Merge pull request #1309 from bousmalis/master

More DSN updates

domain_adaptation/README.md

@@ -30,16 +30,16 @@ MNIST and MNIST-M:
 $ export DSN_DATA_DIR=/your/dir
 ```
 
-Then you need to build the binaries with Bazel:
+Add models and models/slim to your `$PYTHONPATH`:
 
 ```
-$ bazel build -c opt domain_adaptation/domain_separation/...
+$ export PYTHONPATH=$PYTHONPATH:$PWD:$PWD/slim
 ```
 
-Add models and models/slim to your `$PYTHONPATH`:
+Then you need to build the binaries with Bazel:
 
 ```
-$ export PYTHONPATH=$PYTHONPATH:$PWD:$PWD/slim
+$ bazel build -c opt domain_adaptation/domain_separation/...
 ```
 
 You can then train with the following command:

domain_adaptation/domain_separation/dsn_eval.py

@@ -14,22 +14,7 @@
 # ==============================================================================
 
 # pylint: disable=line-too-long
-r"""Evaluation for Domain Separation Networks (DSNs).
-
-To build locally for CPU:
-  blaze build -c opt --copt=-mavx \
-    third_party/tensorflow_models/domain_adaptation/domain_separation:dsn_eval
-
-To build locally for GPU:
-  blaze build -c opt --copt=-mavx --config=cuda_clang \
-    third_party/tensorflow_models/domain_adaptation/domain_separation:dsn_eval
-
-To run locally:
-$
-./blaze-bin/third_party/tensorflow_models/domain_adaptation/domain_separation/dsn_eval
-\
-    --alsologtostderr
-"""
+"""Evaluation for Domain Separation Networks (DSNs)."""
 # pylint: enable=line-too-long
 import math
 
@@ -57,7 +42,7 @@ tf.app.flags.DEFINE_string(
     'eval_dir', '/tmp/da/',
     'Directory where we should write the tf summaries to.')
 
-tf.app.flags.DEFINE_string('dataset_dir', '/cns/ok-d/home/konstantinos/cad_learning/',
+tf.app.flags.DEFINE_string('dataset_dir', None,
                            'The directory where the dataset files are stored.')
 
 tf.app.flags.DEFINE_string('dataset', 'mnist_m',

domain_adaptation/domain_separation/dsn_train.py

@@ -13,30 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-# pylint: disable=line-too-long
-r"""Training for Domain Separation Networks (DSNs).
-
--- Compile:
-$ blaze build -c opt --copt=-mavx --config=cuda \
-    third_party/tensorflow_models/domain_adaptation/domain_separation:dsn_train
-
--- Run:
-$
-./blaze-bin/third_party/tensorflow_models/domain_adaptation/domain_separation/dsn_train
-\
-    --similarity_loss=dann \
-    --basic_tower=dsn_cropped_linemod \
-    --source_dataset=pose_synthetic \
-    --target_dataset=pose_real \
-    --learning_rate=0.012 \
-    --alpha_weight=0.26 \
-    --gamma_weight=0.0115 \
-    --weight_decay=4e-5 \
-    --layers_to_regularize=fc3 \
-    --use_separation \
-    --alsologtostderr
-"""
-# pylint: enable=line-too-long
+"""Training for Domain Separation Networks (DSNs)."""
 from __future__ import division
 
 import tensorflow as tf
@@ -59,7 +36,7 @@ tf.app.flags.DEFINE_string('target_dataset', 'pose_real',
 tf.app.flags.DEFINE_string('target_labeled_dataset', 'none',
                            'Target dataset to train on.')
 
-tf.app.flags.DEFINE_string('dataset_dir', '/cns/ok-d/home/konstantinos/cad_learning/',
+tf.app.flags.DEFINE_string('dataset_dir', None,
                            'The directory where the dataset files are stored.')
 
 tf.app.flags.DEFINE_string('master', '',

domain_adaptation/domain_separation/losses.py

@@ -178,16 +178,14 @@ def dann_loss(source_samples, target_samples, weight, scope=None):
 
   assert_op = tf.Assert(tf.is_finite(domain_loss), [domain_loss])
   with tf.control_dependencies([assert_op]):
-    tag_loss = 'losses/Domain Loss'
-    tag_accuracy = 'losses/Domain Accuracy'
+    tag_loss = 'losses/domain_loss'
+    tag_accuracy = 'losses/domain_accuracy'
     if scope:
       tag_loss = scope + tag_loss
       tag_accuracy = scope + tag_accuracy
 
-    tf.summary.scalar(
-        tag_loss, domain_loss, name='domain_loss_summary')
-    tf.summary.scalar(
-        tag_accuracy, domain_accuracy, name='domain_accuracy_summary')
+    tf.summary.scalar(tag_loss, domain_loss)
+    tf.summary.scalar(tag_accuracy, domain_accuracy)
 
   return domain_loss