test_dnn.py 文件源码

def test_pooling_with_tensor_vars():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    x = T.ftensor4()
    ws = theano.shared(numpy.array([2, 2], dtype='int32'))
    st = theano.shared(numpy.array([1, 1], dtype='int32'))
    pad = theano.shared(numpy.array([0, 0], dtype='int32'))
    mode = 'max'

    def fn(x):
        dnn_op = cuda.dnn.dnn_pool(
            x, ws=ws,
            stride=st,
            pad=pad,
            mode=mode)
        return dnn_op

    for shp in [(1, 1, 2, 2),
                (1, 1, 3, 3)]:
        data = numpy.random.normal(0, 1, shp).astype("float32") * 10
        theano.tests.unittest_tools.verify_grad(
            fn, [data], mode=mode_with_gpu)

    mode_without_gpu2 = mode_without_gpu.including()
    mode_without_gpu2.check_isfinite = False

    # GPU implementation
    f_gpu = theano.function([x], fn(x), mode=mode_with_gpu)
    assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                for node in f_gpu.maker.fgraph.apply_nodes])

    # CPU implementation
    out_cpu = pool_2d(x, ws, ignore_border=True, st=st, padding=pad, mode=mode)
    f_cpu = theano.function([x], out_cpu, mode=mode_without_gpu2)
    assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                   for node in f_cpu.maker.fgraph.apply_nodes])
    assert any([isinstance(node.op, Pool)
                for node in f_cpu.maker.fgraph.apply_nodes])

    i = 1
    for shp in [(1, 10, 100, 100),
                (1, 3, 99, 99),
                (32, 1, 147, 197)]:
        data = numpy.random.normal(0, 1, shp).astype("float32")

        # Change the window size dynamically
        ws.set_value(numpy.array([i, i]).astype('int32'))
        a = f_gpu(data).__array__()
        b = f_cpu(data).__array__()
        utt.assert_allclose(a, b)
        i += 1