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https://github.com/nerfstudio-project/nerfstudio.git
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7e9a56fac4
* starting docs for pipelines * working on an example * clearing output * starting to implement config changes and add documentation * wip: removed cicular imports; porting basic config into python; todo: get python setups workin * circular imports again * wip: pausing at dataloader sort of working * break for today * running vanilla * wip need to get all the config docs working * laptop about to die * instant ngp working * added all configs for all methods; not tested yet: [mipnerf360, nerfw, semantic_nerf] * unpacking later * done with pylinting * mostly done. just need to do docs and one test * added import order to docs * fixing near/far plane and other config things * making loggers also instantiable via configs * docs * lint * making --help work with dcargs * moving configs back into flat * convert list to tuples; fixed post init function with mutable params * lint * made run_eval and benchmarking scripts compatible with new config/dcargs * lint * added option to load configs from presaved config Co-authored-by: evonneng <evonne_ng@berkeley.edu>
69 lines
2.1 KiB
Python
69 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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import nerfactory.configs.base as cfg
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from nerfactory.configs.base_configs import base_configs
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from nerfactory.engine.trainer import train_loop
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BLACKLIST = ["base", "semantic_nerf", "mipnerf_360", "instant_ngp"]
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def set_reduced_config(config: cfg.Config):
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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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# switch to using the vanilla ImageDataset class
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config.pipeline.dataloader.image_dataset_type = "rgb"
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config.pipeline.dataloader.train_dataset = cfg.BlenderDatasetConfig(
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data_directory="tests/data/lego_test", downscale_factor=16
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)
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config.pipeline.dataloader.train_num_images_to_sample_from = 1
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config.pipeline.dataloader.train_num_rays_per_batch = 4
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config.pipeline.dataloader.eval_dataset = cfg.BlenderDatasetConfig(
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data_directory="tests/data/lego_test", downscale_factor=16
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)
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# reduce model factors
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config.pipeline.model.num_coarse_samples = 4
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config.pipeline.model.num_importance_samples = 4
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# set logging to tmp
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for writer in config.logging.writer:
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writer.log_dir = Path("/tmp/")
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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_run_train():
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"""test run train script works properly"""
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all_config_names = base_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 = base_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_run_train()
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