#获取模型权重
for k, v in model_2.state_dict().iteritems():
 print("Layer {}".format(k))
 print(v)

#获取模型权重
for layer in model_2.modules():
 if isinstance(layer, nn.Linear):
  print(layer.weight)

#将一个模型权重载入另一个模型
model = VGG(make_layers(cfg['E']), **kwargs)
if pretrained:
 load = torch.load('/home/huangqk/.torch/models/vgg19-dcbb9e9d.pth')
 load_state = {k: v for k, v in load.items() if k not in ['classifier.0.weight', 'classifier.0.bias', 'classifier.3.weight', 'classifier.3.bias', 'classifier.6.weight', 'classifier.6.bias']}
 model_state = model.state_dict()
 model_state.update(load_state)
 model.load_state_dict(model_state)
return model

# 对特定层注入hook
def hook_layers(model):
 def hook_function(module, inputs, outputs):
  recreate_image(inputs[0])

 print(model.features._modules)
 first_layer = list(model.features._modules.items())[0][1]
 first_layer.register_forward_hook(hook_function) 

#获取层
x = someinput
for l in vgg.features.modules():
 x = l(x)
modulelist = list(vgg.features.modules())
for l in modulelist[:5]:
 x = l(x)
keep = x
for l in modulelist[5:]:
 x = l(x)

# 提取vgg模型的中间层输出
# coding:utf8
import torch
import torch.nn as nn
from torchvision.models import vgg16
from collections import namedtuple


class Vgg16(torch.nn.Module):
 def __init__(self):
  super(Vgg16, self).__init__()
  features = list(vgg16(pretrained=True).features)[:23]
  # features的第3，8，15，22层分别是: relu1_2,relu2_2,relu3_3,relu4_3
  self.features = nn.ModuleList(features).eval()

 def forward(self, x):
  results = []
  for ii, model in enumerate(self.features):
   x = model(x)
   if ii in {3, 8, 15, 22}:
    results.append(x)

  vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3'])
  return vgg_outputs(*results)