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import chainer
import chainer.functions as F
import chainer.links as L
from become_yukarin.config.config import ModelConfig
class Convolution1D(chainer.links.ConvolutionND):
def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
nobias=False, initialW=None, initial_bias=None,
cover_all=False) -> None:
super().__init__(
ndim=1,
in_channels=in_channels,
out_channels=out_channels,
ksize=ksize,
stride=stride,
pad=pad,
nobias=nobias,
initialW=initialW,
initial_bias=initial_bias,
cover_all=cover_all,
)
class Deconvolution1D(chainer.links.DeconvolutionND):
def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
nobias=False, outsize=None,
initialW=None, initial_bias=None) -> None:
super().__init__(
ndim=1,
in_channels=in_channels,
out_channels=out_channels,
ksize=ksize,
stride=stride,
pad=pad,
nobias=nobias,
outsize=outsize,
initialW=initialW,
initial_bias=initial_bias,
)
class CBR(chainer.Chain):
def __init__(self, ch0, ch1, bn=True, sample='down', activation=F.relu, dropout=False) -> None:
super().__init__()
self.bn = bn
self.activation = activation
self.dropout = dropout
w = chainer.initializers.Normal(0.02)
with self.init_scope():
if sample == 'down':
self.c = Convolution1D(ch0, ch1, 4, 2, 1, initialW=w)
elif sample == 'up':
self.c = Deconvolution1D(ch0, ch1, 4, 2, 1, initialW=w)
else:
self.c = Convolution1D(ch0, ch1, 1, 1, 0, initialW=w)
if bn:
self.batchnorm = L.BatchNormalization(ch1)
def __call__(self, x):
h = self.c(x)
if self.bn:
h = self.batchnorm(h)
if self.dropout:
h = F.dropout(h)
if self.activation is not None:
h = self.activation(h)
return h
class Encoder(chainer.Chain):
def __init__(self, in_ch, base=64, extensive_layers=8) -> None:
super().__init__()
w = chainer.initializers.Normal(0.02)
with self.init_scope():
if extensive_layers > 0:
self.c0 = Convolution1D(in_ch, base * 1, 3, 1, 1, initialW=w)
else:
self.c0 = Convolution1D(in_ch, base * 1, 1, 1, 0, initialW=w)
_choose = lambda i: 'down' if i < extensive_layers else 'same'
self.c1 = CBR(base * 1, base * 2, bn=True, sample=_choose(1), activation=F.leaky_relu, dropout=False)
self.c2 = CBR(base * 2, base * 4, bn=True, sample=_choose(2), activation=F.leaky_relu, dropout=False)
self.c3 = CBR(base * 4, base * 8, bn=True, sample=_choose(3), activation=F.leaky_relu, dropout=False)
self.c4 = CBR(base * 8, base * 8, bn=True, sample=_choose(4), activation=F.leaky_relu, dropout=False)
self.c5 = CBR(base * 8, base * 8, bn=True, sample=_choose(5), activation=F.leaky_relu, dropout=False)
self.c6 = CBR(base * 8, base * 8, bn=True, sample=_choose(6), activation=F.leaky_relu, dropout=False)
self.c7 = CBR(base * 8, base * 8, bn=True, sample=_choose(7), activation=F.leaky_relu, dropout=False)
def __call__(self, x):
hs = [F.leaky_relu(self.c0(x))]
for i in range(1, 8):
hs.append(self['c%d' % i](hs[i - 1]))
return hs
class Decoder(chainer.Chain):
def __init__(self, out_ch, base=64, extensive_layers=8) -> None:
super().__init__()
w = chainer.initializers.Normal(0.02)
with self.init_scope():
_choose = lambda i: 'up' if i >= 8 - extensive_layers else 'same'
self.c0 = CBR(base * 8, base * 8, bn=True, sample=_choose(0), activation=F.relu, dropout=True)
self.c1 = CBR(base * 16, base * 8, bn=True, sample=_choose(1), activation=F.relu, dropout=True)
self.c2 = CBR(base * 16, base * 8, bn=True, sample=_choose(2), activation=F.relu, dropout=True)
self.c3 = CBR(base * 16, base * 8, bn=True, sample=_choose(3), activation=F.relu, dropout=False)
self.c4 = CBR(base * 16, base * 4, bn=True, sample=_choose(4), activation=F.relu, dropout=False)
self.c5 = CBR(base * 8, base * 2, bn=True, sample=_choose(5), activation=F.relu, dropout=False)
self.c6 = CBR(base * 4, base * 1, bn=True, sample=_choose(6), activation=F.relu, dropout=False)
if extensive_layers > 0:
self.c7 = Convolution1D(base * 2, out_ch, 3, 1, 1, initialW=w)
else:
self.c7 = Convolution1D(base * 2, out_ch, 1, 1, 0, initialW=w)
def __call__(self, hs):
h = self.c0(hs[-1])
for i in range(1, 8):
h = F.concat([h, hs[-i - 1]])
if i < 7:
h = self['c%d' % i](h)
else:
h = self.c7(h)
return h
class Predictor(chainer.Chain):
def __init__(self, in_ch, out_ch, base=64, extensive_layers=8) -> None:
super().__init__()
with self.init_scope():
self.encoder = Encoder(in_ch, base=base, extensive_layers=extensive_layers)
self.decoder = Decoder(out_ch, base=base, extensive_layers=extensive_layers)
def __call__(self, x):
return self.decoder(self.encoder(x))
class Discriminator(chainer.Chain):
def __init__(self, in_ch, out_ch, base=32, extensive_layers=5, is_weak=False) -> None:
super().__init__()
w = chainer.initializers.Normal(0.02)
with self.init_scope():
_choose = lambda i: 'down' if i < extensive_layers else 'same'
self.c0_0 = CBR(in_ch, base * 1, bn=False, sample=_choose(0), activation=F.leaky_relu, dropout=is_weak)
self.c0_1 = CBR(out_ch, base * 1, bn=False, sample=_choose(0), activation=F.leaky_relu, dropout=is_weak)
self.c1 = CBR(base * 2, base * 4, bn=True, sample=_choose(1), activation=F.leaky_relu, dropout=is_weak)
self.c2 = CBR(base * 4, base * 8, bn=True, sample=_choose(2), activation=F.leaky_relu, dropout=is_weak)
self.c3 = CBR(base * 8, base * 16, bn=True, sample=_choose(3), activation=F.leaky_relu, dropout=is_weak)
if extensive_layers > 4:
self.c4 = Convolution1D(base * 16, 1, 3, 1, 1, initialW=w)
else:
self.c4 = Convolution1D(base * 16, 1, 1, 1, 0, initialW=w)
def __call__(self, x_0, x_1):
h = F.concat([self.c0_0(x_0), self.c0_1(x_1)])
h = self.c1(h)
h = self.c2(h)
h = self.c3(h)
h = self.c4(h)
# h = F.average_pooling_2d(h, h.data.shape[2], 1, 0)
return h
def create_predictor(config: ModelConfig):
return Predictor(
in_ch=config.in_channels,
out_ch=config.out_channels,
base=config.generator_base_channels,
extensive_layers=config.generator_extensive_layers,
)
def create_discriminator(config: ModelConfig):
return Discriminator(
in_ch=config.in_channels,
out_ch=config.out_channels,
base=config.discriminator_base_channels,
extensive_layers=config.discriminator_extensive_layers,
is_weak=config.weak_discriminator,
)
def create(config: ModelConfig):
predictor = create_predictor(config)
discriminator = create_discriminator(config)
return predictor, discriminator
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