def __loadNNets(self, name, includeOptimisers=False):
nNetA = NeuralNet(in_channels=3).to(self.device)
nNetB = NeuralNet(in_channels=4).to(self.device)
nNetC = NeuralNet(in_channels=4).to(self.device)
if name is not None:
name = name.replace(".pth", "a.pth")
nNetA.load(name)
name = name.replace("a.pth", "b.pth")
nNetB.load(name)
name = name.replace("b.pth", "c.pth")
nNetC.load(name)
else:
nNetA.loadMostRecent("a.pth")
nNetB.loadMostRecent("b.pth")
nNetC.loadMostRecent("c.pth")
nnets = nNetA, nNetB, nNetC
if includeOptimisers:
optimisers = tuple(torch.optim.Adam(
N.parameters(), lr=0.001) for N in nnets)
return nnets, optimisers
else:
return nnets
