mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-13 03:14:00 +08:00
evonneng
86b1011ae1
* added skeleton for terminal logger * done skeleton terminal writer logic... todo: actual calls to the write classes * moved lambda to top function * added logic to support timing * prettier printing * prettier printing * lint * separated out timing and other stats variables. printing in dict format * keeping logging history and wiping rest * updated logic to calculate total times instead of average times * made stats tracker configurable * moved stats tracker and profiler to own class file; implemented basic functionality for profiler * lint removing duplicate function * update readme; moved around config variables * profiler prints on sig kill
87 lines
2.1 KiB
YAML
87 lines
2.1 KiB
YAML
defaults:
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# This makes the logging module logs colorful
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- override hydra/job_logging: colorlog
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- override hydra/hydra_logging: colorlog
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logging_configs:
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steps_per_log: 10
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# stats tracker logs configurable training stats per iteration to terminal (e.g. rays/s, PSNR)
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enable_stats: True
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stats_tracker:
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max_history: 10
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stats_to_track: [ITER_LOAD_TIME, ITER_TRAIN_TIME] # see mattport/utils/stats_tracker.py for options
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# writer logs losses and images per iteration
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writer:
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type: TensorboardWriter
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save_dir: './'
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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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hydra:
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run:
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# Configure output dir of each experiment programmatically from the arguments
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# Example "outputs/mnist/classifier/baseline/2021-03-10-141516"
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dir: outputs/${experiment_name}/${method_name}/${now:%Y-%m-%d_%H%M%S}
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experiment_name: blender_lego
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method_name: vanilla_nerf
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# pretrained loading options
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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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max_num_iterations: 1000000
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steps_per_save: null
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steps_per_test: 100
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model_dir: /tmp/mattport_models/
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# dataset options
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dataset:
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data_directory: data/blender/lego
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dataset_type: blender
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downscale_factor: 4
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# dataloader options
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dataloader:
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num_workers: 4
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shuffle: true
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num_images_to_sample_from: 8
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num_rays_per_batch: 1024
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num_times_to_repeat_images: 40
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# optimizer options
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param_groups:
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fields:
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optimizer:
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type: Adam
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lr: 1e-4
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scheduler:
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type: StepLR
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step_size: 30 # Period of learning rate decay.
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gamma: 0.1 # Multiplicative factor of learning rate decay. Default: 0.1.
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cameras:
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optimizer:
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type: SGD
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lr: 1e-4
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scheduler:
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type: ExponentialLR
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gamma: 0.1
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# machine options
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machine_config:
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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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# graph options
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graph:
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# NOTE(ethan): currently setting to nothing b/c we're still designing the config format...
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_target_: mattport.nerf.graph.vanilla_nerf.NeRFGraph
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near_plane: 1.0
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far_plane: 6.0
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num_coarse_samples: 64
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num_importance_samples: 64
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