Porting to TF2

Fixes #75 (closed)

bob/learn/tensorflow/test/test_architectures.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-
-from nose.plugins.attrib import attr
-import tensorflow as tf
-from bob.learn.tensorflow.network import inception_resnet_v2, inception_resnet_v2_batch_norm,\
-    inception_resnet_v1, inception_resnet_v1_batch_norm,\
-    vgg_19, vgg_16, mlp_with_batchnorm_and_dropout
-
-# @attr('slow')
-# def test_inceptionv2():
-
-#     tf.reset_default_graph()
-#     # Testing WITHOUT batch norm
-#     inputs = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-#     graph, _ = inception_resnet_v2(inputs)
-#     assert len(tf.trainable_variables()) == 490
-
-#     tf.reset_default_graph()
-#     assert len(tf.global_variables()) == 0
-
-#     # Testing WITH batch norm
-#     inputs = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-#     graph, _ = inception_resnet_v2_batch_norm(inputs)
-#     assert len(tf.trainable_variables()) == 490, len(tf.trainable_variables())
-
-#     tf.reset_default_graph()
-#     assert len(tf.global_variables()) == 0
-
-# @attr('slow')
-# def test_inceptionv2_adaptation():
-
-#     tf.reset_default_graph()
-#     for n, trainable_variables in [
-#         (490, None),
-#         (0, []),
-#         (2, ['Conv2d_1a_3x3', 'Conv2d_1a_3x3_BN']),
-#         (4, ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_1a_3x3_BN',
-#              'Conv2d_2a_3x3_BN']),
-#         (6, ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
-#              'Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN', 'Conv2d_2b_3x3_BN']),
-#         (1, ['Conv2d_1a_3x3_BN']),
-#         (2, ['Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN']),
-#         (3, ['Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN', 'Conv2d_2b_3x3_BN']),
-#     ]:
-#         input = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-#         net, end_points = inception_resnet_v2_batch_norm(
-#             input, trainable_variables=trainable_variables)
-#         l = len(tf.trainable_variables())
-#         assert l == n, (l, n)
-#         tf.reset_default_graph()
-#     tf.reset_default_graph()
-#     assert len(tf.global_variables()) == 0
-
-# @attr('slow')
-# def test_inceptionv1():
-
-#     tf.reset_default_graph()
-#     # Testing WITHOUT batch norm
-#     inputs = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-#     graph, _ = inception_resnet_v1(inputs)
-#     assert len(tf.trainable_variables()) == 266
-
-#     tf.reset_default_graph()
-#     assert len(tf.global_variables()) == 0
-
-#     # Testing WITH batch norm
-#     inputs = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-#     graph, _ = inception_resnet_v1_batch_norm(inputs)
-#     assert len(tf.trainable_variables()) == 266
-
-#     tf.reset_default_graph()
-#     assert len(tf.global_variables()) == 0
-
-# @attr('slow')
-# def test_inceptionv1_adaptation():
-
-#     tf.reset_default_graph()
-#     for n, trainable_variables in [
-#         (266, None),
-#         (0, []),
-#         (2, ['Conv2d_1a_3x3', 'Conv2d_1a_3x3_BN']),
-#         (4, ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_1a_3x3_BN',
-#              'Conv2d_2a_3x3_BN']),
-#         (6, ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
-#              'Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN', 'Conv2d_2b_3x3_BN']),
-#         (1, ['Conv2d_1a_3x3_BN']),
-#         (2, ['Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN']),
-#         (3, ['Conv2d_1a_3x3_BN', 'Conv2d_2a_3x3_BN', 'Conv2d_2b_3x3_BN']),
-#     ]:
-#         input = tf.placeholder(tf.float32, shape=(1, 160, 160, 1))
-#         net, end_points = inception_resnet_v1_batch_norm(
-#             input, trainable_variables=trainable_variables)
-#         l = len(tf.trainable_variables())
-#         assert l == n, (l, n)
-#         tf.reset_default_graph()
-#     tf.reset_default_graph()
-#     assert len(tf.global_variables()) == 0
-
-
-def test_vgg():
-    tf.reset_default_graph()
-
-    # Testing VGG19 Training mode
-    inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
-    graph, _ = vgg_19(inputs)
-    assert len(tf.trainable_variables()) == 38
-
-    tf.reset_default_graph()
-    assert len(tf.global_variables()) == 0
-
-    # Testing VGG19 predicting mode
-    inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
-    graph, _ = vgg_19(inputs, mode=tf.estimator.ModeKeys.PREDICT)
-    assert len(tf.trainable_variables()) == 0
-
-    tf.reset_default_graph()
-    assert len(tf.global_variables()) == 0
-
-
-    # Testing VGG 16 training mode
-    inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
-    graph, _ = vgg_16(inputs)
-    assert len(tf.trainable_variables()) == 30
-
-    tf.reset_default_graph()
-    assert len(tf.global_variables()) == 0
-
-    # Testing VGG 16 predicting mode
-    inputs = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))
-    graph, _ = vgg_16(inputs, mode=tf.estimator.ModeKeys.PREDICT)
-    assert len(tf.trainable_variables()) == 0
-
-    tf.reset_default_graph()
-    assert len(tf.global_variables()) == 0
-
-
-def test_mlp():
-
-    tf.reset_default_graph()
-    # Testing MLP Training mode
-    inputs = tf.placeholder(tf.float32, shape=(1, 10, 10, 3))
-    graph, _ = mlp_with_batchnorm_and_dropout(inputs, [6, 5])
-    assert len(tf.trainable_variables()) == 4
-
-    tf.reset_default_graph()
-    # Testing MLP Predicting mode
-    inputs = tf.placeholder(tf.float32, shape=(1, 10, 10, 3))
-    graph, _ = mlp_with_batchnorm_and_dropout(inputs, [6, 5], mode=tf.estimator.ModeKeys.PREDICT)
-    assert len(tf.trainable_variables()) == 0
-