Incorrect output with simple custom tensorflow network (tf.add bug?)

idata · ‎11-17-2017

Hi,

To test the running of a custom tensorflow network on the NCS, I've created a small "hello world" type example (see below). Unfortunately, the NCS gives the incorrect output when running this example.

This example network aims to take an input value, multiply it by itself and add 10.

The following script generates a tensorflow graph (tf_maths.meta file). The script also prints the output values for inputs 0 to 10.

import tensorflow as tf

tf.reset_default_graph()

# Name of the graph

name = 'tf_maths'

# Tensorflow graph that multiplies an input number by itself and adds 10

x = tf.get_variable("input", shape=[1,1], initializer = tf.zeros_initializer,  dtype = tf.float16)

x1 = tf.matmul(x, x, name = 'multiply')
v1 = tf.Variable(10.0, name = 'variable1',  dtype = tf.float16)
v2 = tf.Variable(0.0, name = 'variable2',  dtype = tf.float16)
y = tf.add(x1,v1, name = 'add_variable')
y2 =tf.add(y,v2, name = 'output')


with tf.Session() as sess:

    # Initialise variables

    init_op = tf.global_variables_initializer() 

    sess.run(init_op)

    # Test with inputs 0 to 10

    for i in range(0,11):
        assign_op = x.assign([[i]])
        sess.run(assign_op)
        print('%i, %s' %(i,sess.run(y2)))

    # Save the graph

    saver = tf.train.Saver(tf.global_variables())

    saver.save(sess, './' + name)

I then run the following command to compile the graph, ready for loading on to the NCS.

mvNCCompile tf_maths.meta -in input -on output -s 12 -is 1 1

Finally, I run the following script to load the graph on to the NCS and test the output values with input values 0 to 10.

from mvnc import mvncapi as mvnc
import numpy as np

# get input parameters

graph_path = './graph'


devices = mvnc.EnumerateDevices()
if len(devices) == 0:
    print('No devices found')
    quit()

device = mvnc.Device(devices[0])
device.OpenDevice()

#Load graph

with open(graph_path, mode='rb') as f:
    graphfile = f.read()

#Load preprocessing data

graph = device.AllocateGraph(graphfile)

# Testing numbers 0 to 10

for i in range(0,11):
    a = np.array([[i]])

    if (graph.LoadTensor(a.astype(np.float16), 'user object')):
        output, userobj = graph.GetResult()
        print('%i, %s' %(i,output))
    else:
        print("LoadTensor fail")

print('deallocate graph')
graph.DeallocateGraph()
device.CloseDevice()
print('Finished')

Unfortunately, the output values aren't correct. As shown below:

| input | expected output | NCS output |

|---|---|---|

| 0.0 | 10.0 | 0.0 |

| 1.0| 11.0 | 12.0 |

| 2.0| 14.0 | 24.0 |

| 3.0| 19.0 | 36.0 |

| 4.0| 26.0 | 48.0 |

| 5.0| 35.0 | 60.0 |

| 6.0 | 46.0 | 72.0 |

| 7.0 | 59.0 | 84.0 |

| 8.0 | 74.0 | 96.0 |

| 9.0 | 91.0 | 108.0 |

| 10.0 | 110.0 | 120.0 |

As you can see, the NCS looks to be multiplying the input by 12.0, rather than performing the intended operation. If I create a similar tensorflow graph in which I only do matrix multiplications (no add operations) I do get the correct output.

Is there possibly a bug in the compiler when interpreting a tf.add layer? I'd be very grateful if you could confirm the approach that I am taking to run a custom tensorflow network on the NCS is correct, and if so, the cause of the problem when performing add operations

idata · ‎11-22-2017

Any comments on this issue would be very much appreciated.

idata · ‎11-24-2017

I am also facing same issue. I am getting wrong output when I run in Movidius stick.

idata · ‎12-01-2017

Bump for comment

idata · ‎12-07-2017

@tf_movidius If you remove the tf.add layer you also will not get correct results, the problem is elsewhere. The mvNCCompile is working properly with 3 channels input (RGB images) when making graph from tensorflow model. Can you try reworking your TF model to be using 3 channels and test if the TF results match the NCS results then.