Better error message

theano/sandbox/cuda/dnn_fwd.c

@@ -5,6 +5,11 @@ APPLY_SPECIFIC(conv_fwd)(CudaNdarray *input, CudaNdarray *kerns,
                          CudaNdarray *om, cudnnConvolutionDescriptor_t desc,
                          float alpha, float beta, CudaNdarray **output) {
   cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
+  if (CudaNdarray_HOST_DIMS(input)[1] != CudaNdarray_HOST_DIMS(kerns)[1]) {
+    PyErr_SetString(PyExc_ValueError,
+		    "GpuDnnConv images and kernel must have the same stack size\n");
+    return 1;
+  }
 
   if (c_set_tensor4d(input, APPLY_SPECIFIC(input)) == -1)
     return 1;

theano/sandbox/cuda/dnn_gi.c

@@ -6,6 +6,12 @@ APPLY_SPECIFIC(conv_gi)(CudaNdarray *kerns, CudaNdarray *output,
                         float alpha, float beta, CudaNdarray **input) {
   cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
 
+  if (CudaNdarray_HOST_DIMS(im)[1] != CudaNdarray_HOST_DIMS(kerns)[1]) {
+    PyErr_SetString(PyExc_ValueError,
+		    "GpuDnnConv images and kernel must have the same stack size\n");
+    return 1;
+  }
+
   if (c_set_tensor4d(output, APPLY_SPECIFIC(output)) == -1)
     return 1;
   if (c_set_filter(kerns, APPLY_SPECIFIC(kerns)) == -1)

theano/sandbox/cuda/dnn_gw.c

@@ -6,6 +6,12 @@ APPLY_SPECIFIC(conv_gw)(CudaNdarray *input, CudaNdarray *output,
                         float alpha, float beta, CudaNdarray **kerns) {
   cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
 
+  if (CudaNdarray_HOST_DIMS(input)[1] != CudaNdarray_HOST_DIMS(km)[1]) {
+    PyErr_SetString(PyExc_ValueError,
+		    "GpuDnnConv images and kernel must have the same stack size\n");
+    return 1;
+  }
+
   if (c_set_tensor4d(input, APPLY_SPECIFIC(input)) == -1)
     return 1;
   if (c_set_tensor4d(output, APPLY_SPECIFIC(output)) == -1)

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -184,11 +184,6 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
         kern = kern[:, :, ::kern_stride[0], ::kern_stride[1]]
         npy_kern = npy_kern[:, :, ::kern_stride[0], ::kern_stride[1]]
 
-    t2 = None
-
-    t0 = time.time()
-    cpuval = py_conv(npy_img, npy_kern, mode, subsample)
-    t1 = time.time()
     i = cuda.CudaNdarrayType(
         broadcastable=[sh == 1 for sh in npy_img.shape])()
     k = cuda.CudaNdarrayType(
@@ -198,21 +193,28 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
                                           version=version,
                                           verbose=verbose,
                                           kshp=compile_kshp)(i, k)
-    assert [(sh == 1) is br for
-            sh, br in zip(cpuval.shape[:2], op.type.broadcastable[:2])]
     f = theano.function([i, k], op, mode=theano_mode)
     if cls is not None:
         assert any([isinstance(node.op, cls)
                     for node in f.maker.fgraph.toposort()]), "Cannot find class %r in %r" % (cls, f.maker.fgraph.toposort())
-    gpuval = f(img, kern)
     t2 = time.time()
+    gpuval = f(img, kern)
+    t3 = time.time()
     for i in range(nb_iter):
         gpuval2 = f(img, kern)
         assert (numpy.asarray(gpuval) == numpy.asarray(gpuval2)).all()
     gpuval = numpy.asarray(gpuval)
+
+    # CPU val computed after GPU val to get the GPU errors.
+    t0 = time.time()
+    cpuval = py_conv(npy_img, npy_kern, mode, subsample)
+    t1 = time.time()
+
     assert gpuval.shape == cpuval.shape, ("shape mismatch", gpuval.shape, cpuval.shape)
     assert_allclose(cpuval, gpuval, rtol=rtol, atol=atol)
     assert numpy.all(numpy.isfinite(gpuval)), gpuval
+    assert [(sh == 1) is br for
+            sh, br in zip(cpuval.shape[:2], op.type.broadcastable[:2])]
 
     if (t2 is not None):
         if mode == 'valid':
@@ -222,7 +224,7 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
                          kshape[3] * ishape[2] * ishape[3] * 2)
         approx_fp /= 1e6
         cpu_mflops = approx_fp / (t1 - t0)
-        gpu_mflops = approx_fp / (t2 - t1)
+        gpu_mflops = approx_fp / (t3 - t2)
         if verbose > 0:
             print('%15s' % str(ishape), '%15s' % str(kshape), end=' ', file=sys.stdout)
             print('%12.5f  %7.2f %7.2f %7.1f' % (approx_fp,
@@ -410,6 +412,16 @@ def test_dnn_valid():
         yield t
 
 
+def test_dnn_valid_err():
+    try:
+        _params_allgood((1, 2, 4, 4), (1, 1, 2, 2), 'valid',
+                        theano_mode=theano_mode.including("cudnn"),
+                        cls=DnnBase)
+        assert "Shape error not raised"
+    except ValueError:
+        pass
+
+
 def test_default_conv():
     """Just test that we introduce the right GPU convolution
     version.

theano/sandbox/cuda/tests/test_dnn.py

@@ -475,7 +475,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
             dtype='float32'
         )
         kern_vals = numpy.asarray(
-            numpy.random.rand(3, 4, 5, 6),
+            numpy.random.rand(13, 14, 15, 16),
             dtype='float32'
         )