App op (atleast_1d and atleast_2d and atleast_3d) | feat (torchlib) #767
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titaiwangms
added
the
module: torchlib
titaiwangms
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BowenBao,
fatcat-z,
gramalingam,
justinchuby and
xiaowuhu
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gramalingam
reviewed
Jun 6, 2023
| def reshape_to_2d(tensor): | ||
| shape = op.Shape(tensor) | ||
| rank = op.Size(shape) | ||
| if rank == 0: |
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nit: you can simplify this as below:
if rank <= 1:
tensor = op.Reshape(tensor, [1, -1])In reshape, -1 is treated as "whatever size matches input tensor size".
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Also applied to 3d
justinchuby
approved these changes
Jun 6, 2023
justinchuby
reviewed
Jun 6, 2023
| ), | ||
| skip( | ||
| "atleast_1d_single_tensor", | ||
| matcher=lambda sample: isinstance(sample.input, (list, tuple)), |
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So we don’t take a Sequence as input? Where is this op used? How are the inputs produced?
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It accepts both tensor and list.
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Oh I see there are two variants
gramalingam
reviewed
Jun 6, 2023
| shape = op.Shape(tensor) | ||
| rank = op.Size(shape) | ||
| if rank <= 1: | ||
| tensor = op.Reshape(tensor, op.Constant(value_ints=[1, -1, 1])) |
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You may want to double-check the downstream usage to make sure whether you want to reshape a tensor of size N to 1x1xN or Nx1x1 ... the above reshapes it to 1xNx1 .... which may be okay, but just wondering.
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op.SequenceMap comes to the rescue for element iteration cases. (Maybe it's just me that didn't know this...)
This PR helps complete #762