def create_optimizer(self,opts, model: Module):
optimopts = opts.optimizeropts
if opts.epocheropts.gpu:
device = torch.device("cpu")
model = model.to(device=device)
optim = globals()[optimopts.type](model.parameters(),
lr=optimopts.lr,
momentum=optimopts.momentum,
weight_decay=optimopts.weight_decay,
dampening=optimopts.dampening,
nesterov=optimopts.nestrov)
opts.optimizeropts.lr_sched = LambdaLR(optim, opts.optimizeropts.lr_sched_lambda, last_epoch=-1)
return optim
def _top_k_selector(net: Module, img: Tensor, k: int) -> NeuronSelector:
"""Creates a top-k classes selector.
Args:
net: network
img: prepared input image
k: number of classes
Returns:
neuron selector for the top k classes
"""
net = net.to(midnite.get_device())
out = net(img).squeeze(0)
mask = _top_k_mask(out, k)
return SimpleSelector(mask)