def _get_up_layer(self, in_channels, out_channels, strides, is_top):
conv = Convolution(self.dimensions,
in_channels,
out_channels,
strides,
self.up_kernel_size,
self.act,
self.norm,
self.dropout,
conv_only=is_top and self.num_res_units == 0,
is_transposed=True)
if self.num_res_units > 0:
ru = ResidualUnit(self.dimensions,
out_channels,
out_channels,
1,
self.kernel_size,
1,
self.act,
self.norm,
self.dropout,
last_conv_only=is_top)
return nn.Sequential(conv, ru)
else:
return conv
def _get_down_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
"""
Args:
in_channels: number of input channels.
out_channels: number of output channels.
strides: convolution stride.
is_top: True if this is the top block.
"""
if self.num_res_units > 0:
return ResidualUnit(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.kernel_size,
subunits=self.num_res_units,
act=self.act,
norm=self.norm,
dropout=self.dropout,
)
else:
return Convolution(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.kernel_size,
act=self.act,
norm=self.norm,
dropout=self.dropout,
)
def _get_down_layer(self, in_channels: int, out_channels: int,
strides: int, kernel_size: int, is_top: bool):
if self.num_res_units > 0:
return ResidualUnit(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=kernel_size,
subunits=self.num_res_units,
act=self.act,
norm=self.norm,
dropout=self.dropout,
)
else:
return Convolution(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=kernel_size,
act=self.act,
norm=self.norm,
dropout=self.dropout,
)
def _get_up_layer(self, in_channels: int, out_channels: int, strides: int,
kernel_size: int, is_top: bool):
conv = Convolution(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=kernel_size,
act=self.act,
norm=self.norm,
dropout=self.dropout,
conv_only=is_top and self.num_res_units == 0,
is_transposed=True,
)
if self.num_res_units > 0:
ru = ResidualUnit(
self.dimensions,
out_channels,
out_channels,
strides=1,
kernel_size=kernel_size,
subunits=1,
act=self.act,
norm=self.norm,
dropout=self.dropout,
last_conv_only=is_top,
)
return nn.Sequential(conv, ru)
else:
return conv
def _get_down_layer(self, in_channels, out_channels, strides, is_top):
if self.num_res_units > 0:
return ResidualUnit(self.dimensions, in_channels, out_channels,
strides, self.kernel_size, self.num_res_units,
self.act, self.norm, self.dropout)
else:
return Convolution(self.dimensions, in_channels, out_channels,
strides, self.kernel_size, self.act, self.norm,
self.dropout)
def _get_up_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
"""
Returns the decoding (up) part of a layer of the network. This typically will upsample data at some point
in its structure. Its output is used as input to the next layer up.
Args:
in_channels: number of input channels.
out_channels: number of output channels.
strides: convolution stride.
is_top: True if this is the top block.
"""
conv: Union[Convolution, nn.Sequential]
conv = Convolution(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.up_kernel_size,
act=self.act,
norm=self.norm,
dropout=self.dropout,
bias=self.bias,
conv_only=is_top and self.num_res_units == 0,
is_transposed=True,
adn_ordering=self.adn_ordering,
)
if self.num_res_units > 0:
ru = ResidualUnit(
self.dimensions,
out_channels,
out_channels,
strides=1,
kernel_size=self.kernel_size,
subunits=1,
act=self.act,
norm=self.norm,
dropout=self.dropout,
bias=self.bias,
last_conv_only=is_top,
adn_ordering=self.adn_ordering,
)
conv = nn.Sequential(conv, ru)
return conv
def _get_up_layer(self, in_channels, out_channels, kernel_size, is_top):
if self.num_res_units > 0:
ru = ResidualUnit(
self.dimensions,
in_channels,
out_channels,
strides=1,
kernel_size=kernel_size,
subunits=1,
act=self.act,
norm=self.norm,
dropout=self.dropout,
last_conv_only=is_top,
)
return ru # nn.Sequential(ru)
else:
return nn.Sequential()
def _get_up_layer(self, in_channels: int, out_channels: int, strides: int,
is_top: bool) -> nn.Module:
"""
Args:
in_channels: number of input channels.
out_channels: number of output channels.
strides: convolution stride.
is_top: True if this is the top block.
"""
conv: Union[Convolution, nn.Sequential]
conv = Convolution(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.up_kernel_size,
act=self.act,
norm=self.norm,
evonorm=self.evonorm,
dropout=self.dropout,
conv_only=is_top and self.num_res_units == 0,
is_transposed=True,
)
if self.num_res_units > 0:
ru = ResidualUnit(
self.dimensions,
out_channels,
out_channels,
strides=1,
kernel_size=self.kernel_size,
subunits=1,
act=self.act,
norm=self.norm,
evonorm=self.evonorm,
dropout=self.dropout,
last_conv_only=is_top,
)
conv = nn.Sequential(conv, ru)
return conv
def _get_down_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
"""
Returns the encoding (down) part of a layer of the network. This typically will downsample data at some point
in its structure. Its output is used as input to the next layer down and is concatenated with output from the
next layer to form the input for the decode (up) part of the layer.
Args:
in_channels: number of input channels.
out_channels: number of output channels.
strides: convolution stride.
is_top: True if this is the top block.
"""
mod: nn.Module
if self.num_res_units > 0:
mod = ResidualUnit(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.kernel_size,
subunits=self.num_res_units,
act=self.act,
norm=self.norm,
dropout=self.dropout,
bias=self.bias,
adn_ordering=self.adn_ordering,
)
return mod
mod = Convolution(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.kernel_size,
act=self.act,
norm=self.norm,
dropout=self.dropout,
bias=self.bias,
adn_ordering=self.adn_ordering,
)
return mod
def test_dropout1(self):
conv = ResidualUnit(2, 1, self.output_channels, dropout=0.15)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0], self.im_shape[1])
self.assertEqual(out.shape, expected_shape)
def test_stride1(self):
conv = ResidualUnit(2, 1, self.output_channels, strides=2)
out = conv(self.imt)
expected_shape = (1, self.output_channels, self.im_shape[0] // 2, self.im_shape[1] // 2)
self.assertEqual(out.shape, expected_shape)