def get_pair(self, args, fname, train):
partial = load_h5(fname)
gtpts = load_h5(fname.replace('partial', 'gt'))
if train:
gtpts, partial = augment_cloud([gtpts, partial], args)
partial = pad_cloudN(partial, args.inpts)
return partial, gtpts
SHAPE_NAMES = [line.rstrip() for line in \
open( '../training_data/shape_names_combined.txt')]
else:
SHAPE_NAMES = [line.rstrip() for line in \
open( '../training_data/shape_names_ext.txt')]
print("Number of Classes: " + str(NUM_CLASSES))
HOSTNAME = socket.gethostname()
np.random.seed(0)
print("Normalized: " + str(NORMALIZED))
print("Center Data: " + str(CENTER_DATA))
if (".h5" in TEST_FILE):
TEST_DATA, TEST_LABELS = data_utils.load_h5(TEST_FILE)
else:
TEST_DATA, TEST_LABELS = data_utils.load_data(TEST_FILE,
NUM_POINT,
with_bg_pl=WITH_BG)
if (CENTER_DATA):
TEST_DATA = data_utils.center_data(TEST_DATA)
if (NORMALIZED):
TEST_DATA = data_utils.normalize_data(TEST_DATA)
def log_string(out_str):
LOG_FOUT.write(out_str + '\n')
LOG_FOUT.flush()
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
LIMIT_GPU = True
MAX_ACCURACY = 0.0
MAX_CLASS_ACCURACY = 0.0
NUM_CLASSES = FLAGS.num_class
print("Number of Classes: "+str(NUM_CLASSES))
print("Normalized: "+str(NORMALIZED))
print("Center Data: "+str(CENTER_DATA))
if (".h5" in TRAIN_FILE):
TRAIN_DATA, TRAIN_LABELS = data_utils.load_h5(TRAIN_FILE)
else:
TRAIN_DATA, TRAIN_LABELS = data_utils.load_data(TRAIN_FILE, NUM_POINT, with_bg_pl = WITH_BG)
if (".h5" in TEST_FILE):
TEST_DATA, TEST_LABELS = data_utils.load_h5(TEST_FILE)
else:
TEST_DATA, TEST_LABELS = data_utils.load_data(TEST_FILE, NUM_POINT, with_bg_pl = WITH_BG)
if (CENTER_DATA):
TRAIN_DATA = data_utils.center_data(TRAIN_DATA)
TEST_DATA = data_utils.center_data(TEST_DATA)
if (NORMALIZED):
TRAIN_DATA = data_utils.normalize_data(TRAIN_DATA)
TEST_DATA = data_utils.normalize_data(TEST_DATA)
