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https://github.com/nerfstudio-project/nerfstudio.git
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update method configs
This commit is contained in:
maturk committed
1 parent
4ddbee9b08
commit
aa7fd51462
1 file changed
+470
-410
@@ -26,14 +26,23 @@ import tyro
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from nerfstudio.cameras.camera_optimizers import CameraOptimizerConfig
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from nerfstudio.configs.base_config import ViewerConfig
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from nerfstudio.configs.external_methods import get_external_methods
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from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManager, VanillaDataManagerConfig
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from nerfstudio.data.datamanagers.base_datamanager import (
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VanillaDataManager,
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VanillaDataManagerConfig,
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)
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from nerfstudio.data.datamanagers.parallel_datamanager import ParallelDataManagerConfig
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from nerfstudio.data.datamanagers.random_cameras_datamanager import RandomCamerasDataManagerConfig
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from nerfstudio.data.datamanagers.random_cameras_datamanager import (
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RandomCamerasDataManagerConfig,
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)
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from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
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from nerfstudio.data.dataparsers.dnerf_dataparser import DNeRFDataParserConfig
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from nerfstudio.data.dataparsers.instant_ngp_dataparser import InstantNGPDataParserConfig
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from nerfstudio.data.dataparsers.instant_ngp_dataparser import (
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InstantNGPDataParserConfig,
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)
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from nerfstudio.data.dataparsers.nerfstudio_dataparser import NerfstudioDataParserConfig
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from nerfstudio.data.dataparsers.phototourism_dataparser import PhototourismDataParserConfig
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from nerfstudio.data.dataparsers.phototourism_dataparser import (
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PhototourismDataParserConfig,
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)
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from nerfstudio.data.dataparsers.sdfstudio_dataparser import SDFStudioDataParserConfig
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from nerfstudio.data.dataparsers.sitcoms3d_dataparser import Sitcoms3DDataParserConfig
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from nerfstudio.data.datasets.depth_dataset import DepthDataset
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@@ -88,7 +97,6 @@ method_configs["nerfacto"] = TrainerConfig(
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mixed_precision=True,
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pipeline=VanillaPipelineConfig(
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datamanager=ParallelDataManagerConfig(
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# datamanager=VanillaDataManagerConfig(
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dataparser=NerfstudioDataParserConfig(),
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train_num_rays_per_batch=4096,
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eval_num_rays_per_batch=4096,
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@@ -116,51 +124,50 @@ method_configs["nerfacto"] = TrainerConfig(
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vis="viewer_beta",
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)
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# **For now I commented everything out since the datamanager config breaks **
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method_configs["nerfacto-big"] = TrainerConfig(
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method_name="nerfacto",
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steps_per_eval_batch=500,
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steps_per_save=2000,
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max_num_iterations=100000,
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mixed_precision=True,
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pipeline=VanillaPipelineConfig(
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datamanager=ParallelDataManagerConfig(
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dataparser=NerfstudioDataParserConfig(),
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train_num_rays_per_batch=8192,
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eval_num_rays_per_batch=4096,
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),
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model=NerfactoModelConfig(
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eval_num_rays_per_chunk=1 << 15,
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num_nerf_samples_per_ray=128,
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num_proposal_samples_per_ray=(512, 256),
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hidden_dim=128,
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hidden_dim_color=128,
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appearance_embed_dim=128,
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base_res=32,
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max_res=4096,
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proposal_weights_anneal_max_num_iters=5000,
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log2_hashmap_size=21,
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camera_optimizer=CameraOptimizerConfig(mode="SO3xR3"),
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),
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),
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optimizers={
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"proposal_networks": {
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"optimizer": RAdamOptimizerConfig(lr=1e-2, eps=1e-15),
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"scheduler": None,
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},
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"fields": {
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"optimizer": RAdamOptimizerConfig(lr=1e-2, eps=1e-15),
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=50000),
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},
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"camera_opt": {
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"optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=5000),
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},
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},
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viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
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vis="viewer",
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)
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# method_configs["nerfacto-big"] = TrainerConfig(
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# method_name="nerfacto",
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# steps_per_eval_batch=500,
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# steps_per_save=2000,
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# max_num_iterations=100000,
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# mixed_precision=True,
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# pipeline=VanillaPipelineConfig(
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# datamanager=VanillaDataManagerConfig(
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# dataparser=NerfstudioDataParserConfig(),
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# train_num_rays_per_batch=8192,
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# eval_num_rays_per_batch=4096,
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# camera_optimizer=CameraOptimizerConfig(
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# mode="SO3xR3",
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# optimizer=RAdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-3),
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# ),
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# ),
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# model=NerfactoModelConfig(
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# eval_num_rays_per_chunk=1 << 15,
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# num_nerf_samples_per_ray=128,
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# num_proposal_samples_per_ray=(512, 256),
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# hidden_dim=128,
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# hidden_dim_color=128,
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# appearance_embed_dim=128,
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# base_res=32,
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# max_res=4096,
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# proposal_weights_anneal_max_num_iters=5000,
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# log2_hashmap_size=21,
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# ),
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# ),
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# optimizers={
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# "proposal_networks": {
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# "optimizer": RAdamOptimizerConfig(lr=1e-2, eps=1e-15),
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# "scheduler": None,
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# },
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# "fields": {
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# "optimizer": RAdamOptimizerConfig(lr=1e-2, eps=1e-15),
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# "scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=50000),
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# },
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# },
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# viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
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# vis="viewer",
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# )
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method_configs["nerfacto-huge"] = TrainerConfig(
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method_name="nerfacto",
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steps_per_eval_batch=500,
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@@ -168,7 +175,6 @@ method_configs["nerfacto-huge"] = TrainerConfig(
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max_num_iterations=100000,
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mixed_precision=True,
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pipeline=VanillaPipelineConfig(
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# datamanager=VanillaDataManagerConfig(
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datamanager=ParallelDataManagerConfig(
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dataparser=NerfstudioDataParserConfig(),
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train_num_rays_per_batch=16384,
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@@ -190,6 +196,7 @@ method_configs["nerfacto-huge"] = TrainerConfig(
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max_res=8192,
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proposal_weights_anneal_max_num_iters=5000,
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log2_hashmap_size=21,
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camera_optimizer=CameraOptimizerConfig(mode="SO3xR3"),
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),
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),
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optimizers={
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@@ -202,382 +209,435 @@ method_configs["nerfacto-huge"] = TrainerConfig(
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=50000),
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},
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"camera_opt": {
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"optimizer": RAdamOptimizerConfig(lr=1e-3, eps=1e-8),
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=10000),
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"optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=5000),
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},
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},
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viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
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vis="viewer_beta",
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vis="viewer",
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)
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# method_configs["depth-nerfacto"] = TrainerConfig(
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# method_name="depth-nerfacto",
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# steps_per_eval_batch=500,
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# steps_per_save=2000,
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# max_num_iterations=30000,
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# mixed_precision=True,
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# pipeline=VanillaPipelineConfig(
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# datamanager=VanillaDataManagerConfig(
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# _target=VanillaDataManager[DepthDataset],
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# dataparser=NerfstudioDataParserConfig(),
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# train_num_rays_per_batch=4096,
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# eval_num_rays_per_batch=4096,
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# camera_optimizer=CameraOptimizerConfig(
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# mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2)
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# ),
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# ),
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# model=DepthNerfactoModelConfig(eval_num_rays_per_chunk=1 << 15),
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# ),
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# optimizers={
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# "proposal_networks": {
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# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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# "scheduler": None,
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# },
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# "fields": {
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# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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# "scheduler": None,
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# },
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# },
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# viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
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# vis="viewer",
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# )
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method_configs["depth-nerfacto"] = TrainerConfig(
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method_name="depth-nerfacto",
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steps_per_eval_batch=500,
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steps_per_save=2000,
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max_num_iterations=30000,
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mixed_precision=True,
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pipeline=VanillaPipelineConfig(
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datamanager=VanillaDataManagerConfig(
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_target=VanillaDataManager[DepthDataset],
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dataparser=NerfstudioDataParserConfig(),
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train_num_rays_per_batch=4096,
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eval_num_rays_per_batch=4096,
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),
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model=DepthNerfactoModelConfig(
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eval_num_rays_per_chunk=1 << 15,
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camera_optimizer=CameraOptimizerConfig(mode="SO3xR3"),
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),
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),
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optimizers={
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"proposal_networks": {
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"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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"scheduler": None,
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},
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"fields": {
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"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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"scheduler": None,
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},
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"camera_opt": {
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"optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=5000),
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},
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},
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viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
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vis="viewer",
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)
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# method_configs["volinga"] = TrainerConfig(
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# method_name="volinga",
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# steps_per_eval_batch=500,
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# steps_per_save=2000,
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# max_num_iterations=30000,
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# mixed_precision=True,
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# pipeline=VanillaPipelineConfig(
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# datamanager=VanillaDataManagerConfig(
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# dataparser=NerfstudioDataParserConfig(),
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# train_num_rays_per_batch=4096,
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# eval_num_rays_per_batch=4096,
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# camera_optimizer=CameraOptimizerConfig(
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# mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2)
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# ),
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# ),
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# model=NerfactoModelConfig(
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# eval_num_rays_per_chunk=1 << 15,
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# hidden_dim=32,
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# hidden_dim_color=32,
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# hidden_dim_transient=32,
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# num_nerf_samples_per_ray=24,
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# proposal_net_args_list=[
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# {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 128, "use_linear": True},
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# {"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 256, "use_linear": True},
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# ],
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# ),
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# ),
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# optimizers={
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# "proposal_networks": {
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# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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# "scheduler": None,
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# },
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# "fields": {
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# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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# "scheduler": None,
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# },
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# },
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# viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
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# vis="viewer",
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# )
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method_configs["volinga"] = TrainerConfig(
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method_name="volinga",
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steps_per_eval_batch=500,
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steps_per_save=2000,
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max_num_iterations=30000,
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mixed_precision=True,
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pipeline=VanillaPipelineConfig(
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datamanager=VanillaDataManagerConfig(
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dataparser=NerfstudioDataParserConfig(),
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train_num_rays_per_batch=4096,
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eval_num_rays_per_batch=4096,
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),
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model=NerfactoModelConfig(
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eval_num_rays_per_chunk=1 << 15,
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hidden_dim=32,
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hidden_dim_color=32,
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hidden_dim_transient=32,
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num_nerf_samples_per_ray=24,
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proposal_net_args_list=[
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{"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 128, "use_linear": True},
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{"hidden_dim": 16, "log2_hashmap_size": 17, "num_levels": 5, "max_res": 256, "use_linear": True},
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],
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camera_optimizer=CameraOptimizerConfig(mode="SO3xR3"),
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),
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),
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optimizers={
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"proposal_networks": {
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"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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"scheduler": None,
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},
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"fields": {
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"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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"scheduler": None,
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},
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"camera_opt": {
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"optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=5000),
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},
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},
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viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
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vis="viewer",
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)
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# method_configs["instant-ngp"] = TrainerConfig(
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# method_name="instant-ngp",
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# steps_per_eval_batch=500,
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# steps_per_save=2000,
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# max_num_iterations=30000,
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# mixed_precision=True,
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# pipeline=DynamicBatchPipelineConfig(
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# datamanager=VanillaDataManagerConfig(
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# dataparser=NerfstudioDataParserConfig(),
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# train_num_rays_per_batch=4096,
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# eval_num_rays_per_batch=4096,
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# ),
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# model=InstantNGPModelConfig(eval_num_rays_per_chunk=8192),
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# ),
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# optimizers={
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# "fields": {
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# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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# "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
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# }
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# },
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# viewer=ViewerConfig(num_rays_per_chunk=1 << 12),
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# vis="viewer",
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# )
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method_configs["instant-ngp"] = TrainerConfig(
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method_name="instant-ngp",
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steps_per_eval_batch=500,
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steps_per_save=2000,
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max_num_iterations=30000,
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mixed_precision=True,
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pipeline=DynamicBatchPipelineConfig(
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datamanager=VanillaDataManagerConfig(
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dataparser=NerfstudioDataParserConfig(),
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train_num_rays_per_batch=4096,
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eval_num_rays_per_batch=4096,
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),
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model=InstantNGPModelConfig(eval_num_rays_per_chunk=8192),
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),
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optimizers={
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"fields": {
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"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
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}
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},
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viewer=ViewerConfig(num_rays_per_chunk=1 << 12),
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vis="viewer",
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)
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method_configs["instant-ngp-bounded"] = TrainerConfig(
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method_name="instant-ngp-bounded",
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steps_per_eval_batch=500,
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steps_per_save=2000,
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max_num_iterations=30000,
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mixed_precision=True,
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pipeline=DynamicBatchPipelineConfig(
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datamanager=VanillaDataManagerConfig(dataparser=InstantNGPDataParserConfig(), train_num_rays_per_batch=8192),
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model=InstantNGPModelConfig(
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eval_num_rays_per_chunk=8192,
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grid_levels=1,
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alpha_thre=0.0,
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cone_angle=0.0,
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disable_scene_contraction=True,
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near_plane=0.01,
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background_color="black",
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),
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),
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optimizers={
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"fields": {
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"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
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"scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
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}
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},
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viewer=ViewerConfig(num_rays_per_chunk=1 << 12),
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vis="viewer",
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)
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#
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#
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method_configs["mipnerf"] = TrainerConfig(
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method_name="mipnerf",
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pipeline=VanillaPipelineConfig(
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datamanager=VanillaDataManagerConfig(dataparser=NerfstudioDataParserConfig(), train_num_rays_per_batch=1024),
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model=VanillaModelConfig(
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_target=MipNerfModel,
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loss_coefficients={"rgb_loss_coarse": 0.1, "rgb_loss_fine": 1.0},
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num_coarse_samples=128,
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num_importance_samples=128,
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eval_num_rays_per_chunk=1024,
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),
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),
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optimizers={
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"fields": {
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"optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
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"scheduler": None,
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}
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},
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||||
)
|
||||
|
||||
# method_configs["instant-ngp-bounded"] = TrainerConfig(
|
||||
# method_name="instant-ngp-bounded",
|
||||
# steps_per_eval_batch=500,
|
||||
# steps_per_save=2000,
|
||||
# max_num_iterations=30000,
|
||||
# mixed_precision=True,
|
||||
# pipeline=DynamicBatchPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(dataparser=InstantNGPDataParserConfig(), train_num_rays_per_batch=8192),
|
||||
# model=InstantNGPModelConfig(
|
||||
# eval_num_rays_per_chunk=8192,
|
||||
# grid_levels=1,
|
||||
# alpha_thre=0.0,
|
||||
# cone_angle=0.0,
|
||||
# disable_scene_contraction=True,
|
||||
# near_plane=0.01,
|
||||
# background_color="black",
|
||||
# ),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "fields": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
# "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=200000),
|
||||
# }
|
||||
# },
|
||||
# viewer=ViewerConfig(num_rays_per_chunk=1 << 12),
|
||||
# vis="viewer",
|
||||
# )
|
||||
method_configs["semantic-nerfw"] = TrainerConfig(
|
||||
method_name="semantic-nerfw",
|
||||
steps_per_eval_batch=500,
|
||||
steps_per_save=2000,
|
||||
max_num_iterations=30000,
|
||||
mixed_precision=True,
|
||||
pipeline=VanillaPipelineConfig(
|
||||
datamanager=VanillaDataManagerConfig(
|
||||
_target=VanillaDataManager[SemanticDataset],
|
||||
dataparser=Sitcoms3DDataParserConfig(),
|
||||
train_num_rays_per_batch=4096,
|
||||
eval_num_rays_per_batch=8192,
|
||||
),
|
||||
model=SemanticNerfWModelConfig(eval_num_rays_per_chunk=1 << 16),
|
||||
),
|
||||
optimizers={
|
||||
"proposal_networks": {
|
||||
"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
"scheduler": None,
|
||||
},
|
||||
"fields": {
|
||||
"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
"scheduler": None,
|
||||
},
|
||||
},
|
||||
viewer=ViewerConfig(num_rays_per_chunk=1 << 16),
|
||||
vis="viewer",
|
||||
)
|
||||
|
||||
method_configs["vanilla-nerf"] = TrainerConfig(
|
||||
method_name="vanilla-nerf",
|
||||
pipeline=VanillaPipelineConfig(
|
||||
datamanager=VanillaDataManagerConfig(
|
||||
dataparser=BlenderDataParserConfig(),
|
||||
),
|
||||
model=VanillaModelConfig(_target=NeRFModel),
|
||||
),
|
||||
optimizers={
|
||||
"fields": {
|
||||
"optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
"scheduler": None,
|
||||
},
|
||||
"temporal_distortion": {
|
||||
"optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
"scheduler": None,
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
# method_configs["mipnerf"] = TrainerConfig(
|
||||
# method_name="mipnerf",
|
||||
# pipeline=VanillaPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(dataparser=NerfstudioDataParserConfig(), train_num_rays_per_batch=1024),
|
||||
# model=VanillaModelConfig(
|
||||
# _target=MipNerfModel,
|
||||
# loss_coefficients={"rgb_loss_coarse": 0.1, "rgb_loss_fine": 1.0},
|
||||
# num_coarse_samples=128,
|
||||
# num_importance_samples=128,
|
||||
# eval_num_rays_per_chunk=1024,
|
||||
# ),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "fields": {
|
||||
# "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
# "scheduler": None,
|
||||
# }
|
||||
# },
|
||||
# )
|
||||
method_configs["tensorf"] = TrainerConfig(
|
||||
method_name="tensorf",
|
||||
steps_per_eval_batch=500,
|
||||
steps_per_save=2000,
|
||||
max_num_iterations=30000,
|
||||
mixed_precision=False,
|
||||
pipeline=VanillaPipelineConfig(
|
||||
datamanager=ParallelDataManagerConfig(
|
||||
dataparser=BlenderDataParserConfig(),
|
||||
train_num_rays_per_batch=4096,
|
||||
eval_num_rays_per_batch=4096,
|
||||
),
|
||||
model=TensoRFModelConfig(
|
||||
regularization="tv",
|
||||
camera_optimizer=CameraOptimizerConfig(mode="off"),
|
||||
),
|
||||
),
|
||||
optimizers={
|
||||
"fields": {
|
||||
"optimizer": AdamOptimizerConfig(lr=0.001),
|
||||
"scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=30000),
|
||||
},
|
||||
"encodings": {
|
||||
"optimizer": AdamOptimizerConfig(lr=0.02),
|
||||
"scheduler": ExponentialDecaySchedulerConfig(lr_final=0.002, max_steps=30000),
|
||||
},
|
||||
"camera_opt": {
|
||||
"optimizer": AdamOptimizerConfig(lr=1e-4, eps=1e-15),
|
||||
"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-5, max_steps=5000),
|
||||
},
|
||||
},
|
||||
viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
|
||||
vis="viewer",
|
||||
)
|
||||
|
||||
# method_configs["semantic-nerfw"] = TrainerConfig(
|
||||
# method_name="semantic-nerfw",
|
||||
# steps_per_eval_batch=500,
|
||||
# steps_per_save=2000,
|
||||
# max_num_iterations=30000,
|
||||
# mixed_precision=True,
|
||||
# pipeline=VanillaPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(
|
||||
# _target=VanillaDataManager[SemanticDataset],
|
||||
# dataparser=Sitcoms3DDataParserConfig(),
|
||||
# train_num_rays_per_batch=4096,
|
||||
# eval_num_rays_per_batch=8192,
|
||||
# ),
|
||||
# model=SemanticNerfWModelConfig(eval_num_rays_per_chunk=1 << 16),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "proposal_networks": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
# "scheduler": None,
|
||||
# },
|
||||
# "fields": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
# "scheduler": None,
|
||||
# },
|
||||
# },
|
||||
# viewer=ViewerConfig(num_rays_per_chunk=1 << 16),
|
||||
# vis="viewer",
|
||||
# )
|
||||
method_configs["dnerf"] = TrainerConfig(
|
||||
method_name="dnerf",
|
||||
pipeline=VanillaPipelineConfig(
|
||||
datamanager=VanillaDataManagerConfig(dataparser=DNeRFDataParserConfig()),
|
||||
model=VanillaModelConfig(
|
||||
_target=NeRFModel,
|
||||
enable_temporal_distortion=True,
|
||||
temporal_distortion_params={"kind": TemporalDistortionKind.DNERF},
|
||||
),
|
||||
),
|
||||
optimizers={
|
||||
"fields": {
|
||||
"optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
"scheduler": None,
|
||||
},
|
||||
"temporal_distortion": {
|
||||
"optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
"scheduler": None,
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
# method_configs["vanilla-nerf"] = TrainerConfig(
|
||||
# method_name="vanilla-nerf",
|
||||
# pipeline=VanillaPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(
|
||||
# dataparser=BlenderDataParserConfig(),
|
||||
# ),
|
||||
# model=VanillaModelConfig(_target=NeRFModel),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "fields": {
|
||||
# "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
# "scheduler": None,
|
||||
# },
|
||||
# "temporal_distortion": {
|
||||
# "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
# "scheduler": None,
|
||||
# },
|
||||
# },
|
||||
# )
|
||||
method_configs["phototourism"] = TrainerConfig(
|
||||
method_name="phototourism",
|
||||
steps_per_eval_batch=500,
|
||||
steps_per_save=2000,
|
||||
max_num_iterations=30000,
|
||||
mixed_precision=True,
|
||||
pipeline=VanillaPipelineConfig(
|
||||
datamanager=VanillaDataManagerConfig(
|
||||
dataparser=PhototourismDataParserConfig(), # NOTE: one of the only differences with nerfacto
|
||||
train_num_rays_per_batch=4096,
|
||||
eval_num_rays_per_batch=4096,
|
||||
# Large dataset, so using prior values from VariableResDataManager.
|
||||
train_num_images_to_sample_from=40,
|
||||
train_num_times_to_repeat_images=100,
|
||||
eval_num_images_to_sample_from=40,
|
||||
eval_num_times_to_repeat_images=100,
|
||||
),
|
||||
model=NerfactoModelConfig(
|
||||
eval_num_rays_per_chunk=1 << 15,
|
||||
camera_optimizer=CameraOptimizerConfig(mode="SO3xR3"),
|
||||
),
|
||||
),
|
||||
optimizers={
|
||||
"proposal_networks": {
|
||||
"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
"scheduler": None,
|
||||
},
|
||||
"fields": {
|
||||
"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
"scheduler": None,
|
||||
},
|
||||
"camera_opt": {
|
||||
"optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
|
||||
"scheduler": ExponentialDecaySchedulerConfig(lr_final=1e-4, max_steps=5000),
|
||||
},
|
||||
},
|
||||
viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
|
||||
vis="viewer",
|
||||
)
|
||||
|
||||
# method_configs["tensorf"] = TrainerConfig(
|
||||
# method_name="tensorf",
|
||||
# steps_per_eval_batch=500,
|
||||
# steps_per_save=2000,
|
||||
# max_num_iterations=30000,
|
||||
# mixed_precision=False,
|
||||
# pipeline=VanillaPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(
|
||||
# dataparser=BlenderDataParserConfig(),
|
||||
# train_num_rays_per_batch=4096,
|
||||
# eval_num_rays_per_batch=4096,
|
||||
# ),
|
||||
# model=TensoRFModelConfig(
|
||||
# regularization="tv",
|
||||
# ),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "fields": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=0.001),
|
||||
# "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.0001, max_steps=30000),
|
||||
# },
|
||||
# "encodings": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=0.02),
|
||||
# "scheduler": ExponentialDecaySchedulerConfig(lr_final=0.002, max_steps=30000),
|
||||
# },
|
||||
# },
|
||||
# viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
|
||||
# vis="viewer",
|
||||
# )
|
||||
method_configs["generfacto"] = TrainerConfig(
|
||||
method_name="generfacto",
|
||||
experiment_name="",
|
||||
steps_per_eval_batch=50,
|
||||
steps_per_eval_image=50,
|
||||
steps_per_save=200,
|
||||
max_num_iterations=30000,
|
||||
mixed_precision=True,
|
||||
pipeline=VanillaPipelineConfig(
|
||||
datamanager=RandomCamerasDataManagerConfig(
|
||||
horizontal_rotation_warmup=3000,
|
||||
),
|
||||
model=GenerfactoModelConfig(
|
||||
eval_num_rays_per_chunk=1 << 15,
|
||||
distortion_loss_mult=1.0,
|
||||
interlevel_loss_mult=100.0,
|
||||
max_res=256,
|
||||
sphere_collider=True,
|
||||
initialize_density=True,
|
||||
taper_range=(0, 2000),
|
||||
random_background=True,
|
||||
proposal_warmup=2000,
|
||||
proposal_update_every=0,
|
||||
proposal_weights_anneal_max_num_iters=2000,
|
||||
start_lambertian_training=500,
|
||||
start_normals_training=2000,
|
||||
opacity_loss_mult=0.001,
|
||||
positional_prompting="discrete",
|
||||
guidance_scale=25,
|
||||
),
|
||||
),
|
||||
optimizers={
|
||||
"proposal_networks": {
|
||||
"optimizer": AdamOptimizerConfig(lr=1e-3, eps=1e-15),
|
||||
"scheduler": None,
|
||||
},
|
||||
"fields": {
|
||||
"optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
"scheduler": None,
|
||||
},
|
||||
},
|
||||
viewer=ViewerConfig(),
|
||||
vis="viewer",
|
||||
)
|
||||
|
||||
# method_configs["dnerf"] = TrainerConfig(
|
||||
# method_name="dnerf",
|
||||
# pipeline=VanillaPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(dataparser=DNeRFDataParserConfig()),
|
||||
# model=VanillaModelConfig(
|
||||
# _target=NeRFModel,
|
||||
# enable_temporal_distortion=True,
|
||||
# temporal_distortion_params={"kind": TemporalDistortionKind.DNERF},
|
||||
# ),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "fields": {
|
||||
# "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
# "scheduler": None,
|
||||
# },
|
||||
# "temporal_distortion": {
|
||||
# "optimizer": RAdamOptimizerConfig(lr=5e-4, eps=1e-08),
|
||||
# "scheduler": None,
|
||||
# },
|
||||
# },
|
||||
# )
|
||||
method_configs["neus"] = TrainerConfig(
|
||||
method_name="neus",
|
||||
steps_per_eval_image=500,
|
||||
steps_per_eval_batch=5000,
|
||||
steps_per_save=20000,
|
||||
steps_per_eval_all_images=1000000, # set to a very large number so we don't eval with all images
|
||||
max_num_iterations=100000,
|
||||
mixed_precision=False,
|
||||
pipeline=VanillaPipelineConfig(
|
||||
datamanager=VanillaDataManagerConfig(
|
||||
_target=VanillaDataManager[SDFDataset],
|
||||
dataparser=SDFStudioDataParserConfig(),
|
||||
train_num_rays_per_batch=1024,
|
||||
eval_num_rays_per_batch=1024,
|
||||
),
|
||||
model=NeuSModelConfig(eval_num_rays_per_chunk=1024),
|
||||
),
|
||||
optimizers={
|
||||
"fields": {
|
||||
"optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
"scheduler": CosineDecaySchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000),
|
||||
},
|
||||
"field_background": {
|
||||
"optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
"scheduler": CosineDecaySchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000),
|
||||
},
|
||||
},
|
||||
viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
|
||||
vis="viewer",
|
||||
)
|
||||
|
||||
# method_configs["phototourism"] = TrainerConfig(
|
||||
# method_name="phototourism",
|
||||
# steps_per_eval_batch=500,
|
||||
# steps_per_save=2000,
|
||||
# max_num_iterations=30000,
|
||||
# mixed_precision=True,
|
||||
# pipeline=VanillaPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(
|
||||
# dataparser=PhototourismDataParserConfig(), # NOTE: one of the only differences with nerfacto
|
||||
# train_num_rays_per_batch=4096,
|
||||
# eval_num_rays_per_batch=4096,
|
||||
# camera_optimizer=CameraOptimizerConfig(
|
||||
# mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2)
|
||||
# ),
|
||||
# # Large dataset, so using prior values from VariableResDataManager.
|
||||
# train_num_images_to_sample_from=40,
|
||||
# train_num_times_to_repeat_images=100,
|
||||
# eval_num_images_to_sample_from=40,
|
||||
# eval_num_times_to_repeat_images=100,
|
||||
# ),
|
||||
# model=NerfactoModelConfig(eval_num_rays_per_chunk=1 << 15),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "proposal_networks": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
# "scheduler": None,
|
||||
# },
|
||||
# "fields": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
# "scheduler": None,
|
||||
# },
|
||||
# },
|
||||
# viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
|
||||
# vis="viewer",
|
||||
# )
|
||||
|
||||
# method_configs["neus"] = TrainerConfig(
|
||||
# method_name="neus",
|
||||
# steps_per_eval_image=500,
|
||||
# steps_per_eval_batch=5000,
|
||||
# steps_per_save=20000,
|
||||
# steps_per_eval_all_images=1000000, # set to a very large number so we don't eval with all images
|
||||
# max_num_iterations=100000,
|
||||
# mixed_precision=False,
|
||||
# pipeline=VanillaPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(
|
||||
# _target=VanillaDataManager[SDFDataset],
|
||||
# dataparser=SDFStudioDataParserConfig(),
|
||||
# train_num_rays_per_batch=1024,
|
||||
# eval_num_rays_per_batch=1024,
|
||||
# camera_optimizer=CameraOptimizerConfig(
|
||||
# mode="off", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2)
|
||||
# ),
|
||||
# ),
|
||||
# model=NeuSModelConfig(eval_num_rays_per_chunk=1024),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "fields": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
# "scheduler": CosineDecaySchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000),
|
||||
# },
|
||||
# "field_background": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
# "scheduler": CosineDecaySchedulerConfig(warm_up_end=5000, learning_rate_alpha=0.05, max_steps=300000),
|
||||
# },
|
||||
# },
|
||||
# viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
|
||||
# vis="viewer",
|
||||
# )
|
||||
|
||||
# method_configs["neus-facto"] = TrainerConfig(
|
||||
# method_name="neus-facto",
|
||||
# steps_per_eval_image=5000,
|
||||
# steps_per_eval_batch=5000,
|
||||
# steps_per_save=2000,
|
||||
# steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images
|
||||
# max_num_iterations=20001,
|
||||
# mixed_precision=False,
|
||||
# pipeline=VanillaPipelineConfig(
|
||||
# datamanager=VanillaDataManagerConfig(
|
||||
# _target=VanillaDataManager[SDFDataset],
|
||||
# dataparser=SDFStudioDataParserConfig(),
|
||||
# train_num_rays_per_batch=2048,
|
||||
# eval_num_rays_per_batch=2048,
|
||||
# camera_optimizer=CameraOptimizerConfig(
|
||||
# mode="SO3xR3", optimizer=AdamOptimizerConfig(lr=6e-4, eps=1e-8, weight_decay=1e-2)
|
||||
# ),
|
||||
# ),
|
||||
# model=NeuSFactoModelConfig(
|
||||
# # proposal network allows for significantly smaller sdf/color network
|
||||
# sdf_field=SDFFieldConfig(
|
||||
# use_grid_feature=True,
|
||||
# num_layers=2,
|
||||
# num_layers_color=2,
|
||||
# hidden_dim=256,
|
||||
# bias=0.5,
|
||||
# beta_init=0.8,
|
||||
# use_appearance_embedding=False,
|
||||
# ),
|
||||
# background_model="none",
|
||||
# eval_num_rays_per_chunk=2048,
|
||||
# ),
|
||||
# ),
|
||||
# optimizers={
|
||||
# "proposal_networks": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
# "scheduler": MultiStepSchedulerConfig(max_steps=20001, milestones=(10000, 1500, 18000)),
|
||||
# },
|
||||
# "fields": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
# "scheduler": CosineDecaySchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20001),
|
||||
# },
|
||||
# "field_background": {
|
||||
# "optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
# "scheduler": CosineDecaySchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20001),
|
||||
# },
|
||||
# },
|
||||
# viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
|
||||
# vis="viewer",
|
||||
# )
|
||||
method_configs["neus-facto"] = TrainerConfig(
|
||||
method_name="neus-facto",
|
||||
steps_per_eval_image=5000,
|
||||
steps_per_eval_batch=5000,
|
||||
steps_per_save=2000,
|
||||
steps_per_eval_all_images=1000000, # set to a very large model so we don't eval with all images
|
||||
max_num_iterations=20001,
|
||||
mixed_precision=False,
|
||||
pipeline=VanillaPipelineConfig(
|
||||
datamanager=VanillaDataManagerConfig(
|
||||
_target=VanillaDataManager[SDFDataset],
|
||||
dataparser=SDFStudioDataParserConfig(),
|
||||
train_num_rays_per_batch=2048,
|
||||
eval_num_rays_per_batch=2048,
|
||||
),
|
||||
model=NeuSFactoModelConfig(
|
||||
# proposal network allows for significantly smaller sdf/color network
|
||||
sdf_field=SDFFieldConfig(
|
||||
use_grid_feature=True,
|
||||
num_layers=2,
|
||||
num_layers_color=2,
|
||||
hidden_dim=256,
|
||||
bias=0.5,
|
||||
beta_init=0.8,
|
||||
use_appearance_embedding=False,
|
||||
),
|
||||
background_model="none",
|
||||
eval_num_rays_per_chunk=2048,
|
||||
),
|
||||
),
|
||||
optimizers={
|
||||
"proposal_networks": {
|
||||
"optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
|
||||
"scheduler": MultiStepSchedulerConfig(max_steps=20001, milestones=(10000, 1500, 18000)),
|
||||
},
|
||||
"fields": {
|
||||
"optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
"scheduler": CosineDecaySchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20001),
|
||||
},
|
||||
"field_background": {
|
||||
"optimizer": AdamOptimizerConfig(lr=5e-4, eps=1e-15),
|
||||
"scheduler": CosineDecaySchedulerConfig(warm_up_end=500, learning_rate_alpha=0.05, max_steps=20001),
|
||||
},
|
||||
},
|
||||
viewer=ViewerConfig(num_rays_per_chunk=1 << 15),
|
||||
vis="viewer",
|
||||
)
|
||||
|
||||
|
||||
def merge_methods(methods, method_descriptions, new_methods, new_descriptions, overwrite=True):
|
||||
|
||||
Reference in new issue
Block a user