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| """ | |
| Adapted from: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py | |
| """ | |
| import argparse | |
| import os | |
| import random | |
| import numpy as np | |
| import torch | |
| import torch.backends.cudnn as cudnn | |
| import gradio as gr | |
| from video_llama.common.config import Config | |
| from video_llama.common.dist_utils import get_rank | |
| from video_llama.common.registry import registry | |
| from video_llama.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 video_llama.datasets.builders import * | |
| from video_llama.models import * | |
| from video_llama.processors import * | |
| from video_llama.runners import * | |
| from video_llama.tasks import * | |
| #%% | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description="Demo") | |
| parser.add_argument("--cfg-path", default='eval_configs/video_llama_eval_withaudio.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='vicuna', help="The type of LLM") | |
| 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.eval() | |
| 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_ask(user_message, chatbot, chat_state): | |
| print("building prompt...") | |
| 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): | |
| print("generating answer...") | |
| llm_message = chat.answer(conv=chat_state, | |
| img_list=img_list, | |
| num_beams=1, | |
| temperature=temperature, | |
| max_new_tokens=240, | |
| max_length=511)[0] | |
| chatbot[-1][1] = llm_message | |
| print(chat_state.get_prompt()) | |
| print(chat_state) | |
| return chatbot, chat_state, img_list | |
| 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(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,chatbot,audio_flag): | |
| 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_video is not None: | |
| print(gr_video) | |
| chatbot = [((gr_video,), None)] | |
| chat_state = default_conversation.copy() | |
| chat_state = Conversation( | |
| system= "You are able to understand the visual content that the user provides." | |
| "Follow the instructions carefully and explain your answers in detail.", | |
| roles=("Human", "Assistant"), | |
| messages=[], | |
| offset=0, | |
| sep_style=SeparatorStyle.SINGLE, | |
| sep="###", | |
| ) | |
| img_list = [] | |
| llm_message = chat.upload_video(gr_video, chat_state, img_list) | |
| llm_message = chat.ask(text_input, chat_state) | |
| llm_message = chat.answer(conv=chat_state, | |
| img_list=img_list, | |
| num_beams=1, | |
| temperature=1.0, | |
| max_new_tokens=240, | |
| max_length=511)[0] | |
| print(llm_message) | |
| output = [[llm_message]] | |
| return llm_message, output | |
| elif gr_img is not None: | |
| print(gr_img) | |
| chatbot = [((gr_img,), None)] | |
| chat_state = Conversation( | |
| system= "You are able to understand the visual content that the user provides." | |
| "Follow the instructions carefully and explain your answers in detail.", | |
| roles=("Human", "Assistant"), | |
| messages=[], | |
| offset=0, | |
| sep_style=SeparatorStyle.SINGLE, | |
| sep="###", | |
| ) | |
| 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 | |
| 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 | |
| title = """ | |
| <h1 align="center"><a href="https://github.com/DAMO-NLP-SG/Video-LLaMA"><img src="https://s1.ax1x.com/2023/05/22/p9oQ0FP.jpg", alt="Video-LLaMA" border="0" style="margin: 0 auto; height: 200px;" /></a> </h1> | |
| <h1><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/51/IBM_logo.svg/1000px-IBM_logo.svg.png", alt="Video-LLaMA" border="0" style="margin: 0 auto; height: 200px;" /></h1> | |
| <h1 align="center">Video-LLaMA-2: An Instruction-tuned Audio-Visual Language Model for Video Understanding</h1> | |
| <h5 align="center"> Introduction: Video-LLaMA is a multi-model large language model that achieves video-grounded conversations between humans and computers \ | |
| by connecting language decoder with off-the-shelf unimodal pre-trained models. </h5> | |
| Current online demo uses the 7B version of Video-LLaMA-2 due to resource limitations of running on a Nvidia A10. | |
| From the IBM Generative AI Italy team who better adapted the model for LLAMA-2-7B. For any issue contact daniele.comi@ibm.com | |
| """ | |
| cite_markdown = (""" | |
| ## Citation | |
| If you find our project useful, hope you can star our repo and cite our paper as follows: | |
| ``` | |
| @article{damonlpsg2023videollama, | |
| author = {Zhang, Hang and Li, Xin and Bing, Lidong}, | |
| title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding}, | |
| year = 2023, | |
| journal = {arXiv preprint arXiv:2306.02858} | |
| url = {https://arxiv.org/abs/2306.02858} | |
| } | |
| """) | |
| 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") | |
| 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") | |
| with gr.Column(): | |
| chat_state = gr.State() | |
| img_list = gr.State() | |
| chatbot = gr.Chatbot(label='Video-LLaMA') | |
| text_input = gr.Textbox(label='User', placeholder='Upload your image/video first, or directly click the examples at the bottom of the page.', interactive=False) | |
| output = gr.Textbox(label='Output') | |
| gr.Markdown(cite_markdown) | |
| #upload_button.click(upload_imgorvideo, inputs=[video, image, text_input], outputs=[chat_state,chatbot]) | |
| text_input.submit(upload_imgorvideo, inputs=[video, image, text_input], outputs=[output]) | |
| #clear.click(gradio_reset, [chat_state, img_list], [chatbot, video, image, text_input, upload_button, chat_state, img_list], queue=False) | |
| demo.queue().launch(debug=True) | |
| # %% | |