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77 lines
2.4 KiB
77 lines
2.4 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Causal convolusion layer modules."""
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import paddle
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from paddle import nn
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class CausalConv1D(nn.Layer):
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"""CausalConv1D module with customized initialization."""
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size,
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dilation=1,
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bias=True,
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pad="Pad1D",
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pad_params={"value": 0.0}, ):
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"""Initialize CausalConv1d module."""
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super().__init__()
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self.pad = getattr(paddle.nn, pad)((kernel_size - 1) * dilation,
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**pad_params)
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self.conv = nn.Conv1D(
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in_channels,
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out_channels,
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kernel_size,
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dilation=dilation,
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bias_attr=bias)
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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x (Tensor):
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Input tensor (B, in_channels, T).
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Returns:
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Tensor: Output tensor (B, out_channels, T).
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"""
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return self.conv(self.pad(x))[:, :, :x.shape[2]]
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class CausalConv1DTranspose(nn.Layer):
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"""CausalConv1DTranspose module with customized initialization."""
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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bias=True):
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"""Initialize CausalConvTranspose1d module."""
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super().__init__()
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self.deconv = nn.Conv1DTranspose(
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in_channels, out_channels, kernel_size, stride, bias_attr=bias)
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self.stride = stride
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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x (Tensor):
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Input tensor (B, in_channels, T_in).
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Returns:
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Tensor: Output tensor (B, out_channels, T_out).
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"""
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return self.deconv(x)[:, :, :-self.stride]
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