tensorflow:: ops:: DepthToSpace

#include <array_ops.h>

DepthToSpace for tensors of type T.

Summary

Rearranges data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. The attr block_size indicates the input block size and how the data is moved.

  • Chunks of data of size block_size * block_size from depth are rearranged into non-overlapping blocks of size block_size x block_size
  • The width the output tensor is input_depth * block_size , whereas the height is input_height * block_size .
  • The Y, X coordinates within each block of the output image are determined by the high order component of the input channel index.
  • The depth of the input tensor must be divisible by block_size * 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,iY,iX,bY,bX,oC (where n=batch index, iX, iY means X or Y coordinates within the input image, bX, bY means coordinates within the output block, oC means output channels). The output would be the input transposed to the following layout: n,iY,bY,iX,bX,oC

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, 1, 1, 4] , data_format = "NHWC" and block_size = 2: 

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


  

  

   This operation will output a tensor of shape
   
    [1, 2, 2, 1]
   
   :
  

  

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

  

  

   Here, the input has a batch of 1 and each batch element has shape
   
    [1, 1, 4]
   
   , the corresponding output will have 2x2 elements and will have a depth of 1 channel (1 =
   
    4 / (block_size * block_size)
   
   ). The output element shape is
   
    [2, 2, 1]
   
   .
  

  

   For an input tensor with larger depth, here of shape
   
    [1, 1, 1, 12]
   
   , 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, 2, 2, 3]
   
  

  

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

  

  

   

  

  

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

  

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

  

  

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

  

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

  

  

   

  

  

   Args:
   
    
    
  • 
     scope: A
     
      Scope
     
     object
    
    • 
    
  • 
     block_size: The size of the spatial block, same as in Space2Depth.
    
    • 
   

  

  

   Returns:
   

  

  

   

    
     

      Constructors and Destructors
     

    		
   

   

    
     
      
       DepthToSpace
      
      (const ::
      
       tensorflow::Scope
      
      & scope, ::
      
       tensorflow::Input
      
      input, int64 block_size)
     
     

    		
   

   

    
     
      
       DepthToSpace
      
      (const ::
      
       tensorflow::Scope
      
      & scope, ::
      
       tensorflow::Input
      
      input, int64 block_size, const
      
       DepthToSpace::Attrs
      
      & attrs)
     
     

    		
   

  

  

   

    
     

      Public attributes
     

    		
   

   

    
     
      
       operation
      
     
    
    
     
    
   

   

    
     
      
       output
      
     
    
    
     
    
   

  

  

   

    
     

      Public functions
     

    		
   

   

    
     
      
       node
      
      () const
     
    
    
     

      
       ::tensorflow::Node *
      
     

    		
   

   

    
     
      
       operator::tensorflow::Input
      
      () const
     
    
    
     

      
      
     

    		
   

   

    
     
      
       operator::tensorflow::Output
      
      () const
     
    
    
     

      
      
     

    		
   

  

  

   

    
     

      Public static functions
     

    		
   

   

    
     
      
       DataFormat
      
      (StringPiece x)
     
    
    
     
    
   

  

  

   

    
     

      Structs
     

    		
   

   

    
     
      tensorflow::
      
      ops::
      
      DepthToSpace::
      
      Attrs
     
    
    
     

      Optional attribute setters for
      
       DepthToSpace
      
      .
     

    		
   

  

  

   Public attributes
  

  

   

    operation
   

   
Operation operation

   

   

  

  

   

    output
   

   
::tensorflow::Output output

   

   

  

  

   Public functions
  

  

   

    DepthToSpace
   

   
 DepthToSpace(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  int64 block_size
)

   

   

  

  

   

    DepthToSpace
   

   
 DepthToSpace(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  int64 block_size,
  const DepthToSpace::Attrs & attrs
)

   

   

  

  

   

    node
   

   
::tensorflow::Node * node() const 

   

   

  

  

   

    operator::tensorflow::Input
   

   
 operator::tensorflow::Input() const 

   

   

  

  

   

    operator::tensorflow::Output
   

   
 operator::tensorflow::Output() const 

   

   

  

  

   Public static functions
  

  

   

    DataFormat
   

   
Attrs DataFormat(
  StringPiece x
)