paddlespeech.t2s.modules.tade_res_block module

StyleMelGAN's TADEResBlock Modules.

class paddlespeech.t2s.modules.tade_res_block.TADELayer(in_channels: int = 64, aux_channels: int = 80, kernel_size: int = 9, bias: bool = True, upsample_factor: int = 2, upsample_mode: str = 'nearest')[source]

Bases: Layer

TADE Layer module.

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, c)	Calculate forward propagation. Args: x (Tensor): Input tensor (B, in_channels, T). c (Tensor): Auxiliary input tensor (B, aux_channels, T). Returns: Tensor: Output tensor (B, in_channels, T * upsample_factor). Tensor: Upsampled aux tensor (B, in_channels, T * upsample_factor).
`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, c)[source]

Calculate forward propagation. Args:

x (Tensor):
Input tensor (B, in_channels, T).

c (Tensor):
Auxiliary input tensor (B, aux_channels, T).

Returns:

Tensor:: Output tensor (B, in_channels, T * upsample_factor).
Tensor:: Upsampled aux tensor (B, in_channels, T * upsample_factor).

class paddlespeech.t2s.modules.tade_res_block.TADEResBlock(in_channels: int = 64, aux_channels: int = 80, kernel_size: int = 9, dilation: int = 2, bias: bool = True, upsample_factor: int = 2, upsample_mode: str = 'nearest', gated_function: str = 'softmax')[source]