commit 2181d0ac35f30202985a877950c88325ff665f6b
author Isabella Gottardi <isabella.gottardi@arm.com> Wed Apr 07 09:27:38 2021 +0100
committer Isabella Gottardi <isabella.gottardi@arm.com> Fri May 07 12:19:19 2021 +0100
tree e16c50d41d85945e0c2c864323ac1769b02af64f
parent d580eee180be219e118152cedabc9637da8574d6

MLECO-1766: Default build flow helper scripts added
MLECO-1882: Anomaly Detection use-case default model download added 
and tests updated to run with it.

Test data generation cmake logic moved from use-case cmakes to top-level cmake script.

Signed-off-by: Isabella Gottardi <isabella.gottardi@arm.com>
Change-Id: Ifde469e3585c37b9a53810236a92ce52d4fbb407

tests/use_case/kws_asr/InferenceTestDSCNN.cc

diff --git a/tests/use_case/kws_asr/InferenceTestDSCNN.cc b/tests/use_case/kws_asr/InferenceTestDSCNN.cc
index f0e5c02..e210c33 100644
--- a/tests/use_case/kws_asr/InferenceTestDSCNN.cc
+++ b/tests/use_case/kws_asr/InferenceTestDSCNN.cc
@@ -22,34 +22,32 @@
 #include <catch.hpp>
 #include <random>
 
-namespace arm {
-namespace app {
+namespace test {
 namespace kws {
-bool RunInference(arm::app::Model& model, const int8_t vec[])
-{
+
+bool RunInference(arm::app::Model& model, const int8_t vec[]) {
     TfLiteTensor* inputTensor = model.GetInputTensor(0);
     REQUIRE(inputTensor);
 
     const size_t copySz = inputTensor->bytes < IFM_DATA_SIZE ?
-                            inputTensor->bytes :
-                            IFM_DATA_SIZE;
+                          inputTensor->bytes :
+                          IFM_DATA_SIZE;
     memcpy(inputTensor->data.data, vec, copySz);
 
     return model.RunInference();
 }
 
-bool RunInferenceRandom(arm::app::Model& model)
-{
+bool RunInferenceRandom(arm::app::Model& model) {
     TfLiteTensor* inputTensor = model.GetInputTensor(0);
     REQUIRE(inputTensor);
 
     std::random_device rndDevice;
     std::mt19937 mersenneGen{rndDevice()};
-    std::uniform_int_distribution<short> dist {-128, 127};
+    std::uniform_int_distribution<short> dist{-128, 127};
 
-    auto gen = [&dist, &mersenneGen](){
-                   return dist(mersenneGen);
-               };
+    auto gen = [&dist, &mersenneGen]() {
+        return dist(mersenneGen);
+    };
 
     std::vector<int8_t> randomAudio(inputTensor->bytes);
     std::generate(std::begin(randomAudio), std::end(randomAudio), gen);
@@ -59,8 +57,7 @@
 }
 
 template<typename T>
-void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model)
-{
+void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model) {
     REQUIRE(RunInference(model, input_goldenFV));
 
     TfLiteTensor* outputTensor = model.GetOutputTensor(0);
@@ -71,12 +68,11 @@
     REQUIRE(tensorData);
 
     for (size_t i = 0; i < outputTensor->bytes; i++) {
-        REQUIRE((int)tensorData[i] == (int)((T)output_goldenFV[i]));
+        REQUIRE((int) tensorData[i] == (int) ((T) output_goldenFV[i]));
     }
 }
 
-TEST_CASE("Running random inference with Tflu and DsCnnModel Int8", "[DS_CNN]")
-{
+TEST_CASE("Running random inference with Tflu and DsCnnModel Int8", "[DS_CNN]") {
     arm::app::DsCnnModel model{};
 
     REQUIRE_FALSE(model.IsInited());
@@ -86,14 +82,12 @@
     REQUIRE(RunInferenceRandom(model));
 }
 
-TEST_CASE("Running inference with Tflu and DsCnnModel Uint8", "[DS_CNN]")
-{
-    for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
+TEST_CASE("Running inference with Tflu and DsCnnModel Uint8", "[DS_CNN]") {
+    for (uint32_t i = 0; i < NUMBER_OF_FM_FILES; ++i) {
         const int8_t* input_goldenFV = get_ifm_data_array(i);
         const int8_t* output_goldenFV = get_ofm_data_array(i);
 
-        DYNAMIC_SECTION("Executing inference with re-init")
-        {
+        DYNAMIC_SECTION("Executing inference with re-init") {
             arm::app::DsCnnModel model{};
 
             REQUIRE_FALSE(model.IsInited());
@@ -107,5 +101,4 @@
 }
 
 } //namespace
-} //namespace
-} //namespace
+} //namespace
\ No newline at end of file