DSN Updates

domain_adaptation/README.md

@@ -4,17 +4,10 @@
 ## Introduction
 This code is the code used for the "Domain Separation Networks" paper
 by Bousmalis K., Trigeorgis G., et al. which was presented at NIPS 2016. The
-<<<<<<< HEAD
-paper can be found here: https://arxiv.org/abs/1608.06019
-
-## Contact
-This code was open-sourced by Konstantinos Bousmalis (konstantinos@google.com, github:bousmalis)
-=======
 paper can be found here: https://arxiv.org/abs/1608.06019.
 
 ## Contact
 This code was open-sourced by [Konstantinos Bousmalis](https://github.com/bousmalis) (konstantinos@google.com).
->>>>>>> d6bee2c713c6aed6522ab32c34b57412d0216d95
 
 ## Installation
 You will need to have the following installed on your machine before trying out the DSN code.
@@ -26,35 +19,27 @@ You will need to have the following installed on your machine before trying out
 Although we are making the code available, you are only able to use the MNIST
 provider for now. We will soon provide a script to download and convert MNIST-M
 as well. Check back here in a few weeks or wait for a relevant announcement from
-<<<<<<< HEAD
-Twitter @bousmalis.
-=======
 [@bousmalis](https://twitter.com/bousmalis).
->>>>>>> d6bee2c713c6aed6522ab32c34b57412d0216d95
 
 ## Running the code for adapting MNIST to MNIST-M
 In order to run the MNIST to MNIST-M experiments with DANNs and/or DANNs with
 domain separation (DSNs) you will need to set the directory you used to download
-<<<<<<< HEAD
-MNIST and MNIST-M:\
-=======
 MNIST and MNIST-M:
->>>>>>> d6bee2c713c6aed6522ab32c34b57412d0216d95
 
 ```
 $ 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
 

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