frame

Howdy, Stranger!

It looks like you're new here. If you want to get involved, click one of these buttons!

Sign In

Howdy, Stranger!

It looks like you're new here. If you want to get involved, click one of these buttons!

Different results from Original Tensorflow model and Inference from NCS

Hello ,

I have been getting wrong results in comparison with the original tensorflow model. I followed the guidance to create the tensorflow model without unneccesary layers (such as dropout) and the created model works perfectly when predicted with GPU. Then once I create the freeze graph, and use it to predict my dataset, i get completely different results. The things is that mvNCCheck passes every test(see below details). But the graph created with mvNCCompile produces completely wrong results. Kindly help me on this.

`sangathamilan@sangathamilan:~/Project/without_concat$ mvNCCheck ./NCS1/inference.meta -s 12 -in=input -on=softmax_out -is 100 25 -cs 0,1,2 -ec
mvNCCheck v02.00, Copyright @ Movidius Ltd 2016

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py:939: DeprecationWarning: builtin type EagerTensor has no module attribute
EagerTensor = c_api.TFE_Py_InitEagerTensor(_EagerTensorBase)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_inspect.py:55: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
if d.decorator_argspec is not None), _inspect.getargspec(target))
/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_inspect.py:55: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
if d.decorator_argspec is not None), _inspect.getargspec(target))
/usr/local/bin/ncsdk/Controllers/FileIO.py:52: UserWarning: You are using a large type. Consider reducing your data sizes for best performance
"Consider reducing your data sizes for best performance\033[0m")
USB: Transferring Data...
USB: Myriad Execution Finished
USB: Myriad Connection Closing.
USB: Myriad Connection Closed.
Result: (1, 1, 2)
1) 1 0.73438
2) 0 0.26514
Expected: (1, 2)
1) 1 0.728714
2) 0 0.271286


Obtained values

** Obtained Min Pixel Accuracy: 0.8437934331595898% (max allowed=2%), Pass
Obtained Average Pixel Accuracy: 0.8102884516119957% (max allowed=1%), Pass
Obtained Percentage of wrong values: 0.0% (max allowed=0%), Pass
Obtained Pixel-wise L2 error: 0.8109808796745067% (max allowed=1%), Pass

Obtained Global Sum Difference: 0.011809378862380981**

`

Comments

  • 1 Comment sorted by Votes Date Added
  • Hi @sangathamilan
    What application are you using? Can you provide the code you're using? I'd like to try to reproduce this issue to see what kind of results I'm getting. Bad results sometimes mean there could be an error in the pre processing.

    Best Regards,
    Sahira

Sign In or Register to comment.