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1.设置GPU 设置GPU
device torch.device… 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P4周猴痘病识别 原作者K同学啊|接辅导、项目定制 一、前期准备
1.设置GPU 设置GPU
device torch.device(cuda if torch.cuda.is_available() else cpu)
print(Using {} device.format(device))
没有GPU则使用CPU 2.导入数据、数据预处理
import os,PIL,random,pathlib
data_dir rD:\P4
data_dir pathlib.Path(data_dir)data_paths list(data_dir.glob(*))
class_names [path.name for path in data_paths]
print(class_names)import pathlib
import torchvision.transforms as transforms
from torchvision import datasets
total_datadir rD:\P4train_transforms transforms.Compose([transforms.Resize([224, 224]),transforms.ToTensor(),transforms.Normalize(mean[0.485, 0.456, 0.406],std[0.229, 0.224, 0.225])
])total_data datasets.ImageFolder(total_datadir, transformtrain_transforms)
print(total_data)
定义数据预处理操作 train_transforms
transforms.Resize([224, 224])将输入图像的尺寸调整为 224x224 像素这通常是训练深度学习模型所使用的常见图像尺寸。transforms.ToTensor()将图像数据转换为PyTorch张量并将像素值归一化到范围 [0, 1]。transforms.Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225])对图像进行标准化处理将像素值标准化为标准正态分布高斯分布这有助于模型更快地收敛。
使用 datasets.ImageFolder 加载图像数据集
total_datadir 是包含图像数据集的目录路径。transformtrain_transforms 指定了要应用的数据预处理操作。 3.标签映射
在DatasetFolder中class_to_idx是一个字典将类别名映射到类别标签从0开始其中类别名是文件夹的名称类别标签是与之相关联的数字。 为什么要做标签映射呢 将类别名映射到类别标签是因为在训练深度学习模型时通常使用类别标签来表示每个样本的类别。 在训练模型时输入数据被转换为张量并且每个张量的标签是一个数字表示与之相关联的类别。 类别标签使得模型可以根据真实标签和预测标签之间的误差来更新模型权重从而使模型学习到如何将输入数据映射到正确的输出标签。 print(total_data.class_to_idx) 二、划分数据集
import torch
from torch.utils.data import DataLoader# total_data 包含了数据集我们可以从 total_data 中划分出训练集和测试集
# 例如可以按照一定的比例划分数据集或者根据需要自定义训练集和测试集# 划分数据集示例
# 假设数据集总共有100个样本可以将前80个样本用于训练后20个用于测试
train_size int(0.8 * len(total_data))
test_size len(total_data) - train_sizetrain_dataset, test_dataset torch.utils.data.random_split(total_data, [train_size, test_size])batch_size 32train_dl DataLoader(train_dataset, batch_sizebatch_size, shuffleTrue, num_workers1)
test_dl DataLoader(test_dataset, batch_sizebatch_size, shuffleTrue, num_workers1)
三、 构建 CNN 网络
import torch
import torch.nn as nn
import torch.nn.functional as Fclass Network(nn.Module):def __init__(self):super(Network, self).__init__()self.conv1 nn.Conv2d(3, 6, 5)self.conv2 nn.Conv2d(6, 16, 5)self.fc1 nn.Linear(16 * 53 * 53, 120)self.fc2 nn.Linear(120, 84)self.fc3 nn.Linear(84, 2)self.dropout nn.Dropout(0.5) # Adding dropout with a probability of 0.5def forward(self, x):x F.max_pool2d(F.relu(self.conv1(x)), 2)x F.max_pool2d(F.relu(self.conv2(x)), 2)x x.view(-1, 16 * 53 * 53)x F.relu(self.fc1(x))x self.dropout(x) # Applying dropout after the first fully connected layerx F.relu(self.fc2(x))x self.dropout(x) # Applying dropout after the second fully connected layerx self.fc3(x)x F.log_softmax(x, dim1)return xmodel Network()
print(model) 四、训练模型
1. 设置超参数
# 定义损失函数和优化器
loss_fn nn.CrossEntropyLoss()
leaining_rate 0.001
opt torch.optim.Adam(model.parameters(),lrleaining_rate)
2.编写训练函数
# 训练函数
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)# Compute prediction errorpred model(x) # 网络输出loss loss_fn(pred, y) # 计算损失optimizer.zero_grad() # 梯度清零loss.backward() # 反向传播optimizer.step() # 更新参数train_acc (pred.argmax(1) y).type(torch.float).sum().item()train_loss loss.item()train_acc / sizetrain_loss / num_batchesreturn train_acc, train_loss# 测试函数
def test(dataloader, model, loss_fn):size len(dataloader.dataset)num_batches len(dataloader)test_loss, test_acc 0, 0with torch.no_grad():for x, y in dataloader:x, y x.to(device), y.to(device)pred model(x)loss loss_fn(pred, y)test_loss loss_fn(pred, y).item()test_acc (pred.argmax(1) y).type(torch.float).sum().item()test_acc / sizetest_loss / num_batchesreturn test_acc, test_loss3.正式训练
epochs 20
train_loss []
train_acc []
test_loss []
test_acc []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)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)template (Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f})print(template.format(epoch1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print(Done) 代码在运行时出现下列问题可能是以下原因导致 Traceback (most recent call last): File string, line 1, in module File D:\Python\lib\multiprocessing\spawn.py, line 116, in spawn_main exitcode _main(fd, parent_sentinel) File D:\Python\lib\multiprocessing\spawn.py, line 125, in _main prepare(preparation_data) File D:\Python\lib\multiprocessing\spawn.py, line 236, in prepare _fixup_main_from_path(data[init_main_from_path]) File D:\Python\lib\multiprocessing\spawn.py, line 287, in _fixup_main_from_path main_content runpy.run_path(main_path, File D:\Python\lib\runpy.py, line 289, in run_path return _run_module_code(code, init_globals, run_name, File D:\Python\lib\runpy.py, line 96, in _run_module_code _run_code(code, mod_globals, init_globals, File D:\Python\lib\runpy.py, line 86, in _run_code exec(code, run_globals) File c:\Users\刘鸿逸\Desktop\python\01.py, line 156, in module epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, opt) File c:\Users\刘鸿逸\Desktop\python\01.py, line 105, in train for X, y in dataloader: # 获取图片及其标签 File D:\Python\lib\site-packages\torch\utils\data\dataloader.py, line 441, in __iter__ return self._get_iterator() File D:\Python\lib\site-packages\torch\utils\data\dataloader.py, line 388, in _get_iterator return _MultiProcessingDataLoaderIter(self) File D:\Python\lib\site-packages\torch\utils\data\dataloader.py, line 1042, in __init__ w.start() File D:\Python\lib\multiprocessing\process.py, line 121, in start self._popen self._Popen(self) File D:\Python\lib\multiprocessing\context.py, line 224, in _Popen return _default_context.get_context().Process._Popen(process_obj) File D:\Python\lib\multiprocessing\context.py, line 336, in _Popen return Popen(process_obj) File D:\Python\lib\multiprocessing\popen_spawn_win32.py, line 45, in __init__ prep_data spawn.get_preparation_data(process_obj._name) File D:\Python\lib\multiprocessing\spawn.py, line 154, in get_preparation_data _check_not_importing_main() File D:\Python\lib\multiprocessing\spawn.py, line 134, in _check_not_importing_main raise RuntimeError( RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ __main__: freeze_support() ... The freeze_support() line can be omitted if the program is not going to be frozen to produce an executable. 这个错误是由于在Windows操作系统上使用多进程时未按照正确的方式设置了启动子进程的方法引起的。它提示需要在主模块中添加适当的if __name__ __main__:块以正确启动子进程。下面解释一下报错的含义以及如何解决它
为了解决这个问题应该确保在主模块中使用if __name__ __main__:块来包装主要的执行代码这是一种在使用多进程时常见的做法。在主模块中包装代码后子进程将只在主进程中执行而不会在导入模块时执行。这可以防止上述报错。
修改后完整代码
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision
from torchvision import transforms, datasets
import os
import PIL
import pathlib# Your code for data loading, model definition, training, and testing should go hereif __name__ __main__:device torch.device(cuda if torch.cuda.is_available() else cpu)print(device)import os,PIL,random,pathlibdata_dir rD:\P4data_dir pathlib.Path(data_dir)data_paths list(data_dir.glob(*))class_names [path.name for path in data_paths]print(class_names)total_datadir rD:\P4# 关于transforms.Compose的更多介绍可以参考https://blog.csdn.net/qq_38251616/article/details/124878863train_transforms 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(total_datadir, transformtrain_transforms)print(total_data)train_size int(0.8 * len(total_data))test_size len(total_data) - train_sizetrain_dataset, test_dataset torch.utils.data.random_split(total_data, [train_size, test_size])print(train_dataset, test_dataset)batch_size 32train_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)import torch.nn.functional as Fclass Network_bn(nn.Module):def __init__(self):super(Network_bn, self).__init__()nn.Conv2d()函数第一个参数in_channels是输入的channel数量第二个参数out_channels是输出的channel数量第三个参数kernel_size是卷积核大小第四个参数stride是步长默认为1第五个参数padding是填充大小默认为0self.conv1 nn.Conv2d(in_channels3, out_channels12, kernel_size5, stride1, padding0)self.bn1 nn.BatchNorm2d(12)self.conv2 nn.Conv2d(in_channels12, out_channels12, kernel_size5, stride1, padding0)self.bn2 nn.BatchNorm2d(12)self.pool nn.MaxPool2d(2,2)self.conv4 nn.Conv2d(in_channels12, out_channels24, kernel_size5, stride1, padding0)self.bn4 nn.BatchNorm2d(24)self.conv5 nn.Conv2d(in_channels24, out_channels24, kernel_size5, stride1, padding0)self.bn5 nn.BatchNorm2d(24)self.fc1 nn.Linear(24*50*50, len(class_names))def forward(self, x):x F.relu(self.bn1(self.conv1(x))) x F.relu(self.bn2(self.conv2(x))) x self.pool(x) x F.relu(self.bn4(self.conv4(x))) x F.relu(self.bn5(self.conv5(x))) x self.pool(x) x x.view(-1, 24*50*50)x self.fc1(x)return xdevice cuda if torch.cuda.is_available() else cpuprint(Using {} device.format(device))model Network_bn().to(device)print(model)loss_fn nn.CrossEntropyLoss() # 创建损失函数learn_rate 1e-4 # 学习率opt torch.optim.SGD(model.parameters(),lrlearn_rate)# 训练循环def train(dataloader, model, loss_fn, optimizer):size len(dataloader.dataset) # 训练集的大小一共60000张图片num_batches len(dataloader) # 批次数目187560000/32train_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_lossdef test (dataloader, model, loss_fn):size len(dataloader.dataset) # 测试集的大小一共10000张图片num_batches len(dataloader) # 批次数目31310000/32312.5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_lossepochs 17train_loss []train_acc []test_loss []test_acc []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)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)template (Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%Test_loss:{:.3f})print(template.format(epoch1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))print(Done)验证集正确率达到88%以上。
