如何导入冷冻的原生动物以对其进行重新训练？
我在网上找到的所有方法都需要检查点。有没有办法读取protobuf，以便将内核常数和偏差常数转换为变量？
编辑1 ： 这类似于以下问题：How to retrain model in graph (.pb)?
我查看了DeepSpeech，该问题的答案中建议使用它。他们似乎有initialize_from_frozen_model的{{3}}。我找不到原因。
编辑2 ：我尝试创建一个新的GraphDef对象，在其中用变量替换了内核和偏差：
probable_variables = [...] # kernels and biases of Conv2D and MatMul
new_graph_def = tf.GraphDef()
with tf.Session(graph=graph) as sess:
for n in sess.graph_def.node:
if n.name in probable_variables:
# create variable op
nn = new_graph_def.node.add()
nn.name = n.name
nn.op = 'VariableV2'
nn.attr['dtype'].CopyFrom(attr_value_pb2.AttrValue(type=dtype))
nn.attr['shape'].CopyFrom(attr_value_pb2.AttrValue(shape=shape))
else:
nn = new_model.node.add()
nn.CopyFrom(n)
不确定我走的路是否正确。不知道如何在trainable=True对象中设置NodeDef。
答案 0 :(得分：2)
实际上，您提供的代码片段的方向正确：）
最棘手的部分是获取以前可训练的变量的名称。希望该模型是通过一些高级框架（例如keras或tf.slim）创建的-他们将变量很好地包装在诸如conv2d_1/kernel，dense_1/bias，batch_normalization/gamma之类的变量中等
如果不确定，最有用的就是可视化图形...
# read graph definition
with tf.gfile.GFile('frozen.pb', 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# now build the graph in the memory and visualize it
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def, name="prefix")
writer = tf.summary.FileWriter('out', graph)
writer.close()
...使用张量板：
$ tensorboard --logdir out/
亲自观察一下图形的外观和名称。
您需要的只是一个名为tf.contrib.graph_editor的神奇库。现在，假设您已经将先前可训练的操作的名称（以前是变量，但现在是Const）存储在probable_variables中（就像您在 Edit 2中一样）。
注意：请记住ops，tensors和variables之间的区别。操作是图形的元素，张量是包含操作结果的缓冲区，变量wrappers位于张量周围，具有3个操作：assign（在初始化变量时调用），{{ 1}}（由其他操作调用，例如read）和ref tensor（保存值）。
注释2：conv2d只能在会话外运行 –您不能在线进行任何图形修改！
graph_editor PS： 此代码未经测试；但是，最近我一直在使用import numpy as np
import tensorflow.contrib.graph_editor as ge
# load the graphdef into memory, just as in Step 1
graph = load_graph('frozen.pb')
# create a variable for each constant, beware the naming
const_var_name_pairs = []
for name in probable_variables:
var_shape = graph.get_tensor_by_name('{}:0'.format(name)).get_shape()
var_name = '{}_a'.format(name)
var = tf.get_variable(name=var_name, shape=var_shape, dtype='float32')
const_var_name_pairs.append((name, var_name))
# from now we're going to work with GraphDef
name_to_op = dict([(n.name, n) for n in graph.as_graph_def().node])
# magic: now we swap the outputs of const and created variable
for const_name, var_name in const_var_name_pairs:
const_op = name_to_op[const_name]
var_reader_op = name_to_op[var_name + '/read']
ge.swap_outputs(ge.sgv(const_op), ge.sgv(var_reader_op))
# Now we can safely create a session and copy the values
sess = tf.Session(graph=graph)
for const_name, var_name in const_var_name_pairs:
ts = graph.get_tensor_by_name('{}:0'.format(const_name))
var = tf.get_variable(var_name)
var.load(ts.eval(sess))
# All done! Now you can make sure everything is correct by visualizing
# and calculate outputs for some inputs.
并经常进行网络手术，因此我认为它应该基本上是正确的：）
答案 1 :(得分：2)
我已经用经过测试的代码验证了@FalconUA的解决方案。需要稍作修改（特别是，我在initializer中使用了get_variable选项来正确地初始化变量）。在这里！
假设冻结的模型存储在frozen_graph.pb中：
probable_variables = [...] # kernels and biases of Conv2D and MatMul
tf_graph = load_pb('frozen_graph.pb')
const_var_name_pairs = []
with tf_graph.as_default() as g:
for name in probable_variables:
tensor = g.get_tensor_by_name('{}:0'.format(name))
with tf.Session() as sess:
tensor_as_numpy_array = sess.run(tensor)
var_shape = tensor.get_shape()
# Give each variable a name that doesn't already exist in the graph
var_name = '{}_turned_var'.format(name)
# Create TensorFlow variable initialized by values of original const.
var = tf.get_variable(name=var_name, dtype='float32', shape=var_shape, \
initializer=tf.constant_initializer(tensor_as_numpy_array))
# We want to keep track of our variables names for later.
const_var_name_pairs.append((name, var_name))
# At this point, we added a bunch of tf.Variables to the graph, but they're
# not connected to anything.
# The magic: we use TF Graph Editor to swap the Constant nodes' outputs with
# the outputs of our newly created Variables.
for const_name, var_name in const_var_name_pairs:
const_op = g.get_operation_by_name(const_name)
var_reader_op = g.get_operation_by_name(var_name + '/read')
ge.swap_outputs(ge.sgv(const_op), ge.sgv(var_reader_op))
注意：如果保存转换后的模型并在Tensorboard或Netron中查看，您将看到Variables取代了Constants。您还将看到一堆悬空的常量，可以有选择地删除它们。
我已经验证了冻结版本和未冻结版本之间的权重值是相同的。
这是load_pb函数：
import tensorflow as tf
# Load protobuf as graph, given filepath
def load_pb(path_to_pb):
with tf.gfile.GFile(path_to_pb, '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
答案 2 :(得分：1)
感谢@FalconUA和@Max wu。 我添加了一种快速获取变量名称的方法。
import tensorflow as tf
# Load protobuf as graph, given filepath
def load_pb(path_to_pb):
with tf.gfile.GFile(path_to_pb, '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
tf_graph = load_pb('mobilenet_v1_1.0_224_frozen_ccl.pb')
variables = [op.name for op in tf_graph.get_operations() if op.type == "Const"]
print(variables)
答案 3 :(得分：0)
[代码] def protobuf_to_checkpoint_conversion（pb_model，ckpt_dir）：
graph = tf.Graph()
with graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(pb_model, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def,name='')
image_tensor = graph.get_tensor_by_name('image_tensor:0')
dummy = np.random.random((1, 512, 512, 3))
with graph.as_default():
config = tf.ConfigProto()
with tf.Session(graph=graph, config=config) as sess:
constant_ops = [op for op in graph.get_operations() if op.type == "Const"]
vars_dict = {}
ass = []
for constant_op in constant_ops:
name = constant_op.name
const = constant_op.outputs[0]
shape = const.shape
var = tf.get_variable(name, shape, dtype=const.dtype, initializer=tf.zeros_initializer())
vars_dict[name] = var
print('INFO:Initializing variables')
init = tf.global_variables_initializer()
sess.run(init)
print('INFO: Loading vars')
for constant_op in tqdm(constant_ops):
name = constant_op.name
if 'FeatureExtractor' in name or 'BoxPredictor' in name:
const = constant_op.outputs[0]
shape = const.shape
var = vars_dict[name]
var.load(sess.run(const, feed_dict={image_tensor:dummy}), sess)
saver = tf.train.Saver(var_list=vars_dict)
ckpt_path = os.path.join(ckpt_dir, 'model.ckpt')
saver.save(sess, ckpt_path)
return graph, vars_dict
[/ code]
参考：https://github.com/yeephycho/tensorflow-face-detection/issues/42#issuecomment-455325984
答案 4 :(得分：0)
def protobuf_to_checkpoint_conversion（pb_model，ckpt_dir）：
graph = tf.Graph()
with graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(pb_model, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def,name='')
image_tensor = graph.get_tensor_by_name('image_tensor:0')
dummy = np.random.random((1, 512, 512, 3))
with graph.as_default():
config = tf.ConfigProto()
with tf.Session(graph=graph, config=config) as sess:
constant_ops = [op for op in graph.get_operations() if op.type == "Const"]
vars_dict = {}
ass = []
for constant_op in constant_ops:
name = constant_op.name
const = constant_op.outputs[0]
shape = const.shape
var = tf.get_variable(name, shape, dtype=const.dtype, initializer=tf.zeros_initializer())
vars_dict[name] = var
print('INFO:Initializing variables')
init = tf.global_variables_initializer()
sess.run(init)
print('INFO: Loading vars')
for constant_op in tqdm(constant_ops):
name = constant_op.name
if 'FeatureExtractor' in name or 'BoxPredictor' in name:
const = constant_op.outputs[0]
shape = const.shape
var = vars_dict[name]
var.load(sess.run(const, feed_dict={image_tensor:dummy}), sess)
saver = tf.train.Saver(var_list=vars_dict)
ckpt_path = os.path.join(ckpt_dir, 'model.ckpt')
saver.save(sess, ckpt_path)
参考：https://github.com/yeephycho/tensorflow-face-detection/issues/42#issuecomment-455325984