Imported Upstream version 1.18.0

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

diff --git a/compiler/luci-interpreter/src/kernels/Div.test.cpp b/compiler/luci-interpreter/src/kernels/Div.test.cpp
index 1a0c4af..021d68d 100644 (file)
--- a/compiler/luci-interpreter/src/kernels/Div.test.cpp
+++ b/compiler/luci-interpreter/src/kernels/Div.test.cpp
@@ -17,6 +17,7 @@
 
 #include "kernels/Div.h"
 #include "kernels/TestUtils.h"
+#include "luci_interpreter/TestMemoryManager.h"
 
 namespace luci_interpreter
 {
@@ -27,6 +28,14 @@ namespace
 
 using namespace testing;
 
+class DivTest : public ::testing::Test
+{
+protected:
+  void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
+
+  std::unique_ptr<IMemoryManager> _memory_manager;
+};
+
 float GetTolerance(float min, float max)
 {
   const float kQuantizedStep = (max - min) / 255.0f;
@@ -34,7 +43,7 @@ float GetTolerance(float min, float max)
   return kQuantizedTolerance;
 }
 
-TEST(DivTest, Float)
+TEST_F(DivTest, Float)
 {
   Shape base_shape = {2, 3, 1, 1};
 
@@ -44,8 +53,10 @@ TEST(DivTest, Float)
   std::vector<float> input2_data{0.2f, 1.6f, 0.5f, 0.4f, 1.6f, 0.4f};
   std::vector<float> test_outputs{1.5f, 1.4375f, 1.8f, 1.25f, 0.5f, 2.75f};
 
-  Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>(base_shape, input1_data);
-  Tensor input2_tensor = makeInputTensor<DataType::FLOAT32>(base_shape, input2_data);
+  Tensor input1_tensor =
+    makeInputTensor<DataType::FLOAT32>(base_shape, input1_data, _memory_manager.get());
+  Tensor input2_tensor =
+    makeInputTensor<DataType::FLOAT32>(base_shape, input2_data, _memory_manager.get());
 
   Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
 
@@ -54,13 +65,14 @@ TEST(DivTest, Float)
 
   Div kernel(&input1_tensor, &input2_tensor, &output_tensor, params);
   kernel.configure();
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(test_outputs, 0.0001f));
   EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
 }
 
-TEST(DivTest, FloatBroadcast)
+TEST_F(DivTest, FloatBroadcast)
 {
   Shape input1_shape = {1, 3};
   Shape input2_shape = {3, 1};
@@ -69,8 +81,10 @@ TEST(DivTest, FloatBroadcast)
   std::vector<float> input2_data{0.2f, 1.6f, 0.5f};
   std::vector<float> test_outputs{0.f, 11.5f, 4.5f, 0.f, 1.4375f, 0.5625f, 0.f, 4.6f, 1.8f};
 
-  Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>(input1_shape, input1_data);
-  Tensor input2_tensor = makeInputTensor<DataType::FLOAT32>(input2_shape, input2_data);
+  Tensor input1_tensor =
+    makeInputTensor<DataType::FLOAT32>(input1_shape, input1_data, _memory_manager.get());
+  Tensor input2_tensor =
+    makeInputTensor<DataType::FLOAT32>(input2_shape, input2_data, _memory_manager.get());
 
   Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
 
@@ -79,12 +93,13 @@ TEST(DivTest, FloatBroadcast)
 
   Div kernel(&input1_tensor, &input2_tensor, &output_tensor, params);
   kernel.configure();
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(test_outputs, 0.0001f));
 }
 
-TEST(DivTest, Uint8)
+TEST_F(DivTest, Uint8)
 {
   Shape base_shape = {1, 2, 2, 1};
 
@@ -98,10 +113,10 @@ TEST(DivTest, Uint8)
 
   std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-1.f, 1.f);
 
-  Tensor input1_tensor =
-    makeInputTensor<DataType::U8>(base_shape, quant_param.first, quant_param.second, input1_data);
-  Tensor input2_tensor =
-    makeInputTensor<DataType::U8>(base_shape, quant_param.first, quant_param.second, input2_data);
+  Tensor input1_tensor = makeInputTensor<DataType::U8>(
+    base_shape, quant_param.first, quant_param.second, input1_data, _memory_manager.get());
+  Tensor input2_tensor = makeInputTensor<DataType::U8>(
+    base_shape, quant_param.first, quant_param.second, input2_data, _memory_manager.get());
 
   Tensor output_tensor =
     makeOutputTensor(getElementType<uint8_t>(), quant_param.first, quant_param.second);
@@ -111,6 +126,7 @@ TEST(DivTest, Uint8)
 
   Div kernel(&input1_tensor, &input2_tensor, &output_tensor, params);
   kernel.configure();
+  _memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   EXPECT_THAT(dequantizeTensorData(output_tensor),
@@ -118,10 +134,10 @@ TEST(DivTest, Uint8)
   EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
 }
 
-TEST(DivTest, Input_Output_Type_NEG)
+TEST_F(DivTest, Input_Output_Type_NEG)
 {
-  Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1.f});
-  Tensor input2_tensor = makeInputTensor<DataType::S32>({1}, {2});
+  Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1.f}, _memory_manager.get());
+  Tensor input2_tensor = makeInputTensor<DataType::S32>({1}, {2}, _memory_manager.get());
   Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
 
   DivParams params{};
@@ -131,10 +147,10 @@ TEST(DivTest, Input_Output_Type_NEG)
   EXPECT_ANY_THROW(kernel.configure());
 }
 
-TEST(DivTest, Invalid_Input_Type_NEG)
+TEST_F(DivTest, Invalid_Input_Type_NEG)
 {
-  Tensor input1_tensor = makeInputTensor<DataType::S64>({1}, {1});
-  Tensor input2_tensor = makeInputTensor<DataType::S64>({1}, {2});
+  Tensor input1_tensor = makeInputTensor<DataType::S64>({1}, {1}, _memory_manager.get());
+  Tensor input2_tensor = makeInputTensor<DataType::S64>({1}, {2}, _memory_manager.get());
   Tensor output_tensor = makeOutputTensor(DataType::S64);
 
   DivParams params{};
@@ -142,6 +158,7 @@ TEST(DivTest, Invalid_Input_Type_NEG)
 
   Div kernel(&input1_tensor, &input2_tensor, &output_tensor, params);
   kernel.configure();
+  _memory_manager->allocate_memory(output_tensor);
   EXPECT_ANY_THROW(kernel.execute());
 }