| 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) |
| |
| |
| 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 |
|
|
