293 lines
13 KiB
Python
293 lines
13 KiB
Python
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import torch
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import torch.nn as nn
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import numpy as np
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from PIL import Image
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import os
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import random
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import scipy.stats as st
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import copy
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from utils import ROOT_PATH
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from functools import partial
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import copy
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import pickle as pkl
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from torch.autograd import Variable
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import torch.nn.functional as F
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from dataset import params
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from model import get_model
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class BaseAttack(object):
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def __init__(self, attack_name, model_name, target):
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self.attack_name = attack_name
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self.model_name = model_name
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self.target = target
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if self.target:
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self.loss_flag = -1
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else:
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self.loss_flag = 1
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self.used_params = params(self.model_name)
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self.model = get_model(self.model_name)
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self.model.cuda()
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self.model.eval()
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def forward(self, *input):
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raise NotImplementedError
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def _mul_std_add_mean(self, inps):
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dtype = inps.dtype
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mean = torch.as_tensor(self.used_params['mean'], dtype=dtype).cuda()
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std = torch.as_tensor(self.used_params['std'], dtype=dtype).cuda()
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inps.mul_(std[:,None, None]).add_(mean[:,None,None])
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return inps
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def _sub_mean_div_std(self, inps):
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dtype = inps.dtype
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mean = torch.as_tensor(self.used_params['mean'], dtype=dtype).cuda()
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std = torch.as_tensor(self.used_params['std'], dtype=dtype).cuda()
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inps = (inps - mean[:,None,None])/std[:,None,None]
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return inps
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def _save_images(self, inps, filenames, output_dir):
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unnorm_inps = self._mul_std_add_mean(inps)
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for i,filename in enumerate(filenames):
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save_path = os.path.join(output_dir, filename)
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image = unnorm_inps[i].permute([1,2,0])
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image[image<0] = 0
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image[image>1] = 1
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image = Image.fromarray((image.detach().cpu().numpy()*255).astype(np.uint8))
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image.save(save_path)
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def _update_inps(self, inps, grad, step_size):
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unnorm_inps = self._mul_std_add_mean(inps.clone().detach())
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unnorm_inps = unnorm_inps + step_size * grad.sign()
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unnorm_inps = torch.clamp(unnorm_inps, min=0, max=1).detach()
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adv_inps = self._sub_mean_div_std(unnorm_inps)
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return adv_inps
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def _update_perts(self, perts, grad, step_size):
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perts = perts + step_size * grad.sign()
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perts = torch.clamp(perts, -self.epsilon, self.epsilon)
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return perts
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def _return_perts(self, clean_inps, inps):
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clean_unnorm = self._mul_std_add_mean(clean_inps.clone().detach())
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adv_unnorm = self._mul_std_add_mean(inps.clone().detach())
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return adv_unnorm - clean_unnorm
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def __call__(self, *input, **kwargs):
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images = self.forward(*input, **kwargs)
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return images
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class ADG(BaseAttack):
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def __init__(self, model_name, sample_num_batches=130, steps=10, epsilon=16/255, target=False, decay=1.0):
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super(ADG, self).__init__('ADG', model_name, target)
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self.epsilon = epsilon
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self.steps = steps
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self.step_size = self.epsilon/self.steps
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self.decay = decay
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self.image_size = 224
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self.crop_length = 16
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self.sample_num_batches = sample_num_batches
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self.max_num_batches = int((224/16)**2)
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assert self.sample_num_batches <= self.max_num_batches
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self._register_model()
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def _register_model(self):
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def attn_ADG(module, grad_in, grad_out, gamma):
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mask = torch.ones_like(grad_in[0]) * gamma
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out_grad = mask * grad_in[0][:]
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if self.model_name in ['vit_base_patch16_224', 'visformer_small', 'pit_b_224']:
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B,C,H,W = grad_in[0].shape
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out_grad_cpu = out_grad.data.clone().cpu().numpy().reshape(B,C,H*W)
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max_all = np.argmax(out_grad_cpu[0,:,:], axis = 1)
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max_all_H = max_all//H
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max_all_W = max_all%H
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min_all = np.argmin(out_grad_cpu[0,:,:], axis = 1)
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min_all_H = min_all//H
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min_all_W = min_all%H
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out_grad[:,range(C),max_all_H,:] = 0.0
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out_grad[:,range(C),:,max_all_W] = 0.0
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out_grad[:,range(C),min_all_H,:] = 0.0
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out_grad[:,range(C),:,min_all_W] = 0.0
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if self.model_name in ['cait_s24_224']:
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B,H,W,C = grad_in[0].shape
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out_grad_cpu = out_grad.data.clone().cpu().numpy().reshape(B, H*W, C)
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max_all = np.argmax(out_grad_cpu[0,:,:], axis = 0)
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max_all_H = max_all//H
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max_all_W = max_all%H
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min_all = np.argmin(out_grad_cpu[0,:,:], axis = 0)
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min_all_H = min_all//H
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min_all_W = min_all%H
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out_grad[:,max_all_H,:,range(C)] = 0.0
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out_grad[:,:,max_all_W,range(C)] = 0.0
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out_grad[:,min_all_H,:,range(C)] = 0.0
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out_grad[:,:,min_all_W,range(C)] = 0.0
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return (out_grad, )
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def attn_cait_ADG(module, grad_in, grad_out, gamma):
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mask = torch.ones_like(grad_in[0]) * gamma
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out_grad = mask * grad_in[0][:]
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B,H,W,C = grad_in[0].shape
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out_grad_cpu = out_grad.data.clone().cpu().numpy()
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max_all = np.argmax(out_grad_cpu[0,:,0,:], axis = 0)
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min_all = np.argmin(out_grad_cpu[0,:,0,:], axis = 0)
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out_grad[:,max_all,:,range(C)] = 0.0
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out_grad[:,min_all,:,range(C)] = 0.0
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return (out_grad, )
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def q_ADG(module, grad_in, grad_out, gamma):
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mask = torch.ones_like(grad_in[0]) * gamma
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out_grad = mask * grad_in[0][:]
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out_grad[:] = 0.0
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return (out_grad, grad_in[1], grad_in[2])
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def v_ADG(module, grad_in, grad_out, gamma):
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mask = torch.ones_like(grad_in[0]) * gamma
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out_grad = mask * grad_in[0][:]
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if self.model_name in ['visformer_small']:
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B,C,H,W = grad_in[0].shape
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out_grad_cpu = out_grad.data.clone().cpu().numpy().reshape(B,C,H*W)
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max_all = np.argmax(out_grad_cpu[0,:,:], axis = 1)
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max_all_H = max_all//H
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max_all_W = max_all%H
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min_all = np.argmin(out_grad_cpu[0,:,:], axis = 1)
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min_all_H = min_all//H
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min_all_W = min_all%H
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out_grad[:,range(C),max_all_H,max_all_W] = 0.0
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out_grad[:,range(C),min_all_H,min_all_W] = 0.0
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if self.model_name in ['vit_base_patch16_224', 'pit_b_224', 'cait_s24_224']:
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c = grad_in[0].shape[2]
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out_grad_cpu = out_grad.data.clone().cpu().numpy()
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max_all = np.argmax(out_grad_cpu[0,:,:], axis = 0)
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min_all = np.argmin(out_grad_cpu[0,:,:], axis = 0)
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out_grad[:,max_all,range(c)] = 0.0
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out_grad[:,min_all,range(c)] = 0.0
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return (out_grad, grad_in[1])
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def mlp_ADG(module, grad_in, grad_out, gamma):
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mask = torch.ones_like(grad_in[0]) * gamma
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out_grad = mask * grad_in[0][:]
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if self.model_name in ['visformer_small']:
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B,C,H,W = grad_in[0].shape
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out_grad_cpu = out_grad.data.clone().cpu().numpy().reshape(B,C,H*W)
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max_all = np.argmax(out_grad_cpu[0,:,:], axis = 1)
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max_all_H = max_all//H
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max_all_W = max_all%H
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min_all = np.argmin(out_grad_cpu[0,:,:], axis = 1)
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min_all_H = min_all//H
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min_all_W = min_all%H
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out_grad[:,range(C),max_all_H,max_all_W] = 0.0
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out_grad[:,range(C),min_all_H,min_all_W] = 0.0
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if self.model_name in ['vit_base_patch16_224', 'pit_b_224', 'cait_s24_224', 'resnetv2_101']:
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c = grad_in[0].shape[2]
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out_grad_cpu = out_grad.data.clone().cpu().numpy()
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max_all = np.argmax(out_grad_cpu[0,:,:], axis = 0)
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min_all = np.argmin(out_grad_cpu[0,:,:], axis = 0)
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out_grad[:,max_all,range(c)] = 0.0
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out_grad[:,min_all,range(c)] = 0.0
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for i in range(len(grad_in)):
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if i == 0:
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return_dics = (out_grad,)
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else:
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return_dics = return_dics + (grad_in[i],)
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return return_dics
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attn_ADG_hook = partial(attn_ADG, gamma=0.25)
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attn_cait_ADG_hook = partial(attn_cait_ADG, gamma=0.25)
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v_ADG_hook = partial(v_ADG, gamma=0.75)
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q_ADG_hook = partial(q_ADG, gamma=0.75)
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mlp_ADG_hook = partial(mlp_ADG, gamma=0.5)
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if self.model_name in ['vit_base_patch16_224' ,'deit_base_distilled_patch16_224']:
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for i in range(12):
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self.model.blocks[i].attn.attn_drop.register_backward_hook(attn_ADG_hook)
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self.model.blocks[i].attn.qkv.register_backward_hook(v_ADG_hook)
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self.model.blocks[i].mlp.register_backward_hook(mlp_ADG_hook)
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elif self.model_name == 'pit_b_224':
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for block_ind in range(13):
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if block_ind < 3:
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transformer_ind = 0
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used_block_ind = block_ind
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elif block_ind < 9 and block_ind >= 3:
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transformer_ind = 1
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used_block_ind = block_ind - 3
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elif block_ind < 13 and block_ind >= 9:
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transformer_ind = 2
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used_block_ind = block_ind - 9
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self.model.transformers[transformer_ind].blocks[used_block_ind].attn.attn_drop.register_backward_hook(attn_ADG_hook)
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self.model.transformers[transformer_ind].blocks[used_block_ind].attn.qkv.register_backward_hook(v_ADG_hook)
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self.model.transformers[transformer_ind].blocks[used_block_ind].mlp.register_backward_hook(mlp_ADG_hook)
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elif self.model_name == 'cait_s24_224':
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for block_ind in range(26):
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if block_ind < 24:
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self.model.blocks[block_ind].attn.attn_drop.register_backward_hook(attn_ADG_hook)
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self.model.blocks[block_ind].attn.qkv.register_backward_hook(v_ADG_hook)
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self.model.blocks[block_ind].mlp.register_backward_hook(mlp_ADG_hook)
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elif block_ind > 24:
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self.model.blocks_token_only[block_ind-24].attn.attn_drop.register_backward_hook(attn_cait_ADG_hook)
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self.model.blocks_token_only[block_ind-24].attn.q.register_backward_hook(q_ADG_hook)
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self.model.blocks_token_only[block_ind-24].attn.k.register_backward_hook(v_ADG_hook)
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self.model.blocks_token_only[block_ind-24].attn.v.register_backward_hook(v_ADG_hook)
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self.model.blocks_token_only[block_ind-24].mlp.register_backward_hook(mlp_ADG_hook)
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elif self.model_name == 'visformer_small':
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for block_ind in range(8):
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if block_ind < 4:
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self.model.stage2[block_ind].attn.attn_drop.register_backward_hook(attn_ADG_hook)
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self.model.stage2[block_ind].attn.qkv.register_backward_hook(v_ADG_hook)
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self.model.stage2[block_ind].mlp.register_backward_hook(mlp_ADG_hook)
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elif block_ind >=4:
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self.model.stage3[block_ind-4].attn.attn_drop.register_backward_hook(attn_ADG_hook)
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self.model.stage3[block_ind-4].attn.qkv.register_backward_hook(v_ADG_hook)
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self.model.stage3[block_ind-4].mlp.register_backward_hook(mlp_ADG_hook)
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def _generate_samples_for_interactions(self, perts, seed):
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add_noise_mask = torch.zeros_like(perts)
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grid_num_axis = int(self.image_size/self.crop_length)
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ids = [i for i in range(self.max_num_batches)]
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random.seed(seed)
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random.shuffle(ids)
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ids = np.array(ids[:self.sample_num_batches])
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rows, cols = ids // grid_num_axis, ids % grid_num_axis
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flag = 0
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for r, c in zip(rows, cols):
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add_noise_mask[:,:,r*self.crop_length:(r+1)*self.crop_length,c*self.crop_length:(c+1)*self.crop_length] = 1
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add_perturbation = perts * add_noise_mask
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return add_perturbation
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def forward(self, inps, labels):
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inps = inps.cuda()
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labels = labels.cuda()
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loss = nn.CrossEntropyLoss()
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momentum = torch.zeros_like(inps).cuda()
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unnorm_inps = self._mul_std_add_mean(inps)
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perts = torch.zeros_like(unnorm_inps).cuda()
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perts.requires_grad_()
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for i in range(self.steps):
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outputs = self.model((self._sub_mean_div_std(unnorm_inps + perts)))
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cost = self.loss_flag * loss(outputs, labels).cuda()
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cost.backward()
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grad = perts.grad.data
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grad = grad / torch.mean(torch.abs(grad), dim=[1,2,3], keepdim=True)
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grad += momentum*self.decay
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momentum = grad
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perts.data = self._update_perts(perts.data, grad, self.step_size)
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perts.data = torch.clamp(unnorm_inps.data + perts.data, 0.0, 1.0) - unnorm_inps.data
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perts.grad.data.zero_()
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return (self._sub_mean_div_std(unnorm_inps+perts.data)).detach(), None
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