Minor changes

theano/gpuarray/opt.py

@@ -2135,7 +2135,7 @@ class ConvMetaOptimizer(LocalMetaOptimizer):
                                          node.op.subsample,
                                          node.op.filter_dilation)
 
-            result[kshape] = theano.tensor.as_tensor_variable(node.op.kshp[2:])
+            result[kshape] = theano.tensor.as_tensor_variable(node.op.kshp[-2:])
 
             for(var, shape) in zip((img, top), (node.op.imshp, tshp)):
                 result[var] = theano.shared(np.random.random(shape).astype(var.dtype),

theano/gpuarray/tests/test_gemmcorr.py

@@ -8,9 +8,9 @@ from theano.tests import unittest_tools as utt
 
 from theano.tensor.nnet.corr import CorrMM, CorrMM_gradWeights, CorrMM_gradInputs
 
-from theano.gpuarray.type import gpuarray_shared_constructor
-from theano.gpuarray.blas import GpuCorrMM, GpuCorrMM_gradWeights, GpuCorrMM_gradInputs
-from config import mode_with_gpu, mode_without_gpu, ref_cast
+from ..type import gpuarray_shared_constructor
+from ..blas import GpuCorrMM, GpuCorrMM_gradWeights, GpuCorrMM_gradInputs
+from .config import mode_with_gpu, mode_without_gpu, ref_cast
 from theano.tensor.nnet.tests.test_abstract_conv import Grouped_conv_noOptim, TestUnsharedConv
 
 

theano/tensor/nnet/tests/test_abstract_conv.py

@@ -1754,9 +1754,9 @@ class TestUnsharedConv(unittest.TestCase):
     conv2d_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
     conv2d_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
 
-    def setUp(self):
-        self.mode = theano.compile.mode.Mode(optimizer='None')
+    mode = theano.compile.mode.Mode(optimizer='None')
 
+    def setUp(self):
         self.img_shape = [(2, 1, 4, 4), (1, 2, 4, 2), (1, 3, 5, 3), (1, 4, 4, 4)]
         self.kern_shape = [(2, 2, 2, 1, 3, 3), (2, 4, 2, 2, 4, 2), (3, 2, 1, 1, 3, 3), (4, 3, 3, 2, 4, 2)]
         self.topgrad_shape = [(2, 2, 2, 2), (1, 2, 4, 2), (1, 3, 2, 1), (1, 4, 3, 3)]
@@ -1765,7 +1765,9 @@ class TestUnsharedConv(unittest.TestCase):
         self.filter_dilation = (1, 1)
         self.num_groups = [1, 1, 3, 2]
 
-        self.verify_flags = np.random.choice([True, False], 4, [1.0, 0.0])
+        # self.verify_flags = np.random.choice([True, False], 4, [0.5, 0.5])
+        # Above line can be used instead if speed is a concern
+        self.verify_flags = [True] * 4
 
         self.ref_mode = 'FAST_RUN'
         if theano.config.cxx == "":

theano/tensor/nnet/tests/test_corr.py

@@ -452,9 +452,9 @@ class TestGroupCorr2d(Grouped_conv_noOptim):
         utt.assert_allclose(gconv_output, conv_output)
 
 
-class TestUnsharedCorr2D(TestUnsharedConv):
+class TestUnsharedCorr2d(TestUnsharedConv):
     if theano.config.mode == "FAST_COMPILE":
-        mode = theano.compile.get_mode("FAST_RUN").excluding('gpuarray')
+        mode = theano.compile.get_mode("FAST_RUN")
     else:
         mode = None
     conv2d = corr.CorrMM