From 45a2d4c07441d2cf1c453de4deda8e7e50833d9b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Richard=20L=C3=B6wenstr=C3=B6m?= Date: Tue, 28 Nov 2023 16:03:56 +0100 Subject: [PATCH] improve: support pydantic 2 --- datastream/__init__.py | 7 - datastream/datastream.py | 33 +- datastream/samplers/merge_sampler.py | 13 +- poetry.lock | 1027 +++++++++++++------------- pyproject.toml | 2 +- 5 files changed, 520 insertions(+), 562 deletions(-) diff --git a/datastream/__init__.py b/datastream/__init__.py index d4f136e..49302c9 100644 --- a/datastream/__init__.py +++ b/datastream/__init__.py @@ -1,9 +1,2 @@ from datastream.dataset import Dataset from datastream.datastream import Datastream - -from pkg_resources import get_distribution, DistributionNotFound - -try: - __version__ = get_distribution("pytorch-datastream").version -except DistributionNotFound: - pass diff --git a/datastream/datastream.py b/datastream/datastream.py index 5739ee4..f20c31f 100644 --- a/datastream/datastream.py +++ b/datastream/datastream.py @@ -1,29 +1,20 @@ from __future__ import annotations -from pydantic import BaseModel, PositiveInt -from typing import ( - Tuple, - Dict, - List, - Callable, - Optional, - TypeVar, - Generic, - Union, -) + +from typing import Callable, Dict, Generic, List, Optional, Tuple, TypeVar, Union + import numpy as np import torch -from pathlib import Path +from pydantic import BaseModel, PositiveInt from datastream import Dataset from datastream.samplers import ( - StandardSampler, MergeSampler, - ZipSampler, MultiSampler, RepeatSampler, + StandardSampler, + ZipSampler, ) - T = TypeVar("T") R = TypeVar("R") @@ -46,7 +37,7 @@ class Datastream(BaseModel, Generic[T]): 16 """ - dataset: Dataset[T] + dataset: Dataset sampler: Optional[torch.utils.data.Sampler] class Config: @@ -286,7 +277,6 @@ def cache( def test_infinite(): - datastream = Datastream(Dataset.from_subscriptable(list("abc"))) it = iter(datastream.data_loader(batch_size=8, n_batches_per_epoch=10)) for _ in range(10): @@ -294,13 +284,11 @@ def test_infinite(): def test_iter(): - datastream = Datastream(Dataset.from_subscriptable(list("abc"))) assert len(list(datastream)) == 3 def test_empty(): - import pytest with pytest.raises(ValueError): @@ -308,7 +296,6 @@ def test_empty(): def test_datastream_merge(): - datastream = Datastream.merge( [ Datastream(Dataset.from_subscriptable(list("abc"))), @@ -328,7 +315,6 @@ def test_datastream_merge(): def test_datastream_zip(): - datasets = [ Dataset.from_subscriptable([1, 2]), Dataset.from_subscriptable([3, 4, 5]), @@ -384,7 +370,6 @@ def ZippedMergedDatastream(): def test_datastream_simple_weights(): - dataset = Dataset.from_subscriptable([1, 2, 3, 4]) datastream = ( Datastream(dataset) @@ -412,7 +397,6 @@ def test_datastream_simple_weights(): def test_merge_datastream_weights(): - datasets = [ Dataset.from_subscriptable([1, 2]), Dataset.from_subscriptable([3, 4, 5]), @@ -441,7 +425,6 @@ def test_merge_datastream_weights(): def test_multi_sample(): - data = [1, 2, 4] n_multi_sample = 2 @@ -475,7 +458,6 @@ def test_multi_sample(): def test_take(): - import pytest datastream = Datastream(Dataset.from_subscriptable(list("abc"))).take(2) @@ -494,7 +476,6 @@ def test_take(): def test_sequential_sampler(): - from datastream.samplers import SequentialSampler dataset = Dataset.from_subscriptable(list("abc")) diff --git a/datastream/samplers/merge_sampler.py b/datastream/samplers/merge_sampler.py index 0dbc448..534e4d0 100644 --- a/datastream/samplers/merge_sampler.py +++ b/datastream/samplers/merge_sampler.py @@ -1,11 +1,14 @@ from __future__ import annotations -from pydantic import BaseModel -from typing import Tuple, Callable, Iterable + from functools import partial from itertools import chain, islice +from typing import Callable, Iterable, Tuple + import torch -from datastream.tools import repeat_map_chain +from pydantic import BaseModel + from datastream import Dataset +from datastream.tools import repeat_map_chain class MergeSampler(BaseModel, torch.utils.data.Sampler): @@ -39,7 +42,9 @@ def __iter__(self): @staticmethod def merged_samplers_length(samplers, ns): - return min([len(sampler) / n for sampler, n in zip(samplers, ns)]) * sum(ns) + return int( + min([len(sampler) / n for sampler, n in zip(samplers, ns)]) * sum(ns) + ) @staticmethod def merge_samplers(samplers, datasets, ns): diff --git a/poetry.lock b/poetry.lock index 19d7b83..ab61806 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,15 +1,28 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.6.0-py3-none-any.whl", hash = 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