• 🍨 本文为🔗365天深度学习训练营中的学习记录博客
• 🍖 原作者：K同学啊|接辅导、项目定制

一、课题背景和开发环境

📌第J5周：DenseNet+SE-Net实战📌

• 语言：Python3、Pytorch
• 📌本周任务：📌
  – 1. 在DenseNet系列算法中插入SE-Net通道注意力机制，并完成猴痘病识别
  – 2. 改进思路是否可以迁移到其他地方呢
  – 3.测试集accuracy达到89%（拔高，可选）

🔊注： 从前几周开始训练营的难度逐渐提升，具体体现在不再直接提供源代码。任务中会给大家提供一些算法改进的思路、方向，希望大家这一块可以积极探索。（这个探索的过程很重要，也将学到更多）

二、介绍

论文： Squeeze-and-Excitation Networks

SE-Net是ImageNet 2017（ImageNet收官赛）的冠军模型，是由WMW团队发布。具有复杂度低，参数少和计算量小的优点。且SE-Net思路很简单，很容易扩展到已有网络结构如Inception和ResNet中。已经有很多工作在空间维度上来提升网络的性能，如Inception等，而SE-Net将关注点放在了特征通道之间的关细上。其具体策略为：通过学习的方式来自动获取到每个特征通道的重要程度，然后依照这个重要程度去提升有用的特征并抑制对当前任务用处不大的特征，这又叫做“特征重标定”策略。具体的SE模块如下图所示：

给定一个输入 $x$ ,其特征通道数为 $c_1$ ，通过一系列卷积等一般变换 $F_{tr}$ 后得到一个特征通道数为 $c_2$ 的特征。与传统的卷积神经网络不同，我们需要通过下面三个操作来重新标定前面得到的特征。

1. 首先是Squeeze操作，我们顺着空间维度来进行特征压缩，将一个通道数和输入的特征通道数相等，例如将形状为(1, 32, 32, 10)的feature map压缩成(1, 1, 1, 10)。此操作通常采用global average pooling来实现。
2. 得到了全局描述特征后，我们进行Excitation操作来抓取特征通道之间的关系，它是一个类似于循环神经网络中门的机制：
   $$s=F_{ex}(z,W)=\sigma (g(z,W))=\sigma (W_{2}ReLU(W_{1}z))$$
   这里采用包含两个全连接层的bottleneck结构，即中间小两头大的结构：其中第一个全链接层起到即降维的作用，并通过ReLU激活，第二个全链接层用来将其恢复至原始的维度。进行Excitation操作的最终目的是为每个特征通道生成权重，即学习到的各个通道的激活值（sigmoid激活，值在0~1之间）。
3. 最后一个是Scale的操作，我们将Excitation的输出的权重看作是经过特征选择后的每个特征通道的重要性，然后通过乘法逐通道加权到先前的特征上，完成在通道维度上的对原始特征的重标定，从而使得模型对各个通道的特征更有辨别能力，这类似于attention机制。

三、SE模块应用分析

SE模块的灵活性在于它可以直接应用在现有的网络结构中。以Inception和ResNet为例，我们只需要在Inception模块或Residual模块后添加一个SE模块即可。具体如下图所示：

上图分别是将SE模块嵌入到Inception结构与ResNet中的示例，方框旁边的维度信息代表该层的输出， $r$ 表示Excitation操作中的降维系数。

四、SE模块效果对比

SE模块很容易嵌入到其他网络中，为了验证SE模块的作用，在其它流行网络如ResNet和Inception中引入SE模块，测试其在ImageNet上的效果，如下表所示：

首先看一下网络的深度对SE的影响。上表分别展示了ResNet-50、ResNet-101、ResNet-152、ResNeXt-50、ResNeXt-101和VGG-16、BN-Inception、Inception-ResNet-v2嵌入SE模型的结果。第一栏Original是原作者实现的结果，为了进行公平的比较，重新进行了实现实验得到re-implementation的结果。最后一栏SE-module是指嵌入了SE模块的结果，它的训练参数和第二栏re-implementation一致。括号中的红色数值是指相对于re-implementation的精度提升的幅值。
从上表可以看出，SE-ResNets在各种深度上都远远超过了其对应的没有SE的结构版本的精度，这说明无论网络的深度如何，SE模块都能够给网络带来性能上的增益。值得一提的是，SE-ResNet-50可以达到和ResNet-101一样的精度；更甚，SE-ResNet-101远远地超过了更深的ResNet-152。

五、SE模块代码实现

tensorflow

from tensorflow import keras
from keras import layers
from layers import Model, Input, Reshape, Activation, BatchNormalization, GlobalAveragePooling2D, Denseclass SqueezeExcitationLayer(Model):def __init__(self, filter_sq):# filter_sq是Excitation中第一个卷积过程中卷积核的个数super.__init__()self.avgpool = GlobalAveragePooling2D()self.dense = Dense(filter_sq)self.relu = Activation('relu')self.sigmoid = Activation('sigmoid')def call(self, inputs):x = self.avgpool(inputs)x = self.dense(x)x = self.relu(x)x = Dense(inputs.shape[-1])(x)x = self.sigmoid(x)x = Reshape((1,1,inputs.shape[-1]))(x)scale = inputs * xreturn scaleSE = SqueezeExcitationLayer(16)

pytorch

''' Squeeze Excitation Module '''
class SEModule(nn.Module):def __init__(self, in_channel, filter_sq=16):super(SEModule, self).__init__()self.se = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),nn.Flatten(),nn.Linear(in_channel, in_channel//filter_sq),nn.ReLU(True),nn.Linear(in_channel//filter_sq, in_channel),nn.Sigmoid())#self.se = nn.Sequential(#    nn.AdaptiveAvgPool2d((1,1)),#    nn.Conv2d(in_channel, in_channel//filter_sq, kernel_size=1),#    nn.ReLU(),#    nn.Conv2d(in_channel//filter_sq, in_channel, kernel_size=1),#    nn.Sigmoid()#)def forward(self, inputs):x = self.se(inputs)s1, s2 = x.size(0), x.size(1)x = torch.reshape(x, (s1, s2, 1, 1))x = inputs * xreturn x

六、SE模块插入到DenseNet代码实现

tensorflow

''' Basic unit of DenseBlock (using bottleneck layer) '''
def DenseLayer(x, bn_size, growth_rate, drop_rate, name=None):f = BatchNormalization(name=name+'_1_bn')(x)f = Activation('relu', name=name+'_1_relu')(f)f = Conv2D(bn_size*growth_rate, 1, strides=1, use_bias=False, name=name+'_1_conv')(f)f = BatchNormalization(name=name+'_2_bn')(f)f = Activation('relu', name=name+'_2_relu')(f)f = Conv2D(growth_rate, 3, strides=1, padding=1, use_bias=False, name=name+'_2_conv')(f)if drop_rate>0:f = Dropout(drop_rate)(f)x = layers.Concatenate(axis=-1)([x, f])return x''' DenseBlock '''
def DenseBlock(x, num_layers, bn_size, growth_rate, drop_rate, name=None):for i in range(num_layers):x = DenseLayer(x, bn_size, growth_rate, drop_rate, name=name+'_denselayer'+str(i+1))return x''' Transition layer between two adjacent DenseBlock '''
def Transition(x, out_channel):x = BatchNormalization(name=name+'_bn')(x)x = Activation('relu', name=name+'_relu')(x)x = Conv2D(out_channel, 1, strides=1, use_bias=False, name=name+'_conv')(x)x = AveragePooling2D(2, 2, name='pool')(x)return x''' DenseNet-BC model '''
def DenseNet(input_tensor=None,  # 可选的keras张量，用作模型的图像输入input_shape=None,init_channel=64,growth_rate=32,block_config=(6,12,24,16),bn_size=4,compression_rate=0.5,drop_rate=0,classes=1000):  # 用于分类图像的可选类数img_input = Input(shape=input_shape)# first Conv2dx = ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1_conv')(x)x = BatchNormalization(name='conv1_bn')(x)x = Activation('relu', name='conv1_relu')(x)x = MaxPooling2D(3, strides=2, padding=1, name='conv1_pool')(x)# DenseBlocknum_features = init_channelfor i, num_layers in enumerate(block_config):x = DenseBlock(x, num_layers, bn_size, growth_rate, drop_rate, name='denseblock'+str(i+1))num_features += num_layers*growth_rateif i!=len(block_config)-1:x = Transition(x, int(num_features*compression_rate))num_features = int(num_features*compression_rate)# 加SE注意力机制x = SqueezeExcitationLayer(16)(x)# final bn+ReLUx = BatchNormalization(name='final_bn')(x)x = Activation('relu', name='final_relu')(x)x = GlobalAveragePooling2D(name='final_pool')(x)x = Dense(classes, activation='softmax', name='predictions')(x)model = Model(img_input, x, name='DenseNet')return model''' DenseNet121 '''
def densenet121(n_classes=1000, **kwargs):model = DenseNet(init_channel=64, growth_rate=32, block_config=(6,12,24,16),classes=n_classes, **kwargs)return model''' DenseNet169 '''
def DenseNet169(n_classes=1000, **kwargs):model = DenseNet(init_channel=64, growth_rate=32, block_config=(6,12,32,32),classes=n_classes, **kwargs)return model''' DenseNet201 '''
def DenseNet201(n_classes=1000, **kwargs):model = DenseNet(init_channel=64, growth_rate=32, block_config=(6,12,48,32),classes=n_classes, **kwargs)return model

pytorch

''' Basic unit of DenseBlock (using bottleneck layer) '''
class DenseLayer(nn.Sequential):def __init__(self, in_channel, growth_rate, bn_size, drop_rate):super(DenseLayer, self).__init__()self.add_module('norm1', nn.BatchNorm2d(in_channel))self.add_module('relu1', nn.ReLU(inplace=True))self.add_module('conv1', nn.Conv2d(in_channel, bn_size*growth_rate,kernel_size=1, stride=1, bias=False))self.add_module('norm2', nn.BatchNorm2d(bn_size*growth_rate))self.add_module('relu2', nn.ReLU(inplace=True))self.add_module('conv2', nn.Conv2d(bn_size*growth_rate, growth_rate,kernel_size=3, stride=1, padding=1, bias=False))self.drop_rate = drop_ratedef forward(self, x):new_feature = super(DenseLayer, self).forward(x)if self.drop_rate>0:new_feature = F.dropout(new_feature, p=self.drop_rate, training=self.training)return torch.cat([x, new_feature], 1)''' DenseBlock '''
class DenseBlock(nn.Sequential):def __init__(self, num_layers, in_channel, bn_size, growth_rate, drop_rate):super(DenseBlock, self).__init__()for i in range(num_layers):layer = DenseLayer(in_channel+i*growth_rate, growth_rate, bn_size, drop_rate)self.add_module('denselayer%d'%(i+1,), layer)''' Transition layer between two adjacent DenseBlock '''
class Transition(nn.Sequential):def __init__(self, in_channel, out_channel):super(Transition, self).__init__()self.add_module('norm', nn.BatchNorm2d(in_channel))self.add_module('relu', nn.ReLU(inplace=True))self.add_module('conv', nn.Conv2d(in_channel, out_channel,kernel_size=1, stride=1, bias=False))self.add_module('pool', nn.AvgPool2d(2, stride=2))''' DenseNet-BC model '''
class DenseNet(nn.Module):def __init__(self, growth_rate=32, block_config=(6,12,24,16), init_channel=64, bn_size=4, compression_rate=0.5, drop_rate=0, num_classes=1000):''':param growth_rate: (int) number of filters used in DenseLayer, `k` in the paper:param block_config: (list of 4 ints) number of layers in eatch DenseBlock:param init_channel: (int) number of filters in the first Conv2d:param bn_size: (int) the factor using in the bottleneck layer:param compression_rate: (float) the compression rate used in Transition Layer:param drop_rate: (float) the drop rate after each DenseLayer:param num_classes: (int) number of classes for classification'''super(DenseNet, self).__init__()# first Conv2dself.features = nn.Sequential(OrderedDict([('conv0', nn.Conv2d(3, init_channel, kernel_size=7, stride=2, padding=3, bias=False)),('norm0', nn.BatchNorm2d(init_channel)),('relu0', nn.ReLU(inplace=True)),('pool0', nn.MaxPool2d(3, stride=2, padding=1))]))# DenseBlocknum_features = init_channelfor i, num_layers in enumerate(block_config):block = DenseBlock(num_layers, num_features, bn_size, growth_rate, drop_rate)self.features.add_module('denseblock%d'%(i+1), block)num_features += num_layers*growth_rateif i!=len(block_config)-1:transition = Transition(num_features, int(num_features*compression_rate))self.features.add_module('transition%d'%(i+1), transition)num_features = int(num_features*compression_rate)# SE Moduleself.features.add_module('SE-module', SEModule(num_features))# final BN+ReLUself.features.add_module('norm5', nn.BatchNorm2d(num_features))self.features.add_module('relu5', nn.ReLU(inplace=True))# classification layerself.classifier = nn.Linear(num_features, num_classes)# params initializationfor m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1)elif isinstance(m, nn.Linear):nn.init.constant_(m.bias, 0)def forward(self, x):x = self.features(x)x = F.avg_pool2d(x, 7, stride=1).view(x.size(0), -1)x = self.classifier(x)return x

----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1         [-1, 64, 112, 112]           9,408BatchNorm2d-2         [-1, 64, 112, 112]             128ReLU-3         [-1, 64, 112, 112]               0MaxPool2d-4           [-1, 64, 56, 56]               0BatchNorm2d-5           [-1, 64, 56, 56]             128ReLU-6           [-1, 64, 56, 56]               0Conv2d-7          [-1, 128, 56, 56]           8,192BatchNorm2d-8          [-1, 128, 56, 56]             256ReLU-9          [-1, 128, 56, 56]               0Conv2d-10           [-1, 32, 56, 56]          36,864BatchNorm2d-11           [-1, 96, 56, 56]             192ReLU-12           [-1, 96, 56, 56]               0Conv2d-13          [-1, 128, 56, 56]          12,288BatchNorm2d-14          [-1, 128, 56, 56]             256ReLU-15          [-1, 128, 56, 56]               0Conv2d-16           [-1, 32, 56, 56]          36,864BatchNorm2d-17          [-1, 128, 56, 56]             256ReLU-18          [-1, 128, 56, 56]               0Conv2d-19          [-1, 128, 56, 56]          16,384BatchNorm2d-20          [-1, 128, 56, 56]             256ReLU-21          [-1, 128, 56, 56]               0Conv2d-22           [-1, 32, 56, 56]          36,864BatchNorm2d-23          [-1, 160, 56, 56]             320ReLU-24          [-1, 160, 56, 56]               0Conv2d-25          [-1, 128, 56, 56]          20,480BatchNorm2d-26          [-1, 128, 56, 56]             256ReLU-27          [-1, 128, 56, 56]               0Conv2d-28           [-1, 32, 56, 56]          36,864BatchNorm2d-29          [-1, 192, 56, 56]             384ReLU-30          [-1, 192, 56, 56]               0Conv2d-31          [-1, 128, 56, 56]          24,576BatchNorm2d-32          [-1, 128, 56, 56]             256ReLU-33          [-1, 128, 56, 56]               0Conv2d-34           [-1, 32, 56, 56]          36,864BatchNorm2d-35          [-1, 224, 56, 56]             448ReLU-36          [-1, 224, 56, 56]               0Conv2d-37          [-1, 128, 56, 56]          28,672BatchNorm2d-38          [-1, 128, 56, 56]             256ReLU-39          [-1, 128, 56, 56]               0Conv2d-40           [-1, 32, 56, 56]          36,864BatchNorm2d-41          [-1, 256, 56, 56]             512ReLU-42          [-1, 256, 56, 56]               0Conv2d-43          [-1, 128, 56, 56]          32,768AvgPool2d-44          [-1, 128, 28, 28]               0BatchNorm2d-45          [-1, 128, 28, 28]             256ReLU-46          [-1, 128, 28, 28]               0Conv2d-47          [-1, 128, 28, 28]          16,384BatchNorm2d-48          [-1, 128, 28, 28]             256ReLU-49          [-1, 128, 28, 28]               0Conv2d-50           [-1, 32, 28, 28]          36,864BatchNorm2d-51          [-1, 160, 28, 28]             320ReLU-52          [-1, 160, 28, 28]               0Conv2d-53          [-1, 128, 28, 28]          20,480BatchNorm2d-54          [-1, 128, 28, 28]             256ReLU-55          [-1, 128, 28, 28]               0Conv2d-56           [-1, 32, 28, 28]          36,864BatchNorm2d-57          [-1, 192, 28, 28]             384ReLU-58          [-1, 192, 28, 28]               0Conv2d-59          [-1, 128, 28, 28]          24,576BatchNorm2d-60          [-1, 128, 28, 28]             256ReLU-61          [-1, 128, 28, 28]               0Conv2d-62           [-1, 32, 28, 28]          36,864BatchNorm2d-63          [-1, 224, 28, 28]             448ReLU-64          [-1, 224, 28, 28]               0Conv2d-65          [-1, 128, 28, 28]          28,672BatchNorm2d-66          [-1, 128, 28, 28]             256ReLU-67          [-1, 128, 28, 28]               0Conv2d-68           [-1, 32, 28, 28]          36,864BatchNorm2d-69          [-1, 256, 28, 28]             512ReLU-70          [-1, 256, 28, 28]               0Conv2d-71          [-1, 128, 28, 28]          32,768BatchNorm2d-72          [-1, 128, 28, 28]             256ReLU-73          [-1, 128, 28, 28]               0Conv2d-74           [-1, 32, 28, 28]          36,864BatchNorm2d-75          [-1, 288, 28, 28]             576ReLU-76          [-1, 288, 28, 28]               0Conv2d-77          [-1, 128, 28, 28]          36,864BatchNorm2d-78          [-1, 128, 28, 28]             256ReLU-79          [-1, 128, 28, 28]               0Conv2d-80           [-1, 32, 28, 28]          36,864BatchNorm2d-81          [-1, 320, 28, 28]             640ReLU-82          [-1, 320, 28, 28]               0Conv2d-83          [-1, 128, 28, 28]          40,960BatchNorm2d-84          [-1, 128, 28, 28]             256ReLU-85          [-1, 128, 28, 28]               0Conv2d-86           [-1, 32, 28, 28]          36,864BatchNorm2d-87          [-1, 352, 28, 28]             704ReLU-88          [-1, 352, 28, 28]               0Conv2d-89          [-1, 128, 28, 28]          45,056BatchNorm2d-90          [-1, 128, 28, 28]             256ReLU-91          [-1, 128, 28, 28]               0Conv2d-92           [-1, 32, 28, 28]          36,864BatchNorm2d-93          [-1, 384, 28, 28]             768ReLU-94          [-1, 384, 28, 28]               0Conv2d-95          [-1, 128, 28, 28]          49,152BatchNorm2d-96          [-1, 128, 28, 28]             256ReLU-97          [-1, 128, 28, 28]               0Conv2d-98           [-1, 32, 28, 28]          36,864BatchNorm2d-99          [-1, 416, 28, 28]             832ReLU-100          [-1, 416, 28, 28]               0Conv2d-101          [-1, 128, 28, 28]          53,248BatchNorm2d-102          [-1, 128, 28, 28]             256ReLU-103          [-1, 128, 28, 28]               0Conv2d-104           [-1, 32, 28, 28]          36,864BatchNorm2d-105          [-1, 448, 28, 28]             896ReLU-106          [-1, 448, 28, 28]               0Conv2d-107          [-1, 128, 28, 28]          57,344BatchNorm2d-108          [-1, 128, 28, 28]             256ReLU-109          [-1, 128, 28, 28]               0Conv2d-110           [-1, 32, 28, 28]          36,864BatchNorm2d-111          [-1, 480, 28, 28]             960ReLU-112          [-1, 480, 28, 28]               0Conv2d-113          [-1, 128, 28, 28]          61,440BatchNorm2d-114          [-1, 128, 28, 28]             256ReLU-115          [-1, 128, 28, 28]               0Conv2d-116           [-1, 32, 28, 28]          36,864BatchNorm2d-117          [-1, 512, 28, 28]           1,024ReLU-118          [-1, 512, 28, 28]               0Conv2d-119          [-1, 256, 28, 28]         131,072AvgPool2d-120          [-1, 256, 14, 14]               0BatchNorm2d-121          [-1, 256, 14, 14]             512ReLU-122          [-1, 256, 14, 14]               0Conv2d-123          [-1, 128, 14, 14]          32,768BatchNorm2d-124          [-1, 128, 14, 14]             256ReLU-125          [-1, 128, 14, 14]               0Conv2d-126           [-1, 32, 14, 14]          36,864BatchNorm2d-127          [-1, 288, 14, 14]             576ReLU-128          [-1, 288, 14, 14]               0Conv2d-129          [-1, 128, 14, 14]          36,864BatchNorm2d-130          [-1, 128, 14, 14]             256ReLU-131          [-1, 128, 14, 14]               0Conv2d-132           [-1, 32, 14, 14]          36,864BatchNorm2d-133          [-1, 320, 14, 14]             640ReLU-134          [-1, 320, 14, 14]               0Conv2d-135          [-1, 128, 14, 14]          40,960BatchNorm2d-136          [-1, 128, 14, 14]             256ReLU-137          [-1, 128, 14, 14]               0Conv2d-138           [-1, 32, 14, 14]          36,864BatchNorm2d-139          [-1, 352, 14, 14]             704ReLU-140          [-1, 352, 14, 14]               0Conv2d-141          [-1, 128, 14, 14]          45,056BatchNorm2d-142          [-1, 128, 14, 14]             256ReLU-143          [-1, 128, 14, 14]               0Conv2d-144           [-1, 32, 14, 14]          36,864BatchNorm2d-145          [-1, 384, 14, 14]             768ReLU-146          [-1, 384, 14, 14]               0Conv2d-147          [-1, 128, 14, 14]          49,152BatchNorm2d-148          [-1, 128, 14, 14]             256ReLU-149          [-1, 128, 14, 14]               0Conv2d-150           [-1, 32, 14, 14]          36,864BatchNorm2d-151          [-1, 416, 14, 14]             832ReLU-152          [-1, 416, 14, 14]               0Conv2d-153          [-1, 128, 14, 14]          53,248BatchNorm2d-154          [-1, 128, 14, 14]             256ReLU-155          [-1, 128, 14, 14]               0Conv2d-156           [-1, 32, 14, 14]          36,864BatchNorm2d-157          [-1, 448, 14, 14]             896ReLU-158          [-1, 448, 14, 14]               0Conv2d-159          [-1, 128, 14, 14]          57,344BatchNorm2d-160          [-1, 128, 14, 14]             256ReLU-161          [-1, 128, 14, 14]               0Conv2d-162           [-1, 32, 14, 14]          36,864BatchNorm2d-163          [-1, 480, 14, 14]             960ReLU-164          [-1, 480, 14, 14]               0Conv2d-165          [-1, 128, 14, 14]          61,440BatchNorm2d-166          [-1, 128, 14, 14]             256ReLU-167          [-1, 128, 14, 14]               0Conv2d-168           [-1, 32, 14, 14]          36,864BatchNorm2d-169          [-1, 512, 14, 14]           1,024ReLU-170          [-1, 512, 14, 14]               0Conv2d-171          [-1, 128, 14, 14]          65,536BatchNorm2d-172          [-1, 128, 14, 14]             256ReLU-173          [-1, 128, 14, 14]               0Conv2d-174           [-1, 32, 14, 14]          36,864BatchNorm2d-175          [-1, 544, 14, 14]           1,088ReLU-176          [-1, 544, 14, 14]               0Conv2d-177          [-1, 128, 14, 14]          69,632BatchNorm2d-178          [-1, 128, 14, 14]             256ReLU-179          [-1, 128, 14, 14]               0Conv2d-180           [-1, 32, 14, 14]          36,864BatchNorm2d-181          [-1, 576, 14, 14]           1,152ReLU-182          [-1, 576, 14, 14]               0Conv2d-183          [-1, 128, 14, 14]          73,728BatchNorm2d-184          [-1, 128, 14, 14]             256ReLU-185          [-1, 128, 14, 14]               0Conv2d-186           [-1, 32, 14, 14]          36,864BatchNorm2d-187          [-1, 608, 14, 14]           1,216ReLU-188          [-1, 608, 14, 14]               0Conv2d-189          [-1, 128, 14, 14]          77,824BatchNorm2d-190          [-1, 128, 14, 14]             256ReLU-191          [-1, 128, 14, 14]               0Conv2d-192           [-1, 32, 14, 14]          36,864BatchNorm2d-193          [-1, 640, 14, 14]           1,280ReLU-194          [-1, 640, 14, 14]               0Conv2d-195          [-1, 128, 14, 14]          81,920BatchNorm2d-196          [-1, 128, 14, 14]             256ReLU-197          [-1, 128, 14, 14]               0Conv2d-198           [-1, 32, 14, 14]          36,864BatchNorm2d-199          [-1, 672, 14, 14]           1,344ReLU-200          [-1, 672, 14, 14]               0Conv2d-201          [-1, 128, 14, 14]          86,016BatchNorm2d-202          [-1, 128, 14, 14]             256ReLU-203          [-1, 128, 14, 14]               0Conv2d-204           [-1, 32, 14, 14]          36,864BatchNorm2d-205          [-1, 704, 14, 14]           1,408ReLU-206          [-1, 704, 14, 14]               0Conv2d-207          [-1, 128, 14, 14]          90,112BatchNorm2d-208          [-1, 128, 14, 14]             256ReLU-209          [-1, 128, 14, 14]               0Conv2d-210           [-1, 32, 14, 14]          36,864BatchNorm2d-211          [-1, 736, 14, 14]           1,472ReLU-212          [-1, 736, 14, 14]               0Conv2d-213          [-1, 128, 14, 14]          94,208BatchNorm2d-214          [-1, 128, 14, 14]             256ReLU-215          [-1, 128, 14, 14]               0Conv2d-216           [-1, 32, 14, 14]          36,864BatchNorm2d-217          [-1, 768, 14, 14]           1,536ReLU-218          [-1, 768, 14, 14]               0Conv2d-219          [-1, 128, 14, 14]          98,304BatchNorm2d-220          [-1, 128, 14, 14]             256ReLU-221          [-1, 128, 14, 14]               0Conv2d-222           [-1, 32, 14, 14]          36,864BatchNorm2d-223          [-1, 800, 14, 14]           1,600ReLU-224          [-1, 800, 14, 14]               0Conv2d-225          [-1, 128, 14, 14]         102,400BatchNorm2d-226          [-1, 128, 14, 14]             256ReLU-227          [-1, 128, 14, 14]               0Conv2d-228           [-1, 32, 14, 14]          36,864BatchNorm2d-229          [-1, 832, 14, 14]           1,664ReLU-230          [-1, 832, 14, 14]               0Conv2d-231          [-1, 128, 14, 14]         106,496BatchNorm2d-232          [-1, 128, 14, 14]             256ReLU-233          [-1, 128, 14, 14]               0Conv2d-234           [-1, 32, 14, 14]          36,864BatchNorm2d-235          [-1, 864, 14, 14]           1,728ReLU-236          [-1, 864, 14, 14]               0Conv2d-237          [-1, 128, 14, 14]         110,592BatchNorm2d-238          [-1, 128, 14, 14]             256ReLU-239          [-1, 128, 14, 14]               0Conv2d-240           [-1, 32, 14, 14]          36,864BatchNorm2d-241          [-1, 896, 14, 14]           1,792ReLU-242          [-1, 896, 14, 14]               0Conv2d-243          [-1, 128, 14, 14]         114,688BatchNorm2d-244          [-1, 128, 14, 14]             256ReLU-245          [-1, 128, 14, 14]               0Conv2d-246           [-1, 32, 14, 14]          36,864BatchNorm2d-247          [-1, 928, 14, 14]           1,856ReLU-248          [-1, 928, 14, 14]               0Conv2d-249          [-1, 128, 14, 14]         118,784BatchNorm2d-250          [-1, 128, 14, 14]             256ReLU-251          [-1, 128, 14, 14]               0Conv2d-252           [-1, 32, 14, 14]          36,864BatchNorm2d-253          [-1, 960, 14, 14]           1,920ReLU-254          [-1, 960, 14, 14]               0Conv2d-255          [-1, 128, 14, 14]         122,880BatchNorm2d-256          [-1, 128, 14, 14]             256ReLU-257          [-1, 128, 14, 14]               0Conv2d-258           [-1, 32, 14, 14]          36,864BatchNorm2d-259          [-1, 992, 14, 14]           1,984ReLU-260          [-1, 992, 14, 14]               0Conv2d-261          [-1, 128, 14, 14]         126,976BatchNorm2d-262          [-1, 128, 14, 14]             256ReLU-263          [-1, 128, 14, 14]               0Conv2d-264           [-1, 32, 14, 14]          36,864BatchNorm2d-265         [-1, 1024, 14, 14]           2,048ReLU-266         [-1, 1024, 14, 14]               0Conv2d-267          [-1, 512, 14, 14]         524,288AvgPool2d-268            [-1, 512, 7, 7]               0BatchNorm2d-269            [-1, 512, 7, 7]           1,024ReLU-270            [-1, 512, 7, 7]               0Conv2d-271            [-1, 128, 7, 7]          65,536BatchNorm2d-272            [-1, 128, 7, 7]             256ReLU-273            [-1, 128, 7, 7]               0Conv2d-274             [-1, 32, 7, 7]          36,864BatchNorm2d-275            [-1, 544, 7, 7]           1,088ReLU-276            [-1, 544, 7, 7]               0Conv2d-277            [-1, 128, 7, 7]          69,632BatchNorm2d-278            [-1, 128, 7, 7]             256ReLU-279            [-1, 128, 7, 7]               0Conv2d-280             [-1, 32, 7, 7]          36,864BatchNorm2d-281            [-1, 576, 7, 7]           1,152ReLU-282            [-1, 576, 7, 7]               0Conv2d-283            [-1, 128, 7, 7]          73,728BatchNorm2d-284            [-1, 128, 7, 7]             256ReLU-285            [-1, 128, 7, 7]               0Conv2d-286             [-1, 32, 7, 7]          36,864BatchNorm2d-287            [-1, 608, 7, 7]           1,216ReLU-288            [-1, 608, 7, 7]               0Conv2d-289            [-1, 128, 7, 7]          77,824BatchNorm2d-290            [-1, 128, 7, 7]             256ReLU-291            [-1, 128, 7, 7]               0Conv2d-292             [-1, 32, 7, 7]          36,864BatchNorm2d-293            [-1, 640, 7, 7]           1,280ReLU-294            [-1, 640, 7, 7]               0Conv2d-295            [-1, 128, 7, 7]          81,920BatchNorm2d-296            [-1, 128, 7, 7]             256ReLU-297            [-1, 128, 7, 7]               0Conv2d-298             [-1, 32, 7, 7]          36,864BatchNorm2d-299            [-1, 672, 7, 7]           1,344ReLU-300            [-1, 672, 7, 7]               0Conv2d-301            [-1, 128, 7, 7]          86,016BatchNorm2d-302            [-1, 128, 7, 7]             256ReLU-303            [-1, 128, 7, 7]               0Conv2d-304             [-1, 32, 7, 7]          36,864BatchNorm2d-305            [-1, 704, 7, 7]           1,408ReLU-306            [-1, 704, 7, 7]               0Conv2d-307            [-1, 128, 7, 7]          90,112BatchNorm2d-308            [-1, 128, 7, 7]             256ReLU-309            [-1, 128, 7, 7]               0Conv2d-310             [-1, 32, 7, 7]          36,864BatchNorm2d-311            [-1, 736, 7, 7]           1,472ReLU-312            [-1, 736, 7, 7]               0Conv2d-313            [-1, 128, 7, 7]          94,208BatchNorm2d-314            [-1, 128, 7, 7]             256ReLU-315            [-1, 128, 7, 7]               0Conv2d-316             [-1, 32, 7, 7]          36,864BatchNorm2d-317            [-1, 768, 7, 7]           1,536ReLU-318            [-1, 768, 7, 7]               0Conv2d-319            [-1, 128, 7, 7]          98,304BatchNorm2d-320            [-1, 128, 7, 7]             256ReLU-321            [-1, 128, 7, 7]               0Conv2d-322             [-1, 32, 7, 7]          36,864BatchNorm2d-323            [-1, 800, 7, 7]           1,600ReLU-324            [-1, 800, 7, 7]               0Conv2d-325            [-1, 128, 7, 7]         102,400BatchNorm2d-326            [-1, 128, 7, 7]             256ReLU-327            [-1, 128, 7, 7]               0Conv2d-328             [-1, 32, 7, 7]          36,864BatchNorm2d-329            [-1, 832, 7, 7]           1,664ReLU-330            [-1, 832, 7, 7]               0Conv2d-331            [-1, 128, 7, 7]         106,496BatchNorm2d-332            [-1, 128, 7, 7]             256ReLU-333            [-1, 128, 7, 7]               0Conv2d-334             [-1, 32, 7, 7]          36,864BatchNorm2d-335            [-1, 864, 7, 7]           1,728ReLU-336            [-1, 864, 7, 7]               0Conv2d-337            [-1, 128, 7, 7]         110,592BatchNorm2d-338            [-1, 128, 7, 7]             256ReLU-339            [-1, 128, 7, 7]               0Conv2d-340             [-1, 32, 7, 7]          36,864BatchNorm2d-341            [-1, 896, 7, 7]           1,792ReLU-342            [-1, 896, 7, 7]               0Conv2d-343            [-1, 128, 7, 7]         114,688BatchNorm2d-344            [-1, 128, 7, 7]             256ReLU-345            [-1, 128, 7, 7]               0Conv2d-346             [-1, 32, 7, 7]          36,864BatchNorm2d-347            [-1, 928, 7, 7]           1,856ReLU-348            [-1, 928, 7, 7]               0Conv2d-349            [-1, 128, 7, 7]         118,784BatchNorm2d-350            [-1, 128, 7, 7]             256ReLU-351            [-1, 128, 7, 7]               0Conv2d-352             [-1, 32, 7, 7]          36,864BatchNorm2d-353            [-1, 960, 7, 7]           1,920ReLU-354            [-1, 960, 7, 7]               0Conv2d-355            [-1, 128, 7, 7]         122,880BatchNorm2d-356            [-1, 128, 7, 7]             256ReLU-357            [-1, 128, 7, 7]               0Conv2d-358             [-1, 32, 7, 7]          36,864BatchNorm2d-359            [-1, 992, 7, 7]           1,984ReLU-360            [-1, 992, 7, 7]               0Conv2d-361            [-1, 128, 7, 7]         126,976BatchNorm2d-362            [-1, 128, 7, 7]             256ReLU-363            [-1, 128, 7, 7]               0Conv2d-364             [-1, 32, 7, 7]          36,864
AdaptiveAvgPool2d-365           [-1, 1024, 1, 1]               0Flatten-366                 [-1, 1024]               0Linear-367                   [-1, 64]          65,600ReLU-368                   [-1, 64]               0Linear-369                 [-1, 1024]          66,560Sigmoid-370                 [-1, 1024]               0SEModule-371           [-1, 1024, 7, 7]               0BatchNorm2d-372           [-1, 1024, 7, 7]           2,048ReLU-373           [-1, 1024, 7, 7]               0Linear-374                    [-1, 2]           2,050
================================================================
Total params: 7,088,066
Trainable params: 7,088,066
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 294.99
Params size (MB): 27.04
Estimated Total Size (MB): 322.60
----------------------------------------------------------------

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)))(SE-module): SEModule((se): Sequential((0): AdaptiveAvgPool2d(output_size=(1, 1))(1): Flatten(start_dim=1, end_dim=-1)(2): Linear(in_features=1024, out_features=64, bias=True)(3): ReLU(inplace=True)(4): Linear(in_features=64, out_features=1024, bias=True)(5): Sigmoid()))(norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu5): ReLU(inplace=True))(classifier): Linear(in_features=1024, out_features=2, bias=True)
)