Solved: %27pytorch_lightning%27 has no attribute %27metrics%27

%27pytorch_lightning%27 has no attribute %27metrics%27Introduction

In the rapidly evolving world of deep learning and neural networks, libraries and frameworks are essential for simplifying and accelerating the development process. PyTorch Lightning is one such powerful library built on top of the widely popular PyTorch. Lightning is designed to allow Data Scientists and ML Engineers to easily scale their models, avoid boilerplate code, and improve overall readability. However, while working with PyTorch Lightning, you may often find yourself facing issues like the ‘pytorch_lightning.metrics’ attribute error. In this article, we will tackle the problem and walk you through its solution, breaking down the code for better understanding. Moreover, we will discuss related libraries and functions involved in solving this issue.

Solution to the problem

One of the main problems related to the error ‘%27pytorch_lightning%27 has no attribute %27metrics%27’ is that you might have installed the older version of PyTorch Lightning which did not include the metrics module. To fix this, you can simply upgrade your PyTorch Lightning to the latest version by running the following command:

pip install --upgrade pytorch-lightning

Step-by-Step Explanation of the Code

Once you’ve updated the library, we can begin working with PyTorch Lightning-based metrics. The first step is importing the necessary modules from PyTorch Lightning. We will be using the Accuracy metric for illustration purposes in this article.

import torch
from pytorch_lightning import LightningModule
from pytorch_lightning.metrics.functional import accuracy

Next, let’s define our neural network using the LightningModule as base class. Inside the ‘training_step’ and ‘validation_step’ methods, we will compute our prediction and ground truth tensors, and calculate the accuracy using the ‘accuracy’ metric function provided by PyTorch Lightning.

class Classifier(LightningModule):
    def __init__(self):
        self.layer1 = torch.nn.Linear( 32, 128)
        self.layer2 = torch.nn.Linear(128, 10)

    def forward(self, x):
        x = torch.relu(self.layer1(x))
        x = self.layer2(x)
        return x

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = torch.nn.functional.cross_entropy(y_hat, y)
        acc = accuracy(y_hat, y) # Compute accuracy using PyTorch Lightning
        self.log('train_loss', loss)
        self.log('train_acc', acc, prog_bar=True)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = torch.nn.functional.cross_entropy(y_hat, y)
        acc = accuracy(y_hat, y) # Compute accuracy using PyTorch Lightning
        self.log('val_loss', loss, prog_bar=True)
        self.log('val_acc', acc, prog_bar=True)
        return loss

Finally, following this code structure, you should be able to work smoothly with PyTorch Lightning-metrics without encountering the mentioned attribute error.

Related Libraries: Torchmetrics

  • Another library worth mentioning is Torchmetrics, a PyTorch based library specialized in providing metrics for evaluating deep learning models. Torchmetrics library is created by the same developers as PyTorch Lightning, ensuring compatibility and providing a simple and consistent API.
  • Torchmetrics offers various metrics like Accuracy, Precision, Recall, F1 score, and many more. It reduces the strain of implementing these metrics manually and allows you to focus on other aspects of your projects.

Enhancing Code Readability with PyTorch Lightning

One of the key benefits of using PyTorch Lightning is that it significantly simplifies the training loop structure and makes the code more readable. The LightningModule encapsulates the core components of a neural network, such as the model architecture, training logic, and validation logic, giving you the ability to manage these elements in a modular way. As a result, you can develop and scale your models more efficiently, giving you a better understanding of your code while also improving collaboration among team members.

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