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| import os | |
| import json | |
| from tqdm import tqdm | |
| from torch.utils.data import DataLoader | |
| from minigpt4.common.eval_utils import prepare_texts, init_model, eval_parser | |
| from minigpt4.conversation.conversation import CONV_VISION | |
| from minigpt4.processors.blip_processors import Blip2ImageTrainProcessor,BlipCaptionProcessor | |
| from minigpt4.datasets.datasets.video_datasets import VideoChatGPTEvalDataset,VideoChatGPTEval_consistancy,Video_validation_Dataset,TVQAEVAL,TVQAEVAL_Long | |
| parser = eval_parser() | |
| parser.add_argument("--dataset", type=str, default='msvd', help="dataset to evaluate") | |
| parser.add_argument("--add_subtitles",action='store_true',help="whether to add subtitles to the video") | |
| parser.add_argument("--name", type=str, default='3_datasets', help="evaluation name") | |
| parser.add_argument("--batch_size", type=int, default=1, help="batch size") | |
| parser.add_argument("--start", type=int, default=0, help="start from video number") | |
| parser.add_argument("--end", type=int, default=10000000, help="end at video number") | |
| args = parser.parse_args() | |
| print(args.ckpt) | |
| print(args.name) | |
| print(args.cfg_path) | |
| if "test_configs/mistral_test_config.yaml" == args.cfg_path: | |
| llm_name="mistral" | |
| else: | |
| llm_name="llama2" | |
| print("using captions",args.add_subtitles) | |
| model, vis_processor = init_model(args) | |
| conv_temp = CONV_VISION.copy() | |
| conv_temp.system = "" | |
| if args.dataset == 'video_chatgpt_generic': | |
| ann_path="datasets/evaluation_datasets/videochatgpt_benchmark/generic_qa.json" | |
| videos_path="/ibex/project/c2090/datasets/VideoInstruct100K/test_videos/Test_Videos" | |
| subtitles_path="/home/ataallka/minigpt_video/minigpt_multi_img/inference_subtitles" | |
| videos_features_path="/ibex/project/c2106/kirolos/videos_features/evaluation/benchmark/generic" | |
| annotations_keys=['Q','A','video_name'] | |
| data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys,videos_features_path, add_subtitles=args.add_subtitles,llm_name=llm_name) | |
| elif args.dataset == 'video_chatgpt_temporal': | |
| ann_path="datasets/evaluation_datasets/videochatgpt_benchmark/temporal_qa.json" | |
| videos_path="/ibex/project/c2090/datasets/VideoInstruct100K/test_videos/Test_Videos" | |
| subtitles_path="/home/ataallka/minigpt_video/minigpt_multi_img/inference_subtitles" | |
| videos_features_path="/ibex/project/c2106/kirolos/videos_features/evaluation/benchmark/temporal" | |
| annotations_keys=['Q','A','video_name'] | |
| data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys,videos_features_path, add_subtitles=args.add_subtitles,llm_name=llm_name) | |
| elif args.dataset == 'video_chatgpt_consistency': | |
| ann_path="datasets/evaluation_datasets/videochatgpt_benchmark/consistency_qa.json" | |
| videos_path="/ibex/project/c2090/datasets/VideoInstruct100K/test_videos/Test_Videos" | |
| subtitles_path="/home/ataallka/minigpt_video/minigpt_multi_img/inference_subtitles" | |
| annotations_keys=[['Q1','Q2'],'A','video_name'] | |
| data = VideoChatGPTEval_consistancy(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys, add_subtitles=args.add_subtitles,llm_name=llm_name) | |
| elif args.dataset == 'msrvtt': | |
| ann_path="datasets/evaluation_datasets/msrvtt/val_qa_edited.json" | |
| videos_path="/ibex/project/c2090/datasets/VideoInstruct100K/test_videos/MSRVTT/videos/all" | |
| subtitles_path="/home/ataallka/minigpt_video/minigpt_multi_img/inference_subtitles" | |
| videos_features_path="/ibex/project/c2106/kirolos/videos_features/evaluation/msrvtt" | |
| annotations_keys=['question','answer','video_id'] | |
| data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys,videos_features_path, add_subtitles=args.add_subtitles,llm_name=llm_name) | |
| elif args.dataset == 'msvd': | |
| ann_path="datasets/evaluation_datasets/msvd/val_qa_edited.json" | |
| videos_path="/ibex/project/c2090/datasets/VideoInstruct100K/test_videos/MSVD-QA/videos" | |
| subtitles_path="/home/ataallka/minigpt_video/minigpt_multi_img/inference_subtitles" | |
| videos_features_path="/ibex/project/c2106/kirolos/videos_features/evaluation/msvd" | |
| annotations_keys=['question','answer','video_id'] | |
| data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys,videos_features_path, add_subtitles=args.add_subtitles,llm_name=llm_name) | |
| elif args.dataset == 'activitynet': | |
| ann_path="datasets/evaluation_datasets/activityNet/test_qa.json" | |
| videos_path="/ibex/project/c2090/datasets/VideoInstruct100K/test_videos/Activity_net/Activity_net_videos" | |
| subtitles_path="/home/ataallka/minigpt_video/minigpt_multi_img/inference_subtitles/" | |
| videos_features_path="/ibex/project/c2106/kirolos/videos_features/evaluation/activity_net" | |
| annotations_keys=['question','answer','video_id'] | |
| data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys,videos_features_path, add_subtitles=args.add_subtitles,llm_name=llm_name) | |
| elif args.dataset == 'tgif': | |
| ann_path="datasets/evaluation_datasets/tgif/Test_frameqa_question.json" | |
| videos_path="/ibex/project/c2090/datasets/VideoInstruct100K/test_videos/TGIF/mp4s" | |
| subtitles_path="/home/ataallka/minigpt_video/minigpt_multi_img/inference_subtitles" | |
| videos_features_path="/ibex/project/c2106/kirolos/videos_features/evaluation/tgif" | |
| annotations_keys=['question','answer','gif_name'] | |
| # annotations_keys=['question','description','gif_name'] | |
| data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys,videos_features_path, add_subtitles=False,llm_name=llm_name) | |
| elif args.dataset == 'tvqa': | |
| # TVQA dataset | |
| ann_path="datasets/evaluation_datasets/tvqa_short/tvqa_val.json" | |
| videos_path= "/ibex/project/c2090/datasets/TVR_dataset/videos/video_files/frames_hq/" | |
| subtitles_path="/ibex/project/c2090/datasets/TVR_dataset/TVRetrieval/data/tvqa_preprocessed_subtitles.json" | |
| videos_features_path="/ibex/project/c2106/kirolos/videos_features/evaluation/tvqa" | |
| data = TVQAEVAL(vis_processor, videos_path, ann_path,subtitles_path,videos_features_path,add_subtitles=args.add_subtitles,llm_name=llm_name) | |
| eval_dataloader = DataLoader(data, batch_size=args.batch_size, shuffle=False) | |
| minigpt4_predict = [] | |
| sub="subtitles" if args.add_subtitles else "no_subtitles" | |
| if args.start == 0 and args.end == 10000000: | |
| save_path = f'results/{args.name}_{args.dataset}_{sub}.json' | |
| else: | |
| print("start from video number",args.start) | |
| print("end at video number",args.end) | |
| save_path = f'results/{args.name}_{args.dataset}_{sub}_{args.start}_{args.end}.json' | |
| os.makedirs("results", exist_ok=True) | |
| c=0 | |
| pred_result = {} | |
| gt_result = {} | |
| if args.dataset == 'video_chatgpt_consistency': | |
| for images, texts_1,texts_2, gt_answers, lengths,videos_ids in tqdm(eval_dataloader,desc=f"Eval {args.dataset}"): | |
| if args.start<= c <args.end : | |
| texts_q1 = prepare_texts(texts_1, conv_temp, template='', lengths=lengths) # warp the texts with conversation template | |
| texts_q2 = prepare_texts(texts_2, conv_temp, template='', lengths=lengths) # warp the texts with conversation template | |
| models_answers_q1 = model.generate(images, texts_q1, max_new_tokens=args.max_new_tokens, do_sample=False, lengths=lengths,num_beams=1) | |
| models_answers_q2 = model.generate(images, texts_q2, max_new_tokens=args.max_new_tokens, do_sample=False, lengths=lengths,num_beams=1) | |
| for video_id,model_answer_q1,model_answer_q2, gt_answer,text_q1,text_q2 in zip(videos_ids,models_answers_q1,models_answers_q2, gt_answers,texts_q1,texts_q2): | |
| result = dict() | |
| result['video_name'] = video_id | |
| result['Q1'] = text_q1.split('\n')[-1].replace('[/INST]','') | |
| result['Q2'] = text_q2.split('\n')[-1].replace('[/INST]','') | |
| result['A'] = gt_answer | |
| result['pred1'] = model_answer_q1 | |
| result['pred2'] = model_answer_q2 | |
| pred_result[video_id] = [model_answer_q1,model_answer_q2] | |
| gt_result[video_id] = [gt_answer] | |
| minigpt4_predict.append(result) | |
| # save results every 100 videos to avoid losing results | |
| if c%100==0: | |
| with open(save_path, 'w') as f: | |
| json.dump(minigpt4_predict, f) | |
| if c >= args.end : | |
| break | |
| c+=1 | |
| elif args.dataset == 'tvr': | |
| for images, texts, gt_answers, lengths,videos_ids in tqdm(eval_dataloader,desc=f"Eval {args.dataset}"): | |
| if args.start<= c <args.end : | |
| texts = prepare_texts(texts, conv_temp, template='', lengths=lengths) # warp the texts with conversation template | |
| models_answers = model.generate(images, texts, max_new_tokens=args.max_new_tokens, do_sample=False, lengths=lengths,num_beams=1) | |
| for video_id,model_answer, gt_answer,text in zip(videos_ids,models_answers, gt_answers,texts): | |
| result = dict() | |
| result['video_name'] = video_id | |
| result['Q'] = text.split('\n')[-1].replace('[/INST]','') | |
| result['A'] = gt_answer | |
| result['pred'] = model_answer | |
| pred_result[video_id] = [model_answer] | |
| gt_result[video_id] = [gt_answer] | |
| minigpt4_predict.append(result) | |
| # save results every 100 videos to avoid losing results | |
| if c%100==0: | |
| with open(save_path, 'w') as f: | |
| json.dump(minigpt4_predict, f) | |
| if c >= args.end : | |
| break | |
| c+=1 | |
| elif args.dataset == 'ego_schema' or args.dataset == 'tvqa' or args.dataset == 'tvqa_long_videos': | |
| for images, texts, gt_answers, lengths,videos_ids in tqdm(eval_dataloader,desc=f"Eval {args.dataset}"): | |
| if args.start<= c <args.end : | |
| texts = prepare_texts(texts, conv_temp, template='', lengths=lengths) # warp the texts with conversation template | |
| models_answers = model.generate(images, texts, max_new_tokens=args.max_new_tokens, do_sample=False, lengths=lengths,num_beams=1) | |
| for video_id,model_answer, gt_answer,text in zip(videos_ids,models_answers, gt_answers,texts): | |
| result = dict() | |
| result['video_name'] = video_id | |
| if args.dataset == 'tvqa_long_videos': | |
| result['Q'] = text.split('\n\n')[1:] | |
| else: | |
| result['Q'] = text.split('\n')[1:] | |
| result['A'] = gt_answer | |
| result['pred'] = model_answer | |
| pred_result[video_id] = [model_answer] | |
| gt_result[video_id] = [gt_answer] | |
| minigpt4_predict.append(result) | |
| # save results every 100 videos to avoid losing results | |
| if c%100==0: | |
| with open(save_path, 'w') as f: | |
| json.dump(minigpt4_predict, f) | |
| if c >= args.end : | |
| break | |
| c+=1 | |
| else: | |
| for images, texts, gt_answers, lengths,videos_ids in tqdm(eval_dataloader,desc=f"Eval {args.dataset}"): | |
| if args.start<= c <args.end : | |
| texts = prepare_texts(texts, conv_temp, template='', lengths=lengths) # warp the texts with conversation template | |
| models_answers = model.generate(images, texts, max_new_tokens=args.max_new_tokens, do_sample=False, lengths=lengths,num_beams=1) | |
| for video_id,model_answer, gt_answer,text in zip(videos_ids,models_answers, gt_answers,texts): | |
| result = dict() | |
| result['video_name'] = video_id | |
| result['Q'] = text.split('\n')[-1].replace('[/INST]','') | |
| result['A'] = gt_answer | |
| result['pred'] = model_answer | |
| pred_result[video_id] = [model_answer] | |
| gt_result[video_id] = [gt_answer] | |
| minigpt4_predict.append(result) | |
| # save results every 100 videos to avoid losing results | |
| if c%100==0: | |
| with open(save_path, 'w') as f: | |
| json.dump(minigpt4_predict, f) | |
| if c >= args.end : | |
| break | |
| c+=1 | |
| with open(save_path, 'w') as f: | |
| json.dump(minigpt4_predict, f) | |
| print("saved results to",save_path) | |
| # save results | |
| # bleu_save_path = f'results/{args.name}_{args.dataset}_bleu.json' | |
| # cider_save_path = f'results/{args.name}_{args.dataset}_cider.json' | |
| # chatgpt_eval_save_path = f'results/{args.name}_{args.dataset}_chatgpt_eval.json' | |
| # bleu_results=eval_bleu(minigpt4_predict) | |
| # with open(bleu_save_path, 'w') as f: | |
| # json.dump(bleu_results, f) | |
| # print("bleu_results",bleu_results) | |
| # cider_results=eval_cider(pred_result,gt_result) | |
| # with open(cider_save_path, 'w') as f: | |
| # json.dump(cider_results, f) | |
| # print("mean_cider_scores:",cider_results['mean_cider_scores']) | |
| # chatgpt_results=chat_gpt_eval(pred_result,gt_result) | |
| # with open(chatgpt_eval_save_path, 'w') as f: | |
| # json.dump(chatgpt_results, f) | |
| # print("avg_chatgpt_score",chatgpt_results['avg_chatgpt_score']) | |
| # print(chatgpt_results) | |