mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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9053643932
* added volinga model to NeRFStudio * Removed unused dependencies * Modified dockerfile to match main * Corrected typo in core_code_checks.yml * Corrected typo in core_code_checks.yml * updated test with Volinga * Added conversion to NVOL documentation * [WIP] Refactor documentation and removing Volinga config * Volinga use existing NerfactoModelConfig * Added Unreal Engine documentation to extensions * removed unused field class import * Updated docs * Documentation ready * Update Dockerfile --------- Co-authored-by: Matthew Tancik <tancik@berkeley.edu>
86 lines
2.6 KiB
Python
86 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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]
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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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