我已成功地转换预先训练.ckpt模型中使用这个脚本.pb(protobuf的)格式:转换.pb文件来.ckpt(tensorflow)
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,我要的是副verca程序。我如何加载protobuf文件并将其转换为.ckpt(检查点)格式?
我试图做到这一点与下面的脚本,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,这将是有这些转换脚本非常有帮助。
P.S:权重已经嵌入到.pb文件中。
谢谢。
该模型从恢复(sess,tf.train.latest_checkpoint(dir_path))加载检查点(与权重)的位置。 –
好!在第二个脚本中,您没有加载任何模型,只是导入了图形。尽管您在第一个脚本中加载模型,但它不会将变量写入到pb文件中。 –
好吧我在tf.train.write_graph函数中将sess.graph_def更改为sess.graph,但同样的运气。 –