Merge pull request #2398 from ballasn/autoname

theano/gof/graph.py

@@ -10,6 +10,8 @@ __docformat__ = "restructuredtext en"
 
 
 from copy import copy
+from itertools import count
+
 
 import theano
 import warnings
@@ -31,7 +33,6 @@ class Node(utils.object2):
     Variable.owner / Apply.inputs and its children
     via Variable.clients / Apply.outputs.
     """
-
     def get_parents(self):
         """ Return a list of the parents of this node.
         Should return a copy--i.e., modifying the return
@@ -314,9 +315,10 @@ class Variable(Node):
 
     `compile.function` uses each `Apply` instance's `inputs` attribute
     together with each Variable's `owner` field to determine which inputs are necessary to compute the function's outputs.
-
     """
     #__slots__ = ['type', 'owner', 'index', 'name']
+    __count__ = count(0)
+
     def __init__(self, type, owner=None, index=None, name=None):
         """Initialize type, owner, index, name.
 
@@ -334,6 +336,8 @@ class Variable(Node):
         :param name: a string for pretty-printing and debugging
 
         """
+        super(Variable, self).__init__()
+
         self.tag = utils.scratchpad()
         self.type = type
         if owner is not None and not isinstance(owner, Apply):
@@ -345,6 +349,7 @@ class Variable(Node):
         if name is not None and not isinstance(name, basestring):
             raise TypeError("name must be a string", name)
         self.name = name
+        self.auto_name = 'auto_' + str(next(self.__count__))
 
     def __str__(self):
         """WRITEME"""

theano/gof/tests/test_graph.py

 import pickle
 import unittest
+import numpy
+from itertools import count
 
-from theano import tensor
+
+from theano import (
+      clone, sparse,
+      shared, tensor)
 from theano.gof.graph import (
-        Apply, as_string, clone, general_toposort, inputs, io_toposort,
+        Node, Apply, Constant,
+        as_string, clone, general_toposort, inputs, io_toposort,
         is_same_graph, Variable)
 from theano.gof.op import Op
 from theano.gof.type import Type
+from theano.tensor.var import TensorVariable
+from theano.sandbox.cuda.var import (
+        CudaNdarrayVariable, CudaNdarrayConstant, CudaNdarraySharedVariable)
+
+
+
 
 
 def as_variable(x):
@@ -48,7 +60,6 @@ class MyOp(Op):
 
 MyOp = MyOp()
 
-
 ##########
 # inputs #
 ##########
@@ -311,3 +322,87 @@ class TestEval(unittest.TestCase):
                 "variable must have cache after eval")
         self.assertFalse(hasattr(pickle.loads(pickle.dumps(self.w)), '_fn_cache'),
                 "temporary functions must not be serialized")
+
+
+################
+# autoname     #
+################
+class TestAutoName:
+
+    def test_auto_name(self):
+        ## Re-init counter
+        Variable.__count__ = count(0)
+        r1, r2 = MyVariable(1), MyVariable(2)
+        assert r1.auto_name == "auto_0"
+        assert r2.auto_name == "auto_1"
+
+    def test_constant(self):
+        ## Re-init counter
+        Variable.__count__ = count(0)
+        r1 = tensor.constant(1.5)
+        r2 = tensor.constant(1.5)
+        assert r1.auto_name == "auto_0"
+        assert r2.auto_name == "auto_1"
+
+    def test_tensorvariable(self):
+        ## Re-init counter
+        Variable.__count__ = count(0)
+        r1 = tensor.TensorType(dtype='int32', broadcastable=())('myvar')
+        r2 = tensor.TensorVariable(tensor.TensorType(dtype='int32',
+                                                     broadcastable=()))
+        r3 = shared(numpy.random.randn(3,4))
+        assert r1.auto_name == "auto_0"
+        assert r2.auto_name == "auto_1"
+        assert r3.auto_name == "auto_2"
+
+    def test_sparsevariable(self):
+        ## Re-init counter
+        Variable.__count__ = count(0)
+        r1 = sparse.csc_matrix(name='x', dtype='float32')
+        r2 = sparse.dense_from_sparse(r1)
+        r3 = sparse.csc_from_dense(r2)
+        assert r1.auto_name == "auto_0"
+        assert r2.auto_name == "auto_1"
+        assert r3.auto_name == "auto_2"
+
+    def test_cudandarrayvariable(self):
+        ## Re-init counter
+        Variable.__count__ = count(0)
+        mytype = tensor.TensorType(dtype='int32', broadcastable=())
+        r1 = CudaNdarrayVariable(type='int32')
+        r2 = CudaNdarrayVariable(type='int32')
+        r3 = CudaNdarrayConstant(type=mytype,
+                                 data=1)
+        r4 = CudaNdarraySharedVariable(name='x', type=mytype,
+                                       value=1, strict=False)
+        assert r1.auto_name == "auto_0"
+        assert r2.auto_name == "auto_1"
+        assert r3.auto_name == "auto_2"
+        assert r4.auto_name == "auto_3"
+
+    def test_randomvariable(self):
+        ## Re-init counter
+        Variable.__count__ = count(0)
+        mytype = tensor.TensorType(dtype='int32', broadcastable=())
+        r1 = tensor.shared_randomstreams.RandomStateSharedVariable(name='x',
+                                                                   type=mytype,
+                                                                   value=1,
+                                                                   strict=False)
+        r2 = tensor.shared_randomstreams.RandomStateSharedVariable(name='x',
+                                                                   type=mytype,
+                                                                   value=1,
+                                                                   strict=False)
+        assert r1.auto_name == "auto_0"
+        assert r2.auto_name == "auto_1"
+
+
+    def test_clone(self):
+        ## Re-init counter
+        Variable.__count__ = count(0)
+        r1 = MyVariable(1)
+        r2 = r1.clone()
+        assert r1.auto_name == "auto_0"
+        assert r2.auto_name == "auto_1"
+
+
+