『TensorFlow』pad图片

最新推荐文章于 2022-09-15 14:40:01 发布

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本文详细介绍了TensorFlow中tf.pad()函数的用法，包括如何通过paddings参数来指定不同维度上的填充数量，以及不同mode选项（如CONSTANT、REFLECT和SYMMETRIC）产生的效果。通过具体示例展示了如何使用tf.pad()函数进行张量的填充操作。

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tf.pad()文档如下，

pad(tensor, paddings, mode='CONSTANT', name=None, constant_values=0)
    Pads a tensor.

    This operation pads a `tensor` according to the `paddings` you specify.
    `paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of
    `tensor`. For each dimension D of `input`, `paddings[D, 0]` indicates how
    many values to add before the contents of `tensor` in that dimension, and
    `paddings[D, 1]` indicates how many values to add after the contents of
    `tensor` in that dimension. If `mode` is "REFLECT" then both `paddings[D, 0]`
    and `paddings[D, 1]` must be no greater than `tensor.dim_size(D) - 1`. If
    `mode` is "SYMMETRIC" then both `paddings[D, 0]` and `paddings[D, 1]` must be
    no greater than `tensor.dim_size(D)`.

    The padded size of each dimension D of the output is:

    `paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]`

实际使用注意，参数paddings元素数(rank)必须和输入维度一一对应，表示该维度前后填充的层数，文档示例验证如下，

import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)

t = tf.constant([[1, 2, 3], [4, 5, 6]])
paddings = tf.constant([[1, 1,], [2, 2]])
# 'constant_values' is 0.
# rank of 't' is 2.
res = tf.pad(t, paddings, "CONSTANT", constant_values=1)  # [[0, 0, 0, 0, 0, 0, 0],
                                                          #  [0, 0, 1, 2, 3, 0, 0],
                                                          #  [0, 0, 4, 5, 6, 0, 0],
                                                          #  [0, 0, 0, 0, 0, 0, 0]]


print(sess.run(res))

'''
tf.pad(t, paddings, "REFLECT")  # [[6, 5, 4, 5, 6, 5, 4],
                                #  [3, 2, 1, 2, 3, 2, 1],
                                #  [6, 5, 4, 5, 6, 5, 4],
                                #  [3, 2, 1, 2, 3, 2, 1]]

tf.pad(t, paddings, "SYMMETRIC")  # [[2, 1, 1, 2, 3, 3, 2],
                                  #  [2, 1, 1, 2, 3, 3, 2],
                                  #  [5, 4, 4, 5, 6, 6, 5],
                                  #  [5, 4, 4, 5, 6, 6, 5]]
'''