- 🍨 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
我的环境
- 语言环境：Python 3.12
- 编译器：Jupyter Lab
- 深度学习环境：Pytorch 2.4.1 Torchvision 0.19.1
- 数据集：乳腺癌数据集
一、前期准备
今天我们使用前面的DenseNet实现对乳腺癌的识别
1、设置GPU以及库导入
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import os, PIL, pathlib
from collections import OrderedDict
import torchsummary as summarydevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
代码输出：
device(type='cuda')
2、数据的导入以及预处理
data_dir = './data/J3-1-data'
data_dir = pathlib.Path(data_dir)data_path = list(data_dir.glob('*'))
classNames = [path.name for path in data_path]
print(classNames)
代码输出：
['0', '1']
可以看到，我们这次的数据只有两类，0代表不是乳腺癌，1代表是乳腺癌
接下来我们设置transforms：
train_transforms = transforms.Compose([transforms.Resize([224, 224]),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])total_data = datasets.ImageFolder(data_dir, transform=train_transforms)
total_data
代码输出：
Dataset ImageFolderNumber of datapoints: 13403Root location: data\J3-1-dataStandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=True)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))
总共有13403张图片，我们都使用transform对数据进行前期的标准化处理。
随后我们划分训练集，测试集以及验证集：
train_size = int(0.7 * len(total_data))
remain_size = len(total_data) - train_size
train_dataset, remain_dataset = torch.utils.data.random_split(total_data, [train_size, remain_size])
test_size = int(0.6 * len(remain_dataset))
validate_size = len(remain_dataset) - test_size
test_dataset, validate_dataset = torch.utils.data.random_split(remain_dataset, [test_size, validate_size]) #随机分配数据
train_dataset, test_dataset, validate_dataset
代码输出：
(<torch.utils.data.dataset.Subset at 0x22815024c20>,<torch.utils.data.dataset.Subset at 0x2281501d5e0>,<torch.utils.data.dataset.Subset at 0x22815024710>)
这里显示的是内存地址。
接下来使用dataloader对数据集进行加载：
batch_size = 32train_dl = DataLoader(train_dataset, batch_size=batch_size,shuffle=True)test_dl = DataLoader(test_dataset,batch_size = batch_size,shuffle = True
)validate_dl = DataLoader(validate_dataset,batch_size = batch_size,shuffle = False
)for x, y in validate_dl:print("shape of x [N, C, H, W]:", x.shape)print("shape of y:", y.shape, y.dtype)break代码输出：
shape of x [N, C, H, W]: torch.Size([32, 3, 224, 224])
shape of y: torch.Size([32]) torch.int64
3、数据的可视化
# 定义反归一化函数
def unnormalize(img, mean, std):mean = np.array(mean)std = np.array(std)img = img * std + mean # 反归一化return np.clip(img, 0, 1) # 限制值范围到 [0, 1]plt.figure(figsize=(10, 5))
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]for images, labels in validate_dl: # 从 DataLoader 中获取一个批次for i in range(8): # 显示前 8 张图片ax = plt.subplot(2, 4, i + 1) # 创建 2 行 4 列的子图# 反归一化并转换为 (H, W, C)img = images[i].permute(1, 2, 0).numpy() # (C, H, W) -> (H, W, C)img = unnormalize(img, mean, std) # 反归一化# 显示图像plt.imshow(img) # 显示图像，值范围应为 [0, 1]plt.title(classNames[labels[i].item()]) # 使用类别名称作为标题plt.axis("off") # 关闭坐标轴break # 仅显示第一个批次
代码输出：
二、DenseNet网络构建
我们使用上周构建的DenseNet121：
class _DenseLayer(nn.Sequential):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_ratedef 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):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):def __init__(self, num_input_features, num_output_features):super(_Transition,self).__init__()self.add_module('norm',nn.BatchNorm2d(num_input_features))self.add_module('relu',nn.ReLU(inplace=True))self.add_module('conv',nn.Conv2d(num_input_features, num_output_features, kernel_size=1,stride=1, bias=False))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):super(DenseNet, self).__init__()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))]))num_features = num_init_featuresfor i, num_layers in enumerate(block_config):block = _DenseBlock(num_layers, num_features, bn_size, growth_rate, drop_rate)self.features.add_module('denseblock%d' % (i + 1), block)num_features += num_layers * growth_rateif i != len(block_config) - 1:transition = _Transition(num_features, int(num_features * compression_rate))self.features.add_module('transition%d' % (i + 1), transition)num_features = int(num_features * compression_rate)self.features.add_module('norm5', nn.BatchNorm2d(num_features))self.features.add_module('relu5', nn.ReLU(inplace=True))self.classifier = nn.Linear(num_features, num_classes)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, kernel_size=7).view(features.size(0), -1)out = self.classifier(out)return outdensenet121 = DenseNet(num_init_features=64,growth_rate=32,block_config=(6, 12, 24, 6),num_classes=len(classNames))model = densenet121.cuda()
model
代码输出：
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)))(norm5): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(relu5): ReLU(inplace=True))(classifier): Linear(in_features=704, out_features=2, bias=True)
)
我们对模型进行总结：
summary.summary(model, (3, 224, 224))
代码输出：
----------------------------------------------------------------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] 0Linear-307 [-1, 2] 1,410
================================================================
Total params: 5,481,026
Trainable params: 5,481,026
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 286.44
Params size (MB): 20.91
Estimated Total Size (MB): 307.92
----------------------------------------------------------------三、模型训练
def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)num_batches = len(dataloader)train_loss, train_acc = 0, 0for x, y in dataloader:x, y = x.to(device), y.to(device)pred = model(x)loss = loss_fn(pred, y)#backwardoptimizer.zero_grad()loss.backward()optimizer.step()train_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)num_batches = len(dataloader)test_loss, test_acc = 0, 0for x, y in dataloader:x, y = x.to(device), y.to(device)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 /= sizetest_loss /= num_batchesreturn test_acc, test_loss
import copy
from torch.optim.lr_scheduler import ReduceLROnPlateauopt = torch.optim.Adam(model.parameters(), lr= 1e-4)
scheduler = ReduceLROnPlateau(opt, mode='min', factor=0.1, patience=5, verbose=True) # 当指标（如损失）连续 5 次没有改善时，将学习率乘以 0.1
loss_fn = nn.CrossEntropyLoss() # 交叉熵epochs = 32train_loss = []
train_acc = []
test_loss = []
test_acc = []best_acc = 0 # 设置一个最佳准确率，作为最佳模型的判别指标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)scheduler.step(epoch_test_loss)if epoch_test_acc > best_acc:best_acc = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr = opt.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))# 保存最佳模型到文件中
PATH = './best_model.pth' # 保存的参数文件名
torch.save(best_model.state_dict(), PATH)print('Done')
代码输出:
Epoch: 1, Train_acc:84.8%, Train_loss:0.353, Test_acc:88.2%, Test_loss:0.290, Lr:1.00E-04
Epoch: 2, Train_acc:88.1%, Train_loss:0.287, Test_acc:89.8%, Test_loss:0.259, Lr:1.00E-04
Epoch: 3, Train_acc:89.0%, Train_loss:0.269, Test_acc:89.3%, Test_loss:0.278, Lr:1.00E-04
Epoch: 4, Train_acc:90.2%, Train_loss:0.240, Test_acc:90.8%, Test_loss:0.223, Lr:1.00E-04
Epoch: 5, Train_acc:90.5%, Train_loss:0.235, Test_acc:89.1%, Test_loss:0.266, Lr:1.00E-04
Epoch: 6, Train_acc:91.4%, Train_loss:0.218, Test_acc:90.9%, Test_loss:0.226, Lr:1.00E-04
Epoch: 7, Train_acc:91.9%, Train_loss:0.204, Test_acc:91.6%, Test_loss:0.229, Lr:1.00E-04
Epoch: 8, Train_acc:92.5%, Train_loss:0.191, Test_acc:91.2%, Test_loss:0.240, Lr:1.00E-04
Epoch: 9, Train_acc:92.2%, Train_loss:0.189, Test_acc:90.7%, Test_loss:0.227, Lr:1.00E-04
Epoch:10, Train_acc:93.0%, Train_loss:0.176, Test_acc:90.3%, Test_loss:0.244, Lr:1.00E-05
Epoch:11, Train_acc:95.3%, Train_loss:0.126, Test_acc:93.6%, Test_loss:0.178, Lr:1.00E-05
Epoch:12, Train_acc:95.9%, Train_loss:0.113, Test_acc:93.5%, Test_loss:0.170, Lr:1.00E-05
Epoch:13, Train_acc:96.3%, Train_loss:0.100, Test_acc:93.7%, Test_loss:0.179, Lr:1.00E-05
Epoch:14, Train_acc:96.6%, Train_loss:0.093, Test_acc:93.7%, Test_loss:0.176, Lr:1.00E-05
Epoch:15, Train_acc:97.1%, Train_loss:0.085, Test_acc:93.0%, Test_loss:0.185, Lr:1.00E-05
Epoch:16, Train_acc:96.9%, Train_loss:0.082, Test_acc:93.3%, Test_loss:0.182, Lr:1.00E-05
Epoch:17, Train_acc:97.5%, Train_loss:0.069, Test_acc:92.9%, Test_loss:0.184, Lr:1.00E-05
Epoch:18, Train_acc:97.6%, Train_loss:0.068, Test_acc:93.2%, Test_loss:0.195, Lr:1.00E-06
Epoch:19, Train_acc:98.3%, Train_loss:0.054, Test_acc:93.2%, Test_loss:0.187, Lr:1.00E-06
Epoch:20, Train_acc:98.3%, Train_loss:0.058, Test_acc:93.7%, Test_loss:0.186, Lr:1.00E-06
Epoch:21, Train_acc:98.3%, Train_loss:0.053, Test_acc:93.3%, Test_loss:0.185, Lr:1.00E-06
Epoch:22, Train_acc:98.2%, Train_loss:0.056, Test_acc:93.5%, Test_loss:0.187, Lr:1.00E-06
Epoch:23, Train_acc:98.5%, Train_loss:0.051, Test_acc:93.4%, Test_loss:0.191, Lr:1.00E-06
Epoch:24, Train_acc:98.5%, Train_loss:0.051, Test_acc:93.1%, Test_loss:0.184, Lr:1.00E-07
Epoch:25, Train_acc:98.3%, Train_loss:0.052, Test_acc:93.5%, Test_loss:0.184, Lr:1.00E-07
Epoch:26, Train_acc:98.5%, Train_loss:0.051, Test_acc:93.4%, Test_loss:0.186, Lr:1.00E-07
Epoch:27, Train_acc:98.1%, Train_loss:0.057, Test_acc:93.4%, Test_loss:0.187, Lr:1.00E-07
Epoch:28, Train_acc:98.2%, Train_loss:0.052, Test_acc:93.4%, Test_loss:0.191, Lr:1.00E-07
Epoch:29, Train_acc:98.4%, Train_loss:0.052, Test_acc:93.6%, Test_loss:0.188, Lr:1.00E-07
Epoch:30, Train_acc:98.3%, Train_loss:0.054, Test_acc:93.7%, Test_loss:0.184, Lr:1.00E-08
Epoch:31, Train_acc:98.4%, Train_loss:0.050, Test_acc:93.4%, Test_loss:0.184, Lr:1.00E-08
Epoch:32, Train_acc:98.5%, Train_loss:0.052, Test_acc:93.3%, Test_loss:0.184, Lr:1.00E-08
Done四、数据可视化
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='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
代码输出：
可以看到测试集的准确率可以达到93%左右
五、数据的预测
我们使用模型对代码进行测试：
plt.figure(figsize=(10, 5)) # 遍历验证数据集，取第一个批次
for images, labels in validate_dl:for i in range(8): # 只显示前 8 张图片ax = plt.subplot(2, 4, i + 1)# 显示图片img = images[i].permute(1, 2, 0).numpy() # 转换为 (H, W, C)img = unnormalize(img, mean, std) # 反归一化plt.imshow(img) # 显示图像，值范围为 [0, 1]# 增加一个维度用于模型预测img_tensor = images[i].unsqueeze(0).to("cuda" if torch.cuda.is_available() else "cpu")# 使用模型预测类别best_model.eval() # 切换到评估模式with torch.no_grad(): # 禁用梯度计算predictions = best_model(img_tensor) # 预测结果predicted_class_index = predictions.argmax(dim=1).item() # 获取预测类别索引predicted_class = classNames[predicted_class_index] # 获取预测类别名称# 获取真实类别名称true_class = classNames[labels[i].item()]# 设置标题为真实类别和预测类别plt.title(f"T: {true_class}\nP: {predicted_class}")plt.axis("off") # 隐藏坐标轴# 打印真实类别和预测类别print(f"Image {i+1}: True Label = {true_class}, Predicted Label = {predicted_class}")break # 只处理第一个批次
代码输出：
Image 1: True Label = 0, Predicted Label = 1
Image 2: True Label = 0, Predicted Label = 0
Image 3: True Label = 1, Predicted Label = 1
Image 4: True Label = 0, Predicted Label = 0
Image 5: True Label = 0, Predicted Label = 0
Image 6: True Label = 1, Predicted Label = 1
Image 7: True Label = 0, Predicted Label = 0
Image 8: True Label = 0, Predicted Label = 0
最后我们查看验证集的总体正确率：
def validate(dataloader, model):model.eval()size = len(dataloader.dataset)num_batches = len(dataloader)validate_acc = 0for x, y in dataloader:x, y = x.to(device), y.to(device)pred = model(x)validate_acc += (pred.argmax(1) == y).type(torch.float).sum().item()validate_acc /= sizereturn validate_acc# 计算验证集准确率
validate_acc = validate(validate_dl, best_model)
print(f"Validation Accuracy: {validate_acc:.2%}")
代码输出：
Validation Accuracy: 93.23%
准确率达到93.23%总体不错
![Vulhub：Shiro[漏洞复现]](https://i-blog.csdnimg.cn/direct/49e0a29570f64ac8abb316630f5241ec.png)
