icon-filter.yaml

name: icon-filter
ckpt_dir: "./data/ckpt/"
resume_path: "./data/ckpt/icon-filter.ckpt"
normal_path: "./data/ckpt/normal.ckpt"
results_path: "./results"

dataset:
  root: "./data/"
  rotation_num: 36
  num_sample_geo: 8000
  num_sample_color: 0
  num_sample_seg: 0
  num_sample_knn: 0
  sigma_geo: 5.0
  sigma_seg: 0.00
  sigma_color: 0.00
  train_bsize: 1.0
  val_bsize: 1.0
  test_bsize: 1.0
  ray_sample_num: 1
  zray_type: False
  semantic_p: False
  remove_outlier: False

  noise_type: ['pose', 'beta']
  noise_scale: [0.0, 0.0]

  types: ["thuman2"]
  scales: [100.0]

net:
  mlp_dim: [256, 512, 256, 128, 1]
  res_layers: [2,3,4]
  num_stack: 2
  prior_type: "icon"
  use_filter: True
  in_geo: (('normal_F',3), ('normal_B',3))
  in_nml: (('image',3), ('T_normal_F',3), ('T_normal_B',3))
  smpl_feats: ['sdf', 'cmap', 'norm', 'vis']
  gtype: 'HGPIFuNet'
  ctype: 'resnet34'
  norm_mlp: 'batch'
  N_freqs: 10
  geo_w: 0.1
  norm_w: 0.001
  dc_w: 1.0
  hourglass_dim: 6
  voxel_dim: 32
  smpl_dim: 7

lr_G: 1e-4
weight_decay: 0.0 
momentum: 0.0
batch_size: 4
num_threads: 8
gpus: [0]
test_gpus: [0]

sdf: False
sdf_clip: 15.0

fast_dev: 0
resume: False
test_mode: False
mcube_res: 512
clean_mesh: True
num_sanity_val_steps: 1

momentum: 0.0 
optim: RMSprop

# training (batch=4, set=agora, rot-6)
overfit: False
num_epoch: 10
freq_show_train: 0.1
freq_show_val: 0.3
freq_plot: 0.01
freq_eval: 0.2
schedule: [3, 8]