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 3

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: probes

Endpoint Name	Data Format	Nature
probe_isv_offset	system/array_1d_floats/1	Input
probe_statistics	tutorial/gmm_statistics/1	Input
template_ids	system/array_1d_uint64/1	Input
probe_id	system/uint64/1	Input
probe_client_id	system/uint64/1	Input
scores	tutorial/probe_scores/1	Output

Group: templates

Endpoint Name	Data Format	Nature
template_client_id	system/uint64/1	Input
template_id	system/uint64/1	Input
template_model	tpereira/isvmachine/1	Input

Group: train

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

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

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

isvmachine = bob.machine.ISVMachine(isvbase)
    isvmachine.z = data.latent_z
    return isvmachine

class Algorithm:

def __init__(self):
        self.ubm       = None
        self.isvbase   = None
        self.templates = None

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)

# retrieve all the templates once
        if self.templates is None:
            self.templates = {}
            group = inputs.groupOf('template_model')

while group.hasMoreData():
                group.next()

template_id = group['template_id'].data.value

self.templates[template_id] = dict(
                    client_id = group['template_client_id'].data.value,
                    model = isvmachine_from_data(group['template_model'].data, self.isvbase),
                )

# process the probe
        template_ids = inputs['template_ids'].data.value
        statistics = stats_from_data(inputs['probe_statistics'].data)
        ux = inputs['probe_isv_offset'].data.value

scores = []
        for template_id in template_ids:
            template_client_identity = self.templates[template_id]['client_id']
            model = self.templates[template_id]['model']
            
            
            score = model.forward_ux(statistics, ux)

scores.append({
                'template_identity': template_client_identity,
                'score': score,
            })

outputs['scores'].write({
                'client_identity': inputs['probe_client_id'].data.value,
                'scores': scores
            },
            end_data_index=inputs['probe_id'].data_index_end
        )

return True

The code for this algorithm in Python
The ruler at 80 columns indicate suggested POSIX line breaks (for readability).
The editor will automatically enlarge to accomodate the entirety of your input
Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

Could not find any documentation for this object.

No experiments are using this algorithm.

This algorithm was never executed.

algorithms tpereira isv_scoring 1

Endpoint Groups 3

Group: probes

Group: templates

Group: train

algorithms

tpereira

isv_scoring

1