super(TRANS_BLOCK,self).__init__()

这段代码是Python中调用父类构造函数的语法，其中TRANS_BLOCK是当前类的名称。它的作用是在当前类的构造函数中调用父类的构造函数，以便继承父类的属性和方法。具体来说，super(TRANS_BLOCK,self)返回一个代理对象，它会将方法调用转发给当前类的父类。然后我们可以通过这个代理对象调用父类的构造函数，初始化父类的属性。

相关问题

densenet-attention代码实现

以下是使用PyTorch实现的DenseNet-Attention网络的代码：

import torch
import torch.nn as nn

class Bottleneck(nn.Module):
    def __init__(self, in_channels, growth_rate):
        super(Bottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.conv1 = nn.Conv2d(in_channels, 4*growth_rate, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(4*growth_rate)
        self.conv2 = nn.Conv2d(4*growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)

    def forward(self, x):
        out = self.conv1(torch.relu(self.bn1(x)))
        out = self.conv2(torch.relu(self.bn2(out)))
        out = torch.cat((out, x), dim=1)
        return out

class DenseNet(nn.Module):
    def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10):
        super(DenseNet, self).__init__()
        self.growth_rate = growth_rate

        num_planes = 2*growth_rate
        self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False)

        self.dense1 = self._make_dense_layers(block, num_planes, nblocks[0])
        num_planes += nblocks[0]*growth_rate
        out_planes = int(reduction*num_planes)
        self.trans1 = Transition(num_planes, out_planes)
        num_planes = out_planes

        self.dense2 = self._make_dense_layers(block, num_planes, nblocks[1])
        num_planes += nblocks[1]*growth_rate
        out_planes = int(reduction*num_planes)
        self.trans2 = Transition(num_planes, out_planes)
        num_planes = out_planes

        self.dense3 = self._make_dense_layers(block, num_planes, nblocks[2])
        num_planes += nblocks[2]*growth_rate
        out_planes = int(reduction*num_planes)
        self.trans3 = Transition(num_planes, out_planes)
        num_planes = out_planes

        self.dense4 = self._make_dense_layers(block, num_planes, nblocks[3])
        num_planes += nblocks[3]*growth_rate

        self.bn = nn.BatchNorm2d(num_planes)
        self.linear = nn.Linear(num_planes, num_classes)

        self.avgpool = nn.AdaptiveAvgPool2d((1,1))

    def _make_dense_layers(self, block, in_planes, nblock):
        layers = []
        for i in range(nblock):
            layers.append(block(in_planes, self.growth_rate))
            in_planes += self.growth_rate
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.trans1(self.dense1(out))
        out = self.trans2(self.dense2(out))
        out = self.trans3(self.dense3(out))
        out = self.dense4(out)
        out = self.avgpool(torch.relu(self.bn(out)))
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out

class Transition(nn.Module):
    def __init__(self, in_planes, out_planes):
        super(Transition, self).__init__()
        self.bn = nn.BatchNorm2d(in_planes)
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)

    def forward(self, x):
        out = self.conv(torch.relu(self.bn(x)))
        out = nn.functional.avg_pool2d(out, 2)
        return out

class DenseNetAttention(nn.Module):
    def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10):
        super(DenseNetAttention, self).__init__()
        self.growth_rate = growth_rate

        num_planes = 2*growth_rate
        self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False)

        self.dense1 = self._make_dense_layers(block, num_planes, nblocks[0])
        num_planes += nblocks[0]*growth_rate
        out_planes = int(reduction*num_planes)
        self.trans1 = Transition(num_planes, out_planes)
        num_planes = out_planes

        self.dense2 = self._make_dense_layers(block, num_planes, nblocks[1])
        num_planes += nblocks[1]*growth_rate
        out_planes = int(reduction*num_planes)
        self.trans2 = Transition(num_planes, out_planes)
        num_planes = out_planes

        self.dense3 = self._make_dense_layers(block, num_planes, nblocks[2])
        num_planes += nblocks[2]*growth_rate
        out_planes = int(reduction*num_planes)
        self.trans3 = Transition(num_planes, out_planes)
        num_planes = out_planes

        self.dense4 = self._make_dense_layers(block, num_planes, nblocks[3])
        num_planes += nblocks[3]*growth_rate

        self.bn = nn.BatchNorm2d(num_planes)
        self.global_avgpool = nn.AdaptiveAvgPool2d((1,1))
        self.fc1 = nn.Linear(num_planes, num_classes)
        self.fc2 = nn.Linear(num_planes, num_classes)

        self.avgpool = nn.AdaptiveAvgPool2d((1,1))

    def _make_dense_layers(self, block, in_planes, nblock):
        layers = []
        for i in range(nblock):
            layers.append(block(in_planes, self.growth_rate))
            in_planes += self.growth_rate
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.trans1(self.dense1(out))
        out = self.trans2(self.dense2(out))
        out = self.trans3(self.dense3(out))
        out = self.dense4(out)

        attention = self.global_avgpool(torch.relu(self.bn(out)))
        attention = attention.view(attention.size(0), -1)
        attention = self.fc1(attention)
        attention = torch.sigmoid(attention)

        out = out * attention.view(-1, 1024, 1, 1)

        out = self.avgpool(torch.relu(self.bn(out)))
        out = out.view(out.size(0), -1)
        out = self.fc2(out)
        return out

在这个实现中，我们首先定义了一个Bottleneck模块，它是DenseNet的基本构建块。然后我们定义了DenseNet模型，其中包含多个Bottleneck模块和过渡层。最后，我们定义了DenseNetAttention模型，它在DenseNet模型的基础上添加了注意力机制。

在DenseNetAttention模型中，我们首先计算全局平均池化，然后通过一个全连接层得到注意力分数。我们将注意力分数应用于特征图中的每个通道，以产生加权特征图。最后，我们通过全局平均池化和一个全连接层来预测类别。

mindspore densenet代码

以下是用MindSpore实现DenseNet的代码：

import mindspore.nn as nn
from mindspore.ops import operations as P


class DenseLayer(nn.Cell):
    def __init__(self, in_channels, growth_rate):
        super(DenseLayer, self).__init__()
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=growth_rate, kernel_size=3, padding=1, has_bias=False)
        self.relu = nn.ReLU()
        self.concat = P.Concat(axis=1)

    def construct(self, x):
        out = self.conv(x)
        out = self.relu(out)
        out = self.concat((x, out))
        return out


class DenseBlock(nn.Cell):
    def __init__(self, in_channels, growth_rate, num_layers):
        super(DenseBlock, self).__init__()
        self.layers = nn.SequentialCell()
        for i in range(num_layers):
            self.layers.append(DenseLayer(in_channels + i * growth_rate, growth_rate))

    def construct(self, x):
        out = x
        for layer in self.layers:
            out = layer(out)
        return out


class TransitionLayer(nn.Cell):
    def __init__(self, in_channels, out_channels):
        super(TransitionLayer, self).__init__()
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, has_bias=False)
        self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2)
        self.relu = nn.ReLU()

    def construct(self, x):
        out = self.conv(x)
        out = self.avg_pool(out)
        out = self.relu(out)
        return out


class DenseNet(nn.Cell):
    def __init__(self, num_classes=10, growth_rate=12, block_config=(6, 12, 24, 16)):
        super(DenseNet, self).__init__()

        self.conv = nn.Conv2d(in_channels=3, out_channels=2 * growth_rate, kernel_size=3, padding=1, has_bias=False)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')

        self.dense_block1 = DenseBlock(2 * growth_rate, growth_rate, block_config[0])
        in_channels1 = 2 * growth_rate + block_config[0] * growth_rate
        out_channels1 = in_channels1 // 2
        self.trans_layer1 = TransitionLayer(in_channels1, out_channels1)

        self.dense_block2 = DenseBlock(out_channels1, growth_rate, block_config[1])
        in_channels2 = out_channels1 + block_config[1] * growth_rate
        out_channels2 = in_channels2 // 2
        self.trans_layer2 = TransitionLayer(in_channels2, out_channels2)

        self.dense_block3 = DenseBlock(out_channels2, growth_rate, block_config[2])
        in_channels3 = out_channels2 + block_config[2] * growth_rate
        out_channels3 = in_channels3 // 2
        self.trans_layer3 = TransitionLayer(in_channels3, out_channels3)

        self.dense_block4 = DenseBlock(out_channels3, growth_rate, block_config[3])
        in_channels4 = out_channels3 + block_config[3] * growth_rate

        self.avg_pool = nn.AvgPool2d(kernel_size=8)
        self.flatten = nn.Flatten()
        self.fc = nn.Dense(in_channels4, num_classes)

    def construct(self, x):
        out = self.conv(x)
        out = self.relu(out)
        out = self.pool(out)

        out = self.dense_block1(out)
        out = self.trans_layer1(out)

        out = self.dense_block2(out)
        out = self.trans_layer2(out)

        out = self.dense_block3(out)
        out = self.trans_layer3(out)

        out = self.dense_block4(out)

        out = self.avg_pool(out)
        out = self.flatten(out)
        out = self.fc(out)

        return out

这是一个包含了DenseNet各个模块的代码，其中包括了DenseLayer、DenseBlock、TransitionLayer和DenseNet等模块的实现。然后，我们可以通过实例化DenseNet并传入相应的参数来构建模型。