def forward(self, x, state_old):
states = States(state_old)
for m in self.bd:
x_add, state = m(x, states.state_old)
states.update(state)
x = torch.cat([x, x_add], 1)
x, state = self.bd_out(x, states.state_old)
states.update(state)
if self.sa is None:
return x, states.state
#apply multihead self attension module
#[B,C,F,T]
x_add, state = self.sa(x, state_old=states.state_old)
states.update(state)
x = torch.cat([x, x_add], 1)
x, state = self.sa_out(x, states.state_old)
states.update(state)
return x, states.state
def forward(self, x, state_old=None):
states = States(state_old)
tail_size = self.wnd_length - self.hop_length
x_padded = states.pad_left(x, tail_size, 1)
X = self.encode(x_padded)
# [B,2,F,T]
z = X
#DOWN
skips = []
for b in self.blocks_down:
z, state = b(z, states.state_old)
states.update(state)
skips.append(z)
z = self.pool(z)
#BOTTOM
z, state = self.block_bottom(z, states.state_old)
states.update(state)
#UP
for skip, conv_up, block_up in zip(reversed(skips), self.convs_up,
self.blocks_up):
z = torch.nn.functional.interpolate(z,
scale_factor=2,
mode='nearest')
Fs = get_shape(skip)[-2]
Fz = get_shape(z)[-2]
if Fz != Fs:
z = torch.nn.functional.pad(z, (0, 0, 0, 1), mode='replicate')
z = torch.cat([z, skip], 1)
pad = self.convs_up_pad
if pad[0] > 0:
z = states.pad_left(z, pad[0], 3)
pad = (0, ) + pad[1:]
z = torch.nn.functional.pad(z, pad, mode='constant', value=0)
z = conv_up(z)
z, state = block_up(z, states.state_old)
states.update(state)
X = states.pad_left(X, self.ahead, 3, shift_right=True)
# [B,2,F,T] -> [B,F,T],[B,F,T] ->
Mr, Mi = z[:, 0], z[:, 1]
Xr, Xi = X[:, 0], X[:, 1]
# mask in complex space
Yr = Xr * Mr - Xi * Mi
Yi = Xr * Mi + Xi * Mr
#[B,F,T] + [B,F,T] -> [B,2,F,T]
Y = torch.stack([Yr, Yi], 1)
# decode and return only valid samples
Y_paded = states.pad_left(Y, self.ahead_ifft, 3)
y = self.decode(Y_paded)
y = y[:, tail_size:-self.ahead_ifft * self.hop_length]
assert not states.state_old
return y, Y, states.state