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pcpnet007.py
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from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.nn.functional as F
import utils
class STN(nn.Module):
def __init__(self, num_scales=1, num_points=500, dim=3, sym_op='max'):
super(STN, self).__init__()
self.dim = dim
self.sym_op = sym_op
self.num_scales = num_scales
self.num_points = num_points
self.conv1 = torch.nn.Conv1d(self.dim, 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, self.dim * self.dim)
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)
if self.num_scales > 1:
self.fc0 = nn.Linear(1024 * self.num_scales, 1024)
self.bn0 = nn.BatchNorm1d(1024)
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)))
# symmetric operation over all points
if self.num_scales == 1:
x = self.mp1(x)
else:
x_scales = x.new_empty(x.size(0), 1024 * self.num_scales, 1)
for s in range(self.num_scales):
x_scales[:, s * 1024:(s + 1) * 1024, :] = self.mp1(
x[:, :, s * self.num_points:(s + 1) * self.num_points])
x = x_scales
x = x.view(-1, 1024 * self.num_scales)
if self.num_scales > 1:
x = F.relu(self.bn0(self.fc0(x)))
x = F.relu(self.bn4(self.fc1(x)))
x = F.relu(self.bn5(self.fc2(x)))
x = self.fc3(x)
iden = torch.eye(self.dim, dtype=x.dtype, device=x.device).view(1, self.dim * self.dim).repeat(batchsize, 1)
x = x + iden
x = x.view(-1, self.dim, self.dim)
return x
class QSTN(nn.Module):
def __init__(self, num_scales=1, num_points=500, dim=3, sym_op='max'):
super(QSTN, self).__init__()
self.dim = dim
self.sym_op = sym_op
self.num_scales = num_scales
self.num_points = num_points
self.conv1 = torch.nn.Conv1d(self.dim, 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, 4)
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)
if self.num_scales > 1:
self.fc0 = nn.Linear(1024 * self.num_scales, 1024)
self.bn0 = nn.BatchNorm1d(1024)
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)))
# symmetric operation over all points
if self.num_scales == 1:
x = self.mp1(x)
else:
x_scales = x.new_empty(x.size(0), 1024 * self.num_scales, 1)
for s in range(self.num_scales):
x_scales[:, s * 1024:(s + 1) * 1024, :] = self.mp1(
x[:, :, s * self.num_points:(s + 1) * self.num_points])
x = x_scales
x = x.view(-1, 1024 * self.num_scales)
if self.num_scales > 1:
x = F.relu(self.bn0(self.fc0(x)))
x = F.relu(self.bn4(self.fc1(x)))
x = F.relu(self.bn5(self.fc2(x)))
x = self.fc3(x)
# add identity quaternion (so the network can output 0 to leave the point cloud identical)
iden = x.new_tensor([1, 0, 0, 0])
x = x + iden
# convert quaternion to rotation matrix
x = utils.batch_quat_to_rotmat(x)
return x
class PointNetfeat(nn.Module):
def __init__(self, num_scales=1, num_points=500, use_point_stn=True, use_feat_stn=True, sym_op='max',
get_pointfvals=False, point_tuple=1):
super(PointNetfeat, self).__init__()
self.num_points = num_points
self.num_scales = num_scales
self.use_point_stn = use_point_stn
self.use_feat_stn = use_feat_stn
self.sym_op = sym_op
self.get_pointfvals = get_pointfvals
self.point_tuple = point_tuple
if self.use_point_stn:
# self.stn1 = STN(num_scales=self.num_scales, num_points=num_points, dim=3, sym_op=self.sym_op)
self.stn1 = QSTN(num_scales=self.num_scales, num_points=num_points * self.point_tuple, dim=3,
sym_op=self.sym_op)
if self.use_feat_stn:
self.stn2 = STN(num_scales=self.num_scales, num_points=num_points, dim=64, sym_op=self.sym_op)
self.conv0a = torch.nn.Conv1d(3 * self.point_tuple, 64, 1)
self.conv0b = torch.nn.Conv1d(64, 64, 1)
self.bn0a = nn.BatchNorm1d(64)
self.bn0b = nn.BatchNorm1d(64)
self.conv1 = torch.nn.Conv1d(64, 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)
if self.num_scales > 1:
self.conv4 = torch.nn.Conv1d(1024, 1024 * self.num_scales, 1)
self.bn4 = nn.BatchNorm1d(1024 * self.num_scales)
if self.sym_op == 'max':
self.mp1 = torch.nn.MaxPool1d(num_points)
elif self.sym_op == 'sum':
self.mp1 = None
else:
raise ValueError('Unsupported symmetric operation: %s' % (self.sym_op))
def forward(self, x, t0):
# input transform
if self.use_point_stn:
# from tuples to list of single points
x = x.view(x.size(0), 3, -1)
trans = self.stn1(x) # 计算旋转矩阵
x = x.transpose(2, 1)
x = torch.bmm(x, trans)
x = x.transpose(2, 1)
x = x.contiguous().view(x.size(0), 3 * self.point_tuple, -1)
if t0 is not None:
target_c = torch.bmm(t0.unsqueeze(1), trans).squeeze(1)
adjust_ind = torch.nonzero(target_c[:, 2] < 0)
target_c[adjust_ind, :] = -target_c[adjust_ind, :]
else:
target_c = t0
else:
trans = None
target_c = t0
if target_c is not None:
adjust_ind = torch.nonzero(target_c[:, 2] < 0)
target_c[adjust_ind, :] = -target_c[adjust_ind, :]
points = x.clone()
# mlp (64,64)
x = F.relu(self.bn0a(self.conv0a(x)))
x = F.relu(self.bn0b(self.conv0b(x)))
# feature transform
if self.use_feat_stn:
trans2 = self.stn2(x)
x = x.transpose(2, 1)
x = torch.bmm(x, trans2)
x = x.transpose(2, 1)
else:
trans2 = None
# mlp (64,128,1024)
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = self.bn3(self.conv3(x))
# mlp (1024,1024*num_scales)
if self.num_scales > 1:
x = self.bn4(self.conv4(F.relu(x)))
if self.get_pointfvals:
pointfvals = x
else:
pointfvals = None # so the intermediate result can be forgotten if it is not needed
return x, points, target_c, trans, trans2, pointfvals
class PCPNet(nn.Module):
def __init__(self, num_points=500, output_dim=3, use_point_stn=True, use_feat_stn=True, sym_op='max',
get_pointfvals=False, point_tuple=1):
super(PCPNet, self).__init__()
self.num_points = num_points
self.feat = PointNetfeat(
num_points=num_points,
num_scales=1,
use_point_stn=use_point_stn,
use_feat_stn=use_feat_stn,
sym_op=sym_op,
get_pointfvals=get_pointfvals,
point_tuple=point_tuple)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, output_dim)
self.bn1 = nn.BatchNorm1d(512)
self.bn2 = nn.BatchNorm1d(256)
self.do1 = nn.Dropout(p=0.3)
self.do2 = nn.Dropout(p=0.3)
self.convmap1 = nn.Conv2d(1024, 64, kernel_size=5, stride=1, padding=2)
self.bnmap1 = nn.BatchNorm2d(64)
self.convmap2 = nn.Conv2d(64, 1, 1)
@staticmethod
def next_grid(M, row, col):
if col == M - 1:
new_row = row + 1
new_col = 0
else:
new_row = row
new_col = col + 1
return new_row, new_col
def forward(self, grid_norm, weight_null, x1, tc, M):
feat, points, target_c, trans, trans2, pointfvals = self.feat(x1, tc)
dist = torch.abs(torch.bmm(grid_norm[:points.size(0), :, :], points))
mean = (torch.ones(points.size(0), M ** 2, 1) * 0.07).cuda()
weight = torch.exp(-dist ** 2 / (mean ** 2))
weight = torch.mul(weight_null, weight).transpose(2, 1)
x = torch.bmm(feat, weight)
x = x.reshape(points.size(0), 1024, M, M)
x = F.relu(self.bnmap1(self.convmap1(x)))
x = self.convmap2(x)
x = x.squeeze()
return x, points, target_c, trans, trans2, pointfvals
class MSPCPNet(nn.Module):
def __init__(self, num_scales=2, num_points=500, output_dim=3, use_point_stn=True, use_feat_stn=True, sym_op='max',
get_pointfvals=False, point_tuple=1):
super(MSPCPNet, self).__init__()
self.num_points = num_points
self.feat = PointNetfeat(
num_points=num_points,
num_scales=num_scales,
use_point_stn=use_point_stn,
use_feat_stn=use_feat_stn,
sym_op=sym_op,
get_pointfvals=get_pointfvals,
point_tuple=point_tuple)
self.fc0 = nn.Linear(1024 * num_scales ** 2, 1024)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, output_dim)
self.bn0 = nn.BatchNorm1d(1024)
self.bn1 = nn.BatchNorm1d(512)
self.bn2 = nn.BatchNorm1d(256)
self.do1 = nn.Dropout(p=0.3)
self.do2 = nn.Dropout(p=0.3)
def forward(self, x):
x, trans, trans2, pointfvals = self.feat(x)
x = F.relu(self.bn0(self.fc0(x)))
x = F.relu(self.bn1(self.fc1(x)))
x = self.do1(x)
x = F.relu(self.bn2(self.fc2(x)))
x = self.do2(x)
x = self.fc3(x)
return x, trans, trans2, pointfvals