| import cv2 | |
| from transformers import AutoModel | |
| from modeling_internvideo2 import (retrieve_text, vid2tensor, _frame_from_video,) | |
| if __name__ == '__main__': | |
| model = AutoModel.from_pretrained("/mnt/petrelfs/lixinhao/lxh_exp/LongVideo/InternVideo2-Stage2_6B", trust_remote_code=True).eval() | |
| video = cv2.VideoCapture('example1.mp4') | |
| frames = [x for x in _frame_from_video(video)] | |
| text_candidates = ["A playful dog and its owner wrestle in the snowy yard, chasing each other with joyous abandon.", | |
| "A man in a gray coat walks through the snowy landscape, pulling a sleigh loaded with toys.", | |
| "A person dressed in a blue jacket shovels the snow-covered pavement outside their house.", | |
| "A cat excitedly runs through the yard, chasing a rabbit.", | |
| "A person bundled up in a blanket walks through the snowy landscape, enjoying the serene winter scenery."] | |
| texts, probs = retrieve_text(frames, text_candidates, model=model, topk=5) | |
| for t, p in zip(texts, probs): | |
| print(f'text: {t} ~ prob: {p:.4f}') | |
| vidtensor = vid2tensor('example1.mp4', fnum=4) | |
| feat = model.get_vid_feat(vidtensor) |