pointnet2 | Point Sets in a Metric Space | Machine Learning library

by charlesq34 Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(6)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | pointnet2 Summary

pointnet2 is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. pointnet2 has no bugs, it has no vulnerabilities and it has medium support. However pointnet2 build file is not available and it has a Non-SPDX License. You can download it from GitHub.

This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture. PointNet (the v1 model) either transforms features of individual points independently or process global features of the entire point set. However, in many cases there are well defined distance metrics such as Euclidean distance for 3D point clouds collected by 3D sensors or geodesic distance for manifolds like isometric shape surfaces. In PointNet++ we want to respect spatial localities of those point sets. PointNet++ learns hierarchical features with increasing scales of contexts, just like that in convolutional neural networks. Besides, we also observe one challenge that is not present in convnets (with images) -- non-uniform densities in natural point clouds. To deal with those non-uniform densities, we further propose special layers that are able to intelligently aggregate information from different scales. In this repository we release code and data for our PointNet++ classification and segmentation networks as well as a few utility scripts for training, testing and data processing and visualization.

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pointnet2 has a medium active ecosystem.

It has 2670 star(s) with 867 fork(s). There are 70 watchers for this library.

It had no major release in the last 6 months.

There are 171 open issues and 60 have been closed. On average issues are closed in 83 days. There are 10 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pointnet2 is current.

Quality

pointnet2 has 0 bugs and 0 code smells.

Security

pointnet2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pointnet2 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pointnet2 has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

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pointnet2 releases are not available. You will need to build from source code and install.

pointnet2 has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed pointnet2 and discovered the below as its top functions. This is intended to give you an instant insight into pointnet2 implemented functionality, and help decide if they suit your requirements.

Transpose input tensor
Batch function for batchnorm
Create a new variable on cpu
Create a variable with weight decay
Train the model
Log a string
Average gradients of each tower
Evaluate one epoch
Get a model from a point cloud
Embedding module for PointNet
3D convolution layer
Batch normalization for convolution
Returns the next batch of data
Convert a batch of point cloud to a list of volumes
Convert a list of point cloud layers to image
Return True if there is a next batch in the file
Visualize the mouse points
Extracts 3 views of 3 views
Plot a volume
Returns a map of the raw_to_scannets
Convert a list of points to a single volume
Batchnorm template
Evaluate the model
Write labeled points to a file
Gets the next batch of data
Log a string

Get all kandi verified functions for this library.

pointnet2 Key Features

No Key Features are available at this moment for pointnet2.

pointnet2 Examples and Code Snippets

How to Run

Python

Lines of Code : 25

License : No License

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$ python launcher.py -h

usage: launcher.py [-h] [--compile COMPILE] [--download DOWNLOAD]
                   [--list_models LIST_MODELS] [--run RUN] [--train TRAIN]
                   [--use_baseline USE_BASELINE] [--use_limited USE_LIMITED]

MVPNet: Multi-view PointNet for 3D Scene Understanding,Preparation,ScanNet Dataset

Python

Lines of Code : 24

License : Non-SPDX (NOASSERTION)

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https://github.com/ScanNet/ScanNet

python download-scannet.py -o  --type _vh_clean_2.ply
python download-scannet.py -o  --type _vh_clean_2.labels.ply

/scans/                                     % scan id, e.g.: scene0000_00
/scans//_vh_clean_2.ply

Multi-view Neural Human Rendering (NHR) ,Get Started

Python

Lines of Code : 13

License : Non-SPDX (NOASSERTION)

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.
├──  img
│    └── %d    					- the frame number, start from 0.
│        └──mask
│        	└── img_%04d.jpg	- foreground mask of corresponding view. view number start from 0. 
│        └──img_%04d.jpg   		- undistorted RGB images for each view. view

dgl - pointnet2

Python

Lines of Code : 336

License : Non-SPDX (Apache License 2.0)

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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

import dgl
import dgl.function as fn
from dgl.geometry import (
    farthest_point_sampler,
)  # dgl.geometry.pytorch -> dgl

pytorch_geometric - pointnet2 segmentation

Python

Lines of Code : 107

License : Permissive (MIT License)

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import os.path as osp

import torch
import torch.nn.functional as F
from pointnet2_classification import GlobalSAModule, SAModule
from torch_scatter import scatter
from torchmetrics.functional import jaccard_index

import torch_geometric.transforms a

dgl - pointnet2 partseg

Python

Lines of Code : 98

License : Non-SPDX (Apache License 2.0)

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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pointnet2 import PointNet2FP, SAModule, SAMSGModule
from torch.autograd import Variable


class PointNet2SSGPartSeg(nn.Module):
    def __init__(self, output_c

Community Discussions

Trending Discussions on pointnet2

CUDA kernel failed : no kernel image is available for execution on the device, Error when running PyTorch model inside Google Compute VM

QUESTION

CUDA kernel failed : no kernel image is available for execution on the device, Error when running PyTorch model inside Google Compute VM

Asked 2020-Apr-03 at 09:55

I have a docker image of a PyTorch model that returns this error when run inside a google compute engine VM running on debian/Tesla P4 GPU/google deep learning image:

...

ANSWER

Answered 2020-Apr-03 at 09:55

I resolved this in the end by manually deleting all the folders except for "src" in the folder containing setup.py

Then rebuilt the docker image

Then when building the image I ran TORCH_CUDA_ARCH_LIST="6.1" python setup.py install, to install the cuda extensions targeting the correct compute capability for the GPU on the VM

and it worked!

I guess just running setup.py without deleting the folders previously installed doesn't fully overwrite the extension

Source https://stackoverflow.com/questions/60829433

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pointnet2

Install TensorFlow. The code is tested under TF1.2 GPU version and Python 2.7 (version 3 should also work) on Ubuntu 14.04. There are also some dependencies for a few Python libraries for data processing and visualizations like cv2, h5py etc. It's highly recommended that you have access to GPUs. The TF operators are included under tf_ops, you need to compile them (check tf_xxx_compile.sh under each ops subfolder) first. Update nvcc and python path if necessary. The code is tested under TF1.2.0. If you are using earlier version it's possible that you need to remove the -D_GLIBCXX_USE_CXX11_ABI=0 flag in g++ command in order to compile correctly. To compile the operators in TF version >=1.4, you need to modify the compile scripts slightly. First, find Tensorflow include and library paths. Then, add flags of -I$TF_INC/external/nsync/public -L$TF_LIB -ltensorflow_framework to the g++ commands.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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