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fix

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This commit is contained in:
James Betker 2022-06-05 01:27:28 -06:00
parent 38d8b17d18
commit aac92b01b3
1 changed files with 4 additions and 4 deletions
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8
codes/models/audio/music/transformer_diffusion8.py
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@ -224,8 +224,8 @@ class TransformerDiffusionWithQuantizer(nn.Module):
def get_grad_norm_parameter_groups(self): def get_grad_norm_parameter_groups(self):
groups = { groups = {
'attention_layers': [lyr.attn for lyr in self.diff.layers], 'attention_layers': [lyr.attn.parameters() for lyr in self.diff.layers],
'ff_layers': [lyr.ff for lyr in self.diff.layers], 'ff_layers': [lyr.ff.parameters() for lyr in self.diff.layers],
'quantizer_encoder': list(self.quantizer.encoder.parameters()), 'quantizer_encoder': list(self.quantizer.encoder.parameters()),
'quant_codebook': [self.quantizer.quantizer.codevectors], 'quant_codebook': [self.quantizer.quantizer.codevectors],
'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()), 'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
@ -268,9 +268,9 @@ if __name__ == '__main__':
model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6) model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6)
model.get_grad_norm_parameter_groups() model.get_grad_norm_parameter_groups()
#quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant\\models\\18000_generator_ema.pth') quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
#diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth') #diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
#model.quantizer.load_state_dict(quant_weights, strict=False) model.quantizer.load_state_dict(quant_weights, strict=False)
#model.diff.load_state_dict(diff_weights) #model.diff.load_state_dict(diff_weights)
torch.save(model.state_dict(), 'sample.pth') torch.save(model.state_dict(), 'sample.pth')