forked from mrq/tortoise-tts
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
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if bias is not None:
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rest_dim = [1] * (input.ndim - bias.ndim - 1)
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return (
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F.leaky_relu(
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input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
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)
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* scale
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)
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else:
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return F.leaky_relu(input, negative_slope=0.2) * scale
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class EqualLinear(nn.Module):
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def __init__(
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self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
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):
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super().__init__()
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self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
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if bias:
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self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
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else:
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self.bias = None
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self.scale = (1 / math.sqrt(in_dim)) * lr_mul
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self.lr_mul = lr_mul
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def forward(self, input):
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out = F.linear(input, self.weight * self.scale)
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out = fused_leaky_relu(out, self.bias * self.lr_mul)
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return out
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class RandomLatentConverter(nn.Module):
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def __init__(self, channels):
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super().__init__()
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self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
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nn.Linear(channels, channels))
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self.channels = channels
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def forward(self, ref):
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r = torch.randn(ref.shape[0], self.channels, device=ref.device)
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y = self.layers(r)
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return y
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if __name__ == '__main__':
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model = RandomLatentConverter(512)
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model(torch.randn(5,512)) |