mnist_grid2.py

import torch
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader

from torch_geometric.utils import grid
from torch_geometric.nn import SplineConv

train_dataset = MNIST('/tmp/MNIST', train=True, transform=ToTensor())
test_dataset = MNIST('/tmp/MNIST', train=False, transform=ToTensor())
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True,
                          drop_last=True)
test_loader = DataLoader(test_dataset, batch_size=64, drop_last=True)


def to_batch(edge_index, pos, batch_size):
    edge_indices = [edge_index + pos.size(0) * i for i in range(batch_size)]
    edge_index = torch.cat(edge_indices, dim=1)
    pos = torch.cat([pos] * batch_size, dim=0)
    edge_attr = pos[edge_index[0]] - pos[edge_index[1]]
    return edge_index, (edge_attr + 1.) / 2.


edge_index1, edge_attr1 = to_batch(*grid(28, 28), batch_size=64)
edge_index2, edge_attr2 = to_batch(*grid(14, 14), batch_size=64)


class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = SplineConv(1, 32, dim=2, kernel_size=3)
        self.conv2 = SplineConv(32, 64, dim=2, kernel_size=3)
        self.fc1 = torch.nn.Linear(3136, 512)
        self.fc2 = torch.nn.Linear(512, 10)

    def forward(self, x):
        x = x.view(-1, 1)
        x = F.elu(self.conv1(x.view(-1, 1), edge_index1, edge_attr1))
        x = x.view(64, 28, 28, 32).permute(0, 3, 1, 2)
        x = F.max_pool2d(x, kernel_size=2)
        x = x.permute(0, 2, 3, 1).contiguous().view(-1, 32)
        x = F.elu(self.conv2(x, edge_index2, edge_attr2))
        x = x.view(64, 14, 14, 64).permute(0, 3, 1, 2)
        x = F.max_pool2d(x, kernel_size=2)
        x = x.contiguous().view(64, -1)

        x = F.elu(self.fc1(x))
        return F.log_softmax(self.fc2(x), dim=1)


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
edge_index1, edge_attr1 = edge_index1.to(device), edge_attr1.to(device)
edge_index2, edge_attr2 = edge_index2.to(device), edge_attr2.to(device)


def train(epoch):
    model.train()

    for i, (x, y) in enumerate(train_loader):
        x, y = x.to(device), y.to(device)
        optimizer.zero_grad()
        loss = F.nll_loss(model(x), y)
        loss.backward()
        optimizer.step()
        print(i, len(train_loader), loss.item())


def test():
    model.eval()

    correct = 0
    for i, (x, y) in enumerate(test_loader):
        x, y = x.to(device), y.to(device)
        pred = model(x).max(1)[1]
        correct += pred.eq(y).sum().item()
    return correct / len(test_dataset)


for epoch in range(1, 50):
    train(epoch)
    test_acc = test()
    print('Epoch: {:02d}, Test: {:.4f}'.format(epoch, test_acc))

For the grid experiment, we use normal 2D max pooling since we cannot flatten the output features otherwise. To increase accuracy, adding two-hop neighbors to the grid helps. contiguous needs to be called since permute permutes the dimensions of the node features, and GNN operators generally expect inputs with contiguous memory layout.

What's two-hop neighbors? Is it similar to strides in normal CNN? How do I add that to the grid?