parser.add_argument(
'--idx_path',
default=None,
help=
'filename of txt where each line is a data idx, used for rgb detection -- write <id>.txt for all frames. [default: None]'
)
parser.add_argument('--dump_result',
action='store_true',
help='If true, also dump results to .pickle file')
FLAGS = parser.parse_args()
# Set training configurations
BATCH_SIZE = FLAGS.batch_size
MODEL_PATH = FLAGS.model_path
GPU_INDEX = FLAGS.gpu
NUM_POINT = FLAGS.num_point
TEST_DATASET = FrustumDataset(NUM_POINT,
FLAGS.kitti_path,
BATCH_SIZE,
FLAGS.split,
data_dir='./rcnn_data_' + FLAGS.split,
is_training=False,
augmentX=1,
random_shift=False,
rotate_to_center=True,
random_flip=False,
use_gt_prop=False)
test(TEST_DATASET, FLAGS.output + '.pickle', FLAGS.output)
FLAGS = parser.parse_args()
# Set training configurations
BATCH_SIZE = FLAGS.batch_size
MODEL_PATH = FLAGS.model_path
GPU_INDEX = FLAGS.gpu
NUM_POINT = FLAGS.num_point
MODEL = importlib.import_module(FLAGS.model)
# NUM_CLASSES = 2
TEST_DATASET = FrustumDataset(
NUM_POINT,
'/data/ssd/public/jlliu/Kitti/object',
BATCH_SIZE,
'val',
save_dir=
'/data/ssd/public/jlliu/frustum-pointnets/train/rpn_dataset_car_people/val',
augmentX=1,
random_shift=False,
rotate_to_center=True,
random_flip=False,
fill_with_label=False)
kitti_dataset = kitti_object('/data/ssd/public/jlliu/Kitti/object')
def get_session_and_ops(batch_size, num_point):
''' Define model graph, load model parameters,
create session and return session handle and tensors
'''
with tf.Graph().as_default():
with tf.device('/gpu:' + str(GPU_INDEX)):
LOG_DIR = FLAGS.log_dir
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS) + '\n')
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
# load data set in background thread, remember to join data_loading_thread somewhere
TRAIN_DATASET = FrustumDataset(NUM_POINT,
'/data/ssd/public/jlliu/Kitti/object',
BATCH_SIZE,
'train',
data_dir='./rcnn_data_train',
augmentX=5,
random_shift=True,
rotate_to_center=True,
random_flip=True,
use_gt_prop=FLAGS.use_gt_prop)
TEST_DATASET = FrustumDataset(NUM_POINT,
'/data/ssd/public/jlliu/Kitti/object',
BATCH_SIZE,
'val',
data_dir='./rcnn_data_val',
augmentX=1,
random_shift=False,
rotate_to_center=True,
random_flip=False,
use_gt_prop=FLAGS.use_gt_prop)
train_loading_thread = Thread(target=TRAIN_DATASET.load_buffer_repeatedly,