| @@ -21,7 +21,7 @@ | |||||
| #include "minddata/dataset/include/vision.h" | #include "minddata/dataset/include/vision.h" | ||||
| #include "minddata/dataset/kernels/tensor_op.h" | #include "minddata/dataset/kernels/tensor_op.h" | ||||
| #include "include/api/model.h" | #include "include/api/model.h" | ||||
| #include "include/api/serializations.h" | |||||
| #include "include/api/serialization.h" | |||||
| #include "include/api/context.h" | #include "include/api/context.h" | ||||
| using namespace mindspore::api; | using namespace mindspore::api; | ||||
| @@ -86,13 +86,13 @@ TEST_F(TestDE, TestDvpp) { | |||||
| TEST_F(TestDE, TestYoloV3_with_Dvpp) { | TEST_F(TestDE, TestYoloV3_with_Dvpp) { | ||||
| std::vector<std::shared_ptr<Tensor>> images; | std::vector<std::shared_ptr<Tensor>> images; | ||||
| MIndDataEager::LoadImageFromDir("/home/lizhenglong/val2014", &images); | |||||
| MindDataEager::LoadImageFromDir("/home/lizhenglong/val2014", &images); | |||||
| MindDataEager SingleOp({DvppDecodeResizeCropJpeg({416, 416}, {416, 416})}); | MindDataEager SingleOp({DvppDecodeResizeCropJpeg({416, 416}, {416, 416})}); | ||||
| constexpr auto yolo_mindir_file = "/home/zhoufeng/yolov3/yolov3_darknet53.mindir"; | constexpr auto yolo_mindir_file = "/home/zhoufeng/yolov3/yolov3_darknet53.mindir"; | ||||
| Context::Instance().SetDeviceTarget(kDeviceTypeAscend310).SetDeviceID(1); | Context::Instance().SetDeviceTarget(kDeviceTypeAscend310).SetDeviceID(1); | ||||
| auto graph = Serialization::LoadModel(yolo_mindir_file, ModelType::kMindIR); | auto graph = Serialization::LoadModel(yolo_mindir_file, ModelType::kMindIR); | ||||
| Model yolov3((GraphCell(graph))); | Model yolov3((GraphCell(graph))); | ||||
| Status ret = yolov3.Build({{kMOdelOptionInsertOpCfgPath, "/mnt/disk1/yolo_dvpp_result/aipp_resnet50.cfg"}}); | |||||
| Status ret = yolov3.Build({{kModelOptionInsertOpCfgPath, "/mnt/disk1/yolo_dvpp_result/aipp_resnet50.cfg"}}); | |||||
| ASSERT_TRUE(ret == SUCCESS); | ASSERT_TRUE(ret == SUCCESS); | ||||
| std::vector<std::string> names; | std::vector<std::string> names; | ||||
| @@ -107,8 +107,8 @@ TEST_F(TestDE, TestYoloV3_with_Dvpp) { | |||||
| for (auto &img : images) { | for (auto &img : images) { | ||||
| img = SingleOp(img); | img = SingleOp(img); | ||||
| std::vector<float> input_shape = {416, 416}; | std::vector<float> input_shape = {416, 416}; | ||||
| input.clear(); | |||||
| inputs.emplace_back(img->data(), img->DataSize()); | |||||
| inputs.clear(); | |||||
| inputs.emplace_back(img->Data(), img->DataSize()); | |||||
| inputs.emplace_back(input_shape.data(), input_shape.size() * sizeof(float)); | inputs.emplace_back(input_shape.data(), input_shape.size() * sizeof(float)); | ||||
| ret = yolov3.Predict(inputs, &outputs); | ret = yolov3.Predict(inputs, &outputs); | ||||
| for (size_t i = 0; i < outputs.size(); ++i) { | for (size_t i = 0; i < outputs.size(); ++i) { | ||||