BEAT - tpereira/isv

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

Endpoint Groups 2

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: templates

Endpoint Name	Data Format	Nature
statistics	tutorial/gmm_statistics/1	Input
template_id	system/uint64/1	Input
model	tpereira/isvmachine/1	Output

Group: train

Endpoint Name	Data Format	Nature
ubm	tutorial/gmm/1	Input
isvbase	tpereira/isvbase/1	Input

Parameters 1

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name	Description	Type	Default	Range/Choices
isv-enroll-iterations		uint32	1

import bob
import numpy

def gmm_from_data(data):
    """Unmangles a bob.machine.GMMMachine from a BEAT Data object"""

dim_c, dim_d = data.means.shape
    gmm = bob.machine.GMMMachine(dim_c, dim_d)
    gmm.weights = data.weights
    gmm.means = data.means
    gmm.variances = data.variances
    gmm.variance_thresholds = data.variance_thresholds
    return gmm

def isvbase_from_data(data, ubm):
    """Unmangles a bob.machine.ISVBase from a BEAT Data object"""

dim_cd, dim_u = data.subspace_u.shape
    isvbase = bob.machine.ISVBase(ubm, dim_u)
    isvbase.u = data.subspace_u
    isvbase.d = data.subspace_d
    return isvbase

def stats_from_data(data):
    """Unmangles a bob.machine.GMMStats from a BEAT Data object"""

dim_c, dim_d = data.sum_px.shape
    stat = bob.machine.GMMStats(dim_c, dim_d)
    stat.t = long(data.t)
    stat.n = data.n
    stat.sum_px = data.sum_px
    stat.sum_pxx = data.sum_pxx
    return stat

class Algorithm:

def __init__(self):
        self.ubm      = None
        self.isvbase  = None
        self.statistics = []
        self.isv_enroll_iterations = 1

def setup(self, parameters):
        self.isv_enroll_iterations = parameters.get('isv-enroll-iterations', self.isv_enroll_iterations) 
        return True

def process(self, inputs, outputs):
        # retrieve the UBM once
        if self.ubm is None:
            inputs['ubm'].next()
            self.ubm = gmm_from_data(inputs['ubm'].data)

# retrieve the ISVBase once
        if self.isvbase is None:
            inputs['isvbase'].next()
            self.isvbase = isvbase_from_data(inputs['isvbase'].data, self.ubm)

# collect all the features for the current template
        self.statistics.append(stats_from_data(inputs["statistics"].data))

# adapts the UBM GMM for the template (when all the features have been collected)
        if inputs["template_id"].isDataUnitDone():
            model = bob.machine.ISVMachine(self.isvbase)

trainer = bob.trainer.ISVTrainer()
            trainer.enrol(model, self.statistics, int(self.isv_enroll_iterations))

# outputs data
            outputs["model"].write({
                'latent_z':             model.z,
            })

self.statistics = []

return True

The code for this algorithm in Python
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algorithms tpereira isv_enroll 1

Endpoint Groups 2

Group: templates

Group: train

Parameters 1

algorithms

tpereira

isv_enroll

1