Python 对大量图像的张量流预测速度较慢

以下是Tensorflow中的图像分类教程：。对单个图像的训练和测试效果良好。然而，我对大量图像进行预测的代码非常慢，它消耗了100%的CPU，几乎也消耗了最大的内存！2700张图片需要24小时以上！这是不实际的。是否有一种方法可以像我们进行批量培训一样进行批量测试？请注意，我还需要对图像进行标准化。这是我的密码：

import tensorflow as tf
import numpy as np
import os,glob,cv2
import sys,argparse


# First, pass the path of the image
os.chdir("/somepath")

i = 0
files = glob.glob('*.jpg')
files.extend(glob.glob('*.JPG'))
totalNumber = len(files)
print("total number of images is:", totalNumber)

image_size=128
num_channels=3
text_file = open("Results.txt", "w")

for file in files:
    images = []
    filename = file
    print(filename)
    text_file.write("\n")
    text_file.write(filename)
    # Reading the image using OpenCV
    image = cv2.imread(filename)
    # Resizing the image to our desired size and preprocessing will be done exactly as done during training
    image = cv2.resize(image, (image_size, image_size),0,0, cv2.INTER_LINEAR)
    images.append(image)
    images = np.array(images, dtype=np.uint8)
    images = images.astype('float32')
    images = np.multiply(images, 1.0/255.0) 
    #The input to the network is of shape [None image_size image_size num_channels]. Hence we reshape.
    x_batch = images.reshape(1, image_size,image_size,num_channels)

    ## Let us restore the saved model 
    sess = tf.Session()
    # Step-1: Recreate the network graph. At this step only graph is created.
    saver = tf.train.import_meta_graph('pathtomymeta/my_model-9909.meta')
    # Step-2: Now let's load the weights saved using the restore method.
    saver.restore(sess, tf.train.latest_checkpoint('pathtomycheckpoints/checkpoints/'))

    # Accessing the default graph which we have restored
    graph = tf.get_default_graph()

    # Now, let's get hold of the op that we can be processed to get the output.
    # In the original network y_pred is the tensor that is the prediction of the network
    y_pred = graph.get_tensor_by_name("y_pred:0")

    ## Let's feed the images to the input placeholders
    x = graph.get_tensor_by_name("x:0") 
    y_true = graph.get_tensor_by_name("y_true:0") 
    y_test_images = np.zeros((1, 3)) #np.zeros((1, 2)) 


    ### Creating the feed_dict that is required to be fed to calculate y_pred 
    feed_dict_testing = {x: x_batch, y_true: y_test_images}
    result = sess.run(y_pred, feed_dict=feed_dict_testing)
    # result is of this format [probabiliy_of_rose probability_of_sunflower]
    print(result)
    text_file.write("\n")
    text_file.write('%s' % result[i,0])
    text_file.write("\t")
    text_file.write('%s' % result[i,1])
    text_file.write("\t")
    text_file.write('%s' % result[i,2])

text_file.close()

---

我认为你应该在代码中考虑一个非常明显的“优化”。你在做一个for循环，在每次迭代中，你加载一个图像，同时加载模型，构建图形，然后进行预测

但是加载模型和构建图形实际上并不依赖于for循环或其中的任何变量（如输入图像）。for循环中的大部分时间可能都花在加载模型上，而不是进行实际的预测。您可以使用探查器来查找

因此，我建议您在for循环之前加载模型并构建一次图，然后在for循环中使用以下两行：

feed_dict_testing = {x: x_batch, y_true: y_test_images}
result = sess.run(y_pred, feed_dict=feed_dict_testing)

---

它应该快得多。它可能仍然是缓慢的，但那是因为在CPU上评估一个大的神经网络本身是缓慢的。

< P>我认为你应该在代码中考虑一个非常明显的“优化”。你在做一个for循环，在每次迭代中，你加载一个图像，同时加载模型，构建图形，然后进行预测

但是加载模型和构建图形实际上并不依赖于for循环或其中的任何变量（如输入图像）。for循环中的大部分时间可能都花在加载模型上，而不是进行实际的预测。您可以使用探查器来查找

因此，我建议您在for循环之前加载模型并构建一次图，然后在for循环中使用以下两行：

feed_dict_testing = {x: x_batch, y_true: y_test_images}
result = sess.run(y_pred, feed_dict=feed_dict_testing)

---

它应该快得多。它可能仍然很慢，但那是因为在CPU上评估大型神经网络本身就很慢。

谢谢！这使它快了很多，原因很明显：）谢谢！这让它更快了，原因很明显：）