Imported Upstream version 1.18.0

[platform/core/ml/nnfw.git] / compiler / luci-interpreter / src / kernels / Mean.test.cpp

diff --git a/compiler/luci-interpreter/src/kernels/Mean.test.cpp b/compiler/luci-interpreter/src/kernels/Mean.test.cpp
index fa0ba21..d2c0093 100644 (file)
--- a/compiler/luci-interpreter/src/kernels/Mean.test.cpp
+++ b/compiler/luci-interpreter/src/kernels/Mean.test.cpp
@@ -17,6 +17,7 @@
 
 #include "kernels/Mean.h"
 #include "kernels/TestUtils.h"
+#include "luci_interpreter/TestMemoryManager.h"
 
 namespace luci_interpreter
 {
@@ -27,22 +28,39 @@ namespace
 
 using namespace testing;
 
-TEST(MeanTest, FloatKeepDims)
+class MeanTest : public ::testing::Test
+{
+protected:
+  void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
+
+  std::unique_ptr<IMemoryManager> _memory_manager;
+};
+
+TEST_F(MeanTest, FloatKeepDims)
 {
   std::vector<float> input_data = {1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,
                                    9.0,  10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
                                    17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
 
   std::vector<int32_t> axis_data{0, 2};
-  Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({4, 3, 2}, input_data);
-  Tensor axis_tensor = makeInputTensor<DataType::S32>({2}, axis_data);
+  Tensor input_tensor =
+    makeInputTensor<DataType::FLOAT32>({4, 3, 2}, input_data, _memory_manager.get());
+  Tensor axis_tensor = makeInputTensor<DataType::S32>({2}, axis_data, _memory_manager.get());
+  Tensor temp_index(DataType::S32, Shape({}), {}, "");
+  Tensor resolved_axes(DataType::S32, Shape({}), {}, "");
+  Tensor temp_sum(DataType::FLOAT32, Shape({}), {}, "");
   Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
 
   ReducerParams params{};
   params.keep_dims = true;
 
-  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params);
+  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, &temp_index, &resolved_axes, &temp_sum,
+              params);
   kernel.configure();
+  _memory_manager->allocate_memory(temp_index);
+  _memory_manager->allocate_memory(resolved_axes);
+  _memory_manager->allocate_memory(temp_sum);
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   std::vector<float> ref_output_data{10.5, 12.5, 14.5};
@@ -51,22 +69,31 @@ TEST(MeanTest, FloatKeepDims)
   EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
 }
 
-TEST(MeanTest, FloatKeepDims4DMean)
+TEST_F(MeanTest, FloatKeepDims4DMean)
 {
   std::vector<float> input_data = {1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,
                                    9.0,  10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
                                    17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
 
   std::vector<int32_t> axis_data{1, 2};
-  Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({2, 2, 3, 2}, input_data);
-  Tensor axis_tensor = makeInputTensor<DataType::S32>({2}, axis_data);
+  Tensor input_tensor =
+    makeInputTensor<DataType::FLOAT32>({2, 2, 3, 2}, input_data, _memory_manager.get());
+  Tensor axis_tensor = makeInputTensor<DataType::S32>({2}, axis_data, _memory_manager.get());
+  Tensor temp_index(DataType::S32, Shape({}), {}, "");
+  Tensor resolved_axes(DataType::S32, Shape({}), {}, "");
+  Tensor temp_sum(DataType::FLOAT32, Shape({}), {}, "");
   Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
 
   ReducerParams params{};
   params.keep_dims = true;
 
-  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params);
+  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, &temp_index, &resolved_axes, &temp_sum,
+              params);
   kernel.configure();
+  _memory_manager->allocate_memory(temp_index);
+  _memory_manager->allocate_memory(resolved_axes);
+  _memory_manager->allocate_memory(temp_sum);
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   std::vector<float> ref_output_data{6, 7, 18, 19};
@@ -75,22 +102,31 @@ TEST(MeanTest, FloatKeepDims4DMean)
   EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
 }
 
-TEST(MeanTest, FloatNotKeepDims)
+TEST_F(MeanTest, FloatNotKeepDims)
 {
   std::vector<float> input_data = {1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,
                                    9.0,  10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
                                    17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
 
   std::vector<int32_t> axis_data{1, 0, -3, -3};
-  Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({4, 3, 2}, input_data);
-  Tensor axis_tensor = makeInputTensor<DataType::S32>({4}, axis_data);
+  Tensor input_tensor =
+    makeInputTensor<DataType::FLOAT32>({4, 3, 2}, input_data, _memory_manager.get());
+  Tensor axis_tensor = makeInputTensor<DataType::S32>({4}, axis_data, _memory_manager.get());
+  Tensor temp_index(DataType::S32, Shape({}), {}, "");
+  Tensor resolved_axes(DataType::S32, Shape({}), {}, "");
+  Tensor temp_sum(DataType::FLOAT32, Shape({}), {}, "");
   Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
 
   ReducerParams params{};
   params.keep_dims = false;
 
-  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params);
+  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, &temp_index, &resolved_axes, &temp_sum,
+              params);
   kernel.configure();
+  _memory_manager->allocate_memory(temp_index);
+  _memory_manager->allocate_memory(resolved_axes);
+  _memory_manager->allocate_memory(temp_sum);
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   std::vector<float> ref_output_data{12, 13};
@@ -99,23 +135,31 @@ TEST(MeanTest, FloatNotKeepDims)
   EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
 }
 
-TEST(MeanTest, Uint8KeepDims)
+TEST_F(MeanTest, Uint8KeepDims)
 {
   float kQuantizedTolerance = getTolerance(-1.0, 1.0, 255);
   std::vector<float> input_data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
   std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-1.0f, 1.0f);
 
   std::vector<int32_t> axis_data{1};
-  Tensor input_tensor =
-    makeInputTensor<DataType::U8>({3, 2}, quant_param.first, quant_param.second, input_data);
-  Tensor axis_tensor = makeInputTensor<DataType::S32>({1}, axis_data);
+  Tensor input_tensor = makeInputTensor<DataType::U8>({3, 2}, quant_param.first, quant_param.second,
+                                                      input_data, _memory_manager.get());
+  Tensor axis_tensor = makeInputTensor<DataType::S32>({1}, axis_data, _memory_manager.get());
+  Tensor temp_index(DataType::S32, Shape({}), {}, "");
+  Tensor resolved_axes(DataType::S32, Shape({}), {}, "");
+  Tensor temp_sum(DataType::U8, Shape({}), {}, "");
   Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second);
 
   ReducerParams params{};
   params.keep_dims = true;
 
-  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params);
+  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, &temp_index, &resolved_axes, &temp_sum,
+              params);
   kernel.configure();
+  _memory_manager->allocate_memory(temp_index);
+  _memory_manager->allocate_memory(resolved_axes);
+  _memory_manager->allocate_memory(temp_sum);
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   std::vector<float> ref_output_data{0.3, 0.35, 0.55};
@@ -125,23 +169,31 @@ TEST(MeanTest, Uint8KeepDims)
   EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
 }
 
-TEST(MeanTest, Uint8NotKeepDims)
+TEST_F(MeanTest, Uint8NotKeepDims)
 {
   float kQuantizedTolerance = getTolerance(-1.0, 1.0, 255);
   std::vector<float> input_data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
   std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-1.0f, 1.0f);
 
   std::vector<int32_t> axis_data{1};
-  Tensor input_tensor =
-    makeInputTensor<DataType::U8>({1, 3, 2}, quant_param.first, quant_param.second, input_data);
-  Tensor axis_tensor = makeInputTensor<DataType::S32>({1}, axis_data);
+  Tensor input_tensor = makeInputTensor<DataType::U8>(
+    {1, 3, 2}, quant_param.first, quant_param.second, input_data, _memory_manager.get());
+  Tensor axis_tensor = makeInputTensor<DataType::S32>({1}, axis_data, _memory_manager.get());
+  Tensor temp_index(DataType::S32, Shape({}), {}, "");
+  Tensor resolved_axes(DataType::S32, Shape({}), {}, "");
+  Tensor temp_sum(DataType::FLOAT32, Shape({}), {}, "");
   Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second);
 
   ReducerParams params{};
   params.keep_dims = false;
 
-  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params);
+  Mean kernel(&input_tensor, &axis_tensor, &output_tensor, &temp_index, &resolved_axes, &temp_sum,
+              params);
   kernel.configure();
+  _memory_manager->allocate_memory(temp_index);
+  _memory_manager->allocate_memory(resolved_axes);
+  _memory_manager->allocate_memory(temp_sum);
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   std::vector<float> ref_output_data{0.4, 0.4};
@@ -151,7 +203,7 @@ TEST(MeanTest, Uint8NotKeepDims)
   EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
 }
 
-TEST(MeanTest, SInt16KeepDims4D)
+TEST_F(MeanTest, SInt16KeepDims4D)
 {
   std::vector<float> input_data = {1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,
                                    9.0,  10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
@@ -159,15 +211,24 @@ TEST(MeanTest, SInt16KeepDims4D)
   std::vector<int32_t> axes_data{1, 2};
   std::vector<float> ref_output_data{6, 7, 18, 19};
 
-  Tensor input_tensor = makeInputTensor<DataType::S16>({2, 2, 3, 2}, 0.25, 0, input_data);
-  Tensor axes_tensor = makeInputTensor<DataType::S32>({2}, axes_data);
+  Tensor input_tensor =
+    makeInputTensor<DataType::S16>({2, 2, 3, 2}, 0.25, 0, input_data, _memory_manager.get());
+  Tensor axes_tensor = makeInputTensor<DataType::S32>({2}, axes_data, _memory_manager.get());
+  Tensor temp_index(DataType::S32, Shape({}), {}, "");
+  Tensor resolved_axes(DataType::S32, Shape({}), {}, "");
+  Tensor temp_sum(DataType::FLOAT32, Shape({}), {}, "");
   Tensor output_tensor = makeOutputTensor(DataType::S16, 0.2, 0);
 
   ReducerParams params{};
   params.keep_dims = true;
 
-  Mean kernel(&input_tensor, &axes_tensor, &output_tensor, params);
+  Mean kernel(&input_tensor, &axes_tensor, &output_tensor, &temp_index, &resolved_axes, &temp_sum,
+              params);
   kernel.configure();
+  _memory_manager->allocate_memory(temp_index);
+  _memory_manager->allocate_memory(resolved_axes);
+  _memory_manager->allocate_memory(temp_sum);
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({2, 1, 1, 2}));