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pointnet.py
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# -*- coding: utf-8 -*-
import numpy as np
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
class STN3D(nn.Module):
def __init__(self):
super(STN3D, self).__init__()
self.conv1 = nn.Conv1d(3, 64, 1)
self.conv2 = nn.Conv1d(64, 128, 1)
self.conv3 = nn.Conv1d(128, 1024, 1)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 9)
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, 2, 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 = autograd.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 += 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 = nn.Conv1d(3, 64, 1)
self.conv2 = nn.Conv1d(64, 128, 1)
self.conv3 = 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]
n_pts = x.size()[2]
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, 2, 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, n_pts)
return torch.cat([x, pointfeat], 1), trans
class PointNetCls(nn.Module):
def __init__(self, k=2):
super(PointNetCls, self).__init__()
self.k = k
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)
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 PointNetSeg(nn.Module):
def __init__(self, k=2):
super(PointNetSeg, self).__init__()
self.k = k
self.feat = PointNetfeat(global_feat=False)
self.conv1 = nn.Conv1d(1088, 512, 1)
self.conv2 = nn.Conv1d(512, 256, 1)
self.conv3 = nn.Conv1d(256, 128, 1)
self.conv4 = 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]
n_pts = x.size()[2]
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, n_pts, self.k)
return x, trans
if __name__ == '__main__':
sim_data = autograd.Variable(torch.randn(32, 3, 2048))
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=4)
out, _ = cls(sim_data)
print('class', out.size())
seg = PointNetSeg(k=4)
out, _ = seg(sim_data)
print('seg', out.size())