Python 向Tensorflow冻结图添加新层？

这些讨论讨论了（，）关于向Tensorflow图添加新层和重新训练模型的内容

下面的代码显示如何将新层添加到恢复的可训练模型中

import tensorflow as tf

sess=tf.Session()    
#First let's load meta graph and restore weights
saver = tf.train.import_meta_graph('my_test_model-1000.meta')
saver.restore(sess,tf.train.latest_checkpoint('./'))


# Now, let's access and create placeholders variables and
# create feed-dict to feed new data

graph = tf.get_default_graph()
w1 = graph.get_tensor_by_name("w1:0")
w2 = graph.get_tensor_by_name("w2:0")
feed_dict ={w1:13.0,w2:17.0}

#Now, access the op that you want to run. 
op_to_restore = graph.get_tensor_by_name("op_to_restore:0")

#Add more to the current graph
add_on_op = tf.multiply(op_to_restore,2)

print sess.run(add_on_op,feed_dict)
#This will print 120.

但我喜欢在恢复的冻结图中添加图层

我只为一个应用程序冻结了模型。我喜欢在模型中添加图层并再次冻结。 这些层更多用于后处理，不需要训练，因此不在训练模型中

---

原因是我正在将冻结图转换为TensorRT，并且我希望将这些层包含到Int8引擎中。

我希望下面的内容能对您有所帮助。我有一个自定义Op，应该添加到我从.pb文件（冻结模型文件）加载的现有图形中 有了这个，我就可以将新节点附加到现有的图中

Source code below: 

import tensorflow as tf
from tensorflow.python.framework import load_library
from tensorflow.python.platform import resource_loader

from tensorflow.core.protobuf import saved_model_pb2
from tensorflow.python.util import compat


# Utility functions for Loading and Freezing graphs


def load_graph(frozen_graph_filename):

    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def, name="")

    return graph

def freeze_graph(sess, output_graph):

    output_node_names = [
        "custom_op_zero","custom_op_zero_1"
  output_node_names = ",".join(output_node_names)

    output_graph_def = tf.graph_util.convert_variables_to_constants(
        sess,
        tf.get_default_graph().as_graph_def(),
        output_node_names.split(",")
    )

    with tf.gfile.GFile(output_graph, "wb") as f:
        f.write(output_graph_def.SerializeToString())

    print("{} ops written to {}.".format(len(output_graph_def.node), output_graph))


## load custom Ops shared object file

zero_out_ops = load_library.load_op_library(
    resource_loader.get_path_to_datafile('my-op/tensorflow_zero_out/python/ops/_zero_out_ops.so'))
zero_out = zero_out_ops.zero_out

frozen_graph = load_graph("frozen_model.pb")
all_tensors = [tensor for op in frozen_graph.get_operations() for tensor in op.values()]
#print (all_tensors[29])

# Input to the new node is the output of last node

zero_out_custom = zero_out(all_tensors[-1],name="custom_op_zero")
zero_out_custom1 = zero_out(all_tensors[-1],name="custom_op_zero_1")
#print (new_op)

# save new freezed model file
with tf.Session(graph=frozen_graph) as persisted_sess:
  for op in persisted_sess.graph.get_operations():
     print(op)
  freeze_graph(persisted_sess,"new_model.pb")