pytorch_geometric | Graph Neural Network Library for PyTorch | Machine Learning library

import torch
from torch import Tensor
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import MessagePassing

class EdgeConv(MessagePassing):
    def __init__(self, in_channels, out_channels):
        super().__init__(aggr="max")  

import torch
from torch import Tensor
from torch_geometric.nn import GCNConv
from torch_geometric.datasets import Planetoid

dataset = Planetoid(root='.', name='Cora')

class GCN(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels,   

pip install pyg-lib torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html
pip install torch-geometric

pip install torch-cluster torch-spline-conv -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html

pip install pyg-lib t  

"""
An implementation of RandLA-Net based on the paper:
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Reference: https://arxiv.org/abs/1911.11236
"""
import os.path as osp

import torch
import torch.nn.functional as F
from r  

# This code achieves a performance of around 96.60%. However, it is not
# directly comparable to the results reported by the TGN paper since a
# slightly different evaluation setup is used here.
# In particular, predictions in the same batch are made  

""""
Implements the link prediction task on the FB15k237 datasets according to the
`"Modeling Relational Data with Graph Convolutional Networks"
`_ paper.

Caution: This script is executed in a full-batch fashion, and therefore needs
to run on CPU (f  

pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+cpu.html
pip install --no-index torch-cluster -f https://py

def pos_sample(self, batch):
    batch = batch.repeat(self.walks_per_node)
    rowptr, col, _ = self.adj.csr()
    rw = random_walk(rowptr, col, batch, self.walk_length, self.p, self.q)
    if not isinstance(rw, torch.Tensor):
        rw =

FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04

RUN apt-get update && apt-get install -y --no-install-recommends \
        apt-utils \
        python3.6 \
        python-dev \
        python-pip \
        python-setuptools \