mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-10 18:17:31 +08:00
3ffee522e0
Making TrainerConfig an InstantiateConfig so it can be swapped in and out with different methods. Also made the TrainerConfig a subclass of ExperimentConfig (which was changed from a printableconfig to an instantiateconfig), since we were already instantiating the Trainer class with the ExperimentConfig Removed the "config" field from the TrainerCallbackAttributes struct to eliminate circular dependencies when types are checked. Minor scripts and docs changes to accommodate the update to the config struct, meaning the flags that were associated with the TrainerConfig were moved up to the top level of the config.
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
# pylint: disable=protected-access
|
|
"""
|
|
Default test to make sure train runs
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
|
|
from nerfstudio.configs.method_configs import method_configs
|
|
from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
|
|
from nerfstudio.data.dataparsers.minimal_dataparser import MinimalDataParserConfig
|
|
from nerfstudio.engine.trainer import TrainerConfig
|
|
from scripts.train import train_loop
|
|
|
|
BLACKLIST = ["base", "semantic-nerfw", "instant-ngp", "instant-ngp-bounded", "nerfacto", "phototourism"]
|
|
|
|
|
|
def set_reduced_config(config: TrainerConfig):
|
|
"""Reducing the config settings to speedup test"""
|
|
config.machine.num_gpus = 0
|
|
config.max_num_iterations = 2
|
|
# reduce dataset factors; set dataset to test
|
|
config.pipeline.datamanager.dataparser = BlenderDataParserConfig(data=Path("tests/data/lego_test"))
|
|
config.pipeline.datamanager.train_num_images_to_sample_from = 1
|
|
config.pipeline.datamanager.train_num_rays_per_batch = 4
|
|
|
|
# use tensorboard logging instead of wandb
|
|
config.vis = "tensorboard"
|
|
config.logging.relative_log_dir = Path("/tmp/")
|
|
|
|
# reduce model factors
|
|
if hasattr(config.pipeline.model, "num_coarse_samples"):
|
|
config.pipeline.model.num_coarse_samples = 4
|
|
if hasattr(config.pipeline.model, "num_importance_samples"):
|
|
config.pipeline.model.num_importance_samples = 4
|
|
# remove viewer
|
|
config.viewer.enable = False
|
|
|
|
# model specific config settings
|
|
if config.method_name == "instant-ngp":
|
|
config.pipeline.model.field_implementation = "torch"
|
|
|
|
return config
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
|
|
def test_train():
|
|
"""test run train script works properly"""
|
|
all_config_names = method_configs.keys()
|
|
for config_name in all_config_names:
|
|
if config_name in BLACKLIST:
|
|
print("skipping", config_name)
|
|
continue
|
|
print(f"testing run for: {config_name}")
|
|
config = method_configs[config_name]
|
|
config = set_reduced_config(config)
|
|
|
|
train_loop(local_rank=0, world_size=0, config=config)
|
|
|
|
|
|
def test_simple_io():
|
|
"""test to check minimal data IO works correctly"""
|
|
config = method_configs["vanilla-nerf"]
|
|
config.pipeline.datamanager.dataparser = MinimalDataParserConfig(data=Path("tests/data/minimal_parser"))
|
|
config = set_reduced_config(config)
|
|
train_loop(local_rank=0, world_size=0, config=config)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_train()
|
|
test_simple_io()
|