[GitHub] ptrendx opened a new pull request #14153: Fix shape inference pass

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

ptrendx opened a new pull request #14153: Fix shape inference pass
URL: https://github.com/apache/incubator-mxnet/pull/14153
 
 
   ## Description ##
   
   This PR fixes shape inference, which currently results in wrong result for some cases.
   
   An example problematic case (as it works in current version of MXNet, before applying this change):
   If I do
   ```
   import mxnet as mx
   
   data = mx.sym.Variable('data', shape=(1,0,512,512))
   weight = mx.sym.Variable('weight')
   cdata = mx.sym.cast(data, dtype='float16')
   cweight = mx.sym.cast(weight, dtype='float16')
   test = mx.sym.Convolution(data=cdata, weight=cweight,layout='NCHW', pad=(3, 3), num_filter=64, stride=(2, 2), no_bias=True, dilate=(1, 1), kernel=(7, 7), num_group=1)
   
   print(test.infer_shape_partial())
   ```
   I get expected result:
   ```
   ([(1, 0, 512, 512), (64, 0, 7, 7)], [(1, 64, 256, 256)], [])
   ```
   but when I change H and W dimensions in the shape to 0s,
   ```
   import mxnet as mx
   
   data = mx.sym.Variable('data', shape=(1,0,0,0))
   weight = mx.sym.Variable('weight')
   cdata = mx.sym.cast(data, dtype='float16')
   cweight = mx.sym.cast(weight, dtype='float16')
   test = mx.sym.Convolution(data=cdata, weight=cweight,layout='NCHW', pad=(3, 3), num_filter=64, stride=(2, 2), no_bias=True, dilate=(1, 1), kernel=(7, 7), num_group=1)
   
   print(test.infer_shape_partial())
   ```
   I get
   ```
   ([(1, 0, 0, 0), ()], [(1, 64, 0, 0)], [])
   ```
   so the shape of the weight changed to `()`.
   
   ## Checklist ##
   ### Essentials ###
   Please feel free to remove inapplicable items for your PR.
   - [x] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
   - [ ] Code is well-documented: 
   - For new C++ functions in header files, their functionalities and arguments are documented. 
   - [x] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [x] Change to the way passes are done so that both forward and backward inference is performed every time - I'm not sure if this is necessary - @eric-haibin-lin, thoughts? 
   - [x] Change to the way shape inference works. Currently a shape contributes 1 to the `num_unknown` if it has at least 1 zero. After the change number of 0 elements is added to the `num_unknown` - that way shape inference pass does not end prematurely if only some of the elements of shape were deduced.
   
   ## Comments ##
   

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