利用PyTorch如何实现VGG16的方法-创新互联

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这篇文章主要介绍利用PyTorch如何实现VGG16，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！

我就废话不多说了，大家还是直接看代码吧~

import torch
import torch.nn as nn
import torch.nn.functional as F
 
class VGG16(nn.Module):
  
  def __init__(self):
    super(VGG16, self).__init__()
    
    # 3 * 224 * 224
    self.conv1_1 = nn.Conv2d(3, 64, 3) # 64 * 222 * 222
    self.conv1_2 = nn.Conv2d(64, 64, 3, padding=(1, 1)) # 64 * 222* 222
    self.maxpool1 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 64 * 112 * 112
    
    self.conv2_1 = nn.Conv2d(64, 128, 3) # 128 * 110 * 110
    self.conv2_2 = nn.Conv2d(128, 128, 3, padding=(1, 1)) # 128 * 110 * 110
    self.maxpool2 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 128 * 56 * 56
    
    self.conv3_1 = nn.Conv2d(128, 256, 3) # 256 * 54 * 54
    self.conv3_2 = nn.Conv2d(256, 256, 3, padding=(1, 1)) # 256 * 54 * 54
    self.conv3_3 = nn.Conv2d(256, 256, 3, padding=(1, 1)) # 256 * 54 * 54
    self.maxpool3 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 256 * 28 * 28
    
    self.conv4_1 = nn.Conv2d(256, 512, 3) # 512 * 26 * 26
    self.conv4_2 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 26 * 26
    self.conv4_3 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 26 * 26
    self.maxpool4 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 512 * 14 * 14
    
    self.conv5_1 = nn.Conv2d(512, 512, 3) # 512 * 12 * 12
    self.conv5_2 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 12 * 12
    self.conv5_3 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 12 * 12
    self.maxpool5 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 512 * 7 * 7
    
    # view
    
    self.fc1 = nn.Linear(512 * 7 * 7, 4096)
    self.fc2 = nn.Linear(4096, 4096)
    self.fc3 = nn.Linear(4096, 1000)
    # softmax 1 * 1 * 1000
    
  def forward(self, x):
    
    # x.size(0)即为batch_size
    in_size = x.size(0)
    
    out = self.conv1_1(x) # 222
    out = F.relu(out)
    out = self.conv1_2(out) # 222
    out = F.relu(out)
    out = self.maxpool1(out) # 112
    
    out = self.conv2_1(out) # 110
    out = F.relu(out)
    out = self.conv2_2(out) # 110
    out = F.relu(out)
    out = self.maxpool2(out) # 56
    
    out = self.conv3_1(out) # 54
    out = F.relu(out)
    out = self.conv3_2(out) # 54
    out = F.relu(out)
    out = self.conv3_3(out) # 54
    out = F.relu(out)
    out = self.maxpool3(out) # 28
    
    out = self.conv4_1(out) # 26
    out = F.relu(out)
    out = self.conv4_2(out) # 26
    out = F.relu(out)
    out = self.conv4_3(out) # 26
    out = F.relu(out)
    out = self.maxpool4(out) # 14
    
    out = self.conv5_1(out) # 12
    out = F.relu(out)
    out = self.conv5_2(out) # 12
    out = F.relu(out)
    out = self.conv5_3(out) # 12
    out = F.relu(out)
    out = self.maxpool5(out) # 7
    
    # 展平
    out = out.view(in_size, -1)
    
    out = self.fc1(out)
    out = F.relu(out)
    out = self.fc2(out)
    out = F.relu(out)
    out = self.fc3(out)
    
    out = F.log_softmax(out, dim=1)
    return out

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