/
low_level.py
60 lines (57 loc) · 2.13 KB
/
low_level.py
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import chainer
class LowLevelNetwork(chainer.Chain):
def __init__(self):
super(LowLevelNetwork, self).__init__(
conv1_1=chainer.links.Convolution2D(
in_channels=None,
out_channels=64,
ksize=3,
stride=2,
pad=1),
bn1_1=chainer.links.BatchNormalization(64),
conv1_2=chainer.links.Convolution2D(
in_channels=None,
out_channels=128,
ksize=3,
stride=1,
pad=1),
bn1_2=chainer.links.BatchNormalization(128),
conv2_1=chainer.links.Convolution2D(
in_channels=None,
out_channels=128,
ksize=3,
stride=2,
pad=1),
bn2_1=chainer.links.BatchNormalization(128),
conv2_2=chainer.links.Convolution2D(
in_channels=None,
out_channels=256,
ksize=3,
stride=1,
pad=1),
bn2_2=chainer.links.BatchNormalization(256),
conv3_1=chainer.links.Convolution2D(
in_channels=None,
out_channels=256,
ksize=3,
stride=2,
pad=1),
bn3_1=chainer.links.BatchNormalization(256),
conv3_2=chainer.links.Convolution2D(
in_channels=None,
out_channels=512,
ksize=3,
stride=1,
pad=1),
bn3_2=chainer.links.BatchNormalization(512),
)
def __call__(self, x, test=False):
# type: (any, bool) -> any
h = x
h = chainer.functions.relu(self.bn1_1(self.conv1_1(h), test=test))
h = chainer.functions.relu(self.bn1_2(self.conv1_2(h), test=test))
h = chainer.functions.relu(self.bn2_1(self.conv2_1(h), test=test))
h = chainer.functions.relu(self.bn2_2(self.conv2_2(h), test=test))
h = chainer.functions.relu(self.bn3_1(self.conv3_1(h), test=test))
h = chainer.functions.relu(self.bn3_2(self.conv3_2(h), test=test))
return h