1# train ae or vae
2#### a template for pcd encoder
3mode: test
4model:
5 # can be AE, VAE, SeM and DDPM
6 name: ClM
7 # encoder config
8 encoder:
9 name: PointNet
10 in_channel: 3
11 out_channel: 46
12 point_num: 2048
13 building_block: dense
14 # num_neighbors_k: 20
15 local_feature_channels: [64, 64, 128, 256, 512]
16 dense_channels: [1024, 512, 256]
17 activation: lrelu
18 normalization: group
19 classification_losses:
20 - name: CE
21loader:
22 dataset:
23 name: PcdClsDataset
24 data_suffix: .ply
25 cls_label: MedPointS
26 data_path_list:
27 - path/to/datasets/pcd/MedPointS/classification/fold5
28 batch_size: 64
29 num_workers: 8
30 shuffle: true
31 data_transforms:
32 - name: Normalize
33 - name: FixedPoints
34 num: 2048
35 - name: ToTensor
36 dtype: float
37 label_transforms:
38 - name: ToOneHot
39 num_classes: 47
40 ignore_background: true
41 - name: ToTensor
42 dtype: float
43model_path: path/to/flemme-ckp/MedPointS/PointNet/PointNet_CLS/ckp_best_loss.pth
44
45
46evaluation_metrics:
47 cls:
48 - name: ACC
49tsne_visualization:
50 top_n: 10
51 title: t-SNE Visualization of Top-10 Classes
52 vis_dim: 2
53 label_names: MedPointS