From 6cca593f8c2750dd0dc112a562ad9f14c7ab8b77 Mon Sep 17 00:00:00 2001
From: Laurent COLBOIS <lcolbois@.idiap.ch>
Date: Fri, 4 Dec 2020 15:20:10 +0100
Subject: [PATCH] Added TF1/TF2 embedding comparison tests for all Inception
networks. Fixed inconsistencies in input data scaling.
---
bob/bio/face/test/test_embeddings.py | 30 +++++++++++++++++++++++-----
1 file changed, 25 insertions(+), 5 deletions(-)
diff --git a/bob/bio/face/test/test_embeddings.py b/bob/bio/face/test/test_embeddings.py
index 4b0f149d..d4228bef 100644
--- a/bob/bio/face/test/test_embeddings.py
+++ b/bob/bio/face/test/test_embeddings.py
@@ -14,7 +14,7 @@ def test_idiap_inceptionv2_msceleb():
reference = bob.io.base.load(
pkg_resources.resource_filename(
- "bob.bio.face.test", "data/inception_resnet_v2_rgb.hdf5"
+ "bob.bio.face.test", "data/inception_resnet_v2_msceleb_rgb.hdf5"
)
)
np.random.seed(10)
@@ -34,11 +34,18 @@ def test_idiap_inceptionv2_msceleb():
@is_library_available("tensorflow")
def test_idiap_inceptionv2_casia():
- from bob.bio.face.embeddings import InceptionResnetv2_Casia_CenterLoss_2018
+ from bob.bio.face.embeddings.tf2_inception_resnet import (
+ InceptionResnetv2_Casia_CenterLoss_2018,
+ )
+ reference = bob.io.base.load(
+ pkg_resources.resource_filename(
+ "bob.bio.face.test", "data/inception_resnet_v2_casia_rgb.hdf5"
+ )
+ )
np.random.seed(10)
transformer = InceptionResnetv2_Casia_CenterLoss_2018()
- data = np.random.rand(3, 160, 160).astype("uint8")
+ data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
output = transformer.transform([data])[0]
assert output.size == 128, output.shape
@@ -47,6 +54,7 @@ def test_idiap_inceptionv2_casia():
transformer_sample = wrap(["sample"], transformer)
output = [s.data for s in transformer_sample.transform([sample])][0]
+ np.testing.assert_allclose(output, reference.flatten(), rtol=1e-5, atol=1e-4)
assert output.size == 128, output.shape
@@ -56,9 +64,14 @@ def test_idiap_inceptionv1_msceleb():
InceptionResnetv1_MsCeleb_CenterLoss_2018,
)
+ reference = bob.io.base.load(
+ pkg_resources.resource_filename(
+ "bob.bio.face.test", "data/inception_resnet_v1_msceleb_rgb.hdf5"
+ )
+ )
np.random.seed(10)
transformer = InceptionResnetv1_MsCeleb_CenterLoss_2018()
- data = np.random.rand(3, 160, 160).astype("uint8")
+ data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
output = transformer.transform([data])[0]
assert output.size == 128, output.shape
@@ -67,6 +80,7 @@ def test_idiap_inceptionv1_msceleb():
transformer_sample = wrap(["sample"], transformer)
output = [s.data for s in transformer_sample.transform([sample])][0]
+ np.testing.assert_allclose(output, reference.flatten(), rtol=1e-5, atol=1e-4)
assert output.size == 128, output.shape
@@ -76,9 +90,14 @@ def test_idiap_inceptionv1_casia():
InceptionResnetv1_Casia_CenterLoss_2018,
)
+ reference = bob.io.base.load(
+ pkg_resources.resource_filename(
+ "bob.bio.face.test", "data/inception_resnet_v1_casia_rgb.hdf5"
+ )
+ )
np.random.seed(10)
transformer = InceptionResnetv1_Casia_CenterLoss_2018()
- data = np.random.rand(3, 160, 160).astype("uint8")
+ data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
output = transformer.transform([data])[0]
assert output.size == 128, output.shape
@@ -87,6 +106,7 @@ def test_idiap_inceptionv1_casia():
transformer_sample = wrap(["sample"], transformer)
output = [s.data for s in transformer_sample.transform([sample])][0]
+ np.testing.assert_allclose(output, reference.flatten(), rtol=1e-5, atol=1e-4)
assert output.size == 128, output.shape
--
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