hyperparameters.yaml

# Training Parameters -------------------------------
## Loss Computation
batch_size: 1 # batch_size > 1 currently only supported if single image dims are used (vertical and horizontal cells)
# In general: larger batches currently implemented rather primitively
learning_rate: 0.00001 #1e-5
lambda_po2pl: 1.0 # 10.0
## Network training options
use_dropout: False
## Data Augmentation
random_point_cloud_rotations: False # Data augmentation by rotating point cloud during training
random_rotations_only_yaw: False # If set to true, and random_point_cloud_rotations set to true --> rotations only about yaw axis
magnitude_random_rot: 4.0 # in degrees
## Loss Functions
normal_loss: "squared" # "squared" or "linear"
rotation_losses_only_plane_to_plane: False
point_to_point_loss: False
point_to_plane_loss: True
plane_to_plane_loss: True
po2po_alone: False

# Network Parameters ---------------------------------
## Data Format
normalization_scaling: False # If set to true --> lambda_po2pl should be increased
## Model parameters
###############num_input_channels: 8 # No impact yet
activation_fct: "tanh" # "tanh" or "relu"
resnet_outputs: 1000 # Output of the resnet, then fed into the rot/trans fully connected networks
###############conv_size: [3, 5] # No impact yet
pre_feature_extraction: False # Additional feature extraction layers before concatenation of input
layers: [ 2, 2, 2, 2 ] # 4 down-sampling steps, num resnet blocks (2 layers each) per down-sampling (2 here)
                       # [ 1, 1, 1, 1 ] e.g. results in half as many layers
factor_fewer_resnet_channels: 1 # 4 leads to approximately 15 times fewer weights
use_single_mlp_at_output: False # Single big MLP at output