给我一个PyTorch框架实现的VGG16_UNET代码,其输入输出的特征图尺寸与维度一致
时间: 2024-02-11 13:05:30 浏览: 35
好的,以下是一个PyTorch框架实现的VGG16_UNET代码,输入输出的特征图尺寸与维度一致:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class VGG16_UNET(nn.Module):
def __init__(self, n_classes=1):
super(VGG16_UNET, self).__init__()
# VGG16 encoder
self.encoder_conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.encoder_bn1 = nn.BatchNorm2d(64)
self.encoder_conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.encoder_bn2 = nn.BatchNorm2d(64)
self.encoder_conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.encoder_bn3 = nn.BatchNorm2d(128)
self.encoder_conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.encoder_bn4 = nn.BatchNorm2d(128)
self.encoder_conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.encoder_bn5 = nn.BatchNorm2d(256)
self.encoder_conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.encoder_bn6 = nn.BatchNorm2d(256)
self.encoder_conv7 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.encoder_bn7 = nn.BatchNorm2d(512)
self.encoder_conv8 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.encoder_bn8 = nn.BatchNorm2d(512)
self.encoder_conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.encoder_bn9 = nn.BatchNorm2d(512)
self.encoder_conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.encoder_bn10 = nn.BatchNorm2d(512)
# VGG16 decoder
self.decoder_conv1 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.decoder_bn1 = nn.BatchNorm2d(512)
self.decoder_conv2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.decoder_bn2 = nn.BatchNorm2d(512)
self.decoder_conv3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.decoder_bn3 = nn.BatchNorm2d(512)
self.decoder_conv4 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
self.decoder_bn4 = nn.BatchNorm2d(256)
self.decoder_conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.decoder_bn5 = nn.BatchNorm2d(256)
self.decoder_conv6 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
self.decoder_bn6 = nn.BatchNorm2d(128)
self.decoder_conv7 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.decoder_bn7 = nn.BatchNorm2d(128)
self.decoder_conv8 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
self.decoder_bn8 = nn.BatchNorm2d(64)
self.decoder_conv9 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.decoder_bn9 = nn.BatchNorm2d(64)
self.decoder_conv10 = nn.Conv2d(64, n_classes, kernel_size=1)
def forward(self, x):
# VGG16 encoder
x = F.relu(self.encoder_bn1(self.encoder_conv1(x)))
x = F.relu(self.encoder_bn2(self.encoder_conv2(x)))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = F.relu(self.encoder_bn3(self.encoder_conv3(x)))
x = F.relu(self.encoder_bn4(self.encoder_conv4(x)))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = F.relu(self.encoder_bn5(self.encoder_conv5(x)))
x = F.relu(self.encoder_bn6(self.encoder_conv6(x)))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = F.relu(self.encoder_bn7(self.encoder_conv7(x)))
x = F.relu(self.encoder_bn8(self.encoder_conv8(x)))
x = F.relu(self.encoder_bn9(self.encoder_conv9(x)))
x = F.relu(self.encoder_bn10(self.encoder_conv10(x)))
x = F.max_pool2d(x, kernel_size=2, stride=2)
# VGG16 decoder
x = F.interpolate(x, scale_factor=2, mode='nearest')
x = F.relu(self.decoder_bn1(self.decoder_conv1(x)))
x = F.relu(self.decoder_bn2(self.decoder_conv2(x)))
x = F.relu(self.decoder_bn3(self.decoder_conv3(x)))
x = F.interpolate(x, scale_factor=2, mode='nearest')
x = F.relu(self.decoder_bn4(self.decoder_conv4(x)))
x = F.relu(self.decoder_bn5(self.decoder_conv5(x)))
x = F.interpolate(x, scale_factor=2, mode='nearest')
x = F.relu(self.decoder_bn6(self.decoder_conv6(x)))
x = F.relu(self.decoder_bn7(self.decoder_conv7(x)))
x = F.interpolate(x, scale_factor=2, mode='nearest')
x = F.relu(self.decoder_bn8(self.decoder_conv8(x)))
x = F.relu(self.decoder_bn9(self.decoder_conv9(x)))
x = self.decoder_conv10(x)
return x
```
这个模型的输入是一个3通道的图像,输出是一个1通道的分割图像,具体的维度和尺寸取决于输入的图像大小和分割图像大小。