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