torch2trt | An easy to use PyTorch to TensorRT converter | Machine Learning library

by NVIDIA-AI-IOT Python Version: v0.4.0 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | torch2trt Summary

torch2trt is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. torch2trt has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

What models are you using, or hoping to use, with TensorRT? Feel free to join the discussion here. torch2trt is a PyTorch to TensorRT converter which utilizes the TensorRT Python API. The converter is.

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Quality

Security

License

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Support

torch2trt has a medium active ecosystem.

It has 3950 star(s) with 641 fork(s). There are 72 watchers for this library.

It had no major release in the last 12 months.

There are 227 open issues and 421 have been closed. On average issues are closed in 295 days. There are 50 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of torch2trt is v0.4.0

Quality

torch2trt has 0 bugs and 48 code smells.

Security

torch2trt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

torch2trt code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

torch2trt is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

torch2trt releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

torch2trt saves you 219 person hours of effort in developing the same functionality from scratch.

It has 536 lines of code, 44 functions and 22 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed torch2trt and discovered the below as its top functions. This is intended to give you an instant insight into torch2trt implemented functionality, and help decide if they suit your requirements.

Wrapper for torch trtensor
Flattens the given value
Get a list of tensors
Return shape of stat_fn
Convert a tensor getitem getitem getitem
Convert a slice to a trt
Returns the number of slices in slices
Convert group norm
Add trt tensors to network
Convert an interpolation trt tensor
Forward computation
Map MXNet Max Pool2D
Map MXNet s Max Pool3 operator to MXNet
Convert avg pool pool trt tensor
Convert the average pool2d
Convert 1D convolution layer to 2D tensor
Convert a roll operator
Convert a Transposed ConvTranspose 2d tensor
Convert a convolution layer
Converts a convolution layer into a convolution layer
Convert max pooling
Convert convolution layer
Convert from QuantConv
Convert instance norm to batch norm
Convert a layer from a layer
Convert from Conv

Get all kandi verified functions for this library.

torch2trt Key Features

No Key Features are available at this moment for torch2trt.

torch2trt Examples and Code Snippets

2.3. 機械学習ライブラリのインストール

Python

Lines of Code : 53

License : No License

Copy

# インストール手順参考:
# https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-7-0-now-available/72048
# https://github.com/NVIDIA-AI-IOT/torch2trt
# https://github.com/mdegans/nano_build_opencv
# 上記のサイト等を参考にした上で、必要なコマンドを下記に記載しています。


### pytorch

centernet_kinect,Install python packages

Python

Lines of Code : 17

License : Permissive (MIT)

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sudo apt update
sudo apt-get install python3-pip

wget https://nvidia.box.com/shared/static/wa34qwrwtk9njtyarwt5nvo6imenfy26.whl -O torch-1.7.0-cp36-cp36m-linux_aarch64.whl
sudo apt-get install python3-pip libopenblas-base libopenmpi-dev 
pip3 instal

Table of Contents ,Start Your Journey of Self-Driving,3. Model Training

Python

Lines of Code : 15

License : Permissive (MIT)

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$ cd ~/autorace
$ rsync -rv --progress --partial @:~/autorace/data ./

$ cd ~/autorace
$ rsync -rv -e 'ssh -p ' --progress --partial ./data @:~/autorace/

$ ssh -p  @  # if you use the server, connect to it via ssh first on your RC-Car
$ ls ~/autorac

Community Discussions

Trending Discussions on torch2trt

OpenCV 2 imshow not showing the video using python 3.6

QUESTION

OpenCV 2 imshow not showing the video using python 3.6

Asked 2020-May-19 at 13:19

I have code for pose estimated. I am trying to run in real-time but OpenCV not showing the video. How to solve this error? I can not find any issues.

When I use cv2.imshow("Video", dst) that also not working.

My camera is working properly. I tried it with small python code. Then imshow also worked. When I try with this code it is not working. My camera is USB type and I am working on the Ubuntu platform.

This code is running without any errors only problem is imshow window not showing.

Code:

...

ANSWER

Answered 2020-May-19 at 13:19

you need to put

Source https://stackoverflow.com/questions/61890882

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

Vulnerabilities

No vulnerabilities reported

Install torch2trt

torch2trt depends on the TensorRT Python API. On Jetson, this is included with the latest JetPack. For desktop, please follow the TensorRT Installation Guide. You may also try installing torch2trt inside one of the NGC PyTorch docker containers for Desktop or Jetson.

Support

To install torch2trt with experimental community contributed features under torch2trt.contrib, like Quantization Aware Training (QAT)(requires TensorRT>=7.0), call the following,.

Find more information at: