| import os |
| import json |
|
|
| import torch |
| import numpy as np |
| from tqdm import tqdm |
| from vbench.utils import load_video, load_dimension_info, CACHE_DIR |
| 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_pairs): |
| success_frame_count, frame_count = 0,0 |
| video_results = [] |
| for info in tqdm(video_pairs): |
| if 'auxiliary_info' not in info: |
| raise "Auxiliary info is not in json, please check your json." |
| object_info = info['auxiliary_info'] |
| video_path = info['content_path'] |
| query = info["prompt"] |
| |
| 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({'prompt':query, 'video_path': video_path, 'video_results': cur_success_frame_rate}) |
| |
| |
| success_rate = success_frame_count / frame_count |
| |
| return { |
| "score":[success_rate, video_results] |
| } |
|
|
| |
| |
|
|
| def compute_multiple_objects(video_pairs): |
| device = torch.device("cuda") |
| dense_caption_model = DenseCaptioning(device) |
| submodules_dict = { |
| "model_weight": f'{CACHE_DIR}/grit_model/grit_b_densecap_objectdet.pth' |
| } |
| dense_caption_model.initialize_model_det(**submodules_dict) |
| logger.info("Initialize detection model success") |
| |
| results = multiple_objects(dense_caption_model, video_pairs) |
| return results |
|
|
