- 🍨 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
0. 总结
数据导入及处理部分：本次数据导入没有使用torchvision自带的数据集，需要将原始数据进行处理包括数据导入，查看数据分类情况，定义transforms，进行数据类型转换等操作。
划分数据集：划定训练集测试集后，再使用torch.utils.data中的DataLoader()分别加载上一步处理好的训练及测试数据，查看批处理维度.
模型构建部分：DenseNet-121 + SE模块
设置超参数：在这之前需要定义损失函数，学习率（动态学习率），以及根据学习率定义优化器（例如SGD随机梯度下降），用来在训练中更新参数，最小化损失函数。
定义训练函数：函数的传入的参数有四个，分别是设置好的DataLoader(),定义好的模型，损失函数，优化器。函数内部初始化损失准确率为0，接着开始循环，使用DataLoader()获取一个批次的数据，对这个批次的数据带入模型得到预测值，然后使用损失函数计算得到损失值。接下来就是进行反向传播以及使用优化器优化参数，梯度清零放在反向传播之前或者是使用优化器优化之后都是可以的，一般是默认放在反向传播之前。
定义测试函数：函数传入的参数相比训练函数少了优化器，只需传入设置好的DataLoader(),定义好的模型，损失函数。此外除了处理批次数据时无需再设置梯度清零、返向传播以及优化器优化参数，其余部分均和训练函数保持一致。
训练过程：定义训练次数，有几次就使用整个数据集进行几次训练，初始化四个空list分别存储每次训练及测试的准确率及损失。使用model.train()开启训练模式，调用训练函数得到准确率及损失。使用model.eval()将模型设置为评估模式，调用测试函数得到准确率及损失。接着就是将得到的训练及测试的准确率及损失存储到相应list中并合并打印出来，得到每一次整体训练后的准确率及损失。
结果可视化
模型的保存，调取及使用。在PyTorch中，通常使用 torch.save(model.state_dict(), ‘model.pth’) 保存模型的参数，使用 model.load_state_dict(torch.load(‘model.pth’)) 加载参数。
需要改进优化的地方：确保模型和数据的一致性，都存到GPU或者CPU;注意numclasses不要直接用默认的1000，需要根据实际数据集改进；实例化模型也要注意numclasses这个参数；此外注意测试模型需要用（3,224,224）3表示通道数，这和tensorflow定义的顺序是不用的（224,224,3），做代码转换时需要注意。
python">import torch
import torch.nn as nn
import torchvision
from torchvision import datasets,transforms
from torch.utils.data import DataLoader
import torchvision.models as models
import torch.nn.functional as F
from collections import OrderedDict import os,PIL,pathlib
import matplotlib.pyplot as plt
import warningswarnings.filterwarnings('ignore') # 忽略警告信息plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.dpi'] = 100 # 分辨率
1. 设置GPU
python">device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
device(type='cuda')
2. 导入数据及处理部分
python"># 获取数据分布情况
path_dir = './data/mpox_recognize/'
path_dir = pathlib.Path(path_dir)paths = list(path_dir.glob('*'))
# classNames = [str(path).split("\\")[-1] for path in paths] # ['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']
classNames = [path.parts[-1] for path in paths]
classNames
['Monkeypox', 'Others']
python"># 定义transforms 并处理数据
train_transforms = transforms.Compose([transforms.Resize([224,224]), # 将输入图片resize成统一尺寸transforms.RandomHorizontalFlip(), # 随机水平翻转transforms.ToTensor(), # 将PIL Image 或 numpy.ndarray 装换为tensor,并归一化到[0,1]之间transforms.Normalize( # 标准化处理 --> 转换为标准正太分布（高斯分布），使模型更容易收敛mean = [0.485,0.456,0.406], # 其中 mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。std = [0.229,0.224,0.225])
])
test_transforms = transforms.Compose([transforms.Resize([224,224]),transforms.ToTensor(),transforms.Normalize(mean = [0.485,0.456,0.406],std = [0.229,0.224,0.225])
])
total_data = datasets.ImageFolder('./data/mpox_recognize/',transform = train_transforms)
total_data
Dataset ImageFolderNumber of datapoints: 2142Root location: ./data/mpox_recognize/StandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=True)RandomHorizontalFlip(p=0.5)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
python">total_data.class_to_idx
{'Monkeypox': 0, 'Others': 1}
3. 划分数据集
python"># 划分数据集
train_size = int(len(total_data) * 0.8)
test_size = len(total_data) - train_sizetrain_dataset,test_dataset = torch.utils.data.random_split(total_data,[train_size,test_size])
train_dataset,test_dataset
(<torch.utils.data.dataset.Subset at 0x18230109120>,<torch.utils.data.dataset.Subset at 0x182300d2cb0>)
python"># 定义DataLoader用于数据集的加载batch_size = 32train_dl = torch.utils.data.DataLoader(train_dataset,batch_size = batch_size,shuffle = True,num_workers = 1
)
test_dl = torch.utils.data.DataLoader(test_dataset,batch_size = batch_size,shuffle = True,num_workers = 1
)
python"># 观察数据维度
for X,y in test_dl:print("Shape of X [N,C,H,W]: ",X.shape)print("Shape of y: ", y.shape,y.dtype)break
Shape of X [N,C,H,W]: torch.Size([32, 3, 224, 224])
Shape of y: torch.Size([32]) torch.int64
4. 模型构建部分
SE 模块
代码解释：
- Squeeze操作：使用
nn.AdaptiveAvgPool2d(1)来实现全局平均池化，它将输入张量的空间维度（H x W）池化成1x1大小，保留每个通道的平均值。 - Excitation操作：将池化后的输出通过两个全连接层（
fc1和fc2）。第一个全连接层的输出维度是filter_sq，然后通过ReLU激活，再经过第二个全连接层输出1个值，最后通过Sigmoid激活函数将值压缩到[0, 1]之间，表示每个通道的权重。 - Scale操作：对输入的特征图进行按通道加权操作，输出加权后的特征图。
运行示例：
该代码中创建了一个SqueezeExcitationLayer实例并用一个形状为(1, 32, 32, 32)的输入张量进行测试，输出的形状将与输入形状相同，因为SE模块是一个逐通道加权操作，不改变空间维度。
python"># import torch
# import torch.nn as nn
# import torch.nn.functional as F# class SqueezeExcitationLayer(nn.Module):
# def __init__(self, filter_sq):
# # filter_sq 是 Excitation 中第一个全连接层的输出通道数
# super(SqueezeExcitationLayer, self).__init__()
# self.filter_sq = filter_sq
# self.global_avg_pool = nn.AdaptiveAvgPool2d(1) # 等效于全局平均池化
# self.fc1 = nn.Linear(1, filter_sq) # 输入通道数是1（全局池化后的输出），输出通道数是filter_sq
# self.relu = nn.ReLU()
# self.fc2 = nn.Linear(filter_sq, 1) # 最后的输出通道数为1（每个通道的权重）
# self.sigmoid = nn.Sigmoid()# def forward(self, x):
# # Squeeze阶段
# squeeze = self.global_avg_pool(x) # Shape: (batch_size, channels, 1, 1)
# squeeze = squeeze.view(squeeze.size(0), -1) # 拉平成(batch_size, channels)# # Excitation阶段
# excitation = self.fc1(squeeze) # Shape: (batch_size, filter_sq)
# excitation = self.relu(excitation)
# excitation = self.fc2(excitation) # Shape: (batch_size, 1)
# excitation = self.sigmoid(excitation) # Shape: (batch_size, 1)# # Reshape back to match input dimensions for element-wise multiplication
# excitation = excitation.view(excitation.size(0), excitation.size(1), 1, 1) # Shape: (batch_size, channels, 1, 1)# # Scale input with excitation weights
# scale = x * excitation # Element-wise multiplication# return scale# # 示例：创建一个SqueezeExcitation层并通过它传入一个dummy输入
# SE = SqueezeExcitationLayer(16)
# inputs = torch.zeros((1, 32, 32, 32)) # 输入张量，形状为 (batch_size, channels, height, width)
# output = SE(inputs) # 执行前向传播
# print(output.shape) # 输出形状
python">class SqueezeExcitationLayer(nn.Module):def __init__(self, num_input_features, filter_sq):super(SqueezeExcitationLayer, self).__init__()self.filter_sq = filter_sqself.global_avg_pool = nn.AdaptiveAvgPool2d(1) # 等效于全局平均池化self.fc1 = nn.Linear(num_input_features, filter_sq) # 输入特征为num_input_features，输出特征为filter_sqself.relu = nn.ReLU()self.fc2 = nn.Linear(filter_sq, num_input_features) # 最后的输出通道数与输入的通道数相同self.sigmoid = nn.Sigmoid()def forward(self, x):# Squeeze阶段squeeze = self.global_avg_pool(x) # Shape: (batch_size, channels, 1, 1)squeeze = squeeze.view(squeeze.size(0), -1) # 拉平成(batch_size, channels)# Excitation阶段excitation = self.fc1(squeeze) # Shape: (batch_size, filter_sq)excitation = self.relu(excitation)excitation = self.fc2(excitation) # Shape: (batch_size, num_input_features)excitation = self.sigmoid(excitation) # Shape: (batch_size, num_input_features)# Reshape back to match input dimensions for element-wise multiplicationexcitation = excitation.view(excitation.size(0), excitation.size(1), 1, 1) # Shape: (batch_size, channels, 1, 1)# Scale input with excitation weightsscale = x * excitation # Element-wise multiplicationreturn scale# 调用SE模块时，确保传入的参数正确
inputs = torch.zeros((1, 32, 32, 32)) # 示例输入张量，注意channels的位置
inputs = inputs.permute(0, 3, 1, 2) # 将输入的维度从 (batch_size, height, width, channels) 转换为 (batch_size, channels, height, width)se = SqueezeExcitationLayer(32, 16) # 32是输入通道数，16是filter_sq
output = se(inputs)
print(output.shape)
torch.Size([1, 32, 32, 32])
出现 RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x32 and 1x16) 错误是因为在 SE 模块的 fc1 层中，输入的形状和权重矩阵的形状不匹配。这个问题发生在全连接层时，通常是由于输入尺寸不符合全连接层的预期。
问题的根源：
在 SqueezeExcitationLayer 中，我们对输入进行全局平均池化后，得到的输出是 (batch_size, channels, 1, 1)。然后，试图将这个输出展平成 (batch_size, channels)，并传递给全连接层（fc1）。然而，fc1 层的输入特征数应与输入的通道数匹配。错误的根本原因是 fc1 层的输入尺寸不匹配。
解决方法：
要确保输入的形状与 fc1 层的输入特征数匹配，我们应该在 fc1 层的输入时，确保其维度正确。具体地，我们需要使用正确的输入特征大小（即 num_input_features，它应为输入张量的通道数）。
关键修改：
输入维度：在调用 SqueezeExcitationLayer 时，我们确保了输入张量的维度是 (batch_size, channels, height, width)。因为PyTorch通常处理的图像数据格式是 (batch_size, channels, height, width)，而不是 (batch_size, height, width, channels)。
fc1 层的输入：fc1 层的输入特征数应与输入张量的通道数（num_input_features）匹配。在调用 SqueezeExcitationLayer 时，确保了这一点。
这样，您应该能够顺利执行前向传播并得到正确的输出形状。
改进的DenseNET
要在你现有的DenseNet代码中加入SE模块（Squeeze-and-Excitation），我们需要对DenseNet中的每个_DenseLayer做一些修改，确保在每个DenseLayer后加入SE模块。SE模块的作用是通过学习通道重要性来调整每个通道的权重。我们可以将SE模块添加到每个_DenseLayer的输出中。
具体修改步骤：
- 在
_DenseLayer中加入SE模块，使得每个DenseLayer的输出都经过SE模块的加权调整。 - 在
DenseNet构造函数中，对每个_DenseLayer实例化时加入SE模块。
python"># class _DenseLayer(nn.Sequential):
# """Basic unit of DenseBlock (using bottleneck layer) """
# def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
# super(_DenseLayer, self).__init__()
# self.add_module("norm1", nn.BatchNorm2d(num_input_features))
# self.add_module("relu1", nn.ReLU(inplace=True))
# self.add_module("conv1", nn.Conv2d(num_input_features, 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_rate# def forward(self, x):
# new_features = super(_DenseLayer, self).forward(x)
# if self.drop_rate > 0:
# new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
# return torch.cat([x, new_features], 1)# class _DenseBlock(nn.Sequential):
# """DenseBlock"""
# def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
# super(_DenseBlock, self).__init__()
# for i in range(num_layers):
# layer = _DenseLayer(num_input_features+i*growth_rate, growth_rate, bn_size,
# drop_rate)
# self.add_module("denselayer%d" % (i+1,), layer)# class _Transition(nn.Sequential):
# """Transition layer between two adjacent DenseBlock"""
# def __init__(self, num_input_feature, num_output_features):
# super(_Transition, self).__init__()
# self.add_module("norm", nn.BatchNorm2d(num_input_feature))
# self.add_module("relu", nn.ReLU(inplace=True))
# self.add_module("conv", nn.Conv2d(num_input_feature, num_output_features,
# kernel_size=1, stride=1, bias=False))
# self.add_module("pool", nn.AvgPool2d(2, stride=2))# class DenseNet(nn.Module):
# "DenseNet-BC model"
# def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=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 each DenseBlock
# :param num_init_features: (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 Conv2d
# self.features = nn.Sequential(OrderedDict([
# ("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
# ("norm0", nn.BatchNorm2d(num_init_features)),
# ("relu0", nn.ReLU(inplace=True)),
# ("pool0", nn.MaxPool2d(3, stride=2, padding=1))
# ]))# # DenseBlock
# num_features = num_init_features
# for 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_rate
# if 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)# # final bn+ReLU
# self.features.add_module("norm5", nn.BatchNorm2d(num_features))
# self.features.add_module("relu5", nn.ReLU(inplace=True))# # classification layer
# self.classifier = nn.Linear(num_features, num_classes)# # params initialization
# for 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):
# features = self.features(x)
# out = F.avg_pool2d(features, 7, stride=1).view(features.size(0), -1)
# out = self.classifier(out)
# return out
python">import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDictclass SqueezeExcitationLayer(nn.Module):def __init__(self, num_input_features, filter_sq):super(SqueezeExcitationLayer, self).__init__()self.filter_sq = filter_sqself.global_avg_pool = nn.AdaptiveAvgPool2d(1) # 等效于全局平均池化self.fc1 = nn.Linear(num_input_features, filter_sq) # 输入通道数是num_input_features，输出通道数是filter_sqself.relu = nn.ReLU()self.fc2 = nn.Linear(filter_sq, num_input_features) # 最后的输出通道数与输入的通道数相同self.sigmoid = nn.Sigmoid()def forward(self, x):# Squeeze阶段squeeze = self.global_avg_pool(x) # Shape: (batch_size, channels, 1, 1)squeeze = squeeze.view(squeeze.size(0), -1) # 拉平成(batch_size, channels)# Excitation阶段excitation = self.fc1(squeeze) # Shape: (batch_size, filter_sq)excitation = self.relu(excitation)excitation = self.fc2(excitation) # Shape: (batch_size, num_input_features)excitation = self.sigmoid(excitation) # Shape: (batch_size, num_input_features)# Reshape back to match input dimensions for element-wise multiplicationexcitation = excitation.view(excitation.size(0), excitation.size(1), 1, 1) # Shape: (batch_size, channels, 1, 1)# Scale input with excitation weightsscale = x * excitation # Element-wise multiplicationreturn scaleclass _DenseLayer(nn.Sequential):"""Basic unit of DenseBlock (using bottleneck layer) """def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, se_filter_sq=16):super(_DenseLayer, self).__init__()self.add_module("norm1", nn.BatchNorm2d(num_input_features))self.add_module("relu1", nn.ReLU(inplace=True))self.add_module("conv1", nn.Conv2d(num_input_features, 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))# 添加SE模块self.se = SqueezeExcitationLayer(growth_rate, se_filter_sq)self.drop_rate = drop_ratedef forward(self, x):new_features = super(_DenseLayer, self).forward(x)new_features = self.se(new_features) # 将SE模块加到特征图上if self.drop_rate > 0:new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)return torch.cat([x, new_features], 1)class _DenseBlock(nn.Sequential):"""DenseBlock"""def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, se_filter_sq=16):super(_DenseBlock, self).__init__()for i in range(num_layers):layer = _DenseLayer(num_input_features+i*growth_rate, growth_rate, bn_size,drop_rate, se_filter_sq)self.add_module("denselayer%d" % (i+1,), layer)class _Transition(nn.Sequential):"""Transition layer between two adjacent DenseBlock"""def __init__(self, num_input_feature, num_output_features):super(_Transition, self).__init__()self.add_module("norm", nn.BatchNorm2d(num_input_feature))self.add_module("relu", nn.ReLU(inplace=True))self.add_module("conv", nn.Conv2d(num_input_feature, num_output_features,kernel_size=1, stride=1, bias=False))self.add_module("pool", nn.AvgPool2d(2, stride=2))class DenseNet(nn.Module):"DenseNet-BC model"def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64,bn_size=4, compression_rate=0.5, drop_rate=0, num_classes=1000, se_filter_sq=16):""":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 each DenseBlock:param num_init_features: (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:param se_filter_sq: (int) the number of filters used in SE module's fully connected layer"""super(DenseNet, self).__init__()# first Conv2dself.features = nn.Sequential(OrderedDict([ ("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),("norm0", nn.BatchNorm2d(num_init_features)),("relu0", nn.ReLU(inplace=True)),("pool0", nn.MaxPool2d(3, stride=2, padding=1))]))# DenseBlocknum_features = num_init_featuresfor i, num_layers in enumerate(block_config):block = _DenseBlock(num_layers, num_features, bn_size, growth_rate, drop_rate, se_filter_sq)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)# 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):features = self.features(x)out = F.avg_pool2d(features, 7, stride=1).view(features.size(0), -1)out = self.classifier(out)return out
代码解释：
- SqueezeExcitationLayer：实现了SE模块，它根据输入通道的重要性生成一个权重（通过全局平均池化和两个全连接层）。
- _DenseLayer：在每个DenseLayer后添加了SE模块，并将SE模块的输出与输入特征图相乘，从而对每个通道进行加权。
- _DenseBlock：每个DenseBlock中的
_DenseLayer都调用了SE模块。 - DenseNet：在
DenseNet类中，您可以指定se_filter_sq参数，这控制SE模块中的全连接层的大小。
这样，您的DenseNet就可以利用SE模块来提升其表示能力了。
python"># # Now, instantiate and use the model
# densenet121 = DenseNet(num_init_features=64, # init_channel=64,
# growth_rate=32,
# block_config=(6,12,24,16),
# num_classes=len(classNames)) # model = densenet121.to(device)
# model
python"># Now, instantiate and use the model
se_filter_sq = 16 # 可以根据需要调整SE模块的输出大小densenet121 = DenseNet(num_init_features=64, # init_channel=64,growth_rate=32,block_config=(6, 12, 24, 16),num_classes=len(classNames), # 根据您的分类任务设置类别数se_filter_sq=se_filter_sq # 传递SE模块的参数
)model = densenet121.to(device) # 将模型移动到指定的设备上
model
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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid())))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid())))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid())))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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)(se): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): Sigmoid()))(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): SqueezeExcitationLayer((global_avg_pool): AdaptiveAvgPool2d(output_size=1)(fc1): Linear(in_features=32, out_features=16, bias=True)(relu): ReLU()(fc2): Linear(in_features=16, out_features=32, bias=True)(sigmoid): 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)
)
解释：
se_filter_sq：在模型中传递了se_filter_sq参数，用于控制SE模块的内部全连接层的大小。您可以根据实验的需要调整此值。
其余的部分（num_init_features，growth_rate，block_config，num_classes等）可以根据您的需求调整。
通过这样修改，您的模型现在会正确地包括SE模块，并且能够按预期运行。
python"># 查看模型详情
import torchsummary as summary
summary.summary(model,(3,224,224))
----------------------------------------------------------------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] 36,864
AdaptiveAvgPool2d-11 [-1, 32, 1, 1] 0Linear-12 [-1, 16] 528ReLU-13 [-1, 16] 0Linear-14 [-1, 32] 544Sigmoid-15 [-1, 32] 0
SqueezeExcitationLayer-16 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-17 [-1, 32, 1, 1] 0Linear-18 [-1, 16] 528ReLU-19 [-1, 16] 0Linear-20 [-1, 32] 544Sigmoid-21 [-1, 32] 0
SqueezeExcitationLayer-22 [-1, 32, 56, 56] 0BatchNorm2d-23 [-1, 96, 56, 56] 192ReLU-24 [-1, 96, 56, 56] 0Conv2d-25 [-1, 128, 56, 56] 12,288BatchNorm2d-26 [-1, 128, 56, 56] 256ReLU-27 [-1, 128, 56, 56] 0Conv2d-28 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-29 [-1, 32, 1, 1] 0Linear-30 [-1, 16] 528ReLU-31 [-1, 16] 0Linear-32 [-1, 32] 544Sigmoid-33 [-1, 32] 0
SqueezeExcitationLayer-34 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-35 [-1, 32, 1, 1] 0Linear-36 [-1, 16] 528ReLU-37 [-1, 16] 0Linear-38 [-1, 32] 544Sigmoid-39 [-1, 32] 0
SqueezeExcitationLayer-40 [-1, 32, 56, 56] 0BatchNorm2d-41 [-1, 128, 56, 56] 256ReLU-42 [-1, 128, 56, 56] 0Conv2d-43 [-1, 128, 56, 56] 16,384BatchNorm2d-44 [-1, 128, 56, 56] 256ReLU-45 [-1, 128, 56, 56] 0Conv2d-46 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-47 [-1, 32, 1, 1] 0Linear-48 [-1, 16] 528ReLU-49 [-1, 16] 0Linear-50 [-1, 32] 544Sigmoid-51 [-1, 32] 0
SqueezeExcitationLayer-52 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-53 [-1, 32, 1, 1] 0Linear-54 [-1, 16] 528ReLU-55 [-1, 16] 0Linear-56 [-1, 32] 544Sigmoid-57 [-1, 32] 0
SqueezeExcitationLayer-58 [-1, 32, 56, 56] 0BatchNorm2d-59 [-1, 160, 56, 56] 320ReLU-60 [-1, 160, 56, 56] 0Conv2d-61 [-1, 128, 56, 56] 20,480BatchNorm2d-62 [-1, 128, 56, 56] 256ReLU-63 [-1, 128, 56, 56] 0Conv2d-64 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-65 [-1, 32, 1, 1] 0Linear-66 [-1, 16] 528ReLU-67 [-1, 16] 0Linear-68 [-1, 32] 544Sigmoid-69 [-1, 32] 0
SqueezeExcitationLayer-70 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-71 [-1, 32, 1, 1] 0Linear-72 [-1, 16] 528ReLU-73 [-1, 16] 0Linear-74 [-1, 32] 544Sigmoid-75 [-1, 32] 0
SqueezeExcitationLayer-76 [-1, 32, 56, 56] 0BatchNorm2d-77 [-1, 192, 56, 56] 384ReLU-78 [-1, 192, 56, 56] 0Conv2d-79 [-1, 128, 56, 56] 24,576BatchNorm2d-80 [-1, 128, 56, 56] 256ReLU-81 [-1, 128, 56, 56] 0Conv2d-82 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-83 [-1, 32, 1, 1] 0Linear-84 [-1, 16] 528ReLU-85 [-1, 16] 0Linear-86 [-1, 32] 544Sigmoid-87 [-1, 32] 0
SqueezeExcitationLayer-88 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-89 [-1, 32, 1, 1] 0Linear-90 [-1, 16] 528ReLU-91 [-1, 16] 0Linear-92 [-1, 32] 544Sigmoid-93 [-1, 32] 0
SqueezeExcitationLayer-94 [-1, 32, 56, 56] 0BatchNorm2d-95 [-1, 224, 56, 56] 448ReLU-96 [-1, 224, 56, 56] 0Conv2d-97 [-1, 128, 56, 56] 28,672BatchNorm2d-98 [-1, 128, 56, 56] 256ReLU-99 [-1, 128, 56, 56] 0Conv2d-100 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-101 [-1, 32, 1, 1] 0Linear-102 [-1, 16] 528ReLU-103 [-1, 16] 0Linear-104 [-1, 32] 544Sigmoid-105 [-1, 32] 0
SqueezeExcitationLayer-106 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-107 [-1, 32, 1, 1] 0Linear-108 [-1, 16] 528ReLU-109 [-1, 16] 0Linear-110 [-1, 32] 544Sigmoid-111 [-1, 32] 0
SqueezeExcitationLayer-112 [-1, 32, 56, 56] 0BatchNorm2d-113 [-1, 256, 56, 56] 512ReLU-114 [-1, 256, 56, 56] 0Conv2d-115 [-1, 128, 56, 56] 32,768AvgPool2d-116 [-1, 128, 28, 28] 0BatchNorm2d-117 [-1, 128, 28, 28] 256ReLU-118 [-1, 128, 28, 28] 0Conv2d-119 [-1, 128, 28, 28] 16,384BatchNorm2d-120 [-1, 128, 28, 28] 256ReLU-121 [-1, 128, 28, 28] 0Conv2d-122 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-123 [-1, 32, 1, 1] 0Linear-124 [-1, 16] 528ReLU-125 [-1, 16] 0Linear-126 [-1, 32] 544Sigmoid-127 [-1, 32] 0
SqueezeExcitationLayer-128 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-129 [-1, 32, 1, 1] 0Linear-130 [-1, 16] 528ReLU-131 [-1, 16] 0Linear-132 [-1, 32] 544Sigmoid-133 [-1, 32] 0
SqueezeExcitationLayer-134 [-1, 32, 28, 28] 0BatchNorm2d-135 [-1, 160, 28, 28] 320ReLU-136 [-1, 160, 28, 28] 0Conv2d-137 [-1, 128, 28, 28] 20,480BatchNorm2d-138 [-1, 128, 28, 28] 256ReLU-139 [-1, 128, 28, 28] 0Conv2d-140 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-141 [-1, 32, 1, 1] 0Linear-142 [-1, 16] 528ReLU-143 [-1, 16] 0Linear-144 [-1, 32] 544Sigmoid-145 [-1, 32] 0
SqueezeExcitationLayer-146 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-147 [-1, 32, 1, 1] 0Linear-148 [-1, 16] 528ReLU-149 [-1, 16] 0Linear-150 [-1, 32] 544Sigmoid-151 [-1, 32] 0
SqueezeExcitationLayer-152 [-1, 32, 28, 28] 0BatchNorm2d-153 [-1, 192, 28, 28] 384ReLU-154 [-1, 192, 28, 28] 0Conv2d-155 [-1, 128, 28, 28] 24,576BatchNorm2d-156 [-1, 128, 28, 28] 256ReLU-157 [-1, 128, 28, 28] 0Conv2d-158 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-159 [-1, 32, 1, 1] 0Linear-160 [-1, 16] 528ReLU-161 [-1, 16] 0Linear-162 [-1, 32] 544Sigmoid-163 [-1, 32] 0
SqueezeExcitationLayer-164 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-165 [-1, 32, 1, 1] 0Linear-166 [-1, 16] 528ReLU-167 [-1, 16] 0Linear-168 [-1, 32] 544Sigmoid-169 [-1, 32] 0
SqueezeExcitationLayer-170 [-1, 32, 28, 28] 0BatchNorm2d-171 [-1, 224, 28, 28] 448ReLU-172 [-1, 224, 28, 28] 0Conv2d-173 [-1, 128, 28, 28] 28,672BatchNorm2d-174 [-1, 128, 28, 28] 256ReLU-175 [-1, 128, 28, 28] 0Conv2d-176 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-177 [-1, 32, 1, 1] 0Linear-178 [-1, 16] 528ReLU-179 [-1, 16] 0Linear-180 [-1, 32] 544Sigmoid-181 [-1, 32] 0
SqueezeExcitationLayer-182 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-183 [-1, 32, 1, 1] 0Linear-184 [-1, 16] 528ReLU-185 [-1, 16] 0Linear-186 [-1, 32] 544Sigmoid-187 [-1, 32] 0
SqueezeExcitationLayer-188 [-1, 32, 28, 28] 0BatchNorm2d-189 [-1, 256, 28, 28] 512ReLU-190 [-1, 256, 28, 28] 0Conv2d-191 [-1, 128, 28, 28] 32,768BatchNorm2d-192 [-1, 128, 28, 28] 256ReLU-193 [-1, 128, 28, 28] 0Conv2d-194 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-195 [-1, 32, 1, 1] 0Linear-196 [-1, 16] 528ReLU-197 [-1, 16] 0Linear-198 [-1, 32] 544Sigmoid-199 [-1, 32] 0
SqueezeExcitationLayer-200 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-201 [-1, 32, 1, 1] 0Linear-202 [-1, 16] 528ReLU-203 [-1, 16] 0Linear-204 [-1, 32] 544Sigmoid-205 [-1, 32] 0
SqueezeExcitationLayer-206 [-1, 32, 28, 28] 0BatchNorm2d-207 [-1, 288, 28, 28] 576ReLU-208 [-1, 288, 28, 28] 0Conv2d-209 [-1, 128, 28, 28] 36,864BatchNorm2d-210 [-1, 128, 28, 28] 256ReLU-211 [-1, 128, 28, 28] 0Conv2d-212 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-213 [-1, 32, 1, 1] 0Linear-214 [-1, 16] 528ReLU-215 [-1, 16] 0Linear-216 [-1, 32] 544Sigmoid-217 [-1, 32] 0
SqueezeExcitationLayer-218 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-219 [-1, 32, 1, 1] 0Linear-220 [-1, 16] 528ReLU-221 [-1, 16] 0Linear-222 [-1, 32] 544Sigmoid-223 [-1, 32] 0
SqueezeExcitationLayer-224 [-1, 32, 28, 28] 0BatchNorm2d-225 [-1, 320, 28, 28] 640ReLU-226 [-1, 320, 28, 28] 0Conv2d-227 [-1, 128, 28, 28] 40,960BatchNorm2d-228 [-1, 128, 28, 28] 256ReLU-229 [-1, 128, 28, 28] 0Conv2d-230 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-231 [-1, 32, 1, 1] 0Linear-232 [-1, 16] 528ReLU-233 [-1, 16] 0Linear-234 [-1, 32] 544Sigmoid-235 [-1, 32] 0
SqueezeExcitationLayer-236 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-237 [-1, 32, 1, 1] 0Linear-238 [-1, 16] 528ReLU-239 [-1, 16] 0Linear-240 [-1, 32] 544Sigmoid-241 [-1, 32] 0
SqueezeExcitationLayer-242 [-1, 32, 28, 28] 0BatchNorm2d-243 [-1, 352, 28, 28] 704ReLU-244 [-1, 352, 28, 28] 0Conv2d-245 [-1, 128, 28, 28] 45,056BatchNorm2d-246 [-1, 128, 28, 28] 256ReLU-247 [-1, 128, 28, 28] 0Conv2d-248 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-249 [-1, 32, 1, 1] 0Linear-250 [-1, 16] 528ReLU-251 [-1, 16] 0Linear-252 [-1, 32] 544Sigmoid-253 [-1, 32] 0
SqueezeExcitationLayer-254 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-255 [-1, 32, 1, 1] 0Linear-256 [-1, 16] 528ReLU-257 [-1, 16] 0Linear-258 [-1, 32] 544Sigmoid-259 [-1, 32] 0
SqueezeExcitationLayer-260 [-1, 32, 28, 28] 0BatchNorm2d-261 [-1, 384, 28, 28] 768ReLU-262 [-1, 384, 28, 28] 0Conv2d-263 [-1, 128, 28, 28] 49,152BatchNorm2d-264 [-1, 128, 28, 28] 256ReLU-265 [-1, 128, 28, 28] 0Conv2d-266 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-267 [-1, 32, 1, 1] 0Linear-268 [-1, 16] 528ReLU-269 [-1, 16] 0Linear-270 [-1, 32] 544Sigmoid-271 [-1, 32] 0
SqueezeExcitationLayer-272 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-273 [-1, 32, 1, 1] 0Linear-274 [-1, 16] 528ReLU-275 [-1, 16] 0Linear-276 [-1, 32] 544Sigmoid-277 [-1, 32] 0
SqueezeExcitationLayer-278 [-1, 32, 28, 28] 0BatchNorm2d-279 [-1, 416, 28, 28] 832ReLU-280 [-1, 416, 28, 28] 0Conv2d-281 [-1, 128, 28, 28] 53,248BatchNorm2d-282 [-1, 128, 28, 28] 256ReLU-283 [-1, 128, 28, 28] 0Conv2d-284 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-285 [-1, 32, 1, 1] 0Linear-286 [-1, 16] 528ReLU-287 [-1, 16] 0Linear-288 [-1, 32] 544Sigmoid-289 [-1, 32] 0
SqueezeExcitationLayer-290 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-291 [-1, 32, 1, 1] 0Linear-292 [-1, 16] 528ReLU-293 [-1, 16] 0Linear-294 [-1, 32] 544Sigmoid-295 [-1, 32] 0
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AdaptiveAvgPool2d-851 [-1, 32, 1, 1] 0Linear-852 [-1, 16] 528ReLU-853 [-1, 16] 0Linear-854 [-1, 32] 544Sigmoid-855 [-1, 32] 0
SqueezeExcitationLayer-856 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-857 [-1, 32, 1, 1] 0Linear-858 [-1, 16] 528ReLU-859 [-1, 16] 0Linear-860 [-1, 32] 544Sigmoid-861 [-1, 32] 0
SqueezeExcitationLayer-862 [-1, 32, 7, 7] 0BatchNorm2d-863 [-1, 672, 7, 7] 1,344ReLU-864 [-1, 672, 7, 7] 0Conv2d-865 [-1, 128, 7, 7] 86,016BatchNorm2d-866 [-1, 128, 7, 7] 256ReLU-867 [-1, 128, 7, 7] 0Conv2d-868 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-869 [-1, 32, 1, 1] 0Linear-870 [-1, 16] 528ReLU-871 [-1, 16] 0Linear-872 [-1, 32] 544Sigmoid-873 [-1, 32] 0
SqueezeExcitationLayer-874 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-875 [-1, 32, 1, 1] 0Linear-876 [-1, 16] 528ReLU-877 [-1, 16] 0Linear-878 [-1, 32] 544Sigmoid-879 [-1, 32] 0
SqueezeExcitationLayer-880 [-1, 32, 7, 7] 0BatchNorm2d-881 [-1, 704, 7, 7] 1,408ReLU-882 [-1, 704, 7, 7] 0Conv2d-883 [-1, 128, 7, 7] 90,112BatchNorm2d-884 [-1, 128, 7, 7] 256ReLU-885 [-1, 128, 7, 7] 0Conv2d-886 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-887 [-1, 32, 1, 1] 0Linear-888 [-1, 16] 528ReLU-889 [-1, 16] 0Linear-890 [-1, 32] 544Sigmoid-891 [-1, 32] 0
SqueezeExcitationLayer-892 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-893 [-1, 32, 1, 1] 0Linear-894 [-1, 16] 528ReLU-895 [-1, 16] 0Linear-896 [-1, 32] 544Sigmoid-897 [-1, 32] 0
SqueezeExcitationLayer-898 [-1, 32, 7, 7] 0BatchNorm2d-899 [-1, 736, 7, 7] 1,472ReLU-900 [-1, 736, 7, 7] 0Conv2d-901 [-1, 128, 7, 7] 94,208BatchNorm2d-902 [-1, 128, 7, 7] 256ReLU-903 [-1, 128, 7, 7] 0Conv2d-904 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-905 [-1, 32, 1, 1] 0Linear-906 [-1, 16] 528ReLU-907 [-1, 16] 0Linear-908 [-1, 32] 544Sigmoid-909 [-1, 32] 0
SqueezeExcitationLayer-910 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-911 [-1, 32, 1, 1] 0Linear-912 [-1, 16] 528ReLU-913 [-1, 16] 0Linear-914 [-1, 32] 544Sigmoid-915 [-1, 32] 0
SqueezeExcitationLayer-916 [-1, 32, 7, 7] 0BatchNorm2d-917 [-1, 768, 7, 7] 1,536ReLU-918 [-1, 768, 7, 7] 0Conv2d-919 [-1, 128, 7, 7] 98,304BatchNorm2d-920 [-1, 128, 7, 7] 256ReLU-921 [-1, 128, 7, 7] 0Conv2d-922 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-923 [-1, 32, 1, 1] 0Linear-924 [-1, 16] 528ReLU-925 [-1, 16] 0Linear-926 [-1, 32] 544Sigmoid-927 [-1, 32] 0
SqueezeExcitationLayer-928 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-929 [-1, 32, 1, 1] 0Linear-930 [-1, 16] 528ReLU-931 [-1, 16] 0Linear-932 [-1, 32] 544Sigmoid-933 [-1, 32] 0
SqueezeExcitationLayer-934 [-1, 32, 7, 7] 0BatchNorm2d-935 [-1, 800, 7, 7] 1,600ReLU-936 [-1, 800, 7, 7] 0Conv2d-937 [-1, 128, 7, 7] 102,400BatchNorm2d-938 [-1, 128, 7, 7] 256ReLU-939 [-1, 128, 7, 7] 0Conv2d-940 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-941 [-1, 32, 1, 1] 0Linear-942 [-1, 16] 528ReLU-943 [-1, 16] 0Linear-944 [-1, 32] 544Sigmoid-945 [-1, 32] 0
SqueezeExcitationLayer-946 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-947 [-1, 32, 1, 1] 0Linear-948 [-1, 16] 528ReLU-949 [-1, 16] 0Linear-950 [-1, 32] 544Sigmoid-951 [-1, 32] 0
SqueezeExcitationLayer-952 [-1, 32, 7, 7] 0BatchNorm2d-953 [-1, 832, 7, 7] 1,664ReLU-954 [-1, 832, 7, 7] 0Conv2d-955 [-1, 128, 7, 7] 106,496BatchNorm2d-956 [-1, 128, 7, 7] 256ReLU-957 [-1, 128, 7, 7] 0Conv2d-958 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-959 [-1, 32, 1, 1] 0Linear-960 [-1, 16] 528ReLU-961 [-1, 16] 0Linear-962 [-1, 32] 544Sigmoid-963 [-1, 32] 0
SqueezeExcitationLayer-964 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-965 [-1, 32, 1, 1] 0Linear-966 [-1, 16] 528ReLU-967 [-1, 16] 0Linear-968 [-1, 32] 544Sigmoid-969 [-1, 32] 0
SqueezeExcitationLayer-970 [-1, 32, 7, 7] 0BatchNorm2d-971 [-1, 864, 7, 7] 1,728ReLU-972 [-1, 864, 7, 7] 0Conv2d-973 [-1, 128, 7, 7] 110,592BatchNorm2d-974 [-1, 128, 7, 7] 256ReLU-975 [-1, 128, 7, 7] 0Conv2d-976 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-977 [-1, 32, 1, 1] 0Linear-978 [-1, 16] 528ReLU-979 [-1, 16] 0Linear-980 [-1, 32] 544Sigmoid-981 [-1, 32] 0
SqueezeExcitationLayer-982 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-983 [-1, 32, 1, 1] 0Linear-984 [-1, 16] 528ReLU-985 [-1, 16] 0Linear-986 [-1, 32] 544Sigmoid-987 [-1, 32] 0
SqueezeExcitationLayer-988 [-1, 32, 7, 7] 0BatchNorm2d-989 [-1, 896, 7, 7] 1,792ReLU-990 [-1, 896, 7, 7] 0Conv2d-991 [-1, 128, 7, 7] 114,688BatchNorm2d-992 [-1, 128, 7, 7] 256ReLU-993 [-1, 128, 7, 7] 0Conv2d-994 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-995 [-1, 32, 1, 1] 0Linear-996 [-1, 16] 528ReLU-997 [-1, 16] 0Linear-998 [-1, 32] 544Sigmoid-999 [-1, 32] 0
SqueezeExcitationLayer-1000 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-1001 [-1, 32, 1, 1] 0Linear-1002 [-1, 16] 528ReLU-1003 [-1, 16] 0Linear-1004 [-1, 32] 544Sigmoid-1005 [-1, 32] 0
SqueezeExcitationLayer-1006 [-1, 32, 7, 7] 0BatchNorm2d-1007 [-1, 928, 7, 7] 1,856ReLU-1008 [-1, 928, 7, 7] 0Conv2d-1009 [-1, 128, 7, 7] 118,784BatchNorm2d-1010 [-1, 128, 7, 7] 256ReLU-1011 [-1, 128, 7, 7] 0Conv2d-1012 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-1013 [-1, 32, 1, 1] 0Linear-1014 [-1, 16] 528ReLU-1015 [-1, 16] 0Linear-1016 [-1, 32] 544Sigmoid-1017 [-1, 32] 0
SqueezeExcitationLayer-1018 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-1019 [-1, 32, 1, 1] 0Linear-1020 [-1, 16] 528ReLU-1021 [-1, 16] 0Linear-1022 [-1, 32] 544Sigmoid-1023 [-1, 32] 0
SqueezeExcitationLayer-1024 [-1, 32, 7, 7] 0BatchNorm2d-1025 [-1, 960, 7, 7] 1,920ReLU-1026 [-1, 960, 7, 7] 0Conv2d-1027 [-1, 128, 7, 7] 122,880BatchNorm2d-1028 [-1, 128, 7, 7] 256ReLU-1029 [-1, 128, 7, 7] 0Conv2d-1030 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-1031 [-1, 32, 1, 1] 0Linear-1032 [-1, 16] 528ReLU-1033 [-1, 16] 0Linear-1034 [-1, 32] 544Sigmoid-1035 [-1, 32] 0
SqueezeExcitationLayer-1036 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-1037 [-1, 32, 1, 1] 0Linear-1038 [-1, 16] 528ReLU-1039 [-1, 16] 0Linear-1040 [-1, 32] 544Sigmoid-1041 [-1, 32] 0
SqueezeExcitationLayer-1042 [-1, 32, 7, 7] 0BatchNorm2d-1043 [-1, 992, 7, 7] 1,984ReLU-1044 [-1, 992, 7, 7] 0Conv2d-1045 [-1, 128, 7, 7] 126,976BatchNorm2d-1046 [-1, 128, 7, 7] 256ReLU-1047 [-1, 128, 7, 7] 0Conv2d-1048 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-1049 [-1, 32, 1, 1] 0Linear-1050 [-1, 16] 528ReLU-1051 [-1, 16] 0Linear-1052 [-1, 32] 544Sigmoid-1053 [-1, 32] 0
SqueezeExcitationLayer-1054 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-1055 [-1, 32, 1, 1] 0Linear-1056 [-1, 16] 528ReLU-1057 [-1, 16] 0Linear-1058 [-1, 32] 544Sigmoid-1059 [-1, 32] 0
SqueezeExcitationLayer-1060 [-1, 32, 7, 7] 0BatchNorm2d-1061 [-1, 1024, 7, 7] 2,048ReLU-1062 [-1, 1024, 7, 7] 0Linear-1063 [-1, 2] 2,050
================================================================
Total params: 7,080,258
Trainable params: 7,080,258
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 311.15
Params size (MB): 27.01
Estimated Total Size (MB): 338.73
----------------------------------------------------------------
5. 设置超参数：定义损失函数，学习率，以及根据学习率定义优化器等
python"># loss_fn = nn.CrossEntropyLoss() # 创建损失函数# learn_rate = 1e-3 # 初始学习率
# def adjust_learning_rate(optimizer,epoch,start_lr):
# # 每两个epoch 衰减到原来的0.98
# lr = start_lr * (0.92 ** (epoch//2))
# for param_group in optimizer.param_groups:
# param_group['lr'] = lr# optimizer = torch.optim.Adam(model.parameters(),lr=learn_rate)
python"># 调用官方接口示例
loss_fn = nn.CrossEntropyLoss()learn_rate = 1e-4
lambda1 = lambda epoch:(0.92**(epoch//2))optimizer = torch.optim.Adam(model.parameters(),lr = learn_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,lr_lambda=lambda1) # 选定调整方法
6. 训练函数
python"># 训练函数
def train(dataloader,model,loss_fn,optimizer):size = len(dataloader.dataset) # 训练集大小num_batches = len(dataloader) # 批次数目train_loss,train_acc = 0,0for X,y in dataloader:X,y = X.to(device),y.to(device)# 计算预测误差pred = model(X)loss = loss_fn(pred,y)# 反向传播optimizer.zero_grad()loss.backward()optimizer.step()# 记录acc与losstrain_acc += (pred.argmax(1)==y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= sizetrain_loss /= num_batchesreturn train_acc,train_loss
7. 测试函数
python"># 测试函数
def test(dataloader,model,loss_fn):size = len(dataloader.dataset)num_batches = len(dataloader)test_acc,test_loss = 0,0with torch.no_grad():for X,y in dataloader:X,y = X.to(device),y.to(device)# 计算losspred = model(X)loss = loss_fn(pred,y)test_acc += (pred.argmax(1)==y).type(torch.float).sum().item()test_loss += loss.item()test_acc /= sizetest_loss /= num_batchesreturn test_acc,test_loss
8. 正式训练
python">import copyepochs = 40train_acc = []
train_loss = []
test_acc = []
test_loss = []best_acc = 0.0for epoch in range(epochs):# 更新学习率——使用自定义学习率时使用# adjust_learning_rate(optimizer,epoch,learn_rate)model.train()epoch_train_acc,epoch_train_loss = train(train_dl,model,loss_fn,optimizer)scheduler.step() # 更新学习率——调用官方动态学习率时使用model.eval()epoch_test_acc,epoch_test_loss = test(test_dl,model,loss_fn)# 保存最佳模型到 best_modelif epoch_test_acc > best_acc:best_acc = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前学习率lr = optimizer.state_dict()['param_groups'][0]['lr']template = ('Epoch:{:2d},Train_acc:{:.1f}%,Train_loss:{:.3f},Test_acc:{:.1f}%,Test_loss:{:.3f},Lr:{:.2E}')print(template.format(epoch+1,epoch_train_acc*100,epoch_train_loss,epoch_test_acc*100,epoch_test_loss,lr))print('Done')
Epoch: 1,Train_acc:65.3%,Train_loss:0.633,Test_acc:68.5%,Test_loss:0.595,Lr:1.00E-04
Epoch: 2,Train_acc:70.1%,Train_loss:0.578,Test_acc:71.3%,Test_loss:0.562,Lr:9.20E-05
Epoch: 3,Train_acc:72.3%,Train_loss:0.542,Test_acc:75.5%,Test_loss:0.510,Lr:9.20E-05
Epoch: 4,Train_acc:74.0%,Train_loss:0.502,Test_acc:80.0%,Test_loss:0.457,Lr:8.46E-05
Epoch: 5,Train_acc:76.6%,Train_loss:0.474,Test_acc:76.7%,Test_loss:0.488,Lr:8.46E-05
Epoch: 6,Train_acc:79.3%,Train_loss:0.434,Test_acc:79.0%,Test_loss:0.440,Lr:7.79E-05
Epoch: 7,Train_acc:80.9%,Train_loss:0.423,Test_acc:83.0%,Test_loss:0.397,Lr:7.79E-05
Epoch: 8,Train_acc:83.3%,Train_loss:0.375,Test_acc:78.1%,Test_loss:0.433,Lr:7.16E-05
Epoch: 9,Train_acc:83.6%,Train_loss:0.360,Test_acc:82.5%,Test_loss:0.374,Lr:7.16E-05
Epoch:10,Train_acc:84.8%,Train_loss:0.333,Test_acc:88.3%,Test_loss:0.320,Lr:6.59E-05
Epoch:11,Train_acc:88.1%,Train_loss:0.294,Test_acc:87.4%,Test_loss:0.337,Lr:6.59E-05
Epoch:12,Train_acc:87.3%,Train_loss:0.293,Test_acc:84.6%,Test_loss:0.364,Lr:6.06E-05
Epoch:13,Train_acc:89.1%,Train_loss:0.257,Test_acc:88.6%,Test_loss:0.269,Lr:6.06E-05
Epoch:14,Train_acc:90.3%,Train_loss:0.238,Test_acc:84.6%,Test_loss:0.356,Lr:5.58E-05
Epoch:15,Train_acc:91.2%,Train_loss:0.210,Test_acc:84.4%,Test_loss:0.328,Lr:5.58E-05
Epoch:16,Train_acc:91.8%,Train_loss:0.202,Test_acc:89.3%,Test_loss:0.279,Lr:5.13E-05
Epoch:17,Train_acc:93.3%,Train_loss:0.165,Test_acc:89.3%,Test_loss:0.277,Lr:5.13E-05
Epoch:18,Train_acc:93.5%,Train_loss:0.168,Test_acc:89.5%,Test_loss:0.324,Lr:4.72E-05
Epoch:19,Train_acc:93.7%,Train_loss:0.173,Test_acc:87.9%,Test_loss:0.293,Lr:4.72E-05
Epoch:20,Train_acc:93.8%,Train_loss:0.156,Test_acc:90.7%,Test_loss:0.249,Lr:4.34E-05
Epoch:21,Train_acc:95.2%,Train_loss:0.122,Test_acc:89.3%,Test_loss:0.266,Lr:4.34E-05
Epoch:22,Train_acc:96.2%,Train_loss:0.123,Test_acc:90.7%,Test_loss:0.270,Lr:4.00E-05
Epoch:23,Train_acc:95.9%,Train_loss:0.124,Test_acc:89.5%,Test_loss:0.290,Lr:4.00E-05
Epoch:24,Train_acc:96.0%,Train_loss:0.118,Test_acc:91.4%,Test_loss:0.296,Lr:3.68E-05
Epoch:25,Train_acc:95.2%,Train_loss:0.131,Test_acc:91.4%,Test_loss:0.248,Lr:3.68E-05
Epoch:26,Train_acc:95.7%,Train_loss:0.113,Test_acc:90.4%,Test_loss:0.306,Lr:3.38E-05
Epoch:27,Train_acc:97.6%,Train_loss:0.077,Test_acc:93.7%,Test_loss:0.226,Lr:3.38E-05
Epoch:28,Train_acc:96.6%,Train_loss:0.089,Test_acc:91.8%,Test_loss:0.286,Lr:3.11E-05
Epoch:29,Train_acc:97.3%,Train_loss:0.084,Test_acc:92.8%,Test_loss:0.243,Lr:3.11E-05
Epoch:30,Train_acc:96.6%,Train_loss:0.093,Test_acc:91.8%,Test_loss:0.227,Lr:2.86E-05
Epoch:31,Train_acc:97.4%,Train_loss:0.075,Test_acc:93.7%,Test_loss:0.236,Lr:2.86E-05
Epoch:32,Train_acc:97.6%,Train_loss:0.073,Test_acc:92.1%,Test_loss:0.246,Lr:2.63E-05
Epoch:33,Train_acc:97.8%,Train_loss:0.066,Test_acc:93.0%,Test_loss:0.223,Lr:2.63E-05
Epoch:34,Train_acc:98.4%,Train_loss:0.053,Test_acc:92.1%,Test_loss:0.265,Lr:2.42E-05
Epoch:35,Train_acc:98.4%,Train_loss:0.056,Test_acc:91.6%,Test_loss:0.250,Lr:2.42E-05
Epoch:36,Train_acc:98.2%,Train_loss:0.062,Test_acc:92.5%,Test_loss:0.301,Lr:2.23E-05
Epoch:37,Train_acc:97.6%,Train_loss:0.068,Test_acc:93.5%,Test_loss:0.236,Lr:2.23E-05
Epoch:38,Train_acc:98.1%,Train_loss:0.049,Test_acc:91.8%,Test_loss:0.244,Lr:2.05E-05
Epoch:39,Train_acc:98.9%,Train_loss:0.043,Test_acc:94.2%,Test_loss:0.216,Lr:2.05E-05
Epoch:40,Train_acc:98.7%,Train_loss:0.045,Test_acc:92.8%,Test_loss:0.245,Lr:1.89E-05
Done
9. 结果可视化
python">epochs_range = range(epochs)plt.figure(figsize = (12,3))plt.subplot(1,2,1)
plt.plot(epochs_range,train_acc,label = 'Training Accuracy')
plt.plot(epochs_range,test_acc,label = 'Test Accuracy')
plt.legend(loc = 'lower right')
plt.title('Training and Validation Accuracy')plt.subplot(1,2,2)
plt.plot(epochs_range,train_loss,label = 'Test Accuracy')
plt.plot(epochs_range,test_loss,label = 'Test Loss')
plt.legend(loc = 'lower right')
plt.title('Training and validation Loss')
plt.show() 
10. 模型的保存
python"># 自定义模型保存
# 状态字典保存
torch.save(model.state_dict(),'./模型参数/J5_densenet121&SE_model_state_dict.pth') # 仅保存状态字典# 定义模型用来加载参数
best_model = DenseNet(num_init_features=64, # init_channel=64,growth_rate=32,block_config=(6, 12, 24, 16),num_classes=len(classNames), # 根据您的分类任务设置类别数se_filter_sq=se_filter_sq # 传递SE模块的参数
).to(device)best_model.load_state_dict(torch.load('./模型参数/J5_densenet121&SE_model_state_dict.pth')) # 加载状态字典到模型
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11. 使用训练好的模型进行预测
python"># 指定路径图片预测
from PIL import Image
import torchvision.transforms as transformsclasses = list(total_data.class_to_idx) # classes = list(total_data.class_to_idx)def predict_one_image(image_path,model,transform,classes):test_img = Image.open(image_path).convert('RGB')# plt.imshow(test_img) # 展示待预测的图片test_img = transform(test_img)img = test_img.to(device).unsqueeze(0)model.eval()output = model(img)print(output) # 观察模型预测结果的输出数据_,pred = torch.max(output,1)pred_class = classes[pred]print(f'预测结果是:{pred_class}')
python"># 预测训练集中的某张照片
predict_one_image(image_path='./data/mpox_recognize/Monkeypox/M01_01_04.jpg',model = model,transform = test_transforms,classes = classes)
tensor([[ 2.6228, -3.6656]], device='cuda:0', grad_fn=<AddmmBackward0>)
预测结果是:Monkeypox
python">classes
['Monkeypox', 'Others']
python">
