/* Copyright 2017 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 #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; using uint8 = std::uint8_t; class ResizeBilinearOpModel : public SingleOpModel { public: ResizeBilinearOpModel(const TensorData& input, std::initializer_list size_data = {}) { bool const_size = size_data.size() != 0; input_ = AddInput(input); if (const_size) { size_ = AddConstInput(TensorType_INT32, size_data, {2}); } else { size_ = AddInput({TensorType_INT32, {2}}); } output_ = AddOutput(input.type); SetBuiltinOp(BuiltinOperator_RESIZE_BILINEAR, BuiltinOptions_ResizeBilinearOptions, CreateResizeBilinearOptions(builder_).Union()); if (const_size) { BuildInterpreter({GetShape(input_)}); } else { BuildInterpreter({GetShape(input_), GetShape(size_)}); } } template void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } void SetSize(std::initializer_list data) { PopulateTensor(size_, data); } template std::vector GetOutput() { return ExtractVector(output_); } private: int input_; int size_; int output_; }; TEST(ResizeBilinearOpTest, HorizontalResize) { ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 1, 2, 1}}); m.SetInput({3, 6}); m.SetSize({1, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 5, 6}))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 1, 2, 1}}, {1, 3}); const_m.SetInput({3, 6}); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 5, 6}))); } TEST(ResizeBilinearOpTest, HorizontalResize8Bit) { ResizeBilinearOpModel m({TensorType_UINT8, {1, 1, 2, 1}}); m.SetInput({3, 6}); m.SetSize({1, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 5, 6}))); ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 1, 2, 1}}, {1, 3}); const_m.SetInput({3, 6}); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 5, 6}))); } TEST(ResizeBilinearOpTest, VerticalResize) { ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 1, 1}}); m.SetInput({3, 9}); m.SetSize({3, 1}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 7, 9}))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 1, 1}}, {3, 1}); const_m.SetInput({3, 9}); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 7, 9}))); } TEST(ResizeBilinearOpTest, VerticalResize8Bit) { ResizeBilinearOpModel m({TensorType_UINT8, {1, 2, 1, 1}}); m.SetInput({3, 9}); m.SetSize({3, 1}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 7, 9}))); ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 2, 1, 1}}, {3, 1}); const_m.SetInput({3, 9}); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({3, 7, 9}))); } TEST(ResizeBilinearOpTest, TwoDimensionalResize) { ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}); m.SetInput({ 3, 6, // 9, 12 // }); m.SetSize({3, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 5, 6, // 7, 9, 10, // 9, 11, 12, // }))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 2, 1}}, {3, 3}); const_m.SetInput({ 3, 6, // 9, 12 // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 5, 6, // 7, 9, 10, // 9, 11, 12, // }))); } TEST(ResizeBilinearOpTest, TwoDimensionalResize8Bit) { ResizeBilinearOpModel m({TensorType_UINT8, {1, 2, 2, 1}}); m.SetInput({ 3, 6, // 9, 12 // }); m.SetSize({3, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 5, 6, // 7, 9, 10, // 9, 11, 12, // }))); ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 2, 2, 1}}, {3, 3}); const_m.SetInput({ 3, 6, // 9, 12 // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 5, 6, // 7, 9, 10, // 9, 11, 12, // }))); } TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches) { ResizeBilinearOpModel m({TensorType_FLOAT32, {2, 2, 2, 1}}); m.SetInput({ 3, 6, // 9, 12, // 4, 10, // 10, 16 // }); m.SetSize({3, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 5, 6, // 7, 9, 10, // 9, 11, 12, // 4, 8, 10, // 8, 12, 14, // 10, 14, 16, // }))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {2, 2, 2, 1}}, {3, 3}); const_m.SetInput({ 3, 6, // 9, 12, // 4, 10, // 10, 16 // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 5, 6, // 7, 9, 10, // 9, 11, 12, // 4, 8, 10, // 8, 12, 14, // 10, 14, 16, // }))); } TEST(ResizeBilinearOpTest, ThreeDimensionalResize) { ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 2, 2}}); m.SetInput({ 3, 4, 6, 10, // 9, 10, 12, 16, // }); m.SetSize({3, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 4, 5, 8, 6, 10, // 7, 8, 9, 12, 10, 14, // 9, 10, 11, 14, 12, 16, // }))); ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 2, 2}}, {3, 3}); const_m.SetInput({ 3, 4, 6, 10, // 9, 10, 12, 16, // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 4, 5, 8, 6, 10, // 7, 8, 9, 12, 10, 14, // 9, 10, 11, 14, 12, 16, // }))); } TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { ResizeBilinearOpModel m({TensorType_UINT8, {2, 2, 2, 1}}); m.SetInput({ 3, 6, // 9, 12, // 4, 10, // 12, 16 // }); m.SetSize({3, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 5, 6, // 7, 9, 10, // 9, 11, 12, // 4, 8, 10, // 9, 12, 14, // 12, 14, 16, // }))); ResizeBilinearOpModel const_m({TensorType_UINT8, {2, 2, 2, 1}}, {3, 3}); const_m.SetInput({ 3, 6, // 9, 12, // 4, 10, // 12, 16 // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 5, 6, // 7, 9, 10, // 9, 11, 12, // 4, 8, 10, // 9, 12, 14, // 12, 14, 16, // }))); } TEST(ResizeBilinearOpTest, ThreeDimensionalResize8Bit) { ResizeBilinearOpModel m({TensorType_UINT8, {1, 2, 2, 2}}); m.SetInput({ 3, 4, 6, 10, // 10, 12, 14, 16, // }); m.SetSize({3, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 4, 5, 8, 6, 10, // 7, 9, 10, 12, 11, 14, // 10, 12, 12, 14, 14, 16, // }))); ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 2, 2, 2}}, {3, 3}); const_m.SetInput({ 3, 4, 6, 10, // 10, 12, 14, 16, // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 4, 5, 8, 6, 10, // 7, 9, 10, 12, 11, 14, // 10, 12, 12, 14, 14, 16, // }))); } } // namespace } // namespace tflite int main(int argc, char** argv) { ::tflite::LogToStderr(); ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); }