From 83295d7affecdea76403c4d9fde709af559dcc31 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Dec 2022 17:14:06 +0000 Subject: [PATCH 01/24] Bump setuptools from 65.3.0 to 65.6.3 Bumps [setuptools](https://github.com/pypa/setuptools) from 65.3.0 to 65.6.3. - [Release notes](https://github.com/pypa/setuptools/releases) - [Changelog](https://github.com/pypa/setuptools/blob/main/CHANGES.rst) - [Commits](https://github.com/pypa/setuptools/compare/v65.3.0...v65.6.3) --- updated-dependencies: - dependency-name: setuptools dependency-type: indirect ... Signed-off-by: dependabot[bot] --- poetry.lock | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/poetry.lock b/poetry.lock index e11a4be1..e4f158a2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -489,15 +489,15 @@ test = ["pytest"] [[package]] name = "setuptools" -version = "65.3.0" +version = "65.6.3" description = "Easily download, build, install, upgrade, and uninstall Python packages" category = "dev" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] -testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] 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f40c4a68da7d1219576ba3737447dae8b9ac4ba6 Mon Sep 17 00:00:00 2001 From: Myles Bartlett <44702700+MylesBartlett@users.noreply.github.com> Date: Tue, 3 Jan 2023 23:29:53 +0000 Subject: [PATCH 02/24] Pass `num_classes` argument on to `_transform` in `RandomMixUp.__call__`. (#178) * Pass num_classes argument on. * Add .yamllint file. * Format with updated version of black. Co-authored-by: Myles Bartlett --- .yamllint.yaml | 41 ++++++++++++++++++++++++++ pyproject.toml | 49 ++++++++++++++++++++++++++++++++ ranzen/torch/transforms/mixup.py | 3 +- tests/torch_transforms_test.py | 7 +++++ 4 files changed, 99 insertions(+), 1 deletion(-) create mode 100644 .yamllint.yaml diff --git a/.yamllint.yaml b/.yamllint.yaml new file mode 100644 index 00000000..10c0fdb1 --- /dev/null +++ b/.yamllint.yaml @@ -0,0 +1,41 @@ +--- +yaml-files: + - '*.yaml' + - '*.yml' + - '.yamllint' + +rules: + braces: + # Require each key-value pair to be demarcated with identation + forbid: true + brackets: + # Require each element to be on a new line (beginning with - ) + forbud: true + colons: enable + commas: enable + comments: + level: warning + comments-indentation: + level: warning + document-end: disable + document-start: + level: warning + empty-lines: enable + empty-values: disable + float-values: disable + hyphens: enable + indentation: + spaces: consistent + check-multi-line-strings: true + key-duplicates: enable + key-ordering: disable + line-length: + max: 100 + new-line-at-end-of-file: enable + new-lines: enable + octal-values: disable + quoted-strings: disable + trailing-spaces: enable + truthy: + allowed-values: ['true', 'false'] + check-keys: true diff --git a/pyproject.toml b/pyproject.toml index c70553fd..dbe9d8b3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -95,6 +95,55 @@ strictSetInference = true strictParameterNoneValue = true reportUnnecessaryTypeIgnoreComment = "warning" +[tool.ruff] +line-length = 100 +select = [ + "D", # pydocstyle + "E", # pycodestyle + "F", # pyflakes + # "I", # isort + # "UP", # pyupgrade + # "PDV", # pandas-vet + # "ANN", # flake8-annotations +] +ignore = [ + "D105", # magic methods + "D107", # __init__ + "D203", + "D208", # over indentation + "D212", + "D213", + "D214", + "D215", + "D404", + "D405", + "D406", + "D407", + "D408", + "D409", + "D410", + "D411", + "D413", + "D415", + "D416", + "D417", + "TID252", # relative imports + "PDV901", # forbidding "df" as a variable name + "ANN101", + "ANN102", +] +target-version = "py38" + +[tool.ruff.per-file-ignores] +"__init__.py" = ["F403"] + +[tool.ruff.isort] +known-third-party = ["PIL", "black", "git", "fairlearn", "matplotlib", "mypy", "numpy", "pandas", "pylint", "pytest", "scipy", "seaborn", "setuptools", "sklearn", "teext", "torch"] +extra-standard-library = ["__future__", "typing_extensions"] + +[tool.ruff.flake8-annotations] +mypy-init-return = true + [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/ranzen/torch/transforms/mixup.py b/ranzen/torch/transforms/mixup.py index dab62805..09aa6648 100644 --- a/ranzen/torch/transforms/mixup.py +++ b/ranzen/torch/transforms/mixup.py @@ -466,7 +466,7 @@ def __call__( targets: Tensor | None = None, groups_or_edges: Tensor | None = None, cross_group: bool = True, - num_classes: int | None = ..., + num_classes: int | None = None, ) -> Tensor | InputsTargetsPair: """ :param inputs: The samples to apply mixup to. @@ -493,4 +493,5 @@ def __call__( targets=targets, groups_or_edges=groups_or_edges, cross_group=cross_group, + num_classes=num_classes, ) diff --git a/tests/torch_transforms_test.py b/tests/torch_transforms_test.py index 6e8e8700..7d523531 100644 --- a/tests/torch_transforms_test.py +++ b/tests/torch_transforms_test.py @@ -97,6 +97,13 @@ def test_mixup( groups_or_edges=groups_or_edges, cross_group=cross_group, ) + transform( + inputs=inputs, + targets=targets, + groups_or_edges=groups_or_edges, + cross_group=cross_group, + num_classes=num_classes, + ) else: assert targets is None assert res.shape == inputs.shape From 6829ea8233da7e06c4f03552a282371b2810acc2 Mon Sep 17 00:00:00 2001 From: Myles Bartlett <44702700+MylesBartlett@users.noreply.github.com> Date: Wed, 4 Jan 2023 17:29:33 +0000 Subject: [PATCH 03/24] Improve handling of binary targets by CE and MU. (#179) * Improve handling of binary targets by `RandomMixUp` and `CrossEntropyLoss`. Co-authored-by: Myles Bartlett --- ranzen/torch/loss.py | 36 ++++++++++++++++++++++++++++---- ranzen/torch/transforms/mixup.py | 3 ++- tests/torch_loss_test.py | 16 ++++++++++++-- tests/torch_transforms_test.py | 5 ++++- 4 files changed, 52 insertions(+), 8 deletions(-) diff --git a/ranzen/torch/loss.py b/ranzen/torch/loss.py index 886c051c..ca55b183 100644 --- a/ranzen/torch/loss.py +++ b/ranzen/torch/loss.py @@ -81,6 +81,8 @@ class indices. :returns: The (reduced) cross-entropy between ``input`` and ``target``. + :raises ValueError: If 'input' and 'target' have incompatible sizes. + :example: >>> # Example of target with class indices >>> input = torch.randn(3, 5, requires_grad=True) @@ -96,16 +98,42 @@ class indices. """ if isinstance(reduction, str): reduction = str_to_enum(str_=reduction, enum=ReductionType) - if input.ndim == 1 or input.size(1) == 1: # Binary classification - target = target.view_as(input) + + input = input.view(input.size(0), -1).squeeze(-1) + target = target.view(target.size(0), -1).squeeze(-1) + + if input.ndim == 1: # Binary classification + if target.ndim == 2: + if target.size(1) == 2: + target = target[:, 1] + else: + raise ValueError( + "'target' must be of size '2' at dimension '1' if not label encoded." + ) + elif target.ndim > 2: + raise ValueError( + "'target' must be a one- or two-dimensional tensor when 'input' is one-dimensional" + " (excluding dummy dimensions) and corresponds to binary predictions." + ) if not target.is_floating_point(): target = target.float() loss_fn = F.binary_cross_entropy_with_logits else: # Multiclass classification - target = target.view(input.size(0), -1).squeeze(-1) if (target.ndim == 1) and target.is_floating_point(): target = target.long() - loss_fn = partial(F.cross_entropy, ignore_index=ignore_index) + elif target.ndim == 2: + if target.shape != input.shape: + raise ValueError( + "'target' and 'input' must match in size when 'target' is not label encoded." + ) + elif not target.is_floating_point(): + target = target.to(input.dtype) + + loss_fn = partial( + F.cross_entropy, + ignore_index=ignore_index, + label_smoothing=label_smoothing, + ) losses = loss_fn( input=input, target=target, diff --git a/ranzen/torch/transforms/mixup.py b/ranzen/torch/transforms/mixup.py index 09aa6648..23a8e87c 100644 --- a/ranzen/torch/transforms/mixup.py +++ b/ranzen/torch/transforms/mixup.py @@ -417,7 +417,8 @@ def _transform( num_classes = self.num_classes elif num_classes < 1: raise ValueError(f"{ num_classes } must be a positive integer.") - targets = cast(Tensor, F.one_hot(targets, num_classes=num_classes)) + elif num_classes > 2: + targets = cast(Tensor, F.one_hot(targets, num_classes=num_classes)) elif not self.inplace: targets = targets.clone() # Targets need to be floats to be mixed up diff --git a/tests/torch_loss_test.py b/tests/torch_loss_test.py index 44f0170d..ca58df2e 100644 --- a/tests/torch_loss_test.py +++ b/tests/torch_loss_test.py @@ -1,5 +1,6 @@ import pytest import torch +import torch.nn.functional as F from typing_extensions import Final from ranzen.torch import CrossEntropyLoss, ReductionType @@ -10,12 +11,23 @@ @pytest.mark.parametrize("out_dim", [1, 3]) @pytest.mark.parametrize("dtype", ["long", "float"]) @pytest.mark.parametrize("reduction_type", list(ReductionType)) -def test_xent(out_dim: int, dtype: str, reduction_type: ReductionType) -> None: - target = torch.randint(0, max(out_dim, 2), (BATCH_SIZE, 1), dtype=getattr(torch, dtype)) +@pytest.mark.parametrize("le", [True, False]) +def test_xent(out_dim: int, dtype: str, reduction_type: ReductionType, le: bool) -> None: + num_classes = max(out_dim, 2) + if le: + target = torch.randint(0, num_classes, (BATCH_SIZE, 1), dtype=getattr(torch, dtype)) + else: + if dtype == "long": + target_le = torch.randint(0, num_classes, (BATCH_SIZE,), dtype=getattr(torch, dtype)) + target = F.one_hot(target_le, num_classes=num_classes) + else: + target = torch.randn(BATCH_SIZE, num_classes).softmax(dim=1) + pred = torch.randn(BATCH_SIZE, out_dim) iw = torch.randn(BATCH_SIZE) loss_fn = CrossEntropyLoss(reduction=reduction_type) loss_fn(input=pred, target=target, instance_weight=iw) loss_fn(input=pred, target=target.squeeze(), instance_weight=iw) + loss_fn(input=pred.unsqueeze(-1), target=target.unsqueeze(-1), instance_weight=iw) if out_dim == 1: loss_fn(input=pred.squeeze(), target=target, instance_weight=iw) diff --git a/tests/torch_transforms_test.py b/tests/torch_transforms_test.py index 7d523531..d5b9f13a 100644 --- a/tests/torch_transforms_test.py +++ b/tests/torch_transforms_test.py @@ -74,7 +74,10 @@ def test_mixup( mixed_targets = res.targets assert len(mixed_inputs) == len(mixed_targets) == BATCH_SIZE assert mixed_inputs.shape == inputs.shape - assert mixed_targets.size(1) == num_classes + if mixed_targets.ndim == 1: + assert targets.shape == mixed_targets.shape + else: + assert mixed_targets.size(1) == num_classes # Check that setting rows to False in the connectivity matrix excludes samples. if (groups_or_edges is not None) and edges and (p == 1.0): From b3204374751f209026118ff86233a5bb4642cebb Mon Sep 17 00:00:00 2001 From: Myles Bartlett <44702700+MylesBartlett@users.noreply.github.com> Date: Tue, 17 Jan 2023 13:42:48 +0000 Subject: [PATCH 04/24] Add more (general and torch) utility functions. (#180) * Add more (general and torch) utility functions. * Update ranzen/torch/utils.py Co-authored-by: Thomas MK Co-authored-by: Myles Bartlett Co-authored-by: Thomas MK --- ranzen/misc.py | 31 +++++++++++++++++- ranzen/torch/utils.py | 75 +++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 103 insertions(+), 3 deletions(-) diff --git a/ranzen/misc.py b/ranzen/misc.py index c7288bad..d339524a 100644 --- a/ranzen/misc.py +++ b/ranzen/misc.py @@ -6,17 +6,20 @@ import sys from typing import Any, Dict, Iterable, MutableMapping, Type, TypeVar, overload -from typing_extensions import Self +from typing_extensions import Self, TypeGuard from ranzen.types import Addable __all__ = [ "AddDict", "StrEnum", + "default_if_none", "flatten_dict", "gcopy", "reduce_add", + "some", "str_to_enum", + "unwrap_or", ] @@ -270,3 +273,29 @@ def reduce_add(sequence: Iterable[A]) -> A: :returns: The sum of all elements in ``__iterable``. """ return functools.reduce(operator.add, sequence) + + +def some(value: T | None, /) -> TypeGuard[T]: + """ + Returns ``True`` if the input is **not** ``None`` + (that is, if the ``Optional`` monad contains some value). + + :param value: Value to be checked. + :returns: ``True`` if ``value`` is **not** ``None`` else ``False``. + """ + return value is not None + + +def unwrap_or(value: T | None, /, *, default: T) -> T: + """ + Returns the input if the input is **not** None else the specified + ``default`` value. + + :param value: Input to be unwrapped and returned if not ``None``. + :param default: Default value to use if ``value`` is ``None``. + :returns: ``default`` if ``value`` is ``None`` otherwise ``value``. + """ + return default if value is None else value + + +default_if_none = unwrap_or diff --git a/ranzen/torch/utils.py b/ranzen/torch/utils.py index 7b3f7fd2..80597a29 100644 --- a/ranzen/torch/utils.py +++ b/ranzen/torch/utils.py @@ -2,14 +2,27 @@ from collections.abc import Iterable, Iterator from datetime import datetime import random -from typing import Any, List, TypeVar +from typing import Any, List, Optional, TypeVar, Union, overload import numpy as np +import numpy.typing as npt import torch from torch import Tensor import torch.nn as nn +from torch.types import Number -__all__ = ["count_parameters", "random_seed", "inf_generator", "Event", "batchwise_pdist"] +from ranzen.misc import some + +__all__ = [ + "Event", + "batchwise_pdist", + "count_parameters", + "inf_generator", + "random_seed", + "to_item", + "to_numpy", + "torch_eps", +] def count_parameters(model: nn.Module) -> int: @@ -125,3 +138,61 @@ def batchwise_pdist(x: Tensor, chunk_size: int = 1000, p_norm: float = 2.0) -> T del column torch.cuda.empty_cache() return dist + + +DT = TypeVar("DT", bound=Union[np.number, np.bool_]) + + +@overload +def to_numpy(tensor: Tensor, *, dtype: DT) -> npt.NDArray[DT]: + ... + + +@overload +def to_numpy(tensor: Tensor, *, dtype: None = ...) -> npt.NDArray: + ... + + +def to_numpy( + tensor: Tensor, *, dtype: Optional[DT] | None = None +) -> Union[npt.NDArray[DT], npt.NDArray]: + """ + Safely casts a tensor to a numpy ndarray of dtype ``dtype`` + if ``dtype`` is specified. + + :param tensor: Tensor to be cast to a ndarray. + :param dtype: dtype of the cast-to ndarray. If ``None`` then + the dtype will be inherited from ``tensor``. + :returns: ``tensor`` cast to an ndarray of dtype ``dtype`` + if ``dtype`` is specified. + """ + arr = tensor.detach().cpu().numpy() + if some(dtype): + arr.astype(dtype) + return arr + + +def to_item(tensor: Tensor, /) -> Number: + """ + Safely extracts the (int, float or bool) value from a single-element + (scalar) tensor. + + :param tensor: Tensor to extract the item from. + :returns: The value of the scalar ``tensor`` as a built-in float, int, bool. + """ + return tensor.detach().cpu().item() + + +def torch_eps(tensor_or_dtype: Tensor | torch.dtype, /) -> float: + """ + Retrieves the epsilon (the smallest representable number such that 1.0 + eps != 1.0.) + value for the given dtype or the dtype of the given tensor. + + :param tensor_or_dtype: Tensor or :class:`torch.dtype` instance to retrieve + the epislon value for. + + :returns: Epsilon value for the given dtype if ``tensor_or_dtype`` is an + instance of :class:`torch.dtype` else the epsilon value of the dtype of the + given tensor.""" + dtype = tensor_or_dtype.dtype if isinstance(tensor_or_dtype, Tensor) else tensor_or_dtype + return torch.finfo(dtype).eps From dd80e3f1ab7ddecbd121978c03a7152865d22239 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 23 Jan 2023 06:06:22 +0000 Subject: [PATCH 05/24] Bump isort from 5.10.1 to 5.11.4 Bumps [isort](https://github.com/pycqa/isort) from 5.10.1 to 5.11.4. - [Release notes](https://github.com/pycqa/isort/releases) - [Changelog](https://github.com/PyCQA/isort/blob/main/CHANGELOG.md) - [Commits](https://github.com/pycqa/isort/compare/5.10.1...5.11.4) --- updated-dependencies: - dependency-name: isort dependency-type: direct:development update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- poetry.lock | 1074 +++++++++++++++++++++++++-------------------------- 1 file changed, 537 insertions(+), 537 deletions(-) diff --git a/poetry.lock b/poetry.lock index e4f158a2..0f762500 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,3 +1,5 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + [[package]] name = "antlr4-python3-runtime" version = "4.9.3" @@ -5,6 +7,9 @@ description = "ANTLR 4.9.3 runtime for Python 3.7" category = "dev" optional = false python-versions = "*" +files = [ + {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, +] [[package]] name = "attrs" @@ -13,6 +18,10 @@ description = "Classes Without Boilerplate" category = "dev" optional = false python-versions = ">=3.5" +files = [ + {file = "attrs-22.1.0-py2.py3-none-any.whl", hash = "sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c"}, + {file = 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Bumps [gitpython](https://github.com/gitpython-developers/GitPython) from 3.1.27 to 3.1.30. - [Release notes](https://github.com/gitpython-developers/GitPython/releases) - [Changelog](https://github.com/gitpython-developers/GitPython/blob/main/CHANGES) - [Commits](https://github.com/gitpython-developers/GitPython/compare/3.1.27...3.1.30) --- updated-dependencies: - dependency-name: gitpython dependency-type: indirect ... 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Weights and Biases API." category = "dev" optional = false python-versions = ">=3.6" files = [ - {file = "wandb-0.13.3-py2.py3-none-any.whl", hash = "sha256:a246ed531ae405f2c95d299adb7311f2cf333c6c73ce36e3fcaaae60f0817c6c"}, - {file = "wandb-0.13.3.tar.gz", hash = "sha256:8f91a8b67949313769c686e4f5139e4c247de8ce140e72fdcdd7896da2ea1c87"}, + {file = "wandb-0.13.9-py2.py3-none-any.whl", hash = "sha256:b8752e5287aca9f8192eca7be352882975973cd3cd0c88815930498fd357569d"}, + {file = "wandb-0.13.9.tar.gz", hash = "sha256:0a17365ce1f18306ce7a7f16b943094fac7284bb85f4e52c0685705602f9e307"}, ] [package.dependencies] +appdirs = ">=1.4.3" Click = ">=7.0,<8.0.0 || >8.0.0" docker-pycreds = ">=0.4.0" GitPython = ">=1.0.0" pathtools = "*" -promise = ">=2.0,<3" -protobuf = ">=3.12.0,<4.0dev" +protobuf = [ + {version = ">=3.12.0,<4.21.0 || >4.21.0,<5", markers = "python_version < \"3.9\" and sys_platform == \"linux\""}, + {version = ">=3.15.0,<4.21.0 || >4.21.0,<5", markers = "python_version == \"3.9\" and sys_platform == \"linux\""}, + {version = ">=3.19.0,<4.21.0 || >4.21.0,<5", markers = "python_version > \"3.9\" or sys_platform != \"linux\""}, +] psutil = ">=5.0.0" PyYAML = "*" requests = ">=2.0.0,<3" sentry-sdk = ">=1.0.0" setproctitle = "*" setuptools = "*" -shortuuid = ">=0.5.0" -six = ">=1.13.0" +typing-extensions = {version = "*", markers = "python_version < \"3.10\""} [package.extras] aws = ["boto3"] @@ -1136,23 +1215,38 @@ media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfi models = ["cloudpickle"] sweeps = ["sweeps (>=0.2.0)"] +[[package]] +name = "wheel" +version = "0.38.4" +description = "A built-package format for Python" +category = "dev" +optional = false +python-versions = ">=3.7" +files = [ + {file = "wheel-0.38.4-py3-none-any.whl", hash = "sha256:b60533f3f5d530e971d6737ca6d58681ee434818fab630c83a734bb10c083ce8"}, + {file = "wheel-0.38.4.tar.gz", hash = "sha256:965f5259b566725405b05e7cf774052044b1ed30119b5d586b2703aafe8719ac"}, +] + +[package.extras] +test = ["pytest (>=3.0.0)"] + [[package]] name = "zipp" -version = "3.8.1" +version = "3.12.0" description = "Backport of pathlib-compatible object wrapper for zip files" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "zipp-3.8.1-py3-none-any.whl", hash = "sha256:47c40d7fe183a6f21403a199b3e4192cca5774656965b0a4988ad2f8feb5f009"}, - {file = "zipp-3.8.1.tar.gz", hash = "sha256:05b45f1ee8f807d0cc928485ca40a07cb491cf092ff587c0df9cb1fd154848d2"}, + {file = "zipp-3.12.0-py3-none-any.whl", hash = "sha256:9eb0a4c5feab9b08871db0d672745b53450d7f26992fd1e4653aa43345e97b86"}, + {file = "zipp-3.12.0.tar.gz", hash = "sha256:73efd63936398aac78fd92b6f4865190119d6c91b531532e798977ea8dd402eb"}, ] [package.extras] -docs = ["jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx"] -testing = ["func-timeout", "jaraco.itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] [metadata] lock-version = "2.0" python-versions = ">=3.8.0,<3.11" -content-hash = "677ce8e002673b15a37737d925c9d299d12bdfcfe07f47fa175ca316b96d0221" +content-hash = "733e39a75d565320a3ed2b6814e423fbb48ccafe9d5e7104f0d5ed6c84e717d4" diff --git a/pyproject.toml b/pyproject.toml index dbe9d8b3..32b35a9f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,6 +58,7 @@ readme="README.md" # more groups with the --without option: ``poetry install --without torch``. [tool.poetry.dependencies] python = ">=3.8.0,<3.11" +typing-extensions = ">=4.4.0" [tool.poetry.group.wandb.dependencies] pandas = { version = "^1.4.3" } @@ -90,6 +91,7 @@ reportUntypedNamedTuple = "error" reportConstantRedefinition = "warning" reportMissingImports = "error" reportMissingTypeStubs = "warning" +reportUnusedVariable = "warning" strictListInference = true strictSetInference = true strictParameterNoneValue = true @@ -139,7 +141,7 @@ target-version = "py38" [tool.ruff.isort] known-third-party = ["PIL", "black", "git", "fairlearn", "matplotlib", "mypy", "numpy", "pandas", "pylint", "pytest", "scipy", "seaborn", "setuptools", "sklearn", "teext", "torch"] -extra-standard-library = ["__future__", "typing_extensions"] +extra_standard_library = ["dataclasses", "__future__", "typing_extensions"] [tool.ruff.flake8-annotations] mypy-init-return = true diff --git a/ranzen/torch/schedulers.py b/ranzen/torch/schedulers.py index b14dfdd4..752495b1 100644 --- a/ranzen/torch/schedulers.py +++ b/ranzen/torch/schedulers.py @@ -8,8 +8,7 @@ from torch import Tensor from torch.optim import Optimizer from torch.optim.lr_scheduler import CosineAnnealingLR, _LRScheduler - -from ranzen.decorators import implements +from typing_extensions import override __all__ = [ "CosineLRWithLinearWarmup", @@ -35,25 +34,25 @@ class LinearWarmupLR(_LRScheduler): def __init__( self, optimizer: Optimizer, *, warmup_iters: int, lr_start: float = 0, last_epoch: int = -1 ) -> None: + if warmup_iters < 0: + raise AttributeError("'warmup_iters' must be non-negative.") self.lr_start = lr_start self.warmup_iters = warmup_iters super().__init__(optimizer=optimizer, last_epoch=last_epoch) - @implements(_LRScheduler) + @override def get_lr(self) -> list[float]: """ Get the learning rate of each parameter group. :returns: The learning rate for each parameter group in the optimizer. """ - if self.last_epoch > self.warmup_iters: + if self.last_epoch > self.warmup_iters or (self.warmup_iters == 0): return [group["lr"] for group in self.optimizer.param_groups] - - lrs = [ + return [ self.lr_start + self._get_step_size(base_lr) * self.last_epoch - for group, base_lr in zip(self.optimizer.param_groups, self.base_lrs) + for base_lr in self.base_lrs ] - return lrs def _get_step_size(self, base_lr: float) -> float: return (base_lr - self.lr_start) / self.warmup_iters @@ -109,6 +108,8 @@ def __init__( "If 'warmup_iters' is a float, it must be in the range [0, 1]." ) warmup_iters = round(warmup_iters * total_iters) + elif warmup_iters < 0: + raise AttributeError("If 'warmup_iters' is an integer, it must be non-negative.") self.warmup_iters = warmup_iters self._scheduler: Union[LinearWarmupLR, CosineAnnealingLR] = LinearWarmupLR( optimizer=optimizer, warmup_iters=warmup_iters, lr_start=lr_start @@ -133,6 +134,7 @@ def scheduler(self) -> Union[LinearWarmupLR, CosineAnnealingLR]: ) return self._scheduler + @override def get_lr(self) -> list[float]: """ Get the learning rate of each parameter group. @@ -141,6 +143,7 @@ def get_lr(self) -> list[float]: """ return self.scheduler.get_lr() # type: ignore + @override def step(self, epoch: Optional[int] = None) -> None: """ Update the learning rates using the currently-used scheduler. @@ -252,7 +255,7 @@ class WarmupScheduler(Scheduler[T]): warmup_steps: int _curr_step: int = field(init=False) - @implements(Scheduler) + @override def __post_init__(self) -> None: if self.warmup_steps < 0: raise AttributeError("'warmup_steps' must be a non-negative integer.") @@ -266,7 +269,7 @@ def warmed_up(self) -> bool: return self._curr_step == self.warmup_steps @torch.no_grad() - @implements(Scheduler) + @override def step(self) -> None: if not self.warmed_up: super().step() @@ -277,7 +280,7 @@ def step(self) -> None: class LinearWarmup(WarmupScheduler[T]): step_size: T = field(init=False) - @implements(WarmupScheduler) + @override def __post_init__(self) -> None: super().__post_init__() if self.warmup_steps == 0: @@ -285,7 +288,7 @@ def __post_init__(self) -> None: else: self.step_size = (self.end_val - self.start_val) / self.warmup_steps - @implements(WarmupScheduler) + @override def _update(self, value: T) -> T: return value + self.step_size @@ -295,7 +298,7 @@ class ExponentialWarmup(WarmupScheduler[T]): end_val: T step_size: T = field(init=False) - @implements(WarmupScheduler) + @override def __post_init__(self) -> None: super().__post_init__() if self.warmup_steps == 0: @@ -303,7 +306,7 @@ def __post_init__(self) -> None: else: self.step_size = (self.end_val / self.start_val) ** (1 / self.warmup_steps) # type: ignore - @implements(WarmupScheduler) + @override def _update(self, value: T) -> T: return value * self.step_size @@ -313,12 +316,12 @@ class CosineWarmup(WarmupScheduler[T]): end_val: T _coeff: T = field(init=False) - @implements(WarmupScheduler) + @override def __post_init__(self) -> None: super().__post_init__() self._coeff = 0.5 * (self.end_val - self.start_val) - @implements(WarmupScheduler) + @override def _update(self, value: T) -> T: if self.warmup_steps == 0: return value diff --git a/tests/scheduler_test.py b/tests/scheduler_test.py new file mode 100644 index 00000000..b625971f --- /dev/null +++ b/tests/scheduler_test.py @@ -0,0 +1,42 @@ +import pytest +import torch +from torch import optim + +from ranzen.torch.schedulers import LinearWarmupLR + + +def test_linear_warmup_lr() -> None: + params = (torch.randn(1, 1, requires_grad=True),) + base_lr = 1.0 + lr_start = 1.0e-1 + optimizer = optim.SGD(params, lr=base_lr) + scheduler = LinearWarmupLR(optimizer=optimizer, lr_start=lr_start, warmup_iters=1) + for group in optimizer.param_groups: + assert group["lr"] == lr_start + + def _step(): + optimizer.step() + scheduler.step() + + _step() + for group in optimizer.param_groups: + assert group["lr"] == base_lr + + optimizer = optim.AdamW(params, lr=base_lr) + scheduler = LinearWarmupLR(optimizer=optimizer, lr_start=lr_start, warmup_iters=0) + for group in optimizer.param_groups: + assert group["lr"] == base_lr + + _step() + for group in optimizer.param_groups: + assert group["lr"] == base_lr + + optimizer = optim.AdamW(params, lr=base_lr) + scheduler = LinearWarmupLR(optimizer=optimizer, lr_start=lr_start, warmup_iters=2) + _step() + expected_lr_after_one_step = lr_start + 0.5 * (base_lr - lr_start) + for group in optimizer.param_groups: + assert group["lr"] == expected_lr_after_one_step + + with pytest.raises(AttributeError): + scheduler = LinearWarmupLR(optimizer=optimizer, lr_start=1.0e-1, warmup_iters=-1) From d6117aff5370e43c23729efb3ffdf3c82f85330d Mon Sep 17 00:00:00 2001 From: Thomas MK Date: Sun, 29 Jan 2023 17:54:41 +0100 Subject: [PATCH 08/24] Replace @implements with @override And a whole lot of other changes that pyright and mypy pointed out. --- pyproject.toml | 2 +- ranzen/decorators.py | 5 ++++- ranzen/hydra/relay.py | 23 +++++++++++------------ ranzen/misc.py | 7 +++---- ranzen/torch/data.py | 28 ++++++++++++++-------------- ranzen/torch/module.py | 2 +- ranzen/torch/optimizers/lamb.py | 5 ++--- ranzen/torch/optimizers/sam.py | 9 ++++----- ranzen/torch/schedulers.py | 2 +- ranzen/torch/transforms/mixup.py | 4 ++-- ranzen/types.py | 1 - tests/optimizers_test.py | 4 ++-- tests/relay_test.py | 2 +- tests/sequential_sampler_test.py | 6 +++--- tests/stratified_sampler_test.py | 2 +- tests/torch_data_test.py | 6 +++--- tests/torch_loss_test.py | 3 ++- 17 files changed, 55 insertions(+), 56 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 32b35a9f..062e3e9c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ known_third_party = [ "wandb", ] known_future_library = [] -extra_standard_library = ["dataclasses", "__future__"] +extra_standard_library = ["dataclasses", "__future__", "typing_extensions"] line_length = 88 profile = "black" force_sort_within_sections = "True" diff --git a/ranzen/decorators.py b/ranzen/decorators.py index 82ca8158..e8e7038c 100644 --- a/ranzen/decorators.py +++ b/ranzen/decorators.py @@ -9,7 +9,10 @@ class implements: # pylint: disable=invalid-name - """Mark a function as implementing an interface.""" + """Mark a function as implementing an interface. + + Deprecated. Use ``typing_extensions.override`` instead. + """ def __init__(self, interface: type): """Instantiate the decorator. diff --git a/ranzen/hydra/relay.py b/ranzen/hydra/relay.py index e7fe9f5b..67c7493a 100644 --- a/ranzen/hydra/relay.py +++ b/ranzen/hydra/relay.py @@ -4,7 +4,7 @@ from dataclasses import dataclass, is_dataclass, replace from enum import Enum from functools import lru_cache -import importlib +import importlib.util import inspect import logging import os @@ -18,6 +18,7 @@ ClassVar, DefaultDict, Dict, + Final, Generic, List, NamedTuple, @@ -28,7 +29,9 @@ TypeVar, Union, cast, + final, ) +from typing_extensions import Self, TypeAlias import hydra from hydra.core.config_search_path import ConfigSearchPath @@ -36,7 +39,6 @@ from hydra.plugins.search_path_plugin import SearchPathPlugin from hydra.utils import instantiate from omegaconf import OmegaConf -from typing_extensions import Final, Self, TypeAlias, final from .utils import SchemaRegistration @@ -209,8 +211,7 @@ def _init_yaml_files(cls, *, config_dir: Path, config_dict: dict[str, list[Any]] ): continue entry = f"{name}: " - default = param.default - if not default is param.empty: + if (default := param.default) is not param.empty: default_str = _to_yaml_value(default) if default_str is None: entry = f"# {entry}???" @@ -252,10 +253,10 @@ def _generate_conf(cls, output_dir: Path, *, module_class_dict: dict[str, List[s def _load_module_from_path(cls, filepath: Path) -> ModuleType: import sys - spec = importlib.util.spec_from_file_location( # type: ignore - name="", location=str(filepath) - ) - module = importlib.util.module_from_spec(spec) # type: ignore + spec = importlib.util.spec_from_file_location(name="", location=str(filepath)) + assert spec is not None + assert spec.loader is not None + module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) sys.modules[""] = module return module @@ -334,9 +335,7 @@ def _load_schemas( # Load the sub-schemas for group, info_ls in schemas_to_import.items(): for info in info_ls: - module = info.module - if isinstance(module, Path): - module = cls._load_module_from_path(module) + module = cls._load_module_from_path(m) if isinstance(m := info.module, Path) else m schema = getattr(module, info.schema_name) # TODO: figure out why the below kludge (ostensibly) solves the issue of failed @@ -355,7 +354,7 @@ def _launch( root: Path | str, clear_cache: bool = False, instantiate_recursively: bool = True, - **options: list[type[Any] | Option], + **options: Options, ) -> None: root = Path(root) config_dir_name = cls._config_dir_name() diff --git a/ranzen/misc.py b/ranzen/misc.py index d339524a..785d62a4 100644 --- a/ranzen/misc.py +++ b/ranzen/misc.py @@ -5,7 +5,6 @@ import operator import sys from typing import Any, Dict, Iterable, MutableMapping, Type, TypeVar, overload - from typing_extensions import Self, TypeGuard from ranzen.types import Addable @@ -64,6 +63,7 @@ def gcopy( obj: T, *, deep: bool = True, num_copies: int | None = None, **kwargs: Any ) -> T | list[T]: """Generalised (g) copy function. + Allows for switching between deep and shallow copying within a single function as well as for the creation of multiple copies and for copying while simultaneously attribute-setting. @@ -80,8 +80,7 @@ def gcopy( """ if num_copies is not None: return [gcopy(obj=obj, deep=deep, num_copies=None, **kwargs) for _ in range(num_copies)] - copy_fn = copy.deepcopy if deep else copy.copy - obj_cp = copy_fn(obj) + obj_cp = copy.deepcopy(obj) if deep else copy.copy(obj) for attr, value in kwargs.items(): if not hasattr(obj_cp, attr): raise AttributeError( @@ -219,7 +218,7 @@ def __add__( # with th 'no-default' version of``sum``. if isinstance(other, int): return self - copy = AddDict() + copy: AddDict[_KT, _VT | _VT2] = AddDict() copy.update(gcopy(self, deep=False)) for key_o, value_o in other.items(): diff --git a/ranzen/torch/data.py b/ranzen/torch/data.py index a3f1711f..82cf9bd3 100644 --- a/ranzen/torch/data.py +++ b/ranzen/torch/data.py @@ -18,15 +18,14 @@ overload, runtime_checkable, ) +from typing_extensions import Self, override import numpy as np import numpy.typing as npt import torch from torch import Tensor from torch.utils.data import Sampler -from typing_extensions import Self -from ranzen import implements from ranzen.misc import str_to_enum __all__ = [ @@ -97,7 +96,7 @@ def prop_random_split( dataset: SizedDataset, *, props: Sequence[float] | float, - as_indices: Literal[True] = ..., + as_indices: Literal[True], seed: int | None = ..., ) -> List[list[int]]: ... @@ -141,12 +140,13 @@ def prop_random_split( section_sizes.append(len_ - sum(section_sizes)) generator = torch.default_generator if seed is None else torch.Generator().manual_seed(seed) indices = torch.randperm(sum(section_sizes), generator=generator).tolist() - splits = [] - for offset, length in zip(np.cumsum(section_sizes), section_sizes): - split = indices[offset - length : offset] - if not as_indices: - split = Subset(dataset, indices=split) - splits.append(split) + splits = [ + indices[offset - length : offset] + for offset, length in zip(np.cumsum(section_sizes), section_sizes) + ] + + if not as_indices: + return [Subset(dataset, indices=split) for split in splits] return splits @@ -235,7 +235,7 @@ class BatchSamplerBase(Sampler[Sequence[int]]): def __init__(self, epoch_length: int | None = None) -> None: self.epoch_length: Final[int | None] = epoch_length - @implements(Sampler) + @override @abstractmethod def __iter__(self) -> Iterator[list[int]]: ... @@ -325,7 +325,7 @@ def _batch_indexes(self, indexes: Tensor) -> Sequence[Tensor]: """Split the indexes into batches.""" return indexes.split(self.batch_size) - @implements(BatchSamplerBase) + @override def __iter__(self) -> Iterator[list[int]]: generator = _check_generator(self.generator) batched_idxs_iter = iter(self._batch_indexes(self._generate_idx_seq(generator=generator))) @@ -564,7 +564,7 @@ def _random_sampler(self, generator: torch.Generator) -> Iterator[list[int]]: sampled_idxs += list(chunks[:multiplier]) yield torch.cat(sampled_idxs, dim=0).tolist() - @implements(BatchSamplerBase) + @override def __iter__(self) -> Iterator[list[int]]: generator = _check_generator(self.generator) if self.sampler is BaseSampler.random: @@ -614,7 +614,7 @@ def _get_dists(self, batch_idxs: Tensor) -> Tensor: sq = dist_mat.diagonal().view(batch.size(0), 1) return -2 * dist_mat + sq + sq.t() - @implements(BatchSamplerBase) + @override def __iter__(self) -> Iterator[list[int]]: generator = _check_generator(self.generator) # iterative forever (until some externally defined stopping-criterion is reached) @@ -735,7 +735,7 @@ def from_labels( generator=generator, ) - @implements(BatchSamplerBase) + @override def __iter__(self) -> Iterator[list[int]]: generator = _check_generator(self.generator) # Iterate until some stopping criterion is reached diff --git a/ranzen/torch/module.py b/ranzen/torch/module.py index c9bf2b3a..2c4787ef 100644 --- a/ranzen/torch/module.py +++ b/ranzen/torch/module.py @@ -1,9 +1,9 @@ from __future__ import annotations from dataclasses import dataclass from typing import Any +from typing_extensions import Self, final import torch.nn as nn -from typing_extensions import Self, final __all__ = ["DcModule"] diff --git a/ranzen/torch/optimizers/lamb.py b/ranzen/torch/optimizers/lamb.py index fab7cb4f..33069424 100644 --- a/ranzen/torch/optimizers/lamb.py +++ b/ranzen/torch/optimizers/lamb.py @@ -1,13 +1,12 @@ from __future__ import annotations import math from typing import Callable, Iterable +from typing_extensions import override import torch from torch import Tensor from torch.optim.optimizer import Optimizer -from ranzen.decorators import implements - __all__ = ["LAMB"] @@ -66,7 +65,7 @@ def __init__( super().__init__(params=params, defaults=defaults) # type: ignore - @implements(Optimizer) + @override def step(self, closure: Callable[[], Tensor] | None = None) -> Tensor | None: r"""Performs a single optimization step. diff --git a/ranzen/torch/optimizers/sam.py b/ranzen/torch/optimizers/sam.py index f32641b6..eae2e9bc 100644 --- a/ranzen/torch/optimizers/sam.py +++ b/ranzen/torch/optimizers/sam.py @@ -1,12 +1,11 @@ from __future__ import annotations from typing import Any, Callable +from typing_extensions import override import torch from torch import Tensor from torch.optim.optimizer import Optimizer -from ranzen.decorators import implements - __all__ = ["SAM"] @@ -96,7 +95,7 @@ def _second_step(self, zero_grad: bool = False) -> None: self.zero_grad() @torch.no_grad() - @implements(Optimizer) + @override def step(self, closure: Callable[[], Tensor]) -> Tensor: r"""Performs a single optimization step. @@ -110,7 +109,7 @@ def step(self, closure: Callable[[], Tensor]) -> Tensor: self._second_step() return loss - def _grad_norm(self): + def _grad_norm(self) -> Tensor: shared_device = self.param_groups[0]["params"][ 0 ].device # put everything on the same device, in case of model parallelism @@ -129,7 +128,7 @@ def _grad_norm(self): ) return norm - @implements(Optimizer) + @override def load_state_dict(self, state_dict: dict[str, Any]) -> None: r"""Loads the optimizer state. diff --git a/ranzen/torch/schedulers.py b/ranzen/torch/schedulers.py index 752495b1..4616794d 100644 --- a/ranzen/torch/schedulers.py +++ b/ranzen/torch/schedulers.py @@ -3,12 +3,12 @@ from dataclasses import dataclass, field import math from typing import Generic, List, Optional, TypeVar, Union, overload +from typing_extensions import override import torch from torch import Tensor from torch.optim import Optimizer from torch.optim.lr_scheduler import CosineAnnealingLR, _LRScheduler -from typing_extensions import override __all__ = [ "CosineLRWithLinearWarmup", diff --git a/ranzen/torch/transforms/mixup.py b/ranzen/torch/transforms/mixup.py index 23a8e87c..d409bcb5 100644 --- a/ranzen/torch/transforms/mixup.py +++ b/ranzen/torch/transforms/mixup.py @@ -93,7 +93,7 @@ def __init__( :raises ValueError: if ``p`` is not in the range [0, 1] or ``num_classes < 1``. """ super().__init__() - self.lambda_sampler = lambda_sampler + self.lambda_sampler: LS = lambda_sampler if not 0 <= p <= 1: raise ValueError("'p' must be in the range [0, 1].") self.p = p @@ -286,7 +286,7 @@ def _transform( *, targets: Tensor | None = None, groups_or_edges: Tensor | None = None, - cross_group: bool = ..., + cross_group: bool = False, num_classes: int | None = None, ) -> Tensor | InputsTargetsPair: batch_size = len(inputs) diff --git a/ranzen/types.py b/ranzen/types.py index 70da1a3d..7baa0fb7 100644 --- a/ranzen/types.py +++ b/ranzen/types.py @@ -1,5 +1,4 @@ from typing import TypeVar - from typing_extensions import Protocol, Self, runtime_checkable __all__ = ["Sized", "Addable"] diff --git a/tests/optimizers_test.py b/tests/optimizers_test.py index 953197f4..36f50e97 100644 --- a/tests/optimizers_test.py +++ b/tests/optimizers_test.py @@ -7,7 +7,7 @@ @pytest.mark.parametrize("debias", [True, False]) -def test_lamb(debias: bool): +def test_lamb(debias: bool) -> None: params = torch.randn(10, requires_grad=True) optimizer = LAMB(params=[params], debias=debias) old_params = params.data.clone() @@ -19,7 +19,7 @@ def test_lamb(debias: bool): @pytest.mark.parametrize("adaptive", [True, False]) -def test_sam(adaptive: bool): +def test_sam(adaptive: bool) -> None: params = torch.randn(10, requires_grad=True) base_optimizer = AdamW([params]) optimizer = SAM(base_optimizer=base_optimizer, adaptive=adaptive) diff --git a/tests/relay_test.py b/tests/relay_test.py index aaf6d806..ca102927 100644 --- a/tests/relay_test.py +++ b/tests/relay_test.py @@ -2,10 +2,10 @@ from dataclasses import dataclass from pathlib import Path from typing import Any, Dict, Optional, Protocol +from typing_extensions import Self from unittest.mock import patch import pytest -from typing_extensions import Self from ranzen.hydra.relay import Option, Options, Relay diff --git a/tests/sequential_sampler_test.py b/tests/sequential_sampler_test.py index 27f12cfb..e04301f6 100644 --- a/tests/sequential_sampler_test.py +++ b/tests/sequential_sampler_test.py @@ -7,11 +7,11 @@ @pytest.fixture(scope="module") -def data() -> Tensor: # type: ignore[no-any-unimported] +def data() -> Tensor: return torch.arange(200) -def test_regeneration(data: Tensor) -> None: # type: ignore[no-any-unimported] +def test_regeneration(data: Tensor) -> None: batch_size = 175 dataset_size = 200 sampler = SequentialBatchSampler(data_source=data, batch_size=batch_size, shuffle=False) @@ -28,7 +28,7 @@ def test_regeneration(data: Tensor) -> None: # type: ignore[no-any-unimported] @pytest.mark.parametrize("drop_last", [True, False]) @pytest.mark.parametrize("batch_size", [55, 50]) -def test_epoch_mode(data: Tensor, batch_size: int, drop_last: bool) -> None: # type: ignore[no-any-unimported] +def test_epoch_mode(data: Tensor, batch_size: int, drop_last: bool) -> None: sampler = SequentialBatchSampler( data_source=data, batch_size=batch_size, diff --git a/tests/stratified_sampler_test.py b/tests/stratified_sampler_test.py index 1c9bcbe8..05a40bfa 100644 --- a/tests/stratified_sampler_test.py +++ b/tests/stratified_sampler_test.py @@ -88,7 +88,7 @@ def test_with_dataloader(group_ids: list[int], sampler: BaseSampler) -> None: dataset = TensorDataset(torch.as_tensor(group_ids)) data_loader = DataLoader( dataset=dataset, batch_sampler=batch_sampler, drop_last=False, shuffle=False - ) # type: ignore + ) iters = 0 for (x,) in data_loader: assert x.size(0) == batch_size diff --git a/tests/torch_data_test.py b/tests/torch_data_test.py index 3bef306a..7cb4efb6 100644 --- a/tests/torch_data_test.py +++ b/tests/torch_data_test.py @@ -9,7 +9,7 @@ @pytest.fixture(scope="module") -def dummy_ds() -> TensorDataset: # type: ignore[no-any-unimported] +def dummy_ds() -> TensorDataset: return TensorDataset(torch.randn(100)) @@ -17,7 +17,7 @@ def dummy_ds() -> TensorDataset: # type: ignore[no-any-unimported] @pytest.mark.parametrize("props", [0.5, [-0.2, 0.5], [0.1, 0.3, 0.4], [0.5, 0.6]]) def test_prop_random_split( dummy_ds: TensorDataset, props: float | list[float], as_indices: bool -) -> None: # type: ignore[no-any-unimported] +) -> None: sum_ = props if isinstance(props, float) else sum(props) props_ls = [props] if isinstance(props, float) else props if sum_ > 1 or any(not (0 <= prop <= 1) for prop in props_ls): @@ -32,7 +32,7 @@ def test_prop_random_split( assert sizes[-1] == (len(dummy_ds) - (round(sum_ * len(dummy_ds)))) -def test_stratified_split_indices(): +def test_stratified_split_indices() -> None: labels = torch.randint(low=0, high=4, size=(50,)) train_inds, test_inds = stratified_split_indices(labels=labels, default_train_prop=0.5) labels_tr = labels[train_inds] diff --git a/tests/torch_loss_test.py b/tests/torch_loss_test.py index ca58df2e..6bab09e4 100644 --- a/tests/torch_loss_test.py +++ b/tests/torch_loss_test.py @@ -1,7 +1,8 @@ +from typing import Final + import pytest import torch import torch.nn.functional as F -from typing_extensions import Final from ranzen.torch import CrossEntropyLoss, ReductionType From b19432320b5d1e64ea6915922cb4ae27d0b24c42 Mon Sep 17 00:00:00 2001 From: Myles Bartlett <44702700+MylesBartlett@users.noreply.github.com> Date: Mon, 30 Jan 2023 17:17:28 +0000 Subject: [PATCH 09/24] Add `Adafactor` optimizer. (#190) * Add `Adafactor` optimizer. --------- Co-authored-by: Myles Bartlett --- ranzen/torch/optimizers/__init__.py | 2 + ranzen/torch/optimizers/adafactor.py | 224 +++++++++++++++++++++++++++ ranzen/torch/optimizers/common.py | 8 + ranzen/torch/optimizers/lamb.py | 6 +- ranzen/torch/optimizers/sam.py | 11 +- tests/optimizers_test.py | 25 ++- 6 files changed, 269 insertions(+), 7 deletions(-) create mode 100644 ranzen/torch/optimizers/adafactor.py create mode 100644 ranzen/torch/optimizers/common.py diff --git a/ranzen/torch/optimizers/__init__.py b/ranzen/torch/optimizers/__init__.py index 6d841a6c..dca7c4e5 100644 --- a/ranzen/torch/optimizers/__init__.py +++ b/ranzen/torch/optimizers/__init__.py @@ -1,2 +1,4 @@ +from .adafactor import * +from .common import * from .lamb import * from .sam import * diff --git a/ranzen/torch/optimizers/adafactor.py b/ranzen/torch/optimizers/adafactor.py new file mode 100644 index 00000000..f7f54a7d --- /dev/null +++ b/ranzen/torch/optimizers/adafactor.py @@ -0,0 +1,224 @@ +# Most of the code here has been copied from: +# https://github.com/pytorch/fairseq/blob/main/fairseq/optim/adafactor.py +from __future__ import annotations +import math +from typing import Iterable, Sequence, cast +from typing_extensions import TypedDict + +import torch +from torch import Tensor +from torch.optim import Optimizer + +from .common import LossClosure + +__all__ = ["Adafactor"] + + +class ParamGroup(TypedDict): + params: Iterable[Tensor] + lr: float + eps: tuple[float, float] + clipping_threshold: float + decay_rate: float + beta1: float | None + weight_decay: float + multiply_by_parameter_scale: bool + warmup_init: bool + relative_step: bool + + +class ParamState(TypedDict): + lr: float + rms: Tensor + step: int + exp_avg: Tensor + exp_avg_sq: Tensor + exp_avg_sq_row: Tensor + exp_avg_sq_col: Tensor + + +class Adafactor(Optimizer): + """Implements Adafactor algorithm. + This implementation is based on: `Adafactor: Adaptive Learning Rates with + Sublinear Memory Cost `_. Note that + this optimizer internally adjusts the learning rate depending on the + ``multiply_by_parameter_scale*, ``relative_step`` and ``warmup_init`` + options. To use a manual (external) learning rate schedule you should set + ``multiply_by_parameter_scale=False`` and `relative_step=False``. + """ + + param_groups: list[ParamGroup] # type: ignore + state: dict[Tensor, ParamState] + + def __init__( + self, + params: Iterable[Tensor], + *, + lr: float | None = None, + eps: tuple[float, float] = (1e-30, 1e-3), + clipping_threshold: float = 1.0, + decay_rate: float = 0.8, + beta1: float | None = None, + weight_decay: float = 0.0, + multiply_by_parameter_scale: bool = False, + warmup_init: bool = False, + ) -> None: + """ + :param params: iterable of parameters to optimize or dicts defining + parameter groups + + :param lr: learning rate. If ``None``, a time-dependent learning rate + will instead be computed. + + :param eps: regularization constants for square gradient + and parameter scale respectively. + + :param clipping_threshold: threshold of root mean square of + final gradient update. + + :param decay_rate: coefficient used to compute running averages of square + gradient. + + :param beta1: coefficient used for computing running averages of gradient. + + :param weight_decay: weight decay coefficient. + :param multiply_by_parameter_scale: if True, learning rate is scaled by + root mean square of parameter. + + :param warmup_init: time-dependent learning rate computation depends on + whether warm-up initialization is being used. + """ + relative_step = lr is None + defaults = dict( + lr=lr, + eps=eps, + clipping_threshold=clipping_threshold, + decay_rate=decay_rate, + beta1=beta1, + weight_decay=weight_decay, + multiply_by_parameter_scale=multiply_by_parameter_scale, + relative_step=relative_step, + warmup_init=warmup_init, + ) + super().__init__(params, defaults) + + def _get_lr(self, param_group: ParamGroup, param_state) -> float: + rel_step_sz = param_group["lr"] + if param_group["relative_step"]: + min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2 + rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"])) + param_scale = 1.0 + if param_group["multiply_by_parameter_scale"]: + param_scale = max(param_group["eps"][1], param_state["rms"]) + return param_scale * rel_step_sz + + def _get_options(self, param_group: ParamGroup, param_shape: Sequence[int]): + factored = len(param_shape) >= 2 + use_first_moment = param_group["beta1"] is not None + return factored, use_first_moment + + def _rms(self, tensor: Tensor) -> Tensor: + return tensor.norm(p=2) / (tensor.numel() ** 0.5) # type: ignore + + def _approx_sq_grad(self, *, exp_avg_sq_row: Tensor, exp_avg_sq_col: Tensor): + r_factor = ( + (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1) + ) + c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() + return torch.mul(r_factor, c_factor) + + def step(self, closure: LossClosure | None = None) -> Tensor | None: + """Performs a single optimization step. + :param closure: A closure that reevaluates the model and returns the loss. + :returns: loss returned by the closure if ``closure`` is not ``None`` else ``None``. + :raises RuntimeError: if gradients are sparse. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad.data + if grad.dtype in {torch.float16, torch.bfloat16}: + grad = grad.float() + if grad.is_sparse: + raise RuntimeError("Adafactor does not support sparse gradients.") + + state = self.state[p] + grad_shape = grad.shape + + factored, use_first_moment = self._get_options(group, grad_shape) + # State Initialization + if len(state) == 0: + state["step"] = 0 + + if use_first_moment: + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(grad) + if factored: + state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad) + state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to( + grad + ) + else: + state["exp_avg_sq"] = torch.zeros_like(grad) + + state["rms"] = torch.zeros(()) + else: + if use_first_moment: + state["exp_avg"] = state["exp_avg"].to(grad) + if factored: + state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad) + state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad) + else: + state["exp_avg_sq"] = state["exp_avg_sq"].to(grad) + + p_data_fp32 = p.data + if p.data.dtype in {torch.float16, torch.bfloat16}: + p_data_fp32 = p_data_fp32.float() + + state["step"] += 1 + state["rms"] = self._rms(p_data_fp32) + group["lr"] = self._get_lr(group, state) + + beta2t = 1.0 - math.pow(state["step"], -group["decay_rate"]) + update = (grad**2) + group["eps"][0] + if factored: + exp_avg_sq_row = state["exp_avg_sq_row"] + exp_avg_sq_col = state["exp_avg_sq_col"] + + exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=1.0 - beta2t) + exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=1.0 - beta2t) + + # Approximation of exponential moving average of square of gradient + update = self._approx_sq_grad( + exp_avg_sq_row=exp_avg_sq_row, exp_avg_sq_col=exp_avg_sq_col + ) + update.mul_(grad) + else: + exp_avg_sq = state["exp_avg_sq"] + + exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t) + update = exp_avg_sq.rsqrt().mul_(grad) + + update.div_((self._rms(update) / group["clipping_threshold"]).clamp_(min=1.0)) + update.mul_(group["lr"]) + + if use_first_moment: + momentum = cast(float, group["beta1"]) + exp_avg = state["exp_avg"] + exp_avg.mul_(momentum).add_(update, alpha=1 - momentum) + update = exp_avg + + if group["weight_decay"] != 0: + p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"]) + + p_data_fp32.add_(-update) + + if p.data.dtype in {torch.float16, torch.bfloat16}: + p.data.copy_(p_data_fp32) + + return loss diff --git a/ranzen/torch/optimizers/common.py b/ranzen/torch/optimizers/common.py new file mode 100644 index 00000000..f3959c98 --- /dev/null +++ b/ranzen/torch/optimizers/common.py @@ -0,0 +1,8 @@ +from typing import Callable +from typing_extensions import TypeAlias + +from torch import Tensor + +__all__ = ["LossClosure"] + +LossClosure: TypeAlias = Callable[..., Tensor] diff --git a/ranzen/torch/optimizers/lamb.py b/ranzen/torch/optimizers/lamb.py index 33069424..ec07e7fd 100644 --- a/ranzen/torch/optimizers/lamb.py +++ b/ranzen/torch/optimizers/lamb.py @@ -1,12 +1,14 @@ from __future__ import annotations import math -from typing import Callable, Iterable +from typing import Iterable from typing_extensions import override import torch from torch import Tensor from torch.optim.optimizer import Optimizer +from .common import LossClosure + __all__ = ["LAMB"] @@ -66,7 +68,7 @@ def __init__( super().__init__(params=params, defaults=defaults) # type: ignore @override - def step(self, closure: Callable[[], Tensor] | None = None) -> Tensor | None: + def step(self, closure: LossClosure | None = None) -> Tensor | None: r"""Performs a single optimization step. :param closure: A closure that reevaluates the model and returns the loss. diff --git a/ranzen/torch/optimizers/sam.py b/ranzen/torch/optimizers/sam.py index eae2e9bc..1dae8458 100644 --- a/ranzen/torch/optimizers/sam.py +++ b/ranzen/torch/optimizers/sam.py @@ -1,18 +1,21 @@ from __future__ import annotations -from typing import Any, Callable +from typing import Any from typing_extensions import override import torch from torch import Tensor from torch.optim.optimizer import Optimizer +from .common import LossClosure + __all__ = ["SAM"] class SAM(Optimizer): """ - Implements the 'Sharpness Aware Minimization' (SAM) algorithm introducued in - `Sharpness Aware Minimization`_) and the adaptive variant of it introduced in `ASAM`_. + Implements the 'Sharpness Aware Minimization' (SAM) algorithm introducued + in `Sharpness Aware Minimization`_ along with the adaptive variant of it + introduced in `ASAM`_. SAM seeks parameters that lie in neighborhoods having uniformly low loss (rather than parameters that only themselves have low loss value). The adaptive variant of the @@ -96,7 +99,7 @@ def _second_step(self, zero_grad: bool = False) -> None: @torch.no_grad() @override - def step(self, closure: Callable[[], Tensor]) -> Tensor: + def step(self, closure: LossClosure) -> Tensor: r"""Performs a single optimization step. :param closure: A closure that reevaluates the model and returns the loss. diff --git a/tests/optimizers_test.py b/tests/optimizers_test.py index 36f50e97..0b63f9b1 100644 --- a/tests/optimizers_test.py +++ b/tests/optimizers_test.py @@ -1,9 +1,11 @@ +from __future__ import annotations + import pytest import torch from torch import Tensor from torch.optim import AdamW -from ranzen.torch.optimizers import LAMB, SAM +from ranzen.torch.optimizers import LAMB, SAM, Adafactor @pytest.mark.parametrize("debias", [True, False]) @@ -33,3 +35,24 @@ def _closure() -> Tensor: loss.backward() optimizer.step(closure=_closure) assert not torch.allclose(old_params.data, params.data) + + +@pytest.mark.parametrize("lr", [1.0, None]) +@pytest.mark.parametrize("beta1", [0.98, None]) +@pytest.mark.parametrize("multiply_by_parameter_scale", [True, False]) +@pytest.mark.parametrize("warmup_init", [True, False]) +def test_adafactor( + lr: float | None, beta1: float | None, multiply_by_parameter_scale: bool, warmup_init: bool +): + params = torch.randn(10, requires_grad=True) + optimizer = Adafactor( + params=[params], + lr=lr, + beta1=beta1, + multiply_by_parameter_scale=multiply_by_parameter_scale, + warmup_init=warmup_init, + ) + for _ in range(2): + loss = params.norm() + loss.backward() + optimizer.step() From aaa9cde394332b9f29ceae479631ae8ca51cdfb6 Mon Sep 17 00:00:00 2001 From: Thomas M Kehrenberg Date: Wed, 1 Feb 2023 12:58:42 +0100 Subject: [PATCH 10/24] Update to black style 2023 (#191) --- poetry.lock | 51 ++++++++++++++++++++++---------- pyproject.toml | 2 +- ranzen/torch/data.py | 1 - ranzen/torch/optimizers/lamb.py | 3 +- ranzen/torch/transforms/mixup.py | 13 ++++---- tests/relay_test.py | 1 - 6 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a/ranzen/torch/data.py b/ranzen/torch/data.py index 82cf9bd3..f4fd3d89 100644 --- a/ranzen/torch/data.py +++ b/ranzen/torch/data.py @@ -155,7 +155,6 @@ def prop_random_split( @dataclass(frozen=True) class TrainTestSplit(Generic[S]): - train: S test: S diff --git a/ranzen/torch/optimizers/lamb.py b/ranzen/torch/optimizers/lamb.py index ec07e7fd..3614ff11 100644 --- a/ranzen/torch/optimizers/lamb.py +++ b/ranzen/torch/optimizers/lamb.py @@ -87,8 +87,7 @@ def step(self, closure: LossClosure | None = None) -> Tensor | None: grad = p.grad.data if grad.is_sparse: msg = ( - "Lamb does not support sparse gradients, " - "please consider SparseAdam instead" + "Lamb does not support sparse gradients, please consider SparseAdam instead" ) raise RuntimeError(msg) diff --git a/ranzen/torch/transforms/mixup.py b/ranzen/torch/transforms/mixup.py index d409bcb5..cda87230 100644 --- a/ranzen/torch/transforms/mixup.py +++ b/ranzen/torch/transforms/mixup.py @@ -325,7 +325,8 @@ def _transform( if groups_or_edges.ndim == 1: if len(groups_or_edges) != batch_size: raise ValueError( - "The number of elements in 'groups_or_edges' should match the size of dimension 0 of 'inputs'." + "The number of elements in 'groups_or_edges' should match the size of " + "dimension 0 of 'inputs'." ) groups = groups_or_edges.view(batch_size, 1) # [batch_size] @@ -357,8 +358,10 @@ def _transform( num_selected = len(is_connected) if num_selected < len(degrees): warnings.warn( - "One or more samples without valid pairs according to " - "the connectivity defined by 'groups_or_edges'.", + ( + "One or more samples without valid pairs according to " + "the connectivity defined by 'groups_or_edges'." + ), RuntimeWarning, ) # If there are no valid pairs (all vertices are isolated), there's nothing to do. @@ -411,8 +414,8 @@ def _transform( if num_classes is None: if self.num_classes is None: raise RuntimeError( - f"{self.__class__.__name__} can only be applied to label-encoded targets if " - "'num_classes' is specified." + f"{self.__class__.__name__} can only be applied to label-encoded targets " + "if 'num_classes' is specified." ) num_classes = self.num_classes elif num_classes < 1: diff --git a/tests/relay_test.py b/tests/relay_test.py index ca102927..c4055944 100644 --- a/tests/relay_test.py +++ b/tests/relay_test.py @@ -29,7 +29,6 @@ class DummyOptionB(DummyOption): @dataclass class DummyRelay(Relay): - attr1: DummyOption attr2: DummyOption From 78732db0f7abe6037c0ca7737401447fff5416ac Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 6 Feb 2023 06:09:31 +0000 Subject: [PATCH 11/24] Bump pandas-stubs from 1.5.2.230105 to 1.5.3.230203 Bumps [pandas-stubs](https://github.com/pandas-dev/pandas-stubs) from 1.5.2.230105 to 1.5.3.230203. - [Release notes](https://github.com/pandas-dev/pandas-stubs/releases) - [Changelog](https://github.com/pandas-dev/pandas-stubs/blob/main/docs/release_procedure.md) - [Commits](https://github.com/pandas-dev/pandas-stubs/compare/v1.5.2.230105...v1.5.3.230203) --- updated-dependencies: - dependency-name: pandas-stubs dependency-type: direct:development update-type: version-update:semver-patch ... 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Bartlett <44702700+MylesBartlett@users.noreply.github.com> Date: Mon, 6 Feb 2023 16:21:39 +0000 Subject: [PATCH 13/24] Allow `RandomCutMix` to use conditional pair sampling like `RandomMixUp`. (#194) * Add `deprecated` decorator and use it to deprecate `implements`. 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"mypy", + "numpy", + "omegaconf", + "pandas", + "pylint", + "pytest", + "scipy", + "setuptools", + "sklearn", + "torch", + "tqdm", + "wandb", +] +extra-standard-library = ["__future__", "typing_extensions"] +force-sort-within-sections = true +split-on-trailing-comma = false +classes = [ + "SAM", + "LAMB", +] [tool.ruff.flake8-annotations] mypy-init-return = true diff --git a/ranzen/decorators.py b/ranzen/decorators.py index e8e7038c..8b39f031 100644 --- a/ranzen/decorators.py +++ b/ranzen/decorators.py @@ -1,17 +1,113 @@ -"""Functions related to typing.""" +"""Decorator functions.""" from __future__ import annotations from enum import Enum -from typing import Any, Callable, TypeVar, get_type_hints +from functools import partial +import inspect +from typing import Any, Callable, Protocol, TypeVar, cast, get_type_hints, overload -__all__ = ["enum_name_str", "implements", "parsable"] +from loguru import logger +import wrapt # pyright: ignore +from ranzen.misc import some + +__all__ = [ + "deprecated", + "enum_name_str", + "implements", + "parsable", +] + + +_T = TypeVar("_T") _F = TypeVar("_F", bound=Callable[..., Any]) +class IdentityFunction(Protocol[_T]): + def __call__(self, __x: _T) -> _T: + ... + + +# Remember which deprecation warnings have been printed already. +_PRINTED_WARNING = {} + + +@overload +def deprecated( + wrapped: _F, + /, + *, + version: str | None = ..., + explanation: str | None = ..., +) -> _F: + ... + + +@overload +def deprecated( + wrapped: None = ..., + /, + *, + version: str | None = ..., + explanation: str | None = ..., +) -> IdentityFunction: + ... + + +def deprecated( + wrapped: _F | None = None, + /, + *, + version: str | None = None, + explanation: str | None = None, +) -> _F | IdentityFunction: + """ + Decorator which can be used for indicating that a function/class is deprecated and going to be removed. + Tracks down which function/class printed the warning and will print it only once per call. + + :param wrapped: Function/class to be marked as deprecated. + :param version: Version in which the function/class will be removed.. + :param explanation: Additional explanation, e.g. "Please, ``use another_function`` instead." . + + :returns: Function/class wrapped with a deprecation warning. + """ + + if wrapped is None: + return partial(deprecated, version=version, explanation=explanation) + + @wrapt.decorator + def wrapper(wrapped: _F, *args: Any, **kwargs: Any) -> _F: # pyright: ignore + # Check if we already warned about the given function/class. + if wrapped.__name__ not in _PRINTED_WARNING.keys(): + # Add to list so we won't log it again. + _PRINTED_WARNING[wrapped.__name__] = True + + # Prepare the warning message. + entity_name = "Class" if inspect.isclass(wrapped) else "Function" + msg = f"{entity_name} '{wrapped.__name__}' is deprecated" + + # Optionally, add version and explanation. + if some(version): + msg = f"{msg} and will be removed in version {version}" + + msg = f"{msg}." + if some(explanation): + msg = f"{msg} {explanation}" + + # Display the deprecated warning. + logger.warning(msg) + + # Call the function/initialise the class. + return cast(_F, wrapped) + + return wrapper(wrapped) + + +@deprecated(explanation="Use 'typing_extensions.override' instead.") class implements: # pylint: disable=invalid-name """Mark a function as implementing an interface. - Deprecated. Use ``typing_extensions.override`` instead. + .. warning:: + This decorator is deprecated in favour of :func:`typing_extensions.override` instead. """ def __init__(self, interface: type): diff --git a/ranzen/misc.py b/ranzen/misc.py index 785d62a4..1749d634 100644 --- a/ranzen/misc.py +++ b/ranzen/misc.py @@ -165,7 +165,12 @@ def __new__(cls: Type[_S], *values: str) -> _S: def __str__(self) -> str: return str.__str__(self) - def _generate_next_value_(name: str, start: int, count: int, last_values: list[Any]) -> str: + def _generate_next_value_( + name: str, + start: int, # pyright: ignore + count: int, # pyright: ignore + last_values: list[Any], # pyright: ignore + ) -> str: """ Return the lower-cased version of the member name. """ diff --git a/ranzen/torch/module.py b/ranzen/torch/module.py index 2c4787ef..4297842d 100644 --- a/ranzen/torch/module.py +++ b/ranzen/torch/module.py @@ -11,7 +11,7 @@ @dataclass(unsafe_hash=True) class DcModule(nn.Module): @final - def __new__(cls, *args: Any, **kwargs: Any) -> Self: + def __new__(cls, *args: Any, **kwargs: Any) -> Self: # pyright: ignore obj = object.__new__(cls) nn.Module.__init__(obj) return obj diff --git a/ranzen/torch/transforms/__init__.py b/ranzen/torch/transforms/__init__.py index 507efb61..3b8b5c24 100644 --- a/ranzen/torch/transforms/__init__.py +++ b/ranzen/torch/transforms/__init__.py @@ -1,2 +1,3 @@ from .cutmix import * from .mixup import * +from .utils import * diff --git a/ranzen/torch/transforms/cutmix.py b/ranzen/torch/transforms/cutmix.py index 97104915..b4b9c072 100644 --- a/ranzen/torch/transforms/cutmix.py +++ b/ranzen/torch/transforms/cutmix.py @@ -8,6 +8,7 @@ from ranzen.torch.sampling import batched_randint from ranzen.torch.transforms.mixup import InputsTargetsPair +from ranzen.torch.transforms.utils import sample_paired_indices __all__ = ["RandomCutMix"] @@ -31,10 +32,10 @@ def __init__( self, alpha: float = 1.0, *, - p: float = 0.5, - num_classes: int | None = None, + p: float = 1.0, + num_classes: Optional[int] = None, inplace: bool = False, - seed: Optional[int] = None, + generator: Optional[torch.Generator] = None, ) -> None: """ :param alpha: hyperparameter of the Beta distribution used for sampling the areas @@ -48,11 +49,12 @@ def __init__( :param inplace: Whether the transform should be performed in-place. - :param seed: The PRNG seed to use for sampling pairs and bounding-box coordinates. + :param generator: Pseudo-random-number generator to use for sampling. Note that + :class:`torch.distributions.Beta` does not accept such generator object and so + the sampling procedure is only partially deterministic as a function of it. :raises ValueError: if ``p`` is not in the range [0, 1] , if ``num_classes < 1``, or if ``alpha`` is not a positive real number. - """ super().__init__() if not 0 <= p <= 1: @@ -66,7 +68,7 @@ def __init__( self.lambda_sampler = td.Beta(concentration0=alpha, concentration1=alpha) self.num_classes = num_classes self.inplace = inplace - self.seed = seed + self.generator = generator def _sample_masks( self, @@ -97,7 +99,7 @@ def _sample_masks( x_indices >= box_coords_x1.unsqueeze(-1) ) masks = ( - (y_masks.unsqueeze(1) * x_masks.unsqueeze(-1)) + (y_masks.unsqueeze(-1) * x_masks.unsqueeze(1)) .unsqueeze(1) .expand(-1, inputs.size(1), -1, -1) ) @@ -106,15 +108,37 @@ def _sample_masks( return masks, cropped_area_ratios @overload - def _transform(self, inputs: Tensor, *, targets: Tensor) -> InputsTargetsPair: + def _transform( + self, + inputs: Tensor, + *, + targets: Tensor, + groups_or_edges: Tensor | None = ..., + cross_group: bool = ..., + num_classes: int | None = None, + ) -> InputsTargetsPair: ... @overload - def _transform(self, inputs: Tensor, *, targets: None = ...) -> Tensor: + def _transform( + self, + inputs: Tensor, + *, + targets: None = ..., + groups_or_edges: Tensor | None = ..., + cross_group: bool = ..., + num_classes: int | None = None, + ) -> Tensor: ... def _transform( - self, inputs: Tensor, *, targets: Tensor | None = None + self, + inputs: Tensor, + *, + targets: Tensor | None = None, + groups_or_edges: Tensor | None = None, + cross_group: bool = False, + num_classes: int | None = None, ) -> Tensor | InputsTargetsPair: if inputs.ndim != 4: raise ValueError(f"'inputs' must be a batch of image tensors of shape (C, H, W).") @@ -122,76 +146,109 @@ def _transform( if (targets is not None) and (batch_size != len(targets)): raise ValueError(f"'inputs' and 'targets' must match in size at dimension 0.") - generator = ( - None if self.seed is None else torch.Generator(inputs.device).manual_seed(self.seed) + index_pairs = sample_paired_indices( + inputs=inputs, + p=self.p, + groups_or_edges=groups_or_edges, + cross_group=cross_group, ) - if (batch_size == 1) or (self.p == 0): + if index_pairs is None: return inputs if targets is None else InputsTargetsPair(inputs=inputs, targets=targets) - elif self.p < 1: - # Sample a mask determining which samples in the batch are to be transformed - selected = torch.rand(batch_size, device=inputs.device, generator=generator) < self.p - num_selected = int(selected.count_nonzero()) - if num_selected == 0: - return ( - inputs if targets is None else InputsTargetsPair(inputs=inputs, targets=targets) - ) - indices = selected.nonzero(as_tuple=False).long().flatten() - # If p >= 1 then the transform is always applied and we can skip the sampling step above. - else: - num_selected = batch_size - indices = torch.arange(batch_size, device=inputs.device, dtype=torch.long) - - # Pair each selected sample with another sample that will serve as the 'patch donor' - pair_indices = torch.arange(num_selected).roll(1, 0) + num_selected = len(index_pairs) + anchor_indices = index_pairs.anchors + match_indices = index_pairs.matches masks, cropped_area_ratios = self._sample_masks( - inputs=inputs, num_samples=num_selected, generator=generator + inputs=inputs, num_samples=num_selected, generator=self.generator ) if not self.inplace: inputs = inputs.clone() # Trnasplant patches from the paired images to the anchor images as determined by the masks. - inputs[indices] = ~masks * inputs[indices] + masks * inputs[pair_indices] + inputs[anchor_indices] = ~masks * inputs[anchor_indices] + masks * inputs[match_indices] # No targets were recevied so we're done. if targets is None: return inputs # Targets are label-encoded and need to be one-hot encoded prior to mixup. if torch.atleast_1d(targets.squeeze()).ndim == 1: - if self.num_classes is None: - raise RuntimeError( - f"{self.__class__.__name__} can only be applied to label-encoded targets if " - "'num_classes' is specified." - ) - targets = cast(Tensor, F.one_hot(targets, num_classes=self.num_classes)) - elif not self.inplace: - targets = targets.clone() + if num_classes is None: + if self.num_classes is None: + raise RuntimeError( + f"{self.__class__.__name__} can only be applied to label-encoded targets " + "if 'num_classes' is specified." + ) + num_classes = self.num_classes + elif num_classes < 1: + raise ValueError(f"{ num_classes } must be a positive integer.") + if num_classes > 2: + targets = cast(Tensor, F.one_hot(targets.long(), num_classes=num_classes)) # Targets need to be floats to be mixed up. targets = targets.float() target_lambdas = 1.0 - cropped_area_ratios - target_lambdas.unsqueeze_(-1) - targets[indices] *= target_lambdas - targets[indices] += (1.0 - target_lambdas) * targets[pair_indices] + if targets.ndim > 1: + target_lambdas.unsqueeze_(-1) + targets[anchor_indices] *= target_lambdas + targets[anchor_indices] += (1.0 - target_lambdas) * targets[match_indices] return InputsTargetsPair(inputs=inputs, targets=targets) @overload - def __call__(self, inputs: Tensor, *, targets: Tensor) -> InputsTargetsPair: + def __call__( + self, + inputs: Tensor, + *, + targets: Tensor, + groups_or_edges: Tensor | None = ..., + cross_group: bool = ..., + num_classes: int | None = ..., + ) -> InputsTargetsPair: ... @overload - def __call__(self, inputs: Tensor, *, targets: None = ...) -> Tensor: + def __call__( + self, + inputs: Tensor, + *, + targets: None = ..., + groups_or_edges: Tensor | None = ..., + cross_group: bool = ..., + num_classes: int | None = ..., + ) -> Tensor: ... def __call__( - self, inputs: Tensor, *, targets: Tensor | None = None + self, + inputs: Tensor, + *, + targets: Tensor | None = None, + groups_or_edges: Tensor | None = None, + cross_group: bool = True, + num_classes: int | None = None, ) -> Tensor | InputsTargetsPair: """ :param inputs: The samples to apply mixup to. :param targets: The corresponding targets to apply mixup to. If the targets are label-encoded then the 'num_classes' attribute cannot be None. + :param groups_or_edges: Labels indicating which group each sample belongs to or + a boolean connectivity matrix encoding the permissibility of each possible pairing. + In the case of the former, cutmix pairs will be sampled in a cross-group fashion (only + samples belonging to different groups will be paired for mixup) if ``cross_group`` is + ``True`` and sampled in a within-group fashion (only sampled belonging to the same + groups will be paired for cutmix) otherwise. + :param cross_group: Whether to sample cutmix pairs in a cross-group (``True``) or + within-group (``False``) fashion (see ``groups_or_edges``). + :param num_classes: The total number of classes in the dataset that needs to be specified if + wanting to mix up targets that are label-enoded. Passing label-encoded targets without + specifying ``num_classes`` will result in a RuntimeError. :return: If target is None, the Tensor of cutmix-transformed inputs. If target is not None, a namedtuple containing the Tensor of cutmix-transformed inputs (inputs) and the corresponding Tensor of cutmix-transformed targets (targets). """ - return self._transform(inputs=inputs, targets=targets) + return self._transform( + inputs=inputs, + targets=targets, + groups_or_edges=groups_or_edges, + cross_group=cross_group, + num_classes=num_classes, + ) diff --git a/ranzen/torch/transforms/mixup.py b/ranzen/torch/transforms/mixup.py index cda87230..47acc186 100644 --- a/ranzen/torch/transforms/mixup.py +++ b/ranzen/torch/transforms/mixup.py @@ -1,16 +1,14 @@ from __future__ import annotations from enum import Enum, auto -import operator from typing import Generic, NamedTuple, TypeVar, cast, overload -import warnings import torch from torch import Tensor import torch.distributions as td import torch.nn.functional as F -from ranzen.misc import str_to_enum -from ranzen.torch.sampling import batched_randint +from ranzen.misc import some, str_to_enum +from ranzen.torch.transforms.utils import sample_paired_indices __all__ = [ "MixUpMode", @@ -100,7 +98,7 @@ def __init__( if isinstance(mode, str): mode = str_to_enum(str_=mode, enum=MixUpMode) self.mode = mode - if (num_classes is not None) and num_classes < 1: + if some(num_classes) and num_classes < 1: raise ValueError(f"{ num_classes } must be greater than 1.") self.num_classes = num_classes self.featurewise = featurewise or isinstance(lambda_sampler, td.Bernoulli) @@ -289,108 +287,19 @@ def _transform( cross_group: bool = False, num_classes: int | None = None, ) -> Tensor | InputsTargetsPair: - batch_size = len(inputs) - # If the batch is singular or the sampling probability is 0 there's nothing to do. - if (batch_size == 1) or (self.p == 0): - if targets is None: - return inputs - return InputsTargetsPair(inputs=inputs, targets=targets) - elif self.p < 1: - # Sample a mask determining which samples in the batch are to be transformed - selected = ( - torch.rand(batch_size, device=inputs.device, generator=self.generator) < self.p - ) - num_selected = int(selected.count_nonzero()) - indices = selected.nonzero(as_tuple=False).long().flatten() - # if p >= 1 then the transform is always applied and we can skip - # the above step - else: - num_selected = batch_size - indices = torch.arange(batch_size, device=inputs.device, dtype=torch.long) - - if groups_or_edges is None: - # Sample the mixup pairs with the guarantee that a given sample will - # not be paired with itself - offset = torch.randint( - low=1, - high=batch_size, - size=(num_selected,), - device=inputs.device, - dtype=torch.long, - generator=self.generator, - ) - pair_indices = (indices + offset) % batch_size - else: - groups_or_edges = groups_or_edges.squeeze() - if groups_or_edges.ndim == 1: - if len(groups_or_edges) != batch_size: - raise ValueError( - "The number of elements in 'groups_or_edges' should match the size of " - "dimension 0 of 'inputs'." - ) - - groups = groups_or_edges.view(batch_size, 1) # [batch_size] - # Compute the pairwise indicator matrix, indicating whether any two samples - # belong to the same group (0) or different groups (1) - comp = operator.ne if cross_group else operator.eq - connections = comp(groups[indices], groups.t()) # [num_selected, batch_size] - if not cross_group: - # Fill the diagonal with 'False' to prevent self-matches. - connections.fill_diagonal_(False) - # For each sample, compute how many other samples there are that belong - # to a different group. - elif groups_or_edges.ndim == 2: - if groups_or_edges.dtype is not torch.bool: - raise ValueError( - "If 'groups_or_edges' is a matrix, it must have dtype 'torch.bool'." - ) - connections = groups_or_edges[indices] - # Fill the diagonal with 'False' to prevent self-matches. - connections.fill_diagonal_(False) - else: - raise ValueError( - "'groups_or_edges' must be a vector denoting group membership or a boolean-type" - "groups_or_edges matrix with elements denoting the permissibility of each" - "possible pairing in 'inputs'." - ) - degrees = connections.count_nonzero(dim=1) # [num_selected] - is_connected = degrees.nonzero().squeeze(-1) - num_selected = len(is_connected) - if num_selected < len(degrees): - warnings.warn( - ( - "One or more samples without valid pairs according to " - "the connectivity defined by 'groups_or_edges'." - ), - RuntimeWarning, - ) - # If there are no valid pairs (all vertices are isolated), there's nothing to do. - if num_selected == 0: - if targets is None: - return inputs - return InputsTargetsPair(inputs=inputs, targets=targets) - # Update the tensors to account for pairless samples. - indices = indices[is_connected] - degrees = degrees[is_connected] - connections = connections[is_connected] - # Sample the mixup pairs in accordance with the connectivity matrix. - # This can be efficiently done as follows: - # 1) Sample uniformly from {0, ..., diff_group_count - 1} to obtain the groupwise pair indices. - # This involves first drawing samples from the standard uniform distribution, rescaling them to - # [-1/(2*diff_group_count), diff_group_count + (1/(2*diff_group_count)], and then clamping them - # to [0, 1], making it so that 0 and diff_group_count have the same probability of being drawn - # as any other value. The uniform samples are then mapped to indices by multiplying by - # diff_group_counts and rounding. 'randint' is unsuitable here because the groups aren't - # guaranteed to have equal cardinality (using it to sample from the cyclic group, - # Z / diff_group_count Z, as above, leads to biased sampling). - rel_pair_indices = batched_randint(degrees, generator=self.generator) - # 2) Convert the row-wise indices into row-major indices, considering only - # only the postive entries in the rows. - rel_pair_indices[1:] += degrees.cumsum(dim=0)[:-1] - # 3) Finally, map from relative indices to absolute ones. - _, abs_pos_inds = connections.nonzero(as_tuple=True) - pair_indices = abs_pos_inds[rel_pair_indices] + sampled_index_pairs = sample_paired_indices( + inputs=inputs, + p=self.p, + groups_or_edges=groups_or_edges, + cross_group=cross_group, + generator=self.generator, + ) + if sampled_index_pairs is None: + return inputs if targets is None else InputsTargetsPair(inputs=inputs, targets=targets) + num_selected = len(sampled_index_pairs) + anchor_indices = sampled_index_pairs.anchors + match_indices = sampled_index_pairs.matches # Sample the mixing weights if self.featurewise: sample_shape = (num_selected, *inputs.shape[1:]) @@ -403,8 +312,8 @@ def _transform( if not self.inplace: inputs = inputs.clone() # Apply mixup to the inputs - inputs[indices] = self._mix( - tensor_a=inputs[indices], tensor_b=inputs[pair_indices], lambda_=lambdas + inputs[anchor_indices] = self._mix( + tensor_a=inputs[anchor_indices], tensor_b=inputs[match_indices], lambda_=lambdas ) if targets is None: return inputs @@ -420,8 +329,8 @@ def _transform( num_classes = self.num_classes elif num_classes < 1: raise ValueError(f"{ num_classes } must be a positive integer.") - elif num_classes > 2: - targets = cast(Tensor, F.one_hot(targets, num_classes=num_classes)) + if num_classes > 2: + targets = cast(Tensor, F.one_hot(targets.long(), num_classes=num_classes)) elif not self.inplace: targets = targets.clone() # Targets need to be floats to be mixed up @@ -434,8 +343,10 @@ def _transform( # Add singular dimensions to lambdas for broadcasting target_lambdas = lambdas.view(num_selected, *((1,) * (targets.ndim - 1))) # Apply mixup to the targets - targets[indices] = self._mix( - tensor_a=targets[indices], tensor_b=targets[pair_indices], lambda_=target_lambdas + targets[anchor_indices] = self._mix( + tensor_a=targets[anchor_indices], + tensor_b=targets[match_indices], + lambda_=target_lambdas, ) return InputsTargetsPair(inputs, targets) diff --git a/ranzen/torch/transforms/utils.py b/ranzen/torch/transforms/utils.py new file mode 100644 index 00000000..b73e7736 --- /dev/null +++ b/ranzen/torch/transforms/utils.py @@ -0,0 +1,131 @@ +from __future__ import annotations +import operator +from typing import NamedTuple +import warnings + +import torch +from torch import Tensor + +from ranzen.torch.sampling import batched_randint + +__all__ = [ + "sample_paired_indices", + "PairedIndices", +] + + +class PairedIndices(NamedTuple): + anchors: Tensor + matches: Tensor + + def __len__(self) -> int: + return len(self.anchors) + + +def sample_paired_indices( + inputs: Tensor, + *, + p: float, + groups_or_edges: Tensor | None = None, + cross_group: bool = False, + generator: torch.Generator | None = None, +) -> PairedIndices | None: + batch_size = len(inputs) + # If the batch is singular or the sampling probability is 0 there's nothing to do. + if (batch_size == 1) or (p == 0): + return None + if p < 1: + # Sample a mask determining which samples in the batch are to be transformed + selected = torch.rand(batch_size, device=inputs.device, generator=generator) < p + num_selected = int(selected.count_nonzero()) + indices = selected.nonzero(as_tuple=False).long().flatten() + # if p >= 1 then the transform is always applied and we can skip + # the above step + else: + num_selected = batch_size + indices = torch.arange(batch_size, device=inputs.device, dtype=torch.long) + + if groups_or_edges is None: + # Sample the mixup pairs with the guarantee that a given sample will + # not be paired with itself + offset = torch.randint( + low=1, + high=batch_size, + size=(num_selected,), + device=inputs.device, + dtype=torch.long, + generator=generator, + ) + pair_indices = (indices + offset) % batch_size + else: + groups_or_edges = groups_or_edges.squeeze() + if groups_or_edges.ndim == 1: + if len(groups_or_edges) != batch_size: + raise ValueError( + "The number of elements in 'groups_or_edges' should match the size of " + "dimension 0 of 'inputs'." + ) + + groups = groups_or_edges.view(batch_size, 1) # [batch_size] + # Compute the pairwise indicator matrix, indicating whether any two samples + # belong to the same group (0) or different groups (1) + comp = operator.ne if cross_group else operator.eq + connections = comp(groups[indices], groups.t()) # [num_selected, batch_size] + if not cross_group: + # Fill the diagonal with 'False' to prevent self-matches. + connections.fill_diagonal_(False) + # For each sample, compute how many other samples there are that belong + # to a different group. + elif groups_or_edges.ndim == 2: + if groups_or_edges.dtype is not torch.bool: + raise ValueError( + "If 'groups_or_edges' is a matrix, it must have dtype 'torch.bool'." + ) + connections = groups_or_edges[indices] + # Fill the diagonal with 'False' to prevent self-matches. + connections.fill_diagonal_(False) + else: + raise ValueError( + "'groups_or_edges' must be a vector denoting group membership or a boolean-type" + "groups_or_edges matrix with elements denoting the permissibility of each" + "possible pairing in 'inputs'." + ) + degrees = connections.count_nonzero(dim=1) # [num_selected] + is_connected = degrees.nonzero().squeeze(-1) + num_selected = len(is_connected) + if num_selected < len(degrees): + warnings.warn( + ( + "One or more samples without valid pairs according to " + "the connectivity defined by 'groups_or_edges'." + ), + RuntimeWarning, + ) + # If there are no valid pairs (all vertices are isolated), there's nothing to do. + if num_selected == 0: + return None + # Update the tensors to account for pairless samples. + indices = indices[is_connected] + degrees = degrees[is_connected] + connections = connections[is_connected] + # Sample the mixup pairs in accordance with the connectivity matrix. + # This can be efficiently done as follows: + # 1) Sample uniformly from {0, ..., diff_group_count - 1} to obtain the groupwise pair indices. + # This involves first drawing samples from the standard uniform distribution, rescaling them to + # [-1/(2*diff_group_count), diff_group_count + (1/(2*diff_group_count)], and then clamping them + # to [0, 1], making it so that 0 and diff_group_count have the same probability of being drawn + # as any other value. The uniform samples are then mapped to indices by multiplying by + # diff_group_counts and rounding. 'randint' is unsuitable here because the groups aren't + # guaranteed to have equal cardinality (using it to sample from the cyclic group, + # Z / diff_group_count Z, as above, leads to biased sampling). + rel_pair_indices = batched_randint(degrees, generator=generator) + # 2) Convert the row-wise indices into row-major indices, considering only + # only the postive entries in the rows. + rel_pair_indices[1:] += degrees.cumsum(dim=0)[:-1] + # 3) Finally, map from relative indices to absolute ones. + _, abs_pos_inds = connections.nonzero(as_tuple=True) + pair_indices = abs_pos_inds[rel_pair_indices] + return PairedIndices( + anchors=indices, + matches=pair_indices, + ) diff --git a/ranzen/torch/utils.py b/ranzen/torch/utils.py index 80597a29..55e84281 100644 --- a/ranzen/torch/utils.py +++ b/ranzen/torch/utils.py @@ -76,7 +76,7 @@ class Event: def __init__(self) -> None: self.time = 0.0 self._cuda = torch.cuda.is_available() - self._event_start: torch.cuda.Event | datetime # type: ignore + self._event_start: torch.cuda.Event | datetime def __enter__(self) -> Event: """Mark a time. @@ -84,18 +84,18 @@ def __enter__(self) -> Event: Mimics torch.cuda.Event. """ if self._cuda: - self._event_start = torch.cuda.Event(enable_timing=True) # type: ignore + self._event_start = torch.cuda.Event(enable_timing=True) self._event_start.record() else: self._event_start = datetime.now() return self - def __exit__(self, *args: Any) -> None: + def __exit__(self, *args: Any) -> None: # pyright: ignore if self._cuda: - event_end = torch.cuda.Event(enable_timing=True) # type: ignore + event_end = torch.cuda.Event(enable_timing=True) event_end.record(stream=torch.cuda.current_stream()) torch.cuda.synchronize() - assert isinstance(self._event_start, torch.cuda.Event) # type: ignore + assert isinstance(self._event_start, torch.cuda.Event) self.time = self._event_start.elapsed_time(event_end) else: assert isinstance(self._event_start, datetime) diff --git a/ranzen/wandb.py b/ranzen/wandb.py index 631c699c..30241aa0 100644 --- a/ranzen/wandb.py +++ b/ranzen/wandb.py @@ -3,6 +3,7 @@ from functools import lru_cache from typing import Sequence +from loguru import logger import pandas as pd import wandb @@ -38,8 +39,8 @@ def groups(self, *groups_: str) -> pd.DataFrame: path = f"{self.entity}/{self.project}" dfs = [] for group in groups_: - runs = self.api.runs(path, filters={"group": group}) # type: ignore - print(f"'{group}': found {len(runs)} runs.") + runs = self.api.runs(path, filters={"group": group}) + logger.info(f"'{group}': found {len(runs)} runs.") dfs.append(self._runs_to_df(runs)) return pd.concat(dfs, axis=1, sort=False, keys=list(groups_)) @@ -51,12 +52,12 @@ def modify_config( This is not possible with the web UI. """ path = f"{self.entity}/{self.project}" - runs = self.api.runs(path, filters={"group": group}) # type: ignore + runs = self.api.runs(path, filters={"group": group}) i = 0 for i, run in enumerate(runs, start=1): run.config[config_key] = new_value run.update() - print(f"Changed config for {i} runs.") + logger.info(f"Changed config for {i} runs.") @staticmethod def _runs_to_df(runs: Sequence[wandb.wandb_run.Run]) -> pd.DataFrame: # type: ignore diff --git a/tests/decorator_test.py b/tests/decorator_test.py index 2175fac2..54404052 100644 --- a/tests/decorator_test.py +++ b/tests/decorator_test.py @@ -5,59 +5,42 @@ import pytest -from ranzen import enum_name_str, implements, parsable +from ranzen import deprecated, enum_name_str, parsable -def test_implements() -> None: - """Test the implements decorator.""" - - class BaseClass: - def func(self) -> None: - """Do nothing.""" - - def no_docstring(self) -> None: - pass - - class CorrectImplementation(BaseClass): - @implements(BaseClass) - def func(self) -> None: - pass - - with pytest.raises(AssertionError): - - class IncorrectImplementation(BaseClass): - @implements(BaseClass) - def wrong_func(self) -> None: - pass - - # with pytest.raises(AssertionError): +@pytest.mark.parametrize("explanation", [None, "All things that have form eventually decay."]) +@pytest.mark.parametrize("version", [None, "4.2"]) +def test_deprecated(explanation: str | None, version: str | None) -> None: + @deprecated(explanation=explanation, version=version) + class Foo: # pyright: ignore + def __init__(self) -> None: + ... - class NoDocstringImpl(BaseClass): - @implements(BaseClass) - def no_docstring(self) -> None: - pass + @deprecated(explanation=explanation, version=version) + def foo() -> None: # pyright: ignore + ... def test_parsable() -> None: - class Foo: + class Foo: # pyright: ignore @parsable def __init__(self) -> None: ... def test_parsable_valid() -> None: - class Foo: + class Foo: # pyright: ignore @parsable - def __init__(self, a: int, b: Union[int, float], c: List[str]): + def __init__(self, a: int, b: Union[int, float], c: List[str]): # pyright: ignore ... def test_parsable_invalid_union() -> None: with pytest.raises(ValueError): - class Foo: + class Foo: # pyright: ignore @parsable - def __init__(self, a: int, b: int | float, c: List[str]): + def __init__(self, a: int, b: int | float, c: List[str]): # pyright: ignore ... diff --git a/tests/torch_transforms_test.py b/tests/torch_transforms_test.py index d5b9f13a..45c912ac 100644 --- a/tests/torch_transforms_test.py +++ b/tests/torch_transforms_test.py @@ -7,7 +7,7 @@ from torch import Tensor import torch.nn.functional as F -from ranzen.torch.transforms import MixUpMode, RandomMixUp +from ranzen.torch.transforms import MixUpMode, RandomCutMix, RandomMixUp BATCH_SIZE: Final[int] = 20 NUM_CLASSES: Final[int] = 5 @@ -45,6 +45,7 @@ def test_mixup( ) if one_hot: targets = cast(Tensor, F.one_hot(targets, num_classes=num_classes)) + targets = targets.float() if num_groups is None: groups_or_edges = None else: @@ -110,3 +111,98 @@ def test_mixup( else: assert targets is None assert res.shape == inputs.shape + + +@pytest.mark.parametrize("alpha", [0.2, 1]) +@pytest.mark.parametrize("p", [0.8, 1]) +@pytest.mark.parametrize("one_hot", [True, False]) +@pytest.mark.parametrize("num_classes", [2, 3, None]) +@pytest.mark.parametrize("num_groups", [2, None]) +@pytest.mark.parametrize("input_shape", [(3, 28, 28), (3, 16, 19)]) +@pytest.mark.parametrize("cross_group", [True, False]) +@pytest.mark.parametrize("edges", [True, False]) +def test_cutmix( + alpha: float, + p: float, + one_hot: bool, + num_classes: int | None, + num_groups: int | None, + input_shape: tuple[int, ...], + cross_group: bool, + edges: bool, +) -> None: + generator = torch.Generator().manual_seed(47) + inputs = torch.randn(BATCH_SIZE, *input_shape, generator=generator) + if num_classes is None: + targets = None + else: + targets = torch.randint( + low=0, high=num_classes, size=(BATCH_SIZE,), dtype=torch.long, generator=generator + ) + if one_hot: + targets = cast(Tensor, F.one_hot(targets, num_classes=num_classes)) + targets = targets.float() + if num_groups is None: + groups_or_edges = None + else: + groups_or_edges = torch.randint( + low=0, high=num_groups, size=(BATCH_SIZE,), dtype=torch.long + ) + if edges: + comp = operator.ne if cross_group else operator.eq + groups_or_edges = comp(groups_or_edges.unsqueeze(1), groups_or_edges) + + transform = RandomCutMix( + alpha=alpha, + num_classes=num_classes, + p=p, + generator=generator, + ) + res = transform( + inputs=inputs, + targets=targets, + groups_or_edges=groups_or_edges, + cross_group=cross_group, + ) + if isinstance(res, tuple): + assert targets is not None + mixed_inputs = res.inputs + mixed_targets = res.targets + assert len(mixed_inputs) == len(mixed_targets) == BATCH_SIZE + assert mixed_inputs.shape == inputs.shape + if mixed_targets.ndim == 1: + assert targets.shape == mixed_targets.shape + else: + assert mixed_targets.size(1) == num_classes + + # Check that setting rows to False in the connectivity matrix excludes samples. + if (groups_or_edges is not None) and edges and (p == 1.0): + groups_or_edges_t = groups_or_edges.clone() + groups_or_edges_t[[0, -1]] = False + inputs_mu = transform( + inputs=inputs, + targets=targets, + groups_or_edges=groups_or_edges_t, + num_classes=num_classes, + ).inputs + assert torch.allclose(inputs_mu[[0, -1]], inputs[[0, -1]]) + + if not one_hot: + with pytest.raises(RuntimeError): + transform.num_classes = None + transform( + inputs=inputs, + targets=targets, + groups_or_edges=groups_or_edges, + cross_group=cross_group, + ) + transform( + inputs=inputs, + targets=targets, + groups_or_edges=groups_or_edges, + cross_group=cross_group, + num_classes=num_classes, + ) + else: + assert targets is None + assert res.shape == inputs.shape From 23c7090fef88df6d20adf78bb084a5dcd0e406b3 Mon Sep 17 00:00:00 2001 From: Myles Bartlett <44702700+MylesBartlett@users.noreply.github.com> Date: Wed, 8 Feb 2023 18:25:32 +0000 Subject: [PATCH 14/24] Allow `prop_random_split` to operate on integers representing dataset length. (#195) * Allow to operate on integers representing dataset length. * Add additional return-type check to prs test. * Add ``DataclassInstance`` and ``is_td_instance`` to types.py. Use ``DataclassInstance`` to fix newly-introduced pyright error. --------- Co-authored-by: Myles Bartlett --- ranzen/hydra/utils.py | 4 ++- ranzen/torch/data.py | 59 +++++++++++++++++++++++++++++----------- ranzen/types.py | 48 ++++++++++++++++++++++++++++++-- run_darglint.sh | 5 ++++ tests/torch_data_test.py | 17 ++++++++++-- 5 files changed, 110 insertions(+), 23 deletions(-) create mode 100644 run_darglint.sh diff --git a/ranzen/hydra/utils.py b/ranzen/hydra/utils.py index 16086847..2def278b 100644 --- a/ranzen/hydra/utils.py +++ b/ranzen/hydra/utils.py @@ -12,6 +12,8 @@ from hydra.utils import instantiate from omegaconf import DictConfig, OmegaConf +from ranzen.types import DataclassInstance + __all__ = [ "GroupRegistration", "SchemaRegistration", @@ -32,7 +34,7 @@ def _clean_up_dict(obj: Any) -> Any: return obj -def as_pretty_dict(data_class: object) -> dict: +def as_pretty_dict(data_class: DataclassInstance) -> dict: """Convert dataclass to a pretty dictionary.""" return _clean_up_dict(asdict(data_class)) diff --git a/ranzen/torch/data.py b/ranzen/torch/data.py index f4fd3d89..cd46cb77 100644 --- a/ranzen/torch/data.py +++ b/ranzen/torch/data.py @@ -8,7 +8,6 @@ Final, Generic, Iterator, - List, Literal, Protocol, Sequence, @@ -82,7 +81,7 @@ def __len__(self) -> int: @overload def prop_random_split( - dataset: D, + dataset_or_size: D, *, props: Sequence[float] | float, as_indices: Literal[False] = ..., @@ -93,31 +92,55 @@ def prop_random_split( @overload def prop_random_split( - dataset: SizedDataset, + dataset_or_size: SizedDataset, *, props: Sequence[float] | float, as_indices: Literal[True], seed: int | None = ..., -) -> List[list[int]]: +) -> list[list[int]]: + ... + + +@overload +def prop_random_split( + dataset_or_size: int, + *, + props: Sequence[float] | float, + as_indices: bool = ..., + seed: int | None = ..., +) -> list[list[int]]: + ... + + +@overload +def prop_random_split( + dataset_or_size: D | int, + *, + props: Sequence[float] | float, + as_indices: bool = ..., + seed: int | None = ..., +) -> list[Subset[D]] | list[list[int]]: ... def prop_random_split( - dataset: D, + dataset_or_size: D | int, *, props: Sequence[float] | float, as_indices: bool = False, seed: int | None = None, -) -> list[Subset[D]] | List[List[int]]: +) -> list[Subset[D]] | list[list[int]]: """Splits a dataset based on proportions rather than on absolute sizes - :param dataset: Dataset to split. + :param dataset_or_size: Dataset or size (length) of the dataset to split. :param props: The fractional size of each subset into which to randomly split the data. Elements must be non-negative and sum to 1 or less; if less then the size of the final split will be computed by complement. :param as_indices: If ``True`` the raw indices are returned instead of subsets constructed - from them. + from them when `dataset_or_len` is a dataset. This means that when `dataset_or_len` + corresponds to the length of a dataset, this argument has no effect and + the function always returns the split indices. :param seed: The PRNG used for determining the random splits. @@ -125,13 +148,17 @@ def prop_random_split( :raises ValueError: If the dataset does not have a ``__len__`` method or sum(props) > 1. """ - if not hasattr(dataset, "__len__"): - raise ValueError( - "Split proportions can only be computed for datasets with __len__ defined." - ) + if isinstance(dataset_or_size, int): + len_ = dataset_or_size + else: + if not hasattr(dataset_or_size, "__len__"): + raise ValueError( + "Split proportions can only be computed for datasets with __len__ defined." + ) + len_ = len(dataset_or_size) + if isinstance(props, float): props = [props] - len_ = len(dataset) sum_ = np.sum(props) if (sum_ > 1.0) or any(prop < 0 for prop in props): raise ValueError("Values for 'props` must be positive and sum to 1 or less.") @@ -145,9 +172,9 @@ def prop_random_split( for offset, length in zip(np.cumsum(section_sizes), section_sizes) ] - if not as_indices: - return [Subset(dataset, indices=split) for split in splits] - return splits + if as_indices or isinstance(dataset_or_size, int): + return splits + return [Subset(dataset_or_size, indices=split) for split in splits] S = TypeVar("S") diff --git a/ranzen/types.py b/ranzen/types.py index 7baa0fb7..70f17f0c 100644 --- a/ranzen/types.py +++ b/ranzen/types.py @@ -1,7 +1,14 @@ -from typing import TypeVar -from typing_extensions import Protocol, Self, runtime_checkable +from __future__ import annotations +from dataclasses import Field +from typing import Any, ClassVar, TypedDict, TypeVar, get_type_hints +from typing_extensions import Protocol, Self, TypeGuard, runtime_checkable -__all__ = ["Sized", "Addable"] +__all__ = [ + "Addable", + "DataclassInstance", + "Sized", + "is_td_instance", +] T_co = TypeVar("T_co", covariant=True) @@ -17,3 +24,38 @@ def __len__(self) -> int: class Addable(Protocol[T_co]): def __add__(self, other: Self, /) -> Self: ... + + +@runtime_checkable +class DataclassInstance(Protocol): + __dataclass_fields__: ClassVar[dict[str, Field[Any]]] + + +TD = TypeVar("TD", bound=TypedDict) + + +def is_td_instance(dict_: dict[str, Any], cls_: type[TD], *, strict: bool = False) -> TypeGuard[TD]: + """``isinstance`` check for typed dictionaries. + + Returns ``True` if a dictionary conforms to a target ``TypedDict`` class. + When ``strict` is ``False`` 'conforms' means that ``dict_`` contains all + keys defined in ``cls_`` with values of the correct type -- additional keys + are permitted. When ``True``, ``dict_`` must contain those keys **only** + defined by ``cls_``. + + :param dict_: Dictionary to be checked. + :param cls_: ``TypedDict`` class to compare ``dict_` against. + + :param strict: Whether to invalidate a ``dict_`` for containing additional + keys not defined in ``cls_`` + + :returns: ``True`` if ``dict_`` conforms to the target ``TypedDict`` class, ``cls_``, + and ``False`` otherwise. + """ + hints = get_type_hints(cls_) + if strict and (len(dict_) != len(hints)): + return False + for key, type_ in hints.items(): + if (key not in dict_) or (not isinstance(dict_[key], type_)): + return False + return True diff --git a/run_darglint.sh b/run_darglint.sh new file mode 100644 index 00000000..4d1698f7 --- /dev/null +++ b/run_darglint.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +echo "Checking docstrings with darglint..." \ +&& darglint --docstring-style sphinx -v 2 -z long ranzen \ +&& echo "Check complete!" diff --git a/tests/torch_data_test.py b/tests/torch_data_test.py index 7cb4efb6..1bc95def 100644 --- a/tests/torch_data_test.py +++ b/tests/torch_data_test.py @@ -5,7 +5,7 @@ from torch.utils.data import TensorDataset from ranzen.torch import prop_random_split -from ranzen.torch.data import stratified_split_indices +from ranzen.torch.data import Subset, stratified_split_indices @pytest.fixture(scope="module") @@ -22,9 +22,20 @@ def test_prop_random_split( props_ls = [props] if isinstance(props, float) else props if sum_ > 1 or any(not (0 <= prop <= 1) for prop in props_ls): with pytest.raises(ValueError): - splits = prop_random_split(dataset=dummy_ds, props=props, as_indices=as_indices) # type: ignore + splits = prop_random_split(dataset_or_size=dummy_ds, props=props, as_indices=as_indices) else: - splits = prop_random_split(dataset=dummy_ds, props=props, as_indices=as_indices) # type: ignore + splits = prop_random_split(dataset_or_size=dummy_ds, props=props, as_indices=as_indices) + sizes = [len(split) for split in splits] + sum_sizes = sum(sizes) + assert len(splits) == (len(props_ls) + 1) + assert sum_sizes == len(dummy_ds) + assert sizes[-1] == (len(dummy_ds) - (round(sum_ * len(dummy_ds)))) + if not as_indices: + assert all(isinstance(split, Subset) for split in splits) + + splits = prop_random_split( + dataset_or_size=len(dummy_ds), props=props, as_indices=as_indices + ) sizes = [len(split) for split in splits] sum_sizes = sum(sizes) assert len(splits) == (len(props_ls) + 1) From 8b6693a7487609957e635c9b375fe55e042c7480 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 13 Feb 2023 07:02:00 +0000 Subject: [PATCH 15/24] Bump wandb from 0.13.9 to 0.13.10 Bumps [wandb](https://github.com/wandb/wandb) from 0.13.9 to 0.13.10. - [Release notes](https://github.com/wandb/wandb/releases) - [Changelog](https://github.com/wandb/wandb/blob/main/CHANGELOG.md) - [Commits](https://github.com/wandb/wandb/compare/v0.13.9...v0.13.10) --- updated-dependencies: - dependency-name: wandb dependency-type: direct:development update-type: version-update:semver-patch ... 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[[package]] name = "antlr4-python3-runtime" @@ -698,6 +698,7 @@ files = [ numpy = [ {version = ">=1.20.3", markers = "python_version < \"3.10\""}, {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, + {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, ] python-dateutil = ">=2.8.1" pytz = ">=2020.1" @@ -1400,5 +1401,5 @@ testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools" [metadata] lock-version = "2.0" -python-versions = ">=3.8.0,<3.11" -content-hash = "a6aff9ad98f4befdba067b6ea2faca4f57019828e28e336b19ea9453873dbd30" +python-versions = ">=3.8.0,<3.12" +content-hash = "830a3a75fb96705e96a36e1494d190c9a85f6fcd228b3a23e25ffcb60132ab17" diff --git a/pyproject.toml b/pyproject.toml index 8ce5c2d8..df3282ae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,11 +58,11 @@ readme="README.md" # do so with the --only option: ``poetry install --only main``. Equally, you can exclude one or # more groups with the --without option: ``poetry install --without torch``. [tool.poetry.dependencies] -python = ">=3.8.0,<3.11" +python = ">=3.8.0,<3.12" typing-extensions = ">=4.4.0" [tool.poetry.group.wandb.dependencies] -pandas = { version = "^1.4.3" } +pandas = { version = "^1.5.0" } wandb = { version= ">=0.12,<0.14" } [tool.poetry.group.logging.dependencies] @@ -71,10 +71,10 @@ loguru = "^0.6.0" [tool.poetry.group.torch.dependencies] torch = { version = ">=1.12.1" } -numpy = { version = "^1.21.4" } +numpy = { version = "^1.23.2" } [tool.poetry.group.hydra.dependencies] -hydra-core = { version = "^1.2.0" } +hydra-core = { version = "^1.3.0" } neoconfigen = { version = ">=2.3.3" } [tool.poetry.group.dev.dependencies] From d0f0690c5ca923b2958f68e6c9006e5a705fd76b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 16 Mar 2023 10:17:14 +0000 Subject: [PATCH 23/24] Bump pandas-stubs from 1.5.3.230214 to 1.5.3.230304 Bumps [pandas-stubs](https://github.com/pandas-dev/pandas-stubs) from 1.5.3.230214 to 1.5.3.230304. - [Release notes](https://github.com/pandas-dev/pandas-stubs/releases) - [Changelog](https://github.com/pandas-dev/pandas-stubs/blob/main/docs/release_procedure.md) - [Commits](https://github.com/pandas-dev/pandas-stubs/compare/v1.5.3.230214...v1.5.3.230304) --- updated-dependencies: - dependency-name: pandas-stubs dependency-type: direct:development update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- poetry.lock | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/poetry.lock b/poetry.lock index 06bd3daa..00a2beae 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. +# This file is automatically @generated by Poetry and should not be changed by hand. [[package]] name = "antlr4-python3-runtime" @@ -708,14 +708,14 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] [[package]] name = "pandas-stubs" -version = "1.5.3.230214" +version = "1.5.3.230304" description = "Type annotations for pandas" category = "dev" optional = false python-versions = ">=3.8,<3.12" files = [ - {file = "pandas_stubs-1.5.3.230214-py3-none-any.whl", hash = "sha256:be0f3056fef543c37248951a9868cba77e17ace3b815b0a23c72ed2ec13fbd9e"}, - {file = "pandas_stubs-1.5.3.230214.tar.gz", hash = "sha256:5fc1fc9c470d5332f9025a53ecab4c36cb80b2c52a596d597d6c5e33b4078153"}, + {file = "pandas_stubs-1.5.3.230304-py3-none-any.whl", hash = "sha256:ebb545329109e98510cdf6357074497dfbdf62f173d8ec8b5430486716360639"}, + {file = "pandas_stubs-1.5.3.230304.tar.gz", hash = "sha256:853174ea5aac1ee2001b3ae708dea9780c60d546ee490abb5eeb7c37ed57d255"}, ] [package.dependencies] From 384bf5ca0c4379725cd0551261bab30da2b9b905 Mon Sep 17 00:00:00 2001 From: Thomas M Kehrenberg Date: Thu, 16 Mar 2023 11:25:33 +0100 Subject: [PATCH 24/24] A few fixes (#205) * A few fixes * Update pypi classifiers --- README.md | 2 +- pyproject.toml | 1 + ranzen/misc.py | 4 ++-- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 1506543a..053cfe82 100644 --- a/README.md +++ b/README.md @@ -13,5 +13,5 @@ pip install ranzen or install directly from GitHub: ``` -pip install git+https://github.com/predictive-analytics-lab/ranzen.git@main +pip install git+https://github.com/wearepal/ranzen.git@main ``` diff --git a/pyproject.toml b/pyproject.toml index df3282ae..504788b1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,6 +45,7 @@ classifiers=[ "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Typing :: Typed", diff --git a/ranzen/misc.py b/ranzen/misc.py index 759b59ab..1749d634 100644 --- a/ranzen/misc.py +++ b/ranzen/misc.py @@ -159,8 +159,8 @@ def __new__(cls: Type[_S], *values: str) -> _S: raise TypeError("errors must be a string, not %r" % (values[2])) value = str(*values) member = str.__new__(cls, value) - member._value_ = value # pyright: ignore - return member # pyright: ignore + member._value_ = value + return member def __str__(self) -> str: return str.__str__(self)