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2022/09/16阅读：57主题：默认主题
深度学习pytorch训练代码模板(个人习惯)
深度学习pytorch训练代码模板(个人习惯)
来源：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： 导入包以及设置随机种子 以类的方式定义超参数 定义自己的模型 定义早停类(此步骤可以省略) 定义自己的数据集Dataset,DataLoader 实例化模型，设置loss，优化器等 开始训练以及调整lr 绘图 预测 一、导入包以及设置随机种子
import numpy as np
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
seed = 42
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
二、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"),]
三、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
四、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
五、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)
六、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)
七、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))
八、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()
九、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=1
Your_model.eval()
predict = Your_model(data)
【项目推荐】
面向小白的顶会论文核心代码库：https://github.com/xmu-xiaoma666/External-Attention-pytorch[1]
面向小白的YOLO目标检测库：https://github.com/iscyy/yoloair[2]
面向小白的顶刊顶会的论文解析：https://github.com/xmu-xiaoma666/FightingCV-Paper-Reading[3]
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参考资料
https://github.com/xmu-xiaoma666/External-Attention-pytorch: https://github.com/xmu-xiaoma666/External-Attention-pytorch
[2]https://github.com/iscyy/yoloair: https://github.com/iscyy/yoloair
[3]https://github.com/xmu-xiaoma666/FightingCV-Paper-Reading: https://github.com/xmu-xiaoma666/FightingCV-Paper-Reading
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