ESTP-Bench/estp_dataset/model/TimeChat.py

import torch
from PIL import Image
from transformers import AutoConfig, AutoModel, AutoTokenizer, LlamaTokenizer, LlamaForCausalLM
from decord import VideoReader, cpu
import decord
from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_in_model, dispatch_model
import torch.nn as nn
import einops

from .modelclass import Model

import torchshow as ts

import sys
sys.path.insert(0, "/root/videollm-online/baseline/TimeChat")

import argparse
import os
import random
import json
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torchshow as ts
from timechat.common.config import Config
from timechat.common.dist_utils import get_rank
from timechat.common.registry import registry
from timechat.conversation.conversation_video import Chat, Conversation, default_conversation,SeparatorStyle, conv_llava_llama_2
import decord
import cv2
import time
import subprocess
from decord import VideoReader
from timechat.processors.video_processor import ToTHWC, ToUint8, load_video
decord.bridge.set_bridge('torch')

# imports modules for registration
from timechat.datasets.builders import *
from timechat.models import *
from timechat.processors import *
from timechat.runners import *
from timechat.tasks import *

import random as rnd
from transformers import StoppingCriteria, StoppingCriteriaList
from PIL import Image
import gradio as gr

import random as rnd
import numpy as np

import re

import math
def ceil_time_by_fps(time: float, fps: int, min_time: float, max_time: float):
    return min(max(math.ceil(time * fps) / fps, min_time), max_time)

# HACK: need to modifed timechat load video function
def load_video(video_path, start_time, end_time, n_frms=32, height=-1, width=-1, sampling="uniform", return_msg = False):
    decord.bridge.set_bridge("torch")
    vr = VideoReader(uri=video_path, height=height, width=width)
    
    # HACK: need to modifed timechat load video function
    fps = float(vr.get_avg_fps())
    if start_time is not None:
        start_time = ceil_time_by_fps(start_time, fps, min_time=0, max_time=len(vr)/fps)
        start_frame = int(start_time * fps)
    if end_time is not None:
        end_time = ceil_time_by_fps(end_time, fps, min_time=0, max_time=len(vr)/fps)
        end_frame = int(end_time * fps + 1)
    vlen = end_frame - start_frame
    acc_samples = min(n_frms, vlen)
    n_frms = min(n_frms, vlen)

    if sampling == "uniform":
        indices = np.arange(start_frame, end_frame, vlen / n_frms).astype(int).tolist()
    elif sampling == "headtail":
        indices_h = sorted(rnd.sample(range(vlen // 2), n_frms // 2))
        indices_t = sorted(rnd.sample(range(vlen // 2, vlen), n_frms // 2))
        indices = indices_h + indices_t
    elif sampling == 'rand':
        # split the video into `acc_samples` intervals, and sample from each interval.
        intervals = np.linspace(start=0, stop=vlen, num=acc_samples + 1).astype(int)
        ranges = []
        for idx, interv in enumerate(intervals[:-1]):
            ranges.append((interv, intervals[idx + 1] - 1))
        try:
            indices = [rnd.choice(range(x[0], x[1])) for x in ranges]
        except:
            indices = np.random.permutation(vlen)[:acc_samples]
            indices.sort()
            indices = list(indices)
    else:
        raise NotImplementedError

    # get_batch -> T, H, W, C
    temp_frms = vr.get_batch(indices)
    # print(type(temp_frms))
    tensor_frms = torch.from_numpy(temp_frms) if type(temp_frms) is not torch.Tensor else temp_frms
    frms = tensor_frms.permute(3, 0, 1, 2).float()  # (C, T, H, W)

    if not return_msg:
        return frms

    fps = float(vr.get_avg_fps())
    sec = ", ".join([str(round(f / fps, 1)) for f in indices])
    # " " should be added in the start and end
    msg = f"The video contains {len(indices)} frames sampled at {sec} seconds. "
    return frms, msg


class TimeChat(Model):
    def __init__(self, device, config=None):
        """
        Initialize the model by loading the pretrained TimeChat model and tokenizer.
        """
        self.device = device
        self.config = config
        
        # Parse configuration from TimeChat
        cfg = Config(config)
        
        # Load model configuration
        DIR="/home/docker_shared/asus/zhangyl/model/huggingface/hub/models--ShuhuaiRen--TimeChat-7b/snapshots/e12f42c6c9bd114525e99b2d5b1903d86ea3ce43/"
        MODEL_DIR=f"{DIR}/timechat_7b.pth"
        model_config = cfg.model_cfg
        model_config.device_8bit = int(device.split(':')[1]) if ':' in device else 0
        model_config.ckpt = MODEL_DIR
        model_config.vit_model = "/root/videollm-online/baseline/TimeChat/ckpt/eva_vit_g.pth"
        model_config.q_former_model = "/root/videollm-online/baseline/TimeChat/ckpt/instruct_blip_vicuna7b_trimmed.pth"
        
        # Initialize TimeChat model
        model_cls = registry.get_model_class(model_config.arch)
        self.model = model_cls.from_config(model_config).to(device)
        self.model.eval()
        
        # Initialize processor for video frames
        vis_processor_cfg = cfg.datasets_cfg.webvid.vis_processor.train
        self.vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
        self.chat = Chat(self.model, self.vis_processor, device=device)
        
        # Set parameters from config
        self.frame_fps = config.frame_fps
        self.MAX_NUM_FRAMES = config.max_frames_num
        self.num_beams = config.num_beams
        self.temperature = config.temperature
        self.height = config.height
        self.width = config.width
        

    def Run(self, file, inp, start_time, end_time):
        """
        Given the file (video file path) and input prompt (inp), run the model and return the response.
        """
        # Encode video frames
        frames, msg = load_video(
            video_path=file,
            start_time=start_time,
            end_time=end_time,
            n_frms=self.MAX_NUM_FRAMES,
            height=self.height,
            width=self.width,
            sampling ="uniform", return_msg = True
        )


        img_list = []
        chat_state = conv_llava_llama_2.copy()
        chat_state.system =  "You are able to understand the visual content that the user provides. Follow the instructions carefully and explain your answers in detail."
        msg = self.chat.upload_video_without_audio(
            video_path=(frames, msg), 
            conv=chat_state,
            img_list=img_list, 
            n_frms=self.MAX_NUM_FRAMES,
        )
        text_input = "You are given a video. Please watch the video, identify all relevant moments that help answer the question. For each moments, determine the starting and ending times and provide a answer. The format should be: 'start time - end time, answer'. For example, ' 90 - 102 seconds, spread margarine on two slices of white bread'."

        text_input = open('/root/videollm-online/baseline/TimeChat/prompts/tvg_description.txt').read()
        self.chat.ask(text_input.format(inp), chat_state)
        
        
        # self.chat.ask(text_input + "\n" + inp, chat_state)

        num_beams = self.num_beams
        temperature = self.temperature
        llm_message = self.chat.answer(conv=chat_state,img_list=img_list,num_beams=num_beams,temperature=temperature,max_new_tokens=300,max_length=2000)[0]
        response = llm_message.split('\n')[-1]
        
        breakpoint()
        
        # parse response
        pattern = r"(\d+\.\d+) - (\d+\.\d+)\s*seconds,\s*(.*)"
        matches = re.findall(pattern, response)
        answer_pairs = list()
        for match in matches:
            moment_start, moment_end, answer = float(match[0]), float(match[1]), match[2]
            answer_pairs.append(((moment_start + moment_end) / 2, answer))
        
        breakpoint()
        return answer_pairs

    @staticmethod
    def name():
        """
        Return the name of the model
        """
        return "TimeChat"