Python Interpreter.set_tensor方法代码示例

# 需要导入模块: from tensorflow.contrib.lite.python.interpreter import Interpreter [as 别名]
# 或者: from tensorflow.contrib.lite.python.interpreter.Interpreter import set_tensor [as 别名]
  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)

# 需要导入模块: from tensorflow.contrib.lite.python.interpreter import Interpreter [as 别名]
# 或者: from tensorflow.contrib.lite.python.interpreter.Interpreter import set_tensor [as 别名]
  def testFunctionalModel(self):
    """Test a Functional tf.keras model with default inputs."""
    inputs = keras.layers.Input(shape=(3,), name='input')
    x = keras.layers.Dense(2)(inputs)
    output = keras.layers.Dense(3)(x)

    model = keras.models.Model(inputs, output)
    model.compile(
        loss=keras.losses.MSE,
        optimizer=keras.optimizers.RMSprop(),
        metrics=[keras.metrics.categorical_accuracy])
    x = np.random.random((1, 3))
    y = np.random.random((1, 3))
    model.train_on_batch(x, y)

    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.TocoConverter.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('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('dense_1/BiasAdd', output_details[0]['name'])
    self.assertEqual(np.float32, output_details[0]['dtype'])
    self.assertTrue(([1, 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)

# 需要导入模块: from tensorflow.contrib.lite.python.interpreter import Interpreter [as 别名]
# 或者: from tensorflow.contrib.lite.python.interpreter.Interpreter import set_tensor [as 别名]
  def testSequentialModel(self):
    """Test a Sequential tf.keras model with default inputs."""
    keras_file = self._getSequentialModel()

    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)