removed erroneous test default config

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evonneng authored and GitHub committed 2022-05-06 21:57:51 -07:00
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dry_run: true
defaults:
# This makes the logging module logs colorful
- override hydra/job_logging: colorlog
- override hydra/hydra_logging: colorlog
logging_configs:
steps_per_log: 10
# stats tracker logs configurable training stats per iteration to terminal (e.g. rays/s, PSNR)
enable_stats: True
stats_tracker:
max_history: 10
stats_to_track: [ITER_LOAD_TIME, ITER_TRAIN_TIME] # see mattport/utils/stats_tracker.py for options
# writer logs losses and images per iteration
writer:
type: TensorboardWriter
save_dir: './'
# profiler logs run times of functions and prints at end of training
enable_profiler: True
hydra:
run:
# Configure output dir of each experiment programmatically from the arguments
# Example "outputs/mnist/classifier/baseline/2021-03-10-141516"
dir: outputs/${experiment_name}/${method_name}/${now:%Y-%m-%d_%H%M%S}
experiment_name: blender_lego
method_name: vanilla_nerf
# pretrained loading options
resume_train:
load_dir: # pretrained model directory
load_step: # pretrained model's step number
max_num_iterations: 10
steps_per_save: null
steps_per_test: 100
model_dir: /tmp/mattport_models/
# dataset options
dataset:
data_directory: data/blender/lego_test
dataset_type: blender
downscale_factor: 4
# dataloader options
dataloader:
num_workers: 4
shuffle: true
num_images_to_sample_from: 1
num_rays_per_batch: 1024
num_times_to_repeat_images: 40
# optimizer options
param_groups:
fields:
optimizer:
type: Adam
lr: 1e-4
scheduler:
type: StepLR
step_size: 30 # Period of learning rate decay.
gamma: 0.1 # Multiplicative factor of learning rate decay. Default: 0.1.
cameras:
optimizer:
type: SGD
lr: 1e-4
scheduler:
type: ExponentialLR
gamma: 0.1
# machine options
machine_config:
num_gpus: 1
num_machines: 1
machine_rank: 0
dist_url: auto
# graph options
graph:
# NOTE(ethan): currently setting to nothing b/c we're still designing the config format...
_target_: mattport.nerf.graph.vanilla_nerf.NeRFGraph
near_plane: 1.0
far_plane: 6.0
num_coarse_samples: 64
num_importance_samples: 64