- 🍨 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
目录
- 一、课题背景和开发环境
- 二、介绍
- 三、SE模块应用分析
- 四、SE模块效果对比
- 五、SE模块代码实现
- 六、SE模块插入到DenseNet代码实现
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 x x ,其特征通道数为 c 1 c_1 c1 ,通过一系列卷积等一般变换 F t r F_{tr} Ftr 后得到一个特征通道数为 c 2 c_2 c2 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。
- 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。 - 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 e x ( z , W ) = σ ( g ( z , W ) ) = σ ( W 2 R e L U ( W 1 z ) ) s = F_{ex}(z, W) = \sigma(g(z, W)) = \sigma(W_2ReLU(W_1z)) s=Fex(z,W)=σ(g(z,W))=σ(W2ReLU(W1z))
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)。 - 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 r 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)
)