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pipeline for single cluster

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This commit is contained in:
Si11ium
2020-06-24 10:49:10 +02:00
parent 85cf3128f1
commit 965b805ee9
3 changed files with 6 additions and 6 deletions
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2
main_pipeline.py
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@ -72,7 +72,7 @@ if __name__ == '__main__':
test_dataset = ShapeNetPartSegDataset('data', mode=GlobalVar.data_split.predict, collate_per_segment=False, test_dataset = ShapeNetPartSegDataset('data', mode=GlobalVar.data_split.predict, collate_per_segment=False,
refresh=True, transform=transforms) refresh=True, transform=transforms)
grid_clusters = cluster_cubes(test_dataset[1], [3, 3, 3], max_points_per_cluster=1024) grid_clusters = cluster_cubes(test_dataset[1], [1, 1, 1], max_points_per_cluster=32768)
ps.init() ps.init()

2
models/point_net_2.py
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@ -25,7 +25,7 @@ class PointNet2(BaseValMixin,
# Dataset # Dataset
# ============================================================================= # =============================================================================
# rot_max_angle = 15 # rot_max_angle = 15
trans_max_distance = 0.01 trans_max_distance = 0.02
transforms = Compose( transforms = Compose(
[ [
RandomFlip(0, p=0.8), RandomFlip(0, p=0.8),

8
utils/pointcloud.py
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@ -103,10 +103,6 @@ def cluster_per_column(pc, column):
def cluster_cubes(data, cluster_dims, max_points_per_cluster=-1, min_points_per_cluster=-1): def cluster_cubes(data, cluster_dims, max_points_per_cluster=-1, min_points_per_cluster=-1):
if cluster_dims[0] == 1 and cluster_dims[1] == 1 and cluster_dims[2] == 1:
print("no need to cluster.")
return [farthest_point_sampling(data, max_points_per_cluster)]
if isinstance(data, Data): if isinstance(data, Data):
import torch import torch
candidate_list = list() candidate_list = list()
@ -119,6 +115,10 @@ def cluster_cubes(data, cluster_dims, max_points_per_cluster=-1, min_points_per_
data = torch.cat(candidate_list, dim=-1).numpy() data = torch.cat(candidate_list, dim=-1).numpy()
if cluster_dims[0] == 1 and cluster_dims[1] == 1 and cluster_dims[2] == 1:
print("no need to cluster.")
return [farthest_point_sampling(data, max_points_per_cluster)]
max = data[:, :3].max(axis=0) max = data[:, :3].max(axis=0)
max += max * 0.01 max += max * 0.01