code: def train_one_epoch(
      model,
      optimizer,
      data_loader,
      device,
      epoch,
      print_freq=50,
      warmup=False,
  ):
      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 = 5.0 / 10000
          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)
      ):
          # batch inputs information
          images = torch.stack(images, dim=0)
  
          boxes = []
          labels = []
          img_id = []
          for t in targets:
              boxes.append(t["boxes"])
              labels.append(t["labels"])
              img_id.append(t["image_id"])
          targets = {
              "boxes": torch.stack(boxes, dim=0),
              "labels": torch.stack(labels, dim=0),
              "image_id": torch.as_tensor(img_id),
          }
  
          images = images.to(device)
  
          targets = {k: v.to(device) for k, v in targets.items()}
          losses_dict = model(images, targets)
          losses = losses_dict["total_losses"]
  
          # reduce losses over all GPUs for logging purpose
          losses_dict_reduced = utils.reduce_dict(losses_dict)
          losses_reduce = losses_dict_reduced["total_losses"]
  
          loss_value = losses_reduce.detach()
          # 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(losses_dict_reduced)
              sys.exit(1)
  
          optimizer.zero_grad()
          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, **loss_dict_reduced)
          metric_logger.update(**losses_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/ssd/train_utils/train_eval_utils.py
imports: import math
  import sys
  import time
  import torch
  from train_utils import get_coco_api_from_dataset, CocoEvaluator
  import train_utils.distributed_utils
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) and returns: mloss,  now_lr