evi-Genius edited a comment on issue #16929: quantization, excluded_sym_names 
doesn't work
URL: 
https://github.com/apache/incubator-mxnet/issues/16929#issuecomment-559368266
 
 
   > Please specify the version of MXNet you are using. It's best to run the 
diagnosis script that you were requested to run when you opened this report..
   
   ## Environment
   ----------Python Info----------
   Version      : 3.5.2
   Compiler     : GCC 5.4.0 20160609
   Build        : ('default', 'Nov 23 2017 16:37:01')
   Arch         : ('64bit', 'ELF')
   ------------Pip Info-----------
   Version      : 19.1.1
   Directory    : 
/home/xiangyang/.virtualenvs/py35/lib/python3.5/site-packages/pip
   ----------MXNet Info-----------
   Version      : 1.5.1
   Directory    : 
/home/xiangyang/.virtualenvs/py35/lib/python3.5/site-packages/mxnet
   Num GPUs     : 4
   Commit Hash   : c9818480680f84daa6e281a974ab263691302ba8
   ----------System Info----------
   Platform     : Linux-4.4.0-116-generic-x86_64-with-Ubuntu-16.04-xenial
   system       : Linux
   node         : jja-gpu034
   release      : 4.4.0-116-generic
   version      : #140-Ubuntu SMP Mon Feb 12 21:23:04 UTC 2018
   ----------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):                16
   On-line CPU(s) list:   0-15
   Thread(s) per core:    1
   Core(s) per socket:    8
   Socket(s):             2
   NUMA node(s):          1
   Vendor ID:             GenuineIntel
   CPU family:            6
   Model:                 79
   Model name:            Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
   Stepping:              1
   CPU MHz:               2089.582
   CPU max MHz:           3000.0000
   CPU min MHz:           1200.0000
   BogoMIPS:              4201.45
   Virtualization:        VT-x
   L1d cache:             32K
   L1i cache:             32K
   L2 cache:              256K
   L3 cache:              20480K
   NUMA node0 CPU(s):     0-15
   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 aperfmperf eagerfpu 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 epb 
invpcid_single intel_pt retpoline kaiser tpr_shadow vnmi flexpriority ept vpid 
fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdseed adx 
smap xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm arat pln 
pts
   ----------Network Test----------
   Setting timeout: 10
   Timing for GluonNLP GitHub: https://github.com/dmlc/gluon-nlp, DNS: 0.0015 
sec, LOAD: 1.1211 sec.
   Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0008 sec, 
LOAD: 0.0412 sec.
   Timing for D2L (zh-cn): http://zh.d2l.ai, DNS: 0.0008 sec, LOAD: 0.0459 sec.
   Timing for FashionMNIST: 
https://repo.mxnet.io/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, 
DNS: 0.0003 sec, LOAD: 0.6482 sec.
   Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0035 sec, LOAD: 
0.0439 sec.
   Timing for D2L: http://d2l.ai, DNS: 0.0008 sec, LOAD: 0.1977 sec.
   Timing for GluonNLP: http://gluon-nlp.mxnet.io, DNS: 0.0008 sec, LOAD: 
0.0236 sec.
   Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0006 
sec, LOAD: 1.2173 sec.
   
   ### Steps to reproduce
   (Paste the commands you ran that produced the error.)
   
   1.python imagenet_gen_qsym_mkldnn.py --model=mobilenetv2_1.0 
--calib-dataset=./data/val_256_q90.rec --num-calib-batches=5 
--calib-mode=entropy
   2.python imagenet_inference.py 
--symbol-file=./model/mobilenetv2_1.0-quantized-5batches-entropy-symbol.json 
--param-file=./model/mobilenetv2_1.0-0000.params 
--rgb-mean=123.68,116.779,103.939 --num-skipped-batches=50 
--num-inference-batches=500 --dataset=./data/val_256_q90.rec 
--rgb-std=58.393,57.12,57.375
   ### Error Message
   Check failed: dshape[C] % 4 == 0U (3 vs. 0) : for 8bit cudnn conv, the 
number of channel must be multiple of 4
   ## Description
   the first layer is not be excluded, actually I have tested lots of 
`excluded_sym_names` but none of them work. 

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