the result of onnx model validate on target has two different output, why?

[cxf]

hi

I have validate on the STM32N6 board by onnx model in STM32MX,but i have two different output ,like this:

 m_outputs_1: (10, 3)/float64, min/max=[-282.578064, 481.370483], mean/std=[-2.619613, 298.301398], output  m_outputs_2: (10, 1)/float64, min/max=[0.000000, 0.000000], mean/std=[0.000000, 0.000000], node_139  c_outputs_1: (10, 1, 1, 3)/float32, min/max=[-4.547175, 5.813805], mean/std=[0.008044, 4.321293], output  c_outputs_2: (10, 1, 1, 1)/float32, min/max=[0.000800, 0.000800], mean/std=[0.000800, 0.000000], node_139

the  m_outputs_1 is error, and c_outputs_1 is correct,why? what can lead to this problem?

please help me ,thanks very much!!!

I have upload the report.txt and onnx model in the attach.

[Julian E.]

Hello @cxf,

Can you give me more context about what you have done exactly:

• What version of X Cube AI do you use?
• Which board: Nucleo or DK n6?
• Did you use custom data for validation or the random one by default when you do a validate on target?
• Are you using STM32CubeIDE as the IDE when doing the Validation on target or do you use another IDE?

I did test with the Nucleo N6 and N6 DK board, with version 10.1.0 and 10.2.0 of X Cube AI.

I am using STM32CubeIDE and windows, but I never replicated your issue.

Here is an example of report I get, which is correct:

Saving validation data...
 output directory: C:\ST\STEdgeAI\2.2\scripts\N6_scripts\st_ai_output
 creating C:\ST\STEdgeAI\2.2\scripts\N6_scripts\st_ai_output\network_val_io.npz
 m_outputs_1: (10, 3)/float64, min/max=[-282.578064, 481.370483], mean/std=[-2.619613, 298.301398], output
 m_outputs_2: (10, 1)/float64, min/max=[0.000000, 0.000000], mean/std=[0.000000, 0.000000], node_139
 c_outputs_1: (10, 1, 1, 3)/float32, min/max=[-282.578094, 481.370422], mean/std=[-2.619609, 298.301392], output
 c_outputs_2: (10, 1, 1, 1)/float32, min/max=[0.000000, 0.000000], mean/std=[0.000000, 0.000000], node_139

 
Computing the metrics...

 Cross accuracy report #1 (reference vs C-model)
 ----------------------------------------------------------------------------------------------------
 notes: - data type is different: r/float64 instead p/float32
        - ACC metric is not computed ("--classifier" option can be used to force it)
        - the output of the reference model is used as ground truth/reference value
        - 10 samples (3 items per sample)

  acc=n.a. rmse=0.000060476 mae=0.000043488 l2r=0.000000203 mean=-0.000004 std=0.000061 nse=1.000000 cos=1.000000 

 Cross accuracy report #2 (reference vs C-model)
 ----------------------------------------------------------------------------------------------------
 notes: - data type is different: r/float64 instead p/float32
        - the output of the reference model is used as ground truth/reference value
        - 10 samples (1 items per sample)

  acc=n.a. rmse=0.000000000 mae=0.000000000 l2r=0.000000000 mean=0.000000 std=0.000000 nse=1.000000 cos=1.000000 


Evaluation report (summary)
---------------------------------------------------------------------------------------------------------------------------------------------------
Output       acc    rmse          mae           l2r           mean        std        nse        cos        tensor                                  
---------------------------------------------------------------------------------------------------------------------------------------------------
X-cross #1   n.a.   0.000060476   0.000043488   0.000000203   -0.000004   0.000061   1.000000   1.000000   'output', 10 x f32(1x3), m_id=[110]     
X-cross #2   n.a.   0.000000000   0.000000000   0.000000000   0.000000    0.000000   1.000000   1.000000   'node_139', 10 x f32(1x1), m_id=[112]   
---------------------------------------------------------------------------------------------------------------------------------------------------

Have a good day,

Julian