tmp

 def test(test_loader_q, test_loader_g, model, epoch):

      print('>> Evaluating network on test datasets...')

      batch_time = AverageMeter()

      data_time = AverageMeter()

      end = time.time()

      model.eval()

      dataset_q = []

      cluster_q = []

      for step, (x, lbl, _) in enumerate(test_loader_q):

          batch_time.update(time.time() - end)

          end = time.time()

          x = x.cuda()

          x = x.contiguous()

 

          with torch.no_grad():

              vec, _, _ = model(x)

 

 

          dataset_q.extend(vec.unsqueeze(0))

          cluster_q.extend(lbl.cpu().numpy())

 

          if step % 100 == 0:

              print('>> Test: [{0}][{1}/{2}]\t'

                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'

                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'

                  .format(

                  epoch+1, step+1, len(test_loader_q), batch_time=batch_time,

                  data_time=data_time))

 

 

 

      dataset_q = torch.cat(dataset_q, dim = 0)

 

      dataset_g = []

      cluster_g = []

      for step, (x, lbl, _) in enumerate(test_loader_g):

          batch_time.update(time.time() - end)

          end = time.time()

          x = x.cuda()

          x = x.contiguous()

 

          with torch.no_grad():

              vec, _, _ = model(x)

 

 

          dataset_g.extend(vec.unsqueeze(0))

          cluster_g.extend(lbl.cpu().numpy())

 

          if step % 100 == 0:

             print('>> Test: [{0}][{1}/{2}]\t'

                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'

                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'

                  .format(

                  epoch+1, step+1, len(test_loader_g), batch_time=batch_time,

                  data_time=data_time))

 

 

 

      dataset_g = torch.cat(dataset_g, dim = 0)

      mAP,recall = Test(dataset_q,dataset_g,cluster_q,cluster_g)

 

 

      return 0, 0, mAP, recall