使用pytorch搭建AlexNet操作(微调预训练模型及手动搭建)

model = models.alexnet(pretrained=True)
print(model)

import torch.nn as nn
from torchvision import models

class BuildAlexNet(nn.Module):
  def __init__(self, model_type, n_output):
    super(BuildAlexNet, self).__init__()
    self.model_type = model_type
    if model_type == 'pre':
      model = models.alexnet(pretrained=True)
      self.features = model.features
      fc1 = nn.Linear(9216, 4096)
      fc1.bias = model.classifier[1].bias
      fc1.weight = model.classifier[1].weight
      
      fc2 = nn.Linear(4096, 4096)
      fc2.bias = model.classifier[4].bias
      fc2.weight = model.classifier[4].weight
      
      self.classifier = nn.Sequential(
          nn.Dropout(),
          fc1,
          nn.ReLU(inplace=True),
          nn.Dropout(),
          fc2,
          nn.ReLU(inplace=True),
          nn.Linear(4096, n_output)) 
      #或者直接修改为
#      model.classifier[6]==nn.Linear(4096,n_output)
#      self.classifier = model.classifier
    if model_type == 'new':
      self.features = nn.Sequential(
          nn.Conv2d(3, 64, 11, 4, 2),
          nn.ReLU(inplace = True),
          nn.MaxPool2d(3, 2, 0),
          nn.Conv2d(64, 192, 5, 1, 2),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(3, 2, 0),
          nn.Conv2d(192, 384, 3, 1, 1),
          nn.ReLU(inplace = True),
          nn.Conv2d(384, 256, 3, 1, 1),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(3, 2, 0))
      self.classifier = nn.Sequential(
          nn.Dropout(),
          nn.Linear(9216, 4096),
          nn.ReLU(inplace=True),
          nn.Dropout(),
          nn.Linear(4096, 4096),
          nn.ReLU(inplace=True),
          nn.Linear(4096, n_output))
      
  def forward(self, x):
    x = self.features(x)
    x = x.view(x.size(0), -1)
    out = self.classifier(x)
    return out

import numpy as np
from torch.autograd import Variable
import torch

if __name__ == '__main__':
  model_type = 'pre'
  n_output = 10
  alexnet = BuildAlexNet(model_type, n_output)
  print(alexnet)
  
  x = np.random.rand(1,3,224,224)
  x = x.astype(np.float32)
  x_ts = torch.from_numpy(x)
  x_in = Variable(x_ts)
  y = alexnet(x_in)