胶囊网络图像语义分割

胶囊网络在图像语义分割任务中也有应用。下面是一个示例：

import torch
import torch.nn as nn

# 定义胶囊网络模型
class CapsuleNetwork(nn.Module):
    def __init__(self):
        super(CapsuleNetwork, self).__init__()
        # 编码器部分
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=256, kernel_size=9, stride=1)
        self.primary_capsules = PrimaryCapsules()
        self.digit_capsules = DigitCapsules()
        # 解码器部分
        self.decoder = Decoder()

    def forward(self, x):
        x = self.conv1(x)
        x = self.primary_capsules(x)
        x = self.digit_capsules(x)
        classes, reconstructions = self.decoder(x)
        return classes, reconstructions

# 定义主胶囊层
class PrimaryCapsules(nn.Module):
    def __init__(self):
        super(PrimaryCapsules, self).__init__()
        self.capsules = nn.ModuleList([
            nn.Conv2d(in_channels=256, out_channels=32, kernel_size=9, stride=2)
            for _ in range(8)
        ])

    def forward(self, x):
        u = [capsule(x) for capsule in self.capsules]
        u = torch.stack(u, dim=1)
        u = u.view(x.size(0), 32 * 6 * 6, -1)
        u = self.squash(u)
        return u

    def squash(self, x):
        norm = x.norm(dim=-1, keepdim=True)
        scale = norm ** 2 / (1 + norm ** 2)
        return scale * x / norm

# 定义数字胶囊层
class DigitCapsules(nn.Module):
    def __init__(self):
        super(DigitCapsules, self).__init__()
        self.routing_iterations = 3
        self.W = nn.Parameter(torch.randn(1, 32 * 6 * 6, 10, 16, 8))

    def forward(self, x):
        batch_size = x.size(0)
        u_hat = torch.matmul(x[:, None, :, None, :], self.W)
        b = torch.zeros(batch_size, 32 * 6 * 6, 10, 1, device=x.device)
        for _ in range(self.routing_iterations):
            c = torch.softmax(b, dim=2)
            s = (c * u_hat).sum(dim=2, keepdim=True)
            v = self.squash(s)
            if _ < self.routing_iterations - 1:
                b = b + (u_hat * v).sum(dim=-1, keepdim=True)
        return v.squeeze()

    def squash(self, x):
        norm = x.norm(dim=-1, keepdim=True)
        scale = norm ** 2 / (1 + norm ** 2)
        return scale * x / norm

# 定义解码器
class Decoder(nn.Module):
    def __init__(self):
        super(Decoder, self).__init__()
        self.reconstruction_layers = nn.Sequential(
            nn.Linear(16 * 10, 512),
            nn.ReLU(inplace=True),
            nn.Linear(512, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, 784),
            nn.Sigmoid()
        )

    def forward(self, x):
        classes = (x ** 2).sum(dim=-1) ** 0.5
        classes = torch.softmax(classes, dim=-1)
        _, max_length_indices = classes.max(dim=1)
        masked = torch.eye(10, device=x.device)
        masked = masked.index_select(dim=0, index=max_length_indices.squeeze())
        reconstructions = self.reconstruction_layers((x * masked[:, :, None]).view(x.size(0), -1))
        reconstructions = reconstructions.view(-1, 1, 28, 28)
        return classes, reconstructions

# 创建胶囊网络模型实例
model = CapsuleNetwork()

# 加载图像数据并进行预处理
image = torch.randn(1, 3, 28, 28)

# 将图像输入胶囊网络模型
classes, reconstructions = model(image)

# 输出结果
print("Classes:", classes)
print("Reconstructions:", reconstructions)