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Running
on
Zero
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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)
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