torch2trt | convert torch module to tensorrt network or tvm function | Machine Learning library

by traveller59 Python Version: Current License: MIT

X-Ray Key Features Code Snippets 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 vulnerabilities, it has build file available, it has a Permissive License and it has low support. However torch2trt has 1 bugs. You can download it from GitHub.

convert torch module to tensorrt network or tvm function

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Quality

Security

License

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Support

torch2trt has a low active ecosystem.

It has 80 star(s) with 19 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 5 have been closed. On average issues are closed in 0 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of torch2trt is current.

Quality

torch2trt has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 66 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 not available. You will need to build from source code and install.

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

Installation instructions, examples and code snippets are available.

It has 2900 lines of code, 199 functions and 14 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.

Convenience function for aten_convolution
Check if inputs are tvm tensors
Reshape tensor
Checks if inputs are trt tensors
Helper function to build a torch module
Get all input nodes
Append a node to the graph
Parse a graph
Compute atenh
Allocate buffer and device buffers
Slice a tensor
Flatten a tensor
Slice inputs into tensors
Compute the maximum value of the top k
Calculate the mean of the input tensor
Convert input into chunks
Transpose a tensor
Benchmark trt engine
Concatenate layer
Concatenate matrices
Calculate average pooling layer
Compute max pooling layer
Batch norm layer
Attens a tensor
Compute adaptive average across tensors
Benchmark a Tensor v1

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

No Code Snippets are available at this moment for torch2trt.

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

install pytorch 1.2+ (if you use pytorch 1.1, consider torch-1.1 branch). download TensorRT 5.1.2.2+, install tensorrt python package, add TensorRT libraries to LD_LIBRARY_PATH. clone this project, run python setup.py install. (optional) install tvm, you need install tvm with llvm support.
install pytorch 1.2+ (if you use pytorch 1.1, consider torch-1.1 branch)
download TensorRT 5.1.2.2+, install tensorrt python package, add TensorRT libraries to LD_LIBRARY_PATH.
clone this project, run python setup.py install
(optional) install tvm, you need install tvm with llvm support.

Support

We can use torch2trt.TensorRTModuleWrapper to wrap a pytorch module to tensorrt:. Wrapped module will use tensorrt when eval, use pytorch when training. So we can train network in pytorch and eval in tensorrt.

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