If you’re working with Pytorch and want to get the computation graph of your model, there’s a simple way to do it. Just follow these steps and you’ll be able to get the graph in no time.

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## Introduction

Pytorch is a powerful open source tool for deep learning that enables developers to easily create and deploy sophisticated models. One of the great features of Pytorch is its computation graph feature, which allows you to easily visualize the flow of data through your model. In this tutorial, we’ll show you how to get the computation graph in Pytorch.

The first step is to import the necessary packages:

import torch

import torchvision

Then, we need to define the model:

model = torchvision.models.resnet18(pretrained=True)

Finally, we can get the computation graph by calling the .eval() function on our model:

## What is the computation graph?

A computation graph is a way of representing the steps involved in a computation as a directed graph. In Pytorch, the computation graph is built up as you define your model. You can then use the graph to debug your model, or to optimize it for performance.

To get the computation graph for your model, you first need to define the model as a subclass of torch.nn.Module . Once you have done this, you can call the model’s .make_graph() method to get the graph.

Here is an example:

import torch

import torch.nn as nn

class MyModel(nn.Module):

def __init__(self):

super(MyModel, self).__init__()

self.conv1 = nn.Conv2d(3, 16, 3)

self.conv2 = nn.Conv2d(16, 32, 3)

def forward(self, x):

x = self.conv1(x)

x = self.conv2(x)

## What are the benefits of using the computation graph?

There are several benefits of using the computation graph in Pytorch, including the ability to easily calculate gradients, efficient memory usage, and the ability to run code on a GPU. In addition, the computation graph can be used to create dynamic models that can be modified at runtime.

## How to get the computation graph in Pytorch?

Pytorch is a powerful deep learning framework that makes it easy to get the computation graph of your model. Here’s how to do it:

First, import the necessary packages:

import torch

import torch.nn as nn

from torch.autograd import Variable

Then, define your model:

class Model(nn.Module):

def __init__(self):

super(Model, self).__init__()

self.conv1 = nn.Conv2d(1, 10, kernel_size=5)

self.conv2 = nn.Conv2d(10, 20, kernel_size=5)

def forward(self, x):

x = F.relu(F.max_pool2d(self.conv1(x), 2))

x = F.relu(F.max_pool2d(self.conv2(x), 2))

return x

model = Model()Now, define a variable and get the computation graph using the .make_graph() function:

var = Variable(torch.ones((1, 1, 28, 28)))

graph = model(var).make_graph()The output will be a Pytorch computation graph object that you can use to visualize your model’s computation flow:

## Conclusion

The bottom line is, we have seen how to get the computation graph in Pytorch. We have also seen how this can be used to debug our neural networks. I hope you found this guide helpful.

Keyword: How to Get the Computation Graph in Pytorch