def testPointNetClassifier(self):
for feature_dims in [128, 256]:
for input_dims in [3, 6, 9]:
p = pointnet.PointNet().Classifier(input_dims=input_dims,
feature_dims=feature_dims)
# Network should produce a global feature of feature_dims.
self.assertEqual(p.output_dim, feature_dims)
self._testOutShape(p, (8, 128, input_dims), (8, feature_dims))
def testPointNetSegmentationShapeNet(self):
p = pointnet.PointNet().SegmentationShapeNet()
self.assertEqual(p.output_dim, 128)
self._testOutShape(p, (8, 2000, 3), (8, 2000, 128))
def testPointNetSegmentation(self):
p = pointnet.PointNet().Segmentation()
# Network takes batch_size=8 input and produce 128-dim pointwise feature.
self.assertEqual(p.output_dim, 128)
self._testOutShape(p, (8, 100, 3), (8, 100, 128))
