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Bug 292 - Trainable segmentation plugin stops processing without error or output
Trainable segmentation plugin stops processing without error or output
Status: RESOLVED FIXED
Product: Fiji
Classification: Unclassified
Component: Plugins
unspecified
PC Windows
: P4 normal
Assigned To: ImageJ Bugs Mailing List
Depends on:
Blocks:
 
Reported: 2011-03-06 01:19 CST by Rob Baer
Modified: 2011-04-14 22:10 CDT
1 user (show)

See Also:

Description Rob Baer 2011-03-06 01:19:52 CST
[Enter details of the problem or bug and how to reproduce it here.]  I have a rather large stack .tif images that I'm using the trainable segmentation plugin on.  There are about 200 images of size 1388 x 1040.  My machine has 2 GB of memory and ImageJA is set to about 1544 memory.  After processing about 120 images the processing  "progress bar" disappears without comment, error or output.  Unfortunately, the hours of processing (5-6 min per image) are loss because there is no way to recover what has been done.  I'm assuming I'm running into a memory issue, but it is a very "ungraceful landing" and it would at least be nice to have the intermediate work saved to disk.

My next step will be to break up my stacks, but i don't know that this will address the core issues.

Information about your version of Java - this information is useful for the Fiji developers:

  os.arch => x86
  os.name => Windows 7
  os.version => 6.1
  java.version => 1.6.0_20
  java.vendor => Sun Microsystems Inc.
  java.runtime.name => Java(TM) SE Runtime Environment
  java.runtime.version => 1.6.0_20-b02
  java.vm.name => Java HotSpot(TM) Client VM
  java.vm.version => 16.3-b01
  java.vm.vendor => Sun Microsystems Inc.
  java.vm.info => mixed mode
  java.awt.graphicsenv => sun.awt.Win32GraphicsEnvironment
  java.specification.name => Java Platform API Specification
  java.specification.version => 1.6
  sun.cpu.endian => little
  sun.desktop => windows
  file.separator => \

The up-to-date check says: You wanted to be reminded later.

Information relevant to JAVA_HOME related problems:

  JAVA_HOME is set to: C:\Fiji.app/java/win32/jdk1.6.0_20/jre
  fiji.dir => C:\Fiji.app

Information about the version of each plugin:

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Comment 1 Rob Baer 2011-03-07 02:13:01 CST
Indeed making the stacks contain less images helped them complete on 2 GB machine.  I tried the smaller stacks (50 images) on a 1 GB machine and it stalled completely somewhere on image 1. The memory was set at default 760 MB or so.  If I ignored advice and upped the memory to 1460 MB on the 1 GB machine it could get as far as about 8 images before stalling in the middle of an image.  Again, no errors were reported in either case, and the machine just sort of hangs with not end in sight.

It is a shame that at least the work done can't be recovered.  I don't know if the plugin is working on "real" statcks or "hyper" stacks.  Stacks seem the only option for batch processing, but I would be just as happy if I could apply training to a folder of individual images if this were easier to implement in a memory sparing fashion.

Comment 2 Albert Cardona 2011-03-07 16:41:04 CST
Dear Rob,

(In reply to comment #1)
> Indeed making the stacks contain less images helped them complete on 2 GB
> machine.  I tried the smaller stacks (50 images) on a 1 GB machine and it
> stalled completely somewhere on image 1. The memory was set at default 760 MB
> or so.  If I ignored advice and upped the memory to 1460 MB on the 1 GB machine
> it could get as far as about 8 images before stalling in the middle of an
> image.  Again, no errors were reported in either case, and the machine just
> sort of hangs with not end in sight.


Indeed a better error reporting would be nice. At present all interested in the Trainable Segmentation plugin are working on something else, including a new and revamped Trainable Segmentation plugin with far more capabilities. Would you be willing to provide a few patches for the current version, as an interim solution to the problem you identified?

The easiest solution for you is to buy more RAM, which is really cheap nowadays. Performing high-end image processing in a small machine is not a good idea.

Albert
Comment 3 Johannes Schindelin 2011-03-23 15:48:09 CDT
So, how to proceed? Is there anybody with enough interest in the issue to code the nicer error message?
Comment 4 Rob Baer 2011-03-23 22:48:01 CDT
I would label myself "interested by incapable".  My report was not in any way intended to impune this wonderful plugin.  If as Albert implies, people are too busy working on new stuff, maybe we just make a note with the plugin description to keep stack size small relative to memory size, and further note that the possible consequence are that processing may stop functioning with no notification.  For me, a stack of 100 frames of 1388 x 1024 images processed with 2 Mb memory, but 200 frame did not.  For me, the worst part was not the limitation itself, but having to discover it only after losing several batch jobs on several computers at once because I didn't know to anticipate a problem.  I immediately guessed that it was a memory thing even without the error message, so an error message per se would not have helped a lot (except for the additional large stacks that I processed to "verify" my theory).  I only passed it back to you because I like closing circles, not because I expected you to drop everything important to do something.  Keep up the good work on new stuff.  Maybe a next version could GUESS the memory needs are and give a warning if problems are anticipated BEFORE the processing begins?  Further, my wish list would be that whatever intermediate segmentation work had been done up to the point of the crash would not just vaporize.  That said, you guys are a LOT smarter than me, and I'm going with whatever you think.
Comment 5 Johannes Schindelin 2011-03-24 00:49:12 CDT
The idea about adding a note to the documentation is compelling, since it broadens the number of people who could help the issue.