text: # Copyright (c) 2015-present, Facebook, Inc.
  # All rights reserved.
  """
  Train and eval functions used in main.py
  """
  import math
  import sys
  from typing import Iterable, Optional
  
  import torch
  
  from timm.data import Mixup
  from timm.utils import accuracy, ModelEma
  
  from losses import DistillationLoss
  import utils
  
  
  def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss,
                      data_loader: Iterable, optimizer: torch.optim.Optimizer,
                      device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
                      model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
                      set_training_mode=True):
      model.train(set_training_mode)
      metric_logger = utils.MetricLogger(delimiter="  ")
      metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
      header = 'Epoch: [{}]'.format(epoch)
      print_freq = 10
  
      for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
          samples = samples.to(device, non_blocking=True)
          targets = targets.to(device, non_blocking=True)
  
          if mixup_fn is not None:
              samples, targets = mixup_fn(samples, targets)
  
          with torch.cuda.amp.autocast():
              outputs = model(samples)
              loss = criterion(samples, outputs, targets)
  
          loss_value = loss.item()
  
          if not math.isfinite(loss_value):
              print("Loss is {}, stopping training".format(loss_value))
              sys.exit(1)
  
          optimizer.zero_grad()
  
          # this attribute is added by timm on one optimizer (adahessian)
          is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
          loss_scaler(loss, optimizer, clip_grad=max_norm,
                      parameters=model.parameters(), create_graph=is_second_order)
  
          torch.cuda.synchronize()
          if model_ema is not None:
              model_ema.update(model)
  
          metric_logger.update(loss=loss_value)
          metric_logger.update(lr=optimizer.param_groups[0]["lr"])
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print("Averaged stats:", metric_logger)
      return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
  
  
  # To Be delete
  def train_one_epoch_test(model: torch.nn.Module, criterion: DistillationLoss,
                      data_loader: Iterable, optimizer: torch.optim.Optimizer,
                      device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
                      model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
                      set_training_mode=True):
      model.train(set_training_mode)
      metric_logger = utils.MetricLogger(delimiter="  ")
      metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
      header = 'Epoch: [{}]'.format(epoch)
      print_freq = 10
  
      scaler = torch.cuda.amp.GradScaler()
  
      for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
          samples = samples.to(device, non_blocking=True)
          targets = targets.to(device, non_blocking=True)
  
          if mixup_fn is not None:
              samples, targets = mixup_fn(samples, targets)
  
          with torch.cuda.amp.autocast():
              outputs = model(samples)
              loss = criterion(samples, outputs, targets)
  
          loss_value = loss.item()
  
          if not math.isfinite(loss_value):
              print("Loss is {}, stopping training".format(loss_value))
              sys.exit(1)
  
          optimizer.zero_grad()
          scaler.scale(loss).backward()
  
  
          # show gradient sparsity 
          weight_zero = 0
          grad_zero = 0
          elements = 0
  
          for name, p in model.named_parameters():
  
              mask_name = name+'_mask'
              if mask_name in model.state_dict().keys():
                  if p.requires_grad:
                      wzero = (model.state_dict()[mask_name] == 0).float().sum().item()
                      gzero = (p.grad == 0).float().sum().item()
                      ele = p.grad.nelement()
                      assert ele == model.state_dict()[mask_name].nelement()
                      grad_zero += gzero
                      weight_zero += wzero
                      elements += ele 
                      print(name, 'gradient sparsity = {:.2f}%, weight sparsity = {:.2f}%'.format(100*gzero/ele, 100*wzero/ele))
  
          print('total gradient sparsity = {:.2f}%, total weight sparsity = {:.2f}%, all-element = {}'.format(100*grad_zero/elements, 100*weight_zero/elements, elements))
  
          assert False 
  
  
          # this attribute is added by timm on one optimizer (adahessian)
          is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
          loss_scaler(loss, optimizer, clip_grad=max_norm,
                      parameters=model.parameters(), create_graph=is_second_order)
  
          torch.cuda.synchronize()
          if model_ema is not None:
              model_ema.update(model)
  
          metric_logger.update(loss=loss_value)
          metric_logger.update(lr=optimizer.param_groups[0]["lr"])
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print("Averaged stats:", metric_logger)
      return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
  
  
  
  
  @torch.no_grad()
  def evaluate(data_loader, model, device):
      criterion = torch.nn.CrossEntropyLoss()
  
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = 'Test:'
  
      # switch to evaluation mode
      model.eval()
  
      for images, target in metric_logger.log_every(data_loader, 10, header):
          images = images.to(device, non_blocking=True)
          target = target.to(device, non_blocking=True)
  
          # compute output
          with torch.cuda.amp.autocast():
              output = model(images)
              loss = criterion(output, target)
  
          acc1, acc5 = accuracy(output, target, topk=(1, 5))
  
          batch_size = images.shape[0]
          metric_logger.update(loss=loss.item())
          metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
          metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
            .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
  
      return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
  