语义分割fcn源码 paddle

以下是使用PaddlePaddle实现语义分割FCN的示例代码：

import paddle
import paddle.nn as nn

class FCN8s(nn.Layer):
    def __init__(self, num_classes=21):
        super(FCN8s, self).__init__()

        # conv1
        self.conv1_1 = nn.Conv2D(3, 64, 3, 1, 100)
        self.conv1_2 = nn.Conv2D(64, 64, 3, 1, 100)
        self.pool1 = nn.MaxPool2D(2, 2, 'ceil')

        # conv2
        self.conv2_1 = nn.Conv2D(64, 128, 3, 1, 100)
        self.conv2_2 = nn.Conv2D(128, 128, 3, 1, 100)
        self.pool2 = nn.MaxPool2D(2, 2, 'ceil')

        # conv3
        self.conv3_1 = nn.Conv2D(128, 256, 3, 1, 100)
        self.conv3_2 = nn.Conv2D(256, 256, 3, 1, 100)
        self.conv3_3 = nn.Conv2D(256, 256, 3, 1, 100)
        self.pool3 = nn.MaxPool2D(2, 2, 'ceil')

        # conv4
        self.conv4_1 = nn.Conv2D(256, 512, 3, 1, 100)
        self.conv4_2 = nn.Conv2D(512, 512, 3, 1, 100)
        self.conv4_3 = nn.Conv2D(512, 512, 3, 1, 100)
        self.pool4 = nn.MaxPool2D(2, 2, 'ceil')

        # conv5
        self.conv5_1 = nn.Conv2D(512, 512, 3, 1, 100)
        self.conv5_2 = nn.Conv2D(512, 512, 3, 1, 100)
        self.conv5_3 = nn.Conv2D(512, 512, 3, 1, 100)
        self.pool5 = nn.MaxPool2D(2, 2, 'ceil')

        # fc6
        self.fc6 = nn.Conv2D(512, 4096, 7)
        self.drop6 = nn.Dropout(p=0.5)

        # fc7
        self.fc7 = nn.Conv2D(4096, 4096, 1)
        self.drop7 = nn.Dropout(p=0.5)

        # score_fr
        self.score_fr = nn.Conv2D(4096, num_classes, 1)

        # upsample2
        self.upscore2 = nn.Conv2DTranspose(num_classes, num_classes, 4, 2, 1)

        # fuse_pool4
        self.score_pool4 = nn.Conv2D(512, num_classes, 1)
        self.fuse_pool4 = nn.Conv2D(num_classes, num_classes, 3, 1, 1)

        # upsample_pool4
        self.upscore_pool4 = nn.Conv2DTranspose(num_classes, num_classes, 4, 2, 1)

        # fuse_pool3
        self.score_pool3 = nn.Conv2D(256, num_classes, 1)
        self.fuse_pool3 = nn.Conv2D(num_classes, num_classes, 3, 1, 1)

        # upsample8
        self.upscore8 = nn.Conv2DTranspose(num_classes, num_classes, 16, 8, 0)

    def forward(self, x):
        # conv1
        x = self.conv1_1(x)
        x = self.conv1_2(x)
        x = self.pool1(x)

        # conv2
        x = self.conv2_1(x)
        x = self.conv2_2(x)
        x = self.pool2(x)

        # conv3
        x = self.conv3_1(x)
        x = self.conv3_2(x)
        x = self.conv3_3(x)
        x = self.pool3(x)

        # conv4
        conv4_1 = self.conv4_1(x)
        conv4_2 = self.conv4_2(conv4_1)
        conv4_3 = self.conv4_3(conv4_2)
        x = self.pool4(conv4_3)

        # conv5
        conv5_1 = self.conv5_1(x)
        conv5_2 = self.conv5_2(conv5_1)
        conv5_3 = self.conv5_3(conv5_2)
        x = self.pool5(conv5_3)

        # fc6
        x = self.fc6(x)
        x = self.drop6(x)

        # fc7
        x = self.fc7(x)
        x = self.drop7(x)

        # score_fr
        x = self.score_fr(x)

        # upsample2
        upscore2 = self.upscore2(x)

        # fuse_pool4
        score_pool4 = self.score_pool4(conv4_3)
        fuse_pool4 = self.fuse_pool4(score_pool4[:, :, 5:5 + upscore2.shape[2], 5:5 + upscore2.shape[3]])
        x = upscore2 + fuse_pool4

        # upsample_pool4
        upscore_pool4 = self.upscore_pool4(x)

        # fuse_pool3
        score_pool3 = self.score_pool3(conv3_3)
        fuse_pool3 = self.fuse_pool3(score_pool3[:, :, 9:9 + upscore_pool4.shape[2], 9:9 + upscore_pool4.shape[3]])
        x = upscore_pool4 + fuse_pool3

        # upsample8
        x = self.upscore8(x)

        return x

这是一个简单的FCN8s模型，它由卷积层、池化层、全连接层和反卷积层组成。在训练过程中，可以使用交叉熵损失函数和Adam优化器对模型进行训练。具体的代码实现可以参考PaddlePaddle的官方文档和示例。