Function batchToSpaceND

  • batchToSpaceND<T>(x, blockShape, crops): T

  • This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape blockShape + [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 tf.spaceToBatchND. See below for a precise description.

    const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
    const blockShape = [2, 2];
    const crops = [[0, 0], [0, 0]];

    x.batchToSpaceND(blockShape, crops).print();

    Type Parameters

    Parameters

    • x: TensorLike | T

      A tf.Tensor. N-D with x.shape = [batch] + spatialShape + remainingShape, where spatialShape has M dimensions.

    • blockShape: number[]

      A 1-D array. Must have shape [M], all values must be >= 1.

    • crops: number[][]

      A 2-D array. Must have shape [M, 2], all values must be >= 0. crops[i] = [cropStart, cropEnd] specifies the amount to crop from input dimension i + 1, which corresponds to spatial dimension i. It is required that cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]

      This operation is equivalent to the following steps:

      1. Reshape x to reshaped of shape: [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape), x.shape[1], ..., x.shape[N-1]]

      2. Permute dimensions of reshaped to produce permuted of shape [batch / prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M], blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]

      3. Reshape permuted to produce reshapedPermuted of shape [batch / prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]

      4. Crop the start and end of dimensions [1, ..., M] of reshapedPermuted according to crops to produce the output of shape: [batch / prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1], ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] - crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]

    Returns T

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