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import chainer
class DeepConvolution(chainer.link.Chain):
def __init__(self, num_scale: int, base_num_z: int, **kwargs):
super().__init__(**kwargs)
self.num_scale = num_scale
for i in range(num_scale):
l = base_num_z * 2 ** i
self.add_link('conv{}'.format(i + 1),
chainer.links.Convolution2D(None, l, 4, 2, 1, nobias=True))
self.add_link('bn{}'.format(i + 1), chainer.links.BatchNormalization(l))
def get_scaled_width(self, base_width):
return base_width // (2 ** self.num_scale)
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))
chainer.functions.relu(bn(conv(h)))
return h
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