util.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from time import time
import numpy as np
from lib import pointnet2_utils as pointutils
# import lib.pointnet2_utils as pointutils

def quat2mat(quat):
    x, y, z, w = quat[:, 0], quat[:, 1], quat[:, 2], quat[:, 3]

    B = quat.size(0)

    w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
    wx, wy, wz = w*x, w*y, w*z
    xy, xz, yz = x*y, x*z, y*z

    rotMat = torch.stack([w2 + x2 - y2 - z2, 2*xy - 2*wz, 2*wy + 2*xz,
                          2*wz + 2*xy, w2 - x2 + y2 - z2, 2*yz - 2*wx,
                          2*xz - 2*wy, 2*wx + 2*yz, w2 - x2 - y2 + z2], dim=1).reshape(B, 3, 3)
    return rotMat

def transform_point_cloud(point_cloud, rotation, translation):
    if len(rotation.size()) == 2:
        rot_mat = quat2mat(rotation)
    else:
        rot_mat = rotation
    return torch.matmul(rot_mat, point_cloud) + translation.unsqueeze(2)


def npmat2euler(mats, seq='zyx'):
    eulers = []
    for i in range(mats.shape[0]):
        r = Rotation.from_dcm(mats[i])
        eulers.append(r.as_euler(seq, degrees=True))
    return np.asarray(eulers, dtype='float32')

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, C]
        npoint: number of samples
    Return:
        centroids: sampled pointcloud index, [B, 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 knn_point(k, pos1, pos2):
    '''
    Input:
        k: int32, number of k in k-nn search
        pos1: (batch_size, ndataset, c) float32 array, input points
        pos2: (batch_size, npoint, c) float32 array, query points
    Output:
        val: (batch_size, npoint, k) float32 array, L2 distances
        idx: (batch_size, npoint, k) int32 array, indices to input points
    '''
    B, N, C = pos1.shape
    M = pos2.shape[1]
    pos1 = pos1.view(B,1,N,-1).repeat(1,M,1,1)
    pos2 = pos2.view(B,M,1,-1).repeat(1,1,N,1)
    dist = torch.sum(-(pos1-pos2)**2,-1)
    val,idx = dist.topk(k=k,dim = -1)
    return torch.sqrt(-val), idx


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
    mask = group_idx != N
    cnt = mask.sum(dim=-1)
    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, cnt


def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False):
    """
    Input:
        npoint:
        radius:
        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, _ = query_ball_point(radius, 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 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, 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)
    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, mlp2 = None, group_all = False):
        super(PointNetSetAbstraction, self).__init__()
        self.npoint = npoint
        self.radius = radius
        self.nsample = nsample
        self.group_all = group_all
        self.mlp_convs = nn.ModuleList()
        self.mlp_bns = nn.ModuleList()
        self.mlp2_convs = nn.ModuleList()
        last_channel = in_channel+3
        for out_channel in mlp:
            self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1, bias = False))
            self.mlp_bns.append(nn.BatchNorm2d(out_channel))
            last_channel = out_channel
        for out_channel in mlp2:
            self.mlp2_convs.append(nn.Sequential(nn.Conv1d(last_channel, out_channel, 1, bias=False),
                                                nn.BatchNorm1d(out_channel)))
            last_channel = out_channel
        if group_all:
            self.queryandgroup = pointutils.GroupAll()
        else:
            self.queryandgroup = pointutils.QueryAndGroup(radius, nsample)

    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, S, C]
            new_points_concat: sample points feature data, [B, S, D']
        """
        device = xyz.device
        B, C, N = xyz.shape
        xyz_t = xyz.permute(0, 2, 1).contiguous()
        # if points is not None:
        #     points = points.permute(0, 2, 1).contiguous()

        # 选取邻域点
        if self.group_all == False:
            fps_idx = pointutils.furthest_point_sample(xyz_t, self.npoint)  # [B, N]
            new_xyz = pointutils.gather_operation(xyz, fps_idx)  # [B, C, N]
        else:
            new_xyz = xyz
        new_points = self.queryandgroup(xyz_t, new_xyz.transpose(2, 1).contiguous(), points) # [B, 3+C, N, S]
        
        # new_xyz: sampled points position data, [B, C, npoint]
        # new_points: sampled points data, [B, C+D, npoint, nsample]
        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, -1)[0]

        for i, conv in enumerate(self.mlp2_convs):
            new_points = F.relu(conv(new_points))
        return new_xyz, new_points

class FlowEmbedding(nn.Module):
    def __init__(self, radius, nsample, in_channel, mlp, pooling='max', corr_func='concat', knn = True):
        super(FlowEmbedding, self).__init__()
        self.radius = radius
        self.nsample = nsample
        self.knn = knn
        self.pooling = pooling
        self.corr_func = corr_func
        self.mlp_convs = nn.ModuleList()
        self.mlp_bns = nn.ModuleList()
        if corr_func is 'concat':
            last_channel = in_channel*2+3
        for out_channel in mlp:
            self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1, bias=False))
            self.mlp_bns.append(nn.BatchNorm2d(out_channel))
            last_channel = out_channel

    def forward(self, pos1, pos2, feature1, feature2):
        """
        Input:
            xyz1: (batch_size, 3, npoint)
            xyz2: (batch_size, 3, npoint)
            feat1: (batch_size, channel, npoint)
            feat2: (batch_size, channel, npoint)
        Output:
            xyz1: (batch_size, 3, npoint)
            feat1_new: (batch_size, mlp[-1], npoint)
        """
        pos1_t = pos1.permute(0, 2, 1).contiguous()
        pos2_t = pos2.permute(0, 2, 1).contiguous()
        B, N, C = pos1_t.shape
        if self.knn:
            _, idx = pointutils.knn(self.nsample, pos1_t, pos2_t)
        else:
            # If the ball neighborhood points are less than nsample,
            # than use the knn neighborhood points
            idx, cnt = query_ball_point(self.radius, self.nsample, pos2_t, pos1_t)
            # 利用knn取最近的那些点
            _, idx_knn = pointutils.knn(self.nsample, pos1_t, pos2_t)
            cnt = cnt.view(B, -1, 1).repeat(1, 1, self.nsample)
            idx = idx_knn[cnt > (self.nsample-1)]
        
        pos2_grouped = pointutils.grouping_operation(pos2, idx) # [B, 3, N, S]
        pos_diff = pos2_grouped - pos1.view(B, -1, N, 1)    # [B, 3, N, S]
        
        feat2_grouped = pointutils.grouping_operation(feature2, idx)    # [B, C, N, S]
        if self.corr_func=='concat':
            feat_diff = torch.cat([feat2_grouped, feature1.view(B, -1, N, 1).repeat(1, 1, 1, self.nsample)], dim = 1)
        
        feat1_new = torch.cat([pos_diff, feat_diff], dim = 1)  # [B, 2*C+3,N,S]
        for i, conv in enumerate(self.mlp_convs):
            bn = self.mlp_bns[i]
            feat1_new = F.relu(bn(conv(feat1_new)))

        feat1_new = torch.max(feat1_new, -1)[0]  # [B, mlp[-1], npoint]
        return pos1, feat1_new

class PointNetSetUpConv(nn.Module):
    def __init__(self, nsample, radius, f1_channel, f2_channel, mlp, mlp2, knn = True):
        super(PointNetSetUpConv, self).__init__()
        self.nsample = nsample
        self.radius = radius
        self.knn = knn
        self.mlp1_convs = nn.ModuleList()
        self.mlp2_convs = nn.ModuleList()
        last_channel = f2_channel+3
        for out_channel in mlp:
            self.mlp1_convs.append(nn.Sequential(nn.Conv2d(last_channel, out_channel, 1, bias=False),
                                                 nn.BatchNorm2d(out_channel),
                                                 nn.ReLU(inplace=False)))
            last_channel = out_channel
        if len(mlp) is not 0:
            last_channel = mlp[-1] + f1_channel
        else:
            last_channel = last_channel + f1_channel
        for out_channel in mlp2:
            self.mlp2_convs.append(nn.Sequential(nn.Conv1d(last_channel, out_channel, 1, bias=False),
                                                 nn.BatchNorm1d(out_channel),
                                                 nn.ReLU(inplace=False)))
            last_channel = out_channel

    def forward(self, pos1, pos2, feature1, feature2):
        """
            Feature propagation from xyz2 (less points) to xyz1 (more points)

        Inputs:
            xyz1: (batch_size, 3, npoint1)
            xyz2: (batch_size, 3, npoint2)
            feat1: (batch_size, channel1, npoint1) features for xyz1 points (earlier layers, more points)
            feat2: (batch_size, channel1, npoint2) features for xyz2 points
        Output:
            feat1_new: (batch_size, npoint2, mlp[-1] or mlp2[-1] or channel1+3)

            TODO: Add support for skip links. Study how delta(XYZ) plays a role in feature updating.
        """
        pos1_t = pos1.permute(0, 2, 1).contiguous()
        pos2_t = pos2.permute(0, 2, 1).contiguous()
        B,C,N = pos1.shape
        if self.knn:
            _, idx = pointutils.knn(self.nsample, pos1_t, pos2_t)
        else:
            idx, _ = query_ball_point(self.radius, self.nsample, pos2_t, pos1_t)
        
        pos2_grouped = pointutils.grouping_operation(pos2, idx)
        pos_diff = pos2_grouped - pos1.view(B, -1, N, 1)    # [B,3,N1,S]

        feat2_grouped = pointutils.grouping_operation(feature2, idx)
        feat_new = torch.cat([feat2_grouped, pos_diff], dim = 1)   # [B,C1+3,N1,S]
        for conv in self.mlp1_convs:
            feat_new = conv(feat_new)
        # max pooling
        feat_new = feat_new.max(-1)[0]   # [B,mlp1[-1],N1]
        # concatenate feature in early layer
        if feature1 is not None:
            feat_new = torch.cat([feat_new, feature1], dim=1)
        # feat_new = feat_new.view(B,-1,N,1)
        for conv in self.mlp2_convs:
            feat_new = conv(feat_new)
        
        return feat_new

class PointNetFeaturePropogation(nn.Module):
    def __init__(self, in_channel, mlp):
        super(PointNetFeaturePropogation, 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, pos1, pos2, feature1, feature2):
        """
        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]
        """
        pos1_t = pos1.permute(0, 2, 1).contiguous()
        pos2_t = pos2.permute(0, 2, 1).contiguous()
        B, C, N = pos1.shape
        
        # dists = square_distance(pos1, pos2)
        # dists, idx = dists.sort(dim=-1)
        # dists, idx = dists[:, :, :3], idx[:, :, :3]  # [B, N, 3]
        dists,idx = pointutils.three_nn(pos1_t,pos2_t)
        dists[dists < 1e-10] = 1e-10
        weight = 1.0 / dists
        weight = weight / torch.sum(weight, -1,keepdim = True)   # [B,N,3]
        interpolated_feat = torch.sum(pointutils.grouping_operation(feature2, idx) * weight.view(B, 1, N, 3), dim = -1) # [B,C,N,3]

        if feature1 is not None:
            feat_new = torch.cat([interpolated_feat, feature1], 1)
        else:
            feat_new = interpolated_feat
        
        for i, conv in enumerate(self.mlp_convs):
            bn = self.mlp_bns[i]
            feat_new = F.relu(bn(conv(feat_new)))
        return feat_new