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