##################################
# Change model parameters for test
##################################
# Change parameters for the test here. For example, you can stop augmenting the input data.
config.global_fet = False
config.validation_size = 200
config.input_threads = 16
config.n_frames = 4
config.n_test_frames = 4 #it should be smaller than config.n_frames
if config.n_frames < config.n_test_frames:
config.n_frames = config.n_test_frames
config.big_gpu = True
config.dataset_task = '4d_panoptic'
#config.sampling = 'density'
config.sampling = 'importance'
config.decay_sampling = 'None'
config.stride = 1
config.first_subsampling_dl = 0.061
##############
# Prepare Data
##############
print()
print('Data Preparation')
print('****************')
if on_val:
print('\nModel Preparation')
print('*****************')
# Define network model
t1 = time.time()
if config.dataset_task == 'classification':
net = KPCNN(config)
elif config.dataset_task in ['cloud_segmentation', 'slam_segmentation']:
net = KPFCNN(config, test_dataset.label_values, test_dataset.ignored_labels)
else:
raise ValueError('Unsupported dataset_task for testing: ' + config.dataset_task)
# Define a visualizer class
tester = ModelTester(net, chkp_path=chosen_chkp)
print('Done in {:.1f}s\n'.format(time.time() - t1))
print('\nStart test')
print('**********\n')
# Training
config.dataset_task = "rellis_segmentation"
if config.dataset_task == 'classification':
tester.classification_test(net, test_loader, config)
elif config.dataset_task == 'cloud_segmentation':
tester.cloud_segmentation_test(net, test_loader, config)
elif config.dataset_task == 'slam_segmentation':
tester.slam_segmentation_test(net, test_loader, config)
elif config.dataset_task == "rellis_segmentation":
tester.rellis_segmentation_test(net, test_loader, config,num_votes=100)
else:
raise ValueError('Unsupported dataset_task for testing: ' + config.dataset_task)
