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import torch
from tqdm import tqdm
from torchvision import transforms
from pyiqa.archs.musiq_arch import MUSIQ
from vbench.utils import load_video, load_dimension_info, CACHE_DIR
def transform(images, preprocess_mode='shorter'):
if preprocess_mode.startswith('shorter'):
_, _, h, w = images.size()
if min(h,w) > 512:
scale = 512./min(h,w)
images = transforms.Resize(size=( int(scale * h), int(scale * w) ))(images)
if preprocess_mode == 'shorter_centercrop':
images = transforms.CenterCrop(512)(images)
elif preprocess_mode == 'longer':
_, _, h, w = images.size()
if max(h,w) > 512:
scale = 512./max(h,w)
images = transforms.Resize(size=( int(scale * h), int(scale * w) ))(images)
elif preprocess_mode == 'None':
return images / 255.
else:
raise ValueError("Please recheck imaging_quality_mode")
return images / 255.
def technical_quality(model, video_pairs, device, **kwargs):
if 'imaging_quality_preprocessing_mode' not in kwargs:
preprocess_mode = 'longer'
else:
preprocess_mode = kwargs['imaging_quality_preprocessing_mode']
video_results = []
for info in tqdm(video_pairs):
query = info['prompt']
video_path = info['content_path']
images = load_video(video_path)
images = transform(images, preprocess_mode)
acc_score_video = 0.
for i in range(len(images)):
frame = images[i].unsqueeze(0).to(device)
score = model(frame)
acc_score_video += float(score)
video_result = acc_score_video/len(images)
video_results.append({'prompt':query, 'video_path': video_path, 'video_results': video_result/100.})
average_score = sum([o['video_results'] for o in video_results]) / len(video_results)
# average_score = average_score / 100.
return {
"score":[average_score, video_results]
}
def compute_imaging_quality(video_pairs):
device = torch.device("cuda")
model_path = f'{CACHE_DIR}/pyiqa_model/musiq_spaq_ckpt-358bb6af.pth'
kwargs = {
'imaging_quality_preprocessing_mode' : 'longer'
}
model = MUSIQ(pretrained_model_path=model_path)
model.to(device)
model.training = False
results = technical_quality(model, video_pairs, device, **kwargs)
return results
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