mirror of https://github.com/alibaba/MNN.git
55 lines
1.7 KiB
C++
55 lines
1.7 KiB
C++
//
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// ScaleGradTest.cpp
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// MNNTests
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//
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// Created by MNN on 2022/08/11.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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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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#include "../tools/train/source/grad/OpGrad.hpp"
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using namespace MNN;
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using namespace MNN::Express;
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class ScaleGradTest : public MNNTestCase {
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public:
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char name[20] = "Scale";
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virtual ~ScaleGradTest() = default;
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virtual bool run(int precision) {
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const int len = 4;
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auto input = _Input({1, len, 1, 1}, NCHW);
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const float inpudata[] = {-1.0, -2.0, 0.0, 4.0};
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auto inputPtr = input->writeMap<float>();
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memcpy(inputPtr, inpudata, len * sizeof(float));
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std::vector<float> scale = {0.1, 0.2, 0.3, 0.4};
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std::vector<float> bias = {1, 2, 3, 4};
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auto output = _Scale(input, len, std::move(scale), std::move(bias));
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auto opExpr = output->expr().first;
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auto grad = OpGrad::get(opExpr->get()->type());
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float outputDiff[len] = {0.1, -0.2, -0.3, 0.4};
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auto inputGrad = grad->onGrad(opExpr, {_Const(outputDiff, {1, len, 1, 1})});
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const std::vector<float> expectedOutput = {0.01, -0.04, -0.09, 0.16};
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auto gotOutput = inputGrad[0]->readMap<float>();
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for (int i = 0; i < len; ++i) {
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auto diff = ::fabsf(gotOutput[i] - expectedOutput[i]);
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if (diff > 0.0001) {
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MNN_ERROR("%s grad test failed, expected: %f, but got: %f!\n", name, expectedOutput[i], gotOutput[i]);
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return false;
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}
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}
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return true;
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}
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};
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MNNTestSuiteRegister(ScaleGradTest, "grad/scale");
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