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Mods to dvae

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- Add resblock to each layer
- Increase filter size for each layer
- Use SiLU
This commit is contained in:
James Betker 2021-08-15 20:54:10 -06:00
parent b8bec22f1a
commit a826d5f658
1 changed files with 11 additions and 9 deletions
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20
codes/models/gpt_voice/lucidrains_dvae.py
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@ -31,9 +31,9 @@ class ResBlock(nn.Module):
super().__init__()
self.net = nn.Sequential(
conv(chan, chan, 3, padding = 1),
nn.ReLU(),
nn.SiLU(),
conv(chan, chan, 3, padding = 1),
nn.ReLU(),
nn.SiLU(),
conv(chan, chan, 1)
)
@ -74,7 +74,7 @@ class DiscreteVAE(nn.Module):
conv = nn.Conv1d
conv_transpose = nn.ConvTranspose1d
enc_chans = [hidden_dim] * num_layers
enc_chans = [hidden_dim * 2 ** i for i in range(num_layers)]
dec_chans = list(reversed(enc_chans))
enc_chans = [channels, *enc_chans]
@ -88,12 +88,14 @@ class DiscreteVAE(nn.Module):
dec_layers = []
for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io):
enc_layers.append(nn.Sequential(conv(enc_in, enc_out, 4, stride = 2, padding = 1), nn.ReLU()))
dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, 4, stride = 2, padding = 1), nn.ReLU()))
for _ in range(num_resnet_blocks):
dec_layers.append(ResBlock(dec_in, conv))
for _ in range(num_resnet_blocks):
dec_layers.insert(0, ResBlock(dec_chans[1], conv))
enc_layers.append(ResBlock(enc_chans[-1], conv))
enc_layers.append(nn.Sequential(conv(enc_in, enc_out, 4, stride = 2, padding = 1), nn.SiLU()))
dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, 4, stride = 2, padding = 1), nn.SiLU()))
for _ in range(num_resnet_blocks):
enc_layers.append(ResBlock(enc_out, conv))
if num_resnet_blocks > 0:
dec_layers.insert(0, conv(codebook_dim, dec_chans[1], 1))
@ -202,7 +204,7 @@ if __name__ == '__main__':
#v = DiscreteVAE()
#o=v(torch.randn(1,3,256,256))
#print(o.shape)
v = DiscreteVAE(channels=1, normalization=None, positional_dims=1, num_tokens=4096, codebook_dim=2048, hidden_dim=256)
v = DiscreteVAE(channels=1, normalization=None, positional_dims=1, num_tokens=4096, codebook_dim=2048, hidden_dim=256, num_resnet_blocks=2)
v.eval()
o=v(torch.randn(1,1,256))
print(o[-1].shape)