def customRandConvFactory(
poolings=[(3, 3, Const.POOLING_AGGREG_AVG)],
nbSubwindows=10,
subwindowMinSizeRatio=0.5, subwindowMaxSizeRatio=1.,
subwindowTargetWidth=16, subwindowTargetHeight=16,
subwindowInterpolation=SubWindowExtractor.INTERPOLATION_BILINEAR,
includeOriginalImage=False,
nbJobs=-1, verbosity=10, tempFolder=None,
random=True):
"""
Factory method to create :class:`RandConvCoordinator` tuned for RGB images
using predefined well-known filters
Parameters
----------
poolings : iterable of triple (height, width, policy) (default :
[(3, 3, Const.POOLING_AGGREG_AVG)])
A list of parameters to instanciate the according :class:`Pooler`
height : int > 0
the height of the neighborhood window
width : int > 0
the width of the neighborhood window
policy : int in {Const.POOLING_NONE, Const.POOLING_AGGREG_MIN,
Const.POOLING_AGGREG_AVG, Const.POOLING_AGGREG_MAX,
Const.POOLING_CONV_MIN, Const.POOLING_CONV_AVG, Const.POOLING_CONV_MAX}
nbSubwindows : int >= 0 (default : 10)
The number of subwindow to extract
subwindowMinSizeRatio : float > 0 (default : 0.5)
The minimum size of a subwindow expressed as the ratio of the size
of the original image
subwindowMaxSizeRatio : float : subwindowMinSizeRatio
<= subwindowMaxSizeRatio <= 1 (default : 1.)
The maximim size of a subwindow expressed as the ratio of the size
of the original image
subwindowTargetWidth : int > 0 (default : 16)
The width of the subwindows after reinterpolation
subwindowTargetHeight : int > 0 (default : 16)
The height of the subwindows after reinterpolation
subwindowInterpolation : int (default :
SubWindowExtractor.INTERPOLATION_BILINEAR)
The subwindow reinterpolation algorithm. For more information, see
:class:`SubWindowExtractor`
includeOriginalImage : boolean (default : False)
Whether or not to include the original image in the subwindow
extraction process
nbJobs : int >0 or -1 (default : -1)
The number of process to spawn for parallelizing the computation.
If -1, the maximum number is selected. See also :mod:`Joblib`.
verbosity : int >= 0 (default : 10)
The verbosity level
tempFolder : string (directory path) (default : None)
The temporary folder used for memmap. If none, some default folder
will be use (see the :class:`ParallelCoordinator`)
random : bool (default : True)
Whether to use randomness or use a predefined seed
Return
------
coordinator : :class:`Coordinator`
The RandConvCoordinator corresponding to the
set of parameters
Notes
-----
- Convolver
Base instance of :class:`RGBConvolver`
- Subwindow random generator
The subwindow random generator is a :class:`NumberGenerator` base
instance (generate real nubers uniformly).
- Feature extractor
Base instance of :class:`ImageLinearizationExtractor`
"""
#RANDOMNESS
swngSeed = 0
if random is None:
swngSeed = None
#CONVOLUTIONAL EXTRACTOR
filterGenerator = customFinite3sameFilter()
#Convolver
convolver = RGBConvolver()
#Aggregator
poolers = []
for height, width, policy in poolings:
if policy == Const.POOLING_NONE:
poolers.append(IdentityPooler())
elif policy == Const.POOLING_AGGREG_AVG:
poolers.append(AverageAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_AGGREG_MAX:
poolers.append(MaximumAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_AGGREG_MIN:
poolers.append(MinimumAggregator(width, height,
subwindowTargetWidth,
subwindowTargetHeight))
elif policy == Const.POOLING_CONV_MIN:
poolers.append(ConvMinPooler(height, width))
elif policy == Const.POOLING_CONV_AVG:
poolers.append(ConvAvgPooler(height, width))
elif policy == Const.POOLING_CONV_MAX:
poolers.append(ConvMaxPooler(height, width))
multiPooler = getMultiPoolers(subwindowTargetHeight, subwindowTargetWidth)
#SubWindowExtractor
swNumGenerator = NumberGenerator(seed=swngSeed)
swExtractor = SubWindowExtractor(subwindowMinSizeRatio,
subwindowMaxSizeRatio,
subwindowTargetWidth,
subwindowTargetHeight,
subwindowInterpolation, swNumGenerator)
multiSWExtractor = MultiSWExtractor(swExtractor, nbSubwindows, False)
#ConvolutionalExtractor
convolutionalExtractor = ConvolutionalExtractor(filterGenerator,
convolver,
multiSWExtractor,
multiPooler,
includeOriginalImage)
#FEATURE EXTRACTOR
featureExtractor = ImageLinearizationExtractor()
#LOGGER
autoFlush = verbosity >= 45
logger = ProgressLogger(StandardLogger(autoFlush=autoFlush,
verbosity=verbosity))
#COORDINATOR
coordinator = RandConvCoordinator(convolutionalExtractor, featureExtractor,
logger, verbosity)
if nbJobs != 1:
coordinator.parallelize(nbJobs, tempFolder)
return coordinator
def getFilterGenerator(policy, parameters, nbFilters, random=False):
if policy == Const.FGEN_ORDERED:
#Parameters is a list of tuples (policy, parameters)
ls = []
subNbFilters = int(math.ceil(nbFilters/len(parameters)))
for subPolicy, subParameters in parameters:
ls.append(getFilterGenerator(subPolicy, subParameters,
subNbFilters, random))
return OrderedMFF(ls, nbFilters)
if policy is Const.FGEN_CUSTOM:
print "Custom filters"
return customFinite3sameFilter()
#Parameters is a dictionary
valSeed = None
sizeSeed = None
shufflingSeed = None
perturbationSeed = None
cellSeed = None
sparseSeed = 5
if random:
valSeed = 1
sizeSeed = 2
shufflingSeed = 3
perturbationSeed = 4
cellSeed = 5
sparseSeed = 6
minSize = parameters["minSize"]
maxSize = parameters["maxSize"]
sizeGenerator = OddUniformGenerator(minSize, maxSize, seed=sizeSeed)
minVal = parameters["minVal"]
maxVal = parameters["maxVal"]
valGen = parameters["valGen"]
valGenerator = getNumberGenerator(valGen, minVal, maxVal,
valSeed, **parameters)
normalization = None
if "normalization" in parameters:
normalization = parameters["normalization"]
if policy is Const.FGEN_ZEROPERT:
print "Zero perturbation filters"
baseFilterGenerator = FilterGenerator(valGenerator, sizeGenerator,
normalisation=normalization)
elif policy is Const.FGEN_IDPERT:
print "Id perturbation filters"
baseFilterGenerator = IdPerturbatedFG(valGenerator, sizeGenerator,
normalisation=normalization)
elif policy is Const.FGEN_IDDIST:
print "Id distance filters"
maxDist = parameters["maxDist"]
baseFilterGenerator = IdMaxL1DistPerturbFG(valGenerator, sizeGenerator,
maxDist,
normalisation=normalization,
shufflingSeed=shufflingSeed)
elif policy is Const.FGEN_STRAT:
print "Stratified filters"
nbCells = parameters["strat_nbCells"]
minPerturbation = 0
if "minPerturbation" in parameters:
minPerturbation = parameters["minPerturbation"]
maxPerturbation = 1
if "maxPerturbation" in parameters:
maxPerturbation = parameters["maxPerturbation"]
perturbationGenerator = getNumberGenerator(valGen,
minPerturbation,
maxPerturbation,
perturbationSeed)
baseFilterGenerator = StratifiedFG(minVal, maxVal, nbCells,
perturbationGenerator,
sizeGenerator,
normalisation=normalization,
cellSeed=cellSeed)
if "sparseProb" in parameters:
print "Adding sparcity"
sparseProb = parameters["sparseProb"]
baseFilterGenerator = SparsityDecoratorFG(baseFilterGenerator,
sparseProb,
sparseSeed)
print "Returning filters"
return Finite3SameFilter(baseFilterGenerator, nbFilters)
