Add support for dataloader samplers

src/eva/core/data/dataloaders/dataloader.py

@@ -59,17 +59,20 @@ class DataLoader:
     prefetch_factor: int | None = 2
     """Number of batches loaded in advance by each worker."""
 
-    def __call__(self, dataset: datasets.TorchDataset) -> dataloader.DataLoader:
+    def __call__(
+        self, dataset: datasets.TorchDataset, sampler: samplers.Sampler | None = None
+    ) -> dataloader.DataLoader:
         """Returns the dataloader on the provided dataset.
 
         Args:
             dataset: dataset from which to load the data.
+            sampler: defines the strategy to draw samples from the dataset.
         """
         return dataloader.DataLoader(
             dataset=dataset,
             batch_size=self.batch_size,
             shuffle=self.shuffle,
-            sampler=self.sampler,
+            sampler=sampler or self.sampler,
             batch_sampler=self.batch_sampler,
             num_workers=self.num_workers or multiprocessing.cpu_count(),
             collate_fn=self.collate_fn,

src/eva/core/data/datamodules/datamodule.py

@@ -8,6 +8,7 @@
 
 from eva.core.data import dataloaders as dataloaders_lib
 from eva.core.data import datasets as datasets_lib
+from eva.core.data import samplers as samplers_lib
 from eva.core.data.datamodules import call, schemas
 
 
@@ -24,17 +25,20 @@ def __init__(
         self,
         datasets: schemas.DatasetsSchema | None = None,
         dataloaders: schemas.DataloadersSchema | None = None,
+        samplers: schemas.SamplersSchema | None = None,
     ) -> None:
         """Initializes the datamodule.
 
         Args:
             datasets: The desired datasets.
             dataloaders: The desired dataloaders.
+            samplers: The desired samplers for the dataloaders.
         """
         super().__init__()
 
         self.datasets = datasets or self.default_datasets
         self.dataloaders = dataloaders or self.default_dataloaders
+        self.samplers = samplers or self.default_samplers
 
     @property
     def default_datasets(self) -> schemas.DatasetsSchema:
@@ -46,6 +50,11 @@ def default_dataloaders(self) -> schemas.DataloadersSchema:
         """Returns the default dataloader schema."""
         return schemas.DataloadersSchema()
 
+    @property
+    def default_samplers(self) -> schemas.SamplersSchema:
+        """Returns the default samplers schema."""
+        return schemas.SamplersSchema()
+
     @override
     def prepare_data(self) -> None:
         call.call_method_if_exists(self.datasets.tolist(), "prepare_data")
@@ -64,45 +73,73 @@ def train_dataloader(self) -> TRAIN_DATALOADERS:
             raise ValueError(
                 "Train dataloader can not be initialized as `self.datasets.train` is `None`."
             )
-        return self.dataloaders.train(self.datasets.train)
+        if isinstance(self.datasets.train, list) and len(self.datasets.train) > 1:
+            raise ValueError("Train dataloader can not be initialized with multiple datasets.")
+
+        return self._initialize_dataloaders(
+            self.dataloaders.train, self.datasets.train, self.samplers.train
+        )[0]
 
     @override
     def val_dataloader(self) -> EVAL_DATALOADERS:
         if self.datasets.val is None:
             raise ValueError(
                 "Validation dataloader can not be initialized as `self.datasets.val` is `None`."
             )
-        return self._initialize_dataloaders(self.dataloaders.val, self.datasets.val)
+        return self._initialize_dataloaders(
+            self.dataloaders.val, self.datasets.val, self.samplers.val
+        )
 
     @override
     def test_dataloader(self) -> EVAL_DATALOADERS:
         if self.datasets.test is None:
             raise ValueError(
                 "Test dataloader can not be initialized as `self.datasets.test` is `None`."
             )
-        return self._initialize_dataloaders(self.dataloaders.test, self.datasets.test)
+        return self._initialize_dataloaders(
+            self.dataloaders.test, self.datasets.test, self.samplers.test
+        )
 
     @override
     def predict_dataloader(self) -> EVAL_DATALOADERS:
         if self.datasets.predict is None:
             raise ValueError(
                 "Predict dataloader can not be initialized as `self.datasets.predict` is `None`."
             )
-        return self._initialize_dataloaders(self.dataloaders.predict, self.datasets.predict)
+        if isinstance(self.datasets.predict, list) and len(self.datasets.predict) > 1:
+            # Only apply sampler to the first predict dataset (should correspond to train split)
+            train_dataloader = self._initialize_dataloaders(
+                self.dataloaders.predict, self.datasets.predict[0], self.samplers.predict
+            )
+            return train_dataloader + self._initialize_dataloaders(
+                self.dataloaders.predict, self.datasets.predict[1:]
+            )
+
+        return self._initialize_dataloaders(
+            self.dataloaders.predict, self.datasets.predict, self.samplers.predict
+        )
 
     def _initialize_dataloaders(
         self,
         dataloader: dataloaders_lib.DataLoader,
         datasets: datasets_lib.TorchDataset | List[datasets_lib.TorchDataset],
+        sampler: samplers_lib.Sampler | None = None,
     ) -> EVAL_DATALOADERS:
         """Initializes dataloaders from a given set of dataset.
 
         Args:
             dataloader: The dataloader to apply to the provided datasets.
             datasets: The desired dataset(s) to allocate dataloader(s).
+            sampler: The sampler to use for the dataloader.
 
         Returns:
             A list with the dataloaders of the provided dataset(s).
         """
         datasets = datasets if isinstance(datasets, list) else [datasets]
-        return list(map(dataloader, datasets))
+
+        dataloaders = []
+        for dataset in datasets:
+            if sampler is not None and isinstance(sampler, samplers_lib.SamplerWithDataSource):
+                sampler.set_dataset(dataset)  # type: ignore
+            dataloaders.append(dataloader(dataset, sampler=sampler))
+        return dataloaders

src/eva/core/data/datamodules/schemas.py

@@ -3,7 +3,7 @@
 import dataclasses
 from typing import List
 
-from eva.core.data import dataloaders, datasets
+from eva.core.data import dataloaders, datasets, samplers
 
 TRAIN_DATASET = datasets.TorchDataset | None
 """Train dataset."""
@@ -60,3 +60,20 @@ class DataloadersSchema:
 
     predict: dataloaders.DataLoader = dataclasses.field(default_factory=dataloaders.DataLoader)
     """Predict dataloader."""
+
+
+@dataclasses.dataclass(frozen=True)
+class SamplersSchema:
+    """Samplers schema used in DataModule."""
+
+    train: samplers.Sampler | None = None
+    """Train sampler."""
+
+    val: samplers.Sampler | None = None
+    """Validation sampler."""
+
+    test: samplers.Sampler | None = None
+    """Test sampler."""
+
+    predict: samplers.Sampler | None = None
+    """Predict sampler."""

src/eva/core/data/datasets/__init__.py

@@ -1,15 +1,18 @@
 """Datasets API."""
 
-from eva.core.data.datasets.base import Dataset
+from eva.core.data.datasets.base import Dataset, MapDataset
 from eva.core.data.datasets.classification import (
     EmbeddingsClassificationDataset,
     MultiEmbeddingsClassificationDataset,
 )
 from eva.core.data.datasets.dataset import TorchDataset
+from eva.core.data.datasets.typings import DataSample
 
 __all__ = [
     "Dataset",
+    "MapDataset",
     "EmbeddingsClassificationDataset",
     "MultiEmbeddingsClassificationDataset",
     "TorchDataset",
+    "DataSample",
 ]

src/eva/core/data/datasets/base.py

@@ -1,5 +1,7 @@
 """Base dataset class."""
 
+import abc
+
 from eva.core.data.datasets import dataset
 
 
@@ -51,3 +53,28 @@ def teardown(self) -> None:
         of fit (train + validate), validate, test, or predict and it will be
         called from every process (i.e. GPU) across all the nodes in DDP.
         """
+
+
+class MapDataset(Dataset):
+    """Abstract base class for all map-style datasets."""
+
+    @abc.abstractmethod
+    def __getitem__(self, index: int):
+        """Retrieves the item at the given index.
+
+        Args:
+            index (int): Index
+
+        Returns:
+            Any: The data at the given index
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def __len__(self) -> int:
+        """Returns the length of the dataset.
+
+        Returns:
+            int: Length of the dataset
+        """
+        raise NotImplementedError

src/eva/core/data/datasets/typings.py

@@ -0,0 +1,18 @@
+"""Typing definitions for the datasets module."""
+
+from typing import Any, Dict, NamedTuple
+
+import torch
+
+
+class DataSample(NamedTuple):
+    """The default input batch data scheme."""
+
+    data: torch.Tensor
+    """The data batch."""
+
+    targets: torch.Tensor | None = None
+    """The target batch."""
+
+    metadata: Dict[str, Any] | None = None
+    """The associated metadata."""

src/eva/core/data/samplers/__init__.py

@@ -1,5 +1,7 @@
 """Data samplers API."""
 
-from eva.core.data.samplers.sampler import Sampler
+from eva.core.data.samplers.classification.balanced import BalancedSampler
+from eva.core.data.samplers.random import RandomSampler
+from eva.core.data.samplers.sampler import Sampler, SamplerWithDataSource
 
-__all__ = ["Sampler"]
+__all__ = ["Sampler", "SamplerWithDataSource", "RandomSampler", "BalancedSampler"]

src/eva/core/data/samplers/classification/__init__.py

@@ -0,0 +1,5 @@
+"""Classification data samplers API."""
+
+from eva.core.data.samplers.classification.balanced import BalancedSampler
+
+__all__ = ["BalancedSampler"]

src/eva/core/data/samplers/classification/balanced.py

@@ -0,0 +1,96 @@
+"""Random class sampler for data loading."""
+
+from collections import defaultdict
+from typing import Dict, Iterator, List
+
+import numpy as np
+from typing_extensions import override
+
+from eva.core.data import datasets
+from eva.core.data.datasets.typings import DataSample
+from eva.core.data.samplers.sampler import SamplerWithDataSource
+from eva.core.utils.progress_bar import tqdm
+
+
+class BalancedSampler(SamplerWithDataSource[int]):
+    """Balanced class sampler for data loading.
+
+    The sampler ensures that:
+    1. Each class has the same number of samples
+    2. Samples within each class are randomly selected
+    3. Samples of different classes appear in random order
+    """
+
+    def __init__(self, num_samples: int, replacement: bool = False, seed: int | None = 42):
+        """Initializes the balanced sampler.
+
+        Args:
+            num_samples: The number of samples to draw per class.
+            replacement: samples are drawn on-demand with replacement if ``True``, default=``False``
+            seed: Random seed for reproducibility.
+        """
+        self._num_samples = num_samples
+        self._replacement = replacement
+        self._class_indices: Dict[int, List[int]] = defaultdict(list)
+        self._random_generator = np.random.default_rng(seed)
+
+    def __len__(self) -> int:
+        """Returns the total number of samples."""
+        return self._num_samples * len(self._class_indices)
+
+    def __iter__(self) -> Iterator[int]:
+        """Creates an iterator that yields indices in a class balanced way.
+
+        Returns:
+            Iterator yielding dataset indices.
+        """
+        indices = []
+
+        for class_idx in self._class_indices:
+            class_indices = self._class_indices[class_idx]
+            sampled_indices = self._random_generator.choice(
+                class_indices, size=self._num_samples, replace=self._replacement
+            ).tolist()
+            indices.extend(sampled_indices)
+
+        self._random_generator.shuffle(indices)
+
+        return iter(indices)
+
+    @override
+    def set_dataset(self, data_source: datasets.MapDataset):
+        """Sets the dataset and builds class indices.
+
+        Args:
+            data_source: The dataset to sample from.
+
+        Raises:
+            ValueError: If the dataset doesn't have targets or if any class has
+                fewer samples than `num_samples` and `replacement` is `False`.
+        """
+        super().set_dataset(data_source)
+        self._make_indices()
+
+    def _make_indices(self):
+        """Builds indices for each class in the dataset."""
+        self._class_indices.clear()
+
+        for idx in tqdm(
+            range(len(self.data_source)), desc="Fetching class indices for balanced sampler"
+        ):
+            _, target, _ = DataSample(*self.data_source[idx])
+            if target is None:
+                raise ValueError("The dataset must return non-empty targets.")
+            if target.numel() != 1:
+                raise ValueError("The dataset must return a single & scalar target.")
+
+            class_idx = int(target.item())
+            self._class_indices[class_idx].append(idx)
+
+        if not self._replacement:
+            for class_idx, indices in self._class_indices.items():
+                if len(indices) < self._num_samples:
+                    raise ValueError(
+                        f"Class {class_idx} has only {len(indices)} samples, "
+                        f"which is less than the required {self._num_samples} samples."
+                    )