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https://github.com/nerfstudio-project/nerfstudio.git
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60 lines
1.6 KiB
Python
Executable File
60 lines
1.6 KiB
Python
Executable File
#!/usr/bin/env python
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"""
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eval.py
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"""
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from __future__ import annotations
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import json
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import logging
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from dataclasses import dataclass
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from pathlib import Path
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import dcargs
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from rich.console import Console
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from nerfstudio.utils.eval_utils import eval_setup
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console = Console(width=120)
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logging.basicConfig(format="[%(filename)s:%(lineno)d] %(message)s", level=logging.INFO)
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@dataclass
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class ComputePSNR:
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"""Load a checkpoint, compute some PSNR metrics, and save to a JSON."""
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# Path to config YAML file.
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load_config: Path
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# Name of the output file.
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output_path: Path = Path("output.json")
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def main(self) -> None:
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"""Main function."""
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config, pipeline, checkpoint_path = eval_setup(self.load_config)
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assert self.output_path.suffix == ".json"
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metrics_dict = pipeline.get_average_eval_image_metrics()
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# save output to some file
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self.output_path.parent.mkdir(parents=True, exist_ok=True)
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benchmark_info = {
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"experiment_name": config.experiment_name,
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"method_name": config.method_name,
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"checkpoint": str(checkpoint_path),
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"results": metrics_dict,
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}
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self.output_path.write_text(json.dumps(benchmark_info, indent=2), "utf8")
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console.print(f"Saved results to: {self.output_path}")
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def entrypoint():
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"""Entrypoint for use with pyproject scripts."""
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dcargs.extras.set_accent_color("bright_yellow")
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dcargs.cli(ComputePSNR).main()
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if __name__ == "__main__":
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entrypoint()
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# For sphinx docs
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get_parser_fn = lambda: dcargs.extras.get_parser(ComputePSNR) # noqa
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