Description: Repeats the input tensor for the number of times specified by [object Object] along each dimension. Example:
Assume that the input tensor is [[a,b],[c,d],[e,f]]. That is, [object Object] is [3,2] and [object Object] is (2,4). In this case, the value of [object Object] of the generated tensor is [6,8]. The values are as follows:
When [object Object] is (2,4,2), that is, the number of elements in [object Object] exceeds the dimensions in the tensor, the output tensor is equivalent to the following operation: Expand the shape of the input tensor to the dimension [1,3,2] that is the same as the number of [object Object]; continue to expand the tensor based on the corresponding dimension and the value of [object Object], and the tensor is output as [2,12,4]. The result is as follows:
The following conditions must be met during computation:
The number of parameters in [object Object] cannot be less than the number of dimensions of the input tensor.
The value in [object Object] must be greater than or equal to 0.
Each operator has calls. First, [object Object] is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, [object Object] is called to perform computation.
[object Object][object Object]
Parameters:
[object Object](aclTensor*, compute input): aclTensor on the device. are supported. The can be ND. The shape cannot be greater than 8D.- [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be FLOAT, DOUBLE, FLOAT16, COMPLEX64, COMPLEX128, UINT8, INT8, INT16, INT32, INT64, UINT16, UINT32, UINT64, BOOL, or BFLOAT16.
- [object Object]Atlas training products[object Object] and [object Object]Atlas inference products[object Object]: The data type can be FLOAT, DOUBLE, FLOAT16, COMPLEX64, COMPLEX128, UINT8, INT8, INT16, INT32, INT64, UINT16, UINT32, UINT64, or BOOL.
[object Object](aclIntArray*, compute input): aclIntArray on the host. The data type is INT64, indicating the repeats of the input tensor along each dimension. The number of parameters cannot exceed 8. Currently, repeats cannot be performed on more than four dimensions at the same time. For details about the constraints, see [Constraints] (#Constraints).[object Object](aclTensor *, compute output): aclTensor on the device. are supported. The can be ND. The dimensions cannot be greater than 8. The type must be the same as that of[object Object].- [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be FLOAT, DOUBLE, FLOAT16, COMPLEX64, COMPLEX128, UINT8, INT8, INT16, INT32, INT64, UINT16, UINT32, UINT64, BOOL, or BFLOAT16.
- [object Object]Atlas training products[object Object] and [object Object]Atlas inference products[object Object]: The data type can be FLOAT, DOUBLE, FLOAT16, COMPLEX64, COMPLEX128, UINT8, INT8, INT16, INT32, INT64, UINT16, UINT32, UINT64, or BOOL.
[object Object](uint64_t *, output): size of the workspace to be allocated on the device.[object Object](aclOpExecutor **, output): operator executor, containing the operator computation process.
Returns:
x.repeat(2, 3, 4, 5, 6) # Not supported. An error is reported during verification. The repeat value of the first axis is 2, and there are five non-1 repeat parameters. x.repeat(2, 3, 1, 5, 6) # Supported. The repeat value of the first axis is 2, and there are four non-1 repeat parameters.
x.repeat(1, 3, 4, 5, 6) # Not supported. An error is reported during verification. The repeat value of the first axis is 1, and there are four non-1 repeat parameters. x.repeat(1, 3, 1, 5, 6) # Supported. The repeat value of the first axis is 1, and there are three non-1 repeat parameters.