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Commit 9e3a1f03 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
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Created CSV Score writter

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2 merge requests!185Wrappers and aggregators,!180[dask] Preparing bob.bio.base for dask pipelines
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bob/bio/base/pipelines/vanilla_biometrics/abstract_classes.py
+ 35
− 39
View file @ 9e3a1f03
......@@ -13,15 +13,9 @@ class BioAlgorithm(metaclass=ABCMeta):
biometric model enrollement, via ``enroll()`` and scoring, with
``score()``.
Parameters
----------
allow_score_multiple_references: bool
If true, your scoring function can be executed by :any:`BioAlgorithm.score_multiple_biometric_references`
"""
def __init__(self, allow_score_multiple_references=False, **kwargs):
self.allow_score_multiple_references = allow_score_multiple_references
def __init__(self, **kwargs):
self.stacked_biometric_references = None
def enroll_samples(self, biometric_references):
......@@ -66,7 +60,12 @@ class BioAlgorithm(metaclass=ABCMeta):
"""
pass
def score_samples(self, probe_features, biometric_references, allow_scoring_with_all_biometric_references=False):
def score_samples(
self,
probe_features,
biometric_references,
allow_scoring_with_all_biometric_references=False,
):
"""Scores a new sample against multiple (potential) references
Parameters
......@@ -83,7 +82,7 @@ class BioAlgorithm(metaclass=ABCMeta):
allow_scoring_with_all_biometric_references: bool
If true will call `self.score_multiple_biometric_references`, at scoring time, to compute scores in one shot with multiple probes.
This optiization is useful when all probes needs to be compared with all biometric references AND
This optimization is useful when all probes needs to be compared with all biometric references AND
your scoring function allows this broadcast computation.
......@@ -92,18 +91,29 @@ class BioAlgorithm(metaclass=ABCMeta):
scores : list
For each sample in a probe, returns as many scores as there are
samples in the probe, together with the probe's and the
samples in the probe, together with the probes and the
relevant reference's subject identifiers.
"""
retval = []
for p in probe_features:
retval.append(self._score_sample_set(p, biometric_references, allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references))
retval.append(
self._score_sample_set(
p,
biometric_references,
allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references,
)
)
return retval
def _score_sample_set(self, sampleset, biometric_references, allow_scoring_with_all_biometric_references):
"""Given a sampleset for probing, compute the scores and retures a sample set with the scores
def _score_sample_set(
self,
sampleset,
biometric_references,
allow_scoring_with_all_biometric_references,
):
"""Given a sampleset for probing, compute the scores and returns a sample set with the scores
"""
# Stacking the samples from a sampleset
......@@ -111,11 +121,10 @@ class BioAlgorithm(metaclass=ABCMeta):
# Compute scores for each sample inside of the sample set
# TODO: In some cases we want to compute 1 score per sampleset (IJB-C)
# We should add an agregator function here so we can properlly agregate samples from
# We should add an aggregator function here so we can properly aggregator samples from
# a sampleset either after or before scoring.
# To be honest, this should be the default behaviour
# To be honest, this should be the default behavior
retval = []
for subprobe_id, (s, parent) in enumerate(zip(data, sampleset.samples)):
# Creating one sample per comparison
subprobe_scores = []
......@@ -129,9 +138,9 @@ class BioAlgorithm(metaclass=ABCMeta):
scores = self.score_multiple_biometric_references(
self.stacked_biometric_references, s
)
# Wrapping the scores in samples
for ref, score in zip(biometric_references, scores):
# Wrapping the scores in samples
for ref, score in zip(biometric_references, scores):
subprobe_scores.append(Sample(score, parent=ref))
else:
......@@ -141,9 +150,13 @@ class BioAlgorithm(metaclass=ABCMeta):
score = self.score(ref.data, s)
subprobe_scores.append(Sample(score, parent=ref))
# Creating one sampleset per probe
subprobe = SampleSet(subprobe_scores, parent=parent)
subprobe.subject = sampleset.subject
# Fetching metadata from the probe
kwargs = dict(
(metadata, sampleset.__dict__[metadata])
for metadata in sampleset.__dict__.keys()
if metadata not in ["samples", "key", "data", "load", "_data"]
)
subprobe = SampleSet(subprobe_scores, parent=parent, **kwargs)
retval.append(subprobe)
return retval
......@@ -259,7 +272,6 @@ class ScoreWriter(metaclass=ABCMeta):
def write(self, sampleset, path):
pass
@abstractmethod
def read(self, path):
pass
......@@ -267,19 +279,3 @@ class ScoreWriter(metaclass=ABCMeta):
@abstractmethod
def concatenate_write_scores(self, sampleset, path):
pass
def create_score_delayed_sample(path, probe):
"""
Write scores in the four columns format
"""
with open(path, "w") as f:
for score_line in probe.samples:
f.write(score_line.data)
def load():
with open(path) as f:
return f.read()
return DelayedSample(load, parent=probe)
bob/bio/base/pipelines/vanilla_biometrics/score_writers.py
+ 100
− 1
View file @ 9e3a1f03
......@@ -6,6 +6,8 @@ import os
from bob.pipelines import SampleSet, DelayedSample
from .abstract_classes import ScoreWriter
import functools
import csv
class FourColumnsScoreWriter(ScoreWriter):
"""
......@@ -61,4 +63,101 @@ class FourColumnsScoreWriter(ScoreWriter):
for samplesets in samplesets_list:
for sset in samplesets:
for s in sset:
f.writelines(s.data)
\ No newline at end of file
f.writelines(s.data)
class CSVScoreWriter(ScoreWriter):
"""
Read and write scores in CSV format, shipping all metadata with the scores
"""
def write(self, probe_sampleset, path):
"""
Write scores and returns a :any:`bob.pipelines.DelayedSample` containing
the instruction to open the score file
"""
exclude_list = ["samples", "key", "data", "load", "_data", "references"]
def create_csv_header(probe_sampleset):
first_biometric_reference = probe_sampleset[0]
probe_dict = dict(
(k, f"probe_{k}")
for k in probe_sampleset.__dict__.keys()
if k not in exclude_list
)
bioref_dict = dict(
(k, f"bio_ref_{k}")
for k in first_biometric_reference.__dict__.keys()
if k not in exclude_list
)
header = (
["probe_key"]
+ [probe_dict[k] for k in probe_dict]
+ [bioref_dict[k] for k in bioref_dict]
+ ["score"]
)
return header, probe_dict, bioref_dict
os.makedirs(path, exist_ok=True)
checkpointed_scores = []
header, probe_dict, bioref_dict = create_csv_header(probe_sampleset[0])
for probe in probe_sampleset:
filename = os.path.join(path, probe.subject) + ".csv"
with open(filename, "w") as f:
csv_write = csv.writer(f)
csv_write.writerow(header)
rows = []
probe_row = [str(probe.key)] + [
str(probe.__dict__[k]) for k in probe_dict.keys()
]
for biometric_reference in probe:
bio_ref_row = [
str(biometric_reference.__dict__[k])
for k in list(bioref_dict.keys()) + ["data"]
]
rows.append(probe_row + bio_ref_row)
csv_write.writerows(rows)
checkpointed_scores.append(
SampleSet(
[
DelayedSample(
functools.partial(self.read, filename), parent=probe
)
],
parent=probe,
)
)
return checkpointed_scores
def read(self, path):
"""
Base Instruction to load a score file
"""
return open(path).readlines()
def concatenate_write_scores(self, samplesets_list, filename):
"""
Given a list of samplsets, write them all in a single file
"""
os.makedirs(os.path.dirname(filename), exist_ok=True)
f = open(filename, "w")
first = True
for samplesets in samplesets_list:
for sset in samplesets:
for s in sset:
if first:
f.writelines(s.data)
first = False
else:
f.writelines(s.data[1:])
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