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