# 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.
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"""Causal convolusion layer modules."""
import paddle
from paddle import nn
[docs]class CausalConv1D(nn.Layer):
"""CausalConv1D module with customized initialization."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
dilation=1,
bias=True,
pad="Pad1D",
pad_params={"value": 0.0}, ):
"""Initialize CausalConv1d module."""
super().__init__()
self.pad = getattr(paddle.nn, pad)((kernel_size - 1) * dilation,
**pad_params)
self.conv = nn.Conv1D(
in_channels,
out_channels,
kernel_size,
dilation=dilation,
bias_attr=bias)
[docs] def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor):
Input tensor (B, in_channels, T).
Returns:
Tensor: Output tensor (B, out_channels, T).
"""
return self.conv(self.pad(x))[:, :, :x.shape[2]]
[docs]class CausalConv1DTranspose(nn.Layer):
"""CausalConv1DTranspose module with customized initialization."""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
bias=True):
"""Initialize CausalConvTranspose1d module."""
super().__init__()
self.deconv = nn.Conv1DTranspose(
in_channels, out_channels, kernel_size, stride, bias_attr=bias)
self.stride = stride
[docs] def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor):
Input tensor (B, in_channels, T_in).
Returns:
Tensor: Output tensor (B, out_channels, T_out).
"""
return self.deconv(x)[:, :, :-self.stride]