Python neighbours.images2neibs函数代码示例

def test_neibs_gpu():
    if cuda.cuda_available == False:
        raise SkipTest('Optional package cuda disabled')
    for shape, pshape in [((100, 40, 18, 18), (2, 2)),
                          ((100, 40, 6, 18), (3, 2)),
                          ((10, 40, 66, 66), (33, 33)),
                          ((10, 40, 68, 66), (34, 33))
                          ]:

        images = shared(numpy.arange(numpy.prod(shape),
                                     dtype='float32').reshape(shape))
        neib_shape = T.as_tensor_variable(pshape)

        f = function([], images2neibs(images, neib_shape),
                     mode=mode_with_gpu)
        f_gpu = function([], images2neibs(images, neib_shape),
                     mode=mode_with_gpu)
        assert any([isinstance(node.op, GpuImages2Neibs)
                    for node in f_gpu.maker.env.toposort()])
        #print images.get_value(borrow=True)
        neibs = numpy.asarray(f_gpu())
        assert numpy.allclose(neibs, f())
        #print neibs
        g = function([], neibs2images(neibs, neib_shape, images.shape),
                     mode=mode_with_gpu)
        assert any([isinstance(node.op, GpuImages2Neibs)
                    for node in f.maker.env.toposort()])
        #print numpy.asarray(g())
        assert numpy.allclose(images.get_value(borrow=True), g())

def test_neibs_bad_shape():
    shape = (2, 3, 10, 10)
    images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
    neib_shape = T.as_tensor_variable((3, 2))

    try:
        f = function([], images2neibs(images, neib_shape),
                     mode=mode_without_gpu)
        neibs = f()
        #print neibs
        assert False, "An error was expected"
    except TypeError:
        pass

    shape = (2, 3, 10, 10)
    images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
    neib_shape = T.as_tensor_variable((2, 3))

    try:
        f = function([], images2neibs(images, neib_shape),
                     mode=mode_without_gpu)
        neibs = f()
        #print neibs
        assert False, "An error was expected"
    except TypeError:
        pass

    def test_neibs_bad_shape_wrap_centered(self):
        shape = (2, 3, 10, 10)

        for dtype in self.dtypes:
            images = shared(numpy.arange(
                numpy.prod(shape), dtype=dtype
                ).reshape(shape))

            for neib_shape in [(3, 2), (2, 3)]:
                neib_shape = T.as_tensor_variable(neib_shape)

                f = function([], images2neibs(images, neib_shape,
                                              mode="wrap_centered"),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

            for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
                images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
                neib_shape = T.as_tensor_variable((3, 3))
                f = function([], images2neibs(images, neib_shape,
                                              mode="wrap_centered"),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

            # Test a valid shapes
            shape = (2, 3, 3, 3)
            images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
            neib_shape = T.as_tensor_variable((3, 3))

            f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
                         mode=self.mode)
            f()

    def test_neibs(self):
        for shape, pshape in [((100, 40, 18, 18), (2, 2)),
                              ((100, 40, 6, 18), (3, 2)),
                              ((10, 40, 66, 66), (33, 33)),
                              ((10, 40, 68, 66), (34, 33))
                                  ]:
            for border in ['valid', 'ignore_borders']:
                for dtype in self.dtypes:
                    images = shared(
                            numpy.arange(numpy.prod(shape), dtype=dtype
                            ).reshape(shape))
                    neib_shape = T.as_tensor_variable(pshape)

                    f = function([],
                                 images2neibs(images, neib_shape, mode=border),
                                 mode=self.mode)

                    #print images.get_value(borrow=True)
                    neibs = f()
                    #print neibs
                    g = function([],
                                 neibs2images(neibs, neib_shape, images.shape),
                                 mode=self.mode)
                    if border in ['valid']:
                        assert any([isinstance(node.op, self.op)
                                    for node in f.maker.fgraph.toposort()])

                    #print g()
                    assert numpy.allclose(images.get_value(borrow=True), g())

def speed_neibs_wrap_centered():
    shape = (100, 40, 18, 18)
    images = shared(numpy.arange(numpy.prod(shape),
                                 dtype='float32').reshape(shape))
    neib_shape = T.as_tensor_variable((3, 3))

    f = function([], images2neibs(images, neib_shape, mode="wrap_centered"))

    for i in range(1000):
        f()

    def test_neibs_bad_shape(self):
        shape = (2, 3, 10, 10)
        for dtype in self.dtypes:
            images = shared(numpy.arange(
                numpy.prod(shape), dtype=dtype
                ).reshape(shape))

            for neib_shape in [(3, 2), (2, 3)]:
                neib_shape = T.as_tensor_variable(neib_shape)
                f = function([], images2neibs(images, neib_shape),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

                #Test that ignore border work in that case.
                f = function([],
                             images2neibs(images, neib_shape,
                                          mode='ignore_borders'),
                             mode=self.mode)
                f()

def speed_neibs_wrap_centered():
    shape = (100,40,18,18)
    images = shared(numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
    neib_shape = T.as_tensor_variable((3,3))

    from theano.sandbox.cuda.basic_ops import gpu_from_host

    f = function([], images2neibs(images,neib_shape,mode="wrap_centered"))#, mode=mode_without_gpu)

    for i in range(1000):
        f()

def test_neibs_bad_shape_warp_centered():
    shape = (2, 3, 10, 10)
    images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))

    for neib_shape in [(3, 2), (2, 3)]:
        neib_shape = T.as_tensor_variable(neib_shape)

        try:
            f = function([], images2neibs(images, neib_shape,
                                          mode="wrap_centered"),
                         mode=mode_without_gpu)
            f()
            assert False, "An error was expected"
        except TypeError:
            pass

    shape = (2, 3, 2, 3)
    images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
    neib_shape = T.as_tensor_variable((3, 3))

    for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
        try:
            f = function([], images2neibs(images, neib_shape,
                                          mode="wrap_centered"),
                         mode=mode_without_gpu)
            f()
            assert False, "An error was expected"
        except TypeError:
            pass

    # Test a valid shapes
    shape = (2, 3, 3, 3)
    images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
    neib_shape = T.as_tensor_variable((3, 3))

    f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
                 mode=mode_without_gpu)
    f()

def test_neibs():
    shape = (100,40,18,18)
    images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
    neib_shape = T.as_tensor_variable((2,2))#(array((2,2), dtype='float32'))

    f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)

    #print images.get_value(borrow=True)
    neibs = f()
    #print neibs
    g = function([], neibs2images(neibs, neib_shape, images.shape), mode=mode_without_gpu)

    #print g()
    assert numpy.allclose(images.get_value(borrow=True),g())

    def test_neibs_valid_with_inconsistent_borders(self):
        shape = (2, 3, 5, 5)
        images = T.dtensor4()
        images_val = numpy.arange(numpy.prod(shape),
                                  dtype='float32').reshape(shape)

        def fn(images):
            return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
                         axis=[0, 1])

        f = theano.function([images],
                            T.sqr(images2neibs(images, (2, 2), mode='valid')),
                            mode=self.mode)
        self.assertRaises(TypeError, f, images_val)

def test_neibs_step_manual():
    shape = (2, 3, 5, 5)
    images = shared(numpy.asarray(numpy.arange(numpy.prod(
                    shape)).reshape(shape), dtype='float32'))
    neib_shape = T.as_tensor_variable((3, 3))
    neib_step = T.as_tensor_variable((2, 2))
    modes = [mode_without_gpu]
    if cuda.cuda_available:
        modes.append(mode_with_gpu)
    for mode_idx, mode in enumerate(modes):
        f = function([], images2neibs(images, neib_shape, neib_step),
                     mode=mode)

    #print images.get_value(borrow=True)
        neibs = f()
        if mode_idx == 0:
            assert Images2Neibs in [type(node.op)
                                    for node in f.maker.env.toposort()]
        elif mode_idx == 1:
            assert GpuImages2Neibs in [type(node.op)
                                       for node in f.maker.env.toposort()]

        assert numpy.allclose(neibs,
      [[  0,   1,   2,   5,   6,   7,  10,  11,  12],
       [  2,   3,   4,   7,   8,   9,  12,  13,  14],
       [ 10,  11,  12,  15,  16,  17,  20,  21,  22],
       [ 12,  13,  14,  17,  18,  19,  22,  23,  24],
       [ 25,  26,  27,  30,  31,  32,  35,  36,  37],
       [ 27,  28,  29,  32,  33,  34,  37,  38,  39],
       [ 35,  36,  37,  40,  41,  42,  45,  46,  47],
       [ 37,  38,  39,  42,  43,  44,  47,  48,  49],
       [ 50,  51,  52,  55,  56,  57,  60,  61,  62],
       [ 52,  53,  54,  57,  58,  59,  62,  63,  64],
       [ 60,  61,  62,  65,  66,  67,  70,  71,  72],
       [ 62,  63,  64,  67,  68,  69,  72,  73,  74],
       [ 75,  76,  77,  80,  81,  82,  85,  86,  87],
       [ 77,  78,  79,  82,  83,  84,  87,  88,  89],
       [ 85,  86,  87,  90,  91,  92,  95,  96,  97],
       [ 87,  88,  89,  92,  93,  94,  97,  98,  99],
       [100, 101, 102, 105, 106, 107, 110, 111, 112],
       [102, 103, 104, 107, 108, 109, 112, 113, 114],
       [110, 111, 112, 115, 116, 117, 120, 121, 122],
       [112, 113, 114, 117, 118, 119, 122, 123, 124],
       [125, 126, 127, 130, 131, 132, 135, 136, 137],
       [127, 128, 129, 132, 133, 134, 137, 138, 139],
       [135, 136, 137, 140, 141, 142, 145, 146, 147],
       [137, 138, 139, 142, 143, 144, 147, 148, 149]])

def test_neibs_grad():
    shape = (2,3,4,4)
    images = shared(numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))

    cost = T.sum(T.sqr(images2neibs(images, (2,2))), axis=[0,1])

    grad = T.grad(cost, images)

    f = theano.function([], [cost, grad], mode=mode_without_gpu)

    got = f()

    should_get = [numpy.asarray(290320.0, dtype=numpy.float32),
        numpy.asarray([[[[   0.,    2.,    4.,    6.],
         [   8.,   10.,   12.,   14.],
         [  16.,   18.,   20.,   22.],
         [  24.,   26.,   28.,   30.]],

        [[  32.,   34.,   36.,   38.],
         [  40.,   42.,   44.,   46.],
         [  48.,   50.,   52.,   54.],
         [  56.,   58.,   60.,   62.]],

        [[  64.,   66.,   68.,   70.],
         [  72.,   74.,   76.,   78.],
         [  80.,   82.,   84.,   86.],
         [  88.,   90.,   92.,   94.]]],


       [[[  96.,   98.,  100.,  102.],
         [ 104.,  106.,  108.,  110.],
         [ 112.,  114.,  116.,  118.],
         [ 120.,  122.,  124.,  126.]],

        [[ 128.,  130.,  132.,  134.],
         [ 136.,  138.,  140.,  142.],
         [ 144.,  146.,  148.,  150.],
         [ 152.,  154.,  156.,  158.]],

        [[ 160.,  162.,  164.,  166.],
         [ 168.,  170.,  172.,  174.],
         [ 176.,  178.,  180.,  182.],
         [ 184.,  186.,  188.,  190.]]]], dtype=numpy.float32)]

    assert numpy.allclose(got[0], should_get[0])
    assert numpy.allclose(got[1], should_get[1])

    def test_neibs_manual(self):
        shape = (2, 3, 4, 4)
        for dtype in self.dtypes:
            images = shared(
                    numpy.arange(numpy.prod(shape), dtype=dtype
                    ).reshape(shape))
            neib_shape = T.as_tensor_variable((2, 2))

            for border in ['valid', 'ignore_borders']:
                f = function([], images2neibs(images, neib_shape, mode=border),
                             mode=self.mode)
                assert any([isinstance(node.op, self.op)
                            for node in f.maker.fgraph.toposort()])

                #print images.get_value(borrow=True)
                neibs = f()
                #print neibs
                assert numpy.allclose(neibs,
                   [[ 0,  1,  4,  5],
                   [ 2,  3,  6,  7],
                   [ 8,  9, 12, 13],
                   [10, 11, 14, 15],
                   [16, 17, 20, 21],
                   [18, 19, 22, 23],
                   [24, 25, 28, 29],
                   [26, 27, 30, 31],
                   [32, 33, 36, 37],
                   [34, 35, 38, 39],
                   [40, 41, 44, 45],
                   [42, 43, 46, 47],
                   [48, 49, 52, 53],
                   [50, 51, 54, 55],
                   [56, 57, 60, 61],
                   [58, 59, 62, 63],
                   [64, 65, 68, 69],
                   [66, 67, 70, 71],
                   [72, 73, 76, 77],
                   [74, 75, 78, 79],
                   [80, 81, 84, 85],
                   [82, 83, 86, 87],
                   [88, 89, 92, 93],
                   [90, 91, 94, 95]])
                g = function([], neibs2images(neibs, neib_shape, images.shape),
                             mode=self.mode)

                assert numpy.allclose(images.get_value(borrow=True), g())

def test_neibs_valid_with_inconsistent_borders():
    shape = (2, 3, 5, 5)
    images = T.dtensor4()
    images_val = numpy.arange(numpy.prod(shape),
                              dtype='float32').reshape(shape)

    def fn(images):
        return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
                     axis=[0, 1])

    f = theano.function([images],
                        T.sqr(images2neibs(images, (2, 2), mode='valid')),
                        mode=mode_without_gpu)
    try:
        f(images_val)
        assert False, "An error was expected"
    except TypeError, e:
        # This is expected if the assert is there
        pass

    def test_neibs_manual_step(self):
        shape = (2, 3, 5, 5)
        for dtype in self.dtypes:
            images = shared(numpy.asarray(numpy.arange(numpy.prod(
                            shape)).reshape(shape), dtype=dtype))
            neib_shape = T.as_tensor_variable((3, 3))
            neib_step = T.as_tensor_variable((2, 2))
            for border in ['valid', 'ignore_borders']:
                f = function([],
                             images2neibs(images, neib_shape, neib_step,
                                          mode=border),
                             mode=self.mode)

                neibs = f()
                if border in ['valid']:
                    assert self.op in [type(node.op)
                                       for node in f.maker.fgraph.toposort()]

                assert numpy.allclose(neibs,
              [[  0,   1,   2,   5,   6,   7,  10,  11,  12],
               [  2,   3,   4,   7,   8,   9,  12,  13,  14],
               [ 10,  11,  12,  15,  16,  17,  20,  21,  22],
               [ 12,  13,  14,  17,  18,  19,  22,  23,  24],
               [ 25,  26,  27,  30,  31,  32,  35,  36,  37],
               [ 27,  28,  29,  32,  33,  34,  37,  38,  39],
               [ 35,  36,  37,  40,  41,  42,  45,  46,  47],
               [ 37,  38,  39,  42,  43,  44,  47,  48,  49],
               [ 50,  51,  52,  55,  56,  57,  60,  61,  62],
               [ 52,  53,  54,  57,  58,  59,  62,  63,  64],
               [ 60,  61,  62,  65,  66,  67,  70,  71,  72],
               [ 62,  63,  64,  67,  68,  69,  72,  73,  74],
               [ 75,  76,  77,  80,  81,  82,  85,  86,  87],
               [ 77,  78,  79,  82,  83,  84,  87,  88,  89],
               [ 85,  86,  87,  90,  91,  92,  95,  96,  97],
               [ 87,  88,  89,  92,  93,  94,  97,  98,  99],
               [100, 101, 102, 105, 106, 107, 110, 111, 112],
               [102, 103, 104, 107, 108, 109, 112, 113, 114],
               [110, 111, 112, 115, 116, 117, 120, 121, 122],
               [112, 113, 114, 117, 118, 119, 122, 123, 124],
               [125, 126, 127, 130, 131, 132, 135, 136, 137],
               [127, 128, 129, 132, 133, 134, 137, 138, 139],
               [135, 136, 137, 140, 141, 142, 145, 146, 147],
               [137, 138, 139, 142, 143, 144, 147, 148, 149]])