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

 by   arogozhnikov Python Version: 0.7.0rc2 License: MIT

kandi X-RAY | einops Summary

kandi X-RAY | einops Summary

einops is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Numpy applications. einops has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However einops build file is not available. You can install using 'pip install einops' or download it from GitHub, PyPI.

Flexible and powerful tensor operations for readable and reliable code. Supports numpy, pytorch, tensorflow, jax, and others.
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            kandi-support Support

              einops has a medium active ecosystem.
              It has 6832 star(s) with 302 fork(s). There are 66 watchers for this library.
              There were 1 major release(s) in the last 6 months.
              There are 32 open issues and 109 have been closed. On average issues are closed in 362 days. There are 6 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of einops is 0.7.0rc2

            kandi-Quality Quality

              einops has 0 bugs and 0 code smells.

            kandi-Security Security

              einops has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              einops code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              einops is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              einops releases are available to install and integrate.
              Deployable package is available in PyPI.
              einops has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              einops saves you 1612 person hours of effort in developing the same functionality from scratch.
              It has 6308 lines of code, 294 functions and 30 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed einops and discovered the below as its top functions. This is intended to give you an instant insight into einops implemented functionality, and help decide if they suit your requirements.
            • Einsum operator
            • Convert a pattern to a regular expression
            • Returns the backend for the given tensor
            • Validate einsum
            • Evaluate a symbol
            • Convert a tensor to a numpy array
            • Add axis positions
            • Add axis positions to x
            • Add axes to x
            Get all kandi verified functions for this library.

            einops Key Features

            No Key Features are available at this moment for einops.

            einops Examples and Code Snippets

            Jittor-MLP,What's New
            Pythondot img1Lines of Code : 67dot img1License : Permissive (MIT)
            copy iconCopy
            @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},
                
            中国软件杯——行人检测与跟踪,1. 算法详解,1.5 W-MSA
            Pythondot img2Lines of Code : 60dot img2License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            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 **   
            Sensor Transformer (SeT)
            Pythondot img3Lines of Code : 37dot img3License : Permissive (MIT)
            copy iconCopy
            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  
            einops - converter
            Pythondot img4Lines of Code : 83dot img4License : Permissive (MIT License)
            copy iconCopy
            """
            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  
            einops - init
            Pythondot img5Lines of Code : 24dot img5License : Permissive (MIT License)
            copy iconCopy
            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  
            How can I do calculations on tensors that have "requires_grad = true"?
            Pythondot img6Lines of Code : 17dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            >>> 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 +
            Performing simple custom operations with Einsum
            Pythondot img7Lines of Code : 2dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            einops.reduce(x, 'i j -> i', 'prod')
            
            Does @tf.function decorator work with class attributes?
            Pythondot img8Lines of Code : 21dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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)
            
            Generating probabilites from patches of image
            Pythondot img9Lines of Code : 2dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            resf = torch.stack([torch.histc(patch, C, min=0, max=C-1) for patch in x]) / x.size(1)
            
            Best way to concatenate subarrays along an axis?
            Pythondot img10Lines of Code : 14dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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]])
            

            Community Discussions

            QUESTION

            Does @tf.function decorator work with class attributes?
            Asked 2021-Oct-20 at 14:39

            I'm currently developing an Autoencoder class - one of the methods is as follows:

            ...

            ANSWER

            Answered 2021-Oct-20 at 12:23

            The following methods both seem to do the job:

            Source https://stackoverflow.com/questions/69644874

            QUESTION

            Generating probabilites from patches of image
            Asked 2021-Sep-11 at 09:42

            I am working with an image of size 512x512. The image is divided into patches using einops with patch size of 32. The number of patches overall is 256, in other words, we get a new "image" of size 256x1024.

            Since this image is actually a mask for a segmentation problem, the image is actually comprised of only 4 values (4 classes): 0 for the background, 1 for the first class, 2 for the second class, 3 for the third class.

            My goal is to take every patch, and compute for every class C the following:

            Number of pixels in this patch / Number of pixels labeled C.

            This should give me an array of size 4 where the first entry is the total number of pixels in the patch (1024) over the number of background pixels (labeled as 0), the second, third and fourth entries are the same but for the corresponding class.

            In theory, I know that I need to iterate over every single patch and then count how many pixels of each class exists in the current patch, then divide by 1024. Doing this 256 yields exactly what I want. The problem is that I have a (very) large amount of images that I need to do this for, and the size of 512 is just an example to make the question simpler, therefore a for loop is out of question.

            I know that I can get the result that I want using numpy. I tried both: numpy.apply_over_axes and numpy.apply_along_axis but I don't know which one is better suited for this task, also there is numpy.where which I don't know how it applies here.

            Here is what I did:

            ...

            ANSWER

            Answered 2021-Sep-11 at 09:42

            There is a built-in function to count occurrences called torch.histc, it is similar to Python's collections.Counter.

            torch.histc(input, bins=100, min=0, max=0, *, out=None) → Tensor
            Computes the histogram of a tensor.

            The elements are sorted into equal width bins between min and max. If min and max are both zero, the minimum and maximum values of the data are used.

            Elements lower than min and higher than max are ignored.

            You need to specify the number of bins, here the number of classes C. As well as the min and max values for ordering. Also, it won't work with multi-dimensional tensors as such the resulting tensor will contain global statistics of the input tensor regardless of dimensions. As a possible workaround, you can iterate through your patches, calling torch.histc each time, then stacking the results and normalizing:

            Source https://stackoverflow.com/questions/69108737

            QUESTION

            Einops rearrange function basic functionallity
            Asked 2021-Aug-07 at 18:56

            I'm trying to grok the einops syntax for tensor reordering, but am somehow missing the point

            If I have the following matrix:

            ...

            ANSWER

            Answered 2021-Aug-07 at 18:40
            rearrange(mat, '(h n) w -> (n h) w', n = 2)
            and
            rearrange(mat, '(n h) w -> (h n) w', n = 2)
            

            Source https://stackoverflow.com/questions/68685636

            QUESTION

            Rearrange torch 2D tensors ("Tiles") to be in a particular order
            Asked 2021-Apr-02 at 17:36

            I have something that looks like this...

            ...

            ANSWER

            Answered 2021-Mar-23 at 22:56

            I will start with the simple case where we know the number of tensors and which tensors to be positioned etc.:

            Source https://stackoverflow.com/questions/66770797

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install einops

            You can install using 'pip install einops' or download it from GitHub, PyPI.
            You can use einops like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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            pip install einops

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            gh repo clone arogozhnikov/einops

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