Upload directory

README.md

@@ -0,0 +1,89 @@
+---
+language: en
+license: mit
+arxiv: 2403.14852
+---
+
+<div align="center">
+<h1>
+  CVLFace Pretrained Model (ADAFACE VIT BASE KPRPE WEBFACE4M)
+</h1>
+</div>
+
+
+<p align="center">
+ 🌎 <a href="https://github.com/mk-minchul/CVLface" target="_blank">GitHub</a> • 🤗 <a href="https://huggingface.co/minchul" target="_blank">Hugging Face</a> 
+</p>
+
+
+-----
+
+
+##  1. Introduction
+
+Model Name: ADAFACE VIT BASE KPRPE WEBFACE4M
+
+Related Paper: KeyPoint Relative Position Encoding for Face Recognition (https://arxiv.org/abs/2403.14852)
+
+Please cite the orignal paper and follow the license of the training dataset.
+
+##  2. Quick Start
+
+```python
+from transformers import AutoModel
+from huggingface_hub import hf_hub_download
+import shutil
+import os
+import torch
+
+
+# helpfer function to download huggingface repo and use model
+def download(repo_id, path, HF_TOKEN=None):
+    files_path = os.path.join(path, 'files.txt')
+    if not os.path.exists(files_path):
+        hf_hub_download(repo_id, 'files.txt', token=HF_TOKEN, local_dir=path, local_dir_use_symlinks=False)
+    with open(os.path.join(path, 'files.txt'), 'r') as f:
+        files = f.read().split('\n')
+    for file in [f for f in files if f] + ['config.json', 'wrapper.py', 'model.safetensors']:
+        full_path = os.path.join(path, file)
+        if not os.path.exists(full_path):
+            hf_hub_download(repo_id, file, token=HF_TOKEN, local_dir=path, local_dir_use_symlinks=False)
+
+            
+# helpfer function to download huggingface repo and use model
+def load_model_from_local_path(path, HF_TOKEN=None):
+    cwd = os.getcwd()
+    os.chdir(path)
+    model = AutoModel.from_pretrained(path, trust_remote_code=True, token=HF_TOKEN)
+    os.chdir(cwd)
+    return model
+
+
+# helpfer function to download huggingface repo and use model
+def load_model_by_repo_id(repo_id, save_path, HF_TOKEN=None, force_download=False):
+    if force_download:
+        if os.path.exists(save_path):
+            shutil.rmtree(save_path)
+    download(repo_id, save_path, HF_TOKEN)
+    return load_model_from_local_path(save_path, HF_TOKEN)
+
+
+if __name__ == '__main__':
+    
+    HF_TOKEN = 'YOUR_HUGGINGFACE_TOKEN'
+    path = 'path/to/store/model/locally'
+    repo_id = 'minchul/cvlface_adaface_vit_base_kprpe_webface4m'
+    model = load_model_by_repo_id(repo_id, path, HF_TOKEN)
+
+    # input is a rgb image normalized.
+    from torchvision.transforms import Compose, ToTensor, Normalize
+    from PIL import Image
+    img = Image.open('path/to/image.jpg')
+    trans = Compose([ToTensor(), Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
+    input = trans(img).unsqueeze(0)  # torch.randn(1, 3, 112, 112)
+    
+    # KPRPE also takes keypoints locations as input
+    keypoints = torch.randn(1, 5, 2)
+    out = model(input, keypoints)
+```
+