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 npimport torchfrom torchvision import modelsfrom torch.autograd import Variableimport 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。