def conv_evaluate(self, prediction, response):
prediction = prediction.tolist()
response = response.tolist()
for p, r in zip(prediction, response):
p_str = ind2txt(p, self.ind2tok, self.end_token_idx)
r_str = ind2txt(r, self.ind2tok, self.end_token_idx)
self.evaluator.gen_evaluate(p_str, [r_str])
def conv_evaluate(self, prediction, response):
"""
Args:
prediction: torch.LongTensor, shape=(bs, response_truncate-1)
response: torch.LongTensor, shape=(bs, response_truncate)
the first token in response is <|endoftext|>, it is not in prediction
"""
prediction = prediction.tolist()
response = response.tolist()
for p, r in zip(prediction, response):
p_str = ind2txt(p, self.ind2tok, self.end_token_idx)
r_str = ind2txt(r[1:], self.ind2tok, self.end_token_idx)
self.evaluator.gen_evaluate(p_str, [r_str])
def interact(self):
self.init_interact()
input_text = self.get_input(self.language)
while not self.finished:
# rec
if hasattr(self, 'rec_model'):
rec_input = self.process_input(input_text, 'rec')
scores = self.rec_model.forward(rec_input, 'infer')
scores = scores.cpu()[0]
scores = scores[self.item_ids]
_, rank = torch.topk(scores, 10, dim=-1)
item_ids = []
for r in rank.tolist():
item_ids.append(self.item_ids[r])
first_item_id = item_ids[:1]
self.update_context('rec',
entity_ids=first_item_id,
item_ids=first_item_id)
print(f"[Recommend]:")
for item_id in item_ids:
if item_id in self.id2entity:
print(self.id2entity[item_id])
# conv
if hasattr(self, 'conv_model'):
conv_input = self.process_input(input_text, 'conv')
preds = self.conv_model.forward(conv_input,
'infer').tolist()[0]
p_str = ind2txt(preds, self.ind2tok, self.end_token_idx)
token_ids, entity_ids, movie_ids, word_ids = self.convert_to_id(
p_str, 'conv')
self.update_context('conv', token_ids, entity_ids, movie_ids,
word_ids)
print(f"[Response]:\n{p_str}")
# input
input_text = self.get_input(self.language)
