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import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import Linear
import torch_geometric.transforms as T
from torch_geometric.datasets import ModelNet
from torch_geometric.loader import DataLoader
from torch_geometric.nn import MLP, DynamicEdgeConv, global_max_pool
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data/ModelNet10')
pre_transform, transform = T.NormalizeScale(), T.SamplePoints(1024)
train_dataset = ModelNet(path, '10', True, transform, pre_transform)
test_dataset = ModelNet(path, '10', False, transform, pre_transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True,
num_workers=6)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False,
num_workers=6)
class Net(torch.nn.Module):
def __init__(self, out_channels, k=20, aggr='max'):
super().__init__()
self.conv1 = DynamicEdgeConv(MLP([2 * 3, 64, 64, 64]), k, aggr)
self.conv2 = DynamicEdgeConv(MLP([2 * 64, 128]), k, aggr)
self.lin1 = Linear(128 + 64, 1024)
self.mlp = MLP([1024, 512, 256, out_channels], dropout=0.5, norm=None)
def forward(self, data):
pos, batch = data.pos, data.batch
x1 = self.conv1(pos, batch)
x2 = self.conv2(x1, batch)
out = self.lin1(torch.cat([x1, x2], dim=1))
out = global_max_pool(out, batch)
out = self.mlp(out)
return F.log_softmax(out, dim=1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(train_dataset.num_classes, k=20).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
def train():
model.train()
total_loss = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
out = model(data)
loss = F.nll_loss(out, data.y)
loss.backward()
total_loss += loss.item() * data.num_graphs
optimizer.step()
return total_loss / len(train_dataset)
def test(loader):
model.eval()
correct = 0
for data in loader:
data = data.to(device)
with torch.no_grad():
pred = model(data).max(dim=1)[1]
correct += pred.eq(data.y).sum().item()
return correct / len(loader.dataset)
for epoch in range(1, 201):
loss = train()
test_acc = test(test_loader)
print(f'Epoch {epoch:03d}, Loss: {loss:.4f}, Test: {test_acc:.4f}')
scheduler.step()
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