tf.space_to_batch( input, paddings, 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.
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 width_pad = pad_left + width + pad_right
The attr block_size
must be greater than one. It indicates the block size.
block_size x block size
in the height and width dimensions are rearranged into the batch dimension at each location.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).
A Tensor
. Has the same type as input
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/space_to_batch