tjy/BloodPressure/train.py

import os

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm

from dataloader import BPDataLoader
from models.lstm import LSTMModel

# 定义模型
model = LSTMModel()

#定义训练参数
max_epochs = 100
batch_size= 1024
warmup_epochs = 10
lr = 0.0005

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=lr)

# 定义学习率调度器
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=max_epochs - warmup_epochs, eta_min=1e-6)

# 定义TensorBoard写入器
writer = SummaryWriter()

# 训练函数
def train(model, dataloader, epoch, device,batch_size):
    model.train()
    running_loss = 0.0
    pbar = tqdm(dataloader, total=len(dataloader), desc=f"Epoch {epoch+1}/{max_epochs}")
    for i, (inputs, sbp_labels, dbp_labels) in enumerate(pbar):
        inputs = inputs.to(device)
        sbp_labels = sbp_labels.to(device)
        dbp_labels = dbp_labels.to(device)

        optimizer.zero_grad()

        sbp_outputs, dbp_outputs = model(inputs)

        sbp_outputs = sbp_outputs.squeeze(1)  # 将输出形状从(batch_size, 1)变为(batch_size,)
        dbp_outputs = dbp_outputs.squeeze(1)

        loss_sbp = criterion(sbp_outputs, sbp_labels)
        loss_dbp = criterion(dbp_outputs, dbp_labels)

        loss = loss_sbp + loss_dbp

        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        pbar.set_postfix(loss=running_loss / (i + 1))

    scheduler.step()
    writer.add_scalar("Loss/train", running_loss / len(dataloader)/ batch_size, epoch)

    return running_loss / len(dataloader) / batch_size

# 评估函数
def evaluate(model, dataloader, device,batch_size):
    model.eval()
    running_loss_sbp = 0.0
    running_loss_dbp = 0.0
    with torch.no_grad():
        for inputs, sbp_labels, dbp_labels in dataloader:
            inputs = inputs.to(device)
            sbp_labels = sbp_labels.to(device)
            dbp_labels = dbp_labels.to(device)

            sbp_outputs, dbp_outputs = model(inputs)

            sbp_outputs = sbp_outputs.squeeze(1)  # 将输出形状从(batch_size, 1)变为(batch_size,)
            dbp_outputs = dbp_outputs.squeeze(1)

            loss_sbp = criterion(sbp_outputs, sbp_labels)
            loss_dbp = criterion(dbp_outputs, dbp_labels)

            running_loss_sbp += loss_sbp.item()
            running_loss_dbp += loss_dbp.item()

    eval_loss_sbp = running_loss_sbp / len(dataloader) / batch_size
    eval_loss_dbp = running_loss_dbp / len(dataloader) / batch_size

    return eval_loss_sbp, eval_loss_dbp

# 训练循环
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

data_type='MIMIC_full'

#判断权重保存目录是否存在，不存在则创建
if not os.path.exists('weights'):
    os.makedirs('weights')
    #在其中创建data_type同名子文件夹
    os.makedirs(os.path.join('weights',data_type))
else:
    #判断子文件夹是否存在
    if not os.path.exists(os.path.join('weights',data_type)):
        os.makedirs(os.path.join('weights',data_type))


data_loader = BPDataLoader(data_dir='data', val_split=0.2, batch_size=batch_size,data_type=data_type)

train_dataloader, val_dataloader = data_loader.get_dataloaders()

best_val_loss_sbp = float('inf')
best_val_loss_dbp = float('inf')


for epoch in range(max_epochs):
    if epoch < warmup_epochs:
        warmup_lr = 1e-6 + (epoch + 1) * (5e-4 - 1e-6) / warmup_epochs
        for param_group in optimizer.param_groups:
            param_group['lr'] = warmup_lr

    train_loss = train(model, train_dataloader, epoch, device,batch_size)
    val_loss_sbp, val_loss_dbp = evaluate(model, val_dataloader, device,batch_size)

    writer.add_scalar("Loss/val_sbp", val_loss_sbp, epoch)
    writer.add_scalar("Loss/val_dbp", val_loss_dbp, epoch)

    print(f"Epoch {epoch+1}/{max_epochs}, Train Loss: {train_loss:.4f}, Val Loss SBP: {val_loss_sbp:.4f}, Val Loss DBP: {val_loss_dbp:.4f}")

    if val_loss_sbp < best_val_loss_sbp or val_loss_dbp < best_val_loss_dbp:
        best_val_loss_sbp = val_loss_sbp
        best_val_loss_dbp = val_loss_dbp
        torch.save(model.state_dict(), f'weights/{data_type}/best_{epoch}_lstm_model_sbp{val_loss_sbp:.4f}_dbp{val_loss_dbp:.4f}.pth')

    torch.save(model.state_dict(),
               f'weights/{data_type}/last.pth')

writer.close()