def get_active_subnet(self, in_channel, preserve_weight=True):
sub_layer = ConvLayer(
in_channel, self.active_out_channel, self.kernel_size, self.stride, self.dilation,
use_bn=self.use_bn, act_func=self.act_func
)
sub_layer = sub_layer.to(get_net_device(self))
if not preserve_weight:
return sub_layer
sub_layer.conv.weight.data.copy_(self.conv.conv.weight.data[:self.active_out_channel, :in_channel, :, :])
if self.use_bn:
copy_bn(sub_layer.bn, self.bn.bn)
return sub_layer
def get_active_subnet(self, in_channel, preserve_weight=True):
middle_channel = make_divisible(
round(in_channel * self.active_expand_ratio), 8)
# build the new layer
sub_layer = MBInvertedConvLayer(
in_channel,
self.active_out_channel,
self.active_kernel_size,
self.stride,
self.active_expand_ratio,
act_func=self.act_func,
mid_channels=middle_channel,
use_se=self.use_se,
)
sub_layer = sub_layer.to(get_net_device(self))
if not preserve_weight:
return sub_layer
# copy weight from current layer
if sub_layer.inverted_bottleneck is not None:
sub_layer.inverted_bottleneck.conv.weight.data.copy_(
self.inverted_bottleneck.conv.conv.weight.
data[:middle_channel, :in_channel, :, :])
copy_bn(sub_layer.inverted_bottleneck.bn,
self.inverted_bottleneck.bn.bn)
sub_layer.depth_conv.conv.weight.data.copy_(
self.depth_conv.conv.get_active_filter(
middle_channel, self.active_kernel_size).data)
copy_bn(sub_layer.depth_conv.bn, self.depth_conv.bn.bn)
if self.use_se:
se_mid = make_divisible(middle_channel // SEModule.REDUCTION,
divisor=8)
sub_layer.depth_conv.se.fc.reduce.weight.data.copy_(
self.depth_conv.se.fc.reduce.weight.
data[:se_mid, :middle_channel, :, :])
sub_layer.depth_conv.se.fc.reduce.bias.data.copy_(
self.depth_conv.se.fc.reduce.bias.data[:se_mid])
sub_layer.depth_conv.se.fc.expand.weight.data.copy_(
self.depth_conv.se.fc.expand.weight.
data[:middle_channel, :se_mid, :, :])
sub_layer.depth_conv.se.fc.expand.bias.data.copy_(
self.depth_conv.se.fc.expand.bias.data[:middle_channel])
sub_layer.point_linear.conv.weight.data.copy_(
self.point_linear.conv.conv.weight.
data[:self.active_out_channel, :middle_channel, :, :])
copy_bn(sub_layer.point_linear.bn, self.point_linear.bn.bn)
return sub_layer
def get_active_subnet(self, in_channel, preserve_weight=True):
middle_channel = make_divisible(
round(in_channel * self.active_expand_ratio), 8)
# build the new layer
sub_layer = MBInvertedQConvLayer(
in_channel,
self.active_out_channel,
self.active_kernel_size,
self.stride,
self.active_expand_ratio,
act_func=self.act_func,
mid_channels=middle_channel,
pw_w_bit=self.point_linear.conv.w_bit,
pw_a_bit=self.point_linear.conv.a_bit,
dw_w_bit=self.depth_conv.conv.w_bit,
dw_a_bit=self.depth_conv.conv.a_bit,
)
sub_layer = sub_layer.to(get_net_device(self))
if not preserve_weight:
return sub_layer
# copy weight from current layer
if sub_layer.inverted_bottleneck is not None:
sub_layer.inverted_bottleneck.conv.weight.data.copy_(
self.inverted_bottleneck.conv.conv.weight.
data[:middle_channel, :in_channel, :, :])
copy_bn(sub_layer.inverted_bottleneck.bn,
self.inverted_bottleneck.bn.bn)
sub_layer.depth_conv.conv.weight.data.copy_(
self.depth_conv.conv.get_active_filter(
middle_channel, self.active_kernel_size).data)
copy_bn(sub_layer.depth_conv.bn, self.depth_conv.bn.bn)
sub_layer.point_linear.conv.weight.data.copy_(
self.point_linear.conv.conv.weight.
data[:self.active_out_channel, :middle_channel, :, :])
copy_bn(sub_layer.point_linear.bn, self.point_linear.bn.bn)
return sub_layer