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utils.py
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utils.py
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import torch, random
import numpy as np
import nm, network
def set_seed(seed):
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
torch.backends.cudnn.enabled = False
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def vect2net(x):
net = nm.FC_Nm()
d = net.state_dict()
i = 0
for name, W in net.named_parameters():
if 'weight' in name:
w_size = list(W.flatten().size())[0]
d[name] = torch.Tensor(x[i:i+w_size].reshape(W.shape))
i+=w_size
elif 'bias' in name:
w_size = list(W.flatten().size())[0]
d[name] = torch.Tensor(x[i:i+w_size].reshape(W.shape))
i+=w_size
net.load_state_dict(d)
return net
def vect2net_snn(x):
net = network.SNN()
d = net.state_dict()
i = 0
for name, W in net.named_parameters():
if 'weight' in name:
w_size = list(W.flatten().size())[0]
d[name] = torch.Tensor(x[i:i+w_size].reshape(W.shape))
i+=w_size
elif 'bias' in name:
w_size = list(W.flatten().size())[0]
d[name] = torch.Tensor(x[i:i+w_size].reshape(W.shape))
i+=w_size
net.load_state_dict(d)
return net
def net2vect(net):
x = []
for name, W in net.named_parameters():
if 'weight' in name:
x.append(W.cpu().detach().numpy().flatten())
elif 'bias' in name:
x.append(W.cpu().detach().numpy().flatten())
return np.concatenate(x)