Issues about [Error 25] Myriad Error: "3x3 Convolution outputChannels Dimension too small.". & PReLU

[idata]

Hi, I'm trying to use a network that contains layers with output channels < 8, which is forbidden in NCS. Is there any workaround to solver this problem instead of changing the network?

Another problem is that any network contains the PReLU layer can't pass mvNCCheck, and seems produce wrong result.

Thanks!

[idata]

@ms2308 Can you provide a link to your files for testing and debugging purposes?

[idata]

@Tome_at_Intel Thanks for the reply. The models can be get from https://github.com/kpzhang93/MTCNN_face_detection_alignment/tree/master/code/codes/MTCNNv2/model .

The prototxt runs fine without the model, so my guess is the implementation might be correct but data order in NCS-PReLU is wrong.

[idata]

@Tome_at_Intel FYI:

WITH MODEL

mvNCCheck rnet.prototxt -on conv2

mvNCCheck v02.00, Copyright @ Movidius Ltd 2016

/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 FinishedUSB: Myriad Connection Closing.USB: Myriad Connection Closed.Result: (48, 9, 9)1) 3085 2.8342) 3084 2.56843) 3412 2.40234) 2183 2.3345) 3094 2.3008Expected: (48, 9, 9)1) 2657 2.47272) 2649 2.31843) 861 2.30864) 3085 2.27155) 2669 2.2422 Obtained values

Obtained Min Pixel Accuracy: 16.109853982925415% (max allowed=2%), Fail

Obtained Average Pixel Accuracy: 1.488905493170023% (max allowed=1%), Fail Obtained Percentage of wrong values: 26.080246913580247% (max allowed=0%), Fail Obtained Pixel-wise L2 error: 2.0507845160736085% (max allowed=1%), Fail Obtained Global Sum Difference: 412.45660400390625

WITH ZERO VALUE MODEL:

mvNCCheck rnet.prototxt -w zero_weights.caffemodel -on conv2

mvNCCheck v02.00, Copyright @ Movidius Ltd 2016

/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 FinishedUSB: Myriad Connection Closing.USB: Myriad Connection Closed.Result: (48, 9, 9)1) 2592 2.77152) 2610 2.65233) 1133 2.58014) 2675 2.57625) 2674 2.5723Expected: (48, 9, 9)1) 2592 2.77542) 2610 2.64843) 2675 2.57424) 2674 2.57235) 1133 2.5449 Obtained values

Obtained Min Pixel Accuracy: 1.6977481544017792% (max allowed=2%), Pass

Obtained Average Pixel Accuracy: 0.13053794391453266% (max allowed=1%), Pass Obtained Percentage of wrong values: 0.0% (max allowed=0%), Pass Obtained Pixel-wise L2 error: 0.23660713157300328% (max allowed=1%), Pass Obtained Global Sum Difference: 14.085982322692871

[idata]

@ms2308 Thanks for providing your model files. I'll check this out and get back to you.

[idata]

@Tome_at_Intel Thanks. There's another issue in the implementation of SSD's 'Normalize' layer. (This layer seems not appear in the Mobile-SSD). The pixel value error between Caffe and NCS in these layers are high enough to make impact on the final output. I couldn't provide the model for testing for now but please have a check.

Since the parameter for 'Normalize' and 'PReLU' layers are similar (one value per input channel), the bug could be same.

[idata]

@ms2308 Thank you very much for reporting this. We are investigating to confirm if there is a bug/error in the PReLu and Normalize layers.