From 33774cafc99e071b4da046d1a1f8d135c8705d02 Mon Sep 17 00:00:00 2001 From: Thomas M Kehrenberg Date: Sat, 24 Feb 2024 16:10:11 +0100 Subject: [PATCH 1/3] Fix or silence the remaining type errors --- poetry.lock | 135 ++++++++++++++----------- pyproject.toml | 5 +- src/algs/adv/scorer.py | 6 +- src/algs/fs/dro.py | 3 +- src/algs/fs/lff.py | 8 +- src/algs/fs/sd.py | 5 +- src/arch/autoencoder/resnet.py | 14 +-- src/arch/predictors/set_transformer.py | 10 +- src/data/splitter.py | 5 +- 9 files changed, 108 insertions(+), 83 deletions(-) diff --git a/poetry.lock b/poetry.lock index 9399af71..c2247fd2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -532,30 +532,36 @@ files = [ [[package]] name = "ethicml" -version = "1.2.1" +version = "1.3.0" description = "EthicML is a library for performing and assessing algorithmic fairness. 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-3218,5 +3237,5 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" -python-versions = ">=3.10,<3.12" -content-hash = "ab002e6142da0d933f3da4085b997984ea27afb49ad5e2206fdff392b66935a6" +python-versions = ">=3.10,<3.13" +content-hash = "d9315641cde83b4dfcdabdeb8e6288e4f2f0ba8737e29ad9b11071d921c39d3a" diff --git a/pyproject.toml b/pyproject.toml index 3abeeb09..4fa176bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,13 +24,13 @@ neoconfigen = ">=2.3.3" numpy = { version = ">=1.23.2" } pandas = { version = ">=1.5.0" } pillow = "*" -python = ">=3.10,<3.12" +python = ">=3.10,<3.13" ranzen = { version = "^2.1.2" } scikit-image = ">=0.14" scikit_learn = { version = ">=0.20.1" } scipy = { version = ">=1.2.1" } seaborn = { version = ">=0.9.0" } -torch-conduit = { version = "^0.3.4", extras = ["image"] } +torch-conduit = { version = ">=0.3.4", extras = ["image"] } tqdm = { version = ">=4.31.1" } typer = "*" @@ -56,6 +56,7 @@ torchvision = ">=0.15.2" ruff = "*" types-tqdm = "*" pandas-stubs = "*" +python-type-stubs = {git = "https://github.com/wearepal/python-type-stubs.git", rev = "8d5f608"} [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/src/algs/adv/scorer.py b/src/algs/adv/scorer.py index 80f81996..ad3dd720 100644 --- a/src/algs/adv/scorer.py +++ b/src/algs/adv/scorer.py @@ -7,6 +7,7 @@ from conduit.data.datasets import CdtDataLoader, CdtDataset import conduit.metrics as cdtm from conduit.models.utils import prefix_keys +from conduit.types import Loss from loguru import logger from ranzen.misc import gcopy from ranzen.torch.loss import CrossEntropyLoss, ReductionType @@ -141,9 +142,8 @@ def run( score = recon_score = self.recon_score_w * 0.5 * (recon_score_tr + recon_score_dep) logger.info(f"Aggregate reconstruction score: {recon_score}") - classifier = SetClassifier( - model=disc, opt=self.opt, criterion=CrossEntropyLoss(reduction=ReductionType.mean) - ) + cross_entropy: Loss = CrossEntropyLoss(reduction=ReductionType.mean) # type: ignore + classifier = SetClassifier(model=disc, opt=self.opt, criterion=cross_entropy) logger.info("Training invariance-scorer") classifier.fit( dm.train_dataloader(batch_size=self.batch_size_tr), diff --git a/src/algs/fs/dro.py b/src/algs/fs/dro.py index 7507ad82..35d2ae2a 100644 --- a/src/algs/fs/dro.py +++ b/src/algs/fs/dro.py @@ -29,7 +29,8 @@ def __init__( reduction = str_to_enum(str_=reduction, enum=ReductionType) self.reduction = reduction if loss_fn is None: - loss_fn = CrossEntropyLoss(reduction=ReductionType.none) + cross_entropy: Loss = CrossEntropyLoss(reduction=ReductionType.none) # type: ignore + loss_fn = cross_entropy else: loss_fn.reduction = ReductionType.none self.reduction = reduction diff --git a/src/algs/fs/lff.py b/src/algs/fs/lff.py index 56e13709..08b9dac2 100644 --- a/src/algs/fs/lff.py +++ b/src/algs/fs/lff.py @@ -5,8 +5,9 @@ from typing_extensions import Self, override from conduit.data.datasets.base import CdtDataset -from conduit.data.structures import XI, LoadedData, SampleBase, SizedDataset, TernarySample, X +from conduit.data.structures import LoadedData, SampleBase, SizedDataset, TernarySample, X from conduit.types import Indexable, IndexType +import numpy as np from ranzen.misc import gcopy from ranzen.torch import CrossEntropyLoss import torch @@ -74,7 +75,8 @@ def __add__(self, other: Self) -> Self: return copy @override - def __getitem__(self: "IndexedSample[XI]", index: IndexType) -> "IndexedSample[XI]": + def __getitem__(self, index: IndexType) -> Self: + assert isinstance(self.x, (Tensor, np.ndarray)), "x is not indexable" return gcopy( self, deep=False, x=self.x[index], y=self.y[index], s=self.s[index], idx=self.idx[index] ) @@ -101,7 +103,7 @@ def __len__(self) -> int: @dataclass(kw_only=True, repr=False, eq=False, frozen=True) class LfFClassifier(Classifier): - criterion: CrossEntropyLoss + criterion: CrossEntropyLoss # type: ignore sample_loss_ema_b: LabelEma sample_loss_ema_d: LabelEma q: float = 0.7 diff --git a/src/algs/fs/sd.py b/src/algs/fs/sd.py index be46b160..71df51c6 100644 --- a/src/algs/fs/sd.py +++ b/src/algs/fs/sd.py @@ -33,7 +33,8 @@ def __init__( if isinstance(gamma, ListConfig): gamma = list(gamma) if loss_fn is None: - loss_fn = CrossEntropyLoss(reduction=ReductionType.mean) + cross_entropy: Loss = CrossEntropyLoss(reduction=ReductionType.mean) # type: ignore + loss_fn = cross_entropy self.loss_fn = loss_fn if isinstance(lambda_, (tuple, list)): self.register_buffer("lambda_", torch.as_tensor(lambda_, dtype=torch.float)) @@ -49,7 +50,7 @@ def reduction(self) -> Union[ReductionType, str]: return self.loss_fn.reduction @reduction.setter - def reduction(self, value: Union[ReductionType, str]) -> None: + def reduction(self, value: Union[ReductionType, str]) -> None: # type: ignore self.loss_fn.reduction = value @override diff --git a/src/arch/autoencoder/resnet.py b/src/arch/autoencoder/resnet.py index 8d9a29d2..40de3ca0 100644 --- a/src/arch/autoencoder/resnet.py +++ b/src/arch/autoencoder/resnet.py @@ -22,7 +22,7 @@ def __init__( super().__init__() self.size, self.scale_factor = size, scale_factor - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: return F.interpolate(x, size=self.size, scale_factor=self.scale_factor) @@ -72,7 +72,7 @@ def __init__( self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) @@ -112,7 +112,7 @@ def __init__( self.downsample = downsample self.stride = stride - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) @@ -150,7 +150,7 @@ def __init__( self.bn2 = nn.BatchNorm2d(planes) self.upsample = upsample - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) @@ -189,7 +189,7 @@ def __init__( self.upsample = upsample self.scale = scale - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) @@ -270,7 +270,7 @@ def _make_layer( return nn.Sequential(*layers) - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: x = self.conv1(x) x = self.bn1(x) x = self.relu(x) @@ -357,7 +357,7 @@ def _make_layer( return nn.Sequential(*layers) - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: x = self.linear(x) # NOTE: replaced this by Linear(in_channels, 514 * 4 * 4) diff --git a/src/arch/predictors/set_transformer.py b/src/arch/predictors/set_transformer.py index 902fccf8..70c4ef68 100644 --- a/src/arch/predictors/set_transformer.py +++ b/src/arch/predictors/set_transformer.py @@ -19,7 +19,7 @@ def __init__(self, embed_dim: int, num_heads: int = 4) -> None: self.fc = nn.Sequential(nn.Linear(embed_dim, embed_dim), nn.ReLU(inplace=True)) self.mh = nn.MultiheadAttention(embed_dim=embed_dim, num_heads=num_heads) - def forward(self, Q: Tensor, K: Tensor) -> Tensor: # type: ignore + def forward(self, Q: Tensor, K: Tensor) -> Tensor: H = self.norm1(K + self.mh(key=K, query=Q, value=K, need_weights=False)[0]) out = self.norm2(H + self.fc(H)) return out @@ -30,7 +30,7 @@ def __init__(self, dim_in: int, *, num_heads: int) -> None: super().__init__() self.mab = MultiheadAttentionBlock(embed_dim=dim_in, num_heads=num_heads) - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: return self.mab(x, x) @@ -42,7 +42,7 @@ def __init__(self, dim_in: int, *, out_dim: int, num_heads: int, num_inds: int) self.mab1 = MultiheadAttentionBlock(embed_dim=out_dim, num_heads=num_heads) self.mab2 = MultiheadAttentionBlock(embed_dim=dim_in, num_heads=num_heads) - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: H = self.mab1(self.inducing_points.repeat(x.size(0), 1, 1), x) return self.mab2(x, H) @@ -54,7 +54,7 @@ def __init__(self, dim: int, *, num_heads: int, num_seeds: int) -> None: nn.init.xavier_uniform_(self.seed_vectors) self.mab = MultiheadAttentionBlock(embed_dim=dim, num_heads=num_heads) - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: return self.mab(self.seed_vectors.repeat(x.size(0), 1, 1), x) @@ -85,7 +85,7 @@ def __init__( ) self.predictor = nn.Linear(hidden_dim * num_inds, target_dim) - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: out = self.embedder(x).unsqueeze(1) out = self.decoder(self.encoder(out)) out = out.flatten(start_dim=1).sum(0) diff --git a/src/data/splitter.py b/src/data/splitter.py index 952366ba..48c22a18 100644 --- a/src/data/splitter.py +++ b/src/data/splitter.py @@ -7,6 +7,7 @@ from typing_extensions import override from conduit.data.constants import IMAGENET_STATS +from conduit.data.datasets import random_split from conduit.data.datasets.utils import stratified_split from conduit.data.datasets.vision import CdtVisionDataset, ImageTform, PillowTform from loguru import logger @@ -159,8 +160,8 @@ def __post_init__(self) -> None: def split(self, dataset: D) -> TrainDepTestSplit[D]: if self.data_prop < 1: dataset = stratified_split(dataset, default_train_prop=self.data_prop).train - dep_inds, test_inds, train_inds = dataset.random_split( - props=[self.dep_prop, self.test_prop], seed=self.seed, as_indices=True + dep_inds, test_inds, train_inds = random_split( + dataset, props=[self.dep_prop, self.test_prop], seed=self.seed, as_indices=True ) train_inds = torch.as_tensor(train_inds) train_data = dataset.subset(train_inds) From 3ae31f02a1da441a28ee54478a1f12f94dc17509 Mon Sep 17 00:00:00 2001 From: Thomas M Kehrenberg Date: Sat, 24 Feb 2024 16:11:41 +0100 Subject: [PATCH 2/3] Format code --- src/algs/fs/dro.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/algs/fs/dro.py b/src/algs/fs/dro.py index 35d2ae2a..05bcd4da 100644 --- a/src/algs/fs/dro.py +++ b/src/algs/fs/dro.py @@ -29,7 +29,7 @@ def __init__( reduction = str_to_enum(str_=reduction, enum=ReductionType) self.reduction = reduction if loss_fn is None: - cross_entropy: Loss = CrossEntropyLoss(reduction=ReductionType.none) # type: ignore + cross_entropy: Loss = CrossEntropyLoss(reduction=ReductionType.none) # type: ignore loss_fn = cross_entropy else: loss_fn.reduction = ReductionType.none @@ -38,7 +38,7 @@ def __init__( self.eta = eta @override - def forward(self, input: Tensor, *, target: Tensor) -> Tensor: # type: ignore + def forward(self, input: Tensor, *, target: Tensor) -> Tensor: sample_losses = (self.loss_fn(input, target=target) - self.eta).relu().pow(2) return reduce(sample_losses, reduction_type=self.reduction) From 831304a605efc5b9820bf875b30c955a21766481 Mon Sep 17 00:00:00 2001 From: Thomas M Kehrenberg Date: Sat, 24 Feb 2024 16:22:16 +0100 Subject: [PATCH 3/3] One more type:ignore --- src/labelling/encoder.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/labelling/encoder.py b/src/labelling/encoder.py index b8232372..9a44ff4d 100644 --- a/src/labelling/encoder.py +++ b/src/labelling/encoder.py @@ -39,13 +39,13 @@ def __init__( model, self.transforms = clip.load( name=version.value, # type: ignore device="cpu", - download_root=download_root, + download_root=download_root, # type: ignore ) logger.info("Done.") self.encoder = model.visual self.out_dim = cast(int, self.encoder.output_dim) - def forward(self, x: Tensor) -> Tensor: # type: ignore + def forward(self, x: Tensor) -> Tensor: return self.encoder(x) @torch.no_grad() # pyright: ignore