fix map minp metric bugs

ppcls/metric/metrics.py

@@ -41,7 +41,7 @@ class mAP(nn.Layer):
         super().__init__()
 
     def forward(self, similarities_matrix, query_img_id, gallery_img_id,
-                *args):
+                keep_mask):
         metric_dict = dict()
 
         choosen_indices = paddle.argsort(
@@ -55,8 +55,19 @@ class mAP(nn.Layer):
         choosen_label = paddle.index_sample(gallery_labels_transpose,
                                             choosen_indices)
         equal_flag = paddle.equal(choosen_label, query_img_id)
+        if keep_mask is not None:
+            keep_mask = paddle.index_sample(
+                keep_mask.astype('float32'), choosen_indices)
+            equal_flag = paddle.logical_and(equal_flag,
+                                            keep_mask.astype('bool'))
         equal_flag = paddle.cast(equal_flag, 'float32')
 
+        num_rel = paddle.sum(equal_flag, axis=1)
+        num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
+        num_rel_index = paddle.nonzero(num_rel.astype("int"))
+        num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
+        equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)
+
         acc_sum = paddle.cumsum(equal_flag, axis=1)
         div = paddle.arange(acc_sum.shape[1]).astype("float32") + 1
         precision = paddle.divide(acc_sum, div)
@@ -74,7 +85,7 @@ class mINP(nn.Layer):
         super().__init__()
 
     def forward(self, similarities_matrix, query_img_id, gallery_img_id,
-                *args):
+                keep_mask):
         metric_dict = dict()
 
         choosen_indices = paddle.argsort(
@@ -87,15 +98,26 @@ class mINP(nn.Layer):
             ])
         choosen_label = paddle.index_sample(gallery_labels_transpose,
                                             choosen_indices)
-        tmp = paddle.equal(choosen_label, query_img_id)
-        tmp = paddle.cast(tmp, 'float64')
+        equal_flag = paddle.equal(choosen_label, query_img_id)
+        if keep_mask is not None:
+            keep_mask = paddle.index_sample(
+                keep_mask.astype('float32'), choosen_indices)
+            equal_flag = paddle.logical_and(equal_flag,
+                                            keep_mask.astype('bool'))
+        equal_flag = paddle.cast(equal_flag, 'float64')
+
+        num_rel = paddle.sum(equal_flag, axis=1)
+        num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
+        num_rel_index = paddle.nonzero(num_rel.astype("int"))
+        num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
+        equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)
 
         #do accumulative sum
-        div = paddle.arange(tmp.shape[1]).astype("float64") + 2
-        minus = paddle.divide(tmp, div)
-        auxilary = paddle.subtract(tmp, minus)
+        div = paddle.arange(equal_flag.shape[1]).astype("float64") + 2
+        minus = paddle.divide(equal_flag, div)
+        auxilary = paddle.subtract(equal_flag, minus)
         hard_index = paddle.argmax(auxilary, axis=1).astype("float64")
-        all_INP = paddle.divide(paddle.sum(tmp, axis=1), hard_index)
+        all_INP = paddle.divide(paddle.sum(equal_flag, axis=1), hard_index)
         mINP = paddle.mean(all_INP)
         metric_dict["mINP"] = mINP.numpy()[0]
         return metric_dict