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MNN/test/op/BroadcastToTest.cpp

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//
// BroadcastToTest.cpp
// MNNTests
//
// Created by MNN on 2019/12/03.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include <MNN/expr/Expr.hpp>
#include <MNN/expr/ExprCreator.hpp>
#include <MNN/expr/Module.hpp>
#include "MNNTestSuite.h"
#include "TestUtils.h"
#include "MNN_generated.h"
using namespace MNN::Express;
using namespace MNN;
class BroadcastToTest : public MNNTestCase {
virtual ~BroadcastToTest() = default;
virtual bool run(int precision) {
{
const float tensorData[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0};
const int shapeData[] = {2, 3, 2, 2};
const float expectedData[] = {
1.0, 2.0, 1.0, 2.0, 3.0, 4.0, 3.0, 4.0, 5.0, 6.0, 5.0, 6.0,
1.0, 2.0, 1.0, 2.0, 3.0, 4.0, 3.0, 4.0, 5.0, 6.0, 5.0, 6.0,
};
auto tensor = _Const(tensorData, {1, 3, 1, 2}, NHWC, halide_type_of<float>());
auto shape = _Const(shapeData, {4}, NHWC, halide_type_of<int>());
auto result = _BroadcastTo(tensor, shape);
const int size = result->getInfo()->size;
if (size != 24) {
return false;
}
auto& dims = result->getInfo()->dim;
if (dims != std::vector<int>({2, 3, 2, 2})) {
return false;
}
auto resultData = result->readMap<float>();
if (!checkVector<float>(resultData, expectedData, size, 0.0)) {
return false;
}
}
{
const float tensorData[] = {1.0, 2.0, 3.0};
const int shapeData[] = {3, 3};
const float expectedData[] = {1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0};
auto tensor = _Const(tensorData, {1, 3}, NHWC, halide_type_of<float>());
auto shape = _Const(shapeData, {2}, NHWC, halide_type_of<int>());
auto result = _BroadcastTo(tensor, shape);
const int size = result->getInfo()->size;
if (size != 9) {
return false;
}
auto& dims = result->getInfo()->dim;
if (dims != std::vector<int>({3, 3})) {
return false;
}
auto resultData = result->readMap<float>();
if (!checkVector<float>(resultData, expectedData, size, 0.0)) {
return false;
}
}
{
const float tensorData[] = {1.0, 2.0, 3.0};
const int shapeData[] = {3, 3};
const float expectedData[] = {1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0};
auto tensor = _Const(tensorData, {3, 1}, NHWC, halide_type_of<float>());
auto shape = _Const(shapeData, {2}, NHWC, halide_type_of<int>());
auto result = _BroadcastTo(tensor, shape);
const int size = result->getInfo()->size;
if (size != 9) {
return false;
}
auto& dims = result->getInfo()->dim;
if (dims != std::vector<int>({3, 3})) {
return false;
}
auto resultData = result->readMap<float>();
if (!checkVector<float>(resultData, expectedData, size, 0.0)) {
return false;
}
}
{
const float tensorData[] = {1.0, 2.0};
const int shapeData[] = {2, 3, 2, 2};
const float expectedData[] = {1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0,
1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0};
auto tensor = _Const(tensorData, {1, 1, 1, 2}, NHWC, halide_type_of<float>());
auto shape = _Const(shapeData, {4}, NHWC, halide_type_of<int>());
auto result = _BroadcastTo(tensor, shape);
const int size = result->getInfo()->size;
if (size != 24) {
return false;
}
auto& dims = result->getInfo()->dim;
if (dims != std::vector<int>({2, 3, 2, 2})) {
return false;
}
auto resultData = result->readMap<float>();
if (!checkVector<float>(resultData, expectedData, size, 0.0)) {
return false;
}
}
{
const float tensorData[] = {1.0, 2.0, 3.0};
const int shapeData[] = {2, 3, 2, 2};
const float expectedData[] = {1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0,
1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0};
auto tensor = _Const(tensorData, {1, 3, 1, 1}, NHWC, halide_type_of<float>());
auto shape = _Const(shapeData, {4}, NHWC, halide_type_of<int>());
auto result = _BroadcastTo(tensor, shape);
const int size = result->getInfo()->size;
if (size != 24) {
return false;
}
auto& dims = result->getInfo()->dim;
if (dims != std::vector<int>({2, 3, 2, 2})) {
return false;
}
auto resultData = result->readMap<float>();
if (!checkVector<float>(resultData, expectedData, size, 0.0)) {
return false;
}
}
{
const float tensorData[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0};
const int shapeData[] = {1, 1, 1, 1};
const float expectedData[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0};
auto tensor = _Const(tensorData, {1, 3, 1, 2}, NHWC, halide_type_of<float>());
auto shape = _Const(shapeData, {4}, NHWC, halide_type_of<int>());
auto result = _BroadcastTo(tensor, shape);
const int size = result->getInfo()->size;
if (size != 6) {
return false;
}
auto& dims = result->getInfo()->dim;
if (dims != std::vector<int>({1, 3, 1, 2})) {
return false;
}
auto resultData = result->readMap<float>();
if (!checkVector<float>(resultData, expectedData, size, 0.0)) {
return false;
}
}
{
const float tensorData[] = {1.0, 2.0, 3.0};
const int shapeData[] = {2, 1, 2};
const float expectedData[] = {1.0, 1.0, 2.0, 2.0, 3.0, 3.0,
1.0, 1.0, 2.0, 2.0, 3.0, 3.0};
auto tensor = _Const(tensorData, {3, 1}, NHWC, halide_type_of<float>());
auto shape = _Const(shapeData, {3}, NHWC, halide_type_of<int>());
auto result = _BroadcastTo(tensor, shape);
const int size = result->getInfo()->size;
if (size != 12) {
return false;
}
auto& dims = result->getInfo()->dim;
if (dims != std::vector<int>({2, 3, 2})) {
return false;
}
auto resultData = result->readMap<float>();
if (!checkVector<float>(resultData, expectedData, size, 0.0)) {
return false;
}
}
return true;
}
};
class BinaryBroadcastTest : public MNNTestCase {
virtual ~BinaryBroadcastTest() = default;
virtual bool run(int precision) {
auto X = _Input({2, 5, 2}, NHWC, halide_type_of<float>());
X->setName("X");
auto y0 = _Input({}, NHWC, halide_type_of<float>());
y0->writeMap<float>()[0] = 1.0f;
auto y1 = _Input({1, 1, 2}, NHWC, halide_type_of<float>());
y1->writeMap<float>()[0] = 1.0f;
y1->writeMap<float>()[1] = 2.0f;
auto y2 = _Input({2, 1, 2}, NHWC, halide_type_of<float>());
y2->writeMap<float>()[0] = 1.0f;
y2->writeMap<float>()[1] = 2.0f;
y2->writeMap<float>()[2] = 3.0f;
y2->writeMap<float>()[3] = 4.0f;
y0.fix(VARP::CONSTANT);
y1.fix(VARP::CONSTANT);
y2.fix(VARP::CONSTANT);
// Run as Module
flatbuffers::FlatBufferBuilder builderOutput(1024);
{
auto z0 = _Add(X, y0);
z0->setName("z0");
auto z1 = _Add(X, y1);
z1->setName("z1");
auto z2 = _Add(X, y2);
z2->setName("z2");
auto z3 = _Add(y2, X);
z3->setName("z3");
std::unique_ptr<MNN::NetT> net(new NetT);
Variable::save({z0, z1, z2, z3}, net.get());
auto len = MNN::Net::Pack(builderOutput, net.get());
builderOutput.Finish(len);
}
float error = (precision <= MNN::BackendConfig::Precision_High ? 1 : 100) * 0.0005f;
int sizeOutput = builderOutput.GetSize();
auto bufferOutput = builderOutput.GetBufferPointer();
std::shared_ptr<MNN::Express::Module> module(Module::load(std::vector<std::string>{"X"}, std::vector<std::string>{"z0", "z1", "z2", "z3"}, bufferOutput, sizeOutput));
// First
{
auto x0 = _Input({2, 1, 2}, NHWC, halide_type_of<float>());
auto size = x0->getInfo()->size;
auto ptr = x0->writeMap<float>();
for (int i=0; i<size; ++i) {
ptr[i] = (float)(i+1) * 0.1f;
}
auto z = module->onForward({x0});
std::vector<float> z0Target = {
1.1f, 1.2f, 1.3f, 1.4f
};
std::vector<float> z1Target = {
1.1f, 2.2f, 1.3f, 2.4f
};
std::vector<float> z2Target = {
1.1f, 2.2f, 3.3f, 4.4f
};
if (!checkVector(z[0]->readMap<float>(), z0Target.data(), 4, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[1]->readMap<float>(), z1Target.data(), 4, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[2]->readMap<float>(), z2Target.data(), 4, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[3]->readMap<float>(), z2Target.data(), 4, error)) {
FUNC_PRINT(1);
return false;
}
}
{
auto x0 = _Input({2, 5, 2}, NHWC, halide_type_of<float>());
auto size = x0->getInfo()->size;
auto ptr = x0->writeMap<float>();
for (int i=0; i<size; ++i) {
ptr[i] = (float)(i+1) * 0.1f;
}
auto z = module->onForward({x0});
std::vector<float> z0Target(2 * 5 * 2);
std::vector<float> z1Target(2 * 5 * 2);
std::vector<float> z2Target(2 * 5 * 2);
for (int i=0; i<2; ++i) {
for (int j=0; j<5; ++j) {
for (int k=0; k<2; ++k) {
auto index = i*10+j*2+k;
z0Target[index] = (float)(index+1) * 0.1f + 1.0f;
z1Target[index] = (float)(index+1) * 0.1f + (float)(k + 1) * 1.0f;
z2Target[index] = (float)(index+1) * 0.1f + (float)(k + i * 2 + 1) * 1.0f;
}
}
}
auto tsize = 2 * 5 * 2;
if (!checkVector(z[0]->readMap<float>(), z0Target.data(), tsize, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[1]->readMap<float>(), z1Target.data(), tsize, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[2]->readMap<float>(), z2Target.data(), tsize, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[3]->readMap<float>(), z2Target.data(), 4, error)) {
FUNC_PRINT(1);
return false;
}
}
{
auto x0 = _Input({2, 3, 2}, NHWC, halide_type_of<float>());
auto size = x0->getInfo()->size;
auto ptr = x0->writeMap<float>();
for (int i=0; i<size; ++i) {
ptr[i] = (float)(i+1) * 0.1f;
}
auto z = module->onForward({x0});
std::vector<float> z0Target(2 * 3 * 2);
std::vector<float> z1Target(2 * 3 * 2);
std::vector<float> z2Target(2 * 3 * 2);
for (int i=0; i<2; ++i) {
for (int j=0; j<3; ++j) {
for (int k=0; k<2; ++k) {
auto index = i*6+j*2+k;
z0Target[index] = (float)(index+1) * 0.1f + 1.0f;
z1Target[index] = (float)(index+1) * 0.1f + (float)(k + 1) * 1.0f;
z2Target[index] = (float)(index+1) * 0.1f + (float)(k + i * 2 + 1) * 1.0f;
}
}
}
auto tsize = 2 * 3 * 2;
if (!checkVector(z[0]->readMap<float>(), z0Target.data(), tsize, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[1]->readMap<float>(), z1Target.data(), tsize, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[2]->readMap<float>(), z2Target.data(), tsize, error)) {
FUNC_PRINT(1);
return false;
}
if (!checkVector(z[3]->readMap<float>(), z2Target.data(), 4, error)) {
FUNC_PRINT(1);
return false;
}
}
return true;
}
};
MNNTestSuiteRegister(BroadcastToTest, "op/BroadcastToTest");
MNNTestSuiteRegister(BinaryBroadcastTest, "op/BinaryBroadcastTest");
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