MLECO-1904: Update to use latest TFLu
* Now uses seperate TFLu github repo
* Fixes to align with API changes
* Update ASR model ops and re-enable ASR inference tests
* Set default release level to release_with_logs
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I57612088985dece1413c5c00a6e442381e07dd91
diff --git a/tests/common/ClassifierTests.cc b/tests/common/ClassifierTests.cc
index a04e4c2..d950304 100644
--- a/tests/common/ClassifierTests.cc
+++ b/tests/common/ClassifierTests.cc
@@ -21,7 +21,7 @@
template<typename T>
void test_classifier_result(std::vector<std::pair<uint32_t, T>>& selectedResults, T defaultTensorValue) {
- const int dimArray[] = {1, 1001};
+ int dimArray[] = {1, 1001};
std::vector <std::string> labels(1001);
std::vector<T> outputVec(1001, defaultTensorValue);
TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
diff --git a/tests/use_case/asr/AsrClassifierTests.cc b/tests/use_case/asr/AsrClassifierTests.cc
index 12523aa..e2bfb18 100644
--- a/tests/use_case/asr/AsrClassifierTests.cc
+++ b/tests/use_case/asr/AsrClassifierTests.cc
@@ -30,7 +30,7 @@
TEST_CASE("Test valid classifier UINT8") {
- const int dimArray[] = {4, 1, 1, 246, 29};
+ int dimArray[] = {4, 1, 1, 246, 29};
std::vector <std::string> labels(29);
std::vector <uint8_t> outputVec(7134);
TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
@@ -46,7 +46,7 @@
TEST_CASE("Get classification results") {
- const int dimArray[] = {4, 1, 1, 10, 15};
+ int dimArray[] = {4, 1, 1, 10, 15};
std::vector <std::string> labels(15);
std::vector<uint8_t> outputVec(150, static_cast<uint8_t>(1));
TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
diff --git a/tests/use_case/asr/InferenceTestWav2Letter.cc b/tests/use_case/asr/InferenceTestWav2Letter.cc
index 0943db8..d5e6c35 100644
--- a/tests/use_case/asr/InferenceTestWav2Letter.cc
+++ b/tests/use_case/asr/InferenceTestWav2Letter.cc
@@ -54,8 +54,7 @@
return true;
}
-/* Skip this test, Wav2LetterModel if not Vela optimized but only from ML-zoo will fail. */
-TEST_CASE("Running random inference with TensorFlow Lite Micro and Wav2LetterModel Int8", "[Wav2Letter][.]")
+TEST_CASE("Running random inference with TensorFlow Lite Micro and Wav2LetterModel Int8", "[Wav2Letter]")
{
arm::app::Wav2LetterModel model{};
@@ -86,7 +85,7 @@
}
}
-TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter][.]")
+TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter]")
{
for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
auto input_goldenFV = get_ifm_data_array(i);;
diff --git a/tests/use_case/asr/Wav2LetterPreprocessingTest.cc b/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
index 1391011..8af9014 100644
--- a/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
+++ b/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
@@ -108,7 +108,7 @@
/* Constants. */
const uint32_t windowLen = 512;
const uint32_t windowStride = 160;
- const int dimArray[] = {3, 1, numMfccFeatures * 3, numMfccVectors};
+ int dimArray[] = {3, 1, numMfccFeatures * 3, numMfccVectors};
const float quantScale = 0.1410219967365265;
const int quantOffset = -11;
diff --git a/tests/use_case/img_class/InferenceTestMobilenetV2.cc b/tests/use_case/img_class/InferenceTestMobilenetV2.cc
index b2720a8..6fbf374 100644
--- a/tests/use_case/img_class/InferenceTestMobilenetV2.cc
+++ b/tests/use_case/img_class/InferenceTestMobilenetV2.cc
@@ -24,7 +24,7 @@
using namespace test;
-bool RunInference(arm::app::Model& model, const uint8_t imageData[])
+bool RunInference(arm::app::Model& model, const int8_t imageData[])
{
TfLiteTensor* inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
diff --git a/tests/use_case/kws_asr/InferenceTestWav2Letter.cc b/tests/use_case/kws_asr/InferenceTestWav2Letter.cc
index 897ad0a..5f5ad98 100644
--- a/tests/use_case/kws_asr/InferenceTestWav2Letter.cc
+++ b/tests/use_case/kws_asr/InferenceTestWav2Letter.cc
@@ -55,8 +55,7 @@
return true;
}
-/* Skip this test, Wav2LetterModel if not Vela optimized but only from ML-zoo will fail. */
-TEST_CASE("Running random inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter][.]")
+TEST_CASE("Running random inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter]")
{
arm::app::Wav2LetterModel model{};
@@ -88,7 +87,7 @@
}
}
-TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter][.]")
+TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter]")
{
for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
auto input_goldenFV = get_ifm_data_array(i);;
diff --git a/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc b/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
index e71366a..16dbea2 100644
--- a/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
+++ b/tests/use_case/kws_asr/Wav2LetterPreprocessingTest.cc
@@ -108,7 +108,7 @@
/* Constants. */
const uint32_t windowLen = 512;
const uint32_t windowStride = 160;
- const int dimArray[] = {3, 1, numMfccFeatures * 3, numMfccVectors};
+ int dimArray[] = {3, 1, numMfccFeatures * 3, numMfccVectors};
const float quantScale = 0.1410219967365265;
const int quantOffset = -11;