| import os |
| import clip |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import subprocess |
| from urllib.request import urlretrieve |
| from editboard.utils import load_video, load_dimension_info, clip_transform |
| from tqdm import tqdm |
|
|
|
|
| def get_aesthetic_model(cache_folder): |
| """load the aethetic model""" |
| path_to_model = cache_folder + "/sa_0_4_vit_l_14_linear.pth" |
| if not os.path.exists(path_to_model): |
| os.makedirs(cache_folder, exist_ok=True) |
| url_model = ( |
| "https://github.com/LAION-AI/aesthetic-predictor/blob/main/sa_0_4_vit_l_14_linear.pth?raw=true" |
| ) |
| |
| if not os.path.isfile(path_to_model): |
| try: |
| print(f'trying urlretrieve to download {url_model} to {path_to_model}') |
| urlretrieve(url_model, path_to_model) |
| except: |
| print(f'unable to download {url_model} to {path_to_model} using urlretrieve, trying wget') |
| wget_command = ['wget', url_model, '-P', os.path.dirname(path_to_model)] |
| subprocess.run(wget_command) |
| m = nn.Linear(768, 1) |
| s = torch.load(path_to_model) |
| m.load_state_dict(s) |
| m.eval() |
| return m |
|
|
|
|
| def laion_aesthetic(aesthetic_model, clip_model, video_list, device): |
| aesthetic_model.eval() |
| clip_model.eval() |
| num = 0 |
| video_results = {} |
| for video_path in tqdm(video_list): |
| images = load_video(video_path) |
| image_transform = clip_transform(224) |
| images = image_transform(images) |
| images = images.to(device) |
| image_feats = clip_model.encode_image(images).to(torch.float32) |
| image_feats = F.normalize(image_feats, dim=-1, p=2) |
| aesthetic_scores = aesthetic_model(image_feats).squeeze() |
| normalized_aesthetic_scores = aesthetic_scores/10 |
| cur_avg = torch.mean(normalized_aesthetic_scores, dim=0, keepdim=True) |
| num += 1 |
| video_results[os.path.dirname(os.path.dirname(video_path))] = cur_avg.item() |
| return video_results |
|
|
|
|
| def compute_aesthetic_quality(json_dir, device, submodules_list): |
| vit_path = submodules_list[0] |
| aes_path = submodules_list[1] |
| aesthetic_model = get_aesthetic_model(aes_path).to(device) |
| clip_model, preprocess = clip.load(vit_path, device=device) |
| video_list = load_dimension_info(json_dir, dimension='aesthetic_quality') |
| video_results = laion_aesthetic(aesthetic_model, clip_model, video_list, device) |
| return video_results |
|
|
