Updated docu for new anira::InferenceConfig format, new anira version…

… 0.1.0
anira-project · May 20, 2024 · 05fced2 · 05fced2
1 parent 31053af
commit 05fced2
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Showing 3 changed files with 24 additions and 15 deletions.
diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -22,7 +22,7 @@ option(ANIRA_WITH_TFLITE "Build with TensorFlow Lite backend" ON)
 
 set (PROJECT_NAME anira)
 
-project (${PROJECT_NAME} VERSION 0.0.8)
+project (${PROJECT_NAME} VERSION 0.1.0)
 
 # Sets the minimum macOS version, c++20 is only available from macOS 11.0
 if (APPLE)

diff --git a/README.md b/README.md
@@ -28,9 +28,6 @@ anira::InferenceConfig myNNConfig(
  "path/to/your/model.onnx (or *.pt, *.tflite)", // Model path
  {2048, 1, 150}, // Input shape
  {2048, 1}, // Output shape
- 2048, // Batch size
- 150, // Model input size
- 1, // Model output size
  42.66f // Maximum inference time in ms
 );
 

diff --git a/docs/anira-usage.md b/docs/anira-usage.md
@@ -35,9 +35,6 @@ anira::InferenceConfig hybridNNConfig(
  {2048, 150, 1}, // Input shape for TensorFlow Lite (required, when -DANIRA_WITH_TFLITE=ON)
  {2048, 1}, // Output shape for TensorFlow Lite (required, when -DANIRA_WITH_TFLITE=ON)
 #endif
- 2048, // Batch size (required)
- 150, // Model input size (required)
- 1, // Model output size (required)
  42.66f, // Maximum inference time in ms for processing of all batches (required)
 
  0, // Internal model latency in samples for processing of all batches (optional: default = 0)
@@ -73,15 +70,30 @@ When your pre- and post-processing requires to access values from the ```anira::
 
 class MyPrePostProcessor : public anira::PrePostProcessor {
 public:
- void preProcess(anira::RingBuffer& input, anira::AudioBufferF& output, [[maybe_unused]] anira::InferenceBackend currentInferenceBackend) override {
- for (size_t batch = 0; batch < config.m_batch_size; ++batch) {
- size_t baseIdx = batch * config.m_model_input_size;
- popSamplesFromBuffer(input, output, config.m_model_output_size, config.m_model_input_size-config.m_model_output_size, baseIdx);
+ virtual void preProcess(anira::RingBuffer& input, anira::AudioBufferF& output, [[maybe_unused]] anira::InferenceBackend currentInferenceBackend) override {
+ int64_t num_batches;
+ int64_t num_input_samples;
+ int64_t num_output_samples;
+ if (currentInferenceBackend == anira::LIBTORCH) {
+ num_batches = config.m_model_input_shape_torch[0];
+ num_input_samples = config.m_model_input_shape_torch[2];
+ num_output_samples = config.m_model_output_shape_torch[1];
+ } else if (currentInferenceBackend == anira::ONNX) {
+ num_batches = config.m_model_input_shape_onnx[0];
+ num_input_samples = config.m_model_input_shape_onnx[2];
+ num_output_samples = config.m_model_output_shape_onnx[1];
+ } else if (currentInferenceBackend == anira::TFLITE) {
+ num_batches = config.m_model_input_shape_tflite[0];
+ num_input_samples = config.m_model_input_shape_tflite[1];
+ num_output_samples = config.m_model_output_shape_tflite[1];
+ } else {
+ throw std::runtime_error("Invalid inference backend");
+ }
+
+ for (size_t batch = 0; batch < num_batches; batch++) {
+ size_t baseIdx = batch * num_input_samples;
+ popSamplesFromBuffer(input, output, num_output_samples, num_input_samples-num_output_samples, baseIdx);
  }
- };
-
- void postProcess(anira::AudioBufferF& input, anira::RingBuffer& output, [[maybe_unused]] anira::InferenceBackend currentInferenceBackend) {
-  pushSamplesToBuffer(input, output);
  };
 
  anira::InferenceConfig config = myConfig;