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import chainer
class ConvolutionBlock(chainer.Chain):
def __init__(self, in_channels, out_channels):
super(ConvolutionBlock, self).__init__(
conv=chainer.links.Convolution2D(
in_channels,
out_channels,
7,
2,
3,
initialW=chainer.initializers.HeNormal(),
),
bn_conv=chainer.links.BatchNormalization(out_channels),
)
def __call__(self, x):
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,
3,
pad=1,
initialW=chainer.initializers.HeNormal(),
),
bn_branch2a=chainer.links.BatchNormalization(out_channels),
res_branch2b=chainer.links.Convolution2D(
out_channels,
out_channels,
3,
pad=1,
initialW=chainer.initializers.HeNormal(),
),
bn_branch2b=chainer.links.BatchNormalization(out_channels),
)
def __call__(self, x):
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,
2,
initialW=chainer.initializers.HeNormal(),
),
bn_branch1=chainer.links.BatchNormalization(out_channels),
res_branch2a=chainer.links.Convolution2D(
in_channels,
out_channels,
3,
2,
1,
initialW=chainer.initializers.HeNormal(),
),
bn_branch2a=chainer.links.BatchNormalization(out_channels),
res_branch2b=chainer.links.Convolution2D(
out_channels,
out_channels,
3,
pad=1,
initialW=chainer.initializers.HeNormal(),
),
bn_branch2b=chainer.links.BatchNormalization(out_channels),
)
def __call__(self, x):
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
class ResNet18(chainer.Chain):
def __init__(self):
super(ResNet18, self).__init__(
conv1_relu=ConvolutionBlock(3, 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):
chainer.config.train = False
h = self.conv1_relu(x)
h = chainer.functions.max_pooling_2d(h, 3, 2, 1)
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
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