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)