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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 = ("""
<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
<div>
<h1 >Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams</h1>
</div>
</div>
<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
<div style="display:flex; gap: 0.25rem;" align="center">
<a href="https://invinciblewyq.github.io/vstream-page/"><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a href="https://arxiv.org/abs/2406.08085v1"><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
<a href='https://github.com/IVGSZ/Flash-VStream'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
</div>
</div>
""")
block_css = """
#buttons button {
min-width: min(120px,100%);
}
"""
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