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| import os | |
| import json | |
| import numpy as np | |
| import torch | |
| import clip | |
| from tqdm import tqdm | |
| from vbench.utils import load_video, load_dimension_info, clip_transform, read_frames_decord_by_fps, CACHE_DIR | |
| from vbench.third_party.ViCLIP.viclip import ViCLIP | |
| from vbench.third_party.ViCLIP.simple_tokenizer import SimpleTokenizer | |
| def get_text_features(model, input_text, tokenizer, text_feature_dict={}): | |
| if input_text in text_feature_dict: | |
| return text_feature_dict[input_text] | |
| text_template= f"{input_text}" | |
| with torch.no_grad(): | |
| text_features = model.encode_text(text_template).float() | |
| text_features /= text_features.norm(dim=-1, keepdim=True) | |
| text_feature_dict[input_text] = text_features | |
| return text_features | |
| def get_vid_features(model, input_frames): | |
| with torch.no_grad(): | |
| clip_feat = model.encode_vision(input_frames,test=True).float() | |
| clip_feat /= clip_feat.norm(dim=-1, keepdim=True) | |
| return clip_feat | |
| def get_predict_label(clip_feature, text_feats_tensor, top=5): | |
| label_probs = (100.0 * clip_feature @ text_feats_tensor.T).softmax(dim=-1) | |
| top_probs, top_labels = label_probs.cpu().topk(top, dim=-1) | |
| return top_probs, top_labels | |
| def overall_consistency(clip_model, video_dict, tokenizer, device, sample="middle"): | |
| sim = [] | |
| video_results = [] | |
| image_transform = clip_transform(224) | |
| for info in tqdm(video_dict): | |
| query = info['prompt'] | |
| text = clip.tokenize([query]).to(device) | |
| video_list = info['video_list'] | |
| for video_path in video_list: | |
| cur_video = [] | |
| with torch.no_grad(): | |
| images = read_frames_decord_by_fps(video_path, num_frames=8, sample=sample) | |
| images = image_transform(images) | |
| images = images.to(device) | |
| clip_feat = get_vid_features(clip_model,images.unsqueeze(0)) | |
| text_feat = get_text_features(clip_model, query, tokenizer) | |
| logit_per_text = clip_feat @ text_feat.T | |
| score_per_video = float(logit_per_text[0][0].cpu()) | |
| sim.append(score_per_video) | |
| video_results.append({'video_path': video_path, 'video_results': score_per_video}) | |
| avg_score = np.mean(sim) | |
| return avg_score, video_results | |
| def compute_overall_consistency(json_dir, device, submodules_list): | |
| tokenizer = SimpleTokenizer(os.path.join(CACHE_DIR, "ViCLIP/bpe_simple_vocab_16e6.txt.gz")) | |
| viclip = ViCLIP(tokenizer= tokenizer, **submodules_list).to(device) | |
| _, video_dict = load_dimension_info(json_dir, dimension='overall_consistency', lang='en') | |
| all_results, video_results = overall_consistency(viclip, video_dict, tokenizer, device) | |
| return all_results, video_results | |