def test_export_tflite_with_post_processing(self):
saved_model_path = os.path.join(self.tmp_path, 'saved_model')
driver = inference.ServingDriver('efficientdet-lite0',
self.tmp_path,
only_network=False)
driver.export(saved_model_path, tflite='FP32')
self.assertTrue(
tf.io.gfile.exists(os.path.join(saved_model_path, 'fp32.tflite')))
tf.io.gfile.rmtree(saved_model_path)
tfrecord_path = test_util.make_fake_tfrecord(self.get_temp_dir())
driver.export(saved_model_path,
tflite='INT8',
file_pattern=[tfrecord_path],
num_calibration_steps=1)
self.assertTrue(
tf.io.gfile.exists(os.path.join(saved_model_path, 'int8.tflite')))
def test_export_tflite_only_network(self):
saved_model_path = os.path.join(self.lite_tmp_path, 'saved_model')
driver = infer_lib.KerasDriver(self.lite_tmp_path,
False,
'efficientdet-lite0',
only_network=True)
driver.export(saved_model_path, tflite='FP32')
self.assertTrue(
tf.io.gfile.exists(os.path.join(saved_model_path, 'fp32.tflite')))
tf.io.gfile.rmtree(saved_model_path)
driver.export(saved_model_path, tflite='FP16')
self.assertTrue(
tf.io.gfile.exists(os.path.join(saved_model_path, 'fp16.tflite')))
tf.io.gfile.rmtree(saved_model_path)
tfrecord_path = test_util.make_fake_tfrecord(self.get_temp_dir())
driver.export(saved_model_path,
tflite='INT8',
file_pattern=[tfrecord_path],
num_calibration_steps=1)
self.assertTrue(
tf.io.gfile.exists(os.path.join(saved_model_path, 'int8.tflite')))
def test_parser(self):
tf.random.set_seed(111111)
params = hparams_config.get_detection_config(
'efficientdet-d0').as_dict()
input_anchors = anchors.Anchors(params['min_level'],
params['max_level'],
params['num_scales'],
params['aspect_ratios'],
params['anchor_scale'],
params['image_size'])
anchor_labeler = anchors.AnchorLabeler(input_anchors,
params['num_classes'])
example_decoder = tf_example_decoder.TfExampleDecoder(
regenerate_source_id=params['regenerate_source_id'])
tfrecord_path = test_util.make_fake_tfrecord(self.get_temp_dir())
dataset = tf.data.TFRecordDataset([tfrecord_path])
value = next(iter(dataset))
reader = dataloader.InputReader(tfrecord_path, True)
result = reader.dataset_parser(value, example_decoder, anchor_labeler,
params)
self.assertEqual(len(result), 11)
