1 files changed, 101 insertions, 0 deletions
diff --git a/compiler/luci-interpreter/src/kernels/Pow.test.cpp b/compiler/luci-interpreter/src/kernels/Pow.test.cpp
new file mode 100644
index 000000000..69d8946c8
--- /dev/null
+++ b/compiler/luci-interpreter/src/kernels/Pow.test.cpp
@@ -0,0 +1,101 @@
+/*
+ * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "kernels/Pow.h"
+#include "kernels/TestUtils.h"
+
+namespace luci_interpreter
+{
+namespace kernels
+{
+namespace
+{
+
+using namespace testing;
+
+TEST(PowTest, SimplePow)
+{
+  std::initializer_list<int32_t> base_shape = {1, 1, 3, 2};
+
+  std::vector<float> input1_data{0.3f, 2.3f, 0.9f, 0.5f, 0.8f, 1.1f};
+  std::vector<float> input2_data{0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f};
+  std::vector<float> test_outputs{0.786f, 1.2838f, 1.043f, 0.7071f, 0.8f, 1.08956f};
+
+  Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>(base_shape, input1_data);
+  Tensor input2_tensor = makeInputTensor<DataType::FLOAT32>(base_shape, input2_data);
+  Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
+
+  Pow kernel(&input1_tensor, &input2_tensor, &output_tensor);
+  kernel.configure();
+  kernel.execute();
+
+  EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(test_outputs, 0.0001f));
+  EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(base_shape));
+}
+
+TEST(PowTest, FloatBroadcastPow)
+{
+  std::initializer_list<int32_t> input1_shape = {1, 3};
+  std::initializer_list<int32_t> input2_shape = {3, 1};
+
+  std::vector<float> input1_data{0.3f, 2.3f, 0.9f};
+  std::vector<float> input2_data{0.2f, 0.3f, 0.4f};
+  std::vector<float> test_outputs{0.786f,   1.18126f, 0.9791f, 0.6968f, 1.28386f,
+                                  0.96888f, 0.6178f,  1.3953f, 0.9587f};
+
+  Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>(input1_shape, input1_data);
+  Tensor input2_tensor = makeInputTensor<DataType::FLOAT32>(input2_shape, input2_data);
+  Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
+
+  Pow kernel(&input1_tensor, &input2_tensor, &output_tensor);
+  kernel.configure();
+  kernel.execute();
+
+  EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(test_outputs, 0.0001f));
+}
+
+TEST(PowTest, IntPow)
+{
+  std::initializer_list<int32_t> base_shape = {1, 3};
+
+  std::vector<int32_t> input_data{2, 3, 4};
+  std::vector<int32_t> test_outputs{4, 27, 256};
+
+  Tensor input1_tensor = makeInputTensor<DataType::S32>(base_shape, input_data);
+  Tensor input2_tensor = makeInputTensor<DataType::S32>(base_shape, input_data);
+  Tensor output_tensor = makeOutputTensor(DataType::S32);
+
+  Pow kernel(&input1_tensor, &input2_tensor, &output_tensor);
+  kernel.configure();
+  kernel.execute();
+
+  EXPECT_THAT(extractTensorData<int32_t>(output_tensor), ::testing::ElementsAreArray(test_outputs));
+  EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(base_shape));
+}
+
+TEST(PowTest, Input_Output_Type_NEG)
+{
+  Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1.0f});
+  Tensor input2_tensor = makeInputTensor<DataType::S32>({1}, {4});
+  Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
+
+  Pow kernel(&input1_tensor, &input2_tensor, &output_tensor);
+  EXPECT_ANY_THROW(kernel.configure());
+}
+
+} // namespace
+} // namespace kernels
+} // namespace luci_interpreter