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31f43c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | """DGCNN backbone — drop-in replacement for PointNet.
EdgeConv with dynamic graph KNN captures local geometric structure
better than PointNet's global aggregation.
Ref: Wang et al., "Dynamic Graph CNN for Learning on Point Clouds", TOG 2019
https://github.com/antao97/dgcnn.pytorch
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def knn(x, k):
"""Compute KNN graph. x: (B, C, N). Returns (B, N, k) indices."""
inner = -2 * torch.matmul(x.transpose(2, 1), x) # (B, N, N)
xx = torch.sum(x ** 2, dim=1, keepdim=True) # (B, 1, N)
pairwise_dist = -xx - inner - xx.transpose(2, 1) # (B, N, N) negative distances
idx = pairwise_dist.topk(k=k, dim=-1)[1] # (B, N, k)
return idx
def get_graph_feature(x, k=20, idx=None):
"""Build edge features for EdgeConv.
For each point, concatenate [x_j - x_i, x_i] for its k neighbors.
Returns (B, 2*C, N, k).
"""
B, C, N = x.shape
device = x.device
if idx is None:
idx = knn(x, k=k) # (B, N, k)
idx_base = torch.arange(0, B, device=device).view(-1, 1, 1) * N
idx = idx + idx_base
idx = idx.view(-1)
x = x.transpose(2, 1).contiguous() # (B, N, C)
feature = x.view(B * N, -1)[idx, :] # (B*N*k, C)
feature = feature.view(B, N, k, C)
x = x.view(B, N, 1, C).repeat(1, 1, k, 1) # (B, N, k, C)
feature = torch.cat((feature - x, x), dim=3).permute(0, 3, 1, 2).contiguous()
# (B, 2*C, N, k)
return feature
class EdgeConv(nn.Module):
"""Single EdgeConv layer."""
def __init__(self, in_channels, out_channels, k=20):
super().__init__()
self.k = k
self.conv = nn.Sequential(
nn.Conv2d(in_channels * 2, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(0.2, inplace=True),
)
def forward(self, x):
# x: (B, C, N)
feat = get_graph_feature(x, k=self.k) # (B, 2*C, N, k)
feat = self.conv(feat) # (B, out, N, k)
feat = feat.max(dim=-1)[0] # (B, out, N)
return feat
class DGCNNBackbone(nn.Module):
"""DGCNN backbone with multiple EdgeConv layers.
Same interface as PointNetBackbone: (B, C, N) → (B, out_dim).
"""
def __init__(self, in_channels, k=20, emb_dims=1024):
super().__init__()
self.k = k
self.edge_conv1 = EdgeConv(in_channels, 64, k)
self.edge_conv2 = EdgeConv(64, 64, k)
self.edge_conv3 = EdgeConv(64, 128, k)
self.edge_conv4 = EdgeConv(128, 256, k)
# Aggregate all EdgeConv outputs
self.conv5 = nn.Sequential(
nn.Conv1d(64 + 64 + 128 + 256, emb_dims, 1, bias=False),
nn.BatchNorm1d(emb_dims),
nn.LeakyReLU(0.2, inplace=True),
)
self.out_dim = emb_dims * 2 # max + avg pooling
def forward(self, x):
"""
Args:
x: (B, C, N)
Returns:
global_feat: (B, out_dim)
"""
x1 = self.edge_conv1(x) # (B, 64, N)
x2 = self.edge_conv2(x1) # (B, 64, N)
x3 = self.edge_conv3(x2) # (B, 128, N)
x4 = self.edge_conv4(x3) # (B, 256, N)
x_cat = torch.cat([x1, x2, x3, x4], dim=1) # (B, 512, N)
x5 = self.conv5(x_cat) # (B, emb_dims, N)
x_max = x5.max(dim=-1)[0] # (B, emb_dims)
x_avg = x5.mean(dim=-1) # (B, emb_dims)
global_feat = torch.cat([x_max, x_avg], dim=1) # (B, 2*emb_dims)
return global_feat
class DGCNNVertexClassifier(nn.Module):
"""DGCNN vertex classifier — same heads as PointNet version."""
def __init__(self, in_channels=11, k=10, emb_dims=512):
super().__init__()
self.backbone = DGCNNBackbone(in_channels, k, emb_dims)
feat_dim = self.backbone.out_dim
self.cls_head = nn.Sequential(
nn.Linear(feat_dim, 512),
nn.BatchNorm1d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout(0.3),
nn.Linear(512, 128),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(128, 1),
)
self.offset_head = nn.Sequential(
nn.Linear(feat_dim, 512),
nn.BatchNorm1d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout(0.3),
nn.Linear(512, 128),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(128, 3),
)
self.conf_head = nn.Sequential(
nn.Linear(feat_dim, 256),
nn.BatchNorm1d(256),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(256, 1),
nn.Sigmoid(),
)
def forward(self, x):
feat = self.backbone(x)
cls_logits = self.cls_head(feat)
offset = self.offset_head(feat)
confidence = self.conf_head(feat)
return cls_logits, offset, confidence
class DGCNNEdgeClassifier(nn.Module):
"""DGCNN edge classifier — same heads as PointNet version."""
def __init__(self, in_channels=6, k=10, emb_dims=256):
super().__init__()
self.backbone = DGCNNBackbone(in_channels, k, emb_dims)
feat_dim = self.backbone.out_dim
self.head = nn.Sequential(
nn.Linear(feat_dim, 512),
nn.BatchNorm1d(512),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout(0.5),
nn.Linear(512, 256),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout(0.3),
nn.Linear(256, 1),
)
def forward(self, x):
feat = self.backbone(x)
return self.head(feat)
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