remfx/models.py

import torch
import numpy as np
import torchmetrics
import pytorch_lightning as pl
from torch import Tensor, nn
from torchaudio.models import HDemucs
from auraloss.time import SISDRLoss
from auraloss.freq import MultiResolutionSTFTLoss

from remfx.utils import spectrogram
from remfx.tcn import TCN
from remfx.utils import causal_crop
from remfx import effects
from remfx.classifier import Cnn14
import asteroid
import random

ALL_EFFECTS = effects.Pedalboard_Effects


class RemFXChainInference(pl.LightningModule):
    def __init__(
        self,
        models,
        sample_rate,
        num_bins,
        effect_order,
        classifier=None,
        shuffle_effect_order=False,
        use_all_effect_models=False,
    ):
        super().__init__()
        self.model = models
        self.mrstftloss = MultiResolutionSTFTLoss(
            n_bins=num_bins, sample_rate=sample_rate
        )
        self.l1loss = nn.L1Loss()
        self.metrics = nn.ModuleDict(
            {
                "SISDR": SISDRLoss(),
                "STFT": MultiResolutionSTFTLoss(),
            }
        )
        self.sample_rate = sample_rate
        self.effect_order = effect_order
        self.classifier = classifier
        self.shuffle_effect_order = shuffle_effect_order
        self.output_str = "IN_SISDR,OUT_SISDR,IN_STFT,OUT_STFT\n"
        self.use_all_effect_models = use_all_effect_models

    def forward(self, batch, batch_idx, order=None, verbose=False):
        x, y, _, rem_fx_labels = batch
        # Use chain of effects defined in config
        if order:
            effects_order = order
        else:
            effects_order = self.effect_order
        # Use classifier labels
        if self.classifier:
            threshold = 0.5
            with torch.no_grad():
                labels = torch.hstack(self.classifier(x))
                rem_fx_labels = torch.where(labels > threshold, 1.0, 0.0)
        if self.use_all_effect_models:
            effects_present = [
                [ALL_EFFECTS[i] for i, effect in enumerate(effect_label)]
                for effect_label in rem_fx_labels
            ]
        else:
            effects_present = [
                [
                    ALL_EFFECTS[i]
                    for i, effect in enumerate(effect_label)
                    if effect == 1.0
                ]
                for effect_label in rem_fx_labels
            ]
            effects_present_name = [
                [
                    ALL_EFFECTS[i].__name__
                    for i, effect in enumerate(effect_label)
                    if effect == 1.0
                ]
                for effect_label in rem_fx_labels
            ]
            if verbose:
                print("Detected effects:", effects_present_name[0])
                print("Removing effects...")

        output = []
        with torch.no_grad():
            for i, (elem, effects_list) in enumerate(zip(x, effects_present)):
                elem = elem.unsqueeze(0)  # Add batch dim
                # Get the correct effect by search for names in effects_order
                effect_list_names = [effect.__name__ for effect in effects_list]
                effects = [
                    effect for effect in effects_order if effect in effect_list_names
                ]

                for effect in effects:
                    # Sample the model
                    elem = self.model[effect].model.sample(elem)
                output.append(elem.squeeze(0))
        output = torch.stack(output)

        loss = self.mrstftloss(output, y) + self.l1loss(output, y) * 100
        return loss, output

    def test_step(self, batch, batch_idx):
        x, y, _, _ = batch  # x, y = (B, C, T), (B, C, T)
        if self.shuffle_effect_order:
            # Random order
            random.shuffle(self.effect_order)
        loss, output = self.forward(batch, batch_idx, order=self.effect_order)
        # Crop target to match output
        if output.shape[-1] < y.shape[-1]:
            y = causal_crop(y, output.shape[-1])
        self.log("test_loss", loss)
        # Metric logging
        with torch.no_grad():
            for metric in self.metrics:
                # SISDR returns negative values, so negate them
                if metric == "SISDR":
                    negate = -1
                else:
                    negate = 1
                self.log(
                    f"test_{metric}",  # + "".join(self.effect_order).replace("RandomPedalboard", ""),
                    negate * self.metrics[metric](output, y),
                    on_step=False,
                    on_epoch=True,
                    logger=True,
                    prog_bar=True,
                    sync_dist=True,
                )
                self.log(
                    f"Input_{metric}",
                    negate * self.metrics[metric](x, y),
                    on_step=False,
                    on_epoch=True,
                    logger=True,
                    prog_bar=True,
                    sync_dist=True,
                )
        return loss

    def sample(self, batch):
        return self.forward(batch, 0)[1]


class RemFX(pl.LightningModule):
    def __init__(
        self,
        lr: float,
        lr_beta1: float,
        lr_beta2: float,
        lr_eps: float,
        lr_weight_decay: float,
        sample_rate: float,
        network: nn.Module,
    ):
        super().__init__()
        self.lr = lr
        self.lr_beta1 = lr_beta1
        self.lr_beta2 = lr_beta2
        self.lr_eps = lr_eps
        self.lr_weight_decay = lr_weight_decay
        self.sample_rate = sample_rate
        self.model = network
        self.metrics = nn.ModuleDict(
            {
                "SISDR": SISDRLoss(),
                "STFT": MultiResolutionSTFTLoss(),
            }
        )
        # Log first batch metrics input vs output only once
        self.log_train_audio = True
        self.output_str = "IN_SISDR,OUT_SISDR,IN_STFT,OUT_STFT\n"

    @property
    def device(self):
        return next(self.model.parameters()).device

    def configure_optimizers(self):
        optimizer = torch.optim.AdamW(
            list(self.model.parameters()),
            lr=self.lr,
            betas=(self.lr_beta1, self.lr_beta2),
            eps=self.lr_eps,
            weight_decay=self.lr_weight_decay,
        )
        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
            optimizer,
            [0.8 * self.trainer.max_steps, 0.95 * self.trainer.max_steps],
            gamma=0.1,
        )
        return {
            "optimizer": optimizer,
            "lr_scheduler": {
                "scheduler": lr_scheduler,
                "monitor": "val_loss",
                "interval": "step",
                "frequency": 1,
            },
        }

    def training_step(self, batch, batch_idx):
        return self.common_step(batch, batch_idx, mode="train")

    def validation_step(self, batch, batch_idx):
        return self.common_step(batch, batch_idx, mode="valid")

    def test_step(self, batch, batch_idx):
        return self.common_step(batch, batch_idx, mode="test")

    def common_step(self, batch, batch_idx, mode: str = "train"):
        x, y, _, _ = batch  # x, y = (B, C, T), (B, C, T)

        loss, output = self.model((x, y))
        # Crop target to match output
        target = y
        if output.shape[-1] < y.shape[-1]:
            target = causal_crop(y, output.shape[-1])
        self.log(f"{mode}_loss", loss)
        # Metric logging
        with torch.no_grad():
            for metric in self.metrics:
                # SISDR returns negative values, so negate them
                if metric == "SISDR":
                    negate = -1
                else:
                    negate = 1
                # Only Log FAD on test set
                if metric == "FAD" and mode != "test":
                    continue
                self.log(
                    f"{mode}_{metric}",
                    negate * self.metrics[metric](output, target),
                    on_step=False,
                    on_epoch=True,
                    logger=True,
                    prog_bar=True,
                    sync_dist=True,
                )

                self.log(
                    f"Input_{metric}",
                    negate * self.metrics[metric](x, y),
                    on_step=False,
                    on_epoch=True,
                    logger=True,
                    prog_bar=True,
                    sync_dist=True,
                )
        return loss


class DemucsModel(nn.Module):
    def __init__(self, sample_rate, **kwargs) -> None:
        super().__init__()
        self.model = HDemucs(**kwargs)
        self.num_bins = kwargs["nfft"] // 2 + 1
        self.mrstftloss = MultiResolutionSTFTLoss(
            n_bins=self.num_bins, sample_rate=sample_rate
        )
        self.l1loss = nn.L1Loss()

    def forward(self, batch):
        x, target = batch
        output = self.model(x).squeeze(1)
        loss = self.mrstftloss(output, target) + self.l1loss(output, target) * 100
        return loss, output

    def sample(self, x: Tensor) -> Tensor:
        return self.model(x).squeeze(1)


class DPTNetModel(nn.Module):
    def __init__(self, sample_rate, num_bins, **kwargs):
        super().__init__()
        self.model = asteroid.models.dptnet.DPTNet(**kwargs)
        self.num_bins = num_bins
        self.mrstftloss = MultiResolutionSTFTLoss(
            n_bins=self.num_bins, sample_rate=sample_rate
        )
        self.l1loss = nn.L1Loss()

    def forward(self, batch):
        x, target = batch
        output = self.model(x.squeeze(1))
        loss = self.mrstftloss(output, target) + self.l1loss(output, target) * 100
        return loss, output

    def sample(self, x: Tensor) -> Tensor:
        return self.model(x.squeeze(1))


class DCUNetModel(nn.Module):
    def __init__(self, sample_rate, num_bins, **kwargs):
        super().__init__()
        self.model = asteroid.models.DCUNet(**kwargs)
        self.mrstftloss = MultiResolutionSTFTLoss(
            n_bins=num_bins, sample_rate=sample_rate
        )
        self.l1loss = nn.L1Loss()

    def forward(self, batch):
        x, target = batch
        output = self.model(x.squeeze(1))  # B x T
        # Crop target to match output
        if output.shape[-1] < target.shape[-1]:
            target = causal_crop(target, output.shape[-1])
        loss = self.mrstftloss(output, target) + self.l1loss(output, target) * 100
        return loss, output

    def sample(self, x: Tensor) -> Tensor:
        output = self.model(x.squeeze(1))  # B x T
        return output


class TCNModel(nn.Module):
    def __init__(self, sample_rate, num_bins, **kwargs):
        super().__init__()
        self.model = TCN(**kwargs)
        self.mrstftloss = MultiResolutionSTFTLoss(
            n_bins=num_bins, sample_rate=sample_rate
        )
        self.l1loss = nn.L1Loss()

    def forward(self, batch):
        x, target = batch
        output = self.model(x)  # B x 1 x T
        # Crop target to match output
        if output.shape[-1] < target.shape[-1]:
            target = causal_crop(target, output.shape[-1])
        loss = self.mrstftloss(output, target) + self.l1loss(output, target) * 100
        return loss, output

    def sample(self, x: Tensor) -> Tensor:
        output = self.model(x)  # B x 1 x T
        return output


def mixup(x: torch.Tensor, y: torch.Tensor, alpha: float = 1.0):
    """Mixup data augmentation for time-domain signals.
    Args:
        x (torch.Tensor): Batch of time-domain signals, shape [batch, 1, time].
        y (torch.Tensor): Batch of labels, shape [batch, n_classes].
        alpha (float): Beta distribution parameter.
    Returns:
        torch.Tensor: Mixed time-domain signals, shape [batch, 1, time].
        torch.Tensor: Mixed labels, shape [batch, n_classes].
        torch.Tensor: Lambda
    """
    batch_size = x.size(0)
    if alpha > 0:
        # lam = np.random.beta(alpha, alpha)
        lam = np.random.uniform(0.25, 0.75, batch_size)
        lam = torch.from_numpy(lam).float().to(x.device).view(batch_size, 1, 1)
    else:
        lam = 1

    if np.random.rand() > 0.5:
        index = torch.randperm(batch_size).to(x.device)
        mixed_x = lam * x + (1 - lam) * x[index, :]
        mixed_y = torch.logical_or(y, y[index, :]).float()
    else:
        mixed_x = x
        mixed_y = y

    return mixed_x, mixed_y, lam


class FXClassifier(pl.LightningModule):
    def __init__(
        self,
        lr: float,
        lr_weight_decay: float,
        sample_rate: float,
        network: nn.Module,
        mixup: bool = False,
        label_smoothing: float = 0.0,
    ):
        super().__init__()
        self.lr = lr
        self.lr_weight_decay = lr_weight_decay
        self.sample_rate = sample_rate
        self.network = network
        self.effects = ["Reverb", "Chorus", "Delay", "Distortion", "Compressor"]
        self.mixup = mixup
        self.label_smoothing = label_smoothing

        if isinstance(self.network, Cnn14):
            self.loss_fn = torch.nn.BCELoss()

            self.metrics = torch.nn.ModuleDict()
            for effect in self.effects:
                self.metrics[
                    f"train_{effect}_acc"
                ] = torchmetrics.classification.Accuracy(task="binary")
                self.metrics[
                    f"valid_{effect}_acc"
                ] = torchmetrics.classification.Accuracy(task="binary")
                self.metrics[
                    f"test_{effect}_acc"
                ] = torchmetrics.classification.Accuracy(task="binary")
        else:
            self.loss_fn = torch.nn.CrossEntropyLoss(label_smoothing=label_smoothing)
            self.train_f1 = torchmetrics.classification.MultilabelF1Score(
                5, average="none", multidim_average="global"
            )
            self.val_f1 = torchmetrics.classification.MultilabelF1Score(
                5, average="none", multidim_average="global"
            )
            self.test_f1 = torchmetrics.classification.MultilabelF1Score(
                5, average="none", multidim_average="global"
            )

            self.train_f1_avg = torchmetrics.classification.MultilabelF1Score(
                5, threshold=0.5, average="macro", multidim_average="global"
            )
            self.val_f1_avg = torchmetrics.classification.MultilabelF1Score(
                5, threshold=0.5, average="macro", multidim_average="global"
            )
            self.test_f1_avg = torchmetrics.classification.MultilabelF1Score(
                5, threshold=0.5, average="macro", multidim_average="global"
            )

            self.metrics = {
                "train": self.train_f1,
                "valid": self.val_f1,
                "test": self.test_f1,
            }

            self.avg_metrics = {
                "train": self.train_f1_avg,
                "valid": self.val_f1_avg,
                "test": self.test_f1_avg,
            }

    def forward(self, x: torch.Tensor, train: bool = False):
        return self.network(x, train=train)

    def common_step(self, batch, batch_idx, mode: str = "train"):
        train = True if mode == "train" else False
        x, y, dry_label, wet_label = batch

        if mode == "train" and self.mixup:
            x_mixed, label_mixed, lam = mixup(x, wet_label)
            outputs = self(x_mixed, train)
            loss = 0
            for idx, output in enumerate(outputs):
                loss += self.loss_fn(output.squeeze(-1), label_mixed[..., idx])
        else:
            outputs = self(x, train)
            loss = 0
            # Multi-head binary loss
            if isinstance(self.network, Cnn14):
                for idx, output in enumerate(outputs):
                    loss += self.loss_fn(output.squeeze(-1), wet_label[..., idx])
            else:
                # Output is a 2d tensor
                loss = self.loss_fn(outputs, wet_label)

        self.log(
            f"{mode}_loss",
            loss,
            on_step=True,
            on_epoch=True,
            prog_bar=True,
            logger=True,
            sync_dist=True,
        )

        if isinstance(self.network, Cnn14):
            acc_metrics = []
            for idx, effect_name in enumerate(self.effects):
                acc_metric = self.metrics[f"{mode}_{effect_name}_acc"](
                    outputs[idx].squeeze(-1), wet_label[..., idx]
                )
                self.log(
                    f"{mode}_{effect_name}_acc",
                    acc_metric,
                    on_step=True,
                    on_epoch=True,
                    prog_bar=True,
                    logger=True,
                    sync_dist=True,
                )
                acc_metrics.append(acc_metric)

            self.log(
                f"{mode}_avg_acc",
                torch.mean(torch.stack(acc_metrics)),
                on_step=True,
                on_epoch=True,
                prog_bar=True,
                logger=True,
                sync_dist=True,
            )
        else:
            metrics = self.metrics[mode](torch.sigmoid(outputs), wet_label.long())
            for idx, effect_name in enumerate(self.effects):
                self.log(
                    f"{mode}_f1_{effect_name}",
                    metrics[idx],
                    on_step=True,
                    on_epoch=True,
                    prog_bar=True,
                    logger=True,
                    sync_dist=True,
                )
            avg_metrics = self.avg_metrics[mode](
                torch.sigmoid(outputs), wet_label.long()
            )

            self.log(
                f"{mode}_avg_acc",
                avg_metrics,
                on_step=True,
                on_epoch=True,
                prog_bar=True,
                logger=True,
                sync_dist=True,
            )
        return loss

    def training_step(self, batch, batch_idx):
        return self.common_step(batch, batch_idx, mode="train")

    def validation_step(self, batch, batch_idx):
        return self.common_step(batch, batch_idx, mode="valid")

    def test_step(self, batch, batch_idx):
        return self.common_step(batch, batch_idx, mode="test")

    def configure_optimizers(self):
        optimizer = torch.optim.AdamW(
            self.network.parameters(),
            lr=self.lr,
            weight_decay=self.lr_weight_decay,
        )
        return optimizer