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Utility function for PointConv
Originally from : https://github.com/yanx27/Pointnet_Pointnet2_pytorch/blob/master/utils.py
Modify by Wenxuan Wu
Date: September 2019
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from time import time
import numpy as np
from sklearn.neighbors._kde import KernelDensity
def timeit(tag, t):
print("{}: {}s".format(tag, time() - t))
return time()
def square_distance(src, dst):
"""
Calculate Euclid distance between each two points.
src^T * dst = xn * xm + yn * ym + zn * zm；
sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2
= sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst
Input:
src: source points, [B, N, C]
dst: target points, [B, M, C]
Output:
dist: per-point square distance, [B, N, M]
"""
B, N, _ = src.shape
_, M, _ = dst.shape
dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))
dist += torch.sum(src ** 2, -1).view(B, N, 1)
dist += torch.sum(dst ** 2, -1).view(B, 1, M)
return dist
def index_points(points, idx):
"""
Input:
points: input points data, [B, N, C]
idx: sample index data, [B, S]
Return:
new_points:, indexed points data, [B, S, C]
"""
device = points.device
B = points.shape[0]
view_shape = list(idx.shape)
view_shape[1:] = [1] * (len(view_shape) - 1)
repeat_shape = list(idx.shape)
repeat_shape[0] = 1
batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)
new_points = points[batch_indices, idx, :]
return new_points
def farthest_point_sample(xyz, npoint):
"""
Input:
xyz: pointcloud data, [B, N, C]
npoint: number of samples
Return:
centroids: sampled pointcloud index, [B, npoint]
"""
#import ipdb; ipdb.set_trace()
device = xyz.device
B, N, C = xyz.shape
centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)
distance = torch.ones(B, N).to(device) * 1e10
#farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
farthest = torch.zeros(B, dtype=torch.long).to(device)
batch_indices = torch.arange(B, dtype=torch.long).to(device)
for i in range(npoint):
centroids[:, i] = farthest
centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)
dist = torch.sum((xyz - centroid) ** 2, -1)
mask = dist < distance
distance[mask] = dist[mask]
farthest = torch.max(distance, -1)[1]
return centroids
def query_ball_point(radius, nsample, xyz, new_xyz):
"""
Input:
radius: local region radius
nsample: max sample number in local region
xyz: all points, [B, N, C]
new_xyz: query points, [B, S, C]
Return:
group_idx: grouped points index, [B, S, nsample]
"""
device = xyz.device
B, N, C = xyz.shape
_, S, _ = new_xyz.shape
group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])
sqrdists = square_distance(new_xyz, xyz)
group_idx[sqrdists > radius ** 2] = N
group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]
group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])
mask = group_idx == N
group_idx[mask] = group_first[mask]
return group_idx
def knn_point(nsample, xyz, new_xyz):
"""
Input:
nsample: max sample number in local region
xyz: all points, [B, N, C]
new_xyz: query points, [B, S, C]
Return:
group_idx: grouped points index, [B, S, nsample]
"""
sqrdists = square_distance(new_xyz, xyz)
_, group_idx = torch.topk(sqrdists, nsample, dim = -1, largest=False, sorted=False)
return group_idx
def sample_and_group(npoint, nsample, xyz, points, density_scale = None):
"""
Input:
npoint:
nsample:
xyz: input points position data, [B, N, C]
points: input points data, [B, N, D]
Return:
new_xyz: sampled points position data, [B, 1, C]
new_points: sampled points data, [B, 1, N, C+D]
"""
B, N, C = xyz.shape
S = npoint
fps_idx = farthest_point_sample(xyz, npoint) # [B, npoint, C]
new_xyz = index_points(xyz, fps_idx)
idx = knn_point(nsample, xyz, new_xyz)
grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C]
grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C)
if points is not None:
grouped_points = index_points(points, idx)
new_points = torch.cat([grouped_xyz_norm, grouped_points], dim=-1) # [B, npoint, nsample, C+D]
else:
new_points = grouped_xyz_norm
if density_scale is None:
return new_xyz, new_points, grouped_xyz_norm, idx
else:
grouped_density = index_points(density_scale, idx)
return new_xyz, new_points, grouped_xyz_norm, idx, grouped_density
def sample_and_group_all(xyz, points, density_scale = None):
"""
Input:
xyz: input points position data, [B, N, C]
points: input points data, [B, N, D]
Return:
new_xyz: sampled points position data, [B, 1, C]
new_points: sampled points data, [B, 1, N, C+D]
"""
device = xyz.device
B, N, C = xyz.shape
#new_xyz = torch.zeros(B, 1, C).to(device)
new_xyz = xyz.mean(dim = 1, keepdim = True)
grouped_xyz = xyz.view(B, 1, N, C) - new_xyz.view(B, 1, 1, C)
if points is not None:
new_points = torch.cat([grouped_xyz, points.view(B, 1, N, -1)], dim=-1)
else:
new_points = grouped_xyz
if density_scale is None:
return new_xyz, new_points, grouped_xyz
else:
grouped_density = density_scale.view(B, 1, N, 1)
return new_xyz, new_points, grouped_xyz, grouped_density
def group(nsample, xyz, points):
"""
Input:
npoint:
nsample:
xyz: input points position data, [B, N, C]
points: input points data, [B, N, D]
Return:
new_xyz: sampled points position data, [B, 1, C]
new_points: sampled points data, [B, 1, N, C+D]
"""
B, N, C = xyz.shape
S = N
new_xyz = xyz
idx = knn_point(nsample, xyz, new_xyz)
grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C]
grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C)
if points is not None:
grouped_points = index_points(points, idx)
new_points = torch.cat([grouped_xyz_norm, grouped_points], dim=-1) # [B, npoint, nsample, C+D]
else:
new_points = grouped_xyz_norm
return new_points, grouped_xyz_norm
def compute_density(xyz, bandwidth):
'''
xyz: input points position data, [B, N, C]
'''
#import ipdb; ipdb.set_trace()
B, N, C = xyz.shape
sqrdists = square_distance(xyz, xyz)
gaussion_density = torch.exp(- sqrdists / (2.0 * bandwidth * bandwidth)) / (2.5 * bandwidth)
xyz_density = gaussion_density.mean(dim = -1)
return xyz_density
class DensityNet(nn.Module):
def __init__(self, hidden_unit = [16, 8]):
super(DensityNet, self).__init__()
self.mlp_convs = nn.ModuleList()
self.mlp_bns = nn.ModuleList()
self.mlp_convs.append(nn.Conv2d(1, hidden_unit[0], 1))
self.mlp_bns.append(nn.BatchNorm2d(hidden_unit[0]))
for i in range(1, len(hidden_unit)):
self.mlp_convs.append(nn.Conv2d(hidden_unit[i - 1], hidden_unit[i], 1))
self.mlp_bns.append(nn.BatchNorm2d(hidden_unit[i]))
self.mlp_convs.append(nn.Conv2d(hidden_unit[-1], 1, 1))
self.mlp_bns.append(nn.BatchNorm2d(1))
def forward(self, density_scale):
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
density_scale = bn(conv(density_scale))
if i == len(self.mlp_convs):
density_scale = F.sigmoid(density_scale)
else:
density_scale = F.relu(density_scale)
return density_scale
class WeightNet(nn.Module):
def __init__(self, in_channel, out_channel, hidden_unit = [8, 8]):
super(WeightNet, self).__init__()
self.mlp_convs = nn.ModuleList()
self.mlp_bns = nn.ModuleList()
if hidden_unit is None or len(hidden_unit) == 0:
self.mlp_convs.append(nn.Conv2d(in_channel, out_channel, 1))
self.mlp_bns.append(nn.BatchNorm2d(out_channel))
else:
self.mlp_convs.append(nn.Conv2d(in_channel, hidden_unit[0], 1))
self.mlp_bns.append(nn.BatchNorm2d(hidden_unit[0]))
for i in range(1, len(hidden_unit)):
self.mlp_convs.append(nn.Conv2d(hidden_unit[i - 1], hidden_unit[i], 1))
self.mlp_bns.append(nn.BatchNorm2d(hidden_unit[i]))
self.mlp_convs.append(nn.Conv2d(hidden_unit[-1], out_channel, 1))
self.mlp_bns.append(nn.BatchNorm2d(out_channel))
def forward(self, localized_xyz):
#xyz : BxCxKxN
weights = localized_xyz
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
weights = F.relu(bn(conv(weights)))
return weights
class PointConvSetAbstraction(nn.Module):
def __init__(self, npoint, nsample, in_channel, mlp, group_all):
super(PointConvSetAbstraction, self).__init__()
self.npoint = npoint
self.nsample = nsample
self.mlp_convs = nn.ModuleList()
self.mlp_bns = nn.ModuleList()
last_channel = in_channel
for out_channel in mlp:
self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))
self.mlp_bns.append(nn.BatchNorm2d(out_channel))
last_channel = out_channel
self.weightnet = WeightNet(3, 16)
self.linear = nn.Linear(16 * mlp[-1], mlp[-1])
self.bn_linear = nn.BatchNorm1d(mlp[-1])
self.group_all = group_all
def forward(self, xyz, points):
"""
Input:
xyz: input points position data, [B, C, N]
points: input points data, [B, D, N]
Return:
new_xyz: sampled points position data, [B, C, S]
new_points_concat: sample points feature data, [B, D', S]
"""
B = xyz.shape[0]
xyz = xyz.permute(0, 2, 1)
if points is not None:
points = points.permute(0, 2, 1)
if self.group_all:
new_xyz, new_points, grouped_xyz_norm = sample_and_group_all(xyz, points)
else:
new_xyz, new_points, grouped_xyz_norm, _ = sample_and_group(self.npoint, self.nsample, xyz, points)
# new_xyz: sampled points position data, [B, npoint, C]
# new_points: sampled points data, [B, npoint, nsample, C+D]
new_points = new_points.permute(0, 3, 2, 1) # [B, C+D, nsample,npoint]
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
new_points = F.relu(bn(conv(new_points)))
grouped_xyz = grouped_xyz_norm.permute(0, 3, 2, 1)
weights = self.weightnet(grouped_xyz)
new_points = torch.matmul(input=new_points.permute(0, 3, 1, 2), other = weights.permute(0, 3, 2, 1)).view(B, self.npoint, -1)
new_points = self.linear(new_points)
new_points = self.bn_linear(new_points.permute(0, 2, 1))
new_points = F.relu(new_points)
new_xyz = new_xyz.permute(0, 2, 1)
return new_xyz, new_points
class PointConvDensitySetAbstraction(nn.Module):
def __init__(self, npoint, nsample, in_channel, mlp, bandwidth, group_all):
super(PointConvDensitySetAbstraction, self).__init__()
self.npoint = npoint
self.nsample = nsample
self.mlp_convs = nn.ModuleList()
self.mlp_bns = nn.ModuleList()
last_channel = in_channel
for out_channel in mlp:
self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1))
self.mlp_bns.append(nn.BatchNorm2d(out_channel))
last_channel = out_channel
self.weightnet = WeightNet(3, 16)
self.linear = nn.Linear(16 * mlp[-1], mlp[-1])
self.bn_linear = nn.BatchNorm1d(mlp[-1])
self.densitynet = DensityNet()
self.group_all = group_all
self.bandwidth = bandwidth
def forward(self, xyz, points):
"""
Input:
xyz: input points position data, [B, C, N]
points: input points data, [B, D, N]
Return:
new_xyz: sampled points position data, [B, C, S]
new_points_concat: sample points feature data, [B, D', S]
"""
B = xyz.shape[0]
N = xyz.shape[2]
xyz = xyz.permute(0, 2, 1)
if points is not None:
points = points.permute(0, 2, 1)
xyz_density = compute_density(xyz, self.bandwidth)
inverse_density = 1.0 / xyz_density
if self.group_all:
new_xyz, new_points, grouped_xyz_norm, grouped_density = sample_and_group_all(xyz, points, inverse_density.view(B, N, 1))
else:
new_xyz, new_points, grouped_xyz_norm, _, grouped_density = sample_and_group(self.npoint, self.nsample, xyz, points, inverse_density.view(B, N, 1))
# new_xyz: sampled points position data, [B, npoint, C]
# new_points: sampled points data, [B, npoint, nsample, C+D]
new_points = new_points.permute(0, 3, 2, 1) # [B, C+D, nsample,npoint]
for i, conv in enumerate(self.mlp_convs):
bn = self.mlp_bns[i]
new_points = F.relu(bn(conv(new_points)))
inverse_max_density = grouped_density.max(dim = 2, keepdim=True)[0]
density_scale = grouped_density / inverse_max_density
density_scale = self.densitynet(density_scale.permute(0, 3, 2, 1))
new_points = new_points * density_scale
grouped_xyz = grouped_xyz_norm.permute(0, 3, 2, 1)
weights = self.weightnet(grouped_xyz)
new_points = torch.matmul(input=new_points.permute(0, 3, 1, 2), other = weights.permute(0, 3, 2, 1)).view(B, self.npoint, -1)
new_points = self.linear(new_points)
new_points = self.bn_linear(new_points.permute(0, 2, 1))
new_points = F.relu(new_points)
new_xyz = new_xyz.permute(0, 2, 1)
return new_xyz, new_points
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