nmwsharp

Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc.

Now on pip! `pip install arrgh` https://github.com/nmwsharp/arrgh

Nikolaj-K

In this video we come across about 50 online resources for category theory:

https://youtu.be/hEW42ARKNoE

I quickly comment on about 20 major ones. I link to the university sites, arXiv sites or Amazon page - most of the mentioned books are online available.

Here's another category theory list on github

https://github.com/prathyvsh/category-theory-resources

rwightman

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair, _quadruple


class MedianPool2d(nn.Module):
    """ Median pool (usable as median filter when stride=1) module.
    

apaszke

from graphviz import Digraph
import torch
from torch.autograd import Variable, Function

def iter_graph(root, callback):
    queue = [root]
    seen = set()
    while queue:
        fn = queue.pop()
        if fn in seen:

joshbuchea

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

lvzongting

#reference https://unix.stackexchange.com/questions/136371/how-to-download-a-folder-from-google-drive-using-terminal
#get cookie and code
wget --save-cookies cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=FILEID' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/Code: \1\n/p'

#download the file 
wget --load-cookies cookies.txt 'https://docs.google.com/uc?export=download&confirm=CODE_FROM_ABOVE&id=FILEID'

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

fubel

""" Radon Transform as described in Birkfellner, Wolfgang. Applied Medical Image Processing: A Basic Course. [p. 344] """
from scipy import misc
import numpy as np
import matplotlib.pyplot as plt

def discrete_radon_transform(image, steps):
    R = np.zeros((steps, len(image)), dtype='float64')
    for s in range(steps):
        rotation = misc.imrotate(image, -s*180/steps).astype('float64')
        R[:,s] = sum(rotation)

synapticarbors

import numpy as np
import numba as nb

from numba import types
from numba.extending import overload_method


@overload_method(types.Array, 'take')
def array_take(arr, indices):
   if isinstance(indices, types.Array):