Commit 7cf745d4 authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
Browse files

[nose] Decorated some tests

parent da387a3c
Pipeline #45382 failed with stage
in 8 minutes and 13 seconds
......@@ -12,8 +12,8 @@ import os
import bob.io.base
import functools
import copy
import tensorflow as tf
from bob.bio.base.test.utils import mxnet_available, tensorflow_available
images = dict()
images["bioref"] = (
......@@ -54,7 +54,8 @@ def get_fake_samples_for_training():
annotations = {"reye": (131, 176), "leye": (222, 170)}
return [
Sample(x, key=str(i), subject=str(i), annotations=annotations) for i,x in enumerate(data)
Sample(x, key=str(i), subject=str(i), annotations=annotations)
for i, x in enumerate(data)
]
......@@ -72,9 +73,7 @@ def run_baseline(baseline, samples_for_training=[]):
with tempfile.TemporaryDirectory() as d:
cpy = copy.deepcopy(pipeline)
checkpoint_pipeline = checkpoint_vanilla_biometrics(
cpy, base_dir=d
)
checkpoint_pipeline = checkpoint_vanilla_biometrics(cpy, base_dir=d)
checkpoint_scores = checkpoint_pipeline([], biometric_references, probes)
assert len(checkpoint_scores) == 1
......@@ -106,31 +105,40 @@ def run_baseline(baseline, samples_for_training=[]):
assert "samplewrapper-2" in dirs
assert "scores" in dirs
@tensorflow_available
def test_facenet_baseline():
run_baseline("facenet-sanderberg")
@tensorflow_available
def test_inception_resnetv2_msceleb():
run_baseline("inception-resnetv2-msceleb")
@tensorflow_available
def test_inception_resnetv2_casiawebface():
run_baseline("inception-resnetv2-casiawebface")
@tensorflow_available
def test_inception_resnetv1_msceleb():
run_baseline("inception-resnetv1-msceleb")
@tensorflow_available
def test_inception_resnetv1_casiawebface():
run_baseline("inception-resnetv1-casiawebface")
@mxnet_available
def test_arcface_insightface():
run_baseline("arcface-insightface")
def test_gabor_graph():
run_baseline("gabor_graph")
#def test_lda():
# def test_lda():
# run_baseline("lda", get_fake_samples_for_training())
......@@ -3,8 +3,10 @@ import bob.io.base
import numpy as np
from bob.pipelines import Sample, wrap
import pkg_resources
from bob.bio.base.test.utils import mxnet_available, tensorflow_available
@tensorflow_available
def test_idiap_inceptionv2_msceleb():
from bob.bio.face.embeddings import InceptionResnetv2_MsCeleb_CenterLoss_2018
......@@ -28,6 +30,7 @@ def test_idiap_inceptionv2_msceleb():
assert output.size == 128, output.shape
@tensorflow_available
def test_idiap_inceptionv2_casia():
from bob.bio.face.embeddings import InceptionResnetv2_Casia_CenterLoss_2018
......@@ -45,6 +48,7 @@ def test_idiap_inceptionv2_casia():
assert output.size == 128, output.shape
@tensorflow_available
def test_idiap_inceptionv1_msceleb():
from bob.bio.face.embeddings import InceptionResnetv1_MsCeleb_CenterLoss_2018
......@@ -62,6 +66,7 @@ def test_idiap_inceptionv1_msceleb():
assert output.size == 128, output.shape
@tensorflow_available
def test_idiap_inceptionv1_casia():
from bob.bio.face.embeddings import InceptionResnetv1_Casia_CenterLoss_2018
......@@ -78,6 +83,8 @@ def test_idiap_inceptionv1_casia():
assert output.size == 128, output.shape
@tensorflow_available
def test_facenet_sanderberg():
from bob.bio.face.embeddings import FaceNetSanderberg_20170512_110547
......@@ -94,39 +101,18 @@ def test_facenet_sanderberg():
assert output.size == 128, output.shape
@mxnet_available
def test_arcface_insight_face():
from bob.bio.face.embeddings import ArcFaceInsightFace
transformer = ArcFaceInsightFace()
data = np.random.rand(3, 112, 112)*255
data = np.random.rand(3, 112, 112) * 255
data = data.astype("uint8")
output = transformer.transform([data])
assert output.size == 512, output.shape
# Sample Batch
sample = Sample(data)
transformer_sample = wrap(["sample"], transformer)
output = [s.data for s in transformer_sample.transform([sample])][0]
assert output.size == 512, output.shape
"""
def test_arface_insight_tf():
import tensorflow as tf
tf.compat.v1.reset_default_graph()
from bob.bio.face.embeddings import ArcFace_InsightFaceTF
np.random.seed(10)
transformer = ArcFace_InsightFaceTF()
data = np.random.rand(3, 112, 112).astype("uint8")
output = transformer.transform([data])[0]
assert output.size == 512, output.shape
# Sample Batch
sample = Sample(data)
transformer_sample = wrap(["sample"], transformer)
output = [s.data for s in transformer_sample.transform([sample])][0]
assert output.size == 512, output.shape
"""
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