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import argparse
import os.path as osp
import random
import torch
import torch.nn.functional as F
from torch.nn import Linear
import torch_geometric.transforms as T
from torch_geometric.datasets import MedShapeNet, ModelNet
from torch_geometric.loader import DataLoader
from torch_geometric.nn import MLP, DynamicEdgeConv, global_max_pool
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
'--dataset',
type=str,
default='modelnet10',
choices=['modelnet10', 'modelnet40', 'medshapenet'],
help='Dataset name.',
)
parser.add_argument(
'--dataset_dir',
type=str,
default='./data',
help='Root directory of dataset.',
)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--num_workers', type=int, default=6)
parser.add_argument('--epochs', type=int, default=201)
args = parser.parse_args()
num_epochs = args.epochs
num_workers = args.num_workers
batch_size = args.batch_size
root = osp.join(args.dataset_dir, args.dataset)
print('The root is: ', root)
pre_transform, transform = T.NormalizeScale(), T.SamplePoints(1024)
print('The Dataset is: ', args.dataset)
if args.dataset == 'modelnet40':
print('Loading training data')
train_dataset = ModelNet(root, '40', True, transform, pre_transform)
print('Loading test data')
test_dataset = ModelNet(root, '40', False, transform, pre_transform)
elif args.dataset == 'medshapenet':
print('Loading dataset')
dataset = MedShapeNet(root=root, size=50, pre_transform=pre_transform,
transform=transform, force_reload=False)
random.seed(42)
train_indices = []
test_indices = []
for label in range(dataset.num_classes):
by_class = [
i for i, data in enumerate(dataset) if int(data.y) == label
]
random.shuffle(by_class)
split_point = int(0.7 * len(by_class))
train_indices.extend(by_class[:split_point])
test_indices.extend(by_class[split_point:])
train_dataset = dataset[train_indices]
test_dataset = dataset[test_indices]
elif args.dataset == 'modelnet10':
print('Loading training data')
train_dataset = ModelNet(root, '10', True, transform, pre_transform)
print('Loading test data')
test_dataset = ModelNet(root, '10', False, transform, pre_transform)
else:
raise ValueError(
f"Unknown dataset name '{args.dataset}'. "
f"Available options: 'modelnet10', 'modelnet40', 'medshapenet'.")
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True,
num_workers=num_workers)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False,
num_workers=num_workers)
print('Running model')
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, num_epochs):
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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