STT-tensorflow/tensorflow/lite/kernels/add_n_test.cc
Karim Nosir 2a96849f47 Update source files with used includes.
PiperOrigin-RevId: 316589177
Change-Id: I0aba0ed1cf9ff478e7890fa53a7749bf844bd26d
2020-06-15 18:42:14 -07:00

94 lines
3.0 KiB
C++

/* Copyright 2019 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 <stdint.h>
#include <vector>
#include <gtest/gtest.h>
#include "flatbuffers/flatbuffers.h" // from @flatbuffers
#include "tensorflow/lite/kernels/test_util.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
namespace {
using ::testing::ElementsAreArray;
class BaseAddNOpModel : public SingleOpModel {
public:
BaseAddNOpModel(const std::vector<TensorData>& inputs,
const TensorData& output) {
int num_inputs = inputs.size();
std::vector<std::vector<int>> input_shapes;
for (int i = 0; i < num_inputs; ++i) {
inputs_.push_back(AddInput(inputs[i]));
input_shapes.push_back(GetShape(inputs_[i]));
}
output_ = AddOutput(output);
SetBuiltinOp(BuiltinOperator_ADD_N, BuiltinOptions_AddNOptions,
CreateAddNOptions(builder_).Union());
BuildInterpreter(input_shapes);
}
int input(int i) { return inputs_[i]; }
protected:
std::vector<int> inputs_;
int output_;
};
class FloatAddNOpModel : public BaseAddNOpModel {
public:
using BaseAddNOpModel::BaseAddNOpModel;
std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
};
class IntegerAddNOpModel : public BaseAddNOpModel {
public:
using BaseAddNOpModel::BaseAddNOpModel;
std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
};
TEST(FloatAddNOpModel, AddMultipleTensors) {
FloatAddNOpModel m({{TensorType_FLOAT32, {1, 2, 2, 1}},
{TensorType_FLOAT32, {1, 2, 2, 1}},
{TensorType_FLOAT32, {1, 2, 2, 1}}},
{TensorType_FLOAT32, {}});
m.PopulateTensor<float>(m.input(0), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input(1), {0.1, 0.2, 0.3, 0.5});
m.PopulateTensor<float>(m.input(2), {0.5, 0.1, 0.1, 0.2});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.4, 0.5, 1.1, 1.5}));
}
TEST(IntegerAddNOpModel, AddMultipleTensors) {
IntegerAddNOpModel m({{TensorType_INT32, {1, 2, 2, 1}},
{TensorType_INT32, {1, 2, 2, 1}},
{TensorType_INT32, {1, 2, 2, 1}}},
{TensorType_INT32, {}});
m.PopulateTensor<int32_t>(m.input(0), {-20, 2, 7, 8});
m.PopulateTensor<int32_t>(m.input(1), {1, 2, 3, 5});
m.PopulateTensor<int32_t>(m.input(2), {10, -5, 1, -2});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-9, -1, 11, 11}));
}
} // namespace
} // namespace tflite