diff --git a/domain_adaptation/domain_separation/dsn.py b/domain_adaptation/domain_separation/dsn.py index 73760aaed9cba06bd50650780559e74317685f49..3018e8a791840ae465bad493913235cc04c31cff 100644 --- a/domain_adaptation/domain_separation/dsn.py +++ b/domain_adaptation/domain_separation/dsn.py @@ -282,15 +282,17 @@ def add_autoencoders(source_data, source_shared, target_data, target_shared, # Add summaries source_reconstructions = tf.concat( - map(normalize_images, [ + axis=2, + values=map(normalize_images, [ source_data, source_recons, source_shared_recons, source_private_recons - ]), 2) + ])) target_reconstructions = tf.concat( - map(normalize_images, [ + axis=2, + values=map(normalize_images, [ target_data, target_recons, target_shared_recons, target_private_recons - ]), 2) + ])) tf.summary.image( 'Source Images:Recons:RGB', source_reconstructions[:, :, :, :3], diff --git a/domain_adaptation/domain_separation/dsn_test.py b/domain_adaptation/domain_separation/dsn_test.py index 073a8575111d7cf26b0f1f2a09e57936006b9467..3d687398a9b9356455f739417bc96ddb2ca5ad40 100644 --- a/domain_adaptation/domain_separation/dsn_test.py +++ b/domain_adaptation/domain_separation/dsn_test.py @@ -26,7 +26,7 @@ class HelperFunctionsTest(tf.test.TestCase): with self.test_session() as sess: # Test for when global_step < domain_separation_startpoint step = tf.contrib.slim.get_or_create_global_step() - sess.run(tf.initialize_all_variables()) # global_step = 0 + sess.run(tf.global_variables_initializer()) # global_step = 0 params = {'domain_separation_startpoint': 2} weight = dsn.dsn_loss_coefficient(params) weight_np = sess.run(weight) diff --git a/domain_adaptation/domain_separation/losses.py b/domain_adaptation/domain_separation/losses.py index ef59ea457f79af5519bdc2e087f2f2aef52188e3..2f143245b578d4cafa97a4f83a15eddd19cc9791 100644 --- a/domain_adaptation/domain_separation/losses.py +++ b/domain_adaptation/domain_separation/losses.py @@ -100,7 +100,7 @@ def mmd_loss(source_samples, target_samples, weight, scope=None): tag = 'MMD Loss' if scope: tag = scope + tag - tf.contrib.deprecated.scalar_summary(tag, loss_value) + tf.summary.scalar(tag, loss_value) tf.losses.add_loss(loss_value) return loss_value @@ -135,7 +135,7 @@ def correlation_loss(source_samples, target_samples, weight, scope=None): tag = 'Correlation Loss' if scope: tag = scope + tag - tf.contrib.deprecated.scalar_summary(tag, corr_loss) + tf.summary.scalar(tag, corr_loss) tf.losses.add_loss(corr_loss) return corr_loss @@ -155,11 +155,11 @@ def dann_loss(source_samples, target_samples, weight, scope=None): """ with tf.variable_scope('dann'): batch_size = tf.shape(source_samples)[0] - samples = tf.concat([source_samples, target_samples], 0) + samples = tf.concat(axis=0, values=[source_samples, target_samples]) samples = slim.flatten(samples) domain_selection_mask = tf.concat( - [tf.zeros((batch_size, 1)), tf.ones((batch_size, 1))], 0) + axis=0, values=[tf.zeros((batch_size, 1)), tf.ones((batch_size, 1))]) # Perform the gradient reversal and be careful with the shape. grl = grl_ops.gradient_reversal(samples) @@ -184,9 +184,9 @@ def dann_loss(source_samples, target_samples, weight, scope=None): tag_loss = scope + tag_loss tag_accuracy = scope + tag_accuracy - tf.contrib.deprecated.scalar_summary( + tf.summary.scalar( tag_loss, domain_loss, name='domain_loss_summary') - tf.contrib.deprecated.scalar_summary( + tf.summary.scalar( tag_accuracy, domain_accuracy, name='domain_accuracy_summary') return domain_loss @@ -216,7 +216,7 @@ def difference_loss(private_samples, shared_samples, weight=1.0, name=''): cost = tf.reduce_mean(tf.square(correlation_matrix)) * weight cost = tf.where(cost > 0, cost, 0, name='value') - tf.contrib.deprecated.scalar_summary('losses/Difference Loss {}'.format(name), + tf.summary.scalar('losses/Difference Loss {}'.format(name), cost) assert_op = tf.Assert(tf.is_finite(cost), [cost]) with tf.control_dependencies([assert_op]): diff --git a/domain_adaptation/domain_separation/models_test.py b/domain_adaptation/domain_separation/models_test.py index f6e5a16dde286ba1211561509970e984e5c977a7..69d1a27259022569cc5865e49dd6bba5675d834f 100644 --- a/domain_adaptation/domain_separation/models_test.py +++ b/domain_adaptation/domain_separation/models_test.py @@ -115,7 +115,7 @@ class DecoderTest(tf.test.TestCase): width=width, channels=channels, batch_norm_params=batch_norm_params) - sess.run(tf.initialize_all_variables()) + sess.run(tf.global_variables_initializer()) output_np = sess.run(output) self.assertEqual(output_np.shape, (32, height, width, channels)) self.assertTrue(np.any(output_np)) diff --git a/domain_adaptation/domain_separation/utils.py b/domain_adaptation/domain_separation/utils.py index 617bebccd4823dfb0a33c7eed7e4795c03d099d8..e144ee86120bd58eb06b710fb35f3f58b5a05343 100644 --- a/domain_adaptation/domain_separation/utils.py +++ b/domain_adaptation/domain_separation/utils.py @@ -75,15 +75,15 @@ def reshape_feature_maps(features_tensor): num_filters) num_filters_sqrt = int(num_filters_sqrt) conv_summary = tf.unstack(features_tensor, axis=3) - conv_one_row = tf.concat(conv_summary[0:num_filters_sqrt], 2) + conv_one_row = tf.concat(axis=2, values=conv_summary[0:num_filters_sqrt]) ind = 1 conv_final = conv_one_row for ind in range(1, num_filters_sqrt): - conv_one_row = tf.concat(conv_summary[ - ind * num_filters_sqrt + 0:ind * num_filters_sqrt + num_filters_sqrt], - 2) + conv_one_row = tf.concat(axis=2, + values=conv_summary[ + ind * num_filters_sqrt + 0:ind * num_filters_sqrt + num_filters_sqrt]) conv_final = tf.concat( - [tf.squeeze(conv_final), tf.squeeze(conv_one_row)], 1) + axis=1, values=[tf.squeeze(conv_final), tf.squeeze(conv_one_row)]) conv_final = tf.expand_dims(conv_final, -1) return conv_final