ba6919baaf
* modifying some implmentations of mdtc to get the model torch-scripting through * modifications to get the mdtc model torch-scriptable Co-authored-by: lxiao336 <shawl336@163.com>
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
# Copyright (c) 2021 Binbin Zhang
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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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import torch
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# There is no right context or lookahead in our Subsampling design, so
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# If there is CNN in Subsampling, it's a causal CNN.
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class SubsamplingBase(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.subsampling_rate = 1
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class NoSubsampling(SubsamplingBase):
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"""No subsampling in accordance to the 'none' preprocessing
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"""
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def __init__(self):
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super().__init__()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return x
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class LinearSubsampling1(SubsamplingBase):
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"""Linear transform the input without subsampling
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"""
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def __init__(self, idim: int, odim: int):
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super().__init__()
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self.out = torch.nn.Sequential(
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torch.nn.Linear(idim, odim),
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# torch.nn.BatchNorm1d(odim),
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torch.nn.ReLU(),
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)
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self.subsampling_rate = 1
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.out(x)
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return x
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class Conv1dSubsampling1(SubsamplingBase):
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"""Conv1d transform without subsampling
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"""
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def __init__(self, idim: int, odim: int):
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super().__init__()
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self.out = torch.nn.Sequential(
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torch.nn.Conv1d(idim, odim, 3),
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torch.nn.BatchNorm1d(odim),
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torch.nn.ReLU(),
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)
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self.subsampling_rate = 1
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.out(x)
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return x
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