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, "Binary_Protobuf.pb", False)
tf.train.write_graph(sess.graph_def, save_dir, "Text_Protobuf.pbtxt", True)
print("Saving Done .. ")
现在,我想要的是副verca程序。如何加载protobuf文件并将其转换为.ckpt(checkpoint)格式?
我正在尝试使用以下脚本执行此操作,但它总是失败:
import tensorflow as tf
import argparse
# Pass the filename as an argument
parser = argparse.ArgumentParser()
parser.add_argument("--frozen_model_filename", default="/path-to-pb-file/Binary_Protobuf.pb", type=str, help="Pb model file to import")
args = parser.parse_args()
# We load the protobuf file from the disk and parse it to retrieve the
# unserialized graph_def
with tf.gfile.GFile(args.frozen_model_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
#saver=tf.train.Saver()
with tf.Graph().as_default() as graph:
tf.import_graph_def(
graph_def,
input_map=None,
return_elements=None,
name="prefix",
op_dict=None,
producer_op_list=None
)
sess = tf.Session(graph=graph)
saver=tf.train.Saver()
save_path = saver.save(sess, "path-to-ckpt/model.ckpt")
print("Model saved to chkp format")
我相信拥有这些转换脚本会非常有用。
P.S:权重已经嵌入到.pb文件中。
感谢。
答案 0 :(得分:0)
似乎你只在两个文件中得到了图形定义,而不是冻结的模型。
# This two lines only save the graph as proto file; it doesn't save the variables and their values.
tf.train.write_graph(sess.graph_def, save_dir, "Binary_Protobuf.pb", False)
tf.train.write_graph(sess.graph_def, save_dir, "Text_Protobuf.pbtxt", True)
获取冻结图
答案 1 :(得分:0)
如果您在pb文件中加载模型,它是否可以作为预制模型重新训练。 我使用了" tf.global_variables()"获取可以训练的变量,但是当我加载pb模型时没有变量返回。
tf.global_variables()