From 5818e14ebf98005c7e3b13a3f18fade501065c23 Mon Sep 17 00:00:00 2001
From: zz-jacob <zhangz.goal@gmail.com>
Date: Mon, 21 Mar 2022 15:41:56 +0800
Subject: [PATCH] fix bugs
---
convlab2/nlg/scgpt/evaluate.py | 2 +-
convlab2/nlg/scgpt/main.py | 10 ++++++++--
2 files changed, 9 insertions(+), 3 deletions(-)
diff --git a/convlab2/nlg/scgpt/evaluate.py b/convlab2/nlg/scgpt/evaluate.py
index c54b079f..f7435b68 100644
--- a/convlab2/nlg/scgpt/evaluate.py
+++ b/convlab2/nlg/scgpt/evaluate.py
@@ -246,7 +246,7 @@ class GentScorer(object):
## 2. Compute slot error rate
## 3. Detailed illustraction of how differet split
## of data affect performance
- def __init__(self, detectfile):
+ def __init__(self):
self.bleuscorer = BLEUScorer()
def scoreERR(self, parallel_pairs):
diff --git a/convlab2/nlg/scgpt/main.py b/convlab2/nlg/scgpt/main.py
index a48bf7b4..2d69ba2f 100644
--- a/convlab2/nlg/scgpt/main.py
+++ b/convlab2/nlg/scgpt/main.py
@@ -221,6 +221,8 @@ def test(model, nlg_data, ontology, model_path):
test_data = nlg_data['test']
dialog_acts = [act2str(item['dialogue_acts']) for item in test_data]
golden_responses = [item['utterance'] for item in test_data]
+ # dialog_acts = dialog_acts[:10]
+ # golden_responses = golden_responses[:10]
outputs = inference_sents(model, dialog_acts)
if dist.get_rank() == 0:
output_file = './test_output.txt'
@@ -241,13 +243,15 @@ def test(model, nlg_data, ontology, model_path):
domain = ontology['domains'][domain_name]
for slot_name in domain['slots']:
slot = domain['slots'][slot_name]
+ if 'possible_values' not in slot:
+ continue
possible_vals = slot['possible_values']
if len(possible_vals) > 0:
for val in possible_vals:
val2ds_dict[val] = f'{domain_name}-{slot_name}'
## missing values
score_list = []
- for item in nlg_data:
+ for item in test_data:
da = item['dialogue_acts']
utterance = item['utterance']
missing_count = 0
@@ -263,11 +267,13 @@ def test(model, nlg_data, ontology, model_path):
if value.strip().lower() not in utterance.lower():
missing_count += 1
all_count += 1
+ if all_count == 0:
+ continue
## redundant values
for val in val2ds_dict:
if f' {val.strip().lower()} ' in f' {utterance.strip().lower()} ' and val.strip().lower() not in all_values:
redundant_count += 1
- item_score = float(redundant_count + all_count) / all_count
+ item_score = float(redundant_count + redundant_count) / all_count
score_list.append(item_score)
ERR_Score = np.mean(score_list)
print(f'BLEU: {BLEU_Score}\nERR_Score: {ERR_Score}')
--
GitLab