[nose] Decorated some tests

bob/bio/face/test/test_baselines.py

@@ -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())

bob/bio/face/test/test_embeddings.py

@@ -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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