Philip Meier pmeier

This is an overview over the usage of the availability of the transform, target_transform, and transforms arguments for all datasets from torchvision.datasets.

Dataset	`transform`	`target_transform`	`transforms`
`Caltech101`	x	x
`Caltech256`	x	x
`CelebA`	x	x
`CIFAR10`	x	x
`CIFAR100`	x	x
`Cityscapes`	x	x	x

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

Hope this works!

Hopefully you can read this 🎉

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

	import argparse
	import multiprocessing
	from math import ceil
	import torch
	from torch.utils import data
	from torchvision import datasets, transforms


	class FiniteRandomSampler(data.Sampler):
	def __init__(self, data_source, num_samples):

	class Foo:
	def bar(self, *baz):
	raise NotImplementedError


	class SubFoo(Foo):
	def bar(self, baz: float) -> str:
	pass

	80:
	################################################################################
	81:
	#################################################################################
	82:
	##################################################################################
	83:
	###################################################################################
	84:
	####################################################################################

	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(

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


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

	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)