I'm currently working on a project where I need to perform multidimensional slicing on tensors using PyTorch, MXNet, and TensorFlow. Specifically, I have tensors of shape (batch_size, height, width, channels) and I need to slice them along multiple dimensions to extract specific regions from each tensor in c++.
Could someone please provide detailed examples of how to perform multidimensional slicing on tensors using PyTorch, MXNet, and TensorFlow? I'm particularly interested in understanding how to slice tensors along multiple dimensions simultaneously, as the standard slice() or Slice() functions seem to only support slicing along a single dimension (dim0).
Example Scenario:
Let's say I have a tensor image_data with shape (batch_size=2, height=100, width=100, channels=3) representing two RGB images. I want to extract a region of interest (ROI) from each image, defined by the top-left corner (x1, y1) and the bottom-right corner (x2, y2). The ROI has dimensions roi_height and roi_width.
For example, I want to extract a region of size (20x20) starting from coordinates (10, 10) for each image in the batch. How would I perform this operation in PyTorch, MXNet, and TensorFlow?
Expected Output:
I'm looking for detailed code examples that demonstrate how to slice tensors along multiple dimensions in PyTorch, MXNet, and TensorFlow for the given scenario. Additionally, explanations of the functions or methods used and any potential differences or considerations between the libraries would be greatly appreciated.
Thank you in advance!