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]]]]

  

  

    
  

  
Arguments:
    
      
  • 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
)