How to call existing stgcn code

DDD
Release: 2024-08-15 13:50:18
Original
741 people have browsed it

How to call the extant stgcn code

To call the extant stgcn code, you can follow these steps:

  1. Clone the stgcn repository from GitHub.
  2. Install the required dependencies by running the following command in the terminal:

    <code>pip install -r requirements.txt</code>
    Copy after login
  3. Import the stgcn module into your Python script.
  4. Create an instance of the STGCN model.
  5. Load the pre-trained weights into the model.
  6. Call the model's predict() method to make predictions on your data.

How to integrate stgcn code into your own projects?

To integrate stgcn code into your own projects, you can follow these steps:

  1. Clone the stgcn repository from GitHub.
  2. Create a new Python script in your project directory.
  3. Import the stgcn module into your script.
  4. Create an instance of the STGCN model.
  5. Load the pre-trained weights into the model.
  6. Call the model's predict() method to make predictions on your data.

Can I use stgcn code in a different programming language?

The stgcn code is currently available in Python. There are no official plans to port the code to other programming languages, but it is possible to do so if you have the necessary expertise.

Where can I find documentation or tutorials on using stgcn code?

You can find documentation and tutorials on using stgcn code on the following websites:

  • [STGCN GitHub repository](https://github.com/yysijie/STGCN)
  • [PyTorch documentation](https://pytorch.org/)
  • [TensorFlow documentation](https://www.tensorflow.org/)

The above is the detailed content of How to call existing stgcn code. For more information, please follow other related articles on the PHP Chinese website!

source:php.cn
Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Popular Tutorials
More>
Latest Downloads
More>
Web Effects
Website Source Code
Website Materials
Front End Template
About us Disclaimer Sitemap
php.cn:Public welfare online PHP training,Help PHP learners grow quickly!