self.bn=nn.BatchNorm1d(1024)defforward(self,input):n_pts=input.size()[2]matrix3x3=self.input_transform(input)input_transform_output=torch.bmm(torch.transpose(input,1,2),matrix3x3).transpose(1,2)x=self.mlp1(input
下面是一个简化的PointNet模型在PyTorch中的实现示例: import torch import torch.nn as nn class PointNet(nn.Module): def __init__(self, num_classes): super(PointNet, self).__init__() self.conv1 = nn.Conv1d(3, 64, 1) # 输入通道为3(x, y, z),输出通道为64 self.conv2 = nn.Conv1d...
net=PointNet(POINT_NUM).cuda() optimizer=torch.optim.Adam(net.parameters(),weight_decay=0.001) loss_function=nn.CrossEntropyLoss() for epoch in range(EPOCHES): time_start=datetime.datetime.now() net.train() for cloud,label in train_loader: cloud,label=cloud.cuda(),label.cuda() out = n...
import torch.utils.data from pointnet.dataset import ShapeNetDataset, ModelNetDataset from pointnet.model import PointNetCls, feature_transform_regularizer import torch.nn.functional as F from tqdm import tqdm def main(): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") par...
importtorchimporttorch.nnasnnimporttorch.optimasoptim# 定义模型model=PointNet()# 定义损失函数criterion=nn.CrossEntropyLoss()# 定义优化器optimizer=optim.Adam(model.parameters(),lr=0.001)# 训练模型forepochinrange(num_epochs):running_loss=0.0fori,datainenumerate(train_loader,0):inputs,labels=data# ...
import torch.nn as nnimport torch.nn.functional as F class PointNet2SAModule(nn.Module): def init(self, npoint, radius, nsample, inchannels, mlp, groupall=False): super(PointNet2SAModule, self).__init() self.npoint = npoint self.radius = radius self.nsample = nsample self.mlp_con...
按照官网下载命令中提示的网址,自己下载离线包,使用pip install D:\自己离线包路径\torch-1.4.0+cpu-cp36-cp36m-win_amd64.whl安装,目前没有看到可以通过conda install 命令实现离线安装的。 速度快,一般都可以成功,但有时会报错,如下面这种: 出现
=self.radius, npoint=self.npoint, xyz=xyz, points=points) new_points = self.group_point(xyz, points) new_points = new_points.permute(0, 2, 3, 1) for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_points = F.relu(bn(conv(new_points))) new_points = torch...
浅浅记录一下model的复现,之后做好完整的工程放到github上 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 2 import torch.nn as nn import torch import numpy as np class tnet(nn.Module): def __init__(self, inplanes: int): super(tnet, self).__init__() self.k...
首先,你需要理解PointNet网络结构的原理和实现方式。 2. 实现PointNet网络结构 接下来,你需要使用PyTorch来实现PointNet网络结构。以下是具体步骤: 1.导入所需库 ```python import torch import torch.nn as nn import torch.nn.functional as F 1. 2. ...