Sometimes, we need to train models jointly, or use one model to help another model training. So it’s critically for us to konw the name of parameters in the model.
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import torch
import torch.nn as nn
class monet(nn.Module):
def __init__(self):
super(monet, self).__init__()
self.pre_conv = nn.Sequential(
nn.Conv2d(48, 48, 3, 1, 1),
nn.ReLU(inplace=True)
)
self.conv0 = nn.Conv2d(48, 48, 3, 1)
self.layers = nn.ModuleList()
for i in range(3):
self.layers.append(self.conv0)
self.arch_weights = [torch.autograd.Variable(torch.zeros(8).cuda(), requires_grad=True) for i in range(len(self.layers))]
self.softmax = nn.Softmax()
def forward(self, x):
x = self.pre_conv(x)
x = self.layers(x)
x = self.conv0(x)
x = self.softmax(self.arch_weights) * x
return x
net = monet()
for i in net.state_dict():
print(i)
print(net.arch_weights[:2])
print(net.pre_conv)
print(net.conv0.parameters())
outputs:
pre_conv.0.weight
pre_conv.0.bias
conv0.weight
conv0.bias
layers.0.weight
layers.0.bias
layers.1.weight
layers.1.bias
layers.2.weight
layers.2.bias
[tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True), tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True)]
Sequential(
(0): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
)
<generator object Module.parameters at 0x7f22dfa5bb50>
