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import os |
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import argparse |
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from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--results_dir', default='./LaVIN', type=str) |
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eval_type_dict = { |
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"Perception": ["existence", "count", "position", "color", "posters", "celebrity", "scene", "landmark", "artwork", "OCR"], |
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"Cognition": ["commonsense_reasoning", "numerical_calculation", "code_reasoning"] |
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} |
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class calculate_metrics: |
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def divide_chunks(self, l, n=2): |
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for i in range(0, len(l), n): |
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yield l[i:i + n] |
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return |
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def parse_pred_ans(self, pred_ans): |
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pred_label = None |
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if pred_ans in ["Sim", "Não"]: |
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pred_label = pred_ans |
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else: |
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prefix_pred_ans = pred_ans[:4] |
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if "Sim" in prefix_pred_ans or "sim" in prefix_pred_ans: |
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pred_label = "Sim" |
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elif "Não" in prefix_pred_ans or "não" in prefix_pred_ans or "Nao" in prefix_pred_ans or "nao" in prefix_pred_ans: |
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pred_label = "Não" |
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else: |
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pred_label = "other" |
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return pred_label |
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def compute_metric(self, gts, preds): |
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assert len(gts) == len(preds) |
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label_map = { |
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"sim": 1, |
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"não": 0, |
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"other": -1, |
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} |
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gts = [label_map[x] for x in gts] |
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preds = [label_map[x] for x in preds] |
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acc = accuracy_score(gts, preds) |
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clean_gts = [] |
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clean_preds = [] |
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other_num = 0 |
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for gt, pred in zip(gts, preds): |
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if pred == -1: |
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other_num += 1 |
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continue |
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clean_gts.append(gt) |
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clean_preds.append(pred) |
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conf_mat = confusion_matrix(clean_gts, clean_preds, labels=[1,0]) |
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precision = precision_score(clean_gts, clean_preds, average='binary') |
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recall = recall_score(clean_gts, clean_preds, average='binary') |
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tp, fn = conf_mat[0] |
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fp, tn = conf_mat[1] |
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metric_dict = dict() |
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metric_dict = { |
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"TP": tp, |
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"FN": fn, |
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"TN": tn, |
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"FP": fp, |
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"precision": precision, |
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"recall": recall, |
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"other_num": other_num, |
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"acc": acc, |
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} |
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return metric_dict |
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def process_result(self, results_dir): |
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model_score_dict = dict() |
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for eval_type, task_name_list in eval_type_dict.items(): |
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print("===========", eval_type, "===========") |
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scores = 0 |
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task_score_dict = dict() |
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for task_name in task_name_list: |
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task_txt = os.path.join(results_dir, task_name + ".txt") |
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lines = open(task_txt, 'r').readlines() |
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chunk_lines = list(self.divide_chunks(lines)) |
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img_num = len(chunk_lines) |
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task_other_ans_num = 0 |
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task_score = 0 |
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acc_plus_correct_num = 0 |
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gts = [] |
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preds = [] |
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for img_items in chunk_lines: |
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assert len(img_items) == 2 |
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img_correct_num = 0 |
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for img_item in img_items: |
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img_name, question, gt_ans, pred_ans = img_item.split("\t") |
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gt_ans = gt_ans.lower() |
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pred_ans = pred_ans.lower() |
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assert gt_ans in ["sim", "não"] |
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pred_ans = self.parse_pred_ans(pred_ans) |
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assert pred_ans in ["sim", "não", "other"] |
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gts.append(gt_ans) |
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preds.append(pred_ans) |
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if gt_ans == pred_ans: |
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img_correct_num += 1 |
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if pred_ans not in ["sim", "não"]: |
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task_other_ans_num += 1 |
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if img_correct_num == 2: |
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acc_plus_correct_num += 1 |
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metric_dict = self.compute_metric(gts, preds) |
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acc_plus = acc_plus_correct_num / img_num |
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metric_dict["acc_plus"] = acc_plus |
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for k, v in metric_dict.items(): |
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if k in ["acc", "acc_plus"]: |
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task_score += v*100 |
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task_score_dict[task_name] = task_score |
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scores += task_score |
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print("total score:", scores, "\n") |
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for task_name, score in task_score_dict.items(): |
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print("\t", task_name, " score:", score) |
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print("\n") |
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return |
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if __name__ == "__main__": |
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cal = calculate_metrics() |
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args = parser.parse_args() |
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results_dir = args.results_dir |
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cal.process_result(results_dir) |
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