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"""An implementation of RandLA-Net based on the `"RandLA-Net: Efficient
Semantic Segmentation of Large-Scale Point Clouds"
<https://arxiv.org/abs/1911.11236>`_ paper.
"""
import os.path as osp
import torch
import torch.nn.functional as F
from randlanet_classification import DilatedResidualBlock, SharedMLP, decimate
from torch.nn import Linear
from torchmetrics.functional import jaccard_index
from tqdm import tqdm
import torch_geometric.transforms as T
from torch_geometric.datasets import ShapeNet
from torch_geometric.loader import DataLoader
from torch_geometric.nn import 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.
category_num_classes = 4 # 4 for Airplane - see ShapeNet for details
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, 12, shuffle=True, num_workers=6)
test_loader = DataLoader(test_dataset, 12, shuffle=False, num_workers=6)
class FPModule(torch.nn.Module):
"""Upsampling with a skip connection."""
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)
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_features: int,
num_classes: int,
decimation: int = 4,
num_neighbors: int = 16,
return_logits: bool = False,
):
super().__init__()
self.decimation = decimation
# An option to return logits instead of log probabilities:
self.return_logits = return_logits
# Authors use 8, which is a bottleneck
# for the final MLP, and also when num_classes>8
# or num_features>8.
d_bottleneck = max(32, num_classes, num_features)
self.fc0 = Linear(num_features, d_bottleneck)
self.block1 = DilatedResidualBlock(num_neighbors, d_bottleneck, 32)
self.block2 = DilatedResidualBlock(num_neighbors, 32, 128)
self.block3 = DilatedResidualBlock(num_neighbors, 128, 256)
self.block4 = DilatedResidualBlock(num_neighbors, 256, 512)
self.mlp_summit = SharedMLP([512, 512])
self.fp4 = FPModule(1, SharedMLP([512 + 256, 256]))
self.fp3 = FPModule(1, SharedMLP([256 + 128, 128]))
self.fp2 = FPModule(1, SharedMLP([128 + 32, 32]))
self.fp1 = FPModule(1, SharedMLP([32 + 32, d_bottleneck]))
self.mlp_classif = SharedMLP([d_bottleneck, 64, 32],
dropout=[0.0, 0.5])
self.fc_classif = Linear(32, num_classes)
def forward(self, x, pos, batch, ptr):
x = x if x is not None else pos
b1_out = self.block1(self.fc0(x), pos, batch)
b1_out_decimated, ptr1 = decimate(b1_out, ptr, self.decimation)
b2_out = self.block2(*b1_out_decimated)
b2_out_decimated, ptr2 = decimate(b2_out, ptr1, self.decimation)
b3_out = self.block3(*b2_out_decimated)
b3_out_decimated, ptr3 = decimate(b3_out, ptr2, self.decimation)
b4_out = self.block4(*b3_out_decimated)
b4_out_decimated, _ = decimate(b4_out, ptr3, self.decimation)
mlp_out = (
self.mlp_summit(b4_out_decimated[0]),
b4_out_decimated[1],
b4_out_decimated[2],
)
fp4_out = self.fp4(*mlp_out, *b3_out_decimated)
fp3_out = self.fp3(*fp4_out, *b2_out_decimated)
fp2_out = self.fp2(*fp3_out, *b1_out_decimated)
fp1_out = self.fp1(*fp2_out, *b1_out)
x = self.mlp_classif(fp1_out[0])
logits = self.fc_classif(x)
if self.return_logits:
return logits
probas = logits.log_softmax(dim=-1)
return probas
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(3, category_num_classes).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
def train():
model.train()
total_loss = correct_nodes = total_nodes = 0
for i, data in tqdm(enumerate(train_loader)):
data = data.to(device)
optimizer.zero_grad()
out = model(data.x, data.pos, data.batch, data.ptr)
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.x, data.pos, data.batch, data.ptr)
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}')
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