# debug_timing(test_dataset, test_loader) # debug_upsampling(training_dataset, training_loader) print('\nModel Preparation') print('*****************') # Define network model t1 = time.time() net = KPFCNN(config, training_dataset.label_values, training_dataset.ignored_labels) debug = False if debug: print('\n*************************************\n') print(net) print('\n*************************************\n') for param in net.parameters(): if param.requires_grad: print(param.shape) print('\n*************************************\n') print("Model size %i" % sum(param.numel() for param in net.parameters() if param.requires_grad)) print('\n*************************************\n') # Define a trainer class trainer = ModelTrainer(net, config, chkp_path=chosen_chkp) print('Done in {:.1f}s\n'.format(time.time() - t1)) print('\nStart training') print('**************') # Training trainer.train(net, training_loader, test_loader, config, time_limit=36000)
# debug_upsampling(training_dataset, training_loader) print('\nModel Preparation') print('*****************') # Define network model t1 = time.time() net = KPFCNN(config, training_dataset.label_values, training_dataset.ignored_labels) debug = False if debug: print('\n*************************************\n') print(net) print('\n*************************************\n') for param in net.parameters(): if param.requires_grad: print(param.shape) print('\n*************************************\n') print("Model size %i" % sum(param.numel() for param in net.parameters() if param.requires_grad)) print('\n*************************************\n') # Define a trainer class trainer = ModelTrainer(net, config, chkp_path=chosen_chkp) print('Done in {:.1f}s\n'.format(time.time() - t1)) print('\nStart training') print('**************')
# debug_upsampling(training_dataset, training_loader) print('\nModel Preparation') print('*****************') # Define network model t1 = time.time() net = KPFCNN(config, training_dataset.label_values, training_dataset.ignored_labels) debug = False if debug: print('\n*************************************\n') print(net) print('\n*************************************\n') for param in net.parameters(): if param.requires_grad: print(param.shape) print('\n*************************************\n') print("Model size %i" % sum( param.numel() for param in net.parameters() if param.requires_grad)) print('\n*************************************\n') # Define a trainer class trainer = ModelTrainer(net, config, chkp_path=chosen_chkp) print('Done in {:.1f}s\n'.format(time.time() - t1)) print('\nStart training') print('**************') # Training