375 lines
15 KiB
Python
375 lines
15 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# from torch import einsum
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# from einops import rearrange, repeat
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from pointnet2_ops import pointnet2_utils
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def get_activation(activation):
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if activation.lower() == 'gelu':
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return nn.GELU()
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elif activation.lower() == 'rrelu':
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return nn.RReLU(inplace=True)
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elif activation.lower() == 'selu':
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return nn.SELU(inplace=True)
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elif activation.lower() == 'silu':
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return nn.SiLU(inplace=True)
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elif activation.lower() == 'hardswish':
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return nn.Hardswish(inplace=True)
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elif activation.lower() == 'leakyrelu':
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return nn.LeakyReLU(inplace=True)
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else:
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return nn.ReLU(inplace=True)
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def square_distance(src, dst):
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"""
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Calculate Euclid distance between each two points.
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src^T * dst = xn * xm + yn * ym + zn * zm；
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sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
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sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
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dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2
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= sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst
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Input:
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src: source points, [B, N, C]
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dst: target points, [B, M, C]
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Output:
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dist: per-point square distance, [B, N, M]
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"""
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B, N, _ = src.shape
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_, M, _ = dst.shape
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dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))
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dist += torch.sum(src ** 2, -1).view(B, N, 1)
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dist += torch.sum(dst ** 2, -1).view(B, 1, M)
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return dist
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def index_points(points, idx):
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"""
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Input:
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points: input points data, [B, N, C]
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idx: sample index data, [B, S]
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Return:
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new_points:, indexed points data, [B, S, C]
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"""
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device = points.device
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B = points.shape[0]
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view_shape = list(idx.shape)
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view_shape[1:] = [1] * (len(view_shape) - 1)
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repeat_shape = list(idx.shape)
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repeat_shape[0] = 1
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batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)
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new_points = points[batch_indices, idx, :]
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return new_points
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def farthest_point_sample(xyz, npoint):
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"""
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Input:
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xyz: pointcloud data, [B, N, 3]
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npoint: number of samples
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Return:
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centroids: sampled pointcloud index, [B, npoint]
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"""
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device = xyz.device
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B, N, C = xyz.shape
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centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)
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distance = torch.ones(B, N).to(device) * 1e10
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farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
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batch_indices = torch.arange(B, dtype=torch.long).to(device)
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for i in range(npoint):
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centroids[:, i] = farthest
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centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)
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dist = torch.sum((xyz - centroid) ** 2, -1)
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distance = torch.min(distance, dist)
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farthest = torch.max(distance, -1)[1]
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return centroids
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def query_ball_point(radius, nsample, xyz, new_xyz):
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"""
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Input:
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radius: local region radius
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nsample: max sample number in local region
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xyz: all points, [B, N, 3]
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new_xyz: query points, [B, S, 3]
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Return:
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group_idx: grouped points index, [B, S, nsample]
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"""
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device = xyz.device
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B, N, C = xyz.shape
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_, S, _ = new_xyz.shape
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group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1])
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sqrdists = square_distance(new_xyz, xyz)
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group_idx[sqrdists > radius ** 2] = N
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group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample]
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group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample])
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mask = group_idx == N
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group_idx[mask] = group_first[mask]
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return group_idx
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def knn_point(nsample, xyz, new_xyz):
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"""
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Input:
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nsample: max sample number in local region
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xyz: all points, [B, N, C]
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new_xyz: query points, [B, S, C]
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Return:
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group_idx: grouped points index, [B, S, nsample]
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"""
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sqrdists = square_distance(new_xyz, xyz)
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_, group_idx = torch.topk(sqrdists, nsample, dim=-1, largest=False, sorted=False)
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return group_idx
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class LocalGrouper(nn.Module):
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def __init__(self, channel, groups, kneighbors, use_xyz=True, normalize="center", **kwargs):
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"""
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Give xyz[b,p,3] and fea[b,p,d], return new_xyz[b,g,3] and new_fea[b,g,k,d]
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:param groups: groups number
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:param kneighbors: k-nerighbors
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:param kwargs: others
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"""
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super(LocalGrouper, self).__init__()
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self.groups = groups
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self.kneighbors = kneighbors
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self.use_xyz = use_xyz
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if normalize is not None:
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self.normalize = normalize.lower()
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else:
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self.normalize = None
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if self.normalize not in ["center", "anchor"]:
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print(f"Unrecognized normalize parameter (self.normalize), set to None. Should be one of [center, anchor].")
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self.normalize = None
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if self.normalize is not None:
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add_channel=3 if self.use_xyz else 0
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self.affine_alpha = nn.Parameter(torch.ones([1,1,1,channel + add_channel]))
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self.affine_beta = nn.Parameter(torch.zeros([1, 1, 1, channel + add_channel]))
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def forward(self, xyz, points):
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B, N, C = xyz.shape
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S = self.groups
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xyz = xyz.contiguous() # xyz [btach, points, xyz]
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# fps_idx = torch.multinomial(torch.linspace(0, N - 1, steps=N).repeat(B, 1).to(xyz.device), num_samples=self.groups, replacement=False).long()
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# fps_idx = farthest_point_sample(xyz, self.groups).long()
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fps_idx = pointnet2_utils.furthest_point_sample(xyz, self.groups).long() # [B, npoint]
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new_xyz = index_points(xyz, fps_idx) # [B, npoint, 3]
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new_points = index_points(points, fps_idx) # [B, npoint, d]
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idx = knn_point(self.kneighbors, xyz, new_xyz)
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# idx = query_ball_point(radius, nsample, xyz, new_xyz)
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grouped_xyz = index_points(xyz, idx) # [B, npoint, k, 3]
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grouped_points = index_points(points, idx) # [B, npoint, k, d]
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if self.use_xyz:
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grouped_points = torch.cat([grouped_points, grouped_xyz],dim=-1) # [B, npoint, k, d+3]
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if self.normalize is not None:
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if self.normalize =="center":
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mean = torch.mean(grouped_points, dim=2, keepdim=True)
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if self.normalize =="anchor":
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mean = torch.cat([new_points, new_xyz],dim=-1) if self.use_xyz else new_points
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mean = mean.unsqueeze(dim=-2) # [B, npoint, 1, d+3]
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std = torch.std((grouped_points-mean).reshape(B,-1),dim=-1,keepdim=True).unsqueeze(dim=-1).unsqueeze(dim=-1)
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grouped_points = (grouped_points-mean)/(std + 1e-5)
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grouped_points = self.affine_alpha*grouped_points + self.affine_beta
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new_points = torch.cat([grouped_points, new_points.view(B, S, 1, -1).repeat(1, 1, self.kneighbors, 1)], dim=-1)
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return new_xyz, new_points
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class ConvBNReLU1D(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=1, bias=True, activation='relu'):
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super(ConvBNReLU1D, self).__init__()
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self.act = get_activation(activation)
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self.net = nn.Sequential(
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nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, bias=bias),
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nn.BatchNorm1d(out_channels),
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self.act
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)
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def forward(self, x):
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return self.net(x)
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class ConvBNReLURes1D(nn.Module):
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def __init__(self, channel, kernel_size=1, groups=1, res_expansion=1.0, bias=True, activation='relu'):
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super(ConvBNReLURes1D, self).__init__()
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self.act = get_activation(activation)
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self.net1 = nn.Sequential(
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nn.Conv1d(in_channels=channel, out_channels=int(channel * res_expansion),
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kernel_size=kernel_size, groups=groups, bias=bias),
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nn.BatchNorm1d(int(channel * res_expansion)),
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self.act
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)
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if groups > 1:
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self.net2 = nn.Sequential(
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nn.Conv1d(in_channels=int(channel * res_expansion), out_channels=channel,
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kernel_size=kernel_size, groups=groups, bias=bias),
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nn.BatchNorm1d(channel),
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self.act,
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nn.Conv1d(in_channels=channel, out_channels=channel,
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kernel_size=kernel_size, bias=bias),
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nn.BatchNorm1d(channel),
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)
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else:
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self.net2 = nn.Sequential(
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nn.Conv1d(in_channels=int(channel * res_expansion), out_channels=channel,
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kernel_size=kernel_size, bias=bias),
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nn.BatchNorm1d(channel)
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)
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def forward(self, x):
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return self.act(self.net2(self.net1(x)) + x)
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class PreExtraction(nn.Module):
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def __init__(self, channels, out_channels, blocks=1, groups=1, res_expansion=1, bias=True,
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activation='relu', use_xyz=True):
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"""
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input: [b,g,k,d]: output:[b,d,g]
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:param channels:
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:param blocks:
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"""
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super(PreExtraction, self).__init__()
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in_channels = 3+2*channels if use_xyz else 2*channels
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self.transfer = ConvBNReLU1D(in_channels, out_channels, bias=bias, activation=activation)
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operation = []
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for _ in range(blocks):
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operation.append(
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ConvBNReLURes1D(out_channels, groups=groups, res_expansion=res_expansion,
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bias=bias, activation=activation)
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)
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self.operation = nn.Sequential(*operation)
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def forward(self, x):
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b, n, s, d = x.size() # torch.Size([32, 512, 32, 6])
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x = x.permute(0, 1, 3, 2)
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x = x.reshape(-1, d, s)
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x = self.transfer(x)
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batch_size, _, _ = x.size()
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x = self.operation(x) # [b, d, k]
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x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
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x = x.reshape(b, n, -1).permute(0, 2, 1)
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return x
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class PosExtraction(nn.Module):
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def __init__(self, channels, blocks=1, groups=1, res_expansion=1, bias=True, activation='relu'):
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"""
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input[b,d,g]; output[b,d,g]
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:param channels:
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:param blocks:
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"""
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super(PosExtraction, self).__init__()
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operation = []
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for _ in range(blocks):
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operation.append(
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ConvBNReLURes1D(channels, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation)
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)
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self.operation = nn.Sequential(*operation)
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def forward(self, x): # [b, d, g]
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return self.operation(x)
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class Model(nn.Module):
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def __init__(self, points=1024, class_num=40, embed_dim=64, groups=1, res_expansion=1.0,
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activation="relu", bias=True, use_xyz=True, normalize="center",
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dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
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k_neighbors=[32, 32, 32, 32], reducers=[2, 2, 2, 2], **kwargs):
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super(Model, self).__init__()
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self.stages = len(pre_blocks)
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self.class_num = class_num
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self.points = points
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self.embedding = ConvBNReLU1D(3, embed_dim, bias=bias, activation=activation)
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assert len(pre_blocks) == len(k_neighbors) == len(reducers) == len(pos_blocks) == len(dim_expansion), \
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"Please check stage number consistent for pre_blocks, pos_blocks k_neighbors, reducers."
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self.local_grouper_list = nn.ModuleList()
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self.pre_blocks_list = nn.ModuleList()
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self.pos_blocks_list = nn.ModuleList()
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last_channel = embed_dim
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anchor_points = self.points
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for i in range(len(pre_blocks)):
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out_channel = last_channel * dim_expansion[i]
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pre_block_num = pre_blocks[i]
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pos_block_num = pos_blocks[i]
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kneighbor = k_neighbors[i]
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reduce = reducers[i]
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anchor_points = anchor_points // reduce
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# append local_grouper_list
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local_grouper = LocalGrouper(last_channel, anchor_points, kneighbor, use_xyz, normalize) # [b,g,k,d]
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self.local_grouper_list.append(local_grouper)
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# append pre_block_list
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pre_block_module = PreExtraction(last_channel, out_channel, pre_block_num, groups=groups,
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res_expansion=res_expansion,
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bias=bias, activation=activation, use_xyz=use_xyz)
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self.pre_blocks_list.append(pre_block_module)
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# append pos_block_list
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pos_block_module = PosExtraction(out_channel, pos_block_num, groups=groups,
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res_expansion=res_expansion, bias=bias, activation=activation)
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self.pos_blocks_list.append(pos_block_module)
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last_channel = out_channel
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self.act = get_activation(activation)
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self.classifier = nn.Sequential(
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nn.Linear(last_channel, 512),
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nn.BatchNorm1d(512),
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self.act,
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nn.Dropout(0.5),
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nn.Linear(512, 256),
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nn.BatchNorm1d(256),
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self.act,
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nn.Dropout(0.5),
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nn.Linear(256, self.class_num)
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)
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def forward(self, x):
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xyz = x.permute(0, 2, 1)
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batch_size, _, _ = x.size()
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x = self.embedding(x) # B,D,N
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for i in range(self.stages):
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# Give xyz[b, p, 3] and fea[b, p, d], return new_xyz[b, g, 3] and new_fea[b, g, k, d]
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xyz, x = self.local_grouper_list[i](xyz, x.permute(0, 2, 1)) # [b,g,3] [b,g,k,d]
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x = self.pre_blocks_list[i](x) # [b,d,g]
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x = self.pos_blocks_list[i](x) # [b,d,g]
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x = F.adaptive_max_pool1d(x, 1).squeeze(dim=-1)
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x = self.classifier(x)
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return x
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def pointMLP(num_classes=40, **kwargs) -> Model:
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return Model(points=1024, class_num=num_classes, embed_dim=64, groups=1, res_expansion=1.0,
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activation="relu", bias=False, use_xyz=False, normalize="anchor",
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dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2],
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k_neighbors=[24, 24, 24, 24], reducers=[2, 2, 2, 2], **kwargs)
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def pointMLPElite(num_classes=40, **kwargs) -> Model:
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return Model(points=1024, class_num=num_classes, embed_dim=32, groups=1, res_expansion=0.25,
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activation="relu", bias=False, use_xyz=False, normalize="anchor",
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dim_expansion=[2, 2, 2, 1], pre_blocks=[1, 1, 2, 1], pos_blocks=[1, 1, 2, 1],
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k_neighbors=[24,24,24,24], reducers=[2, 2, 2, 2], **kwargs)
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if __name__ == '__main__':
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data = torch.rand(2, 3, 1024).cuda()
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print(data.shape)
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print("===> testing pointMLP ...")
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model = pointMLP().cuda()
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out = model(data)
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print(out.shape)
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print("===> testing pointMLPElite ...")
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model = pointMLPElite().cuda()
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out = model(data)
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print(out.shape)
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