def eval_KITTI(method, args):
dset = KITTIDataset(
root=kitti_dir,
split='test',
descriptor=args.descriptor,
in_dim=6,
inlier_threshold=args.inlier_threshold,
num_node=15000,
use_mutual=args.use_mutual,
augment_axis=0,
augment_rotation=0.00,
augment_translation=0.0,
)
dloader = get_dataloader(dset, batch_size=1, num_workers=8, shuffle=False)
stats = eval_KITTI_scene(method, dloader, args)
# pair level average
allpair_stats = stats
allpair_average = allpair_stats.mean(0)
correct_pair_average = allpair_stats[allpair_stats[:, 0] == 1].mean(0)
logging.info(f"*" * 40)
logging.info(
f"All {allpair_stats.shape[0]} pairs, Mean Reg Recall={allpair_average[0] * 100:.2f}%, Mean Re={correct_pair_average[1]:.2f}, Mean Te={correct_pair_average[2]:.2f}"
)
logging.info(
f"\tInput: Mean Inlier Num={allpair_average[3]:.2f}(ratio={allpair_average[4] * 100:.2f}%)"
)
logging.info(
f"\tOutput: Mean Inlier Num={allpair_average[5]:.2f}(precision={allpair_average[6] * 100:.2f}%, recall={allpair_average[7] * 100:.2f}%, f1={allpair_average[8] * 100:.2f}%)"
)
logging.info(
f"\tMean model time: {allpair_average[9]:.2f}s, Mean data time: {allpair_average[10]:.2f}s"
)
return allpair_stats
def eval_KITTI(model, config, use_icp):
dset = KITTIDataset(root='/data/KITTI',
split='test',
descriptor=config.descriptor,
in_dim=config.in_dim,
inlier_threshold=config.inlier_threshold,
num_node=12000,
use_mutual=config.use_mutual,
augment_axis=0,
augment_rotation=0.00,
augment_translation=0.0,
)
dloader = get_dataloader(dset, batch_size=1, num_workers=16, shuffle=False)
stats = eval_KITTI_per_pair(model, dloader, config, use_icp)
logging.info(f"Max memory allicated: {torch.cuda.max_memory_allocated() / 1024 ** 3:.2f}GB")
# pair level average
allpair_stats = stats
allpair_average = allpair_stats.mean(0)
correct_pair_average = allpair_stats[allpair_stats[:, 0] == 1].mean(0)
logging.info(f"*"*40)
logging.info(f"All {allpair_stats.shape[0]} pairs, Mean Success Rate={allpair_average[0]*100:.2f}%, Mean Re={correct_pair_average[1]:.2f}, Mean Te={correct_pair_average[2]:.2f}")
logging.info(f"\tInput: Mean Inlier Num={allpair_average[3]:.2f}(ratio={allpair_average[4]*100:.2f}%)")
logging.info(f"\tOutput: Mean Inlier Num={allpair_average[5]:.2f}(precision={allpair_average[6]*100:.2f}%, recall={allpair_average[7]*100:.2f}%, f1={allpair_average[8]*100:.2f}%)")
logging.info(f"\tMean model time: {allpair_average[9]:.2f}s, Mean data time: {allpair_average[10]:.2f}s")
return allpair_stats
def calculate_repeatability(desc_name, timestr, num_keypts):
"""
calculate the relative repeatability of {desc_name}_{timestr} under {num_keypts} setting.
"""
from datasets.KITTI import KITTIDataset
dataset = KITTIDataset(1, first_subsampling_dl=0.3, load_test=True)
repeat_list = []
keyptspath = f"geometric_registration_kitti/{desc_name}_{timestr}"
for i in range(len(dataset.files['test'])):
drive = dataset.files['test'][i][0]
t0, t1 = dataset.files['test'][i][1], dataset.files['test'][i][2]
filename = f'{drive}@{t0}-{t1}.npz'
if not os.path.exists(os.path.join(keyptspath, filename)):
continue
data = np.load(os.path.join(keyptspath, filename))
source_keypts = data['anc_pts'][-num_keypts:]
target_keypts = data['pos_pts'][-num_keypts:]
repeat_list += [
deal_with_one_pair(source_keypts,
target_keypts,
data['trans'],
num_keypts,
threshold=0.5)
]
print(
f"Average Repeatability at num_keypts = {num_keypts}: {np.mean(repeat_list)}"
)
return np.mean(repeat_list)
###########################
# Load the model parameters
###########################
config = KITTIConfig()
##############
# Prepare Data
##############
print()
print('Dataset Preparation')
print('*******************')
# Initiate dataset configuration
dataset = KITTIDataset(config.input_threads, config.first_subsampling_dl)
# Create subsampled input clouds
dl0 = config.first_subsampling_dl
# dataset.load_subsampled_clouds(dl0)
# Initialize input pipelines
dataset.init_input_pipeline(config)
# Test the input pipeline alone with this debug function
# dataset.check_input_pipeline_timing(config)
##############
# Define Model
##############
def test_caller(path, step_ind, on_val):
# Disable warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
###########################
# Load the model parameters
###########################
# Load model parameters
config = Config()
config.load(path)
##################################
# Change model parameters for test
##################################
# Change parameters for the test here. For example, you can stop augmenting the input data.
#config.augment_noise = 0.0001
#config.augment_color = 1.0
#config.validation_size = 500
#config.batch_num = 10
##############
# Prepare Data
##############
print()
print('Dataset Preparation')
print('*******************')
# Initiate dataset configuration
dataset = KITTIDataset(1, config.first_subsampling_dl, load_test=True)
# Initialize input pipelines
dataset.init_test_input_pipeline(config)
##############
# Define Model
##############
print('Creating Model')
print('**************\n')
t1 = time.time()
model = KernelPointFCNN(dataset.flat_inputs, config)
# Find all snapshot in the chosen training folder
snap_path = os.path.join(path, 'snapshots')
snap_steps = [
int(f[:-5].split('-')[-1]) for f in os.listdir(snap_path)
if f[-5:] == '.meta'
]
# Find which snapshot to restore
chosen_step = np.sort(snap_steps)[step_ind]
chosen_snap = os.path.join(path, 'snapshots',
'snap-{:d}'.format(chosen_step))
# Create a tester class
tester = ModelTester(model, restore_snap=chosen_snap)
t2 = time.time()
print('\n----------------')
print('Done in {:.1f} s'.format(t2 - t1))
print('----------------\n')
############
# Start test
############
print('Start Test')
print('**********\n')
tester.test_kitti(model, dataset)
# momentum=config.momentum,
weight_decay=config.weight_decay,
)
config.scheduler = optim.lr_scheduler.ExponentialLR(
config.optimizer,
gamma=config.scheduler_gamma,
)
# create dataset and dataloader
train_set = KITTIDataset(
root=config.root,
split='train',
descriptor=config.descriptor,
in_dim=config.in_dim,
inlier_threshold=config.inlier_threshold,
num_node=config.num_node,
use_mutual=config.use_mutual,
augment_axis=config.augment_axis,
augment_rotation=config.augment_rotation,
augment_translation=config.augment_translation,
)
val_set = KITTIDataset(
root=config.root,
split='val',
descriptor=config.descriptor,
in_dim=config.in_dim,
inlier_threshold=config.inlier_threshold,
num_node=config.num_node,
use_mutual=config.use_mutual,
augment_axis=config.augment_axis,
augment_rotation=config.augment_rotation,