pytorch 提取权重_pytorch 获取层权重,对特定层注入hook, 提取中间层输出的方法

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。