问题描述
我发现，要使用步幅1从(X,Y)生成大小为(x,y)的(X - x + 1, Y - y + 1)色块，图像需要我们将步幅参数指定为img.strides * 2或img.strides + img.strides. 我不知道他们是否知道该如何快速计算出来. conv2d中的步幅
I found that for generating (X - x + 1, Y - y + 1) patches of size (x,y) from (X,Y) with stride 1, image requires us to give strides parameter as img.strides * 2 or img.strides + img.strides. I don't know how they quickly compute this knowing the no. of strides in conv2d
但是我应该怎么做才能从stride步幅的相同大小的图像中获得相同大小的((X-x)/stride)+1, ((Y-y)/stride)+1补丁?
But what should I do to get ((X-x)/stride)+1, ((Y-y)/stride)+1 patches of same size from same sized image with stride stride?
由此SO 答案 稍作修改，将通道和图像数量放在前面
def patchify(img, patch_shape):
a,b,X, Y = img.shape # a images and b channels
x, y = patch_shape
shape = (a, b, X - x + 1, Y - y + 1, x, y)
a_str, b_str, X_str, Y_str = img.strides
strides = (a_str, b_str, X_str, Y_str, X_str, Y_str)
return np.lib.stride_tricks.as_strided(img, shape=shape, strides=strides)
我可以看到它创建了一个滑动窗口，其大小为(x，y)，跨度为1(向右移动1个像素，向下移动1个像素).我很难将as_strided使用的strides参数与我们通常用于conv2d的strides相关联.
I can see that it creates a sliding window with size (x,y) and stride 1 (move 1 pixel to the right and move 1 pixel down). I have trouble correlating the strides parameter which as_strided uses and the strides we usually use for conv2d.
如何向上述计算as_strided步幅参数的函数添加参数?
How do I add a parameter to the above function that computes as_strided strides parameter?
def patchify(img, patch_shape, stride): # stride=stepsize in conv2d eg: 1,2,3,...
a,b,X,Y = img.shape # a images and b channels
x, y = patch_shape
shape = (a,b,((X-x)/stride)+1, ((Y-y)/stride)+1, x, y)
strides = ??? # strides for as_strided
return np.lib.stride_tricks.as_strided(img, shape=shape, strides=strides)
img是4d (a, b, X, Y)
-
a=图片数量 -
b=通道数 -
(X,Y)=宽度和高度
a=no.of images,b=no.of channels,(X,Y)= width and height
注意:的意思是stepsize.不幸的是，这也被称为跨步.
注释2 :由于stepsize通常在两个轴上都是相同的，因此在我提供的代码中，我仅提供了一个参数，但是在两个维度上都使用了它.
Note 2: Since stepsize will usually be the same on both axes, in the code I provided, I've provided only one parameter, however used it for both dimensions.
游乐场:strides 此处的内容.我已在stepsize=1 此处上运行.我注意到，该链接可能无法正常工作，但是在粘贴到新的游乐场时，它会起作用a>.
Playground:What goes in for strides here. I have it running for stepsize=1 here. I noticed that it might not work from the link but it works when pasted in new playground.
这应该可以清楚地说明我的需求:
This should give a clear idea of what I need:
[[ 0.5488135 0.71518937 0.60276338 0.54488318]
[ 0.4236548 0.64589411 0.43758721 0.891773 ]
[ 0.96366276 0.38344152 0.79172504 0.52889492]
[ 0.56804456 0.92559664 0.07103606 0.0871293 ]]
# patch_size = 2x2
# stride = 1,1
[[[[ 0.5488135 0.71518937]
[ 0.4236548 0.64589411]]
[[ 0.71518937 0.60276338]
[ 0.64589411 0.43758721]]
[[ 0.60276338 0.54488318]
[ 0.43758721 0.891773 ]]]
[[[ 0.4236548 0.64589411]
[ 0.96366276 0.38344152]]
[[ 0.64589411 0.43758721]
[ 0.38344152 0.79172504]]
[[ 0.43758721 0.891773 ]
[ 0.79172504 0.52889492]]]
[[[ 0.96366276 0.38344152]
[ 0.56804456 0.92559664]]
[[ 0.38344152 0.79172504]
[ 0.92559664 0.07103606]]
[[ 0.79172504 0.52889492]
[ 0.07103606 0.0871293 ]]]]
# stride = 2,2
[[[[[[ 0.5488135 0.71518937]
[ 0.4236548 0.64589411]]
[[ 0.60276338 0.54488318]
[ 0.43758721 0.891773 ]]]
[[[ 0.96366276 0.38344152]
[ 0.56804456 0.92559664]]
[[ 0.79172504 0.52889492]
[ 0.07103606 0.0871293 ]]]]]]
# stride = 2,1
[[[[ 0.5488135 0.71518937]
[ 0.4236548 0.64589411]]
[[ 0.71518937 0.60276338]
[ 0.64589411 0.43758721]]
[[ 0.60276338 0.54488318]
[ 0.43758721 0.891773 ]]]
[[[ 0.96366276 0.38344152]
[ 0.56804456 0.92559664]]
[[ 0.38344152 0.79172504]
[ 0.92559664 0.07103606]]
[[ 0.79172504 0.52889492]
[ 0.07103606 0.0871293 ]]]]
推荐答案
这是一种方法-
def patchify(img, patch_shape, stepsize_x=1, stepsize_y=1):
strided = np.lib.stride_tricks.as_strided
x, y = patch_shape
p,q = img.shape[-2:]
sp,sq = img.strides[-2:]
out_shp = img.shape[:-2] + (p-x+1,q-y+1,x,y)
out_stride = img.strides[:-2] + (sp,sq,sp,sq)
imgs = strided(img, shape=out_shp, strides=out_stride)
return imgs[...,::stepsize_x,::stepsize_y,:,:]
样品运行-
1]输入:
In [156]: np.random.seed(0)
In [157]: img = np.random.randint(11,99,(2,4,4))
In [158]: img
Out[158]:
array([[[55, 58, 75, 78],
[78, 20, 94, 32],
[47, 98, 81, 23],
[69, 76, 50, 98]],
[[57, 92, 48, 36],
[88, 83, 20, 31],
[91, 80, 90, 58],
[75, 93, 60, 40]]])
2]输出-案例1:
In [159]: patchify(img, (2,2), stepsize_x=1, stepsize_y=1)[0]
Out[159]:
array([[[[55, 58],
[78, 20]],
[[58, 75],
[20, 94]],
[[75, 78],
[94, 32]]],
[[[78, 20],
[47, 98]],
[[20, 94],
[98, 81]],
[[94, 32],
[81, 23]]],
[[[47, 98],
[69, 76]],
[[98, 81],
[76, 50]],
[[81, 23],
[50, 98]]]])
3]输出-情况2:
In [160]: patchify(img, (2,2), stepsize_x=2, stepsize_y=1)[0]
Out[160]:
array([[[[55, 58],
[78, 20]],
[[58, 75],
[20, 94]],
[[75, 78],
[94, 32]]],
[[[47, 98],
[69, 76]],
[[98, 81],
[76, 50]],
[[81, 23],
[50, 98]]]])
4]输出-情况3:
In [161]: patchify(img, (2,2), stepsize_x=2, stepsize_y=2)[0]
Out[161]:
array([[[[55, 58],
[78, 20]],
[[75, 78],
[94, 32]]],
[[[47, 98],
[69, 76]],
[[81, 23],
[50, 98]]]])
这篇关于as_strided:使用as_strided strides参数链接stepsize(conv2d的步幅)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！