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>

1 files changed, 53 insertions, 55 deletions

diff --git a/tests/use_case/object_detection/InferenceTestYoloFastest.cc b/tests/use_case/object_detection/InferenceTestYoloFastest.cc
index f1c3719..b3cf37d 100644
--- a/tests/use_case/object_detection/InferenceTestYoloFastest.cc
+++ b/tests/use_case/object_detection/InferenceTestYoloFastest.cc
@@ -1,6 +1,6 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 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.
@@ -14,58 +14,51 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "log_macros.h"
-#include "ImageUtils.hpp"
-#include "YoloFastestModel.hpp"
-#include "TensorFlowLiteMicro.hpp"
+#include "BufAttributes.hpp"
 #include "DetectorPostProcessing.hpp"
+#include "ImageUtils.hpp"
 #include "InputFiles.hpp"
-#include "BufAttributes.hpp"
+#include "TensorFlowLiteMicro.hpp"
+#include "YoloFastestModel.hpp"
+#include "log_macros.h"
 
 namespace arm {
-    namespace app {
-        static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
-        namespace object_detection {
-            extern uint8_t* GetModelPointer();
-            extern size_t GetModelLen();
-        } /* namespace object_detection */
-    } /* namespace app */
+namespace app {
+    static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
+    namespace object_detection {
+        extern uint8_t* GetModelPointer();
+        extern size_t GetModelLen();
+    } /* namespace object_detection */
+} /* namespace app */
 } /* namespace arm */
 
 #include <catch.hpp>
 
-void GetExpectedResults(std::vector<std::vector<arm::app::object_detection::DetectionResult>> &expected_results)
+void GetExpectedResults(
+    std::vector<std::vector<arm::app::object_detection::DetectionResult>>& expected_results)
 {
     /* Img1
     0)  (0.999246) -> Detection box: {x=89,y=17,w=41,h=56}
     1)  (0.995367) -> Detection box: {x=27,y=81,w=48,h=53}
     */
-    expected_results.push_back({
-        arm::app::object_detection::DetectionResult(0.99,89,17,41,56),
-        arm::app::object_detection::DetectionResult(0.99,27,81,48,53)
-    });
+    expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 89, 17, 41, 56),
+                                arm::app::object_detection::DetectionResult(0.99, 27, 81, 48, 53)});
     /* Img2
     0)  (0.998107) -> Detection box: {x=87,y=35,w=53,h=64}
     */
-    expected_results.push_back({
-        arm::app::object_detection::DetectionResult(0.99,87,35,53,64)
-    });
+    expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 87, 35, 53, 64)});
     /* Img3
     0)  (0.999244) -> Detection box: {x=105,y=73,w=58,h=66}
     1)  (0.985984) -> Detection box: {x=34,y=40,w=70,h=95}
     */
-    expected_results.push_back({
-        arm::app::object_detection::DetectionResult(0.99,105,73,58,66),
-        arm::app::object_detection::DetectionResult(0.98,34,40,70,95)
-    });
+    expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 105, 73, 58, 66),
+                                arm::app::object_detection::DetectionResult(0.98, 34, 40, 70, 95)});
     /* Img4
     0)  (0.993294) -> Detection box: {x=22,y=43,w=39,h=53}
     1)  (0.992021) -> Detection box: {x=63,y=60,w=38,h=45}
     */
-    expected_results.push_back({
-        arm::app::object_detection::DetectionResult(0.99,22,43,39,53),
-        arm::app::object_detection::DetectionResult(0.99,63,60,38,45)
-    });
+    expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 22, 43, 39, 53),
+                                arm::app::object_detection::DetectionResult(0.99, 63, 60, 38, 45)});
 }
 
 bool RunInference(arm::app::Model& model, const uint8_t imageData[])
@@ -73,41 +66,43 @@ bool RunInference(arm::app::Model& model, const uint8_t imageData[])
     TfLiteTensor* inputTensor = model.GetInputTensor(0);
     REQUIRE(inputTensor);
 
-    const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ?
-                            inputTensor->bytes : IMAGE_DATA_SIZE;
+    const size_t copySz =
+        inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE;
 
-    arm::app::image::RgbToGrayscale(imageData,inputTensor->data.uint8,copySz);
+    arm::app::image::RgbToGrayscale(imageData, inputTensor->data.uint8, copySz);
 
-    if(model.IsDataSigned()){
+    if (model.IsDataSigned()) {
         arm::app::image::ConvertImgToInt8(inputTensor->data.data, copySz);
     }
 
     return model.RunInference();
 }
 
-template<typename T>
-void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T tolerance) {
+template <typename T>
+void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T tolerance)
+{
 
     std::vector<arm::app::object_detection::DetectionResult> results;
-    auto image = get_img_array(imageIdx);
+    auto image = GetImgArray(imageIdx);
 
     TfLiteIntArray* inputShape = model.GetInputShape(0);
-    auto nCols = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx];
-    auto nRows = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx];
+    auto nCols                 = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx];
+    auto nRows                 = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx];
 
     REQUIRE(RunInference(model, image));
 
-
     std::vector<TfLiteTensor*> output_arr{model.GetOutputTensor(0), model.GetOutputTensor(1)};
-    for (size_t i =0; i < output_arr.size(); i++) {
+    for (size_t i = 0; i < output_arr.size(); i++) {
         REQUIRE(output_arr[i]);
         REQUIRE(tflite::GetTensorData<T>(output_arr[i]));
     }
 
-    const arm::app::object_detection::PostProcessParams postProcessParams {
-            nRows, nCols, arm::app::object_detection::originalImageSize,
-            arm::app::object_detection::anchor1, arm::app::object_detection::anchor2
-    };
+    const arm::app::object_detection::PostProcessParams postProcessParams{
+        nRows,
+        nCols,
+        arm::app::object_detection::originalImageSize,
+        arm::app::object_detection::anchor1,
+        arm::app::object_detection::anchor2};
     arm::app::DetectorPostProcess postp{output_arr[0], output_arr[1], results, postProcessParams};
     postp.DoPostProcess();
 
@@ -117,18 +112,21 @@ void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T toler
     /* Validate got the same number of boxes */
     REQUIRE(results.size() == expected_results[imageIdx].size());
 
-
-    for (int i=0; i < (int)results.size(); i++) {
+    for (int i = 0; i < (int)results.size(); i++) {
         /* Validate confidence and box dimensions */
-        REQUIRE(std::abs(results[i].m_normalisedVal - expected_results[imageIdx][i].m_normalisedVal) < 0.1);
-        REQUIRE(static_cast<int>(results[i].m_x0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance));
-        REQUIRE(static_cast<int>(results[i].m_y0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance));
-        REQUIRE(static_cast<int>(results[i].m_w) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance));
-        REQUIRE(static_cast<int>(results[i].m_h) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance));
+        REQUIRE(std::abs(results[i].m_normalisedVal -
+                         expected_results[imageIdx][i].m_normalisedVal) < 0.1);
+        REQUIRE(static_cast<int>(results[i].m_x0) ==
+                Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance));
+        REQUIRE(static_cast<int>(results[i].m_y0) ==
+                Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance));
+        REQUIRE(static_cast<int>(results[i].m_w) ==
+                Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance));
+        REQUIRE(static_cast<int>(results[i].m_h) ==
+                Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance));
     }
 }
 
-
 TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[YoloFastest]")
 {
     SECTION("Executing inferences sequentially")
@@ -142,12 +140,12 @@ TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[Yolo
                            arm::app::object_detection::GetModelLen()));
         REQUIRE(model.IsInited());
 
-        for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) {
+        for (uint32_t i = 0; i < NUMBER_OF_FILES; ++i) {
             TestInferenceDetectionResults<uint8_t>(i, model, 1);
         }
     }
 
-    for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) {
+    for (uint32_t i = 0; i < NUMBER_OF_FILES; ++i) {
         DYNAMIC_SECTION("Executing inference with re-init")
         {
             arm::app::YoloFastestModel model{};