企业型网站建设怎样收费,行业协会网站建设,网站建设采购项目,如何自己制作网页文章目录 将点特征 转换为 voxel 特征稀疏张量 到 稠密张量#xff0c;反向索引参考博客 将点特征 转换为 voxel 特征
https://github.com/skyhehe123/VoxelNet-pytorch/blob/master/data/kitti.py
【Python】np.unique() 介绍与使用
self.T #xff1a; # maxiumum numbe… 文章目录 将点特征 转换为 voxel 特征稀疏张量 到 稠密张量反向索引参考博客 将点特征 转换为 voxel 特征
https://github.com/skyhehe123/VoxelNet-pytorch/blob/master/data/kitti.py
【Python】np.unique() 介绍与使用
self.T # maxiumum number of points per voxel def preprocess(self, lidar):# shuffling the pointsnp.random.shuffle(lidar)voxel_coords ((lidar[:, :3] - np.array([self.xrange[0], self.yrange[0], self.zrange[0]])) / (self.vw, self.vh, self.vd)).astype(np.int32)# convert to (D, H, W)voxel_coords voxel_coords[:,[2,1,0]]voxel_coords, inv_ind, voxel_counts np.unique(voxel_coords, axis0, \return_inverseTrue, return_countsTrue)voxel_features []for i in range(len(voxel_coords)):voxel np.zeros((self.T, 7), dtypenp.float32)pts lidar[inv_ind i] # 落到同一个voxel上的 点if voxel_counts[i] self.T:pts pts[:self.T, :]voxel_counts[i] self.T# augment the pointsvoxel[:pts.shape[0], :] np.concatenate((pts, pts[:, :3] - np.mean(pts[:, :3], 0)), axis1)voxel_features.append(voxel)return np.array(voxel_features), voxel_coords输入输出解释
稀疏张量 到 稠密张量反向索引
https://github.com/skyhehe123/VoxelNet-pytorch/blob/master/voxelnet.py
和 chatgpt一起学习的代码
import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.autograd import Variable
from config import config as cfg# conv2d bn relu
class Conv2d(nn.Module):def __init__(self,in_channels,out_channels,k,s,p, activationTrue, batch_normTrue):super(Conv2d, self).__init__()self.conv nn.Conv2d(in_channels,out_channels,kernel_sizek,strides,paddingp)if batch_norm:self.bn nn.BatchNorm2d(out_channels)else:self.bn Noneself.activation activationdef forward(self,x):x self.conv(x)if self.bn is not None:xself.bn(x)if self.activation:return F.relu(x,inplaceTrue)else:return x# conv3d bn relu
class Conv3d(nn.Module):def __init__(self, in_channels, out_channels, k, s, p, batch_normTrue):super(Conv3d, self).__init__()self.conv nn.Conv3d(in_channels, out_channels, kernel_sizek, strides, paddingp)if batch_norm:self.bn nn.BatchNorm3d(out_channels)else:self.bn Nonedef forward(self, x):x self.conv(x)if self.bn is not None:x self.bn(x)return F.relu(x, inplaceTrue)# Fully Connected Network
class FCN(nn.Module):def __init__(self,cin,cout):super(FCN, self).__init__()self.cout coutself.linear nn.Linear(cin, cout)self.bn nn.BatchNorm1d(cout)def forward(self,x):# KK is the stacked k across batchkk, t, _ x.shapex self.linear(x.view(kk*t,-1))x F.relu(self.bn(x))return x.view(kk,t,-1)# Voxel Feature Encoding layer
class VFE(nn.Module):def __init__(self,cin,cout):super(VFE, self).__init__()assert cout % 2 0self.units cout // 2self.fcn FCN(cin,self.units)def forward(self, x, mask): # x: [N, T, C] : # N:一个batch voxel 的数量不固定# point-wise feauturepwf self.fcn(x)#locally aggregated featurelaf torch.max(pwf,1)[0].unsqueeze(1).repeat(1,cfg.T,1) # laf[N, T, cout // 2]# point-wise concat featurepwcf torch.cat((pwf,laf),dim2)# apply maskmask mask.unsqueeze(2).repeat(1, 1, self.units * 2) # mask作用 一个voxel T35 个点不够T个点则用0填充但在计算时 不考虑这些0pwcf pwcf * mask.float()return pwcf # [N, T, Cout]# Stacked Voxel Feature Encoding
class SVFE(nn.Module):def __init__(self):super(SVFE, self).__init__()self.vfe_1 VFE(7,32)self.vfe_2 VFE(32,128)self.fcn FCN(128,128)def forward(self, x):mask torch.ne(torch.max(x,2)[0], 0)x self.vfe_1(x, mask)x self.vfe_2(x, mask)x self.fcn(x)# element-wise max poolingx torch.max(x,1)[0]return x # Convolutional Middle Layer
class CML(nn.Module):def __init__(self):super(CML, self).__init__()self.conv3d_1 Conv3d(128, 64, 3, s(2, 1, 1), p(1, 1, 1))self.conv3d_2 Conv3d(64, 64, 3, s(1, 1, 1), p(0, 1, 1))self.conv3d_3 Conv3d(64, 64, 3, s(2, 1, 1), p(1, 1, 1))def forward(self, x): x self.conv3d_1(x)x self.conv3d_2(x)x self.conv3d_3(x)return x# # Region Proposal Network
# class RPN(nn.Module):
# def __init__(self):
# super(RPN, self).__init__()
# self.block_1 [Conv2d(128, 128, 3, 2, 1)]
# self.block_1 [Conv2d(128, 128, 3, 1, 1) for _ in range(3)]
# self.block_1 nn.Sequential(*self.block_1)# self.block_2 [Conv2d(128, 128, 3, 2, 1)]
# self.block_2 [Conv2d(128, 128, 3, 1, 1) for _ in range(5)]
# self.block_2 nn.Sequential(*self.block_2)# self.block_3 [Conv2d(128, 256, 3, 2, 1)]
# self.block_3 [nn.Conv2d(256, 256, 3, 1, 1) for _ in range(5)]
# self.block_3 nn.Sequential(*self.block_3)# self.deconv_1 nn.Sequential(nn.ConvTranspose2d(256, 256, 4, 4, 0),nn.BatchNorm2d(256))
# self.deconv_2 nn.Sequential(nn.ConvTranspose2d(128, 256, 2, 2, 0),nn.BatchNorm2d(256))
# self.deconv_3 nn.Sequential(nn.ConvTranspose2d(128, 256, 1, 1, 0),nn.BatchNorm2d(256))# self.score_head Conv2d(768, cfg.anchors_per_position, 1, 1, 0, activationFalse, batch_normFalse)
# self.reg_head Conv2d(768, 7 * cfg.anchors_per_position, 1, 1, 0, activationFalse, batch_normFalse)# def forward(self,x):
# x self.block_1(x)
# x_skip_1 x
# x self.block_2(x)
# x_skip_2 x
# x self.block_3(x)
# x_0 self.deconv_1(x)
# x_1 self.deconv_2(x_skip_2)
# x_2 self.deconv_3(x_skip_1)
# x torch.cat((x_0,x_1,x_2),1)
# return self.score_head(x),self.reg_head(x)class VoxelNet(nn.Module):def __init__(self):super(VoxelNet, self).__init__()self.svfe SVFE()self.cml CML()# self.rpn RPN()def voxel_indexing(self, sparse_features, coords): # sparse_features:[N, C]: # N: 一个batch voxel的数量不固定dim sparse_features.shape[-1]dense_feature Variable(torch.zeros(dim, cfg.N, cfg.D, cfg.H, cfg.W).cuda()) # cfg.N batch这段代码的操作可以通过一个for循环来实现但是需要注意使用for循环的效率通常会比使用向量化操作低。下面是一个可能的实现for i in range(len(coords)):dense_feature[:, coords[i,0], coords[i,1], coords[i,2], coords[i,3]] sparse_features[i]这个for循环遍历coords的每一行即每一个坐标然后在dense_feature中找到对应的位置将sparse_features中的对应元素赋给这个位置。这与原始代码的操作是一样的。但是需要注意的是这种方法的效率通常会比使用向量化操作低特别是当处理大量数据时。在实际的代码中我们通常会优先使用向量化操作因为它们可以利用现代硬件的并行计算能力从而大大提高计算效率这是一种常见的将稀疏张量转换为密集张量的方法。在稀疏张量中只存储非零元素和它们的位置而在密集张量中所有元素都被存储。
这段代码就是在将 sparse_features 中的元素放入 dense_feature 的对应位置从而将稀疏表示转换为密集表示。dense_feature[:, coords[:,0], coords[:,1], coords[:,2], coords[:,3]] sparse_features# dense_feature:[C, B, D, H, W]return dense_feature.transpose(0, 1) # dense_feature:[B, C, D, H, W] # 这样就转换为稠密张量了 def forward(self, voxel_features, voxel_coords): # voxel_features:[N, T, C] # N:一个batch voxel的数量每个voxel 35个点每个点 C维# voxel_coords:[N, 4] , [batch_id, x, y, z]# feature learning networkvwfs self.svfe(voxel_features)print(fvwfs.shape {vwfs.shape}) # [N, C]vwfs self.voxel_indexing(vwfs,voxel_coords) # index 反向索引print(fvoxel_indexing vwfs.shape {vwfs.shape}) # # convolutional middle network# cml_out self.cml(vwfs)# region proposal network# merge the depth and feature dim into one, output probability score map and regression map# psm,rm self.rpn(cml_out.view(cfg.N,-1,cfg.H, cfg.W))# return psm, rmif __name__ __main__:model VoxelNet()voxel_features torch.rand(100, 35, 7)voxel_coords torch.randint(low0, high10, size(100, 4))model(voxel_features, voxel_coords)参考博客
VoxelNet End-to-End Learning for Point Cloud Based 3D Object Detection 论文学习
VoxelNet基于点云的端到端 3D 物体检测网络
