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import re
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from rouge import Rouge
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import argparse
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import os
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import json
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics.pairwise import cosine_similarity
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spot_the_diff = ["Spot-the-Diff", "Birds-to-Words", "CLEVR-Change"]
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image_edit_instruct = ["IEdit", "HQ-Edit", "MagicBrush"]
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visual_story_telling = ["AESOP", "FlintstonesSV", "PororoSV", "VIST"]
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visual_cloze = ["COMICS_Dialogue", "RecipeQA_VisualCloze"]
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text_rich_vqa = ["WebQA", "TQA", "OCR-VQA", "DocVQA"]
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multi_image_vqa = ["MIT-States_StateCoherence", "MIT-States_PropertyCoherence", "VISION", "RecipeQA_ImageCoherence"]
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puzzle = ["RAVEN"]
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nlrv2 = ["NLVR2_Mantis"]
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qbench = ["QBench"]
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class Eval:
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def __init__(self):
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self.periodStrip = re.compile("(?!<=\d)(\.)(?!\d)")
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self.commaStrip = re.compile("(\d)(\,)(\d)")
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self.punct = [
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";",
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r"/",
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"[",
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"]",
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'"',
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"{",
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"}",
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"(",
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")",
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"=",
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"+",
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"\\",
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"_",
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"-",
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">",
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"<",
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"@",
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"`",
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",",
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"?",
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"!",
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]
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def processPunctuation(self, inText):
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outText = inText
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for p in self.punct:
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if (p + " " in inText or " " + p in inText) or (
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re.search(self.commaStrip, inText) != None
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):
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outText = outText.replace(p, "")
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else:
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outText = outText.replace(p, " ")
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outText = self.periodStrip.sub("", outText, re.UNICODE)
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return outText
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def process(self, answer):
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answer = answer.replace("\n", " ")
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answer = answer.replace("\t", " ")
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answer = answer.strip()
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answer = self.processPunctuation(answer)
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answer = answer.strip('\'')
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answer = answer.strip('\"')
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answer = answer.strip(')')
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answer = answer.strip('(')
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answer = answer.strip().lower()
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return answer
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def evaluate_rouge(self,preds):
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rouge = Rouge()
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acc = {'f': []}
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eval_list = []
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for i, res in enumerate(preds):
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sample_id = res['sample_id']
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gt_ans = self.process(res["gt_response"])
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pred_ans = self.process(res["pred_response"])
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if gt_ans == '':
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continue
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if pred_ans == '':
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s = 0
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else:
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if len(pred_ans) > 512:
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pred_ans = pred_ans[0: 512]
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s = rouge.get_scores(pred_ans, gt_ans)[0]['rouge-l']['f']
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acc['f'].append(s)
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eval_list.append({'id':str(sample_id),'score':str(round(s,3))})
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results = {'Rouge-L f': np.mean(acc['f'])}
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return results,eval_list
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def judge_multi_choice(self,sample):
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sample_id = sample['sample_id']
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gt_ans = sample["gt_response"]
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pred_ans = sample["pred_response"]
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if ":" in pred_ans:
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a_list = pred_ans.split(":")
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a_list = [a.strip() for a in a_list ]
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for a in a_list:
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if len(a) == 1 and a[-1] in ["a", "b", "c", "d", "e", "f", "g", "h"]:
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pred_ans = a
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if pred_ans == gt_ans:
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return 1
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else:
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return 0
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def process_sample(self,sample):
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sample["gt_response"] = self.process(sample["gt_response"])
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sample["pred_response"] = self.process(sample["pred_response"])
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def evaluate_multichoice(self, preditions):
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correct = 0
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eval_list = []
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for i, sample in enumerate(preditions):
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self.process_sample(sample)
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score = self.judge_multi_choice(sample)
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sample_id = sample['sample_id']
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sample['result'] = score
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eval_list.append({'id':str(sample_id),'score':str(score)})
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correct+=score
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return {'Accuracy':correct/len(preditions)},eval_list
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def evaluate_multi_choice_image(self,preditions):
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correct = 0
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eval_list = []
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for i,sample in enumerate(preditions):
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gt_ans = self.process(sample["gt_response"])
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pred_ans = self.process(sample["pred_response"])
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sample_id = sample['sample_id']
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if ":" in pred_ans:
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a_list = pred_ans.split(":")
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a_list = [a.strip() for a in a_list ]
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for a in a_list:
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if len(a) == 1 and a[-1] in ["a", "b", "c", "d", "e", "f", "g", "h"]:
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pred_ans = a
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if gt_ans == pred_ans:
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score = 1
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else:
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score = 0
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sample_id = sample['sample_id']
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sample['result'] = score
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eval_list.append({'id':str(sample_id),'score':str(score)})
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correct+=score
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return {'Accuracy':correct/len(preditions)},eval_list
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--result-dir', type=str, required=True)
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args = parser.parse_args()
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result_file = os.path.join(args.result_dir, "result.jsonl")
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if not os.path.exists(result_file):
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print('No prediction file found')
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exit(0)
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with open(result_file, 'r') as f:
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preds_all = [json.loads(line) for line in f]
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preds_all_dict = dict()
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for pred in preds_all:
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if pred["dataset"] not in preds_all_dict:
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preds_all_dict[pred["dataset"]] = list()
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preds_all_dict[pred["dataset"]].append(pred)
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image_choice_dataset_list = ["recipeqa-RecipeQA_VisualCloze", "RecipeQA_ImageCoherence", "COMICS_Panel"]
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E = Eval()
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eval_result_list = dict()
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eval_result_list_detail = dict()
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for dataset in preds_all_dict:
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preds = preds_all_dict[dataset]
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question_type = preds[0]["question_type"]
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if question_type == 'open-ended':
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eval_result, eval_list = E.evaluate_rouge(preds)
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elif question_type == 'multi-choice' or dataset == 'nlrv2':
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if dataset in image_choice_dataset_list:
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eval_result, eval_list = E.evaluate_multi_choice_image(preds)
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else:
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eval_result, eval_list = E.evaluate_multichoice(preds)
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else:
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eval_result = 'Dataset not supported'
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print('Dataset not supported')
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exit(0)
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print(dataset, end = ': ')
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print(eval_result)
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eval_result_list[dataset] = eval_result
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eval_result_list_detail[dataset] = eval_list
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os.makedirs(args.result_dir, exist_ok=True)
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with open(os.path.join(args.result_dir, 'eval_dataset.json'), 'w') as f:
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json.dump(eval_result_list, f, indent=4)
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with open(os.path.join(args.result_dir,'eval_dataset_details.json'), 'w') as f:
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json.dump(eval_result_list_detail, f, indent=4)
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eval_cat_list = dict()
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print()
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in spot_the_diff:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["spot_the_diff"] = score
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print("spot_the_diff", end = ': ')
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print('{:.2f}'.format(100 * score))
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in image_edit_instruct:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["image_edit_instruct"] = score
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print("image_edit_instruct", end = ': ')
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print('{:.2f}'.format(100 * score))
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in visual_story_telling:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["visual_story_telling"] = score
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print("visual_story_telling", end = ': ')
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print('{:.2f}'.format(100 * score))
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in visual_cloze:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["visual_cloze"] = score
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print("visual_cloze", end = ': ')
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print('{:.2f}'.format(100 * score))
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in text_rich_vqa:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["text_rich_vqa"] = score
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print("text_rich_vqa", end = ': ')
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print('{:.2f}'.format(100 * score))
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in multi_image_vqa:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["multi_image_vqa"] = score
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print("multi_image_vqa", end = ': ')
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print('{:.2f}'.format(100 * score))
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in puzzle:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["puzzle"] = score
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print("puzzle", end = ': ')
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print('{:.2f}'.format(100 * score))
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in nlrv2:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["nlrv2"] = score
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print("nlrv2", end = ': ')
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print('{:.2f}'.format(100 * score))
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score = 0
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count = 0
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for dataset in eval_result_list:
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if dataset in qbench:
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count += 1
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score += list(eval_result_list[dataset].values())[0]
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if count > 0:
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score /= count
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eval_cat_list["qbench"] = score
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print("qbench", end = ': ')
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print('{:.2f}'.format(100 * score))
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with open(os.path.join(args.result_dir,'eval_cat.json'), 'w') as f:
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json.dump(eval_cat_list, f, indent=4) |
