text: import argparse
  import os
  import random
  import shutil
  import time
  import logging
  import warnings
  import json
  
  import torch
  import torch.nn as nn
  import torch.nn.parallel
  import torch.backends.cudnn as cudnn
  import torch.distributed as dist
  import torch.optim
  import torch.multiprocessing as mp
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms as transforms
  import torchvision.datasets as datasets
  import torchvision.models as models
  
  import utils.models.q_jettagger
  from bit_config import *
  from utils import *
  from pytorchcv.model_provider import get_model as ptcv_get_model
  
  from sklearn.metrics import top_k_accuracy_score, accuracy_score
  import train_funcs
  
  parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
  parser.add_argument('--data', metavar='DIR',
                      help='path to dataset')
  parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                      help='model architecture')
  parser.add_argument('--teacher-arch',
                      type=str,
                      default='resnet101',
                      help='teacher network used to do distillation')
  parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                      help='number of data loading workers (default: 4)')
  parser.add_argument('--epochs', default=90, type=int, metavar='N',
                      help='number of total epochs to run')
  parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                      help='manual epoch number (useful on restarts)')
  parser.add_argument('-b', '--batch-size', default=1, type=int,
                      metavar='N',
                      help='mini-batch size (default: 256), this is the total '
                           'batch size of all GPUs on the current node when '
                           'using Data Parallel or Distributed Data Parallel')
  parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                      metavar='LR', help='initial learning rate', dest='lr')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                      help='momentum')
  parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                      metavar='W', help='weight decay (default: 1e-4)',
                      dest='weight_decay')
  parser.add_argument('-p', '--print-freq', default=10, type=int,
                      metavar='N', help='print frequency (default: 10)')
  parser.add_argument('--resume', default='', type=str, metavar='PATH',
                      help='path to latest checkpoint (default: none)')
  parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                      help='evaluate model on validation set')
  parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                      help='use pre-trained model')
  parser.add_argument('--world-size', default=-1, type=int,
                      help='number of nodes for distributed training')
  parser.add_argument('--rank', default=-1, type=int,
                      help='node rank for distributed training')
  parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                      help='url used to set up distributed training')
  parser.add_argument('--dist-backend', default='nccl', type=str,
                      help='distributed backend')
  parser.add_argument('--seed', default=None, type=int,
                      help='seed for initializing training. ')
  parser.add_argument('--gpu', default=None, type=int,
                      help='GPU id to use.')
  parser.add_argument('--multiprocessing-distributed', action='store_true',
                      help='Use multi-processing distributed training to launch '
                           'N processes per node, which has N GPUs. This is the '
                           'fastest way to use PyTorch for either single node or '
                           'multi node data parallel training')
  parser.add_argument('--act-range-momentum',
                      type=float,
                      default=-1,
                      help='momentum of the activation range moving average, '
                           '-1 stands for using minimum of min and maximum of max')
  parser.add_argument('--quant-mode',
                      type=str,
                      default='symmetric',
                      choices=['asymmetric', 'symmetric'],
                      help='quantization mode')
  parser.add_argument('--save-path',
                      type=str,
                      default='checkpoints/imagenet/test/',
                      help='path to save the quantized model')
  parser.add_argument('--data-percentage',
                      type=float,
                      default=1,
                      help='data percentage of training data')
  parser.add_argument('--fix-BN',
                      action='store_true',
                      help='whether to fix BN statistics and fold BN during training')
  parser.add_argument('--fix-BN-threshold',
                      type=int,
                      default=None,
                      help='when to start training with fixed and folded BN,'
                           'after the threshold iteration, the original fix-BN will be overwritten to be True')
  parser.add_argument('--checkpoint-iter',
                      type=int,
                      default=-1,
                      help='the iteration that we save all the featuremap for analysis')
  parser.add_argument('--evaluate-times',
                      type=int,
                      default=-1,
                      help='The number of evaluations during one epoch')
  parser.add_argument('--quant-scheme',
                      type=str,
                      default='uniform4',
                      help='quantization bit configuration')
  parser.add_argument('--resume-quantize',
                      action='store_true',
                      help='if True map the checkpoint to a quantized model,'
                           'otherwise map the checkpoint to an ordinary model and then quantize')
  parser.add_argument('--act-percentile',
                      type=float,
                      default=0,
                      help='the percentage used for activation percentile'
                           '(0 means no percentile, 99.9 means cut off 0.1%)')
  parser.add_argument('--weight-percentile',
                      type=float,
                      default=0,
                      help='the percentage used for weight percentile'
                           '(0 means no percentile, 99.9 means cut off 0.1%)')
  parser.add_argument('--channel-wise',
                      action='store_false',
                      help='whether to use channel-wise quantizaiton or not')
  parser.add_argument('--bias-bit',
                      type=int,
                      default=32,
                      help='quantizaiton bit-width for bias')
  parser.add_argument('--distill-method',
                      type=str,
                      default='None',
                      help='you can choose None or KD_naive')
  parser.add_argument('--distill-alpha',
                      type=float,
                      default=0.95,
                      help='how large is the ratio of normal loss and teacher loss')
  parser.add_argument('--temperature',
                      type=float,
                      default=6,
                      help='how large is the temperature factor for distillation')
  parser.add_argument('--fixed-point-quantization',
                      action='store_true',
                      help='whether to skip deployment-oriented operations and '
                           'use fixed-point rather than integer-only quantization')
  parser.add_argument('--critoptoverride',
                      action='store_true',
                      help='hls4ml criterion and optimizer overrides (temp, shouldnt leave this)')
  best_acc1 = 0
  quantize_arch_dict = {'resnet50': q_resnet50, 'resnet50b': q_resnet50,
                        'resnet18': q_resnet18, 'resnet101': q_resnet101,
                        'inceptionv3': q_inceptionv3,
                        'mobilenetv2_w1': q_mobilenetv2_w1,
                        'hls4ml_jettagger': utils.models.q_jettagger.q_jettagger_model}
  args = parser.parse_args()
  if not os.path.exists(args.save_path):
      os.makedirs(args.save_path)
  
  hook_counter = args.checkpoint_iter
  hook_keys = []
  hook_keys_counter = 0
  
  logging.basicConfig(format='%(asctime)s - %(message)s',
                      datefmt='%d-%b-%y %H:%M:%S', filename=args.save_path + 'log.log')
  logging.getLogger().setLevel(logging.INFO)
  logging.getLogger().addHandler(logging.StreamHandler())
  
  logging.info(args)
  
  
  def main():
      if args.seed is not None:
          random.seed(args.seed)
          torch.manual_seed(args.seed)
          cudnn.deterministic = True
          warnings.warn('You have chosen to seed training. '
                        'This will turn on the CUDNN deterministic setting, '
                        'which can slow down your training considerably! '
                        'You may see unexpected behavior when restarting '
                        'from checkpoints.')
  
      if args.gpu is not None:
          warnings.warn('You have chosen a specific GPU. This will completely '
                        'disable data parallelism.')
  
      if args.dist_url == "env://" and args.world_size == -1:
          args.world_size = int(os.environ["WORLD_SIZE"])
  
      args.distributed = args.world_size > 1 or args.multiprocessing_distributed
  
      ngpus_per_node = torch.cuda.device_count()
      if args.multiprocessing_distributed:
          # Since we have ngpus_per_node processes per node, the total world_size
          # needs to be adjusted accordingly
          args.world_size = ngpus_per_node * args.world_size
          # Use torch.multiprocessing.spawn to launch distributed processes: the
          # main_worker process function
          mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
      else:
          # Simply call main_worker function
          main_worker(args.gpu, ngpus_per_node, args)
  
  
  def main_worker(gpu, ngpus_per_node, args):
      global best_acc1
      args.gpu = gpu
  
      if args.gpu is not None:
          logging.info("Use GPU: {} for training".format(args.gpu))
  
      if args.distributed:
          if args.dist_url == "env://" and args.rank == -1:
              args.rank = int(os.environ["RANK"])
          if args.multiprocessing_distributed:
              # For multiprocessing distributed training, rank needs to be the
              # global rank among all the processes
              args.rank = args.rank * ngpus_per_node + gpu
          dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                  world_size=args.world_size, rank=args.rank)
      # create model
      if args.pretrained and not args.resume:
          logging.info("=> using pre-trained PyTorchCV model '{}'".format(args.arch))
          model = ptcv_get_model(args.arch, pretrained=True)
          if args.distill_method != 'None':
              logging.info("=> using pre-trained PyTorchCV teacher '{}'".format(args.teacher_arch))
              teacher = ptcv_get_model(args.teacher_arch, pretrained=True)
      else:
          logging.info("=> creating PyTorchCV model '{}'".format(args.arch))
          try:
              model = ptcv_get_model(args.arch, pretrained=False)
              if args.distill_method != 'None':
                  logging.info("=> creating PyTorchCV teacher '{}'".format(args.teacher_arch))
                  teacher = ptcv_get_model(args.teacher_arch, pretrained=False)
          except:
              logging.info("=> Warning!! No PytorchCV Model found with arch '{}', assuming local model... (no dist support!!) ".format(args.arch))
              model, teacher = None, None
  
      if args.resume and not args.resume_quantize:
          if os.path.isfile(args.resume):
              logging.info("=> loading checkpoint '{}'".format(args.resume))
  
              checkpoint = torch.load(args.resume)['state_dict']
              model_key_list = list(model.state_dict().keys())
              for key in model_key_list:
                  if 'num_batches_tracked' in key: model_key_list.remove(key)
              i = 0
              modified_dict = {}
              for key, value in checkpoint.items():
                  if 'scaling_factor' in key: continue
                  if 'num_batches_tracked' in key: continue
                  if 'weight_integer' in key: continue
                  if 'min' in key or 'max' in key: continue
                  modified_key = model_key_list[i]
                  modified_dict[modified_key] = value
                  i += 1
              logging.info(model.load_state_dict(modified_dict, strict=False))
          else:
              logging.info("=> no checkpoint found at '{}'".format(args.resume))
  
      quantize_arch = quantize_arch_dict[args.arch]
      model = quantize_arch(model)
  
      #this might explode things? idk
      #model.double()
  
      bit_config = bit_config_dict["bit_config_" + args.arch + "_" + args.quant_scheme]
      name_counter = 0
  
      for name, m in model.named_modules():
          if name in bit_config.keys():
              name_counter += 1
              setattr(m, 'quant_mode', 'symmetric')
              setattr(m, 'bias_bit', args.bias_bit)
              setattr(m, 'quantize_bias', (args.bias_bit != 0))
              setattr(m, 'per_channel', args.channel_wise)
              setattr(m, 'act_percentile', args.act_percentile)
              setattr(m, 'act_range_momentum', args.act_range_momentum)
              setattr(m, 'weight_percentile', args.weight_percentile)
              setattr(m, 'fix_flag', False)
              setattr(m, 'fix_BN', args.fix_BN)
              setattr(m, 'fix_BN_threshold', args.fix_BN_threshold)
              setattr(m, 'training_BN_mode', args.fix_BN)
              setattr(m, 'checkpoint_iter_threshold', args.checkpoint_iter)
              setattr(m, 'save_path', args.save_path)
              setattr(m, 'fixed_point_quantization', args.fixed_point_quantization)
  
              if type(bit_config[name]) is tuple:
                  bitwidth = bit_config[name][0]
                  if bit_config[name][1] == 'hook':
                      m.register_forward_hook(hook_fn_forward)
                      global hook_keys
                      hook_keys.append(name)
              else:
                  bitwidth = bit_config[name]
  
              if hasattr(m, 'activation_bit'):
                  setattr(m, 'activation_bit', bitwidth)
                  if bitwidth == 4:
                      setattr(m, 'quant_mode', 'asymmetric')
              else:
                  setattr(m, 'weight_bit', bitwidth)
  
      logging.info("match all modules defined in bit_config: {}".format(len(bit_config.keys()) == name_counter))
      logging.info(model)
  
      if args.resume and args.resume_quantize:
          if os.path.isfile(args.resume):
              logging.info("=> loading quantized checkpoint '{}'".format(args.resume))
              checkpoint = torch.load(args.resume)['state_dict']
              modified_dict = {}
              for key, value in checkpoint.items():
                  if 'num_batches_tracked' in key: continue
                  if 'weight_integer' in key: continue
                  if 'bias_integer' in key: continue
  
                  modified_key = key.replace("module.", "")
                  modified_dict[modified_key] = value
              model.load_state_dict(modified_dict, strict=False)
          else:
              logging.info("=> no quantized checkpoint found at '{}'".format(args.resume))
  
      if args.distributed:
          # For multiprocessing distributed, DistributedDataParallel constructor
          # should always set the single device scope, otherwise,
          # DistributedDataParallel will use all available devices.
          if args.gpu is not None:
              torch.cuda.set_device(args.gpu)
              model.cuda(args.gpu)
              # When using a single GPU per process and per
              # DistributedDataParallel, we need to divide the batch size
              # ourselves based on the total number of GPUs we have
              args.batch_size = int(args.batch_size / ngpus_per_node)
              args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
              model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
              if args.distill_method != 'None':
                  teacher.cuda(args.gpu)
                  teacher = torch.nn.parallel.DistributedDataParallel(teacher, device_ids=[args.gpu])
          else:
              model.cuda()
              # DistributedDataParallel will divide and allocate batch_size to all
              # available GPUs if device_ids are not set
              model = torch.nn.parallel.DistributedDataParallel(model)
              if args.distill_method != 'None':
                  teacher.cuda()
                  teacher = torch.nn.parallel.DistributedDataParallel(teacher)
      elif args.gpu is not None:
          torch.cuda.set_device(args.gpu)
          model = model.cuda(args.gpu)
          if args.distill_method != 'None':
              teacher = teacher.cuda(args.gpu)
      else:
          # DataParallel will divide and allocate batch_size to all available GPUs
          if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
              model.features = torch.nn.DataParallel(model.features)
              model.cuda()
              # teacher is not alexnet or vgg
              if args.distill_method != 'None':
                  teacher = torch.nn.DataParallel(teacher).cuda()
          else:
              model = torch.nn.DataParallel(model).cuda()
              if args.distill_method != 'None':
                  teacher = torch.nn.DataParallel(teacher).cuda()
  
      # define loss function (criterion) and optimizer
      if args.critoptoverride:
          optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
          criterion = nn.BCELoss().cuda(args.gpu)
      else:
          criterion = nn.CrossEntropyLoss().cuda(args.gpu)
  
          optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                      momentum=args.momentum,
                                      weight_decay=args.weight_decay)
  
      # optionally resume optimizer and meta information from a checkpoint
      if args.resume:
          if os.path.isfile(args.resume):
              if args.gpu is None:
                  checkpoint = torch.load(args.resume)
              else:
                  # Map model to be loaded to specified single gpu.
                  loc = 'cuda:{}'.format(args.gpu)
                  checkpoint = torch.load(args.resume, map_location=loc)
              args.start_epoch = checkpoint['epoch']
              best_acc1 = checkpoint['best_acc1']
              if args.gpu is not None:
                  # best_acc1 may be from a checkpoint from a different GPU
                  best_acc1 = best_acc1.to(args.gpu)
              optimizer.load_state_dict(checkpoint['optimizer'])
              logging.info("=> loaded optimizer and meta information from checkpoint '{}' (epoch {})".
                           format(args.resume, checkpoint['epoch']))
          else:
              logging.info("=> no checkpoint found at '{}'".format(args.resume))
  
      cudnn.benchmark = True
  
  
  
  
      if args.arch == "hls4ml_jettagger":
          train_loader, val_loader, train_sampler = utils.getDataloaders("hls4ml_lhc_jets",
                                                              train_path="datasets/hls4ml_lhc_jets/train",
                                                              test_path="datasets/hls4ml_lhc_jets/test",
                                                              dist_sampler=args.distributed,
                                                              num_workers=1)
      else:
          train_resolution = 224
          if args.arch == "inceptionv3":
              train_resolution = 299
          # Data loading code
          traindir = os.path.join(args.data, 'train')
          valdir = os.path.join(args.data, 'val')
          normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                           std=[0.229, 0.224, 0.225])
          train_dataset = datasets.ImageFolder(
              traindir,
              transforms.Compose([
                  transforms.RandomResizedCrop(train_resolution),
                  transforms.RandomHorizontalFlip(),
                  transforms.ToTensor(),
                  normalize,
              ]))
  
          if args.distributed:
              train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
          else:
              train_sampler = None
  
          dataset_length = int(len(train_dataset) * args.data_percentage)
          if args.data_percentage == 1:
              train_loader = torch.utils.data.DataLoader(
                  train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
                  num_workers=args.workers, pin_memory=True, sampler=train_sampler)
          else:
              partial_train_dataset, _ = torch.utils.data.random_split(train_dataset,
                                                                       [dataset_length,
                                                                        len(train_dataset) - dataset_length])
              train_loader = torch.utils.data.DataLoader(
                  partial_train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
                  num_workers=args.workers, pin_memory=True, sampler=train_sampler)
  
          test_resolution = (256, 224)
          if args.arch == 'inceptionv3':
              test_resolution = (342, 299)
  
          # evaluate on validation set
          val_loader = torch.utils.data.DataLoader(
              datasets.ImageFolder(valdir, transforms.Compose([
                  transforms.Resize(test_resolution[0]),
                  transforms.CenterCrop(test_resolution[1]),
                  transforms.ToTensor(),
                  normalize,
              ])),
              batch_size=args.batch_size, shuffle=False,
              num_workers=args.workers, pin_memory=True)
  
      if args.evaluate:
          validate(val_loader, model, criterion, args)
          return
  
      best_epoch = 0
      '''
      for epoch in range(args.start_epoch, args.epochs):
          train_loss_meter = AverageMeter('Train Loss', ':.4e')
          val_loss_meter = AverageMeter('Valid Loss', ':.4e')
          top1 = AverageMeter('Acc@1', ':6.2f')
  
  
          model, train_losses = train_funcs.train(model, optimizer, criterion, train_loader, L1_factor=0,device='cuda:0')
  
          # Validate
          val_losses, val_avg_acc_list, val_roc_auc_scores_list = train_funcs.val(model, criterion, val_loader,
                                                                            L1_factor=0,device='cuda:0')
  
          # Calculate average epoch statistics
          try:
              train_loss = np.average(train_losses)
          except:
              train_loss = torch.mean(torch.stack(train_losses)).cpu().numpy()
  
          try:
              valid_loss = np.average(val_losses)
          except:
              valid_loss = torch.mean(torch.stack(val_losses)).cpu().numpy()
  
          val_roc_auc_score = np.average(val_roc_auc_scores_list)
          val_avg_acc = np.average(val_avg_acc_list)
  
          train_loss_meter.update(train_loss.tolist(),epoch+1)
          val_loss_meter.update(valid_loss.tolist(),epoch+1)
          top1.update(val_avg_acc,epoch+1)
  
          print('[epoch %d] train batch loss: %.7f' % (epoch + 1, train_loss))
          print('[epoch %d] val batch loss: %.7f' % (epoch + 1, valid_loss))
          print('[epoch %d] val ROC AUC Score: %.7f' % (epoch + 1, val_roc_auc_score))
          print('[epoch %d] val Avg Acc Score: %.7f' % (epoch + 1, val_avg_acc))
  
  
      '''
      #'''
      loss_record = list()
      acc_record = list()
      for epoch in range(args.start_epoch, args.epochs):
          if args.distributed:
              train_sampler.set_epoch(epoch)
          adjust_learning_rate(optimizer, epoch, args)
  
          # train for one epoch
          if args.distill_method != 'None':
              train_kd(train_loader, model, teacher, criterion, optimizer, epoch, val_loader,
                       args, ngpus_per_node, dataset_length)
          else:
              epoch_loss = train(train_loader, model, criterion, optimizer, epoch, args)
  
          acc1 = validate(val_loader, model, criterion, args)
  
          loss_record.append(epoch_loss)
          acc_record.append(acc1)
  
          # remember best acc@1 and save checkpoint
          is_best = acc1 > best_acc1
          best_acc1 = max(acc1, best_acc1)
  
          logging.info(f'Best acc at epoch {epoch}: {best_acc1}')
          if is_best:
              # record the best epoch
              best_epoch = epoch
  
          if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                      and args.rank % ngpus_per_node == 0):
              if not os.path.exists(args.save_path):
                  os.makedirs(args.save_path)
  
              save_checkpoint({
                  'epoch': epoch + 1,
                  'arch': args.arch,
                  'state_dict': model.state_dict(),
                  'best_acc1': best_acc1,
                  'optimizer': optimizer.state_dict(),
              }, is_best, args.save_path)
      filename = 'model_losses_{}_{}.json'.format(args.arch,args.quant_scheme)
      with open(os.path.join(args.save_path, filename), 'w') as fp:
          json.dump(loss_record, fp)
      filename = 'model_acc_{}_{}.json'.format(args.arch,args.quant_scheme)
      with open(os.path.join(args.save_path, filename), 'w') as fp:
          json.dump(acc_record, fp)
      import post_train_plots
      post_train_plots.plot_total_loss([model],[loss_record],[[best_epoch]])
      post_train_plots.plot_total_acc([model], [acc_record], [[best_epoch]])
  
  
  def train(train_loader, model, criterion, optimizer, epoch, args):
      batch_time = AverageMeter('Time', ':6.3f')
      data_time = AverageMeter('Data', ':6.3f')
      losses = AverageMeter('Loss', ':.4e')
      top1 = AverageMeter('Acc@1', ':6.2f')
      #top5 = AverageMeter('Acc@5', ':6.2f')
      progress = ProgressMeter(
          len(train_loader),
          [batch_time, data_time, losses, top1],#, top5],
          prefix="Epoch: [{}]".format(epoch))
  
      # switch to train mode
      if args.fix_BN == True:
          model.eval()
      else:
          model.train()
  
      end = time.time()
      for i, (images, target) in enumerate(train_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          if args.gpu is not None:
              images = images.cuda(args.gpu, non_blocking=True)
          target = target.cuda(args.gpu, non_blocking=True)
  
          # compute output
          output = model(images.float())
          loss = criterion(output, target.float())
  
          #print(target.size())
          #print(np.argmax(output.cpu(), axis=1).size())
  
          # measure accuracy and record loss
          #images = images.cpu().numpy()
          #target = target.cpu().numpy()
          #output = output.cpu().numpy()
          #_, preds = torch.max(output, 1)
          #truth = torch.max(target, 1)[1].view(-1).cpu()
          #acc1 = accuracy_score(truth,preds.view(-1))
          #acc5 = top_k_accuracy_score(target,output,k=5)
          #acc1, acc5 = accuracy(output, target, topk=(1,))#, 5))
          losses.update(loss.item(), images.size(0))
          #top1.update(acc1, images.size(0))
          #top5.update(acc5, images.size(0))
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if i % args.print_freq == 0:
              progress.display(i)
      return losses.avg
  
  def train_kd(train_loader, model, teacher, criterion, optimizer, epoch, val_loader, args, ngpus_per_node,
               dataset_length):
      batch_time = AverageMeter('Time', ':6.3f')
      data_time = AverageMeter('Data', ':6.3f')
      losses = AverageMeter('Loss', ':.4e')
      top1 = AverageMeter('Acc@1', ':6.2f')
      top5 = AverageMeter('Acc@5', ':6.2f')
      progress = ProgressMeter(
          len(train_loader),
          [batch_time, data_time, losses, top1, top5],
          prefix="Epoch: [{}]".format(epoch))
  
      # switch to train mode
      if args.fix_BN == True:
          model.eval()
      else:
          model.train()
      teacher.eval()
  
      end = time.time()
  
      for i, (images, target) in enumerate(train_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          if args.gpu is not None:
              images = images.cuda(args.gpu, non_blocking=True)
          target = target.cuda(args.gpu, non_blocking=True)
  
          # compute output
          output = model(images)
          if args.distill_method != 'None':
              with torch.no_grad():
                  teacher_output = teacher(images)
  
          if args.distill_method == 'None':
              loss = criterion(output, target)
              loss += train_funcs.l1_regularizer(model)
          elif args.distill_method == 'KD_naive':
              loss = loss_kd(output, target, teacher_output, args)
          else:
              raise NotImplementedError
  
          # measure accuracy and record loss
          acc1, acc5 = accuracy(output, target, topk=(1, 5))
          losses.update(loss.item(), images.size(0))
          top1.update(acc1[0], images.size(0))
          top5.update(acc5[0], images.size(0))
  
          # compute gradient and do SGD step
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if i % args.print_freq == 0:
              progress.display(i)
          if i % args.print_freq == 0 and args.rank == 0:
              print('Epoch {epoch_} [{iters}]  Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(epoch_=epoch, iters=i,
                                                                                                 top1=top1, top5=top5))
  
          if i % ((dataset_length // (
                  args.batch_size * args.evaluate_times)) + 2) == 0 and i > 0 and args.evaluate_times > 0:
              acc1 = validate(val_loader, model, criterion, args)
  
              # switch to train mode
              if args.fix_BN == True:
                  model.eval()
              else:
                  model.train()
  
              # remember best acc@1 and save checkpoint
              global best_acc1
              is_best = acc1 > best_acc1
              best_acc1 = max(acc1, best_acc1)
  
              if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                          and args.rank % ngpus_per_node == 0):
                  if not os.path.exists(args.save_path):
                      os.makedirs(args.save_path)
  
                  save_checkpoint({
                      'epoch': epoch + 1,
                      'arch': args.arch,
                      'state_dict': model.state_dict(),
                      'best_acc1': best_acc1,
                      'optimizer': optimizer.state_dict(),
                  }, is_best, args.save_path)
  
  
  def validate(val_loader, model, criterion, args):
      batch_time = AverageMeter('Time', ':6.3f')
      losses = AverageMeter('Loss', ':.4e')
      top1 = AverageMeter('Acc@1', ':6.2f')
      top5 = AverageMeter('Acc@5', ':6.2f')
      progress = ProgressMeter(
          len(val_loader),
          [batch_time, losses, top1, top5],
          prefix='Test: ')
  
      # switch to evaluate mode
      freeze_model(model)
      model.eval()
  
      predlist = torch.zeros(0, dtype=torch.long, device='cpu')
      lbllist = torch.zeros(0, dtype=torch.long, device='cpu')
  
      with torch.no_grad():
          end = time.time()
          for i, (images, target) in enumerate(val_loader):
              if args.gpu is not None:
                  images = images.cuda(args.gpu, non_blocking=True)
              target = target.cuda(args.gpu, non_blocking=True)
  
              # compute output
              output = model(images.float())
              loss = criterion(output, target.float())
  
              # measure accuracy and record loss
              #acc1, acc5 = accuracy(output, target, topk=(1, 5))
              losses.update(loss.item(), images.size(0))
              #top1.update(acc1[0], images.size(0))
              #top5.update(acc5[0], images.size(0))
  
              _, preds = torch.max(output, 1)
              #print(target.size())
              predlist = torch.cat([predlist, preds.view(-1).cpu()])
              lbllist = torch.cat([lbllist, torch.max(target, 1)[1].view(-1).cpu()])
              # measure elapsed time
              batch_time.update(time.time() - end)
              end = time.time()
  
              if i % args.print_freq == 0:
                  progress.display(i)
          outputs = output.cpu()
          local_labels = target.cpu()
          predict_test = outputs.numpy()
          accuracy_value = accuracy_score(np.nan_to_num(lbllist.numpy()), np.nan_to_num(predlist.numpy()))
          top1.update(accuracy_value, 1)
          logging.info(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1, top5=top5))
  
      torch.save({'convbn_scaling_factor': {k: v for k, v in model.state_dict().items() if 'convbn_scaling_factor' in k},
                  'fc_scaling_factor': {k: v for k, v in model.state_dict().items() if 'fc_scaling_factor' in k},
                  'weight_integer': {k: v for k, v in model.state_dict().items() if 'weight_integer' in k},
                  'bias_integer': {k: v for k, v in model.state_dict().items() if 'bias_integer' in k},
                  'act_scaling_factor': {k: v for k, v in model.state_dict().items() if 'act_scaling_factor' in k},
                  }, args.save_path + 'quantized_checkpoint.pth.tar')
      torch.save({'convbn_scaling_factor': {k: v for k, v in model.state_dict().items() if 'convbn_scaling_factor' in k},
                  'fc_scaling_factor': {k: v for k, v in model.state_dict().items() if 'fc_scaling_factor' in k},
                  'weight_integer': {k: v for k, v in model.state_dict().items() if 'weight_integer' in k},
                  'bias_integer': {k: v for k, v in model.state_dict().items() if 'bias_integer' in k},
                  'act_scaling_factor': {k: v for k, v in model.state_dict().items() if 'act_scaling_factor' in k},
                  }, args.save_path + 'quantized_checkpoint.pth')
      unfreeze_model(model)
  
      return accuracy_value #top1.avg
  
  
  def save_checkpoint(state, is_best, filename=None):
      torch.save(state, filename + 'checkpoint.pth.tar')
      if is_best:
          shutil.copyfile(filename + 'checkpoint.pth.tar', filename + 'model_best.pth.tar')
  
  
  
  class AverageMeter(object):
      """Computes and stores the average and current value"""
  
      def __init__(self, name, fmt=':f'):
          self.name = name
          self.fmt = fmt
          self.reset()
          self.list = list()
  
      def reset(self):
          self.val = 0
          self.avg = 0
          self.sum = 0
          self.count = 0
  
      def update(self, val, n=1):
          self.val = val
          self.sum += val * n
          self.count += n
          self.avg = self.sum / self.count
          self.list.append((self.val,self.count))
  
      def __str__(self):
          fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
          return fmtstr.format(**self.__dict__)
  
  
  class ProgressMeter(object):
      def __init__(self, num_batches, meters, prefix=""):
          self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
          self.meters = meters
          self.prefix = prefix
  
      def display(self, batch):
          entries = [self.prefix + self.batch_fmtstr.format(batch)]
          entries += [str(meter) for meter in self.meters]
          logging.info('\t'.join(entries))
  
      def _get_batch_fmtstr(self, num_batches):
          num_digits = len(str(num_batches // 1))
          fmt = '{:' + str(num_digits) + 'd}'
          return '[' + fmt + '/' + fmt.format(num_batches) + ']'
  
  
  def adjust_learning_rate(optimizer, epoch, args):
      """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
      lr = args.lr * (0.1 ** (epoch // 30))
      print('lr = ', lr)
      for param_group in optimizer.param_groups:
          param_group['lr'] = lr
  
  
  def accuracy(output, target, topk=(1,)):
      """Computes the accuracy over the k top predictions for the specified values of k"""
      with torch.no_grad():
          maxk = max(topk)
          batch_size = target.size(0)
  
          _, pred = output.topk(maxk, 1, True, True)
          pred.t()
          pred = pred.t()
          correct = pred.eq(target.view(1, -1).expand_as(pred))
          #correct = pred.eq(target.t().expand_as(pred))
  
          res = []
          for k in topk:
              correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
              res.append(correct_k.mul_(100.0 / batch_size))
          return res
  
  
  def loss_kd(output, target, teacher_output, args):
      """
      Compute the knowledge-distillation (KD) loss given outputs and labels.
      "Hyperparameters": temperature and alpha
      The KL Divergence for PyTorch comparing the softmaxs of teacher and student.
      The KL Divergence expects the input tensor to be log probabilities.
      """
      alpha = args.distill_alpha
      T = args.temperature
      KD_loss = F.kl_div(F.log_softmax(output / T, dim=1), F.softmax(teacher_output / T, dim=1)) * (alpha * T * T) + \
                F.cross_entropy(output, target) * (1. - alpha)
  
      return KD_loss
  
  
  if __name__ == '__main__':
      main()
  