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EOT_Center.py
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# %%
import sys
import numpy as np
from omegaconf import OmegaConf
import PIL
from PIL import Image
from einops import rearrange
import ssl
from tqdm import tqdm
import time
import torch
import os
from PIL import Image, ImageOps
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
from diffusers import DiffusionPipeline
import copy
import torch.nn.functional as F
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image, ImageOps
import matplotlib.pyplot as plt
from torch import optim
import json
import random
random.seed(333)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
model_id = "timbrooks/instruct-pix2pix"
pretrained_model_name_or_path = model_id
torch.cuda.device_count()
from torchvision import transforms
from pathlib import Path
from Functions import *
import pandas as pd
# %%
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_path = './instruct-pix2pix-main/diffuser_cache'
cop_path = './instruct-pix2pix-main/cop_file'
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained('timbrooks/instruct-pix2pix', torch_dtype=torch.float16,
safety_checker=None, cache_dir=model_path,
local_files_only=True)
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# %% PGD parameters
pgd_alpha = 1 / 255
pgd_eps = 0.05
max_steps = 30
# %% Center crop function
def center_crop(images, new_height, new_width):
_, _, height, width = images.shape
startx = width // 2 - (new_width // 2)
starty = height // 2 - (new_height // 2)
endx = startx + new_width
endy = starty + new_height
return images[:, :, starty:endy, startx:endx]
# %% Make necessary directories if not exist
paths = ['./instruct-pix2pix-main/002_Data/EOT-C/Gen',
'./instruct-pix2pix-main/002_Data/EOT-C/Adv',
'./instruct-pix2pix-main/002_Data/EOT-C/AdvGen',
'./instruct-pix2pix-main/002_Data/EOT-C/Ori',
'./instruct-pix2pix-main/002_Data/EOT-C/EXCEL']
for path in paths:
if not os.path.exists(path):
os.makedirs(path)
root_path = './instruct-pix2pix-main/train_data'
train_data_path = os.listdir(root_path)
def perceptual_consistency_loss(perturbed_images, original_images, beta):
l2_norm = F.mse_loss(perturbed_images, original_images)
loss = beta * l2_norm
return loss
# %%
for i in range(len(train_data_path)):
print(train_data_path[i])
save_ori = os.path.join('./instruct-pix2pix-main/002_Data/EOT-C/Ori', train_data_path[i])
save_gen = os.path.join('./instruct-pix2pix-main/002_Data/EOT-C/Gen', train_data_path[i])
save_adv = os.path.join('./instruct-pix2pix-main/002_Data/EOT-C/Adv', train_data_path[i])
save_advgen = os.path.join('./instruct-pix2pix-main/002_Data/EOT-C/AdvGen', train_data_path[i])
save_result = os.path.join('./instruct-pix2pix-main/002_Data/EOT-C/EXCEL', train_data_path[i])
image_path = os.path.join(root_path, train_data_path[i])
image_list = os.listdir(image_path)
resolution = 512
with open(os.path.join(image_path, 'prompt.json'), 'r', encoding='utf-8') as f:
load_json = json.load(f)
prompt = load_json['edit']
name_list = []
sim_image_bef_list = []
sim_image_aft_list = []
sim_image_adv_list = []
for j in range(len(image_list)):
if image_list[j].endswith('_0.jpg'):
input_path = os.path.join(image_path, image_list[j])
name_list.append(image_list[j])
perturbed_data = load_data(input_path, resolution, center_crop=False)
resolution = min(perturbed_data.size(2), perturbed_data.size(3))
perturbed_data = center_crop(perturbed_data, resolution, resolution)
tgt_data = load_data(input_path, resolution, center_crop=False)
original_data = perturbed_data.clone() # Store original image
aaa = original_data.detach().cpu().numpy()[0]
plt.imsave(os.path.join(save_ori, image_list[j]), aaa.transpose(1, 2, 0))
generator = torch.Generator("cuda").manual_seed(33)
images = pipe(prompt, image=Image.open(input_path).resize((512, 512)), num_inference_steps=100,
image_guidance_scale=1.2, generator=generator).images[0]
images.save(os.path.join(save_gen, image_list[j]))
original_images = original_data
perturbed_images = perturbed_data.detach().clone()
tgt_images = tgt_data.detach().clone()
tgt_emb = get_emb(tgt_images).detach().clone()
optimizer = optim.Adam([perturbed_images])
for step in range(max_steps):
perturbed_images.requires_grad = True
img_emb = get_emb(perturbed_images)
optimizer.zero_grad()
loss_perceptual = perceptual_consistency_loss(perturbed_images, original_data, beta=0.1)
loss_mse = -F.mse_loss(img_emb.float(), tgt_emb.float())
loss = loss_mse + loss_perceptual
loss.backward()
optimizer.step()
if step % 10 == 0:
print(
f"PGD loss - step {step}, total loss: {loss.item()}, perceptual loss: {loss_perceptual.item()}, mse loss: {loss_mse.item()}")
noised_imgs = perturbed_images.detach().cpu().numpy()[0]
plt.imsave(os.path.join(save_adv, image_list[j]), np.clip(noised_imgs.transpose(1, 2, 0), 0, 1))
generator = torch.Generator("cuda").manual_seed(33)
images = \
pipe(prompt, image=Image.fromarray(np.uint8(noised_imgs.transpose(1, 2, 0) * 255)), num_inference_steps=100,
image_guidance_scale=1.2, generator=generator).images[0]
images.save(os.path.join(save_advgen, image_list[j]))
x = np.array(Image.open(os.path.join(save_ori, image_list[j])).resize((512, 512))) / 255
x_adv = np.array(Image.open(os.path.join(save_adv, image_list[j])).resize((512, 512))) / 255
x_gen = np.array(Image.open(os.path.join(save_gen, image_list[j])).resize((512, 512))) / 255
x_gen_attack = np.array(Image.open(os.path.join(save_advgen, image_list[j])).resize((512, 512))) / 255
clip_similarity = ClipSimilarity().cuda()
image_features_benign = clip_similarity.encode_image(
torch.tensor(x.transpose(2, 0, 1)).unsqueeze(0).to(device))
image_features_gen = clip_similarity.encode_image(
torch.tensor(x_gen.transpose(2, 0, 1)).unsqueeze(0).to(device))
image_feature_adv = clip_similarity.encode_image(
torch.tensor(x_adv.transpose(2, 0, 1)).unsqueeze(0).to(device))
image_features_attack = clip_similarity.encode_image(
torch.tensor(x_gen_attack.transpose(2, 0, 1)).unsqueeze(0).to(device))
sim_image_bef = F.cosine_similarity(image_features_benign, image_features_gen)[0]
sim_image_aft = F.cosine_similarity(image_features_benign, image_features_attack)[0]
sim_image_adv = F.cosine_similarity(image_features_benign, image_feature_adv)[0]
sim_image_bef_list.append(sim_image_bef.detach().cpu().numpy())
sim_image_aft_list.append(sim_image_aft.detach().cpu().numpy())
sim_image_adv_list.append(sim_image_adv.detach().cpu().numpy())
else:
continue
data = {'file_name': name_list, 'sim_image_bef': sim_image_bef_list, 'sim_image_aft': sim_image_aft_list,
'sim_image_adv': sim_image_adv_list}
df = pd.DataFrame(data)
df.to_csv(os.path.join(save_result, 'result.csv'), index=False)