| import os |
| import json |
|
|
| import torch |
| import numpy as np |
| from tqdm import tqdm |
| from vbench.utils import load_video, load_dimension_info, tag2text_transform |
| from vbench.third_party.tag2Text.tag2text import tag2text_caption |
|
|
| import logging |
|
|
| from .distributed import ( |
| get_world_size, |
| get_rank, |
| all_gather, |
| barrier, |
| distribute_list_to_rank, |
| gather_list_of_dict, |
| ) |
|
|
| logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s') |
| logger = logging.getLogger(__name__) |
|
|
|
|
| def get_caption(model, image_arrays): |
| caption, tag_predict = model.generate(image_arrays, tag_input = None, return_tag_predict = True) |
| return caption |
|
|
| def check_generate(key_info, predictions): |
| cur_cnt = 0 |
| key = key_info['scene'] |
| for pred in predictions: |
| q_flag = [q in pred for q in key.split(' ')] |
| if len(q_flag) == sum(q_flag): |
| cur_cnt +=1 |
| return cur_cnt |
|
|
| def scene(model, video_dict, device): |
| success_frame_count, frame_count = 0, 0 |
| video_results = [] |
| transform = tag2text_transform(384) |
| for info in tqdm(video_dict, disable=get_rank() > 0): |
| if 'auxiliary_info' not in info: |
| raise "Auxiliary info is not in json, please check your json." |
| scene_info = info['auxiliary_info']['scene'] |
| for video_path in info['video_list']: |
| video_array = load_video(video_path, num_frames=16, return_tensor=False, width=384, height=384) |
| video_tensor_list = [] |
| for i in video_array: |
| video_tensor_list.append(transform(i).to(device).unsqueeze(0)) |
| video_tensor = torch.cat(video_tensor_list) |
| cur_video_pred = get_caption(model, video_tensor) |
| cur_success_frame_count = check_generate(scene_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_frame_count': cur_success_frame_count, |
| 'frame_count': len(cur_video_pred)}) |
| success_rate = success_frame_count / frame_count |
| return success_rate, video_results |
| |
|
|
| def compute_scene(json_dir, device, submodules_dict, **kwargs): |
| model = tag2text_caption(**submodules_dict) |
| model.eval() |
| model = model.to(device) |
| logger.info("Initialize caption model success") |
| _, prompt_dict_ls = load_dimension_info(json_dir, dimension='scene', lang='en') |
| prompt_dict_ls = distribute_list_to_rank(prompt_dict_ls) |
| all_results, video_results = scene(model, prompt_dict_ls, device) |
| if get_world_size() > 1: |
| video_results = gather_list_of_dict(video_results) |
| success_frame_count = sum([d['success_frame_count'] for d in video_results]) |
| frame_count = sum([d['frame_count'] for d in video_results]) |
| all_results = success_frame_count / frame_count |
| return all_results, video_results |
|
|
