training_sampler.calibration(training_loader, verbose=True) test_sampler.calibration(test_loader, verbose=True) # Optional debug functions # debug_timing(training_dataset, training_loader) # 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) net = nn.DataParallel(net.cuda()) # net = net.cuda() 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')
dset = KITTIMapDataset("test", cfg, config_d3feat=config, root=config.root) # dset = ThreeDMatchTestset(root=config.root, # downsample=config.downsample, # config=config, # last_scene=False, # ) dloader, _ = get_dataloader( dataset=dset, batch_size=1, shuffle=False, num_workers=config.num_workers, ) generate_features(model.cuda(), dloader, config, args.chosen_snapshot) def test_kitti(model, dataset, config): # self.sess.run(dataset.test_init_op) import sys use_random_points = False icp_save_path = "d3feat_output" if use_random_points: num_keypts = 5000 # icp_save_path = f'geometric_registration_kitti/D3Feat_{self.experiment_str}-rand{num_keypts}' else: num_keypts = 250 # icp_save_path = f'geometric_registration_kitti/D3Feat_{self.experiment_str}-pred{num_keypts}' if not os.path.exists(icp_save_path): os.mkdir(icp_save_path)
# Calibrate samplers training_sampler.calibration(training_loader, verbose=True) test_sampler.calibration(test_loader, verbose=True) # debug_timing(training_dataset, training_loader) # debug_timing(test_dataset, test_loader) # debug_class_w(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) net = torch.nn.DataParallel(net.cuda()) 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