def test_default_conv():
 img = theano.tensor.ftensor4()
 fil = theano.tensor.ftensor4()
 c = theano.tensor.nnet.conv2d(img, fil)
 f = theano.function([img, fil], c, mode=theano_mode)
 if cuda.dnn.dnn_available():
  assert any([isinstance(a.op, GpuDnnConv) for a in f.maker.fgraph.apply_nodes])
 else:
  assert any([isinstance(a.op, cuda.blas.GpuCorrMM) for a in f.maker.fgraph.apply_nodes])

def test_default_conv ( ) : img = theano . tensor . ftensor4 ( ) fil = theano . tensor . ftensor4 ( ) c = theano . tensor . nnet . conv2d ( img , fil ) f = theano . function ( [ img , fil ] , c , mode = theano_mode ) if cuda . dnn . dnn_available ( ) : assert any ( [ isinstance ( a . op , GpuDnnConv ) for a in f . maker . fgraph . apply_nodes ] ) else : assert any ( [ isinstance ( a . op , cuda . blas . GpuCorrMM ) for a in f . maker . fgraph . apply_nodes ] )
just test that we introduce the right gpu convolution version .