| # coding=utf-8 | |
| # Copyright 2022 The IDEA 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. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class MLP(nn.Module): | |
| """Very simple multi-layer perceptron (also called FFN)""" | |
| def __init__(self, input_dim, hidden_dim, output_dim, num_layers): | |
| super().__init__() | |
| self.num_layers = num_layers | |
| h = [hidden_dim] * (num_layers - 1) | |
| self.layers = nn.ModuleList( | |
| nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) | |
| ) | |
| def forward(self, x): | |
| for i, layer in enumerate(self.layers): | |
| x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) | |
| return x | |