daquexian

#!/usr/bin/env bash

set -uexo pipefail

if [ ! -f linuxdeploy-x86_64.AppImage ]
then
  wget https://github.com/linuxdeploy/linuxdeploy/releases/download/continuous/linuxdeploy-x86_64.AppImage
fi
if [ ! -f linuxdeploy-plugin-appimage-x86_64.AppImage ]
then

daquexian

daquexian

import numpy as np  # type: ignore

from typing import List, Callable, Union, Optional, Text


def cartesian(arrays, out=None):
    # type: (List[np.ndarray], np.ndarray) -> np.ndarray
    """
    From https://stackoverflow.com/a/1235363
    Generate a cartesian product of input arrays.

daquexian

import numpy as np

from typing import List, Callable, Union, Optional


def cartesian(arrays, out=None):
    # type: (List[np.ndarray], np.ndarray) -> np.ndarray
    """
    From https://stackoverflow.com/a/1235363

daquexian

# A correct and dabnn-compatible PyTorch implementation of binary convolutions.
# It consists of a implementation of the binary convolution itself, and the way
# to make the implementation both ONNX- and dabnn-compatible
# 1. The input of binary convolutions should only be +1/-1, so we pad -1 instead
#    of 0 by a explicit pad operation.
# 2. Since PyTorch doesn't support exporting Sign ONNX operator (until 
#    https://github.com/pytorch/pytorch/pull/20470 gets merged), we perform sign 
#    operation on input and weight by directly accessing the `data`

import torch

daquexian

This Gist confirms the Linked Identity in my OpenPGP key, and links it to this GitHub account.

Token for proof:
[Verifying my OpenPGP key: openpgp4fpr:ae767adf33d6dbc2746ce7ec845a581542488b59]

daquexian

This Gist confirms the Linked Identity in my OpenPGP key, and links it to this GitHub account.

Token for proof:
[Verifying my OpenPGP key: openpgp4fpr:ae767adf33d6dbc2746ce7ec845a581542488b59]

daquexian

This Gist confirms the Linked Identity in my OpenPGP key, and links it to this GitHub account.

Token for proof:
[Verifying my OpenPGP key: openpgp4fpr:ae767adf33d6dbc2746ce7ec845a581542488b59]