with torch.no_grad()
时间: 2024-04-30 13:18:05 浏览: 16
`with torch.no_grad()` is a Python context manager provided by the PyTorch library. When you wrap a block of code inside this context manager, it tells PyTorch to disable gradient computation. This is useful when you are only interested in performing inference on a trained model and don't need to compute gradients for backpropagation.
When you disable gradient computation, PyTorch skips building the computation graph for any tensor operations that occur inside the `with torch.no_grad()` block. This can lead to faster execution times and reduced memory consumption.
Here's an example of how to use `with torch.no_grad()`:
```
import torch
# Create a tensor with requires_grad=True
x = torch.tensor([1.0, 2.0], requires_grad=True)
# Wrap a block of code in with torch.no_grad()
with torch.no_grad():
# Perform some tensor operations
y = x * 2
z = y.mean()
# Since we're outside the with torch.no_grad() block, gradients will be computed
z.backward()
# Check the gradients of x
print(x.grad)
```
In this example, we perform some tensor operations inside the `with torch.no_grad()` block and compute the mean of the resulting tensor `y`. Since `requires_grad=True` for `x`, the gradients of `z` with respect to `x` can be computed using `z.backward()`. However, since the tensor operations inside the `with torch.no_grad()` block do not require gradients, they are skipped during the backward pass.