import torch.nn as nn
import torch
import torch.nn.functional as F
from models.pointnet_util import PointNetSetAbstractionMsg,PointNetSetAbstraction,PointNetFeaturePropagation
class get_model(nn.Module):
def __init__(self, num_classes, normal_channel=False):
super(get_model, self).__init__()
if normal_channel:
additional_channel = 3
else:
additional_channel = 0
self.normal_channel = normal_channel
self.sa1 = PointNetSetAbstractionMsg(512, [0.1, 0.2, 0.4], [32, 64, 128], 3+additional_channel, [[32, 32, 64], [64, 64, 128], [64, 96, 128]])
self.sa2 = PointNetSetAbstractionMsg(128, [0.4,0.8], [64, 128], 128+128+64, [[128, 128, 256], [128, 196, 256]])
self.sa3 = PointNetSetAbstraction(npoint=None, radius=None, nsample=None, in_channel=512 + 3, mlp=[256, 512, 1024], group_all=True)
self.fp3 = PointNetFeaturePropagation(in_channel=1536, mlp=[256, 256])
self.fp2 = PointNetFeaturePropagation(in_channel=576, mlp=[256, 128])
self.fp1 = PointNetFeaturePropagation(in_channel=150+additional_channel, mlp=[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, cls_label):
# Set Abstraction layers
B,C,N = xyz.shape
if self.normal_channel:
l0_points = xyz
l0_xyz = xyz[:,:3,:]
else:
l0_points = xyz
l0_xyz = xyz
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)
# Feature Propagation layers
l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points)
l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)
cls_label_one_hot = cls_label.view(B,16,1).repeat(1,1,N)
l0_points = self.fp1(l0_xyz, l1_xyz, torch.cat([cls_label_one_hot,l0_xyz,l0_points],1), l1_points)
# FC layers
feat = F.relu(self.bn1(self.conv1(l0_points)))
x = self.drop1(feat)
x = self.conv2(x)
x = F.log_softmax(x, dim=1)
x = x.permute(0, 2, 1)
return x, l3_points
class get_loss(nn.Module):
def __init__(self):
super(get_loss, self).__init__()
def forward(self, pred, target, trans_feat):
total_loss = F.nll_loss(pred, target)
return total_loss
