pointnet2_segmentation.py

import os.path as osp

import torch
import torch.nn.functional as F
from pointnet2_classification import GlobalSAModule, SAModule
from torchmetrics.functional import jaccard_index

import torch_geometric.transforms as T
from torch_geometric.datasets import ShapeNet
from torch_geometric.loader import DataLoader
from torch_geometric.nn import MLP, knn_interpolate
from torch_geometric.typing import WITH_TORCH_CLUSTER
from torch_geometric.utils import scatter

if not WITH_TORCH_CLUSTER:
    quit("This example requires 'torch-cluster'")

category = 'Airplane'  # Pass in `None` to train on all categories.
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'ShapeNet')
transform = T.Compose([
    T.RandomJitter(0.01),
    T.RandomRotate(15, axis=0),
    T.RandomRotate(15, axis=1),
    T.RandomRotate(15, axis=2)
])
pre_transform = T.NormalizeScale()
train_dataset = ShapeNet(path, category, split='trainval', transform=transform,
                         pre_transform=pre_transform)
test_dataset = ShapeNet(path, category, split='test',
                        pre_transform=pre_transform)
train_loader = DataLoader(train_dataset, batch_size=12, shuffle=True,
                          num_workers=6)
test_loader = DataLoader(test_dataset, batch_size=12, shuffle=False,
                         num_workers=6)


class FPModule(torch.nn.Module):
    def __init__(self, k, nn):
        super().__init__()
        self.k = k
        self.nn = nn

    def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip):
        x = knn_interpolate(x, pos, pos_skip, batch, batch_skip, k=self.k)
        if x_skip is not None:
            x = torch.cat([x, x_skip], dim=1)
        x = self.nn(x)
        return x, pos_skip, batch_skip


class Net(torch.nn.Module):
    def __init__(self, num_classes):
        super().__init__()

        # Input channels account for both `pos` and node features.
        self.sa1_module = SAModule(0.2, 0.2, MLP([3 + 3, 64, 64, 128]))
        self.sa2_module = SAModule(0.25, 0.4, MLP([128 + 3, 128, 128, 256]))
        self.sa3_module = GlobalSAModule(MLP([256 + 3, 256, 512, 1024]))

        self.fp3_module = FPModule(1, MLP([1024 + 256, 256, 256]))
        self.fp2_module = FPModule(3, MLP([256 + 128, 256, 128]))
        self.fp1_module = FPModule(3, MLP([128 + 3, 128, 128, 128]))

        self.mlp = MLP([128, 128, 128, num_classes], dropout=0.5, norm=None)

        self.lin1 = torch.nn.Linear(128, 128)
        self.lin2 = torch.nn.Linear(128, 128)
        self.lin3 = torch.nn.Linear(128, num_classes)

    def forward(self, data):
        sa0_out = (data.x, data.pos, data.batch)
        sa1_out = self.sa1_module(*sa0_out)
        sa2_out = self.sa2_module(*sa1_out)
        sa3_out = self.sa3_module(*sa2_out)

        fp3_out = self.fp3_module(*sa3_out, *sa2_out)
        fp2_out = self.fp2_module(*fp3_out, *sa1_out)
        x, _, _ = self.fp1_module(*fp2_out, *sa0_out)

        return self.mlp(x).log_softmax(dim=-1)


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(train_dataset.num_classes).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)


def train():
    model.train()

    total_loss = correct_nodes = total_nodes = 0
    for i, data in enumerate(train_loader):
        data = data.to(device)
        optimizer.zero_grad()
        out = model(data)
        loss = F.nll_loss(out, data.y)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        correct_nodes += out.argmax(dim=1).eq(data.y).sum().item()
        total_nodes += data.num_nodes

        if (i + 1) % 10 == 0:
            print(f'[{i+1}/{len(train_loader)}] Loss: {total_loss / 10:.4f} '
                  f'Train Acc: {correct_nodes / total_nodes:.4f}')
            total_loss = correct_nodes = total_nodes = 0


@torch.no_grad()
def test(loader):
    model.eval()

    ious, categories = [], []
    y_map = torch.empty(loader.dataset.num_classes, device=device).long()
    for data in loader:
        data = data.to(device)
        outs = model(data)

        sizes = (data.ptr[1:] - data.ptr[:-1]).tolist()
        for out, y, category in zip(outs.split(sizes), data.y.split(sizes),
                                    data.category.tolist()):
            category = list(ShapeNet.seg_classes.keys())[category]
            part = ShapeNet.seg_classes[category]
            part = torch.tensor(part, device=device)

            y_map[part] = torch.arange(part.size(0), device=device)

            iou = jaccard_index(out[:, part].argmax(dim=-1), y_map[y],
                                num_classes=part.size(0), absent_score=1.0)
            ious.append(iou)

        categories.append(data.category)

    iou = torch.tensor(ious, device=device)
    category = torch.cat(categories, dim=0)

    mean_iou = scatter(iou, category, reduce='mean')  # Per-category IoU.
    return float(mean_iou.mean())  # Global IoU.


for epoch in range(1, 31):
    train()
    iou = test(test_loader)
    print(f'Epoch: {epoch:02d}, Test IoU: {iou:.4f}')