# pytorchviz

**Repository Path**: baozhiqiang1978/pytorchviz

## Basic Information

- **Project Name**: pytorchviz
- **Description**: A small package to create visualizations of PyTorch execution graphs
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-30
- **Last Updated**: 2021-09-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

PyTorchViz
=======

A small package to create visualizations of PyTorch execution graphs and traces.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/szagoruyko/pytorchviz/blob/master/examples.ipynb)

## Installation

Install graphviz, e.g.:

```
brew install graphviz
```

Install the package itself:

```
pip install torchviz
```


## Usage
Example usage of `make_dot`:
```
model = nn.Sequential()
model.add_module('W0', nn.Linear(8, 16))
model.add_module('tanh', nn.Tanh())
model.add_module('W1', nn.Linear(16, 1))

x = torch.randn(1, 8)
y = model(x)

make_dot(y.mean(), params=dict(model.named_parameters()))
```
![image](https://user-images.githubusercontent.com/13428986/110844921-ff3f7500-8277-11eb-912e-3ba03623fdf5.png)

Set `show_attrs=True` and `show_saved=True` to see what autograd saves for the backward pass. (Note that this is only available for pytorch >= 1.9.)
```
model = nn.Sequential()
model.add_module('W0', nn.Linear(8, 16))
model.add_module('tanh', nn.Tanh())
model.add_module('W1', nn.Linear(16, 1))

x = torch.randn(1, 8)
y = model(x)

make_dot(y.mean(), params=dict(model.named_parameters()), show_attrs=True, show_saved=True)
```
![image](https://user-images.githubusercontent.com/13428986/110845186-4ded0f00-8278-11eb-88d2-cc33413bb261.png)

## Acknowledgements

The script was moved from [functional-zoo](https://github.com/szagoruyko/functional-zoo) where it was created with the help of Adam Paszke, Soumith Chintala, Anton Osokin, and uses bits from [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch).
Other contributors are [@willprice](https://github.com/willprice), [@soulitzer](https://github.com/soulitzer), [@albanD](https://github.com/albanD).