slice

This operator produces a slice of input along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slice uses axes, starts and ends attributes to specify the start and end dimension for each axis in the list of axes and Slice uses this information to slice the input data tensor. If a negative value is passed to starts or ends such as \(-i\), it represents the reverse position of the axis \(i-1\) (here 0 is the initial position). If the value passed to starts or ends is greater than n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. The size of axes must be equal to starts and ends. Following examples will explain how slice works:

           Case1:
    Given:
        data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
        axes = [0, 1]
        starts = [1, 0]
        ends = [2, 3]
    Then:
        result = [ [5, 6, 7], ]

Case2:
    Given:
        data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
        axes = [0, 1]
        starts = [0, 1]
        ends = [-1, 1000]       # -1 denotes the reverse 0th position of dimension 0.
    Then:
        result = [ [2, 3, 4], ] # result = data[0:1, 1:4]

          

The following figure illustrates the first case – a 2D tensor of shape [2, 4] is transformed into a 2D tensor of shape [1, 3] through a slicing operation.

Parameters

input (Tensor) – A Tensor . The data type is float16, float32, float64, int32 or int64.
axes (list|tuple) – The data type is int32 . Axes that starts and ends apply to .
starts (list|tuple|Tensor) – The data type is int32 . If starts is a list or tuple, each element of it should be integer or 0-D int Tensor with shape []. If starts is an Tensor, it should be an 1-D Tensor. It represents starting indices of corresponding axis in axes.
ends (list|tuple|Tensor) – The data type is int32 . If ends is a list or tuple, each element of it should be integer or 0-D int Tensor with shape []. If ends is an Tensor, it should be an 1-D Tensor . It represents ending indices of corresponding axis in axes.

Returns

Tensor, A Tensor. The data type is same as input.

Examples

           >>> import paddle

>>> input = paddle.rand(shape=[4, 5, 6], dtype='float32')
>>> # example 1:
>>> # attr starts is a list which doesn't contain tensor.
>>> axes = [0, 1, 2]
>>> starts = [-3, 0, 2]
>>> ends = [3, 2, 4]
>>> sliced_1 = paddle.slice(input, axes=axes, starts=starts, ends=ends)
>>> # sliced_1 is input[1:3, 0:2, 2:4].

>>> # example 2:
>>> # attr starts is a list which contain tensor.
>>> minus_3 = paddle.full([1], -3, "int32")
>>> sliced_2 = paddle.slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends)
>>> # sliced_2 is input[1:3, 0:2, 2:4].

          