34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
|
#!/usr/bin/env python3
|
||
|
# -*- coding: utf-8 -*-
|
||
|
|
||
|
|
||
|
"""Positionwise feed forward layer definition."""
|
||
|
|
||
|
import torch
|
||
|
|
||
|
from funasr.models.transformer.layer_norm import LayerNorm
|
||
|
|
||
|
|
||
|
class PositionwiseFeedForwardDecoderSANM(torch.nn.Module):
|
||
|
"""Positionwise feed forward layer.
|
||
|
|
||
|
Args:
|
||
|
idim (int): Input dimenstion.
|
||
|
hidden_units (int): The number of hidden units.
|
||
|
dropout_rate (float): Dropout rate.
|
||
|
|
||
|
"""
|
||
|
|
||
|
def __init__(self, idim, hidden_units, dropout_rate, adim=None, activation=torch.nn.ReLU()):
|
||
|
"""Construct an PositionwiseFeedForward object."""
|
||
|
super(PositionwiseFeedForwardDecoderSANM, self).__init__()
|
||
|
self.w_1 = torch.nn.Linear(idim, hidden_units)
|
||
|
self.w_2 = torch.nn.Linear(hidden_units, idim if adim is None else adim, bias=False)
|
||
|
self.dropout = torch.nn.Dropout(dropout_rate)
|
||
|
self.activation = activation
|
||
|
self.norm = LayerNorm(hidden_units)
|
||
|
|
||
|
def forward(self, x):
|
||
|
"""Forward function."""
|
||
|
return self.w_2(self.norm(self.dropout(self.activation(self.w_1(x)))))
|