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| import os | |
| import json | |
| import numpy as np | |
| from tqdm import tqdm | |
| import torch | |
| import clip | |
| from PIL import Image | |
| from vbench.utils import load_video, load_dimension_info, clip_transform, read_frames_decord_by_fps, clip_transform_Image | |
| 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 appearance_style(clip_model, video_dict, device, sample="rand"): | |
| sim = 0.0 | |
| cnt = 0 | |
| video_results = [] | |
| image_transform = clip_transform_Image(224) | |
| for info in tqdm(video_dict): | |
| if 'auxiliary_info' not in info: | |
| raise "Auxiliary info is not in json, please check your json." | |
| query = info['auxiliary_info']['appearance_style'] | |
| text = clip.tokenize([query]).to(device) | |
| video_list = info['video_list'] | |
| for video_path in video_list: | |
| cur_video = [] | |
| with torch.no_grad(): | |
| video_arrays = load_video(video_path, return_tensor=False) | |
| images = [Image.fromarray(i) for i in video_arrays] | |
| for image in images: | |
| image = image_transform(image) | |
| image = image.to(device) | |
| logits_per_image, logits_per_text = clip_model(image.unsqueeze(0), text) | |
| cur_sim = float(logits_per_text[0][0].cpu()) | |
| cur_sim = cur_sim / 100 | |
| cur_video.append(cur_sim) | |
| sim += cur_sim | |
| cnt +=1 | |
| video_sim = np.mean(cur_video) | |
| video_results.append({'video_path': video_path, 'video_results': video_sim, 'frame_results':cur_video}) | |
| sim_per_frame = sim / cnt | |
| return sim_per_frame, video_results | |
| def compute_appearance_style(json_dir, device, submodules_list): | |
| clip_model, preprocess = clip.load(device=device, **submodules_list) | |
| _, video_dict = load_dimension_info(json_dir, dimension='appearance_style', lang='en') | |
| all_results, video_results = appearance_style(clip_model, video_dict, device) | |
| return all_results, video_results | |