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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/06/05 11:41:09 UTC
[GitHub] HuichuanLiu opened a new issue #11149: Unreasonable performance of
resnext models provided in model_zoo, evaluated by score.py
HuichuanLiu opened a new issue #11149: Unreasonable performance of resnext models provided in model_zoo, evaluated by score.py
URL: https://github.com/apache/incubator-mxnet/issues/11149
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## Description
I used the incubator-mxnet/example/image-classification/score.py to evaluate resnext-50、resnext-101、resnext-101-64d，but none of them reached a reasonable result.
```
python score.py --model imagenet1k-resnext-50 --gpus 2 --data-val /data3/liuhuichuan/Data/imagenet/imagenet1k-val.rec
INFO:root:Finished with 439.479926 images per second
INFO:root:('accuracy', 0.00011988491048593351)
INFO:root:('top_k_accuracy_5', 0.0020580242966751917)
```
```
python score.py --model imagenet1k-resnext-101 --gpus 2 --data-val /data3/liuhuichuan/Data/imagenet/imagenet1k-val.rec
INFO:root:Finished with 270.428020 images per second
INFO:root:('accuracy', 7.9923273657289009e-05)
INFO:root:('top_k_accuracy_5', 0.0022778132992327367)
```
```
python score.py --model imagenet1k-resnext-101-64x4d --gpus 2 --data-val /data3/liuhuichuan/Data/imagenet/imagenet1k-val.rec
INFO:root:Finished with 160.059377 images per second
INFO:root:('accuracy', 0.48345588235294118)
INFO:root:('top_k_accuracy_5', 0.6939338235294118)
```
However, the resnet-101 model works perfectly well
```
python score.py --model imagenet1k-resnet-101 --gpus 2 --data-val /data3/liuhuichuan/Data/imagenet/imagenet1k-val.rec
INFO:root:Finished with 383.548030 images per second
INFO:root:('accuracy', 0.76856218030690537)
INFO:root:('top_k_accuracy_5', 0.93300431585677746)
```
It seems the ResNeXt models are not appropriately trained or something(preprocess?) does not fit the model in score.py?
## Environment info (Required)
```
----------Python Info----------
Version : 3.6.4
Compiler : GCC 7.2.0
Build : ('default', 'Jan 16 2018 18:10:19')
Arch : ('64bit', '')
------------Pip Info-----------
Version : 9.0.1
Directory : /home/liuhuichuan/anaconda3/envs/i3d_mxn/lib/python3.6/site-packages/pip
----------MXNet Info-----------
Version : 1.1.0
Directory : /home/liuhuichuan/anaconda3/envs/i3d_mxn/lib/python3.6/site-packages/mxnet
Commit Hash : 07a83a0325a3d782513a04f47d711710972cb144
----------System Info----------
Platform : Linux-4.4.0-62-generic-x86_64-with-debian-stretch-sid
system : Linux
node : jf-gpu003
release : 4.4.0-62-generic
version : #83-Ubuntu SMP Wed Jan 18 14:10:15 UTC 2017
----------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): 32
On-line CPU(s) list: 0-31
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
Stepping: 1
CPU MHz: 1236.867
CPU max MHz: 3000.0000
CPU min MHz: 1200.0000
BogoMIPS: 4201.09
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 20480K
NUMA node0 CPU(s): 0-7,16-23
NUMA node1 CPU(s): 8-15,24-31
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 intel_pt 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 ida arat pln pts
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0259 sec, LOAD: 1.6091 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0223 sec, LOAD: 1.3577 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0249 sec, LOAD: 2.8521 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.3443 sec, LOAD: 1.3495 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0243 sec, LOAD: 5.4109 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0222 sec, LOAD: 0.9340 sec.
```
Package used (Python/R/Scala/Julia):
(I'm using ...)
For Scala user, please provide:
1. Java version: (`java -version`)
2. Maven version: (`mvn -version`)
3. Scala runtime if applicable: (`scala -version`)
For R user, please provide R `sessionInfo()`:
## Build info (Required if built from source)
Installed from pip of python3.6
Compiler (gcc/clang/mingw/visual studio):
MXNet commit hash:
a48480b706763203a294cb76eb8916517ff214c1
Build config:
(Paste the content of config.mk, or the build command.)
## Error Message:
(Paste the complete error message, including stack trace.)
## Minimum reproducible example
(If you are using your own code, please provide a short script that reproduces the error. Otherwise, please provide link to the existing example.)
## Steps to reproduce
(Paste the commands you ran that produced the error.)
1. pack val files in local directory instead of redownloading，It shouldn't be a problem because the resnet uses the same .rec and works well.
```
# if [ ! -e ILSVRC2012_img_val.tar ]; then
# wget $1
# fi
# mkdir -p val
# tar -xf ILSVRC2012_img_val.tar -C val
wget http://data.mxnet.io/models/imagenet/resnet/val.lst -O imagenet1k-val.lst
CUR_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
MX_DIR=${CUR_DIR}/../../../
python ${CUR_DIR}/../../../tools/im2rec.py --resize 256 --quality 90 --num-thread 16 imagenet1k-val my_path_to_store_imagenet_val_data/
# rm -rf val
```
2. run score.py in incubator-mxnet/example/image-classification
```
python score.py --model imagenet1k-resnext-101-64x4d --gpus 2 --data-val /data3/liuhuichuan/Data/imagenet/imagenet1k-val.rec
```
## What have you tried to solve it?
1. switch the git branch from mater to v1.2.0 but nothing changed.
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