configs/neural_volumes/neural_volumes_390.yaml

task: 'if_nerf'
gpus: [0]

train_dataset_module: 'lib.datasets.light_stage.can_smpl'
train_dataset_path: 'lib/datasets/light_stage/can_smpl.py'
test_dataset_module: 'lib.datasets.light_stage.can_smpl'
test_dataset_path: 'lib/datasets/light_stage/can_smpl.py'

network_module: 'lib.networks.latent_xyzc'
network_path: 'lib/networks/latent_xyzc.py'
renderer_module: 'lib.networks.renderer.if_clight_renderer'
renderer_path: 'lib/networks/renderer/if_clight_renderer.py'

trainer_module: 'lib.train.trainers.if_nerf_clight'
trainer_path: 'lib/train/trainers/if_nerf_clight.py'

evaluator_module: 'lib.evaluators.neural_volume'
evaluator_path: 'lib/evaluators/neural_volume.py'

visualizer_module: 'lib.visualizers.if_nerf'
visualizer_path: 'lib/visualizers/if_nerf.py'

human: 390

train:
    dataset: Human390_0001_Train
    batch_size: 1
    collator: ''
    lr: 5e-4
    weight_decay: 0
    epoch: 400
    scheduler:
        type: 'exponential'
        gamma: 0.1
        decay_epochs: 1000
    num_workers: 16

test:
    dataset: Human390_0001_Test
    sampler: 'FrameSampler'
    batch_size: 1
    collator: ''

ep_iter: 500
save_ep: 1000
eval_ep: 1000

# training options
netdepth: 8
netwidth: 256
netdepth_fine: 8
netwidth_fine: 256
netchunk: 65536
chunk: 32768

no_batching: True

precrop_iters: 500
precrop_frac: 0.5

# network options
point_feature: 6

# rendering options
use_viewdirs: True
i_embed: 0
xyz_res: 10
view_res: 4
raw_noise_std: 0

N_samples: 64
N_importance: 128
N_rand: 1024

near: 1
far: 3

perturb: 1
white_bkgd: False

render_views: 50

# data options
res: 256
ratio: 0.5
intv: 6
begin_i: 700
ni: 300
smpl: 'smpl'
params: 'params'

voxel_size: [0.005, 0.005, 0.005]  # dhw

# record options
log_interval: 1