inference_i2t.py

import torch
import argparse
import PIL
from PIL import Image
import os
from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from conversation import conv_templates, SeparatorStyle
from torchvision import transforms

from constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN

from threading import Thread
from unitok.config import Args
from unitok.model import UniTok

from model.builder import load_pretrained_model
from mm_utils import tokenizer_image_token, get_model_name_from_path


IMAGE_TOKEN_INDEX=-200

def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result
    

def main(args):
    
    ckpt = torch.load(args.unitok_path, map_location='cpu')
    vae_cfg = Args()
    vae_cfg.load_state_dict(ckpt['args'])
    vq_model = UniTok(vae_cfg)
    vq_model.load_state_dict(ckpt['trainer']['unitok'])
    vq_model.to('cuda')
    vq_model.eval()

    model_path = os.path.expanduser(args.mllm_path)
    model_name = get_model_name_from_path(model_path)
    tokenizer, vqllm, image_processor, context_len = load_pretrained_model(model_path, model_name, load_8bit=args.load_8bit)

    qs = args.prompt
    qs = '<boi><image><eoi>' + '\n' + qs
    conv = conv_templates['llava_v1'].copy()
    conv.append_message(conv.roles[0], qs)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()
    
    crop_size = 256
    transform = transforms.Compose([
        transforms.Resize((crop_size, crop_size)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
    ])
    
    print(prompt)
    image = Image.open(args.image_path).convert('RGB')
    pad_image = expand2square(image, (122, 116, 104) )
    # import pdb;pdb.set_trace()
    img = transform(pad_image).unsqueeze(0)
    img = img.to('cuda')
    # import pdb;pdb.set_trace()
    with torch.no_grad():
        vq_code = vq_model.img_to_idx(img)
        image_codes = vq_code.unsqueeze(0)
        
        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
        
        # input_ids = torch.cat(text_ids, dim=0)
        # input_embeddings = vqllm.embed_tokens(input_ids)
        inputs =  {
            "inputs":input_ids.unsqueeze(0).to("cuda:0"),
            "images":image_codes.to("cuda:0"),
            "max_new_tokens":1024,
            "bos_token_id":tokenizer.bos_token_id,  # Begin of sequence token
            "eos_token_id":tokenizer.eos_token_id,  # End of sequence token
            "pad_token_id":tokenizer.pad_token_id,  # Pad token
            }
        streamer = TextIteratorStreamer(tokenizer, **{"skip_special_tokens": True, "skip_prompt": True})

        # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
        generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
        thread = Thread(target=vqllm.generate_mllm, kwargs=generation_kwargs)
        thread.start()
        generated_text = ""
        for new_text in streamer:
            generated_text += new_text
        print(generated_text)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Process some integers.')
    parser.add_argument('--unitok_path', type=str, default=r'D:\projects\liquid_app\UniTok\UniTok_weights\unitok_tokenizer\unitok_tokenizer.pth',required=False)
    parser.add_argument('--mllm_path', type=str, default= r'C:\debug_ckpts\unitok_mllm', required=False)
    parser.add_argument('--prompt', type=str, required=True, help='input text prompt')
    parser.add_argument('--image_path', type=str, required=True, help='input image path')
    parser.add_argument('--load_8bit',  action='store_true', default=False, help='use 8bit to save memory')

    args = parser.parse_args()
    main(args)