einops | Deep learning operations reinvented ( for pytorch | Machine Learning library

@article{liu2021we,
  title={Are we ready for a new paradigm shift? A Survey on Visual Deep MLP},
  author={Liu, Ruiyang and Li, Yinghui and Liang, Dun and Tao, Linmi and Hu, Shimin and Zheng, Hai-Tao},
  journal={arXiv preprint arXiv:2111.04060},
    

class WindowAttention(nn.Module):
    def __init__(self, dim, heads, head_dim, shifted, window_size, relative_pos_embedding):
        super().__init__()
        inner_dim = head_dim * heads
        self.heads = heads
        self.scale = head_dim **   

pip install sensortransformer

import argparse
import tensorflow as tf
from sensortransformer import set_network

parser = argparse.ArgumentParser()
parser.add_argument("--signal-length", type=int)
parser.add_argument("--segment-size", type=int)
pars  

"""
just run this script with python converter.py .
It will convert pytorch.ipynb to html page docs/pytorch-examples.html

"""
import nbformat
import markdown

from pygments import highlight
from pygments.lexers import PythonLexer
from pygments.forma  

import numpy as np

from PIL.Image import fromarray
from IPython import get_ipython


def display_np_arrays_as_images():
    def np_to_png(a):
        if 2 <= len(a.shape) <= 3:
            return fromarray(np.array(np.clip(a, 0, 1) * 255, dtyp  

>>> x = torch.rand(1)
>>> y = torch.rand(1)
>>> x
tensor([0.2738])
>>> id(x)
140736259305336
>>> x = x + y   # Normal operation
>>> id(x)
140726604827672 # New location
>>> x +

tf.config.run_functions_eagerly

class Test:
    
    @tf.function
    def build_test(self, inputs):
        self.inp = inputs
        
t = Test()
input_t = tf.keras.layers.Input(shape=(3,3,3))
t.build_test(input_t)

import numpy as np
a = [ [[1,2], [3,4]], [[5,6], [7,8]] ]
an = np.array(a)

from einops import rearrange

ar = rearrange(an, "a b c -> (b c) a")
ar

array([[1, 5],
       [2, 6],
       [3, 7],
       [4, 8]])