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支持numpy和torch
1.einops.rearrange 重新指定维度
2.einops.repeat 重排和重复(增加)维度
3.einops.reduce
def rearrange(tensor, pattern, **axes_lengths):
einops.rearrange is a reader-friendly smart element reordering for multidimensional tensors. This operation includes functionality of transpose (axes permutation), reshape (view), squeeze, unsqueeze, stack, concatenate and other operations.
其中:括号代表合并
import torch
if __name__ == '__main__':
# data = torch.arange(0,27,1)
# print(data)
import numpy as np
from einops import rearrange, repeat
# suppose we have a set of 32 images in "h w c" format (height-width-channel)
images = [np.random.randn(30, 40, 3) for _ in range(32)]
print("data shape",len(images),images[0].shape)
# stack along first (batch) axis, output is a single array :(32, 30, 40, 3)
print(rearrange(images, 'b h w c -> b w h c').shape)
# concatenate images along height (vertical axis), 960 = 32 * 30 :(960, 40, 3)
print(rearrange(images, 'b h w c -> (b h) w c').shape)
# concatenated images along horizontal axis, 1280 = 32 * 40 :(30, 1280, 3)
print(rearrange(images, 'b h w c -> h (b w) c').shape)
# reordered axes to "b c h w" format for deep learning :(32, 3, 30, 40)
print(rearrange(images, 'b h w c -> b c h w').shape)
# flattened each image into a vector, 3600 = 30 * 40 * 3 :(32, 3600)
print(rearrange(images, 'b h w c -> b (c h w)').shape)
print(rearrange(images, 'b h w (c ph) -> b (c h) (w ph)',ph=1).shape)
print(rearrange(images, 'b (h h1) (w w1) c -> (b h1 w1) h w c', h1=2, w1=2).shape)
print(rearrange(images, 'b (h h1) (w w1) c -> b h w (c h1 w1)', h1=2, w1=2).shape)
2.einops.repeat 重排和重复(增加)维度
einops.repeat allows reordering elements and repeating them in arbitrary combinations. This operation includes functionality of repeat, tile, broadcast functions.
import numpy as np
from einops import rearrange, repeat,reduce
# a grayscale image (of shape height x width)
image = np.random.randn(30, 40)
# change it to RGB format by repeating in each channel:(30, 40, 3)
print(repeat(image, 'h w -> h w c', c=3).shape)
# repeat image 2 times along height (vertical axis):(60, 40)
print(repeat(image, 'h w -> (repeat h) w', repeat=2).shape)
# repeat image 2 time along height and 3 times along width:(30, 120)
print(repeat(image, 'h w -> h (repeat w)', repeat=3).shape)
# convert each pixel to a small square 2x2. Upsample image by 2x:(60, 80)
print(repeat(image, 'h w -> (h h2) (w w2)', h2=2, w2=2).shape)
# pixelate image first by downsampling by 2x, then upsampling:(30, 40)
downsampled = reduce(image, '(h h2) (w w2) -> h w', 'mean', h2=2, w2=2)
print(repeat(downsampled, 'h w -> (h h2) (w w2)', h2=2, w2=2).shape)
3.einops.reduce
einops.reduce provides combination of reordering and reduction using reader-friendly notation.
import numpy as np
from einops import rearrange,reduce
x = np.random.randn(100, 32, 64)
# perform max-reduction on the first axis:(32, 64)
print(reduce(x, 't b c -> b c', 'max').shape)
# same as previous, but with clearer axes meaning:(32, 64)
print(reduce(x, 'time batch channel -> batch channel', 'max').shape)
x = np.random.randn(10, 20, 30, 40)
# 2d max-pooling with kernel size = 2 * 2 for image processing:(10, 20, 15, 20)
y1 = reduce(x, 'b c (h1 h2) (w1 w2) -> b c h1 w1', 'max', h2=2, w2=2)
print(y1.shape)
# if one wants to go back to the original height and width, depth-to-space trick can be applied:(10, 5, 30, 40)
y2 = rearrange(y1, 'b (c h2 w2) h1 w1 -> b c (h1 h2) (w1 w2)', h2=2, w2=2)
print(y2.shape)
# Adaptive 2d max-pooling to 3 * 4 grid:(10, 20, 3, 4)
print(reduce(x, 'b c (h1 h2) (w1 w2) -> b c h1 w1', 'max', h1=3, w1=4).shape)
# Global average pooling:(10, 20)
print(reduce(x, 'b c h w -> b c', 'mean').shape)
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版权声明:本文为CSDN博主「马鹏森」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
原文链接:https://blog.csdn.net/weixin_43135178/article/details/118877384
支持numpy和torch 1.einops.rearrange 重新指定维度 2.einops.repeat 重排和重复(增加)维度 3.einops.reduce 1.einops.rearran
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