Spaces:
Sleeping
Sleeping
import argparse | |
import os | |
import random | |
import time | |
import numpy as np | |
import torch | |
import torch.backends.cudnn as cudnn | |
import gradio as gr | |
import esm | |
from minigpt4.common.config import Config | |
from minigpt4.common.dist_utils import get_rank | |
from minigpt4.common.registry import registry | |
from minigpt4.conversation.conversation_esm import Chat, CONV_VISION | |
import json | |
# Imports PIL module | |
from PIL import Image | |
# imports modules for registration | |
from minigpt4.datasets.builders import * | |
from minigpt4.models import * | |
from minigpt4.processors import * | |
from minigpt4.runners import * | |
from minigpt4.tasks import * | |
import esm | |
import esm.inverse_folding | |
def parse_args(): | |
parser = argparse.ArgumentParser(description="Demo") | |
parser.add_argument("--cfg-path", required=True, 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("--json-path", default='/home/h5guo/shared/Mini-GPT4/coco_json/cocoval2014_img_prompt.json', help="path to the classification json file") | |
# parser.add_argument("--caption-save-path", default='/home/h5guo/shared/Mini-GPT4/coco_json_result/results.json', help="path to saved generated captions") | |
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)) | |
vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.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 chat_state, img_list | |
def upload_protein(gr_img): | |
chat_state = CONV_VISION.copy() | |
img_list = [] | |
llm_message = chat.upload_protein(gr_img, chat_state, img_list) | |
return chat_state, img_list | |
def gradio_ask(user_message, chat_state): | |
chat.ask(user_message, chat_state) | |
return chat_state | |
def gradio_answer(chat_state, img_list, num_beams=1, temperature=1e-3): | |
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] | |
return llm_message, chat_state, img_list | |
if __name__ == "__main__": | |
start = time.time() | |
print("******************") | |
protein_embedding_path = "/home/h5guo/data/esm_subset/pt/2wge.pt" | |
protein_embedding = torch.load(protein_embedding_path, map_location=torch.device('cpu')) | |
sample_protein = protein_embedding.to('cuda:{}'.format(args.gpu_id)) | |
user_message = "Describe this protein in a short paragraph." | |
chat_state, img_list = upload_protein(sample_protein) | |
chat_state = gradio_ask(user_message, chat_state) | |
llm_message, chat_state, img_list = gradio_answer(chat_state, img_list) | |
print(f"llm_message: {llm_message}") | |
end = time.time() | |
print(end - start) | |
# i += 1 | |
print("******************") | |
# f.close() | |