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export_rave.py
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export_rave.py
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import torch
import torch.nn as nn
from effortless_config import Config
import logging
from termcolor import colored
import cached_conv
logging.basicConfig(level=logging.INFO,
format=colored("[%(relativeCreated).2f] ", "green") +
"%(message)s")
logging.info("exporting model")
class args(Config):
RUN = None
SR = None
CACHED = False
FIDELITY = .95
NAME = "vae"
STEREO = False
DETERMINISTIC = False
args.parse_args()
cached_conv.use_buffer_conv(args.CACHED)
from rave.model import RAVE
from cached_conv import CachedConv1d, CachedConvTranspose1d, AlignBranches
from rave.resample import Resampling
from rave.pqmf import CachedPQMF
from rave.core import search_for_run
import numpy as np
import math
class TraceModel(nn.Module):
def __init__(self, pretrained: RAVE, resample: Resampling,
fidelity: float):
super().__init__()
latent_size = pretrained.latent_size
self.resample = resample
self.pqmf = pretrained.pqmf
self.encoder = pretrained.encoder
self.decoder = pretrained.decoder
self.register_buffer("latent_pca", pretrained.latent_pca)
self.register_buffer("latent_mean", pretrained.latent_mean)
self.register_buffer("latent_size", torch.tensor(latent_size))
self.register_buffer(
"sampling_rate",
torch.tensor(self.resample.taget_sr),
)
self.trained_cropped = bool(pretrained.cropped_latent_size)
self.deterministic = args.DETERMINISTIC
if self.trained_cropped:
self.cropped_latent_size = pretrained.cropped_latent_size
else:
latent_size = np.argmax(pretrained.fidelity.numpy() > fidelity)
latent_size = 2**math.ceil(math.log2(latent_size))
self.cropped_latent_size = latent_size
x = torch.zeros(1, 1, 2**14)
z = self.encode(x)
ratio = x.shape[-1] // z.shape[-1]
self.register_buffer(
"encode_params",
torch.tensor([
1,
1,
self.cropped_latent_size,
ratio,
]))
self.register_buffer(
"decode_params",
torch.tensor([
self.cropped_latent_size,
ratio,
2 if args.STEREO else 1,
1,
]))
self.register_buffer("forward_params",
torch.tensor([1, 1, 2 if args.STEREO else 1, 1]))
self.stereo = args.STEREO
def post_process_distribution(self, mean, scale):
std = nn.functional.softplus(scale) + 1e-4
return mean, std
def reparametrize(self, mean, std):
var = std * std
logvar = torch.log(var)
z = torch.randn_like(mean) * std + mean
kl = (mean * mean + var - logvar - 1).sum(1).mean()
return z, kl
@torch.jit.export
def encode(self, x):
x = self.resample.from_target_sampling_rate(x)
if self.pqmf is not None:
x = self.pqmf(x)
mean, scale = self.encoder(x)
mean, std = self.post_process_distribution(mean, scale)
if self.deterministic:
z = mean
else:
z = self.reparametrize(mean, std)[0]
z = z - self.latent_mean.unsqueeze(-1)
z = nn.functional.conv1d(z, self.latent_pca.unsqueeze(-1))
z = z[:, :self.cropped_latent_size]
return z
@torch.jit.export
def encode_amortized(self, x):
x = self.resample.from_target_sampling_rate(x)
if self.pqmf is not None:
x = self.pqmf(x)
mean, scale = self.encoder(x)
mean, std = self.post_process_distribution(mean, scale)
var = std * std
mean = mean - self.latent_mean.unsqueeze(-1)
mean = nn.functional.conv1d(mean, self.latent_pca.unsqueeze(-1))
var = nn.functional.conv1d(var, self.latent_pca.unsqueeze(-1).pow(2))
mean = mean[:, :self.cropped_latent_size]
var = var[:, :self.cropped_latent_size]
std = var.sqrt()
return mean, std
@torch.jit.export
def decode(self, z):
if self.trained_cropped: # PERFORM PCA BEFORE PADDING
z = nn.functional.conv1d(z, self.latent_pca.T.unsqueeze(-1))
z = z + self.latent_mean.unsqueeze(-1)
if self.stereo and z.shape[0] == 1: # DUPLICATE LATENT PATH
z = z.expand(2, z.shape[1], z.shape[2])
# CAT WITH SAMPLES FROM PRIOR DISTRIBUTION
pad_size = self.latent_size.item() - z.shape[1]
if self.deterministic:
pad_latent = torch.zeros(
z.shape[0],
pad_size,
z.shape[-1],
device=z.device,
)
else:
pad_latent = torch.randn(
z.shape[0],
pad_size,
z.shape[-1],
device=z.device,
)
z = torch.cat([z, pad_latent], 1)
if not self.trained_cropped: # PERFORM PCA AFTER PADDING
z = nn.functional.conv1d(z, self.latent_pca.T.unsqueeze(-1))
z = z + self.latent_mean.unsqueeze(-1)
x = self.decoder(z, add_noise=not self.deterministic)
if self.pqmf is not None:
x = self.pqmf.inverse(x)
x = self.resample.to_target_sampling_rate(x)
if self.stereo:
x = x.permute(1, 0, 2)
return x
def forward(self, x):
return self.decode(self.encode(x))
logging.info("loading model from checkpoint")
RUN = search_for_run(args.RUN)
logging.info(f"using {RUN}")
model = RAVE.load_from_checkpoint(RUN, strict=False).eval()
logging.info("flattening weights")
for m in model.modules():
if hasattr(m, "weight_g"):
nn.utils.remove_weight_norm(m)
logging.info("warmup forward pass")
x = torch.zeros(1, 1, 2**14)
if model.pqmf is not None:
x = model.pqmf(x)
z, _ = model.reparametrize(*model.encoder(x))
if args.STEREO:
z = z.expand(2, *z.shape[1:])
y = model.decoder(z)
if model.pqmf is not None:
y = model.pqmf.inverse(y)
logging.info("scripting cached modules")
n_cache = 0
cached_modules = [
CachedConv1d,
CachedConvTranspose1d,
CachedPQMF,
AlignBranches,
]
model.discriminator = None
# for n, m in model.named_modules():
# if any(list(map(lambda c: isinstance(m, c),
# cached_modules))) and args.CACHED:
# m.script_cache()
# n_cache += 1
logging.info(f"{n_cache} cached modules found and scripted")
sr = model.sr
if args.SR is not None:
target_sr = int(args.SR)
else:
target_sr = sr
logging.info("build resampling model")
resample = Resampling(target_sr, sr)
x = torch.zeros(1, 1, 2**14)
resample.to_target_sampling_rate(resample.from_target_sampling_rate(x))
if not resample.identity and args.CACHED:
resample.upsample.script_cache()
resample.downsample.script_cache()
logging.info("script model")
model = TraceModel(model, resample, args.FIDELITY)
model(x)
model = torch.jit.script(model)
logging.info(f"save rave_{args.NAME}.ts")
model.save(f"rave_{args.NAME}.ts")