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jlynxdev/magical-drones-model-temp

sourceHugging Faceupdated 2y agoView on Hugging Face

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Model Card

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:

python

    pip install torch omegaconf huggingface_hub

1.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:

python

    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")

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

python

    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.