num_batch =len(dataset) / opt.batchSize# 开始训练模型forepochinrange(opt.nepoch): scheduler.step()fori, datainenumerate(dataloader,0):# 从数据中解析出点和标签points, target = data# 交换维度,便于后续操作points = points.transpose(2,1)# 利用GPU来学习points, target = points.cuda(), target...
import os # 解决 OpenMP 错误(可选,根据需要) os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' import h5py import numpy as np import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torch.utils.data import Dataset, DataLoader import matplotlib.pyplot...
dataset_3d = MNIST3D(dataset, number_of_points) l_data = len(dataset_3d) train_dataset, val_dataset, test_dataset = random_split(dataset_3d, [round(0.8*l_data), round(0.1*l_data), round(0.1*l_data)], generator=torch.Generator().manual_seed(1)) train_dataloader = DataLoader(train_...
all_files.txt 中保存24个数据文件名,room_filelist.txt中数据为23585 行,对应每行的Block所对应的采集area和room。 ·2.如果想要进行测试和可视化,需要下载3D室内解析数据集(S3DIS Dataset数据集介绍)进行模型的测试和可视化工作。作者实验用的是Stanford3dDataset_v1.2_Aligned_Version数据集,填写信息进行下载下载链接。
point_data_set=PointDataSet(train=train) data_loader=DataLoader(dataset=point_data_set,batch_size=16,shuffle=train) return data_loader 第二部分:模型及其训练 import torch import torch.nn as nn import getData import datetime class PointNet(nn.Module): ...
val_loader = paddle.io.DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False) 5. 创建PointNet网络 classPointNet(paddle.nn.Layer):def__init__(self, name_scope='PointNet_', num_classes=4, num_point=1024):super(PointNet, self).__init__() ...
We use five fold cross validation to acquire classification accuracy on this dataset. ScanNet: 1513 scanned and reconstructed indoor scenes. We follow the experiment setting in and use 1201 scenes for training, 312 scenes for test. 注:1. 和PointNet一样,仍然需要每个输入样本的采样点个数一样。2....
point_data_set=PointDataSet(train=train) data_loader=DataLoader(dataset=point_data_set,batch_size=16,shuffle=train) return data_loader 第二部分:模型及其训练 import torch import torch.nn as nn import getData import datetime class PointNet(nn.Module): ...
然后,我们创建了一个自定义数据集PointCloudData,扩展了PyTorch的Dataset类。这个数据集代表了用于训练和测试的点云集合。结构包括: 用数据集详细信息和可选的变换函数进行初始化。 定义数据集的长度。 获取项目,并在指定的情况下应用转换。 class PointCloudData(Dataset):def __init__(self, dataset_path, transfo...
point_data_set=PointDataSet(train=train) data_loader=DataLoader(dataset=point_data_set,batch_size=16,shuffle=train) return data_loader 第二部分:模型及其训练 import torch import torch.nn as nn import getData import datetime class PointNet(nn.Module): ...