part_name, part_type, voxel_dim, voxel_channels,
point_dim)
#Import model architecture
output_dimension = assembly_kccs
dl_model = Bayes_DLModel(model_type, output_dimension, optimizer,
loss_func, regularizer_coeff, output_type)
model = dl_model.bayes_cnn_model_3d(voxel_dim, voxel_channels)
#Inference from simulated data
inference_model = deploy_model.get_model(model, model_path, voxel_dim,
voxel_channels)
kcc_dataset = get_data.data_import(kcc_files, kcc_folder)
input_conv_data, kcc_subset_dump, kpi_subset_dump = get_data.data_convert_voxel_mc(
vrm_system, dataset, point_index, kcc_dataset)
y_pred = np.zeros_like(kcc_dataset)
y_pred, y_std, y_aleatoric_std = deploy_model.model_inference(
input_conv_data, inference_model, y_pred, kcc_dataset.values,
plots_path)
avg_std = np.array(y_std).mean(axis=0)
avg_aleatoric_std = np.array(y_std).mean(axis=0)
print("Average Epistemic Uncertainty of each KCC: ", avg_std)
print("Average Aleatoric Uncertainty of each KCC: ", avg_aleatoric_std)
evalerror = 1
if (evalerror == 1):
metrics_eval = MetricsEval()
eval_metrics, accuracy_metrics_df = metrics_eval.metrics_eval_base(
file_names_y=[file_names_y]
file_names_z=[file_names_z]
np.savetxt(file_path, initial_samples, delimiter=",")
print('Sampling Completed...')
test_samples=initial_samples
cae_status=cae_simulations.run_simulations(run_id=0,type_flag='test')
print("Pre-processing simulated test data")
dataset_test=[]
dataset_test.append(get_data.data_import(file_names_x,data_folder))
dataset_test.append(get_data.data_import(file_names_y,data_folder))
dataset_test.append(get_data.data_import(file_names_z,data_folder))
input_conv_data_test, kcc_subset_dump_test,kpi_subset_dump_test=get_data.data_convert_voxel_mc(vrm_system,dataset_test,point_index,test_samples)
for i in tqdm(range(max_run_length)):
run_id=i
print('Training Run ID: ',i)
file_name=sampling_config['output_file_name_train']+'_'+str(i)+'.csv'
file_names_x=sampling_config['datagen_filename_x']+'train'+'_'+str(i)+'.csv'
file_names_y=sampling_config['datagen_filename_y']+'train'+'_'+str(i)+'.csv'
file_names_z=sampling_config['datagen_filename_z']+'train'+'_'+str(i)+'.csv'
file_names_x=[file_names_x]
file_names_y=[file_names_y]
file_names_z=[file_names_z]
get_data.data_import(test_output_file_names_y, data_folder))
test_output_dataset.append(
get_data.data_import(test_output_file_names_z, data_folder))
kcc_dataset = get_data.data_import(kcc_files, kcc_folder)
test_kcc_dataset = get_data.data_import(test_kcc_files, kcc_folder)
if (kcc_sublist != 0):
print("Sub-setting Process Parameters: ", kcc_sublist)
kcc_dataset = kcc_dataset.iloc[:, kcc_sublist]
test_kcc_dataset = test_kcc_dataset[:, kcc_sublist]
else:
print("Using all Process Parameters")
#Pre-processing to point cloud data
input_conv_data, kcc_subset_dump, kpi_subset_dump = get_data.data_convert_voxel_mc(
vrm_system, input_dataset, point_index, kcc_dataset)
test_input_conv_data, test_kcc_subset_dump, test_kpi_subset_dump = get_data.data_convert_voxel_mc(
vrm_system, test_input_dataset, point_index, test_kcc_dataset)
output_conv_data, kcc_subset_dump, kpi_subset_dump = get_data.data_convert_voxel_mc(
vrm_system, output_dataset, point_index, kcc_dataset)
test_output_conv_data, test_kcc_subset_dump, test_kpi_subset_dump = get_data.data_convert_voxel_mc(
vrm_system, test_output_dataset, point_index, test_kcc_dataset)
unet_train_model = Unet_TrainModel(batch_size, epocs, split_ratio)
trained_model, eval_metrics, accuracy_metrics_df = unet_train_model.unet_run_train_model(
model, input_conv_data, kcc_subset_dump, output_conv_data,
test_input_conv_data, test_kcc_subset_dump, test_output_conv_data,
model_path, logs_path, plots_path, activate_tensorboard)
file_names_z = sampling_config[
'datagen_filename_z'] + 'test' + '_' + str(0) + '.csv'
np.savetxt(file_path, test_samples, delimiter=",")
print('Sampling Completed...')
cae_status = cae_simulations.run_simulations(run_id=0,
type_flag='test')
print("Pre-processing simulated test data")
dataset_test = []
dataset_test.append(get_data.data_import([file_names_x], data_folder))
dataset_test.append(get_data.data_import([file_names_y], data_folder))
dataset_test.append(get_data.data_import([file_names_z], data_folder))
input_conv_data_test, kcc_subset_dump_test, kpi_subset_dump_test = get_data.data_convert_voxel_mc(
vrm_system, dataset_test, point_index, test_samples)
if (sampling_validation_flag == 1):
print('Generating Adaptive Sampling Data...')
print('LHS Sampling for validation samples')
#get prediction errors
#get uncertainty estimates
from cae_simulations import CAESimulations
cae_simulations = CAESimulations(simulation_platform,
simulation_engine, max_run_length,
case_study)
validate_samples = adaptive_sampling.inital_sampling_uniform_random(
kcc_struct, sampling_config['sample_validation_dim'])
file_name = sampling_config['output_file_name_validate'] + ".csv"