Question

我使用重新培训图像再培训示例训练了张量流模型：https://www.tensorflow.org/versions/master/how_tos/image_retraining/index.html

现在我想用它来预测许多图像，我已修改此python script以在许多图像上运行：

import numpy as np
import tensorflow as tf
import glob
import os
modelFullPath = 'output_graph.pb'


def create_graph():
    """Creates a graph from saved GraphDef file and returns a saver."""
    # Creates graph from saved graph_def.pb.                                                                                                                                                                       
    with tf.gfile.FastGFile(modelFullPath, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')

if __name__ == '__main__':

    imagePath = 'MYFOLDERWITHIMAGES/*.jpg'
    testimages=glob.glob(imagePath)

    ## init numpy array to hold all predictions                                                                                                                                                                    
    all_predictions = np.zeros(shape=(len(testimages),121)) ## 121 categories                                                                                                                                      


    # Creates graph from saved GraphDef.                                                                                                                                                                           
    create_graph()

    with tf.Session() as sess:
        softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
        for i in range(len(testimages)):
            image_data1 = tf.gfile.FastGFile(testimages[i], 'rb').read()
            predictions = sess.run(softmax_tensor,
                                   {'DecodeJpeg/contents:0': image_data1})
            all_predictions[i,:] = np.squeeze(predictions)
            if i % 100 == 0:
              print(str(i) +' of a total of '+ str(len(testimages)))

但即使在我的gpu上运行也相当慢（aprox。每500张图像25秒）。我怎样才能加快速度？

Answer 1

加速张量流的标准方法可能是个好主意。例如，使用输入队列可以帮助您保持GPU忙碌，如the reading data section of the tensorflow documentation中所述。另外，为了提高GPU利用率，您希望使用比一次预测一个图像更大的批量大小。

张量流预测许多图像

1 个答案: