Spaces:
Running
Running
File size: 10,713 Bytes
dc80a97 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
import os
import json
from tqdm import tqdm
import sys
project_dir = os.getcwd()
sys.path.append(project_dir)
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
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='test', help="evaluation name")
parser.add_argument("--videos_path", type=str, default='videos path', help="path to videos")
parser.add_argument("--subtitles_path", type=str, default='subtitles path', help="path to subtitles")
parser.add_argument("--ann_path", type=str, default='annotations path', help="path to annotations")
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,whisper_gpu_id,minigpt4_gpu_id,answer_module_gpu_id = init_model(args)
conv_temp = CONV_VISION.copy()
conv_temp.system = ""
if args.dataset == 'video_chatgpt_generic':
ann_path=args.ann_path
videos_path= args.videos_path
subtitles_path=args.subtitles_path
annotations_keys=['Q','A','video_name']
data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys, add_subtitles=args.add_subtitles,llm_name=llm_name)
elif args.dataset == 'video_chatgpt_temporal':
ann_path=args.ann_path
videos_path= args.videos_path
subtitles_path=args.subtitles_path
annotations_keys=['Q','A','video_name']
data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys, add_subtitles=args.add_subtitles,llm_name=llm_name)
elif args.dataset == 'video_chatgpt_consistency':
ann_path=args.ann_path
videos_path= args.videos_path
subtitles_path=args.subtitles_path
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=args.ann_path
videos_path= args.videos_path
subtitles_path=args.subtitles_path
annotations_keys=['question','answer','video_id']
data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys, add_subtitles=args.add_subtitles,llm_name=llm_name)
elif args.dataset == 'msvd':
ann_path=args.ann_path
videos_path= args.videos_path
subtitles_path="" # no subtitles for msvd as these videos don't have audio
annotations_keys=['question','answer','video_id']
data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys, add_subtitles=args.add_subtitles,llm_name=llm_name)
elif args.dataset == 'activitynet':
ann_path=args.ann_path
videos_path= args.videos_path
subtitles_path=args.subtitles_path
annotations_keys=['question','answer','video_id']
data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys, 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= args.videos_path
subtitles_path="" # no subtitles for TGIF as these videos don't have audio
annotations_keys=['question','answer','gif_name']
data = VideoChatGPTEvalDataset(vis_processor, videos_path, ann_path,subtitles_path,annotations_keys, 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= args.videos_path
subtitles_path=args.subtitles_path
data = TVQAEVAL(vis_processor, videos_path, ann_path,subtitles_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)
|