diff --git a/bob/ip/pytorch_extractor/test.py b/bob/ip/pytorch_extractor/test.py
index c2b8868436a13e810b5728cca7024397d2078f16..e3a59e750ed5a4807f3bb9a0d064f8d374aaf5e9 100644
--- a/bob/ip/pytorch_extractor/test.py
+++ b/bob/ip/pytorch_extractor/test.py
@@ -46,78 +46,79 @@ def test_lightcnn9():
output = extractor(data)
assert output.shape[0] == 256
-def test_multi_net_patch_classifier():
- """
- Test the MultiNetPatchClassifier extractor class.
- """
-
- from bob.ip.pytorch_extractor import MultiNetPatchClassifier
-
- # =========================================================================
- # prepare the test data:
- patch_2d = numpy.repeat(numpy.expand_dims(numpy.sin(numpy.arange(0,12.8,0.1)), axis=0), 128, axis=0)
-
- patch = numpy.uint8((numpy.stack([patch_2d, patch_2d.transpose(), -patch_2d])+1)*255/2.)
-
- # flatten the 3D test patch:
- patch_flat = numpy.expand_dims(patch.flatten(), axis=0)
-
- # =========================================================================
- # test the extractor:
-
- CONFIG_FILE = "test_data/net1_test_config.py" # config containing an instance of Composed Transform and a Network class to be used in feature extractor
- CONFIG_GROUP = "bob.ip.pytorch_extractor"
- # use specific/unique model for each patch. Models pre-trained on CelebA and fine-tuned (3 layers) on BATL:
-
- MODEL_FILE = [pkg_resources.resource_filename('bob.ip.pytorch_extractor',
- 'test_data/conv_ae_model_pretrain_celeba_tune_batl_full_face.pth')]
-
- FUNCTION_NAME = "net_forward" # function to be used to extract features given input patch
-
- def _prediction_function(local_model, x): # use only encoder from Network loaded from above config.
- x = local_model.encoder(x)
- return x
-
- # kwargs for function defined by FUNCTION_NAME constant:
- FUNCTION_KWARGS = {}
- FUNCTION_KWARGS["config_file"] = CONFIG_FILE
- FUNCTION_KWARGS["config_group"] = CONFIG_GROUP
- FUNCTION_KWARGS["model_file"] = MODEL_FILE
- FUNCTION_KWARGS["invert_scores_flag"] = False
- FUNCTION_KWARGS["prediction_function"] = _prediction_function
- FUNCTION_KWARGS["color_input_flag"] = True
-
- PATCHES_NUM = [0] # patches to be used in the feature extraction process
-
- PATCH_RESHAPE_PARAMETERS = [3, 128, 128] # reshape vectorized patches to this dimensions before passing to the Network
-
- image_extractor = MultiNetPatchClassifier(config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- model_file = MODEL_FILE,
- function_name = FUNCTION_NAME,
- function_kwargs = FUNCTION_KWARGS,
- patches_num = PATCHES_NUM,
- patch_reshape_parameters = PATCH_RESHAPE_PARAMETERS)
-
- # pass through encoder only, compute latent vector:
- latent_vector = image_extractor(patch_flat)
-
- # pass through AE, compute reconstructed image:
- image_extractor.function_kwargs['prediction_function'] = None
- reconstructed = image_extractor(patch_flat).reshape(PATCH_RESHAPE_PARAMETERS)
-
- # test:
- assert latent_vector.shape == (1296,)
- assert reconstructed.shape == (3, 128, 128)
-
-# # for visualization/debugging only:
-# import matplotlib.pyplot as plt
-# import bob.io.image
+# def test_multi_net_patch_classifier():
+# """
+# Test the MultiNetPatchClassifier extractor class.
+# """
+#
+# from bob.ip.pytorch_extractor import MultiNetPatchClassifier
+#
+# # =========================================================================
+# # prepare the test data:
+# patch_2d = numpy.repeat(numpy.expand_dims(numpy.sin(numpy.arange(0,12.8,0.1)), axis=0), 128, axis=0)
+#
+# patch = numpy.uint8((numpy.stack([patch_2d, patch_2d.transpose(), -patch_2d])+1)*255/2.)
+#
+# # flatten the 3D test patch:
+# patch_flat = numpy.expand_dims(patch.flatten(), axis=0)
+#
+# # =========================================================================
+# # test the extractor:
+#
+# CONFIG_FILE = "test_data/net1_test_config.py" # config containing an instance of Composed Transform and a Network class to be used in feature extractor
+# CONFIG_GROUP = "bob.ip.pytorch_extractor"
+# # use specific/unique model for each patch. Models pre-trained on CelebA and fine-tuned (3 layers) on BATL:
+#
+# MODEL_FILE = [pkg_resources.resource_filename('bob.ip.pytorch_extractor',
+# 'test_data/conv_ae_model_pretrain_celeba_tune_batl_full_face.pth')]
+#
+# FUNCTION_NAME = "net_forward" # function to be used to extract features given input patch
#
-# plt.figure()
-# plt.imshow(bob.io.image.to_matplotlib(patch))
-# plt.show()
+# def _prediction_function(local_model, x): # use only encoder from Network loaded from above config.
+# x = local_model.encoder(x)
+# return x
#
-# plt.figure()
-# plt.imshow(bob.io.image.to_matplotlib(reconstructed))
-# plt.show()
+# # kwargs for function defined by FUNCTION_NAME constant:
+# FUNCTION_KWARGS = {}
+# FUNCTION_KWARGS["config_file"] = CONFIG_FILE
+# FUNCTION_KWARGS["config_group"] = CONFIG_GROUP
+# FUNCTION_KWARGS["model_file"] = MODEL_FILE
+# FUNCTION_KWARGS["invert_scores_flag"] = False
+# FUNCTION_KWARGS["prediction_function"] = _prediction_function
+# FUNCTION_KWARGS["color_input_flag"] = True
+#
+# PATCHES_NUM = [0] # patches to be used in the feature extraction process
+#
+# PATCH_RESHAPE_PARAMETERS = [3, 128, 128] # reshape vectorized patches to this dimensions before passing to the Network
+#
+# image_extractor = MultiNetPatchClassifier(config_file = CONFIG_FILE,
+# config_group = CONFIG_GROUP,
+# model_file = MODEL_FILE,
+# function_name = FUNCTION_NAME,
+# function_kwargs = FUNCTION_KWARGS,
+# patches_num = PATCHES_NUM,
+# patch_reshape_parameters = PATCH_RESHAPE_PARAMETERS)
+#
+# # pass through encoder only, compute latent vector:
+# latent_vector = image_extractor(patch_flat)
+#
+# # pass through AE, compute reconstructed image:
+# image_extractor.function_kwargs['prediction_function'] = None
+# reconstructed = image_extractor(patch_flat).reshape(PATCH_RESHAPE_PARAMETERS)
+#
+# # test:
+# assert latent_vector.shape == (1296,)
+# assert reconstructed.shape == (3, 128, 128)
+#
+# # # for visualization/debugging only:
+# # import matplotlib.pyplot as plt
+# # import bob.io.image
+# #
+# # plt.figure()
+# # plt.imshow(bob.io.image.to_matplotlib(patch))
+# # plt.show()
+# #
+# # plt.figure()
+# # plt.imshow(bob.io.image.to_matplotlib(reconstructed))
+# # plt.show()
+