mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 02:24:38 +08:00
97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
"""
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run_benchmark.py
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"""
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import argparse
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import json
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import logging
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import os
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from hydra import compose, initialize
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from hydra.core.global_hydra import GlobalHydra
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from omegaconf import DictConfig
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from tqdm import tqdm
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from scripts.run_eval import run_inference
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OBJECT_LIST = ["mic", "ficus", "chair", "hotdog", "materials", "drums", "ship", "lego"]
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def _load_best_ckpt(hydra_dir: str, config: DictConfig) -> str:
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"""helper function to update config with latest checkpoint in specified hydra_dir
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Args:
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hydra_dir (str): base directory for the specified run (where the model directories are stored)
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config (DictConfig): configuration for the specified run.
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Returns:
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str: path to the most recent checkpoint sorted by timestamps and step number
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"""
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# get the most recent run in the model directory
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latest_run = os.listdir(hydra_dir)
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latest_run.sort()
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latest_run = latest_run[-1]
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# get the latest checkpoint name
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model_dir = os.path.join(hydra_dir, latest_run, config.model_dir)
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latest_ckpt = os.listdir(model_dir)
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latest_ckpt.sort()
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latest_ckpt = latest_ckpt[-1]
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step = os.path.splitext(latest_ckpt)[0].split("-")[-1]
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config.resume_train.load_dir = model_dir
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config.resume_train.load_step = int(step)
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return os.path.join(model_dir, latest_ckpt)
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def _load_hydra_config(hydra_dir: str) -> DictConfig:
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"""helper function to load the specified hydra config from the specified directory
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Args:
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hydra_dir (str): base directory for the specified run (where the flattened config.yaml is stored)
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Returns:
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DictConfig: returns the loaded hydra dictionary config
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"""
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basename = os.listdir(hydra_dir)[0]
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hydra_dir = f"{hydra_dir}/{basename}"
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initialize(config_path=os.path.join("../../", hydra_dir, ".hydra/"))
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config = compose("config.yaml")
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## set the tensorboard to a /tmp/ output file for now
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config.logging.writer.TensorboardWriter.log_dir = "/tmp/"
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return config
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def main(args):
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"""Main function."""
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benchmarks = {}
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for dataset in tqdm(OBJECT_LIST):
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# set up trainer, config, and checkpoint loading
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hydra_dir = f"{args.hydra_base_dir}/blender_{dataset}_{args.benchmark_date}/{args.graph}/"
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config = _load_hydra_config(hydra_dir)
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ckpt = _load_best_ckpt(hydra_dir, config.trainer)
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# run evaluation
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avg_psnr, avg_rays_per_sec = run_inference(config)
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benchmarks[dataset] = {"avg psnr": avg_psnr, "avg rays/s": avg_rays_per_sec, "checkpoint": ckpt}
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# reset hydra config
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GlobalHydra.instance().clear()
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# output benchmark statistics to a json file
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benchmark_info = {
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"meta info": {"graph": args.graph, "benchmark_date": args.benchmark_date, "hydra": args.hydra_base_dir},
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"results": benchmarks,
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}
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json_file = os.path.join(args.hydra_base_dir, f"{args.timestamp}.json")
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with open(json_file, "w", encoding="utf8") as f:
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json.dump(benchmark_info, f, indent=2)
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logging.info("saved benchmark results to %s", json_file)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-g", "--graph", type=str, required=True, help="name of nerf graph to benchmark")
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parser.add_argument("-d", "--benchmark_date", type=str, required=True, help="timestamp of run to benchmark")
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parser.add_argument("-h", "--hydra_base_dir", type=str, required=True, help="hydra base output path")
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args = parser.parse_args()
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main(args)
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