diff options
Diffstat (limited to 'become_yukarin/model.py')
| -rw-r--r-- | become_yukarin/model.py | 46 |
1 files changed, 33 insertions, 13 deletions
diff --git a/become_yukarin/model.py b/become_yukarin/model.py index 8a727ae..d4fa369 100644 --- a/become_yukarin/model.py +++ b/become_yukarin/model.py @@ -1,3 +1,4 @@ +from functools import partial from typing import List import chainer @@ -10,7 +11,7 @@ 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): - super(Convolution1D, self).__init__( + super().__init__( ndim=1, in_channels=in_channels, out_channels=out_channels, @@ -24,6 +25,29 @@ 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) + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + ksize=(ksize, 1), + stride=(stride, 1), + pad=(pad, 0), + nobias=nobias, + 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): @@ -64,7 +88,7 @@ class Conv1DBank(chainer.link.Chain): super().__init__() self.stacked_channels = out_channels * k self.pads = [ - chainer.functions.Pad(((0, 0), (0, 0), (i // 2, (i + 1) // 2)), mode='constant') + partial(chainer.functions.pad, pad_width=((0, 0), (0, 0), (i // 2, (i + 1) // 2)), mode='constant') for i in range(k) ] @@ -111,8 +135,9 @@ class CBHG(chainer.link.Chain): disable_last_rnn: bool, ): super().__init__() - self.max_pooling_padding = chainer.functions.Pad( - ((0, 0), (0, 0), ((max_pooling_k - 1) // 2, max_pooling_k // 2)), + 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) @@ -201,25 +226,20 @@ class Discriminator(chainer.link.Chain): super().__init__() with self.init_scope(): self.convs = chainer.link.ChainList(*( - Convolution1D(i_c, o_c, ksize=2, stride=2, nobias=True) + 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.lstm_cell = chainer.links.StatelessLSTM(hidden_channels_list[-1], last_channels) - self.last_linear = chainer.links.Linear(last_channels, 1) + self.last_linear = chainer.links.Linear(None, 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)) - - hs = chainer.functions.separate(h, axis=2) - c_next = h_next = None - for h in reversed(hs): - c_next, h_next = self.lstm_cell(c_next, h_next, h) - h = h_next + h = chainer.functions.reshape(h, h.shape[:-1]) h = self.last_linear(h) return h |