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72 lines
2.6 KiB
72 lines
2.6 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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# Modified from espnet(https://github.com/espnet/espnet)
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"""Subsampling layer definition."""
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import paddle
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from paddle import nn
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from paddlespeech.t2s.modules.transformer.embedding import PositionalEncoding
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class Conv2dSubsampling(nn.Layer):
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"""Convolutional 2D subsampling (to 1/4 length).
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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pos_enc (nn.Layer): Custom position encoding layer.
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"""
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def __init__(self, idim, odim, dropout_rate, pos_enc=None):
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"""Construct an Conv2dSubsampling object."""
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2D(1, odim, 3, 2),
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nn.ReLU(),
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nn.Conv2D(odim, odim, 3, 2),
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nn.ReLU(), )
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self.out = nn.Sequential(
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nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim),
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pos_enc if pos_enc is not None else
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PositionalEncoding(odim, dropout_rate), )
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def forward(self, x, x_mask):
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"""Subsample x.
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Args:
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x (Tensor): Input tensor (#batch, time, idim).
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x_mask (Tensor): Input mask (#batch, 1, time).
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Returns:
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Tensor: Subsampled tensor (#batch, time', odim), where time' = time // 4.
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Tensor: Subsampled mask (#batch, 1, time'), where time' = time // 4.
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"""
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# (b, c, t, f)
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x = x.unsqueeze(1)
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x = self.conv(x)
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b, c, t, f = paddle.shape(x)
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x = self.out(x.transpose([0, 2, 1, 3]).reshape([b, t, c * f]))
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if x_mask is None:
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return x, None
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return x, x_mask[:, :, :-2:2][:, :, :-2:2]
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def __getitem__(self, key):
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"""Get item.
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When reset_parameters() is called, if use_scaled_pos_enc is used,
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return the positioning encoding.
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"""
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if key != -1:
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raise NotImplementedError(
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"Support only `-1` (for `reset_parameters`).")
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return self.out[key]
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