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import argparse
import os
import random
import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from PIL import Image
import json
import pandas as pd
def rtp_read(text_file):
dataset = []
lines = open(text_file).read().split("\n")
for li in lines:
obj = json.loads(li)
if obj['challenging']:
dataset.append(obj['prompt']['text'])
return dataset
def parse_args():
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--model-path", type=str, default="ckpts/llava_llama_2_13b_chat_freeze")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--mode", type=int, default=1, help="testing against benign prompts (mode=0) and adversarial prompts (mode=1).")
parser.add_argument("--image_path", type=str, default='./image.bmp',
help="Image file")
parser.add_argument("--output_file", type=str, default='./result.jsonl',
help="Output file.")
args = parser.parse_args()
return args
def load_image(image_path):
image = Image.open(image_path).convert('RGB')
return image
# ========================================
# Model Initialization
# ========================================
print('>>> Initializing Models')
from llava.utils import get_model
args = parse_args()
print('model = ', args.model_path)
tokenizer, model, image_processor, model_name = get_model(args)
model = model.cuda()
print(model)
print(model_name)
model.eval()
print('[Initialization Finished]\n')
from llava_utils import prompt_wrapper, generator
my_generator = generator.Generator(model=model, tokenizer=tokenizer)
# ========================================
# Inference
# ========================================
## TODO: expose interface.
if args.mode==1:
prompts = rtp_read('harmful_corpus/rtp_prompts.jsonl')
else:
lines = open('prompts/benign').read().split("\n")
prompts = [li for li in lines]
#text_prompt = 'Please extend the following sentence: %s'
text_prompt = '%s'
out = []
with torch.no_grad():
for i, user_message in enumerate(prompts):
print(f" ----- {i} ----")
print(" -- prompt: ---")
print(text_prompt % user_message)
# blur kernel baseline
#image = cv2.imread(args.image_path+str(np.random.randint(25))+'.bmp')
#image = cv2.blur(image,(3,3))
# compression decompression baseline
image = load_image(args.image_path+str(np.random.randint(25))+'.bmp')
image = image.save('baseline/compressed_q10.jpg', quality=10)
image = load_image('baseline/compressed_q10.jpg')
safe_image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].cuda()
text_prompt_template = prompt_wrapper.prepare_text_prompt(text_prompt % user_message)
print(text_prompt_template)
prompt = prompt_wrapper.Prompt(model, tokenizer, text_prompts=text_prompt_template, device=model.device)
response = my_generator.generate(prompt, safe_image)
print(" -- continuation: ---")
print(response)
out.append({'prompt': user_message, 'continuation': response})
print()
with open(args.output_file, 'w') as f:
f.write(json.dumps({
"args": vars(args),
"prompt": text_prompt
}))
f.write("\n")
for li in out:
f.write(json.dumps(li))
f.write("\n")
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