# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
import paddle
from paddle import nn
from paddle.nn import functional as F
from paddlespeech.s2t.modules.align import Conv2D
from paddlespeech.s2t.modules.align import Linear
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ["get_activation", "brelu", "LinearGLUBlock", "ConvGLUBlock", "GLU"]
[docs]def brelu(x, t_min=0.0, t_max=24.0, name=None):
# paddle.to_tensor is dygraph_only can not work under JIT
t_min = paddle.full(shape=[1], fill_value=t_min, dtype='float32')
t_max = paddle.full(shape=[1], fill_value=t_max, dtype='float32')
return x.maximum(t_min).minimum(t_max)
[docs]class GLU(nn.Layer):
"""Gated Linear Units (GLU) Layer"""
def __init__(self, dim: int=-1):
super().__init__()
self.dim = dim
[docs] def forward(self, xs):
return F.glu(xs, axis=self.dim)
[docs]class LinearGLUBlock(nn.Layer):
"""A linear Gated Linear Units (GLU) block."""
def __init__(self, idim: int):
""" GLU.
Args:
idim (int): input and output dimension
"""
super().__init__()
self.fc = Linear(idim, idim * 2)
[docs] def forward(self, xs):
return glu(self.fc(xs), dim=-1)
[docs]class ConvGLUBlock(nn.Layer):
def __init__(self, kernel_size, in_ch, out_ch, bottlececk_dim=0,
dropout=0.):
"""A convolutional Gated Linear Units (GLU) block.
Args:
kernel_size (int): kernel size
in_ch (int): number of input channels
out_ch (int): number of output channels
bottlececk_dim (int): dimension of the bottleneck layers for computational efficiency. Defaults to 0.
dropout (float): dropout probability. Defaults to 0..
"""
super().__init__()
self.conv_residual = None
if in_ch != out_ch:
self.conv_residual = nn.utils.weight_norm(
Conv2D(
in_channels=in_ch, out_channels=out_ch, kernel_size=(1, 1)),
name='weight',
dim=0)
self.dropout_residual = nn.Dropout(p=dropout)
self.pad_left = nn.Pad2d((0, 0, kernel_size - 1, 0), 0)
layers = OrderedDict()
if bottlececk_dim == 0:
layers['conv'] = nn.utils.weight_norm(
Conv2D(
in_channels=in_ch,
out_channels=out_ch * 2,
kernel_size=(kernel_size, 1)),
name='weight',
dim=0)
# TODO(hirofumi0810): padding?
layers['dropout'] = nn.Dropout(p=dropout)
layers['glu'] = GLU()
elif bottlececk_dim > 0:
layers['conv_in'] = nn.utils.weight_norm(
nn.Conv2D(
in_channels=in_ch,
out_channels=bottlececk_dim,
kernel_size=(1, 1)),
name='weight',
dim=0)
layers['dropout_in'] = nn.Dropout(p=dropout)
layers['conv_bottleneck'] = nn.utils.weight_norm(
Conv2D(
in_channels=bottlececk_dim,
out_channels=bottlececk_dim,
kernel_size=(kernel_size, 1)),
name='weight',
dim=0)
layers['dropout'] = nn.Dropout(p=dropout)
layers['glu'] = GLU()
layers['conv_out'] = nn.utils.weight_norm(
Conv2D(
in_channels=bottlececk_dim,
out_channels=out_ch * 2,
kernel_size=(1, 1)),
name='weight',
dim=0)
layers['dropout_out'] = nn.Dropout(p=dropout)
self.layers = nn.Sequential(layers)
[docs] def forward(self, xs):
"""Forward pass.
Args:
xs (FloatTensor): `[B, in_ch, T, feat_dim]`
Returns:
out (FloatTensor): `[B, out_ch, T, feat_dim]`
"""
residual = xs
if self.conv_residual is not None:
residual = self.dropout_residual(self.conv_residual(residual))
xs = self.pad_left(xs) # `[B, embed_dim, T+kernel-1, 1]`
xs = self.layers(xs) # `[B, out_ch * 2, T ,1]`
xs = xs + residual
return xs
[docs]def get_activation(act):
"""Return activation function."""
# Lazy load to avoid unused import
activation_funcs = {
"hardshrink": paddle.nn.Hardshrink,
"hardswish": paddle.nn.Hardswish,
"hardtanh": paddle.nn.Hardtanh,
"tanh": paddle.nn.Tanh,
"relu": paddle.nn.ReLU,
"relu6": paddle.nn.ReLU6,
"leakyrelu": paddle.nn.LeakyReLU,
"selu": paddle.nn.SELU,
"swish": paddle.nn.Swish,
"gelu": paddle.nn.GELU,
"glu": GLU,
"elu": paddle.nn.ELU,
}
return activation_funcs[act]()