Deploy() Method

Table of contents

1. Description
2. Code

For this Stage of Deployment, You should modify the following file

MDL_Deploy.cpp

---

Description

In this method, you receive the ModelInput struct received from the ITP thread. Moreover, you have access to the parameters of the plugin. You should use these to prepare the ModelOutput struct and send it to the PPP thread.

Similarly to ITP, whenever you are ready to send the data to the PPP thread, you should return true from this method.

This method is called whenever:

• A ModelInput is received from the ITP thread
• Any of the parameters are changed

Code

bool ModelThread::deploy(bool new_model_input_received,
                         bool did_any_gui_params_change) {

    /*              IF YOU NEED TO PRINT TO CONSOLE FOR DEBUGGING,
    *                  YOU CAN USE THE FOLLOWING METHOD:
    *                      PrintMessage("YOUR MESSAGE HERE");
    */

    // =================================================================================
    // ===         0. LOADING THE MODEL
    // =================================================================================
    // Try loading the model if it hasn't been loaded yet
    if (!isModelLoaded) {
        load("drumLoopVAE.pt");
    }

    // =================================================================================
    // ===         1. ACCESSING GUI PARAMETERS
    // Refer to:
    // https://neuralmidifx.github.io/docs/v1_0_0/datatypes/GuiParams#accessing-the-ui-parameters
    // =================================================================================


    // =================================================================================


    // =================================================================================
    // ===         1.b. ACCESSING REALTIME PLAYBACK INFORMATION
    // Refer to:
    // https://neuralmidifx.github.io/docs/v1_0_0/datatypes/RealtimePlaybackInfo#accessing-the-realtimeplaybackinfo
    // =================================================================================


    // =================================================================================


    // =================================================================================
    // ===         Inference
    // =================================================================================
    // ---       the input is available in model_input
    // ---       the output MUST be placed in model_output
    // ---       if the output is ready for transmission to PPP, return true,
    // ---                                             otherwise return false
    // ---       The API of the model MUST be defined in DeploymentSettings/Model.h
    // =================================================================================
    if (new_model_input_received || did_any_gui_params_change) {

        /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         * Use DisplayTensor to display the data if debugging
         * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
        // DisplayTensor(model_input.tensor1, "INPUT");

        if (isModelLoaded)
        { // =================================================================================
            // ===       Prepare your input
            // =================================================================================
            // Prepare The Input To Your Model
            // example: let's assume the pytorch model has some method that you want to
            // use for inference, e.g.:
            //          def SomeMethod(self, x: float, y: torch.Tensor)
            //             ....
            //             return a, b

            // Step A: To Prepare the inputs, first create a vector of torch::jit::IValue
            // e.g.:
            // >> std::vector<torch::jit::IValue> inputs;

            // Step B: Push the input parameters
            // Remember that if you need data passed from the ITP thread,
            // you can access it from the model_input struct
            // e.g.:
            // >> inputs.emplace_back(float(Slider1)); // this is the x parameter
            // >> inputs.emplace_back(model_input.tensor1); // this is the y parameter
            // =================================================================================

            // =================================================================================
            // ===        Run Inference
            // =================================================================================
            // Get the method for inference or use the forward method
            // auto SomeMethod = model.get_method("SomeMethod");
            // auto outs = SomeMethod(inputs);
            // auto outs = model.forward(inputs);

            // =================================================================================
            // ===        Prepare the model_output struct and return true to signal
            // ===        that the output is ready to be sent to PPP
            // =================================================================================
            // auto outs_as_tuple = outs.toTuple()->elements();
            // model_output.tensor1 = outs_as_tuple[0].toTensor();
            // model_output.tensor2 = outs_as_tuple[1].toTensor();

            return true;
        }
    } else {
        return false;
    }
}