(附代码)经验 | 深度学习pytorch训练代码模板

目标检测与深度学习

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2021-09-20 21:51

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一个专注于目标检测与深度学习知识分享的公众号
作者|wfnian@知乎

链接|https://zhuanlan.zhihu.com/p/396666255


从参数定义,到网络模型定义,再到训练步骤,验证步骤,测试步骤,总结了一套较为直观的模板。目录如下:
1. 导入包以及设置随机种子
2. 以类的方式定义超参数
3. 定义自己的模型
4. 定义早停类(此步骤可以省略)
5. 定义自己的数据集Dataset,DataLoader
6. 实例化模型,设置loss,优化器等
7. 开始训练以及调整lr
8. 绘图
9. 预测


01


导入包以及设置随机种子
import numpy as npimport torchimport torch.nn as nnimport numpy as npimport pandas as pdfrom torch.utils.data import DataLoader, Datasetfrom sklearn.model_selection import train_test_splitimport matplotlib.pyplot as plt
import randomseed = 42torch.manual_seed(seed)np.random.seed(seed)random.seed(seed)



02


以类的方式定义超参数
class argparse():    pass
args = argparse()args.epochs, args.learning_rate, args.patience = [30, 0.001, 4]args.hidden_size, args.input_size= [40, 30]args.device, = [torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),]


03

定义自己的模型


class Your_model(nn.Module):    def __init__(self):        super(Your_model, self).__init__()        pass
def forward(self,x): pass        return x



04


定义早停类(此步骤可以省略)
class EarlyStopping():    def __init__(self,patience=7,verbose=False,delta=0):        self.patience = patience        self.verbose = verbose        self.counter = 0        self.best_score = None        self.early_stop = False        self.val_loss_min = np.Inf        self.delta = delta    def __call__(self,val_loss,model,path):        print("val_loss={}".format(val_loss))        score = -val_loss        if self.best_score is None:            self.best_score = score            self.save_checkpoint(val_loss,model,path)        elif score < self.best_score+self.delta:            self.counter+=1            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')            if self.counter>=self.patience:                self.early_stop = True        else:            self.best_score = score            self.save_checkpoint(val_loss,model,path)            self.counter = 0    def save_checkpoint(self,val_loss,model,path):        if self.verbose:            print(                f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')        torch.save(model.state_dict(), path+'/'+'model_checkpoint.pth')        self.val_loss_min = val_loss



05


定义自己的数据集Dataset,DataLoader
class Dataset_name(Dataset):    def __init__(self, flag='train'):        assert flag in ['train', 'test', 'valid']        self.flag = flag        self.__load_data__()
def __getitem__(self, index): pass def __len__(self): pass
def __load_data__(self, csv_paths: list): pass print( "train_X.shape:{}\ntrain_Y.shape:{}\nvalid_X.shape:{}\nvalid_Y.shape:{}\n" .format(self.train_X.shape, self.train_Y.shape, self.valid_X.shape, self.valid_Y.shape))
train_dataset = Dataset_name(flag='train')train_dataloader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)valid_dataset = Dataset_name(flag='valid')valid_dataloader = DataLoader(dataset=valid_dataset, batch_size=64, shuffle=True)



06


实例化模型,设置loss,优化器等
model = Your_model().to(args.device)criterion = torch.nn.MSELoss()optimizer = torch.optim.Adam(Your_model.parameters(),lr=args.learning_rate)
train_loss = []valid_loss = []train_epochs_loss = []valid_epochs_loss = []
early_stopping = EarlyStopping(patience=args.patience,verbose=True)



07


开始训练以及调整lr
for epoch in range(args.epochs):    Your_model.train()    train_epoch_loss = []    for idx,(data_x,data_y) in enumerate(train_dataloader,0):        data_x = data_x.to(torch.float32).to(args.device)        data_y = data_y.to(torch.float32).to(args.device)        outputs = Your_model(data_x)        optimizer.zero_grad()        loss = criterion(data_y,outputs)        loss.backward()        optimizer.step()        train_epoch_loss.append(loss.item())        train_loss.append(loss.item())        if idx%(len(train_dataloader)//2)==0:            print("epoch={}/{},{}/{}of train, loss={}".format(                epoch, args.epochs, idx, len(train_dataloader),loss.item()))    train_epochs_loss.append(np.average(train_epoch_loss))
#=====================valid============================ Your_model.eval() valid_epoch_loss = [] for idx,(data_x,data_y) in enumerate(valid_dataloader,0): data_x = data_x.to(torch.float32).to(args.device) data_y = data_y.to(torch.float32).to(args.device) outputs = Your_model(data_x) loss = criterion(outputs,data_y) valid_epoch_loss.append(loss.item()) valid_loss.append(loss.item()) valid_epochs_loss.append(np.average(valid_epoch_loss)) #==================early stopping====================== early_stopping(valid_epochs_loss[-1],model=Your_model,path=r'c:\\your_model_to_save') if early_stopping.early_stop: print("Early stopping") break #====================adjust lr======================== lr_adjust = { 2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6, 10: 5e-7, 15: 1e-7, 20: 5e-8 } if epoch in lr_adjust.keys(): lr = lr_adjust[epoch] for param_group in optimizer.param_groups: param_group['lr'] = lr        print('Updating learning rate to {}'.format(lr))



08


绘图
plt.figure(figsize=(12,4))plt.subplot(121)plt.plot(train_loss[:])plt.title("train_loss")plt.subplot(122)plt.plot(train_epochs_loss[1:],'-o',label="train_loss")plt.plot(valid_epochs_loss[1:],'-o',label="valid_loss")plt.title("epochs_loss")plt.legend()plt.show()



09


预测


# 此处可定义一个预测集的Dataloader。也可以直接将你的预测数据reshape,添加batch_size=1Your_model.eval()predict = Your_model(data)


END



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