mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-10 18:17:31 +08:00
457788d689
* renaming dataloader to data. adding some dataloader classes * restructuring configs * fixed instant ngp tcnn direction rescaling * working through test cases * black and lint and tests
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
"""
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run_eval.py
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"""
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import json
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import logging
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import os
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from datetime import date
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import torch
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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 pyrad.engine.trainer import Trainer
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from pyrad.cameras.cameras import Camera
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BENCH = {
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"method": "vanilla_nerf",
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"hydra_base_dir": "outputs/",
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"benchmark_date": "05-26-2022",
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"object_list": ["mic", "ficus", "chair", "hotdog", "materials", "drums", "ship", "lego"],
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}
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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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model_dir = os.path.join(hydra_dir, config.graph.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.graph.resume_train.load_dir = model_dir
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config.graph.resume_train.load_step = int(step)
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config.logging.writer.TensorboardWriter.save_dir = "/tmp/"
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return os.path.join(model_dir, latest_ckpt)
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def main():
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"""Main function."""
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# benchmarks = {}
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# hydra_base_dir = BENCH["hydra_base_dir"]
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# method = BENCH["method"]
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# benchmark_date = BENCH["benchmark_date"]
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# for dataset in tqdm(BENCH["object_list"]):
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# hydra_dir = f"{hydra_base_dir}/blender_{dataset}_{benchmark_date}/{method}/"
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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("configs/flat.yaml")
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with initialize(version_base=None, config_path="../configs"):
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config = compose(config_name="flat.yaml")
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trainer = Trainer(config, local_rank=0, world_size=1)
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trainer.setup(test_mode=False)
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# create a camera trajectory
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print("Running eval.")
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# camera = Camera()
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# trainer.graph.eval()
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# camera_ray_bundle = camera.get_camera_ray_bundle(device=trainer.device)
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image_idx = 0
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camera_ray_bundle, batch = trainer.dataloader_eval.get_data_from_image_idx(image_idx)
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image_height, image_width = camera_ray_bundle.origins.shape[:2]
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print(image_height, image_width)
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outputs = trainer.graph.get_outputs_for_camera_ray_bundle(camera_ray_bundle)
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# print(outputs)
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# avg_psnr = 0
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# for step, image_idx in enumerate(config.data.val_image_indices):
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# with torch.no_grad():
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# psnr = trainer.test_image(image_idx=image_idx, step=step)
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# avg_psnr = (step * avg_psnr + psnr) / (step + 1)
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# benchmarks[dataset] = (avg_psnr, ckpt)
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# GlobalHydra.instance().clear()
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# benchmark_info = {"bench": BENCH, "results": benchmarks}
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# timestamp = date.today().strftime("%b-%d-%Y")
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# json_file = os.path.join(BENCH["hydra_base_dir"], f"{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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main()
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