Flunzmas

    def to_line_digraph(self, data: Data) -> Data:
        """
        TODO can we make this more efficient by removing the for-loop?
        """
        assert data.edge_index is not None
        assert data.is_directed()
        edge_index, edge_attr = data.edge_index, data.edge_attr
        N, E = data.num_nodes, data.num_edges

        edge_index, edge_attr = coalesce(edge_index, edge_attr, num_nodes=data.num_nodes)

Flunzmas

import math
import torch

"""
Differentiable dual quaternion distance metric in PyTorch. 

Acknowledgements: 
- Function q_mul(): https://github.com/facebookresearch/QuaterNet/blob/main/common/quaternion.py
- Other functions related to quaternions: re-implementations based on pip package "pyquaternion"
- Functions related to dual quaternions: re-implementations based on pip package "dual_quaternions"

Flunzmas

from torch_geometric.data import Data as GraphData

# ... load training data
train_data = None

# uses the following DataLoader: https://gist.github.com/Flunzmas/5a5c8c8fd553609359704be3174db793
data_loader = DataLoader(train_data, batch_size=32, shuffle=True, num_workers=4, drop_last=True)

for batch_idx, data in enumerate(data_loader):
    for t, batch_at_timestep in enumerate(data):

Flunzmas

from typing import Union, List, Optional

from collections.abc import Mapping, Sequence

import torch.utils.data
from torch.utils.data.dataloader import default_collate
from torch_geometric.data import Data, HeteroData, Dataset, Batch
from torch_geometric_temporal.signal import StaticGraphTemporalSignal as SGTS
from torch_geometric_temporal.signal import DynamicGraphTemporalSignal as DGTS
from torch_geometric_temporal.signal import DynamicGraphStaticSignal as DGSS

Flunzmas

import math
import torch
import torch.linalg as linalg

def calculate_2_wasserstein_dist(X, Y):
    '''
    Calulates the two components of the 2-Wasserstein metric:
    The general formula is given by: d(P_X, P_Y) = min_{X, Y} E[|X-Y|^2]

    For multivariate gaussian distributed inputs z_X ~ MN(mu_X, cov_X) and z_Y ~ MN(mu_Y, cov_Y),