def get_data(self, data: tuple[torch.Tensor, torch.Tensor], org: bool = False, keep_org: bool = True, poison_label=True, **kwargs) -> tuple[torch.Tensor, torch.Tensor]:
if org:
_input, _label = self.model.get_data(data)
else:
_input, _label = self.attack.get_data(data=data, keep_org=keep_org, poison_label=poison_label, **kwargs)
h, w = _input.shape[-2], _input.shape[-1]
_input_list = []
for single_input in _input:
image = to_pil_image(single_input)
image = F.resize(image, (int(h * self.resize_ratio), int(w * self.resize_ratio)), Image.ANTIALIAS)
image = F.resize(image, (h, w))
_input_list.append(to_tensor(image))
return torch.stack(_input_list), _label
def get_mark(self, conv_ref_img: torch.Tensor):
'''
input is a convolved reflection images, already in same
shape of any input images, this function will legally reshape
this ref_img and give to self.mark.mark.
'''
org_mark_img: Image.Image = to_pil_image(conv_ref_img)
org_mark_img = org_mark_img.resize(
(self.mark.mark_width, self.mark.mark_height), Image.ANTIALIAS)
self.mark.org_mark = byte2float(org_mark_img)
self.mark.org_mask, self.mark.org_alpha_mask = self.mark.org_mask_mark(
self.mark.org_mark, self.mark.edge_color, self.mark.mark_alpha)
self.mark.mark, self.mark.mask, self.mark.alpha_mask = self.mark.mask_mark(
height_offset=self.mark.height_offset,
width_offset=self.mark.width_offset)