import torch from ..constants import * from ..conversation import conv_templates, SeparatorStyle from ..model.builder import load_pretrained_model from ..utils import disable_torch_init from ..mm_utils import tokenizer_image_token, KeywordsStoppingCriteria, get_model_name_from_path from PIL import Image import os from decord import VideoReader, cpu import numpy as np class Chat: def __init__(self, model_path, conv_mode="simple", load_8bit=False, load_4bit=False): disable_torch_init() model_name = get_model_name_from_path(model_path) self.tokenizer, self.model, self.image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=load_8bit, load_4bit=load_4bit) mm_use_im_start_end = getattr(self.model.config, "mm_use_im_start_end", False) mm_use_im_patch_token = getattr(self.model.config, "mm_use_im_patch_token", True) if mm_use_im_patch_token: self.tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) if mm_use_im_start_end: self.tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) self.model.resize_token_embeddings(len(self.tokenizer)) vision_tower = self.model.get_vision_tower() if not vision_tower.is_loaded: vision_tower.load_model() self.image_processor = vision_tower.image_processor self.conv_mode = conv_mode print(self.model) def get_prompt(self, qs, state): state.append_message(state.roles[0], qs) state.append_message(state.roles[1], None) return state def _get_rawvideo_dec(self, video_path, image_processor, max_frames=MAX_IMAGE_LENGTH, image_resolution=224, video_framerate=1, s=None, e=None): if s is None: start_time, end_time = None, None else: start_time = int(s) end_time = int(e) start_time = start_time if start_time >= 0. else 0. end_time = end_time if end_time >= 0. else 0. if start_time > end_time: start_time, end_time = end_time, start_time elif start_time == end_time: end_time = start_time + 1 if os.path.exists(video_path): vreader = VideoReader(video_path, ctx=cpu(0)) else: print(video_path) raise FileNotFoundError fps = vreader.get_avg_fps() f_start = 0 if start_time is None else int(start_time * fps) f_end = int(min(1000000000 if end_time is None else end_time * fps, len(vreader) - 1)) num_frames = f_end - f_start + 1 if num_frames > 0: sample_fps = int(video_framerate) t_stride = int(round(float(fps) / sample_fps)) all_pos = list(range(f_start, f_end + 1, t_stride)) if len(all_pos) > max_frames: sample_pos = [all_pos[_] for _ in np.linspace(0, len(all_pos) - 1, num=max_frames, dtype=int)] else: sample_pos = all_pos patch_numpy = vreader.get_batch(sample_pos).asnumpy() print("patch_numpy", patch_numpy.shape) return patch_numpy @torch.inference_mode() def generate(self, images_tensor, prompt, first_run, state): tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor state = self.get_prompt(prompt, state) prompt = state.get_prompt() print(prompt) input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() temperature = 0.2 max_new_tokens = 1024 stop_str = conv_templates[self.conv_mode].copy().sep if conv_templates[self.conv_mode].copy().sep_style != SeparatorStyle.TWO else \ conv_templates[self.conv_mode].copy().sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images=images_tensor, do_sample=True, temperature=temperature, max_new_tokens=max_new_tokens, use_cache=True, stopping_criteria=[stopping_criteria]) input_token_len = input_ids.shape[1] n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() if n_diff_input_output > 0: print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] outputs = outputs.strip() print('response', outputs) return outputs, state title_markdown = ("""

Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams

""") block_css = """ #buttons button { min-width: min(120px,100%); } """