def predict_dis(self, model, src_snt_ids, dis_sns, alpha=1.0): input_var = ids2var(src_snt_ids, -1, 1, addEOS=True) input_dis = [ids2var(x, -1, 1, addEOS=True) for x in dis_sns] #print(input_dis) output_ids, attention_weights = model.predict_dis( input_var, input_dis, alpha) return output_ids, attention_weights
def predict_one(self, model, src_snt_ids, beam_size=None): input_var = ids2var(src_snt_ids, -1, 1, addEOS=True) # batch_size = 1; cudified #print(input_var) #input_var = input_var.cpu()#.type(torch.cuda.LongTensor) #print(input_var) if beam_size: output_ids, attention_weights = model.predict_beam(input_var, beam_size=beam_size) else: output_ids, attention_weights = model.predict(input_var) return output_ids, attention_weights
def predict_one(self, model, src_snt_ids): input_var = ids2var(src_snt_ids, -1, 1, addEOS=True) # batch_size = 1; cudified output_ids, attention_weights = model.predict(input_var) return output_ids, attention_weights