diff options
Diffstat (limited to 'become_yukarin/model/model.py')
| -rw-r--r-- | become_yukarin/model/model.py | 313 |
1 files changed, 89 insertions, 224 deletions
diff --git a/become_yukarin/model/model.py b/become_yukarin/model/model.py index 71fb805..56870d9 100644 --- a/become_yukarin/model/model.py +++ b/become_yukarin/model/model.py @@ -1,16 +1,14 @@ -from functools import partial -from typing import List - import chainer +import chainer.functions as F +import chainer.links as L -from become_yukarin.config.config import DiscriminatorModelConfig 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): + cover_all=False) -> None: super().__init__( ndim=1, in_channels=in_channels, @@ -25,268 +23,135 @@ class Convolution1D(chainer.links.ConvolutionND): ) -class LegacyConvolution1D(chainer.links.Convolution2D): - def __init__(self, in_channels, out_channels, ksize=None, stride=1, pad=0, - nobias=False, initialW=None, initial_bias=None, **kwargs): - assert ksize is None or isinstance(ksize, int) - assert isinstance(stride, int) - assert isinstance(pad, int) +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, 1), - stride=(stride, 1), - pad=(pad, 0), + ksize=ksize, + stride=stride, + pad=pad, nobias=nobias, + outsize=outsize, initialW=initialW, initial_bias=initial_bias, - **kwargs, ) - def __call__(self, x): - assert x.shape[-1] == 1 - return super().__call__(x) - - -class ConvHighway(chainer.link.Chain): - def __init__(self, in_out_size, nobias=False, activate=chainer.functions.relu, - init_Wh=None, init_Wt=None, init_bh=None, init_bt=-1): - super().__init__() - self.activate = activate - - with self.init_scope(): - self.plain = Convolution1D( - in_out_size, in_out_size, 1, nobias=nobias, - initialW=init_Wh, initial_bias=init_bh) - self.transform = Convolution1D( - in_out_size, in_out_size, 1, nobias=nobias, - initialW=init_Wt, initial_bias=init_bt) - - def __call__(self, x): - out_plain = self.activate(self.plain(x)) - out_transform = chainer.functions.sigmoid(self.transform(x)) - y = out_plain * out_transform + x * (1 - out_transform) - return y - - -class PreNet(chainer.link.Chain): - def __init__(self, in_channels: int, hidden_channels: int, out_channels: int) -> None: - super().__init__() - with self.init_scope(): - self.conv1 = Convolution1D(in_channels, hidden_channels, 1) - self.conv2 = Convolution1D(hidden_channels, out_channels, 1) - - def __call__(self, x): - h = x - h = chainer.functions.dropout((chainer.functions.relu(self.conv1(h)), 0.5)) - h = chainer.functions.dropout((chainer.functions.relu(self.conv2(h)), 0.5)) - return h - -class Conv1DBank(chainer.link.Chain): - def __init__(self, in_channels: int, out_channels: int, k: int) -> None: +class CBR(chainer.Chain): + def __init__(self, ch0, ch1, bn=True, sample='down', activation=F.relu, dropout=False) -> None: super().__init__() - self.stacked_channels = out_channels * k - self.pads = [ - partial(chainer.functions.pad, pad_width=((0, 0), (0, 0), (i // 2, (i + 1) // 2)), mode='constant') - for i in range(k) - ] + self.bn = bn + self.activation = activation + self.dropout = dropout + w = chainer.initializers.Normal(0.02) with self.init_scope(): - self.convs = chainer.link.ChainList( - *(Convolution1D(in_channels, out_channels, i + 1, nobias=True) for i in range(k)) - ) - self.bn = chainer.links.BatchNormalization(out_channels * k) + if sample == 'down': + self.c = Convolution1D(ch0, ch1, 4, 2, 1, initialW=w) + else: + self.c = Deconvolution1D(ch0, ch1, 4, 2, 1, initialW=w) + if bn: + self.batchnorm = L.BatchNormalization(ch1) def __call__(self, x): - h = x - h = chainer.functions.concat([conv(pad(h)) for pad, conv in zip(self.pads, self.convs)]) - h = chainer.functions.relu(self.bn(h)) + 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 Conv1DProjections(chainer.link.Chain): - def __init__(self, in_channels: int, hidden_channels: int, out_channels: int) -> None: +class Encoder(chainer.Chain): + def __init__(self, in_ch) -> None: super().__init__() - + w = chainer.initializers.Normal(0.02) with self.init_scope(): - self.conv1 = Convolution1D(in_channels, hidden_channels, 3, pad=1, nobias=True) - self.bn1 = chainer.links.BatchNormalization(hidden_channels) - self.conv2 = Convolution1D(hidden_channels, out_channels, 3, pad=1, nobias=True) - self.bn2 = chainer.links.BatchNormalization(out_channels) + self.c0 = Convolution1D(in_ch, 64, 3, 1, 1, initialW=w) + self.c1 = CBR(64, 128, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c2 = CBR(128, 256, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c3 = CBR(256, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c4 = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c5 = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c6 = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c7 = CBR(512, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False) def __call__(self, x): - h = x - h = chainer.functions.relu(self.bn1(self.conv1(h))) - h = chainer.functions.relu(self.bn2(self.conv2(h))) - return h + 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 CBHG(chainer.link.Chain): - def __init__( - self, - in_channels: int, - conv_bank_out_channels: int, - conv_bank_k: int, - max_pooling_k: int, - conv_projections_hidden_channels: int, - highway_layers: int, - out_channels: int, - disable_last_rnn: bool, - ) -> None: +class Decoder(chainer.Chain): + def __init__(self, out_ch) -> None: super().__init__() - self.max_pooling_padding = partial( - chainer.functions.pad, - pad_width=((0, 0), (0, 0), ((max_pooling_k - 1) // 2, max_pooling_k // 2)), - mode='constant', - ) - self.max_pooling = chainer.functions.MaxPoolingND(1, max_pooling_k, 1, cover_all=False) - self.out_size = out_channels * (1 if disable_last_rnn else 2) - + w = chainer.initializers.Normal(0.02) with self.init_scope(): - self.conv_bank = Conv1DBank( - in_channels=in_channels, - out_channels=conv_bank_out_channels, - k=conv_bank_k, - ) - self.conv_projectoins = Conv1DProjections( - in_channels=self.conv_bank.stacked_channels, - hidden_channels=conv_projections_hidden_channels, - out_channels=out_channels, - ) - self.highways = chainer.link.ChainList( - *([ConvHighway(out_channels) for _ in range(highway_layers)]) - ) - if not disable_last_rnn: - self.gru = chainer.links.NStepBiGRU( - n_layers=1, - in_size=out_channels, - out_size=out_channels, - dropout=0.0, - ) + self.c0 = CBR(512, 512, bn=True, sample='up', activation=F.relu, dropout=True) + self.c1 = CBR(1024, 512, bn=True, sample='up', activation=F.relu, dropout=True) + self.c2 = CBR(1024, 512, bn=True, sample='up', activation=F.relu, dropout=True) + self.c3 = CBR(1024, 512, bn=True, sample='up', activation=F.relu, dropout=False) + self.c4 = CBR(1024, 256, bn=True, sample='up', activation=F.relu, dropout=False) + self.c5 = CBR(512, 128, bn=True, sample='up', activation=F.relu, dropout=False) + self.c6 = CBR(256, 64, bn=True, sample='up', activation=F.relu, dropout=False) + self.c7 = Convolution1D(128, out_ch, 3, 1, 1, initialW=w) - def __call__(self, x): - h = x - h = self.conv_bank(h) - h = self.max_pooling(self.max_pooling_padding(h)) - h = self.conv_projectoins(h) - h = h + x - for highway in self.highways: - h = highway(h) - - if hasattr(self, 'gru'): - h = chainer.functions.separate(chainer.functions.transpose(h, axes=(0, 2, 1))) - _, h = self.gru(None, h) - h = chainer.functions.transpose(chainer.functions.stack(h), axes=(0, 2, 1)) + 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.link.Chain): - def __init__(self, network, out_size: int) -> None: +class Predictor(chainer.Chain): + def __init__(self, in_ch, out_ch) -> None: super().__init__() with self.init_scope(): - self.network = network - self.last = Convolution1D(network.out_size, out_size, 1) + self.encoder = Encoder(in_ch) + self.decoder = Decoder(out_ch) def __call__(self, x): - h = x - h = self.network(h) - h = self.last(h) - return h + return self.decoder(self.encoder(x)) -class Aligner(chainer.link.Chain): - def __init__(self, in_size: int, out_time_length: int) -> None: +class Discriminator(chainer.Chain): + def __init__(self, in_ch, out_ch) -> None: super().__init__() + w = chainer.initializers.Normal(0.02) with self.init_scope(): - self.gru = chainer.links.NStepBiGRU( - n_layers=1, - in_size=in_size, - out_size=in_size // 2, - dropout=0.0, - ) - self.last = Convolution1D(in_size // 2 * 2, out_time_length, 1) + self.c0_0 = CBR(in_ch, 32, bn=False, sample='down', activation=F.leaky_relu, dropout=False) + self.c0_1 = CBR(out_ch, 32, bn=False, sample='down', activation=F.leaky_relu, dropout=False) + self.c1 = CBR(64, 128, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c2 = CBR(128, 256, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c3 = CBR(256, 512, bn=True, sample='down', activation=F.leaky_relu, dropout=False) + self.c4 = Convolution1D(512, 1, 3, 1, 1, initialW=w) - def __call__(self, x): - """ - :param x: (batch, channel, timeA) - """ - h = x - h = chainer.functions.separate(chainer.functions.transpose(h, axes=(0, 2, 1))) # h: batch * (timeA, channel) - _, h = self.gru(None, h) # h: batch * (timeA, ?) - h = chainer.functions.transpose(chainer.functions.stack(h), axes=(0, 2, 1)) # h: (batch, ?, timeA) - h = chainer.functions.softmax(self.last(h), axis=1) # h: (batch, timeB, timeA) - - h = chainer.functions.matmul(x, h) # h: (batch, channel, time) - return h - - -class Discriminator(chainer.link.Chain): - def __init__(self, in_channels: int, hidden_channels_list: List[int]) -> None: - super().__init__() - with self.init_scope(): - self.convs = chainer.link.ChainList(*( - LegacyConvolution1D(i_c, o_c, ksize=2, stride=2) - for i_c, o_c in zip([in_channels] + hidden_channels_list[:-1], hidden_channels_list) - )) - self.last_conv = LegacyConvolution1D(hidden_channels_list[-1], 1, ksize=1) - - def __call__(self, x): - """ - :param x: (batch, channel, time) - """ - h = x - h = chainer.functions.reshape(h, h.shape + (1,)) - for conv in self.convs.children(): - h = chainer.functions.relu(conv(h)) - h = self.last_conv(h) - h = chainer.functions.reshape(h, h.shape[:-1]) + 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): - network = CBHG( - in_channels=config.in_channels, - conv_bank_out_channels=config.conv_bank_out_channels, - conv_bank_k=config.conv_bank_k, - max_pooling_k=config.max_pooling_k, - conv_projections_hidden_channels=config.conv_projections_hidden_channels, - highway_layers=config.highway_layers, - out_channels=config.out_channels, - disable_last_rnn=config.disable_last_rnn, - ) - predictor = Predictor( - network=network, - out_size=config.out_size, - ) - return predictor - - -def create_aligner(config: ModelConfig): - assert config.enable_aligner - aligner = Aligner( - in_size=config.in_channels, - out_time_length=config.aligner_out_time_length, - ) - return aligner - - -def create_discriminator(config: DiscriminatorModelConfig): - discriminator = Discriminator( - in_channels=config.in_channels, - hidden_channels_list=config.hidden_channels_list, - ) - return discriminator + return Predictor(in_ch=config.in_channels, out_ch=config.out_channels) def create(config: ModelConfig): predictor = create_predictor(config) - if config.enable_aligner: - aligner = create_aligner(config) - else: - aligner = None - if config.discriminator is not None: - discriminator = create_discriminator(config.discriminator) - else: - discriminator = None - return predictor, aligner, discriminator + discriminator = Discriminator(in_ch=config.in_channels, out_ch=config.out_channels) + return predictor, discriminator |