mindspore.ops.fold

mindspore.ops.fold(input, output_size, kernel_size, dilation=1, padding=0, stride=1)[source]

Combines an array of sliding local blocks into a large containing tensor.

Consider a batched input tensor of shape \((N, C \times \prod(\text{kernel_size}), L)\) , where \(N\) is the batch dimension, \(C \times \prod(\text{kernel_size})\) is the total number of values within each block (a block has \(\prod(\text{kernel_size})\) spatial locations each containing a C-channeled vector), and \(L\) is the total number of such blocks:

\[L = \prod_d \left\lfloor\frac{\text{output_size}[d] + 2 \times \text{padding}[d] % - \text{dilations}[d] \times (\text{kernel_size}[d] - 1) - 1}{\text{strides}[d]} + 1\right\rfloor,\]

where \(d\) is over all spatial dimensions.

Therefore, output_size is the spatial shape of the large containing tensor of the sliding local blocks.

The dilation, padding and stride arguments specify how the sliding blocks are retrieved.

Warning

  • The input must be a 3-dimensional Tensor with shape \((N, C \times \prod(\text{kernel_size}), L)\) .

  • The output must be a 4-dimensional Tensor with shape \((N, C, output\_size[0], output\_size[1], ...)\) .

Parameters

  • input (Tensor) – 3-D Tensor, supported dtypes: float16, float32, float64, complex64 and complex128.

  • output_size (Tensor) – 1D tensor with 2 elements of data type int.

  • kernel_size (Union[int, tuple[int], list[int]]) – The size of the kernel, should be two int for height and width. If type is int, it means that height equal with width. Must be specified.

  • dilation (Union[int, tuple[int], list[int]], optional) – The size of the dilation, should be two int for height and width. If type is int, it means that height equal with width. Default: 1 .

  • padding (Union[int, tuple[int], list[int]], optional) – The size of the padding, should be two int for height and width. If type is int, it means that height equal with width. Default: 0 .

  • stride (Union[int, tuple[int], list[int]], optional) – The size of the stride, should be two int for height and width. If type is int, it means that height equal with width. Default: 1 .

Returns

A Tensor, with same type as input . And its shape is as described above.

Raises

  • TypeError – If output_size, kernel_size, stride, dilation, padding data type is not int, tuple or list.

  • ValueError – If output_size, kernel_size, dilation, stride value is not greater than zero or elements number more than 2.

  • ValueError – If padding value is less than zero or elements number more than 2.

  • ValueError – If input.shape[1] != kernel_size[0] * kernel_size[1]

  • ValueError – If input.shape[2] does not match the calculated number of sliding blocks.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x = Tensor(input_data=np.random.rand(16, 64, 25), dtype=mstype.float32)
>>> output_size = Tensor(input_data=[8, 8], dtype=mstype.int32)
>>> output = ops.fold(x, output_size, [2, 2], [2, 2], [2, 2], [2, 2])
>>> print(output.shape)
(16, 16, 8, 8)