def _dilated_bottle_resblock(self,
x,
num_o,
dilation_factor,
name,
identity_connection=True):
assert num_o % 4 == 0, 'Bottleneck number of output ERROR!'
# branch1
if not identity_connection:
o_b1 = self._conv2d(x,
1,
num_o,
1,
name='%s/bottleneck_v1/shortcut' % name)
o_b1 = self._batch_norm(o_b1,
name='%s/bottleneck_v1/shortcut' % name,
is_training=False,
activation_fn=None)
else:
o_b1 = x
# branch2
o_b2a = self._conv2d(x,
1,
num_o / 4,
1,
name='%s/bottleneck_v1/conv1' % name)
o_b2a = self._batch_norm(o_b2a,
name='%s/bottleneck_v1/conv1' % name,
is_training=False,
activation_fn=tf.nn.relu)
o_b2b = _dilated_conv2d(self.dilated_type,
o_b2a,
3,
num_o / 4,
dilation_factor,
name='%s/bottleneck_v1/conv2' % name,
top_scope=self.top_scope)
o_b2b = self._batch_norm(o_b2b,
name='%s/bottleneck_v1/conv2' % name,
is_training=False,
activation_fn=tf.nn.relu)
o_b2c = self._conv2d(o_b2b,
1,
num_o,
1,
name='%s/bottleneck_v1/conv3' % name)
o_b2c = self._batch_norm(o_b2c,
name='%s/bottleneck_v1/conv3' % name,
is_training=False,
activation_fn=None)
# add
outputs = self._add([o_b1, o_b2c], name='%s/bottleneck_v1/add' % name)
# relu
outputs = self._relu(outputs, name='%s/bottleneck_v1/relu' % name)
return outputs
def _ASPP(self, x, num_o, dilations):
o = []
for i, d in enumerate(dilations):
o.append(
_dilated_conv2d('regular',
x,
3,
num_o,
d,
name='aspp/conv%d' % (i + 1),
biased=True))
return self._add(o, name='aspp/add')
def _ASPP(self, x, num_o, dilations):
o = []
for i, d in enumerate(dilations):
o.append(
_dilated_conv2d('regular',
x,
3,
num_o,
d,
name='fc1_voc12_c%d' % i,
biased=True))
return self._add(o, name='fc1_voc12')