def main(_) -> None:
params = exp_factory.get_exp_config(FLAGS.experiment)
if FLAGS.config_file is not None:
for config_file in FLAGS.config_file:
params = hyperparams.override_params_dict(params,
config_file,
is_strict=True)
if FLAGS.params_override:
params = hyperparams.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
params.validate()
params.lock()
logging.info('Converting SavedModel from %s to TFLite model...',
FLAGS.saved_model_dir)
tflite_model = export_tflite_lib.convert_tflite_model(
saved_model_dir=FLAGS.saved_model_dir,
quant_type=FLAGS.quant_type,
params=params,
calibration_steps=FLAGS.calibration_steps)
with tf.io.gfile.GFile(FLAGS.tflite_path, 'wb') as fw:
fw.write(tflite_model)
logging.info('TFLite model converted and saved to %s.', FLAGS.tflite_path)
def test_export_tflite_detection(self, experiment, quant_type,
input_image_size):
test_tfrecord_file = os.path.join(self.get_temp_dir(), 'det_test.tfrecord')
example = tfexample_utils.create_detection_test_example(
image_height=input_image_size[0],
image_width=input_image_size[1],
image_channel=3,
num_instances=10)
self._create_test_tfrecord(
tfrecord_file=test_tfrecord_file, example=example, num_samples=10)
params = exp_factory.get_exp_config(experiment)
params.task.validation_data.input_path = test_tfrecord_file
params.task.train_data.input_path = test_tfrecord_file
temp_dir = self.get_temp_dir()
module = detection_serving.DetectionModule(
params=params,
batch_size=1,
input_image_size=input_image_size,
input_type='tflite')
self._export_from_module(
module=module,
input_type='tflite',
saved_model_dir=os.path.join(temp_dir, 'saved_model'))
tflite_model = export_tflite_lib.convert_tflite_model(
saved_model_dir=os.path.join(temp_dir, 'saved_model'),
quant_type=quant_type,
params=params,
calibration_steps=5)
self.assertIsInstance(tflite_model, bytes)
def test_export_tflite_detection(self, experiment, quant_type,
input_image_size):
params = exp_factory.get_exp_config(experiment)
temp_dir = self.get_temp_dir()
module = detection_serving.DetectionModule(
params=params, batch_size=1, input_image_size=input_image_size)
self._export_from_module(module=module,
input_type='tflite',
saved_model_dir=os.path.join(
temp_dir, 'saved_model'))
tflite_model = export_tflite_lib.convert_tflite_model(
saved_model_dir=os.path.join(temp_dir, 'saved_model'),
quant_type=quant_type,
params=params,
calibration_steps=5)
self.assertIsInstance(tflite_model, bytes)
def test_export_tflite(self, experiment, quant_type, input_image_size):
params = exp_factory.get_exp_config(experiment)
params.task.validation_data.input_path = self._test_tfrecord_file
params.task.train_data.input_path = self._test_tfrecord_file
temp_dir = self.get_temp_dir()
module = image_classification_serving.ClassificationModule(
params=params, batch_size=1, input_image_size=input_image_size)
self._export_from_module(module=module,
input_type='tflite',
saved_model_dir=os.path.join(
temp_dir, 'saved_model'))
tflite_model = export_tflite_lib.convert_tflite_model(
saved_model_dir=os.path.join(temp_dir, 'saved_model'),
quant_type=quant_type,
params=params,
calibration_steps=5)
self.assertIsInstance(tflite_model, bytes)