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([1,1,16,256,256])
if torch.cuda.is_available():
print('cuda is avaliable')
model.cuda()
input = input.cuda()
#打印某一层的参数名
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)
#打印每一层的参数名和参数值
#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])
以上这篇pytorch获取模型某一层参数名及参数值方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
