def famosGeneration(content, noise, templatePatch, bVis=False):
if opt.multiScale > 0:
x = netMix(content, noise, templatePatch)
else:
x = netMix(content, noise)
a5 = x[:, -5:]
A = 4 * nn.functional.tanh(x[:, :-5]) ##smooths probs somehow
A = nn.functional.softmax(1 * (A - A.detach().max()), dim=1)
mixed = getTemplateMixImage(A, templatePatch)
alpha = nn.functional.sigmoid(a5[:, 3:4])
beta = nn.functional.sigmoid(a5[:, 4:5])
fake = blend(nn.functional.tanh(a5[:, :3]), mixed, alpha, beta)
##call second Unet to refine further
if opt.refine:
a5 = netRefine(
torch.cat([content, mixed, fake, a5[:, :3],
tvArray(A)], 1), noise)
alpha = nn.functional.sigmoid(a5[:, 3:4])
beta = nn.functional.sigmoid(a5[:, 4:5])
fake = blend(nn.functional.tanh(a5[:, :3]), mixed, alpha, beta)
if bVis:
return fake, torch.cat([alpha, beta, (alpha + beta) * 0.5],
1), A, mixed #alpha
return fake
def forward(self, input1, input2=None, M=None):
if bfirstNoise and input2 is not None:
x = torch.cat([
input1,
nn.functional.upsample(
input2, scale_factor=2**self.nDep, mode='bilinear')
], 1)
input2 = None
else:
x = input1 ##initial input
skips = []
input1 = input1[:, 3:5] ##only coords
for i in range(self.nDep):
if i > 0 and self.bCopyIn:
input1 = nn.functional.avg_pool2d(input1, int(2))
x = torch.cat([x, input1], 1)
x = self.eblocks[i].forward(x)
if i != self.nDep - 1:
if self.bCopyIn:
skips += [
torch.cat(
[x, nn.functional.avg_pool2d(input1, int(2))], 1)
]
else:
skips += [x]
bottle = x
if input2 is not None:
bottle = torch.cat((x, input2),
1) ##the det. output and the noise appended
x = bottle
with torch.no_grad():
MM = M.view(-1, 3, M.shape[3], M.shape[4]) ##only RGB images
mFeat = [] ##without full length M
for i in range(1, self.nDep):
sc = 2**i
mFeat.append(
nn.functional.avg_pool2d(MM, int(sc)).view(
M.shape[0], M.shape[1], 3, M.shape[3] // sc,
M.shape[4] // sc))
mFeat = mFeat[::-1]
for i in range(len(self.dblocks)):
x = self.dblocks[i].forward(x)
if i < self.nDep - 1 and self.bSkip:
x = torch.cat((x, skips[-1 - i]), 1)
if i < self.nDep - 1:
blendA = 4 * nn.functional.tanh(
x[:, :opt.N]) ##channels for mixing
blendA = nn.functional.softmax(
1 * (blendA - blendA.detach().max()), dim=1)
mixed = getTemplateMixImage(blendA, mFeat[i])
x = torch.cat((x, mixed), 1)
return x