tf.space_to_depth( input, block_size, name=None, data_format='NHWC' )
Defined in tensorflow/python/ops/array_ops.py
.
See the guide: Tensor Transformations > Slicing and Joining
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.
block_size x block size
are rearranged into depth at each location.block_size * block_size * input_depth
.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]]]]
input
: A Tensor
.block_size
: An int
that is >= 2
. The size of the spatial block.data_format
: An optional string
from: "NHWC", "NCHW", "NCHW_VECT_C"
. Defaults to "NHWC"
.name
: A name for the operation (optional).A Tensor
. Has the same type as input
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/space_to_depth