pytorch获取vgg16-feature层输出的例子

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()