torch.topk（）是你正在寻找的。从文件上看，
torch.topk（input，k，dim=None，largest=True，sorted=True，out=None）-〉（Tensor，LongTensor）
返回给定维度沿着给定inputTensor的k最大元素。

• 如果dim未给出，则选择输入的最后一个维度。
• 如果largest是False，则返回k个最小元素。
• 返回（values，indices）的namedtuple，其中索引是原始输入Tensor中元素的索引。
• 布尔选项sorted（如果是True）将确保返回的k个元素本身已排序

如果我没理解错的话，你需要的不是值，而是索引。不幸的是，没有开箱即用的解决方案。有一个argmax()函数，但我不知道如何让它做你想要的。
所以这里有一个小的解决方案，效率也应该是好的，因为我们只是划分Tensor：

n = torch.tensor(4)
d = torch.tensor(4)
x = torch.rand(n, 1, d, d)
m = x.view(n, -1).argmax(1)
# since argmax() does only return the index of the flattened
# matrix block we have to calculate the indices by ourself 
# by using / and % (// would also work, but as we are dealing with
# type torch.long / works as well
indices = torch.cat(((m / d).view(-1, 1), (m % d).view(-1, 1)), dim=1)
print(x)
print(indices)

n表示第一维，d表示最后两个维。我在这里取较小的数字来显示结果。当然，这也适用于n=20和d=120：

n = torch.tensor(20)
d = torch.tensor(120)
x = torch.rand(n, 1, d, d)
m = x.view(n, -1).argmax(1)
indices = torch.cat(((m / d).view(-1, 1), (m % d).view(-1, 1)), dim=1)
#print(x)
print(indices)

以下是n=4和d=4的输出：

tensor([[[[0.3699, 0.3584, 0.4940, 0.8618],
          [0.6767, 0.7439, 0.5984, 0.5499],
          [0.8465, 0.7276, 0.3078, 0.3882],
          [0.1001, 0.0705, 0.2007, 0.4051]]],

        [[[0.7520, 0.4528, 0.0525, 0.9253],
          [0.6946, 0.0318, 0.5650, 0.7385],
          [0.0671, 0.6493, 0.3243, 0.2383],
          [0.6119, 0.7762, 0.9687, 0.0896]]],

        [[[0.3504, 0.7431, 0.8336, 0.0336],
          [0.8208, 0.9051, 0.1681, 0.8722],
          [0.5751, 0.7903, 0.0046, 0.1471],
          [0.4875, 0.1592, 0.2783, 0.6338]]],

        [[[0.9398, 0.7589, 0.6645, 0.8017],
          [0.9469, 0.2822, 0.9042, 0.2516],
          [0.2576, 0.3852, 0.7349, 0.2806],
          [0.7062, 0.1214, 0.0922, 0.1385]]]])
tensor([[0, 3],
        [3, 2],
        [1, 1],
        [1, 0]])

我希望这是你想要的！：）

编辑：

这里有一个稍微修改过的，可能会稍微快一点（我猜不会太多：），但它更简单，更漂亮：
而不是像以前那样：

m = x.view(n, -1).argmax(1)
indices = torch.cat(((m // d).view(-1, 1), (m % d).view(-1, 1)), dim=1)

已经对argmax值进行了必要的整形：

m = x.view(n, -1).argmax(1).view(-1, 1)
indices = torch.cat((m // d, m % d), dim=1)

但正如评论中提到的。我认为不可能从中得到更多。
你可以做的一件事，如果它对你来说真的很重要，那就是将上面的函数实现为pytorch的低级扩展（如C++）。
这将给予你一个函数，你可以调用它，并将避免缓慢的Python代码。
https://pytorch.org/tutorials/advanced/cpp_extension.html

下面是torch中的unravel_index实现：

def unravel_index(
    indices: torch.LongTensor,
    shape: Tuple[int, ...],
) -> torch.LongTensor:
    r"""Converts flat indices into unraveled coordinates in a target shape.

    This is a `torch` implementation of `numpy.unravel_index`.

    Args:
        indices: A tensor of (flat) indices, (*, N).
        shape: The targeted shape, (D,).

    Returns:
        The unraveled coordinates, (*, N, D).
    """

    coord = []

    for dim in reversed(shape):
        coord.append(indices % dim)
        indices = indices // dim

    coord = torch.stack(coord[::-1], dim=-1)

    return coord

然后，您可以使用torch.argmax函数来获得“展平”Tensor的索引。

y = x.view(20, -1)
indices = torch.argmax(y)
indices.shape  # (20,)

用unravel_index函数解出指数。

indices = unravel_index(indices, x.shape[-2:])
indices.shape  # (20, 2)

公认的答案只适用于给定的示例。
tejasvi88的答案很有趣，但无助于回答最初的问题（正如我在那里的评论中所解释的）。
我相信弗朗索瓦的答案是最接近的，因为它处理的是一个更一般的情况（任何数量的维度）。但是，它不与argmax连接，并且所示示例没有说明该函数处理批处理的能力。
因此，我将在Francois的答案的基础上添加代码来连接到argmax。我写了一个新函数batch_argmax，它返回一个批处理中最大值的索引。批次可以在多个维度中组织。我还提供了一些测试用例以供说明：

def batch_argmax(tensor, batch_dim=1):
    """
    Assumes that dimensions of tensor up to batch_dim are "batch dimensions"
    and returns the indices of the max element of each "batch row".
    More precisely, returns tensor `a` such that, for each index v of tensor.shape[:batch_dim], a[v] is
    the indices of the max element of tensor[v].
    """
    if batch_dim >= len(tensor.shape):
        raise NoArgMaxIndices()
    batch_shape = tensor.shape[:batch_dim]
    non_batch_shape = tensor.shape[batch_dim:]
    flat_non_batch_size = prod(non_batch_shape)
    tensor_with_flat_non_batch_portion = tensor.reshape(*batch_shape, flat_non_batch_size)

    dimension_of_indices = len(non_batch_shape)

    # We now have each batch row flattened in the last dimension of tensor_with_flat_non_batch_portion,
    # so we can invoke its argmax(dim=-1) method. However, that method throws an exception if the tensor
    # is empty. We cover that case first.
    if tensor_with_flat_non_batch_portion.numel() == 0:
        # If empty, either the batch dimensions or the non-batch dimensions are empty
        batch_size = prod(batch_shape)
        if batch_size == 0:  # if batch dimensions are empty
            # return empty tensor of appropriate shape
            batch_of_unraveled_indices = torch.ones(*batch_shape, dimension_of_indices).long()  # 'ones' is irrelevant as it will be empty
        else:  # non-batch dimensions are empty, so argmax indices are undefined
            raise NoArgMaxIndices()
    else:   # We actually have elements to maximize, so we search for them
        indices_of_non_batch_portion = tensor_with_flat_non_batch_portion.argmax(dim=-1)
        batch_of_unraveled_indices = unravel_indices(indices_of_non_batch_portion, non_batch_shape)

    if dimension_of_indices == 1:
        # above function makes each unraveled index of a n-D tensor a n-long tensor
        # however indices of 1D tensors are typically represented by scalars, so we squeeze them in this case.
        batch_of_unraveled_indices = batch_of_unraveled_indices.squeeze(dim=-1)
    return batch_of_unraveled_indices

class NoArgMaxIndices(BaseException):

    def __init__(self):
        super(NoArgMaxIndices, self).__init__(
            "no argmax indices: batch_argmax requires non-batch shape to be non-empty")

以下是测试：

def test_basic():
    # a simple array
    tensor = torch.tensor([0, 1, 2, 3, 4])
    batch_dim = 0
    expected = torch.tensor(4)
    run_test(tensor, batch_dim, expected)

    # making batch_dim = 1 renders the non-batch portion empty and argmax indices undefined
    tensor = torch.tensor([0, 1, 2, 3, 4])
    batch_dim = 1
    check_that_exception_is_thrown(lambda: batch_argmax(tensor, batch_dim), NoArgMaxIndices)

    # now a batch of arrays
    tensor = torch.tensor([[1, 2, 3], [6, 5, 4]])
    batch_dim = 1
    expected = torch.tensor([2, 0])
    run_test(tensor, batch_dim, expected)

    # Now we have an empty batch with non-batch 3-dim arrays' shape (the arrays are actually non-existent)
    tensor = torch.ones(0, 3)  # 'ones' is irrelevant since this is empty
    batch_dim = 1
    # empty batch of the right shape: just the batch dimension 0,since indices of arrays are scalar (0D)
    expected = torch.ones(0)
    run_test(tensor, batch_dim, expected)

    # Now we have an empty batch with non-batch matrices' shape (the matrices are actually non-existent)
    tensor = torch.ones(0, 3, 2)  # 'ones' is irrelevant since this is empty
    batch_dim = 1
    # empty batch of the right shape: the batch and two dimension for the indices since we have 2D matrices
    expected = torch.ones(0, 2)
    run_test(tensor, batch_dim, expected)

    # a batch of 2D matrices:
    tensor = torch.tensor([[[1, 2, 3], [6, 5, 4]], [[2, 3, 1], [4, 5, 6]]])
    batch_dim = 1
    expected = torch.tensor([[1, 0], [1, 2]])  # coordinates of two 6's, one in each 2D matrix
    run_test(tensor, batch_dim, expected)

    # same as before, but testing that batch_dim supports negative values
    tensor = torch.tensor([[[1, 2, 3], [6, 5, 4]], [[2, 3, 1], [4, 5, 6]]])
    batch_dim = -2
    expected = torch.tensor([[1, 0], [1, 2]])
    run_test(tensor, batch_dim, expected)

    # Same data, but a 2-dimensional batch of 1D arrays!
    tensor = torch.tensor([[[1, 2, 3], [6, 5, 4]], [[2, 3, 1], [4, 5, 6]]])
    batch_dim = 2
    expected = torch.tensor([[2, 0], [1, 2]])  # coordinates of 3, 6, 3, and 6
    run_test(tensor, batch_dim, expected)

    # same as before, but testing that batch_dim supports negative values
    tensor = torch.tensor([[[1, 2, 3], [6, 5, 4]], [[2, 3, 1], [4, 5, 6]]])
    batch_dim = -1
    expected = torch.tensor([[2, 0], [1, 2]])
    run_test(tensor, batch_dim, expected)

def run_test(tensor, batch_dim, expected):
    actual = batch_argmax(tensor, batch_dim)
    print(f"batch_argmax of {tensor} with batch_dim {batch_dim} is\n{actual}\nExpected:\n{expected}")
    assert actual.shape == expected.shape
    assert actual.eq(expected).all()

def check_that_exception_is_thrown(thunk, exception_type):
    if isinstance(exception_type, BaseException):
        raise Exception(f"check_that_exception_is_thrown received an exception instance rather than an exception type: "
                        f"{exception_type}")
    try:
        thunk()
        raise AssertionError(f"Should have thrown {exception_type}")
    except exception_type:
        pass
    except Exception as e:
        raise AssertionError(f"Should have thrown {exception_type} but instead threw {e}")

我有一个简单的解决方案，但不是批量计算每个项目的最大值的2D坐标的最佳解决方案。简单的解决方法可能是：

# suppose the tensor is of shape (3,2,2), 
>>> a = torch.randn(3, 2, 2)
>>> a
tensor([[[ 0.1450, -1.3480],
         [-0.3339, -0.5133]],

        [[ 0.6867, -0.2972],
         [ 0.8768,  0.0844]],

        [[-2.3115, -0.4549],
         [-1.5074, -0.8706]]])

# then perform batch-wise max
>>> torch.stack([(a[i]==torch.max(a[i])).nonzero() for i in range(a.size(0))], dim=0)

tensor([[[0, 0]],

        [[1, 0]],

        [[0, 1]]])

ps=ps.numpy()
ps=ps.tolist()

mx=[max(l) for l in ps]
mx=max(mx)
for i in range(len(ps[0])):
  if mx==ps[0][i]:
    print("The digit is "+str(i))
    break

这对我很有效