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import os
import json
import logging
import numpy as np
import clip
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
from vbench.utils import load_video, load_dimension_info, clip_transform, CACHE_DIR
from tqdm import tqdm
def background_consistency(clip_model, preprocess, video_pairs, device):
sim = 0.0
cnt = 0
video_results = []
image_transform = clip_transform(224)
for info in tqdm(video_pairs):
video_sim = 0.0
query = info['prompt']
video_path = info['content_path']
images = load_video(video_path)
images = image_transform(images)
images = images.to(device)
image_features = clip_model.encode_image(images)
image_features = F.normalize(image_features, dim=-1, p=2)
for i in range(len(image_features)):
image_feature = image_features[i].unsqueeze(0)
if i == 0:
first_image_feature = image_feature
else:
sim_pre = max(0.0, F.cosine_similarity(former_image_feature, image_feature).item())
sim_fir = max(0.0, F.cosine_similarity(first_image_feature, image_feature).item())
cur_sim = (sim_pre + sim_fir) / 2
video_sim += cur_sim
cnt += 1
former_image_feature = image_feature
sim_per_image = video_sim / (len(image_features) - 1)
sim += video_sim
video_results.append({'prompt':query, 'video_path': video_path, 'video_results': sim_per_image})
sim_per_frame = sim / cnt
return {
"score":[sim_per_frame, video_results]
}
def compute_background_consistency(video_pairs):
device = torch.device("cuda")
vit_path = f'{CACHE_DIR}/clip_model/ViT-B-32.pt'
clip_model, preprocess = clip.load(vit_path, device=device)
results = background_consistency(clip_model, preprocess, video_pairs, device)
return results
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