将CNN张量流模型冻结到.pb文件中

Question

I'm currently experimenting with superresolution using CNNs. To serve my model I'll need to frezze it first, into a .pb file, right? Being a newbie I don't really know how to do that. My model basically goes like this:

low res input image -> bicubic scaling (2x) -> fed to CNN -> CNN output image with the same (2x) resolution.

My model has 3 simple layers. The output layer is called "output". You can find the model here:

https://github.com/pinae/Superresolution

It saves its progress like so:

• checkpoint
• network_params.data-00000-of-00001
• network_params.index
• network_params.meta

I see to ways of doing this.

First: Follow this tutorial: https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc

This seems to be made for multiple output nodes (for identification) and not for superresolution which only has one output. I don't know how to modify that script for my usage.

Second: Use freeze_graph.py

Again, I'm totally lost on how to use this with my model. All examples seem to be based on the MNIST tutorial.

Thanks!

Answer 1

Don't understand what you mean but in the metaflow article, he's also using one output nodes. You can add several depending on how you name your tensor .

In you case, have a look at the network.py . You need to look at the output_layer :

    self.output = self.conv_layer("reconstruction_layer", self.layer_params[-1],
                                  non_linear_mapping_layer, linear=True)

As you can see it's already name due to conv_layer , so in the metaflow code, you need to do something like this:

def freeze_graph(model_folder):
    # We retrieve our checkpoint fullpath
    checkpoint = tf.train.get_checkpoint_state(model_folder)
    input_checkpoint = checkpoint.model_checkpoint_path

    # We precise the file fullname of our freezed graph
    absolute_model_folder = "/".join(input_checkpoint.split('/')[:-1])
    output_graph = absolute_model_folder + "/frozen_model.pb"

    # Before exporting our graph, we need to precise what is our output node
    # This is how TF decides what part of the Graph he has to keep and what part it can dump
    # NOTE: this variable is plural, because you can have multiple output nodes
    output_node_names = "reconstruction_layer"
    ...

Note: Sometimes it has a prefix in the naming like Accuracy is a prefix in the case of the metaflow article, Accuracy/predictions . Therefore, it would make sense to print out all the variables name that you stored in the checkpoint.

By the way, since TF 1.0 you can save your model with the SavedModelBuilder . This is the preferred way as it offers compatibility across multiple languages. The only caveats is that it is still not one single file but works well with Tensorflow Serving.