mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 02:24:38 +08:00
ddfa463f4d
* add initial method configs and data manager * add neus scheduler * add initial surface model and neus * add Neus ray sampler * add sdf field * move scale/shift calc to math utils * add debug and fix sdf_dataset typo * update vscode neus debug * fix dataset and get initial working version * add magic number from original repo * black fixes * clean up get weights/transmittance from alphas * clean up typos in sdf datamanager * type annotations on neus scheduler and remove unused params * clean up and annotate sdf field along w/ other fixes * more clean up * remove unnessary directions norm * resolve comments * update neus scheduler to follow refactor * fix linter errors * fix some typos * remove get_weights_from_alpha * more comment fixes * more linter error fixes * clean up and add comments * more little fixes for types/comments * more comments * rename NeuS Scheduler to DelayedScheduler * rename DelayedScheduler to CosineDecayScheduler
87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
# pylint: disable=protected-access
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"""
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Default test to make sure train runs
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"""
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from __future__ import annotations
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from pathlib import Path
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import pytest
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from nerfstudio.configs.method_configs import method_configs
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from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
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from nerfstudio.data.dataparsers.minimal_dataparser import MinimalDataParserConfig
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from nerfstudio.engine.trainer import TrainerConfig
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from scripts.train import train_loop
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BLACKLIST = [
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"base",
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"semantic-nerfw",
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"instant-ngp",
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"instant-ngp-bounded",
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"nerfacto",
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"volinga",
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"phototourism",
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"depth-nerfacto",
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"nerfplayer-ngp",
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"nerfplayer-nerfacto",
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"neus",
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]
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def set_reduced_config(config: TrainerConfig):
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"""Reducing the config settings to speedup test"""
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config.machine.num_gpus = 0
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config.max_num_iterations = 2
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# reduce dataset factors; set dataset to test
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config.pipeline.datamanager.dataparser = BlenderDataParserConfig(data=Path("tests/data/lego_test"))
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config.pipeline.datamanager.train_num_images_to_sample_from = 1
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config.pipeline.datamanager.train_num_rays_per_batch = 4
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# use tensorboard logging instead of wandb
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config.vis = "tensorboard"
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config.logging.relative_log_dir = Path("/tmp/")
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# reduce model factors
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if hasattr(config.pipeline.model, "num_coarse_samples"):
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config.pipeline.model.num_coarse_samples = 4
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if hasattr(config.pipeline.model, "num_importance_samples"):
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config.pipeline.model.num_importance_samples = 4
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# remove viewer
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config.viewer.enable = False
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# model specific config settings
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if config.method_name == "instant-ngp":
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config.pipeline.model.field_implementation = "torch"
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return config
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@pytest.mark.filterwarnings("ignore::DeprecationWarning")
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def test_train():
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"""test run train script works properly"""
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all_config_names = method_configs.keys()
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for config_name in all_config_names:
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if config_name in BLACKLIST:
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print("skipping", config_name)
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continue
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print(f"testing run for: {config_name}")
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config = method_configs[config_name]
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config = set_reduced_config(config)
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train_loop(local_rank=0, world_size=0, config=config)
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def test_simple_io():
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"""test to check minimal data IO works correctly"""
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config = method_configs["vanilla-nerf"]
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config.pipeline.datamanager.dataparser = MinimalDataParserConfig(data=Path("tests/data/minimal_parser"))
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config = set_reduced_config(config)
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train_loop(local_rank=0, world_size=0, config=config)
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if __name__ == "__main__":
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test_train()
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test_simple_io()
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