hetong007 opened a new issue #10818: Feature Request: Improve ndarray.pad to be an numpy.pad equivalent URL: https://github.com/apache/incubator-mxnet/issues/10818 ## Current Limitations Comparing to `numpy.pad`, the current (https://github.com/apache/incubator-mxnet/commit/3bba4c8f6362df8b3355404002eab6a6c88123d6) `mxnet.ndarray.pad` has the following limitations: 1. It only works with real data type (being fixed in https://github.com/apache/incubator-mxnet/pull/10815) 2. It only works with 4-D or 5-D ndarray: https://github.com/apache/incubator-mxnet/blob/master/src/operator/pad-inl.h#L205 2. It doesn't allow padding on the first two dimensions: https://github.com/apache/incubator-mxnet/blob/master/src/operator/pad-inl.h#L208 ## A Scenario with Difficulties In resnet training on CIFAR10, the standard data augmentation includes: 1. Pad the input 32x32 image to 40x40 with zeros. 2. Randomly crop a 32x32 area from the padded array. In the current `mxnet.image` implementations, the image at the crop stage is of shape `Height x Width x Channel`. To make use of our `nd.pad`, one needs to 1. Cast the image from `int` to `float32`. 2. Expand the dimension of the image to be `Null x Null x Height x Width x Channel` 3. Apply `nd.pad` on the 2nd and 3rd (Height, Width) dimensions 4. Squeeze the array back to `Height x Width x Channel` The above combination of operations introduces unacceptable overhead for CIFAR10 training. A slightly faster workaround is to cast the array to `numpy.array` and apply `np.pad`, but it still introduces unnecessary costs. Without the limitations, one can simply apply `nd.pad` on the 0th and 1st dimension, i.e. Height and Width in the image array, at no extra cost.
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