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hookdef 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 = someinputfor l in vgg.features.modules(): x = l(x)modulelist = list(vgg.features.modules())for l in modulelist[:5]: x = l(x)keep = xfor l in modulelist[5:]: x = l(x)# 提取vgg模型的中间层输出# coding:utf8import torchimport torch.nn as nnfrom torchvision.models import vgg16from collections import namedtupleclass 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。