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forgot other customizations I want to keep

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This commit is contained in:
James Betker 2022-06-10 15:09:05 -06:00
parent 8f40108f5b
commit 7198bd8bd0
1 changed files with 7 additions and 6 deletions
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13
codes/models/audio/music/transformer_diffusion8.py
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@ -196,13 +196,14 @@ class TransformerDiffusion(nn.Module):
class TransformerDiffusionWithQuantizer(nn.Module): class TransformerDiffusionWithQuantizer(nn.Module):
def __init__(self, train_quantizer_reconstruction_until=-1, freeze_quantizer_until=10000, **kwargs): def __init__(self, quantizer_dims=[1024], train_quantizer_reconstruction_until=-1, freeze_quantizer_until=10000, **kwargs):
super().__init__() super().__init__()
self.internal_step = 0 self.internal_step = 0
self.freeze_quantizer_until = freeze_quantizer_until self.freeze_quantizer_until = freeze_quantizer_until
self.train_quantizer_reconstruction_until = train_quantizer_reconstruction_until self.train_quantizer_reconstruction_until = train_quantizer_reconstruction_until
self.diff = TransformerDiffusion(**kwargs) self.diff = TransformerDiffusion(**kwargs)
self.quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256, self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
codevector_dim=quantizer_dims[0], codebook_size=256,
codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5) codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
if train_quantizer_reconstruction_until == -1: if train_quantizer_reconstruction_until == -1:
@ -327,11 +328,11 @@ def register_transformer_diffusion8_with_ar_prior(opt_net, opt):
def test_quant_model(): def test_quant_model():
clip = torch.randn(2, 256, 400) clip = torch.randn(2, 100, 401)
ts = torch.LongTensor([600, 600]) ts = torch.LongTensor([600, 600])
model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, model = TransformerDiffusionWithQuantizer(in_channels=100, model_channels=2048, block_channels=1024, prenet_channels=1024,
input_vec_dim=1024, num_layers=16, prenet_layers=6, input_vec_dim=1024, num_layers=16, prenet_layers=6, quantizer_dims=[1024,896,768,512],
train_quantizer_reconstruction_until=1000) train_quantizer_reconstruction_until=-1)
model.get_grad_norm_parameter_groups() model.get_grad_norm_parameter_groups()
print_network(model) print_network(model)