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import argparse
import torch
import sys
import os
# 添加当前命令行运行的目录到 sys.path
sys.path.append(os.getcwd()+"/dialoggen")
from llava.constants import (
IMAGE_TOKEN_INDEX,
DEFAULT_IMAGE_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN,
IMAGE_PLACEHOLDER,
)
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import (
process_images,
tokenizer_image_token,
get_model_name_from_path,
)
import requests
from PIL import Image
from io import BytesIO
import re
def image_parser(image_file, sep=','):
out = image_file.split(sep)
return out
def load_image(image_file):
if image_file.startswith("http") or image_file.startswith("https"):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert("RGB")
else:
image = Image.open(image_file).convert("RGB")
return image
def load_images(image_files):
out = []
for image_file in image_files:
image = load_image(image_file)
out.append(image)
return out
def init_dialoggen_model(model_path, model_base=None):
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(
model_path, model_base, model_name, llava_type_model=True)
return {"tokenizer": tokenizer,
"model": model,
"image_processor": image_processor}
def eval_model(models,
query='详细描述一下这张图片',
image_file=None,
sep=',',
temperature=0.2,
top_p=None,
num_beams=1,
max_new_tokens=512,
):
# Model
disable_torch_init()
qs = query
image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
if IMAGE_PLACEHOLDER in qs:
if models["model"].config.mm_use_im_start_end:
qs = re.sub(IMAGE_PLACEHOLDER, image_token_se, qs)
else:
qs = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, qs)
else:
if models["model"].config.mm_use_im_start_end:
qs = image_token_se + "\n" + qs
else:
qs = DEFAULT_IMAGE_TOKEN + "\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()
if image_file is not None:
image_files = image_parser(image_file, sep=sep)
images = load_images(image_files)
image_sizes = [x.size for x in images]
images_tensor = process_images(
images,
models["image_processor"],
models["model"].config
).to(models["model"].device, dtype=torch.float16)
else:
# fomatted input as training data
image_sizes = [(1024, 1024)]
images_tensor = torch.zeros(1, 5, 3, models["image_processor"].crop_size["height"], models["image_processor"].crop_size["width"])
images_tensor = images_tensor.to(models["model"].device, dtype=torch.float16)
input_ids = (
tokenizer_image_token(prompt, models["tokenizer"], IMAGE_TOKEN_INDEX, return_tensors="pt")
.unsqueeze(0)
.cuda()
)
with torch.inference_mode():
output_ids = models["model"].generate(
input_ids,
images=images_tensor,
image_sizes=image_sizes,
do_sample=True if temperature > 0 else False,
temperature=temperature,
top_p=top_p,
num_beams=num_beams,
max_new_tokens=max_new_tokens,
use_cache=True,
)
outputs = models["tokenizer"].batch_decode(output_ids, skip_special_tokens=True)[0].strip()
return outputs
def remove_prefix(text):
if text.startswith("<画图>"):
return text[len("<画图>"):], True
elif text.startswith("对不起"):
# 拒绝画图
return "", False
else:
return text, True
class DialogGen(object):
def __init__(self, model_path):
self.models = init_dialoggen_model(model_path)
self.query_template = "请先判断用户的意图，若为画图则在输出前加入<画图>:{}"
def __call__(self, prompt):
enhanced_prompt = eval_model(
models=self.models,
query=self.query_template.format(prompt),
image_file=None,
)
enhanced_prompt, compliance = remove_prefix(enhanced_prompt)
if not compliance:
return False, ""
return True, enhanced_prompt
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str, default='./ckpts/dialoggen')
parser.add_argument('--prompt', type=str, default='画一只小猫')
parser.add_argument('--image_file', type=str, default=None) # 'images/demo1.jpeg'
args = parser.parse_args()
query = f"请先判断用户的意图，若为画图则在输出前加入<画图>:{args.prompt}"
models = init_dialoggen_model(args.model_path)
res = eval_model(models,
query=query,
image_file=args.image_file,
)
print(res)
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