def test_pn2(self):
from torch_points3d.applications.pointnet2 import PointNet2
input_nc = 2
num_layers = 3
output_nc = 5
model = PointNet2(
architecture="unet",
input_nc=input_nc,
output_nc=output_nc,
num_layers=num_layers,
multiscale=True,
config=None,
)
dataset = MockDataset(input_nc, num_points=512)
self.assertEqual(len(model._modules["down_modules"]), num_layers - 1)
self.assertEqual(len(model._modules["inner_modules"]), 1)
self.assertEqual(len(model._modules["up_modules"]), num_layers)
try:
data_out = model.forward(dataset[0])
self.assertEqual(data_out.x.shape[1], output_nc)
except Exception as e:
print("Model failing:")
print(model)
raise e
import torch
from torch_points3d.applications.pointnet2 import PointNet2
from torch_geometric.data import Batch, Data
num_points = 1024
num_classes = 10
input_nc = 5
pos = torch.randn((num_points, 3)).unsqueeze(0)
T = torch.randn((num_points, input_nc)).unsqueeze(0)
data = Data(pos=pos, x=T)
data = Batch.from_data_list([data, data])
print(data)
# Batch(batch=[2], pos=[2, 1024, 3], x=[2, 1024, 5])
model = PointNet2(
architecture="encoder",
input_nc=input_nc,
num_layers=3,
output_nc=num_classes,
)
res = model(data)
print(res)
# Data(x=[2, 10, 1])
import torch
from torch_points3d.applications.pointnet2 import PointNet2
from torch_geometric.data import Batch, Data
num_points = 1024
num_classes = 10
input_nc = 5
pos = torch.randn((num_points, 3)).unsqueeze(0)
T = torch.randn((num_points, input_nc)).unsqueeze(0)
data = Data(pos=pos, x=T)
data = Batch.from_data_list([data, data])
print(data)
# Batch(batch=[2], pos=[2, 1024, 3], x=[2, 1024, 5])
# pos gets concatenated with x features
model = PointNet2(architecture="unet",
input_nc=input_nc,
num_layers=3,
output_nc=num_classes)
res = model(data)
print(res)
# Data(pos=[2, 1024, 3], x=[2, 10, 1024])