用java写一个冒泡排序

由于PWC-Net是一个深度学习模型，其代码需要在深度学习框架中实现。以下是使用PyTorch框架实现PWC-Net的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class PWCNet(nn.Module):

    def __init__(self, input_channels=6, output_channels=2, num_levels=5, num_layers=5, num_filters=32):
        super(PWCNet, self).__init__()

        self.num_levels = num_levels
 self.num_layers = num

        # Feature Extraction
        self.conv1 = nn.Conv2d_channels, num_filters, kernel_size=3, stride=2, padding=1)
        self.conv1b nn.Conv2d(num_filters, num_filters, kernel_size=3,=1, padding=1)
        self.conv2a = nn.Conv2d(num_filters, num_filters*2, kernel_size=3, stride=2, padding=1)
        self.conv2b = nn.Conv2d(num*2, num_filters*2, kernel_size=3, stride1, padding=1)
        self.conv3a nn.Conv2d(num_filters*2, num_filters*4, kernel_size3, stride=2, padding=1)
        self.conv3b = nn.Conv2d(num_filters*4, num_filters*4, kernel_size=3, stride=1, padding=1)
        self.conv4a = nn.Conv2d(num_filters*4, num_filters*8, kernel_size=3, stride=2, padding=1)
        self.conv4b = nn.Conv2d(num_filters*8, num_filters*8, kernel_size=3, stride=1, padding=1)
        self.conv5a = nn.Conv2d(num_filters*8, num_filters*8, kernel_size=3, stride=2, padding=1)
        self.conv5b = nn.Conv2d(num_filters*8,*8, kernel_size=3, stride=1 padding=1)

        # Pyramid Feature
        self.pyramid1 = nn.Conv2d(num_filters*8 num_filters*2, kernel_size=3, stride=1, padding=1)
        self.pyramid2 = nn.Conv2d(num_filters*2 num_filters*2, kernel_size=3, stride=1, padding=1)
        self.pyramid3 = nn.Conv2d(num_filters*4, num_filters*2, kernel_size=3, stride=1, padding=)
        self.pyramid_conv4 = nn.Conv2d(num_filters*2 num_filters*2, kernel_size=3, stride=1, padding=1)
 self.pyramid_conv5 = nn.Conv2d(num_filters, num_filters*2, kernel_size=, stride=1, padding=1)
        self.pyramid_conv6 = nn.Conv2d(num_filters*2, num_filters*2, kernel_size=, stride=1, padding=1)

        # Optical Flow Estimation
 self.flow_conv1 =.Conv2d(num_filters*8*2, num_filters*4, kernel_size=3, stride=1, padding=1        self.flow_conv2 = nn.Conv2d(num_filters*4, num*4, kernel_size=3, stride=1, padding=1)
        self.flow_conv3 = nn.Conv2d(num_filters*4, num_filters4, kernel_size=3, stride=1, padding=1)
        self.flow_conv4 = nn.Conv2d(num_filters*4, num_filters*8, kernel_size=3, stride=1, padding=1)
        self.flow_conv5 = nn.Conv2d(num_filters*8, num_filters*8, kernel_size=3, stride=1, padding=1)
        self.flow_conv6 = nn.Conv2d(num_filters*8, output_channels, kernel_size=3, stride=1, padding=1)

        # Upsampling
        self.upsample_flow6to5 = nn.ConvTranspose2d(output_channels, output_channels, kernel_size=4, stride=2, padding=1, bias=False)
        self.upsample_flow5to4 = nn.ConvTranspose2d(output_channels, output_channels, kernel_size=4, stride=2, padding=1, bias=False)
        self.upsample_flow4to3 = nn.ConvTranspose2d(output_channels, output_channels, kernel_size=4, stride=2, padding=1, bias=False)
        self.upsample_flow3to2 = nn.ConvTranspose2d(output_channels, output_channels, kernel_size=4, stride=2, padding=1, bias=False)
        self.upsample_flow2to1 = nn.ConvTranspose2d(output_channels, output_channels, kernel_size=4, stride=2, padding=1, bias=False)

    def forward(self, x):
        # Feature Extraction
        x1 = F.leaky_relu(self.conv1b(F.leaky_relu(self.conv1a(x))))
        x2 = F.leaky_relu(self.conv2b(F.leaky_relu(self.conv2a(x1))))
        x3 = F.leaky_relu(self.conv3b(F.leaky_relu(self.conv3a(x2))))
        x4 = F.leaky_relu(self.conv4b(F.leaky_relu(self.conv4a(x3))))
        x5 = F.leaky_relu(self.conv5b(F.leaky_relu(self.conv5a(x4))))

        # Pyramid Feature Extraction
        y1 = self.pyramid_conv1(x5)
        y2 = self.pyramid_conv2(F.interpolate(y1, x4.size()[2:], mode='bilinear', align_corners=False))
        y3 = self.pyramid_conv3(F.interpolate(y2, x3.size()[2:], mode='bilinear', align_corners=False))
        y4 = self.pyramid_conv4(F.interpolate(y3, x2.size()[2:], mode='bilinear', align_corners=False))
        y5 = self.pyramid_conv5(F.interpolate(y4, x1.size()[2:], mode='bilinear', align_corners=False))
        y6 = self.pyramid_conv6(y5)

        # Optical Flow Estimation
        z1 = torch.cat((x5, y6), dim=1)
        z2 = F.leaky_relu(self.flow_conv1(z1))
        z3 = F.leaky_relu(self.flow_conv2(z2))
        z4 = F.leaky_relu(self.flow_conv3(z3))
        z5 = F.leaky_relu(self.flow_conv4(z4))
        z6 = F.leaky_relu(self.flow_conv5(z5))
        flow = self.flow_conv6(z6)

        # Upsampling
        flow5 = self.upsample_flow6to5(flow)
        flow4 = self.upsample_flow5to4(flow5)
        flow3 = self.upsample_flow4to3(flow4)
        flow2 = self.upsample_flow3to2(flow3)
        flow1 = self.upsample_flow2to1(flow2)

        return flow1

以上代码实现了PWC-Net的主要部分，包括特征提取、金字塔特征提取、光流估计和上采样等。其输入为两张图像的堆叠张量，输出为一张光流图像。可以根据需要调整模型的输入通道数、输出通道数、金字塔层数、卷积层数和卷积核数等参数。