darknet/new_layers.md

1. silence layer

Usage:

[silence]

PyTorch code:

class Silence(nn.Module):
    def __init__(self):
        super(Silence, self).__init__()
    def forward(self, x):    
        return x

2. implicit_add layer

Usage:

[implicit_add]
filters=128

PyTorch code:

class ImplicitA(nn.Module):
    def __init__(self, channel):
        super(ImplicitA, self).__init__()
        self.channel = channel
        self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
        nn.init.normal_(self.implicit, std=.02)

    def forward(self):
        return self.implicit

3. shift_channels layer

Usage:

[shift_channels]
from=101

PyTorch code:

class ShiftChannel(nn.Module):
    def __init__(self, layers):
        super(ShiftChannel, self).__init__()
        self.layers = layers  # layer indices

    def forward(self, x, outputs):
        a = outputs[self.layers[0]]
        return a.expand_as(x) + x

4. implicit_mul layer

Usage:

[implicit_mul]
filters=128

PyTorch code:

class ImplicitM(nn.Module):
    def __init__(self, channel):
        super(ImplicitM, self).__init__()
        self.channel = channel
        self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1))
        nn.init.normal_(self.implicit, mean=1., std=.02)

    def forward(self):
        return self.implicit

5. control_channels layer

Usage:

[control_channels]
from=101

PyTorch code:

class ControlChannel(nn.Module):
    def __init__(self, layers):
        super(ControlChannel, self).__init__()
        self.layers = layers  # layer indices

    def forward(self, x, outputs):
        a = outputs[self.layers[0]]
        return a.expand_as(x) * x

6. implicit_cat layer

Usage:

[implicit_cat]
filters=128

PyTorch code: (same as ImplicitA)

class ImplicitC(nn.Module):
    def __init__(self, channel):
        super(ImplicitC, self).__init__()
        self.channel = channel
        self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
        nn.init.normal_(self.implicit, std=.02)

    def forward(self):
        return self.implicit

7. alternate_channels layer

Usage:

[alternate_channels]
from=101

PyTorch code:

class AlternateChannel(nn.Module):
    def __init__(self, layers):
        super(AlternateChannel, self).__init__()
        self.layers = layers  # layer indices

    def forward(self, x, outputs):
        a = outputs[self.layers[0]]
        return torch.cat([a.expand_as(x), x], dim=1)

8. implicit_add_2d layer

Usage:

[implicit_add_2d]
filters=128
atoms=128

PyTorch code:

class Implicit2DA(nn.Module):
    def __init__(self, atom, channel):
        super(Implicit2DA, self).__init__()
        self.channel = channel
        self.implicit = nn.Parameter(torch.zeros(1, atom, channel, 1))
        nn.init.normal_(self.implicit, std=.02)

    def forward(self):
        return self.implicit

9. shift_channels_2d layer

Usage:

[shift_channels_2d]
from=101

PyTorch code:

class ShiftChannel2D(nn.Module):
    def __init__(self, layers):
        super(ShiftChannel2D, self).__init__()
        self.layers = layers  # layer indices

    def forward(self, x, outputs):
        a = outputs[self.layers[0]].view(1,-1,1,1)
        return a.expand_as(x) + x

10. implicit_mul_2d layer

Usage:

[implicit_mul_2d]
filters=128
atoms=128

PyTorch code:

class Implicit2DM(nn.Module):
    def __init__(self, atom, channel):
        super(Implicit2DM, self).__init__()
        self.channel = channel
        self.implicit = nn.Parameter(torch.ones(1, atom, channel, 1))
        nn.init.normal_(self.implicit, mean=1., std=.02)

    def forward(self):
        return self.implicit

11. control_channels_2d layer

Usage:

[control_channels_2d]
from=101

PyTorch code:

class ControlChannel2D(nn.Module):
    def __init__(self, layers):
        super(ControlChannel2D, self).__init__()
        self.layers = layers  # layer indices

    def forward(self, x, outputs):
        a = outputs[self.layers[0]].view(1,-1,1,1)
        return a.expand_as(x) * x

12. implicit_cat_2d layer

Usage:

[implicit_cat_2d]
filters=128
atoms=128

PyTorch code: (same as Implicit2DA)

class Implicit2DC(nn.Module):
    def __init__(self, atom, channel):
        super(Implicit2DC, self).__init__()
        self.channel = channel
        self.implicit = nn.Parameter(torch.zeros(1, atom, channel, 1))
        nn.init.normal_(self.implicit, std=.02)

    def forward(self):
        return self.implicit

13. alternate_channels_2d layer

Usage:

[alternate_channels_2d]
from=101

PyTorch code:

class AlternateChannel2D(nn.Module):
    def __init__(self, layers):
        super(AlternateChannel2D, self).__init__()
        self.layers = layers  # layer indices

    def forward(self, x, outputs):
        a = outputs[self.layers[0]].view(1,-1,1,1)
        return torch.cat([a.expand_as(x), x], dim=1)

14. dwt layer

Usage:

[dwt]

PyTorch code:

# https://github.com/fbcotter/pytorch_wavelets
from pytorch_wavelets import DWTForward, DWTInverse
class DWT(nn.Module):
    def __init__(self):
        super(DWT, self).__init__()
        self.xfm = DWTForward(J=1, wave='db1', mode='zero')

    def forward(self, x):
        b,c,w,h = x.shape
        yl, yh = self.xfm(x)
        return torch.cat([yl/2., yh[0].view(b,-1,w//2,h//2)/2.+.5], 1)