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.
    

ASvyatkovskiy

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import sys
import tempfile
import time
import os

jpierson

If you are like me you find yourself cloning a repo, making some proposed changes and then deciding to later contributing back using the GitHub Flow convention. Below is a set of instructions I've developed for myself on how to deal with this scenario and an explanation of why it matters based on jagregory's gist.

To follow GitHub flow you should really have created a fork initially as a public representation of the forked repository and the clone that instead. My understanding is that the typical setup would have your local repository pointing to your fork as origin and the original forked repository as upstream so that you can use these keywords in other git commands.

1. Clone some repo (you've probably already done this step)

   git clone git@github...some-repo.git

raingo

import tensorflow as tf

"""
Multi dimensional softmax,
refer to https://github.com/tensorflow/tensorflow/issues/210

compute softmax along the dimension of target
the native softmax only supports batch_size x dimension
"""
def softmax(target, axis, name=None):

ktnyt

import argparse
import numpy as np
from chainer import Variable, FunctionSet, optimizers, cuda
import chainer.functions  as F
import cv2
import random
import cPickle as pickle
import sys

class ConvolutionalAutoencoder(FunctionSet):

shunsukeaihara

# -*- coding: utf-8 -*-
import numpy as np
import Image
import sys

def from_pil(pimg):
    pimg = pimg.convert(mode='RGB')
    nimg = np.asarray(pimg)
    nimg.flags.writeable = True
    return nimg

bhawkins

#!/usr/bin/env python

import numpy as np


def medfilt (x, k):
    """Apply a length-k median filter to a 1D array x.
    Boundaries are extended by repeating endpoints.
    """
    assert k % 2 == 1, "Median filter length must be odd."