else:
pillar_net = build_point_pillar_graph(params)
pillar_net.load_weights(os.path.join(MODEL_ROOT, "model.h5"))
pillar_net.compile(optimizer, loss=loss.losses())
data_reader = KittiDataReader()
lidar_files = sorted(glob(os.path.join(DATA_ROOT, "velodyne", "*.bin")))
label_files = sorted(glob(os.path.join(DATA_ROOT, "label_2", "*.txt")))
calibration_files = sorted(glob(os.path.join(DATA_ROOT, "calib", "*.txt")))
assert len(lidar_files) == len(label_files) == len(
calibration_files), "Input dirs require equal number of files."
validation_len = int(0.3 * len(label_files))
training_gen = SimpleDataGenerator(data_reader, params.batch_size,
lidar_files[:-validation_len],
label_files[:-validation_len],
calibration_files[:-validation_len])
validation_gen = SimpleDataGenerator(data_reader, params.batch_size,
lidar_files[-validation_len:],
label_files[-validation_len:],
calibration_files[-validation_len:])
log_dir = MODEL_ROOT
epoch_to_decay = int(
np.round(params.iters_to_decay / params.batch_size *
int(np.ceil(float(len(label_files)) / params.batch_size))))
callbacks = [
tf.keras.callbacks.TensorBoard(log_dir=log_dir),
tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(
log_dir, "model.h5"),
monitor='val_loss',
from processors import SimpleDataGenerator
from readers import KittiDataReader
from config import Parameters
from network import build_point_pillar_graph
DATA_ROOT = "../validation_small"
MODEL_ROOT = "./logs"
# os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"]="0"
if __name__ == "__main__":
params = Parameters()
pillar_net = build_point_pillar_graph(params)
pillar_net.load_weights(os.path.join(MODEL_ROOT, "model.h5"))
pillar_net.summary()
data_reader = KittiDataReader()
lidar_files = sorted(glob(os.path.join(DATA_ROOT, "velodyne", "*.bin")))
label_files = sorted(glob(os.path.join(DATA_ROOT, "label_2", "*.txt")))
calibration_files = sorted(glob(os.path.join(DATA_ROOT, "calib", "*.txt")))
eval_gen = SimpleDataGenerator(data_reader, params.batch_size, lidar_files,
label_files, calibration_files)
pred = pillar_net.predict(eval_gen)
print(pred)
label_files_val = label_files[valVal:testVal]
calibration_files_val = calibration_files[valVal:testVal]
lidar_files_test = lidar_files[testVal:]
label_files_test = label_files[testVal:]
calibration_files_test = calibration_files[testVal:]
lidar_files = lidar_files[:valVal]
label_files = label_files[:valVal]
calibration_files = calibration_files[:valVal]
print(lidar_files)
print(lidar_files_val)
print(lidar_files_test)
training_gen = SimpleDataGenerator(data_reader, params.batch_size, lidar_files, label_files, calibration_files)
validation_gen = SimpleDataGenerator(data_reader, params.batch_size, lidar_files_val, label_files_val, calibration_files_val)
test_gen = SimpleDataGenerator(data_reader, params.batch_size, lidar_files_test, label_files_test, calibration_files_test)
try:
pillar_net.fit_generator(training_gen,
len(training_gen),
callbacks=callbacks,
use_multiprocessing=True,
epochs=int(params.total_training_epochs),
workers=6,
validation_data = validation_gen,
validation_steps = len(validation_gen))