sess.run（）太慢

Question

Tensorflow对象检测模块的sess.run（）函数大约需要2.5秒来检测600x600图像中的边界模糊。 如何加快此代码的速度？

def run(image, detection_graph):

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')

        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.
        image_np = image
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        # Actual detection.
        print("2")
        start_time = datetime.datetime.now()
        (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
        end_time = datetime.datetime.now()
        diff = (end_time - start_time).total_seconds()*1000
        print (diff)
        print("3")

        return boxes[0], scores[0]
        #print scores
        #print classes

Answer 1

您的sess.run执行时间对于第一次运行是正常的，此后它可能会运行100倍（不是开玩笑）。

关键是重新使用会话，在您的示例中，我将添加另一个图像评估并测量该时间并检查性能是否有所提高，例如：

# all your prev code here
print (diff)
print("3")

image_np = image2 # get another image from somewhere
image_np_expanded = np.expand_dims(image_np, axis=0)
start_time = datetime.datetime.now()

(boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
end_time = datetime.datetime.now()

diff = (end_time - start_time).total_seconds()*1000
print("Detection #2")
print(diff)

因此，您不需要GPU或较小的图像（尚未），只需“预热”会话并将其用于所有预测。

我目前在测试环境中的安装设置非常适中，最新版本的Ubuntu在VirtualBox上运行，单核且没有GPU（MobileNet2 + COCO数据集），一旦会话“热”，我得到的时间就相当不错了。

--- 3.7862255573272705 seconds ---
--- 0.21631121635437012 seconds ---
--- 0.1784508228302002 seconds ---

注意第一个缓慢的执行时间，最后一个是尺寸为1050 * 600的图像