add upload script

upload_to_hf.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""
+Script to upload PhaseNet-TF model to Hugging Face Hub
+"""
+import os
+import json
+from pathlib import Path
+from huggingface_hub import HfApi, create_repo, upload_file
+from huggingface_hub import hf_hub_download
+import torch
+import yaml
+
+def create_model_card():
+    """Create a comprehensive model card for PhaseNet-TF"""
+    return """---
+language: en
+tags:
+- seismic
+- earthquake
+- phase-picking
+- deep-learning
+- pytorch
+license: mit
+datasets:
+- PS_Alaska
+metrics:
+- f1-score
+- precision
+- recall
+---
+
+# PhaseNet-TF: Advanced Seismic Arrival Time Detection
+
+## Model Description
+
+PhaseNet-TF is an advanced deep learning model for automatic seismic phase picking (P-wave, S-wave, and PS-wave detection) using spectrogram-based image segmentation approaches. The model leverages DeepLabV3Plus architecture to detect seismic arrivals with high accuracy, especially for weak and noisy signals from ocean-bottom seismometers and weak phases such as slab interface refracted PS and SP waves. This Alaska version is specifically trained on the PS_Alaska dataset for P and S phases. For more details, please refer to the paper and the [PhaseNet-TF](https://github.com/swei-seismo/PhaseNet-TF) repository.
+
+## Model Architecture
+
+- **Backbone**: DeepLabV3Plus with ResNet34 encoder
+- **Input**: 3-component seismic waveforms converted to 6-channel spectrograms (real + imaginary)
+- **Output**: Probability maps for P, S, PS phases and noise
+- **Sampling Rate**: 40 Hz (dt_s = 0.025s)
+- **Window Length**: 4800 points (120 seconds)
+- **Spectrogram Size**: 64 × 4800 (frequency × time)
+- **Input Channels**: 6 (3 real + 3 imaginary spectrogram channels)
+- **Output Classes**: 4 (noise, P, S, PS)
+
+## Load the checkpoint
+checkpoint = torch.load("alaska_iter2.ckpt", map_location="cpu")
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@article{jie2025background,
+  title={Background Seismicity and Aftershocks of the 2020-2021 Large Earthquakes at the Alaska Peninsula Revealed by a Deep-learning-based Catalog},
+  author={Jie, Yaqi and Wei, Songqiao Shawn and Zhu, Weiqiang and Freymueller, Jeffrey Todd and Elliott, Julie},
+  journal={Authorea Preprints},
+  year={2025},
+  publisher={Authorea}
+}
+```
+
+## License
+
+This model is licensed under the MIT License.
+"""
+
+def create_config_json(model_path):
+    """Create config.json with model metadata"""
+    config = {
+        "model_type": "phasenet-tf",
+        "architecture": "DeepLabV3Plus with ResNet34 encoder",
+        "input_channels": 6,  # 3-component real + 3-component imaginary spectrograms
+        "output_classes": 4,  # noise, P, S, PS
+        "sampling_rate": 40,  # 1/0.025 = 40 Hz
+        "window_length": 4800,  # 120 seconds at 40 Hz
+        "phases": ["P", "S", "PS"],
+        "framework": "pytorch",
+        "license": "mit",
+        "tags": ["seismic", "earthquake", "phase-picking", "deep-learning", "deeplabv3plus"]
+    }
+    return config
+
+def upload_model_to_hf(
+    checkpoint_path: str,
+    config_path: str = None,
+    repo_name: str = "PhaseNet-TF_Alaska",
+    username: str = None,
+    token: str = None
+):
+    """Upload model to Hugging Face Hub"""
+    
+    # Initialize API
+    if token:
+        api = HfApi(token=token)
+    else:
+        api = HfApi()
+    
+    # Get username if not provided
+    if username is None:
+        try:
+            username = api.whoami()["name"]
+            print(f"Using logged-in username: {username}")
+        except Exception as e:
+            print(f"Error getting username: {e}")
+            print("Please provide username with --username or login with huggingface-cli login")
+            return
+    
+    # Create repository
+    repo_id = f"{username}/{repo_name}"
+    try:
+        if token:
+            create_repo(repo_id, token=token, exist_ok=True)
+        else:
+            create_repo(repo_id, exist_ok=True)
+        print(f"Repository {repo_id} created/accessed successfully")
+    except Exception as e:
+        print(f"Error creating repository: {e}")
+        return
+    
+    # Upload checkpoint
+    print("Uploading model checkpoint...")
+    upload_file(
+        path_or_fileobj=checkpoint_path,
+        path_in_repo="pytorch_model.bin",
+        repo_id=repo_id,
+        token=token
+    )
+    
+    # Upload config
+    if config_path and os.path.exists(config_path):
+        print("Uploading config file...")
+        upload_file(
+            path_or_fileobj=config_path,
+            path_in_repo="config.yaml",
+            repo_id=repo_id,
+            token=token
+        )
+    
+    # Create and upload config.json
+    config_json = create_config_json(checkpoint_path)
+    config_json_path = "config.json"
+    with open(config_json_path, 'w') as f:
+        json.dump(config_json, f, indent=2)
+    
+    upload_file(
+        path_or_fileobj=config_json_path,
+        path_in_repo="config.json",
+        repo_id=repo_id,
+        token=token
+    )
+    
+    # Create and upload README.md
+    model_card = create_model_card()
+    readme_path = "README.md"
+    with open(readme_path, 'w') as f:
+        f.write(model_card)
+    
+    upload_file(
+        path_or_fileobj=readme_path,
+        path_in_repo="README.md",
+        repo_id=repo_id,
+        token=token
+    )
+    
+    # Clean up temporary files
+    os.remove(config_json_path)
+    os.remove(readme_path)
+    
+    print(f"Model uploaded successfully to https://huggingface.co/{repo_id}")
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Upload PhaseNet-TF model to Hugging Face")
+    parser.add_argument("--checkpoint", required=True, help="Path to model checkpoint (.ckpt)")
+    parser.add_argument("--config", help="Path to config file (.yaml)")
+    parser.add_argument("--repo-name", default="PhaseNet-TF_Alaska", help="Repository name")
+    parser.add_argument("--username", help="Hugging Face username (optional if already logged in)")
+    parser.add_argument("--token", help="Hugging Face token (optional if already logged in)")
+    
+    args = parser.parse_args()
+    
+    upload_model_to_hf(
+        checkpoint_path=args.checkpoint,
+        config_path=args.config,
+        repo_name=args.repo_name,
+        username=args.username,
+        token=args.token
+    )