implement top1

models.py

     loss = loss.sum()
     return loss
 
-def top_loss(p_scores, n_values):
-    p1 = p_scores[:, 0, None]
-    p2 = p_scores[:, 1, None]
-
-    num_neg = n_values.shape[1]
-    half_index = int(num_neg / 2)
-
-    d1 = torch.sub(p1, n_values[:, 0:half_index])
-    d2 = torch.sub(p2, n_values[:, half_index:])
-    # print("d1 shape:",d1.shape)
+def top_loss(p_scores, n_values,device):
+    ratio = int(n_values.shape[1] / p_scores.shape[1])
+    temp_pvalues = torch.tensor([]).cuda(device=device)
+    for i in range(p_scores.shape[1]):
+        temp_pvalues = torch.cat((temp_pvalues, p_scores[:, i, None].expand(-1, ratio)), dim=1)
 
-    # print("add shape:",torch.cat((d1,d2),dim=1).shape)
-    t1 = torch.sigmoid(torch.cat((d1,d2),dim=1))
-    # print("t1 shape:",t1.shape)
+    t1 = torch.sigmoid(torch.sub(n_values , temp_pvalues))
     t2 = torch.sigmoid(torch.pow(n_values,2))
-    # print("t2 shape:",t2.shape)
-
-    t3 = torch.add(t1,t2)
-    # print("t3 shape:",t3.shape)
-
-    loss = t3.sum()
-    # print(loss.shape)
-    # loss /= (n_values.shape[1] * p_scores.shape[0])
-    loss /= n_values.shape[1]
+    t = torch.add(t1,t2)
+    t = t.sum(dim=1)
+    loss = t / n_values.shape[1]
+    loss = loss.sum(dim=0)
     return loss
 
 
 
         self.embedding_learner = EmbeddingLearner()
         # self.loss_func = nn.MarginRankingLoss(self.margin)
-        self.loss_func = bpr_loss
+        self.loss_func = top_loss
 
         self.rel_q_sharing = dict()