class FastPyramidPooling(HybridBlock):
def __init__(self,
in_channels,
out_channels,
in_size):
super(FastPyramidPooling, self).__init__()
down_sizes = [1, 2, 3, 6]
mid_channels = in_channels // 4
with self.name_scope():
self.branches = Concurrent()
self.branches.add(Identity())
for down_size in down_sizes:
self.branches.add(PoolingBranch(
in_channels=in_channels,
out_channels=mid_channels,
in_size=in_size,
down_size=down_size))
self.conv = conv1x1_block(
in_channels=(in_channels * 2),
out_channels=out_channels)
def hybrid_forward(self, F, x):
x = self.branches(x)
x = self.conv(x)
return x
class FastPyramidPooling(nn.Layer):
"""
Fast-SCNN specific fast pyramid pooling block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
in_size : tuple of 2 int or None
Spatial size of input image.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
"""
def __init__(self,
in_channels,
out_channels,
in_size,
data_format="channels_last",
**kwargs):
super(FastPyramidPooling, self).__init__(**kwargs)
down_sizes = [1, 2, 3, 6]
mid_channels = in_channels // 4
self.branches = Concurrent(
data_format=data_format,
name="branches")
self.branches.add(Identity(name="branch1"))
for i, down_size in enumerate(down_sizes):
self.branches.add(PoolingBranch(
in_channels=in_channels,
out_channels=mid_channels,
in_size=in_size,
down_size=down_size,
data_format=data_format,
name="branch{}".format(i + 2)))
self.conv = conv1x1_block(
in_channels=(in_channels * 2),
out_channels=out_channels,
data_format=data_format,
name="conv")
def call(self, x, training=None):
x = self.branches(x, training=training)
x = self.conv(x, training=training)
return x