paddlespeech.t2s.models.vits.wavenet.residual_block module

class paddlespeech.t2s.models.vits.wavenet.residual_block.ResidualBlock(kernel_size: int = 3, residual_channels: int = 64, gate_channels: int = 128, skip_channels: int = 64, aux_channels: int = 80, global_channels: int = -1, dropout_rate: float = 0.0, dilation: int = 1, bias: bool = True, scale_residual: bool = False)

Bases: Layer

Residual block module in WaveNet.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(x[, x_mask, c, g])	Calculate forward propagation.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(x: Tensor, x_mask: Optional[Tensor] = None, c: Optional[Tensor] = None, g: Optional[Tensor] = None) → Tuple[Tensor, Tensor]

Calculate forward propagation.

Args:

x (Tensor): Input tensor (B, residual_channels, T). x_mask Optional[paddle.Tensor]: Mask tensor (B, 1, T). c (Optional[Tensor]): Local conditioning tensor (B, aux_channels, T). g (Optional[Tensor]): Global conditioning tensor (B, global_channels, 1).

Returns:

Tensor: Output tensor for residual connection (B, residual_channels, T). Tensor: Output tensor for skip connection (B, skip_channels, T).