mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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6a7c8820b8
* Copy in `viser` communication infrastructure * Update background image * Add build directory to fix viser HTTP server May want to remove these later * Copy in `viser` communication infrastructure * Update background image * Pass dataset vis using viser * initial impl of ViewerParameter * idk what i did but brent and matt know * Suppress eslint typescript warnings * implement ViewerElement base class, then ViewerButton, ViewerSlider, etc * client -> server message setup * multi folder stuff * Stream controls over viser * import * add types to viewer_param.py * Move another message to viser * Update folder color * Use non throttled messages * multiple folders works * Cleanup, more porting * starting impl of control panel in viser * More porting * scenebox to viser * Cleanup * Fix render updates * More cleanup, docs, typing * start control_panel.py * set_hidden logic but not merged in * control_panel replicates behavior of existing control panel (visually), except for setting the list of output options. not yet integrated with viewer_utils.py for logic flow. also implemented set_disabled and set_hidden in javscript/ViewerElement side * Remove some original leva components * add some train util logic, attempt set_value, doesn't work on javascript side fully yet (value updates but not slider visually * bye zmq * missing call to decoder * 'gui_set' works properly, control panel now replicates behavior of original * remove print * Fix status viewer message * More cleanup * support set_options in dropdown * hiding buttons works correctly * fix set_options, switch from cur_value to get_value() * Fix slowdown * output options * colormap logic converted * add handle to cb_hook * fix some lambda bugs * cleanup * Save checkpoint logic readded * Cleanup and minor fixes * snappier ui * Refactoring * Fix state update bugs * Bug fixes * Write less to redux * added comments to reducer code * Render state machine (#1727) * state machine works, but train step throttling not implemented * more step logic, cleanup * remove random try catcH * mutex logic for render * Update lock logic * types and docs * added some state machine logic to help rerenders * merge --------- Co-authored-by: Justin <justin.g.kerr@gmail.com> * Refactor and cleanup * More cleanup and docs * docstring to gui_utils * docstrings, rename viewer_param.py to viewer_elements.py * Fix viewer, remove bridge stuff * Render on connect * Fix lint errors * More eslint fixes * render_freq logic * better rerender logic for crop * More cleanup * Purge * Cleanup * Lint fixes * Fix cache * better parsing logic * Fix infinite test * More cleanup * typo * Update webdocs * Update docs * rerender on custom hooks * add more docs * Use shared utilities in `viser.infra` * rework callback to use elements instead of handles * Fix ESlint errors * change naming of handle to element, add setter for value * cleanup --------- Co-authored-by: Matt Tancik <mattt4001@gmail.com> Co-authored-by: Justin <justin.g.kerr@gmail.com> Co-authored-by: Ethan Weber <eweb0124@gmail.com> Co-authored-by: Matthew Tancik <tancik@berkeley.edu>
85 lines
2.5 KiB
Python
85 lines
2.5 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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"nerfacto-big",
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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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"neus",
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"neus-facto",
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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.quit_on_train_completion = True
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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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