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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
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