Implement TFLite Shape operator

PiperOrigin-RevId: 201389618

1 files changed, 95 insertions, 0 deletions

diff --git a/tensorflow/contrib/lite/kernels/shape_test.cc b/tensorflow/contrib/lite/kernels/shape_test.cc
new file mode 100644
index 0000000000..27b48f4e99
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/shape_test.cc
@@ -0,0 +1,95 @@
+/* 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 <initializer_list>
+
+#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 ShapeOpModel : public SingleOpModel {
+ public:
+  ShapeOpModel(std::initializer_list<int> input_shape, TensorType input_type,
+               TensorType output_type) {
+    input_ = AddInput(input_type);
+    output_ = AddOutput(output_type);
+    SetBuiltinOp(BuiltinOperator_SHAPE, BuiltinOptions_ShapeOptions,
+                 CreateShapeOptions(builder_, output_type).Union());
+    BuildInterpreter({input_shape});
+  }
+
+  TfLiteStatus InvokeWithResult() { return interpreter_->Invoke(); }
+
+  int input() { return input_; }
+
+  int32_t GetOutputSize() { return GetTensorSize(output_); }
+  std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
+  std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
+
+ private:
+  int input_;
+  int output_;
+};
+
+TEST(ShapeOpTest, OutTypeInt) {
+  ShapeOpModel<int32_t> model({1, 3, 1, 3, 5}, TensorType_FLOAT32,
+                              TensorType_INT32);
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 3, 1, 3, 5}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({5}));
+}
+
+TEST(ShapeOpTest, OutTypeInt64) {
+  ShapeOpModel<int64_t> model({1, 3, 1, 3, 5}, TensorType_FLOAT32,
+                              TensorType_INT64);
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 3, 1, 3, 5}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({5}));
+}
+
+TEST(ShapeOpTest, ScalarTensor) {
+  ShapeOpModel<int32_t> model({}, TensorType_FLOAT32, TensorType_INT32);
+  model.Invoke();
+
+  EXPECT_EQ(model.GetOutputSize(), 0);
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({0}));
+}
+
+TEST(ShapeOpTest, EmptyTensor) {
+  ShapeOpModel<int32_t> model({1, 0}, TensorType_FLOAT32, TensorType_INT32);
+  model.Invoke();
+
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0}));
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2}));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}