Hi, thank you very much for your reply, but my model is already trained and model.trainable returns False for me.
What I am trying to do is to convert the dynamic model to tensorflow and finally convert it to Tensorflow Lite.
My Pytorch model is actually a dynamic one, which can dynamically adjust the width by update the parameter of the model with the key ‘width_mult’.  For evaluation, basically what I do in the PyTorch is
model.eval()
for width_mult in sorted(FLAGS.width_mult_list, reverse=True):
      model.apply(
      lambda m: setattr(m, 'width_mult', width_mult))
      outputs = model(input)

say FLAGS.width_mult_list = [0.35, 0.5, 0.7, 1.0]. If the prediction is different then the model can achieve dynamic switching.
Then I used the onnx for the conversion and got the model in TensorFlow. But I don’t know if there is any way for me to verify the dynamics of the model, as in PyTorch.
Many Thanks:)