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This article mainly introduces how to use the inplace field in PyTorch, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let Xiaobian take you to understand.

Many people may have some doubts about the inplace field of pytorch. What exactly is this field for? In fact, this involves the operation mechanism of pytorch, if you use this field, the pytorch data will be processed on the spot, so it will not take up too much memory and save the operation memory, so how to use the inplace field of pytorch? Next, the editor will take you to learn about it!

For example, torch.nn.ReLU (inplace=True) inplace=True

Means to operate in place and directly modify the tensor passed from the previous layer, such as x=x+3

Inplace=False

Indicates that a new variable is created to store the results of the operation, such as y _ variables _ 3j _ x _ y

Inplace=True

You can save computational memory and do not have to store more variables.

Add: the meaning of the inplace=True field in the network in PyTorch

What does the inplace field mean in, for example, nn.LeakyReLU (inplace=True)? What's the use?

Inplace=True means to operate in place, such as x=x+5, which is an in-place operation for x, which performs the same function as x=x+5 but not in-place operation.

The meaning of inplace=True in the above LeakyReLU is the same, which is directly modified to the tensor passed down by the upper network such as Conv2d. The advantage is that it can save operational memory and do not have to store the variable y.

Inplace=True means that it will modify the input directly, without allocating any additional output. It can sometimes slightly decrease the memory usage, but may not always be a valid operation (because the original input is destroyed). However, if you don't see an error, it means that your use case is valid.

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