|---|
| [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/array_ops.py#L3850-L3985) |
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`](https://www.tensorflow.org/api_docs/python/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:
1. Reshape `input` to `reshaped` of shape: \[block_shape\[0\], ..., block_shape\[M-1\], batch / prod(block_shape), input_shape\[1\], ..., input_shape\[N-1\]\] 2. Permute dimensions of `reshaped` to produce `permuted` of shape \[batch / prod(block_shape), input_shape\[1\], block_shape\[0\], ..., input_shape\[M\], block_shape\[M-1\], input_shape\[M+1\], ..., input_shape\[N-1\]\] 3. Reshape `permuted` to produce `reshaped_permuted` of shape \[batch / prod(block_shape), input_shape\[1\] \* block_shape\[0\], ..., input_shape\[M\] \* block_shape\[M-1\], input_shape\[M+1\], ..., input_shape\[N-1\]\] 4. Crop the start and end of dimensions `[1, ..., M]` of `reshaped_permuted` according to `crops` to produce the output of shape: \[batch / prod(block_shape), input_shape\[1\] \* block_shape\[0\] - crops\[0,0\] - crops\[0,1\], ..., input_shape\[M\] \* block_shape\[M-1\] - crops\[M-1,0\] - crops\[M-1,1\], input_shape\[M+1\], ..., input_shape\[N-1\]\] |
| `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]]`:
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:
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]]`:
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:
x = [[[[ 1], [ 2], [ 3], [ 4]],
[[ 5], [ 6], [ 7], [ 8]]],
[[[ 9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `input`. ||