mirror of https://github.com/alibaba/MNN.git
268 lines
11 KiB
C++
268 lines
11 KiB
C++
//
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// GatherTest.cpp
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// MNNTests
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//
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// Created by MNN on 2019/09/17.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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/*
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Test Case From https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/gather-nd
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*/
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#include <math.h>
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#include <MNN/expr/ExprCreator.hpp>
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#include <MNN/expr/Module.hpp>
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#include "MNNTestSuite.h"
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#include "MNN_generated.h"
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using namespace MNN;
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using namespace MNN::Express;
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class GatherExprTest : public MNNTestCase {
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public:
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virtual bool run(int precision) {
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std::unique_ptr<MNN::OpT> gatherOp(new MNN::OpT);
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gatherOp->type = MNN::OpType_GatherND;
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auto parameter = _Input({2, 2}, NHWC, halide_type_of<int32_t>());
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parameter->setName("param");
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auto indice = _Input({2, 2}, NHWC, halide_type_of<int32_t>());
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indice->setName("indice");
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auto y = Variable::create(Expr::create(gatherOp.get(), {parameter, indice}));
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y->setName("y");
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{
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parameter->resize({2, 2});
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auto ptr = parameter->writeMap<float>();
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ptr[0] = 7.0;
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ptr[1] = 2.0;
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ptr[2] = 4.0;
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ptr[3] = 6.0;
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}
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{
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auto indicePtr = indice->writeMap<int32_t>();
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indicePtr[0] = 0;
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indicePtr[1] = 0;
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indicePtr[2] = 1;
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indicePtr[3] = 1;
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auto size = y->getInfo()->size;
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if (size != 2) {
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return false;
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}
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auto yPtr = y->readMap<float>();
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if (fabs(yPtr[0] - 7.0) > 0.001 || fabs(yPtr[1] - 6.0) > 0.001) {
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return false;
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}
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}
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{
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indice->resize({2, 1});
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auto indicePtr = indice->writeMap<int32_t>();
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indicePtr[0] = 1;
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indicePtr[1] = 0;
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auto size = y->getInfo()->size;
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if (4 != size) {
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return false;
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}
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auto yPtr = y->readMap<float>();
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if (fabs(yPtr[0] - 4.0) > 0.001 || fabs(yPtr[1] - 6.0) > 0.001 || fabs(yPtr[2] - 7.0) > 0.001 ||
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fabs(yPtr[3] - 2.0) > 0.001) {
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return false;
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}
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}
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{
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indice->resize({1, 1});
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auto indicePtr = indice->writeMap<int32_t>();
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indicePtr[0] = 1;
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parameter->resize({2, 2, 2});
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auto parameterPtr = parameter->writeMap<float>();
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for (int i = 0; i < parameter->getInfo()->size; ++i) {
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parameterPtr[i] = 1.0 * i;
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}
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auto size = y->getInfo()->size;
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if (4 != size) {
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return false;
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}
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auto yPtr = y->readMap<float>();
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for (int i = 0; i < size; ++i) {
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if (fabs(yPtr[i] - 4.0 - i) > 0.001) {
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return false;
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}
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}
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}
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// Run as Module
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flatbuffers::FlatBufferBuilder builderOutput(1024);
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{
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std::unique_ptr<MNN::NetT> net(new NetT);
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Variable::save({y}, net.get());
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y = nullptr;
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auto len = MNN::Net::Pack(builderOutput, net.get());
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builderOutput.Finish(len);
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}
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int sizeOutput = builderOutput.GetSize();
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auto bufferOutput = builderOutput.GetBufferPointer();
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std::shared_ptr<MNN::Express::Module> module(Module::load(std::vector<std::string>{"param", "indice"}, std::vector<std::string>{"y"}, bufferOutput, sizeOutput));
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{
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{
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parameter->resize({2, 2});
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auto ptr = parameter->writeMap<float>();
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ptr[0] = 7.0;
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ptr[1] = 2.0;
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ptr[2] = 4.0;
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ptr[3] = 6.0;
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}
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{
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indice->resize({2, 2});
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auto indicePtr = indice->writeMap<int32_t>();
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indicePtr[0] = 0;
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indicePtr[1] = 0;
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indicePtr[2] = 1;
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indicePtr[3] = 1;
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auto y2 = module->onForward({parameter, indice})[0];
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auto size = y2->getInfo()->size;
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if (size != 2) {
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return false;
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}
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auto yPtr = y2->readMap<float>();
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if (fabs(yPtr[0] - 7.0) > 0.001 || fabs(yPtr[1] - 6.0) > 0.001) {
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return false;
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}
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}
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{
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indice->resize({2, 1});
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auto indicePtr = indice->writeMap<int32_t>();
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indicePtr[0] = 1;
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indicePtr[1] = 0;
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auto y2 = module->onForward({parameter, indice})[0];
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auto size = y2->getInfo()->size;
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if (4 != size) {
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return false;
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}
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auto yPtr = y2->readMap<float>();
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if (fabs(yPtr[0] - 4.0) > 0.001 || fabs(yPtr[1] - 6.0) > 0.001 || fabs(yPtr[2] - 7.0) > 0.001 ||
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fabs(yPtr[3] - 2.0) > 0.001) {
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return false;
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}
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}
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{
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indice->resize({1, 1});
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auto indicePtr = indice->writeMap<int32_t>();
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indicePtr[0] = 1;
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parameter->resize({2, 2, 2});
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auto parameterPtr = parameter->writeMap<float>();
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for (int i = 0; i < parameter->getInfo()->size; ++i) {
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parameterPtr[i] = 1.0 * i;
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}
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auto y2 = module->onForward({parameter, indice})[0];
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auto yPtr = y2->readMap<float>();
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auto size = y2->getInfo()->size;
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if (4 != size) {
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return false;
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}
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for (int i = 0; i < size; ++i) {
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if (fabs(yPtr[i] - 4.0 - i) > 0.001) {
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return false;
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}
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}
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}
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{
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const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
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const int indices_data[] = {0, 0, 1, 1};
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auto params = _Const(inpudata, {2, 2}, NHWC, halide_type_of<float>());
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auto indices = _Const(indices_data, {2, 2}, NHWC, halide_type_of<int>());
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auto x1 = _GatherND(params, indices);
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x1->setName("input1");
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auto shape = x1->getInfo()->dim;
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auto x0 = _Input(shape, NHWC, halide_type_of<float>());
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float x0data[] = {1.0f, 2.0f};
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::memcpy(x0->writeMap<float>(), x0data, 2 * sizeof(float));
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x0->setName("input0");
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auto res = _Add(x0, x1);
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res->setName("GatherNd_output_0");
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flatbuffers::FlatBufferBuilder builderOutput(1024);
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{
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std::unique_ptr<MNN::NetT> net(new NetT);
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Variable::save({res}, net.get());
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y = nullptr;
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auto len = MNN::Net::Pack(builderOutput, net.get());
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builderOutput.Finish(len);
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}
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int sizeOutput = builderOutput.GetSize();
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auto bufferOutput = builderOutput.GetBufferPointer();
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const char* cacheFileName = ".tempcache";
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MNN::ScheduleConfig config;
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config.numThread = 1;
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BackendConfig bnConfig;
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bnConfig.precision = (MNN::BackendConfig::PrecisionMode)precision;
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config.backendConfig = &bnConfig;
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std::shared_ptr<Executor::RuntimeManager> rtmgr(Executor::RuntimeManager::createRuntimeManager(config));
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rtmgr->setCache(cacheFileName);
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MNN::Express::Module::Config mConfig;
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/*
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ScheduleConfig config;
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BackendConfig bnConfig;
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bnConfig.precision = (MNN::BackendConfig::PrecisionMode)precision;
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config.numThread = 1;
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config.type = ExecutorScope::Current()->getAttr()->firstType.first;
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config.backendConfig = &bnConfig;
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*/
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std::shared_ptr<MNN::Express::Module> module_2(Module::load(std::vector<std::string>{"input0"}, std::vector<std::string>{"GatherNd_output_0"}, bufferOutput, sizeOutput, rtmgr, &mConfig));
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auto y2 = module_2->onForward({x0})[0];
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const float inpudata1[] = {-5.0, -6.0, 7.0, 8.0};
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x0->resize({2, 2});
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auto parameterPtr = params->writeMap<float>();
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::memcpy(parameterPtr, inpudata1, 4*sizeof(float));
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y2 = module_2->onForward({x0})[0];
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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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class GatherNdReComputeTest : public MNNTestCase {
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public:
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virtual bool run(int precision) {
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const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
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const int indices_data[] = {0, 0, 1, 1};
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auto params = _Const(inpudata, {2, 2}, NHWC, halide_type_of<float>());
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auto indices = _Const(indices_data, {2, 2}, NHWC, halide_type_of<int>());
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auto x1 = _GatherND(params, indices);
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x1->setName("input1");
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auto shape = x1->getInfo()->dim;
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auto x0 = _Input(shape, NHWC, halide_type_of<float>());
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x0->setName("input0");
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auto res = _Add(x0, x1);
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res->setName("GatherNd_output_0");
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flatbuffers::FlatBufferBuilder builderOutput(1024);
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{
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std::unique_ptr<MNN::NetT> net(new NetT);
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Variable::save({res}, net.get());
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auto len = MNN::Net::Pack(builderOutput, net.get());
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builderOutput.Finish(len);
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}
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int sizeOutput = builderOutput.GetSize();
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auto bufferOutput = builderOutput.GetBufferPointer();
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std::shared_ptr<MNN::Express::Module> module(Module::load(std::vector<std::string>{"input0"}, std::vector<std::string>{"GatherNd_output_0"}, bufferOutput, sizeOutput));
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// first run, call GatherNd compute function when resize.
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float data0[] = {1.0f, 2.0f};
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::memcpy(x0->writeMap<float>(), data0, 2 * sizeof(float));
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auto y = module->onForward({x0});
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// resize input and test GatherNd recompute function when risize.
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const float data1[] = {-5.0, -6.0, 7.0, 8.0};
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x0->resize({2, 2});
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::memcpy(x0->writeMap<float>(), data1, 4 * sizeof(float));
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y = module->onForward({x0});
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return true;
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}
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};
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MNNTestSuiteRegister(GatherExprTest, "expr/Gather");
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MNNTestSuiteRegister(GatherNdReComputeTest, "expr/GatherNdRecomputeTest");
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