def coordinatorRandConvFactory(
nbFilters=5,
filterPolicy=(Const.FGEN_ZEROPERT,
{"minSize": 2, "maxSize": 32, "minVal": -1, "maxVal": 1,
"valGen": Const.RND_RU,
"normalization": FilterGenerator.NORMALISATION_MEANVAR}),
poolings=[(3, 3, Const.POOLING_AGGREG_AVG)],
extractor=(Const.FEATEXT_ALL, {}),
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
Parameters
----------
nbFilters : int >= 0 (default : 5)
The number of filter
filterPolicy : pair (policyType, parameters)
policyType : one of Const.FGEN_*
The type of filter generation policy to use
parameters : dict
The parameter dictionnary to forward to :func:`getFilterGenerator`
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
-----
- Filter generator
Base instance of :class:`Finite3SameFilter` with a base instance of
:class:`NumberGenerator` for the values and
:class:`OddUniformGenerator` for the sizes
- Filter size
The filter are square (same width as height)
- Convolver
Base instance of :class:`RGBConvolver`
- Subwindow random generator
The subwindow random generator is a :class:`NumberGenerator` base
instance (generate real nubers uniformely).
- Feature extractor
Base instance of :class:`ImageLinearizationExtractor`
"""
#RANDOMNESS
swngSeed = None
if random is False:
swngSeed = 0
#CONVOLUTIONAL EXTRACTOR
#Filter generator
#Type/policy parameters, #filters, random
filterPolicyType, filterPolicyParam = filterPolicy
filterGenerator = getFilterGenerator(filterPolicyType, filterPolicyParam,
nbFilters, random)
#Convolver
convolver = RGBConvolver()
#Aggregator
multiPooler = getMultiPoolers(poolings, 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 = getFeatureExtractor(extractor[0], extractor[1])
#LOGGER
autoFlush = verbosity >= 40
logger = ProgressLogger(StandardLogger(autoFlush=autoFlush,
verbosity=verbosity))
#COORDINATOR
coordinator = RandConvCoordinator(convolutionalExtractor, featureExtractor,
logger, verbosity)
if nbJobs != 1:
coordinator.parallelize(nbJobs, tempFolder)
return coordinator
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 coordinatorRandConvFactory(
nbFilters=5,
filterGenConfiguration=(Const.GEN_REAL, (-1, 1)),
filterMinSize=1, filterMaxSize=17,
filterNormalisation=FilterGenerator.NORMALISATION_MEANVAR,
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
Parameters
----------
nbFilters : int >= 0 (default : 5)
The number of filter
filterGenConfiguration : pair (policy, parameters)
The value filter generation parameters
policy : int in {Const.GEN_REAL, Const.GEN_SET}
The filter value generation policy.
- Const.GEN_REAL (default):
generate real number between two bounds. The parameters must
specify theses bounds :
parameters : pairs (filterMinVal, filterMaxVal)
filterMinVal : float (default : -1)
The minimum value of a filter component
filterMaxVal : float : filterMinVal <= filterMaxVal
(default : 1)
The maximum value of a filter component
- Const.GEN_SET :
generate number from a predefine set with a given probability
parameters : iterable of pairs (number, probability)
number : number
an element of the set from which to draw
probability : float
the probability of the element being chosen at each
draw
seed : int or None (default : None)
if seed is int : initiate the random generator with this seed
filterMinSize : int >= 0 : odd number (default : 1)
The minimum size of a filter
filterMaxSize : int >= 0 : odd number s.t. filterMinSize <= filterMaxSize
(default : 1)
The maximum size of a filter
filterNormalisation : int (default : FilterGenerator.NORMALISATION_MEANVAR)
The filter normalisation policy. See also :class:`FilterGenerator`
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
-----
- Filter generator
Base instance of :class:`Finite3SameFilter` with a base instance of
:class:`NumberGenerator` for the values and
:class:`OddUniformGenerator` for the sizes
- Filter size
The filter are square (same width as height)
- Convolver
Base instance of :class:`RGBConvolver`
- Subwindow random generator
The subwindow random generator is a :class:`NumberGenerator` base
instance (generate real nubers uniformely).
- Feature extractor
Base instance of :class:`ImageLinearizationExtractor`
"""
#RANDOMNESS
swngSeed = 0
filtValGenSeed = 1
filtSizeGenSeed = 2
if random is None:
swngSeed = None
filtValGenSeed = None
filtSizeGenSeed = None
#CONVOLUTIONAL EXTRACTOR
#Filter generator
#--Value
filterGenPolicy, filterGenParam = filterGenConfiguration
if filterGenPolicy == Const.GEN_REAL:
filterMinVal, filterMaxVal = filterGenParam
filterValGenerator = NumberGenerator(filterMinVal, filterMaxVal,
seed=filtValGenSeed)
if filterGenPolicy == Const.GEN_SET:
filterValGenerator = CustomDiscreteNumberGenerator(filterGenParam,
seed=filtValGenSeed)
#--Size
filterSizeGenerator = OddUniformGenerator(filterMinSize, filterMaxSize,
seed=filtSizeGenSeed)
baseFilterGenerator = FilterGenerator(filterValGenerator,
filterSizeGenerator,
normalisation=filterNormalisation)
filterGenerator = Finite3SameFilter(baseFilterGenerator, nbFilters)
#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 = MultiPooler(poolers)
#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