Spaces:
Running
on
Zero
Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from time import time | |
| import numpy as np | |
| # reference https://github.com/yanx27/Pointnet_Pointnet2_pytorch, modified by Yang You | |
| def timeit(tag, t): | |
| print("{}: {}s".format(tag, time() - t)) | |
| return time() | |
| def pc_normalize(pc): | |
| 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] | |
| """ | |
| return torch.sum((src[:, :, None] - dst[:, None]) ** 2, dim=-1) | |
| def index_points(points, idx): | |
| """ | |
| Input: | |
| points: input points data, [B, N, C] | |
| idx: sample index data, [B, S, [K]] | |
| Return: | |
| new_points:, indexed points data, [B, S, [K], C] | |
| """ | |
| raw_size = idx.size() | |
| idx = idx.reshape(raw_size[0], -1) | |
| res = torch.gather(points, 1, idx[..., None].expand(-1, -1, points.size(-1))) | |
| return res.reshape(*raw_size, -1) | |
| def farthest_point_sample(xyz, npoint): | |
| """ | |
| Input: | |
| xyz: pointcloud data, [B, N, 3] | |
| 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) | |
| distance = torch.min(distance, dist) | |
| 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[:, :, 0].view(B, S, 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, knn=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] | |
| torch.cuda.empty_cache() | |
| new_xyz = index_points(xyz, fps_idx) | |
| torch.cuda.empty_cache() | |
| if knn: | |
| dists = square_distance(new_xyz, xyz) # B x npoint x N | |
| idx = dists.argsort()[:, :, :nsample] # B x npoint x K | |
| else: | |
| 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, knn=False): | |
| super(PointNetSetAbstraction, self).__init__() | |
| self.npoint = npoint | |
| self.radius = radius | |
| self.nsample = nsample | |
| self.knn = knn | |
| 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, N, C] | |
| points: input points data, [B, N, C] | |
| Return: | |
| new_xyz: sampled points position data, [B, S, C] | |
| new_points_concat: sample points feature data, [B, S, D'] | |
| """ | |
| 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, knn=self.knn) | |
| # 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].transpose(1, 2) | |
| return new_xyz, new_points | |
| class PointNetSetAbstractionMsg(nn.Module): | |
| def __init__(self, npoint, radius_list, nsample_list, in_channel, mlp_list, knn=False): | |
| super(PointNetSetAbstractionMsg, self).__init__() | |
| self.npoint = npoint | |
| self.radius_list = radius_list | |
| self.nsample_list = nsample_list | |
| self.knn = knn | |
| 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, seed_idx=None): | |
| """ | |
| 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, N, C = xyz.shape | |
| S = self.npoint | |
| new_xyz = index_points(xyz, farthest_point_sample(xyz, S) if seed_idx is None else seed_idx) | |
| new_points_list = [] | |
| for i, radius in enumerate(self.radius_list): | |
| K = self.nsample_list[i] | |
| if self.knn: | |
| dists = square_distance(new_xyz, xyz) # B x npoint x N | |
| group_idx = dists.argsort()[:, :, :K] # B x npoint x K | |
| else: | |
| 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_points_concat = torch.cat(new_points_list, dim=1).transpose(1, 2) | |
| return new_xyz, new_points_concat | |
| # NoteL this function swaps N and C | |
| 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 |