jlynxdev/magical-drones-model-temp

To load and initialize the Generator model from the repository, follow these steps:

1. Install Required Packages: Ensure you have the necessary Python packages installed:

   pip install torch omegaconf huggingface_hub
   

2. Download Model Files: Retrieve the generator.pth, config.json, and model.py files from the Hugging Face repository. You can use the huggingface_hub library for this:

   from huggingface_hub import hf_hub_download
   
   repo_id = "Kiwinicki/sat2map-generator"
   generator_path = hf_hub_download(repo_id=repo_id, filename="generator.pth")
   config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
   model_path = hf_hub_download(repo_id=repo_id, filename="model.py")
   

3. Load the Model: Incorporate the downloaded model.py to define the Generator class, then load the model's state dictionary and configuration:

   import torch
   import json
   from omegaconf import OmegaConf
   import sys
   from pathlib import Path
   from model import Generator
   
   # Load configuration
   with open(config_path, "r") as f:
       config_dict = json.load(f)
   cfg = OmegaConf.create(config_dict)
   
   # Initialize and load the generator model
   generator = Generator(cfg)
   generator.load_state_dict(torch.load(generator_path))
   generator.eval()
   x = torch.randn([1, cfg['channels'], 256, 256])
   out = generator(x)
   

   Here, generator is the initialized model ready for inference.