Fix or silence the remaining type errors

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"]

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),

src/algs/fs/dro.py

@@ -29,15 +29,16 @@ 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
         self.loss_fn = loss_fn
         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)
 

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

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