Hello, I trained a Unet Segmentation Model without Dropout in the SegmentationHead module.
I added the module to my network but I dont want to retrain it from scratch. I was wondering if I could initialize my model with dropout module on and then just load the weights from the model without dropout.
I did this:
model = smp.Unet(
    encoder_name=ENCODER, 
    encoder_depth=ENCODER_DEPTH,
    encoder_weights=ENCODER_WEIGHTS, 
    decoder_channels=DECODER_CHANNELS,
    classes=len(CLASSES), 
    activation=ACTIVATION,
    decoder_attention_type=attention,
    #aux_params=dict(dropout=0,classes=len(CLASSES))
)

Then I loaded the weights:
model.load_state_dict(torch.load(path+"/model_loss.pth"))
However, I am getting the following error:

‘Unet’ object has no attribute ‘copy’

Any idea if this is possible and if so, what am I doing wrong?
Kind regards