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OpenVino Model Optimizer - Can't find the right weights

Hello!

I'm trying to implement a LSTM model for anomaly detection in OpenVino

I got the Keras model from a blog post then converted it to a frozen Tensorflow model following another, but I'm stuck at the "optimising the model and converting it to IR" part.

I'm getting that error:

[ ERROR ]  Shape [ -1  10 256 256   1] is not fully defined for output 0 of "time_distributed_1_input_4". Use --input_shape with positive integers to override model input shapes.
[ ERROR ]  Cannot infer shapes or values for node "time_distributed_1_input_4".

I understand that we need to specify the batch size, but the original model seems to have a flexible one, am I forced to fix it in the code and retrain it? Or is there a workaround?

 

Thanks a lot.

 

Additional info:

 

Shape of the model:

Model

Here is the part related to model creation:

    seq = Sequential()
    seq.add(TimeDistributed(Conv2D(128, (11, 11), strides=4, padding="same"), batch_input_shape=(None, 10, 256, 256, 1)))
    seq.add(LayerNormalization())
    seq.add(TimeDistributed(Conv2D(64, (5, 5), strides=2, padding="same")))
    seq.add(LayerNormalization())
    # # # # #
    seq.add(ConvLSTM2D(64, (3, 3), padding="same", return_sequences=True))
    seq.add(LayerNormalization())
    seq.add(ConvLSTM2D(32, (3, 3), padding="same", return_sequences=True))
    seq.add(LayerNormalization())
    seq.add(ConvLSTM2D(64, (3, 3), padding="same", return_sequences=True))
    seq.add(LayerNormalization())
    # # # # #
    seq.add(TimeDistributed(Conv2DTranspose(64, (5, 5), strides=2, padding="same")))
    seq.add(LayerNormalization())
    seq.add(TimeDistributed(Conv2DTranspose(128, (11, 11), strides=4, padding="same")))
    seq.add(LayerNormalization())
    seq.add(TimeDistributed(Conv2D(1, (11, 11), activation="sigmoid", padding="same")))
    print(seq.summary())
    seq.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=1e-4, decay=1e-5, epsilon=1e-6))
    seq.fit(training_set, training_set,
            batch_size=Config.BATCH_SIZE, epochs=Config.EPOCHS, shuffle=False)

 

Here are the errors I got in the workbench when removing a dimension, or replacing it with 190 and 200:

no_dim 190 200

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