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| import os | |
| import json | |
| import torch | |
| import numpy as np | |
| from tqdm import tqdm | |
| from vbench.utils import load_video, load_dimension_info | |
| from vbench.third_party.grit_model import DenseCaptioning | |
| import logging | |
| logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s') | |
| logger = logging.getLogger(__name__) | |
| def get_dect_from_grit(model, image_arrays): | |
| pred = [] | |
| if type(image_arrays) is not list: | |
| image_arrays = image_arrays.numpy() | |
| with torch.no_grad(): | |
| for frame in image_arrays: | |
| ret = model.run_caption_tensor(frame) | |
| if len(ret[0])>0: | |
| pred.append(set(ret[0][0][2])) | |
| else: | |
| pred.append(set([])) | |
| return pred | |
| def check_generate(key_info, predictions): | |
| cur_cnt = 0 | |
| key_a, key_b = key_info.split(' and ') | |
| key_a = key_a.strip() | |
| key_b = key_b.strip() | |
| for pred in predictions: | |
| if key_a in pred and key_b in pred: | |
| cur_cnt+=1 | |
| return cur_cnt | |
| def multiple_objects(model, video_dict, device): | |
| success_frame_count, frame_count = 0,0 | |
| video_results = [] | |
| for info in tqdm(video_dict): | |
| if 'auxiliary_info' not in info: | |
| raise "Auxiliary info is not in json, please check your json." | |
| object_info = info['auxiliary_info']['object'] | |
| for video_path in info['video_list']: | |
| video_tensor = load_video(video_path, num_frames=16) | |
| cur_video_pred = get_dect_from_grit(model, video_tensor.permute(0,2,3,1)) | |
| cur_success_frame_count = check_generate(object_info, cur_video_pred) | |
| cur_success_frame_rate = cur_success_frame_count/len(cur_video_pred) | |
| success_frame_count += cur_success_frame_count | |
| frame_count += len(cur_video_pred) | |
| video_results.append({'video_path': video_path, 'video_results': cur_success_frame_rate}) | |
| success_rate = success_frame_count / frame_count | |
| return success_rate, video_results | |
| def compute_multiple_objects(json_dir, device, submodules_dict): | |
| dense_caption_model = DenseCaptioning(device) | |
| dense_caption_model.initialize_model_det(**submodules_dict) | |
| logger.info("Initialize detection model success") | |
| _, prompt_dict_ls = load_dimension_info(json_dir, dimension='multiple_objects', lang='en') | |
| all_results, video_results = multiple_objects(dense_caption_model, prompt_dict_ls, device) | |
| return all_results, video_results | |