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idata
Community Manager
304 Views

mvNCCheck some details WONDERED!

I am working on translate mobilenet_v2 on NCS stick,

 

Now, the mvNCCheck tool outputs normally,(two fail does not impact something)

 

the command line : mvNCCheck mobilenet_v2.prototxt -w mobilenet_v2.caffemodel

 

then outputs thh follow:

 

------------------------------------------------------------------------------------------------------------------------------------------------ Result: (1000, 1, 1) 1) 278 0.2666 2) 285 0.2563 3) 282 0.10364 4) 331 0.05457 5) 287 0.0442 Expected: (1000, 1, 1) 1) 278 0.2751 2) 285 0.2505 3) 282 0.10583 4) 331 0.0525 5) 287 0.04297 ------------------------------------------------------------ Obtained values ------------------------------------------------------------ Obtained Min Pixel Accuracy: 3.105590119957924% (max allowed=2%), Fail Obtained Average Pixel Accuracy: 0.01152553886640817% (max allowed=1%), Pass Obtained Percentage of wrong values: 0.2% (max allowed=0%), Fail Obtained Pixel-wise L2 error: 0.12669563700506148% (max allowed=1%), Pass Obtained Global Sum Difference: 0.031712114810943604 --------------------------------------------------------------------------------------------------------------------------------------------------------------

 

I think I have translate successfully,

 

so I try to use the graph on NCS stick:

 

*********************************************************************************************************** #PYTHON CODE from mvnc import mvncapi import numpy import cv2 # Get a list of available device identifiers device_list = mvncapi.enumerate_devices() # Initialize a Device device = mvncapi.Device(device_list[0]) # Initialize the device and open communication device.open() # Load graph file data GRAPH_FILEPATH = './graph' with open(GRAPH_FILEPATH, mode='rb') as f: graph_file_buffer = f.read() graph = mvncapi.Graph('graph1') input_fifo, output_fifo = graph.allocate_with_fifos(device,graph_file_buffer) tensor = cv2.imread('cat.jpg') tensor = tensor.astype(numpy.float32) tensor = cv2.resize(tensor,(224,224)) graph.queue_inference_with_fifo_elem(input_fifo, output_fifo, tensor, 'user object') output, user_obj = output_fifo.read_elem() print(output) # Clean up input_fifo.destroy() output_fifo.destroy() graph.destroy() device.close() device.destroy() ***********************************************************************************************

 

but it outputs all zero matrix:

 

[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ...]

 

but I think mvNCChek has validate the graph,

 

can someone tell me where to start with to solve the problem??
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2 Replies
idata
Community Manager
44 Views

@zufeifei Can you provide a link to the model and prototxt files? I noticed that you didn't do any preprocessing. If you take a look at the image classifier sample from the NCAPPZOO, you can see that in https://github.com/movidius/ncappzoo/blob/master/apps/image-classifier/image-classifier.py#L63 through https://github.com/movidius/ncappzoo/blob/master/apps/image-classifier/image-classifier.py#L68.

 

I tried using our mobilenet model and when I printed out the results, there were a lot of zeroes but there were also some non-zeroes (see below). The zeroes represent categories that have a zero score. This is normal. You should be able to sort the output and get your top score. When printing your output try using print("output: ", sorted(output, reverse=True)) instead of a print(output)

 

output: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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0.0032444, 0.06933594, 0.9277344]
idata
Community Manager
44 Views

@Tome_at_Intel

 

Thanks for your great help,

 

I would take it into my mind to work on image' preprocessing before put it into a NCS stick