mindspore.Tensor.gather
- mindspore.Tensor.gather(input_indices, axis)[源代码]
返回指定 axis 上 input_indices 的元素对应的输入Tensor切片,输入Tensor的形状是 \((x_1, x_2, ..., x_R)\)。为了方便描述,对于输入Tensor记为 input_params。
Note
input_indices 的值必须在 [0, input_params.shape[axis]) 的范围内,结果未定义超出范围。
当前在Ascend平台,input_params的值不能是 bool_ 类型。
- 参数:
input_indices (Tensor) - 待切片的索引张量,其形状为 \((y_1, y_2, ..., y_S)\),代表指定原始张量元素的索引,其数据类型包括:int32,int64。
axis (int) - 指定维度索引的轴以搜集切片。
- 返回:
Tensor,其中shape维度为 \(input\_params.shape[:axis] + input\_indices.shape + input\_params.shape[axis + 1:]\)。
- 异常:
TypeError - 如果 axis 不是一个整数。
TypeError - 如果 input_indices 不是一个整数类型的Tensor。
- 支持平台:
Ascend
GPU
CPU
样例:
>>> # case1: input_indices is a Tensor with shape (5, ). >>> input_params = Tensor(np.array([1, 2, 3, 4, 5, 6, 7]), mindspore.float32) >>> input_indices = Tensor(np.array([0, 2, 4, 2, 6]), mindspore.int32) >>> axis = 0 >>> output = input_params.gather(input_indices, axis) >>> print(output) [1. 3. 5. 3. 7.] >>> # case2: input_indices is a Tensor with shape (2, 2). When the input_params has one dimension, >>> # the output shape is equal to the input_indices shape. >>> input_indices = Tensor(np.array([[0, 2], [2, 6]]), mindspore.int32) >>> axis = 0 >>> output = input_params.gather(input_indices, axis) >>> print(output) [[ 1. 3.] [ 3. 7.]] >>> # case3: input_indices is a Tensor with shape (2, ) and >>> # input_params is a Tensor with shape (3, 4) and axis is 0. >>> input_params = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), mindspore.float32) >>> input_indices = Tensor(np.array([0, 2]), mindspore.int32) >>> axis = 0 >>> output = input_params.gather(input_indices, axis) >>> print(output) [[1. 2. 3. 4.] [9. 10. 11. 12.]] >>> # case4: input_indices is a Tensor with shape (2, ) and >>> # input_params is a Tensor with shape (3, 4) and axis is 1. >>> input_params = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), mindspore.float32) >>> input_indices = Tensor(np.array([0, 2]), mindspore.int32) >>> axis = 1 >>> output = input_params.gather(input_indices, axis) >>> print(output) [[1. 3.] [5. 7.] [9. 11.]]