import torch import torch.nn as nn import torch.nn.functional as F from torch import einsum from einops import rearrange, repeat from pointnet2_ops import pointnet2_utils def get_activation(activation): if activation.lower() == 'gelu': return nn.GELU() elif activation.lower() == 'rrelu': return nn.RReLU(inplace=True) elif activation.lower() == 'selu': return nn.SELU(inplace=True) elif activation.lower() == 'silu': return nn.SiLU(inplace=True) elif activation.lower() == 'hardswish': return nn.Hardswish(inplace=True) elif activation.lower() == 'leakyrelu': return nn.LeakyReLU(inplace=True) elif activation.lower() == 'leakyrelu0.2': return nn.LeakyReLU(negative_slope=0.2, inplace=True) else: return nn.ReLU(inplace=True) 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] """ 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 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 class LocalGrouper(nn.Module): def __init__(self, channel, groups, kneighbors, use_xyz=True, normalize="anchor", **kwargs): """ Give xyz[b,p,3] and fea[b,p,d], return new_xyz[b,g,3] and new_fea[b,g,k,d] :param groups: groups number :param kneighbors: k-nerighbors :param kwargs: others """ super(LocalGrouper, self).__init__() self.groups = groups self.kneighbors = kneighbors self.use_xyz = use_xyz if normalize is not None: self.normalize = normalize.lower() else: self.normalize = None if self.normalize not in ["center", "anchor"]: print(f"Unrecognized normalize parameter (self.normalize), set to None. Should be one of [center, anchor].") self.normalize = None if self.normalize is not None: add_channel=3 if self.use_xyz else 0 self.affine_alpha = nn.Parameter(torch.ones([1,1,1,channel + add_channel])) self.affine_beta = nn.Parameter(torch.zeros([1, 1, 1, channel + add_channel])) def forward(self, xyz, points): B, N, C = xyz.shape S = self.groups xyz = xyz.contiguous() # xyz [btach, points, xyz] # fps_idx = torch.multinomial(torch.linspace(0, N - 1, steps=N).repeat(B, 1).to(xyz.device), num_samples=self.groups, replacement=False).long() # fps_idx = farthest_point_sample(xyz, self.groups).long() fps_idx = pointnet2_utils.furthest_point_sample(xyz, self.groups).long() # [B, npoint] new_xyz = index_points(xyz, fps_idx) # [B, npoint, 3] new_points = index_points(points, fps_idx) # [B, npoint, d] idx = knn_point(self.kneighbors, xyz, new_xyz) # idx = query_ball_point(radius, nsample, xyz, new_xyz) grouped_xyz = index_points(xyz, idx) # [B, npoint, k, 3] grouped_points = index_points(points, idx) # [B, npoint, k, d] if self.use_xyz: grouped_points = torch.cat([grouped_points, grouped_xyz],dim=-1) # [B, npoint, k, d+3] if self.normalize is not None: if self.normalize =="center": mean = torch.mean(grouped_points, dim=2, keepdim=True) if self.normalize =="anchor": mean = torch.cat([new_points, new_xyz],dim=-1) if self.use_xyz else new_points mean = mean.unsqueeze(dim=-2) # [B, npoint, 1, d+3] std = torch.std((grouped_points-mean).reshape(B,-1),dim=-1,keepdim=True).unsqueeze(dim=-1).unsqueeze(dim=-1) grouped_points = (grouped_points-mean)/(std + 1e-5) grouped_points = self.affine_alpha*grouped_points + self.affine_beta new_points = torch.cat([grouped_points, new_points.view(B, S, 1, -1).repeat(1, 1, self.kneighbors, 1)], dim=-1) return new_xyz, new_points class ConvBNReLU1D(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, bias=True, activation='relu'): super(ConvBNReLU1D, self).__init__() self.act = get_activation(activation) self.net = nn.Sequential( nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, bias=bias), nn.BatchNorm1d(out_channels), self.act ) def forward(self, x): return self.net(x) class ConvBNReLURes1D(nn.Module): def __init__(self, channel, kernel_size=1, groups=1, res_expansion=1.0, bias=True, activation='relu'): super(ConvBNReLURes1D, self).__init__() self.act = get_activation(activation) self.net1 = nn.Sequential( nn.Conv1d(in_channels=channel, out_channels=int(channel * res_expansion), kernel_size=kernel_size, groups=groups, bias=bias), nn.BatchNorm1d(int(channel * res_expansion)), self.act ) if groups > 1: self.net2 = nn.Sequential( nn.Conv1d(in_channels=int(channel * res_expansion), out_channels=channel, kernel_size=kernel_size, groups=groups, bias=bias), nn.BatchNorm1d(channel), self.act, nn.Conv1d(in_channels=channel, out_channels=channel, kernel_size=kernel_size, bias=bias), nn.BatchNorm1d(channel), ) else: self.net2 = nn.Sequential( nn.Conv1d(in_channels=int(channel * res_expansion), out_channels=channel, kernel_size=kernel_size, bias=bias), nn.BatchNorm1d(channel) ) def forward(self, x): return self.act(self.net2(self.net1(x)) + x) class PreExtraction(nn.Module): def __init__(self, channels, out_channels, blocks=1, groups=1, res_expansion=1, bias=True, activation='relu', use_xyz=True): """ input: [b,g,k,d]: output:[b,d,g] :param channels: :param blocks: """ super(PreExtraction, self).__init__() in_channels = 3+2*channels if use_xyz else 2*channels self.transfer = ConvBNReLU1D(in_channels, out_channels, bias=bias, activation=activation) operation = [] for _ in range(blocks): operation.append( ConvBNReLURes1D(out_channels, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation) ) self.operation = nn.Sequential(*operation) def forward(self, x): b, n, s, d = x.size() # torch.Size([32, 512, 32, 6]) x = x.permute(0, 1, 3, 2) x = x.reshape(-1, d, s) x = self.transfer(x) batch_size, _, _ = x.size() x = self.operation(x) # [b, d, k] x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1) x = x.reshape(b, n, -1).permute(0, 2, 1) return x class PosExtraction(nn.Module): def __init__(self, channels, blocks=1, groups=1, res_expansion=1, bias=True, activation='relu'): """ input[b,d,g]; output[b,d,g] :param channels: :param blocks: """ super(PosExtraction, self).__init__() operation = [] for _ in range(blocks): operation.append( ConvBNReLURes1D(channels, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation) ) self.operation = nn.Sequential(*operation) def forward(self, x): # [b, d, g] return self.operation(x) class PointNetFeaturePropagation(nn.Module): def __init__(self, in_channel, out_channel, blocks=1, groups=1, res_expansion=1.0, bias=True, activation='relu'): super(PointNetFeaturePropagation, self).__init__() self.fuse = ConvBNReLU1D(in_channel, out_channel, 1, bias=bias) self.extraction = PosExtraction(out_channel, blocks, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation) def forward(self, xyz1, xyz2, points1, points2): """ Input: xyz1: input points position data, [B, N, 3] xyz2: sampled input points position data, [B, S, 3] 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) new_points = self.fuse(new_points) new_points = self.extraction(new_points) return new_points class PointMLP(nn.Module): def __init__(self, num_classes=50,points=2048, embed_dim=64, groups=1, res_expansion=1.0, activation="relu", bias=True, use_xyz=True, normalize="anchor", dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2], k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4], de_dims=[512, 256, 128, 128], de_blocks=[2,2,2,2], gmp_dim=64,cls_dim=64, **kwargs): super(PointMLP, self).__init__() self.stages = len(pre_blocks) self.class_num = num_classes self.points = points self.embedding = ConvBNReLU1D(6, embed_dim, bias=bias, activation=activation) assert len(pre_blocks) == len(k_neighbors) == len(reducers) == len(pos_blocks) == len(dim_expansion), \ "Please check stage number consistent for pre_blocks, pos_blocks k_neighbors, reducers." self.local_grouper_list = nn.ModuleList() self.pre_blocks_list = nn.ModuleList() self.pos_blocks_list = nn.ModuleList() last_channel = embed_dim anchor_points = self.points en_dims = [last_channel] ### Building Encoder ##### for i in range(len(pre_blocks)): out_channel = last_channel * dim_expansion[i] pre_block_num = pre_blocks[i] pos_block_num = pos_blocks[i] kneighbor = k_neighbors[i] reduce = reducers[i] anchor_points = anchor_points // reduce # append local_grouper_list local_grouper = LocalGrouper(last_channel, anchor_points, kneighbor, use_xyz, normalize) # [b,g,k,d] self.local_grouper_list.append(local_grouper) # append pre_block_list pre_block_module = PreExtraction(last_channel, out_channel, pre_block_num, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation, use_xyz=use_xyz) self.pre_blocks_list.append(pre_block_module) # append pos_block_list pos_block_module = PosExtraction(out_channel, pos_block_num, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation) self.pos_blocks_list.append(pos_block_module) last_channel = out_channel en_dims.append(last_channel) ### Building Decoder ##### self.decode_list = nn.ModuleList() en_dims.reverse() de_dims.insert(0,en_dims[0]) assert len(en_dims) ==len(de_dims) == len(de_blocks)+1 for i in range(len(en_dims)-1): self.decode_list.append( PointNetFeaturePropagation(de_dims[i]+en_dims[i+1], de_dims[i+1], blocks=de_blocks[i], groups=groups, res_expansion=res_expansion, bias=bias, activation=activation) ) self.act = get_activation(activation) # class label mapping self.cls_map = nn.Sequential( ConvBNReLU1D(16, cls_dim, bias=bias, activation=activation), ConvBNReLU1D(cls_dim, cls_dim, bias=bias, activation=activation) ) # global max pooling mapping self.gmp_map_list = nn.ModuleList() for en_dim in en_dims: self.gmp_map_list.append(ConvBNReLU1D(en_dim, gmp_dim, bias=bias, activation=activation)) self.gmp_map_end = ConvBNReLU1D(gmp_dim*len(en_dims), gmp_dim, bias=bias, activation=activation) # classifier self.classifier = nn.Sequential( nn.Conv1d(gmp_dim+cls_dim+de_dims[-1], 128, 1, bias=bias), nn.BatchNorm1d(128), nn.Dropout(), nn.Conv1d(128, num_classes, 1, bias=bias) ) self.en_dims = en_dims def forward(self, x, norm_plt, cls_label): xyz = x.permute(0, 2, 1) x = torch.cat([x,norm_plt],dim=1) x = self.embedding(x) # B,D,N xyz_list = [xyz] # [B, N, 3] x_list = [x] # [B, D, N] # here is the encoder for i in range(self.stages): # Give xyz[b, p, 3] and fea[b, p, d], return new_xyz[b, g, 3] and new_fea[b, g, k, d] xyz, x = self.local_grouper_list[i](xyz, x.permute(0, 2, 1)) # [b,g,3] [b,g,k,d] x = self.pre_blocks_list[i](x) # [b,d,g] x = self.pos_blocks_list[i](x) # [b,d,g] xyz_list.append(xyz) x_list.append(x) # here is the decoder xyz_list.reverse() x_list.reverse() x = x_list[0] for i in range(len(self.decode_list)): x = self.decode_list[i](xyz_list[i+1], xyz_list[i], x_list[i+1],x) # here is the global context gmp_list = [] for i in range(len(x_list)): gmp_list.append(F.adaptive_max_pool1d(self.gmp_map_list[i](x_list[i]), 1)) global_context = self.gmp_map_end(torch.cat(gmp_list, dim=1)) # [b, gmp_dim, 1] #here is the cls_token cls_token = self.cls_map(cls_label.unsqueeze(dim=-1)) # [b, cls_dim, 1] x = torch.cat([x, global_context.repeat([1, 1, x.shape[-1]]), cls_token.repeat([1, 1, x.shape[-1]])], dim=1) x = self.classifier(x) x = F.log_softmax(x, dim=1) x = x.permute(0, 2, 1) return x def pointMLP(num_classes=50, **kwargs) -> PointMLP: return PointMLP(num_classes=num_classes, points=2048, embed_dim=64, groups=1, res_expansion=1.0, activation="relu", bias=True, use_xyz=True, normalize="anchor", dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2], k_neighbors=[32, 32, 32, 32], reducers=[4, 4, 4, 4], de_dims=[512, 256, 128, 128], de_blocks=[4,4,4,4], gmp_dim=64,cls_dim=64, **kwargs) if __name__ == '__main__': data = torch.rand(2, 3, 2048).cuda() norm = torch.rand(2, 3, 2048).cuda() cls_label = torch.rand([2, 16]).cuda() print(f"data shape: {data.shape}") print(f"norm shape: {norm.shape}") print(f"cls_label shape: {cls_label.shape}") print("===> testing pointMLP (segmentation) ...") model = pointMLP(50).cuda() out = model(data, norm, cls_label) # [2,2048,50] print(f"out shape: {out.shape}")