Tensorflow在C++中导出和运行图形的不同方式

Question

为了将您训练过的网络导入到C++中，您需要导出您的网络才能这样做。在搜索了很多并且几乎没有找到关于它的信息之后，澄清了我们应该使用freeze_graph()来能够做到这一点。

由于Tensorflow的新0.7版本，他们增加了它的文档。

Answer 1

下面是我的解决方案，它利用TF0.12中引入的V2检查点。

不需要将所有变量转换为常量或冻结图形。

为了清楚起见，V2检查点在我的目录models中如下所示：

checkpoint  # some information on the name of the files in the checkpoint
my-model.data-00000-of-00001  # the saved weights
my-model.index  # probably definition of data layout in the previous file
my-model.meta  # protobuf of the graph (nodes and topology info)

with tf.Session() as sess:
    tf.train.Saver(tf.trainable_variables()).save(sess, 'models/my-model')

with tf.Session() as sess:
    saver = tf.train.import_meta_graph('models/my-model.meta')
    saver.restore(sess, tf.train.latest_checkpoint('models/'))
    outputTensors = sess.run(outputOps, feed_dict=feedDict)

请注意，checkpointpath不是任何现有文件的路径，只是它们的公共前缀。如果您错误地将路径放置到.index文件，TF不会告诉您这是错误的，但它会在推理过程中由于变量未初始化而死亡。

#include <tensorflow/core/public/session.h>
#include <tensorflow/core/protobuf/meta_graph.pb.h>

using namespace std;
using namespace tensorflow;

...
// set up your input paths
const string pathToGraph = "models/my-model.meta"
const string checkpointPath = "models/my-model";
...

auto session = NewSession(SessionOptions());
if (session == nullptr) {
    throw runtime_error("Could not create Tensorflow session.");
}

Status status;

// Read in the protobuf graph we exported
MetaGraphDef graph_def;
status = ReadBinaryProto(Env::Default(), pathToGraph, &graph_def);
if (!status.ok()) {
    throw runtime_error("Error reading graph definition from " + pathToGraph + ": " + status.ToString());
}

// Add the graph to the session
status = session->Create(graph_def.graph_def());
if (!status.ok()) {
    throw runtime_error("Error creating graph: " + status.ToString());
}

// Read weights from the saved checkpoint
Tensor checkpointPathTensor(DT_STRING, TensorShape());
checkpointPathTensor.scalar<std::string>()() = checkpointPath;
status = session->Run(
        {{ graph_def.saver_def().filename_tensor_name(), checkpointPathTensor },},
        {},
        {graph_def.saver_def().restore_op_name()},
        nullptr);
if (!status.ok()) {
    throw runtime_error("Error loading checkpoint from " + checkpointPath + ": " + status.ToString());
}

// and run the inference to your liking
auto feedDict = ...
auto outputOps = ...
std::vector<tensorflow::Tensor> outputTensors;
status = session->Run(feedDict, outputOps, {}, &outputTensors);