# Copyright 2023 AntGroup CO., Ltd.
#
# 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.
# Some of the code implementation is referred from https://github.com/xunzheng/notears
# Modified by Ant Group in 2023
import math
import torch
import torch.nn as nn
[docs]class LocallyConnected(nn.Module):
"""Local linear layer
Argument:
num_linear: num of local linear layers, i.e.
in_features: m1
out_features: m2
bias: whether to include bias or not
Shape:
- Input: [n, d, m1]
- Output: [n, d, m2]
Attributes:
weight: [d, m1, m2]
bias: [d, m2]
"""
[docs] def __init__(self, num_linear, input_features, output_features, bias=True):
super(LocallyConnected, self).__init__()
self.num_linear = num_linear
self.input_features = input_features
self.output_features = output_features
self.weight = nn.Parameter(
torch.Tensor(num_linear, input_features, output_features)
)
if bias:
self.bias = nn.Parameter(torch.Tensor(num_linear, output_features))
else:
self.register_parameter("bias", None)
self.reset_parameters()
[docs] @torch.no_grad()
def reset_parameters(self):
k = 1.0 / self.input_features
bound = math.sqrt(k)
nn.init.uniform_(self.weight, -bound, bound)
if self.bias is not None:
nn.init.uniform_(self.bias, -bound, bound)
[docs] def forward(self, input: torch.Tensor):
# [n, d, 1, m2] = [n, d, 1, m1] @ [1, d, m1, m2]
out = torch.matmul(input.unsqueeze(dim=2), self.weight.unsqueeze(dim=0))
out = out.squeeze(dim=2)
if self.bias is not None:
# [n, d, m2] += [d, m2]
out += self.bias
return out