diff --git a/fastreid/evaluation/pair_evaluator.py b/fastreid/evaluation/pair_evaluator.py
index 989a65d..4837581 100644
--- a/fastreid/evaluation/pair_evaluator.py
+++ b/fastreid/evaluation/pair_evaluator.py
@@ -22,11 +22,11 @@ class PairEvaluator(DatasetEvaluator):
         self._output_dir = output_dir
         self._cpu_device = torch.device('cpu')
         self._predictions = []
-        self._threshold_list = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98] 
+        self._threshold_list = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98]
 
     def reset(self):
         self._predictions = []
-    
+
     def process(self, inputs, outputs):
         embedding = outputs['features'].to(self._cpu_device)
         embedding = embedding.view(embedding.size(0) * 2, -1)
@@ -41,31 +41,31 @@ class PairEvaluator(DatasetEvaluator):
             'labels': inputs["targets"].to(self._cpu_device).numpy()
         }
         self._predictions.append(prediction)
-    
+
     def evaluate(self):
         if comm.get_world_size() > 1:
             comm.synchronize()
             predictions = comm.gather(self._predictions, dst=0)
             predictions = list(itertools.chain(*predictions))
 
-            if not comm.is_main_process(): 
+            if not comm.is_main_process():
                 return {}
         else:
             predictions = self._predictions
-        
+
         all_distances = []
         all_labels = []
         for prediction in predictions:
             all_distances.append(prediction['distances'])
             all_labels.append(prediction['labels'])
-        
+
         all_distances = np.concatenate(all_distances)
         all_labels = np.concatenate(all_labels)
 
         # 计算这3个总体值，还有给定阈值下的precision, recall, f1
         acc = skmetrics.accuracy_score(all_labels, all_distances > 0.5)
         ap = skmetrics.average_precision_score(all_labels, all_distances)
-        auc = skmetrics.roc_auc_score(all_labels, all_distances)  # auc under roc 
+        auc = skmetrics.roc_auc_score(all_labels, all_distances)  # auc under roc
 
         precisions = []
         recalls = []
@@ -74,11 +74,11 @@ class PairEvaluator(DatasetEvaluator):
             precision = skmetrics.precision_score(all_labels, all_distances >= thresh, zero_division=0)
             recall = skmetrics.recall_score(all_labels, all_distances >= thresh, zero_division=0)
             f1 = 2 * precision * recall / (precision + recall + 1e-12)
-            
+
             precisions.append(precision)
             recalls.append(recall)
             f1s.append(f1)
-            
+
         self._results = OrderedDict()
         self._results['Acc'] = acc
         self._results['Ap'] = ap
diff --git a/fastreid/evaluation/pair_score_evaluator.py b/fastreid/evaluation/pair_score_evaluator.py
new file mode 100644
index 0000000..ddf032e
--- /dev/null
+++ b/fastreid/evaluation/pair_score_evaluator.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2021/10/27 15:56:52
+# @Author  : zuchen.wang@vipshop.com
+# @File    : pair_score_evaluator.py
+# coding: utf-8
+
+import logging
+
+import torch
+
+from fastreid.modeling.losses.utils import normalize
+from .pair_evaluator import PairEvaluator
+
+logger = logging.getLogger(__name__)
+
+
+class PairScoreEvaluator(PairEvaluator):
+
+    def process(self, inputs, outputs):
+        prediction = {
+            'distances': outputs['cls_outputs'].to(self._cpu_device).numpy(),
+            'labels': inputs["targets"].to(self._cpu_device).numpy()
+        }
+        self._predictions.append(prediction)
diff --git a/fastreid/evaluation/pari_distance_evaluator.py b/fastreid/evaluation/pari_distance_evaluator.py
new file mode 100644
index 0000000..a1c9e05
--- /dev/null
+++ b/fastreid/evaluation/pari_distance_evaluator.py
@@ -0,0 +1,28 @@
+# coding: utf-8
+
+import logging
+
+import torch
+
+from fastreid.modeling.losses.utils import normalize
+from .pair_evaluator import PairEvaluator
+
+logger = logging.getLogger(__name__)
+
+
+class PairDistanceEvaluator(PairEvaluator):
+
+    def process(self, inputs, outputs):
+        embedding = outputs['features'].to(self._cpu_device)
+        embedding = embedding.view(embedding.size(0) * 2, -1)
+        embedding = normalize(embedding, axis=-1)
+        embed1 = embedding[0:len(embedding):2, :]
+        embed2 = embedding[1:len(embedding):2, :]
+        distances = torch.mul(embed1, embed2).sum(-1).numpy()
+
+        # print(distances)
+        prediction = {
+            'distances': distances,
+            'labels': inputs["targets"].to(self._cpu_device).numpy()
+        }
+        self._predictions.append(prediction)
diff --git a/projects/FastShoe/fastshoe/trainer.py b/projects/FastShoe/fastshoe/trainer.py
index 82eb400..97636c5 100644
--- a/projects/FastShoe/fastshoe/trainer.py
+++ b/projects/FastShoe/fastshoe/trainer.py
@@ -13,7 +13,7 @@ from fastreid.data.datasets import DATASET_REGISTRY
 from fastreid.utils import comm
 from fastreid.data.transforms import build_transforms
 from fastreid.data.build import build_reid_train_loader, build_reid_test_loader
-from fastreid.evaluation.pair_evaluator import PairEvaluator
+from fastreid.evaluation.pair_score_evaluator import PairScoreEvaluator
 from projects.FastShoe.fastshoe.data import PairDataset
 
 
@@ -85,4 +85,4 @@ class PairTrainer(DefaultTrainer):
     @classmethod
     def build_evaluator(cls, cfg, dataset_name, output_dir=None):
         data_loader = cls.build_test_loader(cfg, dataset_name)
-        return data_loader, PairEvaluator(cfg, output_dir)
+        return data_loader, PairScoreEvaluator(cfg, output_dir)