DenseNet算法：口腔癌识别

本文为为🔗365天深度学习训练营内部文章

原作者：K同学啊

一 DenseNet算法结构

其基本思路与ResNet一致，但是它建立的是前面所有层和后面层的密集连接，它的另一大特色是通过特征在channel上的连接来实现特征重用。 

二 设计理念 

 

三 结构 

四 算法代码 

import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasetsimport os,PIL,pathlib,randomdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")device

data_dir = './data/'
data_dir = pathlib.Path(data_dir)data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[1] for path in data_paths]
classeNamesimport matplotlib.pyplot as plt
from PIL import Image# 指定图像文件夹路径
image_folder = './data/OSCC/'# 获取文件夹中的所有图像文件
image_files = [f for f in os.listdir(image_folder) if f.endswith((".jpg", ".png", ".jpeg"))]# 创建Matplotlib图像
fig, axes = plt.subplots(3, 8, figsize=(16, 6))# 使用列表推导式加载和显示图像
for ax, img_file in zip(axes.flat, image_files):img_path = os.path.join(image_folder, img_file)img = Image.open(img_path)ax.imshow(img)ax.axis('off')# 显示图像
plt.tight_layout()
plt.show()

total_datadir = './data/'# 关于transforms.Compose的更多介绍可以参考：https://blog.csdn.net/qq_38251616/article/details/124878863
train_transforms = transforms.Compose([transforms.Resize([224, 224]),  # 将输入图片resize成统一尺寸transforms.ToTensor(),          # 将PIL Image或numpy.ndarray转换为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], std=[0.229, 0.224, 0.225])  # 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。
])total_data = datasets.ImageFolder(total_datadir,transform=train_transforms)
total_data# 划分训练集
train_size = int(0.7 * len(total_data))
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_datasetbatch_size = 32train_dl = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size,shuffle=True,num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,batch_size=batch_size,shuffle=True,num_workers=1)

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

import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import re
import torch
from torch.utils import model_zoo
from torchvision.models.video.resnet import model_urls'''
_DenseLayer 类实现了 DenseNet 的关键机制：
通过使用批归一化、ReLU 激活和卷积层来提取特征，并通过密集连接促进特征的共享和再利用。
'''
class _DenseLayer(nn.Sequential):def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):''':param num_input_features: 输入特征数:param growth_rate: 每层增长的特征数:param bn_size: 批归一化层的大小:param drop_rate: 丢弃率'''super(_DenseLayer, self).__init__()# 添加一个批归一化层（BatchNorm2d），用于对输入特征进行标准化self.add_module("norm1", nn.BatchNorm2d(num_input_features))# 添加一个 ReLU 激活函数self.add_module("relu1", nn.ReLU(inplace=True))# 添加第一个卷积层（Conv2d），其输入通道数为 num_input_features，输出通道数为 bn_size * growth_rate。# 这里使用 1x1 卷积，主要用于减少特征图的维度，并引入更多特征self.add_module("conv1", nn.Conv2d(num_input_features, bn_size * growth_rate,kernel_size=1, stride=1, bias=False))# 添加第二个批归一化层，应用于第一个卷积层的输出self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate))# 添加第二个 ReLU 激活函数。与第一个激活函数相同，提供非线性变换self.add_module("relu2", nn.ReLU(inplace=True))# 添加第二个卷积层，输入通道数为 bn_size * growth_rate，输出通道数为 growth_rate。# 这里使用 3x3 卷积，通常用于提取更复杂的特征self.add_module("conv2", nn.Conv2d(bn_size * growth_rate, growth_rate,kernel_size=3, stride=1, padding=1, bias=False))# 保存丢弃率（drop rate），用于在前向传播中进行 dropout 操作，以防止过拟合self.drop_rate = drop_ratedef forward(self, x):# 调用父类 nn.Sequential 的 forward 方法，将输入 x 传递给之前添加的所有层。# 输出 new_features 是经过所有层处理后的特征new_features = super(_DenseLayer, self).forward(x)# 检查丢弃率是否大于 0，如果是，则进行 dropout 操作if self.drop_rate > 0:# 对新特征应用 dropout，p 是丢弃概率，training 参数指示当前是否在训练模式。这将随机将一部分特征置为零，从而帮助减少过拟合new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)# 将输入 x 和新特征 new_features 在通道维度（即维度 1）上连接。这样可以实现密集连接，允许模型利用前面层的所有特征return torch.cat([x, new_features], 1)'''
创建一个包含多个密集层的模块，每个层都会根据前面层的输出特征动态调整输入特征数量，形成一个密集连接的网络结构。
'''
class _DenseBlock(nn.Sequential):def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):'''num_layers: 该密集块中层的数量。num_input_features: 输入特征的数量。bn_size: 批量归一化的大小。growth_rate: 每层输出特征的增长率。drop_rate: dropout 率，用于防止过拟合'''super(_DenseBlock, self).__init__()# 开始一个循环，迭代 num_layers 次，为每一层创建一个密集层for i in range(num_layers):# 在每次迭代中，创建一个新的 _DenseLayer 实例。该层的输入特征数量为 num_input_features + i * growth_rate，即前面所有层的输出特征总和layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)# 将创建的密集层添加到模块中，并命名为 denselayer1、denselayer2，依此类推。这样可以方便后续访问和调试self.add_module("denselayer%d" % (i + 1,), layer)'''
构建神经网络的一个过渡层,在神经网络中通常用于特征的转换和下采样
'''
class _Transition(nn.Sequential):def __init__(self,num_input_feature,num_output_features):super(_Transition,self).__init__()# 添加一个批归一化层，标准化输入特征self.add_module("norm",nn.BatchNorm2d(num_input_feature))# 添加一个 ReLU 激活函数self.add_module("relu",nn.ReLU(inplace=True))# 添加一个卷积层，使用 1x1 的卷积核，连接输入特征和输出特征。self.add_module("conv",nn.Conv2d(num_input_feature,num_output_features,kernel_size=1,stride=1,bias=False))# 添加一个 2x2 的平均池化层，步幅为 2，用于减少特征图的大小self.add_module("pool",nn.AvgPool2d(2,stride=2))class DenseNet(nn.Module):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):'''growth_rate: 每个DenseBlock中每层输出特征图的增长率。block_config: 一个元组，指定每个DenseBlock中的层数。num_init_features: 第一层卷积的输出特征数量。bn_size: Batch Normalization的大小compression_rate: 每个Transition层中输出特征数量的压缩比例。drop_rate: Dropout的概率num_classes: 最终分类的类别数。'''super(DenseNet,self).__init__()# 第一层卷积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))]))# DenseBlocknum_features = num_init_features# 遍历block_config，为每个DenseBlock构建模型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)# 更新当前特征数量，每个DenseBlock后增加num_layers * growth_ratenum_features += num_layers*growth_rateif i != len(block_config) - 1:# 定义Transition层，连接DenseBlock，减小特征图尺寸（通过compression_ratetransition = _Transition(num_features,int(num_features*compression_rate))# 将DenseBlock和Transition层添加到模型中self.features.add_module("transition%d"%(i+1),transition)num_features = int(num_features * compression_rate)# final bn+relu# 在所有DenseBlock和Transition层之后，添加一个Batch Normalization层和ReLU激活层self.features.add_module("norm5",nn.BatchNorm2d(num_features))self.features.add_module("relu5",nn.ReLU(inplace=True))# classification layer# 定义全连接层，将特征映射到类别数self.classifier = nn.Linear(num_features,num_classes)# 参数初始化'''遍历所有模块，初始化权重。卷积层: 使用Kaiming正态分布初始化。BatchNorm层: 将偏置初始化为0，权重初始化为1。全连接层: 将偏置初始化为0。'''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):'''self.features(x): 将输入x传递通过所有特征层。F.avg_pool2d: 在特征图上进行全局平均池化。view(features.size(0), -1): 将池化后的特征展平。self.classifier(out): 通过分类层得到输出。return out: 返回最终的分类结果。'''features = self.features(x)out = F.avg_pool2d(features,7,stride=1).view(features.size(0),-1)out = self.classifier(out)return outdef densetnet121(pretrained=False, **kwargs):model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), num_classes=len(classeNames))if pretrained:pattern = re.compile(r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')# 从指定的 URL 加载 DenseNet-121 的预训练权重，存储在 state_dictstate_dict = model_zoo.load_url(model_urls['densenet121'])for key in list(state_dict.keys()):res = pattern.match(key)if res:# 创建一个新键，组合匹配结果的前半部分和后半部分new_key = res.group(1) + res.group(2)state_dict[new_key] = state_dict[key]del state_dict[key]# 将处理后的权重加载到模型中model.load_state_dict(state_dict)return modelmodel = densetnet121()
model

import torchsummary as summary
summary.summary(model,(3,224,224))

----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1         [-1, 64, 112, 112]           9,408BatchNorm2d-2         [-1, 64, 112, 112]             128ReLU-3         [-1, 64, 112, 112]               0MaxPool2d-4           [-1, 64, 56, 56]               0BatchNorm2d-5           [-1, 64, 56, 56]             128ReLU-6           [-1, 64, 56, 56]               0Conv2d-7          [-1, 128, 56, 56]           8,192BatchNorm2d-8          [-1, 128, 56, 56]             256ReLU-9          [-1, 128, 56, 56]               0Conv2d-10           [-1, 32, 56, 56]          36,864BatchNorm2d-11           [-1, 96, 56, 56]             192ReLU-12           [-1, 96, 56, 56]               0Conv2d-13          [-1, 128, 56, 56]          12,288BatchNorm2d-14          [-1, 128, 56, 56]             256ReLU-15          [-1, 128, 56, 56]               0Conv2d-16           [-1, 32, 56, 56]          36,864BatchNorm2d-17          [-1, 128, 56, 56]             256ReLU-18          [-1, 128, 56, 56]               0Conv2d-19          [-1, 128, 56, 56]          16,384BatchNorm2d-20          [-1, 128, 56, 56]             256ReLU-21          [-1, 128, 56, 56]               0Conv2d-22           [-1, 32, 56, 56]          36,864BatchNorm2d-23          [-1, 160, 56, 56]             320ReLU-24          [-1, 160, 56, 56]               0Conv2d-25          [-1, 128, 56, 56]          20,480BatchNorm2d-26          [-1, 128, 56, 56]             256ReLU-27          [-1, 128, 56, 56]               0Conv2d-28           [-1, 32, 56, 56]          36,864BatchNorm2d-29          [-1, 192, 56, 56]             384ReLU-30          [-1, 192, 56, 56]               0Conv2d-31          [-1, 128, 56, 56]          24,576BatchNorm2d-32          [-1, 128, 56, 56]             256ReLU-33          [-1, 128, 56, 56]               0Conv2d-34           [-1, 32, 56, 56]          36,864BatchNorm2d-35          [-1, 224, 56, 56]             448ReLU-36          [-1, 224, 56, 56]               0Conv2d-37          [-1, 128, 56, 56]          28,672BatchNorm2d-38          [-1, 128, 56, 56]             256ReLU-39          [-1, 128, 56, 56]               0Conv2d-40           [-1, 32, 56, 56]          36,864BatchNorm2d-41          [-1, 256, 56, 56]             512ReLU-42          [-1, 256, 56, 56]               0Conv2d-43          [-1, 128, 56, 56]          32,768AvgPool2d-44          [-1, 128, 28, 28]               0BatchNorm2d-45          [-1, 128, 28, 28]             256ReLU-46          [-1, 128, 28, 28]               0Conv2d-47          [-1, 128, 28, 28]          16,384BatchNorm2d-48          [-1, 128, 28, 28]             256ReLU-49          [-1, 128, 28, 28]               0Conv2d-50           [-1, 32, 28, 28]          36,864BatchNorm2d-51          [-1, 160, 28, 28]             320ReLU-52          [-1, 160, 28, 28]               0Conv2d-53          [-1, 128, 28, 28]          20,480BatchNorm2d-54          [-1, 128, 28, 28]             256ReLU-55          [-1, 128, 28, 28]               0Conv2d-56           [-1, 32, 28, 28]          36,864BatchNorm2d-57          [-1, 192, 28, 28]             384ReLU-58          [-1, 192, 28, 28]               0Conv2d-59          [-1, 128, 28, 28]          24,576BatchNorm2d-60          [-1, 128, 28, 28]             256ReLU-61          [-1, 128, 28, 28]               0Conv2d-62           [-1, 32, 28, 28]          36,864BatchNorm2d-63          [-1, 224, 28, 28]             448ReLU-64          [-1, 224, 28, 28]               0Conv2d-65          [-1, 128, 28, 28]          28,672BatchNorm2d-66          [-1, 128, 28, 28]             256ReLU-67          [-1, 128, 28, 28]               0Conv2d-68           [-1, 32, 28, 28]          36,864BatchNorm2d-69          [-1, 256, 28, 28]             512ReLU-70          [-1, 256, 28, 28]               0Conv2d-71          [-1, 128, 28, 28]          32,768BatchNorm2d-72          [-1, 128, 28, 28]             256ReLU-73          [-1, 128, 28, 28]               0Conv2d-74           [-1, 32, 28, 28]          36,864BatchNorm2d-75          [-1, 288, 28, 28]             576ReLU-76          [-1, 288, 28, 28]               0Conv2d-77          [-1, 128, 28, 28]          36,864BatchNorm2d-78          [-1, 128, 28, 28]             256ReLU-79          [-1, 128, 28, 28]               0Conv2d-80           [-1, 32, 28, 28]          36,864BatchNorm2d-81          [-1, 320, 28, 28]             640ReLU-82          [-1, 320, 28, 28]               0Conv2d-83          [-1, 128, 28, 28]          40,960BatchNorm2d-84          [-1, 128, 28, 28]             256ReLU-85          [-1, 128, 28, 28]               0Conv2d-86           [-1, 32, 28, 28]          36,864BatchNorm2d-87          [-1, 352, 28, 28]             704ReLU-88          [-1, 352, 28, 28]               0Conv2d-89          [-1, 128, 28, 28]          45,056BatchNorm2d-90          [-1, 128, 28, 28]             256ReLU-91          [-1, 128, 28, 28]               0Conv2d-92           [-1, 32, 28, 28]          36,864BatchNorm2d-93          [-1, 384, 28, 28]             768ReLU-94          [-1, 384, 28, 28]               0Conv2d-95          [-1, 128, 28, 28]          49,152BatchNorm2d-96          [-1, 128, 28, 28]             256ReLU-97          [-1, 128, 28, 28]               0Conv2d-98           [-1, 32, 28, 28]          36,864BatchNorm2d-99          [-1, 416, 28, 28]             832ReLU-100          [-1, 416, 28, 28]               0Conv2d-101          [-1, 128, 28, 28]          53,248BatchNorm2d-102          [-1, 128, 28, 28]             256ReLU-103          [-1, 128, 28, 28]               0Conv2d-104           [-1, 32, 28, 28]          36,864BatchNorm2d-105          [-1, 448, 28, 28]             896ReLU-106          [-1, 448, 28, 28]               0Conv2d-107          [-1, 128, 28, 28]          57,344BatchNorm2d-108          [-1, 128, 28, 28]             256ReLU-109          [-1, 128, 28, 28]               0Conv2d-110           [-1, 32, 28, 28]          36,864BatchNorm2d-111          [-1, 480, 28, 28]             960ReLU-112          [-1, 480, 28, 28]               0Conv2d-113          [-1, 128, 28, 28]          61,440BatchNorm2d-114          [-1, 128, 28, 28]             256ReLU-115          [-1, 128, 28, 28]               0Conv2d-116           [-1, 32, 28, 28]          36,864BatchNorm2d-117          [-1, 512, 28, 28]           1,024ReLU-118          [-1, 512, 28, 28]               0Conv2d-119          [-1, 256, 28, 28]         131,072AvgPool2d-120          [-1, 256, 14, 14]               0BatchNorm2d-121          [-1, 256, 14, 14]             512ReLU-122          [-1, 256, 14, 14]               0Conv2d-123          [-1, 128, 14, 14]          32,768BatchNorm2d-124          [-1, 128, 14, 14]             256ReLU-125          [-1, 128, 14, 14]               0Conv2d-126           [-1, 32, 14, 14]          36,864BatchNorm2d-127          [-1, 288, 14, 14]             576ReLU-128          [-1, 288, 14, 14]               0Conv2d-129          [-1, 128, 14, 14]          36,864BatchNorm2d-130          [-1, 128, 14, 14]             256ReLU-131          [-1, 128, 14, 14]               0Conv2d-132           [-1, 32, 14, 14]          36,864BatchNorm2d-133          [-1, 320, 14, 14]             640ReLU-134          [-1, 320, 14, 14]               0Conv2d-135          [-1, 128, 14, 14]          40,960BatchNorm2d-136          [-1, 128, 14, 14]             256ReLU-137          [-1, 128, 14, 14]               0Conv2d-138           [-1, 32, 14, 14]          36,864BatchNorm2d-139          [-1, 352, 14, 14]             704ReLU-140          [-1, 352, 14, 14]               0Conv2d-141          [-1, 128, 14, 14]          45,056BatchNorm2d-142          [-1, 128, 14, 14]             256ReLU-143          [-1, 128, 14, 14]               0Conv2d-144           [-1, 32, 14, 14]          36,864BatchNorm2d-145          [-1, 384, 14, 14]             768ReLU-146          [-1, 384, 14, 14]               0Conv2d-147          [-1, 128, 14, 14]          49,152BatchNorm2d-148          [-1, 128, 14, 14]             256ReLU-149          [-1, 128, 14, 14]               0Conv2d-150           [-1, 32, 14, 14]          36,864BatchNorm2d-151          [-1, 416, 14, 14]             832ReLU-152          [-1, 416, 14, 14]               0Conv2d-153          [-1, 128, 14, 14]          53,248BatchNorm2d-154          [-1, 128, 14, 14]             256ReLU-155          [-1, 128, 14, 14]               0Conv2d-156           [-1, 32, 14, 14]          36,864BatchNorm2d-157          [-1, 448, 14, 14]             896ReLU-158          [-1, 448, 14, 14]               0Conv2d-159          [-1, 128, 14, 14]          57,344BatchNorm2d-160          [-1, 128, 14, 14]             256ReLU-161          [-1, 128, 14, 14]               0Conv2d-162           [-1, 32, 14, 14]          36,864BatchNorm2d-163          [-1, 480, 14, 14]             960ReLU-164          [-1, 480, 14, 14]               0Conv2d-165          [-1, 128, 14, 14]          61,440BatchNorm2d-166          [-1, 128, 14, 14]             256ReLU-167          [-1, 128, 14, 14]               0Conv2d-168           [-1, 32, 14, 14]          36,864BatchNorm2d-169          [-1, 512, 14, 14]           1,024ReLU-170          [-1, 512, 14, 14]               0Conv2d-171          [-1, 128, 14, 14]          65,536BatchNorm2d-172          [-1, 128, 14, 14]             256ReLU-173          [-1, 128, 14, 14]               0Conv2d-174           [-1, 32, 14, 14]          36,864BatchNorm2d-175          [-1, 544, 14, 14]           1,088ReLU-176          [-1, 544, 14, 14]               0Conv2d-177          [-1, 128, 14, 14]          69,632BatchNorm2d-178          [-1, 128, 14, 14]             256ReLU-179          [-1, 128, 14, 14]               0Conv2d-180           [-1, 32, 14, 14]          36,864BatchNorm2d-181          [-1, 576, 14, 14]           1,152ReLU-182          [-1, 576, 14, 14]               0Conv2d-183          [-1, 128, 14, 14]          73,728BatchNorm2d-184          [-1, 128, 14, 14]             256ReLU-185          [-1, 128, 14, 14]               0Conv2d-186           [-1, 32, 14, 14]          36,864BatchNorm2d-187          [-1, 608, 14, 14]           1,216ReLU-188          [-1, 608, 14, 14]               0Conv2d-189          [-1, 128, 14, 14]          77,824BatchNorm2d-190          [-1, 128, 14, 14]             256ReLU-191          [-1, 128, 14, 14]               0Conv2d-192           [-1, 32, 14, 14]          36,864BatchNorm2d-193          [-1, 640, 14, 14]           1,280ReLU-194          [-1, 640, 14, 14]               0Conv2d-195          [-1, 128, 14, 14]          81,920BatchNorm2d-196          [-1, 128, 14, 14]             256ReLU-197          [-1, 128, 14, 14]               0Conv2d-198           [-1, 32, 14, 14]          36,864BatchNorm2d-199          [-1, 672, 14, 14]           1,344ReLU-200          [-1, 672, 14, 14]               0Conv2d-201          [-1, 128, 14, 14]          86,016BatchNorm2d-202          [-1, 128, 14, 14]             256ReLU-203          [-1, 128, 14, 14]               0Conv2d-204           [-1, 32, 14, 14]          36,864BatchNorm2d-205          [-1, 704, 14, 14]           1,408ReLU-206          [-1, 704, 14, 14]               0Conv2d-207          [-1, 128, 14, 14]          90,112BatchNorm2d-208          [-1, 128, 14, 14]             256ReLU-209          [-1, 128, 14, 14]               0Conv2d-210           [-1, 32, 14, 14]          36,864BatchNorm2d-211          [-1, 736, 14, 14]           1,472ReLU-212          [-1, 736, 14, 14]               0Conv2d-213          [-1, 128, 14, 14]          94,208BatchNorm2d-214          [-1, 128, 14, 14]             256ReLU-215          [-1, 128, 14, 14]               0Conv2d-216           [-1, 32, 14, 14]          36,864BatchNorm2d-217          [-1, 768, 14, 14]           1,536ReLU-218          [-1, 768, 14, 14]               0Conv2d-219          [-1, 128, 14, 14]          98,304BatchNorm2d-220          [-1, 128, 14, 14]             256ReLU-221          [-1, 128, 14, 14]               0Conv2d-222           [-1, 32, 14, 14]          36,864BatchNorm2d-223          [-1, 800, 14, 14]           1,600ReLU-224          [-1, 800, 14, 14]               0Conv2d-225          [-1, 128, 14, 14]         102,400BatchNorm2d-226          [-1, 128, 14, 14]             256ReLU-227          [-1, 128, 14, 14]               0Conv2d-228           [-1, 32, 14, 14]          36,864BatchNorm2d-229          [-1, 832, 14, 14]           1,664ReLU-230          [-1, 832, 14, 14]               0Conv2d-231          [-1, 128, 14, 14]         106,496BatchNorm2d-232          [-1, 128, 14, 14]             256ReLU-233          [-1, 128, 14, 14]               0Conv2d-234           [-1, 32, 14, 14]          36,864BatchNorm2d-235          [-1, 864, 14, 14]           1,728ReLU-236          [-1, 864, 14, 14]               0Conv2d-237          [-1, 128, 14, 14]         110,592BatchNorm2d-238          [-1, 128, 14, 14]             256ReLU-239          [-1, 128, 14, 14]               0Conv2d-240           [-1, 32, 14, 14]          36,864BatchNorm2d-241          [-1, 896, 14, 14]           1,792ReLU-242          [-1, 896, 14, 14]               0Conv2d-243          [-1, 128, 14, 14]         114,688BatchNorm2d-244          [-1, 128, 14, 14]             256ReLU-245          [-1, 128, 14, 14]               0Conv2d-246           [-1, 32, 14, 14]          36,864BatchNorm2d-247          [-1, 928, 14, 14]           1,856ReLU-248          [-1, 928, 14, 14]               0Conv2d-249          [-1, 128, 14, 14]         118,784BatchNorm2d-250          [-1, 128, 14, 14]             256ReLU-251          [-1, 128, 14, 14]               0Conv2d-252           [-1, 32, 14, 14]          36,864BatchNorm2d-253          [-1, 960, 14, 14]           1,920ReLU-254          [-1, 960, 14, 14]               0Conv2d-255          [-1, 128, 14, 14]         122,880BatchNorm2d-256          [-1, 128, 14, 14]             256ReLU-257          [-1, 128, 14, 14]               0Conv2d-258           [-1, 32, 14, 14]          36,864BatchNorm2d-259          [-1, 992, 14, 14]           1,984ReLU-260          [-1, 992, 14, 14]               0Conv2d-261          [-1, 128, 14, 14]         126,976BatchNorm2d-262          [-1, 128, 14, 14]             256ReLU-263          [-1, 128, 14, 14]               0Conv2d-264           [-1, 32, 14, 14]          36,864BatchNorm2d-265         [-1, 1024, 14, 14]           2,048ReLU-266         [-1, 1024, 14, 14]               0Conv2d-267          [-1, 512, 14, 14]         524,288AvgPool2d-268            [-1, 512, 7, 7]               0BatchNorm2d-269            [-1, 512, 7, 7]           1,024ReLU-270            [-1, 512, 7, 7]               0Conv2d-271            [-1, 128, 7, 7]          65,536BatchNorm2d-272            [-1, 128, 7, 7]             256ReLU-273            [-1, 128, 7, 7]               0Conv2d-274             [-1, 32, 7, 7]          36,864BatchNorm2d-275            [-1, 544, 7, 7]           1,088ReLU-276            [-1, 544, 7, 7]               0Conv2d-277            [-1, 128, 7, 7]          69,632BatchNorm2d-278            [-1, 128, 7, 7]             256ReLU-279            [-1, 128, 7, 7]               0Conv2d-280             [-1, 32, 7, 7]          36,864BatchNorm2d-281            [-1, 576, 7, 7]           1,152ReLU-282            [-1, 576, 7, 7]               0Conv2d-283            [-1, 128, 7, 7]          73,728BatchNorm2d-284            [-1, 128, 7, 7]             256ReLU-285            [-1, 128, 7, 7]               0Conv2d-286             [-1, 32, 7, 7]          36,864BatchNorm2d-287            [-1, 608, 7, 7]           1,216ReLU-288            [-1, 608, 7, 7]               0Conv2d-289            [-1, 128, 7, 7]          77,824BatchNorm2d-290            [-1, 128, 7, 7]             256ReLU-291            [-1, 128, 7, 7]               0Conv2d-292             [-1, 32, 7, 7]          36,864BatchNorm2d-293            [-1, 640, 7, 7]           1,280ReLU-294            [-1, 640, 7, 7]               0Conv2d-295            [-1, 128, 7, 7]          81,920BatchNorm2d-296            [-1, 128, 7, 7]             256ReLU-297            [-1, 128, 7, 7]               0Conv2d-298             [-1, 32, 7, 7]          36,864BatchNorm2d-299            [-1, 672, 7, 7]           1,344ReLU-300            [-1, 672, 7, 7]               0Conv2d-301            [-1, 128, 7, 7]          86,016BatchNorm2d-302            [-1, 128, 7, 7]             256ReLU-303            [-1, 128, 7, 7]               0Conv2d-304             [-1, 32, 7, 7]          36,864BatchNorm2d-305            [-1, 704, 7, 7]           1,408ReLU-306            [-1, 704, 7, 7]               0Conv2d-307            [-1, 128, 7, 7]          90,112BatchNorm2d-308            [-1, 128, 7, 7]             256ReLU-309            [-1, 128, 7, 7]               0Conv2d-310             [-1, 32, 7, 7]          36,864BatchNorm2d-311            [-1, 736, 7, 7]           1,472ReLU-312            [-1, 736, 7, 7]               0Conv2d-313            [-1, 128, 7, 7]          94,208BatchNorm2d-314            [-1, 128, 7, 7]             256ReLU-315            [-1, 128, 7, 7]               0Conv2d-316             [-1, 32, 7, 7]          36,864BatchNorm2d-317            [-1, 768, 7, 7]           1,536ReLU-318            [-1, 768, 7, 7]               0Conv2d-319            [-1, 128, 7, 7]          98,304BatchNorm2d-320            [-1, 128, 7, 7]             256ReLU-321            [-1, 128, 7, 7]               0Conv2d-322             [-1, 32, 7, 7]          36,864BatchNorm2d-323            [-1, 800, 7, 7]           1,600ReLU-324            [-1, 800, 7, 7]               0Conv2d-325            [-1, 128, 7, 7]         102,400BatchNorm2d-326            [-1, 128, 7, 7]             256ReLU-327            [-1, 128, 7, 7]               0Conv2d-328             [-1, 32, 7, 7]          36,864BatchNorm2d-329            [-1, 832, 7, 7]           1,664ReLU-330            [-1, 832, 7, 7]               0Conv2d-331            [-1, 128, 7, 7]         106,496BatchNorm2d-332            [-1, 128, 7, 7]             256ReLU-333            [-1, 128, 7, 7]               0Conv2d-334             [-1, 32, 7, 7]          36,864BatchNorm2d-335            [-1, 864, 7, 7]           1,728ReLU-336            [-1, 864, 7, 7]               0Conv2d-337            [-1, 128, 7, 7]         110,592BatchNorm2d-338            [-1, 128, 7, 7]             256ReLU-339            [-1, 128, 7, 7]               0Conv2d-340             [-1, 32, 7, 7]          36,864BatchNorm2d-341            [-1, 896, 7, 7]           1,792ReLU-342            [-1, 896, 7, 7]               0Conv2d-343            [-1, 128, 7, 7]         114,688BatchNorm2d-344            [-1, 128, 7, 7]             256ReLU-345            [-1, 128, 7, 7]               0Conv2d-346             [-1, 32, 7, 7]          36,864BatchNorm2d-347            [-1, 928, 7, 7]           1,856ReLU-348            [-1, 928, 7, 7]               0Conv2d-349            [-1, 128, 7, 7]         118,784BatchNorm2d-350            [-1, 128, 7, 7]             256ReLU-351            [-1, 128, 7, 7]               0Conv2d-352             [-1, 32, 7, 7]          36,864BatchNorm2d-353            [-1, 960, 7, 7]           1,920ReLU-354            [-1, 960, 7, 7]               0Conv2d-355            [-1, 128, 7, 7]         122,880BatchNorm2d-356            [-1, 128, 7, 7]             256ReLU-357            [-1, 128, 7, 7]               0Conv2d-358             [-1, 32, 7, 7]          36,864BatchNorm2d-359            [-1, 992, 7, 7]           1,984ReLU-360            [-1, 992, 7, 7]               0Conv2d-361            [-1, 128, 7, 7]         126,976BatchNorm2d-362            [-1, 128, 7, 7]             256ReLU-363            [-1, 128, 7, 7]               0Conv2d-364             [-1, 32, 7, 7]          36,864BatchNorm2d-365           [-1, 1024, 7, 7]           2,048ReLU-366           [-1, 1024, 7, 7]               0Linear-367                    [-1, 2]           2,050
================================================================
Total params: 6,955,906
Trainable params: 6,955,906
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 294.57
Params size (MB): 26.53
Estimated Total Size (MB): 321.68
----------------------------------------------------------------

loss_fn    = nn.CrossEntropyLoss() # 创建损失函数
learn_rate = 1e-4 # 学习率
opt        = torch.optim.SGD(model.parameters(),lr=learn_rate)# 训练循环
def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)  # 训练集的大小，一共60000张图片num_batches = len(dataloader)   # 批次数目，1875（60000/32）train_loss, train_acc = 0, 0  # 初始化训练损失和正确率for X, y in dataloader:  # 获取图片及其标签X, y = X.to(device), y.to(device)# 计算预测误差pred = model(X)          # 网络输出loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失# 反向传播optimizer.zero_grad()  # grad属性归零loss.backward()        # 反向传播optimizer.step()       # 每一步自动更新# 记录acc与losstrain_acc  += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc  /= sizetrain_loss /= num_batchesreturn train_acc, train_lossdef test (dataloader, model, loss_fn):size        = len(dataloader.dataset)  # 测试集的大小，一共10000张图片num_batches = len(dataloader)          # 批次数目，313（10000/32=312.5，向上取整）test_loss, test_acc = 0, 0# 当不进行训练时，停止梯度更新，节省计算内存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target = imgs.to(device), target.to(device)# 计算losstarget_pred = model(imgs)loss        = loss_fn(target_pred, target)test_loss += loss.item()test_acc  += (target_pred.argmax(1) == target).type(torch.float).sum().item()test_acc  /= sizetest_loss /= num_batchesreturn test_acc, test_lossepochs     = 20
train_loss = []
train_acc  = []
test_loss  = []
test_acc   = []for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')

Epoch: 1, Train_acc:47.7%, Train_loss:0.725, Test_acc:47.4%，Test_loss:0.708
Epoch: 2, Train_acc:50.2%, Train_loss:0.697, Test_acc:52.7%，Test_loss:0.690
Epoch: 3, Train_acc:56.1%, Train_loss:0.686, Test_acc:59.9%，Test_loss:0.681
Epoch: 4, Train_acc:58.5%, Train_loss:0.679, Test_acc:60.7%，Test_loss:0.675
Epoch: 5, Train_acc:60.9%, Train_loss:0.673, Test_acc:60.1%，Test_loss:0.671
Epoch: 6, Train_acc:61.7%, Train_loss:0.670, Test_acc:62.6%，Test_loss:0.664
Epoch: 7, Train_acc:62.4%, Train_loss:0.665, Test_acc:63.5%，Test_loss:0.659
Epoch: 8, Train_acc:63.0%, Train_loss:0.660, Test_acc:64.8%，Test_loss:0.653
Epoch: 9, Train_acc:64.2%, Train_loss:0.656, Test_acc:65.5%，Test_loss:0.649
Epoch:10, Train_acc:64.9%, Train_loss:0.652, Test_acc:65.6%，Test_loss:0.644
Epoch:11, Train_acc:65.4%, Train_loss:0.649, Test_acc:66.6%，Test_loss:0.641
Epoch:12, Train_acc:65.0%, Train_loss:0.646, Test_acc:66.6%，Test_loss:0.638
Epoch:13, Train_acc:64.8%, Train_loss:0.643, Test_acc:67.5%，Test_loss:0.634
Epoch:14, Train_acc:65.7%, Train_loss:0.641, Test_acc:67.3%，Test_loss:0.633
Epoch:15, Train_acc:65.9%, Train_loss:0.638, Test_acc:67.8%，Test_loss:0.629
Epoch:16, Train_acc:66.3%, Train_loss:0.635, Test_acc:67.6%，Test_loss:0.626
Epoch:17, Train_acc:67.3%, Train_loss:0.632, Test_acc:67.8%，Test_loss:0.624
Epoch:18, Train_acc:67.1%, Train_loss:0.628, Test_acc:68.2%，Test_loss:0.618
Epoch:19, Train_acc:67.3%, Train_loss:0.628, Test_acc:68.9%，Test_loss:0.618
Epoch:20, Train_acc:67.9%, Train_loss:0.624, Test_acc:68.4%，Test_loss:0.614
Done

import matplotlib.pyplot as plt
#隐藏警告
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='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()