SpaceToBatch for N-D tensors of type T.

# Aliases:

• tf.compat.v2.nn.space_to_batch
• tf.compat.v2.space_to_batch
• tf.nn.space_to_batch

 tf.space_to_batch(
    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:

• 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 [1, 2, 2, 1], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]:
• 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 The output tensor has shape [4, 1, 1, 1] and value: (2) For the following input of shape [1, 2, 2, 3], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]: The output tensor has shape [4, 1, 1, 3] and value: (3) For the following input of shape [1, 4, 4, 1], block_shape = [2, 2], and paddings = [[0, 0], [0, 0]]: The output tensor has shape [4, 2, 2, 1] and value: (4) For the following input of shape [2, 2, 4, 1], block_shape = [2, 2], and paddings = [[0, 0], [2, 0]]: The output tensor has shape [8, 1, 3, 1] and value: 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.