logs_path=train_path+'/logs'
pathlib.Path(logs_path).mkdir(parents=True, exist_ok=True)
plots_path=train_path+'/plots'
pathlib.Path(plots_path).mkdir(parents=True, exist_ok=True)
deployment_path=train_path+'/deploy'
pathlib.Path(deployment_path).mkdir(parents=True, exist_ok=True)
#Objects of Measurement System, Assembly System, Get Inference Data
print('Initializing the Assembly System and Measurement System....')
measurement_system=HexagonWlsScanner(data_type,application,system_noise,part_type,data_format)
vrm_system=VRMSimulationModel(assembly_type,assembly_kccs,assembly_kpis,part_name,part_type,voxel_dim,voxel_channels,point_dim,aritifical_noise)
get_data=GetTrainData();
deploy_model=DeployModel();
metrics_eval=MetricsEval();
print('Importing and Preprocessing Cloud-of-Point Data')
dataset=[]
dataset.append(get_data.data_import(file_names_x,data_folder))
dataset.append(get_data.data_import(file_names_y,data_folder))
dataset.append(get_data.data_import(file_names_z,data_folder))
dataset_test=[]
dataset_test.append(get_data.data_import(test_file_names_x,data_folder))
dataset_test.append(get_data.data_import(test_file_names_y,data_folder))
dataset_test.append(get_data.data_import(test_file_names_z,data_folder))
point_index=get_data.load_mapping_index(mapping_index)
kcc_dataset=get_data.data_import(kcc_files,kcc_folder)
model=dl_model.bayes_cnn_model_3d(voxel_dim,voxel_channels)
print('Model summary used for training')
print(model.summary())
train_model=BayesTrainModel(batch_size,epocs,split_ratio)
trained_model=train_model.run_train_model(model,combined_conv_data,combined_kcc_data,model_path,logs_path,plots_path,activate_tensorboard,run_id)
print('Training Complete')
if(model_type=='3D Convolution Neural Network'):
from core_model import DLModel
from model_deployment import DeployModel
from model_train import TrainModel
deploy_model=DeployModel();
if(learning_type=='Basic'):
dl_model=DLModel(model_type,output_dimension,optimizer,loss_func,regularizer_coeff,output_type)
model=dl_model.cnn_model_3d(voxel_dim,voxel_channels)
if(learning_type=='Transfer Learning'):
transfer_learning=TransferLearning(tl_type,tl_base,tl_app,model_type,assembly_kccs,optimizer,loss_func,regularizer_coeff,output_type)
base_model=transfer_learning.get_trained_model()
print('Base Model used for Transfer Learning...')
print(base_model.summary())
#plot_model(base_model, to_file='model.png')
transfer_model=transfer_learning.build_transfer_model(base_model)
file_names_z = config.assembly_system['test_data_files_z']
system_noise = config.assembly_system['system_noise']
aritifical_noise = config.assembly_system['aritifical_noise']
data_folder = config.assembly_system['data_folder']
kcc_folder = config.assembly_system['kcc_folder']
kcc_files = config.assembly_system['test_kcc_files']
print('Initializing the Assembly System and Measurement System....')
measurement_system = HexagonWlsScanner(data_type, application,
system_noise, part_type,
data_format)
vrm_system = VRMSimulationModel(assembly_type, assembly_kccs,
assembly_kpis, part_name, part_type,
voxel_dim, voxel_channels, point_dim,
aritifical_noise)
deploy_model = DeployModel()
get_data = GetTrainData()
#Generate Paths
train_path = '../trained_models/' + part_type
model_path = train_path + '/model' + '/trained_model_0.h5'
logs_path = train_path + '/logs'
deploy_path = train_path + '/deploy/'
#Import all static resources
#import Model
inference_model = deploy_model.get_model(model_path)
point_index = get_data.load_mapping_index(mapping_index)
cop_file_name = vc.voxel_parameters['nominal_cop_filename']
file_path = '../resources/nominal_cop_files/' + cop_file_name
#Read cop from csv file
print('Model summary used for training')
print(model.summary())
train_model = BayesTrainModel(batch_size, epocs, split_ratio)
trained_model = train_model.run_train_model(
model, combined_conv_data, combined_kcc_data, model_path,
logs_path, plots_path, activate_tensorboard, run_id)
print('Training Complete')
if (model_type == '3D Convolution Neural Network'):
from core_model import DLModel
from model_deployment import DeployModel
from model_train import TrainModel
deploy_model = DeployModel()
if (learning_type == 'Basic'):
dl_model = DLModel(model_type, output_dimension, optimizer,
loss_func, regularizer_coeff, output_type)
model = dl_model.cnn_model_3d(voxel_dim, voxel_channels)
if (learning_type == 'Transfer Learning'):
transfer_learning = TransferLearning(tl_type, tl_base, tl_app,
model_type, assembly_kccs,
optimizer, loss_func,
regularizer_coeff,
output_type)
base_model = transfer_learning.get_trained_model()
print('Base Model used for Transfer Learning...')