mirror of https://github.com/alibaba/MNN.git
59 lines
2.1 KiB
C++
59 lines
2.1 KiB
C++
//
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// TileTest.cpp
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// MNNTests
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//
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// Created by MNN on 2019/01/15.
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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 <MNN/expr/ExecutorScope.hpp>
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using namespace MNN::Express;
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class TileTest : public MNNTestCase {
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public:
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virtual ~TileTest() = default;
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bool _run(int precision, bool lazy) {
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auto input = _Input({2, 2}, NCHW);
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input->setName("input_tensor");
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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->unMap();
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const int mul_data[] = {2, 2};
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auto mul = _Const(mul_data, {2}, NCHW, halide_type_of<int>());
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auto output = _Tile(input, mul);
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const std::vector<float> expectedOutput = {-1.0, -2.0, -1.0, -2.0, 3.0, 4.0, 3.0, 4.0,
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-1.0, -2.0, -1.0, -2.0, 3.0, 4.0, 3.0, 4.0};
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auto gotOutput = output->readMap<float>();
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if (!checkVector<float>(gotOutput, expectedOutput.data(), 16, 0.0001)) {
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MNN_ERROR("TileTest 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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virtual bool run(int precision) {
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ExecutorScope::Current()->lazyEval = false;
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auto res = _run(precision, false);
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if (!res) {
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FUNC_PRINT(1);
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return false;
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}
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ExecutorScope::Current()->lazyEval = true;
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ExecutorScope::Current()->setLazyComputeMode(MNN::Express::Executor::LAZY_CONTENT);
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res = _run(precision, true);
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if (!res) {
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FUNC_PRINT(1);
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return false;
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}
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ExecutorScope::Current()->setLazyComputeMode(MNN::Express::Executor::LAZY_FULL);
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res = _run(precision, true);
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return res;
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}
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};
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MNNTestSuiteRegister(TileTest, "op/tile");
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