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)
model = PolypDetectionModel()
model.build_model(False)
model.load_weights(FLAGS.weights)
eval_model = model.get_model()
class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
logging.info('classes loaded')
if FLAGS.tfrecord:
dataset = load_tfrecord_dataset(FLAGS.tfrecord, FLAGS.classes,
FLAGS.size)
dataset = dataset.shuffle(512)
img_raw, _label = next(iter(dataset.take(1)))
else:
img_raw = tf.image.decode_image(open(FLAGS.image, 'rb').read(),
channels=3)
img = tf.expand_dims(img_raw, 0)
img = PolypDataset.transform_images(img)
t1 = time.time()
boxes, scores, classes, nums = eval_model(img)
t2 = time.time()
logging.info('time: {}'.format(t2 - t1))
logging.info('detections:')
for i in range(nums[0]):
logging.info('\t{}, {}, {}'.format(class_names[int(classes[0][i])],
np.array(scores[0][i]),
np.array(boxes[0][i])))
img = cv2.cvtColor(img_raw.numpy(), cv2.COLOR_RGB2BGR)
img = draw_outputs(img, (boxes, scores, classes, nums), class_names)
cv2.imwrite(FLAGS.output, img)
logging.info('output saved to: {}'.format(FLAGS.output))
def convert(model_name, tf_weights_input, pb_weights_output):
model = PolypDetectionModel(model_name=model_name)
model.build_model(True)
model.load_weights(tf_weights_input)
tf_model = model.get_model()
tf.saved_model.save(tf_model, pb_weights_output)