pmeier

import numpy as np

def haversine_distance_matrix(lats, longs):
    lats = np.radians(lats.reshape(-1, 1))
    longs = np.radians(longs.reshape(1, -1))
    return 2 * np.arcsin(np.sqrt((1 - np.cos(lats - lats.T) + np.cos(lats) * np.cos(lats.T) * (1 - np.cos(longs - longs.T))) / 2))
  

pmeier

import unittest
from concurrent.futures import ThreadPoolExecutor


def multi_threaded(*, threads=2):
    def decorator(test_cls):
        for name, test_fn in test_cls.__dict__.copy().items():
            if not (name.startswith("test") and callable(test_fn)):
                continue

pmeier

from typing import Any

from metadata_filter import MetadataFilter, MetadataFilterOperator

# https://docs.trychroma.com/usage-guide#using-where-filters
OPERATOR_MAP = {
    MetadataFilterOperator.AND: "$and",
    MetadataFilterOperator.OR: "$or",
    MetadataFilterOperator.EQ: "$eq",
    MetadataFilterOperator.NE: "$ne",

pmeier

import functools
import pathlib
import sys

import libcst as cst


def main(root):
    root = pathlib.Path(root)
    fn = functools.partial(remove_annotations, annotations_remover=AnnotationRemover())

pmeier

Benchmark script and results for torchvision.transforms.functional v1 vs v2

Run benchmark.py to reproduce the results.

To add a new benchmark, add a BenchmarkConfig to BENCHMARK_CONFIGS in configs.py

pmeier

import pathlib
from urllib.parse import urlparse

import requests
import tqdm


def download(url, root=".", *, name=None, chunk_size=32 * 1024):
    root = pathlib.Path(root)
    root.mkdir(exist_ok=True, parents=True)

pmeier

Hope this works!

Hopefully you can read this 🎉

pmeier

import os
import pathlib
import re
import shlex
import subprocess
import sys


def main():
    if git("status", "--porcelain"):

pmeier

import itertools
from typing import Collection

import networkx as nx

# overwrite this with the array API that you want to test
import numpy as array_api


def maybe_add_dtype(

pmeier

A case for pytest in PyTorch

This is a short post about why I think it would be beneficial for PyTorch to not only use pytest as test runner, but also rely on the other features it provides.

Disclaimer

My experience with the PyTorch test suite is limited as of now. Thus, it might very well be that my view on things is too naive. In that case I'm happy to hear about examples where and adoption of pytest would make a use case significantly harder or outright impossible.

Setup