geometric_linear.py

from typing import Optional

import copy
import math

import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn.parameter import Parameter

from torch_geometric.nn import inits


class Linear(torch.nn.Module):
    r"""Applies a linear tranformation to the incoming data
    .. math::
        \mathbf{x}^{\prime} = \mathbf{x} \mathbf{W}^{\top} + \mathbf{b}
    similar to :class:`torch.nn.Linear`.
    It supports lazy initialization and customizable weight and bias
    initialization.
    Args:
        in_channels (int): Size of each input sample.
            Will be initialized lazily in case :obj:`-1`.
        out_channels (int): Size of each output sample.
        bias (bool, optional): If set to :obj:`False`, the layer will not learn
            an additive bias. (default: :obj:`True`)
        weight_initializer (str, optional): The initializer for the weight
            matrix (:obj:`"glorot"`, :obj:`"uniform"`, :obj:`"kaiming_uniform"`
            or :obj:`None`).
            If set to :obj:`None`, will match default weight initialization of
            :class:`torch.nn.Linear`. (default: :obj:`None`)
        bias_initializer (str, optional): The initializer for the bias
            vector (:obj:`"zeros"` or :obj:`None`).
            If set to :obj:`None`, will match default bias initialization of
            :class:`torch.nn.Linear`. (default: :obj:`None`)
    """
    def __init__(self, in_channels: int, out_channels: int, bias: bool = True,
                 weight_initializer: Optional[str] = None,
                 bias_initializer: Optional[str] = None):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.weight_initializer = weight_initializer
        self.bias_initializer = bias_initializer

        if in_channels > 0:
            self.weight = Parameter(torch.Tensor(out_channels, in_channels))
        else:
            self.weight = torch.nn.parameter.UninitializedParameter()
            self._hook = self.register_forward_pre_hook(
                self.initialize_parameters)

        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)

        self.reset_parameters()

    def __deepcopy__(self, memo):
        out = Linear(self.in_channels, self.out_channels, self.bias
                     is not None, self.weight_initializer,
                     self.bias_initializer)
        if self.in_channels > 0:
            out.weight = copy.deepcopy(self.weight, memo)
        if self.bias is not None:
            out.bias = copy.deepcopy(self.bias, memo)
        return out

    def reset_parameters(self):
        if self.in_channels > 0:
            if self.weight_initializer == 'glorot':
                inits.glorot(self.weight)
            elif self.weight_initializer == 'uniform':
                bound = 1.0 / math.sqrt(self.weight.size(-1))
                torch.nn.init.uniform_(self.weight.data, -bound, bound)
            elif self.weight_initializer == 'kaiming_uniform':
                inits.kaiming_uniform(self.weight, fan=self.in_channels,
                                      a=math.sqrt(5))
            elif self.weight_initializer is None:
                inits.kaiming_uniform(self.weight, fan=self.in_channels,
                                      a=math.sqrt(5))
            else:
                raise RuntimeError(
                    f"Linear layer weight initializer "
                    f"'{self.weight_initializer}' is not supported")

        if self.in_channels > 0 and self.bias is not None:
            if self.bias_initializer == 'zeros':
                inits.zeros(self.bias)
            elif self.bias_initializer is None:
                inits.uniform(self.in_channels, self.bias)
            else:
                raise RuntimeError(
                    f"Linear layer bias initializer "
                    f"'{self.bias_initializer}' is not supported")

    def forward(self, x: Tensor) -> Tensor:
        #print("neighbourhod feature:", x.shape)
        #print("self.weight:", self.weight.shape)
        return F.linear(x, self.weight, self.bias)

    @torch.no_grad()
    def initialize_parameters(self, module, input):
        if isinstance(self.weight, torch.nn.parameter.UninitializedParameter):
            self.in_channels = input[0].size(-1)
            self.weight.materialize((self.out_channels, self.in_channels))
            self.reset_parameters()
        module._hook.remove()
        delattr(module, '_hook')

    def __repr__(self) -> str:
        return (f'{self.__class__.__name__}({self.in_channels}, '
                f'{self.out_channels}, bias={self.bias is not None})')