DenseNet代码复现+超详细注释(PyTorch)

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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)

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 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。
 

DenseNet代码复现+超详细注释(PyTorch)


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

DenseNet代码复现+超详细注释(PyTorch)

 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

DenseNet代码复现+超详细注释(PyTorch)

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 Conv
class _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。

DenseNet代码复现+超详细注释(PyTorch)

代码

'''-------------三、构造Transition层-----------------------------'''
#BN+1×1Conv+2×2AveragePooling
class _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

DenseNet代码复现+超详细注释(PyTorch)

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文章来源地址https://www.toymoban.com/news/detail-422758.html

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+cu113
Torchvision 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: 6,962,058
Trainable params: 6,962,058
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 383.87
Params size (MB): 26.56
Estimated Total Size (MB): 411.01
----------------------------------------------------------------

Process finished with exit code 0

完整代码 

import torch
import torch.nn as nn
import torchvision
# noinspection PyUnresolvedReferences
from torchsummary import summary


print("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 Conv
class _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+2×2AveragePooling
class _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 x

def 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))

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