103 lines
3.3 KiB
Python
Executable File
103 lines
3.3 KiB
Python
Executable File
import torch
|
|
import torch.nn as nn
|
|
import sys
|
|
sys.path.append('..')
|
|
from pointnet2.pointnet2_modules import PointnetFPModule, PointnetSAModuleMSG
|
|
import pointnet2.pytorch_utils as pt_utils
|
|
|
|
|
|
def get_model(input_channels=0):
|
|
return Pointnet2MSG(input_channels=input_channels)
|
|
|
|
|
|
NPOINTS = [4096, 1024, 256, 64]
|
|
RADIUS = [[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]]
|
|
NSAMPLE = [[16, 32], [16, 32], [16, 32], [16, 32]]
|
|
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]]]
|
|
FP_MLPS = [[128, 128], [256, 256], [512, 512], [512, 512]]
|
|
CLS_FC = [128]
|
|
DP_RATIO = 0.5
|
|
|
|
|
|
class Pointnet2MSG(nn.Module):
|
|
def __init__(self, input_channels=6):
|
|
super().__init__()
|
|
|
|
self.SA_modules = nn.ModuleList()
|
|
channel_in = input_channels
|
|
|
|
skip_channel_list = [input_channels]
|
|
for k in range(NPOINTS.__len__()):
|
|
mlps = 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=NPOINTS[k],
|
|
radii=RADIUS[k],
|
|
nsamples=NSAMPLE[k],
|
|
mlps=mlps,
|
|
use_xyz=True,
|
|
bn=True
|
|
)
|
|
)
|
|
skip_channel_list.append(channel_out)
|
|
channel_in = channel_out
|
|
|
|
self.FP_modules = nn.ModuleList()
|
|
|
|
for k in range(FP_MLPS.__len__()):
|
|
pre_channel = FP_MLPS[k + 1][-1] if k + 1 < len(FP_MLPS) else channel_out
|
|
self.FP_modules.append(
|
|
PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] + FP_MLPS[k])
|
|
)
|
|
|
|
cls_layers = []
|
|
pre_channel = FP_MLPS[0][-1]
|
|
for k in range(0, CLS_FC.__len__()):
|
|
cls_layers.append(pt_utils.Conv1d(pre_channel, CLS_FC[k], bn=True))
|
|
pre_channel = CLS_FC[k]
|
|
cls_layers.append(pt_utils.Conv1d(pre_channel, 1, activation=None))
|
|
cls_layers.insert(1, nn.Dropout(0.5))
|
|
self.cls_layer = nn.Sequential(*cls_layers)
|
|
|
|
def _break_up_pc(self, pc):
|
|
xyz = pc[..., 0:3].contiguous()
|
|
features = (
|
|
pc[..., 3:].transpose(1, 2).contiguous()
|
|
if pc.size(-1) > 3 else None
|
|
)
|
|
|
|
return xyz, features
|
|
|
|
def forward(self, pointcloud: torch.cuda.FloatTensor):
|
|
xyz, features = self._break_up_pc(pointcloud)
|
|
|
|
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])
|
|
|
|
print(li_xyz.shape, li_features.shape)
|
|
|
|
l_xyz.append(li_xyz)
|
|
l_features.append(li_features)
|
|
|
|
for i in range(-1, -(len(self.FP_modules) + 1), -1):
|
|
l_features[i - 1] = self.FP_modules[i](
|
|
l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i]
|
|
)
|
|
|
|
pred_cls = self.cls_layer(l_features[0]).transpose(1, 2).contiguous() # (B, N, 1)
|
|
return pred_cls
|
|
|
|
if __name__ == '__main__':
|
|
net = Pointnet2MSG(0).cuda()
|
|
pts = torch.randn(2, 1024, 3).cuda()
|
|
|
|
pre = net(pts)
|
|
print(pre.shape)
|