fn_generate = masked_fake_output #A function that takes distorted_A as input and outputs fake_A.
fn_mask = torch.cat(
(alpha, alpha, alpha),
0) #A function that takes distorted_A as input and outputs mask_A.
fn_abgr = torch.cat(
(alpha, rgb), 0
) #A function that takes distorted_A as input and outputs concat([mask_A, fake_A]).
return input, fake_output, alpha, fn_generate, fn_mask, fn_abgr
A, fake_A, mask_A, path_A, path_mask_A, path_abgr_A = cycle_variables(
input, netG_A)
B, fake_B, mask_B, path_B, path_mask_B, path_abgr_B = cycle_variables(
input, netG_B)
real_A = Image.Resize(real_A, (64, 64))
real_B = Image.Resize(real_B, (64, 64))
################################################################
whom2whom = "BtoA" # default trainsforming faceB to faceA
if whom2whom is "AtoB":
path_func = path_abgr_B
elif whom2whom is "BtoA":
path_func = path_abgr_A
else:
print("whom2whom should be either AtoB or BtoA")
###############################################################
use_smoothed_mask = True
use_smoothed_bbox = True
