Safran/PointMLP
1
0
Fork 0
Code Issues Pull requests Packages Projects Releases Wiki Activity

update

Browse source
This commit is contained in:
Xu Ma 2021-10-04 12:50:22 -04:00
parent 5fb50b9eda
commit 4a38583069
2 changed files with 6 additions and 142 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

5
part_segmentation/models/__init__.py
View file

@ -1,5 +1,2 @@
from __future__ import absolute_import
from .curvenet_seg import CurveNet
from .pointMLP31 import model31G, model31J, model31K, model31D, model31A
from .pointMLP32 import model32A, model32B, model32C, model32D, model32E, model32F, model32G
from .pointMLP33 import model33A, model33B, model33C, model33D, model33E, model33F, model33G
from .pointMLP import pointMLP

143
part_segmentation/models/pointMLP31.py → part_segmentation/models/pointMLP.py
View file

@ -1,13 +1,3 @@
"""
Based on PointMLP9, change to 128, 256, 512, 512
Based on PointMLP4, use fps replace random sample.
Based on PointMLP3, change dropout to 0.1
Based on PointMLP2, add blocks in decode, change channel numbers.
Based on PointMLP1, using poseExtraction to replace MLP in decode.
PointsformerE2, 1)change relu to GELU, 2) change backbone to model24.
"""
import torch
import torch.nn as nn
@ -337,14 +327,14 @@ class PointNetFeaturePropagation(nn.Module):
class PointMLP31(nn.Module):
class PointMLP(nn.Module):
def __init__(self, num_classes=50,points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[2,2,2,2],
gmp_dim=64,cls_dim=64, **kwargs):
super(PointMLP31, self).__init__()
super(PointMLP, self).__init__()
self.stages = len(pre_blocks)
self.class_num = num_classes
self.points = points
@ -455,143 +445,20 @@ class PointMLP31(nn.Module):
return x
def model31A(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=False, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31B(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes,points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=False, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[3, 3, 3, 3], pos_blocks=[3, 3, 3, 3],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[3, 3, 3, 3],
gmp_dim=64,cls_dim=64, **kwargs)
def model31C(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes,points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=False, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[24, 24, 24, 24], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[3, 3, 3, 3],
gmp_dim=64,cls_dim=64, **kwargs)
def model31D(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=32, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=False, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31E(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=False, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[4, 4, 4, 4], pos_blocks=[4, 4, 4, 4],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[6,6,6,6],
gmp_dim=64,cls_dim=64, **kwargs)
def model31F(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=False, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 2, 4, 2],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31G(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
def pointMLP(num_classes=50, **kwargs) -> PointMLP:
return PointMLP(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31G1(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="leakyrelu0.2", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31G2(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="leakyrelu", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31G3(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="gelu", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31H(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="gelu", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31I(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="relu", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 1], pre_blocks=[3, 3, 3, 3], pos_blocks=[3, 3, 3, 3],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4, 4, 4, 4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31J(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="leakyrelu", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
def model31K(num_classes=50, **kwargs) -> PointMLP31:
return PointMLP31(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0,
activation="leakyrelu0.2", bias=True, use_xyz=True, normalize="anchor",
dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4],
gmp_dim=64,cls_dim=64, **kwargs)
if __name__ == '__main__':
data = torch.rand(2, 3, 2048)
norm = torch.rand(2, 3, 2048)
cls_label = torch.rand([2, 16])
print("===> testing modelD ...")
model = model31D(50)
model = model31G(50)
out = model(data, cls_label) # [2,2048,50]
print(out.shape)
model = model31A(50)
out = model(data, cls_label) # [2,2048,50]
print(out.shape)
model = model31B(50)
out = model(data, cls_label) # [2,2048,50]
print(out.shape)
model = model31C(50)
out = model(data, cls_label) # [2,50, 2048]
print(out.shape)