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-rw-r--r--source/use_case/vww/include/VisualWakeWordProcessing.hpp25
-rw-r--r--source/use_case/vww/src/UseCaseHandler.cc22
-rw-r--r--source/use_case/vww/src/VisualWakeWordProcessing.cc33
3 files changed, 37 insertions, 43 deletions
diff --git a/source/use_case/vww/include/VisualWakeWordProcessing.hpp b/source/use_case/vww/include/VisualWakeWordProcessing.hpp
index b1d68ce..bef161f 100644
--- a/source/use_case/vww/include/VisualWakeWordProcessing.hpp
+++ b/source/use_case/vww/include/VisualWakeWordProcessing.hpp
@@ -34,9 +34,9 @@ namespace app {
public:
/**
* @brief Constructor
- * @param[in] model Pointer to the the Image classification Model object.
+ * @param[in] inputTensor Pointer to the TFLite Micro input Tensor.
**/
- explicit VisualWakeWordPreProcess(Model* model);
+ explicit VisualWakeWordPreProcess(TfLiteTensor* inputTensor);
/**
* @brief Should perform pre-processing of 'raw' input image data and load it into
@@ -46,6 +46,9 @@ namespace app {
* @return true if successful, false otherwise.
**/
bool DoPreProcess(const void* input, size_t inputSize) override;
+
+ private:
+ TfLiteTensor* m_inputTensor;
};
/**
@@ -56,6 +59,7 @@ namespace app {
class VisualWakeWordPostProcess : public BasePostProcess {
private:
+ TfLiteTensor* m_outputTensor;
Classifier& m_vwwClassifier;
const std::vector<std::string>& m_labels;
std::vector<ClassificationResult>& m_results;
@@ -63,19 +67,20 @@ namespace app {
public:
/**
* @brief Constructor
- * @param[in] classifier Classifier object used to get top N results from classification.
- * @param[in] model Pointer to the VWW classification Model object.
- * @param[in] labels Vector of string labels to identify each output of the model.
- * @param[out] results Vector of classification results to store decoded outputs.
+ * @param[in] outputTensor Pointer to the TFLite Micro output Tensor.
+ * @param[in] classifier Classifier object used to get top N results from classification.
+ * @param[in] model Pointer to the VWW classification Model object.
+ * @param[in] labels Vector of string labels to identify each output of the model.
+ * @param[out] results Vector of classification results to store decoded outputs.
**/
- VisualWakeWordPostProcess(Classifier& classifier, Model* model,
+ VisualWakeWordPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
const std::vector<std::string>& labels,
std::vector<ClassificationResult>& results);
/**
- * @brief Should perform post-processing of the result of inference then
- * populate classification result data for any later use.
- * @return true if successful, false otherwise.
+ * @brief Should perform post-processing of the result of inference then
+ * populate classification result data for any later use.
+ * @return true if successful, false otherwise.
**/
bool DoPostProcess() override;
};
diff --git a/source/use_case/vww/src/UseCaseHandler.cc b/source/use_case/vww/src/UseCaseHandler.cc
index 7681f89..267e6c4 100644
--- a/source/use_case/vww/src/UseCaseHandler.cc
+++ b/source/use_case/vww/src/UseCaseHandler.cc
@@ -53,7 +53,7 @@ namespace app {
}
TfLiteTensor* inputTensor = model.GetInputTensor(0);
-
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
if (!inputTensor->dims) {
printf_err("Invalid input tensor dims\n");
return false;
@@ -75,15 +75,13 @@ namespace app {
const uint32_t displayChannels = 3;
/* Set up pre and post-processing. */
- VisualWakeWordPreProcess preprocess = VisualWakeWordPreProcess(&model);
+ VisualWakeWordPreProcess preProcess = VisualWakeWordPreProcess(inputTensor);
std::vector<ClassificationResult> results;
- VisualWakeWordPostProcess postprocess = VisualWakeWordPostProcess(
- ctx.Get<Classifier&>("classifier"), &model,
+ VisualWakeWordPostProcess postProcess = VisualWakeWordPostProcess(outputTensor,
+ ctx.Get<Classifier&>("classifier"),
ctx.Get<std::vector<std::string>&>("labels"), results);
- UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
-
do {
hal_lcd_clear(COLOR_BLACK);
@@ -115,17 +113,18 @@ namespace app {
inputTensor->bytes : IMAGE_DATA_SIZE;
/* Run the pre-processing, inference and post-processing. */
- if (!runner.PreProcess(imgSrc, imgSz)) {
+ if (!preProcess.DoPreProcess(imgSrc, imgSz)) {
+ printf_err("Pre-processing failed.");
return false;
}
- profiler.StartProfiling("Inference");
- if (!runner.RunInference()) {
+ if (!RunInference(model, profiler)) {
+ printf_err("Inference failed.");
return false;
}
- profiler.StopProfiling();
- if (!runner.PostProcess()) {
+ if (!postProcess.DoPostProcess()) {
+ printf_err("Post-processing failed.");
return false;
}
@@ -138,7 +137,6 @@ namespace app {
ctx.Set<std::vector<ClassificationResult>>("results", results);
#if VERIFY_TEST_OUTPUT
- TfLiteTensor* outputTensor = model.GetOutputTensor(0);
arm::app::DumpTensor(outputTensor);
#endif /* VERIFY_TEST_OUTPUT */
diff --git a/source/use_case/vww/src/VisualWakeWordProcessing.cc b/source/use_case/vww/src/VisualWakeWordProcessing.cc
index 94eae28..a9863c0 100644
--- a/source/use_case/vww/src/VisualWakeWordProcessing.cc
+++ b/source/use_case/vww/src/VisualWakeWordProcessing.cc
@@ -22,13 +22,9 @@
namespace arm {
namespace app {
- VisualWakeWordPreProcess::VisualWakeWordPreProcess(Model* model)
- {
- if (!model->IsInited()) {
- printf_err("Model is not initialised!.\n");
- }
- this->m_model = model;
- }
+ VisualWakeWordPreProcess::VisualWakeWordPreProcess(TfLiteTensor* inputTensor)
+ :m_inputTensor{inputTensor}
+ {}
bool VisualWakeWordPreProcess::DoPreProcess(const void* data, size_t inputSize)
{
@@ -37,9 +33,8 @@ namespace app {
}
auto input = static_cast<const uint8_t*>(data);
- TfLiteTensor* inputTensor = this->m_model->GetInputTensor(0);
- auto unsignedDstPtr = static_cast<uint8_t*>(inputTensor->data.data);
+ auto unsignedDstPtr = static_cast<uint8_t*>(this->m_inputTensor->data.data);
/* VWW model has one channel input => Convert image to grayscale here.
* We expect images to always be RGB. */
@@ -47,10 +42,10 @@ namespace app {
/* VWW model pre-processing is image conversion from uint8 to [0,1] float values,
* then quantize them with input quantization info. */
- QuantParams inQuantParams = GetTensorQuantParams(inputTensor);
+ QuantParams inQuantParams = GetTensorQuantParams(this->m_inputTensor);
- auto signedDstPtr = static_cast<int8_t*>(inputTensor->data.data);
- for (size_t i = 0; i < inputTensor->bytes; i++) {
+ auto signedDstPtr = static_cast<int8_t*>(this->m_inputTensor->data.data);
+ for (size_t i = 0; i < this->m_inputTensor->bytes; i++) {
auto i_data_int8 = static_cast<int8_t>(
((static_cast<float>(unsignedDstPtr[i]) / 255.0f) / inQuantParams.scale) + inQuantParams.offset
);
@@ -62,22 +57,18 @@ namespace app {
return true;
}
- VisualWakeWordPostProcess::VisualWakeWordPostProcess(Classifier& classifier, Model* model,
+ VisualWakeWordPostProcess::VisualWakeWordPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
const std::vector<std::string>& labels, std::vector<ClassificationResult>& results)
- :m_vwwClassifier{classifier},
+ :m_outputTensor{outputTensor},
+ m_vwwClassifier{classifier},
m_labels{labels},
m_results{results}
- {
- if (!model->IsInited()) {
- printf_err("Model is not initialised!.\n");
- }
- this->m_model = model;
- }
+ {}
bool VisualWakeWordPostProcess::DoPostProcess()
{
return this->m_vwwClassifier.GetClassificationResults(
- this->m_model->GetOutputTensor(0), this->m_results,
+ this->m_outputTensor, this->m_results,
this->m_labels, 1, true);
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-17 22:46:33 +0000