package renamed to tensorflow_node

CMakeLists.txt

@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 2.8.3)
-project(ros_destin)
+project(tensorflow_node)
 
 ## Find catkin macros and libraries
 find_package(catkin REQUIRED COMPONENTS
@@ -16,8 +16,8 @@ catkin_python_setup()
 ## Generate messages in the 'msg' folder
 add_message_files(
     FILES
-    DestinNodeState.msg
-    DestinStatus.msg
+    TFNodeState.msg
+    TFStatus.msg
 )
 
 ## Generate services in the 'srv' folder
@@ -90,4 +90,4 @@ install(PROGRAMS
 #############
 
 ## Add folders to be run by python nosetests
-catkin_add_nosetests(src/destin/tests)
+catkin_add_nosetests(src/tensorflow_node/tests)

config/small-ae-destin.yaml

@@ -1,4 +1,4 @@
-destin:
+tensorflow_node:
   inputlayer: 
     type: OpenCVInputLayer
     params:

launch/mnist.launch

@@ -1,5 +1,5 @@
 <launch>
    <!-- Destin Configuration -->
    <rosparam command="load" file="$(find ros_destin)/config/small-ae-destin.yaml" />
-   <node name="destin" pkg="ros_destin" type="daemon" output="screen" />
+   <node name="tf_node" pkg="tensorflow_node" type="daemon" output="screen" />
 </launch>

msg/DestinNodeState.msg → msg/TFNodeState.msg

@@ -1,4 +1,4 @@
-# DestinNodeState.msg
+# TFNodeState.msg
 Header header
 string id                   # might be 'ae-e239j0-1-3-2'
 string type                 # might be 'autoencoder'

msg/DestinStatus.msg → msg/TFStatus.msg

@@ -1,4 +1,4 @@
-# DestinStatus.msg
+# TFStatus.msg
 Header header
 string id                   # might be 'chest_camera'
 bool running

package.xml

@@ -1,7 +1,7 @@
 <package format="2">
-  <name>ros_destin</name>
+  <name>tensorflow_node</name>
   <version>0.0.1</version>
-  <description>Tensorflow implementation of DeSTIN</description>
+  <description>Persistent Tensorflow node</description>
   <maintainer email="[email protected]">Ralf Mayet</maintainer>
   <license>Unlicense</license>
 

scripts/daemon

@@ -7,48 +7,46 @@ import rospy
 import tensorflow as tf
 import numpy as np
 
-from destin import *
+from tensorflow_node import *
 
 from std_msgs.msg import Header
-from ros_destin.msg import DestinNodeState
+from tensorflow_node.msg import TFNodeState
 
 def str_to_class(str):
     return getattr(sys.modules[__name__], str)
 
 with tf.Session() as sess:
     # initialize ROS node
-    rospy.init_node('destin', anonymous=False, log_level=rospy.INFO)
-    rospy.loginfo("Destin ROS node launching")
+    rospy.init_node('tensorflow_daemon', anonymous=False, log_level=rospy.INFO)
+    rospy.loginfo("Tensorflow daemon ROS node launching")
 
-    # TODO Assertions: 
-    #   - inputlayer size and receptive field / stride fit together...
-    #   - ...?
+    config_prefix = "tensorflow_node"
 
     # initialize input layer from yaml
-    inputlayer_type = rospy.get_param("destin/inputlayer/type")
+    inputlayer_type = rospy.get_param(config_prefix+"/inputlayer/type")
     inputlayer_class = str_to_class(inputlayer_type)
-    inputlayer_params = rospy.get_param("destin/inputlayer/params")
+    inputlayer_params = rospy.get_param(config_prefix+"/inputlayer/params")
     inputlayer = inputlayer_class(**inputlayer_params)
 
     # initialize network from yaml
-    architecture_type = rospy.get_param("destin/architecture/type")
+    architecture_type = rospy.get_param(config_prefix+"/architecture/type")
     architecture_class = str_to_class(architecture_type)
-    architecture_params = rospy.get_param("destin/architecture/params")
+    architecture_params = rospy.get_param(config_prefix+"/architecture/params")
     architecture = architecture_class(sess, inputlayer, **architecture_params)
 
     # initialize summary writer
-    if (rospy.get_param("destin/publishing/summaries")):
+    if (rospy.get_param(config_prefix+"/publishing/summaries")):
         rospy.loginfo("recording summaries to " + SummaryWriter().get_summary_folder())
         # initialize summary writer with graph
         SummaryWriter().writer.add_graph(sess.graph)
         merged_summary_op = tf.merge_all_summaries()
 
     # initialize publishers for network
     publishers = {}
-    topic_name = rospy.get_param("destin/publishing/topic")
-    queue_size = rospy.get_param("/destin/inputlayer/params/batch_size")
+    topic_name = rospy.get_param(config_prefix+"/publishing/topic")
+    queue_size = rospy.get_param(config_prefix+"/inputlayer/params/batch_size")
     for node in architecture.nodes:
-        publishers[node.name] = rospy.Publisher('/'+topic_name+'/'+node.name, DestinNodeState, queue_size=queue_size)
+        publishers[node.name] = rospy.Publisher('/'+topic_name+'/'+node.name, TFNodeState, queue_size=queue_size)
 
     # main callback to evaluate architecture and publish states
     iteration = 0
@@ -67,7 +65,7 @@ with tf.Session() as sess:
 
             for state in ae_state:
                 # formulate message
-                msg = DestinNodeState()
+                msg = TFNodeState()
 
                 msg.header = Header()
                 msg.header.stamp = rospy.Time.now()
@@ -80,7 +78,7 @@ with tf.Session() as sess:
                 publishers[node.name].publish(msg)
 
         # publish summary output
-        if (rospy.get_param("destin/publishing/summaries")):
+        if (rospy.get_param(config_prefix+"/publishing/summaries")):
             summary_str = merged_summary_op.eval(feed_dict=feed_dict, session=sess)
             SummaryWriter().writer.add_summary(summary_str, iteration)
             SummaryWriter().writer.flush()

scripts/tensorboard

@@ -1,3 +1,3 @@
 #!/bin/bash
 
-tensorboard --logdir=runs:`rosparam get destin/publishing/summary_folder` --port 6006 --reload_interval 5
+tensorboard --logdir=runs:`rosparam get tensorflow_node/publishing/summary_folder` --port 6006 --reload_interval 5

setup.py

@@ -2,7 +2,7 @@
 from catkin_pkg.python_setup import generate_distutils_setup
 
 d = generate_distutils_setup(
-    packages=['destin'],
+    packages=['tensorflow_node'],
     package_dir={'': 'src'}
 )
 

src/destin/architectures/__init__.py → src/tensorflow_node/architectures/__init__.py

@@ -1,3 +1,3 @@
 from .architecture import NetworkArchitecture
-from .destin_arch import DestinArchitecture
+from .destin import DestinArchitecture
 from .handcoded_destin import HandcodedDestinArchitecture

src/destin/architectures/architecture.py → src/tensorflow_node/architectures/architecture.py

@@ -5,7 +5,7 @@
 import rospy
 import tensorflow as tf
 
-from destin.nodes import *
+from tensorflow_node.nodes import *
 
 class NetworkArchitecture(object):
     __metaclass__ = abc.ABCMeta

src/destin/architectures/destin_arch.py → src/tensorflow_node/architectures/destin.py

@@ -6,13 +6,16 @@
 import tensorflow as tf
 import numpy as np
 
-from destin.nodes import *
-from destin.architectures import NetworkArchitecture
+from tensorflow_node.nodes import *
+from tensorflow_node.architectures import NetworkArchitecture
 
 class DestinArchitecture(NetworkArchitecture):
 
     def __init__(self, session, inputlayer, node_type, node_params, receptive_field=[14,14], stride=[7,7]):
-
+        # TODO Assertions: 
+        #   - inputlayer size and receptive field / stride fit together...
+        #   - ...?
+
         self.nodes = []
         self.train_op = []
 

src/destin/architectures/handcoded_destin.py → src/tensorflow_node/architectures/handcoded_destin.py

@@ -5,7 +5,7 @@
 import rospy
 import tensorflow as tf
 
-from destin.architectures import NetworkArchitecture
+from tensorflow_node.architectures import NetworkArchitecture
 
 class HandcodedDestinArchitecture(NetworkArchitecture):
 

src/destin/input/opencv.py → src/tensorflow_node/input/opencv.py

@@ -6,7 +6,7 @@
 import os.path
 import rospy
 
-from destin.input import InputLayer
+from tensorflow_node.input import InputLayer
 
 
 class OpenCVInputLayer(InputLayer):
@@ -22,8 +22,8 @@ def __init__(self, batch_size=1, output_size=[28, 28], input="", number_of_frame
     def feed_to(self, feed_callback):
 
         # TODO: there should be clearer distinction here, get these params via daemon.
-        frames = rospy.get_param("/destin/inputlayer/params/number_of_frames")
-        repeat = rospy.get_param("/destin/inputlayer/params/repeat")
+        frames = rospy.get_param("tensorflow_node/inputlayer/params/number_of_frames")
+        repeat = rospy.get_param("tensorflow_node/inputlayer/params/repeat")
 
         # check if file exists
         if not os.path.isfile(self.input) or self.input == 0:

src/destin/input/ros.py → src/tensorflow_node/input/ros.py

@@ -5,7 +5,7 @@
 import os.path
 from skimage import transform
 
-from destin.input import InputLayer
+from tensorflow_node.input import InputLayer
 
 from sensor_msgs.msg import Image
 

src/destin/nodes/autoencoder.py → src/tensorflow_node/nodes/autoencoder.py

@@ -12,7 +12,7 @@
 import numpy as np
 import rospy
 
-from destin import SummaryWriter
+from tensorflow_node import SummaryWriter
 
 
 class AutoEncoderNode(object):

src/destin/tests/test_autoencoder.py → src/tensorflow_node/tests/test_autoencoder.py

@@ -6,9 +6,9 @@
 import tensorflow as tf
 import numpy as np
 
-from destin import AutoEncoderNode
-from destin import SummaryWriter
-from destin import OpenCVInputLayer
+from tensorflow_node import AutoEncoderNode
+from tensorflow_node import SummaryWriter
+from tensorflow_node import OpenCVInputLayer
 
 
 # this tests very basic functionality of the autoencoder

src/destin/tests/test_inputlayer.py → src/tensorflow_node/tests/test_inputlayer.py

@@ -6,9 +6,9 @@
 import tensorflow as tf
 import numpy as np
 
-from destin import AutoEncoderNode
-from destin import SummaryWriter
-from destin import OpenCVInputLayer
+from tensorflow_node import AutoEncoderNode
+from tensorflow_node import SummaryWriter
+from tensorflow_node import OpenCVInputLayer
 
 
 # this tests the cropping of the input layer.

src/destin/tests/test_stacked_autoencoder.py → src/tensorflow_node/tests/test_stacked_autoencoder.py

@@ -6,9 +6,9 @@
 import tensorflow as tf
 import numpy as np
 
-from destin import StackedAutoEncoderNode
-from destin import SummaryWriter
-from destin import OpenCVInputLayer
+from tensorflow_node import StackedAutoEncoderNode
+from tensorflow_node import SummaryWriter
+from tensorflow_node import OpenCVInputLayer
 
 
 # this tests very basic functionality of the stacked autoencoder

src/destin/utils/summary_writer.py → src/tensorflow_node/utils/summary_writer.py

@@ -27,7 +27,7 @@ def __init__(self):
 
     def get_output_folder(self, path):
         #output_path = pjoin(os.getcwd(), 'output', path)
-        output_path = rospy.get_param("destin/publishing/summary_folder")
+        output_path = rospy.get_param("tensorflow_node/publishing/summary_folder")
         if not os.path.exists(output_path):
             os.makedirs(output_path)
         return output_path