[GitHub] [incubator-mxnet] ZheyuYe edited a comment on issue #17327: np.ndarray indexing after hybridize

[GitBox <gi...@apache.org>]

ZheyuYe edited a comment on issue #17327: np.ndarray indexing after hybridize
URL: https://github.com/apache/incubator-mxnet/issues/17327#issuecomment-575180346
 
 
   @sxjscience It would be great if hybridized indexing could be supported as the last line in the code snippet.
   
   ```
   import numpy as np
   import mxnet as mx
   import numpy.testing as npt
   
   # new implementation in deep numpy
   mx.npx.set_np()
   sequence = mx.np.array(np.random.normal(0, 1, (8, 32, 768)), dtype=np.float32)
   # pick_ids: [batch_size, picked_index]
   pick_ids = mx.np.random.randint(0, 31, (8,2), dtype=np.int32)
   
   idx_arange = mx.npx.arange_like(pick_ids.reshape((-1, )), axis=0)
   batch_idx = mx.np.floor(idx_arange / 2).astype(np.int32)
   
   encoded = sequence[batch_idx, pick_ids.reshape((-1,))]
   ```
   I was aimed to pick the items from the sequence with shape (8, 2, 768) whereas the `mn.npx.pick` can not handle it. Under the deep numpy enviorment, I used the basic indexing as numpy operation as `encoded = sequence[batch_idx, pick_ids.reshape((-1,))]` which would fail after hybridize() raising the below **Error**
   
   > IndexError: Only integer, slice, or tuple of these types are supported! Received key=(<_Symbol albertmodel0_floor0>, <_Symbol albertmodel0_reshape4>)
   
   The full testing code can be found in [here](https://gist.github.com/ZheyuYe/63862dfea57dea95ea32ac8b51741c5e)

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