text: from __future__ import division, print_function, absolute_import
  
  from torchreid import metrics
  from torchreid.losses import CrossEntropyLoss
  
  from ..engine import Engine
  
  
  class ImageSoftmaxEngine(Engine):
      r"""Softmax-loss engine for image-reid.
  
      Args:
          datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
              or ``torchreid.data.VideoDataManager``.
          model (nn.Module): model instance.
          optimizer (Optimizer): an Optimizer.
          scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
          use_gpu (bool, optional): use gpu. Default is True.
          label_smooth (bool, optional): use label smoothing regularizer. Default is True.
  
      Examples::
          
          import torchreid
          datamanager = torchreid.data.ImageDataManager(
              root='path/to/reid-data',
              sources='market1501',
              height=256,
              width=128,
              combineall=False,
              batch_size=32
          )
          model = torchreid.models.build_model(
              name='resnet50',
              num_classes=datamanager.num_train_pids,
              loss='softmax'
          )
          model = model.cuda()
          optimizer = torchreid.optim.build_optimizer(
              model, optim='adam', lr=0.0003
          )
          scheduler = torchreid.optim.build_lr_scheduler(
              optimizer,
              lr_scheduler='single_step',
              stepsize=20
          )
          engine = torchreid.engine.ImageSoftmaxEngine(
              datamanager, model, optimizer, scheduler=scheduler
          )
          engine.run(
              max_epoch=60,
              save_dir='log/resnet50-softmax-market1501',
              print_freq=10
          )
      """
  
      def __init__(
          self,
          datamanager,
          model,
          optimizer,
          scheduler=None,
          use_gpu=True,
          label_smooth=True
      ):
          super(ImageSoftmaxEngine, self).__init__(datamanager, use_gpu)
  
          self.model = model
          self.optimizer = optimizer
          self.scheduler = scheduler
          self.register_model('model', model, optimizer, scheduler)
  
          self.criterion = CrossEntropyLoss(
              num_classes=self.datamanager.num_train_pids,
              use_gpu=self.use_gpu,
              label_smooth=label_smooth
          )
  
      def forward_backward(self, data):
          imgs, pids = self.parse_data_for_train(data)
  
          if self.use_gpu:
              imgs = imgs.cuda()
              pids = pids.cuda()
  
          outputs = self.model(imgs)
          loss = self.compute_loss(self.criterion, outputs, pids)
  
          self.optimizer.zero_grad()
          loss.backward()
          self.optimizer.step()
  
          loss_summary = {
              'loss': loss.item(),
              'acc': metrics.accuracy(outputs, pids)[0].item()
          }
  
          return loss_summary
  