def forward(self, x, add_noise=False):
h = x
if self.blur_k is None:
k = np.asarray([1, 2, 1]).astype('f')
k = k[:, None] * k[None, :]
k = k / np.sum(k)
self.blur_k = self.xp.asarray(k)[None, None, :]
if self.enable_blur:
h = blur(upscale2x(h), self.blur_k)
else:
h = upscale2x(h)
h = self.c0(h)
# h should be (batch, ch, size, size)
if add_noise:
h = self.n0(h)
h = F.leaky_relu(self.b0(h))
h = F.normalize(h)
h = self.c1(h)
if add_noise:
h = self.n1(h)
h = F.leaky_relu(self.b1(h))
h = F.normalize(h)
return h
def __call__(self, w, x=None, add_noise=False):
h = x
batch_size, _ = w.shape
if self.upsample:
assert h is not None
if self.blur_k is None:
k = np.asarray([1, 2, 1]).astype('f')
k = k[:, None] * k[None, :]
k = k / np.sum(k)
self.blur_k = self.xp.asarray(k)[None, None, :]
if self.enable_blur:
h = blur(upscale2x(h), self.blur_k)
else:
h = upscale2x(h)
h = self.c0(h)
else:
h = F.broadcast_to(self.W, (batch_size, self.ch_in, 4, 4))
# h should be (batch, ch, size, size)
if add_noise:
h = self.n0(h)
h = F.leaky_relu(self.b0(h))
h = self.s0(w, h)
h = self.c1(h)
if add_noise:
h = self.n1(h)
h = F.leaky_relu(self.b1(h))
h = self.s1(w, h)
return h
def __call__(self, x):
h = x
h = F.leaky_relu((self.c0(h)))
h = F.leaky_relu((self.c1(h)))
if self.blur_k is None:
k = np.asarray([1, 2, 1]).astype('f')
k = k[:, None] * k[None, :]
k = k / np.sum(k)
self.blur_k = self.xp.asarray(k)[None, None, :]
if self.enable_blur:
h = blur(downscale2x(h), self.blur_k)
else:
h = downscale2x(h)
return h
def forward(self, x):
shortcut = self.c_sc(x)
res = F.leaky_relu((self.c0(x)))
h = F.leaky_relu((self.c1(res) + shortcut))
if self.blur_k is None:
k = np.asarray([1, 2, 1]).astype('f')
k = k[:, None] * k[None, :]
k = k / np.sum(k)
self.blur_k = self.xp.asarray(k)[None, None, :]
if self.enable_blur:
h = blur(downscale2x(h), self.blur_k)
else:
h = downscale2x(h)
return h