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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/11/29 07:47:32 UTC
[GitHub] shuokay opened a new issue #8863: [gluon] segmentation error when getting the shape of output ndarray of transpose conv
shuokay opened a new issue #8863: [gluon] segmentation error when getting the shape of output ndarray of transpose conv
URL: https://github.com/apache/incubator-mxnet/issues/8863
## Environment info (Required)
```
----------Python Info----------
('Version :', '2.7.12')
('Compiler :', 'GCC 5.4.0 20160609')
('Build :', ('default', 'Nov 19 2016 06:48:10'))
('Arch :', ('64bit', 'ELF'))
------------Pip Info-----------
('Version :', '9.0.1')
('Directory :', '/usr/local/lib/python2.7/dist-packages/pip')
----------MXNet Info-----------
('Version :', '0.12.1')
('Directory :', '/home/luban/incubator-mxnet/python/mxnet')
Traceback (most recent call last):
File "diagnose.py", line 171, in <module>
check_mxnet()
File "diagnose.py", line 113, in check_mxnet
except FileNotFoundError:
NameError: global name 'FileNotFoundError' is not defined
```
Package used (Python/R/Scala/Julia): Python2.7.12
MXNet commit hash: 91ffd691bf865833a51ca793eda125b7f9befc18
Build config:
```
#-------------------------------------------------------------------------------
# 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 signal handler (e.g. segfault logger)
USE_SIGNAL_HANDLER =
# the additional link flags you want to add
ADD_LDFLAGS = -L/usr/local/nvidia/lib64
# the additional compile flags you want to add
ADD_CFLAGS =
#---------------------------------------------
# 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 = /usr/local/cuda
# 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 = 1
#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=/usr/local
# 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 = openblas
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 =
# by default, disable lapack when using MKL
# switch on when there is a full installation of MKL available (not just MKL2017/MKL_ML)
ifeq ($(USE_BLAS), mkl)
USE_LAPACK = 0
endif
# add path to intel library, you may need it for MKL, if you did not add the path
# to environment variable
USE_INTEL_PATH = NONE
# If use MKL only for BLAS, choose static link automatically to allow python wrapper
ifeq ($(USE_MKL2017), 0)
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
endif
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 = 0
#----------------------------
# 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
# whether to use torch integration. This requires installing torch.
# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
# TORCH_PATH = $(HOME)/torch
# MXNET_PLUGINS += plugin/torch/torch.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:
[1] 17341 segmentation fault (core dumped) python resnet-fcn.py
## Minimum reproducible example
```python
import mxnet as mx
from mxnet import gluon
from mxnet import nd
ctx = mx.gpu(1)
net4 = gluon.nn.Sequential()
conv_t4 = gluon.nn.Conv2DTranspose(1024, kernel_size=(4, 4), strides=(2, 2), padding=(1, 1))
conv_t4.initialize(init=mx.init.Xavier(), ctx=ctx)
x4 = nd.uniform(low=0, high=255, shape=(2, 1024, 14, 14), ctx=ctx)
net4.add(conv_t4)
print (net4(x4)).shape
```
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