[英]How to use Python concurrent multiprocess feature but calling this session only once
我有一个类似这样的代码:
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()
但我只想调用此代码一次。
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')
但我需要processImage
的sess
变量。 有什么办法,我该如何修改这段代码,所以我只调用with detection_graph.as_default(): and with tf.Session(graph=detection_graph) as sess:
part 一次?
假设检测图和会话对象是可选择的(即,可以被序列化以发送到不同的进程)并且可以安全地分发(即,在不同副本上的操作是有意义且安全的),您可以执行这样的操作。
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)
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