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auditory_stream.py
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auditory_stream.py
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import chainer
### BLOCK ###
class ConvolutionBlock(chainer.Chain):
def __init__(self, in_channels, out_channels):
super(ConvolutionBlock, self).__init__(
conv=chainer.links.Convolution2D(in_channels, out_channels, (1, 49), (1, 4), (0, 24),
initialW=chainer.initializers.HeNormal()),
bn_conv=chainer.links.BatchNormalization(out_channels),
)
def __call__(self, x):
# Set Train to False.
chainer.config.train = False
h = self.conv(x)
h = self.bn_conv(h)
y = chainer.functions.relu(h)
return y
class ResidualBlock(chainer.Chain):
def __init__(self, in_channels, out_channels):
super(ResidualBlock, self).__init__(
res_branch2a=chainer.links.Convolution2D(in_channels, out_channels, (1, 9), pad=(0, 4),
initialW=chainer.initializers.HeNormal()),
bn_branch2a=chainer.links.BatchNormalization(out_channels),
res_branch2b=chainer.links.Convolution2D(out_channels, out_channels, (1, 9), pad=(0, 4),
initialW=chainer.initializers.HeNormal()),
bn_branch2b=chainer.links.BatchNormalization(out_channels)
)
def __call__(self, x):
# Set Train to False.
chainer.config.train = False
h = self.res_branch2a(x)
h = self.bn_branch2a(h)
h = chainer.functions.relu(h)
h = self.res_branch2b(h)
h = self.bn_branch2b(h)
h = x + h
y = chainer.functions.relu(h)
return y
class ResidualBlockA():
def __init__(self):
pass
def __call__(self):
pass
class ResidualBlockB(chainer.Chain):
def __init__(self, in_channels, out_channels):
super(ResidualBlockB, self).__init__(
res_branch1=chainer.links.Convolution2D(in_channels, out_channels, (1, 1), (1, 4),
initialW=chainer.initializers.HeNormal()),
bn_branch1=chainer.links.BatchNormalization(out_channels),
res_branch2a=chainer.links.Convolution2D(in_channels, out_channels, (1, 9), (1, 4), (0, 4),
initialW=chainer.initializers.HeNormal()),
bn_branch2a=chainer.links.BatchNormalization(out_channels),
res_branch2b=chainer.links.Convolution2D(out_channels, out_channels, (1, 9), pad=(0, 4),
initialW=chainer.initializers.HeNormal()),
bn_branch2b=chainer.links.BatchNormalization(out_channels)
)
def __call__(self, x):
# Set Train to False.
chainer.config.train = False
temp = self.res_branch1(x)
temp = self.bn_branch1(temp)
h = self.res_branch2a(x)
h = self.bn_branch2a(h)
h = chainer.functions.relu(h)
h = self.res_branch2b(h)
h = self.bn_branch2b(h)
h = temp + h
y = chainer.functions.relu(h)
return y
### BLOCK ###
### MODEL ###
class ResNet18(chainer.Chain):
def __init__(self):
super(ResNet18, self).__init__(
conv1_relu=ConvolutionBlock(1, 32),
res2a_relu=ResidualBlock(32, 32),
res2b_relu=ResidualBlock(32, 32),
res3a_relu=ResidualBlockB(32, 64),
res3b_relu=ResidualBlock(64, 64),
res4a_relu=ResidualBlockB(64, 128),
res4b_relu=ResidualBlock(128, 128),
res5a_relu=ResidualBlockB(128, 256),
res5b_relu=ResidualBlock(256, 256)
)
def __call__(self, x):
# Set Train to False.
chainer.config.train = False
h = self.conv1_relu(x)
h = chainer.functions.max_pooling_2d(h, (1, 9), (1, 4), (0, 4))
h = self.res2a_relu(h)
h = self.res2b_relu(h)
h = self.res3a_relu(h)
h = self.res3b_relu(h)
h = self.res4a_relu(h)
h = self.res4b_relu(h)
h = self.res5a_relu(h)
h = self.res5b_relu(h)
y = chainer.functions.average_pooling_2d(h, h.data.shape[2:])
return y
### MODEL ###