输入是【1，3，600，700】的tensor，Unet网络有3个encoder,每个encoder对应3个decoder，如何添加attention机制提高模型准确度，请给出paddle的示例

可以在Unet的decoder部分添加attention机制来提高模型准确度。具体实现方法是在decoder的每一层后面添加一个attention模块，将encoder对应层的输出作为attention模块的输入，然后将attention模块的输出与decoder的输出相加。这样可以使decoder更加关注encoder中与当前层相关的特征，从而提高模型的准确度。

PaddlePaddle的实现示例代码如下：

import paddle
import paddle.nn as nn

class AttentionBlock(nn.Layer):
    def __init__(self, in_channels, out_channels):
        super(AttentionBlock, self).__init__()
        self.query_conv = nn.Conv2D(in_channels, out_channels, kernel_size=1)
        self.key_conv = nn.Conv2D(in_channels, out_channels, kernel_size=1)
        self.value_conv = nn.Conv2D(in_channels, out_channels, kernel_size=1)
        self.gamma = paddle.create_parameter(shape=[1], dtype='float32', default_initializer=nn.initializer.Constant(0.0))

    def forward(self, x, encoder_output):
        batch_size, channels, height, width = x.shape
        query = self.query_conv(x).reshape([batch_size, -1, width * height]).transpose([0, 2, 1])
        key = self.key_conv(encoder_output).reshape([batch_size, -1, width * height])
        value = self.value_conv(encoder_output).reshape([batch_size, -1, width * height])
        attention = nn.functional.softmax(paddle.bmm(query, key), axis=-1)
        context = paddle.bmm(value, attention.transpose([0, 2, 1])).reshape([batch_size, -1, height, width])
        out = x + self.gamma * context
        return out

class DecoderBlock(nn.Layer):
    def __init__(self, in_channels, out_channels):
        super(DecoderBlock, self).__init__()
        self.conv1 = nn.Conv2D(in_channels, out_channels, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2D(out_channels, out_channels, kernel_size=3, padding=1)
        self.attention = AttentionBlock(in_channels, in_channels)

    def forward(self, x, encoder_output):
        x = nn.functional.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
        x = paddle.concat([x, encoder_output], axis=1)
        x = self.attention(x, encoder_output)
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        return x

class Unet(nn.Layer):
    def __init__(self, in_channels, out_channels):
        super(Unet, self).__init__()
        self.encoder1 = nn.Sequential(
            nn.Conv2D(in_channels, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2D(64, 64, kernel_size=3, padding=1),
            nn.ReLU()
        )
        self.encoder2 = nn.Sequential(
            nn.MaxPool2D(kernel_size=2, stride=2),
            nn.Conv2D(64, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2D(128, 128, kernel_size=3, padding=1),
            nn.ReLU()
        )
        self.encoder3 = nn.Sequential(
            nn.MaxPool2D(kernel_size=2, stride=2),
            nn.Conv2D(128, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2D(256, 256, kernel_size=3, padding=1),
            nn.ReLU()
        )
        self.decoder3 = nn.Sequential(
            DecoderBlock(256 + 128, 128),
            DecoderBlock(128, 64),
            nn.Conv2D(64, out_channels, kernel_size=1)
        )
        self.decoder2 = nn.Sequential(
            DecoderBlock(128 + 64, 64),
            DecoderBlock(64, 32),
            nn.Conv2D(32, out_channels, kernel_size=1)
        )
        self.decoder1 = nn.Sequential(
            DecoderBlock(64 + 32, 32),
            DecoderBlock(32, 32),
            nn.Conv2D(32, out_channels, kernel_size=1)
        )

    def forward(self, x):
        encoder1_output = self.encoder1(x)
        encoder2_output = self.encoder2(encoder1_output)
        encoder3_output = self.encoder3(encoder2_output)
        decoder3_output = self.decoder3(encoder3_output, encoder2_output)
        decoder2_output = self.decoder2(decoder3_output, encoder1_output)
        decoder1_output = self.decoder1(decoder2_output, x)
        return decoder1_output

在上述代码中，我们首先定义了一个AttentionBlock类来实现attention机制。在forward函数中，我们首先将输入x和encoder_output通过三个卷积层分别转换成query、key和value，然后计算得到注意力矩阵attention，再将attention与value相乘得到上下文向量context，最后将context和原始输入x加权相加得到输出out。

接着，我们定义了一个DecoderBlock类来实现decoder部分的每一个层。在forward函数中，我们首先对输入x进行上采样，然后将上采样后的结果和encoder对应层的输出encoder_output进行concat，再通过attention机制得到注意力加权的encoder_output，最后经过两个卷积层得到输出。

最后，我们定义了整个Unet模型，包括三个encoder和三个decoder。在forward函数中，我们首先将输入x经过encoder1、encoder2和encoder3，然后将encoder3的输出和encoder2的输出作为decoder3的输入，将decoder3的输出和encoder1的输出作为decoder2的输入，将decoder2的输出和输入x作为decoder1的输入，得到最终的输出。

这样，在Unet的decoder部分添加attention机制后，可以有效提高模型的准确度。