mindspore.ops.Unique
- class mindspore.ops.Unique[源代码]
Returns the unique elements of input tensor and also return a tensor containing the index of each value of input tensor corresponding to the output unique tensor.
The output contains Tensor y and Tensor idx, the format is probably similar to (y, idx). The shape of Tensor y and Tensor idx is different in most cases, because Tensor y will be duplicated, and the shape of Tensor idx is consistent with the input.
To get the same shape between idx and y, please ref to ‘UniqueWithPad’ operator.
- Inputs:
input_x (Tensor) - The input tensor. The shape is \((N,*)\) where \(*\) means, any number of additional dimensions.
- Outputs:
Tuple, containing Tensor objects (y, idx), y is a tensor with the same type as input_x, and contains the unique elements in x. idx is a tensor containing indices of elements in the input corresponding to the output tensor.
- Raises
TypeError – If input_x is not a Tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> input_x = Tensor(np.array([1, 2, 5, 2]), mindspore.int32) >>> output = ops.Unique()(input_x) >>> print(output) (Tensor(shape=[3], dtype=Int32, value= [1, 2, 5]), Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 1])) >>> y = output[0] >>> print(y) [1 2 5] >>> idx = output[1] >>> print(idx) [0 1 2 1] >>> # As can be seen from the above, y and idx shape >>> # note that for GPU, this operator must be wrapped inside a model, and executed in graph mode. >>> class UniqueNet(nn.Cell): ... def __init__(self): ... super(UniqueNet, self).__init__() ... self.unique_op = ops.Unique() ... ... def construct(self, x): ... output, indices = self.unique_op(x) ... return output, indices ... >>> input_x = Tensor(np.array([1, 2, 5, 2]), mindspore.int32) >>> net = UniqueNet() >>> output = net(input_x) >>> print(output) (Tensor(shape=[3], dtype=Int32, value= [1, 2, 5]), Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 1]))