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import io
import os
import cv2
import json
import numpy as np
from PIL import Image
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from vbench.utils import load_video, load_dimension_info, dino_transform, dino_transform_Image
import logging
logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def subject_consistency(model, video_pairs, device):
sim = 0.0
cnt = 0
video_results = []
image_transform = dino_transform(224)
for info in tqdm(video_pairs):
query = info['prompt']
video_path = info['content_path']
video_sim = 0.0
images = load_video(video_path)
images = image_transform(images)
for i in range(len(images)):
with torch.no_grad():
image = images[i].unsqueeze(0)
image = image.to(device)
image_features = model(image)
image_features = F.normalize(image_features, dim=-1, p=2)
if i == 0:
first_image_features = image_features
else:
sim_pre = max(0.0, F.cosine_similarity(former_image_features, image_features).item())
sim_fir = max(0.0, F.cosine_similarity(first_image_features, image_features).item())
cur_sim = (sim_pre + sim_fir) / 2
video_sim += cur_sim
cnt += 1
former_image_features = image_features
sim_per_images = video_sim / (len(images) - 1)
sim += video_sim
video_results.append({'prompt':query, 'video_path': video_path, 'video_results': sim_per_images})
sim_per_frame = sim / cnt
return {
"score":[sim_per_frame, video_results]
}
def compute_subject_consistency(video_pairs):
device = torch.device("cuda")
submodules_list = {
'repo_or_dir':'facebookresearch/dino:main',
'source':'github',
'model': 'dino_vitb16',
}
dino_model = torch.hub.load(**submodules_list).to(device)
logger.info("Initialize DINO success")
results = subject_consistency(dino_model, video_pairs, device)
return results
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