# tf.space_to_batch

tf.space_to_batch(
input,
block_size,
name=None
)


Defined in tensorflow/python/ops/array_ops.py.

See the guide: Tensor Transformations > Slicing and Joining

SpaceToBatch for 4-D tensors of type T.

This is a legacy version of the more general SpaceToBatchND.

Zero-pads and then rearranges (permutes) blocks of spatial data into batch. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the batch dimension. After the zero-padding, both height and width of the input must be divisible by the block size.

#### Args:

• input: A Tensor. 4-D with shape [batch, height, width, depth].
• paddings: A Tensor. Must be one of the following types: int32, int64. 2-D tensor of non-negative integers with shape [2, 2]. It specifies the padding of the input with zeros across the spatial dimensions as follows:

  paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]


The effective spatial dimensions of the zero-padded input tensor will be:

  height_pad = pad_top + height + pad_bottom


The attr block_size must be greater than one. It indicates the block size.

• Non-overlapping blocks of size block_size x block size in the height and width dimensions are rearranged into the batch dimension at each location.
• The batch of the output tensor is batch * block_size * block_size.

The shape of the output will be:

[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
depth]


Some examples:

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

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] and block_size of 2:

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] and block_size of 2:

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] and block_size of 2:

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


The output tensor has shape [8, 1, 2, 1] and value:

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


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

• block_size: An int that is >= 2.

• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as input.