Method testScalarValid

tensorflow/lite/python/lite_test.py:283–319

(self)

                     str(error.exception))

  def testScalarValid(self):
    # Construct a graph using a scalar (empty shape) input.
    with ops.Graph().as_default():
      in_tensor = array_ops.placeholder(dtype=dtypes.float32, shape=[])
      out_tensor = in_tensor + in_tensor
      sess = session.Session()

    # Test conversion with the scalar input shape.
    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
                                                  [out_tensor])
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Check values from converted model.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

    input_details = interpreter.get_input_details()
    self.assertEqual(1, len(input_details))
    self.assertEqual('Placeholder', input_details[0]['name'])
    self.assertEqual(np.float32, input_details[0]['dtype'])
    self.assertTrue(([] == input_details[0]['shape']).all())

    output_details = interpreter.get_output_details()
    self.assertEqual(1, len(output_details))
    self.assertEqual('add', output_details[0]['name'])
    self.assertEqual(np.float32, output_details[0]['dtype'])
    self.assertTrue(([] == input_details[0]['shape']).all())

    # Validate inference using the scalar inputs/outputs.
    test_input = np.array(4.0, dtype=np.float32)
    expected_output = np.array(8.0, dtype=np.float32)
    interpreter.set_tensor(input_details[0]['index'], test_input)
    interpreter.invoke()

    output_data = interpreter.get_tensor(output_details[0]['index'])
    self.assertTrue((expected_output == output_data).all())

  def testSizeInvalid(self):
    with ops.Graph().as_default():