wehub-resource-sync
294 lines
8.5 KiB
Python
294 lines
8.5 KiB
Python
import dgl
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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 dgl.geometry import farthest_point_sampler
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"""
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Part of the code are adapted from
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https://github.com/yanx27/Pointnet_Pointnet2_pytorch
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"""
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def square_distance(src, dst):
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"""
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Adapted from https://github.com/yanx27/Pointnet_Pointnet2_pytorch
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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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Adapted from https://github.com/yanx27/Pointnet_Pointnet2_pytorch
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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 = (
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torch.arange(B, dtype=torch.long)
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.to(device)
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.view(view_shape)
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.repeat(repeat_shape)
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)
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new_points = points[batch_indices, idx, :]
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return new_points
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class KNearNeighbors(nn.Module):
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"""
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Find the k nearest neighbors
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"""
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def __init__(self, n_neighbor):
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super(KNearNeighbors, self).__init__()
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self.n_neighbor = n_neighbor
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def forward(self, pos, centroids):
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"""
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Adapted from https://github.com/yanx27/Pointnet_Pointnet2_pytorch
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"""
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center_pos = index_points(pos, centroids)
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sqrdists = square_distance(center_pos, pos)
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group_idx = sqrdists.argsort(dim=-1)[:, :, : self.n_neighbor]
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return group_idx
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class KNNGraphBuilder(nn.Module):
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"""
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Build NN graph
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"""
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def __init__(self, n_neighbor):
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super(KNNGraphBuilder, self).__init__()
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self.n_neighbor = n_neighbor
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self.knn = KNearNeighbors(n_neighbor)
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def forward(self, pos, centroids, feat=None):
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dev = pos.device
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group_idx = self.knn(pos, centroids)
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B, N, _ = pos.shape
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glist = []
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for i in range(B):
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center = torch.zeros((N)).to(dev)
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center[centroids[i]] = 1
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src = group_idx[i].contiguous().view(-1)
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dst = (
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centroids[i]
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.view(-1, 1)
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.repeat(
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1, min(self.n_neighbor, src.shape[0] // centroids.shape[1])
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)
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.view(-1)
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)
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unified = torch.cat([src, dst])
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uniq, inv_idx = torch.unique(unified, return_inverse=True)
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src_idx = inv_idx[: src.shape[0]]
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dst_idx = inv_idx[src.shape[0] :]
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g = dgl.graph((src_idx, dst_idx))
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g.ndata["pos"] = pos[i][uniq]
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g.ndata["center"] = center[uniq]
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if feat is not None:
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g.ndata["feat"] = feat[i][uniq]
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glist.append(g)
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bg = dgl.batch(glist)
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return bg
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class RelativePositionMessage(nn.Module):
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"""
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Compute the input feature from neighbors
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"""
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def __init__(self, n_neighbor):
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super(RelativePositionMessage, self).__init__()
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self.n_neighbor = n_neighbor
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def forward(self, edges):
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pos = edges.src["pos"] - edges.dst["pos"]
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if "feat" in edges.src:
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res = torch.cat([pos, edges.src["feat"]], 1)
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else:
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res = pos
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return {"agg_feat": res}
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class KNNConv(nn.Module):
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"""
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Feature aggregation
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"""
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def __init__(self, sizes, batch_size):
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super(KNNConv, self).__init__()
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self.batch_size = batch_size
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self.conv = nn.ModuleList()
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self.bn = nn.ModuleList()
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for i in range(1, len(sizes)):
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self.conv.append(nn.Conv2d(sizes[i - 1], sizes[i], 1))
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self.bn.append(nn.BatchNorm2d(sizes[i]))
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def forward(self, nodes):
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shape = nodes.mailbox["agg_feat"].shape
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h = (
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nodes.mailbox["agg_feat"]
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.view(self.batch_size, -1, shape[1], shape[2])
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.permute(0, 3, 2, 1)
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)
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for conv, bn in zip(self.conv, self.bn):
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h = conv(h)
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h = bn(h)
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h = F.relu(h)
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h = torch.max(h, 2)[0]
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feat_dim = h.shape[1]
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h = h.permute(0, 2, 1).reshape(-1, feat_dim)
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return {"new_feat": h}
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def group_all(self, pos, feat):
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"""
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Feature aggregation and pooling for the non-sampling layer
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"""
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if feat is not None:
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h = torch.cat([pos, feat], 2)
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else:
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h = pos
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B, N, D = h.shape
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_, _, C = pos.shape
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new_pos = torch.zeros(B, 1, C)
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h = h.permute(0, 2, 1).view(B, -1, N, 1)
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for conv, bn in zip(self.conv, self.bn):
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h = conv(h)
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h = bn(h)
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h = F.relu(h)
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h = torch.max(h[:, :, :, 0], 2)[0] # [B,D]
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return new_pos, h
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class TransitionDown(nn.Module):
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"""
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The Transition Down Module
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"""
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def __init__(self, n_points, batch_size, mlp_sizes, n_neighbors=64):
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super(TransitionDown, self).__init__()
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self.n_points = n_points
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self.frnn_graph = KNNGraphBuilder(n_neighbors)
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self.message = RelativePositionMessage(n_neighbors)
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self.conv = KNNConv(mlp_sizes, batch_size)
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self.batch_size = batch_size
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def forward(self, pos, feat):
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centroids = farthest_point_sampler(pos, self.n_points)
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g = self.frnn_graph(pos, centroids, feat)
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g.update_all(self.message, self.conv)
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mask = g.ndata["center"] == 1
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pos_dim = g.ndata["pos"].shape[-1]
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feat_dim = g.ndata["new_feat"].shape[-1]
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pos_res = g.ndata["pos"][mask].view(self.batch_size, -1, pos_dim)
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feat_res = g.ndata["new_feat"][mask].view(self.batch_size, -1, feat_dim)
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return pos_res, feat_res
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class FeaturePropagation(nn.Module):
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"""
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The FeaturePropagation Layer
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"""
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def __init__(self, input_dims, sizes):
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super(FeaturePropagation, self).__init__()
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self.convs = nn.ModuleList()
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self.bns = nn.ModuleList()
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sizes = [input_dims] + sizes
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for i in range(1, len(sizes)):
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self.convs.append(nn.Conv1d(sizes[i - 1], sizes[i], 1))
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self.bns.append(nn.BatchNorm1d(sizes[i]))
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def forward(self, x1, x2, feat1, feat2):
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"""
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Adapted from https://github.com/yanx27/Pointnet_Pointnet2_pytorch
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Input:
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x1: input points position data, [B, N, C]
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x2: sampled input points position data, [B, S, C]
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feat1: input points data, [B, N, D]
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feat2: input points data, [B, S, D]
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Return:
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new_feat: upsampled points data, [B, D', N]
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"""
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B, N, C = x1.shape
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_, S, _ = x2.shape
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if S == 1:
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interpolated_feat = feat2.repeat(1, N, 1)
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else:
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dists = square_distance(x1, x2)
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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_feat = torch.sum(
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index_points(feat2, idx) * weight.view(B, N, 3, 1), dim=2
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)
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if feat1 is not None:
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new_feat = torch.cat([feat1, interpolated_feat], dim=-1)
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else:
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new_feat = interpolated_feat
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new_feat = new_feat.permute(0, 2, 1) # [B, D, S]
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for i, conv in enumerate(self.convs):
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bn = self.bns[i]
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new_feat = F.relu(bn(conv(new_feat)))
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return new_feat
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class SwapAxes(nn.Module):
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def __init__(self, dim1=1, dim2=2):
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super(SwapAxes, self).__init__()
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self.dim1 = dim1
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self.dim2 = dim2
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def forward(self, x):
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return x.transpose(self.dim1, self.dim2)
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class TransitionUp(nn.Module):
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"""
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The Transition Up Module
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"""
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def __init__(self, dim1, dim2, dim_out):
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super(TransitionUp, self).__init__()
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self.fc1 = nn.Sequential(
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nn.Linear(dim1, dim_out),
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SwapAxes(),
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nn.BatchNorm1d(dim_out), # TODO
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SwapAxes(),
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nn.ReLU(),
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)
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self.fc2 = nn.Sequential(
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nn.Linear(dim2, dim_out),
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SwapAxes(),
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nn.BatchNorm1d(dim_out), # TODO
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SwapAxes(),
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nn.ReLU(),
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)
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self.fp = FeaturePropagation(-1, [])
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def forward(self, pos1, feat1, pos2, feat2):
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h1 = self.fc1(feat1)
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h2 = self.fc2(feat2)
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h1 = self.fp(pos2, pos1, None, h1).transpose(1, 2)
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return h1 + h2
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