In the given MNIST_CNN example in owl, if we set batch size as 4, the input dimension is something like [28,28,1,4], since the pic is a 28*28 square matrix. However, I found that when input matrix is non-square, the output dimension is confusing. I am wondering if it is a bug or it is implemented that way intentionally.
For example, if input.shape is [4, 2, 1, 4] in owl format, while I set "pooling = conv.Pooler(2, 2, 2, 2, 0, 0, conv.pool_op.max)" and after I did "pooling.ff(input)" I am expected to have [2,1,1,4]. But actually I got [1, 2, 1, 4] as the output dimension in owl. Should I expect to have [1, 2, 1, 4] as the result or there is something going wrong inside the pooling function?
Great appreciation for any comments and suggestions. Thanks!
[dimension-fix] dimension order (refer #43)
Thanks for reporting. This is indeed a bug on our side.