vq/module.py

import torch.nn as nn
from einops import rearrange
from . import activations
from .alias_free_torch import *
from torch.nn.utils import weight_norm

from typing import Optional, Tuple
 
from torch.nn.utils import weight_norm, remove_weight_norm


def WNConv1d(*args, **kwargs):
    return weight_norm(nn.Conv1d(*args, **kwargs))


def WNConvTranspose1d(*args, **kwargs):
    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))

class ResidualUnit(nn.Module):
    def __init__(self, dim: int = 16, dilation: int = 1):
        super().__init__()
        pad = ((7 - 1) * dilation) // 2
        self.block = nn.Sequential(
            Activation1d(activation=activations.SnakeBeta(dim, alpha_logscale=True)),
            WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
            Activation1d(activation=activations.SnakeBeta(dim, alpha_logscale=True)),
            WNConv1d(dim, dim, kernel_size=1),
        )

    def forward(self, x):
        return x + self.block(x)

class EncoderBlock(nn.Module):
    def __init__(self, dim: int = 16, stride: int = 1, dilations = (1, 3, 9)):
        super().__init__()
        runits = [ResidualUnit(dim // 2, dilation=d) for d in dilations]
        self.block = nn.Sequential(
            *runits,
            Activation1d(activation=activations.SnakeBeta(dim//2, alpha_logscale=True)),
            WNConv1d(
                dim // 2,
                dim,
                kernel_size=2 * stride,
                stride=stride,
                padding=stride // 2 + stride % 2,
            ),
        )

    def forward(self, x):
        return self.block(x)
    
class DecoderBlock(nn.Module):
    def __init__(self, input_dim: int = 16, output_dim: int = 8, stride: int = 1, dilations = (1, 3, 9)):
        super().__init__()
        self.block = nn.Sequential(
            Activation1d(activation=activations.SnakeBeta(input_dim, alpha_logscale=True)),
            WNConvTranspose1d(
                input_dim,
                output_dim,
                kernel_size=2 * stride,
                stride=stride,
                padding=stride // 2 + stride % 2,
                output_padding= stride % 2,
            )
        )
        self.block.extend([ResidualUnit(output_dim, dilation=d) for d in dilations])

    def forward(self, x):
        return self.block(x)
    
class ResLSTM(nn.Module):
    def __init__(self, dimension: int,
                 num_layers: int = 2,
                 bidirectional: bool = False,
                 skip: bool = True):
        super().__init__()
        self.skip = skip
        self.lstm = nn.LSTM(dimension, dimension if not bidirectional else dimension // 2,
                            num_layers, batch_first=True,
                            bidirectional=bidirectional)

    def forward(self, x):
        """
        Args:
            x: [B, F, T]

        Returns:
            y: [B, F, T]
        """
        x = rearrange(x, "b f t -> b t f")
        y, _ = self.lstm(x)
        if self.skip:
            y = y + x
        y = rearrange(y, "b t f -> b f t")
        return y



class ConvNeXtBlock(nn.Module):
    """ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal.

    Args:
        dim (int): Number of input channels.
        intermediate_dim (int): Dimensionality of the intermediate layer.
        layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
            Defaults to None.
        adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
            None means non-conditional LayerNorm. Defaults to None.
    """

    def __init__(
        self,
        dim: int,
        intermediate_dim: int,
        layer_scale_init_value: float,
        adanorm_num_embeddings: Optional[int] = None,
    ):
        super().__init__()
        self.dwconv = nn.Conv1d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise conv
        self.adanorm = adanorm_num_embeddings is not None
        if adanorm_num_embeddings:
            self.norm = AdaLayerNorm(adanorm_num_embeddings, dim, eps=1e-6)
        else:
            self.norm = nn.LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(intermediate_dim, dim)
        self.gamma = (
            nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
            if layer_scale_init_value > 0
            else None
        )

    def forward(self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None) -> torch.Tensor:
        residual = x
        x = self.dwconv(x)
        x = x.transpose(1, 2)  # (B, C, T) -> (B, T, C)
        if self.adanorm:
            assert cond_embedding_id is not None
            x = self.norm(x, cond_embedding_id)
        else:
            x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.gamma is not None:
            x = self.gamma * x
        x = x.transpose(1, 2)  # (B, T, C) -> (B, C, T)

        x = residual + x
        return x


class AdaLayerNorm(nn.Module):
    """
    Adaptive Layer Normalization module with learnable embeddings per `num_embeddings` classes

    Args:
        num_embeddings (int): Number of embeddings.
        embedding_dim (int): Dimension of the embeddings.
    """

    def __init__(self, num_embeddings: int, embedding_dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.dim = embedding_dim
        self.scale = nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim)
        self.shift = nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim)
        torch.nn.init.ones_(self.scale.weight)
        torch.nn.init.zeros_(self.shift.weight)

    def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor) -> torch.Tensor:
        scale = self.scale(cond_embedding_id)
        shift = self.shift(cond_embedding_id)
        x = nn.functional.layer_norm(x, (self.dim,), eps=self.eps)
        x = x * scale + shift
        return x


class ResBlock1(nn.Module):
    """
    ResBlock adapted from HiFi-GAN V1 (https://github.com/jik876/hifi-gan) with dilated 1D convolutions,
    but without upsampling layers.

    Args:
        dim (int): Number of input channels.
        kernel_size (int, optional): Size of the convolutional kernel. Defaults to 3.
        dilation (tuple[int], optional): Dilation factors for the dilated convolutions.
            Defaults to (1, 3, 5).
        lrelu_slope (float, optional): Negative slope of the LeakyReLU activation function.
            Defaults to 0.1.
        layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
            Defaults to None.
    """

    def __init__(
        self,
        dim: int,
        kernel_size: int = 3,
        dilation: Tuple[int, int, int] = (1, 3, 5),
        lrelu_slope: float = 0.1,
        layer_scale_init_value: Optional[float] = None,
    ):
        super().__init__()
        self.lrelu_slope = lrelu_slope
        self.convs1 = nn.ModuleList(
            [
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=dilation[0],
                        padding=self.get_padding(kernel_size, dilation[0]),
                    )
                ),
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=dilation[1],
                        padding=self.get_padding(kernel_size, dilation[1]),
                    )
                ),
                weight_norm(
                    nn.Conv1d(
                        dim,
                        dim,
                        kernel_size,
                        1,
                        dilation=dilation[2],
                        padding=self.get_padding(kernel_size, dilation[2]),
                    )
                ),
            ]
        )

        self.convs2 = nn.ModuleList(
            [
                weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))),
                weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))),
                weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))),
            ]
        )

        self.gamma = nn.ParameterList(
            [
                nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True)
                if layer_scale_init_value is not None
                else None,
                nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True)
                if layer_scale_init_value is not None
                else None,
                nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True)
                if layer_scale_init_value is not None
                else None,
            ]
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        for c1, c2, gamma in zip(self.convs1, self.convs2, self.gamma):
            xt = torch.nn.functional.leaky_relu(x, negative_slope=self.lrelu_slope)
            xt = c1(xt)
            xt = torch.nn.functional.leaky_relu(xt, negative_slope=self.lrelu_slope)
            xt = c2(xt)
            if gamma is not None:
                xt = gamma * xt
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)

    @staticmethod
    def get_padding(kernel_size: int, dilation: int = 1) -> int:
        return int((kernel_size * dilation - dilation) / 2)


def safe_log(x: torch.Tensor, clip_val: float = 1e-7) -> torch.Tensor:
    """
    Computes the element-wise logarithm of the input tensor with clipping to avoid near-zero values.

    Args:
        x (Tensor): Input tensor.
        clip_val (float, optional): Minimum value to clip the input tensor. Defaults to 1e-7.

    Returns:
        Tensor: Element-wise logarithm of the input tensor with clipping applied.
    """
    return torch.log(torch.clip(x, min=clip_val))


def symlog(x: torch.Tensor) -> torch.Tensor:
    return torch.sign(x) * torch.log1p(x.abs())


def symexp(x: torch.Tensor) -> torch.Tensor:
    return torch.sign(x) * (torch.exp(x.abs()) - 1)



class SemanticEncoder(nn.Module):
    def __init__(
        self,
        input_channels: int,
        code_dim: int,
        encode_channels: int,
        kernel_size: int = 3,
        bias: bool = True,
    ):
        super(SemanticEncoder, self).__init__()

        # 初始卷积，将 input_channels 映射到 encode_channels
        self.initial_conv = nn.Conv1d(
            in_channels=input_channels,
            out_channels=encode_channels,
            kernel_size=kernel_size,
            stride=1,
            padding=(kernel_size - 1) // 2,
            bias=False
        )

        # 残差块
        self.residual_blocks = nn.Sequential(
            nn.ReLU(inplace=True),
            nn.Conv1d(
                encode_channels,
                encode_channels,
                kernel_size=kernel_size,
                stride=1,
                padding=(kernel_size - 1) // 2,
                bias=bias
            ),
            nn.ReLU(inplace=True),
            nn.Conv1d(
                encode_channels,
                encode_channels,
                kernel_size=kernel_size,
                stride=1,
                padding=(kernel_size - 1) // 2,
                bias=bias
            )
        )

        # 最终卷积，将 encode_channels 映射到 code_dim
        self.final_conv = nn.Conv1d(
            in_channels=encode_channels,
            out_channels=code_dim,
            kernel_size=kernel_size,
            stride=1,
            padding=(kernel_size - 1) // 2,
            bias=False
        )

    def forward(self, x):
        """
        前向传播方法。

        Args:
            x (Tensor): 输入张量，形状为 (Batch, Input_channels, Length)

        Returns:
            Tensor: 编码后的张量，形状为 (Batch, Code_dim, Length)
        """
        x = self.initial_conv(x)           # (Batch, Encode_channels, Length)
        x = self.residual_blocks(x) + x   # 残差连接
        x = self.final_conv(x)             # (Batch, Code_dim, Length)
        return x

class SemanticDecoder(nn.Module):
    def __init__(
        self,
        code_dim: int,
        output_channels: int,
        decode_channels: int,
        kernel_size: int = 3,
        bias: bool = True,
    ):
        super(SemanticDecoder, self).__init__()
        
        # Initial convolution to map code_dim to decode_channels
        self.initial_conv = nn.Conv1d(
            in_channels=code_dim,
            out_channels=decode_channels,
            kernel_size=kernel_size,
            stride=1,
            padding=(kernel_size - 1) // 2,
            bias=False
        )
        
        # Residual Blocks
        self.residual_blocks = nn.Sequential(
            nn.ReLU(inplace=True),
            nn.Conv1d(decode_channels, decode_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias),
            nn.ReLU(inplace=True),
            nn.Conv1d(decode_channels, decode_channels, kernel_size=kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias)
        )
        
        # Final convolution to map decode_channels to output_channels
        self.final_conv = nn.Conv1d(
            in_channels=decode_channels,
            out_channels=output_channels,
            kernel_size=kernel_size,
            stride=1,
            padding=(kernel_size - 1) // 2,
            bias=False
        )
        
    def forward(self, z):
        # z: (Batch, Code_dim, Length)
        x = self.initial_conv(z)  # (Batch, Decode_channels, Length)
        x = self.residual_blocks(x) + x  # Residual connection
        x = self.final_conv(x)  # (Batch, Output_channels, Length)
        return x