From 05fced237bfe991ce4328f1f95ec72f916bcb113 Mon Sep 17 00:00:00 2001
From: Fares Schulz <schulzfares@gmail.com>
Date: Mon, 20 May 2024 16:10:56 +0200
Subject: [PATCH] Updated docu for new anira::InferenceConfig format, new anira
 version 0.1.0

---
 CMakeLists.txt      |  2 +-
 README.md           |  3 ---
 docs/anira-usage.md | 34 +++++++++++++++++++++++-----------
 3 files changed, 24 insertions(+), 15 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4bef544..5a1f4bd 100644
--- 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
index 1471e35..d9c96c9 100644
--- 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
index 27379ec..389d72d 100644
--- 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;