深度学习每周学习总结J3（DenseNet-121算法实战与解析

🍨 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

- - DenseNet 与 ResNet
  - - 1. 设计理念
    - 2. 网络结构
    - 3. 与CNN和ResNet的对比
    - 补充：一些网络结构对比的网站（重要）
  - 0. 总结
  - 1. 设置GPU
  - 2. 导入数据及处理部分
  - 3. 划分数据集
  - 4. 模型构建部分
  - 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
  - 6. 训练函数
  - 7. 测试函数
  - 8. 正式训练
  - 9. 结果可视化
  - 10. 模型的保存
  - 11. 使用训练好的模型进行预测

DenseNet 与 ResNet

在计算机视觉领域，卷积神经网络（CNN）已经成为最主流的方法，比如GoogLenet，VGG-16，Incepetion等模型。CNN史上的一个里程碑事件是ResNet模型的出现，ResNet可以训练出更深的CNN模型，从而实现更高的准确度。ResNet模型的核心是通过建立前面层与后面层之间的“短路连接”（shortcuts，skip connection），进而训练出更深的CNN网络。

今天我们要介绍的是DenseNet模型，它的基本思路与ResNet一致，但是它建立的是前面所有层与后面层的密集连接（dense connection），它的名称也是由此而来。DenseNet的另一大特色是通过特征在channel上的连接来实现特征重用（feature reuse）。这些特点让DenseNet在参数和计算成本更少的情形下实现比ResNet更优的性能，DenseNet也因此斩获CVPR 2017的最佳论文奖。

DenseNet（Densely Connected Convolutional Networks）是一种用于图像分类和其他视觉任务的深度学习架构。它于2017年由Gao Huang及其同事提出。以下是DenseNet的设计理念、网络结构及其与CNN和ResNet的对比。

1. 设计理念

DenseNet的核心设计理念是“密集连接”（Dense Connectivity）。其目标是通过在每一层与所有前面层的特征图进行连接，来更有效地利用特征，缓解深度网络中的梯度消失问题。具体来说，DenseNet的主要优点包括：

特征重用：每一层都直接连接到前面的所有层，使得特征的重用变得更加高效，提高了信息流动性。
降低参数数量：通过密集连接，DenseNet显著减少了需要学习的参数数量，从而降低了过拟合的风险。
改善梯度流动：更好的梯度流动使得网络训练更加稳定，即使在极深的网络结构中也能保持良好的性能。

2. 网络结构

DenseNet的基本结构由多个稠密块（Dense Block）组成，每个稠密块内的每一层都与前面所有层进行连接。DenseNet的结构通常包含以下几个部分：

输入层：输入原始图像。
卷积层：初始卷积层用于提取特征。
稠密块：由多个卷积层组成，每个卷积层的输出都会被连接到下一层。
过渡层（Transition Layer）：通常用于降低特征图的尺寸，采用1x1卷积和平均池化。
分类层：最终的全局平均池化层和全连接层进行分类。

以下是DenseNet的一个简单结构示意图：

在这里插入图片描述

3. 与CNN和ResNet的对比

CNN（卷积神经网络）：传统的CNN采用每层单独连接的结构，深度较大时会面临梯度消失和特征梯度消失问题。DenseNet通过密集连接，有效地解决了这些问题。
ResNet（残差网络）：ResNet引入了残差学习，通过跳跃连接（skip connections）来缓解深度网络中的梯度消失问题。与ResNet不同，DenseNet将每一层都连接起来，这意味着每层的信息都可以从所有前面层中获得。DenseNet通常比同深度的ResNet具有更少的参数，从而提高了计算效率和分类精度。

以下是一个简单的对比图示：

在这里插入图片描述

DenseNet通过密集连接层间的特征图，有效地利用了信息流，降低了对参数的需求，并且改善了梯度流动。与传统的CNN和ResNet相比，DenseNet在许多应用上能够提供更好的性能和更高的效率。

如果你需要更深入的图形说明和具体的实现细节，请告诉我！

补充：一些网络结构对比的网站（重要）

如果你想找到关于DenseNet、ResNet和CNN架构的比较图，可以参考以下几个来源：

ResearchGate - 这个网站提供了一些关于网络架构比较的科学图表，你可以查看比较CNN、ResNet和DenseNet的结构图。
- 网络架构比较图
Comet - 这篇文章对各种深度学习网络进行了介绍，包括DenseNet和ResNet的结构图。
- 直观了解卷积神经网络
Medium - 在这篇文章中，作者讲解了不同的CNN架构，包括ResNet和DenseNet的对比。
- CNN架构概述
Nature - 提供一项研究比较不同深度学习架构的结果，包括CNN和ResNet。
- 比较不同深度学习架构的研究

这些链接中可能包含你所需的简笔画或结构图，使你能更好地理解它们之间的区别。如果需要进一步的帮助或者具体的图示，请告诉我！

要找到单独的DenseNet架构图，可以参阅以下资源：

Github - 许多开源项目在其文档中包含架构图，搜索“DenseNet architecture”可以找到相关的实现和图示。例如，搜索以下GitHub链接：
- DenseNet GitHub Repository 搜索 “DenseNet”
Google Scholar - 在Google Scholar上搜索DenseNet的原始论文（如：Huang et al.，2017年发表的《Densely Connected Convolutional Networks》），论文中通常包含架构图。链接如下：
- Densely Connected Convolutional Networks
Medium 和 Towards Data Science - 这类平台上有许多关于数据科学的博文，很多博主在文章中会详细描述DenseNet，并附上架构图。可以搜索“DenseNet architecture diagram”。
- Medium - DenseNet介绍搜索 “DenseNet architecture diagram”
Kaggle - 在Kaggle上搜索与DenseNet相关的竞赛和项目，很多数据科学家和机器学习工程师会在其内附加架构图。
- Kaggle 搜索 “DenseNet”
图像搜索 - 通过Google图像搜索，输入“DenseNet architecture”也可以快速找到多种示意图：
- Google Images - DenseNet Architecture

这些资源将帮助你找到DenseNet架构的示意图。如果你需要特定的图示或更直接的链接，请告诉我！

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

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/bird_photos/'
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

['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']

# 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/bird_photos/',transform = train_transforms)
total_data

Dataset ImageFolder
    Number of datapoints: 565
    Root location: ./data/bird_photos/
    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

{'Bananaquit': 0,
 'Black Skimmer': 1,
 'Black Throated Bushtiti': 2,
 'Cockatoo': 3}

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 0x24b5c8a54e0>,
 <torch.utils.data.dataset.Subset at 0x24b5c8a5720>)

# 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

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

# 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

DenseNet(
  (features): Sequential(
    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu0): ReLU(inplace=True)
    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (denseblock1): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition1): _Transition(
      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock2): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition2): _Transition(
      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock3): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer17): _DenseLayer(
        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer18): _DenseLayer(
        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer19): _DenseLayer(
        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer20): _DenseLayer(
        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer21): _DenseLayer(
        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer22): _DenseLayer(
        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer23): _DenseLayer(
        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer24): _DenseLayer(
        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition3): _Transition(
      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock4): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace=True)
        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace=True)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu5): ReLU(inplace=True)
  )
  (classifier): Linear(in_features=1024, out_features=4, bias=True)
)

# 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
      BatchNorm2d-11           [-1, 96, 56, 56]             192
             ReLU-12           [-1, 96, 56, 56]               0
           Conv2d-13          [-1, 128, 56, 56]          12,288
      BatchNorm2d-14          [-1, 128, 56, 56]             256
             ReLU-15          [-1, 128, 56, 56]               0
           Conv2d-16           [-1, 32, 56, 56]          36,864
      BatchNorm2d-17          [-1, 128, 56, 56]             256
             ReLU-18          [-1, 128, 56, 56]               0
           Conv2d-19          [-1, 128, 56, 56]          16,384
      BatchNorm2d-20          [-1, 128, 56, 56]             256
             ReLU-21          [-1, 128, 56, 56]               0
           Conv2d-22           [-1, 32, 56, 56]          36,864
      BatchNorm2d-23          [-1, 160, 56, 56]             320
             ReLU-24          [-1, 160, 56, 56]               0
           Conv2d-25          [-1, 128, 56, 56]          20,480
      BatchNorm2d-26          [-1, 128, 56, 56]             256
             ReLU-27          [-1, 128, 56, 56]               0
           Conv2d-28           [-1, 32, 56, 56]          36,864
      BatchNorm2d-29          [-1, 192, 56, 56]             384
             ReLU-30          [-1, 192, 56, 56]               0
           Conv2d-31          [-1, 128, 56, 56]          24,576
      BatchNorm2d-32          [-1, 128, 56, 56]             256
             ReLU-33          [-1, 128, 56, 56]               0
           Conv2d-34           [-1, 32, 56, 56]          36,864
      BatchNorm2d-35          [-1, 224, 56, 56]             448
             ReLU-36          [-1, 224, 56, 56]               0
           Conv2d-37          [-1, 128, 56, 56]          28,672
      BatchNorm2d-38          [-1, 128, 56, 56]             256
             ReLU-39          [-1, 128, 56, 56]               0
           Conv2d-40           [-1, 32, 56, 56]          36,864
      BatchNorm2d-41          [-1, 256, 56, 56]             512
             ReLU-42          [-1, 256, 56, 56]               0
           Conv2d-43          [-1, 128, 56, 56]          32,768
        AvgPool2d-44          [-1, 128, 28, 28]               0
      BatchNorm2d-45          [-1, 128, 28, 28]             256
             ReLU-46          [-1, 128, 28, 28]               0
           Conv2d-47          [-1, 128, 28, 28]          16,384
      BatchNorm2d-48          [-1, 128, 28, 28]             256
             ReLU-49          [-1, 128, 28, 28]               0
           Conv2d-50           [-1, 32, 28, 28]          36,864
      BatchNorm2d-51          [-1, 160, 28, 28]             320
             ReLU-52          [-1, 160, 28, 28]               0
           Conv2d-53          [-1, 128, 28, 28]          20,480
      BatchNorm2d-54          [-1, 128, 28, 28]             256
             ReLU-55          [-1, 128, 28, 28]               0
           Conv2d-56           [-1, 32, 28, 28]          36,864
      BatchNorm2d-57          [-1, 192, 28, 28]             384
             ReLU-58          [-1, 192, 28, 28]               0
           Conv2d-59          [-1, 128, 28, 28]          24,576
      BatchNorm2d-60          [-1, 128, 28, 28]             256
             ReLU-61          [-1, 128, 28, 28]               0
           Conv2d-62           [-1, 32, 28, 28]          36,864
      BatchNorm2d-63          [-1, 224, 28, 28]             448
             ReLU-64          [-1, 224, 28, 28]               0
           Conv2d-65          [-1, 128, 28, 28]          28,672
      BatchNorm2d-66          [-1, 128, 28, 28]             256
             ReLU-67          [-1, 128, 28, 28]               0
           Conv2d-68           [-1, 32, 28, 28]          36,864
      BatchNorm2d-69          [-1, 256, 28, 28]             512
             ReLU-70          [-1, 256, 28, 28]               0
           Conv2d-71          [-1, 128, 28, 28]          32,768
      BatchNorm2d-72          [-1, 128, 28, 28]             256
             ReLU-73          [-1, 128, 28, 28]               0
           Conv2d-74           [-1, 32, 28, 28]          36,864
      BatchNorm2d-75          [-1, 288, 28, 28]             576
             ReLU-76          [-1, 288, 28, 28]               0
           Conv2d-77          [-1, 128, 28, 28]          36,864
      BatchNorm2d-78          [-1, 128, 28, 28]             256
             ReLU-79          [-1, 128, 28, 28]               0
           Conv2d-80           [-1, 32, 28, 28]          36,864
      BatchNorm2d-81          [-1, 320, 28, 28]             640
             ReLU-82          [-1, 320, 28, 28]               0
           Conv2d-83          [-1, 128, 28, 28]          40,960
      BatchNorm2d-84          [-1, 128, 28, 28]             256
             ReLU-85          [-1, 128, 28, 28]               0
           Conv2d-86           [-1, 32, 28, 28]          36,864
      BatchNorm2d-87          [-1, 352, 28, 28]             704
             ReLU-88          [-1, 352, 28, 28]               0
           Conv2d-89          [-1, 128, 28, 28]          45,056
      BatchNorm2d-90          [-1, 128, 28, 28]             256
             ReLU-91          [-1, 128, 28, 28]               0
           Conv2d-92           [-1, 32, 28, 28]          36,864
      BatchNorm2d-93          [-1, 384, 28, 28]             768
             ReLU-94          [-1, 384, 28, 28]               0
           Conv2d-95          [-1, 128, 28, 28]          49,152
      BatchNorm2d-96          [-1, 128, 28, 28]             256
             ReLU-97          [-1, 128, 28, 28]               0
           Conv2d-98           [-1, 32, 28, 28]          36,864
      BatchNorm2d-99          [-1, 416, 28, 28]             832
            ReLU-100          [-1, 416, 28, 28]               0
          Conv2d-101          [-1, 128, 28, 28]          53,248
     BatchNorm2d-102          [-1, 128, 28, 28]             256
            ReLU-103          [-1, 128, 28, 28]               0
          Conv2d-104           [-1, 32, 28, 28]          36,864
     BatchNorm2d-105          [-1, 448, 28, 28]             896
            ReLU-106          [-1, 448, 28, 28]               0
          Conv2d-107          [-1, 128, 28, 28]          57,344
     BatchNorm2d-108          [-1, 128, 28, 28]             256
            ReLU-109          [-1, 128, 28, 28]               0
          Conv2d-110           [-1, 32, 28, 28]          36,864
     BatchNorm2d-111          [-1, 480, 28, 28]             960
            ReLU-112          [-1, 480, 28, 28]               0
          Conv2d-113          [-1, 128, 28, 28]          61,440
     BatchNorm2d-114          [-1, 128, 28, 28]             256
            ReLU-115          [-1, 128, 28, 28]               0
          Conv2d-116           [-1, 32, 28, 28]          36,864
     BatchNorm2d-117          [-1, 512, 28, 28]           1,024
            ReLU-118          [-1, 512, 28, 28]               0
          Conv2d-119          [-1, 256, 28, 28]         131,072
       AvgPool2d-120          [-1, 256, 14, 14]               0
     BatchNorm2d-121          [-1, 256, 14, 14]             512
            ReLU-122          [-1, 256, 14, 14]               0
          Conv2d-123          [-1, 128, 14, 14]          32,768
     BatchNorm2d-124          [-1, 128, 14, 14]             256
            ReLU-125          [-1, 128, 14, 14]               0
          Conv2d-126           [-1, 32, 14, 14]          36,864
     BatchNorm2d-127          [-1, 288, 14, 14]             576
            ReLU-128          [-1, 288, 14, 14]               0
          Conv2d-129          [-1, 128, 14, 14]          36,864
     BatchNorm2d-130          [-1, 128, 14, 14]             256
            ReLU-131          [-1, 128, 14, 14]               0
          Conv2d-132           [-1, 32, 14, 14]          36,864
     BatchNorm2d-133          [-1, 320, 14, 14]             640
            ReLU-134          [-1, 320, 14, 14]               0
          Conv2d-135          [-1, 128, 14, 14]          40,960
     BatchNorm2d-136          [-1, 128, 14, 14]             256
            ReLU-137          [-1, 128, 14, 14]               0
          Conv2d-138           [-1, 32, 14, 14]          36,864
     BatchNorm2d-139          [-1, 352, 14, 14]             704
            ReLU-140          [-1, 352, 14, 14]               0
          Conv2d-141          [-1, 128, 14, 14]          45,056
     BatchNorm2d-142          [-1, 128, 14, 14]             256
            ReLU-143          [-1, 128, 14, 14]               0
          Conv2d-144           [-1, 32, 14, 14]          36,864
     BatchNorm2d-145          [-1, 384, 14, 14]             768
            ReLU-146          [-1, 384, 14, 14]               0
          Conv2d-147          [-1, 128, 14, 14]          49,152
     BatchNorm2d-148          [-1, 128, 14, 14]             256
            ReLU-149          [-1, 128, 14, 14]               0
          Conv2d-150           [-1, 32, 14, 14]          36,864
     BatchNorm2d-151          [-1, 416, 14, 14]             832
            ReLU-152          [-1, 416, 14, 14]               0
          Conv2d-153          [-1, 128, 14, 14]          53,248
     BatchNorm2d-154          [-1, 128, 14, 14]             256
            ReLU-155          [-1, 128, 14, 14]               0
          Conv2d-156           [-1, 32, 14, 14]          36,864
     BatchNorm2d-157          [-1, 448, 14, 14]             896
            ReLU-158          [-1, 448, 14, 14]               0
          Conv2d-159          [-1, 128, 14, 14]          57,344
     BatchNorm2d-160          [-1, 128, 14, 14]             256
            ReLU-161          [-1, 128, 14, 14]               0
          Conv2d-162           [-1, 32, 14, 14]          36,864
     BatchNorm2d-163          [-1, 480, 14, 14]             960
            ReLU-164          [-1, 480, 14, 14]               0
          Conv2d-165          [-1, 128, 14, 14]          61,440
     BatchNorm2d-166          [-1, 128, 14, 14]             256
            ReLU-167          [-1, 128, 14, 14]               0
          Conv2d-168           [-1, 32, 14, 14]          36,864
     BatchNorm2d-169          [-1, 512, 14, 14]           1,024
            ReLU-170          [-1, 512, 14, 14]               0
          Conv2d-171          [-1, 128, 14, 14]          65,536
     BatchNorm2d-172          [-1, 128, 14, 14]             256
            ReLU-173          [-1, 128, 14, 14]               0
          Conv2d-174           [-1, 32, 14, 14]          36,864
     BatchNorm2d-175          [-1, 544, 14, 14]           1,088
            ReLU-176          [-1, 544, 14, 14]               0
          Conv2d-177          [-1, 128, 14, 14]          69,632
     BatchNorm2d-178          [-1, 128, 14, 14]             256
            ReLU-179          [-1, 128, 14, 14]               0
          Conv2d-180           [-1, 32, 14, 14]          36,864
     BatchNorm2d-181          [-1, 576, 14, 14]           1,152
            ReLU-182          [-1, 576, 14, 14]               0
          Conv2d-183          [-1, 128, 14, 14]          73,728
     BatchNorm2d-184          [-1, 128, 14, 14]             256
            ReLU-185          [-1, 128, 14, 14]               0
          Conv2d-186           [-1, 32, 14, 14]          36,864
     BatchNorm2d-187          [-1, 608, 14, 14]           1,216
            ReLU-188          [-1, 608, 14, 14]               0
          Conv2d-189          [-1, 128, 14, 14]          77,824
     BatchNorm2d-190          [-1, 128, 14, 14]             256
            ReLU-191          [-1, 128, 14, 14]               0
          Conv2d-192           [-1, 32, 14, 14]          36,864
     BatchNorm2d-193          [-1, 640, 14, 14]           1,280
            ReLU-194          [-1, 640, 14, 14]               0
          Conv2d-195          [-1, 128, 14, 14]          81,920
     BatchNorm2d-196          [-1, 128, 14, 14]             256
            ReLU-197          [-1, 128, 14, 14]               0
          Conv2d-198           [-1, 32, 14, 14]          36,864
     BatchNorm2d-199          [-1, 672, 14, 14]           1,344
            ReLU-200          [-1, 672, 14, 14]               0
          Conv2d-201          [-1, 128, 14, 14]          86,016
     BatchNorm2d-202          [-1, 128, 14, 14]             256
            ReLU-203          [-1, 128, 14, 14]               0
          Conv2d-204           [-1, 32, 14, 14]          36,864
     BatchNorm2d-205          [-1, 704, 14, 14]           1,408
            ReLU-206          [-1, 704, 14, 14]               0
          Conv2d-207          [-1, 128, 14, 14]          90,112
     BatchNorm2d-208          [-1, 128, 14, 14]             256
            ReLU-209          [-1, 128, 14, 14]               0
          Conv2d-210           [-1, 32, 14, 14]          36,864
     BatchNorm2d-211          [-1, 736, 14, 14]           1,472
            ReLU-212          [-1, 736, 14, 14]               0
          Conv2d-213          [-1, 128, 14, 14]          94,208
     BatchNorm2d-214          [-1, 128, 14, 14]             256
            ReLU-215          [-1, 128, 14, 14]               0
          Conv2d-216           [-1, 32, 14, 14]          36,864
     BatchNorm2d-217          [-1, 768, 14, 14]           1,536
            ReLU-218          [-1, 768, 14, 14]               0
          Conv2d-219          [-1, 128, 14, 14]          98,304
     BatchNorm2d-220          [-1, 128, 14, 14]             256
            ReLU-221          [-1, 128, 14, 14]               0
          Conv2d-222           [-1, 32, 14, 14]          36,864
     BatchNorm2d-223          [-1, 800, 14, 14]           1,600
            ReLU-224          [-1, 800, 14, 14]               0
          Conv2d-225          [-1, 128, 14, 14]         102,400
     BatchNorm2d-226          [-1, 128, 14, 14]             256
            ReLU-227          [-1, 128, 14, 14]               0
          Conv2d-228           [-1, 32, 14, 14]          36,864
     BatchNorm2d-229          [-1, 832, 14, 14]           1,664
            ReLU-230          [-1, 832, 14, 14]               0
          Conv2d-231          [-1, 128, 14, 14]         106,496
     BatchNorm2d-232          [-1, 128, 14, 14]             256
            ReLU-233          [-1, 128, 14, 14]               0
          Conv2d-234           [-1, 32, 14, 14]          36,864
     BatchNorm2d-235          [-1, 864, 14, 14]           1,728
            ReLU-236          [-1, 864, 14, 14]               0
          Conv2d-237          [-1, 128, 14, 14]         110,592
     BatchNorm2d-238          [-1, 128, 14, 14]             256
            ReLU-239          [-1, 128, 14, 14]               0
          Conv2d-240           [-1, 32, 14, 14]          36,864
     BatchNorm2d-241          [-1, 896, 14, 14]           1,792
            ReLU-242          [-1, 896, 14, 14]               0
          Conv2d-243          [-1, 128, 14, 14]         114,688
     BatchNorm2d-244          [-1, 128, 14, 14]             256
            ReLU-245          [-1, 128, 14, 14]               0
          Conv2d-246           [-1, 32, 14, 14]          36,864
     BatchNorm2d-247          [-1, 928, 14, 14]           1,856
            ReLU-248          [-1, 928, 14, 14]               0
          Conv2d-249          [-1, 128, 14, 14]         118,784
     BatchNorm2d-250          [-1, 128, 14, 14]             256
            ReLU-251          [-1, 128, 14, 14]               0
          Conv2d-252           [-1, 32, 14, 14]          36,864
     BatchNorm2d-253          [-1, 960, 14, 14]           1,920
            ReLU-254          [-1, 960, 14, 14]               0
          Conv2d-255          [-1, 128, 14, 14]         122,880
     BatchNorm2d-256          [-1, 128, 14, 14]             256
            ReLU-257          [-1, 128, 14, 14]               0
          Conv2d-258           [-1, 32, 14, 14]          36,864
     BatchNorm2d-259          [-1, 992, 14, 14]           1,984
            ReLU-260          [-1, 992, 14, 14]               0
          Conv2d-261          [-1, 128, 14, 14]         126,976
     BatchNorm2d-262          [-1, 128, 14, 14]             256
            ReLU-263          [-1, 128, 14, 14]               0
          Conv2d-264           [-1, 32, 14, 14]          36,864
     BatchNorm2d-265         [-1, 1024, 14, 14]           2,048
            ReLU-266         [-1, 1024, 14, 14]               0
          Conv2d-267          [-1, 512, 14, 14]         524,288
       AvgPool2d-268            [-1, 512, 7, 7]               0
     BatchNorm2d-269            [-1, 512, 7, 7]           1,024
            ReLU-270            [-1, 512, 7, 7]               0
          Conv2d-271            [-1, 128, 7, 7]          65,536
     BatchNorm2d-272            [-1, 128, 7, 7]             256
            ReLU-273            [-1, 128, 7, 7]               0
          Conv2d-274             [-1, 32, 7, 7]          36,864
     BatchNorm2d-275            [-1, 544, 7, 7]           1,088
            ReLU-276            [-1, 544, 7, 7]               0
          Conv2d-277            [-1, 128, 7, 7]          69,632
     BatchNorm2d-278            [-1, 128, 7, 7]             256
            ReLU-279            [-1, 128, 7, 7]               0
          Conv2d-280             [-1, 32, 7, 7]          36,864
     BatchNorm2d-281            [-1, 576, 7, 7]           1,152
            ReLU-282            [-1, 576, 7, 7]               0
          Conv2d-283            [-1, 128, 7, 7]          73,728
     BatchNorm2d-284            [-1, 128, 7, 7]             256
            ReLU-285            [-1, 128, 7, 7]               0
          Conv2d-286             [-1, 32, 7, 7]          36,864
     BatchNorm2d-287            [-1, 608, 7, 7]           1,216
            ReLU-288            [-1, 608, 7, 7]               0
          Conv2d-289            [-1, 128, 7, 7]          77,824
     BatchNorm2d-290            [-1, 128, 7, 7]             256
            ReLU-291            [-1, 128, 7, 7]               0
          Conv2d-292             [-1, 32, 7, 7]          36,864
     BatchNorm2d-293            [-1, 640, 7, 7]           1,280
            ReLU-294            [-1, 640, 7, 7]               0
          Conv2d-295            [-1, 128, 7, 7]          81,920
     BatchNorm2d-296            [-1, 128, 7, 7]             256
            ReLU-297            [-1, 128, 7, 7]               0
          Conv2d-298             [-1, 32, 7, 7]          36,864
     BatchNorm2d-299            [-1, 672, 7, 7]           1,344
            ReLU-300            [-1, 672, 7, 7]               0
          Conv2d-301            [-1, 128, 7, 7]          86,016
     BatchNorm2d-302            [-1, 128, 7, 7]             256
            ReLU-303            [-1, 128, 7, 7]               0
          Conv2d-304             [-1, 32, 7, 7]          36,864
     BatchNorm2d-305            [-1, 704, 7, 7]           1,408
            ReLU-306            [-1, 704, 7, 7]               0
          Conv2d-307            [-1, 128, 7, 7]          90,112
     BatchNorm2d-308            [-1, 128, 7, 7]             256
            ReLU-309            [-1, 128, 7, 7]               0
          Conv2d-310             [-1, 32, 7, 7]          36,864
     BatchNorm2d-311            [-1, 736, 7, 7]           1,472
            ReLU-312            [-1, 736, 7, 7]               0
          Conv2d-313            [-1, 128, 7, 7]          94,208
     BatchNorm2d-314            [-1, 128, 7, 7]             256
            ReLU-315            [-1, 128, 7, 7]               0
          Conv2d-316             [-1, 32, 7, 7]          36,864
     BatchNorm2d-317            [-1, 768, 7, 7]           1,536
            ReLU-318            [-1, 768, 7, 7]               0
          Conv2d-319            [-1, 128, 7, 7]          98,304
     BatchNorm2d-320            [-1, 128, 7, 7]             256
            ReLU-321            [-1, 128, 7, 7]               0
          Conv2d-322             [-1, 32, 7, 7]          36,864
     BatchNorm2d-323            [-1, 800, 7, 7]           1,600
            ReLU-324            [-1, 800, 7, 7]               0
          Conv2d-325            [-1, 128, 7, 7]         102,400
     BatchNorm2d-326            [-1, 128, 7, 7]             256
            ReLU-327            [-1, 128, 7, 7]               0
          Conv2d-328             [-1, 32, 7, 7]          36,864
     BatchNorm2d-329            [-1, 832, 7, 7]           1,664
            ReLU-330            [-1, 832, 7, 7]               0
          Conv2d-331            [-1, 128, 7, 7]         106,496
     BatchNorm2d-332            [-1, 128, 7, 7]             256
            ReLU-333            [-1, 128, 7, 7]               0
          Conv2d-334             [-1, 32, 7, 7]          36,864
     BatchNorm2d-335            [-1, 864, 7, 7]           1,728
            ReLU-336            [-1, 864, 7, 7]               0
          Conv2d-337            [-1, 128, 7, 7]         110,592
     BatchNorm2d-338            [-1, 128, 7, 7]             256
            ReLU-339            [-1, 128, 7, 7]               0
          Conv2d-340             [-1, 32, 7, 7]          36,864
     BatchNorm2d-341            [-1, 896, 7, 7]           1,792
            ReLU-342            [-1, 896, 7, 7]               0
          Conv2d-343            [-1, 128, 7, 7]         114,688
     BatchNorm2d-344            [-1, 128, 7, 7]             256
            ReLU-345            [-1, 128, 7, 7]               0
          Conv2d-346             [-1, 32, 7, 7]          36,864
     BatchNorm2d-347            [-1, 928, 7, 7]           1,856
            ReLU-348            [-1, 928, 7, 7]               0
          Conv2d-349            [-1, 128, 7, 7]         118,784
     BatchNorm2d-350            [-1, 128, 7, 7]             256
            ReLU-351            [-1, 128, 7, 7]               0
          Conv2d-352             [-1, 32, 7, 7]          36,864
     BatchNorm2d-353            [-1, 960, 7, 7]           1,920
            ReLU-354            [-1, 960, 7, 7]               0
          Conv2d-355            [-1, 128, 7, 7]         122,880
     BatchNorm2d-356            [-1, 128, 7, 7]             256
            ReLU-357            [-1, 128, 7, 7]               0
          Conv2d-358             [-1, 32, 7, 7]          36,864
     BatchNorm2d-359            [-1, 992, 7, 7]           1,984
            ReLU-360            [-1, 992, 7, 7]               0
          Conv2d-361            [-1, 128, 7, 7]         126,976
     BatchNorm2d-362            [-1, 128, 7, 7]             256
            ReLU-363            [-1, 128, 7, 7]               0
          Conv2d-364             [-1, 32, 7, 7]          36,864
     BatchNorm2d-365           [-1, 1024, 7, 7]           2,048
            ReLU-366           [-1, 1024, 7, 7]               0
          Linear-367                    [-1, 4]           4,100
================================================================
Total params: 6,957,956
Trainable params: 6,957,956
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 294.57
Params size (MB): 26.54
Estimated Total Size (MB): 321.69
----------------------------------------------------------------

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:57.5%,Train_loss:1.125,Test_acc:67.3%,Test_loss:0.976,Lr:1.00E-04
Epoch: 2,Train_acc:74.8%,Train_loss:0.812,Test_acc:78.8%,Test_loss:0.694,Lr:9.20E-05
Epoch: 3,Train_acc:81.9%,Train_loss:0.677,Test_acc:78.8%,Test_loss:0.528,Lr:9.20E-05
Epoch: 4,Train_acc:86.5%,Train_loss:0.463,Test_acc:84.1%,Test_loss:0.457,Lr:8.46E-05
Epoch: 5,Train_acc:86.5%,Train_loss:0.427,Test_acc:90.3%,Test_loss:0.311,Lr:8.46E-05
Epoch: 6,Train_acc:90.5%,Train_loss:0.350,Test_acc:89.4%,Test_loss:0.417,Lr:7.79E-05
Epoch: 7,Train_acc:92.7%,Train_loss:0.287,Test_acc:85.8%,Test_loss:0.303,Lr:7.79E-05
Epoch: 8,Train_acc:92.0%,Train_loss:0.339,Test_acc:90.3%,Test_loss:0.313,Lr:7.16E-05
Epoch: 9,Train_acc:93.1%,Train_loss:0.231,Test_acc:92.0%,Test_loss:0.243,Lr:7.16E-05
Epoch:10,Train_acc:94.9%,Train_loss:0.286,Test_acc:90.3%,Test_loss:0.285,Lr:6.59E-05
Epoch:11,Train_acc:95.4%,Train_loss:0.185,Test_acc:88.5%,Test_loss:0.334,Lr:6.59E-05
Epoch:12,Train_acc:95.6%,Train_loss:0.151,Test_acc:92.9%,Test_loss:0.226,Lr:6.06E-05
Epoch:13,Train_acc:96.9%,Train_loss:0.118,Test_acc:91.2%,Test_loss:0.263,Lr:6.06E-05
Epoch:14,Train_acc:98.5%,Train_loss:0.114,Test_acc:94.7%,Test_loss:0.177,Lr:5.58E-05
Epoch:15,Train_acc:98.0%,Train_loss:0.145,Test_acc:92.9%,Test_loss:0.244,Lr:5.58E-05
Epoch:16,Train_acc:96.9%,Train_loss:0.126,Test_acc:94.7%,Test_loss:0.200,Lr:5.13E-05
Epoch:17,Train_acc:97.8%,Train_loss:0.196,Test_acc:93.8%,Test_loss:0.166,Lr:5.13E-05
Epoch:18,Train_acc:97.6%,Train_loss:0.167,Test_acc:93.8%,Test_loss:0.236,Lr:4.72E-05
Epoch:19,Train_acc:98.0%,Train_loss:0.102,Test_acc:92.9%,Test_loss:0.258,Lr:4.72E-05
Epoch:20,Train_acc:98.7%,Train_loss:0.093,Test_acc:93.8%,Test_loss:0.207,Lr:4.34E-05
Epoch:21,Train_acc:98.9%,Train_loss:0.070,Test_acc:96.5%,Test_loss:0.180,Lr:4.34E-05
Epoch:22,Train_acc:99.1%,Train_loss:0.115,Test_acc:93.8%,Test_loss:0.213,Lr:4.00E-05
Epoch:23,Train_acc:99.3%,Train_loss:0.051,Test_acc:92.9%,Test_loss:0.241,Lr:4.00E-05
Epoch:24,Train_acc:99.1%,Train_loss:0.064,Test_acc:92.9%,Test_loss:0.201,Lr:3.68E-05
Epoch:25,Train_acc:99.3%,Train_loss:0.070,Test_acc:91.2%,Test_loss:0.313,Lr:3.68E-05
Epoch:26,Train_acc:98.5%,Train_loss:0.083,Test_acc:92.0%,Test_loss:0.288,Lr:3.38E-05
Epoch:27,Train_acc:99.1%,Train_loss:0.125,Test_acc:97.3%,Test_loss:0.146,Lr:3.38E-05
Epoch:28,Train_acc:99.3%,Train_loss:0.109,Test_acc:94.7%,Test_loss:0.259,Lr:3.11E-05
Epoch:29,Train_acc:98.7%,Train_loss:0.073,Test_acc:92.0%,Test_loss:0.268,Lr:3.11E-05
Epoch:30,Train_acc:99.6%,Train_loss:0.097,Test_acc:91.2%,Test_loss:0.254,Lr:2.86E-05
Epoch:31,Train_acc:98.9%,Train_loss:0.083,Test_acc:90.3%,Test_loss:0.255,Lr:2.86E-05
Epoch:32,Train_acc:99.1%,Train_loss:0.058,Test_acc:90.3%,Test_loss:0.326,Lr:2.63E-05
Epoch:33,Train_acc:99.6%,Train_loss:0.042,Test_acc:92.9%,Test_loss:0.155,Lr:2.63E-05
Epoch:34,Train_acc:100.0%,Train_loss:0.037,Test_acc:94.7%,Test_loss:0.157,Lr:2.42E-05
Epoch:35,Train_acc:99.1%,Train_loss:0.140,Test_acc:97.3%,Test_loss:0.144,Lr:2.42E-05
Epoch:36,Train_acc:99.3%,Train_loss:0.059,Test_acc:93.8%,Test_loss:0.174,Lr:2.23E-05
Epoch:37,Train_acc:99.8%,Train_loss:0.038,Test_acc:91.2%,Test_loss:0.215,Lr:2.23E-05
Epoch:38,Train_acc:99.8%,Train_loss:0.045,Test_acc:92.9%,Test_loss:0.163,Lr:2.05E-05
Epoch:39,Train_acc:99.1%,Train_loss:0.076,Test_acc:93.8%,Test_loss:0.185,Lr:2.05E-05
Epoch:40,Train_acc:98.9%,Train_loss:0.090,Test_acc:93.8%,Test_loss:0.176,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()

在这里插入图片描述

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/J3_densenet121_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)).to(device)

best_model.load_state_dict(torch.load('./模型参数/J3_densenet121_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/bird_photos/Bananaquit/007.jpg',
                 model = model,
                 transform = test_transforms,
                 classes = classes
                 )

tensor([[ 5.3812, -3.3856, -0.2259, -2.2854]], device='cuda:0',
       grad_fn=<AddmmBackward0>)
预测结果是:Bananaquit

classes

['Bananaquit', 'Black Skimmer', 'Black Throated Bushtiti', 'Cockatoo']