PyTorch permute vs transpose: Why using reshape on tensors can corrupt your data

In PyTorch, developers often need to reorder tensor dimensions — for example, converting image data from (batch, height, width, channels) to (batch, channels, height, width) for convolution layers. The transpose() function swaps exactly two dimensions, while permute() reorders all dimensions at once, making it the preferred choice for multi-axis rearrangements. Both transpose and permute return a non-contiguous view of the original data without copying it, meaning only the stride metadata changes. The reshape() function, by contrast, reinterprets flat memory under a new shape and can produce the correct-looking dimensions while silently scrambling the underlying data values. Understanding the distinction is critical for machine learning workflows, particularly when handling image batches, as reshape misuse can corrupt data without raising any errors.