def __init__(self,
in_channels,
out_channels,
strides,
bottleneck,
conv1_stride,
data_format="channels_last",
**kwargs):
super(SEPreResUnit, self).__init__(**kwargs)
self.resize_identity = (in_channels != out_channels) or (strides != 1)
if bottleneck:
self.body = PreResBottleneck(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
conv1_stride=conv1_stride,
data_format=data_format,
name="body")
else:
self.body = PreResBlock(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
data_format=data_format,
name="body")
self.se = SEBlock(channels=out_channels,
data_format=data_format,
name="se")
if self.resize_identity:
self.identity_conv = conv1x1(in_channels=in_channels,
out_channels=out_channels,
strides=strides,
data_format=data_format,
name="identity_conv")
def __init__(self,
in_channels_low,
in_channels_high,
out_channels,
classes,
data_format="channels_last",
**kwargs):
super(CFFBlock, self).__init__(**kwargs)
self.up = InterpolationBlock(scale_factor=2,
data_format=data_format,
name="up")
self.conv_low = conv3x3_block(in_channels=in_channels_low,
out_channels=out_channels,
padding=2,
dilation=2,
activation=None,
data_format=data_format,
name="conv_low")
self.conv_hign = conv1x1_block(in_channels=in_channels_high,
out_channels=out_channels,
activation=None,
data_format=data_format,
name="conv_hign")
self.activ = nn.ReLU()
self.conv_cls = conv1x1(in_channels=out_channels,
out_channels=classes,
data_format=data_format,
name="conv_cls")
def __init__(self, classes, data_format="channels_last", **kwargs):
super(ICHeadBlock, self).__init__(**kwargs)
self.cff_12 = CFFBlock(in_channels_low=128,
in_channels_high=64,
out_channels=128,
classes=classes,
data_format=data_format,
name="cff_12")
self.cff_24 = CFFBlock(in_channels_low=256,
in_channels_high=256,
out_channels=128,
classes=classes,
data_format=data_format,
name="cff_24")
self.up_x2 = InterpolationBlock(scale_factor=2,
data_format=data_format,
name="up_x2")
self.up_x8 = InterpolationBlock(scale_factor=4,
data_format=data_format,
name="up_x8")
self.conv_cls = conv1x1(in_channels=128,
out_channels=classes,
data_format=data_format,
name="conv_cls")
