Hey there,
I would like take advantage of mixed-precision to efficiently train a model, and then use my CPU for inference.
I’ve trained a model using apex (O2), and followed the instructions to save the checkpoint:
checkpoint = {
	'model': model.state_dict(),
	'optimizer': optimizer.state_dict(),
	'amp': amp.state_dict()
}

torch.save(checkpoint, 'checkpoint.pt')


Now I would like to load the trained model for CPU based inference…
I’ve defined a function that sums all the tensors from the state_dict, presumably this sum should be the same for both the saved and loaded model:
def calculate_checksum(model):
	return sum(model.state_dict()[key].sum().item() for key in model.state_dict().keys())

device = torch.device('cpu')
checkpoint = torch.load("checkpoint.pt", map_location=device)

Now, it turns out that the checksum values don’t match unless I initialize amp, which requires both the optimizer to be loaded and the model to be moved to the gpu.
model, optimizer = apex.amp.initialize(model, optimizer, opt_level=“O2”)
apex.amp.load_state_dict(best_checkpoint[‘amp’])
How do i save the trained model so that I can properly load it onto the CPU for inference? I shouldn’t need to initialize amp or load the optimizer. Thanks!