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demo.py
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import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from PIL import Image
# from minigpt4.common.config import Config
from util.misc import get_rank
# from minigpt4.common.registry import registry
from conversation.conversation import Chat, CONV_VISION
from torchvision.transforms import transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from eval import load
from fairscale.nn.model_parallel.initialize import initialize_model_parallel
from typing import Tuple
def parse_args():
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--server_name", type=str, default="127.0.0.1", help="server name")
parser.add_argument("--ckpt_dir", type=str, default="../data/weights/", help="dir of pre-trained weights.")
parser.add_argument("--llm_model", type=str, default="13B", help="the type of llm.")
parser.add_argument("--max_seq_len", type=int, default=512, help="decoder length")
parser.add_argument('--adapter_type', type=str, default='attn', metavar='LENGTH',choices=['block','attn'],
help='the insert position of adapter layer')
parser.add_argument('--adapter_path', type=str, default='./15-eph-pretrain.pth', help='path of pre-trained adapter')
parser.add_argument('--temperature', type=float, default=5., metavar='LENGTH',
help='the temperature of router')
parser.add_argument('--use_vicuna', action='store_true', help='use vicuna weights')
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_model_parallel() -> Tuple[int, int]:
local_rank = int(os.environ.get("LOCAL_RANK", -1))
world_size = int(os.environ.get("WORLD_SIZE", -1))
torch.distributed.init_process_group("nccl")
initialize_model_parallel(world_size)
torch.cuda.set_device(local_rank)
# seed must be the same in all processes
torch.manual_seed(1)
return local_rank, world_size
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()
local_rank, world_size = setup_model_parallel()
lavin=load(
ckpt_dir=args.ckpt_dir,
llm_model=args.llm_model,
adapter_path=args.adapter_path,
max_seq_len=512,
max_batch_size=4,
adapter_type='attn',
adapter_dim=8,
adapter_scale=1,
hidden_proj=128,
visual_adapter_type='router',
temperature=args.temperature,
tokenizer_path='',
local_rank=local_rank,
world_size=world_size,
use_vicuna=args.use_vicuna
)
vis_processor = transforms.Compose([transforms.Resize((224, 224), interpolation=Image.BICUBIC),transforms.ToTensor(), transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)])
chat = Chat(lavin, vis_processor, device=torch.device('cuda'))
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='Type and press Enter',
interactive=True), gr.update(
value="Upload & Start Chat", interactive=True), chat_state, img_list
def upload_img(gr_img, text_input, chat_state):
if gr_img is None:
return None, None, gr.update(interactive=True), chat_state, None
chat_state = CONV_VISION.copy()
img_list = []
llm_message = chat.upload_img(gr_img, 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
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
if chat_state is None:
chat_state=CONV_VISION.copy()
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)
chatbot[-1][1] = llm_message
return chatbot, chat_state, img_list
title = """<h1 align="center">Demo of LaVIN</h1>"""
description = """<h3>This is the demo of LaVIN. Upload your images and start chatting!</h3>"""
with gr.Blocks() as demo:
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column(scale=0.5):
image = gr.Image(type="pil")
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",
)
with gr.Column():
chat_state = gr.State()
img_list = gr.State()
chatbot = gr.Chatbot(label='LaVIN-13B')
text_input = gr.Textbox(label='User', placeholder='Type and press Enter', interactive=True)
upload_button.click(upload_img, [image, text_input, chat_state],
[image, text_input, upload_button, chat_state, img_list])
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, image, text_input, upload_button, chat_state, img_list],
queue=False)
demo.launch(share=True, enable_queue=True,server_name=args.server_name)