Remove things specific to GpuConv2d in tests

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -439,18 +439,6 @@ def test_default_conv():
         assert any([isinstance(a.op, cuda.blas.GpuCorrMM)
                     for a in f.maker.fgraph.apply_nodes])
 
-    mode = theano_mode.excluding('local_conv_dnn', 'local_conv_gemm')
-    f = theano.function([img, fil], c, mode=mode)
-
-    assert any([isinstance(a.op, cuda.blas.GpuConv)
-                for a in f.maker.fgraph.apply_nodes])
-
-    mode = theano_mode.excluding('conv_dnn', 'conv_gemm')
-    f = theano.function([img, fil], c, mode=mode)
-
-    assert any([isinstance(a.op, cuda.blas.GpuConv)
-                for a in f.maker.fgraph.apply_nodes])
-
 
 def _test_full(cls, mode=None, version=[-1], extra_shapes=[],
                test_bigger_kernels=True):
@@ -830,17 +818,8 @@ def conv_grad(mode, bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsample, op):
 
     # TODO: also test custom pad values
     corr_op = op(mode, subsample)(i, k)
-    # try to compile reference implementation without shape,
-    # so we don't have to compile hundreds of versions
     conv_op = tensor.nnet.conv2d(i, k[:, :, ::-1, ::-1],
                                  border_mode=mode, subsample=subsample)
-    try:
-        conv_op_di = theano.grad(conv_op.sum(), i)
-        conv_op_dk = theano.grad(conv_op.sum(), k)
-    except Exception:
-        # compile with shape information only when needed
-        conv_op = tensor.nnet.conv2d(i, k[:, :, ::-1, ::-1],
-                                     ishape, kshape, mode, subsample)
     conv_op_di = theano.grad(conv_op.sum(), i)
     conv_op_dk = theano.grad(conv_op.sum(), k)
     corr_op_di = theano.grad(corr_op.sum(), i)
@@ -848,18 +827,15 @@ def conv_grad(mode, bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsample, op):
     outputs = [corr_op, conv_op,
                corr_op_di, conv_op_di,
                corr_op_dk, conv_op_dk]
-    try:
+
     conv_op_dik = theano.grad(conv_op_di.sum(), k)
     conv_op_dki = theano.grad(conv_op_dk.sum(), i)
     corr_op_dik = theano.grad(corr_op_di.sum(), k)
     corr_op_dki = theano.grad(corr_op_dk.sum(), i)
     outputs.extend([corr_op_dik, conv_op_dik,
                     corr_op_dki, conv_op_dki])
-    except Exception:
-        # skip if the reference implementation can't do it
-        pass
 
-    f = theano.function([i, k], outputs, mode=theano_mode.excluding('conv_dnn', 'conv_gemm'))
+    f = theano.function([i, k], outputs, mode=theano_mode)
 
     allvals = f(npy_img, npy_kern)