| from .simple_tokenizer import SimpleTokenizer as _Tokenizer |
| from .viclip import ViCLIP |
| import torch |
| import numpy as np |
| import cv2 |
| import os |
|
|
|
|
| def get_viclip(size='l', |
| pretrain=os.path.join(os.path.dirname(os.path.abspath(__file__)), "ViClip-InternVid-10M-FLT.pth")): |
| |
| tokenizer = _Tokenizer() |
| vclip = ViCLIP(tokenizer=tokenizer, size=size, pretrain=pretrain) |
| m = {'viclip':vclip, 'tokenizer':tokenizer} |
| |
| return m |
|
|
| def get_text_feat_dict(texts, clip, tokenizer, text_feat_d={}): |
| for t in texts: |
| feat = clip.get_text_features(t, tokenizer, text_feat_d) |
| text_feat_d[t] = feat |
| return text_feat_d |
|
|
| def get_vid_feat(frames, clip): |
| return clip.get_vid_features(frames) |
|
|
| def _frame_from_video(video): |
| while video.isOpened(): |
| success, frame = video.read() |
| if success: |
| yield frame |
| else: |
| break |
|
|
| v_mean = np.array([0.485, 0.456, 0.406]).reshape(1,1,3) |
| v_std = np.array([0.229, 0.224, 0.225]).reshape(1,1,3) |
| def normalize(data): |
| return (data/255.0-v_mean)/v_std |
|
|
| def frames2tensor(vid_list, fnum=8, target_size=(224, 224), device=torch.device('cuda')): |
| assert(len(vid_list) >= fnum) |
| step = len(vid_list) // fnum |
| vid_list = vid_list[::step][:fnum] |
| vid_list = [cv2.resize(x[:,:,::-1], target_size) for x in vid_list] |
| vid_tube = [np.expand_dims(normalize(x), axis=(0, 1)) for x in vid_list] |
| vid_tube = np.concatenate(vid_tube, axis=1) |
| vid_tube = np.transpose(vid_tube, (0, 1, 4, 2, 3)) |
| vid_tube = torch.from_numpy(vid_tube).to(device, non_blocking=True).float() |
| return vid_tube |
|
|
| def retrieve_text(frames, |
| texts, |
| models={'viclip':None, |
| 'tokenizer':None}, |
| topk=5, |
| device=torch.device('cuda')): |
| |
| assert(type(models)==dict and models['viclip'] is not None and models['tokenizer'] is not None) |
| clip, tokenizer = models['viclip'], models['tokenizer'] |
| clip = clip.to(device) |
| frames_tensor = frames2tensor(frames, device=device) |
| vid_feat = get_vid_feat(frames_tensor, clip) |
|
|
| text_feat_d = {} |
| text_feat_d = get_text_feat_dict(texts, clip, tokenizer, text_feat_d) |
| text_feats = [text_feat_d[t] for t in texts] |
| text_feats_tensor = torch.cat(text_feats, 0) |
| |
| probs, idxs = clip.get_predict_label(vid_feat, text_feats_tensor, top=topk) |
|
|
| ret_texts = [texts[i] for i in idxs.numpy()[0].tolist()] |
| return ret_texts, probs.numpy()[0] |
