mazeltovlee opened a new issue #20875:
URL: https://github.com/apache/incubator-mxnet/issues/20875
## Description
The `infer_shape` cannot infer the shape when the `batch_size` (first dim of
input) is 0. However, `infer_shape_partial` can still output the correct shape.
Given the same graph:
```
net = mx.sym.Variable('data', shape=(0,10,10,3))
net = mx.sym.reshape(data=net, shape=(0, 100, 3))
net = mx.sym.expand_dims(data=net, axis=2)
```
When I run `net.infer_shape()`, it raises an UserWarning and output `(None,
None, None)`
```
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: UserWarning:
Cannot decide shape for the following arguments (0s in shape means unknown
dimensions). Consider providing them as input:
data: (0, 10, 10, 3)
"""
(None, None, None)
```
But when I run `net.infer_shape_partial()`, it outputs a reasonable output
shape:
```
([(0, 10, 10, 3)], [(0, 100, 1, 3)], [])
```
I find out that if I change the shape of the first input from `(0,10,10,3)`
to `(10,0,10,3)`, both `shape_infer` and `shape_infer_partial` can output
correct output shape: `([(10, 10, 10, 3)], [(10, 100, 1, 3)], [])`. I expected
that mxnet can still infer the symbol's shape if the `batch_size` (first dim of
the symbol) is 0. If setting `batch_size` to 0 is not acceptable for shape
inference, maybe mxnet can make the behavior between `shape_infer` and
`shape_infer_partial` consistent.
Moreover, another issue I found is that if I add another operator:
`mx.sym.flatten()`, `net.infer_shape_partial()` will also unable to infer the
output shape:
```
import mxnet as mx
net = mx.sym.Variable('data', shape=(0,10,10,3))
net = mx.sym.flatten(data=net, axes=[0,3,2,1])
net = mx.sym.reshape(data=net, shape=(0, 100, 3))
net = mx.sym.expand_dims(data=net, axis=2)
print(net.infer_shape_partial()) # ([(0, 10, 10, 3)], [()], [])
```
## To Reproduce
You can either copy-and-paste the above code or directly run this colab link
to reproduce these issues:
https://colab.research.google.com/drive/1KZp5Qw4vuMi9aA6kNIkPQBWGQlbTcDtv?usp=sharing
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