I have two models that are built with the same structure except for a single layer placed in the middle of the second net which is not present in the first one, like this:
I trained the first model and stored the weights in a file, I would now like to initialise the second model with these weights and perform some predictions with it.
To do so I instantiate the model and pass some parameters that are required by the layer in the middle but then I don’t know how to manually copy the weights from the first model into the second one.
I don’t think that I can use the usual load_state_dict function since the two models have different structures but I would like to do so while ignoring the single different layer.
Does anyone know if there is a way to manually copy the weights? In keras I would do something like
for layer_name in model_1: model_2.get_layer(layer_name).set_weights(model_1.get_layer(layer_name).get_weights())
Does PyTorch have something similar?
It was easier than expected, the solution seems to be the following
with torch.no_grad(): model2.layer_name.weight.copy_(model1.layer_name.weight) model2.layer_name.bias.copy_(model1.layer_name.bias)
This process must be applied to each layer.