import torch import torch.nn as nn import torch.nn.functional as F from time import time import numpy as np #import dgl.geometry def timeit(tag, t): print("{}: {}s".format(tag, time() - t)) return time() def pc_normalize(pc): l = pc.shape[0] centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc**2, axis=1))) pc = pc / m return pc 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, 3] npoint: number of samples Return: centroids: sampled pointcloud index, [B, npoint] """ #return dgl.geometry.farthest_point_sampler(xyz, npoint) 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) 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, 3] new_xyz: query points, [B, S, 3] 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[..., :1].repeat([1, 1, nsample]) mask = group_idx == N group_idx[mask] = group_first[mask] return group_idx def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False): """ Input: npoint: radius: nsample: xyz: input points position data, [B, N, 3] points: input points data, [B, N, D] Return: new_xyz: sampled points position data, [B, npoint, nsample, 3] new_points: sampled points data, [B, npoint, nsample, 3+D] """ B, N, C = xyz.shape S = npoint fps_idx = farthest_point_sample(xyz, npoint) # [B, npoint, C] # torch.cuda.empty_cache() new_xyz = index_points(xyz, fps_idx) # torch.cuda.empty_cache() idx = query_ball_point(radius, nsample, xyz, new_xyz) # torch.cuda.empty_cache() grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C] # torch.cuda.empty_cache() grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C) # torch.cuda.empty_cache() 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 returnfps: return new_xyz, new_points, grouped_xyz, fps_idx else: return new_xyz, new_points def sample_and_group_all(xyz, points): """ Input: xyz: input points position data, [B, N, 3] points: input points data, [B, N, D] Return: new_xyz: sampled points position data, [B, 1, 3] new_points: sampled points data, [B, 1, N, 3+D] """ device = xyz.device B, N, C = xyz.shape new_xyz = torch.zeros(B, 1, C).to(device) grouped_xyz = xyz.view(B, 1, N, 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 return new_xyz, new_points class PointNetSetAbstraction(nn.Module): def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all): super(PointNetSetAbstraction, self).__init__() self.npoint = npoint self.radius = radius 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.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] """ 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 = sample_and_group_all(xyz, points) else: new_xyz, new_points = sample_and_group(self.npoint, self.radius, 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))) new_points = torch.max(new_points, 2)[0] new_xyz = new_xyz.permute(0, 2, 1) return new_xyz, new_points class PointNetSetAbstractionMsg(nn.Module): def __init__(self, npoint, radius_list, nsample_list, in_channel, mlp_list): super(PointNetSetAbstractionMsg, self).__init__() self.npoint = npoint self.radius_list = radius_list self.nsample_list = nsample_list self.conv_blocks = nn.ModuleList() self.bn_blocks = nn.ModuleList() for i in range(len(mlp_list)): convs = nn.ModuleList() bns = nn.ModuleList() last_channel = in_channel + 3 for out_channel in mlp_list[i]: convs.append(nn.Conv2d(last_channel, out_channel, 1)) bns.append(nn.BatchNorm2d(out_channel)) last_channel = out_channel self.conv_blocks.append(convs) self.bn_blocks.append(bns) 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] """ xyz = xyz.permute(0, 2, 1) if points is not None: points = points.permute(0, 2, 1) B, N, C = xyz.shape S = self.npoint new_xyz = index_points(xyz, farthest_point_sample(xyz, S)) new_points_list = [] for i, radius in enumerate(self.radius_list): K = self.nsample_list[i] group_idx = query_ball_point(radius, K, xyz, new_xyz) grouped_xyz = index_points(xyz, group_idx) grouped_xyz -= new_xyz.view(B, S, 1, C) if points is not None: grouped_points = index_points(points, group_idx) grouped_points = torch.cat([grouped_points, grouped_xyz], dim=-1) else: grouped_points = grouped_xyz grouped_points = grouped_points.permute(0, 3, 2, 1) # [B, D, K, S] for j in range(len(self.conv_blocks[i])): conv = self.conv_blocks[i][j] bn = self.bn_blocks[i][j] grouped_points = F.relu(bn(conv(grouped_points))) new_points = torch.max(grouped_points, 2)[0] # [B, D', S] new_points_list.append(new_points) new_xyz = new_xyz.permute(0, 2, 1) new_points_concat = torch.cat(new_points_list, dim=1) return new_xyz, new_points_concat class PointNetFeaturePropagation(nn.Module): def __init__(self, in_channel, mlp): super(PointNetFeaturePropagation, self).__init__() self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() last_channel = in_channel for out_channel in mlp: self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm1d(out_channel)) last_channel = out_channel def forward(self, xyz1, xyz2, points1, points2): """ Input: xyz1: input points position data, [B, C, N] xyz2: sampled input points position data, [B, C, S] points1: input points data, [B, D, N] points2: input points data, [B, D, S] Return: new_points: upsampled points data, [B, D', N] """ xyz1 = xyz1.permute(0, 2, 1) xyz2 = xyz2.permute(0, 2, 1) points2 = points2.permute(0, 2, 1) B, N, C = xyz1.shape _, S, _ = xyz2.shape if S == 1: interpolated_points = points2.repeat(1, N, 1) else: dists = square_distance(xyz1, xyz2) dists, idx = dists.sort(dim=-1) dists, idx = dists[:, :, :3], idx[:, :, :3] # [B, N, 3] dist_recip = 1.0 / (dists + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2) if points1 is not None: points1 = points1.permute(0, 2, 1) new_points = torch.cat([points1, interpolated_points], dim=-1) else: new_points = interpolated_points new_points = new_points.permute(0, 2, 1) for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_points = F.relu(bn(conv(new_points))) return new_points