text: #!/usr/bin/env python
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  import argparse
  import builtins
  import math
  import os
  import random
  import shutil
  import time
  import warnings
  import pandas as pd
  from sklearn.feature_extraction.text import TfidfVectorizer
  from PIL import Image
  import numpy as np
  from tqdm import tqdm
  
  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 torch.utils.data as data
  
  import moco.loader
  import moco.builder
  
  model_names = sorted(name for name in models.__dict__
      if name.islower() and not name.startswith("__")
      and callable(models.__dict__[name]))
  
  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='resnet50',
                      choices=model_names,
                      help='model architecture: ' +
                          ' | '.join(model_names) +
                          ' (default: resnet50)')
  parser.add_argument('-j', '--workers', default=0, type=int, metavar='N',
                      help='number of data loading workers (default: 32)')
  parser.add_argument('--epochs', default=50, 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=256, 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=0.03, type=float,
                      metavar='LR', help='initial learning rate', dest='lr')
  parser.add_argument('--schedule', default=[120, 160], nargs='*', type=int,
                      help='learning rate schedule (when to drop lr by 10x)')
  parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                      help='momentum of SGD solver')
  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('--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')
  
  # moco specific configs:
  parser.add_argument('--moco-dim', default=128, type=int,
                      help='feature dimension (default: 128)')
  parser.add_argument('--moco-k', default=8192, type=int,
                      help='queue size; number of negative keys (default: 65536)')
  parser.add_argument('--moco-m', default=0.999, type=float,
                      help='moco momentum of updating key encoder (default: 0.999)')
  parser.add_argument('--moco-t', default=0.07, type=float,
                      help='softmax temperature (default: 0.07)')
  
  # options for moco v2
  parser.add_argument('--mlp', action='store_true',
                      help='use mlp head')
  parser.add_argument('--aug-plus', action='store_true',
                      help='use moco v2 data augmentation')
  parser.add_argument('--cos', action='store_true',
                      help='use cosine lr schedule')
  
  
  class product_image_retrieval(data.Dataset):
      def __init__(self, root, train, debug=False, transform=None):
          self.root = root
          self.images = []
          self.labels = []
          self.transform = transform
  
          if train:
              csv_file = os.path.join(self.root, 'splitted', 'train.csv')
          else:
              csv_file = os.path.join(self.root, 'splitted', 'test.csv')
  
          data = pd.read_csv(csv_file)
          if debug:
              data = data.iloc[:20]
          corpus = list(data['title'])
          vector = TfidfVectorizer()
          print('preparing tfidf...')
          tfidf = vector.fit_transform(corpus)
          self.weightlist = tfidf.toarray()
          print('tfidf ready...')
          data['image'] = data['image'].apply(lambda image: os.path.join(self.root, 'train_images', image))
          vc = list(set(data['label_group']))
          vc.sort()
          group2label = dict(zip(vc, range(len(vc))))
          import operator
          self.images = list(data['image'])
          self.labels = operator.itemgetter(*list(data['label_group']))(group2label)
          data['label_group'] = self.labels
          self.data = data
          group = []
          print('preparing data...')
          for i in tqdm(range(len(self.images))):
              sub_group_dict = {}
              subgroup = self.data[self.data['label_group'].isin([self.labels[i]])]['image'].tolist()
              subindex = []
              for j in range(len(subgroup)):
                  subindex.append(self.data[self.data['image'] == subgroup[j]].index.tolist()[0])
              sub_group_dict['name'] = subgroup
              sub_group_dict['index'] = subindex
              group.append(sub_group_dict)
          self.group = group
  
      def __getitem__(self, index):
          image_path = self.images[index]
          img = Image.open(image_path).convert('RGB')
          target = int(self.labels[index])
          title = self.weightlist[index]
  
          sg = self.group[index]['name']
          random_idx = random.randint(0, len(sg)-1)
          sg_path = sg[random_idx]
          sg_img = Image.open(sg_path).convert('RGB')
          sg_index = self.group[index]['index'][random_idx]
          sg_title = self.weightlist[sg_index]
  
          if self.transform is not None:
              img = self.transform(img)
              sg_img = self.transform(sg_img)
  
          sample = {'img': img, 'sg_img': sg_img, 'title': title, 'sg_title': sg_title, 'target': target}  # 根据图片和标签创建字典
  
          return sample
  
      def __len__(self):
          return len(self.labels)
  
  
  def main():
      args = parser.parse_args()
  
      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):
      args.gpu = gpu
  
      # suppress printing if not master
      if args.multiprocessing_distributed and args.gpu != 0:
          def print_pass(*args):
              pass
          builtins.print = print_pass
  
      if args.gpu is not None:
          print("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
      print("=> creating model '{}'".format(args.arch))
      model = moco.builder.MoCo(
          models.__dict__[args.arch],
          args.moco_dim, args.moco_k, args.moco_m, args.moco_t, args.mlp)
      print(model)
  
      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])
          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)
      elif args.gpu is not None:
          torch.cuda.set_device(args.gpu)
          model = model.cuda(args.gpu)
          # comment out the following line for debugging
          raise NotImplementedError("Only DistributedDataParallel is supported.")
      else:
          # AllGather implementation (batch shuffle, queue update, etc.) in
          # this code only supports DistributedDataParallel.
          raise NotImplementedError("Only DistributedDataParallel is supported.")
  
      # define loss function (criterion) and optimizer
      criterion = nn.CrossEntropyLoss().cuda(args.gpu)
  
      optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                  momentum=args.momentum,
                                  weight_decay=args.weight_decay)
  
      # optionally resume from a checkpoint
      if args.resume:
          if os.path.isfile(args.resume):
              print("=> loading checkpoint '{}'".format(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']
              model.load_state_dict(checkpoint['state_dict'])
              optimizer.load_state_dict(checkpoint['optimizer'])
              print("=> loaded checkpoint '{}' (epoch {})"
                    .format(args.resume, checkpoint['epoch']))
          else:
              print("=> no checkpoint found at '{}'".format(args.resume))
  
      cudnn.benchmark = True
  
      # Data loading code
      traindir = args.data
      normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                       std=[0.229, 0.224, 0.225])
      if args.aug_plus:
          # MoCo v2's aug: similar to SimCLR https://arxiv.org/abs/2002.05709
          augmentation = [
              transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
              transforms.RandomApply([
                  transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)  # not strengthened
              ], p=0.8),
              transforms.RandomGrayscale(p=0.2),
              transforms.RandomApply([moco.loader.GaussianBlur([.1, 2.])], p=0.5),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              normalize
          ]
      else:
          # MoCo v1's aug: the same as InstDisc https://arxiv.org/abs/1805.01978
          augmentation = [
              transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
              transforms.RandomGrayscale(p=0.2),
              transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              normalize
          ]
  
      train_dataset = product_image_retrieval(
          traindir,
          True,
          transform=transforms.Compose(augmentation))
  
      if args.distributed:
          train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
      else:
          train_sampler = None
  
      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, drop_last=True)
  #     print('data loaded')
      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
  #         print('training')
          train(train_loader, model, criterion, optimizer, epoch, args)
          # if not os.path.exists('./expr/checkpoints/'):
          #     os.makedirs('./expr/checkpoints/')
  
          if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                  and args.rank % ngpus_per_node == 0):
              save_checkpoint({
                  'epoch': epoch + 1,
                  'arch': args.arch,
                  'state_dict': model.state_dict(),
                  'optimizer' : optimizer.state_dict(),
              }, is_best=False, filename='./expr/checkpoints/checkpoint_{:04d}.pth.tar'.format(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
      model.train()
  
      end = time.time()
  #     print('training data loading')
      for i, samples in enumerate(train_loader):
          # measure data loading time
          data_time.update(time.time() - end)
  
          if args.gpu is not None:
              img = samples['img'].cuda(args.gpu, non_blocking=True)
              sg_img = samples['sg_img'].cuda(args.gpu, non_blocking=True)
              title = samples['title'].cuda(args.gpu, non_blocking=True)
              sg_title = samples['sg_title'].cuda(args.gpu, non_blocking=True)
              target = samples['target'].cuda(args.gpu, non_blocking=True)
  #         print('batch samples loaded')
          # compute output
          output, target = model(im_q=img, im_k=sg_img, title=title, sg_title=sg_title, target=target)
  #         print('samples forward done')
          loss = criterion(output, target)
  
          # acc1/acc5 are (K+1)-way contrast classifier accuracy
          # measure accuracy and record loss
          acc1, acc5 = accuracy(output, target, topk=(1, 5))
          losses.update(loss.item(), img.size(0))
          top1.update(acc1[0], img.size(0))
          top5.update(acc5[0], img.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)
  
  
  def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
      torch.save(state, filename)
      if is_best:
          shutil.copyfile(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()
  
      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
  
      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]
          print('\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):
      """Decay the learning rate based on schedule"""
      lr = args.lr
      if args.cos:  # cosine lr schedule
          lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))
      else:  # stepwise lr schedule
          for milestone in args.schedule:
              lr *= 0.1 if epoch >= milestone else 1.
      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 = pred.t()
          correct = pred.eq(target.view(1, -1).expand_as(pred))
  
          res = []
          for k in topk:
              correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
              res.append(correct_k.mul_(100.0 / batch_size))
          return res
  
  
  if __name__ == '__main__':
      main()
  