深度学习Week9-YOLOv5-C3模块实现（Pytorch）

🍨 本文为🔗365天深度学习训练营 中的学习记录博客🍦 参考文章：Pytorch实战 | 第P8天：YOLOv5-C3模块实现(训练营内部成员可读)🍖 原作者：K同学啊|接辅导、项目定制

了解C3的结构，方便后续YOLOv5算法的学习。采用的数据集是天气识别的数据集。 

一、 前期准备 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,warnings warnings.filterwarnings("ignore") #忽略警告信息 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(device)

输出：cuda

2. 导入数据 import os,PIL,random,pathlib 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] print(classeNames)

图形变换，输出一下：用到torchvision.transforms.Compose()类

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("./data/",transform=train_transforms) print(total_data.class_to_idx)

输出：{'cloudy': 0, 'rain': 1, 'shine': 2, 'sunrise': 3}

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]) batch_size = 32 train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0) test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=0) 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、搭建YOLOv5-C3模型

 1.搭建模型 import torch.nn.functional as F def 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-pad return p class Conv(nn.Module): # Standard convolution def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super().__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))) def forward_fuse(self, x): return self.act(self.conv(x)) class Bottleneck(nn.Module): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c2, 3, 1, g=g) self.add = shortcut and c1 == c2 def 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 convolutions def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__() c_ = int(c2 * e) # hidden channels self.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1 self.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 x device = "cuda" if torch.cuda.is_available() else "cpu" print("Using {} device".format(device)) model = model_K().to(device) print(model) 2.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))  三、 训练模型 1. 编写训练和测试函数

和之前cnn网络、vgg一样

# 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 def test (dataloader, model, loss_fn): size = len(dataloader.dataset) # 测试集的大小 num_batches = len(dataloader) # 批次数目 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loss target_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 /= size test_loss /= num_batches return test_acc, test_loss 2. 正式训练

这里也设置了训练器，结合前几次实验经验，使用Adam模型

import copy optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) loss_fn = nn.CrossEntropyLoss() # 创建损失函数 epochs = 20 train_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): # 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接口时使用） 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(model.state_dict(), PATH) print('Done')

遇到了问题：RuntimeError: CUDA out of memory。这个在之前也遇到过，我显卡（3050ti）性能一般，但是可以把batch_size减小一半，本实验由32改为16即可运行。

四、 结果可视化 1. Loss与Accuracy图 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()

 2. 指定图片进行预测 from PIL import Image 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) # 展示预测的图片 test_img = transform(test_img) img = test_img.to(device).unsqueeze(0) model.eval() output = model(img) _, 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/sunrise/sunrise8.jpg', model=model, transform=train_transforms, classes=classes)  3. 模型评估

 以往都是看看最后几轮得到准确率，但是跳动比较大就不太好找准确率最高的一回，所以我们用函数返回进行比较。

best_model.eval() epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn) print(epoch_test_acc, epoch_test_loss) print(epoch_test_acc) *五、优化模型

C3模块 作用： 1 在新版yolov5中,作者将BottleneckCSP(瓶颈层)模块转变为了C3模块，其结构作用基本相同均为CSP架构，只是在修正单元的选择上有所不同，其包含了3个标准卷积层以及多个Bottleneck模块（数量由配置文件.yaml的n和depth_multiple参数乘积决定）

2 C3相对于BottleneckCSP模块不同的是，经历过残差输出后的Conv模块被去掉了，concat后的标准卷积模块中的激活函数也由LeakyRelu变为了SiLU（同上）。

3 该模块是对残差特征进行学习的主要模块,其结构分为两支,一支使用了上述指定多个Bottleneck堆叠和3个标准卷积层，另一支仅经过一个基本卷积模块，最后将两支进行concat操作。

class C3(nn.Module): # CSP Bottleneck with 3 convolutions def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__() c_ = int(c2 * e) # hidden channels self.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))

提升：

修改BottleNeck层数为4

 最后准确率提升了7%左右