parser.add_argument(
'--result_path',
default=None,
help='result path for .pickle file from test.py [default: None]')
parser.add_argument('--viz',
action='store_true',
help='to visualize error result.')
parser.add_argument('--from_rgb_detection',
action='store_true',
help='test from data file from rgb detection.')
FLAGS = parser.parse_args()
IMG_DIR = '/home/rqi/Data/mysunrgbd/training/image'
TEST_DATASET = roi_seg_box3d_dataset.ROISegBoxDataset(
npoints=2048,
split='val',
rotate_to_center=True,
overwritten_data_path=FLAGS.data_path,
from_rgb_detection=FLAGS.from_rgb_detection)
dataset = sunrgbd_object('/home/rqi/Data/mysunrgbd', 'training')
VISU = FLAGS.viz
if VISU:
import mayavi.mlab as mlab
from view_pc import draw_lidar, draw_gt_boxes3d
#with open(FLAGS.result_path, 'rb') as fp:
# ps_list = pickle.load(fp)
# segp_list = pickle.load(fp)
# center_list = pickle.load(fp)
# heading_cls_list = pickle.load(fp)
# heading_res_list = pickle.load(fp)
# size_cls_list = pickle.load(fp)
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
HOSTNAME = socket.gethostname()
NUM_CLASSES = 2
TRAIN_DATASET = roi_seg_box3d_dataset.ROISegBoxDataset(
npoints=NUM_POINT,
split='train',
rotate_to_center=True,
random_flip=True,
random_shift=True,
overwritten_data_path='train_1002_aug5x.zip.pickle',
one_hot=True)
TEST_DATASET = roi_seg_box3d_dataset.ROISegBoxDataset(
npoints=NUM_POINT,
split='val',
rotate_to_center=True,
overwritten_data_path='val_1002.zip.pickle',
one_hot=True)
print(len(TRAIN_DATASET))
print(len(TEST_DATASET))
def log_string(out_str):
LOG_FOUT.write(out_str + '\n')
classes3D=FLAGS.TRAIN_CLS,
classes2D=classes2D,
data3D_keep_prob=FLAGS.train_data3D_keep_prob,
add3D_for_classes2D_prob=FLAGS.add3D_for_classes2D_prob,
npoints=NUM_POINT,
rotate_to_center=True,
random_flip=aug_data,
random_shift=aug_data,
overwritten_data_path=pjoin('frustums', train_file))
print('Length of Train Dataset: (2D: %d, 3D: %d)' % \
(TRAIN_DATASET.get_len_classes2D(), TRAIN_DATASET.get_len_classes3D()))
TEST_DATASET = roi_seg_box3d_dataset.ROISegBoxDataset(
FLAGS.TEST_CLS,
npoints=NUM_POINT,
split='val',
rotate_to_center=True,
overwritten_data_path=pjoin('frustums', test_file),
one_hot=True)
print('Length of Final Test Dataset : %d' % len(TEST_DATASET))
# For Evaluation
from sunrgbd_data import sunrgbd_object
from test_semisup import main_batch, main_batch_from_rgb_detection
from evaluate import evaluate_predictions, get_ap_info
SUNRGBD_DATASET_DIR = '/home/yewsiang/Transferable3D/dataset/mysunrgbd'
DATASET = sunrgbd_object(SUNRGBD_DATASET_DIR, 'training')
def log_string(out_str):
# Make sure all of the classes specified are valid
WHITE_LIST = []
for cls_name in FLAGS.test:
if not (cls_name in ALL_CLASSES): assert (False)
WHITE_LIST.append(cls_name)
MODEL_PATH = FLAGS.model_path
GPU_INDEX = FLAGS.gpu
NUM_POINT = FLAGS.num_point
MODEL = importlib.import_module(FLAGS.model) # import network module
NUM_CHANNEL = 3 if FLAGS.no_rgb else 6
num_classes = len(ALL_CLASSES)
TEST_DATASET = roi_seg_box3d_dataset.ROISegBoxDataset(WHITE_LIST, npoints=NUM_POINT,
split='training', rotate_to_center=True, \
overwritten_data_path=FLAGS.data_path, \
from_rgb_detection=FLAGS.from_rgb_detection, \
one_hot=True) # Always take one hot from dataset but may not use it
print('Test Dataset size: %d' % len(TEST_DATASET))
# Detections
print('3D Object Detection...')
if FLAGS.from_rgb_detection:
main_batch_from_rgb_detection(
TEST_DATASET,
WHITE_LIST,
num_classes,
NUM_POINT,
NUM_CHANNEL,
prefix=FLAGS.pred_prefix,
semi_type=FLAGS.semi_type,
