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import chainer
from .config import ModelConfig
class DeepConvolution1D(chainer.link.Chain):
def __init__(self, in_size: int, num_scale: int, base_num_z: int, **kwargs):
super().__init__(**kwargs)
self.num_scale = num_scale
self.out_size = base_num_z * 2 ** (num_scale - 1)
for i in range(num_scale):
l = base_num_z * 2 ** i
self.add_link('conv{}'.format(i + 1), chainer.links.ConvolutionND(1, in_size, l, 3, 1, 1, nobias=True))
self.add_link('bn{}'.format(i + 1), chainer.links.BatchNormalization(l))
in_size = l
def __call__(self, x):
h = x
for i in range(self.num_scale):
conv = getattr(self, 'conv{}'.format(i + 1))
bn = getattr(self, 'bn{}'.format(i + 1))
h = chainer.functions.relu(bn(conv(h)))
return h
class Model(chainer.link.Chain):
def __init__(self, convs: DeepConvolution1D, out_size: int):
super().__init__()
with self.init_scope():
self.convs = convs
self.last = chainer.links.ConvolutionND(1, convs.out_size, out_size, 1)
def __call__(self, x):
h = x
h = self.convs(h)
h = self.last(h)
return h
def create(config: ModelConfig):
convs = DeepConvolution1D(
in_size=config.in_size,
num_scale=config.num_scale,
base_num_z=config.base_num_z,
)
model = Model(
convs=convs,
out_size=config.out_size,
)
return model
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