Question

我的张量为BST<T>::iterators：

[batch_size, channel, depth, height, width]

带有数据：

torch.Size([1, 1, 32, 64, 64])

现在我要从中获得预测

tensor([[[[[-1.8540, -2.8068, -2.7348, ..., -1.9074, -1.8227, -1.4540], [-2.7012, -4.2785, -3.7421, ..., -3.1961, -2.7786, -1.8042], [-2.1924, -4.2202, -4.4361, ..., -3.1203, -2.9282, -2.3800], ..., [-2.7429, -4.3133, -4.4029, ..., -4.4971, -5.3288, -2.8659], [-3.0169, -4.0198, -3.6886, ..., -3.7542, -4.5010, -2.4040], [-1.6174, -2.5340, -2.3974, ..., -1.9249, -2.4107, -1.2664]], [[-2.7840, -3.2442, -3.6118, ..., -3.1365, -2.8342, -1.9516], [-3.5764, -4.9253, -5.9196, ..., -4.8373, -4.2233, -3.3809], [-3.1701, -5.0826, -5.6424, ..., -5.2955, -4.6438, -3.4820], ..., [-4.0111, -6.1946, -5.6582, ..., -6.7947, -6.5305, -4.2866], [-4.2103, -6.6177, -6.0420, ..., -5.8076, -6.2128, -3.2093], [-2.3174, -4.1081, -3.7369, ..., -3.5552, -3.1871, -1.9736]], [[-2.8441, -4.1575, -3.8233, ..., -3.5065, -3.4313, -2.3030], [-4.0076, -5.4939, -6.2451, ..., -4.6663, -4.9835, -3.1530], [-3.4737, -5.6347, -6.0232, ..., -5.6191, -5.2626, -3.6109], ..., [-3.8026, -5.3676, -6.1460, ..., -7.6695, -6.7640, -4.1681], [-4.4012, -6.1293, -6.1859, ..., -6.0011, -6.1012, -3.5307], [-2.7917, -4.2264, -4.1388, ..., -4.2080, -3.5555, -1.6384]], ..., [[-2.2204, -3.5705, -4.3114, ..., -4.2249, -3.9628, -2.9190], [-3.6343, -5.3445, -6.1638, ..., -6.3998, -6.7561, -4.8491], [-3.4870, -5.5835, -5.6436, ..., -6.8527, -7.2536, -4.8143], ..., [-2.4492, -3.7896, -5.4344, ..., -6.2853, -6.0766, -3.7538], [-2.4723, -3.8393, -4.8480, ..., -5.6503, -5.0375, -3.5580], [-1.6161, -2.9843, -3.2865, ..., -3.2627, -3.2887, -2.5750]], [[-2.1509, -3.8303, -4.2807, ..., -3.7945, -3.7561, -3.0863], [-3.1012, -5.1321, -6.1387, ..., -6.5191, -6.3268, -4.4283], [-2.8346, -5.0640, -5.4868, ..., -6.6515, -6.5529, -4.3672], ..., [-2.7278, -4.2538, -4.9776, ..., -6.4153, -6.0100, -3.9929], [-2.8002, -4.0473, -4.7455, ..., -5.4203, -4.7286, -3.4111], [-1.7964, -3.2307, -3.6329, ..., -3.2750, -2.3952, -1.9714]], [[-1.4447, -2.1572, -2.4487, ..., -2.3859, -2.9540, -1.8451], [-1.8075, -2.8380, -3.5621, ..., -3.8641, -3.5828, -2.7304], [-1.7862, -2.9849, -3.8364, ..., -4.3380, -4.4745, -2.8476], ..., [-1.8043, -2.5662, -2.7296, ..., -4.2772, -3.9882, -2.8654], [-1.2364, -2.5228, -2.7190, ..., -4.1142, -3.6160, -2.2325], [-1.0395, -1.7621, -2.5738, ..., -2.0349, -1.5140, -1.1625]]]]]

这应该给我最大值在通道维中的位置，但是我得到的张量只包含零。甚至torch.argmax(data, 1)也会产生max(torch.argmax())。

这怎么可能，张量只有一个维度和一个批处理，它如何返回0？

要摆脱负值，我在其上应用了0，但torch.nn.Sigmoid()仍然找不到最大值。我不明白，怎么没有最大值？

argmax给出相同的输出，所有numpy.argmax(output.detach().numpy(), 1)。

我不是正确使用argmax吗？

Answer 1

我不确定您的预测在这里是什么意思（通常是针对矢量或XN大小张量进行预测），所以我无法告诉您应该做什么，但我会尽力解释为什么到处都是零。

正如您已经提到的，通道维只有1行，因此所有argmax均为0，因为在第0个位置仅需要检查一个值。因此所有argmax为0都是有道理的。

Sigmoid是单调函数，因此不会改变结果。

Answer 2

在this page上，argmax令人困惑。他们选择的示例为4x4，因此您无法发现差异

a = torch.randn(5, 3)
print(a)
print(torch.argmax(a, dim=0))
print(torch.argmax(a, dim=1))

出

tensor([[-1.0329,  0.2042,  2.5499],
        [ 0.9893,  0.3913,  0.5096],
        [ 0.4951,  0.2260, -0.3810],
        [-1.8953, -0.6823,  0.8349],
        [-0.6217,  0.4068, -1.0846]])
tensor([1, 4, 0])
tensor([2, 0, 0, 2, 1])

看看在dim=0中我们有3个值。这是列的尺寸。因此，它告诉您第一列中索引为1的元素是该列中的最大值。另一个dim=1是行的维度，因此我们有5个值。

对于您的示例，您可以计算argmax的结果形状：

for i in range(data.dim()):
    print("dim", i)
    r =torch.argmax(data,i)
    print(r.shape)

dim 0
torch.Size([1, 32, 64, 64])
dim 1
torch.Size([1, 32, 64, 64])
dim 2
torch.Size([1, 1, 64, 64])
dim 3
torch.Size([1, 1, 32, 64])
dim 4
torch.Size([1, 1, 32, 64])

对于dim=0和dim=0，您应该全为0正弦，暗角为1（索引= 0）。

我尝试过，但是如何从argmax中提取最大值，以及应该针对哪个维度呢？

data = torch.randn(32, 64, 64)

values, indices = data.max(0)
print(values, indices)

values, indices = values.max(0)
print(values, indices)

values, indices = values.max(0)
print(values, indices

)

tensor([[1.9918, 1.6041, 2.6535,  ..., 1.5768, 1.7320, 1.8234],
        [1.6700, 2.4574, 1.8548,  ..., 1.8770, 1.7674, 1.6194],
        [1.8361, 1.6800, 1.8982,  ..., 1.7983, 2.7109, 2.2166],
        ...,
        [2.7439, 1.6215, 2.9740,  ..., 1.7031, 1.4445, 1.6681],
        [1.9437, 1.4507, 1.8551,  ..., 2.5853, 1.9753, 2.4046],
        [1.4198, 2.5250, 1.8949,  ..., 3.2618, 2.8547, 2.0487]]) tensor([[ 4,  7, 21,  ..., 27, 28, 17],
        [16, 27, 18,  ..., 29, 30, 19],
        [ 6, 16, 14,  ..., 22, 24, 29],
        ...,
        [16, 16,  8,  ..., 21, 27, 22],
        [15,  0,  0,  ...,  9, 12,  3],
        [30, 14,  9,  ..., 23, 20, 14]])
tensor([3.2089, 4.1386, 3.2650, 3.3497, 4.4210, 3.0439, 3.5144, 3.2356, 3.3058,
        3.2702, 2.9981, 3.6997, 3.1719, 3.4962, 3.0889, 3.6220, 3.9256, 4.1314,
        3.0804, 3.3636, 3.5517, 3.2052, 3.6548, 3.7064, 3.6531, 4.5144, 3.1287,
        4.1465, 3.1906, 3.1493, 3.1996, 3.6754, 3.7610, 3.5968, 3.2109, 3.6037,
        3.2799, 3.0069, 3.0386, 3.0240, 3.5372, 3.6539, 3.5571, 3.2047, 3.1218,
        4.2479, 3.1230, 3.0372, 3.0258, 3.8679, 3.6409, 3.0938, 3.1246, 2.9426,
        4.0824, 3.8124, 3.4226, 3.3459, 4.1600, 3.6566, 3.0351, 3.3969, 3.5842,
        3.0997]) tensor([17, 21, 30, 62, 62, 63, 43, 31, 45, 63, 20,  4, 58, 23, 22, 43, 54, 30,
        15, 28, 13,  4,  4, 28,  6, 52, 53, 19, 33, 20,  3,  1, 14, 40,  0,  0,
        46, 62, 58, 45, 28, 50,  4, 55, 25,  5, 21, 16, 27, 32, 10, 19, 38, 30,
        48, 27, 20,  9,  2, 39, 55, 58, 32,  6])
tensor(4.5144) tensor(25)

这是按尺寸或简单的

m = values.max()

将为您提供最大价值。

a = torch.argmax(values)
idx = np.unravel_index(a, values.shape)

将为您提供索引。

torch.argmax（）无法在包含数据的张量中找到最大值

2 个答案: