def main(_argv):
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.summary()
logging.info('model created')
load_darknet_weights(yolo, FLAGS.weights, FLAGS.tiny)
logging.info('weights loaded')
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
logging.info('sanity check passed')
yolo.save_weights(FLAGS.output)
logging.info('weights saved')
def main(_argv):
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.summary()
logging.info("model created")
load_darknet_weights(yolo, FLAGS.weights,
False) # False for absence of yolo-TinY
logging.info("weights loaded")
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo(img)
logging.info("sanity check passed")
yolo.save_weights(FLAGS.output)
logging.info("weights saved")
def main(_argv):
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.summary()
logging.info('model created')
load_darknet_weights(yolo, FLAGS.weights, FLAGS.tiny)
logging.info('weights loaded')
img = np.random.random((1, 320, 320, 3)).astype(np.float32)
output = yolo.predict(img)
logging.info('sanity check passed')
yolo.save_weights(FLAGS.output)
logging.info('weights saved')