mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-16 04:43:52 +08:00
Ethan WeberandJake Austin
0b042be956
* Model class done, pipeline class still needs more visualization stuffs * Renaming the BaseDataContainer to DataloaderOutputs and adding documentation * Pushing more documentation, removing some trivial functions * Updating dict typing * Removing config dicts for collider * Passing tests * Pushing shell new dataloader * Saving work, need to fix NGP model's get_loss_dict and Pipeline * V1 of the ngp pipeline should be done. Need train script and debugging * Updating with latest instant-ngp code * Syntax updates from latest ngp graph code * Linter, removing unneeded code in new NGP graph code * Style * Should be good now. Linter is happy and the code is verbatum current instant-ngp * Everything done except novel view testing code * Fixing some nitpicks * Debugging dataloader * merged master * WOP. integrating pipeline with configs and notebook demo * removing model folder * a lot of name changing from Graph to Model * in progress... changing trainer to use pipeline... broken right now * pipeline code runs * switched to val dataset * makes run_eval.py work * updated configs for pipeline change * fixing test_run_train script * fixing black and linting * fix docs * small updates to trainer Co-authored-by: Jake Austin <jake-austin@berkeley.edu>
100 lines
2.7 KiB
YAML
100 lines
2.7 KiB
YAML
defaults:
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- override hydra/job_logging: colorlog
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- override hydra/hydra_logging: colorlog
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- _self_
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machine:
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seed: 42
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num_gpus: 1
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num_machines: 1
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machine_rank: 0
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dist_url: auto
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logging:
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steps_per_log: 10
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max_buffer_size: 20
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# writer logs losses and images per iteration
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writer:
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TensorboardWriter:
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log_dir: './'
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LocalWriter:
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log_dir: './'
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stats_to_track:
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[ITER_LOAD_TIME, ITER_TRAIN_TIME, RAYS_PER_SEC, CURR_TEST_PSNR]
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max_log_size: 10 # if 0, logs everything with no erasing
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# profiler logs run times of functions and prints at end of training
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enable_profiler: True
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trainer:
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model_dir: './checkpoints'
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steps_per_save: 1000
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steps_per_test: 500
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max_num_iterations: 1000000
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mixed_precision: False
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resume_train:
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load_dir: # pretrained model directory
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load_step: # pretrained model's step number
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experiment_name: blender_lego
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method_name: vanilla_nerf
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# hydra settings
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hydra:
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mode: RunMode.RUN
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run:
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dir: outputs/${experiment_name}/${method_name}/${now:%Y-%m-%d_%H%M%S}
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job:
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chdir: True
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pipeline:
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_target_: nerfactory.pipelines.base.Pipeline
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dataloader:
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_target_: nerfactory.dataloaders.base.VanillaDataloader
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image_dataset_type: rgb
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train_dataset:
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_target_: nerfactory.dataloaders.datasets.Dataset
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train_num_rays_per_batch: 1024
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train_num_images_to_sample_from: -1
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eval_dataset:
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_target_: nerfactory.dataloaders.datasets.Dataset
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eval_image_indices: [0]
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eval_num_rays_per_chunk: 4096
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model:
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_target_: nerfactory.models.vanilla_nerf.NeRFModel
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enable_collider: True
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collider_config:
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_target_: nerfactory.models.modules.scene_colliders.NearFarCollider
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near_plane: 2.0
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far_plane: 6.0
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num_coarse_samples: 64
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num_importance_samples: 128
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loss_coefficients:
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rgb_loss_coarse: 1.0
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rgb_loss_fine: 1.0
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enable_density_field: false
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density_field_config:
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# Please see the docstr for DensityGrid on how to set those arguments.
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_target_: nerfactory.fields.density_fields.density_grid.DensityGrid
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center: 0.0 # simply set it as the center of the scene bbox
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base_scale: 3.0 # simply set it as the scale of the scene bbox
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num_cascades: 1 # if using more than 1 cascade, the `base_scale` can be smaller than scene scale.
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resolution: 128
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update_every_num_iters: 16
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optimizers:
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fields:
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optimizer:
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_target_: torch.optim.RAdam
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lr: 0.0005
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scheduler:
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_target_: nerfactory.optimizers.optimizers.ExponentialDecaySchedule
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lr_final: 0.000005
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max_steps: 1000000
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viewer:
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enable: False
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zmq_url: tcp://127.0.0.1:6000
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min_render_image_height: 64
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max_render_image_height: 1024
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num_rays_per_chunk: 4096
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