-
Notifications
You must be signed in to change notification settings - Fork 1
/
pgd_attack_eval_vo.py
501 lines (413 loc) · 21.1 KB
/
pgd_attack_eval_vo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
# Copyright © NavInfo Europe 2022.
from imageio import imread
import numpy as np
from path import Path
import argparse
from tqdm import tqdm
import custom_transforms
from loss_functions import compute_smooth_loss, compute_photo_and_geometry_loss
import torch.utils.data
from inverse_warp import *
from kitti_eval.kitti_odometry import KittiEvalOdom
from PIL import Image
import models
import random
import os
import math
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(description='Script for visualizing depth map and masks',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--pretrained-posenet", required=True, type=str, help="pretrained PoseNet path")
parser.add_argument("--pretrained-dispnet", required=True, type=str, help="pretrained DispNet path")
parser.add_argument("--img-height", default=256, type=int, help="Image height")
parser.add_argument("--img-width", default=832, type=int, help="Image width")
parser.add_argument("--no-resize", action='store_true', help="no resizing is done")
parser.add_argument("--save-imgs", action='store_true', help="To save adv imgs")
parser.add_argument("--min-depth", default=1e-3)
parser.add_argument("--max-depth", default=80)
parser.add_argument("--dataset-dir", required=True, type=str, help="Dataset directory")
parser.add_argument("--output-dir", required=True, type=str, help="Output directory for saving predictions in a big 3D numpy file")
parser.add_argument("--img-exts", default=['png', 'jpg', 'bmp'], nargs='*', type=str, help="images extensions to glob")
parser.add_argument("--rotation-mode", default='euler', choices=['euler', 'quat'], type=str)
parser.add_argument("--stats-fname", help="expt_name", type=str, default="PGD")
parser.add_argument("--num-workers", type=int, help="number of dataloader workers", default=12)
parser.add_argument('--resnet-layers', type=int, default=50, choices=[18, 50], help='depth network architecture.')
parser.add_argument("--sequence", default='09', type=str, help="sequence to test", choices=['09', '10'])
parser.add_argument('-p', '--photo-loss-weight', type=float, help='weight for photometric loss', metavar='W', default=1)
parser.add_argument('-s', '--smooth-loss-weight', type=float, help='weight for disparity smoothness loss', metavar='W', default=0.1)
parser.add_argument('-c', '--geometry-consistency-weight', type=float, help='weight for depth consistency loss', metavar='W', default=0.5)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
def load_as_float(path):
return imread(path).astype(np.float32)
class SequenceFolder(torch.utils.data.Dataset):
"""A sequence data loader where the files are arranged in this way:
root/scene_1/0000000.jpg
root/scene_1/0000001.jpg
..
root/scene_1/cam.txt
root/scene_2/0000000.jpg
.
transform functions must take in a list a images and a numpy array (usually intrinsics matrix)
"""
def __init__(self, root, seed=None, seq='09', sequence_length=3, transform=None, skip_frames=1, dataset='kitti'):
np.random.seed(seed)
random.seed(seed)
self.root = Path(root)
scene_list_path = self.root/seq + '.txt'
self.scenes = [self.root/folder.strip() for folder in open(scene_list_path) if len(folder.strip()) > 0]
self.transform = transform
self.dataset = dataset
self.k = skip_frames
self.crawl_folders(sequence_length)
def crawl_folders(self, sequence_length):
# k skip frames
sequence_set = []
demi_length = (sequence_length-1)//2
shifts = list(range(-demi_length * self.k, demi_length * self.k + 1, self.k))
shifts.pop(demi_length)
for scene in self.scenes:
intrinsics = np.genfromtxt(scene/'cam.txt').astype(np.float32).reshape((3, 3))
imgs = sorted(scene.files('*.jpg'))
if len(imgs) < sequence_length:
continue
for i in range(demi_length * self.k, len(imgs)-demi_length * self.k):
sample = {'intrinsics': intrinsics, 'tgt': imgs[i], 'ref_imgs': []}
for j in shifts:
sample['ref_imgs'].append(imgs[i+j])
sequence_set.append(sample)
self.samples = sequence_set
def __getitem__(self, index):
sample = self.samples[index]
tgt_img = load_as_float(sample['tgt'])
ref_imgs = [load_as_float(ref_img) for ref_img in sample['ref_imgs']]
if self.transform is not None:
imgs, intrinsics = self.transform([tgt_img] + ref_imgs, np.copy(sample['intrinsics']))
tgt_img = imgs[0]
ref_imgs = imgs[1:]
else:
intrinsics = np.copy(sample['intrinsics'])
return tgt_img, ref_imgs, intrinsics, np.linalg.inv(intrinsics)
def __len__(self):
return len(self.samples)
class PGDAttack:
def __init__(self,
data_path,
pose_model_pth,
depth_model_pth,
sequence,
eval_out_dir,
no_resize,
height=256,
width=832,
img_exts="PNG",
save_adv_imgs=False,
min_depth=0.1,
max_depth=80.0,
resnet_layers=50,
w1=1,
w2=1,
w3=1
):
self.data_path = data_path
self.eval_split = sequence
self.sequence_id = self.eval_split.split("_")[-1]
self.eval_out_dir = eval_out_dir
self.save_adv_imgs = save_adv_imgs
self.img_exts = img_exts
self.no_resize = no_resize
self.height = height
self.width = width
self.min_depth = min_depth
self.max_depth = max_depth
self.device = torch.device("cuda")
self.resnet_layers = resnet_layers
self.w1 = w1
self.w2 = w2
self.w3 = w3
output_dir = Path(self.eval_out_dir)
output_dir.makedirs_p()
self.eval_tool = KittiEvalOdom()
self.gt_dir = "./kitti_eval/gt_poses/"
print("gt path", self.gt_dir)
normalize = custom_transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transform = custom_transforms.Compose([custom_transforms.ArrayToTensor(), normalize])
# SC-SfM structure means we need to keep train=True
self.test_set = SequenceFolder(
data_path,
transform=test_transform,
seq=self.sequence_id,
sequence_length=3,
dataset='kitti'
)
print("data_path:", data_path)
weights_pose = torch.load(pose_model_pth)
self.pose_net = models.PoseResNet().to(device)
self.pose_net.load_state_dict(weights_pose['state_dict'], strict=False)
self.pose_net.eval()
weights = torch.load(depth_model_pth)
self.disp_net = models.DispResNet(self.resnet_layers, False).to(device)
self.disp_net.load_state_dict(weights['state_dict'])
self.disp_net.eval()
self.models = {}
self.ivt = [
torch.tensor([0.485, 0.456, 0.406]).view(1, -1, 1, 1).cuda(),
torch.tensor([0.229, 0.224, 0.225]).view(1, -1, 1, 1).cuda()
]
def process(self, epsilon, num_workers=12):
dataloader = torch.utils.data.DataLoader(
self.test_set, batch_size=1, shuffle=False,
num_workers=num_workers, pin_memory=True)
self.save_dir = os.path.join(self.eval_out_dir, "pgd", "adv_" + str(epsilon))
os.makedirs(os.path.join(self.save_dir, self.eval_split), exist_ok=True)
print("save dir: ", self.save_dir)
self.results_dir = os.path.join(self.save_dir, self.eval_split)
os.makedirs(self.results_dir, exist_ok=True)
if self.save_adv_imgs:
self.adv_dir = os.path.join(self.results_dir, "adv_examples")
self.noise_dir = os.path.join(self.results_dir, "noise")
os.makedirs(self.adv_dir, exist_ok=True)
os.makedirs(self.noise_dir, exist_ok=True)
self.evaluate(dataloader, self.results_dir, epsilon=epsilon)
def evaluate(self, dataloader, results_dir, epsilon):
"""Evaluates a pretrained model using a specified test set
"""
num_iters = min(epsilon + 4, math.ceil(1.25 * epsilon))
num_iters = int(np.max([np.ceil(num_iters), 1]))
self.disp_net.eval()
self.pose_net.eval()
print("-> Computing predictions with size {}x{}".format(
self.width, self.height))
print("len dataloader: ", len(dataloader))
global_pose = np.eye(4)
poses = [global_pose[0:3, :].reshape(1, 12)]
for i, data in tqdm(enumerate(dataloader), total=len(dataloader)):
tgt, ref1, ref2 = self.fgsm_untargetted(data, eps=epsilon, num_iters=num_iters, visualize=False,
save_img=self.save_adv_imgs, im_num=i+1)
with torch.no_grad():
if i == 0:
pose = self.pose_net(ref1, tgt)
pose_mat = pose_vec2mat(pose).squeeze(0).cpu().numpy()
pose_mat = np.vstack([pose_mat, np.array([0, 0, 0, 1])])
global_pose = global_pose @ np.linalg.inv(pose_mat)
poses.append(global_pose[0:3, :].reshape(1, 12))
if self.save_adv_imgs:
save_adv_name = os.path.join(self.adv_dir, str(i) + ".png")
save_noise_name = os.path.join(self.noise_dir, str(i) + ".png")
save_adv_name_npy = os.path.join(self.adv_dir, str(i) + ".npy")
Image.fromarray(
np.transpose(255 * (ref1 * self.ivt[1] + self.ivt[
0]).detach().cpu().squeeze().numpy(),
(1, 2, 0)).astype(np.uint8)
).save(save_adv_name)
Image.fromarray(
np.transpose(
((ref1 * self.ivt[1] + self.ivt[0]).detach().cpu().squeeze().numpy() -
(data[1][0].to(device) * self.ivt[1] + self.ivt[
0]).cpu().squeeze().numpy()) * 255.0,
(1, 2, 0)
).astype(np.uint8)
).save(save_noise_name)
np.save(save_adv_name_npy,
(ref1 * self.ivt[1] + self.ivt[0]).detach().cpu().numpy()
)
pose = self.pose_net(tgt, ref2)
pose_mat = pose_vec2mat(pose).squeeze(0).cpu().numpy()
pose_mat = np.vstack([pose_mat, np.array([0, 0, 0, 1])])
global_pose = global_pose @ np.linalg.inv(pose_mat)
poses.append(global_pose[0:3, :].reshape(1, 12))
if i == len(dataloader) - 1:
if self.save_adv_imgs:
save_adv_name = os.path.join(self.adv_dir, str(i + 2) + ".png")
save_noise_name = os.path.join(self.noise_dir, str(i + 2) + ".png")
save_adv_name_npy = os.path.join(self.adv_dir, str(i + 2) + ".npy")
Image.fromarray(
np.transpose(255 * (ref2 * self.ivt[1] + self.ivt[
0]).detach().cpu().squeeze().numpy(),
(1, 2, 0)).astype(np.uint8)
).save(save_adv_name)
Image.fromarray(
np.transpose(
((ref2 * self.ivt[1] + self.ivt[0]).detach().cpu().squeeze().numpy() -
(data[1][1].to(device) * self.ivt[1] + self.ivt[
0]).cpu().squeeze().numpy()) * 255.0,
(1, 2, 0)
).astype(np.uint8)
).save(save_noise_name)
np.save(save_adv_name_npy,
(ref2 * self.ivt[1] + self.ivt[0]).detach().cpu().numpy()
)
print("len poses:", len(poses))
poses = np.concatenate(poses, axis=0)
filename = os.path.join(results_dir, self.eval_split+".txt")
np.savetxt(filename, poses, delimiter=' ', fmt='%1.8e')
self.eval_tool.eval(
self.gt_dir,
results_dir,
alignment='7dof'
)
def compute_depth(self, tgt_img, ref_imgs):
tgt_depth = [1 / disp for disp in self.disp_net(tgt_img)]
if len(tgt_depth[0].shape) == 3:
tgt_depth = [depth.unsqueeze(1) for depth in tgt_depth]
ref_depths = []
for ref_img in ref_imgs:
ref_depth = [1 / disp for disp in self.disp_net(ref_img)]
if len(ref_depth[0].shape) == 3:
ref_depth = [depth.unsqueeze(1) for depth in ref_depth]
ref_depths.append(ref_depth)
return tgt_depth, ref_depths
def compute_pose_with_inv(self, tgt_img, ref_imgs):
poses = []
poses_inv = []
for ref_img in ref_imgs:
poses.append(self.pose_net(tgt_img, ref_img))
poses_inv.append(self.pose_net(ref_img, tgt_img))
return poses, poses_inv
def process_inputs(self, tgt_img, ref_img1, ref_img2, intrinsics):
"""Pass a minibatch through the network and generate images and losses
"""
ref_imgs = [ref_img1, ref_img2]
tgt_img = tgt_img.to(device)
ref_imgs = [img.to(device) for img in ref_imgs]
intrinsics = intrinsics.to(device)
# compute output
tgt_depth, ref_depths = self.compute_depth(tgt_img, ref_imgs)
poses, poses_inv = self.compute_pose_with_inv(tgt_img, ref_imgs)
loss_1, loss_3 = compute_photo_and_geometry_loss(tgt_img, ref_imgs, intrinsics, tgt_depth, ref_depths,
poses, poses_inv, max_scales=1, with_ssim=1,
with_mask=1, with_auto_mask=1, padding_mode='zeros')
loss_2 = compute_smooth_loss(tgt_depth, tgt_img, ref_depths, ref_imgs)
loss = w1 * loss_1 + w2 * loss_2 + w3 * loss_3
return loss
def fgsm_untargetted(self, inputs, eps, num_iters, alpha=1,
visualize=True, save_img=False, using_noise=True,
im_num=None):
tgt_img, ref_imgs, intrinsics, intrinsics_inv = inputs
ref_img1, ref_img2 = ref_imgs
tgt_img = tgt_img.to(device)
ref_img1 = ref_img1.to(device)
ref_img2 = ref_img2.to(device)
intrinsics = intrinsics.to(device)
if save_img:
save_adv_name = os.path.join(self.adv_dir, str(im_num) + ".png")
save_noise_name = os.path.join(self.noise_dir, str(im_num) + ".png")
save_adv_name_npy = os.path.join(self.adv_dir, str(im_num) + ".npy")
if eps == 0:
if save_img:
Image.fromarray(
np.transpose(255 * (tgt_img * self.ivt[1] + self.ivt[
0]).detach().cpu().squeeze().numpy(),
(1, 2, 0)).astype(np.uint8)
).save(save_adv_name)
Image.fromarray(
np.transpose(
((tgt_img * self.ivt[1] + self.ivt[0]).detach().cpu().squeeze().numpy() -
(tgt_img * self.ivt[1] + self.ivt[0]).cpu().squeeze().numpy()) * 255.0, (1, 2, 0)
).astype(np.uint8)
).save(save_noise_name)
np.save(save_adv_name_npy,
(tgt_img * self.ivt[1] + self.ivt[0]).detach().cpu().numpy()
)
return tgt_img, ref_img1, ref_img2
eps /= 255.0
eps_depth = torch.ones_like(tgt_img.to(device)) * eps / self.ivt[1]
eps_pose = torch.ones_like(tgt_img.to(device)) * eps / self.ivt[1]
alpha /= 255.0
alpha_depth = alpha / self.ivt[1]
alpha_depth = alpha_depth.view(1, 3, 1, 1).to(device)
alpha_pose = alpha / self.ivt[1]
alpha_pose = alpha_pose.view(1, 3, 1, 1).to(device)
adv_tgt_img = tgt_img.clone().to(device)
adv_ref_img1 = ref_img1.clone().to(device)
adv_ref_img2 = ref_img2.clone().to(device)
ub_max_depth = (torch.ones_like(adv_tgt_img) - self.ivt[0]) / self.ivt[1]
lb_min_depth = (torch.zeros_like(adv_tgt_img) - self.ivt[0]) / self.ivt[1]
ub_depth = torch.min(adv_tgt_img + eps_depth, ub_max_depth)
lb_depth = torch.max(adv_tgt_img - eps_depth, lb_min_depth)
if using_noise:
adv_tgt_img = adv_tgt_img + \
torch.FloatTensor(adv_tgt_img.size()).uniform_(-eps, eps).cuda()
adv_tgt_img = torch.max(torch.min(adv_tgt_img, ub_depth), lb_depth)
del ub_max_depth, lb_min_depth, eps_depth
ub_max_pose = (torch.ones_like(adv_ref_img1) - self.ivt[0]) / self.ivt[1]
lb_min_pose = (torch.zeros_like(adv_ref_img1) - self.ivt[0]) / self.ivt[1]
ub_pose_1 = torch.min(adv_ref_img1 + eps_pose, ub_max_pose)
lb_pose_1 = torch.max(adv_ref_img1 - eps_pose, lb_min_pose)
ub_pose_2 = torch.min(adv_ref_img2 + eps_pose, ub_max_pose)
lb_pose_2 = torch.max(adv_ref_img2 - eps_pose, lb_min_pose)
if using_noise:
adv_ref_img1 = adv_ref_img1 + \
torch.FloatTensor(adv_ref_img1.size()).uniform_(-eps, eps).cuda()
adv_ref_img1 = torch.max(torch.min(adv_ref_img1, ub_pose_1), lb_pose_1)
adv_ref_img2 = adv_ref_img2 + \
torch.FloatTensor(adv_ref_img2.size()).uniform_(-eps, eps).cuda()
adv_ref_img2 = torch.max(torch.min(adv_ref_img2, ub_pose_2), lb_pose_2)
del ub_max_pose, lb_min_pose, eps_pose
if visualize:
plt.ion()
plt.show()
for i in range(num_iters):
adv_tgt_img.requires_grad = True
adv_ref_img1.requires_grad = True
adv_ref_img2.requires_grad = True
loss = self.process_inputs(adv_tgt_img, adv_ref_img1, adv_ref_img2, intrinsics)
loss.backward()
noise_depth = alpha_depth * torch.sign(adv_tgt_img.grad)
noise_pose_1 = alpha_pose * torch.sign(adv_ref_img1.grad)
noise_pose_2 = alpha_pose * torch.sign(adv_ref_img2.grad)
adv_tgt_img = adv_tgt_img.detach() + noise_depth
adv_tgt_img = torch.max(torch.min(adv_tgt_img, ub_depth), lb_depth)
adv_ref_img1 = adv_ref_img1.detach() + noise_pose_1
adv_ref_img1 = torch.max(torch.min(adv_ref_img1, ub_pose_1), lb_pose_1)
adv_ref_img2 = adv_ref_img2.detach() + noise_pose_2
adv_ref_img2 = torch.max(torch.min(adv_ref_img2, ub_pose_2), lb_pose_2)
if (i == num_iters - 1) and (visualize or save_img):
if visualize:
plt.imshow(
np.transpose(
(adv_tgt_img * self.ivt[1] + self.ivt[0]).detach().cpu().squeeze().numpy() *
255.0, (1, 2, 0)).astype(np.uint8)
)
plt.pause(1)
if save_img:
Image.fromarray(
np.transpose(255 * (adv_tgt_img * self.ivt[1] + self.ivt[
0]).detach().cpu().squeeze().numpy(),
(1, 2, 0)).astype(np.uint8)
).save(save_adv_name)
Image.fromarray(
np.transpose(
((adv_tgt_img * self.ivt[1] + self.ivt[0]).detach().cpu().squeeze().numpy() -
(tgt_img * self.ivt[1] + self.ivt[0]).cpu().squeeze().numpy()) * 255.0, (1, 2, 0)
).astype(np.uint8)
).save(save_noise_name)
np.save(save_adv_name_npy,
(adv_tgt_img * self.ivt[1] + self.ivt[0]).detach().cpu().numpy()
)
return adv_tgt_img.detach(), adv_ref_img1.detach(), adv_ref_img2.detach()
if __name__ == '__main__':
args = parser.parse_args()
stats_all = []
w1, w2, w3 = args.photo_loss_weight, args.smooth_loss_weight, args.geometry_consistency_weight
attack = PGDAttack(
pose_model_pth=args.pretrained_posenet,
depth_model_pth=args.pretrained_dispnet,
data_path=args.dataset_dir,
sequence=args.sequence,
eval_out_dir=args.output_dir,
height=args.img_height,
width=args.img_width,
img_exts=args.img_exts,
save_adv_imgs=args.save_imgs,
min_depth=args.min_depth,
max_depth=args.max_depth,
no_resize=args.no_resize,
resnet_layers=args.resnet_layers,
w1=w1,
w2=w2,
w3=w3
)
epsilons = [0, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0]
for epsilon in epsilons:
attack.process(epsilon=epsilon, num_workers=args.num_workers)