Add support for select (via tf.where) support to tflite.

PiperOrigin-RevId: 195734246

1 files changed, 143 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/select_test.cc b/tensorflow/contrib/lite/kernels/select_test.cc
new file mode 100644
index 0000000000..cfe24a5fc9
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/select_test.cc
@@ -0,0 +1,143 @@
+/* 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;
+
+class SelectOpModel : public SingleOpModel {
+ public:
+  SelectOpModel(std::initializer_list<int> input1_shape,
+                std::initializer_list<int> input2_shape,
+                std::initializer_list<int> input3_shape,
+                TensorType input_type) {
+    input1_ = AddInput(TensorType_BOOL);
+    input2_ = AddInput(input_type);
+    input3_ = AddInput(input_type);
+    output_ = AddOutput(input_type);
+    SetBuiltinOp(BuiltinOperator_SELECT, BuiltinOptions_SelectOptions,
+                 CreateSelectOptions(builder_).Union());
+    BuildInterpreter({input1_shape, input2_shape, input3_shape});
+  }
+
+  int input1() { return input1_; }
+  int input2() { return input2_; }
+  int input3() { return input3_; }
+
+  template <typename T>
+  std::vector<T> GetOutput() {
+    return ExtractVector<T>(output_);
+  }
+
+  std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int input1_;
+  int input2_;
+  int input3_;
+  int output_;
+};
+
+TEST(SelectOpTest, SelectBool) {
+  SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4},
+                      TensorType_BOOL);
+
+  model.PopulateTensor<bool>(model.input1(), {true, false, true, false});
+  model.PopulateTensor<bool>(model.input2(), {false, false, false, false});
+  model.PopulateTensor<bool>(model.input3(), {true, true, true, true});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput<bool>(),
+              ElementsAreArray({false, true, false, true}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(SelectOpTest, SelectFloat) {
+  SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4},
+                      TensorType_FLOAT32);
+
+  model.PopulateTensor<bool>(model.input1(), {true, false, true, false});
+  model.PopulateTensor<float>(model.input2(), {0.1, 0.2, 0.3, 0.4});
+  model.PopulateTensor<float>(model.input3(), {0.5, 0.6, 0.7, 0.8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput<float>(), ElementsAreArray({0.1, 0.6, 0.3, 0.8}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(SelectOpTest, SelectUInt8) {
+  SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4},
+                      TensorType_UINT8);
+
+  model.PopulateTensor<bool>(model.input1(), {false, true, false, false});
+  model.PopulateTensor<uint8>(model.input2(), {1, 2, 3, 4});
+  model.PopulateTensor<uint8>(model.input3(), {5, 6, 7, 8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput<uint8>(), ElementsAreArray({5, 2, 7, 8}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(SelectOpTest, SelectInt32) {
+  SelectOpModel model({1, 1, 1, 4}, {1, 1, 1, 4}, {1, 1, 1, 4},
+                      TensorType_INT32);
+
+  model.PopulateTensor<bool>(model.input1(), {false, true, false, false});
+  model.PopulateTensor<int32>(model.input2(), {1, 2, 3, 4});
+  model.PopulateTensor<int32>(model.input3(), {5, 6, 7, 8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput<int32>(), ElementsAreArray({5, 2, 7, 8}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 1, 1, 4}));
+}
+
+TEST(SelectOpTest, RankOneSelectInt32) {
+  SelectOpModel model({2}, {2, 1, 2, 1}, {2, 1, 2, 1}, TensorType_INT32);
+
+  model.PopulateTensor<bool>(model.input1(), {false, true});
+  model.PopulateTensor<int32>(model.input2(), {1, 2, 3, 4});
+  model.PopulateTensor<int32>(model.input3(), {5, 6, 7, 8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput<int32>(), ElementsAreArray({5, 6, 3, 4}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 2, 1}));
+}
+
+TEST(SelectOpTest, RankZeroSelectInt32) {
+  SelectOpModel model({1}, {1, 2, 2, 1}, {1, 2, 2, 1}, TensorType_INT32);
+
+  model.PopulateTensor<bool>(model.input1(), {false});
+  model.PopulateTensor<int32>(model.input2(), {1, 2, 3, 4});
+  model.PopulateTensor<int32>(model.input3(), {5, 6, 7, 8});
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput<int32>(), ElementsAreArray({5, 6, 7, 8}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 2, 2, 1}));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}