小编Kon*_*los的帖子

我已设法使用此脚本将预先训练的.ckpt模型转换为.pb(protobuf)格式:

import os
import tensorflow as tf

# Get the current directory
dir_path = os.path.dirname(os.path.realpath(__file__))
print "Current directory : ", dir_path
save_dir = dir_path + '/Protobufs'

graph = tf.get_default_graph()

# Create a session for running Ops on the Graph.
sess = tf.Session()

print("Restoring the model to the default graph ...")
saver = tf.train.import_meta_graph(dir_path + '/model.ckpt.meta')
saver.restore(sess,tf.train.latest_checkpoint(dir_path))
print("Restoring Done .. ")

print "Saving the model to Protobuf format: ", save_dir

#Save the model to protobuf  (pb and pbtxt) file.
tf.train.write_graph(sess.graph_def, save_dir, …

我已经将预训练的 .ckpt 文件转换为 .pb 文件，冻结模型并保存重量。我现在要做的是使用该 .pb 文件进行简单的推断，然后提取并保存输出图像。该模型是从这里下载的（用于语义分割的完全卷积网络）：https : //github.com/MarvinTeichmann/KittiSeg。到目前为止，我已经设法加载图像，设置默认的 tf 图并导入模型定义的图，读取输入和输出张量并运行会话（此处为错误）。

import tensorflow as tf
import os
import numpy as np
from tensorflow.python.platform import gfile
from PIL import Image

# Read the image & get statstics
img=Image.open('/path-to-image/demoImage.png')
img.show()
width, height = img.size
print(width)
print(height)

#Plot the image
#image.show()

with tf.Graph().as_default() as graph:

        with tf.Session() as sess:

                # Load the graph in graph_def
                print("load graph")

                # We load the protobuf file from the disk and parse it to retrive …