mvNCcheck fails when I was trying to check mobilenet ssd

[idata]

mvNCcheck fails when I was trying to check mobilenet ssd. Is there anyone run mobilenet ssd succcessfully with the latest 1.11 NCSDK release?

the command I used: "mvNCCheck MobileNetSSD_deploy.prototxt -w MobileNetSSD_deploy.caffemodel -s 12 -is 300 300 -i /opt/movidius/ncappzoo/data/images/512_Monitor.jpg"the caffemodel and prototxt are from https://github.com/chuanqi305/MobileNet-SSD.the error message is as follows:mvNCCheck v02.00, Copyright @ Movidius Ltd 2016

/usr/local/lib/python3.5/dist-packages/scipy/lib/decorator.py:219: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead

first = inspect.getargspec(caller)[0][0] # first arg/usr/local/lib/python3.5/dist-packages/scipy/optimize/nonlin.py:1498: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead args, varargs, varkw, defaults = inspect.getargspec(jac.init)/usr/local/lib/python3.5/dist-packages/scipy/stats/_distn_infrastructure.py:611: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead sign = inspect.getargspec(self._stats)/usr/local/lib/python3.5/dist-packages/scipy/stats/_distn_infrastructure.py:648: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead shapes_args = inspect.getargspec(meth)/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: (1, 25, 7)1) 44 nan2) 9 nan3) 23 nan4) 30 nan5) 149 nanExpected: (1, 1, 7)1) 1 20.02) 2 0.999513) 5 0.992194) 6 0.91265) 3 0.059631/usr/local/bin/ncsdk/Controllers/Metrics.py:75: RuntimeWarning: invalid value encountered in greater diff)) / total_values * 100 Obtained values

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

Obtained Average Pixel Accuracy: nan% (max allowed=1%), Fail Obtained Percentage of wrong values: 1700.0% (max allowed=0%), Fail Obtained Pixel-wise L2 error: nan% (max allowed=1%), Fail Obtained Global Sum Difference: nan

[idata]

I tried to run inference with the compiled graph file and get result of length 707, some values are inf or nan.

The following is the script I ran:dim=(300, 300)

mvnc.SetGlobalOption(mvnc.GlobalOption.LOG_LEVEL, 2)

devices = mvnc.EnumerateDevices()

device = mvnc.Device(devices[0])

device.OpenDevice()

network_blob="/home/intel/workspace/ncappzoo/caffe/MobileNetSSD/graph"

with open(network_blob, mode="rb") as f:

blob = f.read()

graph = device.AllocateGraph(blob)

img = cv2.imread("/home/intel/workspace/ncappzoo/data/images/cat.jpg")

img = cv2.resize(img, dim)img = np.subtract(img, 127.5)img = np.multiply(img, 0.007843)

graph.LoadTensor(img.astype(np.float16), 'user object')

output, userobj = graph.GetResult()print (len(output))

print (output)

graph.DeallocateGraph()

device.CloseDevice()

[idata]

some of the results as follows:

2.50000000e+01 8.13125000e+01 5.33203125e+00 4.25312500e+01 8.11219215e-05 8.03125000e+01 8.53125000e+01 0.00000000e+00 1.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 2.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 3.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 4.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 5.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 6.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 7.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 8.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 8.00000000e+00 1.00000000e+00 5.37109375e-03 -2.92968750e-03 9.48242188e-01 9.67285156e-01 0.00000000e+00 9.00000000e+00 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 1.00000000e+01 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 1.10000000e+01 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 1.20000000e+01 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 1.30000000e+01 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 1.40000000e+01 nan -inf -5.27187500e+01 inf -5.27187500e+01 0.00000000e+00 1.50000000e+01 nan -inf

[idata]

I face the same problem. Have you solved it?@xhuan28