118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import os
|
||
|
|
import sys
|
||
|
|
path = os.path.abspath(__file__)
|
||
|
|
for i in range(3):
|
||
|
|
path = os.path.dirname(path)
|
||
|
|
PROJECT_ROOT = path
|
||
|
|
sys.path.append(PROJECT_ROOT)
|
||
|
|
from modules.module_lib.pointnet2_utils.pointnet2.pointnet2_modules import PointnetSAModuleMSG
|
||
|
|
from modules.pts_encoder.abstract_pts_encoder import PointsEncoder
|
||
|
|
|
||
|
|
ClsMSG_CFG_Dense = {
|
||
|
|
'NPOINTS': [512, 256, 128, None],
|
||
|
|
'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
|
||
|
|
'NSAMPLE': [[32, 64], [16, 32], [8, 16], [None, None]],
|
||
|
|
'MLPS': [[[16, 16, 32], [32, 32, 64]],
|
||
|
|
[[64, 64, 128], [64, 96, 128]],
|
||
|
|
[[128, 196, 256], [128, 196, 256]],
|
||
|
|
[[256, 256, 512], [256, 384, 512]]],
|
||
|
|
'DP_RATIO': 0.5,
|
||
|
|
}
|
||
|
|
|
||
|
|
ClsMSG_CFG_Light = {
|
||
|
|
'NPOINTS': [512, 256, 128, None],
|
||
|
|
'RADIUS': [[0.02, 0.04], [0.04, 0.08], [0.08, 0.16], [None, None]],
|
||
|
|
'NSAMPLE': [[16, 32], [16, 32], [16, 32], [None, None]],
|
||
|
|
'MLPS': [[[16, 16, 32], [32, 32, 64]],
|
||
|
|
[[64, 64, 128], [64, 96, 128]],
|
||
|
|
[[128, 196, 256], [128, 196, 256]],
|
||
|
|
[[256, 256, 512], [256, 384, 512]]],
|
||
|
|
'DP_RATIO': 0.5,
|
||
|
|
}
|
||
|
|
|
||
|
|
ClsMSG_CFG_Lighter = {
|
||
|
|
'NPOINTS': [512, 256, 128, 64, None],
|
||
|
|
'RADIUS': [[0.01], [0.02], [0.04], [0.08], [None]],
|
||
|
|
'NSAMPLE': [[64], [32], [16], [8], [None]],
|
||
|
|
'MLPS': [[[32, 32, 64]],
|
||
|
|
[[64, 64, 128]],
|
||
|
|
[[128, 196, 256]],
|
||
|
|
[[256, 256, 512]],
|
||
|
|
[[512, 512, 1024]]],
|
||
|
|
'DP_RATIO': 0.5,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
def select_params(name):
|
||
|
|
if name == 'light':
|
||
|
|
return ClsMSG_CFG_Light
|
||
|
|
elif name == 'lighter':
|
||
|
|
return ClsMSG_CFG_Lighter
|
||
|
|
elif name == 'dense':
|
||
|
|
return ClsMSG_CFG_Dense
|
||
|
|
else:
|
||
|
|
raise NotImplementedError
|
||
|
|
|
||
|
|
|
||
|
|
def break_up_pc(pc):
|
||
|
|
xyz = pc[..., 0:3].contiguous()
|
||
|
|
features = (
|
||
|
|
pc[..., 3:].transpose(1, 2).contiguous()
|
||
|
|
if pc.size(-1) > 3 else None
|
||
|
|
)
|
||
|
|
|
||
|
|
return xyz, features
|
||
|
|
|
||
|
|
|
||
|
|
class PointNet2Encoder(PointsEncoder):
|
||
|
|
def encode_points(self, pts):
|
||
|
|
return self.forward(pts)
|
||
|
|
|
||
|
|
def __init__(self, input_channels=6, params_name="light"):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.SA_modules = nn.ModuleList()
|
||
|
|
channel_in = input_channels
|
||
|
|
selected_params = select_params(params_name)
|
||
|
|
for k in range(selected_params['NPOINTS'].__len__()):
|
||
|
|
mlps = selected_params['MLPS'][k].copy()
|
||
|
|
channel_out = 0
|
||
|
|
for idx in range(mlps.__len__()):
|
||
|
|
mlps[idx] = [channel_in] + mlps[idx]
|
||
|
|
channel_out += mlps[idx][-1]
|
||
|
|
|
||
|
|
self.SA_modules.append(
|
||
|
|
PointnetSAModuleMSG(
|
||
|
|
npoint=selected_params['NPOINTS'][k],
|
||
|
|
radii=selected_params['RADIUS'][k],
|
||
|
|
nsamples=selected_params['NSAMPLE'][k],
|
||
|
|
mlps=mlps,
|
||
|
|
use_xyz=True,
|
||
|
|
bn=True
|
||
|
|
)
|
||
|
|
)
|
||
|
|
channel_in = channel_out
|
||
|
|
|
||
|
|
def forward(self, point_cloud: torch.cuda.FloatTensor):
|
||
|
|
xyz, features = break_up_pc(point_cloud)
|
||
|
|
|
||
|
|
l_xyz, l_features = [xyz], [features]
|
||
|
|
for i in range(len(self.SA_modules)):
|
||
|
|
li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i])
|
||
|
|
l_xyz.append(li_xyz)
|
||
|
|
l_features.append(li_features)
|
||
|
|
return l_features[-1].squeeze(-1)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
seed = 100
|
||
|
|
torch.manual_seed(seed)
|
||
|
|
torch.cuda.manual_seed(seed)
|
||
|
|
net = PointNet2Encoder(0).cuda()
|
||
|
|
pts = torch.randn(2, 1024, 3).cuda()
|
||
|
|
print(torch.mean(pts, dim=1))
|
||
|
|
pre = net.encode_points(pts)
|
||
|
|
print(pre.shape)
|