Python Tensorflow同时运行2个冻结图（并行）

是否可以同时运行多个tensorflow对象检测模型？（我已经培训了两个模型，并希望两者并行运行） 我编写了这段代码并尝试运行，但它不起作用

# First Frozen
detection_graph1 = tf.Graph()
with detection_graph1.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH1, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')

# Second Frozen
detection_graph2 = tf.Graph()
with detection_graph2.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH2, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')

def run_inference_for_multiple_images(path,graph1,graph2):
  with graph1.as_default():
    with tf.Session() as sess1:
      with graph2.as_default():
        with tf.Session() as sess2:
          #detection code..

---

是的，这是绝对可能的，但你做错了。 不要在两个单独的图中定义这两个模型，只需将它们加载到同一个图中（并添加适当的名称范围以避免命名冲突）：

注意：我假设提要是

dict

s，带有

tensor\u name:values

或

placeholder\u tensor:values

键/值对

graph = tf.Graph() # just one graph, with both models loaded
with graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH1, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='first_graph')
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH2, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='second_graph')

# [...] get the correct input and output tensors for the two graphs via their names

with tf.Session(graph=graph) as sess: # just one session
  # Running only one of the two at a time
  res_1 = sess.run(outputs_from_graph_1, feed_dict=graph_1_feeds)
  res_2 = sess.run(outputs_from_graph_2, feed_dict=graph_2_feeds)

  # Actually running them in parallel (might not fit in memory!)
  res_1_and_2 = sess.run( outputs_from_graph_1 + outputs_from_graph_2, {**graph_1_feeds, **graph_2_feeds} )