mirror of https://github.com/alibaba/MNN.git
84 lines
3.6 KiB
C++
84 lines
3.6 KiB
C++
//
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// CumTest.cpp
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// MNNTests
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//
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// Created by MNN on 2022/05/10.
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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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using namespace MNN::Express;
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class CumProdTest : public MNNTestCase {
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public:
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virtual ~CumProdTest() = default;
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virtual bool run(int precision) {
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auto input = _Input({2, 2, 2},NCHW);
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input->setName("input_tensor");
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const float inpudata[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
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auto inputPtr = input->writeMap<float>();
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memcpy(inputPtr, inpudata, 8 * sizeof(float));
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auto output0 = _CumProd(input, 0);
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const std::vector<float> expectedOutput0 = {1., 2., 3., 4., 5., 12., 21., 32.};
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auto gotOutput0 = output0->readMap<float>();
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if (!checkVector<float>(gotOutput0, expectedOutput0.data(), 8, 0.01)) {
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MNN_ERROR("CumProdTest axis=0 test failed!\n");
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return false;
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}
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auto output1 = _CumProd(input, 1);
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const std::vector<float> expectedOutput1 = {1., 2., 3., 8., 5., 6., 35., 48.};
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auto gotOutput1 = output1->readMap<float>();
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if (!checkVector<float>(gotOutput1, expectedOutput1.data(), 8, 0.01)) {
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MNN_ERROR("CumProdTest axis=1 test failed!\n");
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return false;
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}
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auto output2 = _CumProd(input, 2);
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const std::vector<float> expectedOutput2 = {1., 2., 3., 12., 5., 30., 7., 56.};
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auto gotOutput2 = output2->readMap<float>();
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if (!checkVector<float>(gotOutput2, expectedOutput2.data(), 8, 0.01)) {
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MNN_ERROR("CumProdTest axis=2 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(CumProdTest, "op/cumprod");
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class CumSumTest : public MNNTestCase {
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public:
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virtual ~CumSumTest() = default;
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virtual bool run(int precision) {
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auto input = _Input({2, 2, 2},NCHW);
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input->setName("input_tensor");
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const float inpudata[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
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auto inputPtr = input->writeMap<float>();
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memcpy(inputPtr, inpudata, 8 * sizeof(float));
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auto output0 = _CumSum(input, 0);
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const std::vector<float> expectedOutput0 = {1., 2., 3., 4., 6., 8., 10., 12.};
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auto gotOutput0 = output0->readMap<float>();
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if (!checkVector<float>(gotOutput0, expectedOutput0.data(), 8, 0.01)) {
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MNN_ERROR("CumSumTest axis=0 test failed!\n");
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return false;
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}
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auto output1 = _CumSum(input, 1);
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const std::vector<float> expectedOutput1 = {1., 2., 4., 6., 5., 6., 12., 14.};
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auto gotOutput1 = output1->readMap<float>();
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if (!checkVector<float>(gotOutput1, expectedOutput1.data(), 8, 0.01)) {
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MNN_ERROR("CumSumTest axis=1 test failed!\n");
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return false;
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}
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auto output2 = _CumSum(input, 2);
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const std::vector<float> expectedOutput2 = {1., 3., 3., 7., 5., 11., 7., 15.};
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auto gotOutput2 = output2->readMap<float>();
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if (!checkVector<float>(gotOutput2, expectedOutput2.data(), 8, 0.01)) {
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MNN_ERROR("CumSumTest axis=2 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(CumSumTest, "op/cumsum");
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