strided_slice¶

paddle. strided_slice ( x, axes, starts, ends, strides, name=None ) [source]

This operator produces a slice of x 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 \(i-1\) th(here 0 is the initial position). The strides represents steps of slicing and if the strides is negative, slice operation is in the opposite direction. 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 , ends and strides. Following examples will explain how strided_slice works:

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

Case2:
    Given:
        data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
        axes = [0, 1]
        starts = [0, 1]
        ends = [2, 0]
        strides = [1, -1]
    Then:
        result = [ [8, 7, 6], ]
Case3:
    Given:
        data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
        axes = [0, 1]
        starts = [0, 1]
        ends = [-1, 1000]
        strides = [1, 3]
    Then:
        result = [ [2], ]

Parameters

• x (Tensor) – An N-D Tensor. The data type is bool, float16, float32, float64, int32 or int64.

• axes (list|tuple) – The data type is int32 . Axes that starts and ends apply to. It’s optional. If it is not provides, it will be treated as \([0,1,...,len(starts)-1]\).

• starts (list|tuple|Tensor) – The data type is int32 . If starts is a list or tuple, the elements of it should be integers or Tensors 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, the elements of it should be integers or Tensors with shape []. If ends is an Tensor, it should be an 1-D Tensor. It represents ending indices of corresponding axis in axes.

• strides (list|tuple|Tensor) – The data type is int32 . If strides is a list or tuple, the elements of it should be integers or Tensors with shape []. If strides is an Tensor, it should be an 1-D Tensor. It represents slice step of corresponding axis in axes.

• name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name .

Returns

Tensor, A Tensor with the same dimension as x. The data type is same as x.

Examples

>>> import paddle
>>> x = paddle.zeros(shape=[3,4,5,6], dtype="float32")
>>> # example 1:
>>> # attr starts is a list which doesn't contain Tensor.
>>> axes = [1, 2, 3]
>>> starts = [-3, 0, 2]
>>> ends = [3, 2, 4]
>>> strides_1 = [1, 1, 1]
>>> strides_2 = [1, 1, 2]
>>> sliced_1 = paddle.strided_slice(x, axes=axes, starts=starts, ends=ends, strides=strides_1)
>>> # sliced_1 is x[:, 1:3:1, 0:2:1, 2:4:1].
>>> # example 2:
>>> # attr starts is a list which contain tensor Tensor.
>>> minus_3 = paddle.full(shape=[1], fill_value=-3, dtype='int32')
>>> sliced_2 = paddle.strided_slice(x, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2)
>>> # sliced_2 is x[:, 1:3:1, 0:2:1, 2:4:2].