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pointnet.py
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pointnet.py
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import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.autograd import Variable
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, 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 = 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 STNkd(nn.Module):
def __init__(self, k=64):
super(STNkd, self).__init__()
self.conv1 = torch.nn.Conv1d(k, 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, k * k)
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)
self.k = k
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 = Variable(torch.from_numpy(np.eye(self.k).flatten().astype(np.float32))).view(1, self.k * self.k).repeat(
batchsize, 1)
if x.is_cuda:
iden = iden.cuda()
x = x + iden
x = x.view(-1, self.k, self.k)
return x
class PointNetEncoder(nn.Module):
def __init__(self, global_feat=True, feature_transform=False, semseg = False):
super(PointNetEncoder, self).__init__()
self.stn = STN3d() if not semseg else STNkd(k=9)
self.conv1 = torch.nn.Conv1d(3, 64, 1) if not semseg else torch.nn.Conv1d(9, 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
self.feature_transform = feature_transform
if self.feature_transform:
self.fstn = STNkd(k=64)
def forward(self, x):
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)))
if self.feature_transform:
trans_feat = self.fstn(x)
x = x.transpose(2, 1)
x = torch.bmm(x, trans_feat)
x = x.transpose(2, 1)
else:
trans_feat = None
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, trans_feat, pointfeat
else:
x = x.view(-1, 1024, 1).repeat(1, 1, n_pts)
return torch.cat([x, pointfeat], 1), trans, trans_feat
class PointNetCls(nn.Module):
def __init__(self, k=2, feature_transform=True):
super(PointNetCls, self).__init__()
self.feature_transform = feature_transform
self.feat = PointNetEncoder(global_feat=True, feature_transform=feature_transform)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, k)
self.dropout = nn.Dropout(p=0.3)
self.dp1 = nn.Dropout(p=0.3)
self.dp2 = nn.Dropout(p=0.3)
self.bn1 = nn.BatchNorm1d(512)
self.bn2 = nn.BatchNorm1d(256)
self.relu = nn.ReLU()
def forward(self, x):
x, trans, trans_feat, pointfeat = self.feat(x)
global_feat = x
x = F.relu(self.bn1(self.fc1(x)))
# x = self.dp1(x)
# x = F.relu(self.bn2(self.fc2(x)))
# x = self.dp2(x)
x = F.relu(self.bn2(self.dropout(self.fc2(x))))
x = self.fc3(x)
#x = F.log_softmax(x, dim=1)
return x, trans_feat, global_feat
class PointNetDenseCls(nn.Module):
def __init__(self, cat_num=16,part_num=50):
super(PointNetDenseCls, self).__init__()
self.cat_num = cat_num
self.part_num = part_num
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, 128, 1)
self.conv4 = torch.nn.Conv1d(128, 512, 1)
self.conv5 = torch.nn.Conv1d(512, 2048, 1)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(128)
self.bn4 = nn.BatchNorm1d(512)
self.bn5 = nn.BatchNorm1d(2048)
self.fstn = STNkd(k=128)
# classification network
self.fc1 = nn.Linear(2048, 256)
self.fc2 = nn.Linear(256, 256)
self.fc3 = nn.Linear(256, cat_num)
self.dropout = nn.Dropout(p=0.3)
self.bnc1 = nn.BatchNorm1d(256)
self.bnc2 = nn.BatchNorm1d(256)
# segmentation network
self.convs1 = torch.nn.Conv1d(4944, 256, 1)
self.dp1 = nn.Dropout(p=0.2)
self.convs2 = torch.nn.Conv1d(256, 256, 1)
self.dp2 = nn.Dropout(p=0.2)
self.convs3 = torch.nn.Conv1d(256, 128, 1)
self.convs4 = torch.nn.Conv1d(128, part_num, 1)
self.bns1 = nn.BatchNorm1d(256)
self.bns2 = nn.BatchNorm1d(256)
self.bns3 = nn.BatchNorm1d(128)
def forward(self, point_cloud,label):
batchsize,_ , n_pts = point_cloud.size()
# point_cloud_transformed
trans = self.stn(point_cloud)
point_cloud = point_cloud.transpose(2, 1)
point_cloud_transformed = torch.bmm(point_cloud, trans)
point_cloud_transformed = point_cloud_transformed.transpose(2, 1)
# MLP
out1 = F.relu(self.bn1(self.conv1(point_cloud_transformed)))
out2 = F.relu(self.bn2(self.conv2(out1)))
out3 = F.relu(self.bn3(self.conv3(out2)))
# net_transformed
trans_feat = self.fstn(out3)
x = out3.transpose(2, 1)
net_transformed = torch.bmm(x, trans_feat)
net_transformed = net_transformed.transpose(2, 1)
# MLP
out4 = F.relu(self.bn4(self.conv4(net_transformed)))
out5 = F.relu(self.bn5(self.conv5(out4)))
#out5 = self.bn5(self.conv5(out4))
out_max = torch.max(out5, 2, keepdim=True)[0]
out_max = out_max.view(-1, 2048)
# classification network
net = F.relu(self.bnc1(self.fc1(out_max)))
net = F.relu(self.bnc2(self.dropout(self.fc2(net))))
net = self.fc3(net) # [B,16]
# segmentation network
out_max = torch.cat([out_max,label],1)
expand = out_max.view(-1, 2048+16, 1).repeat(1, 1, n_pts)
concat = torch.cat([expand, out1, out2, out3, out4, out5], 1)
net2 = F.relu(self.bns1(self.convs1(concat)))
net2 = self.dp1(net2)
net2 = F.relu(self.bns2(self.convs2(net2)))
net2 = self.dp2(net2)
net2 = F.relu(self.bns3(self.convs3(net2)))
net2 = self.convs4(net2)
net2 = net2.transpose(2, 1).contiguous()
net2 = F.log_softmax(net2.view(-1, self.part_num), dim=-1)
net2 = net2.view(batchsize, n_pts, self.part_num) # [B, N 50]
return net, net2, trans_feat
def feature_transform_reguliarzer(trans):
d = trans.size()[1]
I = torch.eye(d)[None, :, :]
if trans.is_cuda:
I = I.cuda()
loss = torch.mean(torch.norm(torch.bmm(trans, trans.transpose(2, 1)) - I, dim=(1, 2)))
return loss
class PointNetLoss(torch.nn.Module):
def __init__(self, weight=1,mat_diff_loss_scale=0.001):
super(PointNetLoss, self).__init__()
self.mat_diff_loss_scale = mat_diff_loss_scale
self.weight = weight
def forward(self, labels_pred, label, seg_pred,seg, trans_feat):
seg_loss = F.nll_loss(seg_pred, seg)
mat_diff_loss = feature_transform_reguliarzer(trans_feat)
label_loss = F.nll_loss(labels_pred, label)
loss = self.weight * seg_loss + (1-self.weight) * label_loss + mat_diff_loss * self.mat_diff_loss_scale
return loss, seg_loss, label_loss
class PointNetSeg(nn.Module):
def __init__(self,num_class,feature_transform=True, semseg = True):
super(PointNetSeg, self).__init__()
self.k = num_class
self.feat = PointNetEncoder(global_feat=False,feature_transform=feature_transform, semseg = semseg)
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.bn1_1 = nn.BatchNorm1d(1024)
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, trans_feat = 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_feat
if __name__ == '__main__':
point = torch.randn(8,3,1024)
label = torch.randn(8,16)
model = PointNetDenseCls()
net, net2, trans_feat = model(point,label)
print('net',net.shape)
print('net2',net2.shape)
print('trans_feat',trans_feat.shape)