如何使用 Python 并发多进程功能但仅调用此会话一次

Question

I have a code something like this:

def processImage(filename):
    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            # Definite input and output Tensors for detection_graph
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            # Each box represents a part of the image where a particular object was detected.
            detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            # Each score represent how level of confidence for each of the objects.
            # Score is shown on the result image, together with the class label.
            detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
            detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
            num_detections = detection_graph.get_tensor_by_name('num_detections:0')

            #Do Other Stuff Here with "sess" variable like:
            sess.run([abc, xyz, stuff])


def main():
with concurrent.futures.ProcessPoolExecutor(max_workers=2) as executor:
                #{executor.map(processImage, filesToProcess):  filesToProcess for filesToProcess in filesToProcess}
                {executor.submit(processImage, filesToProcess): filesToProcess for filesToProcess in filesToProcess}
if __name__ == '__main__':
    main()

But I want to call this code only once.

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        # Definite input and output Tensors for detection_graph
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')

But I need the sess variable inside processImage . Is there any way, how can I modify this code, so I call the with detection_graph.as_default(): and with tf.Session(graph=detection_graph) as sess: part only once?

Answer 1

Assuming detection graph and session objects are pickable (ie, can be serialized to be sent to different processes) and safe to be distributed (ie, operations on different copies are meaningful and safe), you can do something like this.

def processImage(f, detection_graph, sess):
   ...

def main():
    with detection_graph.as_default() as dg:
        with tf.Session(graph=detection_graph) as sess:
            with concurrent.futures.ProcessPoolExecutor(max_workers=2) as executor:
                for f in filesToProcess:
                    executor.submit(processImage, f, dg, sess)