mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 02:24:38 +08:00
b17268e83b
* starting the framework for dreamfusion * Removed view direction from RGB to calculate albedo * Started diffusion * Added dreamfusion to CLI, removed direction embeddings * Normals in dreamfusion * Lambertian shading added * Train normals in Dreamfusion * Stable diffusion from Stable Dreamfusion * Fix normal orientation loss * Configurable light direction sampling * adding density initalization at origin * V1 datamanager * Don't train normals for first 1k iters * SDS loss added to dreamfusion * Dreamfusion pipeline * Background MLP and SphereCollider * Normals prediction training fixed * Sigmoid on background, prompt in config * Shading hyperparameter adjustements * Shading hyperparameter tuning * Location based prompting, density initialization fades, LR change * Cleaned imports, more config options * Tuning, more config changes * remove unused sampler, normalize predicted normals from network * change position encoding to direction encoding for background color mlp * Updating dreamfusion to use custom trainer, fixed bug, multi-gpu support (put diffusion on one specific device), merged with main * Hard coding less, offloading to config * Appeasing the linter * First pass opacity / sparsity losses * Small patches and configs * Accidentally pushed logs * Removing logs for real this time * Transmittance loss bug fix * 12 GB Dreamfusion! * Lint * Remove unecissary collider changes * Start SD cleanup * Cleanup SD code * adding overhead location prompting * Use lambertian renderer * turn grad accum off by default, decrease background mlp size, add overhead text embedding * Cleanup * Fix normals vis * change uniform sampler to prediction network, add option to jit unet for faster sds_loss * Use shaders * cleaning up jit code * add warning if using non jit unet * add proposal network optimizer to config * speed up render and cleanup code * Clean up datamanager * Add distortion loss * fix wanb * adding interpolated location sampling * Update configs * interpolated location prompting option, lowering init density, transmittance loss interpolation flipped * rename alphas mult to alphas loss mult * Mess around with normals * add option to use random bg color + bg mlp * Remove Dreamfusion Trainer (#1423) * Dreamfusion trainer no longer needed * Added typing for grad scaler * SDS return loss * Fix CFG * Cleanup * Update configs * Fix opacity loss, use unform sampling first round of prop * add prompt edditing to viewer * Choose your own diffusion * remove dreamfusion pipeline file * merging + remove dreamfusion pipeline file * Update configs * add stable diff utils * cleanup * cleanup * Fix test * positional prompting bugfix * Warmups * tune * lint * Remove dataparser * merging + text embedding bugfix * Set data path to launch viewer for DreamFusion (#1776) * Set data path to launch viewer for DreamFusion * Apply formatter * Fallback to 'base_dir' if data manager has no path * Make if statement non-inline * Reformat --------- Co-authored-by: Matthew Tancik <mattt4001@gmail.com> * add deepfloyd option first try * merging * merging from main * merging * removing comments, cleaning code, making stable diff utils usable for deepfloyd, remove deepfloyd text encoder after use * undo chagnes in data folder * cleaning code, removing unneccesary changes from main, removed normals prediction network * remove old scripts files, rename generative pipeline flag to no_input_data, revert to default histogram padding in proposal sampler * changing pipeline flag * move no input data flag from pipeline to datamanager * undo config and merge in deepfloyd from main * merging grad scaler pipeline addition * merging + setting proposal net scheduler * revert nerfacto config change * typing + linting * rename dreamfusion datamanager to random camras datamanager, check datamanager typing in pipeline and trainer * fix test import * fix test import * changing datamanager check in trainer, add typing in random cameras datamanager * add Tensor to randomcamerasdatamanager * remove old comments * resolve tyro warning * update fields to use tcnn * lint * add packages for deepfloyd * add versions * docs first draft * rename to generfacto, clean docs * fix import error * minor changes * fix orientation loss bug, retune params * cleanup * typing * trying docs fix * fixing generative package imports * fix pyright * docs fix * merge w main * docs minor change * tuning + bugfixes * remove torch.backends --------- Co-authored-by: Abhik Ahuja <ahujaabhik@berkeley.edu> Co-authored-by: David McAllister <davidrmc2@gmail.com> Co-authored-by: Jake Austin <jake-austin@berkeley.edu> Co-authored-by: Matthew Tancik <tancik@berkeley.edu> Co-authored-by: Matt Tancik <mattt4001@gmail.com> Co-authored-by: SeungWoo Yoo <dreamy1534@gmail.com> Co-authored-by: ayaanzhaque <ayaanzhaque@gmail.com> Co-authored-by: Ayaan Haque <ayaanzhaque@berkeley.edu>
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
"""
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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 nerfstudio.models.vanilla_nerf import VanillaModelConfig
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from nerfstudio.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-big",
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"volinga",
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"phototourism",
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"depth-nerfacto",
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"neus",
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"generfacto",
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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.device_type = "cpu"
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if hasattr(config.pipeline.model, "implementation"):
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setattr(config.pipeline.model, "implementation", "torch")
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config.mixed_precision = False
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config.use_grad_scaler = False
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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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assert isinstance(config.pipeline.model, VanillaModelConfig)
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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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assert isinstance(config.pipeline.model, VanillaModelConfig)
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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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