'''
@作者:你遇了我321640253@qq.com
@文件:VGG16Net.py
@创建时间:2023 11 20

模型网络结构
'''
import torch
from torch import nn
from torchsummary import summary

# 定义 VGG16 网络结构
class VGG16(nn.Module):
  def __init__(self):
      super(VGG16, self).__init__()

      self.conv1 = nn.Sequential(
         #32*1*28*28
          nn.Conv2d(1, 16, kernel_size=3, padding=1),
          #16*28*28
          nn.ReLU(),
          nn.Conv2d(16, 16, kernel_size=3, padding=1),
          #16*28*28
          nn.ReLU()
      )

      self.conv2 = nn.Sequential(
          nn.Conv2d(16, 32, kernel_size=3, padding=1),
          nn.ReLU(),
          nn.Conv2d(32, 32, kernel_size=3, padding=1),
          nn.ReLU()
      )

      self.conv3 = nn.Sequential(
          nn.Conv2d(32, 64, kernel_size=3, padding=1),
          nn.ReLU(),
          nn.Conv2d(64, 64, kernel_size=3, padding=1),
          nn.ReLU(),
          nn.Conv2d(64, 64, kernel_size=3, padding=1),
          nn.ReLU()
      )

      self.conv4 = nn.Sequential(
          nn.Conv2d(64, 128, kernel_size=3, padding=1),
          nn.ReLU(),
          nn.Conv2d(128, 128, kernel_size=3, padding=1),
          nn.ReLU()
      )
      
      self.fc = nn.Linear(128*28*28, 100)
      self.fc1 = nn.Linear(100, 10)

  def forward(self, x):
      x = self.conv1(x)
      x = self.conv2(x)
      x = self.conv3(x)
      x = self.conv4(x)
      x = nn.Flatten()(x)
      x = self.fc(x)
      x = self.fc1(x)
      return x




def getSummary(size:tuple):
    model = VGG16()
    model = model.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
    summary(model, size)