In PyTorch, model.eval() and with torch.no_grad(): are not equivalent, but they are often used together for model evaluation or inference. Here’s how they differ and how they interact:

1. model.eval()

• Purpose: Sets the model to evaluation mode.
• What it does:
  • Disables layers like dropout and batch normalization, which behave differently during training vs. evaluation.
  • Affects the model’s internal state (e.g., running statistics for batch norm).
• When to use: Before running inference or evaluation, to ensure the model behaves correctly.

2. with torch.no_grad():

• Purpose: Temporarily disables gradient computation.
• What it does:
  • Stops PyTorch from tracking gradients, which reduces memory usage and speeds up computation.
  • Does not change the model’s evaluation/training mode.
• When to use: During inference or evaluation to avoid unnecessary gradient calculations.

Key Differences

How They Work Together

• Best practice: Use both for inference/evaluation:
  python

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  model.eval()
  with torch.no_grad():
      outputs = model(inputs)

  This ensures:
  • The model is in evaluation mode (e.g., dropout disabled).
  • Gradients are not computed (saves memory and time).

Summary

• model.eval() changes the model’s behavior (e.g., dropout, batch norm).
• with torch.no_grad(): disables gradient computation.
• They are complementary, not equivalent.