Module `audiocraft.adversarial.discriminators.msd`

Classes

class MultiScaleDiscriminator (in_channels: int = 1, out_channels: int = 1, downsample_factor: int = 2, scale_norms: Sequence[str] = ['weight_norm', 'weight_norm', 'weight_norm'], **kwargs)

Expand source code

class MultiScaleDiscriminator(MultiDiscriminator):
    """Multi-Scale (MSD) Discriminator,

    Args:
        in_channels (int): Number of input channels.
        out_channels (int): Number of output channels.
        downsample_factor (int): Downsampling factor between the different scales.
        scale_norms (Sequence[str]): Normalization for each sub-discriminator.
        **kwargs: Additional args for ScaleDiscriminator.
    """
    def __init__(self, in_channels: int = 1, out_channels: int = 1, downsample_factor: int = 2,
                 scale_norms: tp.Sequence[str] = ['weight_norm', 'weight_norm', 'weight_norm'], **kwargs):
        super().__init__()
        self.discriminators = nn.ModuleList([
            ScaleDiscriminator(in_channels, out_channels, norm=norm, **kwargs) for norm in scale_norms
        ])
        self.downsample = nn.AvgPool1d(downsample_factor * 2, downsample_factor, padding=downsample_factor)

    @property
    def num_discriminators(self):
        return len(self.discriminators)

    def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType:
        logits = []
        fmaps = []
        for i, disc in enumerate(self.discriminators):
            if i != 0:
                self.downsample(x)
            logit, fmap = disc(x)
            logits.append(logit)
            fmaps.append(fmap)
        return logits, fmaps

Multi-Scale (MSD) Discriminator,

Args

in_channels : int: Number of input channels.
out_channels : int: Number of output channels.
downsample_factor : int: Downsampling factor between the different scales.
scale_norms : Sequence[str]: Normalization for each sub-discriminator.
**kwargs: Additional args for ScaleDiscriminator.

Ancestors

MultiDiscriminator
abc.ABC
torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Inherited members

MultiDiscriminator:
- forward
- num_discriminators

class ScaleDiscriminator (in_channels=1, out_channels=1, kernel_sizes: Sequence[int] = [5, 3], filters: int = 16, max_filters: int = 1024, downsample_scales: Sequence[int] = [4, 4, 4, 4], inner_kernel_sizes: Sequence[int] | None = None, groups: Sequence[int] | None = None, strides: Sequence[int] | None = None, paddings: Sequence[int] | None = None, norm: str = 'weight_norm', activation: str = 'LeakyReLU', activation_params: dict = {'negative_slope': 0.2}, pad: str = 'ReflectionPad1d', pad_params: dict = {})

Expand source code

class ScaleDiscriminator(nn.Module):
    """Waveform sub-discriminator.

    Args:
        in_channels (int): Number of input channels.
        out_channels (int): Number of output channels.
        kernel_sizes (Sequence[int]): Kernel sizes for first and last convolutions.
        filters (int): Number of initial filters for convolutions.
        max_filters (int): Maximum number of filters.
        downsample_scales (Sequence[int]): Scale for downsampling implemented as strided convolutions.
        inner_kernel_sizes (Sequence[int] or None): Kernel sizes for inner convolutions.
        groups (Sequence[int] or None): Groups for inner convolutions.
        strides (Sequence[int] or None): Strides for inner convolutions.
        paddings (Sequence[int] or None): Paddings for inner convolutions.
        norm (str): Normalization method.
        activation (str): Activation function.
        activation_params (dict): Parameters to provide to the activation function.
        pad (str): Padding for initial convolution.
        pad_params (dict): Parameters to provide to the padding module.
    """
    def __init__(self, in_channels=1, out_channels=1, kernel_sizes: tp.Sequence[int] = [5, 3],
                 filters: int = 16, max_filters: int = 1024, downsample_scales: tp.Sequence[int] = [4, 4, 4, 4],
                 inner_kernel_sizes: tp.Optional[tp.Sequence[int]] = None, groups: tp.Optional[tp.Sequence[int]] = None,
                 strides: tp.Optional[tp.Sequence[int]] = None, paddings: tp.Optional[tp.Sequence[int]] = None,
                 norm: str = 'weight_norm', activation: str = 'LeakyReLU',
                 activation_params: dict = {'negative_slope': 0.2}, pad: str = 'ReflectionPad1d',
                 pad_params: dict = {}):
        super().__init__()
        assert len(kernel_sizes) == 2
        assert kernel_sizes[0] % 2 == 1
        assert kernel_sizes[1] % 2 == 1
        assert (inner_kernel_sizes is None or len(inner_kernel_sizes) == len(downsample_scales))
        assert (groups is None or len(groups) == len(downsample_scales))
        assert (strides is None or len(strides) == len(downsample_scales))
        assert (paddings is None or len(paddings) == len(downsample_scales))
        self.activation = getattr(torch.nn, activation)(**activation_params)
        self.convs = nn.ModuleList()
        self.convs.append(
            nn.Sequential(
                getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
                NormConv1d(in_channels, filters, kernel_size=np.prod(kernel_sizes), stride=1, norm=norm)
            )
        )

        in_chs = filters
        for i, downsample_scale in enumerate(downsample_scales):
            out_chs = min(in_chs * downsample_scale, max_filters)
            default_kernel_size = downsample_scale * 10 + 1
            default_stride = downsample_scale
            default_padding = (default_kernel_size - 1) // 2
            default_groups = in_chs // 4
            self.convs.append(
                NormConv1d(in_chs, out_chs,
                           kernel_size=inner_kernel_sizes[i] if inner_kernel_sizes else default_kernel_size,
                           stride=strides[i] if strides else default_stride,
                           groups=groups[i] if groups else default_groups,
                           padding=paddings[i] if paddings else default_padding,
                           norm=norm))
            in_chs = out_chs

        out_chs = min(in_chs * 2, max_filters)
        self.convs.append(NormConv1d(in_chs, out_chs, kernel_size=kernel_sizes[0], stride=1,
                                     padding=(kernel_sizes[0] - 1) // 2, norm=norm))
        self.conv_post = NormConv1d(out_chs, out_channels, kernel_size=kernel_sizes[1], stride=1,
                                    padding=(kernel_sizes[1] - 1) // 2, norm=norm)

    def forward(self, x: torch.Tensor):
        fmap = []
        for layer in self.convs:
            x = layer(x)
            x = self.activation(x)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        # x = torch.flatten(x, 1, -1)
        return x, fmap

Waveform sub-discriminator.

Args

in_channels : int: Number of input channels.
out_channels : int: Number of output channels.
kernel_sizes : Sequence[int]: Kernel sizes for first and last convolutions.
filters : int: Number of initial filters for convolutions.
max_filters : int: Maximum number of filters.
downsample_scales : Sequence[int]: Scale for downsampling implemented as strided convolutions.
inner_kernel_sizes : Sequence[int] or None: Kernel sizes for inner convolutions.
groups : Sequence[int] or None: Groups for inner convolutions.
strides : Sequence[int] or None: Strides for inner convolutions.
paddings : Sequence[int] or None: Paddings for inner convolutions.
norm : str: Normalization method.
activation : str: Activation function.
activation_params : dict: Parameters to provide to the activation function.
pad : str: Padding for initial convolution.
pad_params : dict: Parameters to provide to the padding module.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Methods

def forward(self, x: torch.Tensor) ‑> Callable[..., Any]

Expand source code

def forward(self, x: torch.Tensor):
    fmap = []
    for layer in self.convs:
        x = layer(x)
        x = self.activation(x)
        fmap.append(x)
    x = self.conv_post(x)
    fmap.append(x)
    # x = torch.flatten(x, 1, -1)
    return x, fmap

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.