import torch.nn as nn
import torch.nn.functional as F
from models.pointnet_util import PointNetSetAbstraction,PointNetFeaturePropagation
class get_model(nn.Module):
def __init__(self, num_classes):
super(get_model, self).__init__()
self.sa1 = PointNetSetAbstraction(1024, 0.1, 32, 9 + 3, [32, 32, 64], False)
self.sa2 = PointNetSetAbstraction(256, 0.2, 32, 64 + 3, [64, 64, 128], False)
self.sa3 = PointNetSetAbstraction(64, 0.4, 32, 128 + 3, [128, 128, 256], False)
self.sa4 = PointNetSetAbstraction(16, 0.8, 32, 256 + 3, [256, 256, 512], False)
self.fp4 = PointNetFeaturePropagation(768, [256, 256])
self.fp3 = PointNetFeaturePropagation(384, [256, 256])
self.fp2 = PointNetFeaturePropagation(320, [256, 128])
self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128])
self.conv1 = nn.Conv1d(128, 128, 1)
self.bn1 = nn.BatchNorm1d(128)
self.drop1 = nn.Dropout(0.5)
self.conv2 = nn.Conv1d(128, num_classes, 1)
def forward(self, xyz):
l0_points = xyz
l0_xyz = xyz[:,:3,:]
l1_xyz, l1_points = self.sa1(l0_xyz, l0_points)
l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)
l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)
l4_xyz, l4_points = self.sa4(l3_xyz, l3_points)
l3_points = self.fp4(l3_xyz, l4_xyz, l3_points, l4_points)
l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points)
l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)
l0_points = self.fp1(l0_xyz, l1_xyz, None, l1_points)
x = self.drop1(F.relu(self.bn1(self.conv1(l0_points))))
x = self.conv2(x)
x = F.log_softmax(x, dim=1)
x = x.permute(0, 2, 1)
return x, l4_points
class get_loss(nn.Module):
def __init__(self):
super(get_loss, self).__init__()
def forward(self, pred, target, trans_feat, weight):
total_loss = F.nll_loss(pred, target, weight=weight)
return total_loss
if __name__ == '__main__':
import torch
model = get_model(13)
xyz = torch.rand(6, 9, 2048)
(model(xyz))
