Hi,
I’m working on a code that fine tune Mask-RCNN model on a dataset, then save it, and load the model to integrate it in a GAN framework as generator, thus the Mask-RCNN model that is loaded, has to be trained further.
Anyway when I load the model and launch the training, it doesn’t update like if the parameters are freezed, how can it be possible?
I add a code to better understand:
# save
PATH = "./saved_models/generator"+str(epoch)
torch.save({
            'epoch': epoch,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            }, PATH)

# load 
epoch = 0 # last epoch for which the model was saved

model = get_instance_segmentation_model(num_classes)
optimizer = optimizer = torch.optim.SGD(params, lr=0.005,
                            momentum=0.9, weight_decay=0.0005)

checkpoint = torch.load(PATH, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']


model.train()

Am I missing something? If I train the model without saving it and proceed to the GAN training phase, the model update works and I can see change in results, while If I do the same but with a loaded model, results don’t change.