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:/ oh well.

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James Betker 2022-06-10 15:17:41 -06:00
parent 33178e89c4
commit 6d85fe05f6
1 changed files with 31 additions and 31 deletions
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codes/models/audio/music/transformer_diffusion8.py
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@ -196,18 +196,18 @@ class TransformerDiffusion(nn.Module):
class TransformerDiffusionWithQuantizer(nn.Module): class TransformerDiffusionWithQuantizer(nn.Module):
def __init__(self, quantizer_dims=[1024], train_quantizer_reconstruction_until=-1, freeze_quantizer_until=10000, **kwargs): def __init__(self, freeze_quantizer_until=20000, quantizer_dims=[1024], no_reconstruction=True, **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.diff = TransformerDiffusion(**kwargs) self.diff = TransformerDiffusion(**kwargs)
self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims, self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
codevector_dim=quantizer_dims[0], codebook_size=256, codevector_dim=quantizer_dims[0], checkpoint=False,
codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5) codebook_size=256, 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 no_reconstruction:
# We won't be using the upsampler, so delete it.
del self.quantizer.up del self.quantizer.up
def update_for_step(self, step, *args): def update_for_step(self, step, *args):
@ -219,38 +219,30 @@ class TransformerDiffusionWithQuantizer(nn.Module):
) )
def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False): def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
diff_disabled = self.internal_step < self.train_quantizer_reconstruction_until mse, diversity_loss, proj = self.quantizer(truth_mel, return_decoder_latent=True)
if diff_disabled:
mse, diversity_loss = self.quantizer(truth_mel)
# Use the diff parameters so DDP doesn't give us grief.
unused = 0
for p in self.diff.parameters():
unused = unused + p.mean() * 0
mse = mse + unused
return x.repeat(1,2,1), diversity_loss, mse
quant_grad_enabled = self.internal_step >= self.freeze_quantizer_until
with torch.set_grad_enabled(quant_grad_enabled):
proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
proj = proj.permute(0,2,1) proj = proj.permute(0,2,1)
quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
if not quant_grad_enabled: if not quant_grad_enabled:
proj = proj.detach()
# Make sure this does not cause issues in DDP by explicitly using the parameters for nothing. # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
unused = 0 unused = 0
for p in self.quantizer.parameters(): for p in self.quantizer.parameters():
unused = unused + p.mean() * 0 unused = unused + p.mean() * 0
proj = proj + unused proj = proj + unused
diversity_loss = diversity_loss * 0
diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free) diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input,
conditioning_free=conditioning_free)
if disable_diversity: if disable_diversity:
return diff return diff
return diff, diversity_loss, None if mse is None:
return diff, diversity_loss
return diff, diversity_loss, mse
def get_debug_values(self, step, __): def get_debug_values(self, step, __):
if self.quantizer.total_codes > 0: if self.quantizer.total_codes > 0:
return {'histogram_codes': self.quantizer.codes[:self.quantizer.total_codes], return {'histogram_quant_codes': self.quantizer.codes[:self.quantizer.total_codes],
'gumbel_temperature': self.quantizer.quantizer.temperature} 'gumbel_temperature': self.quantizer.quantizer.temperature}
else: else:
return {} return {}
@ -330,17 +322,24 @@ def register_transformer_diffusion8_with_ar_prior(opt_net, opt):
def test_quant_model(): def test_quant_model():
clip = torch.randn(2, 100, 401) clip = torch.randn(2, 100, 401)
ts = torch.LongTensor([600, 600]) ts = torch.LongTensor([600, 600])
model = TransformerDiffusionWithQuantizer(in_channels=100, model_channels=2048, block_channels=1024, prenet_channels=1024, model = TransformerDiffusionWithQuantizer(in_channels=100, out_channels=200, quantizer_dims=[1024,768,512,384],
input_vec_dim=1024, num_layers=16, prenet_layers=6, quantizer_dims=[1024,896,768,512], model_channels=2048, block_channels=1024, prenet_channels=1024,
train_quantizer_reconstruction_until=-1) input_vec_dim=1024, num_layers=16, prenet_layers=6,
model.get_grad_norm_parameter_groups() no_reconstruction=False)
#model.get_grad_norm_parameter_groups()
#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')
#model.quantizer.load_state_dict(quant_weights, strict=False)
#model.diff.load_state_dict(diff_weights)
#torch.save(model.state_dict(), 'sample.pth')
print_network(model) print_network(model)
o = model(clip, ts, clip) o = model(clip, ts, clip)
def test_ar_model(): def test_ar_model():
clip = torch.randn(2, 256, 400) clip = torch.randn(2, 256, 401)
cond = torch.randn(2, 256, 400) cond = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600]) ts = torch.LongTensor([600, 600])
model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1024, model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1024,
@ -358,7 +357,8 @@ def test_ar_model():
model.diff.load_state_dict(pruned_diff_weights, strict=False) model.diff.load_state_dict(pruned_diff_weights, strict=False)
torch.save(model.state_dict(), 'sample.pth') torch.save(model.state_dict(), 'sample.pth')
model(clip, ts, cond) model(clip, ts, cond, conditioning_input=cond)
if __name__ == '__main__': if __name__ == '__main__':