"""
Run the following command to start the demo:
python demo_video.py \
--cfg-path /remote-home/share/jiaqitang/Hawk_Ours/configs/eval_configs/eval.yaml \
--model_type llama_v2 \
--gpu-id 0
"""
import requests
import argparse
import os
import random
import subprocess
import sys
import io
import spaces
subprocess.check_call([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from hawk.common.config import Config
from hawk.common.dist_utils import get_rank
from hawk.common.registry import registry
from hawk.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 hawk.datasets.builders import *
from hawk.models import *
from hawk.processors import *
from hawk.runners import *
from hawk.tasks import *
import time
def parse_args():
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--cfg-path", required=False, default='./configs/eval_configs/eval.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="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 = model_cls.from_config(model_config).to('cuda')
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))
chat = Chat(model, vis_processor, device='cuda')
print('Initialization Finished')
have_video = 0
# ========================================
# Gradio Setting
# ========================================
def gradio_reset(chat_state, img_list):
global have_video
have_video = 0
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(interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
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()
if 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:
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
# 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 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
@spaces.GPU
def gradio_answer(video, chatbot, chat_state, img_list, num_beams, temperature):
global have_video
if have_video == 0:
llm_message = chat.upload_video_without_audio(video, chat_state, img_list)
have_video = 1
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, []
title = """
Hawk: Learning to Understand Open-World Video Anomalies
"Have eyes like a Hawk!"
"""
cite_markdown = ("""
## Citation
The following is a BibTeX reference:
```
@inproceedings{atang2024hawk,
title = {Hawk: Learning to Understand Open-World Video Anomalies},
author = {Tang, Jiaqi and Lu, Hao and Wu, Ruizheng and Xu, Xiaogang and Ma, Ke and Fang, Cheng and Guo, Bin and Lu, Jiangbo and Chen, Qifeng and Chen, Ying-Cong},
year = {2024},
booktitle = {Neural Information Processing Systems (NeurIPS)}
}
""")
# 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='Hawk')
text_input = gr.Textbox(label='User', placeholder='Upload your video first and start to chat.', interactive=False)
with gr.Column():
gr.Examples(examples=[
[f"figs/examples/explosion2.mp4", "What happened in this video? "],
[f"figs/examples/car.mp4", "What is the anomaly for the car in this video? "],
], 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])
start_time = time.time()
text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
gradio_answer, [video, chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]
)
end_time = time.time()
print('Time:', end_time - start_time)
clear.click(gradio_reset, [chat_state, img_list], [chatbot, video, text_input, upload_button, chat_state, img_list], queue=False)
demo.launch()
