https://cv-tricks.com/image-segmentation/transpose-convolution-in-tensorflow/

https://zhuanlan.zhihu.com/p/38964806

``````tf.nn.conv2d_transpose（
conv,   卷积后的结果  ，假设为 （16,375,250,64）
权重的初始化，   使用线性插值，请参考后面，  [3,3,3,64] [kernel,kernel,输出特征个数，输入特征个数]，
输出的初始化，    [16,750,500,3]   [batch,height,width,chanel]   chanel必须与输出特征个数相等
）

``````from math import ceil
import numpy as np

import tensorflow as tf

def __get_deconv_filter(f_shape):
"""
Compute bilinear filter and return it
"""
filt_width = f_shape[0]  #计算kernel的宽
filt_height = f_shape[1] #计算kernel的长
half_width = ceil(filt_width /2.0)
center = (2 * half_width - 1 - half_width % 2) / (2.0 * half_width) # 计算某点的权值  对这个点进行插值
bilinear = np.zeros([filt_width, filt_height])
for x in range(filt_width):
for y in range(filt_height):
value = (1 - abs(x / half_width - center)) * (1 - abs(y / half_width - center))
bilinear[x, y] = value
weights = np.zeros(f_shape)
for i in range(f_shape[2]):
weights[:, :, i, i] = bilinear
print(weights[:, :, i, i])

init = tf.constant_initializer(value=weights,
dtype=tf.float32)
return tf.get_variable(name="up_filter", initializer=init,
shape=weights.shape)
a = __get_deconv_filter([3, 3, 3, 3])``````

``````def get_kernel_size(factor):
"""
Find the kernel size given the desired factor of upsampling.
"""
#获取kernel的大小
return 2 * factor - factor % 2

def upsample_filt(size):
"""
Make a 2D bilinear kernel suitable for upsampling of the given (h, w) size.
"""
factor = (size + 1) // 2
if size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:size, :size]
return (1 - abs(og[0] - center) / factor) * \
(1 - abs(og[1] - center) / factor)

def bilinear_upsample_weights(factor, number_of_classes):
"""
Create weights matrix for transposed convolution with bilinear filter
initialization.
"""

filter_size = get_kernel_size(factor)

weights = np.zeros((filter_size,
filter_size,
3,
4), dtype=np.float32)

upsample_kernel = upsample_filt(filter_size)

for i in range(3):
weights[:, :, i, i] = upsample_kernel

return weights

print(bilinear_upsample_weights(2,21).shape)``````

``````import tensorflow as tf
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

from math import ceil

'''
一张图片的反卷积
'''

im = Image.open('timg.jpg')
images = np.asarray(im)
print(images.shape)

images = np.reshape(images,[1,750,500,3])

img = tf.Variable(images,dtype=tf.float32)
# kernel = tf.get_variable(name='a',shape=[3, 3, 3, 3], dtype=tf.float32,
#                                   initializer=tf.contrib.layers.xavier_initializer())

# 卷积核
kernel = tf.get_variable(name='a',shape=[3, 3, 3, 64], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())

def __get_deconv_filter(f_shape):
"""
Compute bilinear filter and return it
"""
filt_width = f_shape[0]  #计算kernel的宽
filt_height = f_shape[1] #计算kernel的长
half_width = ceil(filt_width /2.0)
center = (2 * half_width - 1 - half_width % 2) / (2.0 * half_width) # 计算某点的权值  对这个点进行插值
bilinear = np.zeros([filt_width, filt_height])
for x in range(filt_width):
for y in range(filt_height):
value = (1 - abs(x / half_width - center)) * (1 - abs(y / half_width - center))
bilinear[x, y] = value
weights = np.zeros(f_shape)
for i in range(f_shape[2]):
weights[:, :, i, i] = bilinear
print(weights[:, :, i, i])

init = tf.constant_initializer(value=weights,
dtype=tf.float32)
return  init

# 反卷积核
kernel2 = tf.get_variable(name='a1',shape=[3, 3, 3, 64], dtype=tf.float32,
initializer=__get_deconv_filter([3,3,3,64]))

#tf.nn.conv2d(input=input_op, filter=weights, strides=[1, dh, dw, 1], padding="SAME")

# 卷积
conv1 = tf.nn.conv2d(input=img, filter=kernel,strides=[1, 1, 1, 1], padding="SAME")
# print(conv1)
# 池化
pool = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

shape_ = pool.get_shape().as_list()
print(shape_) #[1, 375, 250, 64]
output_shape = [shape_[0], shape_[1] * 2, shape_[2] * 2, 3]

print('pool:',pool.get_shape())
# 反卷积操作

# print(conv1.get_shape())

a = tf.transpose(conts, [0, 3, 1, 2])

b = tf.transpose(tf.squeeze(a) , [1,2,0])

with tf.Session() as sess:
sess.run(tf.global_variables_initializer())

# conv1_convert = sess.run(tf.transpose(conts, [0, 3, 1, 2]))

# fig6, ax6 = plt.subplots(nrows=3, ncols=8, figsize=(8, 8))
# plt.title('Pool2 32x7x7')
# for i in range(8):
#     for j in range(8):
#         ax6[i][j].imshow(conv1_convert[0][(i + 1) * j])

# plt.show()

plt.imshow(sess.run(b))

plt.show()``````