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a95f6c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | from tqdm import tqdm
import torch
import numpy as np
from nara_wpe.wpe import wpe
from nara_wpe.utils import stft, istft
from utils.losses import get_loss
from testing.EulerHeunSampler import EulerHeunSampler
class EulerHeunSamplerDPS(EulerHeunSampler):
"""
Euler Heun sampler for DPS
inverse problem solver
"""
def __init__(self, model, diff_params, args):
super().__init__(model, diff_params, args)
self.zeta = self.args.tester.posterior_sampling.zeta
def initialize_x(self, shape, device, schedule):
if self.args.tester.posterior_sampling.warm_initialization.mode == "none":
x = schedule[0]*torch.randn(shape).to(device)
elif self.args.tester.posterior_sampling.warm_initialization.mode == "reverb_scaled":
x = self.args.tester.posterior_sampling.warm_initialization.scaling_factor * self.y.clone() / self.y.std() + schedule[0] * torch.randn(shape).to(device)
elif self.args.tester.posterior_sampling.warm_initialization.mode == "wpe_scaled":
print("Processing WPE")
stft_options = dict(size=512, shift=128)
delay = self.args.tester.posterior_sampling.warm_initialization.wpe.delay
iterations = self.args.tester.posterior_sampling.warm_initialization.wpe.iterations
taps = self.args.tester.posterior_sampling.warm_initialization.wpe.taps
Y = stft(self.y.cpu().numpy(), **stft_options)
Y = Y.transpose(2, 0, 1)
Z = wpe(
Y,
taps=taps,
delay=delay,
iterations=iterations,
statistics_mode='full'
).transpose(1, 2, 0)
x_pred = torch.from_numpy(istft(Z, size=stft_options['size'], shift=stft_options['shift'])).to(self.y.device).type(self.y.dtype)
if x_pred.shape[-1] > self.y.shape[-1]:
x_pred = x_pred[..., :self.y.shape[-1]]
x_pred = self.args.tester.posterior_sampling.warm_initialization.scaling_factor * x_pred / x_pred.std()
x = x_pred + schedule[0] * torch.randn(shape).to(device)
else:
raise NotImplementedError
return x
def get_likelihood_score(self, x_den, x, t):
y_hat = self.operator.degradation(x_den, mode="waveform")
rec = self.rec_loss(self.y, y_hat)
rec_grads = torch.autograd.grad(outputs=rec, inputs=x)[0]
# Normalize weighting parameter zeta
normguide = torch.norm(rec_grads)/(self.args.exp.audio_len**0.5)
return self.zeta / (normguide+1e-8) * rec_grads, rec
def optimize_op(self, x_den, t):
"""
Optimize the operator parameters
"""
for _ in range(self.args.tester.posterior_sampling.blind_hp.op_updates_per_step):
for k in range(len(self.operator.params)):
self.operator.params[k].requires_grad=True
for k in range(len(self.operator.params_phases)):
self.operator.params_phases[k].requires_grad=True
self.operator.update_H()
# Reconstruction loss
y_hat = self.operator.degradation(x_den, mode="waveform")
if self.rec_loss_params is not None:
rec_loss = self.rec_loss_params(self.y, y_hat)
loss = rec_loss
assert (torch.isnan(rec_loss).any()==False), f"rec_loss is Nan"
else:
loss = 0.
# RIR noise regularization
if self.RIR_noise_regularization_loss is not None:
rir_time = self.operator.get_time_RIR()
rir_noise = torch.randn_like(rir_time).to(x_den.device)
t_op = max(min(t, self.args.tester.posterior_sampling.RIR_noise_regularization.crop_sigma_max), self.args.tester.posterior_sampling.RIR_noise_regularization.crop_sigma_min)
rir_noisy = rir_time + t_op * rir_noise
reg_loss = self.RIR_noise_regularization_loss(rir_time, rir_noisy.detach()) #detach gradients so that we do not backpropagate through the RIR operator
loss += reg_loss
assert (torch.isnan(loss).any()==False), f"loss is Nan"
self.optimizer_operator.zero_grad()
loss.backward()
self.optimizer_operator.step()
for p in self.operator.params:
p.detach_()
self.operator.project_params()
for p in self.operator.params:
p.requires_grad=True
def step(self, x_i, t_i, t_iplus1, gamma_i, blind=False):
x_hat, t_hat = self.stochastic_timestep(x_i, t_i, gamma_i)
x_hat.requires_grad = True
x_den = self.get_Tweedie_estimate(x_hat, t_hat)
if blind:
self.optimize_op(x_den.clone().detach(), t_hat)
lh_score, rec_loss_value = self.get_likelihood_score(x_den, x_hat, t_hat)
x_hat.detach_()
# Rescale denoised speech estimate magnitude to constraint absolute magnitudes of RIR / speech estimate
if self.args.tester.posterior_sampling.constraint_speech_magnitude.use:
x_den = self.args.tester.posterior_sampling.constraint_speech_magnitude.speech_scaling / x_den.detach().std() * x_den #Match the sigma_data of dataset
score = self.Tweedie2score(x_den, x_hat, t_hat)
ode_integrand = self.diff_params._ode_integrand(x_hat, t_hat, score) + lh_score
dt = t_iplus1 - t_hat
if t_iplus1 !=0 and self.order == 2: #second order correction
t_prime = t_iplus1
x_prime = x_hat + dt * ode_integrand
x_prime.requires_grad_(True)
x_den = self.get_Tweedie_estimate(x_prime, t_prime)
if blind:
self.optimize_op(x_den.clone().detach(), t_prime)
lh_score_next, rec_loss_value = self.get_likelihood_score(x_den, x_prime, t_prime)
x_prime.detach_()
score = self.Tweedie2score(x_den, x_prime, t_prime)
ode_integrand_next = self.diff_params._ode_integrand(x_prime, t_prime, score) + lh_score_next
ode_integrand_midpoint = .5 * (ode_integrand + ode_integrand_next)
x_iplus1 = x_hat + dt * ode_integrand_midpoint
else:
x_iplus1 = x_hat + dt * ode_integrand
return x_iplus1.detach_(), x_den.detach()
def predict(
self,
shape,
device,
blind=False
):
# get the noise schedule
t = self.create_schedule().to(device)
# sample prior
x = self.initialize_x(shape,device, t)
# parameter for langevin stochasticity, if Schurn is 0, gamma will be 0 to, so the sampler will be deterministic
gamma = self.get_gamma(t).to(device)
for i in tqdm(range(0, self.T, 1)):
self.step_counter=i
x, x_den = self.step(x, t[i] , t[i+1], gamma[i], blind)
return x_den.detach()
def predict_unconditional(self, *args, **kwargs):
raise ValueError("DPS not made for unconditional sampling")
def predict_conditional(
self,
y, #observations
operator, #degradation operator (assuming we define it in the tester)
shape=None,
blind=False,
**kwargs
):
self.operator = operator
self.y = y
self.rec_loss = get_loss(self.args.tester.posterior_sampling.rec_loss, operator=self.operator)
if blind:
self.rec_loss_params = get_loss(self.args.tester.posterior_sampling.rec_loss_params, operator=self.operator)
self.optimizer_operator = torch.optim.Adam(self.operator.params + self.operator.params_phases, lr=self.args.tester.posterior_sampling.blind_hp.lr_op, weight_decay=self.args.tester.posterior_sampling.blind_hp.weight_decay, betas=(self.args.tester.posterior_sampling.blind_hp.beta1, self.args.tester.posterior_sampling.blind_hp.beta2))
self.RIR_noise_regularization_loss = get_loss(self.args.tester.posterior_sampling.RIR_noise_regularization.loss, operator=self.operator)
if shape is None:
shape = y.shape
return self.predict(shape, y.device, blind)
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