mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-10 18:17:31 +08:00
e6cd95109b
* Moved Volinga as an external method * Update external_methods.py for black * Formatted with black --------- Co-authored-by: jperezh <jperezh@arquimea.com>
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
"""
|
|
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 nerfstudio.models.vanilla_nerf import VanillaModelConfig
|
|
from nerfstudio.scripts.train import train_loop
|
|
|
|
BLACKLIST = [
|
|
"base",
|
|
"semantic-nerfw",
|
|
"instant-ngp",
|
|
"instant-ngp-bounded",
|
|
"nerfacto-big",
|
|
"phototourism",
|
|
"depth-nerfacto",
|
|
"neus",
|
|
"generfacto",
|
|
"neus-facto",
|
|
]
|
|
|
|
|
|
def set_reduced_config(config: TrainerConfig):
|
|
"""Reducing the config settings to speedup test"""
|
|
config.machine.device_type = "cpu"
|
|
if hasattr(config.pipeline.model, "implementation"):
|
|
setattr(config.pipeline.model, "implementation", "torch")
|
|
config.mixed_precision = False
|
|
config.use_grad_scaler = False
|
|
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"):
|
|
assert isinstance(config.pipeline.model, VanillaModelConfig)
|
|
config.pipeline.model.num_coarse_samples = 4
|
|
if hasattr(config.pipeline.model, "num_importance_samples"):
|
|
assert isinstance(config.pipeline.model, VanillaModelConfig)
|
|
config.pipeline.model.num_importance_samples = 4
|
|
# remove viewer
|
|
config.viewer.quit_on_train_completion = True
|
|
|
|
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()
|