449 lines
18 KiB
Python
449 lines
18 KiB
Python
from typing import Optional
|
|
import torch
|
|
import torchaudio.transforms as T
|
|
import torch.nn.functional as F
|
|
|
|
from ..utils import gc_collect
|
|
from ..infer_pack.commons import compute_correlation, median_pool1d, minmax_scale
|
|
|
|
class LossBalancer:
|
|
model: torch.nn.Module
|
|
|
|
def __init__(self, model: torch.nn.Module, initial_weights: dict={}, historical_losses: dict={}, ema_weights: dict={}, epsilon=1e-8, weights_decay=0., loss_decay=.0, active=True, use_pareto=True, use_norm=False):
|
|
"""
|
|
Initializes the LossBalancer with optional initial weights, partial gradient computation, and EMA.
|
|
|
|
Args:
|
|
model (torch.nn.Module): The model for which gradients will be computed.
|
|
initial_weights (dict): Initial weights for each loss term.
|
|
epsilon (float): A small value to avoid division by zero.
|
|
partial (bool): Whether to compute gradients only for the output layer.
|
|
"""
|
|
self.model = model
|
|
self.epsilon = epsilon
|
|
self.weights_decay = weights_decay
|
|
self.loss_decay = loss_decay
|
|
self.initial_weights = initial_weights
|
|
self.ema_weights = ema_weights
|
|
self.historical_losses = historical_losses
|
|
self.active = active
|
|
self.use_pareto = use_pareto # apply the 20/80 rule to loss balancing
|
|
self.use_norm = use_norm
|
|
|
|
def to_dict(self):
|
|
return dict(
|
|
epsilon = self.epsilon,
|
|
weights_decay = self.weights_decay,
|
|
loss_decay = self.loss_decay,
|
|
ema_weights = self.ema_weights,
|
|
initial_weights = self.initial_weights,
|
|
historical_losses = self.historical_losses,
|
|
active = self.active,
|
|
use_pareto = self.use_pareto, # apply the 20/80 rule to loss balancing
|
|
use_norm = self.use_norm
|
|
)
|
|
|
|
def update_ema_weights(self, new_weights):
|
|
"""Updates the EMA of the weights."""
|
|
if not self.ema_weights: self.ema_weights = dict(**new_weights)
|
|
else: self.ema_weights = {
|
|
k: self.weights_decay * self.ema_weights.get(k, 1.) + (1 - self.weights_decay) * new_weights[k]
|
|
for k in new_weights
|
|
}
|
|
return dict(**self.ema_weights)
|
|
|
|
def update_historical_losses(self, new_losses: dict):
|
|
"""Updates the EMA of the weights."""
|
|
if not self.historical_losses: self.historical_losses = dict(**new_losses)
|
|
else:
|
|
for k in new_losses:
|
|
self.historical_losses[k]=self.loss_decay*self.historical_losses.get(k, 0.) + (1 - self.loss_decay)*new_losses[k]
|
|
return dict(**self.historical_losses)
|
|
|
|
def calculate_loss_slope(self, key, current_loss):
|
|
"""Calculates the slope of the loss using the current loss and its EMA."""
|
|
ema_loss = self.historical_losses.get(key, current_loss)+self.epsilon
|
|
slope = (current_loss-ema_loss)/ema_loss # relative loss change
|
|
return slope
|
|
|
|
def calculate_gradients(self, key, current_loss, input):
|
|
"""Calculates the gradient norm of the current loss wrt the model params."""
|
|
self.model.zero_grad() # Clear previous gradients
|
|
|
|
# Backward pass to output layer
|
|
output_params = torch.autograd.grad(current_loss, [input], retain_graph=True, allow_unused=True, materialize_grads=True)[0]
|
|
|
|
# Compute L2 gradient norm
|
|
# grad_norm = output_params.view(output_params.size(0),-1).norm(2, dim=-1).mean()
|
|
if output_params.ndim>1: grad_norm = output_params.view(output_params.size(0), -1).norm(2, dim=-1).mean()
|
|
else: grad_norm = output_params.norm(2)
|
|
|
|
slope = self.calculate_loss_slope(key, current_loss)
|
|
return grad_norm * slope
|
|
|
|
def pareto_normalizer(self, data: dict):
|
|
# Sort the data in descending order based on the values
|
|
sorted_data = sorted(data.items(), key=lambda x: x[1], reverse=True)
|
|
|
|
# Calculate the number of elements in the top 20%
|
|
top_20_count = max(int(0.2 * len(sorted_data)),1)
|
|
|
|
# Calculate the weights
|
|
weights = [0] * len(sorted_data)
|
|
total_weight = 1.0
|
|
top_20_weight = 0.8 * total_weight
|
|
remaining_weight = total_weight - top_20_weight
|
|
|
|
# Assign weights to the top 20%
|
|
for i in range(top_20_count):
|
|
weights[i] = top_20_weight / top_20_count
|
|
|
|
# Assign weights to the remaining 80%
|
|
for i in range(top_20_count, len(sorted_data)):
|
|
weights[i] = remaining_weight / (len(sorted_data) - top_20_count)
|
|
|
|
# Normalize the weights
|
|
total_weight = sum(weights)
|
|
normalized_weights = [w / total_weight for w in weights]
|
|
|
|
# Combine the keys with their normalized weights
|
|
normalized_data = {sorted_data[i][0]: normalized_weights[i]*len(data) for i in range(len(sorted_data))}
|
|
|
|
return normalized_data
|
|
|
|
def on_train_batch_start(self, losses: dict, input: Optional[torch.Tensor]=None):
|
|
"""
|
|
Balances the loss terms based on their gradients before each training batch.
|
|
|
|
Args:
|
|
losses (dict): Dictionary of individual loss terms (scalar tensors).
|
|
|
|
Returns:
|
|
balanced_loss (torch.Tensor): Weighted sum of losses.
|
|
new_weights (dict): Updated weights for each loss term.
|
|
"""
|
|
|
|
if len(losses)==0: return 0. # no losses to balance
|
|
if not self.initial_weights: self.initial_weights = {k: 1. for k in losses} # initialize weights
|
|
if not self.ema_weights: self.ema_weights = {k: 1. for k in losses} # initialize ema weights
|
|
if not self.active:
|
|
self.update_historical_losses({k: v.item()*self.initial_weights.get(k,1.) for k,v in losses.items() if v>0})
|
|
return sum(v*self.initial_weights.get(k,1.) for k,v in losses.items())
|
|
|
|
gradients = {}
|
|
valid_losses = {}
|
|
|
|
# Process each loss, skip if the corresponding weight is 0
|
|
for key, loss in losses.items():
|
|
weight = self.initial_weights.get(key, 1.)
|
|
if weight == 0 or loss == 0 or not isinstance(loss,torch.Tensor): continue # Skip loss with weight 0 or constants
|
|
weighted_loss = loss * weight
|
|
|
|
if self.use_norm and input is not None:
|
|
# Compute L2 gradient norm
|
|
input.requires_grad_(True)
|
|
grad_norm = self.calculate_gradients(key, weighted_loss, input)
|
|
gradients[key] = max(grad_norm.item(), self.epsilon)
|
|
else:
|
|
# Use loss plateau detection with EMA
|
|
loss_slope = self.calculate_loss_slope(key, weighted_loss)
|
|
gradients[key] = max(loss_slope.item(), self.epsilon)
|
|
valid_losses[key] = weighted_loss
|
|
|
|
if not valid_losses: return torch.tensor(0.0) # If all losses are skipped
|
|
|
|
# Calculate loss weights based on gradient magnitudes
|
|
if self.use_pareto: #20% hardest tasks gets 80% weight
|
|
normalized_weights = self.pareto_normalizer(gradients)
|
|
else:
|
|
total_gradient = sum(gradients.values()) + self.epsilon
|
|
normalized_weights = {k: w/total_gradient*len(gradients) for k, w in gradients.items()}
|
|
|
|
# Update EMA weights
|
|
self.update_ema_weights(normalized_weights)
|
|
balanced_loss = 0
|
|
for k, loss in valid_losses.items():
|
|
balanced_loss += self.ema_weights[k] * loss
|
|
|
|
# update historical losses
|
|
self.update_historical_losses({k: loss.item()})
|
|
|
|
return balanced_loss
|
|
|
|
def on_epoch_end(self, weights_decay=None, loss_decay=None):
|
|
"""
|
|
Optional method to call at the end of each epoch for logging or further adjustments.
|
|
"""
|
|
if weights_decay is not None: self.weights_decay=weights_decay
|
|
if loss_decay is not None: self.loss_decay=loss_decay
|
|
weights = dict(sorted(self.ema_weights.items(),key=lambda x:x[1],reverse=True))
|
|
losses = dict(sorted(self.historical_losses.items(),key=lambda x:x[1],reverse=True))
|
|
print(f"|| EMA weights: {weights} ({self.weights_decay=})")
|
|
print(f"|| EMA losses: {losses} ({self.loss_decay=})")
|
|
print(f"===> Weighted EMA loss: {self.weighted_ema_loss:.3f} <====")
|
|
gc_collect()
|
|
|
|
@property
|
|
def weighted_ema_loss(self):
|
|
return sum(v*self.ema_weights.get(k,1./len(self.historical_losses)) for k,v in self.historical_losses.items())
|
|
|
|
def compute_tsi_loss(original_log_magnitude: torch.Tensor, generated_log_magnitude: torch.Tensor, dim=-1, eps=1e-8):
|
|
"""
|
|
Computes the correlation loss between the original and generated log-magnitude spectrograms.
|
|
:param original_envelope: Original log magnitude spectrogram (batch, time)
|
|
:param generated_envelope: Generated log magnitude spectrogram (batch, time)
|
|
:return: Correlation loss.
|
|
"""
|
|
original_envelope = compute_envelope(original_log_magnitude, eps=eps, dim=dim)
|
|
generated_envelope = compute_envelope(generated_log_magnitude, eps=eps, dim=dim)
|
|
|
|
# Normalize the envelope
|
|
original_envelope = minmax_scale(original_envelope, eps=eps)
|
|
generated_envelope = minmax_scale(generated_envelope, eps=eps)
|
|
|
|
# Compute the correlation
|
|
correlation = compute_correlation(original_envelope, generated_envelope, eps=eps)
|
|
|
|
# Compute the loss as negative correlation
|
|
loss = 1-correlation.abs()
|
|
|
|
return loss.mean()
|
|
|
|
def compute_envelope(log_magnitude: torch.Tensor, dim=-1, kernel_size=3, eps=1e-8):
|
|
"""
|
|
Compute the envelope of the log-magnitude spectrum of an audio signal.
|
|
|
|
Args:
|
|
log_magnitude (torch.Tensor): The log-magnitude spectrum of the audio signal.
|
|
dim (int): The dimension along which to average. Default is -1.
|
|
kernel_size (int): The size of the max pooling kernel. Default is 3.
|
|
|
|
Returns:
|
|
torch.Tensor: The computed envelope of the log-magnitude spectrum.
|
|
"""
|
|
|
|
# Normalize the pooled magnitude
|
|
log_magnitude = F.normalize(log_magnitude, dim=dim, eps=eps)
|
|
|
|
# Use max pooling to get the peaks
|
|
max_filtered_tensor = F.max_pool1d(log_magnitude, kernel_size=kernel_size, stride=1, padding=kernel_size//2)
|
|
|
|
# Replace NaNs with zeros and sum along the second-to-last dimension
|
|
return max_filtered_tensor.nan_to_num(0).sum(dim)
|
|
|
|
def compute_tefs(audio_signal: torch.Tensor, eps=1e-8):
|
|
"""
|
|
Computes the Hilbert transform of a batched mono audio signal to extract the envelope.
|
|
|
|
Args:
|
|
audio_signal (torch.Tensor): Batched mono audio signal of shape (batch_size, signal_length).
|
|
|
|
Returns:
|
|
torch.Tensor: The envelope of the audio signal of shape (batch_size, signal_length).
|
|
"""
|
|
# Perform FFT on the input signal
|
|
audio_fft = torch.fft.fft(audio_signal.float(), dim=-1)
|
|
|
|
# Create a filter to zero out negative frequencies
|
|
signal_length = audio_signal.shape[-1]
|
|
h = torch.zeros(signal_length, device=audio_signal.device)
|
|
|
|
# First component is left untouched
|
|
h[0] = 1
|
|
|
|
if signal_length % 2 == 0:
|
|
# For even signal length
|
|
h[1:signal_length // 2] = 2
|
|
h[signal_length // 2] = 1
|
|
else:
|
|
# For odd signal length
|
|
h[1:(signal_length + 1) // 2] = 2
|
|
|
|
# Apply the filter in the frequency domain
|
|
hilbert_fft = audio_fft * h
|
|
|
|
# Perform inverse FFT to obtain the analytic signal
|
|
analytic_signal = torch.fft.ifft(hilbert_fft, dim=-1)
|
|
|
|
# Extract the envelope by taking the magnitude of the analytic signal
|
|
envelope = torch.abs(analytic_signal)
|
|
|
|
# Normalize the envelope
|
|
envelope = minmax_scale(envelope, eps=eps)
|
|
|
|
# Compute the instantaneous phase from the analytic signal
|
|
phase = torch.angle(analytic_signal).cos()
|
|
|
|
return envelope.nan_to_num(0), phase.nan_to_num(0)
|
|
|
|
def compute_harmonics(mag: torch.Tensor, harmonic_kernel_sizes=[11,17,23], percussive_kernel_sizes=[3,7,13], eps=1e-8):
|
|
# Calculate log-magnitude spectrograms
|
|
|
|
harmonic_list = []
|
|
percussive_list = []
|
|
|
|
for kernel_size in harmonic_kernel_sizes:
|
|
harmonic_list.append(median_pool1d(mag, kernel_size=kernel_size, dim=-1).nan_to_num(0).view(mag.size(0),-1))
|
|
|
|
for kernel_size in percussive_kernel_sizes:
|
|
percussive_list.append(median_pool1d(mag, kernel_size=kernel_size, dim=-2).nan_to_num(0).view(mag.size(0),-1))
|
|
|
|
# Concatenate the results
|
|
harmonic = torch.cat(harmonic_list, dim=-1)
|
|
percussive = torch.cat(percussive_list, dim=-1)
|
|
|
|
# normalize values
|
|
harmonic = minmax_scale(harmonic, eps=eps)
|
|
percussive = minmax_scale(percussive, eps=eps)
|
|
|
|
return harmonic.nan_to_num(0), percussive.nan_to_num(0)
|
|
|
|
def combined_aux_loss(
|
|
original_audio: torch.Tensor, generated_audio: torch.Tensor,
|
|
c_tefs=1., c_hd=1., c_tsi=1., n_mels=128, sample_rate=40000,
|
|
n_fft=1024, hop_length=320, win_length=1024,
|
|
eps=None):
|
|
|
|
kernel_size = n_fft//hop_length+1
|
|
if kernel_size % 2 == 0: kernel_size+=1 #enforce odd kernel size
|
|
if eps is None: eps = torch.finfo(original_audio.dtype).eps
|
|
|
|
# Compute STFT once
|
|
if c_hd+c_tsi>0:
|
|
|
|
hann_window = torch.hann_window(win_length).to(
|
|
dtype=original_audio.dtype, device=original_audio.device
|
|
)
|
|
generated_stft = torch.stft(
|
|
generated_audio.view(-1,generated_audio.size(-1)),
|
|
n_fft=n_fft,
|
|
hop_length=hop_length,
|
|
win_length=win_length,
|
|
window=hann_window,
|
|
return_complex=True,
|
|
onesided=True,
|
|
center=False)
|
|
original_stft = torch.stft(
|
|
original_audio.view(-1,original_audio.size(-1)),
|
|
n_fft=n_fft,
|
|
hop_length=hop_length,
|
|
win_length=win_length,
|
|
window=hann_window,
|
|
return_complex=True,
|
|
onesided=True,
|
|
center=False)
|
|
|
|
MelScaler = T.MelScale(n_mels=n_mels, sample_rate=sample_rate, n_stft=n_fft // 2 + 1).to(original_audio.device)
|
|
org_mag = MelScaler(original_stft.abs()+eps)
|
|
gen_mag = MelScaler(generated_stft.abs()+eps)
|
|
|
|
# Harmonic Loss
|
|
if c_hd>0:
|
|
original_harmonics, original_percussives = compute_harmonics(org_mag, eps=eps)
|
|
generated_harmonics, generated_percussives = compute_harmonics(gen_mag, eps=eps)
|
|
# Define loss terms
|
|
harmonic_loss = F.l1_loss(generated_harmonics, original_harmonics)
|
|
harmonic_loss += F.l1_loss(generated_percussives, original_percussives)
|
|
else: harmonic_loss = 0
|
|
|
|
# temporal invariant phase
|
|
if c_tsi>0:
|
|
freq_tsi = compute_tsi_loss(org_mag,gen_mag,dim=-1, eps=eps)
|
|
temp_tsi = compute_tsi_loss(org_mag,gen_mag,dim=-2, eps=eps)
|
|
tsi_loss = (freq_tsi+temp_tsi)
|
|
else: tsi_loss = 0
|
|
|
|
# Temperol Envelope and Fine Structure Loss
|
|
if c_tefs>0:
|
|
# temporal envelope
|
|
gen_te, gen_tfs = compute_tefs(generated_audio, eps=eps)
|
|
org_te, org_tfs = compute_tefs(original_audio, eps=eps)
|
|
correlation = 1-compute_correlation(gen_tfs,org_tfs, eps=eps).abs()
|
|
|
|
tefs_loss = F.l1_loss(gen_te, org_te) + correlation.mean()
|
|
else: tefs_loss = 0
|
|
|
|
return harmonic_loss, tefs_loss, tsi_loss
|
|
|
|
def gradient_norm_loss(original_audio: torch.Tensor, generated_audio: torch.Tensor, net_d: torch.nn.Module, eps=1e-8):
|
|
loss=0
|
|
# Compute the gradient penalty
|
|
# Randomly interpolate between real and generated data
|
|
size = [1]*original_audio.ndim
|
|
size[0] = original_audio.size(0)
|
|
alpha = torch.rand(*size, device=original_audio.device)
|
|
interpolated = alpha * original_audio + (1 - alpha) * generated_audio
|
|
interpolated.requires_grad_(True)
|
|
# Get the discriminator output for the interpolated data
|
|
_, disc_interpolated_output, _, _ = net_d(original_audio, interpolated)
|
|
# Compute gradients of discriminator output w.r.t. interpolated data
|
|
for output in disc_interpolated_output:
|
|
net_d.zero_grad()
|
|
gradients = torch.autograd.grad(
|
|
outputs=output,
|
|
inputs=interpolated,
|
|
grad_outputs=torch.ones_like(output, device=original_audio.device),
|
|
retain_graph=True,
|
|
allow_unused=True,
|
|
materialize_grads=True
|
|
)[0]
|
|
if gradients.ndim>1: grad_norm = gradients.view(gradients.size(0), -1).norm(2, dim=-1).mean()
|
|
else: grad_norm = gradients.norm(2)
|
|
loss += torch.log1p((grad_norm - 1) ** 2)
|
|
return loss/len(disc_interpolated_output)
|
|
|
|
def feature_loss(fmap_r, fmap_g):
|
|
loss = 0
|
|
for dr, dg in zip(fmap_r, fmap_g):
|
|
for rl, gl in zip(dr, dg):
|
|
rl = rl.float()
|
|
gl = gl.float()
|
|
loss += torch.mean(torch.abs(rl - gl))
|
|
|
|
return loss
|
|
|
|
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
|
loss = 0
|
|
r_losses = []
|
|
g_losses = []
|
|
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
|
dr = dr.float()
|
|
dg = dg.float()
|
|
r_loss = torch.mean((1 - dr) ** 2)
|
|
g_loss = torch.mean(dg**2)
|
|
loss += r_loss + g_loss
|
|
r_losses.append(r_loss.item())
|
|
g_losses.append(g_loss.item())
|
|
# loss /= len(disc_generated_outputs) #average
|
|
return loss, r_losses, g_losses
|
|
|
|
|
|
def generator_loss(disc_outputs):
|
|
loss = 0
|
|
gen_losses = []
|
|
for dg in disc_outputs:
|
|
dg = dg.float()
|
|
l = torch.mean((1 - dg) ** 2)
|
|
gen_losses.append(l)
|
|
loss += l
|
|
# loss /= len(disc_outputs) #average
|
|
return loss, gen_losses
|
|
|
|
|
|
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
|
|
"""
|
|
z_p, logs_q: [b, h, t_t]
|
|
m_p, logs_p: [b, h, t_t]
|
|
"""
|
|
z_p = z_p.float()
|
|
logs_q = logs_q.float()
|
|
m_p = m_p.float()
|
|
logs_p = logs_p.float()
|
|
z_mask = z_mask.float()
|
|
|
|
kl = logs_p - logs_q - 0.5
|
|
kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
|
|
kl = torch.sum(kl * z_mask)
|
|
l = kl / torch.sum(z_mask)
|
|
return l
|