def __init__(self,
in_channels,
out_channels,
strides,
data_format="channels_last",
**kwargs):
super(LinearBottleneck, self).__init__(**kwargs)
self.residual = (in_channels == out_channels) and (strides == 1)
mid_channels = in_channels * 6
self.conv1 = conv1x1_block(
in_channels=in_channels,
out_channels=mid_channels,
data_format=data_format,
name="conv1")
self.conv2 = dwconv3x3_block(
in_channels=mid_channels,
out_channels=mid_channels,
strides=strides,
data_format=data_format,
name="conv2")
self.conv3 = conv1x1_block(
in_channels=mid_channels,
out_channels=out_channels,
activation=None,
data_format=data_format,
name="conv3")
def __init__(self,
in_channels,
out_channels,
strides,
bn_use_global_stats=False,
expansion=True,
remove_exp_conv=False,
activation=(lambda: nn.Activation("relu")),
**kwargs):
super(LinearBottleneck, self).__init__(**kwargs)
self.residual = (in_channels == out_channels) and (strides == 1)
mid_channels = in_channels * 6 if expansion else in_channels
self.use_exp_conv = (expansion or (not remove_exp_conv))
with self.name_scope():
if self.use_exp_conv:
self.conv1 = conv1x1_block(
in_channels=in_channels,
out_channels=mid_channels,
bn_use_global_stats=bn_use_global_stats,
activation=activation)
self.conv2 = dwconv3x3_block(
in_channels=mid_channels,
out_channels=mid_channels,
strides=strides,
bn_use_global_stats=bn_use_global_stats,
activation=activation)
self.conv3 = conv1x1_block(in_channels=mid_channels,
out_channels=out_channels,
bn_use_global_stats=bn_use_global_stats,
activation=None)
def __init__(self,
highter_in_channels,
lower_in_channels,
out_channels,
height,
width,
scale_factor=4,
**kwargs):
super(FeatureFusionModule, self).__init__()
self.scale_factor = scale_factor
self._up_kwargs = {'height': height, 'width': width}
with self.name_scope():
self.dwconv = dwconv3x3_block(in_channels=lower_in_channels,
out_channels=out_channels)
self.conv_lower_res = conv1x1_block(in_channels=out_channels,
out_channels=out_channels,
use_bias=True,
activation=None)
self.conv_higher_res = conv1x1_block(
in_channels=highter_in_channels,
out_channels=out_channels,
use_bias=True,
activation=None)
self.activ = nn.Activation("relu")
def __init__(self,
x_in_channels,
y_in_channels,
out_channels,
x_in_size,
data_format="channels_last",
**kwargs):
super(FeatureFusion, self).__init__(**kwargs)
self.x_in_size = x_in_size
self.data_format = data_format
self.up = InterpolationBlock(
scale_factor=None,
out_size=x_in_size,
data_format=data_format,
name="up")
self.low_dw_conv = dwconv3x3_block(
in_channels=y_in_channels,
out_channels=out_channels,
data_format=data_format,
name="low_dw_conv")
self.low_pw_conv = conv1x1_block(
in_channels=out_channels,
out_channels=out_channels,
use_bias=True,
activation=None,
data_format=data_format,
name="low_pw_conv")
self.high_conv = conv1x1_block(
in_channels=x_in_channels,
out_channels=out_channels,
use_bias=True,
activation=None,
data_format=data_format,
name="high_conv")
self.activ = nn.ReLU()