TensorFlow中 tf.space_to_depth()函数的用法_space2depth

目录

一、函数定义

二、解释范例

三、代码验证

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一、函数定义

通俗易懂些，就是把输入为[batch, height, width, channels]形式的Tensor，其在height和width维的值将移至depth维

space_to_depth(
    input, 
    block_size, 
    data_format="NHWC", 
    name=None)

input：就是输入的Tensor

block_size：就是块大小，一般≥2

data_format：数据形式，如下表，默认“NHWC”

name：名字

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*输出张量的深度是block_size * block_size * input_depth

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二、解释范例

一共三种Tensor：

（1）输入大小为：[1, 2, 2, 1]

test_tensor_1 = [[[[1], [2]],
                  [[3], [4]]]]

输出大小：[1, 1, 1, 4]

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

（2）输入大小为：[1, 2, 2, 3]

test_tensor_2 = [[[[1, 2, 3], [4, 5, 6]],
                  [[7, 8, 9], [10, 11, 12]]]]

输出大小：[1, 1, 1, 12]

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

（3）输入大小为：[1, 4, 4, 1]

test_tensor_3 = [[[[1],   [2],  [5],  [6]],
                  [[3],   [4],  [7],  [8]],
                  [[9],  [10], [13],  [14]],
                  [[11], [12], [15],  [16]]]]

输出大小：[1, 2, 2, 4]

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

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三、代码验证

 test_space_to_depth.py

import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
test_tensor_1 = tf.placeholder('float32', [1, 2, 2, 1], name='test_tensor_1')
test_tensor_1 = [[[[1], [2]],
                  [[3], [4]]]]
test_tensor_1 = tf.space_to_depth(test_tensor_1, 2)
 
test_tensor_2 = tf.placeholder('float32', [1, 2, 2, 3], name='test_tensor_1')
test_tensor_2 = [[[[1, 2, 3], [4, 5, 6]],
                  [[7, 8, 9], [10, 11, 12]]]]
test_tensor_2 = tf.space_to_depth(test_tensor_2, 2)
 
test_tensor_3 = tf.placeholder('float32', [1, 4, 4, 1], name='test_tensor_1')
test_tensor_3 = [[[[1],   [2],  [5],  [6]],
                  [[3],   [4],  [7],  [8]],
                  [[9],  [10], [13],  [14]],
                  [[11], [12], [15],  [16]]]]
test_tensor_3 = tf.space_to_depth(test_tensor_3, 2)
 
sess = tf.Session()
print(test_tensor_1)
print(sess.run(test_tensor_1))
 
print(test_tensor_2)
print(sess.run(test_tensor_2))
 
print(test_tensor_3)
print(sess.run(test_tensor_3))

结果：