Implemented test case for InceptionResnetV2

bob/bio/face/test/test_embeddings.py

 import bob.bio.face
+import bob.io.base
 import numpy as np
 from bob.pipelines import Sample, wrap
+import pkg_resources
+
 
 def test_facenet():
     from bob.bio.face.embeddings import FaceNetSanderberg
 
     np.random.seed(10)
 
-    transformer = FaceNetSanderberg()    
+    transformer = FaceNetSanderberg()
     # Raw data
     data = np.random.rand(3, 160, 160).astype("uint8")
     output = transformer.transform(data)
     assert output.size == 128, output.shape
-    
 
     # Sample Batch
     sample = Sample(data)
@@ -25,9 +27,15 @@ def test_facenet():
 def test_idiap_inceptionv2_msceleb():
     from bob.bio.face.embeddings import InceptionResnetv2_MsCeleb
 
+    reference = bob.io.base.load(
+        pkg_resources.resource_filename(
+            "bob.bio.face.test", "data/inception_resnet_v2_rgb.hdf5"
+        )
+    )
+
     np.random.seed(10)
     transformer = InceptionResnetv2_MsCeleb()
-    data = np.random.rand(3, 160, 160).astype("uint8")
+    data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
     output = transformer.transform(data)
     assert output.size == 128, output.shape
 
@@ -36,6 +44,7 @@ def test_idiap_inceptionv2_msceleb():
     transformer_sample = wrap(["sample"], transformer)
     output = [s.data for s in transformer_sample.transform([sample])][0]
 
+    assert np.allclose(output, reference)
     assert output.size == 128, output.shape
 
 
@@ -48,7 +57,6 @@ def test_idiap_inceptionv2_casia():
     output = transformer.transform(data)
     assert output.size == 128, output.shape
 
-
     # Sample Batch
     sample = Sample(data)
     transformer_sample = wrap(["sample"], transformer)
@@ -93,6 +101,7 @@ def test_idiap_inceptionv1_casia():
 
 def test_arface_insight_tf():
     import tensorflow as tf
+
     tf.compat.v1.reset_default_graph()
 
     from bob.bio.face.embeddings import ArcFace_InsightFaceTF