深度学习pytorch训练代码模板(个人习惯)

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)

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"),]

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

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

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)

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)

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))

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()