# 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
