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vgg16.py
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import torch
import torch.nn as nn
import numpy as np
# 定义VGG16网络类
class VGG16(nn.Module):
def __init__(self):
super(VGG16, self).__init__()
# 卷积层部分
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.relu2 = nn.ReLU(inplace=True)
self.max_pooling1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.relu4 = nn.ReLU(inplace=True)
self.max_pooling2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.relu5 = nn.ReLU(inplace=True)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.relu6 = nn.ReLU(inplace=True)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.relu7 = nn.ReLU(inplace=True)
self.max_pooling3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.relu8 = nn.ReLU(inplace=True)
self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu9 = nn.ReLU(inplace=True)
self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu10 = nn.ReLU(inplace=True)
self.max_pooling4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu11 = nn.ReLU(inplace=True)
self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu12 = nn.ReLU(inplace=True)
self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu13 = nn.ReLU(inplace=True)
self.max_pooling5 = nn.MaxPool2d(kernel_size=2, stride=2)
# 全连接层部分
self.fc1 = nn.Linear(512 * 7 * 7, 4096)
self.relu14 = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(4096, 4096)
self.relu15 = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(),
self.fc3 = nn.Linear(4096, 1000)
# 前向传播函数
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.max_pooling1(x)
x = self.conv3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.relu4(x)
x = self.max_pooling2(x)
x = self.conv5(x)
x = self.relu5(x)
x = self.conv6(x)
x = self.relu6(x)
x = self.conv7(x)
x = self.relu7(x)
x = self.max_pooling3(x)
x = self.conv8(x)
x = self.relu8(x)
x = self.conv9(x)
x = self.relu9(x)
x = self.conv10(x)
x = self.relu10(x)
x = self.max_pooling4(x)
x = self.conv11(x)
x = self.relu11(x)
x = self.conv12(x)
x = self.relu12(x)
x = self.conv13(x)
x = self.relu13(x)
x = self.max_pooling5(x)
print(x.shape)
x = x.view(-1, 512*7*7)
print(x.shape)
x = self.fc1(x)
x = self.relu14(x)
x = self.fc2(x)
x = self.relu15(x)
x = self.fc3(x)
return x
class VGG16(nn.Module):
def __init__(self, num_classes=1000):
super(VGG16, self).__init__()
# 卷积层部分
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
# 全连接层部分
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
# 前向传播函数
def forward(self, x):
x = self.features(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
# 生成随机的224x224x3大小的数据
if __name__ == '__main__':
random_data = np.random.rand(1, 3, 224, 224) # 调整数据形状为 (batch_size, channels, height, width)
random_data_tensor = torch.from_numpy(random_data.astype(np.float32)) # 将NumPy数组转换为PyTorch的Tensor类型，并确保数据类型为float32
print("输入数据的数据维度", random_data_tensor.size()) # 检查数据形状是否正确
# 创建VGG16网络实例
vgg16 = VGG16()
output = vgg16(random_data_tensor)
print("输出数据维度", output.shape)
print(output)