469 lines
19 KiB
Python
469 lines
19 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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elif activation.lower() == 'leakyrelu0.2':
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return nn.LeakyReLU(negative_slope=0.2, 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="anchor", **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 PointNetFeaturePropagation(nn.Module):
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def __init__(self, in_channel, out_channel, blocks=1, groups=1, res_expansion=1.0, bias=True, activation='relu'):
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super(PointNetFeaturePropagation, self).__init__()
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self.fuse = ConvBNReLU1D(in_channel, out_channel, 1, bias=bias)
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self.extraction = PosExtraction(out_channel, blocks, groups=groups,
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res_expansion=res_expansion, bias=bias, activation=activation)
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def forward(self, xyz1, xyz2, points1, points2):
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"""
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Input:
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xyz1: input points position data, [B, N, 3]
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xyz2: sampled input points position data, [B, S, 3]
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points1: input points data, [B, D', N]
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points2: input points data, [B, D'', S]
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Return:
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new_points: upsampled points data, [B, D''', N]
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"""
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# xyz1 = xyz1.permute(0, 2, 1)
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# xyz2 = xyz2.permute(0, 2, 1)
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points2 = points2.permute(0, 2, 1)
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B, N, C = xyz1.shape
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_, S, _ = xyz2.shape
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if S == 1:
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interpolated_points = points2.repeat(1, N, 1)
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else:
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dists = square_distance(xyz1, xyz2)
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dists, idx = dists.sort(dim=-1)
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dists, idx = dists[:, :, :3], idx[:, :, :3] # [B, N, 3]
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dist_recip = 1.0 / (dists + 1e-8)
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norm = torch.sum(dist_recip, dim=2, keepdim=True)
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weight = dist_recip / norm
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interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2)
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if points1 is not None:
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points1 = points1.permute(0, 2, 1)
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new_points = torch.cat([points1, interpolated_points], dim=-1)
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else:
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new_points = interpolated_points
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new_points = new_points.permute(0, 2, 1)
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new_points = self.fuse(new_points)
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new_points = self.extraction(new_points)
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return new_points
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class PointMLP(nn.Module):
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def __init__(self, num_classes=50,points=2048, embed_dim=64, groups=1, res_expansion=1.0,
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activation="relu", bias=True, use_xyz=True, 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=[32, 32, 32, 32], reducers=[4, 4, 4, 4],
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de_dims=[512, 256, 128, 128], de_blocks=[2,2,2,2],
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gmp_dim=64,cls_dim=64, **kwargs):
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super(PointMLP, self).__init__()
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self.stages = len(pre_blocks)
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self.class_num = num_classes
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self.points = points
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self.embedding = ConvBNReLU1D(6, 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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en_dims = [last_channel]
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### Building Encoder #####
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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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en_dims.append(last_channel)
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### Building Decoder #####
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self.decode_list = nn.ModuleList()
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en_dims.reverse()
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de_dims.insert(0,en_dims[0])
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assert len(en_dims) ==len(de_dims) == len(de_blocks)+1
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for i in range(len(en_dims)-1):
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self.decode_list.append(
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PointNetFeaturePropagation(de_dims[i]+en_dims[i+1], de_dims[i+1],
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blocks=de_blocks[i], groups=groups, res_expansion=res_expansion,
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bias=bias, activation=activation)
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)
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self.act = get_activation(activation)
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# class label mapping
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self.cls_map = nn.Sequential(
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ConvBNReLU1D(16, cls_dim, bias=bias, activation=activation),
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ConvBNReLU1D(cls_dim, cls_dim, bias=bias, activation=activation)
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)
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# global max pooling mapping
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self.gmp_map_list = nn.ModuleList()
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for en_dim in en_dims:
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self.gmp_map_list.append(ConvBNReLU1D(en_dim, gmp_dim, bias=bias, activation=activation))
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self.gmp_map_end = ConvBNReLU1D(gmp_dim*len(en_dims), gmp_dim, bias=bias, activation=activation)
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# classifier
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self.classifier = nn.Sequential(
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nn.Conv1d(gmp_dim+cls_dim+de_dims[-1], 128, 1, bias=bias),
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nn.BatchNorm1d(128),
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nn.Dropout(),
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nn.Conv1d(128, num_classes, 1, bias=bias)
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)
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self.en_dims = en_dims
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def forward(self, x, norm_plt, cls_label):
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xyz = x.permute(0, 2, 1)
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x = torch.cat([x,norm_plt],dim=1)
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x = self.embedding(x) # B,D,N
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xyz_list = [xyz] # [B, N, 3]
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x_list = [x] # [B, D, N]
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# here is the encoder
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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]
|
||
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}")
|