paddlespeech.s2t.models.ds2.conv module

class paddlespeech.s2t.models.ds2.conv.Conv2dSubsampling4Pure(idim: int, odim: int, dropout_rate: float)

Bases: Conv2dSubsampling4

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_len)	Subsample x. Args: x (paddle.Tensor): Input tensor (#batch, time, idim). x_mask (paddle.Tensor): Input mask (#batch, 1, time). offset (int): position encoding offset. Returns: paddle.Tensor: Subsampled tensor (#batch, time', odim), where time' = time // 4. paddle.Tensor: positional encoding paddle.Tensor: Subsampled mask (#batch, 1, time'), where time' = time // 4.
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
position_encoding
register_state_dict_hook

forward(x: Tensor, x_len: Tensor) → [<class 'paddle.Tensor'>, <class 'paddle.Tensor'>]

Subsample x. Args:

x (paddle.Tensor): Input tensor (#batch, time, idim). x_mask (paddle.Tensor): Input mask (#batch, 1, time). offset (int): position encoding offset.

Returns:

paddle.Tensor: Subsampled tensor (#batch, time', odim),

where time' = time // 4.

paddle.Tensor: positional encoding paddle.Tensor: Subsampled mask (#batch, 1, time'),

where time' = time // 4.