TasNET_model.py

import torch
import torch.nn as nn
import torch.nn.functional as F

from utils import overlap_and_add

EPS = 1e-8


class TasNET(nn.Module):
    def __init__(self, N=256, L=20, B=256, H=512, P=3,
                 X=8, R=4, C=2, norm_type="gLN", causal=False,
                 mask_nonlinear='relu'):

        """
        Args:
            N: Number of filters in autoencoder
            L: Length of the filters (in samples)
            B: Number of channels in bottleneck 1 × 1-conv block
            H: Number of channels in convolutional blocks
            P: Kernel size in convolutional blocks
            X: Number of convolutional blocks in each repeat
            R: Number of repeats
            C: Number of speakers
            norm_type: BN, gLN, cLN
            causal: causal or non-causal
            mask_nonlinear: use which non-linear function to generate mask
        """
        super(TasNET, self).__init__()
        # Hyper-parameter
        self.N, self.L, self.B, self.H, self.P, self.X, self.R, self.C = N, L, B, H, P, X, R, C
        self.norm_type = norm_type
        self.causal = causal
        self.mask_nonlinear = mask_nonlinear
        # Components
        self.encoder = Encoder(L, N)
        self.separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type, causal, mask_nonlinear)
        self.decoder = Decoder(N, L)
        # init
        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_normal_(p)

    def forward(self, mixture):
        """
        Args:
            mixture: [M, N, L], M is batch size, #T is #samples
        Returns:
            x1: [M, T]
            x2: [M, T]
            #est_source: [M, C, T]
        """
        mixture_w = self.encoder(mixture)
        est_mask = self.separator(mixture_w)
        est_source = self.decoder(mixture_w, est_mask)

        # T changed after conv1d in encoder, fix it here
        T_origin = mixture.size(-1)*mixture.size(-2)
        T_conv = est_source.size(-1)
        est_source = F.pad(est_source, (0, T_origin - T_conv))
        M = est_source.size()[0]
        x1 = est_source[:,0,:]
        x1 = x1.view(M, -1)
        x2 = est_source[:,1,:]
        x2 = x2.view(M, -1)
        # print(x1.size())
        return x1, x2

    @classmethod
    def load_model(cls, path):
        # Load to CPU
        package = torch.load(path, map_location=lambda storage, loc: storage)
        model = cls.load_model_from_package(package)
        return model

    @classmethod
    def load_model_from_package(cls, package):
        model = cls(package['N'], package['L'], package['B'], package['H'],
                    package['P'], package['X'], package['R'], package['C'],
                    norm_type=package['norm_type'], causal=package['causal'],
                    mask_nonlinear=package['mask_nonlinear'])
        model.load_state_dict(package['state_dict'])
        return model

    @staticmethod
    def serialize(model, optimizer, epoch, tr_loss=None, cv_loss=None):
        package = {
            # hyper-parameter
            'N': model.N, 'L': model.L, 'B': model.B, 'H': model.H,
            'P': model.P, 'X': model.X, 'R': model.R, 'C': model.C,
            'norm_type': model.norm_type, 'causal': model.causal,
            'mask_nonlinear': model.mask_nonlinear,
            # state
            'state_dict': model.state_dict(),
            'optim_dict': optimizer.state_dict(),
            'epoch': epoch
        }
        if tr_loss is not None:
            package['tr_loss'] = tr_loss
            package['cv_loss'] = cv_loss
        return package


class Encoder(nn.Module):
    """Estimation of the nonnegative mixture weight by a 1-D conv layer.
    """
    def __init__(self, L, N):
        super(Encoder, self).__init__()
        # Hyper-parameter
        self.L, self.N = L, N
        # Components
        # 50% overlap
        self.conv1d_U = nn.Conv1d(1, N, kernel_size=L, stride=L // 2, bias=False)

    def forward(self, mixture):
        """
        Args:
            mixture: [M, T], M is batch size, T is #samples
        Returns:
            mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1
        """
        M, _, _ = mixture.size()
        mixture = mixture.view(M, -1)
        mixture = torch.unsqueeze(mixture, 1)  # [M, 1, T]
        mixture_w = F.relu(self.conv1d_U(mixture))  # [M, N, K]
        return mixture_w


class Decoder(nn.Module):
    def __init__(self, N, L):
        super(Decoder, self).__init__()
        # Hyper-parameter
        self.N, self.L = N, L
        # Components
        self.basis_signals = nn.Linear(N, L, bias=False)

    def forward(self, mixture_w, est_mask):
        """
        Args:
            mixture_w: [M, N, K]
            est_mask: [M, C, N, K]
        Returns:
            est_source: [M, C, T]
        """
        # D = W * M
        source_w = torch.unsqueeze(mixture_w, 1) * est_mask  # [M, C, N, K]
        source_w = torch.transpose(source_w, 2, 3) # [M, C, K, N]
        # S = DV
        est_source = self.basis_signals(source_w)  # [M, C, K, L]
        est_source = overlap_and_add(est_source, self.L//2) # M x C x T
        return est_source


class TemporalConvNet(nn.Module):
    def __init__(self, N, B, H, P, X, R, C, norm_type="gLN", causal=False,
                 mask_nonlinear='relu'):
        """
        Args:
            N: Number of filters in autoencoder
            B: Number of channels in bottleneck 1 × 1-conv block
            H: Number of channels in convolutional blocks
            P: Kernel size in convolutional blocks
            X: Number of convolutional blocks in each repeat
            R: Number of repeats
            C: Number of speakers
            norm_type: BN, gLN, cLN
            causal: causal or non-causal
            mask_nonlinear: use which non-linear function to generate mask
        """
        super(TemporalConvNet, self).__init__()
        # Hyper-parameter
        self.C = C
        self.mask_nonlinear = mask_nonlinear
        # Components
        # [M, N, K] -> [M, N, K]
        layer_norm = ChannelwiseLayerNorm(N)
        # [M, N, K] -> [M, B, K]
        bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)
        # [M, B, K] -> [M, B, K]
        repeats = []
        for r in range(R):
            blocks = []
            for x in range(X):
                dilation = 2**x
                padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2
                blocks += [TemporalBlock(B, H, P, stride=1,
                                         padding=padding,
                                         dilation=dilation,
                                         norm_type=norm_type,
                                         causal=causal)]
            repeats += [nn.Sequential(*blocks)]
        temporal_conv_net = nn.Sequential(*repeats)
        # [M, B, K] -> [M, C*N, K]
        mask_conv1x1 = nn.Conv1d(B, C*N, 1, bias=False)
        # Put together
        self.network = nn.Sequential(layer_norm,
                                     bottleneck_conv1x1,
                                     temporal_conv_net,
                                     mask_conv1x1)

    def forward(self, mixture_w):
        """
        Keep this API same with TasNet
        Args:
            mixture_w: [M, N, K], M is batch size
        returns:
            est_mask: [M, C, N, K]
        """
        M, N, K = mixture_w.size()
        score = self.network(mixture_w)  # [M, N, K] -> [M, C*N, K]
        score = score.view(M, self.C, N, K) # [M, C*N, K] -> [M, C, N, K]
        if self.mask_nonlinear == 'softmax':
            est_mask = F.softmax(score, dim=1)
        elif self.mask_nonlinear == 'relu':
            est_mask = F.relu(score)
        else:
            raise ValueError("Unsupported mask non-linear function")
        return est_mask


class TemporalBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size,
                 stride, padding, dilation, norm_type="gLN", causal=False):
        super(TemporalBlock, self).__init__()
        # [M, B, K] -> [M, H, K]
        conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)
        prelu = nn.PReLU()
        norm = chose_norm(norm_type, out_channels)
        # [M, H, K] -> [M, B, K]
        dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size,
                                        stride, padding, dilation, norm_type,
                                        causal)
        # Put together
        self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)

    def forward(self, x):
        """
        Args:
            x: [M, B, K]
        Returns:
            [M, B, K]
        """
        residual = x
        out = self.net(x)
        # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad?
        return out + residual  # look like w/o F.relu is better than w/ F.relu
        # return F.relu(out + residual)


class DepthwiseSeparableConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size,
                 stride, padding, dilation, norm_type="gLN", causal=False):
        super(DepthwiseSeparableConv, self).__init__()
        # Use `groups` option to implement depthwise convolution
        # [M, H, K] -> [M, H, K]
        depthwise_conv = nn.Conv1d(in_channels, in_channels, kernel_size,
                                   stride=stride, padding=padding,
                                   dilation=dilation, groups=in_channels,
                                   bias=False)
        if causal:
            chomp = Chomp1d(padding)
        prelu = nn.PReLU()
        norm = chose_norm(norm_type, in_channels)
        # [M, H, K] -> [M, B, K]
        pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)
        # Put together
        if causal:
            self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm,
                                     pointwise_conv)
        else:
            self.net = nn.Sequential(depthwise_conv, prelu, norm,
                                     pointwise_conv)

    def forward(self, x):
        """
        Args:
            x: [M, H, K]
        Returns:
            result: [M, B, K]
        """
        return self.net(x)


class Chomp1d(nn.Module):
    """To ensure the output length is the same as the input.
    """
    def __init__(self, chomp_size):
        super(Chomp1d, self).__init__()
        self.chomp_size = chomp_size

    def forward(self, x):
        """
        Args:
            x: [M, H, Kpad]
        Returns:
            [M, H, K]
        """
        return x[:, :, :-self.chomp_size].contiguous()


def chose_norm(norm_type, channel_size):
    """The input of normlization will be (M, C, K), where M is batch size,
       C is channel size and K is sequence length.
    """
    if norm_type == "gLN":
        return GlobalLayerNorm(channel_size)
    elif norm_type == "cLN":
        return ChannelwiseLayerNorm(channel_size)
    else: # norm_type == "BN":
        # Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics
        # along M and K, so this BN usage is right.
        return nn.BatchNorm1d(channel_size)


# TODO: Use nn.LayerNorm to impl cLN to speed up
class ChannelwiseLayerNorm(nn.Module):
    """Channel-wise Layer Normalization (cLN)"""
    def __init__(self, channel_size):
        super(ChannelwiseLayerNorm, self).__init__()
        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]
        self.beta = nn.Parameter(torch.Tensor(1, channel_size,1 ))  # [1, N, 1]
        self.reset_parameters()

    def reset_parameters(self):
        self.gamma.data.fill_(1)
        self.beta.data.zero_()

    def forward(self, y):
        """
        Args:
            y: [M, N, K], M is batch size, N is channel size, K is length
        Returns:
            cLN_y: [M, N, K]
        """
        mean = torch.mean(y, dim=1, keepdim=True)  # [M, 1, K]
        var = torch.var(y, dim=1, keepdim=True, unbiased=False)  # [M, 1, K]
        cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
        return cLN_y


class GlobalLayerNorm(nn.Module):
    """Global Layer Normalization (gLN)"""
    def __init__(self, channel_size):
        super(GlobalLayerNorm, self).__init__()
        self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]
        self.beta = nn.Parameter(torch.Tensor(1, channel_size,1 ))  # [1, N, 1]
        self.reset_parameters()

    def reset_parameters(self):
        self.gamma.data.fill_(1)
        self.beta.data.zero_()

    def forward(self, y):
        """
        Args:
            y: [M, N, K], M is batch size, N is channel size, K is length
        Returns:
            gLN_y: [M, N, K]
        """
        # TODO: in torch 1.0, torch.mean() support dim list
        mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) #[M, 1, 1]
        var = (torch.pow(y-mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)
        gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
        return gLN_y


if __name__ == "__main__":
    torch.manual_seed(123)
    M, N, L, T = 2, 3, 4, 12
    K = 2*T//L-1
    B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, "gLN", False
    mixture = torch.randint(3, (M, T))
    # test Encoder
    encoder = Encoder(L, N)
    encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size())
    mixture_w = encoder(mixture)
    print('mixture', mixture)
    print('U', encoder.conv1d_U.weight)
    print('mixture_w', mixture_w)
    print('mixture_w size', mixture_w.size())

    # test TemporalConvNet
    separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal)
    est_mask = separator(mixture_w)
    print('est_mask', est_mask)

    # test Decoder
    decoder = Decoder(N, L)
    est_mask = torch.randint(2, (B, K, C, N))
    est_source = decoder(mixture_w, est_mask)
    print('est_source', est_source)

    # test Conv-TasNet
    conv_tasnet = TasNET(N, L, B, H, P, X, R, C, norm_type=norm_type)
    est_source = conv_tasnet(mixture)
    print('est_source', est_source)
    print('est_source size', est_source.size())