wehub-resource-sync
112 lines
6.0 KiB
C++
112 lines
6.0 KiB
C++
//
|
|
// PReLUTest.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 PreluTest : public MNNTestCase {
|
|
public:
|
|
virtual ~PreluTest() = default;
|
|
virtual bool run(int precision) {
|
|
auto input = _Input({1, 4, 1, 1}, NCHW);
|
|
input->setName("input_tensor");
|
|
// set input data
|
|
const float inpudata[] = {-1.0, 2.0, -3.0, 4.0};
|
|
auto inputPtr = input->writeMap<float>();
|
|
memcpy(inputPtr, inpudata, 4 * sizeof(float));
|
|
input->unMap();
|
|
input = _Convert(input, NC4HW4);
|
|
auto output = _PRelu(input, {3.0, 1.5, 1.5, 1.5});
|
|
output = _Convert(output, NCHW);
|
|
const std::vector<float> expectedOutput = {-3.0, 2.0, -4.5, 4.0};
|
|
auto gotOutput = output->readMap<float>();
|
|
if (!checkVector<float>(gotOutput, expectedOutput.data(), 4, 0.01)) {
|
|
MNN_ERROR("PreluTest test failed!\n");
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
};
|
|
|
|
class PreluTestInt8 : public MNNTestCase {
|
|
public:
|
|
virtual ~PreluTestInt8() = default;
|
|
virtual bool run(int precision) {
|
|
auto input = _Input({1, 12, 4, 2}, NCHW);
|
|
input->setName("input_tensor");
|
|
// set input data
|
|
input->writeScaleMap(0.02745, -18.714);
|
|
const float inpudata[] = {-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0,
|
|
-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0,
|
|
-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0};
|
|
auto inputPtr = input->writeMap<float>();
|
|
memcpy(inputPtr, inpudata, 96 * sizeof(float));
|
|
input->unMap();
|
|
input = _Convert(input, NC4HW4);
|
|
auto output = _PRelu(input, {3.0, 1.5, 1.5, 1.5, 3.0, 1.5, 1.5, 1.5, 3.0, 1.5, 1.5, 1.5});
|
|
output = _Convert(output, NCHW);
|
|
const std::vector<float> expectedOutput = {-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0,
|
|
-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0,
|
|
-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5, -4.5,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0
|
|
};
|
|
output->writeScaleMap(0.03333, 7.f);
|
|
auto gotOutput = output->readMap<float>();
|
|
if (!checkVector<float>(gotOutput, expectedOutput.data(), 96, 0.1)) {
|
|
MNN_ERROR("PreluTest test 1 failed!\n");
|
|
return false;
|
|
}
|
|
// prelu: one slope
|
|
auto output1 = _PRelu(input, {3.0});
|
|
output1 = _Convert(output1, NCHW);
|
|
const std::vector<float> expectedOutput1 = {-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-9.0, -9.0, -9.0, -9.0, -9.0, -9.0, -9.0, -9.0,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0,
|
|
-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-9.0, -9.0, -9.0, -9.0, -9.0, -9.0, -9.0, -9.0,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0,
|
|
-3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0, -3.0,
|
|
2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
|
-9.0, -9.0, -9.0, -9.0, -9.0, -9.0, -9.0, -9.0,
|
|
4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0,
|
|
};
|
|
output1->writeScaleMap(0.05098, 48.54);
|
|
auto gotOutput1 = output1->readMap<float>();
|
|
if (!checkVector<float>(gotOutput1, expectedOutput1.data(), 96, 0.1)) {
|
|
MNN_ERROR("PreluTest test 2 failed!\n");
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
};
|
|
|
|
MNNTestSuiteRegister(PreluTest, "op/prelu");
|
|
MNNTestSuiteRegister(PreluTestInt8, "op/preluInt8");
|