Update metrics.py

ppcls/metric/metrics.py

@@ -38,14 +38,14 @@ class mAP(nn.Layer):
     def __init__(self):
         super().__init__()
 
-    def forward(self, similarities_matrix, query_labels, gallery_labels):
+    def forward(self, similarities_matrix, query_img_id, gallery_img_id):
         metric_dict = dict()
         
         choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True) 
-        gallery_labels_transpose = paddle.transpose(gallery_labels, [1,0])
+        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1,0])
         gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0],  gallery_labels_transpose.shape[1]])
         choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)        
-        equal_flag = paddle.equal(choosen_label, query_labels)
+        equal_flag = paddle.equal(choosen_label, query_img_id)
         equal_flag = paddle.cast(equal_flag, 'float32')
 
         acc_sum = paddle.cumsum(equal_flag, axis=1)
@@ -62,14 +62,14 @@ class mINP(nn.Layer):
     def __init__(self):
         super().__init__()
 
-    def forward(self, similarities_matrix, query_labels, gallery_labels):
+    def forward(self, similarities_matrix, query_img_id, gallery_img_id):
         metric_dict = dict()
         
         choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True) 
-        gallery_labels_transpose = paddle.transpose(gallery_labels, [1,0])
+        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1,0])
         gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0],  gallery_labels_transpose.shape[1]])
         choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)        
-        tmp = paddle.equal(choosen_label, query_labels)
+        tmp = paddle.equal(choosen_label, query_img_id)
         tmp = paddle.cast(tmp, 'float64')
 
         #do accumulative sum
@@ -90,15 +90,15 @@ class Recallk(nn.Layer):
             topk = [topk]
         self.topk = topk
 
-    def forward(self, similarities_matrix, query_labels, gallery_labels):
+    def forward(self, similarities_matrix, query_img_id, gallery_img_id):
         metric_dict = dict()
 
         #get cmc
         choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True) 
-        gallery_labels_transpose = paddle.transpose(gallery_labels, [1,0])
+        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1,0])
         gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0],  gallery_labels_transpose.shape[1]])
         choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)        
-        equal_flag = paddle.equal(choosen_label, query_labels)
+        equal_flag = paddle.equal(choosen_label, query_img_id)
         equal_flag = paddle.cast(equal_flag, 'float32')
         
         acc_sum = paddle.cumsum(equal_flag, axis=1)