x = x.view(B, H, W, C) x1 = x[:, 1::2, 0::2, :]
时间: 2024-05-30 20:13:00 浏览: 8
These two lines of code perform reshaping and slicing operations on a tensor 'x'.
The first line reshapes the tensor 'x' into a new shape of dimensions (B, H, W, C). Here, 'B' represents the batch size, 'H' represents the height of the image, 'W' represents the width of the image, and 'C' represents the number of channels.
The second line slices the tensor 'x' and creates a new tensor 'x1'. The slicing operation selects every alternate row (starting from the second row) and every alternate column (starting from the first column) of the tensor 'x'. The resulting tensor 'x1' has the same batch size, height, and width as 'x', but only half the number of channels. This slicing operation is often used in downsampling or pooling operations, where we want to reduce the spatial dimensions of the input image while preserving some important features.