# tf.compat.v1.batch_to_space_nd
BatchToSpace for N-D tensors of type T.
#### View aliases
**Compat aliases for migration**
See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.
[`tf.compat.v1.manip.batch_to_space_nd`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/batch_to_space_nd)
tf.compat.v1.batch_to_space_nd(
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 below for a precise description.
| ## Args ||
|---|---|
| `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. |
| `crops` | A `Tensor`. Must be one of the following types: `int32`, `int64`. 2-D with shape `[M, 2]`, 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\[N-1\]\] Some 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]]]]
|
| `name` | A name for the operation (optional). |
| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `input`. ||