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import os |
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import clip |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import subprocess |
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from urllib.request import urlretrieve |
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from vbench.utils import load_video, load_dimension_info, clip_transform, CACHE_DIR |
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from tqdm import tqdm |
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def get_aesthetic_model(cache_folder): |
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"""load the aethetic model""" |
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path_to_model = cache_folder + "/sa_0_4_vit_l_14_linear.pth" |
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if not os.path.exists(path_to_model): |
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os.makedirs(cache_folder, exist_ok=True) |
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url_model = ( |
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"https://github.com/LAION-AI/aesthetic-predictor/blob/main/sa_0_4_vit_l_14_linear.pth?raw=true" |
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) |
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if not os.path.isfile(path_to_model): |
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try: |
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print(f'trying urlretrieve to download {url_model} to {path_to_model}') |
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urlretrieve(url_model, path_to_model) |
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except: |
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print(f'unable to download {url_model} to {path_to_model} using urlretrieve, trying wget') |
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wget_command = ['wget', url_model, '-P', os.path.dirname(path_to_model)] |
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subprocess.run(wget_command) |
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m = nn.Linear(768, 1) |
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s = torch.load(path_to_model) |
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m.load_state_dict(s) |
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m.eval() |
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return m |
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def laion_aesthetic(aesthetic_model, clip_model, video_pairs, device): |
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aesthetic_model.eval() |
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clip_model.eval() |
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aesthetic_avg = 0.0 |
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num = 0 |
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video_results = [] |
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for video_info in tqdm(video_pairs): |
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video_path = video_info["content_path"] |
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prompt = video_info["prompt"] |
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images = load_video(video_path) |
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image_transform = clip_transform(224) |
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images = image_transform(images) |
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images = images.to(device) |
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image_feats = clip_model.encode_image(images).to(torch.float32) |
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image_feats = F.normalize(image_feats, dim=-1, p=2) |
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aesthetic_scores = aesthetic_model(image_feats).squeeze() |
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normalized_aesthetic_scores = aesthetic_scores/10 |
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cur_avg = torch.mean(normalized_aesthetic_scores, dim=0, keepdim=True) |
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aesthetic_avg += cur_avg.item() |
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num += 1 |
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video_results.append({'prompt':prompt, 'video_path': video_path, 'video_results': cur_avg.item()}) |
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aesthetic_avg /= num |
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return { |
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"score":[aesthetic_avg, video_results] |
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} |
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def compute_aesthetic_quality(video_pairs): |
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device = torch.device("cuda") |
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vit_path = 'ViT-L/14' |
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aes_path = f'{CACHE_DIR}/aesthetic_model/emb_reader' |
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aesthetic_model = get_aesthetic_model(aes_path).to(device) |
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clip_model, preprocess = clip.load(vit_path, device=device) |
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results = laion_aesthetic(aesthetic_model, clip_model, video_pairs, device) |
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return results |
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