Revert "Network compression"

deepplantphenomics/__init__.py

@@ -5,4 +5,4 @@
 
 from .deepplantpheno import *
 from .tools import *
-from .networks import *
+from .networks import *

deepplantphenomics/layers.py

@@ -165,7 +165,6 @@ def add_to_graph(self):
         if self.__initializer == 'xavier':
             self.weights = tf.get_variable(self.name + '_weights', shape=[vec_size, self.output_size],
                                            initializer=tf.contrib.layers.xavier_initializer())
-
         else:
             self.weights = tf.get_variable(self.name + '_weights',
                                            shape=[vec_size, self.output_size],
@@ -230,10 +229,6 @@ def forward_pass(self, x, deterministic):
         else:
             return tf.nn.dropout(x, self.p)
 
-    def set_p(self, p):
-        print("dropout: %f -> %f" % (self.p, p))
-        self.p = p
-
 
 class moderationLayer(object):
     """Layer for fusing moderating data into the input vector"""

deepplantphenomics/loaders.py

@@ -10,10 +10,10 @@ def split_raw_data(images, labels, ratio, moderation_features=None, augmentation
     if isinstance(labels, list):
         if split_labels:
             labels = [' '.join(map(str, label)) for label in labels]
-        total_samples = len(labels)
-    else:
-        total_samples = labels.get_shape().as_list()[0]
+        #else:
+            #total_samples = labels.get_shape().as_list()[0]
 
+    total_samples = len(labels)
 
 
     # calculate and perform random split

deepplantphenomics/tools.py

@@ -1,4 +1,4 @@
-from deepplantphenomics import networks
+from . import networks
 import numpy as np
 import cv2
 

examples/arabidopsis_strain_classifier_test.py

@@ -1,5 +1,3 @@
-from __future__ import print_function
-
 #
 # Demonstrates the use of tools.classify_arabidopsis_strain on images of rosettes.
 # Loads filenames from the IPPN dataset by default.
@@ -21,6 +19,6 @@
 y = dpp.tools.classify_arabidopsis_strain(images)
 
 for k,v in zip(images, y):
-    print('%s: %s' % (os.path.basename(k), v))
+    print '%s: %s' % (os.path.basename(k), v)
 
 print('Done')

examples/rosette_leaf_count_test.py

@@ -1,5 +1,3 @@
-from __future__ import print_function
-
 #
 # Demonstrates the use of tools.predict_rosette_leaf_count on images of rosettes.
 # Loads filenames from the IPPN dataset by default.
@@ -23,4 +21,4 @@
 for k,v in zip(images, y):
     print('%s: %d' % (os.path.basename(k), v))
 
-print('Done')
+print('Done')

mkdocs.yml

@@ -10,7 +10,6 @@ pages:
         - Model Options: Model-Options.md
         - Data Fusion Options: Data-Fusion-Options.md
         - Semantic Segmentation: Semantic-Segmentation.md
-        - Compression: Compression.md
       - Tools: Tools.md
       - Hyperparameter  Optimization: Hyperparameter-Optimization.md
       - Tutorials: