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Depthwise Conv Not Working Correctly

I am using Tensorflow 1.9.0 and NCSDK (v2) v2.8.1.2 in Ubuntu 16.04.

============================================================================================
I have the very simple tensorflow graph:
import tensorflow as tf
import numpy as np
import sys

inputs = tf.placeholder(tf.float32, (1,402,202,60), name="input")
weights = tf.get_variable("weights", [3, 3, 60, 1], dtype=tf.float32, initializer=tf.random_uniform_initializer)
outputs = tf.nn.depthwise_conv2d(inputs, weights, [1, 1, 1, 1], "VALID", name="output", data_format='NHWC')

testIn = np.ones((1,402,202,60))
saver = tf.train.Saver()
with tf.Session() as sess:
............sess.run(tf.global_variables_initializer())
............tfOut = sess.run(outputs, feed_dict={inputs:testIn})
............save_path = saver.save(sess, "./testGraph/depthwise")
print(tfOut.shape)

============================================================================================

The tensorflow program runs OK and created the "meta" graph in "./testGraph/depthwise.meta"

Now, when I run the following commands:
"cd testGraph && mvNCCheck depthwise.meta -s 12 -in input -on output"

I got the following output:

============================================================================================

mvNCCheck v02.00, Copyright @ Intel Corporation 2017

****** Info: No Weights provided. inferred path: depthwise.data-00000-of-00001******
shape: (1, 402, 202, 60)
res.shape: (1, 400, 200, 60)
TensorFlow output shape: (400, 200, 60)
Blob generated
USB: Transferring Data...
/usr/lib/python3.5/site-packages/mvnc/mvncapi.py:418: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
USB: Myriad Execution Finished
USB: Myriad Connection Closing.
USB: Myriad Connection Closed.
Result: (400, 200, 60)
1) 2219925 6.094
2) 4589229 5.863
3) 3984105 5.82
4) 480525 5.773
5) 1113525 5.73
Expected: (400, 200, 60)
1) 2219925 6.0941
2) 4589229 5.863187
3) 3984105 5.821101
4) 480525 5.773321
5) 1113525 5.7299337


Obtained values


Obtained Min Pixel Accuracy: 68.20873022079468% (max allowed=2%), Fail
Obtained Average Pixel Accuracy: 2.201648987829685% (max allowed=1%), Fail
Obtained Percentage of wrong values: 6.6650625% (max allowed=0%), Fail
Obtained Pixel-wise L2 error: 8.806281379565066% (max allowed=1%), Fail
Obtained Global Sum Difference: 644019.375


============================================================================================

Noticed that, there are lots of errors in the sum of difference and most of the test have failed. I am using the 3x3 depthwise filter in this case.

Any ideas on how to address this issue in the NCS???

Thanks a lot for all the helps and supports....

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