SpaceToBatchND

tensorflow C++ API

ensorflow::ops::SpaceToBatchND

SpaceToBatch for N-D tensors of type T.

Summary

This operation divides "spatial" dimensions[1, ..., M]of the input into a grid of blocks of shapeblock_shape, and interleaves these blocks with the "batch" dimension (0) such that in the output, the spatial dimensions[1, ..., M]correspond to the position within the grid, and the batch dimension combines both the position within a spatial block and the original batch position. Prior to division into blocks, the spatial dimensions of the input are optionally zero padded according topaddings. See below for a precise description.

Arguments:

• scope: A Scope object
• input: N-D with shape input_shape = [batch] + spatial_shape + remaining_shape, where spatial_shape has M dimensions.
• block_shape: 1-D with shape [M], all values must be >= 1.
• paddings: 2-D with shape [M, 2], all values must be >= 0. paddings[i] = [pad_start, pad_end] specifies the padding for input dimension i + 1, which corresponds to spatial dimensioni. It is required that block_shape[i] divides input_shape[i + 1] + pad_start + pad_end.

This operation is equivalent to the following steps:

1. Zero-pad the start and end of dimensions [1, ..., M] of the input according to paddings to produce padded of shape padded_shape.
2. Reshape padded to reshaped_padded of shape:[batch] + [padded_shape[1] / block_shape[0], block_shape[0], ..., padded_shape[M] / block_shape[M-1], block_shape[M-1]] + remaining_shape
3. Permute dimensions of reshaped_padded to produce permuted_reshaped_padded of shape:block_shape + [batch] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape
4. Reshape permuted_reshaped_padded to flatten block_shape into the batch dimension, producing an output tensor of shape:[batch * prod(block_shape)] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape

Some examples:

(1) For the following input of shape[1, 2, 2, 1],block_shape = [2, 2], andpaddings = [[0, 0], [0, 0]]:

``` x = [[[[1], [2]], [[3], [4]]]] ```

The output tensor has shape[4, 1, 1, 1]and value:

``` [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ```

(2) For the following input of shape[1, 2, 2, 3],block_shape = [2, 2], andpaddings = [[0, 0], [0, 0]]:

``` x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ```

The output tensor has shape[4, 1, 1, 3]and value:

``` [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ```

(3) For the following input of shape[1, 4, 4, 1],block_shape = [2, 2], andpaddings = [[0, 0], [0, 0]]:

``` x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], [[13], [14], [15], [16]]]] ```

The output tensor has shape[4, 2, 2, 1]and value:

``` x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]] ```

(4) For the following input of shape[2, 2, 4, 1], block_shape =[2, 2], and paddings =[[0, 0], [2, 0]]:

``` x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]] ```

The output tensor has shape[8, 1, 3, 1]and value:

``` x = [[[[0], [1], [3]]], [[[0], [9], [11]]], [[[0], [2], [4]]], [[[0], [10], [12]]], [[[0], [5], [7]]], [[[0], [13], [15]]], [[[0], [6], [8]]], [[[0], [14], [16]]]] ```

Among others, this operation is useful for reducing atrous convolution into regular convolution.

Returns:

• Output : The output tensor.

SpaceToBatchND block

Source link :https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_array_ops.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input input: N-D with shape input_shape = [batch] + spatial_shape + remaining_shape, where spatial_shape has Mdimensions.
• Input block_shape: 1-D with shape [M], all values must be >= 1.
• Input paddings: 2-D tensor of non-negative integers with shape [M, 2]

Output:

• Output output: Output object of SpaceToBatchND class object.

Result:

• std::vector(Tensor) result_output: The output tensor.

Using Method

※ N차원의 input 을 재배치하는 기능을 한다. summary의 공식대로 진행된다.