hszhao / pspnet Goto Github PK
View Code? Open in Web Editor NEWPyramid Scene Parsing Network, CVPR2017.
Home Page: https://hszhao.github.io/projects/pspnet
License: Other
Pyramid Scene Parsing Network, CVPR2017.
Home Page: https://hszhao.github.io/projects/pspnet
License: Other
hi @hszhao, after i do cp Makefile.config.example Makefile.config
, and then make -j9
, i met this problem: ./include/caffe/common.cuh(9): error: function "atomicAdd(double *, double)" has already been defined
. how to fix it? thanks.
I have an error when I run eval_all.m
, some codes as flows:
case 'VOC2012'
isVal = true; %evaluation on testset
step = 364; %364=1456/4
data_root = '/home/simon/DEEPLEARNING/DATASETS/VOCdevkit/VOC2012/Test';
eval_list = 'samplelist/VOC2012_test.txt';
save_root = 'mc_result/VOC2012/test/pspnet101_473/';
model_weights = 'model/pspnet101_VOC2012.caffemodel';
model_deploy = 'prototxt/pspnet101_VOC2012_473.prototxt';
fea_cha = 21;
base_size = 512;
crop_size = 473;
data_class = 'objectName21.mat';
data_colormap = 'colormapvoc.mat';
because I have some result pictures, so some codes are commented.
%parfor i = 1:gpu_num %change 'parfor' to 'for' if singe GPU testing is used
% eval_sub(data_name,data_root,eval_list,model_weights,model_deploy,fea_cha,base_size,crop_size,data_class,data_colormap, is_save_feat,save_gray_folder,save_color_folder,save_feat_folder,gpu_id_array(i),index_array(i),step,skipsize,scale_array,mean_r,mean_g,mean_b);
% end
if(isVal)
eval_acc(data_name,data_root,eval_list,save_gray_folder,data_class,fea_cha);
end
run eval_all.m
and the erros happened:
Matrix dimensions must agree.
Error in intersectionAndUnion (line 14)
imPred = imPred.*uint16(imLab>0);
Error in eval_acc (line 55)
[area_intersection(:,cnt), area_union(:,cnt)] = intersectionAndUnion(imPred, imAnno, numClass);
Error in eval_all (line 75)
eval_acc(data_name,data_root,eval_list,save_gray_folder,data_class,fea_cha);
before that, I have fixed an error in eval_acc.m
:
strPred = strsplit(str{1},'/');
strPred = strPred{end};
if(strcmp(data_name,'cityscapes'))
strPred = strrep(strPred,'gtFine_labelTrainIds','leftImg8bit');
end
filePred = fullfile(pathPred, strPred);
fileAnno = fullfile(pathAnno, str{1});
I dont know what to do, could any body help me ? Thanks!
Hi, @hszhao ,
Which version of protobuf do you use? Mine is 2.5.0 and it isn't compatible with PSPNet package.
root@milton-OptiPlex-9010:/data/code/PSPNet# make all
CXX .build_release/src/caffe/proto/caffe.pb.cc
In file included from .build_release/src/caffe/proto/caffe.pb.cc:5:0:
.build_release/src/caffe/proto/caffe.pb.h:12:2: error: #error This file was generated by a newer version of protoc which is
#error This file was generated by a newer version of protoc which is
^
.build_release/src/caffe/proto/caffe.pb.h:13:2: error: #error incompatible with your Protocol Buffer headers. Please update
#error incompatible with your Protocol Buffer headers. Please update
^
.build_release/src/caffe/proto/caffe.pb.h:14:2: error: #error your headers.
#error your headers.
^
.build_release/src/caffe/proto/caffe.pb.h:22:35: fatal error: google/protobuf/arena.h: No such file or directory
#include <google/protobuf/arena.h>
^
compilation terminated.
make: *** [.build_release/src/caffe/proto/caffe.pb.o] Error 1
root@milton-OptiPlex-9010:/data/code/PSPNet# ldconfig
root@milton-OptiPlex-9010:/data/code/PSPNet# make all
CXX .build_release/src/caffe/proto/caffe.pb.cc
In file included from .build_release/src/caffe/proto/caffe.pb.cc:5:0:
.build_release/src/caffe/proto/caffe.pb.h:12:2: error: #error This file was generated by a newer version of protoc which is
#error This file was generated by a newer version of protoc which is
^
.build_release/src/caffe/proto/caffe.pb.h:13:2: error: #error incompatible with your Protocol Buffer headers. Please update
#error incompatible with your Protocol Buffer headers. Please update
^
.build_release/src/caffe/proto/caffe.pb.h:14:2: error: #error your headers.
#error your headers.
^
.build_release/src/caffe/proto/caffe.pb.h:22:35: fatal error: google/protobuf/arena.h: No such file or directory
#include <google/protobuf/arena.h>
^
compilation terminated.
make: *** [.build_release/src/caffe/proto/caffe.pb.o] Error 1
root@milton-OptiPlex-9010:/data/code/PSPNet#
cudnn_bn_layer.cu is same to cudnn_bn_layer.cpp,please check
Hi, I tried to run 'eval_all.m' code to see the performance of pretrained model for Cityscapes dataset
However, even if I followed the instruction, the performance is very very low(See as below)
This is the result of 5 images of Cityscapes. As you can see, the performance is really bad.
I would like to ask what is the problem. Below is the code. I just changed paths for database.
I suspect label png files I used are wrong. I used png files included in Cityscapes dataset. The file format is like 'frankfurt_000000_000294_gtFine_labelIds.png' ... and so on.
Thanks!
Hi @hszhao , i hope to know the time profiling for a typical model running on CPU and GPU, could it be used in real time ?
Hello,
I followed the instructions and ran eval_all.m on the ADE20K Validation Dataset (the one from the Challenge). Unfortunately the results are very bad:
Mean IoU over 150 classes: 0.0007
Pixel-wise Accuracy: 2.15%
(see also attached file)
PSPNet_ADE20K_val_results.txt
Tested on 2 different machines. First one on CPU; second one on GPU (Titan X) with Cuda 8.0 and without cuDNN.
The segmentation images don't look that bad but are a lot worse than the ones presented in the paper...
Any hints? How can I check whether the pretrained caffemodel is loaded successfully or not?
Thanks in advance
Best regards,
Johannes
Hi! First of all thanks for the code! But I tried to make the densecrf, but got the following error. Cannot figure why?
In file included from libDenseCRF/bipartitedensecrf.cpp:27:0:
libDenseCRF/densecrf.h:66:23: warning: unused parameter ‘o’ [-Wunused-parameter]
DenseCRF(DenseCRF & o) {}
^
In file included from libDenseCRF/bipartitedensecrf.cpp:27:0:
libDenseCRF/densecrf.h:140:41: warning: unused parameter ‘o’ [-Wunused-parameter]
BipartiteDenseCRF(BipartiteDenseCRF & o){}
^
libDenseCRF/densecrf.h:185:25: warning: unused parameter ‘filter’ [-Wunused-parameter]
Filter( const Filter& filter ){}
^
In file included from libDenseCRF/util.h:31:0,
from libDenseCRF/bipartitedensecrf.cpp:28:
libDenseCRF/permutohedral.h: In member function ‘void HashTable::grow()’:
libDenseCRF/permutohedral.h:83:20: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
for( int i=0; i<old_capacity; i++ )
^
libDenseCRF/bipartitedensecrf.cpp: At global scope:
libDenseCRF/bipartitedensecrf.cpp:33:44: warning: unused parameter ‘o’ [-Wunused-parameter]
BPPottsPotential( const BPPottsPotential&o ){}
^
In file included from libDenseCRF/densecrf.cpp:33:0:
libDenseCRF/densecrf.h:66:23: warning: unused parameter ‘o’ [-Wunused-parameter]
DenseCRF(DenseCRF & o) {}
^
In file included from libDenseCRF/densecrf.cpp:33:0:
libDenseCRF/densecrf.h:140:41: warning: unused parameter ‘o’ [-Wunused-parameter]
BipartiteDenseCRF(BipartiteDenseCRF & o){}
^
libDenseCRF/densecrf.h:185:25: warning: unused parameter ‘filter’ [-Wunused-parameter]
Filter( const Filter& filter ){}
^
In file included from libDenseCRF/densecrf.cpp:35:0:
libDenseCRF/permutohedral.h: In member function ‘void HashTable::grow()’:
libDenseCRF/permutohedral.h:83:20: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
for( int i=0; i<old_capacity; i++ )
^
libDenseCRF/densecrf.cpp: At global scope:
libDenseCRF/densecrf.cpp:47:40: warning: unused parameter ‘o’ [-Wunused-parameter]
PottsPotential( const PottsPotential&o ){}
^
In file included from libDenseCRF/filter.cpp:27:0:
libDenseCRF/permutohedral.h: In member function ‘void HashTable::grow()’:
libDenseCRF/permutohedral.h:83:20: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
for( int i=0; i<old_capacity; i++ )
^
In file included from libDenseCRF/filter.cpp:28:0:
libDenseCRF/densecrf.h: At global scope:
libDenseCRF/densecrf.h:66:23: warning: unused parameter ‘o’ [-Wunused-parameter]
DenseCRF(DenseCRF & o) {}
^
In file included from libDenseCRF/filter.cpp:28:0:
libDenseCRF/densecrf.h:140:41: warning: unused parameter ‘o’ [-Wunused-parameter]
BipartiteDenseCRF(BipartiteDenseCRF & o){}
^
libDenseCRF/densecrf.h:185:25: warning: unused parameter ‘filter’ [-Wunused-parameter]
Filter( const Filter& filter ){}
^
In file included from libDenseCRF/permutohedral.cpp:28:0:
libDenseCRF/permutohedral.h: In member function ‘void HashTable::grow()’:
libDenseCRF/permutohedral.h:83:20: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
for( int i=0; i<old_capacity; i++ )
^
In file included from libDenseCRF/util.h:30:0,
from libDenseCRF/util.cpp:31:
libDenseCRF/densecrf.h:66:23: warning: unused parameter ‘o’ [-Wunused-parameter]
DenseCRF(DenseCRF & o) {}
^
In file included from libDenseCRF/util.h:30:0,
from libDenseCRF/util.cpp:31:
libDenseCRF/densecrf.h:140:41: warning: unused parameter ‘o’ [-Wunused-parameter]
BipartiteDenseCRF(BipartiteDenseCRF & o){}
^
libDenseCRF/densecrf.h:185:25: warning: unused parameter ‘filter’ [-Wunused-parameter]
Filter( const Filter& filter ){}
^
In file included from libDenseCRF/util.h:31:0,
from libDenseCRF/util.cpp:31:
libDenseCRF/permutohedral.h: In member function ‘void HashTable::grow()’:
libDenseCRF/permutohedral.h:83:20: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
for( int i=0; i<old_capacity; i++ )
^
libDenseCRF/util.cpp: In function ‘unsigned char* readPPM(const char*, int&, int&)’:
libDenseCRF/util.cpp:80:31: warning: ignoring return value of ‘char* fgets(char*, int, FILE*)’, declared with attribute warn_unused_result [-Wunused-result]
fgets ( hdr+l, 256-l, fp );
^
libDenseCRF/util.cpp:97:30: warning: ignoring return value of ‘size_t fread(void*, size_t, size_t, FILE*)’, declared with attribute warn_unused_result [-Wunused-result]
fread ( r, 1, W*H*3, fp );
^
libDenseCRF/util.cpp:101:30: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result [-Wunused-result]
fscanf ( fp, "%d", &c );
^
ar rcs libDenseCRF.a bipartitedensecrf.o densecrf.o filter.o permutohedral.o util.o
g++ test_densecrf/simple_dense_inference.cpp -o prog_test_densecrf -W -Wall -O2 -L. -lDenseCRF
In file included from test_densecrf/simple_dense_inference.cpp:31:0:
test_densecrf/../libDenseCRF/densecrf.h:66:23: warning: unused parameter ‘o’ [-Wunused-parameter]
DenseCRF(DenseCRF & o) {}
^
In file included from test_densecrf/simple_dense_inference.cpp:31:0:
test_densecrf/../libDenseCRF/densecrf.h:140:41: warning: unused parameter ‘o’ [-Wunused-parameter]
BipartiteDenseCRF(BipartiteDenseCRF & o){}
^
test_densecrf/../libDenseCRF/densecrf.h:185:25: warning: unused parameter ‘filter’ [-Wunused-parameter]
Filter( const Filter& filter ){}
^
In file included from test_densecrf/../libDenseCRF/util.h:31:0,
from test_densecrf/simple_dense_inference.cpp:32:
test_densecrf/../libDenseCRF/permutohedral.h: In member function ‘void HashTable::grow()’:
test_densecrf/../libDenseCRF/permutohedral.h:83:20: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
for( int i=0; i<old_capacity; i++ )
^
make[1]: se sale del directorio '/home/poto/Escritorio/deeplabv2/PSPNet/densecrf'
make prog_refine_pascal
make[1]: se entra en el directorio '/home/poto/Escritorio/deeplabv2/PSPNet/densecrf'
g++ refine_pascal/dense_inference.cpp -o prog_refine_pascal -W -Wall -O2 -L. -lDenseCRF -I./refine_pascal/ -I./util/
In file included from refine_pascal/dense_inference.cpp:46:0:
refine_pascal/../libDenseCRF/densecrf.h:66:23: warning: unused parameter ‘o’ [-Wunused-parameter]
DenseCRF(DenseCRF & o) {}
^
In file included from refine_pascal/dense_inference.cpp:46:0:
refine_pascal/../libDenseCRF/densecrf.h:140:41: warning: unused parameter ‘o’ [-Wunused-parameter]
BipartiteDenseCRF(BipartiteDenseCRF & o){}
^
refine_pascal/../libDenseCRF/densecrf.h:185:25: warning: unused parameter ‘filter’ [-Wunused-parameter]
Filter( const Filter& filter ){}
^
In file included from refine_pascal/../libDenseCRF/util.h:31:0,
from refine_pascal/dense_inference.cpp:47:
refine_pascal/../libDenseCRF/permutohedral.h: In member function ‘void HashTable::grow()’:
refine_pascal/../libDenseCRF/permutohedral.h:83:20: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
for( int i=0; i<old_capacity; i++ )
^
make[1]: se sale del directorio '/home/poto/Escritorio/deeplabv2/PSPNet/densecrf'
make prog_refine_pascal_v4
make[1]: se entra en el directorio '/home/poto/Escritorio/deeplabv2/PSPNet/densecrf'
g++ refine_pascal_v4/dense_inference.cpp -o prog_refine_pascal_v4 -W -Wall -O2 -L. -lDenseCRF -lmatio -I./util/
In file included from refine_pascal_v4/dense_inference.cpp:48:0:
refine_pascal_v4/../libDenseCRF/densecrf.h:66:23: warning: unused parameter ‘o’ [-Wunused-parameter]
DenseCRF(DenseCRF & o) {}
^
In file included from refine_pascal_v4/dense_inference.cpp:48:0:
refine_pascal_v4/../libDenseCRF/densecrf.h:140:41: warning: unused parameter ‘o’ [-Wunused-parameter]
BipartiteDenseCRF(BipartiteDenseCRF & o){}
^
refine_pascal_v4/../libDenseCRF/densecrf.h:185:25: warning: unused parameter ‘filter’ [-Wunused-parameter]
Filter( const Filter& filter ){}
^
In file included from refine_pascal_v4/../libDenseCRF/util.h:31:0,
from refine_pascal_v4/dense_inference.cpp:49:
refine_pascal_v4/../libDenseCRF/permutohedral.h: In member function ‘void HashTable::grow()’:
refine_pascal_v4/../libDenseCRF/permutohedral.h:83:20: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
for( int i=0; i<old_capacity; i++ )
maybe I have a dependences problem? Thanks in advance!
Hi,
I was wandering what are the hardware requirements on running this net/model (pspnet101_VOC2012.caffemodel) with or without GPU (does USE_CUDNN cahnges this)?
What are the typical runtimes in CPU/GPU modes? (for a single 473x473 image)?
Thanks
I am using the ground truth data downloaded from the Cityscapes webpage
https://www.cityscapes-dataset.com/
The filenames of the ground truth annotations from Cityscapes end in "gtFine_labelIds", while in the evaluation file for the validation set I have noticed you were looking for annotations ending in "gtFine_labelTrainIds". Where did you get those for the validation set?
I am sure you were using different indices for the classes compared to what can be downloaded from cityscapes now. For example the sky class in the cityscapes ground truths has a value of 23, while the PSP-predicted label for sky is 10. And that happens for all classes.
When running the eval_acc function the performance metrics computed are then obviously wrong.
Do you have a remapping from the current cityscapes labels to the ones you were using?
I have been trying to test pspnet101_VOC2012.caffemodel, pspnet101_cityscapes.caffemodel, and pspnet50_ADE20K.caffemodel with my set of input images of size 1280*600, Each time I run eval_all.m, matlab crashes with the error as show below
I don't understand what the actual problem is, I am running it on a GPU with configuration
have made my makefile.config with openmpi.
On trying to run it with pspnet101_VOC2012.caffemodel with a sample of images I am able to run it without crashing, but the result saved in /home/PSPNet/evaluation/mc_result/VOC2012/test/pspnet101_473/color shows images with all pixel values 11.
Please let me know, What is wrong, and guide me to successfully test PSPNet with my data set.
Hello
I have annotated a custom dataset and I want to train in using a VOC pretrained model (Fine tuning).
I downloaded and install matio by following commands:
./configure
make
sudo make install
I also changed the MATLAB_DIR := /usr/local/MATLAB/R2014b
in Makefile.config
file. Then I compile the PSPnet using
make -j8 && make matcaffe
I got the success message
MEX matlab/+caffe/private/caffe_.cpp
Building with 'g++'.
Warning: You are using gcc version '5.4.1'. The version of gcc is not supported. The version currently supported with MEX is '4.7.x'. For a list of currently supported compilers see: http://www.mathworks.com/support/compilers/current_release.
MEX completed successfully.
However, when I run the eval_all.m file, I got the error
Invalid MEX-file '/home/john/PSPNet/matlab/+caffe/private/caffe_.mexa64': libmatio.so.2: cannot open shared object file: No such file or directory
Error in caffe.reset_all (line 5)
caffe_('reset');
Error in eval_sub (line 20)
caffe.reset_all();
Error in eval_all (line 72) eval_sub(data_name,data_root,eval_list,model_weights,model_deploy,fea_cha,base_size,crop_size,data_class,data_colormap, ..
```
How can I fix it? Thank you
I am on a shared cluster and not a root user.
While doing make matcaffe
, I encountered a warning
Warning: You are using gcc version "5.4.0". The version
currently supported with MEX is "4.4.x".
For a list of currently supported compilers see:
http://www.mathworks.com/support/compilers/current_release/
Since it was only a warning, I ignored it.
I added caffe path to matlab by addpath('/path/to/caffe/matlab')
I got the following error while trying to execute the file eval_all.m
by ./run.sh
/usr/local/MATLAB/R2013a/bin/glnxa64/../../sys/os/glnxa64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by
/users/tejaswi.k/caffe_path/caffe/matlab/+caffe/private/caffe_.mexa64)
From https://github.com/BVLC/caffe/issues/827, I added this to .bashrc
: export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
And now, if I try to run ./run.sh
I get another error as below
/users/tejaswi.k/caffe_path/caffe/matlab/+caffe/private/caffe_.mexa64: undefined symbol:
_ZN2cv8imencodeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_11_InputArrayERSt6vectorIhSaIhEERKSB_IiSaIiEE
From https://github.com/BVLC/caffe/issues/3934, I understood that it is issue with the caffe being built from system's opencv and matlab having its own opencv libraries.
There they suggested to change symbolic links in /bin/glnxa64
, but since I am not a root user on the shared cluster, cannot change the symbolic links in /bin/glnxa64
.
I instead added following to .bashrc
:
export LD_PRELOAD = /usr/lib/x86_64-linux-gnu/libopencv_[core,highgui,imgproc]
Now, it also requires libtiff.so.5
location for the /usr/lib of image processing libraries, so I added that too.
Doing this throws more errors and matlab force closes.
The errors are given below:
malloc: unknown:0: assertion botched
free: called with unallocated block argument
last command: (null)
Aborting...
< M A T L A B (R) >
Copyright 1984-2013 The MathWorks, Inc.
R2013a (8.1.0.604) 64-bit (glnxa64)
February 15, 2013
[libprotobuf ERROR google/protobuf/text_format.cc:274] Error parsing text-format caffe.NetParameter: 49:12: Message type "caffe.LayerParameter" has no field named "bn_param".
WARNING: Logging before InitGoogleLogging() is written to STDERR
F0312 04:16:05.166391 26629 upgrade_proto.cpp:88] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: prototxt/pspnet101_cityscapes_713.prototxt
*** Check failure stack trace: ***
------------------------------------------------------------------------
Abort signal detected at Sun Mar 12 00:55:05 2017
------------------------------------------------------------------------
Configuration:
Crash Decoding : Disabled
Current Visual : None
Default Encoding : UTF-8
GNU C Library : 2.23 stable
MATLAB Architecture: glnxa64
MATLAB Root : /usr/local/MATLAB/R2013a
MATLAB Version : 8.1.0.604 (R2013a)
Operating System : Linux 4.4.0-59-generic #80-Ubuntu SMP Fri Jan 6 17:47:47 UTC 2017 x86_64
Processor ID : x86 Family 6 Model 42 Stepping 7, GenuineIntel
Virtual Machine : Java 1.6.0_17-b04 with Sun Microsystems Inc. Java HotSpot(TM) 64-Bit Server VM mixed mode
Window System : No active display
Fault Count: 1
Abnormal termination:
Abort signal
Register State (from fault):
RAX = 0000000000000000 RBX = 00007f960059f420
RCX = 00007f9698685428 RDX = 0000000000000006
RSP = 00007f9672711158 RBP = 00007f9672711430
RSI = 00000000000067d5 RDI = 000000000000677d
R8 = 0000000000000081 R9 = 00007f960059f440
R10 = 0000000000000008 R11 = 0000000000000206
R12 = 00007f960059f480 R13 = 00000000000000b7
R14 = 00007f960059f420 R15 = 00007f96005a6de0
RIP = 00007f9698685428 EFL = 0000000000000206
CS = 0033 FS = 0000 GS = 0000
Stack Trace (from fault):
[ 0] 0x00007f9698685428 /lib/x86_64-linux-gnu/libc.so.6+00218152 gsignal+00000056
[ 1] 0x00007f969868702a /lib/x86_64-linux-gnu/libc.so.6+00225322 abort+00000362
[ 2] 0x00007f960038ae49 /usr/lib/x86_64-linux-gnu/libglog.so.0+00040521
[ 3] 0x00007f960038c5cd /usr/lib/x86_64-linux-gnu/libglog.so.0+00046541
[ 4] 0x00007f960038e433 /usr/lib/x86_64-linux-gnu/libglog.so.0+00054323 _ZN6google10LogMessage9SendToLogEv+00000643
[ 5] 0x00007f960038c15b /usr/lib/x86_64-linux-gnu/libglog.so.0+00045403 _ZN6google10LogMessage5FlushEv+00000187
[ 6] 0x00007f960038ee1e /usr/lib/x86_64-linux-gnu/libglog.so.0+00056862 _ZN6google15LogMessageFatalD2Ev+00000014
[ 7] 0x00007f95fb71df51 /users/tejaswi.k/caffe_path/caffe/matlab/+caffe/private/caffe_.mexa64+00954193
[ 8] 0x00007f95fb6f7292 /users/tejaswi.k/caffe_path/caffe/matlab/+caffe/private/caffe_.mexa64+00795282
[ 9] 0x00007f95fb67c99a /users/tejaswi.k/caffe_path/caffe/matlab/+caffe/private/caffe_.mexa64+00293274
[ 10] 0x00007f95fb67cc16 /users/tejaswi.k/caffe_path/caffe/matlab/+caffe/private/caffe_.mexa64+00293910 mexFunction+00000169
[ 11] 0x00007f968c3d5f8a /usr/local/MATLAB/R2013a/bin/glnxa64/libmex.so+00110474 mexRunMexFile+00000090
[ 12] 0x00007f968c3d20f9 /usr/local/MATLAB/R2013a/bin/glnxa64/libmex.so+00094457
[ 13] 0x00007f968c3d2f1c /usr/local/MATLAB/R2013a/bin/glnxa64/libmex.so+00098076
[ 14] 0x00007f969a3196b2 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_dispatcher.so+00562866 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000594
[ 15] 0x00007f9699da3bf6 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04262902
[ 16] 0x00007f9699da437a /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04264826
[ 17] 0x00007f9699da4eea /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04267754
[ 18] 0x00007f9699c07bbd /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02575293
[ 19] 0x00007f9699c33412 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02753554
[ 20] 0x00007f9699c3353f /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02753855
[ 21] 0x00007f9699d50500 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+03921152
[ 22] 0x00007f9699b69868 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01927272
[ 23] 0x00007f9699bd4e50 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02367056
[ 24] 0x00007f969a3196b2 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_dispatcher.so+00562866 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000594
[ 25] 0x00007f968c9ae53a /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01672506
[ 26] 0x00007f968c94f13a /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01282362
[ 27] 0x00007f968c94f3be /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01283006
[ 28] 0x00007f968c95112c /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01290540
[ 29] 0x00007f968c9bc246 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01729094
[ 30] 0x00007f968ca3bcd8 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+02251992
[ 31] 0x00007f969a2cbaf8 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_dispatcher.so+00244472 _ZN13Mfh_MATLAB_fn11dispatch_fhEiPP11mxArray_tagiS2_+00000488
[ 32] 0x00007f9699bb7256 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02245206
[ 33] 0x00007f9699b67a86 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01919622
[ 34] 0x00007f9699b6c374 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01938292
[ 35] 0x00007f9699b68993 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01923475
[ 36] 0x00007f9699b69797 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01927063
[ 37] 0x00007f9699bd4e50 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02367056
[ 38] 0x00007f969a3196b2 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_dispatcher.so+00562866 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000594
[ 39] 0x00007f968c9b2a2f /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01690159
[ 40] 0x00007f968c94e5c4 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01279428
[ 41] 0x00007f968c94f0b9 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01282233
[ 42] 0x00007f968c94f3be /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01283006
[ 43] 0x00007f968c95068d /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01287821
[ 44] 0x00007f968c9507ad /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01288109
[ 45] 0x00007f968c950a4c /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01288780 _Z27omConstructObjectWithClientN4mcos9COSNameIDEiPPK11mxArray_tagPKNS_9COSClientE+00000476
[ 46] 0x00007f968c9bbb3d /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+01727293
[ 47] 0x00007f968ca3d673 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcos.so+02258547
[ 48] 0x00007f969a2cbaf8 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_dispatcher.so+00244472 _ZN13Mfh_MATLAB_fn11dispatch_fhEiPP11mxArray_tagiS2_+00000488
[ 49] 0x00007f9699bb7256 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02245206
[ 50] 0x00007f9699b67a86 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01919622
[ 51] 0x00007f9699b6c374 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01938292
[ 52] 0x00007f9699b68993 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01923475
[ 53] 0x00007f9699b69797 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01927063
[ 54] 0x00007f9699bd4e50 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02367056
[ 55] 0x00007f969a3196b2 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_dispatcher.so+00562866 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000594
[ 56] 0x00007f9699da3bf6 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04262902
[ 57] 0x00007f9699da437a /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04264826
[ 58] 0x00007f9699da4eea /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04267754
[ 59] 0x00007f9699c07bbd /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02575293
[ 60] 0x00007f9699c33412 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02753554
[ 61] 0x00007f9699c3353f /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02753855
[ 62] 0x00007f9699d50500 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+03921152
[ 63] 0x00007f9699b6c8ac /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01939628
[ 64] 0x00007f9699b68993 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01923475
[ 65] 0x00007f9699b69797 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01927063
[ 66] 0x00007f9699bd4e50 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02367056
[ 67] 0x00007f969a3196b2 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_dispatcher.so+00562866 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000594
[ 68] 0x00007f9699da3bf6 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04262902
[ 69] 0x00007f9699da437a /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04264826
[ 70] 0x00007f9699da4eea /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+04267754
[ 71] 0x00007f9699c07bbd /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02575293
[ 72] 0x00007f9699c33412 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02753554
[ 73] 0x00007f9699c3353f /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02753855
[ 74] 0x00007f9699d50500 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+03921152
[ 75] 0x00007f9699b69868 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01927272
[ 76] 0x00007f9699bd4e50 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02367056
[ 77] 0x00007f969a3196b2 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_dispatcher.so+00562866 _ZN8Mfh_file11dispatch_fhEiPP11mxArray_tagiS2_+00000594
[ 78] 0x00007f9699ba3dcb /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+02166219
[ 79] 0x00007f9699b617cc /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01894348
[ 80] 0x00007f9699b5de1d /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01879581
[ 81] 0x00007f9699b5e255 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01880661
[ 82] 0x00007f9699b605d0 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwm_interpreter.so+01889744
[ 83] 0x00007f968d210f13 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwiqm.so+03284755 _ZNK3iqm18InternalEvalPlugin24inEvalCmdWithLocalReturnERKSbItSt11char_traitsItESaItEEP15inWorkSpace_tag+00000147
[ 84] 0x00007f968d2118b8 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwiqm.so+03287224 _ZN3iqm18InternalEvalPlugin7executeEP15inWorkSpace_tagRN5boost10shared_ptrIN14cmddistributor17IIPCompletedEventEEE+00000120
[ 85] 0x00007f969a5a3a15 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcr.so+00391701
[ 86] 0x00007f968d1924fa /usr/local/MATLAB/R2013a/bin/glnxa64/libmwiqm.so+02766074
[ 87] 0x00007f968d17fe24 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwiqm.so+02690596
[ 88] 0x00007f968c5f93fd /usr/local/MATLAB/R2013a/bin/glnxa64/libmwbridge.so+00115709 _Z10ioReadLinebP8_IO_FILERKN5boost8optionalIKP15inWorkSpace_tagEEb+00000429
[ 89] 0x00007f968c5f9a84 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwbridge.so+00117380
[ 90] 0x00007f968c5ff49d /usr/local/MATLAB/R2013a/bin/glnxa64/libmwbridge.so+00140445
[ 91] 0x00007f968c5ff59e /usr/local/MATLAB/R2013a/bin/glnxa64/libmwbridge.so+00140702
[ 92] 0x00007f968c5ffc7f /usr/local/MATLAB/R2013a/bin/glnxa64/libmwbridge.so+00142463 _Z8mnParserv+00000623
[ 93] 0x00007f969a5b13d2 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcr.so+00447442 _ZN11mcrInstance30mnParser_on_interpreter_threadEv+00000034
[ 94] 0x00007f969a5909ac /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcr.so+00313772
[ 95] 0x00007f969a590b88 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcr.so+00314248
[ 96] 0x00007f969abd5cab /usr/local/MATLAB/R2013a/bin/glnxa64/libmwservices.so+01166507 _ZN10eventqueue18UserEventQueueImpl5flushEv+00000395
[ 97] 0x00007f9689b205fd /usr/local/MATLAB/R2013a/bin/glnxa64/libmwuix.so+00534013
[ 98] 0x00007f969ac7ba9d /usr/local/MATLAB/R2013a/bin/glnxa64/libmwservices.so+01845917
[ 99] 0x00007f969ac7c50f /usr/local/MATLAB/R2013a/bin/glnxa64/libmwservices.so+01848591 _Z25svWS_ProcessPendingEventsiib+00001615
[100] 0x00007f969a5915ef /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcr.so+00316911
[101] 0x00007f969a591f5c /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcr.so+00319324
[102] 0x00007f969a58b592 /usr/local/MATLAB/R2013a/bin/glnxa64/libmwmcr.so+00292242
[103] 0x00007f9698a206ba /lib/x86_64-linux-gnu/libpthread.so.0+00030394
[104] 0x00007f969875682d /lib/x86_64-linux-gnu/libc.so.6+01075245 clone+00000109
This error was detected while a MEX-file was running. If the MEX-file
is not an official MathWorks function, please examine its source code
for errors. Please consult the External Interfaces Guide for information
on debugging MEX-files.
If this problem is reproducible, please submit a Service Request via:
http://www.mathworks.com/support/contact_us/
A technical support engineer might contact you with further information.
Thank you for your help.
Please help in resolving the issue.
hi @hszhao , why do you set mult_lr
so large in the succeed conv layers after conv5_3/relu
layer?
e.g.
layer {
name: "conv6"
type: "Convolution"
bottom: "conv5_4"
top: "conv6"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 1
}
convolution_param {
num_output: 21
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
and what the differences between your BN layer and the one from BLVC/caffe ?
thanks.
Hi,
I don't understand about the bin size of the pyramid pooling module (11, 22, 33, 66) in the paper. Does it mean that, for instance of bin size 3*3, the width and height of each feature map after pooling are both 3? If yes, each feature map is square? Thx.
Hi,
I came across a bug "matio.h could not found" while trying to make -j8. I also check the src/caffe/util/ and there is also no matio.h both in your version of caffe and BVLC version vaffe.
The following is Error Message:
CXX src/caffe/util/matio_io.cpp
src/caffe/util/matio_io.cpp:10:19: 致命错误: matio.h:没有那个文件或目录
编译中断。
Makefile:575: recipe for target '.build_release/src/caffe/util/matio_io.o' failed
make: *** [.build_release/src/caffe/util/matio_io.o] Error 1
Best Wishes~
Error in eval_sub (line 3)
list = importdata(fullfile(data_root,eval_list));
Error in eval_all (line 72)
eval_sub(data_name,data_root,eval_list,model_weights,model_deploy,fea_cha,base_size,crop_size,data_class,data_colormap,
Hi As per step 3. evaluation,
Starting parallel pool (parpool) using the 'local' profile ...
connected to 4 workers.
Error using importdata (line 137)
Unable to open file.
Error in eval_sub (line 3)
list = importdata(fullfile(data_root,eval_list));
Error in eval_all (line 71)
parfor i = 1:gpu_num %change 'parfor' to 'for' if singe GPU testing is used
One thing I observed is whatever filenames mentioned in VOC2012_test.txt are missing inside path VOC2012/JPEGImages.
Ex:
The first 5 lines of VOC2012_test.txt file are
/JPEGImages/2008_000006.jpg
/JPEGImages/2008_000011.jpg
/JPEGImages/2008_000012.jpg
/JPEGImages/2008_000018.jpg
/JPEGImages/2008_000024.jpg
But the available files in VOC2012/JPEGImages are like
/JPEGImages/2008_000002.jpg
/JPEGImages/2008_000003.jpg
/JPEGImages/2008_000007.jpg
/JPEGImages/2008_000008.jpg
/JPEGImages/2008_000009.jpg
Why the filenames like 6, 11, 12, 18, 24, ... are missing inside VOC2012/JPEGImages is unclear.
Do I need to download VOC2012 from some other place?, please let me know.
FYI, I tried downloading ADE20K, but the folder structures are totally different & don't know how to modify ADE20K_val.txt (big manual work) & gave up.
Hi, I'm confused with parameter settings when using multi-scale input
the interpolation layer in PSP module is as follows:
layer {
name: "conv5_3_pool1_interp"
type: "Interp"
bottom: "conv5_3_pool1_conv"
top: "conv5_3_pool1_interp"
interp_param {
height: 60
width: 60
}
}
this can make scale of feature after pooling layers be the same as conv5_3 so they can be concatenated when input is 473 x 473
but how does it change when dealing with multi-scale input?
I'm wondering about that, thank you if you can solve my question.
Would you provide the train.prototxt file and solver.prototxt file? Thanks.
Getting the following error when make matcaffe
ukrdailo@rcv:~/Desktop/PSPNet/PSPNet$ make matcaffe -j8
make: /usr/local/bin/mexext: Command not found
MEX matlab/+caffe/private/caffe_.cpp
Building with 'g++'.
Warning: You are using gcc version '4.8.4'. The version of gcc is not supported. The version currently supported with MEX is '4.7.x'. For a list of currently supported compilers see: http://www.mathworks.com/support/compilers/current_release.
.build_release/lib/libcaffe.a(window_data_layer.o): In function `caffe::WindowDataLayer<float>::load_batch(caffe::Batch<float>*)':
window_data_layer.cpp:(.text._ZN5caffe15WindowDataLayerIfE10load_batchEPNS_5BatchIfEE[_ZN5caffe15WindowDataLayerIfE10load_batchEPNS_5BatchIfEE]+0xb81): undefined reference to `cv::imread(cv::String const&, int)'
.build_release/lib/libcaffe.a(window_data_layer.o): In function `caffe::WindowDataLayer<double>::load_batch(caffe::Batch<double>*)':
window_data_layer.cpp:(.text._ZN5caffe15WindowDataLayerIdE10load_batchEPNS_5BatchIdEE[_ZN5caffe15WindowDataLayerIdE10load_batchEPNS_5BatchIdEE]+0xbb1): undefined reference to `cv::imread(cv::String const&, int)'
.build_release/lib/libcaffe.a(io.o): In function `caffe::ReadImageToCVMat(std::string const&, int, int, bool, int*, int*)':
io.cpp:(.text+0x597): undefined reference to `cv::imread(cv::String const&, int)'
.build_release/lib/libcaffe.a(io.o): In function `caffe::DecodeDatumToCVMatNative(caffe::Datum const&)':
io.cpp:(.text+0x1294): undefined reference to `cv::imdecode(cv::_InputArray const&, int)'
.build_release/lib/libcaffe.a(io.o): In function `caffe::DecodeDatumToCVMat(caffe::Datum const&, bool)':
io.cpp:(.text+0x1688): undefined reference to `cv::imdecode(cv::_InputArray const&, int)'
.build_release/lib/libcaffe.a(io.o): In function `caffe::ReadImageToDatum(std::string const&, int, int, int, bool, std::string const&, caffe::Datum*)':
io.cpp:(.text+0x1e02): undefined reference to `cv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator<unsigned char> >&, std::vector<int, std::allocator<int> > const&)'
collect2: error: ld returned 1 exit status
make: *** [matlab/+caffe/private/caffe_.] Error 255
I am guessing the problem is with OpenCV version? Are you using OpenCV2 or OpenCV3?
Thanks in andvance!
I have merged interp_layer.cpp/.hpp interp,hpp/.cpp/.cu common.cuh into caffe for Faster R-CNN.
this will cause compilation errors .
error: function "atomicAdd(double *,double)" has already been defined
Hi, I found that there are some errors when compiling with CUDA 8.0.
1 error detected in the compilation of "/tmp/tmpxft_00000a2e_00000000-5_domain_transform_forward_only_layer.cpp4.ii".
make: *** [.build_release/cuda/src/caffe/layers/domain_transform_forward_only_layer.o] Error 1
make: *** Waiting for unfinished jobs....
This problem can be also seen when compiling deeplab with CUDA 8.0.
Do you have any solution for this problem? Thank you.
Hi,
I am trying to train PSPNet50.
With a Titan X, I could only train it with the batch size of 1. I cannot train it with the batch size of 2 even using 2 Titan GPUs.
Could please let me know how many gpus you used to train PSPNet and how many gpus are needed to train PSPNet successfully?
Thank you so much.
When I try to find why I cannot compile PSPNet with CUDNN v5 successfully, I find that the author is changing the README to say it is compatible with CUDNN v4...
So, can anybody give me a hint how to modify the code to make it compatible with CUDNN v5?
src/caffe/layers/domain_transform_forward_only_layer.cu(8): error: function "atomicAdd(double *, double)" has already been defined
Hi, I was trying to install caffe and matcaffe before running the code. When I specified my matlab path and ran matcaffe, I get the following error:
The error basically says that the mex file cannot be found, but it is located exactly at the place it's pointing at. May I get some suggestions about solving this? Thanks!!
Hi @hszhao, what is the layer setting before the auxiliary loss layer? A simple 3x3 conv layer or another pyramid pooling module?
Getting the following error when compiling Caffe with make
:
./include/caffe/common.cuh(9): error: function "atomicAdd(double *, double)" has already been defined
1 error detected in the compilation of "/tmp/tmpxft_00003eec_00000000-5_domain_transform_layer.cpp4.ii".
Any solutions?
Hi, you said you use resnet-101 with dilated convolution, but I can't find any pre-trained ResNet with dilated convolution on the Internet. I want to know more in details.
...
./include/caffe/test/test_gradient_check_util.hpp:175: Failure
The difference between computed_gradient and estimated_gradient is 0.033336639404296875, which exceeds threshold_ * scale, where
computed_gradient evaluates to 1.9999799728393555,
estimated_gradient evaluates to 1.9666433334350586, and
threshold_ * scale evaluates to 0.00019999798678327352.
debug: (top_id, top_data_id, blob_id, feat_id)=0,119,3,119; feat = 0.96758121252059937; objective+ = 2.1408424377441406; objective- = 2.1015095710754395
...
...
[ FAILED ] BatchNormLayerTest/2.TestGradient, where TypeParam = caffe::GPUDevice<float> (3116 ms)
[ RUN ] BatchNormLayerTest/2.TestForward
src/caffe/test/test_batch_norm_layer.cpp:75: Failure
The difference between 1 and var is 111861.75, which exceeds kErrorBound, where
1 evaluates to 1,
var evaluates to 111862.75, and
kErrorBound evaluates to 0.0010000000474974513.
src/caffe/test/test_batch_norm_layer.cpp:75: Failure
The difference between 1 and var is 118616.2578125, which exceeds kErrorBound, where
1 evaluates to 1,
var evaluates to 118617.2578125, and
kErrorBound evaluates to 0.0010000000474974513.
[ FAILED ] BatchNormLayerTest/2.TestForward, where TypeParam = caffe::GPUDevice<float> (2 ms)
[ RUN ] BatchNormLayerTest/2.TestForwardInplace
src/caffe/test/test_batch_norm_layer.cpp:119: Failure
The difference between 1 and var is 114000.234375, which exceeds kErrorBound, where
1 evaluates to 1,
var evaluates to 114001.234375, and
kErrorBound evaluates to 0.0010000000474974513.
src/caffe/test/test_batch_norm_layer.cpp:119: Failure
The difference between 1 and var is 91060.625, which exceeds kErrorBound, where
1 evaluates to 1,
var evaluates to 91061.625, and
kErrorBound evaluates to 0.0010000000474974513.
[ FAILED ] BatchNormLayerTest/2.TestForwardInplace, where TypeParam = caffe::GPUDevice<float> (1 ms)
[----------] 3 tests from BatchNormLayerTest/2 (3119 ms total)
[----------] 8 tests from SliceLayerTest/3, where TypeParam = caffe::GPUDevice<double>
[ RUN ] SliceLayerTest/3.TestSliceAcrossChannels
[ OK ] SliceLayerTest/3.TestSliceAcrossChannels (2 ms)
...
...
[ OK ] ContrastiveLossLayerTest/0.TestGradientLegacy (127 ms)
[----------] 4 tests from ContrastiveLossLayerTest/0 (266 ms total)
[----------] 1 test from LayerFactoryTest/0, where TypeParam = caffe::CPUDevice<float>
[ RUN ] LayerFactoryTest/0.TestCreateLayer
*** Aborted at 1489289221 (unix time) try "date -d @1489289221" if you are using GNU date ***
PC: @ 0x7fd3894b5962 cfree
*** SIGSEGV (@0x1f9) received by PID 11176 (TID 0x7fd393215740) from PID 505; stack trace: ***
@ 0x7fd38980c390 (unknown)
@ 0x7fd3894b5962 cfree
@ 0x7fd38a1302e1 deallocate()
@ 0x7fd38a18c0e0 caffe::DenseCRFLayer<>::DeAllocateAllData()
@ 0x7fd38a190e66 caffe::DenseCRFLayer<>::~DenseCRFLayer()
@ 0x7fd38a1914a9 caffe::DenseCRFLayer<>::~DenseCRFLayer()
@ 0x71fa18 caffe::LayerFactoryTest_TestCreateLayer_Test<>::TestBody()
@ 0x8b5e43 testing::internal::HandleExceptionsInMethodIfSupported<>()
@ 0x8af45a testing::Test::Run()
@ 0x8af5a8 testing::TestInfo::Run()
@ 0x8af685 testing::TestCase::Run()
@ 0x8b095f testing::internal::UnitTestImpl::RunAllTests()
@ 0x8b0c83 testing::UnitTest::Run()
@ 0x46645d main
@ 0x7fd389452830 __libc_start_main
@ 0x46d7e9 _start
@ 0x0 (unknown)
Makefile:526: recipe for target 'runtest' failed
make: *** [runtest] Segmentation fault
Please help!
CUDA8,cuDNN5,ubuntu16,GTX950m
Hi, @hszhao, thanks for the release of your work.
I have run the evaluation of ADE model but get 37.4/73.44(ss) and 38.57/74.19(ms) performance, which is a bit lower than what you reported. Do you have any idea about it?
Thanks for any help you may offer in advance!
Thanks to yifan254 & mjohn123, I'm finally able to run "run.sh".
However, the memory of my gpu is not big enough for the model.
Is there anyway to reduce the size of the model or size of the data used for evaluation.
Here is my configuration:
case 'VOC2012'
isVal = false; %evaluation on testset
step = 364; %=1456/4
data_root = '/home/ubuntu16/my_user/workspace/thesis/Framework/PSPNet-segmentation/data/test/VOCdevkit/VOC2012';
eval_list = 'samplelist/VOC2012_test.txt';
save_root = 'mc_result/VOC2012/test/pspnet101_473/';
model_weights = 'model/pspnet101_VOC2012.caffemodel';
model_deploy = 'prototxt/pspnet101_VOC2012_473.prototxt';
fea_cha = 21;
base_size = 512;
crop_size = 473;
data_class = 'objectName21.mat';
data_colormap = 'colormapvoc.mat';
I want to try run this model in tensorflow, and is using
caffe-tensorflow converter.
There was some minor errors which was fixed, but the main trouble is the interpolation layer.
If i am right - this is the custom layer made specifically for the semantic segmentation networks, and is absent in the master branch of caffe, so its not implemented in any converters.
Has anyone tried to convert it? And how did you face this problem?
Take a look at my repo - implementation in Keras, there are some mistakes which are leading to bad results. Help highly appreciated
Vladkryvoruchko/PSPNet-Keras-tensorflow
Now it is working Keras project with good results
eval_all.m assumes there are 4 GPUs on the user's PC. However when there are < 4 GPUs, matcaffe will crash.
At line 61:
gpu_id_array=[0:3]; %multi-GPUs for parfor testing
The array should be set according to the number of GPUs of the user's machine.
(This bug cost me almost a day to spot, because caffe's log is suppressed, and the workers run in parallel. Therefore I can only guess where it goes wrong, and insert a lot of disp to trace the crash point.)
Hi everybody,
Did anyone run the code completely?
If Yes, please tell me the what is the minimum required hardware?
How can I run the code eval_all.m on a system with a GPU with 3 GB of memory successfully?
I want to know how to train the model. Could you give some scripts on that?
mistaken post, delete/disregard this.
Why I use cpu to run the eval_all.m got correct pictures of prediction, but using gpu can not get what I want that it output whole black or whole yellow images?
Is installation of DeepLab first necessary?
I am trying to build your network with cuDNN support enabled. I'm using CUDA 8 and cudnn5.1.
I've also tried out Cuda 7.5 with cudnn3.
I keep getting the following error:
CXX src/caffe/internal_thread.cpp
CXX src/caffe/layer_factory.cpp
CXX src/caffe/solver.cpp
CXX src/caffe/data_transformer.cpp
CXX src/caffe/net.cpp
CXX src/caffe/common.cpp
CXX src/caffe/interp_layer.cpp
CXX src/caffe/syncedmem.cpp
CXX src/caffe/util/interp.cpp
CXX src/caffe/util/math_functions.cpp
CXX src/caffe/util/benchmark.cpp
CXX src/caffe/util/confusion_matrix.cpp
CXX src/caffe/util/upgrade_proto.cpp
CXX src/caffe/util/mpi_functions.cpp
CXX src/caffe/util/insert_splits.cpp
CXX src/caffe/util/channel.cpp
CXX src/caffe/util/db.cpp
CXX src/caffe/util/io.cpp
CXX src/caffe/util/db_lmdb.cpp
CXX src/caffe/util/im2col.cpp
CXX src/caffe/util/db_leveldb.cpp
CXX src/caffe/util/cudnn.cpp
CXX src/caffe/layers/cudnn_bn_layer.cpp
CXX src/caffe/layers/argmax_layer.cpp
CXX src/caffe/layers/bnll_layer.cpp
CXX src/caffe/layers/flatten_layer.cpp
CXX src/caffe/layers/euclidean_loss_layer.cpp
CXX src/caffe/layers/im2_col_layer.cpp
CXX src/caffe/layers/data_layer.cpp
CXX src/caffe/layers/image_seg_data_layer.cpp
src/caffe/layers/cudnn_bn_layer.cpp: In instantiation of ‘void caffe::CuDNNBNLayer<Dtype>::Reshape(const std::vector<caffe::Blob<Dtype>*>&, const std::vector<caffe::Blob<Dtype>*>&) [with Dtype = float]’:
src/caffe/layers/cudnn_bn_layer.cpp:79:1: required from here
src/caffe/layers/cudnn_bn_layer.cpp:39:3: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘height_’
src/caffe/layers/cudnn_bn_layer.cpp:40:3: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘width_’
src/caffe/layers/cudnn_bn_layer.cpp:45:3: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘height_’
src/caffe/layers/cudnn_bn_layer.cpp:45:3: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘width_’
src/caffe/layers/cudnn_bn_layer.cpp:47:3: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘height_’
src/caffe/layers/cudnn_bn_layer.cpp:47:3: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘width_’
src/caffe/layers/cudnn_bn_layer.cpp:53:3: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘frozen_’
src/caffe/layers/cudnn_bn_layer.cpp:54:5: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘broadcast_buffer_’
src/caffe/layers/cudnn_bn_layer.cpp:58:5: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘height_’
src/caffe/layers/cudnn_bn_layer.cpp:58:5: error: ‘class caffe::CuDNNBNLayer<float>’ has no member named ‘width_’
src/caffe/layers/cudnn_bn_layer.cpp: In instantiation of ‘void caffe::CuDNNBNLayer<Dtype>::Reshape(const std::vector<caffe::Blob<Dtype>*>&, const std::vector<caffe::Blob<Dtype>*>&) [with Dtype = double]’:
src/caffe/layers/cudnn_bn_layer.cpp:79:1: required from here
src/caffe/layers/cudnn_bn_layer.cpp:39:3: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘height_’
src/caffe/layers/cudnn_bn_layer.cpp:40:3: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘width_’
src/caffe/layers/cudnn_bn_layer.cpp:45:3: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘height_’
src/caffe/layers/cudnn_bn_layer.cpp:45:3: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘width_’
src/caffe/layers/cudnn_bn_layer.cpp:47:3: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘height_’
src/caffe/layers/cudnn_bn_layer.cpp:47:3: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘width_’
src/caffe/layers/cudnn_bn_layer.cpp:53:3: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘frozen_’
src/caffe/layers/cudnn_bn_layer.cpp:54:5: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘broadcast_buffer_’
src/caffe/layers/cudnn_bn_layer.cpp:58:5: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘height_’
src/caffe/layers/cudnn_bn_layer.cpp:58:5: error: ‘class caffe::CuDNNBNLayer<double>’ has no member named ‘width_’
make: *** [.build_release/src/caffe/layers/cudnn_bn_layer.o] Error 1
This is an issue with the caffe version you are using, but I do not know how to fix this.
Any help would be greatly appreciated. It would also be great if you could specify the required versions of CUDA and CuDNN in the Readme.
Thank you!
Hi,
I've compiled the net with Cuda 7.0 and and Cudnn 4.
This compiles, but every image that it segments is labeled "dining table", for every pixel.
Using Cudnn 5, like you recommended, it does not compile with the following error message:
make -j1
PROTOC src/caffe/proto/caffe.proto
CXX .build_release/src/caffe/proto/caffe.pb.cc
CXX src/caffe/internal_thread.cpp
In file included from ./include/caffe/util/device_alternate.hpp:40:0,
from ./include/caffe/common.hpp:19,
from ./include/caffe/internal_thread.hpp:4,
from src/caffe/internal_thread.cpp:4:
./include/caffe/util/cudnn.hpp: In function ‘void caffe::cudnn::createPoolingDesc(cudnnPoolingStruct**, caffe::PoolingParameter_PoolMethod, cudnnPoolingMode_t*, int, int, int, int, int, int)’:
./include/caffe/util/cudnn.hpp:126:3: error: too few arguments to function ‘cudnnStatus_t cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t, cudnnPoolingMode_t, cudnnNanPropagation_t, int, int, int, int, int, int)’
In file included from ./include/caffe/util/cudnn.hpp:5:0,
from ./include/caffe/util/device_alternate.hpp:40,
from ./include/caffe/common.hpp:19,
from ./include/caffe/internal_thread.hpp:4,
from src/caffe/internal_thread.cpp:4:
/usr/local/cuda/include/cudnn.h:799:27: note: declared here
make: *** [.build_release/src/caffe/internal_thread.o] Error 1
It works fine if I compile without Cudnn, then everything is segmented fine.
Any idea why this mad bug may be? I've been trying to find out for a week now, without success.
Greetings,
Veith
hello,
Would you please update the repo for Ubuntu 16.04
, Cuda 8.0
and cuDNN 5.1
or cuDNN 6
?
I realize that your model is a bit different the original residual network. Do you train the residual network by yourself?
Is it possible the initialization model?
error during run.sh:
Invalid MEX-file '/home/gopi/PSPNet/matlab/+caffe/private/caffe_.mexa64':
Missing symbol 'ZN6google8protobuf11MessageLite15ParseFromStringERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE'
required by '/home/gopi/PSPNet/matlab/+caffe/private/caffe.mexa64'
Missing symbol
'_ZN6google8protobuf14MessageFactory29InternalRegisterGeneratedFileEPKcPFvRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE'
.....
Note: The output of ldd -d /home/gopi/PSPNet/matlab/+caffe/private/caffe_.mexa64 below.
libmx.so => not found
libmex.so => not found
Anyone faced this issue?
There was a bug when I tried to make all.
The details are as follows:
LD -o .build_release/lib/libcaffe.so.1.0.0-rc3
.build_release/src/caffe/layers/densecrf_layer.o:无法识别文件: 文件被截断
collect2: 错误: ld 返回 1
Makefile:567: recipe for target '.build_release/lib/libcaffe.so.1.0.0-rc3' failed
make: *** [.build_release/lib/libcaffe.so.1.0.0-rc3] Error 1
Could you help me about this problem? Thank you!
Best wishes
I want to known how to train it?
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