modules/discriminator.py

# -*- coding: utf-8 -*-

# Copyright 2020 Tomoki Hayashi
#  MIT License (https://opensource.org/licenses/MIT)

"""MelGAN Modules."""

import logging

import numpy as np
import torch

from modules.base import BaseModule

class MelGANDiscriminator(BaseModule):
    """MelGAN discriminator module."""

    def __init__(
        self,
        in_channels=1,
        out_channels=1,
        kernel_sizes=[5, 3],
        channels=16,
        max_downsample_channels=1024,
        bias=True,
        downsample_scales=[4, 4, 4, 4],
        nonlinear_activation="LeakyReLU",
        nonlinear_activation_params={"negative_slope": 0.2},
        pad="ReflectionPad1d",
        pad_params={},
    ):
        """Initilize MelGAN discriminator module.

        Args:
            in_channels (int): Number of input channels.
            out_channels (int): Number of output channels.
            kernel_sizes (list): List of two kernel sizes. The prod will be used for the first conv layer,
                and the first and the second kernel sizes will be used for the last two layers.
                For example if kernel_sizes = [5, 3], the first layer kernel size will be 5 * 3 = 15,
                the last two layers' kernel size will be 5 and 3, respectively.
            channels (int): Initial number of channels for conv layer.
            max_downsample_channels (int): Maximum number of channels for downsampling layers.
            bias (bool): Whether to add bias parameter in convolution layers.
            downsample_scales (list): List of downsampling scales.
            nonlinear_activation (str): Activation function module name.
            nonlinear_activation_params (dict): Hyperparameters for activation function.
            pad (str): Padding function module name before dilated convolution layer.
            pad_params (dict): Hyperparameters for padding function.

        """
        super(MelGANDiscriminator, self).__init__()
        self.layers = torch.nn.ModuleList()

        # check kernel size is valid
        assert len(kernel_sizes) == 2
        assert kernel_sizes[0] % 2 == 1
        assert kernel_sizes[1] % 2 == 1

        # add first layer
        self.layers += [
            torch.nn.Sequential(
                getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
                torch.nn.Conv1d(
                    in_channels, channels, np.prod(kernel_sizes), bias=bias
                ),
                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
            )
        ]

        # add downsample layers
        in_chs = channels
        for downsample_scale in downsample_scales:
            out_chs = min(in_chs * downsample_scale, max_downsample_channels)
            self.layers += [
                torch.nn.Sequential(
                    torch.nn.Conv1d(
                        in_chs,
                        out_chs,
                        kernel_size=downsample_scale * 10 + 1,
                        stride=downsample_scale,
                        padding=downsample_scale * 5,
                        groups=in_chs // 4,
                        bias=bias,
                    ),
                    getattr(torch.nn, nonlinear_activation)(
                        **nonlinear_activation_params
                    ),
                )
            ]
            in_chs = out_chs

        # add final layers
        out_chs = min(in_chs * 2, max_downsample_channels)
        self.layers += [
            torch.nn.Sequential(
                torch.nn.Conv1d(
                    in_chs,
                    out_chs,
                    kernel_sizes[0],
                    padding=(kernel_sizes[0] - 1) // 2,
                    bias=bias,
                ),
                getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
            )
        ]
        self.layers += [
            torch.nn.Conv1d(
                out_chs,
                out_channels,
                kernel_sizes[1],
                padding=(kernel_sizes[1] - 1) // 2,
                bias=bias,
            ),
        ]

    def forward(self, x):
        """Calculate forward propagation.

        Args:
            x (Tensor): Input noise signal (B, 1, T).

        Returns:
            List: List of output tensors of each layer.

        """
        outs = []
        for f in self.layers:
            x = f(x)
            outs += [x]

        return outs


class MelGANMultiScaleDiscriminator(BaseModule):
    """MelGAN multi-scale discriminator module."""

    def __init__(
        self,
        in_channels=1,
        out_channels=1,
        scales=3,
        downsample_pooling="AvgPool1d",
        # follow the official implementation setting
        downsample_pooling_params={
            "kernel_size": 4,
            "stride": 2,
            "padding": 1,
            "count_include_pad": False,
        },
        kernel_sizes=[5, 3],
        channels=16,
        max_downsample_channels=1024,
        bias=True,
        downsample_scales=[4, 4, 4, 4],
        nonlinear_activation="LeakyReLU",
        nonlinear_activation_params={"negative_slope": 0.2},
        pad="ReflectionPad1d",
        pad_params={},
        use_weight_norm=True,
    ):
        """Initilize MelGAN multi-scale discriminator module.

        Args:
            in_channels (int): Number of input channels.
            out_channels (int): Number of output channels.
            scales (int): Number of multi-scales.
            downsample_pooling (str): Pooling module name for downsampling of the inputs.
            downsample_pooling_params (dict): Parameters for the above pooling module.
            kernel_sizes (list): List of two kernel sizes. The sum will be used for the first conv layer,
                and the first and the second kernel sizes will be used for the last two layers.
            channels (int): Initial number of channels for conv layer.
            max_downsample_channels (int): Maximum number of channels for downsampling layers.
            bias (bool): Whether to add bias parameter in convolution layers.
            downsample_scales (list): List of downsampling scales.
            nonlinear_activation (str): Activation function module name.
            nonlinear_activation_params (dict): Hyperparameters for activation function.
            pad (str): Padding function module name before dilated convolution layer.
            pad_params (dict): Hyperparameters for padding function.
            use_causal_conv (bool): Whether to use causal convolution.

        """
        super(MelGANMultiScaleDiscriminator, self).__init__()
        self.discriminators = torch.nn.ModuleList()

        # add discriminators
        for _ in range(scales):
            self.discriminators += [
                MelGANDiscriminator(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    kernel_sizes=kernel_sizes,
                    channels=channels,
                    max_downsample_channels=max_downsample_channels,
                    bias=bias,
                    downsample_scales=downsample_scales,
                    nonlinear_activation=nonlinear_activation,
                    nonlinear_activation_params=nonlinear_activation_params,
                    pad=pad,
                    pad_params=pad_params,
                )
            ]
        self.pooling = getattr(torch.nn, downsample_pooling)(
            **downsample_pooling_params
        )

        # apply weight norm
        if use_weight_norm:
            self.apply_weight_norm()

        # reset parameters
        self.reset_parameters()

    def forward(self, x):
        """Calculate forward propagation.

        Args:
            x (Tensor): Input noise signal (B, 1, T).

        Returns:
            List: List of list of each discriminator outputs, which consists of each layer output tensors.

        """
        outs = []
        for f in self.discriminators:
            outs += [f(x)]
            x = self.pooling(x)

        return outs

    def remove_weight_norm(self):
        """Remove weight normalization module from all of the layers."""

        def _remove_weight_norm(m):
            try:
                logging.debug(f"Weight norm is removed from {m}.")
                torch.nn.utils.remove_weight_norm(m)
            except ValueError:  # this module didn't have weight norm
                return

        self.apply(_remove_weight_norm)

    def apply_weight_norm(self):
        """Apply weight normalization module from all of the layers."""

        def _apply_weight_norm(m):
            if isinstance(m, torch.nn.Conv1d) or isinstance(
                m, torch.nn.ConvTranspose1d
            ):
                torch.nn.utils.weight_norm(m)
                logging.debug(f"Weight norm is applied to {m}.")

        self.apply(_apply_weight_norm)

    def reset_parameters(self):
        """Reset parameters.

        This initialization follows official implementation manner.
        https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py

        """

        def _reset_parameters(m):
            if isinstance(m, torch.nn.Conv1d) or isinstance(
                m, torch.nn.ConvTranspose1d
            ):
                m.weight.data.normal_(0.0, 0.02)
                logging.debug(f"Reset parameters in {m}.")

        self.apply(_reset_parameters)