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This commit is contained in:
James Betker 2022-10-10 11:30:20 -06:00
parent 3cb14123bc
commit cc74a43675
5 changed files with 64 additions and 17 deletions
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7
codes/models/audio/music/transformer_diffusion12.py
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@ -609,7 +609,6 @@ def test_vqvae_model():
o = model(clip, ts, cond)
pg = model.get_grad_norm_parameter_groups()
"""
with torch.no_grad():
proj = torch.randn(2, 100, 512).cuda()
clip = clip.cuda()
@ -618,10 +617,9 @@ def test_vqvae_model():
model = model.cuda().eval()
model.diff.enable_fp16 = True
ti = model.diff.timestep_independent(proj, clip.shape[2])
for k in range(100):
for k in range(1000):
model.diff(clip, ts, precomputed_code_embeddings=ti)
print(f"Elapsed: {time()-start}")
"""
def test_multi_vqvae_model():
@ -690,4 +688,5 @@ def extract_diff(in_f, out_f, remove_head=False):
if __name__ == '__main__':
#extract_diff('X:\\dlas\\experiments\\train_music_diffusion_tfd12\\models\\41000_generator_ema.pth', 'extracted_diff.pth', True)
#test_cheater_model()
extract_diff('X:\\dlas\experiments\\train_music_diffusion_tfd_cheater_from_scratch\\models\\56500_generator_ema.pth', 'extracted.pth', remove_head=True)
test_vqvae_model()
#extract_diff('X:\\dlas\experiments\\train_music_diffusion_tfd_cheater_from_scratch\\models\\56500_generator_ema.pth', 'extracted.pth', remove_head=True)

4
codes/models/audio/music/transformer_diffusion13.py
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@ -19,8 +19,8 @@ class SubBlock(nn.Module):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.attn = AttentionBlock(inp_dim, out_channels=contraction_dim, num_heads=heads)
self.register_buffer('mask', build_local_attention_mask(n=4000, l=64), persistent=False)
self.pos_bias = RelativeQKBias(l=64)
self.register_buffer('mask', build_local_attention_mask(n=6000, l=64), persistent=False)
self.pos_bias = RelativeQKBias(l=64, max_positions=6000)
ff_contract = contraction_dim//2
self.ff1 = nn.Sequential(nn.Conv1d(inp_dim+contraction_dim, ff_contract, kernel_size=1),
nn.GroupNorm(8, ff_contract),

51
codes/models/audio/tts/unet_diffusion_tts_flat.py
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@ -7,7 +7,8 @@ import torch.nn.functional as F
from torch import autocast
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock
from models.diffusion.unet_diffusion import TimestepEmbedSequential, TimestepBlock, QKVAttentionLegacy
from models.lucidrains.x_transformers import RelativePositionBias
from trainer.networks import register_model
from utils.util import checkpoint
@ -19,6 +20,52 @@ def is_sequence(t):
return t.dtype == torch.long
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other.
Originally ported from here, but adapted to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
"""
def __init__(
self,
channels,
num_heads=1,
num_head_channels=-1,
do_checkpoint=True,
relative_pos_embeddings=False,
):
super().__init__()
self.channels = channels
self.do_checkpoint = do_checkpoint
if num_head_channels == -1:
self.num_heads = num_heads
else:
assert (
channels % num_head_channels == 0
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
self.num_heads = channels // num_head_channels
self.norm = normalization(channels)
self.qkv = nn.Conv1d(channels, channels * 3, 1)
# split heads before split qkv
self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
if relative_pos_embeddings:
self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
else:
self.relative_pos_embeddings = None
def forward(self, x, mask=None):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
h = self.attention(qkv, mask, self.relative_pos_embeddings)
h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial)
class ResBlock(TimestepBlock):
def __init__(
self,
@ -336,7 +383,7 @@ if __name__ == '__main__':
start = time()
model = model.cuda().eval()
ti = model.timestep_independent(proj, clip, clip.shape[2], False)
for k in range(100):
for k in range(1000):
model(clip, ts, precomputed_aligned_embeddings=ti)
print(f"Elapsed: {time()-start}")

17
codes/trainer/eval/music_diffusion_fid.py
View File

@ -137,8 +137,8 @@ class MusicDiffusionFid(evaluator.Evaluator):
# s = q9.clamp(1, 9999999999)
# x = x.clamp(-s, s) / s
# return x
perp = self.diffuser.p_sample_loop_for_log_perplexity(self.model, mel_norm,
model_kwargs = {'truth_mel': mel_norm})
#perp = self.diffuser.p_sample_loop_for_log_perplexity(self.model, mel_norm,
# model_kwargs = {'truth_mel': mel_norm})
sampler = self.diffuser.ddim_sample_loop if self.ddim else self.diffuser.p_sample_loop
gen_mel = sampler(self.model, mel_norm.shape, model_kwargs={'truth_mel': mel_norm})
@ -155,7 +155,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
model_kwargs={'codes': mel})
real_wav = pixel_shuffle_1d(real_wav, 16)
return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate, perp
return gen_wav, real_wav.squeeze(0), gen_mel, mel_norm, sample_rate, torch.tensor([0])
def perform_reconstruction_from_cheater_gen(self, audio, sample_rate=22050):
audio = audio.unsqueeze(0)
@ -303,7 +303,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
gen_projections = torch.stack(gen_projections, dim=0)
real_projections = torch.stack(real_projections, dim=0)
frechet_distance = torch.tensor(self.compute_frechet_distance(gen_projections, real_projections), device=self.env['device'])
perplexity = torch.stack(perplexities, dim=0).mean()
perplexity = torch.stack(perplexities, dim=0).float().mean()
if distributed.is_initialized() and distributed.get_world_size() > 1:
distributed.all_reduce(frechet_distance)
@ -338,16 +338,17 @@ if __name__ == '__main__':
# For TFD+cheater trainer
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd_and_cheater.yml', 'generator',
also_load_savepoint=False, strict_load=False,
load_path='X:\\dlas\\experiments\\train_music_diffusion_tfd14_and_cheater_g2\\models\\120000_generator_ema.pth'
load_path='X:\\dlas\\experiments\\tfd14_and_cheater.pth'
).cuda()
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
opt_eval = {#'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
'path': 'Y:\\separated\\tfd14_test',
'diffusion_steps': 256,
'conditioning_free': False, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': True,
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': True,
'diffusion_schedule': 'cosine', 'diffusion_type': 'from_codes_quant',
}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 13, 'device': 'cuda', 'opt': {}}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 18, 'device': 'cuda', 'opt': {}}
eval = MusicDiffusionFid(diffusion, opt_eval, env)
fds = []
for i in range(2):

2
codes/trainer/injectors/audio_injectors.py
View File

@ -11,7 +11,7 @@ from utils.util import opt_get, load_model_from_config, pad_or_truncate
MEL_MIN = -11.512925148010254
TACOTRON_MEL_MAX = 2.3143386840820312
TORCH_MEL_MAX = 4.82
TORCH_MEL_MAX = 4.82 # FYI: this STILL isn't assertive enough...
def normalize_torch_mel(mel):
return 2 * ((mel - MEL_MIN) / (TORCH_MEL_MAX - MEL_MIN)) - 1