Regression training folder

operators.ml3d.regression_training_folder(client, folder_in_folder_points='/folder_in_folder_points', folder_in_folder_gt='/folder_in_folder_gt', out_model_path='parameters_model', voxel_size=0.02, zero_centering='True', point_names='X,Y,Z', feature_names='ones', learning_rate=3e-06, weight_decay=1e-09, num_epochs=500, batch_size=2, save_after_epochs=100, network_type='FCNN128', criterion_type='L1Mean', num_classes=1, worker_instance_type='x2large', manager_instance_type='small', extension_in_folder_points='.laz', extension_in_folder_gt='.laz', skip_existing_files=False)

regression_training_folder(client,
in_folder_points=’/in_folder_points’,
in_folder_gt=’/in_folder_gt’,
out_model_path=’parameters_model’,
voxel_size=0.02,
zero_centering=’True’,
point_names=’X,Y,Z’,
feature_names=’ones’,
learning_rate=3e-6,
weight_decay=1e-9,
num_epochs=500,
batch_size=2,
save_after_epochs=100,
network_type=’FCNN128’,
criterion_type=’L1Mean’,
num_classes=1,
worker_instance_type=’x2large’,
manager_instance_type=”small”,
extension_in_folder_points=”.data_train/points”,
extension_in_folder_gt=”.data_train/gt”,
skip_existing_files = False )
Parameters:

  • out_model_path – model path

  • voxel_size – voxel size

  • zero_centering – zero centering

  • point_names – point names

  • feature_names – feature names

  • learning_rate – learning rate

  • weight_decay – regularization decay

  • num_epochs – number of epochs

  • batch_size – batch size for training

  • save_after_epochs – save after epochs

  • network_type – model type of backbone network

  • criterion_type – model type of criterion

  • num_classes – number of classes

  • folder_in_folder_points – input directory with training data

  • folder_in_folder_gt – input directory with training data

  • worker_instance_type – cloud instance type of worker nodes

  • manager_instance_type – cloud instance type of manager node

  • extension_in_folder_points – File extension of files in folder for folder_in_folder_points

  • extension_in_folder_gt – File extension of files in folder for folder_in_folder_gt

  • skip_existing_files – skip files that already exist in the output folder