export_generator.py

Code Explained

The provided main function is the entry point for a Python script designed to export a trained model checkpoint into a format suitable for inference. This script is particularly useful in machine learning workflows where a model trained for a specific task needs to be prepared for deployment or further use in production.

The function begins by setting up an argument parser using the argparse module. It defines three command-line arguments:

1. checkpoint: A required positional argument specifying the path to the model checkpoint file (with a .ckpt extension). This file contains the saved state of the model after training.
2. output: A required positional argument specifying the path where the exported model (in .pt format) will be saved.
3. --debug: An optional flag that, when set, enables debug-level logging to provide detailed information during execution.

After parsing the arguments, the script configures the logging system using logging.basicConfig. If the --debug flag is provided, the logging level is set to DEBUG, otherwise it defaults to INFO. This allows the script to provide detailed feedback during execution, which is especially useful for debugging.

The script then converts the checkpoint and output paths into Path objects using Python’s pathlib module. This ensures that the paths are handled in a platform-independent manner. It also creates the parent directory for the output path if it does not already exist, using the mkdir method with the parents=True and exist_ok=True options.

Next, the script loads the model checkpoint using the VitsModel.load_from_checkpoint method. This method initializes the model and restores its state from the checkpoint file. The dataset=None argument indicates that no specific dataset is required for this operation. The script then extracts the generator component of the model (model.model_g), which is responsible for generating outputs during inference.

To prepare the model for inference, the script sets it to evaluation mode using the eval method. This disables certain behaviors specific to training, such as dropout. It also removes weight normalization from the model’s decoder (model_g.dec.remove_weight_norm()) to optimize it for inference. The script then reassigns the model’s forward method to its infer method, ensuring that the model uses the appropriate logic for inference.

Finally, the script saves the prepared model to the specified output path using torch.save. This exports the model in a format that can be easily loaded for inference in other scripts or applications. A log message is generated to confirm the successful export of the model, providing the path to the saved file.

In summary, this script automates the process of converting a trained model checkpoint into an inference-ready format. It ensures that the model is properly configured for deployment and provides detailed logging to aid in debugging and monitoring. This makes it a valuable tool in machine learning workflows.

Source Code

#!/usr/bin/env python3
import argparse
import logging
from pathlib import Path

import torch

from .vits.lightning import VitsModel

_LOGGER = logging.getLogger("piper_train.export_generator")


def main():
    """Main entry point"""
    parser = argparse.ArgumentParser()
    parser.add_argument("checkpoint", help="Path to model checkpoint (.ckpt)")
    parser.add_argument("output", help="Path to output model (.pt)")

    parser.add_argument(
        "--debug", action="store_true", help="Print DEBUG messages to the console"
    )
    args = parser.parse_args()

    if args.debug:
        logging.basicConfig(level=logging.DEBUG)
    else:
        logging.basicConfig(level=logging.INFO)

    _LOGGER.debug(args)

    # -------------------------------------------------------------------------

    args.checkpoint = Path(args.checkpoint)
    args.output = Path(args.output)
    args.output.parent.mkdir(parents=True, exist_ok=True)

    model = VitsModel.load_from_checkpoint(args.checkpoint, dataset=None)
    model_g = model.model_g

    # Inference only
    model_g.eval()

    with torch.no_grad():
        model_g.dec.remove_weight_norm()

    model_g.forward = model_g.infer

    torch.save(model_g, args.output)

    _LOGGER.info("Exported model to %s", args.output)


# -----------------------------------------------------------------------------

if __name__ == "__main__":
    main()