深度学习每周学习总结J5（DenseNet-121 +SE 算法实战与解析 - 猴痘识别）
- 🍨 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。
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 warnings
warnings.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
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
# 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']
# 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 ImageFolder
Number of datapoints: 2142
Root 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])
)
total_data.class_to_idx
{'Monkeypox': 0, 'Others': 1}
3. 划分数据集
# 划分数据集
train_size = int(len(total_data) * 0.8)
test_size = len(total_data) - train_size
train_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>)
# 定义DataLoader用于数据集的加载
batch_size = 32
train_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
)
# 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。
# 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
class SqueezeExcitationLayer(nn.Module):
def __init__(self, num_input_features, filter_sq):
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(num_input_features, filter_sq) # 输入特征为num_input_features，输出特征为filter_sq
self.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 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
# 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。
# 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
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class SqueezeExcitationLayer(nn.Module):
def __init__(self, num_input_features, filter_sq):
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(num_input_features, filter_sq) # 输入通道数是num_input_features，输出通道数是filter_sq
self.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 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
class _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_rate
def 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 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, se_filter_sq)
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
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。
# # 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
# 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。
# 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] 9,408
BatchNorm2d-2 [-1, 64, 112, 112] 128
ReLU-3 [-1, 64, 112, 112] 0
MaxPool2d-4 [-1, 64, 56, 56] 0
BatchNorm2d-5 [-1, 64, 56, 56] 128
ReLU-6 [-1, 64, 56, 56] 0
Conv2d-7 [-1, 128, 56, 56] 8,192
BatchNorm2d-8 [-1, 128, 56, 56] 256
ReLU-9 [-1, 128, 56, 56] 0
Conv2d-10 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-11 [-1, 32, 1, 1] 0
Linear-12 [-1, 16] 528
ReLU-13 [-1, 16] 0
Linear-14 [-1, 32] 544
Sigmoid-15 [-1, 32] 0
SqueezeExcitationLayer-16 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-17 [-1, 32, 1, 1] 0
Linear-18 [-1, 16] 528
ReLU-19 [-1, 16] 0
Linear-20 [-1, 32] 544
Sigmoid-21 [-1, 32] 0
SqueezeExcitationLayer-22 [-1, 32, 56, 56] 0
BatchNorm2d-23 [-1, 96, 56, 56] 192
ReLU-24 [-1, 96, 56, 56] 0
Conv2d-25 [-1, 128, 56, 56] 12,288
BatchNorm2d-26 [-1, 128, 56, 56] 256
ReLU-27 [-1, 128, 56, 56] 0
Conv2d-28 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-29 [-1, 32, 1, 1] 0
Linear-30 [-1, 16] 528
ReLU-31 [-1, 16] 0
Linear-32 [-1, 32] 544
Sigmoid-33 [-1, 32] 0
SqueezeExcitationLayer-34 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-35 [-1, 32, 1, 1] 0
Linear-36 [-1, 16] 528
ReLU-37 [-1, 16] 0
Linear-38 [-1, 32] 544
Sigmoid-39 [-1, 32] 0
SqueezeExcitationLayer-40 [-1, 32, 56, 56] 0
BatchNorm2d-41 [-1, 128, 56, 56] 256
ReLU-42 [-1, 128, 56, 56] 0
Conv2d-43 [-1, 128, 56, 56] 16,384
BatchNorm2d-44 [-1, 128, 56, 56] 256
ReLU-45 [-1, 128, 56, 56] 0
Conv2d-46 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-47 [-1, 32, 1, 1] 0
Linear-48 [-1, 16] 528
ReLU-49 [-1, 16] 0
Linear-50 [-1, 32] 544
Sigmoid-51 [-1, 32] 0
SqueezeExcitationLayer-52 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-53 [-1, 32, 1, 1] 0
Linear-54 [-1, 16] 528
ReLU-55 [-1, 16] 0
Linear-56 [-1, 32] 544
Sigmoid-57 [-1, 32] 0
SqueezeExcitationLayer-58 [-1, 32, 56, 56] 0
BatchNorm2d-59 [-1, 160, 56, 56] 320
ReLU-60 [-1, 160, 56, 56] 0
Conv2d-61 [-1, 128, 56, 56] 20,480
BatchNorm2d-62 [-1, 128, 56, 56] 256
ReLU-63 [-1, 128, 56, 56] 0
Conv2d-64 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-65 [-1, 32, 1, 1] 0
Linear-66 [-1, 16] 528
ReLU-67 [-1, 16] 0
Linear-68 [-1, 32] 544
Sigmoid-69 [-1, 32] 0
SqueezeExcitationLayer-70 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-71 [-1, 32, 1, 1] 0
Linear-72 [-1, 16] 528
ReLU-73 [-1, 16] 0
Linear-74 [-1, 32] 544
Sigmoid-75 [-1, 32] 0
SqueezeExcitationLayer-76 [-1, 32, 56, 56] 0
BatchNorm2d-77 [-1, 192, 56, 56] 384
ReLU-78 [-1, 192, 56, 56] 0
Conv2d-79 [-1, 128, 56, 56] 24,576
BatchNorm2d-80 [-1, 128, 56, 56] 256
ReLU-81 [-1, 128, 56, 56] 0
Conv2d-82 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-83 [-1, 32, 1, 1] 0
Linear-84 [-1, 16] 528
ReLU-85 [-1, 16] 0
Linear-86 [-1, 32] 544
Sigmoid-87 [-1, 32] 0
SqueezeExcitationLayer-88 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-89 [-1, 32, 1, 1] 0
Linear-90 [-1, 16] 528
ReLU-91 [-1, 16] 0
Linear-92 [-1, 32] 544
Sigmoid-93 [-1, 32] 0
SqueezeExcitationLayer-94 [-1, 32, 56, 56] 0
BatchNorm2d-95 [-1, 224, 56, 56] 448
ReLU-96 [-1, 224, 56, 56] 0
Conv2d-97 [-1, 128, 56, 56] 28,672
BatchNorm2d-98 [-1, 128, 56, 56] 256
ReLU-99 [-1, 128, 56, 56] 0
Conv2d-100 [-1, 32, 56, 56] 36,864
AdaptiveAvgPool2d-101 [-1, 32, 1, 1] 0
Linear-102 [-1, 16] 528
ReLU-103 [-1, 16] 0
Linear-104 [-1, 32] 544
Sigmoid-105 [-1, 32] 0
SqueezeExcitationLayer-106 [-1, 32, 56, 56] 0
AdaptiveAvgPool2d-107 [-1, 32, 1, 1] 0
Linear-108 [-1, 16] 528
ReLU-109 [-1, 16] 0
Linear-110 [-1, 32] 544
Sigmoid-111 [-1, 32] 0
SqueezeExcitationLayer-112 [-1, 32, 56, 56] 0
BatchNorm2d-113 [-1, 256, 56, 56] 512
ReLU-114 [-1, 256, 56, 56] 0
Conv2d-115 [-1, 128, 56, 56] 32,768
AvgPool2d-116 [-1, 128, 28, 28] 0
BatchNorm2d-117 [-1, 128, 28, 28] 256
ReLU-118 [-1, 128, 28, 28] 0
Conv2d-119 [-1, 128, 28, 28] 16,384
BatchNorm2d-120 [-1, 128, 28, 28] 256
ReLU-121 [-1, 128, 28, 28] 0
Conv2d-122 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-123 [-1, 32, 1, 1] 0
Linear-124 [-1, 16] 528
ReLU-125 [-1, 16] 0
Linear-126 [-1, 32] 544
Sigmoid-127 [-1, 32] 0
SqueezeExcitationLayer-128 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-129 [-1, 32, 1, 1] 0
Linear-130 [-1, 16] 528
ReLU-131 [-1, 16] 0
Linear-132 [-1, 32] 544
Sigmoid-133 [-1, 32] 0
SqueezeExcitationLayer-134 [-1, 32, 28, 28] 0
BatchNorm2d-135 [-1, 160, 28, 28] 320
ReLU-136 [-1, 160, 28, 28] 0
Conv2d-137 [-1, 128, 28, 28] 20,480
BatchNorm2d-138 [-1, 128, 28, 28] 256
ReLU-139 [-1, 128, 28, 28] 0
Conv2d-140 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-141 [-1, 32, 1, 1] 0
Linear-142 [-1, 16] 528
ReLU-143 [-1, 16] 0
Linear-144 [-1, 32] 544
Sigmoid-145 [-1, 32] 0
SqueezeExcitationLayer-146 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-147 [-1, 32, 1, 1] 0
Linear-148 [-1, 16] 528
ReLU-149 [-1, 16] 0
Linear-150 [-1, 32] 544
Sigmoid-151 [-1, 32] 0
SqueezeExcitationLayer-152 [-1, 32, 28, 28] 0
BatchNorm2d-153 [-1, 192, 28, 28] 384
ReLU-154 [-1, 192, 28, 28] 0
Conv2d-155 [-1, 128, 28, 28] 24,576
BatchNorm2d-156 [-1, 128, 28, 28] 256
ReLU-157 [-1, 128, 28, 28] 0
Conv2d-158 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-159 [-1, 32, 1, 1] 0
Linear-160 [-1, 16] 528
ReLU-161 [-1, 16] 0
Linear-162 [-1, 32] 544
Sigmoid-163 [-1, 32] 0
SqueezeExcitationLayer-164 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-165 [-1, 32, 1, 1] 0
Linear-166 [-1, 16] 528
ReLU-167 [-1, 16] 0
Linear-168 [-1, 32] 544
Sigmoid-169 [-1, 32] 0
SqueezeExcitationLayer-170 [-1, 32, 28, 28] 0
BatchNorm2d-171 [-1, 224, 28, 28] 448
ReLU-172 [-1, 224, 28, 28] 0
Conv2d-173 [-1, 128, 28, 28] 28,672
BatchNorm2d-174 [-1, 128, 28, 28] 256
ReLU-175 [-1, 128, 28, 28] 0
Conv2d-176 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-177 [-1, 32, 1, 1] 0
Linear-178 [-1, 16] 528
ReLU-179 [-1, 16] 0
Linear-180 [-1, 32] 544
Sigmoid-181 [-1, 32] 0
SqueezeExcitationLayer-182 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-183 [-1, 32, 1, 1] 0
Linear-184 [-1, 16] 528
ReLU-185 [-1, 16] 0
Linear-186 [-1, 32] 544
Sigmoid-187 [-1, 32] 0
SqueezeExcitationLayer-188 [-1, 32, 28, 28] 0
BatchNorm2d-189 [-1, 256, 28, 28] 512
ReLU-190 [-1, 256, 28, 28] 0
Conv2d-191 [-1, 128, 28, 28] 32,768
BatchNorm2d-192 [-1, 128, 28, 28] 256
ReLU-193 [-1, 128, 28, 28] 0
Conv2d-194 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-195 [-1, 32, 1, 1] 0
Linear-196 [-1, 16] 528
ReLU-197 [-1, 16] 0
Linear-198 [-1, 32] 544
Sigmoid-199 [-1, 32] 0
SqueezeExcitationLayer-200 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-201 [-1, 32, 1, 1] 0
Linear-202 [-1, 16] 528
ReLU-203 [-1, 16] 0
Linear-204 [-1, 32] 544
Sigmoid-205 [-1, 32] 0
SqueezeExcitationLayer-206 [-1, 32, 28, 28] 0
BatchNorm2d-207 [-1, 288, 28, 28] 576
ReLU-208 [-1, 288, 28, 28] 0
Conv2d-209 [-1, 128, 28, 28] 36,864
BatchNorm2d-210 [-1, 128, 28, 28] 256
ReLU-211 [-1, 128, 28, 28] 0
Conv2d-212 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-213 [-1, 32, 1, 1] 0
Linear-214 [-1, 16] 528
ReLU-215 [-1, 16] 0
Linear-216 [-1, 32] 544
Sigmoid-217 [-1, 32] 0
SqueezeExcitationLayer-218 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-219 [-1, 32, 1, 1] 0
Linear-220 [-1, 16] 528
ReLU-221 [-1, 16] 0
Linear-222 [-1, 32] 544
Sigmoid-223 [-1, 32] 0
SqueezeExcitationLayer-224 [-1, 32, 28, 28] 0
BatchNorm2d-225 [-1, 320, 28, 28] 640
ReLU-226 [-1, 320, 28, 28] 0
Conv2d-227 [-1, 128, 28, 28] 40,960
BatchNorm2d-228 [-1, 128, 28, 28] 256
ReLU-229 [-1, 128, 28, 28] 0
Conv2d-230 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-231 [-1, 32, 1, 1] 0
Linear-232 [-1, 16] 528
ReLU-233 [-1, 16] 0
Linear-234 [-1, 32] 544
Sigmoid-235 [-1, 32] 0
SqueezeExcitationLayer-236 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-237 [-1, 32, 1, 1] 0
Linear-238 [-1, 16] 528
ReLU-239 [-1, 16] 0
Linear-240 [-1, 32] 544
Sigmoid-241 [-1, 32] 0
SqueezeExcitationLayer-242 [-1, 32, 28, 28] 0
BatchNorm2d-243 [-1, 352, 28, 28] 704
ReLU-244 [-1, 352, 28, 28] 0
Conv2d-245 [-1, 128, 28, 28] 45,056
BatchNorm2d-246 [-1, 128, 28, 28] 256
ReLU-247 [-1, 128, 28, 28] 0
Conv2d-248 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-249 [-1, 32, 1, 1] 0
Linear-250 [-1, 16] 528
ReLU-251 [-1, 16] 0
Linear-252 [-1, 32] 544
Sigmoid-253 [-1, 32] 0
SqueezeExcitationLayer-254 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-255 [-1, 32, 1, 1] 0
Linear-256 [-1, 16] 528
ReLU-257 [-1, 16] 0
Linear-258 [-1, 32] 544
Sigmoid-259 [-1, 32] 0
SqueezeExcitationLayer-260 [-1, 32, 28, 28] 0
BatchNorm2d-261 [-1, 384, 28, 28] 768
ReLU-262 [-1, 384, 28, 28] 0
Conv2d-263 [-1, 128, 28, 28] 49,152
BatchNorm2d-264 [-1, 128, 28, 28] 256
ReLU-265 [-1, 128, 28, 28] 0
Conv2d-266 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-267 [-1, 32, 1, 1] 0
Linear-268 [-1, 16] 528
ReLU-269 [-1, 16] 0
Linear-270 [-1, 32] 544
Sigmoid-271 [-1, 32] 0
SqueezeExcitationLayer-272 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-273 [-1, 32, 1, 1] 0
Linear-274 [-1, 16] 528
ReLU-275 [-1, 16] 0
Linear-276 [-1, 32] 544
Sigmoid-277 [-1, 32] 0
SqueezeExcitationLayer-278 [-1, 32, 28, 28] 0
BatchNorm2d-279 [-1, 416, 28, 28] 832
ReLU-280 [-1, 416, 28, 28] 0
Conv2d-281 [-1, 128, 28, 28] 53,248
BatchNorm2d-282 [-1, 128, 28, 28] 256
ReLU-283 [-1, 128, 28, 28] 0
Conv2d-284 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-285 [-1, 32, 1, 1] 0
Linear-286 [-1, 16] 528
ReLU-287 [-1, 16] 0
Linear-288 [-1, 32] 544
Sigmoid-289 [-1, 32] 0
SqueezeExcitationLayer-290 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-291 [-1, 32, 1, 1] 0
Linear-292 [-1, 16] 528
ReLU-293 [-1, 16] 0
Linear-294 [-1, 32] 544
Sigmoid-295 [-1, 32] 0
SqueezeExcitationLayer-296 [-1, 32, 28, 28] 0
BatchNorm2d-297 [-1, 448, 28, 28] 896
ReLU-298 [-1, 448, 28, 28] 0
Conv2d-299 [-1, 128, 28, 28] 57,344
BatchNorm2d-300 [-1, 128, 28, 28] 256
ReLU-301 [-1, 128, 28, 28] 0
Conv2d-302 [-1, 32, 28, 28] 36,864
AdaptiveAvgPool2d-303 [-1, 32, 1, 1] 0
Linear-304 [-1, 16] 528
ReLU-305 [-1, 16] 0
Linear-306 [-1, 32] 544
Sigmoid-307 [-1, 32] 0
SqueezeExcitationLayer-308 [-1, 32, 28, 28] 0
AdaptiveAvgPool2d-309 [-1, 32, 1, 1] 0
Linear-310 [-1, 16] 528
ReLU-311 [-1, 16] 0
Linear-312 [-1, 32] 544
Sigmoid-313 [-1, 32] 0
SqueezeExcitationLayer-314 [-1, 32, 28, 28] 0
BatchNorm2d-315 [-1, 480, 28, 28] 960
ReLU-316 [-1, 480, 28, 28] 0
Conv2d-317 [-1, 128, 28, 28] 61,440
BatchNorm2d-318 [-1, 128, 28, 28] 256
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AdaptiveAvgPool2d-321 [-1, 32, 1, 1] 0
Linear-322 [-1, 16] 528
ReLU-323 [-1, 16] 0
Linear-324 [-1, 32] 544
Sigmoid-325 [-1, 32] 0
SqueezeExcitationLayer-326 [-1, 32, 28, 28] 0
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Linear-328 [-1, 16] 528
ReLU-329 [-1, 16] 0
Linear-330 [-1, 32] 544
Sigmoid-331 [-1, 32] 0
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ReLU-334 [-1, 512, 28, 28] 0
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AvgPool2d-336 [-1, 256, 14, 14] 0
BatchNorm2d-337 [-1, 256, 14, 14] 512
ReLU-338 [-1, 256, 14, 14] 0
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BatchNorm2d-340 [-1, 128, 14, 14] 256
ReLU-341 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-343 [-1, 32, 1, 1] 0
Linear-344 [-1, 16] 528
ReLU-345 [-1, 16] 0
Linear-346 [-1, 32] 544
Sigmoid-347 [-1, 32] 0
SqueezeExcitationLayer-348 [-1, 32, 14, 14] 0
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Linear-350 [-1, 16] 528
ReLU-351 [-1, 16] 0
Linear-352 [-1, 32] 544
Sigmoid-353 [-1, 32] 0
SqueezeExcitationLayer-354 [-1, 32, 14, 14] 0
BatchNorm2d-355 [-1, 288, 14, 14] 576
ReLU-356 [-1, 288, 14, 14] 0
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BatchNorm2d-358 [-1, 128, 14, 14] 256
ReLU-359 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-361 [-1, 32, 1, 1] 0
Linear-362 [-1, 16] 528
ReLU-363 [-1, 16] 0
Linear-364 [-1, 32] 544
Sigmoid-365 [-1, 32] 0
SqueezeExcitationLayer-366 [-1, 32, 14, 14] 0
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ReLU-369 [-1, 16] 0
Linear-370 [-1, 32] 544
Sigmoid-371 [-1, 32] 0
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ReLU-374 [-1, 320, 14, 14] 0
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BatchNorm2d-376 [-1, 128, 14, 14] 256
ReLU-377 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-379 [-1, 32, 1, 1] 0
Linear-380 [-1, 16] 528
ReLU-381 [-1, 16] 0
Linear-382 [-1, 32] 544
Sigmoid-383 [-1, 32] 0
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ReLU-387 [-1, 16] 0
Linear-388 [-1, 32] 544
Sigmoid-389 [-1, 32] 0
SqueezeExcitationLayer-390 [-1, 32, 14, 14] 0
BatchNorm2d-391 [-1, 352, 14, 14] 704
ReLU-392 [-1, 352, 14, 14] 0
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BatchNorm2d-394 [-1, 128, 14, 14] 256
ReLU-395 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-397 [-1, 32, 1, 1] 0
Linear-398 [-1, 16] 528
ReLU-399 [-1, 16] 0
Linear-400 [-1, 32] 544
Sigmoid-401 [-1, 32] 0
SqueezeExcitationLayer-402 [-1, 32, 14, 14] 0
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ReLU-405 [-1, 16] 0
Linear-406 [-1, 32] 544
Sigmoid-407 [-1, 32] 0
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BatchNorm2d-409 [-1, 384, 14, 14] 768
ReLU-410 [-1, 384, 14, 14] 0
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BatchNorm2d-412 [-1, 128, 14, 14] 256
ReLU-413 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-415 [-1, 32, 1, 1] 0
Linear-416 [-1, 16] 528
ReLU-417 [-1, 16] 0
Linear-418 [-1, 32] 544
Sigmoid-419 [-1, 32] 0
SqueezeExcitationLayer-420 [-1, 32, 14, 14] 0
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Linear-422 [-1, 16] 528
ReLU-423 [-1, 16] 0
Linear-424 [-1, 32] 544
Sigmoid-425 [-1, 32] 0
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BatchNorm2d-427 [-1, 416, 14, 14] 832
ReLU-428 [-1, 416, 14, 14] 0
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BatchNorm2d-430 [-1, 128, 14, 14] 256
ReLU-431 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-433 [-1, 32, 1, 1] 0
Linear-434 [-1, 16] 528
ReLU-435 [-1, 16] 0
Linear-436 [-1, 32] 544
Sigmoid-437 [-1, 32] 0
SqueezeExcitationLayer-438 [-1, 32, 14, 14] 0
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Linear-440 [-1, 16] 528
ReLU-441 [-1, 16] 0
Linear-442 [-1, 32] 544
Sigmoid-443 [-1, 32] 0
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BatchNorm2d-445 [-1, 448, 14, 14] 896
ReLU-446 [-1, 448, 14, 14] 0
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BatchNorm2d-448 [-1, 128, 14, 14] 256
ReLU-449 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-451 [-1, 32, 1, 1] 0
Linear-452 [-1, 16] 528
ReLU-453 [-1, 16] 0
Linear-454 [-1, 32] 544
Sigmoid-455 [-1, 32] 0
SqueezeExcitationLayer-456 [-1, 32, 14, 14] 0
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Linear-458 [-1, 16] 528
ReLU-459 [-1, 16] 0
Linear-460 [-1, 32] 544
Sigmoid-461 [-1, 32] 0
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BatchNorm2d-463 [-1, 480, 14, 14] 960
ReLU-464 [-1, 480, 14, 14] 0
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BatchNorm2d-466 [-1, 128, 14, 14] 256
ReLU-467 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-469 [-1, 32, 1, 1] 0
Linear-470 [-1, 16] 528
ReLU-471 [-1, 16] 0
Linear-472 [-1, 32] 544
Sigmoid-473 [-1, 32] 0
SqueezeExcitationLayer-474 [-1, 32, 14, 14] 0
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Linear-476 [-1, 16] 528
ReLU-477 [-1, 16] 0
Linear-478 [-1, 32] 544
Sigmoid-479 [-1, 32] 0
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BatchNorm2d-481 [-1, 512, 14, 14] 1,024
ReLU-482 [-1, 512, 14, 14] 0
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BatchNorm2d-484 [-1, 128, 14, 14] 256
ReLU-485 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-487 [-1, 32, 1, 1] 0
Linear-488 [-1, 16] 528
ReLU-489 [-1, 16] 0
Linear-490 [-1, 32] 544
Sigmoid-491 [-1, 32] 0
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Linear-494 [-1, 16] 528
ReLU-495 [-1, 16] 0
Linear-496 [-1, 32] 544
Sigmoid-497 [-1, 32] 0
SqueezeExcitationLayer-498 [-1, 32, 14, 14] 0
BatchNorm2d-499 [-1, 544, 14, 14] 1,088
ReLU-500 [-1, 544, 14, 14] 0
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BatchNorm2d-502 [-1, 128, 14, 14] 256
ReLU-503 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-505 [-1, 32, 1, 1] 0
Linear-506 [-1, 16] 528
ReLU-507 [-1, 16] 0
Linear-508 [-1, 32] 544
Sigmoid-509 [-1, 32] 0
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Linear-512 [-1, 16] 528
ReLU-513 [-1, 16] 0
Linear-514 [-1, 32] 544
Sigmoid-515 [-1, 32] 0
SqueezeExcitationLayer-516 [-1, 32, 14, 14] 0
BatchNorm2d-517 [-1, 576, 14, 14] 1,152
ReLU-518 [-1, 576, 14, 14] 0
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BatchNorm2d-520 [-1, 128, 14, 14] 256
ReLU-521 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-523 [-1, 32, 1, 1] 0
Linear-524 [-1, 16] 528
ReLU-525 [-1, 16] 0
Linear-526 [-1, 32] 544
Sigmoid-527 [-1, 32] 0
SqueezeExcitationLayer-528 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-529 [-1, 32, 1, 1] 0
Linear-530 [-1, 16] 528
ReLU-531 [-1, 16] 0
Linear-532 [-1, 32] 544
Sigmoid-533 [-1, 32] 0
SqueezeExcitationLayer-534 [-1, 32, 14, 14] 0
BatchNorm2d-535 [-1, 608, 14, 14] 1,216
ReLU-536 [-1, 608, 14, 14] 0
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BatchNorm2d-538 [-1, 128, 14, 14] 256
ReLU-539 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-541 [-1, 32, 1, 1] 0
Linear-542 [-1, 16] 528
ReLU-543 [-1, 16] 0
Linear-544 [-1, 32] 544
Sigmoid-545 [-1, 32] 0
SqueezeExcitationLayer-546 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-547 [-1, 32, 1, 1] 0
Linear-548 [-1, 16] 528
ReLU-549 [-1, 16] 0
Linear-550 [-1, 32] 544
Sigmoid-551 [-1, 32] 0
SqueezeExcitationLayer-552 [-1, 32, 14, 14] 0
BatchNorm2d-553 [-1, 640, 14, 14] 1,280
ReLU-554 [-1, 640, 14, 14] 0
Conv2d-555 [-1, 128, 14, 14] 81,920
BatchNorm2d-556 [-1, 128, 14, 14] 256
ReLU-557 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-559 [-1, 32, 1, 1] 0
Linear-560 [-1, 16] 528
ReLU-561 [-1, 16] 0
Linear-562 [-1, 32] 544
Sigmoid-563 [-1, 32] 0
SqueezeExcitationLayer-564 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-565 [-1, 32, 1, 1] 0
Linear-566 [-1, 16] 528
ReLU-567 [-1, 16] 0
Linear-568 [-1, 32] 544
Sigmoid-569 [-1, 32] 0
SqueezeExcitationLayer-570 [-1, 32, 14, 14] 0
BatchNorm2d-571 [-1, 672, 14, 14] 1,344
ReLU-572 [-1, 672, 14, 14] 0
Conv2d-573 [-1, 128, 14, 14] 86,016
BatchNorm2d-574 [-1, 128, 14, 14] 256
ReLU-575 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-577 [-1, 32, 1, 1] 0
Linear-578 [-1, 16] 528
ReLU-579 [-1, 16] 0
Linear-580 [-1, 32] 544
Sigmoid-581 [-1, 32] 0
SqueezeExcitationLayer-582 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-583 [-1, 32, 1, 1] 0
Linear-584 [-1, 16] 528
ReLU-585 [-1, 16] 0
Linear-586 [-1, 32] 544
Sigmoid-587 [-1, 32] 0
SqueezeExcitationLayer-588 [-1, 32, 14, 14] 0
BatchNorm2d-589 [-1, 704, 14, 14] 1,408
ReLU-590 [-1, 704, 14, 14] 0
Conv2d-591 [-1, 128, 14, 14] 90,112
BatchNorm2d-592 [-1, 128, 14, 14] 256
ReLU-593 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-595 [-1, 32, 1, 1] 0
Linear-596 [-1, 16] 528
ReLU-597 [-1, 16] 0
Linear-598 [-1, 32] 544
Sigmoid-599 [-1, 32] 0
SqueezeExcitationLayer-600 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-601 [-1, 32, 1, 1] 0
Linear-602 [-1, 16] 528
ReLU-603 [-1, 16] 0
Linear-604 [-1, 32] 544
Sigmoid-605 [-1, 32] 0
SqueezeExcitationLayer-606 [-1, 32, 14, 14] 0
BatchNorm2d-607 [-1, 736, 14, 14] 1,472
ReLU-608 [-1, 736, 14, 14] 0
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BatchNorm2d-610 [-1, 128, 14, 14] 256
ReLU-611 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-613 [-1, 32, 1, 1] 0
Linear-614 [-1, 16] 528
ReLU-615 [-1, 16] 0
Linear-616 [-1, 32] 544
Sigmoid-617 [-1, 32] 0
SqueezeExcitationLayer-618 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-619 [-1, 32, 1, 1] 0
Linear-620 [-1, 16] 528
ReLU-621 [-1, 16] 0
Linear-622 [-1, 32] 544
Sigmoid-623 [-1, 32] 0
SqueezeExcitationLayer-624 [-1, 32, 14, 14] 0
BatchNorm2d-625 [-1, 768, 14, 14] 1,536
ReLU-626 [-1, 768, 14, 14] 0
Conv2d-627 [-1, 128, 14, 14] 98,304
BatchNorm2d-628 [-1, 128, 14, 14] 256
ReLU-629 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-631 [-1, 32, 1, 1] 0
Linear-632 [-1, 16] 528
ReLU-633 [-1, 16] 0
Linear-634 [-1, 32] 544
Sigmoid-635 [-1, 32] 0
SqueezeExcitationLayer-636 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-637 [-1, 32, 1, 1] 0
Linear-638 [-1, 16] 528
ReLU-639 [-1, 16] 0
Linear-640 [-1, 32] 544
Sigmoid-641 [-1, 32] 0
SqueezeExcitationLayer-642 [-1, 32, 14, 14] 0
BatchNorm2d-643 [-1, 800, 14, 14] 1,600
ReLU-644 [-1, 800, 14, 14] 0
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BatchNorm2d-646 [-1, 128, 14, 14] 256
ReLU-647 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-649 [-1, 32, 1, 1] 0
Linear-650 [-1, 16] 528
ReLU-651 [-1, 16] 0
Linear-652 [-1, 32] 544
Sigmoid-653 [-1, 32] 0
SqueezeExcitationLayer-654 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-655 [-1, 32, 1, 1] 0
Linear-656 [-1, 16] 528
ReLU-657 [-1, 16] 0
Linear-658 [-1, 32] 544
Sigmoid-659 [-1, 32] 0
SqueezeExcitationLayer-660 [-1, 32, 14, 14] 0
BatchNorm2d-661 [-1, 832, 14, 14] 1,664
ReLU-662 [-1, 832, 14, 14] 0
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BatchNorm2d-664 [-1, 128, 14, 14] 256
ReLU-665 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-667 [-1, 32, 1, 1] 0
Linear-668 [-1, 16] 528
ReLU-669 [-1, 16] 0
Linear-670 [-1, 32] 544
Sigmoid-671 [-1, 32] 0
SqueezeExcitationLayer-672 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-673 [-1, 32, 1, 1] 0
Linear-674 [-1, 16] 528
ReLU-675 [-1, 16] 0
Linear-676 [-1, 32] 544
Sigmoid-677 [-1, 32] 0
SqueezeExcitationLayer-678 [-1, 32, 14, 14] 0
BatchNorm2d-679 [-1, 864, 14, 14] 1,728
ReLU-680 [-1, 864, 14, 14] 0
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BatchNorm2d-682 [-1, 128, 14, 14] 256
ReLU-683 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-685 [-1, 32, 1, 1] 0
Linear-686 [-1, 16] 528
ReLU-687 [-1, 16] 0
Linear-688 [-1, 32] 544
Sigmoid-689 [-1, 32] 0
SqueezeExcitationLayer-690 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-691 [-1, 32, 1, 1] 0
Linear-692 [-1, 16] 528
ReLU-693 [-1, 16] 0
Linear-694 [-1, 32] 544
Sigmoid-695 [-1, 32] 0
SqueezeExcitationLayer-696 [-1, 32, 14, 14] 0
BatchNorm2d-697 [-1, 896, 14, 14] 1,792
ReLU-698 [-1, 896, 14, 14] 0
Conv2d-699 [-1, 128, 14, 14] 114,688
BatchNorm2d-700 [-1, 128, 14, 14] 256
ReLU-701 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-703 [-1, 32, 1, 1] 0
Linear-704 [-1, 16] 528
ReLU-705 [-1, 16] 0
Linear-706 [-1, 32] 544
Sigmoid-707 [-1, 32] 0
SqueezeExcitationLayer-708 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-709 [-1, 32, 1, 1] 0
Linear-710 [-1, 16] 528
ReLU-711 [-1, 16] 0
Linear-712 [-1, 32] 544
Sigmoid-713 [-1, 32] 0
SqueezeExcitationLayer-714 [-1, 32, 14, 14] 0
BatchNorm2d-715 [-1, 928, 14, 14] 1,856
ReLU-716 [-1, 928, 14, 14] 0
Conv2d-717 [-1, 128, 14, 14] 118,784
BatchNorm2d-718 [-1, 128, 14, 14] 256
ReLU-719 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-721 [-1, 32, 1, 1] 0
Linear-722 [-1, 16] 528
ReLU-723 [-1, 16] 0
Linear-724 [-1, 32] 544
Sigmoid-725 [-1, 32] 0
SqueezeExcitationLayer-726 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-727 [-1, 32, 1, 1] 0
Linear-728 [-1, 16] 528
ReLU-729 [-1, 16] 0
Linear-730 [-1, 32] 544
Sigmoid-731 [-1, 32] 0
SqueezeExcitationLayer-732 [-1, 32, 14, 14] 0
BatchNorm2d-733 [-1, 960, 14, 14] 1,920
ReLU-734 [-1, 960, 14, 14] 0
Conv2d-735 [-1, 128, 14, 14] 122,880
BatchNorm2d-736 [-1, 128, 14, 14] 256
ReLU-737 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-739 [-1, 32, 1, 1] 0
Linear-740 [-1, 16] 528
ReLU-741 [-1, 16] 0
Linear-742 [-1, 32] 544
Sigmoid-743 [-1, 32] 0
SqueezeExcitationLayer-744 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-745 [-1, 32, 1, 1] 0
Linear-746 [-1, 16] 528
ReLU-747 [-1, 16] 0
Linear-748 [-1, 32] 544
Sigmoid-749 [-1, 32] 0
SqueezeExcitationLayer-750 [-1, 32, 14, 14] 0
BatchNorm2d-751 [-1, 992, 14, 14] 1,984
ReLU-752 [-1, 992, 14, 14] 0
Conv2d-753 [-1, 128, 14, 14] 126,976
BatchNorm2d-754 [-1, 128, 14, 14] 256
ReLU-755 [-1, 128, 14, 14] 0
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AdaptiveAvgPool2d-757 [-1, 32, 1, 1] 0
Linear-758 [-1, 16] 528
ReLU-759 [-1, 16] 0
Linear-760 [-1, 32] 544
Sigmoid-761 [-1, 32] 0
SqueezeExcitationLayer-762 [-1, 32, 14, 14] 0
AdaptiveAvgPool2d-763 [-1, 32, 1, 1] 0
Linear-764 [-1, 16] 528
ReLU-765 [-1, 16] 0
Linear-766 [-1, 32] 544
Sigmoid-767 [-1, 32] 0
SqueezeExcitationLayer-768 [-1, 32, 14, 14] 0
BatchNorm2d-769 [-1, 1024, 14, 14] 2,048
ReLU-770 [-1, 1024, 14, 14] 0
Conv2d-771 [-1, 512, 14, 14] 524,288
AvgPool2d-772 [-1, 512, 7, 7] 0
BatchNorm2d-773 [-1, 512, 7, 7] 1,024
ReLU-774 [-1, 512, 7, 7] 0
Conv2d-775 [-1, 128, 7, 7] 65,536
BatchNorm2d-776 [-1, 128, 7, 7] 256
ReLU-777 [-1, 128, 7, 7] 0
Conv2d-778 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-779 [-1, 32, 1, 1] 0
Linear-780 [-1, 16] 528
ReLU-781 [-1, 16] 0
Linear-782 [-1, 32] 544
Sigmoid-783 [-1, 32] 0
SqueezeExcitationLayer-784 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-785 [-1, 32, 1, 1] 0
Linear-786 [-1, 16] 528
ReLU-787 [-1, 16] 0
Linear-788 [-1, 32] 544
Sigmoid-789 [-1, 32] 0
SqueezeExcitationLayer-790 [-1, 32, 7, 7] 0
BatchNorm2d-791 [-1, 544, 7, 7] 1,088
ReLU-792 [-1, 544, 7, 7] 0
Conv2d-793 [-1, 128, 7, 7] 69,632
BatchNorm2d-794 [-1, 128, 7, 7] 256
ReLU-795 [-1, 128, 7, 7] 0
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AdaptiveAvgPool2d-797 [-1, 32, 1, 1] 0
Linear-798 [-1, 16] 528
ReLU-799 [-1, 16] 0
Linear-800 [-1, 32] 544
Sigmoid-801 [-1, 32] 0
SqueezeExcitationLayer-802 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-803 [-1, 32, 1, 1] 0
Linear-804 [-1, 16] 528
ReLU-805 [-1, 16] 0
Linear-806 [-1, 32] 544
Sigmoid-807 [-1, 32] 0
SqueezeExcitationLayer-808 [-1, 32, 7, 7] 0
BatchNorm2d-809 [-1, 576, 7, 7] 1,152
ReLU-810 [-1, 576, 7, 7] 0
Conv2d-811 [-1, 128, 7, 7] 73,728
BatchNorm2d-812 [-1, 128, 7, 7] 256
ReLU-813 [-1, 128, 7, 7] 0
Conv2d-814 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-815 [-1, 32, 1, 1] 0
Linear-816 [-1, 16] 528
ReLU-817 [-1, 16] 0
Linear-818 [-1, 32] 544
Sigmoid-819 [-1, 32] 0
SqueezeExcitationLayer-820 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-821 [-1, 32, 1, 1] 0
Linear-822 [-1, 16] 528
ReLU-823 [-1, 16] 0
Linear-824 [-1, 32] 544
Sigmoid-825 [-1, 32] 0
SqueezeExcitationLayer-826 [-1, 32, 7, 7] 0
BatchNorm2d-827 [-1, 608, 7, 7] 1,216
ReLU-828 [-1, 608, 7, 7] 0
Conv2d-829 [-1, 128, 7, 7] 77,824
BatchNorm2d-830 [-1, 128, 7, 7] 256
ReLU-831 [-1, 128, 7, 7] 0
Conv2d-832 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-833 [-1, 32, 1, 1] 0
Linear-834 [-1, 16] 528
ReLU-835 [-1, 16] 0
Linear-836 [-1, 32] 544
Sigmoid-837 [-1, 32] 0
SqueezeExcitationLayer-838 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-839 [-1, 32, 1, 1] 0
Linear-840 [-1, 16] 528
ReLU-841 [-1, 16] 0
Linear-842 [-1, 32] 544
Sigmoid-843 [-1, 32] 0
SqueezeExcitationLayer-844 [-1, 32, 7, 7] 0
BatchNorm2d-845 [-1, 640, 7, 7] 1,280
ReLU-846 [-1, 640, 7, 7] 0
Conv2d-847 [-1, 128, 7, 7] 81,920
BatchNorm2d-848 [-1, 128, 7, 7] 256
ReLU-849 [-1, 128, 7, 7] 0
Conv2d-850 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-851 [-1, 32, 1, 1] 0
Linear-852 [-1, 16] 528
ReLU-853 [-1, 16] 0
Linear-854 [-1, 32] 544
Sigmoid-855 [-1, 32] 0
SqueezeExcitationLayer-856 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-857 [-1, 32, 1, 1] 0
Linear-858 [-1, 16] 528
ReLU-859 [-1, 16] 0
Linear-860 [-1, 32] 544
Sigmoid-861 [-1, 32] 0
SqueezeExcitationLayer-862 [-1, 32, 7, 7] 0
BatchNorm2d-863 [-1, 672, 7, 7] 1,344
ReLU-864 [-1, 672, 7, 7] 0
Conv2d-865 [-1, 128, 7, 7] 86,016
BatchNorm2d-866 [-1, 128, 7, 7] 256
ReLU-867 [-1, 128, 7, 7] 0
Conv2d-868 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-869 [-1, 32, 1, 1] 0
Linear-870 [-1, 16] 528
ReLU-871 [-1, 16] 0
Linear-872 [-1, 32] 544
Sigmoid-873 [-1, 32] 0
SqueezeExcitationLayer-874 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-875 [-1, 32, 1, 1] 0
Linear-876 [-1, 16] 528
ReLU-877 [-1, 16] 0
Linear-878 [-1, 32] 544
Sigmoid-879 [-1, 32] 0
SqueezeExcitationLayer-880 [-1, 32, 7, 7] 0
BatchNorm2d-881 [-1, 704, 7, 7] 1,408
ReLU-882 [-1, 704, 7, 7] 0
Conv2d-883 [-1, 128, 7, 7] 90,112
BatchNorm2d-884 [-1, 128, 7, 7] 256
ReLU-885 [-1, 128, 7, 7] 0
Conv2d-886 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-887 [-1, 32, 1, 1] 0
Linear-888 [-1, 16] 528
ReLU-889 [-1, 16] 0
Linear-890 [-1, 32] 544
Sigmoid-891 [-1, 32] 0
SqueezeExcitationLayer-892 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-893 [-1, 32, 1, 1] 0
Linear-894 [-1, 16] 528
ReLU-895 [-1, 16] 0
Linear-896 [-1, 32] 544
Sigmoid-897 [-1, 32] 0
SqueezeExcitationLayer-898 [-1, 32, 7, 7] 0
BatchNorm2d-899 [-1, 736, 7, 7] 1,472
ReLU-900 [-1, 736, 7, 7] 0
Conv2d-901 [-1, 128, 7, 7] 94,208
BatchNorm2d-902 [-1, 128, 7, 7] 256
ReLU-903 [-1, 128, 7, 7] 0
Conv2d-904 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-905 [-1, 32, 1, 1] 0
Linear-906 [-1, 16] 528
ReLU-907 [-1, 16] 0
Linear-908 [-1, 32] 544
Sigmoid-909 [-1, 32] 0
SqueezeExcitationLayer-910 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-911 [-1, 32, 1, 1] 0
Linear-912 [-1, 16] 528
ReLU-913 [-1, 16] 0
Linear-914 [-1, 32] 544
Sigmoid-915 [-1, 32] 0
SqueezeExcitationLayer-916 [-1, 32, 7, 7] 0
BatchNorm2d-917 [-1, 768, 7, 7] 1,536
ReLU-918 [-1, 768, 7, 7] 0
Conv2d-919 [-1, 128, 7, 7] 98,304
BatchNorm2d-920 [-1, 128, 7, 7] 256
ReLU-921 [-1, 128, 7, 7] 0
Conv2d-922 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-923 [-1, 32, 1, 1] 0
Linear-924 [-1, 16] 528
ReLU-925 [-1, 16] 0
Linear-926 [-1, 32] 544
Sigmoid-927 [-1, 32] 0
SqueezeExcitationLayer-928 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-929 [-1, 32, 1, 1] 0
Linear-930 [-1, 16] 528
ReLU-931 [-1, 16] 0
Linear-932 [-1, 32] 544
Sigmoid-933 [-1, 32] 0
SqueezeExcitationLayer-934 [-1, 32, 7, 7] 0
BatchNorm2d-935 [-1, 800, 7, 7] 1,600
ReLU-936 [-1, 800, 7, 7] 0
Conv2d-937 [-1, 128, 7, 7] 102,400
BatchNorm2d-938 [-1, 128, 7, 7] 256
ReLU-939 [-1, 128, 7, 7] 0
Conv2d-940 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-941 [-1, 32, 1, 1] 0
Linear-942 [-1, 16] 528
ReLU-943 [-1, 16] 0
Linear-944 [-1, 32] 544
Sigmoid-945 [-1, 32] 0
SqueezeExcitationLayer-946 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-947 [-1, 32, 1, 1] 0
Linear-948 [-1, 16] 528
ReLU-949 [-1, 16] 0
Linear-950 [-1, 32] 544
Sigmoid-951 [-1, 32] 0
SqueezeExcitationLayer-952 [-1, 32, 7, 7] 0
BatchNorm2d-953 [-1, 832, 7, 7] 1,664
ReLU-954 [-1, 832, 7, 7] 0
Conv2d-955 [-1, 128, 7, 7] 106,496
BatchNorm2d-956 [-1, 128, 7, 7] 256
ReLU-957 [-1, 128, 7, 7] 0
Conv2d-958 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-959 [-1, 32, 1, 1] 0
Linear-960 [-1, 16] 528
ReLU-961 [-1, 16] 0
Linear-962 [-1, 32] 544
Sigmoid-963 [-1, 32] 0
SqueezeExcitationLayer-964 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-965 [-1, 32, 1, 1] 0
Linear-966 [-1, 16] 528
ReLU-967 [-1, 16] 0
Linear-968 [-1, 32] 544
Sigmoid-969 [-1, 32] 0
SqueezeExcitationLayer-970 [-1, 32, 7, 7] 0
BatchNorm2d-971 [-1, 864, 7, 7] 1,728
ReLU-972 [-1, 864, 7, 7] 0
Conv2d-973 [-1, 128, 7, 7] 110,592
BatchNorm2d-974 [-1, 128, 7, 7] 256
ReLU-975 [-1, 128, 7, 7] 0
Conv2d-976 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-977 [-1, 32, 1, 1] 0
Linear-978 [-1, 16] 528
ReLU-979 [-1, 16] 0
Linear-980 [-1, 32] 544
Sigmoid-981 [-1, 32] 0
SqueezeExcitationLayer-982 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-983 [-1, 32, 1, 1] 0
Linear-984 [-1, 16] 528
ReLU-985 [-1, 16] 0
Linear-986 [-1, 32] 544
Sigmoid-987 [-1, 32] 0
SqueezeExcitationLayer-988 [-1, 32, 7, 7] 0
BatchNorm2d-989 [-1, 896, 7, 7] 1,792
ReLU-990 [-1, 896, 7, 7] 0
Conv2d-991 [-1, 128, 7, 7] 114,688
BatchNorm2d-992 [-1, 128, 7, 7] 256
ReLU-993 [-1, 128, 7, 7] 0
Conv2d-994 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-995 [-1, 32, 1, 1] 0
Linear-996 [-1, 16] 528
ReLU-997 [-1, 16] 0
Linear-998 [-1, 32] 544
Sigmoid-999 [-1, 32] 0
SqueezeExcitationLayer-1000 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-1001 [-1, 32, 1, 1] 0
Linear-1002 [-1, 16] 528
ReLU-1003 [-1, 16] 0
Linear-1004 [-1, 32] 544
Sigmoid-1005 [-1, 32] 0
SqueezeExcitationLayer-1006 [-1, 32, 7, 7] 0
BatchNorm2d-1007 [-1, 928, 7, 7] 1,856
ReLU-1008 [-1, 928, 7, 7] 0
Conv2d-1009 [-1, 128, 7, 7] 118,784
BatchNorm2d-1010 [-1, 128, 7, 7] 256
ReLU-1011 [-1, 128, 7, 7] 0
Conv2d-1012 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-1013 [-1, 32, 1, 1] 0
Linear-1014 [-1, 16] 528
ReLU-1015 [-1, 16] 0
Linear-1016 [-1, 32] 544
Sigmoid-1017 [-1, 32] 0
SqueezeExcitationLayer-1018 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-1019 [-1, 32, 1, 1] 0
Linear-1020 [-1, 16] 528
ReLU-1021 [-1, 16] 0
Linear-1022 [-1, 32] 544
Sigmoid-1023 [-1, 32] 0
SqueezeExcitationLayer-1024 [-1, 32, 7, 7] 0
BatchNorm2d-1025 [-1, 960, 7, 7] 1,920
ReLU-1026 [-1, 960, 7, 7] 0
Conv2d-1027 [-1, 128, 7, 7] 122,880
BatchNorm2d-1028 [-1, 128, 7, 7] 256
ReLU-1029 [-1, 128, 7, 7] 0
Conv2d-1030 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-1031 [-1, 32, 1, 1] 0
Linear-1032 [-1, 16] 528
ReLU-1033 [-1, 16] 0
Linear-1034 [-1, 32] 544
Sigmoid-1035 [-1, 32] 0
SqueezeExcitationLayer-1036 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-1037 [-1, 32, 1, 1] 0
Linear-1038 [-1, 16] 528
ReLU-1039 [-1, 16] 0
Linear-1040 [-1, 32] 544
Sigmoid-1041 [-1, 32] 0
SqueezeExcitationLayer-1042 [-1, 32, 7, 7] 0
BatchNorm2d-1043 [-1, 992, 7, 7] 1,984
ReLU-1044 [-1, 992, 7, 7] 0
Conv2d-1045 [-1, 128, 7, 7] 126,976
BatchNorm2d-1046 [-1, 128, 7, 7] 256
ReLU-1047 [-1, 128, 7, 7] 0
Conv2d-1048 [-1, 32, 7, 7] 36,864
AdaptiveAvgPool2d-1049 [-1, 32, 1, 1] 0
Linear-1050 [-1, 16] 528
ReLU-1051 [-1, 16] 0
Linear-1052 [-1, 32] 544
Sigmoid-1053 [-1, 32] 0
SqueezeExcitationLayer-1054 [-1, 32, 7, 7] 0
AdaptiveAvgPool2d-1055 [-1, 32, 1, 1] 0
Linear-1056 [-1, 16] 528
ReLU-1057 [-1, 16] 0
Linear-1058 [-1, 32] 544
Sigmoid-1059 [-1, 32] 0
SqueezeExcitationLayer-1060 [-1, 32, 7, 7] 0
BatchNorm2d-1061 [-1, 1024, 7, 7] 2,048
ReLU-1062 [-1, 1024, 7, 7] 0
Linear-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
# 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)
# 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
# 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 = 0,0
for 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loss
train_acc += (pred.argmax(1)==y).type(torch.float).sum().item()
train_loss += loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc,train_loss
7. 测试函数
# 测试函数
def test(dataloader,model,loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_acc,test_loss = 0,0
with torch.no_grad():
for X,y in dataloader:
X,y = X.to(device),y.to(device)
# 计算loss
pred = model(X)
loss = loss_fn(pred,y)
test_acc += (pred.argmax(1)==y).type(torch.float).sum().item()
test_loss += loss.item()
test_acc /= size
test_loss /= num_batches
return test_acc,test_loss
8. 正式训练
import copy
epochs = 40
train_acc = []
train_loss = []
test_acc = []
test_loss = []
best_acc = 0.0
for 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_model
if epoch_test_acc > best_acc:
best_acc = epoch_test_acc
best_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
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
# 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
<All keys matched successfully>
11. 使用训练好的模型进行预测
# 指定路径图片预测
from PIL import Image
import torchvision.transforms as transforms
classes = 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}')
# 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
classes
['Monkeypox', 'Others']