mirror of https://github.com/alibaba/MNN.git
110 lines
4.5 KiB
C++
110 lines
4.5 KiB
C++
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//
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// ExecutorResetTest.cpp
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// MNNTests
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//
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// Created by MNN on 2023/01/11.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include <MNN/expr/Executor.hpp>
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#include <MNN/expr/ExprCreator.hpp>
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#include <MNN/expr/ExecutorScope.hpp>
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#include "MNNTestSuite.h"
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using namespace MNN::Express;
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class ExecutorResetTest : public MNNTestCase {
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public:
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static VARP convBlock(VARP x, INTS channels, int stride) {
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int inputChannel = channels[0], outputChannel = channels[1];
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int group = inputChannel;
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x = _Conv(0.0f, 0.0f, x, {inputChannel, inputChannel}, {3, 3}, SAME, {stride, stride}, {1, 1}, group);
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x = _Conv(0.0f, 0.0f, x, {inputChannel, outputChannel}, {1, 1}, SAME, {1, 1}, {1, 1}, 1);
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return x;
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}
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static VARP convBlocTemp(VARP x, INTS channels, int stride) {
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int inputChannel = channels[0], outputChannel = channels[1];
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int group = inputChannel;
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x = _Conv(0.0f, 0.0f, x, {inputChannel, inputChannel}, {3, 3}, SAME, {stride, stride}, {1, 1});
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x = _Conv(0.0f, 0.0f, x, {inputChannel, outputChannel}, {1, 1}, SAME, {1, 1}, {1, 1}, 1);
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return x;
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}
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static VARP _mobileNetV1Expr(VARP x) {
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int inputSize = 224, poolSize; // MobileNet_224, MobileNet_192, MobileNet_160, MobileNet_128
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{
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inputSize = 224;
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poolSize = inputSize / 32;
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}
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int channels[6]; // MobileNet_100, MobileNet_075, MobileNet_050, MobileNet_025
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{ channels[0] = 32; }
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for (int i = 1; i < 6; ++i) {
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channels[i] = channels[0] * (1 << i);
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}
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x->setName("Input");
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x = _Conv(0.0f, 0.0f, x, {3, channels[0]}, {3, 3}, SAME, {2, 2}, {1, 1}, 1);
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x = convBlock(x, {channels[0], channels[1]}, 1);
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x = convBlock(x, {channels[1], channels[2]}, 2);
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x = convBlock(x, {channels[2], channels[2]}, 1);
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x = convBlock(x, {channels[2], channels[3]}, 2);
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x = convBlock(x, {channels[3], channels[3]}, 1);
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x = convBlock(x, {channels[3], channels[4]}, 2);
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x = convBlock(x, {channels[4], channels[4]}, 1);
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x = convBlocTemp(x, {channels[4], channels[4]}, 1);
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x = convBlock(x, {channels[4], channels[4]}, 1);
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x = convBlock(x, {channels[4], channels[4]}, 1);
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x = convBlock(x, {channels[4], channels[4]}, 1);
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x = convBlock(x, {channels[4], channels[5]}, 2);
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x = convBlock(x, {channels[5], channels[5]}, 1);
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x = _AvePool(x, {poolSize, poolSize}, {1, 1}, VALID);
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x = _Conv(0.0f, 0.0f, x, {channels[5], 1001}, {1, 1}, VALID, {1, 1}, {1, 1}, 1); // reshape FC with Conv1x1
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x = _Softmax(x, -1);
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x = _Convert(x, NCHW);
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x->setName("Prob");
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return x;
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}
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virtual bool run(int precision) {
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int numberThread = 0;
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MNN::BackendConfig bnConfig;
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auto exe = Executor::newExecutor(MNN_FORWARD_CPU, bnConfig, 1);
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ExecutorScope scope(exe);
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exe->setGlobalExecutorConfig(MNN_FORWARD_CPU, bnConfig, 4);
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auto x = _Input({1, 3, 224, 224}, NC4HW4);
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auto y = _ReduceSum(_Multiply(x, x), {});
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::memset(x->writeMap<float>(), 0, x->getInfo()->size * sizeof(float));
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y->readMap<float>();
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auto res = Executor::getComputeInfo(y->expr().first, MNN::Interpreter::THREAD_NUMBER, &numberThread);
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if (numberThread != 4 || res == false) {
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FUNC_PRINT(1);
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return false;
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}
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exe->setGlobalExecutorConfig(MNN_FORWARD_CPU, bnConfig, 4);
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::memset(x->writeMap<float>(), 0, x->getInfo()->size * sizeof(float));
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y->readMap<float>();
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res = Executor::getComputeInfo(y->expr().first, MNN::Interpreter::THREAD_NUMBER, &numberThread);
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if (numberThread != 4 || res == false) {
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FUNC_PRINT(1);
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return false;
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}
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exe->setGlobalExecutorConfig(MNN_FORWARD_CPU, bnConfig, 1);
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// Reset x, y
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x = _Input({1, 3, 224, 224}, NC4HW4);
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y = _ReduceSum(_Multiply(x, x), {});
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::memset(x->writeMap<float>(), 0, x->getInfo()->size * sizeof(float));
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y->readMap<float>();
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res = Executor::getComputeInfo(y->expr().first, MNN::Interpreter::THREAD_NUMBER, &numberThread);
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if (numberThread != 1 || res == false) {
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FUNC_PRINT(1);
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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(ExecutorResetTest, "expr/ExecutorReset");
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