AI Vision ONNXRuntime Loading model issue

[Jonvy]

Hello everyone,

I tried  ONNXRuntime sample from github:https://github.com/hshatti/TONNXRuntime/tree/main/examples/Lazarus/FasterRCNN10

When I select the model, I get this error, can anyone help me what's wrong with the code?

Jonvy

[domasz]

Move you .EXE to the folder with your .ONNX

[Jonvy]

Yes,they're in same folder.

[Jonvy]

Looks like sample's onnx model format has problem, I get error information from Netron.

But if I choose my own correct model,best_simply.onnx, it will raise another error.

Can anyone succesfully use this example?

[myisjwj]

Your model looks like a YOLO. Instead of using the FasterRCNN10 example, you can refer to the YoloV7_GPU example to implement it. You can also look at this address
https://www.cnblogs.com/jwjss/p/18263154

[Jonvy]

Yes,my model is YOLOv5, I wonder why Haitham Shatti's example not working. I just use his FasterRCNN-10 model, still not working.

For YoloV7_GPU model, I can not use it, it's for Delphi, even convert from lazaurs, it will lost many units.

Follow your blog, I modified line 2487 in onnxruntime.pas, is that what your mean?

After this, still get error when loading model FasterRCNN-10:Protobuf parsing failed.

I download another model,yolov7-tiny_640x640, it can be loaded, after loading model, when I load source Image, it will get exception: Access violation.

[Jonvy]

exception when load image.

[myisjwj]

Lazarus calls the Yolo example

Code: Pascal  [Select][+][-]

1. {$mode delphi}{$H+}
2. interface
3. uses
4. onnxruntime_pas_api,onnxruntime;
5. var
6.   session:TORTSession;
7. InputNames,OutputNames:array of string;
8. input_w,input_h,output_w,output_h:Integer;
9. img: TBitmap;
10. input:TOrtTensor<single>;
11. tempp:PByte;
12. inputs,outputs:TORTNameValueList;
13. outTensor: TOrtTensor<single>;
14.  
15.  
16. //InitOnnxrun
17. session := TORTSession.Create(YouModFile);
18. setLength(InputNames, Session.GetInputCount());
19.     for i := 0 to High(InputNames) do
20.     begin
21.       Info := Session.GetInputTypeInfo(i);
22.       InputNames[i] := string(session.GetInputNameAllocated(i, DefaultAllocator)
23.         {$ifndef NO_SMARTPTR}
24.         .Instance
25.         {$endif}
26.         );
27.       tensorInfo := Info.GetTensorTypeAndShapeInfo();
28.       ints := tensorInfo.GetShape();
29.       input_w := ints[3];
30.       input_h := ints[2];
31.     end;
32.     setLength(OutputNames, Session.GetOutputCount());
33.     for i := 0 to High(OutputNames) do
34.     begin
35.       Info := Session.GetOutputTypeInfo(i);
36.       OutputNames[i] := string(session.GetOutputNameAllocated(i, DefaultAllocator)
37.         {$ifndef NO_SMARTPTR}
38.         .Instance
39.         {$endif}
40.         );
41.       if OutputNames[i].IndexOf('output') >= 0 then
42.       begin
43.         tensorInfo := Info.GetTensorTypeAndShapeInfo();
44.         ints := tensorInfo.GetShape();
45.         output_w := ints[2];
46.         output_h := ints[1];
47.         outst:=OutputNames[i];
48.       end;
49.     end;
50.  
51. //run
52. img:=TBitmap.Create;
53.   img.Width:=input_w;
54.   img.Height:=input_h;
55.   img.PixelFormat:=pf24bit;
56. .....
57. Input := TOrtTensor<single>.Create([input_w, input_h, 3, 1]);
58.     for yy := 0 to input_h - 1 do
59.     begin
60.       tempp := img.ScanLine[yy];
61.       for xx := 0 to input_w - 1 do
62.       begin
63.         input.Index3[xx, yy, 0] := tempp[xx * 3 ] / 255;
64.         input.Index3[xx, yy, 1] := tempp[xx * 3 + 1] / 255;
65.         input.Index3[xx, yy, 2] := tempp[xx * 3+ 2] / 255;
66.       end;
67.     end;
68.     inputs.AddOrSetValue('images', input);
69. Outputs:=session.Run(Inputs);

[Jonvy]

OK, I'll study this example,thanks myisjwj!