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Python interpreter.Interpreter类代码示例

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

本文整理汇总了Python中tensorflow.contrib.lite.python.interpreter.Interpreter类的典型用法代码示例。如果您正苦于以下问题：Python Interpreter类的具体用法？Python Interpreter怎么用？Python Interpreter使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。



在下文中一共展示了Interpreter类的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: testPbtxt

  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'])
开发者ID:becster，项目名称:tensorflow，代码行数:34，代码来源:lite_test.py


示例2: testDefaultRangesStats

  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
开发者ID:becster，项目名称:tensorflow，代码行数:32，代码来源:lite_test.py


示例3: testSequentialModelInputShape

  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())
开发者ID:becster，项目名称:tensorflow，代码行数:26，代码来源:lite_test.py


示例4: testOrderInputArrays

  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'])
开发者ID:becster，项目名称:tensorflow，代码行数:31，代码来源:lite_test.py


示例5: testNoneBatchSize

  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'])
开发者ID:becster，项目名称:tensorflow，代码行数:30，代码来源:lite_test.py


示例6: testGraphDefBasic

  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"])
开发者ID:AnishShah，项目名称:tensorflow，代码行数:28，代码来源:convert_test.py


示例7: testFloatWithShapesArray

  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())
开发者ID:becster，项目名称:tensorflow，代码行数:25，代码来源:lite_test.py


示例8: testDumpGraphviz

  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)
开发者ID:becster，项目名称:tensorflow，代码行数:33，代码来源:lite_test.py


示例9: testSequentialModel

  def testSequentialModel(self):
    """Test a Sequential tf.keras model with default inputs."""
    keras_file = self._getSequentialModel()

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

    os.remove(keras_file)

    # 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('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'])
开发者ID:Eagle732，项目名称:tensorflow，代码行数:27，代码来源:lite_test.py


示例10: testFunctionalSequentialModel

  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)
开发者ID:becster，项目名称:tensorflow，代码行数:60，代码来源:lite_test.py


示例11: testFreezeGraph

  def testFreezeGraph(self):
    in_tensor = array_ops.placeholder(
        shape=[1, 16, 16, 3], dtype=dtypes.float32)
    var = variable_scope.get_variable(
        'weights', shape=[1, 16, 16, 3], dtype=dtypes.float32)
    out_tensor = in_tensor + var
    sess = session.Session()
    sess.run(_global_variables_initializer())

    # 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)

    # 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'])
开发者ID:mbrukman，项目名称:tensorflow，代码行数:31，代码来源:lite_test.py


示例12: testInferenceInputType

  def testInferenceInputType(self):
    in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3], dtype=dtypes.uint8)
    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])
    converter.inference_input_type = lite_constants.QUANTIZED_UINT8
    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((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.uint8, output_details[0]['dtype'])
    self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
    self.assertEqual((0., 0.), input_details[0]['quantization'])
开发者ID:jfreedman0，项目名称:tensorflow，代码行数:28，代码来源:lite_test.py


示例13: testFloat

  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'])
开发者ID:becster，项目名称:tensorflow，代码行数:29，代码来源:lite_test.py


示例14: testSimpleModel

  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'])
开发者ID:becster，项目名称:tensorflow，代码行数:30，代码来源:lite_test.py


示例15: testQuantization

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

    # Convert model and ensure model is not None.
    converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
    converter.inference_type = lite_constants.QUANTIZED_UINT8
    converter.quantized_input_stats = [(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(1, len(input_details))
    self.assertEqual('input', 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

    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
开发者ID:jinxin0924，项目名称:tensorflow，代码行数:32，代码来源:lite_test.py


示例16: testFunctionalModel

  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)
开发者ID:mrlittlepig，项目名称:tensorflow，代码行数:56，代码来源:lite_test.py


示例17: testSequentialModelTocoConverter

  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()
开发者ID:becster，项目名称:tensorflow，代码行数:11，代码来源:lite_test.py


示例18: testSimpleModelTocoConverter

  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()
开发者ID:becster，项目名称:tensorflow，代码行数:12，代码来源:lite_test.py


示例19: testFloatTocoConverter

  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()
开发者ID:becster，项目名称:tensorflow，代码行数:15，代码来源:lite_test.py


示例20: testFunctionalSequentialModel

  def testFunctionalSequentialModel(self):
    """Test a Functional tf.keras model containing a Sequential model."""
    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')
    keras.models.save_model(model, keras_file)

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

    os.close(fd)
    os.remove(keras_file)

    # 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('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'])
开发者ID:Eagle732，项目名称:tensorflow，代码行数:47，代码来源:lite_test.py



注：本文中的tensorflow.contrib.lite.python.interpreter.Interpreter类示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

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