def train(self):
"""
Optimize a patch to generate an adversarial example.
:return: Nothing
"""
img_size = 800
batch_size = 1
n_t_op_steps = 5000
max_lab = 14
ATTACK_TASK = 'target'
# TARGET_CLASS = 'dog'
TARGET_CLASS = 16
# ATTACK_TASK = 'untarget'
time_str = time.strftime("%Y%m%d-%H%M%S")
conv_size = 2
kernel1 = gkern(2*conv_size+1, 3).astype(np.float32)
stack_kernel1 = np.stack([kernel1, kernel1, kernel1]).swapaxes(2, 0)
stack_kernel1 = np.expand_dims(stack_kernel1, 3)
stack_kernel1 = torch.from_numpy(stack_kernel1).permute(2,3,0,1).float()
# Dataset prepare
data_obj = CocoTrainPerson(dataType='train2017',num_use=100)
dataloader_obj = DataLoader(data_obj, batch_size=1,shuffle=False) #使用DataLoader加载数据
# img info prepare
img_frcn = get_Image_ready(self.Faster_RCNN, '1016.png')
img_frcn['img_metas'][0][0]['filename'] = None
img_frcn['img_metas'][0][0]['ori_filename'] = None
img_frcn['img_metas'][0][0]['ori_shape'] = None
img_frcn['img_metas'][0][0]['pad_shape'] = None
img_frcn['img_metas'][0][0]['scale_factor'] = None
# attack_area_rate = 0.2
ATTACK_AREA_RATE = 0.1
decay_t_op_step = 100
batch_size_sp = 3
population_num = 300 # 36
optim_step_num = 300
k = 0
for i_batch, batch_data in enumerate(dataloader_obj):
img, mask, bbox, class_label = batch_data[0][0], batch_data[1][0], batch_data[2][0], batch_data[3][0]
# img : 3,500,500
# mask : 500,500
# bbox : x1,y1,w,h
# class_label : tensor[]
img_name = batch_data[4][0]
mask_area = torch.sum(mask)
# if img_name.split('_')[0] != '000000001815':
# continue
print('---------------')
print(img_name)
print('---------------')
# use segment SLIC
base_SLIC_seed_num = 3000
img_np = img.numpy().transpose(1,2,0)
mask_np = mask.numpy()
numSegments = int(base_SLIC_seed_num/(500*500)*torch.sum(mask))
segments_np = slic(image=img_np, n_segments=numSegments, sigma=0, slic_zero=True, mask=mask_np)
segments_tensor = torch.from_numpy(segments_np).float().cuda()
segments_label = torch.unique(segments_tensor)
segments_label = segments_label[1:]
# define theta_m
# pay attention to the center and the boundary
# (0) prepare stack of sp
# (1) find the center sp
# (2) find the boundary sp
# # (0) prepare stack of sp
zero_layer = torch.zeros_like(segments_tensor)
one_layer = torch.ones_like(segments_tensor)
# segments_stack = torch.stack([torch.where(segments_tensor==segments_label[j], segments_tensor, zero_layer) for j in range(segments_label.shape[0])], dim=0)
# # (1) find the center sp
bbox_x1 = bbox[0]
bbox_y1 = bbox[1]
bbox_w = bbox[2]
bbox_h = bbox[3]
bbox_x_c = bbox_x1 + bbox_w/2
bbox_y_c = bbox_y1 + bbox_h/2
bbox_x_c_int = int(bbox_x_c)
bbox_y_c_int = int(bbox_y_c)
# 3 load attack region
load_patch_dir = '../common_data/NES_search_test_1107/'+img_name.split('_')[0]
load_patch_list = os.listdir(load_patch_dir)
load_patch_list.sort()
wat_num_max = 0
for i_name in load_patch_list:
wat_num = int(i_name.split('_')[0])
if wat_num > wat_num_max:
wat_num_max = wat_num
for i_name in load_patch_list:
wat_num = int(i_name.split('_')[0])
if wat_num == wat_num_max:
max_name = i_name
break
load_patch = os.path.join(load_patch_dir, max_name)
load_img = Image.open(load_patch).convert('RGB')
load_img = transforms.ToTensor()(load_img)
region_mask = 2*load_img - img.cpu()
region_mask = torch.sum(region_mask,dim=0)/3
region_mask = torch.where(mask>0, region_mask,torch.zeros_like(region_mask))
attack_region_tmp_pil = transforms.ToPILImage()(region_mask.cpu())
attack_region_tmp_pil.save('013k.png')
# process mask
region_mask_new = torch.zeros_like(region_mask).cuda()
for i in range(segments_label.shape[0]):
sp = segments_label[i]
right_color = (torch.where(segments_tensor==sp,region_mask.cuda(),one_layer*(-10))).cpu()
right_color = torch.mean(right_color[right_color!=-10])
color_layer = torch.ones_like(segments_tensor).fill_(right_color)
region_mask_new = torch.where(segments_tensor==sp, color_layer, region_mask_new)
region_mask_new = region_mask_new
region_mask = region_mask_new
region_mask_unique = torch.unique(region_mask)
for i in range(region_mask_unique.shape[0]):
thres = region_mask_unique[i]
# region_mask_tmp = torch.zeros_like(region_mask)
region_mask_tmp = torch.where(region_mask>thres, one_layer, zero_layer)
pixel_num = torch.sum(region_mask_tmp)
if pixel_num < mask_area * ATTACK_AREA_RATE:
break
attack_region_search_top = region_mask_tmp
attack_region_search_top = get_conv_envl(attack_region_search_top)
attack_region_tmp = attack_region_search_top
attack_region_tmp = attack_region_tmp.cuda()
print('---------------')
print('You have used ', float(torch.sum(attack_region_tmp)/mask_area), 'area.')
print('---------------')
## start at gray
adv_patch_w = torch.zeros(3,500,500).cuda()
adv_patch_w.requires_grad_(True)
optimizer = optim.Adam([
{'params': adv_patch_w, 'lr': 0.1}
], amsgrad=True)
t_op_num = 800
min_max_iou_record = 1
for t_op_step in range(t_op_num):
adv_patch = torch.sigmoid(adv_patch_w)
patched_img = torch.where(attack_region_tmp>0, adv_patch, img.cuda()).unsqueeze(0)
patched_img_255 = patched_img * 255.
patched_img_rsz = F.interpolate(patched_img_255, (416, 416), mode='bilinear').cuda()
patched_img_nom_rsz = (patched_img_rsz - self.mean) / self.std
batch_size_now = patched_img_255.shape[0]
# output
img_new = copy.deepcopy(img_frcn)
img_new['img'][0] = patched_img_nom_rsz
yolo_output = self.YOLOv3(return_loss=False, rescale=False, **img_new)
# output formate is [x1,y1,x2,y2]
# anaylize yolo_output [batch_size]
# [
# ( multi_lvl_bboxes, multi_lvl_cls_scores, multi_lvl_conf_scores )
# multi_lvl_bboxes [ 3 layers ]
# [ [0] 1875, 4
# [1] 7500, 4
# [2] 30000,4 ]
#
# multi_lvl_cls_scores
# [ [0] 1875, 80
# [1] 7500, 80
# [2] 30000,80 ]
#
# multi_lvl_conf_scores
# [ [0] 1875
# [1] 7500
# [2] 30000 ]
# * batch_size
# ]
# merge yolo output
multi_lvl_bboxes_batch = []
multi_lvl_cls_scores_batch = []
multi_lvl_conf_scores_batch = []
for i_b in range(batch_size_now):
multi_lvl_bboxes_batch += yolo_output[i_b][0]
multi_lvl_cls_scores_batch += yolo_output[i_b][1]
multi_lvl_conf_scores_batch += yolo_output[i_b][2]
multi_lvl_bboxes_batch = torch.cat(multi_lvl_bboxes_batch, dim=0)
multi_lvl_cls_scores_batch = torch.cat(multi_lvl_cls_scores_batch, dim=0)
multi_lvl_conf_scores_batch = torch.cat(multi_lvl_conf_scores_batch, dim=0)
# objectness loss
objectness_loss = torch.sum(multi_lvl_conf_scores_batch[multi_lvl_conf_scores_batch>0.05])
# class loss
attack_class_score = multi_lvl_cls_scores_batch[:,class_label]
# attack_class_score = attack_class_score[attack_class_score>0.5]
attack_class_score = torch.sort(attack_class_score, descending=True)[0][:30]
cls_loss = torch.sum(attack_class_score)
# target class loss
attack_class_score_target = multi_lvl_cls_scores_batch[:,16]
attack_class_score_target = attack_class_score_target[multi_lvl_conf_scores_batch>0.5]
attack_class_score_target = attack_class_score_target[attack_class_score_target<0.9]
attack_class_score_target = torch.sort(attack_class_score_target, descending=True)[0][:30]
cls_target_loss = - torch.sum(attack_class_score_target)
# iou loss
bbox_x1 = bbox[0]/500*416
bbox_y1 = bbox[1]/500*416
bbox_w = bbox[2]/500*416
bbox_h = bbox[3]/500*416
ground_truth_bbox = [bbox_x1, bbox_y1, bbox_x1+bbox_w, bbox_y1 + bbox_h]
ground_truth_bbox = torch.Tensor(ground_truth_bbox).unsqueeze(0).cuda()
iou_all = compute_iou_tensor(multi_lvl_bboxes_batch, ground_truth_bbox)
iou_positive = iou_all[iou_all>0.05]
iou_loss = torch.sum(iou_all)
# class loss selected by IoU
attack_class_score = multi_lvl_cls_scores_batch[:,class_label]
attack_class_score_iou = attack_class_score[iou_all>0.05]
attack_class_score_iou_sort = torch.sort(attack_class_score_iou, descending=True)[0][:30]
cls_iou_loss = torch.sum(attack_class_score_iou_sort)
# rpn loss
# : to make every proposal smaller to its center
rpn_ctx = (multi_lvl_bboxes_batch[:,0] + multi_lvl_bboxes_batch[:,2])/2
rpn_cty = (multi_lvl_bboxes_batch[:,1] + multi_lvl_bboxes_batch[:,3])/2
rpn_box = multi_lvl_bboxes_batch[:,:4]
rpn_ctx = rpn_ctx.unsqueeze(-1)
rpn_cty = rpn_cty.unsqueeze(-1)
rpn_box_target = torch.cat([rpn_ctx,rpn_cty,rpn_ctx,rpn_cty], dim=-1)
rpn_loss = l1_norm(multi_lvl_conf_scores_batch.unsqueeze(-1).repeat(1,4)*(multi_lvl_bboxes_batch - rpn_box_target))
# total_loss = cls_loss + objectness_loss + rpn_loss + cls_target_loss + cls_iou_loss
# total_loss = cls_target_loss*100 + cls_iou_loss*100 #+ rpn_loss
total_loss = cls_iou_loss*100 + rpn_loss
total_loss.backward()
optimizer.step()
optimizer.zero_grad()
# ----------------------------------
# ------------------------
# early stop
if t_op_step %30:
print( t_op_step,
'iou', float(torch.max(iou_all)),
'cls', float(torch.max(attack_class_score)),
'obj', float(torch.max(multi_lvl_conf_scores_batch)))
#test
patched_img_cpu = patched_img.cpu().squeeze()
test_confidence_threshold = 0.45
iou_max = torch.max(iou_all)
if iou_max < 0.05 or torch.max(multi_lvl_conf_scores_batch) < 0.45:
print('Break at',t_op_step,'iou final max:', torch.max(iou_all))
# save image
patched_img_cpu_pil = transforms.ToPILImage()(patched_img_cpu)
out_file_path = os.path.join('../common_data/NES_attack/YOLO3/success'+str(int(ATTACK_AREA_RATE*100)), img_name)
patched_img_cpu_pil.save(out_file_path)
break
# report
max_iou = torch.max(iou_all)
if max_iou < min_max_iou_record:
min_max_iou_record = max_iou
txt_save_dir = '../common_data/NES_attack/YOLO3/iou'+str(int(ATTACK_AREA_RATE*100))
txt_save_path = os.path.join(txt_save_dir, img_name.split('.')[0]+'.txt')
with open(txt_save_path,'w') as f:
text = str(float(max_iou))
f.write(text)
if t_op_step % 100 == 0:
iou_sort = torch.sort(iou_all,descending=True)[0][:6].detach().clone().cpu()
print(t_op_step, 'iou t-cls :', max_iou)
print()
def train(self):
"""
Optimize a patch to generate an adversarial example.
:return: Nothing
"""
img_size = 800
batch_size = 1
n_t_op_steps = 5000
max_lab = 14
ATTACK_TASK = 'target'
# TARGET_CLASS = 'dog'
TARGET_CLASS = 16
# ATTACK_TASK = 'untarget'
time_str = time.strftime("%Y%m%d-%H%M%S")
conv_size = 2
kernel1 = gkern(2 * conv_size + 1, 3).astype(np.float32)
stack_kernel1 = np.stack([kernel1, kernel1, kernel1]).swapaxes(2, 0)
stack_kernel1 = np.expand_dims(stack_kernel1, 3)
stack_kernel1 = torch.from_numpy(stack_kernel1).permute(2, 3, 0,
1).float()
# Dataset prepare
data_obj = CocoTrainPerson(dataType='train2017', num_use=100)
dataloader_obj = DataLoader(data_obj, batch_size=1,
shuffle=False) #使用DataLoader加载数据
# img info prepare
img_frcn = get_Image_ready(self.Mask_RCNN, '1016.png')
img_frcn['img_metas'][0][0]['filename'] = None
img_frcn['img_metas'][0][0]['ori_filename'] = None
img_frcn['img_metas'][0][0]['ori_shape'] = None
img_frcn['img_metas'][0][0]['pad_shape'] = None
img_frcn['img_metas'][0][0]['scale_factor'] = None
# attack_area_rate = 0.2
ATTACK_AREA_RATE = 0.0
decay_t_op_step = 100
batch_size_sp = 3
population_num = 300 # 36
optim_step_num = 300
k = 0
for i_batch, batch_data in enumerate(dataloader_obj):
img, mask, bbox, class_label = batch_data[0][0], batch_data[1][
0], batch_data[2][0], batch_data[3][0]
# img : 3,500,500
# mask : 500,500
# bbox : x1,y1,w,h
# class_label : tensor[]
img_name = batch_data[4][0]
mask_area = torch.sum(mask)
# if img_name.split('_')[0] != '000000001815':
# continue
print('---------------')
print(img_name)
print('---------------')
# use segment SLIC
base_SLIC_seed_num = 3000
img_np = img.numpy().transpose(1, 2, 0)
mask_np = mask.numpy()
numSegments = int(base_SLIC_seed_num / (500 * 500) *
torch.sum(mask))
segments_np = slic(image=img_np,
n_segments=numSegments,
sigma=0,
slic_zero=True,
mask=mask_np)
segments_tensor = torch.from_numpy(segments_np).float().cuda()
segments_label = torch.unique(segments_tensor)
segments_label = segments_label[1:]
zero_layer = torch.zeros_like(segments_tensor)
one_layer = torch.ones_like(segments_tensor)
# segments_stack = torch.stack([torch.where(segments_tensor==segments_label[j], segments_tensor, zero_layer) for j in range(segments_label.shape[0])], dim=0)
# # (1) find the center sp
bbox_x1 = bbox[0]
bbox_y1 = bbox[1]
bbox_w = bbox[2]
bbox_h = bbox[3]
bbox_x_c = bbox_x1 + bbox_w / 2
bbox_y_c = bbox_y1 + bbox_h / 2
bbox_x_c_int = int(bbox_x_c)
bbox_y_c_int = int(bbox_y_c)
load_patch_dir = '../common_data/NES_search_test_1107/' + img_name.split(
'_')[0]
load_patch_list = os.listdir(load_patch_dir)
load_patch_list.sort()
wat_num_max = 0
for i_name in load_patch_list:
wat_num = int(i_name.split('_')[0])
if wat_num > wat_num_max:
wat_num_max = wat_num
for i_name in load_patch_list:
wat_num = int(i_name.split('_')[0])
if wat_num == wat_num_max:
max_name = i_name
break
load_patch = os.path.join(load_patch_dir, max_name)
load_img = Image.open(load_patch).convert('RGB')
load_img = transforms.ToTensor()(load_img)
region_mask = 2 * load_img - img.cpu()
region_mask = torch.sum(region_mask, dim=0) / 3
region_mask = torch.where(mask > 0, region_mask,
torch.zeros_like(region_mask))
attack_region_tmp_pil = transforms.ToPILImage()(region_mask.cpu())
attack_region_tmp_pil.save('013k.png')
# process mask
region_mask_new = torch.zeros_like(region_mask).cuda()
for i in range(segments_label.shape[0]):
sp = segments_label[i]
right_color = (torch.where(segments_tensor == sp,
region_mask.cuda(),
one_layer * (-10))).cpu()
right_color = torch.mean(right_color[right_color != -10])
color_layer = torch.ones_like(segments_tensor).fill_(
right_color)
region_mask_new = torch.where(segments_tensor == sp,
color_layer, region_mask_new)
region_mask_new = region_mask_new
region_mask = region_mask_new
region_mask_unique = torch.unique(region_mask)
ATTACK_AREA_RATE = 0.0
for enlarge_i in range(50):
ATTACK_AREA_RATE = ATTACK_AREA_RATE + 0.01
for i in range(region_mask_unique.shape[0]):
thres = region_mask_unique[i]
# region_mask_tmp = torch.zeros_like(region_mask)
region_mask_tmp = torch.where(region_mask > thres,
one_layer, zero_layer)
pixel_num = torch.sum(region_mask_tmp)
if pixel_num < mask_area * ATTACK_AREA_RATE:
break
attack_region_search_top = region_mask_tmp
attack_region_search_top = get_conv_envl(
attack_region_search_top)
attack_region_tmp = attack_region_search_top.cuda()
print('---------------')
print('You have used ',
float(torch.sum(attack_region_tmp) / mask_area), 'area.')
print('---------------')
## start at gray
adv_patch_w = torch.zeros(3, 500, 500).cuda()
adv_patch_w.requires_grad_(True)
optimizer = optim.Adam([{
'params': adv_patch_w,
'lr': 0.1
}],
amsgrad=True)
t_op_num = 50
min_max_iou_record = 1
for t_op_step in range(t_op_num):
adv_patch = torch.sigmoid(adv_patch_w)
patched_img = torch.where(attack_region_tmp > 0, adv_patch,
img.cuda()).unsqueeze(0)
patched_img_255 = patched_img * 255.
patched_img_rsz = F.interpolate(patched_img_255,
(800, 800),
mode='bilinear').cuda()
patched_img_nom_rsz = (patched_img_rsz -
self.mean) / self.std
# output
img_new = copy.deepcopy(img_frcn)
img_new['img'][0] = patched_img_nom_rsz
frcn_output = self.Mask_RCNN(return_loss=False,
rescale=False,
**img_new)
# compute loss
proposals_4507 = frcn_output[1]
proposals_score_4507 = frcn_output[2]
det_bboxes, det_labels, proposals = frcn_output[0]
det_bboxes = torch.cat(det_bboxes, dim=0) / 800 * 500
proposals = torch.cat(proposals, dim=0) / 800 * 500
det_labels = torch.cat(det_labels, dim=0)
attack_prob = det_labels[:, class_label]
training_confidence_threshold = 0.05
ov_thrs_index = torch.where(
attack_prob > training_confidence_threshold)[
0] # for certain class
pbbox_attack_cls = det_bboxes[:, class_label *
4:(class_label + 1) * 4]
# cls loss
attack_class_score = det_labels[:, class_label]
top_sort_class_score = torch.sort(attack_class_score,
descending=True)[0][:20]
cls_loss = torch.sum(top_sort_class_score)
# iou loss
bbox_x1 = bbox[0]
bbox_y1 = bbox[1]
bbox_w = bbox[2]
bbox_h = bbox[3]
ground_truth_bbox = [
bbox_x1, bbox_y1, bbox_x1 + bbox_w, bbox_y1 + bbox_h
]
ground_truth_bbox = torch.Tensor(
ground_truth_bbox).unsqueeze(0).cuda()
iou_all = compute_iou_tensor(
det_bboxes[:, class_label * 4:(class_label + 1) * 4],
ground_truth_bbox)
iou_positive = iou_all[iou_all > 0.05]
iou_loss = torch.sum(iou_all)
# class loss selected by IoU
attack_class_score = det_labels[:, class_label]
attack_class_score_iou = attack_class_score[iou_all > 0.25]
attack_class_score_iou_sort = torch.sort(
attack_class_score_iou, descending=True)[0][:30]
cls_iou_loss = torch.sum(attack_class_score_iou_sort)
final_roi = pbbox_attack_cls[
ov_thrs_index] # for certain class
final_roi = final_roi[:, :4]
final_ctx = (final_roi[:, 0] + final_roi[:, 2]) / 2
final_cty = (final_roi[:, 1] + final_roi[:, 3]) / 2
final_ctx = final_ctx.unsqueeze(-1)
final_cty = final_cty.unsqueeze(-1)
final_roi_target = torch.cat(
[final_ctx, final_cty, final_ctx, final_cty], dim=-1)
reg_loss = 10 * l1_norm(final_roi - final_roi_target
) / final_roi.shape[0] / 500
# RPN loss
# r1 : from score
# r2 : from x,y,w,h
# rpn score target is 0
rpn_score = proposals[:, 4]
loss_r1 = l2_norm(rpn_score - 0)
# rpn box target is smaller the proposal boxes
rpn_ctx = (proposals[:, 0] + proposals[:, 2]) / 2
rpn_cty = (proposals[:, 1] + proposals[:, 3]) / 2
rpn_box = proposals[:, :4]
rpn_ctx = rpn_ctx.unsqueeze(-1)
rpn_cty = rpn_cty.unsqueeze(-1)
rpn_box_target = torch.cat(
[rpn_ctx, rpn_cty, rpn_ctx, rpn_cty], dim=-1)
# loss_r2 = l1_norm(rpn_score.unsqueeze(-1).repeat(1,4)*(rpn_box - rpn_box_target)) / 500
loss_r2 = l1_norm((rpn_box - rpn_box_target)) / 500
lambda_balance1 = 0.02
# rpn_loss = loss_r1 + lambda_balance1 * loss_r2
rpn_loss = lambda_balance1 * loss_r2
# rpn_loss = loss_r1
total_loss = cls_loss + cls_iou_loss + reg_loss + rpn_loss
total_loss = cls_iou_loss + reg_loss + rpn_loss
total_loss.backward()
optimizer.step()
optimizer.zero_grad()
# ----------------------------------
# ------------------------
# early stop
#test
patched_img_cpu = patched_img.cpu().squeeze()
test_confidence_threshold = 0.5
iou_threshold = 0.5
ov_test_thrs_index = torch.where(
attack_prob > test_confidence_threshold)[0]
final_pbbox = det_bboxes[:, class_label *
4:(class_label + 1) * 4]
ground_truth_bboxs_final = ground_truth_bbox.repeat(
final_pbbox.shape[0], 1)
iou = compute_iou_tensor(final_pbbox,
ground_truth_bboxs_final)
attack_prob_select_by_iou_ = attack_prob[
iou > iou_threshold]
attack_prob_select_by_iou_ = attack_prob_select_by_iou_[
attack_prob_select_by_iou_ > test_confidence_threshold]
# break if attack success
# stop if no such class found
if attack_prob_select_by_iou_.shape[0] == 0:
print('Break at', t_op_step, 'no bbox found')
# save image
txt_save_dir = '../common_data/NES_attack/disappear/FRCN/area'
txt_save_path = os.path.join(
txt_save_dir,
img_name.split('.')[0] + '.txt')
with open(txt_save_path, 'w') as f:
text = str(
float(
torch.sum(attack_region_tmp).cpu() /
mask_area))
f.write(text)
patched_img_cpu_pil = transforms.ToPILImage()(
patched_img_cpu)
out_file_path = os.path.join(
'../common_data/NES_attack/disappear/FRCN/img',
img_name)
patched_img_cpu_pil.save(out_file_path)
break
if attack_prob_select_by_iou_.shape[0] == 0:
# stop enlarge
break
def train(self):
"""
Optimize a patch to generate an adversarial example.
:return: Nothing
"""
img_size = 800
batch_size = 1
n_epochs = 5000
max_lab = 14
ATTACK_TASK = 'target'
# TARGET_CLASS = 'dog'
TARGET_CLASS = 16
# ATTACK_TASK = 'untarget'
time_str = time.strftime("%Y%m%d-%H%M%S")
conv_size = 2
kernel1 = gkern(2 * conv_size + 1, 3).astype(np.float32)
stack_kernel1 = np.stack([kernel1, kernel1, kernel1]).swapaxes(2, 0)
stack_kernel1 = np.expand_dims(stack_kernel1, 3)
stack_kernel1 = torch.from_numpy(stack_kernel1).permute(2, 3, 0,
1).float()
# Dataset prepare
data_obj = CocoTrainPerson(dataType='train2017', num_use=500)
dataloader_obj = DataLoader(data_obj, batch_size=1,
shuffle=False) #使用DataLoader加载数据
# img info prepare
img_frcn = get_Image_ready(self.Faster_RCNN.module, '1016.png')
img_frcn['img_metas'][0][0]['filename'] = None
img_frcn['img_metas'][0][0]['ori_filename'] = None
img_frcn['img_metas'][0][0]['ori_shape'] = None
img_frcn['img_metas'][0][0]['pad_shape'] = None
img_frcn['img_metas'][0][0]['scale_factor'] = None
# attack_area_rate = 0.2
ATTACK_AREA_RATE = 0.1
evo_step_num = 20
batch_size_sp = 12
population_num = 72 # 36
optim_step_num = 300
k = 0
for i_batch, batch_data in enumerate(dataloader_obj):
img, mask, bbox, class_label = batch_data[0][0], batch_data[1][
0], batch_data[2][0], batch_data[3][0]
# img : 3,500,500
# mask : 500,500
# bbox : x1,y1,w,h
# class_label : tensor[]
img_name = batch_data[4][0]
print('---------------')
print(img_name)
print('---------------')
save_dir = os.path.join('../common_data/NES_search_test_1107/',
img_name.split('.')[0])
if os.path.exists(save_dir):
continue
time0 = time.time()
# use segment SLIC
base_SLIC_seed_num = 3000
img_np = img.numpy().transpose(1, 2, 0)
mask_np = mask.numpy()
numSegments = int(base_SLIC_seed_num / (500 * 500) *
torch.sum(mask))
segments_np = slic(image=img_np,
n_segments=numSegments,
sigma=0,
slic_zero=True,
mask=mask_np)
segments_tensor = torch.from_numpy(segments_np).float().cuda()
segments_label = torch.unique(segments_tensor)
segments_label = segments_label[1:]
# define theta_m
# pay attention to the center and the boundary
# (0) prepare stack of sp
# (1) find the center sp
# (2) find the boundary sp
# (0) prepare stack of sp
zero_layer = torch.zeros_like(segments_tensor)
one_layer = torch.ones_like(segments_tensor)
segments_stack = torch.stack([
torch.where(segments_tensor == segments_label[j],
segments_tensor, zero_layer)
for j in range(segments_label.shape[0])
],
dim=0)
segments_stack = segments_stack.cpu()
mask_area = torch.sum(mask)
# (1) find the center sp
bbox_x1 = bbox[0]
bbox_y1 = bbox[1]
bbox_w = bbox[2]
bbox_h = bbox[3]
bbox_x_c = bbox_x1 + bbox_w / 2
bbox_y_c = bbox_y1 + bbox_h / 2
bbox_x_c_int = int(bbox_x_c)
bbox_y_c_int = int(bbox_y_c)
# if segments_tensor[bbox_y_c_int, bbox_x_c_int] == 0:
# # no sp in center
# center_sp = torch.Tensor().cuda()
# else:
# center_sp = segments_tensor[bbox_y_c_int, bbox_x_c_int].unsqueeze(0)
# center_sp_layer = torch.where(segments_tensor==center_sp, one_layer, zero_layer)
# center_sp_layer_np = center_sp_layer.cpu().numpy()
# kernel = np.ones((3,3),np.uint8)
# center_sp_layer_dilate = cv2.dilate(center_sp_layer_np, kernel, iterations = 2)
# center_sp_layer_dilate = torch.from_numpy(center_sp_layer_dilate)
# center_sp_layer_dilate_stack = center_sp_layer_dilate.unsqueeze(0).repeat(segments_stack.shape[0],1,1)
# cross_stack = center_sp_layer_dilate_stack * segments_stack
# cross_ = torch.sum(cross_stack, dim=0)
# neighborhoods = torch.unique(cross_)[1:]
# center_sps = neighborhoods
# cross_stack = cross_stack.cpu()
# center_sp_layer_dilate_stack = center_sp_layer_dilate_stack.cpu()
# # we also need center sp's neighborhoods
# # (2) find the boundary sp
# # boundary_erode
# kernel = np.ones((3,3),np.uint8)
# mask_erosion = cv2.erode(mask_np, kernel, iterations = 2)
# boundary_erode = mask_np - mask_erosion
# boundary_erode = torch.from_numpy(boundary_erode)
# # boundary_erode_pil = transforms.ToPILImage()(boundary_erode.cpu())
# # boundary_erode_pil.show()
# boundary_erode_stack = boundary_erode.unsqueeze(0).repeat(segments_stack.shape[0],1,1)
# boundary_mul_segments_stack = boundary_erode_stack * segments_stack
# boundary_mul_segments = torch.sum(boundary_mul_segments_stack, dim=0)
# # boundary_mul_segments_pil = transforms.ToPILImage()(boundary_mul_segments.cpu())
# # boundary_mul_segments_pil.show()
# boundary_mul_segments_unique = torch.unique(boundary_mul_segments)
# boundary_mul_segments_unique = boundary_mul_segments_unique[1:]
# boundary_sp = boundary_mul_segments_unique
# shan dian
# init grid
densy = 7
unit_w = 13 * densy
unit_h = 13 * densy
sandian = torch.zeros(unit_w, unit_h)
'''
log:
10,5,10,5 : 0.04 work! at 700
10,5,10,6 : 0.0333 work! at 2040
'''
sandian = sandian.reshape(13, densy, 13, densy)
sandian[:, int((densy - 1) / 2), :, int((densy - 1) / 2)] = 1
sandian = sandian.reshape(unit_w, unit_h)
sandian = sandian.unsqueeze(0).unsqueeze(0)
sandian = F.interpolate(sandian, (500, 500),
mode='nearest').squeeze()
sandian_stack = sandian.unsqueeze(0).repeat(
segments_stack.shape[0], 1, 1)
sandian_mul_segments_stack = sandian_stack * segments_stack
sandian_mul_segments = torch.sum(sandian_mul_segments_stack, dim=0)
sandian_mul_segments_pil = transforms.ToPILImage()(
sandian_mul_segments.cpu())
sandian_mul_segments_pil.show()
sandian_mul_segments_unique = torch.unique(sandian_mul_segments)
sandian_mul_segments_unique = sandian_mul_segments_unique[1:]
sandian_sp = sandian_mul_segments_unique
# pay attention
# spot_sp = torch.cat((center_sps, boundary_sp), dim=0)
spot_sp = sandian_sp
spot_sp = torch.unique(spot_sp).long()
sandian_stack = sandian_stack.cpu()
# boundary_erode_stack = boundary_erode_stack.cpu()
# boundary_mul_segments_stack = boundary_mul_segments_stack.cpu()
segments_stack = segments_stack.cpu()
torch.cuda.empty_cache()
# show_tensor = img.clone().cuda()
# for i in range(spot_sp.shape[0]):
# sp = spot_sp[i]
# show_tensor = torch.where(segments_tensor==sp, zero_layer.fill_(1).unsqueeze(0).repeat(3,1,1),show_tensor)
# show_tensor_pil = transforms.ToPILImage()(show_tensor.cpu())
# show_tensor_pil.show()
# generate theta_m
# for sp id from 1 to 128
uniform_ratio = torch.Tensor([ATTACK_AREA_RATE])[0]
higher_ratio = uniform_ratio * 1.5
uniform_ratio_theta = 1 / 2 * torch.log(uniform_ratio /
(1 - uniform_ratio))
higher_ratio_theta = 1 / 2 * torch.log(higher_ratio /
(1 - higher_ratio))
theta_m = torch.zeros_like(segments_label).cpu().fill_(
uniform_ratio_theta)
theta_m[spot_sp - 1] = higher_ratio_theta
for evo_step in range(evo_step_num):
# prepare sp dataset
g_theta_m = 1 / 2 * (torch.tanh(theta_m) + 1)
data_sp = SuperPixelGet(segments_label=segments_label,
segments_tensor=segments_tensor,
g_theta_m=g_theta_m,
data_num=population_num)
dataloader_sp = DataLoader(data_sp,
batch_size=batch_size_sp,
shuffle=False) #使用DataLoader加载数据
F_value_restore = torch.Tensor()
select_m_restore = torch.Tensor()
for j_sp_batch, sp_batch_data in enumerate(dataloader_sp):
attack_mask_batch, select_m = sp_batch_data[0].unsqueeze(
1), sp_batch_data[1]
select_m_restore = torch.cat(
(select_m_restore, select_m.cpu()))
batch_size_now = attack_mask_batch.shape[0]
## start at gray
adv_patch_w_batch = torch.zeros(batch_size_now, 3, 500,
500).cuda()
adv_patch_w_batch.requires_grad_(True)
## optimizer and scheduler
# optimizer = optim.Adam([
# {'params': adv_patch, 'lr': 0.01*255}
# ], amsgrad=True)
optimizer = optim.Adam([{
'params': adv_patch_w_batch,
'lr': 0.1
}],
amsgrad=True)
L_value_step = torch.ones(batch_size_now,
optim_step_num) * 1000
for step in tqdm(range(optim_step_num)):
# prepare batch data to feed the frcn
img_batch = img.cuda().unsqueeze(0).repeat(
batch_size_now, 1, 1, 1)
adv_patch_batch = torch.sigmoid(adv_patch_w_batch / 2)
patched_img_batch = torch.where(
attack_mask_batch > 0, adv_patch_batch, img_batch)
patched_img_batch_255 = patched_img_batch * 255.
patched_img_batch_rsz = F.interpolate(
patched_img_batch_255, (800, 800),
mode='bilinear').cuda()
patched_img_batch_nom_rsz = (patched_img_batch_rsz -
self.mean) / self.std
# output
img_new = copy.deepcopy(img_frcn)
img_new['img'][0] = patched_img_batch_nom_rsz
# [0] = patched_img_batch_nom_rsz
img_new['img_metas'][0] = [
img_new['img_metas'][0][0]
for i in range(batch_size_now)
]
frcn_output = self.Faster_RCNN(return_loss=False,
rescale=False,
**img_new)
# output formate is [x1,y1,x2,y2]
# compute loss
proposals_4507 = frcn_output[1]
proposals_score_4507 = frcn_output[2]
det_bboxes, det_labels, proposals = frcn_output[0]
det_bboxes = torch.cat(det_bboxes, dim=0) / 800 * 500
proposals = torch.cat(proposals, dim=0) / 800 * 500
det_labels = torch.cat(det_labels, dim=0)
attack_prob = det_labels[:, class_label]
training_confidence_threshold = 0.28
ov_thrs_index = torch.where(
attack_prob > training_confidence_threshold)[
0] # for certain class
pbbox_attack_cls = det_bboxes[:, class_label *
4:(class_label + 1) * 4]
# cls loss
attack_class_score = det_labels[:, class_label]
top_sort_class_score = torch.sort(
attack_class_score, descending=True)[0][:10]
cls_loss = torch.sum(top_sort_class_score)
# iou loss
bbox_x1 = bbox[0] / 500 * 416
bbox_y1 = bbox[1] / 500 * 416
bbox_w = bbox[2] / 500 * 416
bbox_h = bbox[3] / 500 * 416
ground_truth_bbox = [
bbox_x1, bbox_y1, bbox_x1 + bbox_w,
bbox_y1 + bbox_h
]
ground_truth_bbox = torch.Tensor(
ground_truth_bbox).unsqueeze(0).cuda()
iou_all = compute_iou_tensor(
det_bboxes[:,
class_label * 4:(class_label + 1) * 4],
ground_truth_bbox)
iou_positive = iou_all[iou_all > 0.15]
iou_loss = torch.sum(iou_all)
# class loss selected by IoU
attack_class_score = det_labels[:, class_label]
attack_class_score_iou = attack_class_score[
iou_all > 0.45]
attack_class_score_iou_sort = torch.sort(
attack_class_score_iou, descending=True)[0][:30]
cls_iou_loss = torch.sum(attack_class_score_iou_sort)
final_roi = pbbox_attack_cls[
ov_thrs_index] # for certain class
final_roi = final_roi[:, :4]
final_ctx = (final_roi[:, 0] + final_roi[:, 2]) / 2
final_cty = (final_roi[:, 1] + final_roi[:, 3]) / 2
final_ctx = final_ctx.unsqueeze(-1)
final_cty = final_cty.unsqueeze(-1)
final_roi_target = torch.cat(
[final_ctx, final_cty, final_ctx, final_cty],
dim=-1)
reg_loss = 10 * l1_norm(final_roi - final_roi_target
) / final_roi.shape[0] / 500
# RPN loss
# r1 : from score
# r2 : from x,y,w,h
# rpn score target is 0
rpn_score = proposals[:, 4]
loss_r1 = l2_norm(rpn_score - 0)
# rpn box target is smaller the boxes
rpn_ctx = (proposals[:, 0] + proposals[:, 2]) / 2
rpn_cty = (proposals[:, 1] + proposals[:, 3]) / 2
rpn_box = proposals[:, :4]
rpn_ctx = rpn_ctx.unsqueeze(-1)
rpn_cty = rpn_cty.unsqueeze(-1)
rpn_box_target = torch.cat(
[rpn_ctx, rpn_cty, rpn_ctx, rpn_cty], dim=-1)
# loss_r2 = l1_norm(rpn_score.unsqueeze(-1).repeat(1,4)*(rpn_box - rpn_box_target)) / 500
loss_r2 = l1_norm((rpn_box - rpn_box_target)) / 500
lambda_balance1 = 0.02
# rpn_loss = loss_r1 + lambda_balance1 * loss_r2
rpn_loss = lambda_balance1 * loss_r2
# rpn_loss = loss_r1
total_loss = cls_loss + reg_loss + rpn_loss
total_loss = cls_loss + cls_iou_loss + reg_loss + rpn_loss
# if epoch > 500:
# total_loss = rpn_loss + cls_loss_new + reg_loss
total_loss.backward()
optimizer.step()
optimizer.zero_grad()
# compute F(m,t*;y)
# L in disappearing
# i think we better use iou>0.45 in prediction, all bbox's person class confidence sum
bbox_x1 = bbox[0]
bbox_y1 = bbox[1]
bbox_w = bbox[2]
bbox_h = bbox[3]
ground_truth_bbox = [
bbox_x1, bbox_y1, bbox_x1 + bbox_w,
bbox_y1 + bbox_h
]
ground_truth_bbox = torch.Tensor(
ground_truth_bbox).unsqueeze(0).cuda()
iou_all = compute_iou_tensor(
det_bboxes[:,
class_label * 4:(class_label + 1) * 4],
ground_truth_bbox)
iou_select_index = iou_all > 0.45
for ib in range(batch_size_now):
det_labels_one = det_labels[ib:(ib + 1) * 1000,
class_label]
iou_select_index_one = iou_select_index[ib:(ib +
1) *
1000]
cls_conf_iou_select = det_labels_one[
iou_select_index_one]
cls_conf_iou_select = cls_conf_iou_select[
cls_conf_iou_select > 0.25]
cls_conf_iou_select_top = torch.sort(
cls_conf_iou_select, descending=True)[0][:10]
L_value_step[ib, step] = torch.sum(
cls_conf_iou_select_top).detach().clone()
pass
if (torch.min(L_value_step,
dim=1)[0] == torch.zeros(batch_size_now)
).all():
break
pass
L_value = -torch.min(L_value_step, dim=1)[0]
F_value = L_value - torch.sum(
torch.sum(sp_batch_data[0], dim=-1),
dim=-1).cpu() / mask_area * 10 ###!!!!
F_value_restore = torch.cat((F_value_restore, F_value))
pass
# print(F_value_restore)
# now we have F value
delta_J_theta = 1 / population_num * F_value_restore.unsqueeze(
1) * 2 * (select_m_restore.cpu().float() - g_theta_m)
delta_J_theta = torch.sum(delta_J_theta, dim=0)
theta_m = theta_m + delta_J_theta
g_theta_m = 1 / 2 * (torch.tanh(theta_m) + 1)
select_sp_index = torch.sort(
theta_m)[1][:int(theta_m.shape[0] * ATTACK_AREA_RATE)]
attack_region_show = zero_layer.clone().squeeze().cuda()
flag = torch.zeros_like(segments_label)
flag[select_sp_index] = 1
for i in range(segments_label.shape[0]):
color = g_theta_m[i]
color_layer = torch.zeros_like(segments_tensor).fill_(
color)
sp = segments_label[i]
attack_region_show = torch.where(segments_tensor == sp,
color_layer,
attack_region_show)
attack_region_show = attack_region_show / torch.max(
attack_region_show)
attack_region_show = (attack_region_show + img.cuda()) / 2
attack_region_show_pil = transforms.ToPILImage()(
attack_region_show.cpu())
save_dir = os.path.join('../common_data/NES_search_test_1107/',
img_name.split('.')[0])
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(
save_dir,
str(evo_step) + '_pop_' + str(population_num) + '_' +
str(base_SLIC_seed_num) + '.png')
attack_region_show_pil.save(save_path)
time1 = time.time()
time_cost = time1 - time0
if time_cost > 1800:
break
print(g_theta_m)
print(assasasasasasa)
pass
