1 files changed, 181 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/kernels/arg_min_max_test.cc b/tensorflow/contrib/lite/kernels/arg_min_max_test.cc
new file mode 100644
index 0000000000..90e5fdc532
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/arg_min_max_test.cc
@@ -0,0 +1,181 @@
+/* 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::ElementsAreArray;
+
+template <typename T>
+class ArgBaseOpModel : public SingleOpModel {
+ public:
+  ArgBaseOpModel(std::initializer_list<int> input_shape, TensorType input_type,
+                 TensorType output_type, TensorType index_output_type) {
+    input_ = AddInput(input_type);
+    axis_ = AddInput(TensorType_INT32);
+    output_ = AddOutput(output_type);
+  }
+
+  int input() { return input_; }
+  int axis() { return axis_; }
+
+  std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
+  std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
+
+ protected:
+  int input_;
+  int axis_;
+  int output_;
+};
+
+template <typename T>
+class ArgMaxOpModel : public ArgBaseOpModel<T> {
+ public:
+  ArgMaxOpModel(std::initializer_list<int> input_shape, TensorType input_type,
+                TensorType output_type, TensorType index_output_type)
+      : ArgBaseOpModel<T>(input_shape, input_type, output_type,
+                          index_output_type) {
+    ArgBaseOpModel<T>::SetBuiltinOp(
+        BuiltinOperator_ARG_MAX, BuiltinOptions_ArgMaxOptions,
+        CreateArgMaxOptions(ArgBaseOpModel<T>::builder_, index_output_type)
+            .Union());
+    ArgBaseOpModel<T>::BuildInterpreter({input_shape, {1, 1, 1, 1}});
+  }
+};
+
+template <typename T>
+class ArgMinOpModel : public ArgBaseOpModel<T> {
+ public:
+  ArgMinOpModel(std::initializer_list<int> input_shape, TensorType input_type,
+                TensorType output_type, TensorType index_output_type)
+      : ArgBaseOpModel<T>(input_shape, input_type, output_type,
+                          index_output_type) {
+    ArgBaseOpModel<T>::SetBuiltinOp(
+        BuiltinOperator_ARG_MIN, BuiltinOptions_ArgMinOptions,
+        CreateArgMinOptions(ArgBaseOpModel<T>::builder_, index_output_type)
+            .Union());
+    ArgBaseOpModel<T>::BuildInterpreter({input_shape, {1, 1, 1, 1}});
+  }
+};
+
+TEST(ArgMaxOpTest, GetMaxArgFloat) {
+  ArgMaxOpModel<int32_t> model({1, 1, 1, 4}, TensorType_FLOAT32,
+                               TensorType_INT32, TensorType_INT32);
+  model.PopulateTensor<float>(model.input(), {0.1, 0.9, 0.7, 0.3});
+  // Currently only support the last dimension.
+  model.PopulateTensor<int>(model.axis(), {3});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 1}));
+}
+
+TEST(ArgMaxOpTest, GetMaxArgInt) {
+  ArgMaxOpModel<int32_t> model({1, 1, 1, 4}, TensorType_INT32, TensorType_INT32,
+                               TensorType_INT32);
+  model.PopulateTensor<int>(model.input(), {1, 9, 7, 3});
+  // Currently only support the last dimension.
+  model.PopulateTensor<int>(model.axis(), {3});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 1}));
+}
+
+TEST(ArgMaxOpTest, GetMaxArgMulDimensions) {
+  ArgMaxOpModel<int32_t> model({1, 1, 2, 4}, TensorType_INT32, TensorType_INT32,
+                               TensorType_INT32);
+  model.PopulateTensor<int>(model.input(), {1, 2, 7, 8, 1, 9, 7, 3});
+  // Currently only support the last dimension.
+  model.PopulateTensor<int>(model.axis(), {3});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({3, 1}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 1}));
+}
+
+TEST(ArgMaxOpTest, GetMaxArgOutput64) {
+  ArgMaxOpModel<int64_t> model({1, 1, 2, 4}, TensorType_INT32, TensorType_INT64,
+                               TensorType_INT64);
+  model.PopulateTensor<int>(model.input(), {10, 2, 7, 8, 1, 9, 7, 3});
+  // Currently only support the last dimension.
+  model.PopulateTensor<int>(model.axis(), {3});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({0, 1}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 1}));
+}
+
+TEST(ArgMinOpTest, GetMinArgFloat) {
+  ArgMinOpModel<int32_t> model({1, 1, 1, 4}, TensorType_FLOAT32,
+                               TensorType_INT32, TensorType_INT32);
+  model.PopulateTensor<float>(model.input(), {0.1, 0.9, 0.7, 0.3});
+  // Currently only support the last dimension.
+  model.PopulateTensor<int>(model.axis(), {3});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({0}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 1}));
+}
+
+TEST(ArgMinOpTest, GetMinArgInt) {
+  ArgMinOpModel<int32_t> model({1, 1, 1, 4}, TensorType_INT32, TensorType_INT32,
+                               TensorType_INT32);
+  model.PopulateTensor<int>(model.input(), {1, 9, 7, 3});
+  // Currently only support the last dimension.
+  model.PopulateTensor<int>(model.axis(), {3});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({0}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 1}));
+}
+
+TEST(ArgMinOpTest, GetMinArgMulDimensions) {
+  ArgMinOpModel<int32_t> model({1, 1, 2, 4}, TensorType_INT32, TensorType_INT32,
+                               TensorType_INT32);
+  model.PopulateTensor<int>(model.input(), {1, 2, 7, 8, 1, 9, 7, 3});
+  // Currently only support the last dimension.
+  model.PopulateTensor<int>(model.axis(), {3});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({0, 0}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 1}));
+}
+
+TEST(ArgMinOpTest, GetMinArgOutput64) {
+  ArgMinOpModel<int64_t> model({1, 1, 2, 4}, TensorType_INT32, TensorType_INT64,
+                               TensorType_INT64);
+  model.PopulateTensor<int>(model.input(), {10, 2, 7, 8, 1, 9, 7, 3});
+  // Currently only support the last dimension.
+  model.PopulateTensor<int>(model.axis(), {3});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 2, 1}));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}