hanluzhi0429@gmail.com
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
from llava.model.builder import load_pretrained_model
|
|
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
|
|
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
|
|
from llava.conversation import conv_templates, SeparatorStyle
|
|
from llava.actiontokenizer import ActionTokenizer
|
|
from PIL import Image
|
|
import requests
|
|
import copy
|
|
import torch
|
|
|
|
import sys
|
|
import warnings
|
|
|
|
warnings.filterwarnings("ignore")
|
|
pretrained = "/mnt/d/ckpt2/llava-openvla-qwen"
|
|
model_name = "llava_qwen"
|
|
device = "cuda"
|
|
device_map = "auto"
|
|
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map) # Add any other thing you want to pass in llava_model_args
|
|
actiontokenizer = ActionTokenizer(tokenizer)
|
|
|
|
print(model.model.embed_tokens.weight.shape)
|
|
print(model.lm_head.weight.shape)
|
|
|
|
model.eval()
|
|
image_path = '/root/code/openvla-llava/LLaVA-Next/images/images/random_image.jpg'
|
|
image = Image.open(image_path)
|
|
image_tensor = process_images([image], image_processor, model.config)
|
|
image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
|
|
|
|
conv_template = "qwen_1_5"
|
|
question = DEFAULT_IMAGE_TOKEN + "\n, the next position is:"
|
|
conv = copy.deepcopy(conv_templates[conv_template])
|
|
conv.append_message(conv.roles[0], question)
|
|
conv.append_message(conv.roles[1], None)
|
|
prompt_question = conv.get_prompt()
|
|
|
|
input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
|
|
image_sizes = [image.size]
|
|
|
|
cont = model.generate(
|
|
input_ids,
|
|
images=image_tensor,
|
|
image_sizes=image_sizes,
|
|
do_sample=False,
|
|
temperature=0,
|
|
max_new_tokens=4096,
|
|
)
|
|
token_ids = cont.cpu().tolist()[0]
|
|
decoded_text = []
|
|
|
|
for token_id in token_ids:
|
|
if 151647 <= token_id <= 151903:
|
|
decoded_token = actiontokenizer.decode_token_ids_to_actions([token_id])
|
|
else:
|
|
decoded_token = tokenizer.decode([token_id])
|
|
decoded_text.append(decoded_token)
|
|
final_text = ''.join(str(item) for item in decoded_text)
|
|
print(final_text)
|