tf.space_to_batch_nd(input, block_shape, paddings, name=None)
See the guide: Tensor Transformations > Slicing and Joining
SpaceToBatch for N-D tensors of type T.
This operation divides "spatial" dimensions [1, ..., M]
of the input into a grid of blocks of shape block_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 to paddings
. See below for a precise description.
input
: A Tensor
. N-D with shape input_shape = [batch] + spatial_shape + remaining_shape
, where spatial_shape has M
dimensions.block_shape
: A Tensor
. Must be one of the following types: int32
, int64
. 1-D with shape [M]
, all values must be >= 1.paddings
: A Tensor
. Must be one of the following types: int32
, int64
. 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 dimension i
. It is required that block_shape[i]
divides input_shape[i + 1] + pad_start + pad_end
.
This operation is equivalent to the following steps:
Zero-pad the start and end of dimensions [1, ..., M]
of the input according to paddings
to produce padded
of shape padded_shape
.
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
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
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]
, and paddings = [[0, 0], [0, 0]]
:
prettyprint x = [[[[1], [2]], [[3], [4]]]]
The output tensor has shape [4, 1, 1, 1]
and value:
prettyprint [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
(2) For the following input of shape [1, 2, 2, 3]
, block_shape = [2, 2]
, and paddings = [[0, 0], [0, 0]]
:
prettyprint x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
The output tensor has shape [4, 1, 1, 3]
and value:
prettyprint [[[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]
, and paddings = [[0, 0], [0, 0]]
:
prettyprint 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:
prettyprint x = [[[[1], [3]], [[5], [7]]], [[[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]]
:
prettyprint 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:
prettyprint 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. * name
: A name for the operation (optional).
A Tensor
. Has the same type as input
.
Defined in tensorflow/python/ops/gen_array_ops.py
.
© 2017 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_nd