1、Motivation：

I wanna modify the value of some param;

I wanna check the value of some param.

The needed function:

2、state_dict() #generator type

model.modules()#generator type

named_parameters()#OrderDict type

from torch import nn
import torch
#creat a simple model
model = nn.Sequential(
  nn.Conv3d(1,16,kernel_size=1),nn.Conv3d(16,2,kernel_size=1))#tend to print the W of this layer
input = torch.randn([1,1,256,256])
if torch.cuda.is_available():
  print('cuda is avaliable')
  model.cuda()
  input = input.cuda()
#列印某一層的引數名
for name in model.state_dict():
  print(name)
#Then I konw that the name of target layer is '1.weight'

#schemem1(recommended)
print(model.state_dict()['1.weight'])

#scheme2
params = list(model.named_parameters())#get the index by debuging
print(params[2][0])#name
print(params[2][1].data)#data

#scheme3
params = {}#change the tpye of 'generator' into dict
for name,param in model.named_parameters():
params[name] = param.detach().cpu().numpy()
print(params['0.weight'])

#scheme4
for layer in model.modules():
if(isinstance(layer,nn.Conv3d)):
  print(layer.weight)

#列印每一層的引數名和引數值
#schemem1(recommended)
for name,param in model.named_parameters():
  print(name，param)

#scheme2
for name in model.state_dict():
  print(name)
  print(model.state_dict()[name])

以上這篇pytorch獲取模型某一層引數名及引數值方式就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。