Issue with converting tensorflow model to Intel Movidius graph

[idata]

Hello I faced with the problem when trying to use Movidius stick with tensorflow. I have keras model and I convert it to tensorflow model. When I convert it to Movidius graph I got error:

Traceback (most recent call last):

File "/usr/local/bin/mvNCCompile", line 118, in create_graph(args.network, args.inputnode, args.outputnode, args.outfile, args.nshaves, args.inputsize, args.weights) File "/usr/local/bin/mvNCCompile", line 104, in create_graph net = parse_tensor(args, myriad_config) File "/usr/local/bin/ncsdk/Controllers/TensorFlowParser.py", line 290, in parse_tensor if have_first_input(strip_tensor_id(node.outputs[0].name)):IndexError: list index out of range

Here is my code:

from keras.models import model_from_json

from keras.models import load_modelfrom keras import backend as Kimport tensorflow as tfimport nnimport os

weights_file = "weights.h5"

sess = K.get_session()

K.set_learning_phase(0)model = nn.alexnet_model() # get keras modelmodel.load_weights(weights_file)

saver = tf.train.Saver()

saver.save(sess, "./TF_Model/tf_model") # convert keras to tensorflow model

tf_model_path = "./TF_Model/tf_model"

fw = tf.summary.FileWriter('logs', sess.graph)

fw.close()

os.system('mvNCCompile TF_Model/tf_model.meta -in=conv2d_1_input -on=activation_7/Softmax') # get Movidius graph

Python version: 2.7

OS: Ubuntu 16.04Tensorflow version: 1.12

[idata]

@jenamax Thanks for providing your code. Can you Which version of the NCSDK are you using? If you could provide me with your model files, that would be helpful and save me a lot of time also.

[idata]

Hello im facing the same problem

i work with keras/tensorflow. just finetune a yolov3 (416) for custom data. everything fine in the host with the camera.

i use the same code of @jenamax for transform keras->tf. I get the .meta .index .dataXXXX checkpoint as expected.

i run in vm ubuntu16.04 8gb with ncsdk 1 the command

mvNCCompile tfmodel.meta -s 12 -in input_1 -on conv2d_59/conv2d_67/conv2d_75

In the model.json says that outputs layers are those 3

conv2d_59conv2d_67conv2d_75

but getting [ERROR 13] outputs conv2d_59/conv2d_67/conv2d_75 not as expect…

any help?

[idata]

@Tome_at_Intel

hello im facing the same problem here is my model.json (yolov3) : https://drive.google.com/file/d/1syNkwSEro0mElDQ34uihPZDIBGpw4bor/view?usp=sharing

when I type " mvNCCompile TF_Model/tf_model.meta -in input_1 -on conv2d_75/kernel -s 12 "

It gives me :"input_data = np.random.uniform(0, 1, shape) File "mtrand.pyx", line 1302, in mtrand.RanomState.uniform File "mtrand.pyx", line 242, in mtrand.cont2_array_scTypeError: 'NoneType' object cannot be interpreted as an integer"I'm not sure if the output_node 's name is "conv2d_75", because the model.json says that outputs layers are those 3conv2d_59conv2d_67conv2d_75

but i think it's doesn't matter. now i just want to know how to solve this "Type Error"

and i don't know if the yolov3 is supported by ncs? it seems to have three outputs.Thank you very much!