* -------------------------------------------------------------------------
* 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 "MxBase/Log/Log.h"
#include "SegmentPostProcessors/Deeplabv3Post.h"
#include "MxBase/MxBase.h"
namespace {
using namespace MxBase;
const std::vector<uint32_t> SHAPE1 = {1, 255, 255};
const std::vector<uint32_t> SHAPE2 = {1, 255, 255, 21};
const std::vector<uint32_t> SHAPE3 = {2, 255, 255, 21};
const int HEIGHT = 32;
const int WIDTH = 16;
class Deeplabv3PostProcessTest : public testing::Test {};
TEST_F(Deeplabv3PostProcessTest, Deeplabv3Test)
{
Deeplabv3Post instanceSrc;
Deeplabv3Post instanceDst(instanceSrc);
instanceSrc = instanceDst;
EXPECT_TRUE(1);
}
TEST_F(Deeplabv3PostProcessTest, TestDeeplabv3_Should_Return_Success_When_Config_is_Ok)
{
Deeplabv3Post deeplabv3Post;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"21\","
"\"CHECK_MODEL\": \"true\","
"\"MODEL_TYPE\": \"0\"}"}
};
APP_ERROR ret = deeplabv3Post.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(Deeplabv3PostProcessTest, TestDeeplabv3_Should_Return_Fail_When_Config_is_Invalid)
{
Deeplabv3Post deeplabv3Post;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "\"CLASS_NUM\": \"80\","
"\"BIASES_NUM\": \"18\","
"\"BIASES\": \"10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326\","
"\"SCORE_THRESH\": \"0.3\","
"\"OBJECTNESS_THRESH\": \"0.3\","
"\"IOU_THRESH\": \"0.45\","
"\"YOLO_TYPE\": \"3\","
"\"ANCHOR_DIM\": \"3\","
"\"MODEL_TYPE\": \"0\","
"\"RESIZE_FLAG\": \"0\"}"}
};
APP_ERROR ret = deeplabv3Post.Init(postConfig);
EXPECT_NE(ret, APP_ERR_OK);
}
TEST_F(Deeplabv3PostProcessTest, TestDeeplabv3_Should_Return_Fail_When_TensorBase_Dim_is_3)
{
Deeplabv3Post deeplabv3Post;
TensorBase tensor(SHAPE1, TensorDataType::TENSOR_DTYPE_FLOAT32, 0);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> tensors = {tensor};
std::vector<SemanticSegInfo> semanticSegInfos;
ResizedImageInfo resizedImageInfo = {WIDTH, WIDTH, HEIGHT, HEIGHT, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> paramMap;
APP_ERROR ret = deeplabv3Post.Process(tensors, semanticSegInfos, resizedImageInfos, paramMap);
EXPECT_EQ(ret, APP_ERR_COMM_INVALID_PARAM);
}
TEST_F(Deeplabv3PostProcessTest, TestDeeplabv3_Should_Return_Fail_When_Process_before_Init)
{
Deeplabv3Post deeplabv3Post;
TensorBase tensor(SHAPE2, TensorDataType::TENSOR_DTYPE_FLOAT32, 0);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> tensors = {tensor};
std::vector<SemanticSegInfo> semanticSegInfos;
ResizedImageInfo resizedImageInfo = {WIDTH, WIDTH, HEIGHT, HEIGHT, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> paramMap;
APP_ERROR ret = deeplabv3Post.Process(tensors, semanticSegInfos, resizedImageInfos, paramMap);
EXPECT_EQ(ret, APP_ERR_COMM_INVALID_PARAM);
}
TEST_F(Deeplabv3PostProcessTest, TestDeeplabv3_Should_Return_Success_when_FRAMEWORK_TYPE_is_0)
{
Deeplabv3Post deeplabv3Post;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"21\","
"\"CHECK_MODEL\": \"true\","
"\"MODEL_TYPE\": \"0\","
"\"FRAMEWORK_TYPE\": \"0\"}"},
{"labelPath", "/home/simon/models/deeplabv3/deeplabv3.names"}
};
APP_ERROR ret = deeplabv3Post.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
TensorBase tensor(SHAPE2, TensorDataType::TENSOR_DTYPE_FLOAT32, 0);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> tensors = {tensor};
std::vector<SemanticSegInfo> semanticSegInfos;
ResizedImageInfo resizedImageInfo = {WIDTH, WIDTH, HEIGHT, HEIGHT, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> paramMap;
ret = deeplabv3Post.Process(tensors, semanticSegInfos, resizedImageInfos, paramMap);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(Deeplabv3PostProcessTest, TestDeeplabv3_Should_Return_Success_when_FRAMEWORK_TYPE_is_1)
{
Deeplabv3Post deeplabv3Post;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"21\","
"\"CHECK_MODEL\": \"true\","
"\"MODEL_TYPE\": \"0\","
"\"FRAMEWORK_TYPE\": \"1\"}"},
{"labelPath", "/home/simon/models/deeplabv3/deeplabv3.names"}
};
APP_ERROR ret = deeplabv3Post.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
TensorBase tensor(SHAPE2, TensorDataType::TENSOR_DTYPE_FLOAT32, 0);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> tensors = {tensor};
std::vector<SemanticSegInfo> semanticSegInfos;
ResizedImageInfo resizedImageInfo = {WIDTH, WIDTH, HEIGHT, HEIGHT, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> paramMap;
ret = deeplabv3Post.Process(tensors, semanticSegInfos, resizedImageInfos, paramMap);
EXPECT_EQ(ret, APP_ERR_OK);
}
TEST_F(Deeplabv3PostProcessTest, TestDeeplabv3_Should_Return_Failed_When_BatchSize_Is_2)
{
Deeplabv3Post deeplabv3Post;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"21\","
"\"CHECK_MODEL\": \"true\","
"\"MODEL_TYPE\": \"0\","
"\"FRAMEWORK_TYPE\": \"1\"}"},
{"labelPath", "/home/simon/models/deeplabv3/deeplabv3.names"}
};
APP_ERROR ret = deeplabv3Post.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
TensorBase tensor(SHAPE3, TensorDataType::TENSOR_DTYPE_FLOAT32, 0);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> tensors = {tensor};
std::vector<SemanticSegInfo> semanticSegInfos;
ResizedImageInfo resizedImageInfo = {WIDTH, WIDTH, HEIGHT, HEIGHT, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> paramMap;
ret = deeplabv3Post.Process(tensors, semanticSegInfos, resizedImageInfos, paramMap);
EXPECT_EQ(ret, APP_ERR_COMM_INVALID_PARAM);
}
TEST_F(Deeplabv3PostProcessTest, TestDeeplabv3_Should_Return_Success_when_FRAMEWORK_TYPE_is_2)
{
Deeplabv3Post deeplabv3Post;
std::map<std::string, std::string> postConfig = {
{"postProcessConfigContent", "{\"CLASS_NUM\": \"21\","
"\"CHECK_MODEL\": \"true\","
"\"MODEL_TYPE\": \"0\","
"\"FRAMEWORK_TYPE\": \"2\"}"},
{"labelPath", "/home/simon/models/deeplabv3/deeplabv3.names"}
};
APP_ERROR ret = deeplabv3Post.Init(postConfig);
EXPECT_EQ(ret, APP_ERR_OK);
TensorBase tensor(SHAPE2, TensorDataType::TENSOR_DTYPE_FLOAT32, 0);
TensorBase::TensorBaseMalloc(tensor);
std::vector<TensorBase> tensors = {tensor};
std::vector<SemanticSegInfo> semanticSegInfos;
ResizedImageInfo resizedImageInfo = {WIDTH, WIDTH, HEIGHT, HEIGHT, ResizeType::RESIZER_STRETCHING, 1.0};
std::vector<ResizedImageInfo> resizedImageInfos = {resizedImageInfo};
std::map<std::string, std::shared_ptr<void>> paramMap;
ret = deeplabv3Post.Process(tensors, semanticSegInfos, resizedImageInfos, paramMap);
EXPECT_EQ(ret, APP_ERR_OK);
}
}
int main(int argc, char* argv[])
{
MxInit();
testing::InitGoogleTest(&argc, argv);
int ret = RUN_ALL_TESTS();
MxDeInit();
return ret;
}
