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TensorFlow 1 version
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View source on GitHub
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BatchToSpace for N-D tensors of type T.
tf.batch_to_space(
input, block_shape, crops, name=None
)
This operation reshapes the "batch" dimension 0 into M + 1 dimensions of
shape block_shape + [batch], interleaves these blocks back into the grid
defined by the spatial dimensions [1, ..., M], to obtain a result with the
same rank as the input. The spatial dimensions of this intermediate result
are then optionally cropped according to crops to produce the output. This
is the reverse of SpaceToBatch (see tf.space_to_batch).
Args | |
|---|---|
input
|
A N-D Tensor with shape input_shape = [batch] + spatial_shape +
remaining_shape, where spatial_shape has M dimensions.
|
block_shape
|
A 1-D Tensor with shape [M]. Must be one of the following
types: int32, int64. All values must be >= 1. For backwards
compatibility with TF 1.0, this parameter may be an int, in which case it
is converted to
numpy.array([block_shape, block_shape],
dtype=numpy.int64).
|
crops
|
A 2-D Tensor with shape [M, 2]. Must be one of the
following types: int32, int64. All values must be >= 0.
crops[i] = [crop_start, crop_end] specifies the amount to crop from
input dimension i + 1, which corresponds to spatial dimension i.
It is required that
crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1].
This operation is equivalent to the following steps:
|
name
|
A name for the operation (optional). |
Examples:
(1) For the following input of shape [4, 1, 1, 1],
block_shape = [2, 2], and crops = [[0, 0], [0, 0]]:
[[[[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],
block_shape = [2, 2], and crops = [[0, 0], [0, 0]]:
[[[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]`,
`block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:
```python
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:
```python
x = [[[1], [2], [ 3], [ 4]],
[[5], [6], [ 7], [ 8]],
[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]
```
(4) For the following input of shape
`[8, 1, 3, 1]`,
`block_shape = [2, 2]`, and `crops = [[0, 0], [2, 0]]`:
```python
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]]]]
```
The output tensor has shape `[2, 2, 4, 1]` and value:
```python
x = [[[[ 1], [ 2], [ 3], [ 4]],
[[ 5], [ 6], [ 7], [ 8]]],
[[[ 9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
```
<!-- Tabular view -->
<table class="responsive fixed orange">
<colgroup><col width="214px"><col></colgroup>
<tr><th colspan="2"><h2 class="add-link">Returns</h2></th></tr>
<tr class="alt">
<td colspan="2">
A `Tensor`. Has the same type as `input`.
</td>
</tr>
</table>