pointnet.py

from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.utils as vutils
from torch.autograd import Variable
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import pdb
import torch.nn.functional as F
if torch.cuda.is_available():
    import torch.backends.cudnn as cudnn


class STN3d(nn.Module):
    def __init__(self, num_points = 2500):
        super(STN3d, self).__init__()
        self.num_points = num_points
        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.mp1 = torch.nn.MaxPool1d(num_points)
        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 = self.mp1(x)
        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 = Variable(torch.from_numpy(np.array([1,0,0,0,1,0,0,0,1]).astype(np.float32))).view(1,9).repeat(batchsize,1)
        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, num_points = 2500, global_feat = True):
        super(PointNetfeat, self).__init__()
        self.stn = STN3d(num_points = num_points)
        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.mp1 = torch.nn.MaxPool1d(num_points)
        self.num_points = num_points
        self.global_feat = global_feat
    def forward(self, x):
        batchsize = x.size()[0]
        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 = self.mp1(x)
        x = x.view(-1, 1024)
        if self.global_feat:
            return x, trans
        else:
            x = x.view(-1, 1024, 1).repeat(1, 1, self.num_points)
            return torch.cat([x, pointfeat], 1), trans

class PointNetCls(nn.Module):
    def __init__(self, num_points = 2500, k = 2):
        super(PointNetCls, self).__init__()
        self.num_points = num_points
        self.feat = PointNetfeat(num_points, 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, num_points = 2500, k = 2):
        super(PointNetDenseCls, self).__init__()
        self.num_points = num_points
        self.k = k
        self.feat = PointNetfeat(num_points, 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]
        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, self.num_points, self.k)
        return x, trans


if __name__ == '__main__':
    sim_data = Variable(torch.rand(32,3,2500))
    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())