code: def train_one_epoch(
      model,
      optimizer,
      data_loader,
      device,
      epoch,
      print_freq=50,
      warmup=False,
      scaler=None,
  ):
      model.train()
      metric_logger = utils.MetricLogger(delimiter="  ")
      metric_logger.add_meter(
          "lr", utils.SmoothedValue(window_size=1, fmt="{value:.6f}")
      )
      header = "Epoch: [{}]".format(epoch)
  
      lr_scheduler = None
      if (
          epoch == 0 and warmup is True
      ):  # 当训练第一轮（epoch=0）时，启用warmup训练方式，可理解为热身训练
          warmup_factor = 1.0 / 1000
          warmup_iters = min(1000, len(data_loader) - 1)
  
          lr_scheduler = utils.warmup_lr_scheduler(
              optimizer, warmup_iters, warmup_factor
          )
  
      mloss = torch.zeros(1).to(device)  # mean losses
      for i, [images, targets] in enumerate(
          metric_logger.log_every(data_loader, print_freq, header)
      ):
          images = list(image.to(device) for image in images)
          targets = [
              {k: v.to(device) for k, v in t.items()} for t in targets
          ]
  
          # 混合精度训练上下文管理器，如果在CPU环境中不起任何作用
          with torch.cuda.amp.autocast(enabled=scaler is not None):
              loss_dict = model(images, targets)
  
              losses = sum(loss for loss in loss_dict.values())
  
          # reduce losses over all GPUs for logging purpose
          loss_dict_reduced = utils.reduce_dict(loss_dict)
          losses_reduced = sum(
              loss for loss in loss_dict_reduced.values()
          )
  
          loss_value = losses_reduced.item()
          # 记录训练损失
          mloss = (mloss * i + loss_value) / (
              i + 1
          )  # update mean losses
  
          if not math.isfinite(loss_value):  # 当计算的损失为无穷大时停止训练
              print("Loss is {}, stopping training".format(loss_value))
              print(loss_dict_reduced)
              sys.exit(1)
  
          optimizer.zero_grad()
          if scaler is not None:
              scaler.scale(losses).backward()
              scaler.step(optimizer)
              scaler.update()
          else:
              losses.backward()
              optimizer.step()
  
          if lr_scheduler is not None:  # 第一轮使用warmup训练方式
              lr_scheduler.step()
  
          metric_logger.update(loss=losses_reduced, **loss_dict_reduced)
          now_lr = optimizer.param_groups[0]["lr"]
          metric_logger.update(lr=now_lr)
  
      return mloss, now_lr
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/mask_rcnn/train_utils/train_eval_utils.py
imports: import math
  import sys
  import time
  import torch
  import train_utils.distributed_utils
  from .coco_eval import EvalCOCOMetric
label_desc: Use image node_id 1 for calling a global function with example usage: train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq, warmup, scaler) and returns: mloss,  now_lr