cf543c0ff3366add6bf55eb2b
+size 13383277

ImageBind/model_card.md

@@ -0,0 +1,94 @@
+# Model Card for ImageBind
+
+Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images.
+Input any of the six modalities and get the same sized embedding that can be used for cross-modal and multimodal tasks.
+
+# Model Details
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is/does. -->
+Multimodal joint embedding model for image/video, text, audio, depth, IMU, and thermal images
+
+- **Developed by:** Meta AI
+- **Model type:** Multimodal model
+- **Language(s) (NLP):** en
+- **License:** CC BY-NC-SA 4.0
+- **Resources for more information:**
+    - [GitHub Repo](https://github.com/facebookresearch/ImageBind)
+
+
+# Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is intended only for research purposes. It provides a joint embedding space for different modalities -- image/video, text, audio, depth, IMU and thermal images.
+We hope that these joint embeddings can be used for a variety of different cross-modal research, e.g., cross-modal retrieval and combining embeddings from different modalities.
+
+## Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
+
+This model is *NOT* intended to be used in any real world application -- commercial or otherwise.
+It may produce harmful associations with different inputs.
+The model needs to be investigated and likely re-trained on specific data for any such application.
+The model is expected to work better on web-based visual data since it was trained on such data.
+The text encoder is likely to work only on English language text because of the underlying training datasets.
+
+# Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Open-domain joint embedding models are prone to producing specific biases, e.g., study from [CLIP](https://github.com/openai/CLIP/blob/main/model-card.md#bias-and-fairness).
+Since our model uses such models as initialization, it will exhibit such biases too.
+Moreover, for learning joint embeddings for other modalities such as audio, thermal, depth, and IMU we leverage datasets that are relatively small. These joint embeddings are thus limited to the concepts present in the datasets. For example, the thermal datasets we used are limited to outdoor street scenes, while the depth datasets are limited to indoor scenes.
+
+
+
+# Training Details
+
+## Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+ImageBind uses image-paired data for training -- (image, X) where X is one of text, audio, depth, IMU or thermal data.
+In particular, we initialize and freeze the image and text encoders using an OpenCLIP ViT-H encoder.
+We train audio embeddings using Audioset, depth embeddings using the SUN RGB-D dataset, IMU using the Ego4D dataset and thermal embeddings using the LLVIP dataset.
+We provide the exact training data details in the paper.
+
+
+## Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Please refer to the research paper and github repo for exact details on this.
+
+# Evaluation
+
+## Testing Data, Factors & Metrics
+
+We evaluate the model on a variety of different classification benchmarks for each modality.
+The evaluation details are presented in the paper.
+The models performance is measured using standard classification metrics such as accuracy and mAP.
+
+# Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+```
+@inproceedings{girdhar2023imagebind,
+  title={ImageBind: One Embedding Space To Bind Them All},
+  author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
+and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
+  booktitle={CVPR},
+  year={2023}
+}
+```
+
+
+# Model Card Contact
+
+Please reach out to the authors at: rgirdhar@meta.com imisra@meta.com alaaelnouby@gmail.com
+
+# How to Get Started with the Model
+
+Our github repo provides a simple example to extract embeddings from images, audio etc.

ImageBind/requirements.txt

@@ -0,0 +1,11 @@
+torch>=2.0.0
+torchvision  # because torch version already specific, the right torchvision will be derived automatically
+torchaudio  # because torch version already specific, the right torchaudio will be derived automatically
+pytorchvideo @ git+https://github.com/facebookresearch/pytorchvideo.git@6cdc929315aab1b5674b6dcf73b16ec99147735f
+timm
+ftfy
+regex
+einops
+iopath
+numpy>=1.19
+types-regex

ImageBind/setup.py

@@ -0,0 +1,23 @@
+from setuptools import setup, find_packages
+
+with open('requirements.txt') as f:
+    required = f.read().splitlines()
+
+setup(
+    name='imagebind',
+    version='0.1.0',
+    packages=find_packages(),
+    package_data={
+        'imagebind': ['bpe/bpe_simple_vocab_16e6.txt.gz'],
+    },
+    description='A brief description of the package',
+    long_description=open('README.md', encoding='utf-8').read(),
+    long_description_content_type="text/markdown",
+    url='https://github.com/facebookresearch/ImageBind',
+    classifiers=[
+        'Programming Language :: Python :: 3',
+        'License :: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International',
+    ],
+    install_requires=required,
+    dependency_links=['https://download.pytorch.org/whl/cu113'],
+)