| # MIT License | |
| # Copyright (c) 2022 Karl Stelzner | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # This file comes from https://github.com/stelzner/srt | |
| from torch import nn | |
| class FeedForward(nn.Module): | |
| def __init__(self, dim, hidden_dim, dropout=0.0): | |
| super().__init__() | |
| self.net = nn.Sequential( | |
| nn.Linear(dim, hidden_dim), | |
| nn.GELU(), | |
| nn.Dropout(dropout), | |
| nn.Linear(hidden_dim, dim), | |
| nn.Dropout(dropout), | |
| ) | |
| def forward(self, x): | |
| return self.net(x) | |