在tensorflow中使用两种不同的模型

Question

我试着使用两种不同的移动网络模型。下面是我如何初始化模型的代码。

def initialSetup():
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    start_time = timeit.default_timer()

    # This takes 2-5 seconds to run
    # Unpersists graph from file
    with tf.gfile.FastGFile('age/output_graph.pb', 'rb') as f:
        age_graph_def = tf.GraphDef()
        age_graph_def.ParseFromString(f.read())
        tf.import_graph_def(age_graph_def, name='')

    with tf.gfile.FastGFile('output_graph.pb', 'rb') as f:
        gender_graph_def = tf.GraphDef()
        gender_graph_def.ParseFromString(f.read())
        tf.import_graph_def(gender_graph_def, name='')

    print ('Took {} seconds to unpersist the graph'.format(timeit.default_timer() - start_time))

由于两者都是不同的模型，我如何使用它来预测呢？

更新

initialSetup()

age_session = tf.Session(graph=age_graph_def)
gender_session = tf.Session(graph=gender_graph_def)

with tf.Session() as sess:
    start_time = timeit.default_timer()

    # Feed the image_data as input to the graph and get first prediction
    softmax_tensor = age_session.graph.get_tensor_by_name('final_result:0')

    print ('Took {} seconds to feed data to graph'.format(timeit.default_timer() - start_time))

    while True:
        # Capture frame-by-frame
        ret, frame = video_capture.read()

误差

追溯(最近一次调用)：文件"C:/Users/Desktop/untitled/testimg/testimg/combo.py"，第48行，在age_session = tf.Session(graph=age_graph_def)文件"C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py"，第1292行“中，在init超级(会话，self).init(target，图，文件“C：\ Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py"，Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py"，第529行，在init raise (‘图形必须是tf.Graph，但获得%s’%类型(图形)) TypeError:图必须是tf.Graph，但是在：> Traceback (最近调用的最后一次调用)：文件”C:Program第587行中忽略了异常。在del中，如果self._session不是None: AttributeError：'Session‘对象没有属性'_session’

Answer 1

当您在同一个图中处理多个模型时，请使用名称作用域为单个张量提供可预测的名称。例如，您可以重写initial_setup()如下：

def initialSetup():
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    start_time = timeit.default_timer()

    # This takes 2-5 seconds to run
    # Unpersists graph from file
    with tf.gfile.FastGFile('age/output_graph.pb', 'rb') as f:
        age_graph_def = tf.GraphDef()
        age_graph_def.ParseFromString(f.read())
        tf.import_graph_def(age_graph_def, name='age_model')

    with tf.gfile.FastGFile('output_graph.pb', 'rb') as f:
        gender_graph_def = tf.GraphDef()
        gender_graph_def.ParseFromString(f.read())
        tf.import_graph_def(gender_graph_def, name='gender_model')

    print ('Took {} seconds to unpersist the graph'.format(timeit.default_timer() - start_time))

现在，来自age_graph_def的所有节点的名称将以"age_model/"作为前缀，而来自gender_graph_def的所有节点的名称将以"gender_model/"作为前缀。它们都是同一个默认图的一部分，因此您可以使用一个没有tf.Session参数的graph来访问这两个模型。

initialSetup()

with tf.Session() as sess:
    start_time = timeit.default_timer()

    # Feed the image_data as input to the graph and get first prediction
    softmax_tensor = sess.graph.get_tensor_by_name('age_model/final_result:0')

    # Alternatively, to get a tensor from the gender model:
    # tensor = sess.graph.get_tensor_by_name('gender_model/...')

    print ('Took {} seconds to feed data to graph'.format(timeit.default_timer() - start_time))

    while True:
        # Capture frame-by-frame
        ret, frame = video_capture.read()

Answer 2

tf.Session需要一个tf.Graph实例而不是tf.GraphDef，下面的步骤修复了这个问题。

def initialSetup():
    with tf.gfile.FastGFile('age/output_graph.pb', 'rb') as f:
        age_graph_def = tf.GraphDef()
        age_graph_def.ParseFromString(f.read())
        with tf.Graph().as_default() as graph:
            tf.import_graph_def(age_graph_def, name='')
            age_graph = graph

   ...
   return age_graph, gender_graph

age_graph, gender_graph = initial_setup() 
age_session = tf.Session(graph=age_graph)
...
# also delete the following line, as it creates another new context 
with tf.Session() as sess: