- 🍨 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：Python3.8
● 编译器：Jupyter Lab
● 数据集：天气识别数据集
● 深度学习环境：Pytorch
○ torch1.12.1+cu113
○ torchvision0.13.1+cu113
一、前期工作
1、设置GPU
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os,PIL,pathlib,warningswarnings.filterwarnings("ignore") #忽略警告信息device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
2、导入数据
import os,PIL,random,pathlibdata_dir = './weather_photos/'
data_dir = pathlib.Path(data_dir)data_paths = list(data_dir.glob('*'))
classeNames = [str(path).split("\\")[1] for path in data_paths]
classeNames
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。
])test_transform = 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("./weather_photos/",transform=train_transforms)
total_data
total_data.class_to_idx1
3、划分数据集
train_size = int(0.8 * 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_dataset
batch_size = 4train_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
二、构建包含C3模块的模型
1、搭建模型
#搭建模型
import torch.nn.functional as Fdef autopad(k, p=None): # kernel, padding# Pad to 'same'if p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-padreturn pclass Conv(nn.Module):# Standard convolutiondef __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groupssuper().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())def forward(self, x):return self.act(self.bn(self.conv(x)))class Bottleneck(nn.Module):# Standard bottleneckdef __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c_, c2, 3, 1, g=g)self.add = shortcut and c1 == c2def forward(self, x):return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class C3(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))class model_K(nn.Module):def __init__(self):super(model_K, self).__init__()# 卷积模块self.Conv = Conv(3, 32, 3, 2) # C3模块1self.C3_1 = C3(32, 64, 3, 2)# 全连接网络层，用于分类self.classifier = nn.Sequential(nn.Linear(in_features=802816, out_features=100),nn.ReLU(),nn.Linear(in_features=100, out_features=4))def forward(self, x):x = self.Conv(x)x = self.C3_1(x)x = torch.flatten(x, start_dim=1)x = self.classifier(x)return xdevice = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))model = model_K().to(device)
model```python
Using cuda device
model_K((Conv): Conv((conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(C3_1): C3((cv1): Conv((conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv3): Conv((conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(m): Sequential((0): Bottleneck((cv1): Conv((conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU()))(1): Bottleneck((cv1): Conv((conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU()))(2): Bottleneck((cv1): Conv((conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU())(cv2): Conv((conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)(bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(act): SiLU()))))(classifier): Sequential((0): Linear(in_features=802816, out_features=100, bias=True)(1): ReLU()(2): Linear(in_features=100, out_features=4, bias=True))
)
2、查看模型详情
```python
#查看模型详情
# 统计模型参数量以及其他指标
import torchsummary as summary
summary.summary(model, (3, 224, 224))
----------------------------------------------------------------Layer (type) Output Shape Param #
================================================================Conv2d-1 [-1, 32, 112, 112] 864BatchNorm2d-2 [-1, 32, 112, 112] 64SiLU-3 [-1, 32, 112, 112] 0Conv-4 [-1, 32, 112, 112] 0Conv2d-5 [-1, 32, 112, 112] 1,024BatchNorm2d-6 [-1, 32, 112, 112] 64SiLU-7 [-1, 32, 112, 112] 0Conv-8 [-1, 32, 112, 112] 0Conv2d-9 [-1, 32, 112, 112] 1,024BatchNorm2d-10 [-1, 32, 112, 112] 64SiLU-11 [-1, 32, 112, 112] 0Conv-12 [-1, 32, 112, 112] 0Conv2d-13 [-1, 32, 112, 112] 9,216BatchNorm2d-14 [-1, 32, 112, 112] 64SiLU-15 [-1, 32, 112, 112] 0Conv-16 [-1, 32, 112, 112] 0Bottleneck-17 [-1, 32, 112, 112] 0Conv2d-18 [-1, 32, 112, 112] 1,024BatchNorm2d-19 [-1, 32, 112, 112] 64SiLU-20 [-1, 32, 112, 112] 0Conv-21 [-1, 32, 112, 112] 0Conv2d-22 [-1, 32, 112, 112] 9,216BatchNorm2d-23 [-1, 32, 112, 112] 64SiLU-24 [-1, 32, 112, 112] 0Conv-25 [-1, 32, 112, 112] 0Bottleneck-26 [-1, 32, 112, 112] 0Conv2d-27 [-1, 32, 112, 112] 1,024BatchNorm2d-28 [-1, 32, 112, 112] 64SiLU-29 [-1, 32, 112, 112] 0Conv-30 [-1, 32, 112, 112] 0Conv2d-31 [-1, 32, 112, 112] 9,216BatchNorm2d-32 [-1, 32, 112, 112] 64SiLU-33 [-1, 32, 112, 112] 0Conv-34 [-1, 32, 112, 112] 0Bottleneck-35 [-1, 32, 112, 112] 0Conv2d-36 [-1, 32, 112, 112] 1,024BatchNorm2d-37 [-1, 32, 112, 112] 64SiLU-38 [-1, 32, 112, 112] 0Conv-39 [-1, 32, 112, 112] 0Conv2d-40 [-1, 64, 112, 112] 4,096BatchNorm2d-41 [-1, 64, 112, 112] 128SiLU-42 [-1, 64, 112, 112] 0Conv-43 [-1, 64, 112, 112] 0C3-44 [-1, 64, 112, 112] 0Linear-45 [-1, 100] 80,281,700ReLU-46 [-1, 100] 0Linear-47 [-1, 4] 404
================================================================
Total params: 80,320,536
Trainable params: 80,320,536
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 150.06
Params size (MB): 306.40
Estimated Total Size (MB): 457.04
----------------------------------------------------------------
三、训练模型
1、编写训练函数
# 训练循环
def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset) # 训练集的大小num_batches = len(dataloader) # 批次数目, (size/batch_size，向上取整)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_loss
2、编写测试函数
def test (dataloader, model, loss_fn):size = len(dataloader.dataset) # 测试集的大小num_batches = len(dataloader) # 批次数目, (size/batch_size，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_loss
3、正式训练
import copyoptimizer = torch.optim.Adam(model.parameters(), lr= 1e-4)
loss_fn = nn.CrossEntropyLoss() # 创建损失函数epochs = 20train_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, optimizer)model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)# 保存最佳模型到 best_modelif epoch_test_acc > best_acc:best_acc = epoch_test_accbest_model = copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率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))# 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')
四、可视化结果
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from datetime import datetime
current_time = datetime.now() # 获取当前时间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.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效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()
五、模型评估
# 模型评估
best_model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn)
epoch_test_acc, epoch_test_loss
# 查看是否与我们记录的最高准确率一致
epoch_test_acc
六、总结
目标检测：YOLO系列变体（如YOLOv6、YOLOv8）尝试在Backbone中融合C3和注意力机制。
图像分类：在轻量级分类网络（如MobileViT）中结合C3模块，平衡精度与速度。
医学图像分析：需同时关注局部病变细节和全局解剖结构，C3-Transformer模型表现优异。
C3-Transformer类模型通过融合卷积的局部归纳偏置和注意力的全局建模能力，在视觉任务中展现了更强的特征表达能力。未来发展方向可能集中在动态架构设计、轻量化部署和跨模态扩展上。这类模型尤其适合需要兼顾精度与速度的场景（如自动驾驶、实时视频分析）
