MLECO-3611: Formatting fixes for generated files.
Template files updated for generated files to adhere to
coding guidelines and clang format configuration. There
will still be unavoidable violations, but most of the
others have been fixed.
Change-Id: Ia03db40f8c62a369f2b07fe02eea65e41993a523
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>
diff --git a/tests/use_case/img_class/InferenceTestMobilenetV2.cc b/tests/use_case/img_class/InferenceTestMobilenetV2.cc
index 7921959..09eba00 100644
--- a/tests/use_case/img_class/InferenceTestMobilenetV2.cc
+++ b/tests/use_case/img_class/InferenceTestMobilenetV2.cc
@@ -1,6 +1,6 @@
/*
- * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates
+ * <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
@@ -14,22 +14,22 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
+#include "BufAttributes.hpp"
#include "ImageUtils.hpp"
#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 img_class {
- extern uint8_t* GetModelPointer();
- extern size_t GetModelLen();
- } /* namespace img_class */
- } /* namespace app */
+namespace app {
+ static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
+ namespace img_class {
+ extern uint8_t* GetModelPointer();
+ extern size_t GetModelLen();
+ } /* namespace img_class */
+} /* namespace app */
} /* namespace arm */
using namespace test;
@@ -39,22 +39,22 @@
TfLiteTensor* inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
- const size_t copySz = inputTensor->bytes < IFM_0_DATA_SIZE ?
- inputTensor->bytes :
- IFM_0_DATA_SIZE;
+ const size_t copySz =
+ inputTensor->bytes < IFM_0_DATA_SIZE ? inputTensor->bytes : IFM_0_DATA_SIZE;
memcpy(inputTensor->data.data, imageData, copySz);
- if(model.IsDataSigned()){
+ if (model.IsDataSigned()) {
arm::app::image::ConvertImgToInt8(inputTensor->data.data, copySz);
}
return model.RunInference();
}
-template<typename T>
-void TestInference(int imageIdx, arm::app::Model& model, T tolerance) {
- auto image = get_ifm_data_array(imageIdx);
- auto goldenFV = get_ofm_data_array(imageIdx);
+template <typename T>
+void TestInference(int imageIdx, arm::app::Model& model, T tolerance)
+{
+ auto image = GetIfmDataArray(imageIdx);
+ auto goldenFV = GetOfmDataArray(imageIdx);
REQUIRE(RunInference(model, image));
@@ -66,11 +66,11 @@
REQUIRE(tensorData);
for (size_t i = 0; i < outputTensor->bytes; i++) {
- REQUIRE(static_cast<int>(tensorData[i]) == Approx(static_cast<int>((T)goldenFV[i])).epsilon(tolerance));
+ REQUIRE(static_cast<int>(tensorData[i]) ==
+ Approx(static_cast<int>((T)goldenFV[i])).epsilon(tolerance));
}
}
-
TEST_CASE("Running inference with TensorFlow Lite Micro and MobileNeV2 Uint8", "[MobileNetV2]")
{
SECTION("Executing inferences sequentially")
@@ -84,12 +84,12 @@
arm::app::img_class::GetModelLen()));
REQUIRE(model.IsInited());
- for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
+ for (uint32_t i = 0; i < NUMBER_OF_IFM_FILES; ++i) {
TestInference<uint8_t>(i, model, 1);
}
}
- for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
+ for (uint32_t i = 0; i < NUMBER_OF_IFM_FILES; ++i) {
DYNAMIC_SECTION("Executing inference with re-init")
{
arm::app::MobileNetModel model{};