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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/08/15 18:12:47 UTC

[GitHub] [incubator-mxnet] leeyeetonn opened a new issue #18933: Floating point exception in mxnet.ndarray.InstanceNorm

leeyeetonn opened a new issue #18933:
URL: https://github.com/apache/incubator-mxnet/issues/18933


   ## Description
   (A clear and concise description of what the bug is.)
   `mxnet.ndarray.InstanceNorm` has floating point exception when the given data's `batch = 0` or `channel = 0`. Please see the provided code snippets for example.
   
   ### Error Message
   (Paste the complete error message. Please also include stack trace by setting environment variable `DMLC_LOG_STACK_TRACE_DEPTH=10` before running your script.)
   >Floating point exception (core dumped)
   ## To Reproduce
   (If you developed your own code, please provide a short script that reproduces the error. For existing examples, please provide link.)
   ```python
   import mxnet
   import numpy as np
   
   input = mxnet.nd.array(np.random.rand(0,1,1)) # batch = 0
   gamma = mxnet.nd.array(np.random.rand(1))
   beta = mxnet.nd.array(np.random.rand(1))
   mxnet.ndarray.InstanceNorm(input, gamma, beta)
   ```
   
   ```python
   import mxnet
   import numpy as np
   
   input = mxnet.nd.array(np.random.rand(1,0,1)) # channel = 0
   gamma = mxnet.nd.array(np.random.rand(0))
   beta = mxnet.nd.array(np.random.rand(0))
   mxnet.ndarray.InstanceNorm(input, gamma, beta)
   ```
   
   ### Steps to reproduce
   (Paste the commands you ran that produced the error.)
   
   1. run the provided code in python interpreter or as a script
   2.
   
   ## What have you tried to solve it?
   
   1.
   2.
   
   ## Environment
   
   We recommend using our script for collecting the diagnositc information. Run the following command and paste the outputs below:
   ```
   curl --retry 10 -s https://raw.githubusercontent.com/dmlc/gluon-nlp/master/tools/diagnose.py | python
   
   # paste outputs here
   ```
   Got 404 when trying to get the script.
   
   Some environment information:
   
   * OS: ubuntu 18.04
   * Python: 3.7.6
   * pip: 20.0.2
   * numpy: 1.18.5
   * mxnet: 1.6.0
   


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[GitHub] [incubator-mxnet] szha edited a comment on issue #18933: Floating point exception in mxnet.ndarray.InstanceNorm

Posted by GitBox <gi...@apache.org>.
szha edited a comment on issue #18933:
URL: https://github.com/apache/incubator-mxnet/issues/18933#issuecomment-677959144


   Confirmed that this issue also happens in numpy branch. Since instance norm was using a legacy operator interface, I will refactor it to use the new operator interface and register an npx namespace alias.
   
   ```
   from mxnet import np, npx
   
   npx.set_np()
   
   input = np.random.rand(0,1,1) # batch = 0
   gamma = np.random.rand(1)
   beta = np.random.rand(1)
   npx.instance_norm(input, gamma, beta)
   ```
   
   ```
   from mxnet import np, npx
   
   npx.set_np()
   
   input = np.random.rand(1,0,1) # channel = 0
   gamma = np.random.rand(1)
   beta = np.random.rand(1)
   npx.instance_norm(input, gamma, beta)
   ```


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[GitHub] [incubator-mxnet] szha closed issue #18933: Floating point exception in mxnet.ndarray.InstanceNorm

Posted by GitBox <gi...@apache.org>.
szha closed issue #18933:
URL: https://github.com/apache/incubator-mxnet/issues/18933


   


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[GitHub] [incubator-mxnet] szha commented on issue #18933: Floating point exception in mxnet.ndarray.InstanceNorm

Posted by GitBox <gi...@apache.org>.
szha commented on issue #18933:
URL: https://github.com/apache/incubator-mxnet/issues/18933#issuecomment-677959144


   Confirmed that this issue also happens in numpy branch. Since instance norm was using a legacy operator interface, I will refactor it to use the new operator interface and register an npx namespace alias.
   
   ```
   from mxnet import np, npx
   
   npx.set_np()
   
   input = np.random.rand(0,1,1) # batch = 0
   gamma = np.random.rand(1)
   beta = np.random.rand(1)
   npx.instance_norm(input, gamma, beta)
   ```
   
   ```
   from mxnet import np, npx
   
   npx.set_np()
   
   input = mxnet.nd.array(np.random.rand(1,0,1)) # channel = 0
   gamma = np.random.rand(1)
   beta = np.random.rand(1)
   npx.instance_norm(input, gamma, beta)
   ```


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