cuda_devices: '1'

# Network hyper-parameters
batch_size: 4
max_epoch: 150
lr: 0.001
seed: 0
momentum: 0.9
weight_decay: 0.0005
optimizer: 'adamw' # sgd/adamw/adam
scheduler: 'cos' # step/cos/multistep
milestones: [30, 60, 90]
gamma: 0.1
retrieval_on: true   # enable evaluating retrieval performance during training
save_steps: 50

# Model configuration
curve_descriptor:
  num_kernel: 64
graph_conv:
  num_kernel: 64
structural_descriptor:
  num_kernel: 64
  sigma: 0.2
mesh_convolution:
  aggregation_method: 'Aggregation'  # Concat/Max/Average/Aggregation/
num_kernel: 8
ConvSurface:
  num_samples_per_neighbor: 4
  rs_mode: 'Weighted' # Uniform/Weighted
  num_kernel: 16
MeshBlock:
  blocks: [3, 4, 4]
pool_rate: 2

# dataset
dataset:
  data_root: './dataset'
  max_faces: 32868
  augment_data: true
  jitter_sigma: 0.001
  jitter_clip: 0.001

# result
ckpt_root: 'ckpt_root'

mamba : {
  NAME: MeshMamba,
  input_channel: 256,
  trans_dim: 8,
  depth: 5,
  drop_path_rate: 0.1,
  cls_dim: 15,
  num_heads: 6,
  group_size: 36,
  num_group: 1024,
  encoder_dims: 8,
  bimamba_type: "v4",
  center_local_k: 4,
  ordering: False,
  label_smooth: 0.0,
  lr_ratio_cls: 1.,
  lr_ratio_lfa: 1.,
}