def random_apply_affine(img, p, G=None, antialiasing_kernel=SYM6):
kernel = antialiasing_kernel
len_k = len(kernel)
pad_k = (len_k + 1) // 2
kernel = torch.as_tensor(kernel)
kernel = torch.ger(kernel, kernel).to(img)
kernel_flip = torch.flip(kernel, (0, 1))
img_pad, G, (pad_x1, pad_x2, pad_y1,
pad_y2) = try_sample_affine_and_pad(img, p, pad_k, G)
p_ux1 = pad_x1
p_ux2 = pad_x2 + 1
p_uy1 = pad_y1
p_uy2 = pad_y2 + 1
w_p = img_pad.shape[3] - len_k + 1
h_p = img_pad.shape[2] - len_k + 1
h_o = img.shape[2]
w_o = img.shape[3]
img_2x = upfirdn2d(img_pad, kernel_flip, up=2)
grid = make_grid(
img_2x.shape,
-2 * p_ux1 / w_o - 1,
2 * (w_p - p_ux1) / w_o - 1,
-2 * p_uy1 / h_o - 1,
2 * (h_p - p_uy1) / h_o - 1,
device=img_2x.device,
).to(img_2x)
grid = affine_grid(grid, torch.inverse(G)[:, :2, :].to(img_2x))
grid = grid * torch.tensor([w_o / w_p, h_o / h_p],
device=grid.device) + torch.tensor(
[(w_o + 2 * p_ux1) / w_p - 1,
(h_o + 2 * p_uy1) / h_p - 1],
device=grid.device)
img_affine = F.grid_sample(img_2x,
grid,
mode="bilinear",
align_corners=False,
padding_mode="zeros")
img_down = upfirdn2d(img_affine, kernel, down=2)
end_y = -pad_y2 - 1
if end_y == 0:
end_y = img_down.shape[2]
end_x = -pad_x2 - 1
if end_x == 0:
end_x = img_down.shape[3]
img = img_down[:, :, pad_y1:end_y, pad_x1:end_x]
return img, G
def forward(self, input):
out = upfirdn2d(input,
self.kernel,
up=1,
down=self.factor,
pad=self.pad)
return out
def forward(self, input):
out = upfirdn2d(input, self.kernel, pad=self.pad)
return out
