def compute_object_fabrication_gradient(self, x, detections=None):
x_local = x.copy() * 255
x_tensor = t.from_numpy(preprocess(x_local[0].transpose(
(2, 0, 1))))[None].cuda().float()
x_tensor.requires_grad = True
_bboxes = t.from_numpy(np.zeros((1, 1, 4))).float()
_labels = t.from_numpy(np.zeros((1, 1))).int()
_scale = at.scalar(np.asarray([1.]))
losses = self.forward(x_tensor, _bboxes, _labels, _scale)
self.optimizer.zero_grad()
self.faster_rcnn.zero_grad()
losses.object_fabrication_loss.backward()
return x_tensor.grad.data.cpu().numpy().transpose((0, 2, 3, 1))
def compute_object_mislabeling_gradient(self, x, detections):
x_local = x.copy() * 255
x_tensor = t.from_numpy(preprocess(x_local[0].transpose(
(2, 0, 1))))[None].cuda().float()
x_tensor.requires_grad = True
_bboxes = t.from_numpy(detections[np.newaxis, :,
[-3, -4, -1, -2]]).float()
_labels = t.from_numpy(detections[np.newaxis, :, 0]).int()
_scale = at.scalar(np.asarray([1.]))
losses = self.forward(x_tensor, _bboxes, _labels, _scale)
self.optimizer.zero_grad()
self.faster_rcnn.zero_grad()
losses.object_mislabeling_loss.backward()
return x_tensor.grad.data.cpu().numpy().transpose((0, 2, 3, 1))
def compute_object_untargeted_gradient(self, x, detections):
x_local = x.copy() * 255
x_tensor = t.from_numpy(preprocess(x_local[0].transpose(
(2, 0, 1))))[None].cuda().float()
x_tensor.requires_grad = True
if detections is not None and len(detections) > 0:
_bboxes = t.from_numpy(detections[np.newaxis, :,
[-3, -4, -1, -2]]).float()
_labels = t.from_numpy(detections[np.newaxis, :, 0]).int()
else:
_bboxes = t.from_numpy(np.zeros((1, 1, 4))).float()
_labels = t.from_numpy(np.zeros((1, 1))).int()
_scale = at.scalar(np.asarray([1.]))
losses = self.forward(x_tensor, _bboxes, _labels, _scale)
self.optimizer.zero_grad()
self.faster_rcnn.zero_grad()
if len(detections) > 0:
losses.object_untargeted_loss.backward()
else:
losses.object_fabrication_loss.backward()
return x_tensor.grad.data.cpu().numpy().transpose((0, 2, 3, 1))
