text: import numpy as np
  import torch
  from torchvision.utils import make_grid
  from base import BaseTrainer
  from utils import inf_loop, MetricTracker
  from sklearn.metrics import confusion_matrix
  import matplotlib.pyplot as plt
  import seaborn as sns
  
  
  class Trainer(BaseTrainer):
      """
      Trainer class
      """
  
      def __init__(self, model, criterion, metric_ftns, optimizer, config, device,
                   data_loader, valid_data_loader=None, lr_scheduler=None, len_epoch=None):
          super().__init__(model, criterion, metric_ftns, optimizer, config)
          self.config = config
          self.device = device
          self.data_loader = data_loader
          if len_epoch is None:
              # epoch-based training
              self.len_epoch = len(self.data_loader)
          else:
              # iteration-based training
              self.data_loader = inf_loop(data_loader)
              self.len_epoch = len_epoch
          self.valid_data_loader = valid_data_loader
          self.do_validation = self.valid_data_loader is not None
          self.lr_scheduler = lr_scheduler
          self.log_step = int(np.sqrt(data_loader.batch_size))
  
          self.train_metrics = MetricTracker('loss', *[m.__name__ for m in self.metric_ftns], writer=self.writer)
          self.valid_metrics = MetricTracker('loss', *[m.__name__ for m in self.metric_ftns], writer=self.writer)
  
      def _train_epoch(self, epoch):
          """
          Training logic for an epoch
  
          :param epoch: Integer, current training epoch.
          :return: A log that contains average loss and metric in this epoch.
          """
          self.model.train()
          self.train_metrics.reset()
          for batch_idx, (data, target) in enumerate(self.data_loader):
              data, target = data.to(self.device), target.to(self.device)
  
              self.optimizer.zero_grad()
              output = self.model(data)
              loss = self.criterion(output, target)
              loss.backward()
              self.optimizer.step()
  
              self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
              self.train_metrics.update('loss', loss.item())
              for met in self.metric_ftns:
                  self.train_metrics.update(met.__name__, met(output, target))
  
              if batch_idx % self.log_step == 0:
                  self.logger.debug('Train Epoch: {} {} Loss: {:.6f}'.format(
                      epoch,
                      self._progress(batch_idx),
                      loss.item()))
                  self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
  
              if batch_idx == self.len_epoch:
                  break
          log = self.train_metrics.result()
  
          if self.do_validation:
              val_log = self._valid_epoch(epoch)
              log.update(**{'val_' + k: v for k, v in val_log.items()})
  
          if self.lr_scheduler is not None:
              self.lr_scheduler.step()
          return log
  
      def _valid_epoch(self, epoch):
          """
          Validate after training an epoch
  
          :param epoch: Integer, current training epoch.
          :return: A log that contains information about validation
          """
          self.model.eval()
          self.valid_metrics.reset()
          with torch.no_grad():
              for batch_idx, (data, target) in enumerate(self.valid_data_loader):
                  data, target = data.to(self.device), target.to(self.device)
  
                  output = self.model(data)
                  loss = self.criterion(output, target)
  
                  self.writer.set_step((epoch - 1) * len(self.valid_data_loader) + batch_idx, 'valid')
                  self.valid_metrics.update('loss', loss.item())
                  for met in self.metric_ftns:
                      self.valid_metrics.update(met.__name__, met(output, target))
                  self.writer.add_image('input', make_grid(data.cpu(), nrow=8, normalize=True))
  
                  # Confusion Matrix
                  conf_mat = confusion_matrix(
                      target.cpu(), torch.argmax(output, dim=1).cpu())
                  fig = plt.figure()
                  sns.heatmap(conf_mat, cmap=plt.cm.Blues, annot=True, fmt='g')
                  plt.show()
                  plt.xlabel('predicted label')
                  plt.ylabel('true label')
                  self.writer.add_figure('fig', fig)
  
          # add histogram of model parameters to the tensorboard
          for name, p in self.model.named_parameters():
              self.writer.add_histogram(name, p, bins='auto')
          return self.valid_metrics.result()
  
      def _progress(self, batch_idx):
          base = '[{}/{} ({:.0f}%)]'
          if hasattr(self.data_loader, 'n_samples'):
              current = batch_idx * self.data_loader.batch_size
              total = self.data_loader.n_samples
          else:
              current = batch_idx
              total = self.len_epoch
          return base.format(current, total, 100.0 * current / total)
  