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Nifti1Image design prevents efficient caching #1362

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arthurmensch opened this issue Jan 23, 2017 · 3 comments
Open

Nifti1Image design prevents efficient caching #1362

arthurmensch opened this issue Jan 23, 2017 · 3 comments
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Performance issues related to computation time or memory usage

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@arthurmensch
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arthurmensch commented Jan 23, 2017 •
edited

This is a long-standing issue, that we have already tried to adress without success.

from nilearn.datasets import fetch_adhd
from sklearn.externals.joblib import Memory
from nilearn.input_data import MultiNiftiMasker

mem = Memory(cachedir='joblib')
mem.clear()

dataset = fetch_adhd().func[:2]

masker = MultiNiftiMasker(n_jobs=2, memory=mem).fit(dataset)
masker.transform(dataset)
masker = MultiNiftiMasker(n_jobs=1, memory=mem).fit(dataset)
masker.transform(dataset)

yields

/opt/miniconda3/bin/python /home/arthur/work/repos/nilearn/examples/test.py
/opt/miniconda3/lib/python3.5/site-packages/sklearn/externals/joblib/logger.py:77: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead
  logging.warn("[%s]: %s" % (self, msg))
WARNING:root:[Memory(cachedir='joblib/joblib')]: Flushing completely the cache
/home/arthur/work/repos/nilearn/nilearn/_utils/cache_mixin.py:272: UserWarning: memory_level is currently set to 0 but a Memory object has been provided. Setting memory_level to 1.
  warnings.warn("memory_level is currently set to 0 but "
________________________________________________________________________________
[Memory] Calling nilearn.masking.compute_multi_background_mask...
compute_multi_background_mask([ '/home/arthur/data/nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni.nii.gz',
  '/home/arthur/data/nilearn_data/adhd/data/0010064/0010064_rest_tshift_RPI_voreg_mni.nii.gz'], n_jobs=2, memory=Memory(cachedir='joblib/joblib'), target_shape=None, verbose=0, target_affine=None)
________________________________________________________________________________
[Memory] Calling nilearn.image.image._compute_mean...
_compute_mean(<nibabel.nifti1.Nifti1Image object at 0x7f22ac2f9f60>, target_shape=None, smooth=False, target_affine=None)
________________________________________________________________________________
[Memory] Calling nilearn.image.image._compute_mean...
_compute_mean(<nibabel.nifti1.Nifti1Image object at 0x7f22ac2f9d30>, target_shape=None, smooth=False, target_affine=None)
_____________________________________________________compute_mean - 0.4s, 0.0min
_____________________________________________________compute_mean - 0.4s, 0.0min
____________________________________compute_multi_background_mask - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.image.resampling.resample_img...
resample_img(<nibabel.nifti1.Nifti1Image object at 0x7f22ad366908>, target_shape=None, interpolation='nearest', copy=False, target_affine=None)
_____________________________________________________resample_img - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f22ac307f98>, <nibabel.nifti1.Nifti1Image object at 0x7f22ac307160>, { 'detrend': False,
  'high_pass': None,
  'low_pass': None,
  'smoothing_fwhm': None,
  'standardize': False,
  't_r': None,
  'target_affine': None,
  'target_shape': None}, confounds=None, copy=True, memory=Memory(cachedir='joblib/joblib'), verbose=0, memory_level=1)
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f22ac307eb8>, <nibabel.nifti1.Nifti1Image object at 0x7f22ac307780>, { 'detrend': False,
  'high_pass': None,
  'low_pass': None,
  'smoothing_fwhm': None,
  'standardize': False,
  't_r': None,
  'target_affine': None,
  'target_shape': None}, confounds=None, copy=True, memory=Memory(cachedir='joblib/joblib'), verbose=0, memory_level=1)
__________________________________________________filter_and_mask - 1.6s, 0.0min
__________________________________________________filter_and_mask - 1.6s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f22ac3072b0>, <nibabel.nifti1.Nifti1Image object at 0x7f22ac2f9a90>, { 'detrend': False,
  'high_pass': None,
  'low_pass': None,
  'smoothing_fwhm': None,
  'standardize': False,
  't_r': None,
  'target_affine': None,
  'target_shape': None}, confounds=None, memory=Memory(cachedir='joblib/joblib'), memory_level=1, verbose=0, copy=True)
__________________________________________________filter_and_mask - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f22ac344c18>, <nibabel.nifti1.Nifti1Image object at 0x7f22ac2f9a90>, { 'detrend': False,
  'high_pass': None,
  'low_pass': None,
  'smoothing_fwhm': None,
  'standardize': False,
  't_r': None,
  'target_affine': None,
  'target_shape': None}, confounds=None, memory=Memory(cachedir='joblib/joblib'), memory_level=1, verbose=0, copy=True)
__________________________________________________filter_and_mask - 1.0s, 0.0min

Process finished with exit code 0

This indicates that the cached mask function is not properly recalled the second time.

With the following monkey-patch, it works properly:

import nibabel
from nibabel import Nifti1Image as NibabelNifti1Image
from nibabel import load as nibabel_load
import numpy as np


def load(filename, **kwargs):
    img = nibabel_load(filename, **kwargs)
    img.__class__ = Nifti1Image
    return img


class Nifti1Image(NibabelNifti1Image):
    def __getstate__(self):
        state = {'dataobj': np.asarray(self._dataobj),
                 'header': self.header,
                 'affine': self.affine,
                 'extra': self.extra}
        return state

    def __setstate__(self, state):
        new_self = Nifti1Image(dataobj=state['dataobj'],
                               affine=state['affine'],
                               header=state['header'],
                               extra=state['extra'])
        self.__dict__ = new_self.__dict__


def monkey_patch_nifti_image():
    nibabel.load = load

from nilearn.datasets import fetch_adhd
from sklearn.externals.joblib import Memory
from nilearn.input_data import MultiNiftiMasker

monkey_patch_nifti_image()

mem = Memory(cachedir='joblib')
mem.clear()

dataset = fetch_adhd().func[:2]

masker = MultiNiftiMasker(n_jobs=2, memory=mem).fit(dataset)
masker.transform(dataset)
masker = MultiNiftiMasker(n_jobs=1, memory=mem).fit(dataset)
masker.transform(dataset)
/opt/miniconda3/bin/python /home/arthur/work/repos/nilearn/examples/test.py
/opt/miniconda3/lib/python3.5/site-packages/sklearn/externals/joblib/logger.py:77: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead
  logging.warn("[%s]: %s" % (self, msg))
WARNING:root:[Memory(cachedir='joblib/joblib')]: Flushing completely the cache
/home/arthur/work/repos/nilearn/nilearn/_utils/cache_mixin.py:272: UserWarning: memory_level is currently set to 0 but a Memory object has been provided. Setting memory_level to 1.
  warnings.warn("memory_level is currently set to 0 but "
________________________________________________________________________________
[Memory] Calling nilearn.masking.compute_multi_background_mask...
compute_multi_background_mask([ '/home/arthur/data/nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni.nii.gz',
  '/home/arthur/data/nilearn_data/adhd/data/0010064/0010064_rest_tshift_RPI_voreg_mni.nii.gz'], n_jobs=2, verbose=0, target_shape=None, target_affine=None, memory=Memory(cachedir='joblib/joblib'))
________________________________________________________________________________
[Memory] Calling nilearn.image.image._compute_mean...
_compute_mean(<__main__.Nifti1Image object at 0x7f3ea9140cf8>, target_affine=None, target_shape=None, smooth=False)
________________________________________________________________________________
[Memory] Calling nilearn.image.image._compute_mean...
_compute_mean(<__main__.Nifti1Image object at 0x7f3ea9140f98>, target_affine=None, target_shape=None, smooth=False)
_____________________________________________________compute_mean - 1.2s, 0.0min
_____________________________________________________compute_mean - 1.2s, 0.0min
____________________________________compute_multi_background_mask - 3.3s, 0.1min
________________________________________________________________________________
[Memory] Calling nilearn.image.resampling.resample_img...
resample_img(<__main__.Nifti1Image object at 0x7f3eaa1aa8d0>, copy=False, target_affine=None, target_shape=None, interpolation='nearest')
_____________________________________________________resample_img - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<__main__.Nifti1Image object at 0x7f3ea9140fd0>, <__main__.Nifti1Image object at 0x7f3ea914b7f0>, { 'detrend': False,
  'high_pass': None,
  'low_pass': None,
  'smoothing_fwhm': None,
  'standardize': False,
  't_r': None,
  'target_affine': None,
  'target_shape': None}, copy=True, verbose=0, memory=Memory(cachedir='joblib/joblib'), confounds=None, memory_level=1)
________________________________________________________________________________
[Memory] Calling nilearn.input_data.nifti_masker.filter_and_mask...
filter_and_mask(<__main__.Nifti1Image object at 0x7f3ea914b828>, <__main__.Nifti1Image object at 0x7f3ea914b048>, { 'detrend': False,
  'high_pass': None,
  'low_pass': None,
  'smoothing_fwhm': None,
  'standardize': False,
  't_r': None,
  'target_affine': None,
  'target_shape': None}, copy=True, verbose=0, memory=Memory(cachedir='joblib/joblib'), confounds=None, memory_level=1)
__________________________________________________filter_and_mask - 1.8s, 0.0min
__________________________________________________filter_and_mask - 1.6s, 0.0min

Process finished with exit code 0

Integrating the patch in nilearn does not break any test. Should I try to merge this ? I guess we should do a non regression test based on the code snippet above.

The issue is pretty preocuppying as all users are likely to have double-size masking cache on their disk
@arthurmensch arthurmensch changed the title Nifti1Image have states, which prevents efficient caching Nifti1Image design prevents efficient caching Jan 23, 2017
@arthurmensch
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arthurmensch commented Jan 23, 2017 •
edited

Otherwise we could use (ping@ogrisel @GaelVaroquaux). It would be safe and efficient.

class UnsafeNifti1Image(Nifti1Image):
    def __getstate__(self):
        if self.get_filename() is not None:
            filename = self.get_filename()
            stat = os.stat(filename)
            last_modified = stat.st_mtime
            state = {'filename': filename,
                     'last_modified': last_modified}
        else:
            state = {'dataobj': np.asarray(self._dataobj),
                     'header': self.header,
                     'affine': self.affine,
                     'extra': self.extra}
        return state

    def __setstate__(self, state):
        if 'filename' in state:
            print('unpickling')
            new_self = Nifti1Image.from_filename(state['filename'])
        else:
            new_self = Nifti1Image(dataobj=state['dataobj'],
                                   affine=state['affine'],
                                   header=state['header'],
                                   extra=state['extra'])
        self.__dict__ = new_self.__dict__

@arthurmensch
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arthurmensch commented Jan 24, 2017 •
edited

Here is the full gist that:

  • Pickles and unpickles Nifti1Image using only their relevant state
  • Hash them using either these states or, if they exist, the filenames and the modification time of the associated .nii(.gz) files.

https://gist.github.com/arthurmensch/3331024db4ce08510b3bd51a0469c322

The real proper way to integrate it would be to

  • be able to provide a custom hash function for each of the arguments of a cache function in joblib (a feature that has already been requested according to @ogrisel)
  • Propose the modification to the nibabel team - they should take it as it is the proper way of pickling Nifti1Image...
  • Use monkey-patches in nilearn for previous versions of nibabel and joblib

ping @GaelVaroquaux what's your opinion on this ?

@arthurmensch arthurmensch mentioned this issue Mar 3, 2017
Closed
@NicolasGensollen NicolasGensollen self-assigned this Jan 27, 2021
@Remi-Gau Remi-Gau added the Performance issues related to computation time or memory usage label Dec 23, 2023
@Remi-Gau
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Maybe worth rechecking if this issue is still valid

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