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import os
import io
import torch
import PIL
from PIL import Image
from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
import bitsandbytes
import accelerate
from my_model.config import captioning_config as config
from my_model.utilities.gen_utilities import free_gpu_resources
class ImageCaptioningModel:
def __init__(self):
self.model_type = config.MODEL_TYPE
self.processor = None
self.model = None
self.prompt = config.PROMPT
self.max_image_size = config.MAX_IMAGE_SIZE
self.min_length = config.MIN_LENGTH
self.max_new_tokens = config.MAX_NEW_TOKENS
self.model_path = config.MODEL_PATH
self.device_map = config.DEVICE_MAP
self.torch_dtype = config.TORCH_DTYPE
self.load_in_8bit = config.LOAD_IN_8BIT
self.load_in_4bit = config.LOAD_IN_4BIT
self.low_cpu_mem_usage = config.LOW_CPU_MEM_USAGE
self.skip_secial_tokens = config.SKIP_SPECIAL_TOKENS
def load_model(self):
if self.load_in_4bit and self.load_in_8bit: # check if in case both set to True by mistake.
self.load_in_4bit = False
if self.model_type == 'i_blip':
self.processor = InstructBlipProcessor.from_pretrained(self.model_path,
load_in_8bit=self.load_in_8bit,
load_in_4bit=self.load_in_4bit,
torch_dtype=self.torch_dtype,
device_map=self.device_map
)
self.model = InstructBlipForConditionalGeneration.from_pretrained(self.model_path,
load_in_8bit=self.load_in_8bit,
load_in_4bit=self.load_in_4bit,
torch_dtype=self.torch_dtype,
low_cpu_mem_usage=self.low_cpu_mem_usage,
device_map=self.device_map
)
def resize_image(self, image, max_image_size=None):
if max_image_size is None:
max_image_size = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
h, w = image.size
scale = max_image_size / max(h, w)
if scale < 1:
new_w = int(w * scale)
new_h = int(h * scale)
image = image.resize((new_w, new_h), resample=PIL.Image.Resampling.LANCZOS)
return image
def generate_caption(self, image_path):
if isinstance(image_path, str) or isinstance(image_path, io.IOBase):
# If it's a file path or file-like object, open it as a PIL Image
image = Image.open(image_path)
elif isinstance(image_path, Image.Image):
image = image_path
image = self.resize_image(image)
inputs = self.processor(image, self.prompt, return_tensors="pt").to("cuda", self.torch_dtype)
outputs = self.model.generate(**inputs, min_length=self.min_length, max_new_tokens=self.max_new_tokens)
caption = self.processor.decode(outputs[0], skip_special_tokens=self.skip_secial_tokens).strip()
return caption
def generate_captions_for_multiple_images(self, image_paths):
return [self.generate_caption(image_path) for image_path in image_paths]
def get_caption(img):
captioner = ImageCaptioningModel()
captioner.load_model()
caption = captioner.generate_caption(img)
return caption
if __name__ == "__main__":
pass
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