""" Adapted from: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py """ import argparse import os import sys import random import numpy as np import torch import torch.backends.cudnn as cudnn import gradio as gr from global_local.common.config import Config from global_local.common.dist_utils import get_rank from global_local.common.registry import registry from global_local.conversation.conversation_video import Chat, Conversation, default_conversation,SeparatorStyle,conv_llava_llama_2 import decord decord.bridge.set_bridge('torch') #%% # imports modules for registration from global_local.datasets.builders import * from global_local.models import * from global_local.processors import * from global_local.runners import * from global_local.tasks import * #%% def parse_args(): parser = argparse.ArgumentParser(description="Demo") #parser.add_argument("--cfg-path", required=True, help="path to configuration file.") parser.add_argument("--cfg-path", type=str, default='./eval_configs/conversation_demo.yaml', help="path to configuration file.") parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.") parser.add_argument("--model_type", type=str, default='llama_v2', help="specify LLM") parser.add_argument('--pretrained_weight_path', type=str, default="./ckpt/finetuned_model.pth", metavar='PWP', help='path to pretrained weight path') parser.add_argument('--num_frames_per_clip', type=int, default=16, metavar='NPPC', help='specify how frames to use per clip') parser.add_argument('--num_segments', type=int, default=4, metavar='NS', help='specify number of video segments') parser.add_argument('--hierarchical_agg_function', type=str, default="without-top-final-global-prompts-region-segment-full-dis-spatiotemporal-prompts-attn-early-attn-linear-learned", metavar='HAF', help='specify function to merge global and clip visual representations') parser.add_argument( "--options", nargs="+", help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file (deprecate), " "change to --cfg-options instead.", ) args = parser.parse_args() return args def setup_seeds(config): seed = config.run_cfg.seed + get_rank() random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) cudnn.benchmark = False cudnn.deterministic = True # ======================================== # Model Initialization # ======================================== print('Initializing Chat') args = parse_args() cfg = Config(args) model_config = cfg.model_cfg model_config.device_8bit = args.gpu_id model_cls = registry.get_model_class(model_config.arch) model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id)) model.num_frames_per_clip = args.num_frames_per_clip model.num_segments = args.num_segments model.hierarchical_agg_function = args.hierarchical_agg_function model.global_region_embed_weight = None model.initialize_visual_agg_function() best_checkpoint = torch.load(args.pretrained_weight_path, map_location='cpu')['model_state_dict'] pretrained_dict = {} for k, v in best_checkpoint.items(): pretrained_dict[k.replace('module.', '')] = v model_dict = model.state_dict() model_dict.update(pretrained_dict) model.load_state_dict(model_dict) model.cuda().eval() #vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train vis_processor_cfg = cfg.datasets_cfg.webvid.vis_processor.train vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg) chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id)) print('Initialization Finished') # ======================================== # Gradio Setting # ======================================== def gradio_reset(chat_state, img_list): if chat_state is not None: chat_state.messages = [] if img_list is not None: img_list = [] return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your video first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list '''def upload_imgorvideo(gr_video, gr_img, text_input, chat_state,chatbot): if args.model_type == 'vicuna': chat_state = default_conversation.copy() else: chat_state = conv_llava_llama_2.copy() if gr_img is None and gr_video is None: return None, None, None, gr.update(interactive=True), chat_state, None elif gr_img is not None and gr_video is None: print(gr_img) chatbot = chatbot + [((gr_img,), None)] 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." img_list = [] llm_message = chat.upload_img(gr_img, chat_state, img_list) return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot elif gr_video is not None and gr_img is None: print(gr_video) chatbot = chatbot + [((gr_video,), None)] 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." img_list = [] llm_message = chat.upload_video_without_audio(gr_video, chat_state, img_list) return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot else: # img_list = [] return gr.update(interactive=False), gr.update(interactive=False, placeholder='Currently, only one input is supported'), gr.update(value="Currently, only one input is supported", interactive=False), chat_state, None,chatbot''' def upload_imgorvideo(gr_video, text_input, chat_state, chatbot): if args.model_type == 'vicuna': chat_state = default_conversation.copy() else: chat_state = conv_llava_llama_2.copy() print(gr_video) chatbot = chatbot + [((gr_video,), None)] 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." img_list = [] llm_message = chat.upload_video_without_audio(gr_video, chat_state, img_list) return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list,chatbot def gradio_ask(user_message, chatbot, chat_state): if len(user_message) == 0: return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state chat.ask(user_message, chat_state) chatbot = chatbot + [[user_message, None]] return '', chatbot, chat_state def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature): llm_message = chat.answer(conv=chat_state, img_list=img_list, num_beams=num_beams, temperature=temperature, max_new_tokens=300, max_length=2000)[0] chatbot[-1][1] = llm_message print(chat_state.get_prompt()) print(chat_state) return chatbot, chat_state, img_list title = """

Global-Local QFormer for Long Video Understanding with LLMs

Introduction: We introduce a Global-Local QFormer video model that is connected with a Large Language Model to understand and answer questions about long videos. To try out this demo, please upload a video and start the chat.
Thank you for using the Global-Local QFormer Demo Page! If you have any questions or feedback, please feel free to contact us. Current online demo uses the 7B version of Llama-2 due to resource limitations. """ Note_markdown = (""" ### We note that our Global-Local QFormer model may be limited at understanding videos from rare domains. Due to the pretraining data, the \ model may be susceptible to hallucinations We would like to acknowledge the Video-LLama repository which we copied the demo layout from. **Boston University** """) cite_markdown = (""" """) #case_note_upload = (""" ### We provide some examples at the bottom of the page. Simply click on them to try them out directly. #""") #TODO show examples below with gr.Blocks() as demo: gr.Markdown(title) with gr.Row(): with gr.Column(scale=0.5): video = gr.Video() #image = gr.Image(type="filepath") image = None #gr.Markdown(case_note_upload) upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary") clear = gr.Button("Restart") num_beams = gr.Slider( minimum=1, maximum=10, value=1, step=1, interactive=True, label="beam search numbers)", ) temperature = gr.Slider( minimum=0.1, maximum=2.0, value=1.0, step=0.1, interactive=True, label="Temperature", ) audio = gr.Checkbox(interactive=True, value=False, label="Audio") gr.Markdown(Note_markdown) with gr.Column(): chat_state = gr.State() img_list = gr.State() chatbot = gr.Chatbot(label='Global-Local QFormer') text_input = gr.Textbox(label='User', placeholder='Please upload your video first.', interactive=False) '''with gr.Column(): gr.Examples(examples=[ [f"examples/skateboarding_dog.mp4", "What is the dog doing? "], [f"examples/birthday.mp4", "What is the boy doing? "], [f"examples/IronMan.mp4", "Is the guy in the video Iron Man? "], ], inputs=[video, text_input])''' gr.Markdown(cite_markdown) upload_button.click(upload_imgorvideo, [video, text_input, chat_state,chatbot], [video, text_input, upload_button, chat_state, img_list,chatbot]) #upload_button.click(upload_imgorvideo, [video, image, text_input, chat_state,chatbot], [video, image, text_input, upload_button, chat_state, img_list,chatbot]) text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then( gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list] ) clear.click(gradio_reset, [chat_state, img_list], [chatbot, video, text_input, upload_button, chat_state, img_list], queue=False) #demo.launch(share=False, enable_queue=True, debug=True) demo.queue(max_size=10) demo.launch(share=True)