| import os |
| import argparse |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix |
|
|
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument('--results_dir', default='./LaVIN', type=str) |
|
|
| eval_type_dict = { |
| "Perception": ["existence", "count", "position", "color", "posters", "celebrity", "scene", "landmark", "artwork", "OCR"], |
| "Cognition": ["commonsense_reasoning", "numerical_calculation", "text_translation", "code_reasoning"] |
| } |
|
|
|
|
| class calculate_metrics: |
| def divide_chunks(self, l, n=2): |
| |
| for i in range(0, len(l), n): |
| yield l[i:i + n] |
| |
| return |
|
|
| def parse_pred_ans(self, pred_ans): |
| pred_label = None |
| if pred_ans in ["yes", "no"]: |
| pred_label = pred_ans |
| else: |
| prefix_pred_ans = pred_ans[:4] |
|
|
| if "yes" in prefix_pred_ans: |
| pred_label = "yes" |
| elif "no" in prefix_pred_ans: |
| pred_label = "no" |
| else: |
| pred_label = "other" |
|
|
| return pred_label |
|
|
|
|
| def compute_metric(self, gts, preds): |
| assert len(gts) == len(preds) |
|
|
| label_map = { |
| "yes": 1, |
| "no": 0, |
| "other": -1, |
| } |
| |
| gts = [label_map[x] for x in gts] |
| preds = [label_map[x] for x in preds] |
|
|
| acc = accuracy_score(gts, preds) |
|
|
| clean_gts = [] |
| clean_preds = [] |
| other_num = 0 |
| for gt, pred in zip(gts, preds): |
| if pred == -1: |
| other_num += 1 |
| continue |
| clean_gts.append(gt) |
| clean_preds.append(pred) |
| |
|
|
| conf_mat = confusion_matrix(clean_gts, clean_preds, labels=[1,0]) |
| precision = precision_score(clean_gts, clean_preds, average='binary') |
| recall = recall_score(clean_gts, clean_preds, average='binary') |
| tp, fn = conf_mat[0] |
| fp, tn = conf_mat[1] |
|
|
| metric_dict = dict() |
| metric_dict = { |
| "TP": tp, |
| "FN": fn, |
| "TN": tn, |
| "FP": fp, |
| "precision": precision, |
| "recall": recall, |
| "other_num": other_num, |
| "acc": acc, |
| } |
|
|
| return metric_dict |
|
|
|
|
| def process_result(self, results_dir): |
|
|
| model_score_dict = dict() |
| for eval_type, task_name_list in eval_type_dict.items(): |
| print("===========", eval_type, "===========") |
| |
| scores = 0 |
| task_score_dict = dict() |
|
|
| for task_name in task_name_list: |
|
|
| task_txt = os.path.join(results_dir, task_name + ".txt") |
| lines = open(task_txt, 'r', encoding='utf-8').readlines() |
| chunk_lines = list(self.divide_chunks(lines)) |
| |
| img_num = len(chunk_lines) |
| task_other_ans_num = 0 |
| task_score = 0 |
| acc_plus_correct_num = 0 |
| gts = [] |
| preds = [] |
|
|
| for img_items in chunk_lines: |
| assert len(img_items) == 2 |
| img_correct_num = 0 |
|
|
| for img_item in img_items: |
| img_name, question, gt_ans, pred_ans = img_item.split("\t") |
|
|
| gt_ans = gt_ans.lower() |
| pred_ans = pred_ans.lower() |
|
|
| assert gt_ans in ["yes", "no"] |
|
|
| pred_ans = self.parse_pred_ans(pred_ans) |
| assert pred_ans in ["yes", "no", "other"] |
|
|
| gts.append(gt_ans) |
| preds.append(pred_ans) |
| |
| if gt_ans == pred_ans: |
| img_correct_num += 1 |
| |
| if pred_ans not in ["yes", "no"]: |
| task_other_ans_num += 1 |
|
|
| if img_correct_num == 2: |
| acc_plus_correct_num += 1 |
|
|
| |
| metric_dict = self.compute_metric(gts, preds) |
| acc_plus = acc_plus_correct_num / img_num |
| metric_dict["acc_plus"] = acc_plus |
| |
| |
| for k, v in metric_dict.items(): |
| if k in ["acc", "acc_plus"]: |
| task_score += v*100 |
| |
| task_score_dict[task_name] = task_score |
| |
| scores += task_score |
|
|
| print("total score:", scores, "\n") |
| for task_name, score in task_score_dict.items(): |
| print("\t", task_name, " score:", score) |
| print("\n") |
| |
| return |
|
|
|
|
|
|
|
|
| if __name__ == "__main__": |
| cal = calculate_metrics() |
|
|
| args = parser.parse_args() |
| results_dir = args.results_dir |
| cal.process_result(results_dir) |
|
|
|
|
