first inference in prepare to play

Torsion-Audio · Oct 24, 2023 · ccde79c · ccde79c
1 parent 350a9d6
commit ccde79c
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Showing 5 changed files with 11 additions and 2 deletions.
diff --git a/source/dsp/inference/InferenceConfig.h b/source/dsp/inference/InferenceConfig.h
@@ -22,6 +22,8 @@ enum InferenceBackend {
 
 
 #define MODEL_OUTPUT_SIZE_BACKEND 1
+#define MODEL_OUTPUT_SHAPE {BATCH_SIZE, MODEL_OUTPUT_SIZE_BACKEND}
+
 
 #define MAX_INFERENCE_TIME 2048
 #define MODEL_LATENCY 0

diff --git a/source/dsp/inference/InferenceThread.cpp b/source/dsp/inference/InferenceThread.cpp
@@ -34,7 +34,7 @@ void InferenceThread::run() {
 
     processingTime.store(duration.count());
 
-    std::cout << "Inference took " << duration.count() << "ms" << std::endl;
+    // std::cout << "Inference took " << duration.count() << "ms" << std::endl;
 }
 
 void InferenceThread::inference() {

diff --git a/source/dsp/inference/InferenceThread.h b/source/dsp/inference/InferenceThread.h
@@ -42,5 +42,4 @@ class InferenceThread : public juce::Thread {
     juce::ListenerList<Listener> listeners;
 };
 
-
 #endif //NN_INFERENCE_TEMPLATE_INFERENCETHREAD_H
diff --git a/source/dsp/inference/backends/LibtorchProcessor.cpp b/source/dsp/inference/backends/LibtorchProcessor.cpp
@@ -16,6 +16,10 @@ LibtorchProcessor::~LibtorchProcessor() {
 void LibtorchProcessor::prepareToPlay() {
     inputs.clear();
     inputs.push_back(torch::zeros(MODEL_INPUT_SHAPE_LIBTORCH));
+    // first run takes longest, so we do it here
+    std::array<float, BATCH_SIZE * MODEL_INPUT_SIZE_BACKEND> input;
+    std::array<float, BATCH_SIZE * MODEL_OUTPUT_SIZE_BACKEND> output;
+    processBlock(input, output);
 }
 
 void LibtorchProcessor::processBlock(std::array<float, BATCH_SIZE * MODEL_INPUT_SIZE_BACKEND>& input, std::array<float, BATCH_SIZE * MODEL_OUTPUT_SIZE_BACKEND>& output) {

diff --git a/source/dsp/inference/backends/OnnxRuntimeProcessor.cpp b/source/dsp/inference/backends/OnnxRuntimeProcessor.cpp
@@ -12,6 +12,10 @@ OnnxRuntimeProcessor::~OnnxRuntimeProcessor()
 void OnnxRuntimeProcessor::prepareToPlay() {
     // Define the shape of input tensor
     inputShape = MODEL_INPUT_SHAPE_ONNX;
+    // first run takes longest, so we do it here
+    std::array<float, BATCH_SIZE * MODEL_INPUT_SIZE_BACKEND> input;
+    std::array<float, BATCH_SIZE * MODEL_OUTPUT_SIZE_BACKEND> output;
+    processBlock(input, output);
 }
 
 void OnnxRuntimeProcessor::processBlock(std::array<float, BATCH_SIZE * MODEL_INPUT_SIZE_BACKEND>& input, std::array<float, BATCH_SIZE * MODEL_OUTPUT_SIZE_BACKEND>& output) {
Original file line number	Diff line number	Diff line change
Expand Up		@@ -42,5 +42,4 @@ class InferenceThread : public juce::Thread {
		juce::ListenerList<Listener> listeners;
		};


		#endif //NN_INFERENCE_TEMPLATE_INFERENCETHREAD_H