企业官网建设流程全解析

• 🍨本文为🔗365天深度学习训练营中的学习记录博客
• 🍖原作者：K同学啊

一、前置知识

1、YOLOv5算法中的Backbone模块介绍

很高兴能和你一起探索 YOLOv5 的奥秘。YOLOv5 是一个非常经典且高效的目标检测算法，而Backbone（主干网络）是它最基础也最重要的部分。

你可以把 Backbone 想象成读书笔记的“提炼者”

• 读一本厚厚的书（原始图片），你不可能把每一个字都背下来。
• Backbone 就像是你大脑中负责提炼重点的机制。
• 它在阅读过程中，过滤掉了“的、地、得”这些无意义的连接词（背景噪声），只把书中的核心观点、关键数据、人物关系（关键特征）提取出来。
• 最后你得到的一张薄薄的思维导图，就是 Backbone 输出的成果——它比原书薄得多，但包含了所有关键信息。

二、代码实现

1、设置GPU

若设备支持GPU就使用GPU,否则使用CPU

import torch import torch.nn as nn import matplotlib.pyplot as plt import torchvision import warnings import torchvision.transforms as transforms from torchvision import transforms, datasets # 忽略来自 torch.cuda 的 pynvml 弃用警告 warnings.filterwarnings( "ignore", message="The pynvml package is deprecated.*", category=FutureWarning, module="torch.cuda" ) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device

device(type='cuda')

2、数据准备

2.1、识别数据路径

import os import pathlib # 查看当前工作路径（确认路径是否正确） print("当前工作路径：", os.getcwd()) # 定义数据目录（建议用绝对路径更稳妥，相对路径依赖当前工作路径） data_dir = './data/天气识别数据集/' data_dir = pathlib.Path(data_dir) # 获取数据目录下的所有子路径（文件夹或文件） data_paths = list(data_dir.glob('*')) # 提取每个子路径的名称（即类别名，自动适配系统分隔符） classeNames = [path.name for path in data_paths] classeNames

当前工作路径： /root/365天训练营/Pytorch实战 ['cloudy', 'rain', 'shine', 'sunrise']

2.2、获取数据

data_dir = './data/天气识别数据集/' # 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.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_dir, transform=train_transforms) total_data

Dataset ImageFolder Number of datapoints: 1125 Root location: ./data/天气识别数据集/ StandardTransform Transform: Compose( Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=warn) ToTensor() Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) )

total_data.class_to_idx

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

2.3、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 = 4 train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1) test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=1) 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([4, 3, 224, 224]) Shape of y: torch.Size([4]) torch.int64

3、模型搭建

3.1、搭建Backbone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))) 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 SPPF(nn.Module): # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) """ 这个是YOLOv5, 6.0版本的主干网络，这里进行复现 （注：有部分删改，详细讲解将在后续进行展开） """ class YOLOv5_backbone(nn.Module): def __init__(self): super(YOLOv5_backbone, self).__init__() self.Conv_1 = Conv(3, 64, 3, 2, 2) self.Conv_2 = Conv(64, 128, 3, 2) self.C3_3 = C3(128,128) self.Conv_4 = Conv(128, 256, 3, 2) self.C3_5 = C3(256,256) self.Conv_6 = Conv(256, 512, 3, 2) self.C3_7 = C3(512,512) self.Conv_8 = Conv(512, 1024, 3, 2) self.C3_9 = C3(1024, 1024) self.SPPF = SPPF(1024, 1024, 5) # 全连接网络层，用于分类 self.classifier = nn.Sequential( nn.Linear(in_features=65536, out_features=100), nn.ReLU(), nn.Linear(in_features=100, out_features=4) ) def forward(self, x): x = self.Conv_1(x) x = self.Conv_2(x) x = self.C3_3(x) x = self.Conv_4(x) x = self.C3_5(x) x = self.Conv_6(x) x = self.C3_7(x) x = self.Conv_8(x) x = self.C3_9(x) x = self.SPPF(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 = YOLOv5_backbone().to(device) model

Using cuda device YOLOv5_backbone( (Conv_1): Conv( (conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2), bias=False) (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): SiLU() ) (Conv_2): Conv( (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): SiLU() ) ...... (SPPF): SPPF( (cv1): Conv( (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): SiLU() ) (cv2): Conv( (conv): Conv2d(2048, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act): SiLU() ) (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) ) (classifier): Sequential( (0): Linear(in_features=65536, out_features=100, bias=True) (1): ReLU() (2): Linear(in_features=100, out_features=4, bias=True) ) )

3.2、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))

---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 64, 113, 113] 1,728 BatchNorm2d-2 [-1, 64, 113, 113] 128 SiLU-3 [-1, 64, 113, 113] 0 Conv-4 [-1, 64, 113, 113] 0 Conv2d-5 [-1, 128, 57, 57] 73,728 ...... Linear-121 [-1, 100] 6,553,700 ReLU-122 [-1, 100] 0 Linear-123 [-1, 4] 404 ================================================================ Total params: 21,729,592 Trainable params: 21,729,592 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 0.57 Forward/backward pass size (MB): 137.59 Params size (MB): 82.89 Estimated Total Size (MB): 221.06 ----------------------------------------------------------------

4、训练模型

4.1、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

4.2、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

4.3、正式训练

import copy optimizer = torch.optim.Adam(model.parameters(), lr= 1e-4) loss_fn = nn.CrossEntropyLoss() # 创建损失函数 epochs = 60 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): 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_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 = './model/p9_best_model.pth' # 保存的参数文件名 torch.save(best_model.state_dict(), PATH) print('Done')

Epoch: 1, Train_acc:57.4%, Train_loss:1.130, Test_acc:64.9%, Test_loss:0.745, Lr:1.00E-04 Epoch: 2, Train_acc:65.9%, Train_loss:0.850, Test_acc:73.8%, Test_loss:0.663, Lr:1.00E-04 Epoch: 3, Train_acc:74.6%, Train_loss:0.672, Test_acc:83.1%, Test_loss:0.430, Lr:1.00E-04 Epoch: 4, Train_acc:75.9%, Train_loss:0.618, Test_acc:84.9%, Test_loss:0.386, Lr:1.00E-04 Epoch: 5, Train_acc:83.4%, Train_loss:0.445, Test_acc:82.2%, Test_loss:0.492, Lr:1.00E-04 ...... Epoch:56, Train_acc:96.7%, Train_loss:0.101, Test_acc:88.4%, Test_loss:0.578, Lr:1.00E-04 Epoch:57, Train_acc:97.1%, Train_loss:0.080, Test_acc:89.8%, Test_loss:0.468, Lr:1.00E-04 Epoch:58, Train_acc:98.4%, Train_loss:0.052, Test_acc:91.1%, Test_loss:0.450, Lr:1.00E-04 Epoch:59, Train_acc:99.1%, Train_loss:0.032, Test_acc:91.1%, Test_loss:0.513, Lr:1.00E-04 Epoch:60, Train_acc:99.4%, Train_loss:0.023, Test_acc:89.8%, Test_loss:0.563, Lr:1.00E-04 Done

5、结果可视化

5.1、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 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()

6、模型评估

best_model.load_state_dict(torch.load(PATH, map_location=device)) epoch_test_acc, epoch_test_loss = test(test_dl, best_model, loss_fn) epoch_test_acc, epoch_test_loss

(0.9377777777777778, 0.3814523629184725)