MNN/test/op/ReLU6Test.cpp

68 lines
2.1 KiB
C++

//
// ReLU6Test.cpp
// MNNTests
//
// Created by MNN on 2019/01/15.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include <MNN/expr/Expr.hpp>
#include <MNN/expr/ExprCreator.hpp>
#include "MNNTestSuite.h"
#include "TestUtils.h"
using namespace MNN::Express;
class Relu6Test : public MNNTestCase {
public:
virtual ~Relu6Test() = default;
virtual bool run(int precision) {
auto input = _Input(
{
4,
},
NCHW);
input->setName("input_tensor");
// set input data
const float inpudata[] = {-1.0, 3.0, 6.0, 9.0};
auto inputPtr = input->writeMap<float>();
memcpy(inputPtr, inpudata, 4 * sizeof(float));
input->unMap();
auto output = _Relu6(input);
const std::vector<float> expectedOutput = {0.0, 3.0, 6.0, 6.0};
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 4, 0.01)) {
MNN_ERROR("Relu6Test test failed!\n");
return false;
}
return true;
}
};
MNNTestSuiteRegister(Relu6Test, "op/relu6");
class ClampTest : public MNNTestCase {
public:
virtual ~ClampTest() = default;
virtual bool run(int precision) {
auto input = _Input(
{
4,
},
NCHW);
input->setName("input_tensor");
// set input data
const float inpudata[] = {-1.0, 3.0, 6.0, 9.0};
auto inputPtr = input->writeMap<float>();
memcpy(inputPtr, inpudata, 4 * sizeof(float));
input->unMap();
auto output = _Relu6(input, 1.0f, 3.0f);
const std::vector<float> expectedOutput = {1.0, 3.0, 3.0, 3.0};
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 4, 0.01)) {
MNN_ERROR("ClampTest test failed!\n");
return false;
}
return true;
}
};
MNNTestSuiteRegister(ClampTest, "op/clamp");