实际应用时可能比较想获取VGG中间层的输出，
那么就可以如下操作：
import numpy as np
import torch
from torchvision import models
from torch.autograd import Variable
import torchvision.transforms as transforms
class CNNShow():
def __init__(self, model):
self.model = model
self.model.eval()
self.created_image = self.image_for_pytorch(np.uint8(np.random.uniform(150, 180, (224, 224, 3))))
def show(self):
x = self.created_image
for index, layer in enumerate(self.model):
print(index,layer)
x = layer(x)
def image_for_pytorch(self,Data):
transform = transforms.Compose([
transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
]
)
imData = transform(Data)
imData = Variable(torch.unsqueeze(imData, dim=0), requires_grad=True)
return imData
if __name__ == '__main__':
pretrained_model = models.vgg16(pretrained=True).features
CNN = CNNShow(pretrained_model)
CNN.show()
以上这篇pytorch获取vgg16-feature层输出的例子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脚本之家。
