test

main.py

@@ -96,7 +96,6 @@ class Main(object):
         # self.ent2id = {ent: idx for idx, ent in enumerate(ent_set)}
         # self.rel2id = {rel: idx for idx, rel in enumerate(rel_set)}
         
-        print("Num rel1: " + str(len(self.rel2id)))
         self.rel2id.update({rel+'_reverse': idx+len(self.rel2id)
                            for idx, rel in enumerate(rel_set)})
 
@@ -105,7 +104,6 @@ class Main(object):
 
         self.p.num_ent = len(self.ent2id)
         self.p.num_rel = len(self.rel2id) // 2
-        print("Num rel: " + str(self.p.num_rel))
         self.p.embed_dim = self.p.k_w * \
             self.p.k_h if self.p.embed_dim is None else self.p.embed_dim
 
@@ -280,11 +278,9 @@ class Main(object):
             if self.p.train_strategy == 'one_to_x':
                 triple, label, neg_ent, sub_samp = [
                     _.to(self.device) for _ in batch]
-                print(triple.shape)
                 return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label, neg_ent, sub_samp
             else:
                 triple, label = [_.to(self.device) for _ in batch]
-                print(triple.shape)
                 return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label, None, None
         else:
             triple, label = [_.to(self.device) for _ in batch]
@@ -483,7 +479,6 @@ class Main(object):
 
             sub, rel, obj, nt_rel, label, neg_ent, sub_samp = self.read_batch(
                 batch, 'train')
-            print(nt_rel)
 
             pred = self.model.forward(sub, rel, nt_rel, neg_ent, self.p.train_strategy)
             loss = self.model.loss(pred, label, sub_samp)

models.py

@@ -570,7 +570,6 @@ class FouriER(torch.nn.Module):
         z = z.mean([-2, -1])
 
         nt_rel_emb = self.rel_fusion(self.rel_embed(nt_rel))
-        print(nt_rel)
         comb_emb_1 = torch.stack([sub_emb.view(-1, self.p.image_h, self.p.image_w), nt_rel_emb.view(-1, self.p.image_h, self.p.image_w)], dim=1)
         y_1 = comb_emb_1.view(-1, 2, self.p.image_h, self.p.image_w)
         y_1 = self.bn0(y_1)