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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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@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]
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@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
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