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import re
import logging
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
def create_message(sample):
query = sample['query']
all_contents = []
matches = re.findall(r"<(image_\d+)>", query)
split_text = re.split(r"<image_\d+>", query)
images = []
for i, fragment in enumerate(split_text):
if fragment.strip():
all_contents.extend([
{"type": "text", "text": fragment}
])
if i < len(matches):
if sample[matches[i]]:
all_contents.extend([
{"type": "image"}
])
images.append(sample[matches[i]])
else:
logging.error(
f"The image token {matches[i]} is in the query, but there is no corresponding image provided by the data")
messages = [
{
"role": "user",
"content": all_contents
}
]
return messages, images
class Llava_Model:
def __init__(
self,
model_path,
temperature=0,
max_tokens=1024
):
self.temperature = temperature
self.max_tokens = max_tokens
self.model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
use_flash_attention_2=True
)
self.processor = AutoProcessor.from_pretrained(model_path)
def get_response(self, sample):
model = self.model
processor = self.processor
try:
messages, images = create_message(sample)
input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(
images=images,
text=input_text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device, torch.float16)
output = model.generate(**inputs, do_sample=True, temperature=self.temperature, max_new_tokens=self.max_tokens)
response = processor.decode(output[0], skip_special_tokens=True)
assistant_index = response.find("assistant")
if assistant_index != -1:
final_answer = response[assistant_index + len("assistant"):].strip()
else:
final_answer = response.strip()
return final_answer
except Exception as e:
print(e)
return None |