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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/07/15 13:39:23 UTC

[GitHub] [incubator-mxnet] paulbauriegel opened a new issue #15540: Inference Error with ONNX model: leaky_relu-inl.h Cannot broadcast gamma to data

paulbauriegel opened a new issue #15540: Inference Error with ONNX  model: leaky_relu-inl.h Cannot broadcast gamma to data
URL: https://github.com/apache/incubator-mxnet/issues/15540
 
 
   ## Description
   The inference of an ONNX model from the model-zoo (`onnx-model-zoo/arcface/resnet100/resnet100.onnx`) throws following exception when using mxnet:
   `MXNetError: [11:42:53] src/operator/./leaky_relu-inl.h:311: Check failed: s == -1 Cannot broadcast gamma to data. gamma: [1,64,1,1], data: [1,64,112,112]`
   
   ## Environment info (Required)
   
   ```
   ----------Python Info----------
   Version      : 3.7.3
   Compiler     : GCC 7.3.0
   Build        : ('default', 'Mar 27 2019 23:01:00')
   Arch         : ('64bit', '')
   ------------Pip Info-----------
   Version      : 19.1
   Directory    : /opt/conda/lib/python3.7/site-packages/pip
   ----------MXNet Info-----------
   Version      : 1.4.1
   Directory    : /opt/conda/lib/python3.7/site-packages/mxnet
   Commit hash file "/opt/conda/lib/python3.7/site-packages/mxnet/COMMIT_HASH" not found. Not installed from pre-built package or built from source.
   Library      : ['/opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so']
   Build features:
   No runtime build feature info available
   ----------System Info----------
   Platform     : Linux-4.15.0-45-generic-x86_64-with-debian-buster-sid
   system       : Linux
   node         : scratgpu323
   release      : 4.15.0-45-generic
   version      : #48-Ubuntu SMP Tue Jan 29 16:28:13 UTC 2019
   ----------Hardware Info----------
   machine      : x86_64
   processor    : x86_64
   Architecture:        x86_64
   CPU op-mode(s):      32-bit, 64-bit
   Byte Order:          Little Endian
   CPU(s):              80
   On-line CPU(s) list: 0-79
   Thread(s) per core:  2
   Core(s) per socket:  20
   Socket(s):           2
   NUMA node(s):        2
   Vendor ID:           GenuineIntel
   CPU family:          6
   Model:               79
   Model name:          Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
   Stepping:            1
   CPU MHz:             3063.389
   CPU max MHz:         3600.0000
   CPU min MHz:         1200.0000
   BogoMIPS:            4390.15
   Virtualization:      VT-x
   L1d cache:           32K
   L1i cache:           32K
   L2 cache:            256K
   L3 cache:            51200K
   NUMA node0 CPU(s):   0-19,40-59
   NUMA node1 CPU(s):   20-39,60-79
   Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts flush_l1d
   ----------Network Test----------
   Setting timeout: 10
   Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0236 sec, LOAD: 0.9274 sec.
   Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0468 sec, LOAD: 0.7662 sec.
   Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0558 sec, LOAD: 0.7003 sec.
   Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0331 sec, LOAD: 0.2299 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0178 sec, LOAD: 0.3609 sec.
   Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0116 sec, LOAD: 0.0587 sec.
   ```
   ## Error Message:
   ```
   Traceback (most recent call last):
     File "test.py", line 30, in <module>
       print(model.get_outputs())
     File "/opt/conda/lib/python3.7/site-packages/mxnet/ndarray/ndarray.py", line 189, in __repr__
       return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()),
     File "/opt/conda/lib/python3.7/site-packages/mxnet/ndarray/ndarray.py", line 1980, in asnumpy
       ctypes.c_size_t(data.size)))
     File "/opt/conda/lib/python3.7/site-packages/mxnet/base.py", line 252, in check_call
       raise MXNetError(py_str(_LIB.MXGetLastError()))
   mxnet.base.MXNetError: [12:22:17] src/operator/./leaky_relu-inl.h:311: Check failed: s == -1 Cannot broadcast gamma to data. gamma: [1,64,1,1], data: [1,64,112,112]
   
   Stack trace returned 10 entries:
   [bt] (0) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x3f935a) [0x7f11b345435a]
   [bt] (1) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x3f9981) [0x7f11b3454981]
   [bt] (2) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x3151296) [0x7f11b61ac296]
   [bt] (3) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x31833b4) [0x7f11b61de3b4]
   [bt] (4) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x30700b4) [0x7f11b60cb0b4]
   [bt] (5) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x2dcd33f) [0x7f11b5e2833f]
   [bt] (6) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x2dd3b5f) [0x7f11b5e2eb5f]
   [bt] (7) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x2db2124) [0x7f11b5e0d124]
   [bt] (8) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x2db62b2) [0x7f11b5e112b2]
   [bt] (9) /opt/conda/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x2db2854) [0x7f11b5e0d854]
   ```
   
   ## Minimum reproducible example
   [script.zip](https://github.com/apache/incubator-mxnet/files/3392230/script.zip)
   
   ## Steps to reproduce
   (Paste the commands you ran that produced the error.)
   
   1. `wget https://s3.amazonaws.com/onnx-model-zoo/arcface/resnet100/resnet100.onnx`
   2. `wget https://github.com/apache/incubator-mxnet/files/3392230/script.zip`
   3. `unzip script.zip`
   4. `python3 test.py`
   
   ## What have you tried to solve it?
   
   1. Different machines
   2. Install different versions of mxnet via pip: `mxnet`,`mxnet-cu100`, `mxnet-cu100mkl`
   3. Test on GPU and CPU `ctx = mx.gpu(0)`, `ctx = mx.cpu()`
   

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