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more cleanup

pull/9/head
James Betker 2022-06-19 21:04:51 +07:00
parent fef1066687
commit a5d2123daa
2 changed files with 11 additions and 37 deletions
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43
codes/models/audio/music/unet_diffusion_waveform_gen3.py
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@ -146,8 +146,6 @@ class DiffusionWaveformGen(nn.Module):
:param num_res_blocks: number of residual blocks per downsample.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
@ -165,39 +163,24 @@ class DiffusionWaveformGen(nn.Module):
num_res_blocks=(1,1,0),
token_conditioning_resolutions=(1,4),
mid_resnet_depth=10,
conv_resample=True,
dims=1,
use_fp16=False,
kernel_size=3,
scale_factor=2,
time_embed_dim_multiplier=1,
freeze_main_net=False,
use_scale_shift_norm=True,
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
# Parameters for super-sampling.
super_sampling=False,
super_sampling_max_noising_factor=.1,
):
super().__init__()
if super_sampling:
in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.dims = dims
self.super_sampling_enabled = super_sampling
self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.alignment_size = 2 ** (len(channel_mult)+1)
self.freeze_main_net = freeze_main_net
self.in_mel_channels = in_mel_channels
padding = 1 if kernel_size == 3 else 2
time_embed_dim = model_channels * time_embed_dim_multiplier
self.time_embed = nn.Sequential(
@ -217,7 +200,7 @@ class DiffusionWaveformGen(nn.Module):
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
@ -242,8 +225,8 @@ class DiffusionWaveformGen(nn.Module):
dropout,
out_channels=int(mult * model_channels),
dims=dims,
kernel_size=kernel_size,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=3,
use_scale_shift_norm=True,
)
]
ch = int(mult * model_channels)
@ -255,7 +238,7 @@ class DiffusionWaveformGen(nn.Module):
self.input_blocks.append(
TimestepEmbedSequential(
Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=3, pad=1
ch, True, dims=dims, out_channels=out_ch, factor=2, ksize=3, pad=1
)
)
)
@ -279,15 +262,15 @@ class DiffusionWaveformGen(nn.Module):
dropout,
out_channels=int(model_channels * mult),
dims=dims,
kernel_size=kernel_size,
use_scale_shift_norm=use_scale_shift_norm,
kernel_size=3,
use_scale_shift_norm=True,
)
]
ch = int(model_channels * mult)
if level and i == num_blocks:
out_ch = ch
layers.append(
Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
Upsample(ch, True, dims=dims, out_channels=out_ch, factor=2)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
@ -296,20 +279,10 @@ class DiffusionWaveformGen(nn.Module):
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
)
if self.freeze_main_net:
mains = [self.time_embed, self.contextual_embedder, self.unconditioned_embedding, self.conditioning_timestep_integrator,
self.input_blocks, self.middle_block, self.output_blocks, self.out]
for m in mains:
for p in m.parameters():
p.requires_grad = False
p.DO_NOT_TRAIN = True
def get_grad_norm_parameter_groups(self):
if self.freeze_main_net:
return {}
groups = {
'input_blocks': list(self.input_blocks.parameters()),
'output_blocks': list(self.output_blocks.parameters()),

5
codes/trainer/eval/music_diffusion_fid.py
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@ -61,6 +61,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
if mode == 'spec_decode':
self.diffusion_fn = self.perform_diffusion_spec_decode
self.squeeze_ratio = opt_eval['squeeze_ratio']
elif 'from_codes' == mode:
self.diffusion_fn = self.perform_diffusion_from_codes
self.local_modules['codegen'] = get_music_codegen()
@ -81,11 +82,11 @@ class MusicDiffusionFid(evaluator.Evaluator):
def perform_diffusion_spec_decode(self, audio, sample_rate=22050):
real_resampled = audio
audio = audio.unsqueeze(0)
output_shape = (1, 256, audio.shape[-1] // 256)
output_shape = (1, self.squeeze_ratio, audio.shape[-1] // self.squeeze_ratio)
mel = self.spec_fn({'in': audio})['out']
gen = self.diffuser.p_sample_loop(self.model, output_shape,
model_kwargs={'codes': mel})
gen = pixel_shuffle_1d(gen, 256)
gen = pixel_shuffle_1d(gen, self.squeeze_ratio)
return gen, real_resampled, normalize_mel(self.spec_fn({'in': gen})['out']), normalize_mel(mel), sample_rate