def testFloatWithShapesArray(self):
    in_tensor = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32)
    _ = in_tensor + in_tensor
    sess = session.Session()

    # Write graph to file.
    graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
    write_graph(sess.graph_def, '', graph_def_file, False)
    sess.close()

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_frozen_graph(
        graph_def_file, ['Placeholder'], ['add'],
        input_shapes={'Placeholder': [1, 16, 16, 3]})
    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.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())

  def testPostTrainingCalibrateAndQuantize(self):
    func, calibration_gen = self._getCalibrationQuantizeModel()

    # Convert float model.
    float_converter = lite.TFLiteConverterV2.from_concrete_functions([func])
    float_tflite = float_converter.convert()
    self.assertTrue(float_tflite)

    # Convert quantized model.
    quantized_converter = lite.TFLiteConverterV2.from_concrete_functions([func])
    quantized_converter.optimizations = [lite.Optimize.DEFAULT]
    quantized_converter.representative_dataset = calibration_gen
    quantized_tflite = quantized_converter.convert()
    self.assertTrue(quantized_tflite)

    # The default input and output types should be float.
    interpreter = Interpreter(model_content=quantized_tflite)
    interpreter.allocate_tensors()
    input_details = interpreter.get_input_details()
    self.assertEqual(1, len(input_details))
    self.assertEqual(np.float32, input_details[0]['dtype'])
    output_details = interpreter.get_output_details()
    self.assertEqual(1, len(output_details))
    self.assertEqual(np.float32, output_details[0]['dtype'])

    # Ensure that the quantized weights tflite model is smaller.
    self.assertLess(len(quantized_tflite), len(float_tflite))

  def testCalibrateAndQuantizeBuiltinInt8(self):
    func, calibration_gen = self._getCalibrationQuantizeModel()

    # Convert float model.
    float_converter = lite.TFLiteConverterV2.from_concrete_functions([func])
    float_tflite = float_converter.convert()
    self.assertTrue(float_tflite)

    # Convert model by specifying target spec (instead of optimizations), since
    # when targeting an integer only backend, quantization is mandatory.
    quantized_converter = lite.TFLiteConverterV2.from_concrete_functions([func])
    quantized_converter.target_spec.supported_ops = [
        lite.OpsSet.TFLITE_BUILTINS_INT8
    ]
    quantized_converter.representative_dataset = calibration_gen
    quantized_tflite = quantized_converter.convert()
    self.assertTrue(quantized_tflite)

    # The default input and output types should be float.
    interpreter = Interpreter(model_content=quantized_tflite)
    interpreter.allocate_tensors()
    input_details = interpreter.get_input_details()
    self.assertEqual(1, len(input_details))
    self.assertEqual(np.float32, input_details[0]['dtype'])
    output_details = interpreter.get_output_details()
    self.assertEqual(1, len(output_details))
    self.assertEqual(np.float32, output_details[0]['dtype'])

    # Ensure that the quantized weights tflite model is smaller.
    self.assertLess(len(quantized_tflite), len(float_tflite))

  def testNoneBatchSize(self):
    """Test a SavedModel, with None in input tensor's shape."""
    saved_model_dir = self._createSavedModel(shape=[None, 16, 16, 3])

    converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
    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(2, len(input_details))
    self.assertEqual('inputA', input_details[0]['name'])
    self.assertEqual(np.float32, input_details[0]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
    self.assertEqual((0., 0.), input_details[0]['quantization'])

    self.assertEqual('inputB', input_details[1]['name'])
    self.assertEqual(np.float32, input_details[1]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[1]['shape']).all())
    self.assertEqual((0., 0.), input_details[1]['quantization'])

    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(([1, 16, 16, 3] == output_details[0]['shape']).all())
    self.assertEqual((0., 0.), output_details[0]['quantization'])

  def testFloat(self):
    in_tensor = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32)
    out_tensor = in_tensor + in_tensor
    sess = session.Session()

    # Convert model and ensure model is not None.
    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(([1, 16, 16, 3] == input_details[0]['shape']).all())
    self.assertEqual((0., 0.), input_details[0]['quantization'])

    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(([1, 16, 16, 3] == output_details[0]['shape']).all())
    self.assertEqual((0., 0.), output_details[0]['quantization'])

  def testOrderInputArrays(self):
    """Test a SavedModel ordering of input arrays."""
    saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])

    converter = lite.TFLiteConverter.from_saved_model(
        saved_model_dir, input_arrays=['inputB', 'inputA'])
    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(2, len(input_details))
    self.assertEqual('inputA', input_details[0]['name'])
    self.assertEqual(np.float32, input_details[0]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
    self.assertEqual((0., 0.), input_details[0]['quantization'])

    self.assertEqual('inputB', input_details[1]['name'])
    self.assertEqual(np.float32, input_details[1]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[1]['shape']).all())
    self.assertEqual((0., 0.), input_details[1]['quantization'])

    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(([1, 16, 16, 3] == output_details[0]['shape']).all())
    self.assertEqual((0., 0.), output_details[0]['quantization'])

  def testDumpGraphviz(self):
    in_tensor = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32)
    out_tensor = in_tensor + in_tensor
    sess = session.Session()

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
                                                  [out_tensor])
    graphviz_dir = self.get_temp_dir()
    converter.dump_graphviz_dir = graphviz_dir
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Ensure interpreter is able to allocate and check graphviz data.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

    num_items_graphviz = len(os.listdir(graphviz_dir))
    self.assertTrue(num_items_graphviz)

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
                                                  [out_tensor])
    graphviz_dir = self.get_temp_dir()
    converter.dump_graphviz_dir = graphviz_dir
    converter.dump_graphviz_video = True
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Ensure graphviz folder has more data after using video flag.
    num_items_graphviz_video = len(os.listdir(graphviz_dir))
    self.assertTrue(num_items_graphviz_video > num_items_graphviz)

  def testDefaultRangesStats(self):
    in_tensor = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32)
    out_tensor = in_tensor + in_tensor
    sess = session.Session()

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
                                                  [out_tensor])
    converter.inference_type = lite_constants.QUANTIZED_UINT8
    converter.quantized_input_stats = {'Placeholder': (0., 1.)}  # mean, std_dev
    converter.default_ranges_stats = (0, 6)  # min, max
    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.uint8, input_details[0]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
    self.assertEqual((1., 0.), input_details[0]['quantization'])

    output_details = interpreter.get_output_details()
    self.assertEqual(1, len(output_details))
    self.assertEqual('add', output_details[0]['name'])
    self.assertEqual(np.uint8, output_details[0]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
    self.assertTrue(output_details[0]['quantization'][0] > 0)  # scale

  def testSequentialModelInputShape(self):
    """Test a Sequential tf.keras model testing input shapes argument."""
    keras_file = self._getSequentialModel()

    # Passing in shape of invalid input array raises error.
    with self.assertRaises(ValueError) as error:
      converter = lite.TFLiteConverter.from_keras_model_file(
          keras_file, input_shapes={'invalid-input': [2, 3]})
    self.assertEqual(
        "Invalid tensor 'invalid-input' found in tensor shapes map.",
        str(error.exception))

    # Passing in shape of valid input array.
    converter = lite.TFLiteConverter.from_keras_model_file(
        keras_file, input_shapes={'dense_input': [2, 3]})
    tflite_model = converter.convert()
    os.remove(keras_file)
    self.assertTrue(tflite_model)

    # Check input shape 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('dense_input', input_details[0]['name'])
    self.assertTrue(([2, 3] == input_details[0]['shape']).all())

  def testFunctionalSequentialModel(self):
    """Test a Functional tf.keras model containing a Sequential model."""
    with session.Session().as_default():
      model = keras.models.Sequential()
      model.add(keras.layers.Dense(2, input_shape=(3,)))
      model.add(keras.layers.RepeatVector(3))
      model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
      model = keras.models.Model(model.input, model.output)

      model.compile(
          loss=keras.losses.MSE,
          optimizer=keras.optimizers.RMSprop(),
          metrics=[keras.metrics.categorical_accuracy],
          sample_weight_mode='temporal')
      x = np.random.random((1, 3))
      y = np.random.random((1, 3, 3))
      model.train_on_batch(x, y)
      model.predict(x)

      model.predict(x)
      fd, keras_file = tempfile.mkstemp('.h5')
      try:
        keras.models.save_model(model, keras_file)
      finally:
        os.close(fd)

    # Convert to TFLite model.
    converter = lite.TFLiteConverter.from_keras_model_file(keras_file)
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Check tensor details of 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('dense_input', input_details[0]['name'])
    self.assertEqual(np.float32, input_details[0]['dtype'])
    self.assertTrue(([1, 3] == input_details[0]['shape']).all())
    self.assertEqual((0., 0.), input_details[0]['quantization'])

    output_details = interpreter.get_output_details()
    self.assertEqual(1, len(output_details))
    self.assertEqual('time_distributed/Reshape_1', output_details[0]['name'])
    self.assertEqual(np.float32, output_details[0]['dtype'])
    self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all())
    self.assertEqual((0., 0.), output_details[0]['quantization'])

    # Check inference of converted model.
    input_data = np.array([[1, 2, 3]], dtype=np.float32)
    interpreter.set_tensor(input_details[0]['index'], input_data)
    interpreter.invoke()
    tflite_result = interpreter.get_tensor(output_details[0]['index'])

    keras_model = keras.models.load_model(keras_file)
    keras_result = keras_model.predict(input_data)

    np.testing.assert_almost_equal(tflite_result, keras_result, 5)
    os.remove(keras_file)

  def testGraphDefBasic(self):
    in_tensor = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32, name="input")
    _ = in_tensor + in_tensor
    sess = session.Session()

    tflite_model = convert.toco_convert_graph_def(
        sess.graph_def, [("input", [1, 16, 16, 3])], ["add"],
        inference_type=lite_constants.FLOAT)
    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("input", input_details[0]["name"])
    self.assertEqual(np.float32, input_details[0]["dtype"])
    self.assertTrue(([1, 16, 16, 3] == input_details[0]["shape"]).all())
    self.assertEqual((0., 0.), input_details[0]["quantization"])

    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(([1, 16, 16, 3] == output_details[0]["shape"]).all())
    self.assertEqual((0., 0.), output_details[0]["quantization"])

  def testSequentialModelInputShape(self):
    """Test a Sequential tf.keras model testing input shapes argument."""
    keras_file = self._getSequentialModel()

    # Passing in shape of invalid input array has no impact as long as all input
    # arrays have a shape.
    converter = lite.TFLiteConverter.from_keras_model_file(
        keras_file, input_shapes={'invalid-input': [2, 3]})
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Passing in shape of valid input array.
    converter = lite.TFLiteConverter.from_keras_model_file(
        keras_file, input_shapes={'dense_input': [2, 3]})
    tflite_model = converter.convert()
    os.remove(keras_file)
    self.assertTrue(tflite_model)

    # Check input shape 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('dense_input', input_details[0]['name'])
    self.assertTrue(([2, 3] == input_details[0]['shape']).all())

  def testSimpleModel(self):
    """Test a SavedModel."""
    saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

    input_details = interpreter.get_input_details()
    self.assertEqual(2, len(input_details))
    self.assertEqual('inputA', input_details[0]['name'])
    self.assertEqual(np.float32, input_details[0]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
    self.assertEqual((0., 0.), input_details[0]['quantization'])

    self.assertEqual('inputB', input_details[1]['name'])
    self.assertEqual(np.float32, input_details[1]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[1]['shape']).all())
    self.assertEqual((0., 0.), input_details[1]['quantization'])

    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(([1, 16, 16, 3] == output_details[0]['shape']).all())
    self.assertEqual((0., 0.), output_details[0]['quantization'])

  def testPbtxt(self):
    in_tensor = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32)
    _ = in_tensor + in_tensor
    sess = session.Session()

    # Write graph to file.
    graph_def_file = os.path.join(self.get_temp_dir(), 'model.pbtxt')
    write_graph(sess.graph_def, '', graph_def_file, True)
    sess.close()

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_frozen_graph(graph_def_file,
                                                       ['Placeholder'], ['add'])
    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(([1, 16, 16, 3] == input_details[0]['shape']).all())
    self.assertEqual((0., 0.), input_details[0]['quantization'])

    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(([1, 16, 16, 3] == output_details[0]['shape']).all())
    self.assertEqual((0., 0.), output_details[0]['quantization'])

  def testSequentialModelTocoConverter(self):
    """Test a Sequential tf.keras model with deprecated TocoConverter."""
    keras_file = self._getSequentialModel()

    converter = lite.TocoConverter.from_keras_model_file(keras_file)
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Ensure the model is able to load.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

  def _evaluateTFLiteModel(self, tflite_model, input_data):
    """Evaluates the model on the `input_data`."""
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    for input_tensor, tensor_data in zip(input_details, input_data):
      interpreter.set_tensor(input_tensor['index'], tensor_data.numpy())
    interpreter.invoke()
    return interpreter.get_tensor(output_details[0]['index'])

  def testSimpleModelTocoConverter(self):
    """Test a SavedModel with deprecated TocoConverter."""
    saved_model_dir = self._createSavedModel(shape=[1, 16, 16, 3])

    # Convert model and ensure model is not None.
    converter = lite.TocoConverter.from_saved_model(saved_model_dir)
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Ensure the model is able to load.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

  def testFloatTocoConverter(self):
    """Tests deprecated test TocoConverter."""
    in_tensor = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32)
    out_tensor = in_tensor + in_tensor
    sess = session.Session()

    # Convert model and ensure model is not None.
    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
    tflite_model = converter.convert()
    self.assertTrue(tflite_model)

    # Ensure the interpreter is able to load.
    interpreter = Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

  def testQuantization(self):
    in_tensor_1 = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
    in_tensor_2 = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
    out_tensor = array_ops.fake_quant_with_min_max_args(
        in_tensor_1 + in_tensor_2, min=0., max=1., name='output')
    sess = session.Session()

    # Convert model and ensure model is not None.
    converter = lite.TFLiteConverter.from_session(
        sess, [in_tensor_1, in_tensor_2], [out_tensor])
    converter.inference_type = lite_constants.QUANTIZED_UINT8
    converter.quantized_input_stats = {
        'inputA': (0., 1.),
        'inputB': (0., 1.)
    }  # mean, std_dev
    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(2, len(input_details))
    self.assertEqual('inputA', input_details[0]['name'])
    self.assertEqual(np.uint8, input_details[0]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
    self.assertEqual((1., 0.),
                     input_details[0]['quantization'])  # scale, zero_point

    self.assertEqual('inputB', input_details[1]['name'])
    self.assertEqual(np.uint8, input_details[1]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == input_details[1]['shape']).all())
    self.assertEqual((1., 0.),
                     input_details[1]['quantization'])  # scale, zero_point

    output_details = interpreter.get_output_details()
    self.assertEqual(1, len(output_details))
    self.assertEqual('output', output_details[0]['name'])
    self.assertEqual(np.uint8, output_details[0]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
    self.assertTrue(output_details[0]['quantization'][0] > 0)  # scale

  def testGraphDefQuantization(self):
    in_tensor_1 = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32, name="inputA")
    in_tensor_2 = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32, name="inputB")
    _ = array_ops.fake_quant_with_min_max_args(
        in_tensor_1 + in_tensor_2, min=0., max=1., name="output")
    sess = session.Session()

    input_arrays_map = [("inputA", [1, 16, 16, 3]), ("inputB", [1, 16, 16, 3])]
    output_arrays = ["output"]
    tflite_model = convert.toco_convert_graph_def(
        sess.graph_def,
        input_arrays_map,
        output_arrays,
        inference_type=lite_constants.QUANTIZED_UINT8,
        quantized_input_stats=[(0., 1.), (0., 1.)])
    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(2, len(input_details))
    self.assertEqual("inputA", input_details[0]["name"])
    self.assertEqual(np.uint8, input_details[0]["dtype"])
    self.assertTrue(([1, 16, 16, 3] == input_details[0]["shape"]).all())
    self.assertEqual((1., 0.),
                     input_details[0]["quantization"])  # scale, zero_point

    self.assertEqual("inputB", input_details[1]["name"])
    self.assertEqual(np.uint8, input_details[1]["dtype"])
    self.assertTrue(([1, 16, 16, 3] == input_details[1]["shape"]).all())
    self.assertEqual((1., 0.),
                     input_details[1]["quantization"])  # scale, zero_point

    output_details = interpreter.get_output_details()
    self.assertEqual(1, len(output_details))
    self.assertEqual("output", output_details[0]["name"])
    self.assertEqual(np.uint8, output_details[0]["dtype"])
    self.assertTrue(([1, 16, 16, 3] == output_details[0]["shape"]).all())
    self.assertTrue(output_details[0]["quantization"][0] > 0)  # scale