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))