PointNet that supports arbitrary amount of points

pointnet.py

import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F


class STN3d(nn.Module):
    def __init__(self):
        super(STN3d, self).__init__()
        self.conv1 = torch.nn.Conv1d(3, 64, 1)
        self.conv2 = torch.nn.Conv1d(64, 128, 1)
        self.conv3 = torch.nn.Conv1d(128, 1024, 1)
        self.fc1 = nn.Linear(1024, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 9)
        self.relu = nn.ReLU()

        self.bn1 = nn.BatchNorm1d(64)
        self.bn2 = nn.BatchNorm1d(128)
        self.bn3 = nn.BatchNorm1d(1024)
        self.bn4 = nn.BatchNorm1d(512)
        self.bn5 = nn.BatchNorm1d(256)


    def forward(self, x):
        batchsize = x.size()[0]
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.relu(self.bn3(self.conv3(x)))
        x = torch.max(x, dim=-1, keepdim=True)[0]
        x = x.view(-1, 1024)

        x = F.relu(self.bn4(self.fc1(x)))
        x = F.relu(self.bn5(self.fc2(x)))
        x = self.fc3(x)

        iden = torch.from_numpy(np.array([1,0,0,0,1,0,0,0,1]).astype(np.float32)).view(1,9).repeat(batchsize,1).requires_grad_()
        if x.is_cuda:
            iden = iden.cuda()
        x = x + iden
        x = x.view(-1, 3, 3)
        return x


class PointNetfeat(nn.Module):
    def __init__(self, global_feat = True):
        super(PointNetfeat, self).__init__()
        self.stn = STN3d()
        self.conv1 = torch.nn.Conv1d(3, 64, 1)
        self.conv2 = torch.nn.Conv1d(64, 128, 1)
        self.conv3 = torch.nn.Conv1d(128, 1024, 1)
        self.bn1 = nn.BatchNorm1d(64)
        self.bn2 = nn.BatchNorm1d(128)
        self.bn3 = nn.BatchNorm1d(1024)
        self.global_feat = global_feat
    def forward(self, x):
        batchsize = x.size()[0]
        num_points = x.size()[-1]
        trans = self.stn(x)
        x = x.transpose(2,1)
        x = torch.bmm(x, trans)
        x = x.transpose(2,1)
        x = F.relu(self.bn1(self.conv1(x)))
        pointfeat = x
        x = F.relu(self.bn2(self.conv2(x)))
        x = self.bn3(self.conv3(x))
        x = torch.max(x, dim=-1, keepdim=True)[0]
        x = x.view(-1, 1024)
        if self.global_feat:
            return x, trans
        else:
            x = x.view(-1, 1024, 1).repeat(1, 1, num_points)
            return torch.cat([x, pointfeat], 1), trans

class PointNetCls(nn.Module):
    def __init__(self, k = 2):
        super(PointNetCls, self).__init__()
        self.feat = PointNetfeat(global_feat=True)
        self.fc1 = nn.Linear(1024, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, k)
        self.bn1 = nn.BatchNorm1d(512)
        self.bn2 = nn.BatchNorm1d(256)
        self.relu = nn.ReLU()
    def forward(self, x):
        x, trans = self.feat(x)
        x = F.relu(self.bn1(self.fc1(x)))
        x = F.relu(self.bn2(self.fc2(x)))
        x = self.fc3(x)
        return F.log_softmax(x, dim=-1), trans

class PointNetDenseCls(nn.Module):
    def __init__(self, k = 2):
        super(PointNetDenseCls, self).__init__()
        self.k = k
        self.feat = PointNetfeat(global_feat=False)
        self.conv1 = torch.nn.Conv1d(1088, 512, 1)
        self.conv2 = torch.nn.Conv1d(512, 256, 1)
        self.conv3 = torch.nn.Conv1d(256, 128, 1)
        self.conv4 = torch.nn.Conv1d(128, self.k, 1)
        self.bn1 = nn.BatchNorm1d(512)
        self.bn2 = nn.BatchNorm1d(256)
        self.bn3 = nn.BatchNorm1d(128)

    def forward(self, x):
        batchsize = x.size()[0]
        num_points = x.size()[-1]
        x, trans = self.feat(x)
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.relu(self.bn3(self.conv3(x)))
        x = self.conv4(x)
        x = x.transpose(2,1).contiguous()
        x = F.log_softmax(x.view(-1,self.k), dim=-1)
        x = x.view(batchsize, num_points, self.k)
        return x, trans


if __name__ == '__main__':
    sim_data = torch.rand(32,3,2500).requires_grad_()
    trans = STN3d()
    out = trans(sim_data)
    print('stn', out.size())

    pointfeat = PointNetfeat(global_feat=True)
    out, _ = pointfeat(sim_data)
    print('global feat', out.size())

    pointfeat = PointNetfeat(global_feat=False)
    out, _ = pointfeat(sim_data)
    print('point feat', out.size())

    cls = PointNetCls(k = 5)
    out, _ = cls(sim_data)
    print('class', out.size())

    seg = PointNetDenseCls(k = 3)
    out, _ = seg(sim_data)
    print('seg', out.size())