BatchToSpace

tensorflow C++ API

tensorflow::ops::BatchToSpace

BatchToSpace for 4-D tensors of type T.

Summary

This is a legacy version of the more general BatchToSpaceND.

Rearranges (permutes) data from batch into blocks of spatial data, followed by cropping. This is the reverse transformation of SpaceToBatch. More specifically, this op outputs a copy of the input tensor where values from the batch dimension are moved in spatial blocks to the height and width dimensions, followed by cropping along the height and width dimensions.

Arguments:

• scope: A Scope object
• input: 4-D tensor with shape(shape must be integer type) [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth] Note that the batch size of the input tensor must be divisible by block_size * block_size
• crops: 2-D tensor of non-negative integers with shape(shape must be integer type) [2, 2]. It specifies how many elements to crop from the intermediate result across the spatial dimensions as follows:

  crops = [[crop_top, crop_bottom],[crop_left, crop_right]]
  

Returns:

• Output: 4-D with shape [batch, height, width, depth] , where:

  height = height_pad - crop_top - crop_bottom
  width = width_pad - crop_left - crop_right

The attr block_size must be greater than one( block_size > 1 ). It indicates the block size.

Some examples:

(1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2:

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

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

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

(2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2:

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

The output tensor has shape `[1, 2, 2, 3]` and value:

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

(3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2:

x = [[[[1], [3]], [[9], [11]]],

  \[\[\[2\], \[4\]\], \[\[10\], \[12\]\]\],

  \[\[\[5\], \[7\]\], \[\[13\], \[15\]\]\],

  \[\[\[6\], \[8\]\], \[\[14\], \[16\]\]\]\]

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

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

  \[\[5\],   \[6\],  \[7\],  \[8\]\],

  \[\[9\],  \[10\], \[11\],  \[12\]\],

  \[\[13\], \[14\], \[15\],  \[16\]\]\]

BatchToSpace 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 : 4-D tensor with shape(shape must be integer type) [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth] Note that the batch size of the input tensor must be divisible by block_size * block_size

• Input crops : 2-D tensor of non-negative integers with shape(shape must be integer type) [2, 2]. It specifies how many elements to crop from the intermediate result across the spatial dimensions as follows:

  crops = [[crop_top, crop_bottom],[crop_left, crop_right]]
  

• Int64 block_size: determines the size of the image block.

Return:

• Output output : Output object of BatchToSpace class object.

Result:

• std::vector(Tensor) result_output : 4-D with shape [batch, height, width, depth] , where: height = height_pad - crop_top - crop_bottom, width = width_pad - crop_left - crop_right

Using Method

※ input을 재배치할때 쓰는 기능이다. 위의 공식대로 shape가 결정되며 depth는 input과 output이 동일하다.