-
Notifications
You must be signed in to change notification settings - Fork 0
/
Module.py
39 lines (32 loc) · 1.17 KB
/
Module.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
import torch
import torch.nn as nn
import torchvision
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.register_parameter('p1', torch.nn.Parameter(torch.zeros((2)), requires_grad=True))
# self.register_parameter('p2', torch.zeros((3)))
self.register_buffer('mean', torch.zeros((1)), persistent=True)
model = Module1()
self.add_module('model', model)
# self.register_module("model1", model)
def forward(self, inputs):
return inputs
class Module1(nn.Module):
def __init__(self):
super(Module1, self).__init__()
self.register_parameter('model1', torch.nn.Parameter(torch.ones((2)), requires_grad=True))
def forward(self, inputs):
return inputs
if __name__ == '__main__':
net = Net()
print('parameter', list(net.parameters()))
print(net.state_dict())
print(net.state_dict()['p1'])
print('buffer', net.get_buffer('mean'))
sub_model = net.get_submodule('model')
for para in sub_model.state_dict().values():
para.require_grad = False
print('model', sub_model.state_dict())
p1 = net.get_parameter('p1')
print('p1', p1)