Implementation of pow.

PiperOrigin-RevId: 202262513

1 files changed, 117 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/pow_test.cc b/tensorflow/contrib/lite/kernels/pow_test.cc
new file mode 100644
index 0000000000..474d323bc3
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/pow_test.cc
@@ -0,0 +1,117 @@
+/* Copyright 2018 The TensorFlow Authors. 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 <gtest/gtest.h>
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAre;
+using ::testing::ElementsAreArray;
+
+template <typename T>
+class PowOpModel : public SingleOpModel {
+ public:
+  PowOpModel(const TensorData& input1, const TensorData& input2,
+             const TensorData& output) {
+    input1_ = AddInput(input1);
+    input2_ = AddInput(input2);
+    output_ = AddOutput(output);
+    SetBuiltinOp(BuiltinOperator_POW, BuiltinOptions_PowOptions,
+                 CreatePowOptions(builder_).Union());
+    BuildInterpreter({GetShape(input1_), GetShape(input2_)});
+  }
+
+  int input1() { return input1_; }
+  int input2() { return input2_; }
+
+  std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
+  std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int input1_;
+  int input2_;
+  int output_;
+};
+
+TEST(PowOpModel, Simple) {
+  PowOpModel<int32> model({TensorType_INT32, {1, 2, 2, 1}},
+                          {TensorType_INT32, {1, 2, 2, 1}},
+                          {TensorType_INT32, {}});
+  model.PopulateTensor<int32>(model.input1(), {12, 2, 7, 8});
+  model.PopulateTensor<int32>(model.input2(), {1, 2, 3, 1});
+  model.Invoke();
+  EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
+  EXPECT_THAT(model.GetOutput(), ElementsAre(12, 4, 343, 8));
+}
+
+TEST(PowOpModel, NegativeAndZeroValue) {
+  PowOpModel<int32> model({TensorType_INT32, {1, 2, 2, 1}},
+                          {TensorType_INT32, {1, 2, 2, 1}},
+                          {TensorType_INT32, {}});
+  model.PopulateTensor<int32>(model.input1(), {0, 2, -7, 8});
+  model.PopulateTensor<int32>(model.input2(), {1, 2, 3, 0});
+  model.Invoke();
+  EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
+  EXPECT_THAT(model.GetOutput(), ElementsAre(0, 4, -343, 1));
+}
+
+TEST(PowOpModel, Float) {
+  PowOpModel<float> model({TensorType_FLOAT32, {1, 2, 2, 1}},
+                          {TensorType_FLOAT32, {1, 2, 2, 1}},
+                          {TensorType_FLOAT32, {}});
+  model.PopulateTensor<float>(model.input1(), {0.3, 0.4, 0.7, 5.8});
+  model.PopulateTensor<float>(model.input2(), {0.5, 2.7, 3.1, 3.2});
+  model.Invoke();
+  EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
+  EXPECT_THAT(model.GetOutput(),
+              ElementsAreArray(ArrayFloatNear(
+                  {0.5477226, 0.08424846, 0.33098164, 277.313}, 1e-3)));
+}
+
+TEST(PowOpModel, NegativeFloatTest) {
+  PowOpModel<float> model({TensorType_FLOAT32, {1, 2, 2, 1}},
+                          {TensorType_FLOAT32, {1, 2, 2, 1}},
+                          {TensorType_FLOAT32, {}});
+  model.PopulateTensor<float>(model.input1(), {0.3, 0.4, 0.7, 5.8});
+  model.PopulateTensor<float>(model.input2(), {0.5, -2.7, 3.1, -3.2});
+  model.Invoke();
+  EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
+  EXPECT_THAT(model.GetOutput(),
+              ElementsAreArray(ArrayFloatNear(
+                  {0.5477226, 11.869653, 0.33098164, 0.003606}, 1e-3)));
+}
+
+TEST(PowOpModel, BroadcastTest) {
+  PowOpModel<int32> model({TensorType_INT32, {1, 2, 2, 1}},
+                          {TensorType_INT32, {1}}, {TensorType_INT32, {}});
+  model.PopulateTensor<int32>(model.input1(), {12, 2, 7, 8});
+  model.PopulateTensor<int32>(model.input2(), {4});
+  model.Invoke();
+  EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 2, 2, 1));
+  EXPECT_THAT(model.GetOutput(), ElementsAre(20736, 16, 2401, 4096));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}