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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/03/05 02:52:07 UTC
[GitHub] samhodge opened a new issue #9989: Cannot train example gluon style transfer
samhodge opened a new issue #9989: Cannot train example gluon style transfer
URL: https://github.com/apache/incubator-mxnet/issues/9989
Note: Providing complete information in the most concise form is the best way to get help. This issue template serves as the checklist for essential information to most of the technical issues and bug reports. For non-technical issues and feature requests, feel free to present the information in what you believe is the best form.
For Q & A and discussion, please start a discussion thread at https://discuss.mxnet.io
## Description
Cannot train gluon style transfer, needs to be outside of autograd.record() block or need to call backward.
## Environment info (Required)
----------Python Info----------
('Version :', '2.7.10')
('Compiler :', 'GCC 4.1.2')
('Build :', ('default', 'Jun 29 2015 12:45:31'))
('Arch :', ('64bit', 'ELF'))
------------Pip Info-----------
No corresponding pip install for current python.
----------MXNet Info-----------
/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet/optimizer.py:136: UserWarning: WARNING: New optimizer mxnet.optimizer.NAG is overriding existing optimizer mxnet.optimizer.NAG
Optimizer.opt_registry[name].__name__))
('Version :', '1.1.0')
('Directory :', '/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet')
Hashtag not found. Not installed from pre-built package.
----------System Info----------
('Platform :', 'Linux-3.10.105-1.el6.elrepo.x86_64-x86_64-with-centos-6.2-Final')
('system :', 'Linux')
('node :', 'bladerunner')
('release :', '3.10.105-1.el6.elrepo.x86_64')
('version :', '#1 SMP Fri Feb 10 10:48:08 EST 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): 12
On-line CPU(s) list: 0-11
Thread(s) per core: 1
Core(s) per socket: 6
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 63
Model name: Intel(R) Xeon(R) CPU E5-2609 v3 @ 1.90GHz
Stepping: 2
CPU MHz: 1900.000
BogoMIPS: 3796.70
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 15360K
NUMA node0 CPU(s): 0-5
NUMA node1 CPU(s): 6-11
----------Network Test----------
Setting timeout: 10
Error open MXNet: https://github.com/apache/incubator-mxnet, <urlopen error timed out>, DNS finished in 0.0260591506958 sec.
Error open PYPI: https://pypi.python.org/pypi/pip, <urlopen error [Errno 101] Network is unreachable>, DNS finished in 0.170429944992 sec.
Error open FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, <urlopen error [Errno 101] Network is unreachable>, DNS finished in 0.204452037811 sec.
Error open Conda: https://repo.continuum.io/pkgs/free/, <urlopen error [Errno 101] Network is unreachable>, DNS finished in 0.154680967331 sec.
Error open Gluon Tutorial(en): http://gluon.mxnet.io, <urlopen error [Errno 101] Network is unreachable>, DNS finished in 0.381160974503 sec.
Error open Gluon Tutorial(cn): https://zh.gluon.ai, <urlopen error [Errno 101] Network is unreachable>, DNS finished in 0.432467937469 sec.
Package used (Python/R/Scala/Julia):
Python
## Build info (Required if built from source)
Compiler (gcc/clang/mingw/visual studio): GCC-4.8.5 on Centos 6.2
MXNet commit hash:
b73c57c526396d6485bdf65986e3819c54eb7bd9
Build config:
```
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#-------------------------------------------------------------------------------
# Template configuration for compiling mxnet
#
# If you want to change the configuration, please use the following
# steps. Assume you are on the root directory of mxnet. First copy the this
# file so that any local changes will be ignored by git
#
# $ cp make/config.mk .
#
# Next modify the according entries, and then compile by
#
# $ make
#
# or build in parallel with 8 threads
#
# $ make -j8
#-------------------------------------------------------------------------------
#---------------------
# choice of compiler
#--------------------
export CC = gcc
export CXX = g++
export NVCC = nvcc
# whether compile with options for MXNet developer
DEV = 0
# whether compile with debug
DEBUG = 0
# whether compile with profiler
USE_PROFILER =
# whether to turn on segfault signal handler to log the stack trace
USE_SIGNAL_HANDLER =
# the additional link flags you want to add
ADD_LDFLAGS = -L /asset/common/software/thirdparty/cudnn/5.1-build1/cuda/lib64/ -L /asset/common/software/thirdparty/cuda/8.0.61-build1/lib64
# the additional compile flags you want to add
ADD_CFLAGS = -I /asset/common/software/thirdparty/mkl/2018.0.128-build2/mkl/include/ -I /asset/common/software/thirdparty/cudnn/5.1-build1/cuda/include/
#---------------------------------------------
# matrix computation libraries for CPU/GPU
#---------------------------------------------
# whether use CUDA during compile
USE_CUDA = 1
# add the path to CUDA library to link and compile flag
# if you have already add them to environment variable, leave it as NONE
# USE_CUDA_PATH = /usr/local/cuda
USE_CUDA_PATH = /asset/common/software/thirdparty/cuda/8.0.61-build1/
# whether to enable CUDA runtime compilation
ENABLE_CUDA_RTC = 1
# whether use CuDNN R3 library
USE_CUDNN = 1
#whether to use NCCL library
USE_NCCL = 0
#add the path to NCCL library
USE_NCCL_PATH = NONE
# whether use opencv during compilation
# you can disable it, however, you will not able to use
# imbin iterator
USE_OPENCV = 0
#whether use libjpeg-turbo for image decode without OpenCV wrapper
USE_LIBJPEG_TURBO = 0
#add the path to libjpeg-turbo library
USE_LIBJPEG_TURBO_PATH = NONE
# use openmp for parallelization
USE_OPENMP = 1
# MKL ML Library for Intel CPU/Xeon Phi
# Please refer to MKL_README.md for details
# MKL ML Library folder, need to be root for /usr/local
# Change to User Home directory for standard user
# For USE_BLAS!=mkl only
MKLML_ROOT=/asset/common/software/thirdparty/mkl/2018.0.128-build2/
# whether use MKL2017 library
USE_MKL2017 = 0
# whether use MKL2017 experimental feature for high performance
# Prerequisite USE_MKL2017=1
USE_MKL2017_EXPERIMENTAL = 0
# whether use NNPACK library
USE_NNPACK = 0
# choose the version of blas you want to use
# can be: mkl, blas, atlas, openblas
# in default use atlas for linux while apple for osx
UNAME_S := $(shell uname -s)
ifeq ($(UNAME_S), Darwin)
USE_BLAS = apple
else
USE_BLAS = mkl
endif
# whether use lapack during compilation
# only effective when compiled with blas versions openblas/apple/atlas/mkl
USE_LAPACK = 1
# path to lapack library in case of a non-standard installation
USE_LAPACK_PATH =
# add path to intel library, you may need it for MKL, if you did not add the path
# to environment variable
USE_INTEL_PATH = /asset/common/software/thirdparty/mkl/2018.0.128-build2/
# If use MKL only for BLAS, choose static link automatically to allow python wrapper
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
else
USE_STATIC_MKL = NONE
endif
#----------------------------
# Settings for power and arm arch
#----------------------------
ARCH := $(shell uname -a)
ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
USE_SSE=0
else
USE_SSE=1
endif
#----------------------------
# distributed computing
#----------------------------
# whether or not to enable multi-machine supporting
USE_DIST_KVSTORE = 0
# whether or not allow to read and write HDFS directly. If yes, then hadoop is
# required
USE_HDFS = 0
# path to libjvm.so. required if USE_HDFS=1
LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
# whether or not allow to read and write AWS S3 directly. If yes, then
# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
# sudo apt-get install -y libcurl4-openssl-dev
USE_S3 = 0
#----------------------------
# performance settings
#----------------------------
# Use operator tuning
USE_OPERATOR_TUNING = 1
# Use gperftools if found
USE_GPERFTOOLS = 1
# Use JEMalloc if found, and not using gperftools
USE_JEMALLOC = 1
#----------------------------
# additional operators
#----------------------------
# path to folders containing projects specific operators that you don't want to put in src/operators
EXTRA_OPERATORS =
#----------------------------
# other features
#----------------------------
# Create C++ interface package
USE_CPP_PACKAGE = 1
#----------------------------
# plugins
#----------------------------
# whether to use caffe integration. This requires installing caffe.
# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
# CAFFE_PATH = $(HOME)/caffe
# MXNET_PLUGINS += plugin/caffe/caffe.mk
# WARPCTC_PATH = $(HOME)/warp-ctc
# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
# whether to use sframe integration. This requires build sframe
# git@github.com:dato-code/SFrame.git
# SFRAME_PATH = $(HOME)/SFrame
# MXNET_PLUGINS += plugin/sframe/plugin.mk
```
## Error Message:
```
samh@bladerunner ~/dev/mxnet/example/gluon/style_transfer/ run python with mxnet pillow/latest : main.py train --dataset ~/dev/coco/dataset/ --style-folder images/styles --save-model-dir models
/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet/optimizer.py:136: UserWarning: WARNING: New optimizer mxnet.optimizer.NAG is overriding existing optimizer mxnet.optimizer.NAG
Optimizer.opt_registry[name].__name__))
('len(style_loader):', 21)
('style_model:', Net(
(gram): GramMatrix(
)
(model): Sequential(
(0): Sequential(
(0): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(3 -> 64, kernel_size=(7, 7), stride=(1, 1))
)
(1): InstanceNorm(eps=1e-05, in_channels=64)
(2): Activation(relu)
(3): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=64)
(1): Activation(relu)
(2): Conv2D(64 -> 32, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=32)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(2, 2))
)
(6): InstanceNorm(eps=1e-05, in_channels=32)
(7): Activation(relu)
(8): Conv2D(32 -> 128, kernel_size=(1, 1), stride=(1, 1))
)
(residual_layer): Conv2D(64 -> 128, kernel_size=(1, 1), stride=(2, 2))
)
(4): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=128)
(1): Activation(relu)
(2): Conv2D(128 -> 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=128)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(2, 2))
)
(6): InstanceNorm(eps=1e-05, in_channels=128)
(7): Activation(relu)
(8): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))
)
(residual_layer): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(2, 2))
)
)
(1): Inspiration(N x 512)
(2): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=512)
(1): Activation(relu)
(2): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=128)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm(eps=1e-05, in_channels=128)
(7): Activation(relu)
(8): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(3): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=512)
(1): Activation(relu)
(2): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=128)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm(eps=1e-05, in_channels=128)
(7): Activation(relu)
(8): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(4): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=512)
(1): Activation(relu)
(2): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=128)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm(eps=1e-05, in_channels=128)
(7): Activation(relu)
(8): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(5): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=512)
(1): Activation(relu)
(2): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=128)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm(eps=1e-05, in_channels=128)
(7): Activation(relu)
(8): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(6): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=512)
(1): Activation(relu)
(2): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=128)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm(eps=1e-05, in_channels=128)
(7): Activation(relu)
(8): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(7): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=512)
(1): Activation(relu)
(2): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=128)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm(eps=1e-05, in_channels=128)
(7): Activation(relu)
(8): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(8): UpBottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=512)
(1): Activation(relu)
(2): Conv2D(512 -> 32, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=32)
(4): Activation(relu)
(5): UpsampleConvLayer(
(conv2d): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(6): InstanceNorm(eps=1e-05, in_channels=32)
(7): Activation(relu)
(8): Conv2D(32 -> 128, kernel_size=(1, 1), stride=(1, 1))
)
(residual_layer): UpsampleConvLayer(
(conv2d): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1))
)
)
(9): UpBottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=128)
(1): Activation(relu)
(2): Conv2D(128 -> 16, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=16)
(4): Activation(relu)
(5): UpsampleConvLayer(
(conv2d): Conv2D(16 -> 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(6): InstanceNorm(eps=1e-05, in_channels=16)
(7): Activation(relu)
(8): Conv2D(16 -> 64, kernel_size=(1, 1), stride=(1, 1))
)
(residual_layer): UpsampleConvLayer(
(conv2d): Conv2D(128 -> 64, kernel_size=(1, 1), stride=(1, 1))
)
)
(10): InstanceNorm(eps=1e-05, in_channels=64)
(11): Activation(relu)
(12): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(64 -> 3, kernel_size=(7, 7), stride=(1, 1))
)
)
(ins): Inspiration(N x 512)
(model1): Sequential(
(0): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(3 -> 64, kernel_size=(7, 7), stride=(1, 1))
)
(1): InstanceNorm(eps=1e-05, in_channels=64)
(2): Activation(relu)
(3): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=64)
(1): Activation(relu)
(2): Conv2D(64 -> 32, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=32)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(32 -> 32, kernel_size=(3, 3), stride=(2, 2))
)
(6): InstanceNorm(eps=1e-05, in_channels=32)
(7): Activation(relu)
(8): Conv2D(32 -> 128, kernel_size=(1, 1), stride=(1, 1))
)
(residual_layer): Conv2D(64 -> 128, kernel_size=(1, 1), stride=(2, 2))
)
(4): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm(eps=1e-05, in_channels=128)
(1): Activation(relu)
(2): Conv2D(128 -> 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm(eps=1e-05, in_channels=128)
(4): Activation(relu)
(5): ConvLayer(
(pad): ReflectancePadding(
)
(conv2d): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(2, 2))
)
(6): InstanceNorm(eps=1e-05, in_channels=128)
(7): Activation(relu)
(8): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1))
)
(residual_layer): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(2, 2))
)
)
))
[13:10:54] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:107: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
Traceback (most recent call last):
File "main.py", line 228, in <module>
main()
File "main.py", line 213, in main
train(args)
File "main.py", line 82, in train
style_model.setTarget(style_image)
File "/home/samh/dev/mxnet/example/gluon/style_transfer/net.py", line 228, in setTarget
self.ins.setTarget(G)
File "/home/samh/dev/mxnet/example/gluon/style_transfer/net.py", line 252, in setTarget
self.gram.set_data(target)
File "/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet/gluon/parameter.py", line 374, in set_data
arr[:] = data
File "/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet/ndarray/ndarray.py", line 437, in __setitem__
self._set_nd_basic_indexing(key, value)
File "/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet/ndarray/ndarray.py", line 691, in _set_nd_basic_indexing
value.copyto(self)
File "/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet/ndarray/ndarray.py", line 1884, in copyto
return _internal._copyto(self, out=other)
File "<string>", line 25, in _copyto
File "/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet/_ctypes/ndarray.py", line 92, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/asset/common/software/thirdparty/mxnet/1.0.0-build1/python2.7/mxnet/base.py", line 148, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [13:11:10] src/imperative/imperative.cc:192: Check failed: AGInfo::IsNone(*(outputs[i])) Assigning to NDArrays that are already in a computational graph will cause undefined behavior when evaluating gradients. Please call backward first to clear the graph or do this out side of a record section.
Stack trace returned 10 entries:
[bt] (0) /asset/common/software/thirdparty/mxnet/1.0.0-build1/lib/libmxnet.so(dmlc::StackTrace()+0x38) [0x7f937e3b46d8]
[bt] (1) /asset/common/software/thirdparty/mxnet/1.0.0-build1/lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x18) [0x7f937e3b4ad8]
[bt] (2) /asset/common/software/thirdparty/mxnet/1.0.0-build1/lib/libmxnet.so(mxnet::Imperative::RecordOp(nnvm::NodeAttrs&&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, mxnet::OpStatePtr const&, std::vector<bool, std::allocator<bool> >*, std::vector<bool, std::allocator<bool> >*)+0x10b) [0x7f938085e7cb]
[bt] (3) /asset/common/software/thirdparty/mxnet/1.0.0-build1/lib/libmxnet.so(MXImperativeInvokeImpl(void*, int, void**, int*, void***, int, char const**, char const**)+0x756) [0x7f9380790f36]
[bt] (4) /asset/common/software/thirdparty/mxnet/1.0.0-build1/lib/libmxnet.so(MXImperativeInvokeEx+0x63) [0x7f93807911e3]
[bt] (5) /asset/common/software/thirdparty/python/2.7.10-build1/arch/linux-centos6/x86_64/ucs4/ndebug/lib/python2.7/lib-dynload/_ctypes.so(ffi_call_unix64+0x4c) [0x7f939064f6e4]
[bt] (6) /asset/common/software/thirdparty/python/2.7.10-build1/arch/linux-centos6/x86_64/ucs4/ndebug/lib/python2.7/lib-dynload/_ctypes.so(ffi_call+0x1f9) [0x7f939064f4e9]
[bt] (7) /asset/common/software/thirdparty/python/2.7.10-build1/arch/linux-centos6/x86_64/ucs4/ndebug/lib/python2.7/lib-dynload/_ctypes.so(_ctypes_callproc+0x416) [0x7f9390646fb6]
[bt] (8) /asset/common/software/thirdparty/python/2.7.10-build1/arch/linux-centos6/x86_64/ucs4/ndebug/lib/python2.7/lib-dynload/_ctypes.so(+0x9fef) [0x7f939063efef]
[bt] (9) /asset/common/software/thirdparty/python/2.7.10-build1/arch/linux-centos6/x86_64/ucs4/ndebug/lib/libpython2.7.so.1.0(PyObject_Call+0x67) [0x7f939780b427]
```
## Minimum reproducible example
Run the main.py in
https://github.com/apache/incubator-mxnet/tree/master/example/gluon/style_transfer
as follows
main.py train --dataset ~/dev/coco/dataset/ --style-folder images/styles --save-model-dir models
after download the coco dataset and the style images
## Steps to reproduce
1. Install mxnet
2. get the installed version into the environment
3. cd example/gluon/style_transfer/
4. python main.py train --dataset ~/dev/coco/dataset/ --style-folder images/styles --save-model-dir models
## What have you tried to solve it?
1. move https://github.com/apache/incubator-mxnet/blob/master/example/gluon/style_transfer/main.py#L82
2. to between L79 and L80
3. Model will train but produces bad result
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