导入一个简单的Tensorflow frozen_model.pb文件并在C++中进行预测

Question

我正在尝试将从Tensorflow Python导出的图形导入Tensorflow C++.我已经成功地将图形重新导入Python.我现在唯一想要的是用C++编写相同的代码,但我不确定C++ api函数和用法,因为Tensorflow网站上的文档不够好.

这是我到目前为止找到的C++代码.

C++:

namespace tf = tensorflow;

tf::Session* session;

tf::Status status = tf::NewSession(tf::SessionOptions(), &session);
checkStatus(status);

tf::GraphDef graph_def;
status = ReadBinaryProto(tf::Env::Default(), "./models/frozen_model.pb", &graph_def);
checkStatus(status);

status = session->Create(graph_def);
checkStatus(status);

tf::Tensor x(tf::DT_FLOAT, tf::TensorShape());
tf::Tensor y(tf::DT_FLOAT, tf::TensorShape());

x.scalar<float>()() = 23.0;
y.scalar<float>()() = 19.0;

std::vector<std::pair<tf::string, tf::Tensor>> input_tensors = {{"x", x}, {"y", y}};
std::vector<string> vNames; // vector of names for required graph nodes
vNames.push_back("prefix/input_neurons:0");
vNames.push_back("prefix/prediction_restore:0");
std::vector<tf::Tensor> output_tensors;

status = session->Run({}, vNames,  {}, &output_tensors);
checkStatus(status);

tf::Tensor output = output_tensors[0];
std::cout << "Success: " << output.scalar<float>() << "!" << std::endl;
session->Close();
return 0;

我在上面的当前c ++代码中遇到的问题是它说它无法通过名称找到任何操作prefix/input_neurons:0.虽然图中有一个操作,因为当我在Python代码中导入这个图形时(如下所示),它可以很好地工作.

这是成功导入图形的Python代码.

Python :(完美无缺)

def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the 
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the 
    # current default Graph
    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
        )
    return graph

# We use our "load_graph" function
graph = load_graph("./models/frozen_model.pb")

# We can verify that we can access the list of operations in the graph
for op in graph.get_operations():
    print(op.name)     # <--- printing the operations snapshot below
    # prefix/Placeholder/inputs_placeholder
    # ...
    # prefix/Accuracy/predictions

# We access the input and output nodes
x = graph.get_tensor_by_name('prefix/input_neurons:0')
y = graph.get_tensor_by_name('prefix/prediction_restore:0')

# We launch a Session
with tf.Session(graph=graph) as sess:

    test_features = [[0.377745556,0.009904444,0.063231111,0.009904444,0.003734444,0.002914444,0.008633333,0.000471111,0.009642222,0.05406,0.050163333,7e-05,0.006528889,0.000314444,0.00649,0.043956667,0.016816667,0.001644444,0.016906667,0.00204,0.027342222,0.13864]]
        # compute the predicted output for test_x
    pred_y = sess.run( y, feed_dict={x: test_features} )
    print(pred_y)

更新

我可以从python脚本打印操作.这是截图.

这是我得到的错误.

Answer 1

请参阅Run 函数参考：在 c++ 中，输入首先是输入字典，然后是输出节点，然后是需要运行的其他操作，然后是输出向量（可选地带有额外参数，但看起来您不需要他们）。这个调用应该有效：

status = session->Run({{"prefix/input_neurons:0", x}}, {"prefix/prediction_restore:0"}, {}, &output_tensors);

如果你想设置x为与 python 中相同的值（很可能有一种方法可以在不复制数据的情况下做到这一点，但我不知道如何），你可以在调用之前执行此操作Run()：

std::vector<float> test_features = {0.377745556,0.009904444,0.063231111,0.009904444,0.003734444,0.002914444,0.008633333,0.000471111,0.009642222,0.05406,0.050163333,7e-05,0.006528889,0.000314444,0.00649,0.043956667,0.016816667,0.001644444,0.016906667,0.00204,0.027342222,0.13864};
int n_features = test_features.size();
x= tf::Tensor(tf::DT_FLOAT, tf::TensorShape({1,n_features}));
auto x_mapped = x.tensor<float, 2>();

for (int i = 0; i< n_features; i++)
{
    x_mapped(0, i) = test_features[i];
}

告诉我这个是否更好！