Hi there,
I have converted a Pytorch pre-trained model into a TensorFlow model via onnx, but I need to update the weights of some of the fields, is there a way to do this?
I am using TensorFlow 2.4.1, and tf.get_variable() does not work for me.

what do you mean by update the weights of some fields?, do you want to take those weights and train a new model with them?

you can get the weights of a model using model.trainable_variables, that returns a list with the tensors, pretty much like model.named_parameters() in Pytorch, you can loop through that list and get the weights you want

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.