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
