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author | Richard Burton <richard.burton@arm.com> | 2021-08-12 17:26:30 +0100 |
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committer | Richard Burton <richard.burton@arm.com> | 2021-08-12 17:26:30 +0100 |
commit | 0d110594b8a50ce3311be5187f01de2e3b8fe995 (patch) | |
tree | 1e56414f491f1bbd29df4912e2354ac5e1682133 /tests/use_case | |
parent | d2b9853ca848f11dee55beedbb9d650763b3ed53 (diff) | |
download | ml-embedded-evaluation-kit-0d110594b8a50ce3311be5187f01de2e3b8fe995.tar.gz |
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
Diffstat (limited to 'tests/use_case')
6 files changed, 9 insertions, 11 deletions
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 invalid classifier") 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("Test valid classifier UINT8") { 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 @@ bool RunInferenceRandom(arm::app::Model& model) 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 @@ void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app:: } } -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 @@ TEST_CASE("Preprocessing calculation INT8") /* 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 @@ bool RunInferenceRandom(arm::app::Model& model) 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 @@ void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app:: } } -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 @@ TEST_CASE("Preprocessing calculation INT8") /* 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; |