Pytorch下查看各层名字及根据layers的name冻结层进行finetune训练；_model = net().cuda() for name, param in model.name

from model_ori1 import resnet34  # model_ori1:your model
import torch
import torch.nn as nn
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        model = resnet34()
        self.resnet = model
    def forward(self, img):
        out = self.resnet(img)
        # print('out is {}'.format(out))
        return out
model = Net().cuda()
for name, param in model.named_parameters():  # 查看可优化的参数有哪些
    if param.requires_grad:
        print(name)

# 冻结某个参数预训练
from ResNeSt.resnest.torch.resnest import resnest101,resnest200,resnest269
 
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        model = resnest269(pretrained=True)
        model.fc = nn.Linear(2048,102)
        self.resnet = model
        
    def forward(self, img):        
        out = self.resnet(img)
        # print('out is {}'.format(out))
        return out
 
model = Net().cuda()
for name, param in model.named_parameters():  # 查看可优化的参数有哪些
    if param.requires_grad:
        print(name)

新model == net:
读取旧pt，将其layer，name参数给新model中的       
 model = resnet34()
        # inchannel = model.fc.in_features
        # print("fad",inchannel)
        # model.fc = nn.Linear(inchannel, 5)
        # m1,m = model.load_state_dict(torch.load("./rep2.pt"),strict= False)