def forward(self, laf: torch.Tensor,
img: torch.Tensor) -> torch.Tensor: # type: ignore
"""
Args:
laf: (torch.Tensor) shape [BxNx2x3]
img: (torch.Tensor) shape [Bx1xHxW]
Returns:
laf_out: (torch.Tensor) shape [BxNx2x3]"""
raise_error_if_laf_is_not_valid(laf)
img_message: str = "Invalid img shape, we expect BxCxHxW. Got: {}".format(
img.shape)
if not torch.is_tensor(img):
raise TypeError("img type is not a torch.Tensor. Got {}".format(
type(img)))
if len(img.shape) != 4:
raise ValueError(img_message)
if laf.size(0) != img.size(0):
raise ValueError(
"Batch size of laf and img should be the same. Got {}, {}".
format(img.size(0), laf.size(0)))
B, N = laf.shape[:2]
PS: int = self.patch_size
patches: torch.Tensor = extract_patches_from_pyramid(
img, make_upright(laf), PS, True).view(-1, 1, PS, PS)
ellipse_shape: torch.Tensor = self.affine_shape_detector(patches)
ellipses = torch.cat(
[laf.view(-1, 2, 3)[..., 2].unsqueeze(1), ellipse_shape],
dim=2).view(B, N, 5)
scale_orig = get_laf_scale(laf)
laf_out = ellipse_to_laf(ellipses)
ellipse_scale = get_laf_scale(laf_out)
laf_out = scale_laf(laf_out, scale_orig / ellipse_scale)
return laf_out
def forward(self, laf: torch.Tensor, img: torch.Tensor) -> torch.Tensor:
"""
Args:
laf: shape [BxNx2x3]
img: shape [Bx1xHxW]
Returns:
laf_out shape [BxNx2x3]
"""
raise_error_if_laf_is_not_valid(laf)
img_message: str = "Invalid img shape, we expect BxCxHxW. Got: {}".format(img.shape)
if not torch.is_tensor(img):
raise TypeError("img type is not a torch.Tensor. Got {}".format(type(img)))
if len(img.shape) != 4:
raise ValueError(img_message)
if laf.size(0) != img.size(0):
raise ValueError(
"Batch size of laf and img should be the same. Got {}, {}".format(img.size(0), laf.size(0))
)
B, N = laf.shape[:2]
PS: int = self.patch_size
patches: torch.Tensor = extract_patches_from_pyramid(img, make_upright(laf), PS, True).view(-1, 1, PS, PS)
xy = self.features(self._normalize_input(patches)).view(-1, 3)
a1 = torch.cat([1.0 + xy[:, 0].reshape(-1, 1, 1), 0 * xy[:, 0].reshape(-1, 1, 1)], dim=2)
a2 = torch.cat([xy[:, 1].reshape(-1, 1, 1), 1.0 + xy[:, 2].reshape(-1, 1, 1)], dim=2)
new_laf_no_center = torch.cat([a1, a2], dim=1).reshape(B, N, 2, 2)
new_laf = torch.cat([new_laf_no_center, laf[:, :, :, 2:3]], dim=3)
scale_orig = get_laf_scale(laf)
ellipse_scale = get_laf_scale(new_laf)
laf_out = scale_laf(make_upright(new_laf), scale_orig / ellipse_scale)
return laf_out