$ apt install dkmsTODO
Download the source code.
| class A: | |
| pass | |
| class B: | |
| def __get__(self, instance, cls=None): | |
| raise AttributeError() | |
| class C: | |
| a = A() | |
| b = B() |
| import getpass | |
| import os | |
| import pathlib | |
| import sys | |
| import sh | |
| BASE_DIR = os.path.abspath(os.path.join(__file__, '..')) | |
| OPENCV_VERSION = '4.5.2' |
| from dataclasses import asdict, fields | |
| from typing import Any, Dict | |
| class DataClassBase: | |
| @classmethod | |
| def fromdict(cls, d: Dict[str, Any]): | |
| """Return a dataclass from a dict which may include unexpected keys.""" | |
| class_fields = {f.name for f in fields(cls)} | |
| return cls(**{k: v for k, v in d.items() if k in class_fields}) |
| # -*- coding: utf-8 -*- | |
| # ref. https://qiita.com/k-jimon/items/f02fae75e853a9c02127 | |
| from collections import deque, defaultdict, Counter | |
| from itertools import chain, islice, takewhile | |
| import MeCab | |
| import os | |
| import random | |
| import re |
| // The original idea comes from a post by Alex Krush. | |
| // https://medium.com/@akrush95/global-cached-state-in-react-using-hooks-context-and-local-storage-166eacf8ab46 | |
| import React, { useCallback, useEffect, useReducer, useRef } from 'react'; | |
| export const createCachedContext = ({ storageKey, defaultValue }) => { | |
| const initializer = (initialState) => { | |
| const localState = localStorage.getItem(storageKey); | |
| if (localState) { | |
| try { |
| # -*- coding: utf-8 -*- | |
| import FreeCAD | |
| import importCSG | |
| import importDXF | |
| def csg_to_dxf(src, dst): | |
| doc = importCSG.open(src) | |
| importDXF.export([doc.TopologicalSortedObjects[0]], dst) # assumes it has single root object | |
| FreeCAD.closeDocument(doc.Name) |
| #!/usr/bin/env python | |
| import argparse | |
| import chainer | |
| import chainer.functions as F | |
| import chainer.links as L | |
| from chainer import training | |
| from chainer.training import extensions | |
| import numpy as np |
| >>> np.array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]], dtype='b') | |
| array([[[0, 4], | |
| [2, 6]], | |
| [[1, 5], | |
| [3, 7]]], dtype=int8) | |
| >>> ctypes.string_at(np.array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]], dtype='b').ctypes.data, 8) # .data.tobytes() doesn't work properly | |
| b'\x00\x04\x02\x06\x01\x05\x03\x07' | |
| >>> np.array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]], dtype='b').__array_interface__ | |
| {'data': (23426096, False), 'strides': None, 'descr': [('', '|i1')], 'typestr': '|i1', 'shape': (2, 2, 2), 'version': 3} | |
| >>> np.array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]], dtype='b').flags |
$ python xp_nested_array.py --src-xp numpy --dst-xp numpy --shape "(3, 224, 224)" --batch-size 10
Shape: (3, 224, 224)
Batch size: 10
Running numpy.array(<List[numpy.ndarray]>) in 10000 times...
3.857709832955152