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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

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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

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80:
################################################################################
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class Foo:
    def bar(self, *baz):
        raise NotImplementedError


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

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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):