RobustDpatch doesn't work

[YXU300]

`attack = RobustDPatch( estimator=detector, patch_shape=(3, 100, 100), patch_location=(0,0), crop_range=[0, 0], brightness_range=[1.0, 1.0], rotation_weights=[1, 0, 0, 0], sample_size=1, learning_rate=1.99, max_iter=200, batch_size=1, verbose=True, targeted=True )

loss_history = []

for images, image_names in get_images(data_dir): dets = detector.predict(images) y = [] for j in range(len(dets)):

    highest_box = filter_highest_score_box([dets[j]])
    y.append(highest_box)

patch = attack.generate(x=images,y=y)
patched_images = attack.apply_patch(images)

loss_components, _ = detector._get_losses(patched_images, dets)
losses = {}
for loss in loss_components.keys():
    if 'loss' in loss:
        losses[loss] = loss_components[loss].item()
loss_history.append(losses)
print(f"Current Losses: {losses}")

for i in range(len(patched_images)):
    adversarial_image_pgd_bgr = cv2.cvtColor(
        patched_images[i].transpose((1, 2, 0)).astype(np.uint8),
        cv2.COLOR_RGB2BGR
    )
    cv2.imwrite(os.path.join(save_dir, image_names[i]), adversarial_image_pgd_bgr)

print(f"Loss history over all batches: {losshistory}") ` I tried to attack yolov5s, but it didn't work. The dataset is coco2017. 

[beat-buesser]

Hi @YXU300 Thank you very much for your interest in ART! How did you define detector in your script above?

[YXU300]

class Yolo(torch.nn.Module): def init(self, model): super().init() self.model = model self.model.hyp = {'box': 0.1, 'obj': 0.1, 'cls': 0.1, 'anchor_t': 1.0, 'cls_pw': 1.0, 'obj_pw': 1.0, 'fl_gamma': 0.0} self.compute_loss = ComputeLoss(self.model.model.model)

def forward(self, x, targets=None):
    if self.training:
        outputs = self.model.model.model(x)
        loss, loss_items = self.compute_loss(outputs, targets)
        loss_components_dict = {"loss_total": loss, 'loss_box': loss_items[0], 'loss_obj': loss_items[1], 'loss_cls': loss_items[2]}
        return loss_components_dict
    else:
        return self.model(x)

model = torch.hub.load('ultralytics/yolov5', 'yolov5s') model = Yolo(model) detector = PyTorchYolo(model=model, device_type='cuda', input_shape=(3, 256, 256), clip_values=(0, 255), attack_losses=("loss_total", "loss_cls", "loss_box", "loss_obj"))

attack = RobustDPatch( estimator=detector, patch_shape=(3, 100, 100), patch_location=(0,0), crop_range=[0, 0], brightness_range=[1.0, 1.0], rotation_weights=[1, 0, 0, 0], sample_size=1, learning_rate=1.99, max_iter=200, batch_size=5, verbose=True, targeted=False )

loss_history = []

for images, image_names in get_images(data_dir): dets = detector.predict(images) y = [] for j in range(len(dets)):

    highest_box = filter_highest_score_box([dets[j]])
    y.append(highest_box)

patch = attack.generate(x=images)
patched_images = attack.apply_patch(images)

loss_components, _ = detector._get_losses(patched_images, dets)
losses = {}
for loss in loss_components.keys():
    if 'loss' in loss:
        losses[loss] = loss_components[loss].item()
loss_history.append(losses)
print(f"Current Losses: {losses}")

for i in range(len(patched_images)):
    adversarial_image_pgd_bgr = cv2.cvtColor(
        patched_images[i].transpose((1, 2, 0)).astype(np.uint8),
        cv2.COLOR_RGB2BGR
    )
    cv2.imwrite(os.path.join(save_dir, image_names[i]), adversarial_image_pgd_bgr)

print(f"Loss history over all batches: {loss_history}")

Thanks for your reply, here is my code. This code is modified from adversarial patch example.

[YXU300]

I've also tried to run the example code and it also has no work.