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import streamlit as st
import torch
import copy
import os
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from typing import Tuple, Optional
from my_model.utilities.gen_utilities import free_gpu_resources
from my_model.captioner.image_captioning import ImageCaptioningModel
from my_model.object_detection import ObjectDetector
import my_model.config.kbvqa_config as config
class KBVQA:
"""
The KBVQA class encapsulates the functionality for the Knowledge-Based Visual Question Answering (KBVQA) model.
It integrates various components such as an image captioning model, object detection model, and a fine-tuned
language model (LLAMA2) on OK-VQA dataset for generating answers to visual questions.
Attributes:
kbvqa_model_name (str): Name of the fine-tuned language model used for KBVQA.
quantization (str): The quantization setting for the model (e.g., '4bit', '8bit').
max_context_window (int): The maximum number of tokens allowed in the model's context window.
add_eos_token (bool): Flag to indicate whether to add an end-of-sentence token to the tokenizer.
trust_remote (bool): Flag to indicate whether to trust remote code when using the tokenizer.
use_fast (bool): Flag to indicate whether to use the fast version of the tokenizer.
low_cpu_mem_usage (bool): Flag to optimize model loading for low CPU memory usage.
kbvqa_tokenizer (Optional[AutoTokenizer]): The tokenizer for the KBVQA model.
captioner (Optional[ImageCaptioningModel]): The model used for generating image captions.
detector (Optional[ObjectDetector]): The object detection model.
detection_model (Optional[str]): The name of the object detection model.
detection_confidence (Optional[float]): The confidence threshold for object detection.
kbvqa_model (Optional[AutoModelForCausalLM]): The fine-tuned language model for KBVQA.
bnb_config (BitsAndBytesConfig): Configuration for BitsAndBytes optimized model.
access_token (str): Access token for Hugging Face API.
Methods:
create_bnb_config: Creates a BitsAndBytes configuration based on the quantization setting.
load_caption_model: Loads the image captioning model.
get_caption: Generates a caption for a given image.
load_detector: Loads the object detection model.
detect_objects: Detects objects in a given image.
load_fine_tuned_model: Loads the fine-tuned KBVQA model along with its tokenizer.
all_models_loaded: Checks if all the required models are loaded.
force_reload_model: Forces a reload of all models, freeing up GPU resources.
format_prompt: Formats the prompt for the KBVQA model.
generate_answer: Generates an answer to a given question using the KBVQA model.
"""
def __init__(self):
self.col1, self.col2, self.col3 = st.columns([0.2, 0.6, 0.2])
self.kbvqa_model_name: str = config.KBVQA_MODEL_NAME
self.quantization: str = config.QUANTIZATION
self.max_context_window: int = config.MAX_CONTEXT_WINDOW
self.add_eos_token: bool = config.ADD_EOS_TOKEN
self.trust_remote: bool = config.TRUST_REMOTE
self.use_fast: bool = config.USE_FAST
self.low_cpu_mem_usage: bool = config.LOW_CPU_MEM_USAGE
self.kbvqa_tokenizer: Optional[AutoTokenizer] = None
self.captioner: Optional[ImageCaptioningModel] = None
self.detector: Optional[ObjectDetector] = None
self.detection_model: Optional[str] = None
self.detection_confidence: Optional[float] = None
self.kbvqa_model: Optional[AutoModelForCausalLM] = None
self.bnb_config: BitsAndBytesConfig = self.create_bnb_config()
self.access_token: str = config.HUGGINGFACE_TOKEN
def create_bnb_config(self) -> BitsAndBytesConfig:
"""
Creates a BitsAndBytes configuration based on the quantization setting.
Returns:
BitsAndBytesConfig: Configuration for BitsAndBytes optimized model.
"""
if self.quantization == '4bit':
return BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
elif self.quantization == '8bit':
return BitsAndBytesConfig(
load_in_8bit=True,
bnb_8bit_use_double_quant=True,
bnb_8bit_quant_type="nf4",
bnb_8bit_compute_dtype=torch.bfloat16
)
def load_caption_model(self) -> None:
"""
Loads the image captioning model into the KBVQA instance.
"""
self.captioner = ImageCaptioningModel()
self.captioner.load_model()
def get_caption(self, img: Image.Image) -> str:
"""
Generates a caption for a given image using the image captioning model.
Args:
img (PIL.Image.Image): The image for which to generate a caption.
Returns:
str: The generated caption for the image.
"""
return self.captioner.generate_caption(img)
def load_detector(self, model: str) -> None:
"""
Loads the object detection model.
Args:
model (str): The name of the object detection model to load.
"""
self.detector = ObjectDetector()
self.detector.load_model(model)
def detect_objects(self, img: Image.Image) -> Tuple[Image.Image, str]:
"""
Detects objects in a given image using the loaded object detection model.
Args:
img (PIL.Image.Image): The image in which to detect objects.
Returns:
tuple: A tuple containing the image with detected objects drawn and a string representation of detected objects.
"""
image = self.detector.process_image(img)
detected_objects_string, detected_objects_list = self.detector.detect_objects(image, threshold=self.detection_confidence)
image_with_boxes = self.detector.draw_boxes(img, detected_objects_list)
return image_with_boxes, detected_objects_string
def load_fine_tuned_model(self) -> None:
"""
Loads the fine-tuned KBVQA model along with its tokenizer.
"""
self.kbvqa_model = AutoModelForCausalLM.from_pretrained(self.kbvqa_model_name,
device_map="auto",
low_cpu_mem_usage=True,
quantization_config=self.bnb_config,
token=self.access_token)
self.kbvqa_tokenizer = AutoTokenizer.from_pretrained(self.kbvqa_model_name,
use_fast=self.use_fast,
low_cpu_mem_usage=True,
trust_remote_code=self.trust_remote,
add_eos_token=self.add_eos_token,
token=self.access_token)
@property
def all_models_loaded(self):
"""
Checks if all the required models (KBVQA, captioner, detector) are loaded.
Returns:
bool: True if all models are loaded, False otherwise.
"""
return self.kbvqa_model is not None and self.captioner is not None and self.detector is not None
def delete_model(self):
"""
Forces a reload of all models, freeing up GPU resources. This method deletes the current models and calls `free_gpu_resources`.
"""
free_gpu_resources()
if self.kbvqa_model is not None:
del self.kbvqa_model
free_gpu_resources()
if self.captioner is not None:
del self.captioner
free_gpu_resources()
if self.detector is not None:
del self.detector
free_gpu_resources()
free_gpu_resources()
def format_prompt(self, current_query: str, history: Optional[str] = None, sys_prompt: Optional[str] = None, caption: str = None, objects: Optional[str] = None) -> str:
"""
Formats the prompt for the KBVQA model based on the provided parameters.
Args:
current_query (str): The current question to be answered.
history (str, optional): The history of previous interactions.
sys_prompt (str, optional): The system prompt or instructions for the model.
caption (str, optional): The caption of the image.
objects (str, optional): The detected objects in the image.
Returns:
str: The formatted prompt for the KBVQA model.
"""
B_SENT = '<s>'
E_SENT = '</s>'
B_INST = '[INST]'
E_INST = '[/INST]'
B_SYS = '<<SYS>>\n'
E_SYS = '\n<</SYS>>\n\n'
B_CAP = '[CAP]'
E_CAP = '[/CAP]'
B_QES = '[QES]'
E_QES = '[/QES]'
B_OBJ = '[OBJ]'
E_OBJ = '[/OBJ]'
current_query = current_query.strip()
if sys_prompt is None:
sys_prompt = config.SYSTEM_PROMPT.strip()
if history is None:
if objects is None:
p = f"""{B_SENT}{B_INST} {B_SYS}{sys_prompt}{E_SYS}{B_CAP}{caption}{E_CAP}{B_QES}{current_query}{E_QES}{E_INST}"""
else:
p = f"""{B_SENT}{B_INST} {B_SYS}{sys_prompt}{E_SYS}{B_CAP}{caption}{E_CAP}{B_OBJ}{objects}{E_OBJ}{B_QES}taking into consideration the objects with high certainty, {current_query}{E_QES}{E_INST}"""
else:
p = f"""{history}\n{B_SENT}{B_INST} {B_QES}{current_query}{E_QES}{E_INST}"""
return p
def generate_answer(self, question: str, caption: str, detected_objects_str: str) -> str:
"""
Generates an answer to a given question using the KBVQA model.
Args:
question (str): The question to be answered.
caption (str): The caption of the image related to the question.
detected_objects_str (str): The string representation of detected objects in the image.
Returns:
str: The generated answer to the question.
"""
prompt = self.format_prompt(question, caption=caption, objects=detected_objects_str)
num_tokens = len(self.kbvqa_tokenizer.tokenize(prompt))
if num_tokens > self.max_context_window:
self.col2.write(f"Prompt too long with {num_tokens} tokens, consider increasing the confidence threshold for the object detector")
return
model_inputs = self.kbvqa_tokenizer(prompt, add_special_tokens=False, return_tensors="pt").to('cuda')
input_ids = model_inputs["input_ids"]
output_ids = self.kbvqa_model.generate(input_ids)
index = input_ids.shape[1] # needed to avoid printing the input prompt
history = self.kbvqa_tokenizer.decode(output_ids[0], skip_special_tokens=False)
output_text = self.kbvqa_tokenizer.decode(output_ids[0][index:], skip_special_tokens=True)
return output_text.capitalize()
def prepare_kbvqa_model(only_reload_detection_model: bool = False, force_reload: bool = False) -> KBVQA:
"""
Prepares the KBVQA model for use, including loading necessary sub-models.
Args:
only_reload_detection_model (bool): If True, only the object detection model is reloaded.
Returns:
KBVQA: An instance of the KBVQA model ready for inference.
"""
free_gpu_resources()
kbvqa = KBVQA()
kbvqa.detection_model = st.session_state.detection_model
# Progress bar for model loading
with kbvqa.col1:
if force_reload:
self.delete_model()
loading_message = 'Force Reloading model.. this should take no more than a few minutes!'
else: loading_message = 'Looading model.. this should take no more than a few minutes!'
with st.spinner(loading_message):
if not only_reload_detection_model:
progress_bar = st.progress(0)
kbvqa.load_detector(kbvqa.detection_model)
progress_bar.progress(33)
kbvqa.load_caption_model()
free_gpu_resources()
progress_bar.progress(75)
st.text('Almost there :)')
kbvqa.load_fine_tuned_model()
free_gpu_resources()
progress_bar.progress(100)
else:
progress_bar = st.progress(0)
kbvqa.load_detector(kbvqa.detection_model)
progress_bar.progress(100)
if kbvqa.all_models_loaded:
st.success('Model loaded successfully and ready for inferecne!')
kbvqa.kbvqa_model.eval()
free_gpu_resources()
return kbvqa
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