mindspore.ops.Im2Col

class mindspore.ops.Im2Col(ksizes, strides=1, dilations=1, pads=0)

Extracts sliding local blocks from a batched input tensor.

Consider a batched input tensor of shape $(N,C,\ast )$, where $N$ is the batch dimension, $C$ is the channel dimension, and $\ast$ represent arbitrary spatial dimensions. This operation flattens each sliding ksizes- sized block within the spatial dimensions of input x into a column (i.e., last dimension) of a 4-D output tensor of shape $(N,C,\prod (\text{kernel\_size}),L)$, where $C\times \prod (\text{kernel\_size})$ 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:

$$L=\prod _d\lfloor \frac{\text{spatial\_size}[d]+2\times \text{pads}[d]-\text{dilations}[d]\times (\text{kernel\_size}[d]-1)-1}{\text{strides}[d]}+1\rfloor ,$$

where $\text{spatial\_size}$ is formed by the spatial dimensions of input x ($\ast$ above), and $d$ is over all spatial dimensions.

Therefore, indexing output at the last dimension (column dimension) gives all values within a certain block.

The pads, strides and dilations arguments specify how the sliding blocks are retrieved.

Note

Currently, only 4-D input tensors (batched image-like tensors) are supported.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

• ksizes (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.

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

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

• pads (Union[int, tuple[int], list[int]], optional) –

  The pad of the window, that must be a tuple of one or two int for height and width. Default: 0 .

  • If one int, $pad\mathrm{\_}height=pad\mathrm{\_}width$.

  • If two int, $pad\mathrm{\_}height=pads[0]$, $pad\mathrm{\_}width=pads[1]$.

Inputs:

• x (Tensor) - input tensor, only 4-D input tensors (batched image-like tensors) are supported.

Outputs:

Tensor, a 4-D Tensor with same type of input x.

Raises

• TypeError – If ksizes data type is not in Union[int, tuple[int], list[int]].

• TypeError – If strides data type is not in Union[int, tuple[int], list[int]].

• TypeError – If dilations data type is not in Union[int, tuple[int], list[int]].

• TypeError – If pads data type isnot in Union[int, tuple[int], list[int]].

• ValueError – If ksizes value is not greater than zero or elements number more than 2.

• ValueError – If strides value is not greater than zero or elements number more than 2.

• ValueError – If dilations value is not greater than zero or elements number more than 2.

• ValueError – If pads value is not greater than zero.

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(4, 4, 32, 32), dtype=mstype.float64)
>>> im2col = ops.Im2Col(ksizes=3, strides=1, dilations=1)
>>> y = im2col(x)
>>> print(y.shape)
(4, 4, 9, 900)