使用gpu加速库，最好是Tensorflow？

Question

我正在寻找一个gpu加速的n维滑动窗口操作在Python中使用Tensorflow实现。您可以在Torch、Caffe或Theano中发布您的实现，但我将选择Tensorflow实现作为接受的答案。请发布执行2d中值滤波操作的工作代码段（希望在没有代码更改或代码更改最小的情况下，可以应用于n维图像）

以我对张量流的有限知识，我相信两个潜在的模块是^{}或{a2}，然后是{}，apply，reshape魔法？在

我的失败尝试贴在下面，作为娱乐。请注意，我在2年前发布了一个类似的question，要求实现Theano，现在，大多数人都在使用tf/keras或torch。在

import time
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import tensorflow as tf
from tensorflow.contrib.data.python.ops import sliding
from skimage import img_as_float, data
from scipy.signal import medfilt

imgs = img_as_float(data.camera())

### SCIPY median ###
stime = time.time()
scipysmoothed = medfilt(imgs,(9,9))
etime = time.time()
print('scipy smoothed: {:1.4f} seconds'.format(etime-stime))

### Failed attempt of TF median ###
method = 'Tensorflow'
stime = time.time()

window_func = lambda x: tf.contrib.distributions.percentile(x, 50.0)

# create TensorFlow Dataset object
data = tf.data.Dataset.from_tensor_slices(imgs)

# sliding window - only 1d is allowed?
window = 3
stride = 1
data = data.apply(sliding.sliding_window_batch(window, stride)).map(lambda x: window_func(x))

# create TensorFlow Iterator object
iterator =  tf.data.Iterator.from_structure(data.output_types)
next_element = iterator.get_next()

# create initialization ops 
init_op = iterator.make_initializer(data)
c=0
smoothed = np.zeros(imgs.shape)
with tf.Session() as sess:
    # initialize the iterator on the data
    sess.run(init_op)
    while True:
        try:
            elem = sess.run(next_element)
            smoothed[c,:]=elem
            # obviously WRONG.
            c+=1
        except tf.errors.OutOfRangeError:
            #print("End of dataset.")
            break
#print(c)
etime = time.time()
print('tf smoothed: {:1.4f} seconds'.format(etime-stime))


plt.figure(figsize=(20,20))
plt.subplot(131)
plt.imshow(imgs,cmap='gray',interpolation='none')
plt.title('original')
plt.subplot(132)
plt.imshow(smoothed,cmap='gray',interpolation='none')
plt.title('actual smoothed\nwith {}'.format(method))
plt.subplot(133)
plt.imshow(scipysmoothed,cmap='gray',interpolation='none')
_=plt.title('expected smoothed')

一。在

^{pr2}$

Answer 1

建议1：我的尝试如下，因为它只使用tf.image.extract_image_patches和{}，所以该实现只支持2d和3d图像。在

建议2：可以将数据格式化为一个预处理步骤（通过tf.data.Dataset.map），但是这也需要很多时间，我还不知道为什么（例如https://gist.github.com/pangyuteng/ca5cb07fe383ebe59b521c832f2e2918）。在

建议3：使用卷积块来并行化处理，参见“超列用于对象分割和细粒度定位”https://arxiv.org/abs/1411.5752。在

在

方案1代码：

import time
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import tensorflow as tf
from tensorflow.contrib.data.python.ops import sliding
from skimage import img_as_float, data
from scipy.signal import medfilt

dtype = 2
if dtype==2:
    imgs = img_as_float(data.camera())
elif dtype==3:
    imgs = np.random.rand(28,28,28)

imgs = img_as_float(data.camera())

### SCIPY median ###
stime = time.time()
scipysmoothed = medfilt(imgs,(9,9))
etime = time.time()
print('scipy smoothed: {:1.4f} seconds'.format(etime-stime))

### TF median ###
method = 'Tensorflow'
imgs = np.expand_dims(imgs,axis=-1)
imgs = np.expand_dims(imgs,axis=0)
print('imgs.shape:{}'.format(imgs.shape))
imgs = tf.cast(imgs,tf.float32)

stime = time.time()

if len(imgs.shape) == 4:
    kernel=(1,9,9,1)
    stride=(1,1,1,1)
    rates=(1,1,1,1)
    padding='SAME'
    patches=tf.image.extract_image_patches(
        imgs,kernel,stride,rates,padding,
    )    
    _,x,y,n = patches.shape
    _,sx,sy,_ = kernel
    window_func = lambda x: tf.contrib.distributions.percentile(x, 50.0)
    patches = tf.reshape(patches,[x*y,sx,sy])
    smoothed = tf.map_fn(lambda x: window_func(patches[x,:,:]), tf.range(x*y), dtype=tf.float32)
    smoothed = tf.reshape(smoothed,[x,y])

elif len(imgs.shape) == 5:

    kernel=(1,12,12,12,1)
    stride=(1,1,1,1,1)    
    padding='SAME'
    patches=tf.extract_volume_patches(
        imgs,kernel,stride,padding,
    )
    _,x,y,z,n = patches.shape
    _,sx,sy,sz,_ = kernel
    window_func = lambda x: tf.contrib.distributions.percentile(x, 50.0)
    patches = tf.reshape(patches,[x*y*z,sx,sy,sz])
    smoothed = tf.map_fn(lambda x: window_func(patches[x,:,:]), tf.range(x*y*z), dtype=tf.float32)
    smoothed = tf.reshape(smoothed,[x,y,z])

else:
    raise NotImplemented()

with tf.Session() as sess:
    output = sess.run(smoothed)

etime = time.time()
print('tf smoothed: {:1.4f} seconds'.format(etime-stime))

print(output.shape)

plt.figure(figsize=(20,20))
plt.subplot(131)
imgs = img_as_float(data.camera())
plt.imshow(imgs.squeeze(),cmap='gray',interpolation='none')
plt.title('original')
plt.subplot(132)
plt.imshow(output.squeeze(),cmap='gray',interpolation='none')
plt.title('actual smoothed\nwith {}'.format(method))
plt.subplot(133)
plt.imshow(scipysmoothed,cmap='gray',interpolation='none')
_=plt.title('expected smoothed')