Array newbie

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1

Hello

I hate it, but I could not figure this out so I’m hoping I might find some help here. I am trying to rotate images for a given rotation using mxnet. I know I can do it more easily in other frameworks, but if nothing else this is a good learning exercise. With that said, here is what I have got so far.

Given a simple 4x4 pixel image,

# batch 1
in_data = nd.arange(4).reshape((1,2,2))
print('in_data is ', in_data)

Also given a simple rotation matrix,

# setup a rotation matrix
rotate_data = rotate_90
print('rotate_90 ', rotate_data)

This is what I have so far:

# NDArrayIter(data, label=None, batch_size=1, shuffle=False, 
#            last_batch_handle='pad', data_name='data', 
#            label_name='softmax_label')
#
# Ignore the label parameter.
dataiter = mx.io.NDArrayIter(in_data, batch_size=1, shuffle=False, last_batch_handle='discard')
#batch_index = [0]
for batch in dataiter:
    # Does this copy or get an alias to the input image?
    input_img = batch.data[0]

    # This will get the pixel indices/indexes of the image
    input_img_indexes = mx.nd.contrib.index_array(input_img, axes=(1, 2))
    
    # Rewrite to four rows of two colums omitting the batch
    orig_indexes = mx.nd.reshape(input_img_indexes, shape=(4,2))
    orig_indexes = orig_indexes.astype("float32")
    
    # This will do the matrix multiply using the rotation matrix.
   # afterwards, it will retyupe the float results to integer indices/indexes
    new_indexes = nd.dot(orig_indexes, rotate_data) 
    new_indexes = new_indexes.astype('int64')
    # slide the indices back to quadrant since I have some negatives
    new_indexes = new_indexes + nd.array(nd.array([0, 1])).astype('int64')
  
   # Hmm. I have the original indices, pixel values, and the new indices/indexes
   # but no way to reassign the values given the rotated indices

The result of the data after this step looks like this.

print('in_data is ', in_data)
print('rotate_data is ', rotate_data)
print('orig_indexes is ', orig_indexes)
print('new_indexes is ', new_indexes)
print('new_indexes.dtype is ', new_indexes.dtype)

in_data is  
[[[0. 1.]
  [2. 3.]]]
<NDArray 1x2x2 @cpu(0)>
rotate_data is  
[[ 6.123234e-17 -1.000000e+00]
 [ 1.000000e+00  6.123234e-17]]
<NDArray 2x2 @cpu(0)>
orig_indexes is  
[[0. 0.]
 [0. 1.]
 [1. 0.]
 [1. 1.]]
<NDArray 4x2 @cpu(0)>
new_indexes is  
[[0 1]
 [1 1]
 [0 0]
 [1 0]]
<NDArray 4x2 @cpu(0)>
new_indexes.dtype is  <class 'numpy.int64'>

So, my problem is that in the last step, I can not figure out how to assign the output based upon the
given results. I have the rotated indices, the original data and the original indices. I thought it might
just be somethine like this:

But this is a fail

output_data[0,new_indices] = in_data[0,original_indices]

I also tried some with the other contrib api

# x = mx.nd.zeros((5,3))
# t = mx.nd.array([[1,2,3],[4,5,6],[7,8,9]])
# index = mx.nd.array([0,4,2])

mx.nd.contrib.index_copy(out_data[0], new_indexes, in_data[0])

But that is not quite it either.

Any advice is appreciated.