Tensorflow Different ways to Export and Run graph in C++

Here’s my solution utilizing the V2 checkpoints introduced in TF 0.12.

There’s no need to convert all variables to constants or freeze the graph.

Just for clarity, a V2 checkpoint looks like this in my directory models:

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)

Python part (saving)

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

If you create the Saver with tf.trainable_variables(), you can save yourself some headache and storage space. But maybe some more complicated models need all data to be saved, then remove this argument to Saver, just make sure you’re creating the Saver after your graph is created. It is also very wise to give all variables/layers unique names, otherwise you can run in different problems.

Python part (inference)

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)

C++ part (inference)

Note that checkpointPath isn’t a path to any of the existing files, just their common prefix. If you mistakenly put there path to the .index file, TF won’t tell you that was wrong, but it will die during inference due to uninitialized variables.

#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);