torch.max和torch.maximum的区别

torch.max和torch.maximum是PyTorch中用于求最大值的函数，但它们有一些区别。

torch.max是一个函数，可以用来计算给定输入张量中的最大值。它可以接受多个输入张量作为参数，并返回一个包含这些张量中元素的最大值的张量。当给定一个输入张量时，torch.max会返回该张量中的最大值。

例如，对于输入张量x，torch.max(x)将返回x中的最大值。

torch.maximum是一个逐元素的函数，用于计算两个输入张量中对应元素的最大值。它需要两个输入张量作为参数，并返回一个张量，其中每个元素都是对应位置上两个输入张量中的最大值。

例如，对于输入张量x和y，torch.maximum(x, y)将返回一个张量，其中每个元素都是x和y对应位置上的最大值。

简而言之，torch.max用于求一个张量中的最大值，而torch.maximum用于求两个张量中对应位置元素的最大值。

相关问题

torch.max(r,1./r).max(2)[0]

This expression uses PyTorch functions to compute the maximum values of a tensor `r` along two dimensions.

Here's a breakdown of what's happening:

1. `torch.max(r, 1./r)` computes the element-wise maximum between `r` and `1./r`. This essentially creates a new tensor where each element is the maximum of the corresponding elements in `r` and `1./r`.

2. `max(2)` applies the `max` function along the second dimension of the tensor, returning a tuple of two tensors: the maximum values along that dimension, and their corresponding indices.

3. `[0]` selects the first tensor in the tuple, which contains the maximum values along the second dimension.

So, the overall expression computes the maximum values of `r` along the second dimension, but also takes into account the reciprocal values of `r` by computing the maximum with `1./r`.

torch.nn.MaxPool2d

torch.nn.MaxPool2d is a module in PyTorch that performs 2D max pooling operation. It is used to downsample the input tensor along the spatial dimensions, i.e., width and height. The operation replaces each spatial area of the input tensor with the maximum value of that area.

The module takes the following inputs:

- kernel_size: The size of the window used for pooling.
- stride: The stride of the window used for pooling.
- padding: The amount of padding applied to the input tensor before the pooling operation.
- dilation: The spacing between the values within the kernel.
- ceil_mode: When set to True, the output size is rounded up instead of rounded down.
- return_indices: When set to True, the indices of the maximum values are returned along with the output tensor.

The output of the module is a tensor with dimensions (batch_size, channels, output_height, output_width), where output_height and output_width are calculated based on the input tensor dimensions, kernel_size, stride, padding, and dilation.