init

local_coh_ppl.py

@@ -244,7 +244,8 @@ class LocalCohPPL(evaluate.Measurement):
                 for i in range(1,len(stl)):
                     ppl = torch.exp( loss_out[b,stl[i-1]:stl[i]].sum() / shift_attention_mask_batch[b,stl[i-1]:stl[i]].sum() ).detach().cpu().item()
                     indv.append(ppl)
-                norm_ppl.append( perplexity_all[b] / sum(indv) )
+                nppl = perplexity_all[b] / sum(indv) if len(indv)>1 else 0.0
+                norm_ppl.append(nppl)
             #
             all_norm_ppl.extend(norm_ppl)