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

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.

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.
  • 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:

    1. Zero-pad the start and end of dimensions [1, ..., M] of the input according to paddings to produce padded of shape padded_shape.

    2. 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

    3. 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

    4. 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).

Returns:

A Tensor. Has the same type as input.

Defined in tensorflow/python/ops/gen_array_ops.py.