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import torch
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import os
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import json
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from tqdm import tqdm
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from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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from llava.conversation import conv_templates, SeparatorStyle
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from llava.model.builder import load_pretrained_model
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from llava.utils import disable_torch_init
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from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
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from PIL import Image
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import math
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import time
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import glob as gb
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class LLavaAgent:
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def __init__(self, model_path, device='cuda', conv_mode='vicuna_v1', load_8bit=False, load_4bit=False):
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self.device = device
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if torch.device(self.device).index is not None:
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device_map = {'model': torch.device(self.device).index, 'lm_head': torch.device(self.device).index}
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else:
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device_map = 'auto'
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model_path = os.path.expanduser(model_path)
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model_name = get_model_name_from_path(model_path)
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tokenizer, model, image_processor, context_len = load_pretrained_model(
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model_path, None, model_name, device=self.device, device_map=device_map,
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load_8bit=load_8bit, load_4bit=load_4bit)
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self.model = model
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self.image_processor = image_processor
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self.tokenizer = tokenizer
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self.context_len = context_len
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self.qs = 'Describe this image and its style in a very detailed manner.'
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self.conv_mode = conv_mode
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if self.model.config.mm_use_im_start_end:
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self.qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + self.qs
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else:
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self.qs = DEFAULT_IMAGE_TOKEN + '\n' + self.qs
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self.conv = conv_templates[self.conv_mode].copy()
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self.conv.append_message(self.conv.roles[0], self.qs)
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self.conv.append_message(self.conv.roles[1], None)
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prompt = self.conv.get_prompt()
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self.input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(
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0).to(self.device)
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def update_qs(self, qs=None):
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if qs is None:
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qs = self.qs
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else:
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if self.model.config.mm_use_im_start_end:
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qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
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else:
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qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
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self.conv = conv_templates[self.conv_mode].copy()
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self.conv.append_message(self.conv.roles[0], qs)
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self.conv.append_message(self.conv.roles[1], None)
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prompt = self.conv.get_prompt()
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self.input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(
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0).to(self.device)
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def gen_image_caption(self, imgs, temperature=0.2, top_p=0.7, num_beams=1, qs=None):
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'''
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[PIL.Image, ...]
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'''
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self.update_qs(qs)
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bs = len(imgs)
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input_ids = self.input_ids.repeat(bs, 1)
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img_tensor_list = []
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for image in imgs:
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_image_tensor = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
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img_tensor_list.append(_image_tensor)
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image_tensor = torch.stack(img_tensor_list, dim=0).half().to(self.device)
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stop_str = self.conv.sep if self.conv.sep_style != SeparatorStyle.TWO else self.conv.sep2
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with torch.inference_mode():
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output_ids = self.model.generate(
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input_ids,
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images=image_tensor,
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do_sample=True if temperature > 0 else False,
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temperature=temperature,
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top_p=top_p,
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num_beams=num_beams,
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max_new_tokens=512,
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use_cache=True)
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input_token_len = input_ids.shape[1]
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outputs = self.tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)
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img_captions = []
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for output in outputs:
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output = output.strip()
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if output.endswith(stop_str):
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output = output[:-len(stop_str)]
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output = output.strip().replace('\n', ' ').replace('\r', ' ')
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img_captions.append(output)
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return img_captions
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if __name__ == '__main__':
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llava_agent = LLavaAgent("/opt/data/private/AIGC_pretrain/LLaVA1.5/llava-v1.5-13b")
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img = [Image.open('/opt/data/private/LV_Dataset/DiffGLV-Test-All/RealPhoto60/LQ/02.png')]
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caption = llava_agent.gen_image_caption(img)
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