Conversion from Tensorflow model to Intel Movidius Graph error

idata · ‎12-14-2018

Hello! I've got problem with conversion of tensorflow model to movidius graph. Initially model was designed in keras then I convert it to tensorflow and finally to movidius graph. I' ve got following error:

[Error 5] Toolkit Error: Stage Details Not Supported: IsVariableInitialized

Here is the code for conversion:

    import nn
    import os

    square_width = 16
    square_height = 16

    classes_num = 9

    model = nn.alexnet_model((square_width, square_height, 3))

    model.load_weights('weights.h5')

    with open("model.json", "w") as file:
        file.write(model.to_json())


    from keras.models import model_from_json
    from keras import backend as K
    import tensorflow as tf

    model_file = "model.json"
    weights_file = "weights.h5"

    with open(model_file, "r") as file:
        config = file.read()

    K.set_learning_phase(0)
    model = model_from_json(config)
    model.load_weights(weights_file)

    saver = tf.train.Saver()
    sess = K.get_session()
    saver.save(sess, "./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

That's how I define the model:

def alexnet_model(img_shape=(256, 256, 3), n_classes=9, l2_reg=0.,weights=None):
    ### taken from https://github.com/eweill/keras-deepcv/blob/master/models/classification/alexnet.py ###


    # Initialize model
    alexnet = Sequential()

    # Layer 1
    alexnet.add(Conv2D(96, (11, 11), input_shape=img_shape,
        padding='same', kernel_regularizer=l2(l2_reg)))
    alexnet.add(BatchNormalization())
    alexnet.add(Activation('relu'))
    alexnet.add(MaxPooling2D(pool_size=(2, 2)))

    # Layer 2
    alexnet.add(Conv2D(256, (5, 5), padding='same'))
    alexnet.add(BatchNormalization())
    alexnet.add(Activation('relu'))
    alexnet.add(MaxPooling2D(pool_size=(2, 2)))

    # Layer 3
    alexnet.add(ZeroPadding2D((1, 1)))
    alexnet.add(Conv2D(512, (3, 3), padding='same'))
    alexnet.add(BatchNormalization())
    alexnet.add(Activation('relu'))
    alexnet.add(MaxPooling2D(pool_size=(2, 2)))

    # Layer 4
    alexnet.add(ZeroPadding2D((1, 1)))
    alexnet.add(Conv2D(1024, (3, 3), padding='same'))
    alexnet.add(BatchNormalization())
    alexnet.add(Activation('relu'))

    # Layer 5
    #alexnet.add(ZeroPadding2D((1, 1)))
    #alexnet.add(Conv2D(1024, (3, 3), padding='same'))
    #alexnet.add(BatchNormalization())
    #alexnet.add(Activation('relu'))
    #alexnet.add(MaxPooling2D(pool_size=(2, 2)))

    # Layer 6
    alexnet.add(Flatten())
    alexnet.add(Dense(3072))
    alexnet.add(BatchNormalization())
    alexnet.add(Activation('relu'))
    alexnet.add(Dropout(0.5))

    # Layer 7
    alexnet.add(Dense(4096))
    alexnet.add(BatchNormalization())
    alexnet.add(Activation('relu'))
    alexnet.add(Dropout(0.5))

    # Layer 8
    alexnet.add(Dense(n_classes))
    alexnet.add(BatchNormalization())
    alexnet.add(Activation('softmax'))

    if weights is not None:
        alexnet.load_weights(weights)

    return alexnet

OS: Ubuntu 16.04

Pyhon version: 3.5.2

Tensorflow version 1.12.0

Keras version: 2.2.0

NCSDK version: 1.12.01.01