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如下所示：
#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)
以上这篇pytorch 获取层权重,对特定层注入hook, 提取中间层输出的方法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。
