mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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96f269cc1a
* inerted a new model configuration instant-ngp-bounded * added more remarks and small fixes * went back to old near plane * added the oprtion to get a file as well as directory in nerfstudio data parser * pretify prev commit * review fixes * more clear explanation in README.md * reverted readme changes * fixed merge bug * review changes. update config.data docs support for json files inputs Co-authored-by: dror <you@example.com>
65 lines
2.1 KiB
Python
65 lines
2.1 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.experiment_config import ExperimentConfig
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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 scripts.train import train_loop
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BLACKLIST = ["base", "semantic-nerfw", "instant-ngp", "instant-ngp-bounded", "nerfacto", "phototourism"]
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def set_reduced_config(config: ExperimentConfig):
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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.trainer.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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if __name__ == "__main__":
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test_train()
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