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| author | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2022-05-06 09:13:03 +0100 |
|---|---|---|
| committer | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2022-05-06 17:11:41 +0100 |
| commit | aa4bcb14d0cbee910331545dd2fc086b58c37170 (patch) | |
| tree | e67a43a43f61c6f8b6aad19018b0827baf7e31a6 /tests/use_case/img_class/InferenceTestMobilenetV2.cc | |
| parent | fcca863bafd5f33522bc14c23dde4540e264ec94 (diff) | |
| download | ml-embedded-evaluation-kit-aa4bcb14d0cbee910331545dd2fc086b58c37170.tar.gz | |
MLECO-3183: Refactoring application sources
Platform agnostic application sources are moved into application
api module with their own independent CMake projects.
Changes for MLECO-3080 also included - they create CMake projects
individial API's (again, platform agnostic) that dependent on the
common logic. The API for KWS_API "joint" API has been removed and
now the use case relies on individual KWS, and ASR API libraries.
Change-Id: I1f7748dc767abb3904634a04e0991b74ac7b756d
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>
Diffstat (limited to 'tests/use_case/img_class/InferenceTestMobilenetV2.cc')
| -rw-r--r-- | tests/use_case/img_class/InferenceTestMobilenetV2.cc | 20 |
1 files changed, 18 insertions, 2 deletions
diff --git a/tests/use_case/img_class/InferenceTestMobilenetV2.cc b/tests/use_case/img_class/InferenceTestMobilenetV2.cc index 7e7508b..30ce19f 100644 --- a/tests/use_case/img_class/InferenceTestMobilenetV2.cc +++ b/tests/use_case/img_class/InferenceTestMobilenetV2.cc @@ -18,9 +18,19 @@ #include "MobileNetModel.hpp" #include "TensorFlowLiteMicro.hpp" #include "TestData_img_class.hpp" +#include "BufAttributes.hpp" #include <catch.hpp> +namespace arm { + namespace app { + static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; + } /* namespace app */ +} /* namespace arm */ + +extern uint8_t* GetModelPointer(); +extern size_t GetModelLen(); + using namespace test; bool RunInference(arm::app::Model& model, const int8_t imageData[]) @@ -67,7 +77,10 @@ TEST_CASE("Running inference with TensorFlow Lite Micro and MobileNeV2 Uint8", " arm::app::MobileNetModel model{}; REQUIRE_FALSE(model.IsInited()); - REQUIRE(model.Init()); + REQUIRE(model.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + GetModelPointer(), + GetModelLen())); REQUIRE(model.IsInited()); for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) { @@ -81,7 +94,10 @@ TEST_CASE("Running inference with TensorFlow Lite Micro and MobileNeV2 Uint8", " arm::app::MobileNetModel model{}; REQUIRE_FALSE(model.IsInited()); - REQUIRE(model.Init()); + REQUIRE(model.Init(arm::app::tensorArena, + sizeof(arm::app::tensorArena), + GetModelPointer(), + GetModelLen())); REQUIRE(model.IsInited()); TestInference<uint8_t>(i, model, 1); |