* -------------------------------------------------------------------------
* This file is part of the Vision SDK project.
* Copyright (c) 2025 Huawei Technologies Co.,Ltd.
*
* Vision SDK is licensed under Mulan PSL v2.
* You can use this software according to the terms and conditions of the Mulan PSL v2.
* You may obtain a copy of Mulan PSL v2 at:
*
* http://license.coscl.org.cn/MulanPSL2
*
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
* EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
* MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
* See the Mulan PSL v2 for more details.
* -------------------------------------------------------------------------
* Description: Gtest unit cases.
* Author: MindX SDK
* Create: 2020
* History: NA
*/
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "SegmentPostProcessors/UNetMindSporePostProcess.h"
#include "MxBase/Log/Log.h"
namespace {
using namespace MxBase;
class UNetMindSporePostProcessTest : public testing::Test {};
TEST_F(UNetMindSporePostProcessTest, Test_UNetMindSporePostProcess_Construct_Should_Success)
{
UNetMindSporePostProcess unetPostSrc;
UNetMindSporePostProcess unetPostDst(unetPostSrc);
unetPostDst = unetPostSrc;
EXPECT_TRUE(1);
}
TEST_F(UNetMindSporePostProcessTest, Test_UNetMindSporePostProcess_Init_Should_Success)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"3\","
"\"MODEL_TYPE\": \"2\","
"\"POST_TYPE\": \"1\","
"\"RESIZE_TYPE\": \"1\"}"}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(UNetMindSporePostProcessTest, Test_UNetMindSporePostProcess_Init_Should_Success_When_CLASS_NUM_Is_NULL)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"RESIZE_TYPE\": \"3\","
"\"MODEL_TYPE\": \"2\","
"\"POST_TYPE\": \"1\"}"}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(UNetMindSporePostProcessTest, Test_UNetMindSporePostProcess_Init_Should_Success_When_MODEL_TYPE_Is_NULL)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"3\","
"\"POST_TYPE\": \"1\","
"\"RESIZE_TYPE\": \"1\"}"}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(UNetMindSporePostProcessTest, Test_UNetMindSporePostProcess_Init_Should_Success_When_POST_TYPE_Is_NULL)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"3\","
"\"MODEL_TYPE\": \"2\","
"\"RESIZE_TYPE\": \"1\"}"}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(UNetMindSporePostProcessTest, Test_UNetMindSporePostProcess_Init_Should_Success_When_RESIZE_TYPE_Is_NULL)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"3\","
"\"MODEL_TYPE\": \"2\","
"\"POST_TYPE\": \"1\"}"}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(UNetMindSporePostProcessTest,
Test_UNetMindSporePostProcess_Init_Should_Success_When_PostConfig_Is_Json_Format)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"3\","
"\"MODEL_TYPE\": \"2\","
"\"POST_TYPE\": "}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_COMM_INVALID_PARAM);
}
TEST_F(UNetMindSporePostProcessTest,
Test_UNetMindSporePostProcess_Process_Should_Failed_When_Size_Of_ResizedImageInfo_Is_Wrong)
{
UNetMindSporePostProcess unetPostSrc;
std::vector<SemanticSegInfo> semanticSegInfos;
std::vector<uint32_t> shape = {1, 80000, 5, 1};
auto type = TensorDataType::TENSOR_DTYPE_FLOAT32;
TensorBase tensor(shape, type);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> vec = {tensor, tensor, tensor, tensor};
ResizedImageInfo resizedImageInfo = {16, 16, 32, 32, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo, resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> configParamMap;
APP_ERROR ret = unetPostSrc.Process(vec, semanticSegInfos, resizedImageInfos, configParamMap);
EXPECT_EQ(ret, APP_ERR_COMM_FAILURE);
}
TEST_F(UNetMindSporePostProcessTest, Test_UNetMindSporePostProcess_Process_Should_Success_When_PostType_Is_0)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"3\","
"\"MODEL_TYPE\": \"0\","
"\"POST_TYPE\": \"0\","
"\"RESIZE_TYPE\": \"1\"}"}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
std::vector<SemanticSegInfo> semanticSegInfos;
std::vector<uint32_t> shape = {2, 80000, 5, 3};
auto type = TensorDataType::TENSOR_DTYPE_FLOAT32;
TensorBase tensor(shape, type);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> vec = {tensor, tensor, tensor, tensor};
ResizedImageInfo resizedImageInfo = {16, 16, 32, 32, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo, resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> configParamMap;
ret = unetPostSrc.Process(vec, semanticSegInfos, resizedImageInfos, configParamMap);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(UNetMindSporePostProcessTest,
Test_UNetMindSporePostProcess_Process_Should_Failed_When_Post_Type_Is_Not_Match)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"3\","
"\"MODEL_TYPE\": \"0\","
"\"POST_TYPE\": \"1\","
"\"RESIZE_TYPE\": \"1\"}"}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
std::vector<SemanticSegInfo> semanticSegInfos;
std::vector<uint32_t> shape = {2, 80000, 5, 3};
auto type = TensorDataType::TENSOR_DTYPE_FLOAT32;
TensorBase tensor(shape, type);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> vec = {tensor, tensor, tensor, tensor};
ResizedImageInfo resizedImageInfo = {16, 16, 32, 32, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo, resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> configParamMap;
ret = unetPostSrc.Process(vec, semanticSegInfos, resizedImageInfos, configParamMap);
EXPECT_EQ(ret, APP_ERR_COMM_INVALID_PARAM);
}
TEST_F(UNetMindSporePostProcessTest, Test_UNetMindSporePostProcess_Process_Should_Success_When_PostType_Is_1)
{
UNetMindSporePostProcess unetPostSrc;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"3\","
"\"MODEL_TYPE\": \"1\","
"\"POST_TYPE\": \"1\","
"\"RESIZE_TYPE\": \"1\"}"}
};
APP_ERROR ret = unetPostSrc.Init(postConfig);
std::vector<SemanticSegInfo> semanticSegInfos;
std::vector<uint32_t> shape = {2, 3, 5};
auto type = TensorDataType::TENSOR_DTYPE_FLOAT32;
TensorBase tensor(shape, type);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> vec = {tensor, tensor, tensor, tensor};
ResizedImageInfo resizedImageInfo = {16, 16, 32, 32, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo, resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> configParamMap;
ret = unetPostSrc.Process(vec, semanticSegInfos, resizedImageInfos, configParamMap);
EXPECT_EQ(ret, APP_ERR_OK);
}
}
int main(int argc, char *argv[])
{
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
