pytorch训练中内存一直增加的原因

代码中存在累加loss,但是每步的loss没加item()
例如下面例子;

import torch
import torch.nn as nn
from collections import defaultdict

if torch.cuda.is_available():
    device = 'cuda'
else:
    device = 'cpu'

model = nn.Linear(100, 400).to(device)
criterion = nn.L1Loss(reduction='mean').to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

train_loss = defaultdict(float)
eval_loss = defaultdict(float)

for i in range(10000):
    model.train()
    x = torch.rand(50, 100, device=device)
    y_pred = model(x) # 50 * 400
    y_tgt = torch.rand(50, 400, device=device)

    loss = criterion(y_pred, y_tgt)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    # 会导致内存一直增加，需改为train_loss['loss'] += loss.item()
    train_loss['loss'] += loss

    if i % 100 == 0:
        train_loss = defaultdict(float)
        model.eval()
        x = torch.rand(50, 100, device=device)
        y_pred = model(x) # 50 * 400

        y_tgt = torch.rand(50, 400, device=device)
        loss = criterion(y_pred, y_tgt)

        # 会导致内存一直增加，需改为eval_loss['loss'] += loss.item()
        eval_loss['loss'] += loss

对于以上代码会导致内存占用越来越大,解决的方法是train_loss[‘loss’] += loss.item() 以及 eval_loss[‘loss’] += loss.item()。值得注意的是，要复现内存越来越大的问题，模型中需要切换model.train() 和 model.eval()，train_loss以及eval_loss的作用是保存模型的平均误差(这里是累积误差)，保存到tensorboard中

pytorch中，.item()方法 是得到一个元素张量里面的元素值
具体就是 用于将一个零维张量转换成浮点数