FCN分割python

FCN（Fully Convolutional Networks）是一种用于图像分割的深度学习模型。在Python中，可以使用TensorFlow或PyTorch等框架来实现FCN分割。

以下是使用PyTorch实现FCN分割的示例代码：

import torch
import torch.nn as nn

class FCN(nn.Module):
    def __init__(self, num_classes):
        super(FCN, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=100)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        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.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        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.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        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.pool4 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        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.pool5 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        self.fc14 = nn.Conv2d(512, 4096, kernel_size=7)
        self.relu14 = nn.ReLU(inplace=True)
        self.drop14 = nn.Dropout2d()
        self.fc15 = nn.Conv2d(4096, 4096, kernel_size=1)
        self.relu15 = nn.ReLU(inplace=True)
        self.drop15 = nn.Dropout2d()
        self.score_fr = nn.Conv2d(4096, num_classes, kernel_size=1)
        self.upscore = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=64, stride=32, bias=False)

    def forward(self, x):
        h = self.relu1(self.conv1(x))
        h = self.relu2(self.conv2(h))
        h = self.pool1(h)
        h = self.relu3(self.conv3(h))
        h = self.relu4(self.conv4(h))
        h = self.pool2(h)
        h = self.relu5(self.conv5(h))
        h = self.relu6(self.conv6(h))
        h = self.relu7(self.conv7(h))
        h = self.pool3(h)
        h = self.relu8(self.conv8(h))
        h = self.relu9(self.conv9(h))
        h = self.relu10(self.conv10(h))
        h = self.pool4(h)
        h = self.relu11(self.conv11(h))
        h = self.relu12(self.conv12(h))
        h = self.relu13(self.conv13(h))
        h = self.pool5(h)
        h = self.drop14(self.relu14(self.fc14(h)))
        h = self.drop15(self.relu15(self.fc15(h)))
        h = self.score_fr(h)
        h = self.upscore(h)
        return h[:, :, 19:19+x.size()[2], 19:19+x.size()[3]].contiguous()

# 使用示例
model = FCN(num_classes=21)
input_tensor = torch.randn(1, 3, 224, 224)
output_tensor = model(input_tensor)
print(output_tensor.shape) # 输出 (1, 21, 224, 224)