Merge pull request #1891 from nouiz/signal_conv2d

NEWS_DEV.txt

@@ -13,6 +13,9 @@ Done up to PR 1608
 
 * https://github.com/Theano/Theano/pull/1591 # need info
 
+Interface change:
+ - theano.tensor.signal.conv2d(2d,2d) output 2d answer. (Frederic B., reported by Alexander Izvorski)
+
 
 Theano Development version
 ==========================

theano/configdefaults.py

@@ -400,6 +400,12 @@ AddConfigVar('warn.vm_gc_bug',
         BoolParam(False),
         in_c_key=False)
 
+AddConfigVar('warn.signal_conv2d_interface',
+             ("Warn we use the new signal.conv2d() when its interface"
+              " changed mid June 2014"),
+             BoolParam(warn_default('0.7')),
+             in_c_key=False)
+
 AddConfigVar('compute_test_value',
         ("If 'True', Theano will run each op at graph build time, using "
          "Constants, SharedVariables and the tag 'test_value' as inputs "

theano/sandbox/cuda/tests/test_driver.py

@@ -2,11 +2,15 @@ import numpy
 import theano
 
 # Skip test if cuda_ndarray is not available.
-from nose.plugins.skip import SkipTest
-import theano.sandbox.cuda as cuda_ndarray
-if cuda_ndarray.cuda_available == False:
-    raise SkipTest('Optional package cuda disabled')
-
+try:
+    from nose.plugins.skip import SkipTest
+    import theano.sandbox.cuda as cuda_ndarray
+    if cuda_ndarray.cuda_available == False:
+        raise SkipTest('Optional package cuda disabled')
+except ImportError:
+    # To have the GPU back-end work without nose, we need this file to
+    # be importable without nose.
+    pass
 from theano.gof.python25 import any
 import theano.sandbox.cuda as cuda
 import theano.sandbox.cuda.basic_ops as B

theano/tensor/signal/conv.py

@@ -5,6 +5,9 @@ generic 2D convolution.
 
 __docformat__ = "restructuredtext en"
 
+import warnings
+
+import theano
 import theano.tensor as tensor
 from theano.tensor.nnet import conv
 
@@ -82,7 +85,16 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
     output = op(input4D, filters4D)
 
     # flatten to 3D tensor if convolving with single filter or single image
-    if input.ndim==2 or filters.ndim==2:
+    if input.ndim == 2 and filters.ndim == 2:
+        if theano.config.warn.signal_conv2d_interface:
+            warnings.warn(
+                "theano.tensor.signal.conv2d() now output 2d tensor when both"
+                " inputs are 2d. To disable this warning, set the Theano flag"
+                " warn.signal_conv2d_interface to False",
+                stacklevel=3)
+
+        output = tensor.flatten(output.T, outdim=2).T
+    elif input.ndim == 2 or filters.ndim == 2:
         output = tensor.flatten(output.T, outdim=3).T
 
     return output

theano/tensor/signal/tests/test_conv.py

@@ -17,7 +17,7 @@ class TestSignalConv2D(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()
 
-    def validate(self, image_shape, filter_shape, verify_grad=True):
+    def validate(self, image_shape, filter_shape, out_dim, verify_grad=True):
 
         image_dim = len(image_shape)
         filter_dim = len(filter_shape)
@@ -36,6 +36,7 @@ class TestSignalConv2D(unittest.TestCase):
         def sym_conv2d(input, filters):
             return conv.conv2d(input, filters)
         output = sym_conv2d(input, filters)
+        assert output.ndim == out_dim
         theano_conv = theano.function([input, filters], output)
 
         # initialize input and compute result
@@ -90,10 +91,10 @@ class TestSignalConv2D(unittest.TestCase):
             theano.config.cxx == ""):
             raise SkipTest("conv2d tests need SciPy or a c++ compiler")
 
-        self.validate((1, 4, 5), (2, 2, 3), verify_grad=True)
-        self.validate((7, 5), (5, 2, 3), verify_grad=False)
-        self.validate((3, 7, 5), (2, 3), verify_grad=False)
-        self.validate((7, 5), (2, 3), verify_grad=False)
+        self.validate((1, 4, 5), (2, 2, 3), out_dim=4, verify_grad=True)
+        self.validate((7, 5), (5, 2, 3), out_dim=3, verify_grad=False)
+        self.validate((3, 7, 5), (2, 3), out_dim=3, verify_grad=False)
+        self.validate((7, 5), (2, 3), out_dim=2, verify_grad=False)
 
     def test_fail(self):
         """