SpaceToBatch for N-D tensors of type T.

tf.space_to_batch_nd( input, block_shape, paddings, name=None )

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.

Args | |
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`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 x = [[[[1], [2]], [[3], [4]]]] The output tensor has shape [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] (2) For the following input of shape x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] The output tensor has shape [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]] (3) For the following input of shape x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], [[13], [14], [15], [16]]]] The output tensor has shape x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]] (4) For the following input of shape x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]] The output tensor has shape 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). |

Returns | |
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A `Tensor` . Has the same type as `input` . |

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/space_to_batch_nd