mirror of https://github.com/alibaba/MNN.git
58 lines
2.2 KiB
C++
58 lines
2.2 KiB
C++
//
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// NormalizeTest.cpp
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// MNNTests
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//
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// Created by MNN on 2021/10/22.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include <cmath>
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#include <MNN/expr/Expr.hpp>
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#include <MNN/expr/ExprCreator.hpp>
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#include "MNNTestSuite.h"
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#include "TestUtils.h"
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using namespace MNN::Express;
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class NormalizeTest : public MNNTestCase {
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public:
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static void _refNormalize(float* dst, const float* src, int batch, int channel, int area, float* scale, float eps) {
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// Normalize
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for (int b=0; b<batch; ++b) {
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for (int x=0; x<area; ++x) {
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auto dstX = dst + b * area * channel + x;
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auto srcX = src + b * area * channel + x;
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float sumSquare = 0.0f;
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for (int c=0; c<channel; ++c) {
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sumSquare += (srcX[area * c] * srcX[area * c]);
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}
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float normalValue = 1.0f / sqrtf(sumSquare + eps);
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for (int c=0; c<channel; ++c) {
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dstX[area*c] = srcX[area * c] * normalValue * scale[c];
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}
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}
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}
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}
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virtual ~NormalizeTest() = default;
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virtual bool run(int precision) {
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auto input = _Input({1, 2, 2, 1}, NCHW);
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// set input data
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const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
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auto inputPtr = input->writeMap<float>();
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memcpy(inputPtr, inpudata, 4 * sizeof(float));
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input = _Convert(input, NC4HW4);
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std::vector<float> scaleData = {0.5f, 0.5f};
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float eps = 0.00f;
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auto output = _Normalize(input, 0, 0, eps, scaleData);
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output = _Convert(output, NCHW);
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std::vector<float> expectedOutput(4);
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_refNormalize(expectedOutput.data(), inpudata, 1, 2, 2, scaleData.data(), eps);
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auto gotOutput = output->readMap<float>();
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float errorScale = precision <= MNN::BackendConfig::Precision_High ? 1 : 1000;
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if (!checkVectorByRelativeError<float>(gotOutput, expectedOutput.data(), 4, 1e-5 * errorScale)) {
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MNN_ERROR("NormalizeTest test failed!\n");
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return false;
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}
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return true;
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}
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};
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MNNTestSuiteRegister(NormalizeTest, "op/normalize");
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