From 049a8364211ca91d73e10b2002c18f10fe89b8b2 Mon Sep 17 00:00:00 2001
From: Peng Yu <peng.yu@shopify.com>
Date: Fri, 16 Feb 2018 10:59:14 -0500
Subject: [PATCH] add inference support for tree and forest variables

---
 .../contrib/tensor_forest/python/tensor_forest.py  | 31 +++++++++++++---------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
index 7a35a70..0feca52 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
@@ -295,7 +295,7 @@ def get_epoch_variable():
 
 
 # A simple container to hold the training variables for a single tree.
-class TreeTrainingVariables(object):
+class TreeVariables(object):
   """Stores tf.Variables for training a single random tree.
 
   Uses tf.get_variable to get tree-specific names so that this can be used
@@ -303,7 +303,7 @@ class TreeTrainingVariables(object):
   then relies on restoring that model to evaluate).
   """
 
-  def __init__(self, params, tree_num, training):
+  def __init__(self, params, tree_num, training, tree_config='', tree_stat=''):
     if (not hasattr(params, 'params_proto') or
         not isinstance(params.params_proto,
                        _params_proto.TensorForestParams)):
@@ -315,27 +315,27 @@ class TreeTrainingVariables(object):
       # TODO(gilberth): Manually shard this to be able to fit it on
       # multiple machines.
       self.stats = stats_ops.fertile_stats_variable(
-          params, '', self.get_tree_name('stats', tree_num))
+          params, tree_stat, self.get_tree_name('stats', tree_num))
     self.tree = model_ops.tree_variable(
-        params, '', self.stats, self.get_tree_name('tree', tree_num))
+        params, tree_config, self.stats, self.get_tree_name('tree', tree_num))
 
   def get_tree_name(self, name, num):
     return '{0}-{1}'.format(name, num)
 
 
-class ForestTrainingVariables(object):
+class ForestVariables(object):
   """A container for a forests training data, consisting of multiple trees.
 
-  Instantiates a TreeTrainingVariables object for each tree. We override the
+  Instantiates a TreeVariables object for each tree. We override the
   __getitem__ and __setitem__ function so that usage looks like this:
 
-    forest_variables = ForestTrainingVariables(params)
+    forest_variables = ForestVariables(params)
 
     ... forest_variables.tree ...
   """
 
   def __init__(self, params, device_assigner, training=True,
-               tree_variables_class=TreeTrainingVariables):
+               tree_variables_class=TreeVariables, tree_configs=None, tree_stats=None):
     self.variables = []
     # Set up some scalar variables to run through the device assigner, then
     # we can use those to colocate everything related to a tree.
@@ -347,7 +347,12 @@ class ForestTrainingVariables(object):
 
     for i in range(params.num_trees):
       with ops.device(self.device_dummies[i].device):
-        self.variables.append(tree_variables_class(params, i, training))
+        kwargs = {}
+        if tree_configs is not None:
+            kwargs.update(dict(tree_config=tree_configs[i]))
+        if tree_stats is not None:
+            kwargs.update(dict(tree_stat=tree_stats[i]))
+        self.variables.append(tree_variables_class(params, i, training, **kwargs))
 
   def __setitem__(self, t, val):
     self.variables[t] = val
@@ -361,9 +366,11 @@ class RandomForestGraphs(object):
 
   def __init__(self,
                params,
+               tree_configs=None,
+               tree_stats=None,
                device_assigner=None,
                variables=None,
-               tree_variables_class=TreeTrainingVariables,
+               tree_variables_class=TreeVariables,
                tree_graphs=None,
                training=True):
     self.params = params
@@ -371,9 +378,9 @@ class RandomForestGraphs(object):
         device_assigner or framework_variables.VariableDeviceChooser())
     logging.info('Constructing forest with params = ')
     logging.info(self.params.__dict__)
-    self.variables = variables or ForestTrainingVariables(
+    self.variables = variables or ForestVariables(
         self.params, device_assigner=self.device_assigner, training=training,
-        tree_variables_class=tree_variables_class)
+        tree_variables_class=tree_variables_class, tree_configs=tree_configs, tree_stats=tree_stats)
     tree_graph_class = tree_graphs or RandomTreeGraphs
     self.trees = [
         tree_graph_class(self.variables[i], self.params, i)
-- 
2.7.4