DenseNet代码复现＋超详细注释（PyTorch）(densenet详解)

DenseNet代码复现＋超详细注释（PyTorch）

关于DenseNet的原理和具体细节，可参见上篇解读：经典神经网络论文超详细解读（六）——DenseNet学习笔记（翻译＋精读＋代码复现）

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接下来我们就来复现一下代码。

DenseNet模型简介

整个DenseNet模型主要包含三个核心细节结构，分别是DenseLayer(整个模型最基础的原子单元，完成一次最基础的特征提取，如下图第三行)、DenseBlock(整个模型密集连接的基础单元，如下图第二行左侧部分)和Transition(不同密集连接之间的过渡单元，如下图第二行右侧部分)，通过以上结构的拼接+分类层即可完成整个模型的搭建。

DenseLayer层 包含BN + Relu + 1*1Conv + BN + Relu + 3*3Conv。在DenseBlock中，各个层的特征图大小一致，可以在channel维度上连接。所有DenseBlock中各个层卷积之后均输出 k个特征图，即得到的特征图的channel数为k ，或者说采用 k 个卷积核。 其中，k 在DenseNet称为growth rate，这是一个超参数。一般情况下使用较小的k（比如12），就可以得到较佳的性能。

DenseBlock模块 其实就是堆叠一定数量的DenseLayer层，在整个DenseBlock模块内不同DenseLayer层之间会发生密集连接，在DenseBlock模块内特征层宽度不变，不存在stride=2或者池化的情况。

Transition模块 包含BN + Relu + 1*1Conv + 2*2AvgPool，1*1Conv负责降低通道数，2*2AvgPool负责降低特征层宽度，降低到1/2。另外，Transition层可以起到压缩模型的作用。假定Transition的上接DenseBlock得到的特征图channels数为m，Transition层可以产生 ⌊θm⌋个特征（通过卷积层），其中θ∈(0,1] 是压缩系数（compression rate）。当θ=1 时，特征个数经过Transition层没有变化，即无压缩，而当压缩系数小于1时，这种结构称为DenseNet-C，文中使用θ=0.5 。对于使用bottleneck层的DenseBlock结构和压缩系数小于1的Transition组合结构称为DenseNet-BC。  

一、构造初始卷积层

初始卷积层是由一个7*7的conv层和3*3的pooling层组成，stride都为2

 代码'''-------------一、构造初始卷积层-----------------------------'''def Conv1(in_planes, places, stride=2): return nn.Sequential( nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False), nn.BatchNorm2d(places), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1) )二、构造Dense Block模块

2.1 构造Dense Block内部结构

DenserLayer是Dense Block内部结构，BN+ReLU+1x1 Conv+BN+ReLU+3x3 Conv，最后也加入dropout层以用于训练过程。主要用于特征的提取等工作，控制输入经过网络后，输入的模型的特征数量，比如第一个模型输入是5个特征层，后面一个是四个特征层等。

但是可以发现一点，这个和别的网络有所不同的是，每一个DenseLayer虽然特征提取的函数一样的，因为要结合前面的特征最为新的网络的输入，所以模型每次的输入的维度是不同。比如groth_rate = 32,每次输入特征都会在原来的基础上增加32个通道。因此需要在函数中定义 num_layer个不同输入的网络模型，这也正是模型函数有意思的一点。

代码'''---（1）构造Dense Block内部结构---'''#BN+ReLU+1x1 Conv+BN+ReLU+3x3 Convclass _DenseLayer(nn.Module): def __init__(self, inplace, growth_rate, bn_size, drop_rate=0): super(_DenseLayer, self).__init__() self.drop_rate = drop_rate self.dense_layer = nn.Sequential( nn.BatchNorm2d(inplace), nn.ReLU(inplace=True), # growth_rate：增长率。一层产生多少个特征图 nn.Conv2d(in_channels=inplace, out_channels=bn_size * growth_rate, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(bn_size * growth_rate), nn.ReLU(inplace=True), nn.Conv2d(in_channels=bn_size * growth_rate, out_channels=growth_rate, kernel_size=3, stride=1, padding=1, bias=False), ) self.dropout = nn.Dropout(p=self.drop_rate) def forward(self, x): y = self.dense_layer(x) if self.drop_rate > 0: y = self.dropout(y) return torch.cat([x, y], 1)2.2 构造Dense Block模块

再实现DenseBlock模块，内部是密集连接方式（输入特征数线性增长）

代码'''---（2）构造Dense Block模块---'''class DenseBlock(nn.Module): def __init__(self, num_layers, inplances, growth_rate, bn_size , drop_rate=0): super(DenseBlock, self).__init__() layers = [] # 随着layer层数的增加，每增加一层，输入的特征图就增加一倍growth_rate for i in range(num_layers): layers.append(_DenseLayer(inplances + i * growth_rate, growth_rate, bn_size, drop_rate)) self.layers = nn.Sequential(*layers) def forward(self, x): return self.layers(x)三、构造Transition模块

Transition模块降低模型复杂度。包含BN + Relu + 1*1Conv + 2*2AvgPool结构由于每个稠密块都会带来通道数的增加，使用过多则会带来过于复杂的模型。过渡层用来控制模型复杂度。它通过1 × 1 卷积层来减小通道数，并使用步幅为2的平均池化层减半高和宽。

代码'''-------------三、构造Transition层-----------------------------'''#BN+1×1Conv+2×2AveragePoolingclass _TransitionLayer(nn.Module): def __init__(self, inplace, plance): super(_TransitionLayer, self).__init__() self.transition_layer = nn.Sequential( nn.BatchNorm2d(inplace), nn.ReLU(inplace=True), nn.Conv2d(in_channels=inplace,out_channels=plance,kernel_size=1,stride=1,padding=0,bias=False), nn.AvgPool2d(kernel_size=2,stride=2), ) def forward(self, x): return self.transition_layer(x)四、搭建DenseNet网络

DenseNet如下图所示，主要是由多个DenseBlock组成

代码 '''-------------四、搭建DenseNet网络-----------------------------'''class DenseNet(nn.Module): def __init__(self, init_channels=64, growth_rate=32, blocks=[6, 12, 24, 16],num_classes=10): super(DenseNet, self).__init__() bn_size = 4 drop_rate = 0 self.conv1 = Conv1(in_planes=3, places=init_channels) blocks*4 #第一次执行特征的维度来自于前面的特征提取 num_features = init_channels # 第1个DenseBlock有6个DenseLayer, 执行DenseBlock（6,64,32,4） self.layer1 = DenseBlock(num_layers=blocks[0], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[0] * growth_rate # 第1个transition 执行 _TransitionLayer（256,128） self.transition1 = _TransitionLayer(inplace=num_features, plance=num_features // 2) #num_features减少为原来的一半，执行第1回合之后，第2个DenseBlock的输入的feature应该是：num_features = 128 num_features = num_features // 2 # 第2个DenseBlock有12个DenseLayer, 执行DenseBlock（12,128,32,4） self.layer2 = DenseBlock(num_layers=blocks[1], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[1] * growth_rate # 第2个transition 执行 _TransitionLayer（512,256） self.transition2 = _TransitionLayer(inplace=num_features, plance=num_features // 2) # num_features减少为原来的一半，执行第2回合之后，第3个DenseBlock的输入的feature应该是：num_features = 256 num_features = num_features // 2 # 第3个DenseBlock有24个DenseLayer, 执行DenseBlock（24,256,32,4） self.layer3 = DenseBlock(num_layers=blocks[2], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[2] * growth_rate # 第3个transition 执行 _TransitionLayer（1024,512） self.transition3 = _TransitionLayer(inplace=num_features, plance=num_features // 2) # num_features减少为原来的一半，执行第3回合之后，第4个DenseBlock的输入的feature应该是：num_features = 512 num_features = num_features // 2 # 第4个DenseBlock有16个DenseLayer, 执行DenseBlock（16,512,32,4） self.layer4 = DenseBlock(num_layers=blocks[3], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[3] * growth_rate self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = nn.Linear(num_features, num_classes) def forward(self, x): x = self.conv1(x) x = self.layer1(x) x = self.transition1(x) x = self.layer2(x) x = self.transition2(x) x = self.layer3(x) x = self.transition3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x五、网络模型的创建和测试5.1 网络模型创建打印 densenet121

 Q: densenet121 中的121 是如何来的？ 从设置的参数可以看出来，block_num=4,且每个Block的num_layer=(6, 12, 24, 16),则总共有58个denselayer。 从代码中可以知道每个denselayer包含两个卷积。总共三个 _Transition层，每个层一个卷积。在最开始的时候一个卷积，结束的时候一个全连接层。则总计：58*2+3+1+1=121

代码if __name__=='__main__': # model = torchvision.models.densenet121() model = DenseNet121() print(model) input = torch.randn(1, 3, 224, 224) out = model(input) print(out.shape)打印模型如下DenseNet( (features): Sequential( (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu0): ReLU(inplace=True) (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (denseblock1): _DenseBlock( (denselayer1): _DenseLayer( (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer( (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer( (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer( (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer( (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer( (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (transition1): _Transition( (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) ) (denseblock2): _DenseBlock( (denselayer1): _DenseLayer( (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer( (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer( (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer( (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer( (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer( (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer( (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer( (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer9): _DenseLayer( (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer( (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer( (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer( (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (transition2): _Transition( (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) ) (denseblock3): _DenseBlock( (denselayer1): _DenseLayer( (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer( (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer( (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer( (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer( (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer( (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer( (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer( (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer9): _DenseLayer( (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer( (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer( (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer( (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer13): _DenseLayer( (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer14): _DenseLayer( (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer15): _DenseLayer( (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer16): _DenseLayer( (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer17): _DenseLayer( (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer18): _DenseLayer( (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer19): _DenseLayer( (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer20): _DenseLayer( (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer21): _DenseLayer( (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer22): _DenseLayer( (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer23): _DenseLayer( (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer24): _DenseLayer( (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (transition3): _Transition( (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d(kernel_size=2, stride=2, padding=0) ) (denseblock4): _DenseBlock( (denselayer1): _DenseLayer( (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer( (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer( (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer( (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer( (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer( (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer( (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer( (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer9): _DenseLayer( (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer( (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer( (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer( (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer13): _DenseLayer( (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer14): _DenseLayer( (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer15): _DenseLayer( (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer16): _DenseLayer( (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu1): ReLU(inplace=True) (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu2): ReLU(inplace=True) (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (classifier): Linear(in_features=1024, out_features=1000, bias=True))5.2 使用torchsummary打印每个网络模型的详细信息 代码if __name__ == '__main__': net = DenseNet(num_classes=10).cuda() summary(net, (3, 224, 224))打印模型如下PyTorch Version: 1.12.0+cu113Torchvision Version: 0.13.0+cu113---------------------------------------------------------------- Layer (type) Output Shape Param #================================================================ Conv2d-1 [-1, 64, 112, 112] 9,408 BatchNorm2d-2 [-1, 64, 112, 112] 128 ReLU-3 [-1, 64, 112, 112] 0 MaxPool2d-4 [-1, 64, 56, 56] 0 BatchNorm2d-5 [-1, 64, 56, 56] 128 ReLU-6 [-1, 64, 56, 56] 0 Conv2d-7 [-1, 128, 56, 56] 8,192 BatchNorm2d-8 [-1, 128, 56, 56] 256 ReLU-9 [-1, 128, 56, 56] 0 Conv2d-10 [-1, 32, 56, 56] 36,864 _DenseLayer-11 [-1, 96, 56, 56] 0 BatchNorm2d-12 [-1, 96, 56, 56] 192 ReLU-13 [-1, 96, 56, 56] 0 Conv2d-14 [-1, 128, 56, 56] 12,288 BatchNorm2d-15 [-1, 128, 56, 56] 256 ReLU-16 [-1, 128, 56, 56] 0 Conv2d-17 [-1, 32, 56, 56] 36,864 _DenseLayer-18 [-1, 128, 56, 56] 0 BatchNorm2d-19 [-1, 128, 56, 56] 256 ReLU-20 [-1, 128, 56, 56] 0 Conv2d-21 [-1, 128, 56, 56] 16,384 BatchNorm2d-22 [-1, 128, 56, 56] 256 ReLU-23 [-1, 128, 56, 56] 0 Conv2d-24 [-1, 32, 56, 56] 36,864 _DenseLayer-25 [-1, 160, 56, 56] 0 BatchNorm2d-26 [-1, 160, 56, 56] 320 ReLU-27 [-1, 160, 56, 56] 0 Conv2d-28 [-1, 128, 56, 56] 20,480 BatchNorm2d-29 [-1, 128, 56, 56] 256 ReLU-30 [-1, 128, 56, 56] 0 Conv2d-31 [-1, 32, 56, 56] 36,864 _DenseLayer-32 [-1, 192, 56, 56] 0 BatchNorm2d-33 [-1, 192, 56, 56] 384 ReLU-34 [-1, 192, 56, 56] 0 Conv2d-35 [-1, 128, 56, 56] 24,576 BatchNorm2d-36 [-1, 128, 56, 56] 256 ReLU-37 [-1, 128, 56, 56] 0 Conv2d-38 [-1, 32, 56, 56] 36,864 _DenseLayer-39 [-1, 224, 56, 56] 0 BatchNorm2d-40 [-1, 224, 56, 56] 448 ReLU-41 [-1, 224, 56, 56] 0 Conv2d-42 [-1, 128, 56, 56] 28,672 BatchNorm2d-43 [-1, 128, 56, 56] 256 ReLU-44 [-1, 128, 56, 56] 0 Conv2d-45 [-1, 32, 56, 56] 36,864 _DenseLayer-46 [-1, 256, 56, 56] 0 DenseBlock-47 [-1, 256, 56, 56] 0 BatchNorm2d-48 [-1, 256, 56, 56] 512 ReLU-49 [-1, 256, 56, 56] 0 Conv2d-50 [-1, 128, 56, 56] 32,768 AvgPool2d-51 [-1, 128, 28, 28] 0 _TransitionLayer-52 [-1, 128, 28, 28] 0 BatchNorm2d-53 [-1, 128, 28, 28] 256 ReLU-54 [-1, 128, 28, 28] 0 Conv2d-55 [-1, 128, 28, 28] 16,384 BatchNorm2d-56 [-1, 128, 28, 28] 256 ReLU-57 [-1, 128, 28, 28] 0 Conv2d-58 [-1, 32, 28, 28] 36,864 _DenseLayer-59 [-1, 160, 28, 28] 0 BatchNorm2d-60 [-1, 160, 28, 28] 320 ReLU-61 [-1, 160, 28, 28] 0 Conv2d-62 [-1, 128, 28, 28] 20,480 BatchNorm2d-63 [-1, 128, 28, 28] 256 ReLU-64 [-1, 128, 28, 28] 0 Conv2d-65 [-1, 32, 28, 28] 36,864 _DenseLayer-66 [-1, 192, 28, 28] 0 BatchNorm2d-67 [-1, 192, 28, 28] 384 ReLU-68 [-1, 192, 28, 28] 0 Conv2d-69 [-1, 128, 28, 28] 24,576 BatchNorm2d-70 [-1, 128, 28, 28] 256 ReLU-71 [-1, 128, 28, 28] 0 Conv2d-72 [-1, 32, 28, 28] 36,864 _DenseLayer-73 [-1, 224, 28, 28] 0 BatchNorm2d-74 [-1, 224, 28, 28] 448 ReLU-75 [-1, 224, 28, 28] 0 Conv2d-76 [-1, 128, 28, 28] 28,672 BatchNorm2d-77 [-1, 128, 28, 28] 256 ReLU-78 [-1, 128, 28, 28] 0 Conv2d-79 [-1, 32, 28, 28] 36,864 _DenseLayer-80 [-1, 256, 28, 28] 0 BatchNorm2d-81 [-1, 256, 28, 28] 512 ReLU-82 [-1, 256, 28, 28] 0 Conv2d-83 [-1, 128, 28, 28] 32,768 BatchNorm2d-84 [-1, 128, 28, 28] 256 ReLU-85 [-1, 128, 28, 28] 0 Conv2d-86 [-1, 32, 28, 28] 36,864 _DenseLayer-87 [-1, 288, 28, 28] 0 BatchNorm2d-88 [-1, 288, 28, 28] 576 ReLU-89 [-1, 288, 28, 28] 0 Conv2d-90 [-1, 128, 28, 28] 36,864 BatchNorm2d-91 [-1, 128, 28, 28] 256 ReLU-92 [-1, 128, 28, 28] 0 Conv2d-93 [-1, 32, 28, 28] 36,864 _DenseLayer-94 [-1, 320, 28, 28] 0 BatchNorm2d-95 [-1, 320, 28, 28] 640 ReLU-96 [-1, 320, 28, 28] 0 Conv2d-97 [-1, 128, 28, 28] 40,960 BatchNorm2d-98 [-1, 128, 28, 28] 256 ReLU-99 [-1, 128, 28, 28] 0 Conv2d-100 [-1, 32, 28, 28] 36,864 _DenseLayer-101 [-1, 352, 28, 28] 0 BatchNorm2d-102 [-1, 352, 28, 28] 704 ReLU-103 [-1, 352, 28, 28] 0 Conv2d-104 [-1, 128, 28, 28] 45,056 BatchNorm2d-105 [-1, 128, 28, 28] 256 ReLU-106 [-1, 128, 28, 28] 0 Conv2d-107 [-1, 32, 28, 28] 36,864 _DenseLayer-108 [-1, 384, 28, 28] 0 BatchNorm2d-109 [-1, 384, 28, 28] 768 ReLU-110 [-1, 384, 28, 28] 0 Conv2d-111 [-1, 128, 28, 28] 49,152 BatchNorm2d-112 [-1, 128, 28, 28] 256 ReLU-113 [-1, 128, 28, 28] 0 Conv2d-114 [-1, 32, 28, 28] 36,864 _DenseLayer-115 [-1, 416, 28, 28] 0 BatchNorm2d-116 [-1, 416, 28, 28] 832 ReLU-117 [-1, 416, 28, 28] 0 Conv2d-118 [-1, 128, 28, 28] 53,248 BatchNorm2d-119 [-1, 128, 28, 28] 256 ReLU-120 [-1, 128, 28, 28] 0 Conv2d-121 [-1, 32, 28, 28] 36,864 _DenseLayer-122 [-1, 448, 28, 28] 0 BatchNorm2d-123 [-1, 448, 28, 28] 896 ReLU-124 [-1, 448, 28, 28] 0 Conv2d-125 [-1, 128, 28, 28] 57,344 BatchNorm2d-126 [-1, 128, 28, 28] 256 ReLU-127 [-1, 128, 28, 28] 0 Conv2d-128 [-1, 32, 28, 28] 36,864 _DenseLayer-129 [-1, 480, 28, 28] 0 BatchNorm2d-130 [-1, 480, 28, 28] 960 ReLU-131 [-1, 480, 28, 28] 0 Conv2d-132 [-1, 128, 28, 28] 61,440 BatchNorm2d-133 [-1, 128, 28, 28] 256 ReLU-134 [-1, 128, 28, 28] 0 Conv2d-135 [-1, 32, 28, 28] 36,864 _DenseLayer-136 [-1, 512, 28, 28] 0 DenseBlock-137 [-1, 512, 28, 28] 0 BatchNorm2d-138 [-1, 512, 28, 28] 1,024 ReLU-139 [-1, 512, 28, 28] 0 Conv2d-140 [-1, 256, 28, 28] 131,072 AvgPool2d-141 [-1, 256, 14, 14] 0_TransitionLayer-142 [-1, 256, 14, 14] 0 BatchNorm2d-143 [-1, 256, 14, 14] 512 ReLU-144 [-1, 256, 14, 14] 0 Conv2d-145 [-1, 128, 14, 14] 32,768 BatchNorm2d-146 [-1, 128, 14, 14] 256 ReLU-147 [-1, 128, 14, 14] 0 Conv2d-148 [-1, 32, 14, 14] 36,864 _DenseLayer-149 [-1, 288, 14, 14] 0 BatchNorm2d-150 [-1, 288, 14, 14] 576 ReLU-151 [-1, 288, 14, 14] 0 Conv2d-152 [-1, 128, 14, 14] 36,864 BatchNorm2d-153 [-1, 128, 14, 14] 256 ReLU-154 [-1, 128, 14, 14] 0 Conv2d-155 [-1, 32, 14, 14] 36,864 _DenseLayer-156 [-1, 320, 14, 14] 0 BatchNorm2d-157 [-1, 320, 14, 14] 640 ReLU-158 [-1, 320, 14, 14] 0 Conv2d-159 [-1, 128, 14, 14] 40,960 BatchNorm2d-160 [-1, 128, 14, 14] 256 ReLU-161 [-1, 128, 14, 14] 0 Conv2d-162 [-1, 32, 14, 14] 36,864 _DenseLayer-163 [-1, 352, 14, 14] 0 BatchNorm2d-164 [-1, 352, 14, 14] 704 ReLU-165 [-1, 352, 14, 14] 0 Conv2d-166 [-1, 128, 14, 14] 45,056 BatchNorm2d-167 [-1, 128, 14, 14] 256 ReLU-168 [-1, 128, 14, 14] 0 Conv2d-169 [-1, 32, 14, 14] 36,864 _DenseLayer-170 [-1, 384, 14, 14] 0 BatchNorm2d-171 [-1, 384, 14, 14] 768 ReLU-172 [-1, 384, 14, 14] 0 Conv2d-173 [-1, 128, 14, 14] 49,152 BatchNorm2d-174 [-1, 128, 14, 14] 256 ReLU-175 [-1, 128, 14, 14] 0 Conv2d-176 [-1, 32, 14, 14] 36,864 _DenseLayer-177 [-1, 416, 14, 14] 0 BatchNorm2d-178 [-1, 416, 14, 14] 832 ReLU-179 [-1, 416, 14, 14] 0 Conv2d-180 [-1, 128, 14, 14] 53,248 BatchNorm2d-181 [-1, 128, 14, 14] 256 ReLU-182 [-1, 128, 14, 14] 0 Conv2d-183 [-1, 32, 14, 14] 36,864 _DenseLayer-184 [-1, 448, 14, 14] 0 BatchNorm2d-185 [-1, 448, 14, 14] 896 ReLU-186 [-1, 448, 14, 14] 0 Conv2d-187 [-1, 128, 14, 14] 57,344 BatchNorm2d-188 [-1, 128, 14, 14] 256 ReLU-189 [-1, 128, 14, 14] 0 Conv2d-190 [-1, 32, 14, 14] 36,864 _DenseLayer-191 [-1, 480, 14, 14] 0 BatchNorm2d-192 [-1, 480, 14, 14] 960 ReLU-193 [-1, 480, 14, 14] 0 Conv2d-194 [-1, 128, 14, 14] 61,440 BatchNorm2d-195 [-1, 128, 14, 14] 256 ReLU-196 [-1, 128, 14, 14] 0 Conv2d-197 [-1, 32, 14, 14] 36,864 _DenseLayer-198 [-1, 512, 14, 14] 0 BatchNorm2d-199 [-1, 512, 14, 14] 1,024 ReLU-200 [-1, 512, 14, 14] 0 Conv2d-201 [-1, 128, 14, 14] 65,536 BatchNorm2d-202 [-1, 128, 14, 14] 256 ReLU-203 [-1, 128, 14, 14] 0 Conv2d-204 [-1, 32, 14, 14] 36,864 _DenseLayer-205 [-1, 544, 14, 14] 0 BatchNorm2d-206 [-1, 544, 14, 14] 1,088 ReLU-207 [-1, 544, 14, 14] 0 Conv2d-208 [-1, 128, 14, 14] 69,632 BatchNorm2d-209 [-1, 128, 14, 14] 256 ReLU-210 [-1, 128, 14, 14] 0 Conv2d-211 [-1, 32, 14, 14] 36,864 _DenseLayer-212 [-1, 576, 14, 14] 0 BatchNorm2d-213 [-1, 576, 14, 14] 1,152 ReLU-214 [-1, 576, 14, 14] 0 Conv2d-215 [-1, 128, 14, 14] 73,728 BatchNorm2d-216 [-1, 128, 14, 14] 256 ReLU-217 [-1, 128, 14, 14] 0 Conv2d-218 [-1, 32, 14, 14] 36,864 _DenseLayer-219 [-1, 608, 14, 14] 0 BatchNorm2d-220 [-1, 608, 14, 14] 1,216 ReLU-221 [-1, 608, 14, 14] 0 Conv2d-222 [-1, 128, 14, 14] 77,824 BatchNorm2d-223 [-1, 128, 14, 14] 256 ReLU-224 [-1, 128, 14, 14] 0 Conv2d-225 [-1, 32, 14, 14] 36,864 _DenseLayer-226 [-1, 640, 14, 14] 0 BatchNorm2d-227 [-1, 640, 14, 14] 1,280 ReLU-228 [-1, 640, 14, 14] 0 Conv2d-229 [-1, 128, 14, 14] 81,920 BatchNorm2d-230 [-1, 128, 14, 14] 256 ReLU-231 [-1, 128, 14, 14] 0 Conv2d-232 [-1, 32, 14, 14] 36,864 _DenseLayer-233 [-1, 672, 14, 14] 0 BatchNorm2d-234 [-1, 672, 14, 14] 1,344 ReLU-235 [-1, 672, 14, 14] 0 Conv2d-236 [-1, 128, 14, 14] 86,016 BatchNorm2d-237 [-1, 128, 14, 14] 256 ReLU-238 [-1, 128, 14, 14] 0 Conv2d-239 [-1, 32, 14, 14] 36,864 _DenseLayer-240 [-1, 704, 14, 14] 0 BatchNorm2d-241 [-1, 704, 14, 14] 1,408 ReLU-242 [-1, 704, 14, 14] 0 Conv2d-243 [-1, 128, 14, 14] 90,112 BatchNorm2d-244 [-1, 128, 14, 14] 256 ReLU-245 [-1, 128, 14, 14] 0 Conv2d-246 [-1, 32, 14, 14] 36,864 _DenseLayer-247 [-1, 736, 14, 14] 0 BatchNorm2d-248 [-1, 736, 14, 14] 1,472 ReLU-249 [-1, 736, 14, 14] 0 Conv2d-250 [-1, 128, 14, 14] 94,208 BatchNorm2d-251 [-1, 128, 14, 14] 256 ReLU-252 [-1, 128, 14, 14] 0 Conv2d-253 [-1, 32, 14, 14] 36,864 _DenseLayer-254 [-1, 768, 14, 14] 0 BatchNorm2d-255 [-1, 768, 14, 14] 1,536 ReLU-256 [-1, 768, 14, 14] 0 Conv2d-257 [-1, 128, 14, 14] 98,304 BatchNorm2d-258 [-1, 128, 14, 14] 256 ReLU-259 [-1, 128, 14, 14] 0 Conv2d-260 [-1, 32, 14, 14] 36,864 _DenseLayer-261 [-1, 800, 14, 14] 0 BatchNorm2d-262 [-1, 800, 14, 14] 1,600 ReLU-263 [-1, 800, 14, 14] 0 Conv2d-264 [-1, 128, 14, 14] 102,400 BatchNorm2d-265 [-1, 128, 14, 14] 256 ReLU-266 [-1, 128, 14, 14] 0 Conv2d-267 [-1, 32, 14, 14] 36,864 _DenseLayer-268 [-1, 832, 14, 14] 0 BatchNorm2d-269 [-1, 832, 14, 14] 1,664 ReLU-270 [-1, 832, 14, 14] 0 Conv2d-271 [-1, 128, 14, 14] 106,496 BatchNorm2d-272 [-1, 128, 14, 14] 256 ReLU-273 [-1, 128, 14, 14] 0 Conv2d-274 [-1, 32, 14, 14] 36,864 _DenseLayer-275 [-1, 864, 14, 14] 0 BatchNorm2d-276 [-1, 864, 14, 14] 1,728 ReLU-277 [-1, 864, 14, 14] 0 Conv2d-278 [-1, 128, 14, 14] 110,592 BatchNorm2d-279 [-1, 128, 14, 14] 256 ReLU-280 [-1, 128, 14, 14] 0 Conv2d-281 [-1, 32, 14, 14] 36,864 _DenseLayer-282 [-1, 896, 14, 14] 0 BatchNorm2d-283 [-1, 896, 14, 14] 1,792 ReLU-284 [-1, 896, 14, 14] 0 Conv2d-285 [-1, 128, 14, 14] 114,688 BatchNorm2d-286 [-1, 128, 14, 14] 256 ReLU-287 [-1, 128, 14, 14] 0 Conv2d-288 [-1, 32, 14, 14] 36,864 _DenseLayer-289 [-1, 928, 14, 14] 0 BatchNorm2d-290 [-1, 928, 14, 14] 1,856 ReLU-291 [-1, 928, 14, 14] 0 Conv2d-292 [-1, 128, 14, 14] 118,784 BatchNorm2d-293 [-1, 128, 14, 14] 256 ReLU-294 [-1, 128, 14, 14] 0 Conv2d-295 [-1, 32, 14, 14] 36,864 _DenseLayer-296 [-1, 960, 14, 14] 0 BatchNorm2d-297 [-1, 960, 14, 14] 1,920 ReLU-298 [-1, 960, 14, 14] 0 Conv2d-299 [-1, 128, 14, 14] 122,880 BatchNorm2d-300 [-1, 128, 14, 14] 256 ReLU-301 [-1, 128, 14, 14] 0 Conv2d-302 [-1, 32, 14, 14] 36,864 _DenseLayer-303 [-1, 992, 14, 14] 0 BatchNorm2d-304 [-1, 992, 14, 14] 1,984 ReLU-305 [-1, 992, 14, 14] 0 Conv2d-306 [-1, 128, 14, 14] 126,976 BatchNorm2d-307 [-1, 128, 14, 14] 256 ReLU-308 [-1, 128, 14, 14] 0 Conv2d-309 [-1, 32, 14, 14] 36,864 _DenseLayer-310 [-1, 1024, 14, 14] 0 DenseBlock-311 [-1, 1024, 14, 14] 0 BatchNorm2d-312 [-1, 1024, 14, 14] 2,048 ReLU-313 [-1, 1024, 14, 14] 0 Conv2d-314 [-1, 512, 14, 14] 524,288 AvgPool2d-315 [-1, 512, 7, 7] 0_TransitionLayer-316 [-1, 512, 7, 7] 0 BatchNorm2d-317 [-1, 512, 7, 7] 1,024 ReLU-318 [-1, 512, 7, 7] 0 Conv2d-319 [-1, 128, 7, 7] 65,536 BatchNorm2d-320 [-1, 128, 7, 7] 256 ReLU-321 [-1, 128, 7, 7] 0 Conv2d-322 [-1, 32, 7, 7] 36,864 _DenseLayer-323 [-1, 544, 7, 7] 0 BatchNorm2d-324 [-1, 544, 7, 7] 1,088 ReLU-325 [-1, 544, 7, 7] 0 Conv2d-326 [-1, 128, 7, 7] 69,632 BatchNorm2d-327 [-1, 128, 7, 7] 256 ReLU-328 [-1, 128, 7, 7] 0 Conv2d-329 [-1, 32, 7, 7] 36,864 _DenseLayer-330 [-1, 576, 7, 7] 0 BatchNorm2d-331 [-1, 576, 7, 7] 1,152 ReLU-332 [-1, 576, 7, 7] 0 Conv2d-333 [-1, 128, 7, 7] 73,728 BatchNorm2d-334 [-1, 128, 7, 7] 256 ReLU-335 [-1, 128, 7, 7] 0 Conv2d-336 [-1, 32, 7, 7] 36,864 _DenseLayer-337 [-1, 608, 7, 7] 0 BatchNorm2d-338 [-1, 608, 7, 7] 1,216 ReLU-339 [-1, 608, 7, 7] 0 Conv2d-340 [-1, 128, 7, 7] 77,824 BatchNorm2d-341 [-1, 128, 7, 7] 256 ReLU-342 [-1, 128, 7, 7] 0 Conv2d-343 [-1, 32, 7, 7] 36,864 _DenseLayer-344 [-1, 640, 7, 7] 0 BatchNorm2d-345 [-1, 640, 7, 7] 1,280 ReLU-346 [-1, 640, 7, 7] 0 Conv2d-347 [-1, 128, 7, 7] 81,920 BatchNorm2d-348 [-1, 128, 7, 7] 256 ReLU-349 [-1, 128, 7, 7] 0 Conv2d-350 [-1, 32, 7, 7] 36,864 _DenseLayer-351 [-1, 672, 7, 7] 0 BatchNorm2d-352 [-1, 672, 7, 7] 1,344 ReLU-353 [-1, 672, 7, 7] 0 Conv2d-354 [-1, 128, 7, 7] 86,016 BatchNorm2d-355 [-1, 128, 7, 7] 256 ReLU-356 [-1, 128, 7, 7] 0 Conv2d-357 [-1, 32, 7, 7] 36,864 _DenseLayer-358 [-1, 704, 7, 7] 0 BatchNorm2d-359 [-1, 704, 7, 7] 1,408 ReLU-360 [-1, 704, 7, 7] 0 Conv2d-361 [-1, 128, 7, 7] 90,112 BatchNorm2d-362 [-1, 128, 7, 7] 256 ReLU-363 [-1, 128, 7, 7] 0 Conv2d-364 [-1, 32, 7, 7] 36,864 _DenseLayer-365 [-1, 736, 7, 7] 0 BatchNorm2d-366 [-1, 736, 7, 7] 1,472 ReLU-367 [-1, 736, 7, 7] 0 Conv2d-368 [-1, 128, 7, 7] 94,208 BatchNorm2d-369 [-1, 128, 7, 7] 256 ReLU-370 [-1, 128, 7, 7] 0 Conv2d-371 [-1, 32, 7, 7] 36,864 _DenseLayer-372 [-1, 768, 7, 7] 0 BatchNorm2d-373 [-1, 768, 7, 7] 1,536 ReLU-374 [-1, 768, 7, 7] 0 Conv2d-375 [-1, 128, 7, 7] 98,304 BatchNorm2d-376 [-1, 128, 7, 7] 256 ReLU-377 [-1, 128, 7, 7] 0 Conv2d-378 [-1, 32, 7, 7] 36,864 _DenseLayer-379 [-1, 800, 7, 7] 0 BatchNorm2d-380 [-1, 800, 7, 7] 1,600 ReLU-381 [-1, 800, 7, 7] 0 Conv2d-382 [-1, 128, 7, 7] 102,400 BatchNorm2d-383 [-1, 128, 7, 7] 256 ReLU-384 [-1, 128, 7, 7] 0 Conv2d-385 [-1, 32, 7, 7] 36,864 _DenseLayer-386 [-1, 832, 7, 7] 0 BatchNorm2d-387 [-1, 832, 7, 7] 1,664 ReLU-388 [-1, 832, 7, 7] 0 Conv2d-389 [-1, 128, 7, 7] 106,496 BatchNorm2d-390 [-1, 128, 7, 7] 256 ReLU-391 [-1, 128, 7, 7] 0 Conv2d-392 [-1, 32, 7, 7] 36,864 _DenseLayer-393 [-1, 864, 7, 7] 0 BatchNorm2d-394 [-1, 864, 7, 7] 1,728 ReLU-395 [-1, 864, 7, 7] 0 Conv2d-396 [-1, 128, 7, 7] 110,592 BatchNorm2d-397 [-1, 128, 7, 7] 256 ReLU-398 [-1, 128, 7, 7] 0 Conv2d-399 [-1, 32, 7, 7] 36,864 _DenseLayer-400 [-1, 896, 7, 7] 0 BatchNorm2d-401 [-1, 896, 7, 7] 1,792 ReLU-402 [-1, 896, 7, 7] 0 Conv2d-403 [-1, 128, 7, 7] 114,688 BatchNorm2d-404 [-1, 128, 7, 7] 256 ReLU-405 [-1, 128, 7, 7] 0 Conv2d-406 [-1, 32, 7, 7] 36,864 _DenseLayer-407 [-1, 928, 7, 7] 0 BatchNorm2d-408 [-1, 928, 7, 7] 1,856 ReLU-409 [-1, 928, 7, 7] 0 Conv2d-410 [-1, 128, 7, 7] 118,784 BatchNorm2d-411 [-1, 128, 7, 7] 256 ReLU-412 [-1, 128, 7, 7] 0 Conv2d-413 [-1, 32, 7, 7] 36,864 _DenseLayer-414 [-1, 960, 7, 7] 0 BatchNorm2d-415 [-1, 960, 7, 7] 1,920 ReLU-416 [-1, 960, 7, 7] 0 Conv2d-417 [-1, 128, 7, 7] 122,880 BatchNorm2d-418 [-1, 128, 7, 7] 256 ReLU-419 [-1, 128, 7, 7] 0 Conv2d-420 [-1, 32, 7, 7] 36,864 _DenseLayer-421 [-1, 992, 7, 7] 0 BatchNorm2d-422 [-1, 992, 7, 7] 1,984 ReLU-423 [-1, 992, 7, 7] 0 Conv2d-424 [-1, 128, 7, 7] 126,976 BatchNorm2d-425 [-1, 128, 7, 7] 256 ReLU-426 [-1, 128, 7, 7] 0 Conv2d-427 [-1, 32, 7, 7] 36,864 _DenseLayer-428 [-1, 1024, 7, 7] 0 DenseBlock-429 [-1, 1024, 7, 7] 0 AvgPool2d-430 [-1, 1024, 1, 1] 0 Linear-431 [-1, 10] 10,250================================================================Total params: 6,962,058Trainable params: 6,962,058Non-trainable params: 0----------------------------------------------------------------Input size (MB): 0.57Forward/backward pass size (MB): 383.87Params size (MB): 26.56Estimated Total Size (MB): 411.01----------------------------------------------------------------Process finished with exit code 0完整代码 import torchimport torch.nn as nnimport torchvision# noinspection PyUnresolvedReferencesfrom torchsummary import summaryprint("PyTorch Version: ",torch.__version__)print("Torchvision Version: ",torchvision.__version__)#可选择的densenet模型__all__ = ['DenseNet121', 'DenseNet169','DenseNet201','DenseNet264']'''-------------一、构造初始卷积层-----------------------------'''def Conv1(in_planes, places, stride=2): return nn.Sequential( nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False), nn.BatchNorm2d(places), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1) )'''-------------二、定义Dense Block模块-----------------------------''''''---（1）构造Dense Block内部结构---'''#BN+ReLU+1x1 Conv+BN+ReLU+3x3 Convclass _DenseLayer(nn.Module): def __init__(self, inplace, growth_rate, bn_size, drop_rate=0): super(_DenseLayer, self).__init__() self.drop_rate = drop_rate self.dense_layer = nn.Sequential( nn.BatchNorm2d(inplace), nn.ReLU(inplace=True), # growth_rate：增长率。一层产生多少个特征图 nn.Conv2d(in_channels=inplace, out_channels=bn_size * growth_rate, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(bn_size * growth_rate), nn.ReLU(inplace=True), nn.Conv2d(in_channels=bn_size * growth_rate, out_channels=growth_rate, kernel_size=3, stride=1, padding=1, bias=False), ) self.dropout = nn.Dropout(p=self.drop_rate) def forward(self, x): y = self.dense_layer(x) if self.drop_rate > 0: y = self.dropout(y) return torch.cat([x, y], 1)'''---（2）构造Dense Block模块---'''class DenseBlock(nn.Module): def __init__(self, num_layers, inplances, growth_rate, bn_size , drop_rate=0): super(DenseBlock, self).__init__() layers = [] # 随着layer层数的增加，每增加一层，输入的特征图就增加一倍growth_rate for i in range(num_layers): layers.append(_DenseLayer(inplances + i * growth_rate, growth_rate, bn_size, drop_rate)) self.layers = nn.Sequential(*layers) def forward(self, x): return self.layers(x)'''-------------三、构造Transition层-----------------------------'''#BN+1×1Conv+2×2AveragePoolingclass _TransitionLayer(nn.Module): def __init__(self, inplace, plance): super(_TransitionLayer, self).__init__() self.transition_layer = nn.Sequential( nn.BatchNorm2d(inplace), nn.ReLU(inplace=True), nn.Conv2d(in_channels=inplace,out_channels=plance,kernel_size=1,stride=1,padding=0,bias=False), nn.AvgPool2d(kernel_size=2,stride=2), ) def forward(self, x): return self.transition_layer(x)'''-------------四、搭建DenseNet网络-----------------------------'''class DenseNet(nn.Module): def __init__(self, init_channels=64, growth_rate=32, blocks=[6, 12, 24, 16],num_classes=10): super(DenseNet, self).__init__() bn_size = 4 drop_rate = 0 self.conv1 = Conv1(in_planes=3, places=init_channels) blocks*4 #第一次执行特征的维度来自于前面的特征提取 num_features = init_channels # 第1个DenseBlock有6个DenseLayer, 执行DenseBlock（6,64,32,4） self.layer1 = DenseBlock(num_layers=blocks[0], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[0] * growth_rate # 第1个transition 执行 _TransitionLayer（256,128） self.transition1 = _TransitionLayer(inplace=num_features, plance=num_features // 2) #num_features减少为原来的一半，执行第1回合之后，第2个DenseBlock的输入的feature应该是：num_features = 128 num_features = num_features // 2 # 第2个DenseBlock有12个DenseLayer, 执行DenseBlock（12,128,32,4） self.layer2 = DenseBlock(num_layers=blocks[1], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[1] * growth_rate # 第2个transition 执行 _TransitionLayer（512,256） self.transition2 = _TransitionLayer(inplace=num_features, plance=num_features // 2) # num_features减少为原来的一半，执行第2回合之后，第3个DenseBlock的输入的feature应该是：num_features = 256 num_features = num_features // 2 # 第3个DenseBlock有24个DenseLayer, 执行DenseBlock（24,256,32,4） self.layer3 = DenseBlock(num_layers=blocks[2], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[2] * growth_rate # 第3个transition 执行 _TransitionLayer（1024,512） self.transition3 = _TransitionLayer(inplace=num_features, plance=num_features // 2) # num_features减少为原来的一半，执行第3回合之后，第4个DenseBlock的输入的feature应该是：num_features = 512 num_features = num_features // 2 # 第4个DenseBlock有16个DenseLayer, 执行DenseBlock（16,512,32,4） self.layer4 = DenseBlock(num_layers=blocks[3], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[3] * growth_rate self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = nn.Linear(num_features, num_classes) def forward(self, x): x = self.conv1(x) x = self.layer1(x) x = self.transition1(x) x = self.layer2(x) x = self.transition2(x) x = self.layer3(x) x = self.transition3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return xdef DenseNet121(): return DenseNet(init_channels=64, growth_rate=32, blocks=[6, 12, 24, 16])def DenseNet169(): return DenseNet(init_channels=64, growth_rate=32, blocks=[6, 12, 32, 32])def DenseNet201(): return DenseNet(init_channels=64, growth_rate=32, blocks=[6, 12, 48, 32])def DenseNet264(): return DenseNet(init_channels=64, growth_rate=32, blocks=[6, 12, 64, 48])'''if __name__=='__main__': # model = torchvision.models.densenet121() model = DenseNet121() print(model) input = torch.randn(1, 3, 224, 224) out = model(input) print(out.shape) '''if __name__ == '__main__': net = DenseNet(num_classes=10).cuda() summary(net, (3, 224, 224))