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CITATION.cff

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+cff-version: 1.2.0
+message: "If you find this work useful in your research, please cite it."
+preferred-citation:
+  type: conference-paper
+  title: "MARLIN: Masked Autoencoder for facial video Representation LearnINg"
+  authors:
+  - family-names: "Cai"
+    given-names: "Zhixi"
+  - family-names: "Ghosh"
+    given-names: "Shreya"
+  - family-names: "Stefanov"
+    given-names: "Kalin"
+  - family-names: "Dhall"
+    given-names: "Abhinav"
+  - family-names: "Cai"
+    given-names: "Jianfei"
+  - family-names: "Rezatofighi"
+    given-names: "Hamid"
+  - family-names: "Haffari"
+    given-names: "Reza"
+  - family-names: "Hayat"
+    given-names: "Munawar"
+  collection-title: "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition"
+  year: 2023
+  location:
+    name: "Vancouver, Canada"
+  start: 1493
+  end: 1504
+  doi: 10.1109/CVPR52729.2023.00150

LICENSE.txt

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README.md

@@ -1,3 +1,298 @@
----
-license: cc-by-nc-4.0
----
+# MARLIN: Masked Autoencoder for facial video Representation LearnINg
+
+<div>
+    <img src="assets/teaser.svg">
+    <p></p>
+</div>
+
+<div align="center">
+    <a href="https://github.com/ControlNet/MARLIN/network/members">
+        <img src="https://img.shields.io/github/forks/ControlNet/MARLIN?style=flat-square">
+    </a>
+    <a href="https://github.com/ControlNet/MARLIN/stargazers">
+        <img src="https://img.shields.io/github/stars/ControlNet/MARLIN?style=flat-square">
+    </a>
+    <a href="https://github.com/ControlNet/MARLIN/issues">
+        <img src="https://img.shields.io/github/issues/ControlNet/MARLIN?style=flat-square">
+    </a>
+    <a href="https://github.com/ControlNet/MARLIN/blob/master/LICENSE">
+        <img src="https://img.shields.io/badge/license-CC%20BY--NC%204.0-97ca00?style=flat-square">
+    </a>
+    <a href="https://arxiv.org/abs/2211.06627">
+        <img src="https://img.shields.io/badge/arXiv-2211.06627-b31b1b.svg?style=flat-square">
+    </a>
+</div>
+
+<div align="center">    
+    <a href="https://pypi.org/project/marlin-pytorch/">
+        <img src="https://img.shields.io/pypi/v/marlin-pytorch?style=flat-square">
+    </a>
+    <a href="https://pypi.org/project/marlin-pytorch/">
+        <img src="https://img.shields.io/pypi/dm/marlin-pytorch?style=flat-square">
+    </a>
+    <a href="https://www.python.org/"><img src="https://img.shields.io/pypi/pyversions/marlin-pytorch?style=flat-square"></a>
+    <a href="https://pytorch.org/"><img src="https://img.shields.io/badge/PyTorch-%3E%3D1.8.0-EE4C2C?style=flat-square&logo=pytorch"></a>
+</div>
+
+<div align="center">
+    <a href="https://github.com/ControlNet/MARLIN/actions"><img src="https://img.shields.io/github/actions/workflow/status/ControlNet/MARLIN/unittest.yaml?branch=dev&label=unittest&style=flat-square"></a>
+    <a href="https://github.com/ControlNet/MARLIN/actions"><img src="https://img.shields.io/github/actions/workflow/status/ControlNet/MARLIN/release.yaml?branch=master&label=release&style=flat-square"></a>
+    <a href="https://coveralls.io/github/ControlNet/MARLIN"><img src="https://img.shields.io/coverallsCoverage/github/ControlNet/MARLIN?style=flat-square"></a>
+</div>
+
+This repo is the official PyTorch implementation for the paper 
+[MARLIN: Masked Autoencoder for facial video Representation LearnINg](https://openaccess.thecvf.com/content/CVPR2023/html/Cai_MARLIN_Masked_Autoencoder_for_Facial_Video_Representation_LearnINg_CVPR_2023_paper) (CVPR 2023).
+
+## Repository Structure
+
+The repository contains 2 parts:
+ - `marlin-pytorch`: The PyPI package for MARLIN used for inference.
+ - The implementation for the paper including training and evaluation scripts.
+
+```
+.
+├── assets                # Images for README.md
+├── LICENSE
+├── README.md
+├── MODEL_ZOO.md
+├── CITATION.cff
+├── .gitignore
+├── .github
+
+# below is for the PyPI package marlin-pytorch
+├── src                   # Source code for marlin-pytorch
+├── tests                 # Unittest
+├── requirements.lib.txt
+├── setup.py
+├── init.py
+├── version.txt
+
+# below is for the paper implementation
+├── configs              # Configs for experiments settings
+├── model                # Marlin models
+├── preprocess           # Preprocessing scripts
+├── dataset              # Dataloaders
+├── utils                # Utility functions
+├── train.py             # Training script
+├── evaluate.py          # Evaluation script
+├── requirements.txt
+
+```
+
+## Use `marlin-pytorch` for Feature Extraction
+
+Requirements:
+- Python >= 3.6, < 3.12
+- PyTorch >= 1.8
+- ffmpeg
+
+
+Install from PyPI:
+```bash
+pip install marlin-pytorch
+```
+
+Load MARLIN model from online
+```python
+from marlin_pytorch import Marlin
+# Load MARLIN model from GitHub Release
+model = Marlin.from_online("marlin_vit_base_ytf")
+```
+
+Load MARLIN model from file
+```python
+from marlin_pytorch import Marlin
+# Load MARLIN model from local file
+model = Marlin.from_file("marlin_vit_base_ytf", "path/to/marlin.pt")
+# Load MARLIN model from the ckpt file trained by the scripts in this repo
+model = Marlin.from_file("marlin_vit_base_ytf", "path/to/marlin.ckpt")
+```
+
+Current model name list:
+- `marlin_vit_small_ytf`: ViT-small encoder trained on YTF dataset. Embedding 384 dim.
+- `marlin_vit_base_ytf`: ViT-base encoder trained on YTF dataset. Embedding 768 dim.
+- `marlin_vit_large_ytf`: ViT-large encoder trained on YTF dataset. Embedding 1024 dim.
+
+For more details, see [MODEL_ZOO.md](MODEL_ZOO.md).
+
+When MARLIN model is retrieved from GitHub Release, it will be cached in `.marlin`. You can remove marlin cache by
+```python
+from marlin_pytorch import Marlin
+Marlin.clean_cache()
+```
+
+Extract features from cropped video file
+```python
+# Extract features from facial cropped video with size (224x224)
+features = model.extract_video("path/to/video.mp4")
+print(features.shape)  # torch.Size([T, 768]) where T is the number of windows
+
+# You can keep output of all elements from the sequence by setting keep_seq=True
+features = model.extract_video("path/to/video.mp4", keep_seq=True)
+print(features.shape)  # torch.Size([T, k, 768]) where k = T/t * H/h * W/w = 8 * 14 * 14 = 1568
+```
+
+Extract features from in-the-wild video file
+```python
+# Extract features from in-the-wild video with various size
+features = model.extract_video("path/to/video.mp4", crop_face=True)
+print(features.shape)  # torch.Size([T, 768])
+```
+
+Extract features from video clip tensor
+```python
+# Extract features from clip tensor with size (B, 3, 16, 224, 224)
+x = ...  # video clip
+features = model.extract_features(x)  # torch.Size([B, k, 768])
+features = model.extract_features(x, keep_seq=False)  # torch.Size([B, 768])
+```
+
+## Paper Implementation
+
+### Requirements
+- Python >= 3.7, < 3.12
+- PyTorch ~= 1.11
+- Torchvision ~= 0.12
+
+### Installation
+
+Firstly, make sure you have installed PyTorch and Torchvision with or without CUDA. 
+
+Clone the repo and install the requirements:
+```bash
+git clone https://github.com/ControlNet/MARLIN.git
+cd MARLIN
+pip install -r requirements.txt
+```
+
+### MARLIN Pretraining
+
+Download the [YoutubeFaces](https://www.cs.tau.ac.il/~wolf/ytfaces/) dataset (only `frame_images_DB` is required). 
+
+Download the face parsing model from [face_parsing.farl.lapa](https://github.com/FacePerceiver/facer/releases/download/models-v1/face_parsing.farl.lapa.main_ema_136500_jit191.pt)
+and put it in `utils/face_sdk/models/face_parsing/face_parsing_1.0`.
+
+Download the VideoMAE pretrained [checkpoint](https://github.com/ControlNet/MARLIN/releases/misc) 
+for initializing the weights. (ps. They updated their models in this 
+[commit](https://github.com/MCG-NJU/VideoMAE/commit/2b56a75d166c619f71019e3d1bb1c4aedafe7a90), but we are using the 
+old models which are not shared anymore by the authors. So we uploaded this model by ourselves.)
+
+Then run scripts to process the dataset:
+```bash
+python preprocess/ytf_preprocess.py --data_dir /path/to/youtube_faces --max_workers 8
+```
+After processing, the directory structure should be like this:
+```
+├── YoutubeFaces
+│   ├── frame_images_DB
+│   │   ├── Aaron_Eckhart
+│   │   │   ├── 0
+│   │   │   │   ├── 0.555.jpg
+│   │   │   │   ├── ...
+│   │   │   ├── ...
+│   │   ├── ...
+│   ├── crop_images_DB
+│   │   ├── Aaron_Eckhart
+│   │   │   ├── 0
+│   │   │   │   ├── 0.555.jpg
+│   │   │   │   ├── ...
+│   │   │   ├── ...
+│   │   ├── ...
+│   ├── face_parsing_images_DB
+│   │   ├── Aaron_Eckhart
+│   │   │   ├── 0
+│   │   │   │   ├── 0.555.npy
+│   │   │   │   ├── ...
+│   │   │   ├── ...
+│   │   ├── ...
+│   ├── train_set.csv
+│   ├── val_set.csv
+```
+
+Then, run the training script:
+```bash
+python train.py \
+    --config config/pretrain/marlin_vit_base.yaml \
+    --data_dir /path/to/youtube_faces \
+    --n_gpus 4 \
+    --num_workers 8 \
+    --batch_size 16 \
+    --epochs 2000 \
+    --official_pretrained /path/to/videomae/checkpoint.pth
+```
+
+After trained, you can load the checkpoint for inference by
+
+```python
+from marlin_pytorch import Marlin
+from marlin_pytorch.config import register_model_from_yaml
+
+register_model_from_yaml("my_marlin_model", "path/to/config.yaml")
+model = Marlin.from_file("my_marlin_model", "path/to/marlin.ckpt")
+```
+
+## Evaluation
+
+<details>
+<summary>CelebV-HQ</summary>
+
+#### 1. Download the dataset
+Download dataset from [CelebV-HQ](https://github.com/CelebV-HQ/CelebV-HQ) and the file structure should be like this:
+```
+├── CelebV-HQ
+│   ├── downloaded
+│   │   ├── ***.mp4
+│   │   ├── ...
+│   ├── celebvhq_info.json
+│   ├── ...
+```
+#### 2. Preprocess the dataset
+Crop the face region from the raw video and split the train val and test sets.
+```bash
+python preprocess/celebvhq_preprocess.py --data_dir /path/to/CelebV-HQ 
+```
+
+#### 3. Extract MARLIN features (Optional, if linear probing)
+Extract MARLIN features from the cropped video and saved to `<backbone>` directory in `CelebV-HQ` directory.
+```bash
+python preprocess/celebvhq_extract.py --data_dir /path/to/CelebV-HQ --backbone marlin_vit_base_ytf
+```
+
+#### 4. Train and evaluate
+Train and evaluate the model adapted from MARLIN to CelebV-HQ.
+
+Please use the configs in `config/celebv_hq/*/*.yaml` as the config file.
+```bash
+python evaluate.py \
+    --config /path/to/config \
+    --data_path /path/to/CelebV-HQ 
+    --num_workers 4 
+    --batch_size 16
+```
+
+</details>
+
+
+## License
+
+This project is under the CC BY-NC 4.0 license. See [LICENSE](LICENSE) for details.
+
+## References
+If you find this work useful for your research, please consider citing it.
+```bibtex
+@inproceedings{cai2022marlin,
+  title = {MARLIN: Masked Autoencoder for facial video Representation LearnINg},
+  author = {Cai, Zhixi and Ghosh, Shreya and Stefanov, Kalin and Dhall, Abhinav and Cai, Jianfei and Rezatofighi, Hamid and Haffari, Reza and Hayat, Munawar},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year = {2023},
+  month = {June},
+  pages = {1493-1504},
+  doi = {10.1109/CVPR52729.2023.00150},
+  publisher = {IEEE},
+}
+```
+
+## Acknowledgements
+
+Some code about model is based on [MCG-NJU/VideoMAE](https://github.com/MCG-NJU/VideoMAE). The code related to preprocessing
+is borrowed from [JDAI-CV/FaceX-Zoo](https://github.com/JDAI-CV/FaceX-Zoo).

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