What is the difference between `eval()` and `train()` modes in PyTorch?

Q: What is the difference between `eval()` and `train()` modes in PyTorch?

Learn the answer to "What is the difference between `eval()` and `train()` modes in PyTorch?" with detailed explanations, code examples, and best practices on DeployU.

The Scenario

You are an ML engineer at a self-driving car company. You have trained a new computer vision model to detect pedestrians in a video feed. The model has a validation accuracy of 95%, but when you deploy it to a test vehicle, the performance is much worse.

You have double-checked the data and the model architecture, and you are confident that they are correct. You suspect that there might be an issue with the way the model is being evaluated.

The Challenge

Explain the difference between the train() and eval() modes in PyTorch. Why is it important to use the correct mode when training and evaluating a model? How would you debug the issue with the inconsistent performance?

Wrong Approach

A junior engineer might not be aware of the `train()` and `eval()` modes. They might try to debug the problem by re-training the model or by changing the model architecture, which would not address the root cause of the problem.

Addresses symptoms, not root cause

Right Approach

A senior engineer would immediately suspect that the problem is with the use of the `train()` and `eval()` modes. They would be able to explain the difference between the two modes and would have a clear plan for how to debug the issue.

Step 1: `train()` vs. `eval()`

The train() and eval() methods are used to set the model to either training or evaluation mode. This is important because some layers behave differently in each mode.

Layer	`train()` mode	`eval()` mode
Dropout	Active, randomly zeros out some of the activations to prevent overfitting.	Inactive, does not zero out any activations.
Batch Normalization	Uses the mean and variance of the current batch to normalize the activations.	Uses the running mean and variance that were computed during training to normalize the activations.

Step 2: Diagnose the Problem

The most likely cause of the inconsistent performance is that you are forgetting to call model.eval() before evaluating the model. This would cause the dropout layers to be active and the batch normalization layers to use the statistics of the test batch, which would lead to a lower accuracy.

Here’s how you can verify this:

# Create a dummy model with dropout and batch normalization
model = nn.Sequential(
    nn.Linear(10, 20),
    nn.BatchNorm1d(20),
    nn.Dropout(0.5),
    nn.Linear(20, 1)
)

# Create some dummy data
data = torch.randn(100, 10)

# Get the output in train() mode
model.train()
train_output = model(data)

# Get the output in eval() mode
model.eval()
eval_output = model(data)

# Check if the outputs are different
print(torch.allclose(train_output, eval_output)) # False

Step 3: Fix the Problem

The fix for this problem is simple: just remember to call model.eval() before evaluating the model.

def evaluate(model, dataloader):
    model.eval() # Set the model to evaluation mode
    # ... your evaluation code ...

By using the correct mode, you can ensure that you get accurate and reproducible results when evaluating your model.

Systematic, production-ready debugging

Practice Question

You are training a model and you want to use the running mean and variance of the batch normalization layers. Which mode should you use?

Questions