104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
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import torch
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import torch.nn as nn
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def weights_init(m):
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classname = m.__class__.__name__
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if classname.find('Linear') != -1 or classname.find('Conv') != -1:
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nn.init.constant_(m.weight, 0)
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nn.init.normal_(m.bias, 0, 0.01)
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class IgnoreLinear(nn.Module):
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def __init__(self, dim_in, dim_out, dim_c):
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super(IgnoreLinear, self).__init__()
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self._layer = nn.Linear(dim_in, dim_out)
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def forward(self, context, x):
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return self._layer(x)
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class ConcatLinear(nn.Module):
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def __init__(self, dim_in, dim_out, dim_c):
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super(ConcatLinear, self).__init__()
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self._layer = nn.Linear(dim_in + 1 + dim_c, dim_out)
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def forward(self, context, x, c):
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if x.dim() == 3:
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context = context.unsqueeze(1).expand(-1, x.size(1), -1)
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x_context = torch.cat((x, context), dim=2)
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return self._layer(x_context)
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class ConcatLinear_v2(nn.Module):
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def __init__(self, dim_in, dim_out, dim_c):
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super(ConcatLinear_v2, self).__init__()
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self._layer = nn.Linear(dim_in, dim_out)
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self._hyper_bias = nn.Linear(1 + dim_c, dim_out, bias=False)
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def forward(self, context, x):
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bias = self._hyper_bias(context)
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if x.dim() == 3:
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bias = bias.unsqueeze(1)
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return self._layer(x) + bias
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class SquashLinear(nn.Module):
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def __init__(self, dim_in, dim_out, dim_c):
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super(SquashLinear, self).__init__()
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self._layer = nn.Linear(dim_in, dim_out)
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self._hyper = nn.Linear(1 + dim_c, dim_out)
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def forward(self, context, x):
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gate = torch.sigmoid(self._hyper(context))
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if x.dim() == 3:
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gate = gate.unsqueeze(1)
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return self._layer(x) * gate
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class ScaleLinear(nn.Module):
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def __init__(self, dim_in, dim_out, dim_c):
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super(ScaleLinear, self).__init__()
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self._layer = nn.Linear(dim_in, dim_out)
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self._hyper = nn.Linear(1 + dim_c, dim_out)
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def forward(self, context, x):
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gate = self._hyper(context)
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if x.dim() == 3:
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gate = gate.unsqueeze(1)
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return self._layer(x) * gate
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class ConcatSquashLinear(nn.Module):
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def __init__(self, dim_in, dim_out, dim_c):
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super(ConcatSquashLinear, self).__init__()
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self._layer = nn.Linear(dim_in, dim_out)
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self._hyper_bias = nn.Linear(1 + dim_c, dim_out, bias=False)
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self._hyper_gate = nn.Linear(1 + dim_c, dim_out)
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def forward(self, context, x):
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gate = torch.sigmoid(self._hyper_gate(context))
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bias = self._hyper_bias(context)
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if x.dim() == 3:
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gate = gate.unsqueeze(1)
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bias = bias.unsqueeze(1)
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ret = self._layer(x) * gate + bias
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return ret
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class ConcatScaleLinear(nn.Module):
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def __init__(self, dim_in, dim_out, dim_c):
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super(ConcatScaleLinear, self).__init__()
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self._layer = nn.Linear(dim_in, dim_out)
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self._hyper_bias = nn.Linear(1 + dim_c, dim_out, bias=False)
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self._hyper_gate = nn.Linear(1 + dim_c, dim_out)
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def forward(self, context, x):
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gate = self._hyper_gate(context)
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bias = self._hyper_bias(context)
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if x.dim() == 3:
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gate = gate.unsqueeze(1)
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bias = bias.unsqueeze(1)
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ret = self._layer(x) * gate + bias
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return ret
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