Imported Upstream version 1.18.0

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

diff --git a/compiler/luci-interpreter/src/kernels/Pad.test.cpp b/compiler/luci-interpreter/src/kernels/Pad.test.cpp
index 75b2e56..7994263 100644 (file)
--- a/compiler/luci-interpreter/src/kernels/Pad.test.cpp
+++ b/compiler/luci-interpreter/src/kernels/Pad.test.cpp
@@ -16,6 +16,7 @@
 
 #include "kernels/Pad.h"
 #include "kernels/TestUtils.h"
+#include "luci_interpreter/TestMemoryManager.h"
 
 namespace luci_interpreter
 {
@@ -30,17 +31,20 @@ float GetTolerance(float min, float max) { return (max - min) / 255.0; }
 
 TEST(Pad, Uint8)
 {
+  std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
   float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
   std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-1.0f, 1.0f);
   std::vector<float> input_data{-0.8, 0.2, 0.9, 0.7, 0.1, -0.3};
   std::vector<int32_t> paddings_data{0, 0, 0, 2, 1, 3, 0, 0};
-  Tensor input_tensor =
-    makeInputTensor<DataType::U8>({1, 2, 3, 1}, quant_param.first, quant_param.second, input_data);
-  Tensor paddings_tensor = makeInputTensor<DataType::S32>({4, 2}, paddings_data);
+  Tensor input_tensor = makeInputTensor<DataType::U8>(
+    {1, 2, 3, 1}, quant_param.first, quant_param.second, input_data, memory_manager.get());
+  Tensor paddings_tensor =
+    makeInputTensor<DataType::S32>({4, 2}, paddings_data, memory_manager.get());
   Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second);
 
   Pad kernel(&input_tensor, &paddings_tensor, &output_tensor);
   kernel.configure();
+  memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   std::vector<float> ref_output_data{0, -0.8, 0.2, 0.9, 0, 0, 0, 0, 0.7, 0.1, -0.3, 0, 0, 0,
@@ -52,14 +56,18 @@ TEST(Pad, Uint8)
 
 TEST(Pad, Float)
 {
+  std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
   std::vector<float> input_data{1, 2, 3, 4, 5, 6};
   std::vector<int32_t> paddings_data{1, 0, 0, 2, 0, 3, 0, 0};
-  Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1, 2, 3, 1}, input_data);
-  Tensor paddings_tensor = makeInputTensor<DataType::S32>({4, 2}, paddings_data);
+  Tensor input_tensor =
+    makeInputTensor<DataType::FLOAT32>({1, 2, 3, 1}, input_data, memory_manager.get());
+  Tensor paddings_tensor =
+    makeInputTensor<DataType::S32>({4, 2}, paddings_data, memory_manager.get());
   Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
 
   Pad kernel(&input_tensor, &paddings_tensor, &output_tensor);
   kernel.configure();
+  memory_manager->allocate_memory(output_tensor);
   kernel.execute();
 
   std::vector<float> ref_output_data{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,