Class SpaceToDepth<T extends TType>

java.lang.Object
org.tensorflow.op.RawOp
org.tensorflow.op.nn.SpaceToDepth<T>
All Implemented Interfaces:
Shaped, Op, Operand<T>
@Operator(group="nn") public final class SpaceToDepth<T extends TType> extends RawOp implements Operand<T>
SpaceToDepth for tensors of type T. Rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. The attr block_size indicates the input block size.
  • Non-overlapping blocks of size block_size x block size are rearranged into depth at each location.
  • The depth of the output tensor is block_size * block_size * input_depth.
  • The Y, X coordinates within each block of the input become the high order component of the output channel index.
  • The input tensor's height and width must be divisible by block_size.

The data_format attr specifies the layout of the input and output tensors with the following options: "NHWC": [ batch, height, width, channels ] "NCHW": [ batch, channels, height, width ] "NCHW_VECT_C": qint8 [ batch, channels / 4, height, width, 4 ]

It is useful to consider the operation as transforming a 6-D Tensor. e.g. for data_format = NHWC, Each element in the input tensor can be specified via 6 coordinates, ordered by decreasing memory layout significance as: n,oY,bY,oX,bX,iC (where n=batch index, oX, oY means X or Y coordinates within the output image, bX, bY means coordinates within the input block, iC means input channels). The output would be a transpose to the following layout: n,oY,oX,bY,bX,iC

This operation is useful for resizing the activations between convolutions (but keeping all data), e.g. instead of pooling. It is also useful for training purely convolutional models.

For example, given an input of shape [1, 2, 2, 1], data_format = "NHWC" and block_size = 2: 

x = [[[[1], [2]],
      [[3], [4]]]]

This operation will output a tensor of shape [1, 1, 1, 4]: 

[[[[1, 2, 3, 4]]]]

Here, the input has a batch of 1 and each batch element has shape [2, 2, 1], the corresponding output will have a single element (i.e. width and height are both 1) and will have a depth of 4 channels (1 * block_size * block_size). The output element shape is [1, 1, 4].

For an input tensor with larger depth, here of shape [1, 2, 2, 3], e.g. 

x = [[[[1, 2, 3], [4, 5, 6]],
      [[7, 8, 9], [10, 11, 12]]]]

This operation, for block_size of 2, will return the following tensor of shape [1, 1, 1, 12] 

[[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]

Similarly, for the following input of shape [1 4 4 1], and a block size of 2: 

x = [[[[1],   [2],  [5],  [6]],
      [[3],   [4],  [7],  [8]],
      [[9],  [10], [13],  [14]],
      [[11], [12], [15],  [16]]]]

the operator will return the following tensor of shape [1 2 2 4]: 

x = [[[[1, 2, 3, 4],
       [5, 6, 7, 8]],
      [[9, 10, 11, 12],
       [13, 14, 15, 16]]]]

  • Field Details

  • Constructor Details

    • SpaceToDepth

      public SpaceToDepth(Operation operation)

  • Method Details

    • create

      @Endpoint(describeByClass=true) public static <T extends TType> SpaceToDepth<T> create(Scope scope, Operand<T> input, Long blockSize, SpaceToDepth.Options... options)
      Factory method to create a class wrapping a new SpaceToDepth operation.
      Type Parameters:
      T - data type for SpaceToDepth output and operands
      Parameters:
      scope - current scope
      input - The input value
      blockSize - The size of the spatial block.
      options - carries optional attribute values
      Returns:
      a new instance of SpaceToDepth

    • dataFormat

      public static SpaceToDepth.Options dataFormat(String dataFormat)
      Sets the dataFormat option.
      Parameters:
      dataFormat - the dataFormat option
      Returns:
      this Options instance.

    • output

      public Output<T> output()
      Gets output.
      Returns:
      output.

    • asOutput

      public Output<T> asOutput()
      Description copied from interface: Operand
      Returns the symbolic handle of the tensor.

      Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

      Specified by:
      asOutput in interface Operand<T extends TType>
      See Also: