/* 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 #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 DequantizeOpModel : public SingleOpModel { public: DequantizeOpModel(std::initializer_list shape, float min, float max) { input_ = AddInput({TensorType_UINT8, shape, min, max}); output_ = AddOutput({TensorType_FLOAT32, shape}); SetBuiltinOp(BuiltinOperator_DEQUANTIZE, BuiltinOptions_DequantizeOptions, CreateDequantizeOptions(builder_).Union()); BuildInterpreter({GetShape(input_)}); } void SetInput(std::initializer_list data) { PopulateTensor(input_, data); } std::vector GetOutput() { return ExtractVector(output_); } private: int input_; int output_; }; TEST(SplitOpTest, FourDimensional) { DequantizeOpModel m({2, 5}, -63.5, 64); m.SetInput({0, 1, 2, 3, 4, 251, 252, 253, 254, 255}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear( {-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64}))); } } // namespace } // namespace tflite int main(int argc, char** argv) { ::tflite::LogToStderr(); ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); }