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手机网站模板 html5,威海外贸网站建设,免费做logo的网站,wordpress 文件结构#x1f368; 本文为#x1f517;365天深度学习训练营中的学习记录博客#x1f356; 原作者#xff1a;K同学啊 一、前置知识 1、YOLOv5算法中的C3模块介绍 先引用一个生活化的案例图快速理解一下 C3 模块的全称是 CSP Bottleneck with 3 convolutions。它是 YOLOv5 在 …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C3模块介绍先引用一个生活化的案例图快速理解一下C3 模块的全称是CSP Bottleneck with 3 convolutions。它是 YOLOv5 在 YOLOv4 的 CSPCross Stage Partial架构基础上改进而来的。核心思想CSP跨阶段局部网络C3 的核心在于“分治”。它将输入特征图分为两部分一部分通过一系列的残差块Bottleneck进行深度的特征变换负责学习复杂的特征。另一部分直接通过一个卷积层走“捷径”去和后面汇合。这种设计可以让梯度的传播更加顺畅减少了计算量同时保证了特征的丰富性。为什么叫 C3因为它主要包含3 个标准卷积层Conv以及多个 Bottleneck 模块Conv 1主干分支的入口。Conv 2侧路分支捷径的入口。Conv 3最后负责融合两个分支输出的出口。注中间的 Bottleneck 里虽然也有卷积但不算在 C3 命名的这“3”个主控制卷积里。C3 的作用轻量化相比于之前的 CSP 结构C3 去掉了一些不必要的卷积参数更少速度更快。特征提取能力强它是 YOLOv5 网络主干Backbone和颈部Neck中的核心组件网络的深度depth主要就是通过控制 C3 中 Bottleneck 的重复次数N来实现的。二、代码实现1、设置GPU若设备支持GPU就使用GPU,否则使用CPUimport 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, messageThe pynvml package is deprecated.*, categoryFutureWarning, moduletorch.cuda ) device torch.device(cuda if torch.cuda.is_available() else cpu) devicedevice(typecuda)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, transformtrain_transforms) total_dataDataset ImageFolder Number of datapoints: 1125 Root location: ./data/天气识别数据集/ StandardTransform Transform: Compose( Resize(size[224, 224], interpolationbilinear, max_sizeNone, antialiaswarn) 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_sizebatch_size, shuffleTrue, num_workers1) test_dl torch.utils.data.DataLoader(test_dataset, batch_sizebatch_size, shuffleTrue, num_workers1) for X, y in test_dl: print(Shape of X [N, C, H, W]: , X.shape) print(Shape of y: , y.shape, y.dtype) breakShape of X [N, C, H, W]: torch.Size([4, 3, 224, 224]) Shape of y: torch.Size([4]) torch.int643、模型搭建3.1、搭建C3模型import torch.nn.functional as F def autopad(k, pNone): # 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, k1, s1, pNone, g1, actTrue): # ch_in, ch_out, kernel, stride, padding, groups super().__init__() self.conv nn.Conv2d(c1, c2, k, s, autopad(k, p), groupsg, biasFalse) 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, shortcutTrue, g1, e0.5): # 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, gg) 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, n1, shortcutTrue, g1, e0.5): # 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) # actFReLU(c2) self.m nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e1.0) for _ in range(n))) def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim1)) 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_features802816, out_features100), nn.ReLU(), nn.Linear(in_features100, out_features4) ) def forward(self, x): x self.Conv(x) x self.C3_1(x) x torch.flatten(x, start_dim1) 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) modelUsing cuda device model_K( (Conv): Conv( (conv): Conv2d(3, 32, kernel_size(3, 3), stride(2, 2), padding(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) (C3_1): C3( (cv1): Conv( (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) (cv2): Conv( (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) (cv3): Conv( (conv): Conv2d(64, 64, kernel_size(1, 1), stride(1, 1), biasFalse) (bn): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) (m): Sequential( (0): Bottleneck( (cv1): Conv( (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) (cv2): Conv( (conv): Conv2d(32, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) ) (1): Bottleneck( (cv1): Conv( (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) (cv2): Conv( (conv): Conv2d(32, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) ) (2): Bottleneck( (cv1): Conv( (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) (cv2): Conv( (conv): Conv2d(32, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse) (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue) (act): SiLU() ) ) ) ) (classifier): Sequential( (0): Linear(in_features802816, out_features100, biasTrue) (1): ReLU() (2): Linear(in_features100, out_features4, biasTrue) ) )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, 32, 112, 112] 864 BatchNorm2d-2 [-1, 32, 112, 112] 64 SiLU-3 [-1, 32, 112, 112] 0 Conv-4 [-1, 32, 112, 112] 0 Conv2d-5 [-1, 32, 112, 112] 1,024 BatchNorm2d-6 [-1, 32, 112, 112] 64 SiLU-7 [-1, 32, 112, 112] 0 Conv-8 [-1, 32, 112, 112] 0 Conv2d-9 [-1, 32, 112, 112] 1,024 BatchNorm2d-10 [-1, 32, 112, 112] 64 SiLU-11 [-1, 32, 112, 112] 0 Conv-12 [-1, 32, 112, 112] 0 Conv2d-13 [-1, 32, 112, 112] 9,216 BatchNorm2d-14 [-1, 32, 112, 112] 64 SiLU-15 [-1, 32, 112, 112] 0 Conv-16 [-1, 32, 112, 112] 0 Bottleneck-17 [-1, 32, 112, 112] 0 Conv2d-18 [-1, 32, 112, 112] 1,024 BatchNorm2d-19 [-1, 32, 112, 112] 64 SiLU-20 [-1, 32, 112, 112] 0 Conv-21 [-1, 32, 112, 112] 0 Conv2d-22 [-1, 32, 112, 112] 9,216 BatchNorm2d-23 [-1, 32, 112, 112] 64 SiLU-24 [-1, 32, 112, 112] 0 Conv-25 [-1, 32, 112, 112] 0 Bottleneck-26 [-1, 32, 112, 112] 0 Conv2d-27 [-1, 32, 112, 112] 1,024 BatchNorm2d-28 [-1, 32, 112, 112] 64 SiLU-29 [-1, 32, 112, 112] 0 Conv-30 [-1, 32, 112, 112] 0 Conv2d-31 [-1, 32, 112, 112] 9,216 BatchNorm2d-32 [-1, 32, 112, 112] 64 SiLU-33 [-1, 32, 112, 112] 0 Conv-34 [-1, 32, 112, 112] 0 Bottleneck-35 [-1, 32, 112, 112] 0 Conv2d-36 [-1, 32, 112, 112] 1,024 BatchNorm2d-37 [-1, 32, 112, 112] 64 SiLU-38 [-1, 32, 112, 112] 0 Conv-39 [-1, 32, 112, 112] 0 Conv2d-40 [-1, 64, 112, 112] 4,096 BatchNorm2d-41 [-1, 64, 112, 112] 128 SiLU-42 [-1, 64, 112, 112] 0 Conv-43 [-1, 64, 112, 112] 0 C3-44 [-1, 64, 112, 112] 0 Linear-45 [-1, 100] 80,281,700 ReLU-46 [-1, 100] 0 Linear-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 ----------------------------------------------------------------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_loss4.2、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_loss4.3、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 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): 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(epoch1, 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/p8_best_model.pth # 保存的参数文件名 torch.save(best_model.state_dict(), PATH) print(Done)Epoch: 1, Train_acc:74.1%, Train_loss:1.327, Test_acc:86.2%, Test_loss:0.431, Lr:1.00E-04 Epoch: 2, Train_acc:89.6%, Train_loss:0.306, Test_acc:90.2%, Test_loss:0.374, Lr:1.00E-04 Epoch: 3, Train_acc:94.3%, Train_loss:0.165, Test_acc:87.1%, Test_loss:0.356, Lr:1.00E-04 Epoch: 4, Train_acc:97.9%, Train_loss:0.073, Test_acc:91.1%, Test_loss:0.297, Lr:1.00E-04 ... Epoch:18, Train_acc:99.7%, Train_loss:0.006, Test_acc:92.0%, Test_loss:0.297, Lr:1.00E-04 Epoch:19, Train_acc:99.9%, Train_loss:0.002, Test_acc:94.7%, Test_loss:0.204, Lr:1.00E-04 Epoch:20, Train_acc:99.9%, Train_loss:0.002, Test_acc:94.2%, Test_loss:0.212, Lr:1.00E-04 Done5、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, labelTraining Accuracy) plt.plot(epochs_range, test_acc, labelTest Accuracy) plt.legend(loclower 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, labelTraining Loss) plt.plot(epochs_range, test_loss, labelTest Loss) plt.legend(locupper right) plt.title(Training and Validation Loss) plt.show()6、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(0.9466666666666667, 0.20435180879150325)