Merge pull request #802 from FredHuang16/patch-8

fix retrieval metrics

ppcls/metric/__init__.py

@@ -16,8 +16,7 @@ from paddle import nn
 import copy
 from collections import OrderedDict
 
-from .metrics import TopkAcc, mAP, mINP, Recallk
-
+from .metrics import TopkAcc, mAP, mINP, Recallk, RetriMetric
 
 class CombinedMetrics(nn.Layer):
     def __init__(self, config_list):
@@ -25,12 +24,20 @@ class CombinedMetrics(nn.Layer):
         self.metric_func_list = []
         assert isinstance(config_list, list), (
             'operator config should be a list')
+        
+        self.retri_config = dict()  # retrieval metrics config
         for config in config_list:
             assert isinstance(config,
                               dict) and len(config) == 1, "yaml format error"
             metric_name = list(config)[0]
+            if metric_name in ["Recallk", "mAP", "mINP"]:
+                self.retri_config[metric_name] = config[metric_name]
+                continue
             metric_params = config[metric_name]
             self.metric_func_list.append(eval(metric_name)(**metric_params))
+            
+        if self.retri_config:
+            self.metric_func_list.append(RetriMetric(self.retri_config))
 
     def __call__(self, *args, **kwargs):
         metric_dict = OrderedDict()

ppcls/metric/metrics.py

@@ -84,6 +84,29 @@ class Recallk(nn.Layer):
             metric_dict["recall{}".format(k)] = all_cmc[k - 1]
         return metric_dict
 
+# retrieval metrics
+class RetriMetric(nn.Layer):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.max_rank = 50  #max(self.topk) if max(self.topk) > 50 else 50
+
+    def forward(self, similarities_matrix, query_img_id, gallery_img_id):
+        metric_dict = dict()
+        all_cmc, all_AP, all_INP = get_metrics(similarities_matrix, query_img_id,
+                                   gallery_img_id, self.max_rank)
+        if "Recallk" in self.config.keys():
+            topk = self.config['Recallk']['topk']
+            for k in topk:
+                metric_dict["recall{}".format(k)] = all_cmc[k - 1]
+        if "mAP" in self.config.keys():
+            mAP = np.mean(all_AP)
+            metric_dict["mAP"] = mAP
+        if "mINP" in self.config.keys():
+            mINP = np.mean(all_INP)
+            metric_dict["mINP"] = mINP
+        return metric_dict
+    
 
 @lru_cache()
 def get_metrics(similarities_matrix, query_img_id, gallery_img_id,