layer tf.shape forbidden in opencv?

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Hi,

i have got this error :

cv2.error: OpenCV(4.1.0-pre) G:\Lib\opencv\modules\dnn\src\dnn.cpp:524: error: (-2:Unspecified error) Can't create layer "mydataShape" of type "Shape" in function 'cv::dnn::dnn4_v20190122::LayerData::getLayerInstance'

in my graph I have got this :

batch_size = tf.shape(trainDataNode,name="mydataShape")[0]

I read this https://github.com/opencv/opencv/issu... and http://answers.opencv.org/question/17...

Does it mean that tf.shape cannot be used in opencv?

@dkurt

My code (sorry it is 200 lines)is a fork of https://github.com/matroid/dlwithtf . When NOSHAPE is True I can use .pb but when it is false I cannot.

def model(data,train=False):
    # couche de convolution tf.conv2d
    conv1 = tf.nn.conv2d(trainDataNode,conv1Filtres,strides=[1, 1, 1, 1],padding='SAME',name = 'nConv2d1')
    # ajout du biais tf.nn.bias_add
    conv1plusbiais = tf.nn.bias_add(conv1, conv1Biais,name = 'nC1plusB1')
    # Couche RELU pour non linéarité.
    relu1 = tf.nn.relu(conv1plusbiais)
    # Couche Max pooling. recherche du maximum dans un tenseur de taille 1x2x2x1 at décalage de 1x2x2x1
    # résultat un tenseur de taille à peu près égal à la moitié du tenseur d'entrée
    # padding same si un noyau est incomplet il sera complété 
    groupement1 = tf.nn.max_pool(relu1,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME',name = 'maxPool1')
    conv2 = tf.nn.conv2d(groupement1,conv2Filtres,strides=[1, 1, 1, 1],padding='SAME')
    relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2Biais))
    groupement2 = tf.nn.max_pool(relu2,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME',name = 'maxPool2')
    # Redimensionnement de la couche groupement1 pour
    # fully connected layers.
    tailleGroupement2 = groupement2.get_shape().as_list()
    reshape = tf.reshape(groupement2,[tailleLots, tailleGroupement2[1] * tailleGroupement2[2] * tailleGroupement2[3]], name = 'reshape1')
    # Couche de réseau de neurones connectés Fully connected layer. 
    # broadcasts the biases.
    hidden1= tf.nn.relu(tf.matmul(reshape, poidsRN1) + biaisRN1,name='hiddenRN1')
    hidden1d = tf.layers.dropout(hidden1, 0.5, seed=SEEDINIT,name = 'nDrop')
    hidden2 = tf.matmul(hidden1d, poidsRN2,name='matmulRN2') + biaisRN2
    # Predictions for the current training minibatch.
    train_prediction = tf.nn.softmax(hidden2,name = 'noeudPrediction')

    if train:
        train_prediction = tf.nn.softmax(hidden2)
        loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
            labels=trainLabelsNode, logits=hidden2),name = 'noeudPerte')

        # L2 regularization for the fully connected parameters.
        regularizers = (tf.nn.l2_loss(poidsRN1)+ tf.nn.l2_loss(biaisRN1)+tf.nn.l2_loss(poidsRN2)+ tf.nn.l2_loss(biaisRN2))
        # Add the regularization term to the loss.
        loss += 5e-4 * regularizers

        # Optimizer: set up a variable that's incremented once per batch and
        # controls the learning rate decay.
        batch = tf.Variable(0, dtype=tf.float32)
        # Decay once per epoch, using an exponential schedule starting at 0.01.
        learning_rate = tf.train.exponential_decay(
            0.01,                # Base learning rate.
            batch * BATCH_SIZE,  # Current index into the dataset.
            trainSize,          # Decay step.
            0.95,                # Decay rate.
            staircase=True, name = 'noeudTaux')
        # Use simple momentum for the optimization.
        optimizer = tf.train.MomentumOptimizer(learning_rate,0.9).minimize(loss,global_step=batch,name = 'noeudOptimiseur')
        return optimizer, loss, learning_rate
    train_prediction = tf.nn.softmax(hidden2, name='noeudPrediction')
    return train_prediction, None, None


volume = 'f:/'
#Chargement des données ...