diff --git a/src/mednet/libs/classification/config/data/hivtb/datamodule.py b/src/mednet/libs/classification/config/data/hivtb/datamodule.py
index 5d8170b77d50d658b22f3716ed2890a2eb395545..a93606bb6547b5cd196ce58100613a41ed130169 100644
--- a/src/mednet/libs/classification/config/data/hivtb/datamodule.py
+++ b/src/mednet/libs/classification/config/data/hivtb/datamodule.py
@@ -51,7 +51,7 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
@@ -67,22 +67,22 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
# to_pil_image(tensor).show()
# __import__("pdb").set_trace()
- return tensor, dict(label=sample[1], name=sample[0]) # type: ignore[arg-type]
+ return tensor, dict(target=sample[1], name=sample[0]) # type: ignore[arg-type]
- def label(self, sample: tuple[str, int]) -> int:
- """Load a single image sample label from the disk.
+ def target(self, sample: tuple[str, int]) -> int:
+ """Load a single image sample target from the disk.
Parameters
----------
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
int
- The integer label associated with the sample.
+ The integer target associated with the sample.
"""
return sample[1]
diff --git a/src/mednet/libs/classification/config/data/montgomery/datamodule.py b/src/mednet/libs/classification/config/data/montgomery/datamodule.py
index 83753205a142a5c06548527dc224a75849718768..2afbb8d6cd478371bff6f2828a737a5d93959183 100644
--- a/src/mednet/libs/classification/config/data/montgomery/datamodule.py
+++ b/src/mednet/libs/classification/config/data/montgomery/datamodule.py
@@ -57,7 +57,7 @@ class RawDataLoader(_BaseRawDataLoader):
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
@@ -75,22 +75,22 @@ class RawDataLoader(_BaseRawDataLoader):
# to_pil_image(tensor).show()
# __import__("pdb").set_trace()
- return tensor, dict(label=sample[1], name=sample[0]) # type: ignore[arg-type]
+ return tensor, dict(target=sample[1], name=sample[0]) # type: ignore[arg-type]
- def label(self, sample: tuple[str, int]) -> int:
- """Load a single image sample label from the disk.
+ def target(self, sample: tuple[str, int]) -> int:
+ """Load a single image sample target from the disk.
Parameters
----------
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
int
- The integer label associated with the sample.
+ The integer target associated with the sample.
"""
return sample[1]
diff --git a/src/mednet/libs/classification/config/data/nih_cxr14/datamodule.py b/src/mednet/libs/classification/config/data/nih_cxr14/datamodule.py
index 900dcc9d0091a8571629bf283ee6c1829fa62286..0edb1aef91c0f0a19acbd83bf6dfcc1103b89fae 100644
--- a/src/mednet/libs/classification/config/data/nih_cxr14/datamodule.py
+++ b/src/mednet/libs/classification/config/data/nih_cxr14/datamodule.py
@@ -74,7 +74,7 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
@@ -99,22 +99,22 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
# to_pil_image(tensor).show()
# __import__("pdb").set_trace()
- return tensor, dict(label=sample[1], name=sample[0]) # type: ignore[arg-type]
+ return tensor, dict(target=sample[1], name=sample[0]) # type: ignore[arg-type]
- def label(self, sample: tuple[str, list[int]]) -> list[int]:
- """Load a single image sample label from the disk.
+ def target(self, sample: tuple[str, list[int]]) -> list[int]:
+ """Load a single image sample target from the disk.
Parameters
----------
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing the
- sample label.
+ sample target.
Returns
-------
list[int]
- The integer labels associated with the sample.
+ The integer targets associated with the sample.
"""
return sample[1]
diff --git a/src/mednet/libs/classification/config/data/padchest/datamodule.py b/src/mednet/libs/classification/config/data/padchest/datamodule.py
index 0fed49ab85d409ed6ae4b248de849cb31851a3eb..51a20bbdbc9392ce7917cb44b6db3a0233f9a857 100644
--- a/src/mednet/libs/classification/config/data/padchest/datamodule.py
+++ b/src/mednet/libs/classification/config/data/padchest/datamodule.py
@@ -52,7 +52,7 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
@@ -70,22 +70,22 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
# to_pil_image(tensor).show()
# __import__("pdb").set_trace()
- return tensor, dict(label=sample[1], name=sample[0]) # type: ignore[arg-type]
+ return tensor, dict(target=sample[1], name=sample[0]) # type: ignore[arg-type]
- def label(self, sample: tuple[str, int | list[int]]) -> int | list[int]:
- """Load a single image sample label from the disk.
+ def target(self, sample: tuple[str, int | list[int]]) -> int | list[int]:
+ """Load a single image sample target from the disk.
Parameters
----------
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
list[int]
- The integer labels associated with the sample.
+ The integer targets associated with the sample.
"""
return sample[1]
diff --git a/src/mednet/libs/classification/config/data/shenzhen/datamodule.py b/src/mednet/libs/classification/config/data/shenzhen/datamodule.py
index 7ab4d171825e4894c4d25eb0dfe35c72ba7229a9..45aa327e06abbb22d9f7bbbc6f957a49291a4a06 100644
--- a/src/mednet/libs/classification/config/data/shenzhen/datamodule.py
+++ b/src/mednet/libs/classification/config/data/shenzhen/datamodule.py
@@ -57,7 +57,7 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
@@ -75,22 +75,22 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
# to_pil_image(tensor).show()
# __import__("pdb").set_trace()
- return tensor, dict(label=sample[1], name=sample[0]) # type: ignore[arg-type]
+ return tensor, dict(target=sample[1], name=sample[0]) # type: ignore[arg-type]
- def label(self, sample: tuple[str, int]) -> int:
- """Load a single image sample label from the disk.
+ def target(self, sample: tuple[str, int]) -> int:
+ """Load a single image sample target from the disk.
Parameters
----------
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
int
- The integer label associated with the sample.
+ The integer target associated with the sample.
"""
return sample[1]
diff --git a/src/mednet/libs/classification/config/data/tbpoc/datamodule.py b/src/mednet/libs/classification/config/data/tbpoc/datamodule.py
index 2ba9f688a1abc5a3fc38a45ae92d4d6f7f2d38be..f92afc9d2e2ac141f7c4889db32e72c187f93f13 100644
--- a/src/mednet/libs/classification/config/data/tbpoc/datamodule.py
+++ b/src/mednet/libs/classification/config/data/tbpoc/datamodule.py
@@ -47,7 +47,7 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
@@ -65,21 +65,21 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
# to_pil_image(tensor).show()
# __import__("pdb").set_trace()
- return tensor, dict(label=sample[1], name=sample[0]) # type: ignore[arg-type]
+ return tensor, dict(target=sample[1], name=sample[0]) # type: ignore[arg-type]
- def label(self, sample: tuple[str, int]) -> int:
- """Load a single image sample label from the disk.
+ def target(self, sample: tuple[str, int]) -> int:
+ """Load a single image sample target from the disk.
Parameters
----------
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, and an integer, representing
- the sample label.
+ the sample target.
Returns
-------
- The integer label associated with the sample
+ The integer target associated with the sample
"""
return sample[1]
diff --git a/src/mednet/libs/classification/config/data/tbx11k/datamodule.py b/src/mednet/libs/classification/config/data/tbx11k/datamodule.py
index fb86030754971e0dc6551009d161a4965f029bac..47e97db8d085176e94c9d111ee17d63dece108dc 100644
--- a/src/mednet/libs/classification/config/data/tbx11k/datamodule.py
+++ b/src/mednet/libs/classification/config/data/tbx11k/datamodule.py
@@ -199,7 +199,7 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, an integer, representing the
- sample label, and possible radiological findings represented by
+ sample target, and possible radiological findings represented by
bounding boxes.
Returns
@@ -222,26 +222,26 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
)
return tensor, dict(
- label=sample[1],
name=sample[0],
+ target=sample[1],
bounding_boxes=self.bounding_boxes(sample),
)
- def label(self, sample: DatabaseSample) -> int:
- """Load a single image sample label from the disk.
+ def target(self, sample: DatabaseSample) -> int:
+ """Load a single image sample target from the disk.
Parameters
----------
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, an integer, representing the
- sample label, and possible radiological findings represented by
+ sample target, and possible radiological findings represented by
bounding boxes.
Returns
-------
int
- The integer label associated with the sample.
+ The integer target associated with the sample.
"""
return sample[1]
@@ -254,7 +254,7 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
sample
A tuple containing the path suffix, within the dataset root folder,
where to find the image to be loaded, an integer, representing the
- sample label, and possible radiological findings represented by
+ sample target, and possible radiological findings represented by
bounding boxes.
Returns
diff --git a/src/mednet/libs/classification/models/alexnet.py b/src/mednet/libs/classification/models/alexnet.py
index 03802839900ee4bd3f5d8b368cb8d611cbc6b006..674046ca050d695ff3c81916e1e8fc2b31fe0367 100644
--- a/src/mednet/libs/classification/models/alexnet.py
+++ b/src/mednet/libs/classification/models/alexnet.py
@@ -13,6 +13,7 @@ import torchvision.models as models
import torchvision.transforms
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
+
from .separate import separate
from .transforms import RGB, SquareCenterPad
@@ -129,7 +130,7 @@ class Alexnet(Model):
def training_step(self, batch, _):
images = batch[0]
- labels = batch[1]["label"]
+ labels = batch[1]["target"]
# Increase label dimension if too low
# Allows single and multiclass usage
@@ -143,7 +144,7 @@ class Alexnet(Model):
def validation_step(self, batch, batch_idx, dataloader_idx=0):
images = batch[0]
- labels = batch[1]["label"]
+ labels = batch[1]["target"]
# Increase label dimension if too low
# Allows single and multiclass usage
diff --git a/src/mednet/libs/classification/models/densenet.py b/src/mednet/libs/classification/models/densenet.py
index 69ac44b72d22b9cd0ca932e00a6dee51fd1f93bd..a4e948609cb5f4273fab491985d2d85c7339000c 100644
--- a/src/mednet/libs/classification/models/densenet.py
+++ b/src/mednet/libs/classification/models/densenet.py
@@ -13,6 +13,7 @@ import torchvision.models as models
import torchvision.transforms
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
+
from .separate import separate
from .transforms import RGB, SquareCenterPad
@@ -136,7 +137,7 @@ class Densenet(Model):
def training_step(self, batch, _):
images = batch[0]
- labels = batch[1]["label"]
+ labels = batch[1]["target"]
# Increase label dimension if too low
# Allows single and multiclass usage
@@ -150,7 +151,7 @@ class Densenet(Model):
def validation_step(self, batch, batch_idx, dataloader_idx=0):
images = batch[0]
- labels = batch[1]["label"]
+ labels = batch[1]["target"]
# Increase label dimension if too low
# Allows single and multiclass usage
diff --git a/src/mednet/libs/classification/models/loss_weights.py b/src/mednet/libs/classification/models/loss_weights.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f646e322fcdf4ac60303ff41bf35e0037e2074f
--- /dev/null
+++ b/src/mednet/libs/classification/models/loss_weights.py
@@ -0,0 +1,88 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import logging
+
+import torch
+import torch.utils.data
+from mednet.libs.common.data.typing import DataLoader
+
+logger = logging.getLogger("mednet")
+
+
+def _get_label_weights(
+ dataloader: torch.utils.data.DataLoader,
+) -> torch.Tensor:
+ """Compute the weights of each class of a DataLoader.
+
+ This function inputs a pytorch DataLoader and computes the ratio between
+ number of negative and positive samples (scalar). The weight can be used
+ to adjust minimisation criteria to in cases there is a huge data imbalance.
+
+ It returns a vector with weights (inverse counts) for each label.
+
+ Parameters
+ ----------
+ dataloader
+ A DataLoader from which to compute the positive weights. Entries must
+ be a dictionary which must contain a ``label`` key.
+
+ Returns
+ -------
+ torch.Tensor
+ The positive weight of each class in the dataset given as input.
+ """
+
+ targets = torch.tensor(
+ [sample for batch in dataloader for sample in batch[1]["target"]],
+ )
+
+ # Binary labels
+ if len(list(targets.shape)) == 1:
+ class_sample_count = [
+ float((targets == t).sum().item())
+ for t in torch.unique(targets, sorted=True)
+ ]
+
+ # Divide negatives by positives
+ positive_weights = torch.tensor(
+ [class_sample_count[0] / class_sample_count[1]],
+ ).reshape(-1)
+
+ # Multiclass labels
+ else:
+ class_sample_count = torch.sum(targets, dim=0)
+ negative_class_sample_count = (
+ torch.full((targets.size()[1],), float(targets.size()[0]))
+ - class_sample_count
+ )
+
+ positive_weights = negative_class_sample_count / (
+ class_sample_count + negative_class_sample_count
+ )
+
+ return positive_weights
+
+
+def make_balanced_bcewithlogitsloss(
+ dataloader: DataLoader,
+) -> torch.nn.BCEWithLogitsLoss:
+ """Return a balanced binary-cross-entropy loss.
+
+ The loss is weighted using the ratio between positives and total examples
+ available.
+
+ Parameters
+ ----------
+ dataloader
+ The DataLoader to use to compute the BCE weights.
+
+ Returns
+ -------
+ torch.nn.BCEWithLogitsLoss
+ An instance of the weighted loss.
+ """
+
+ weights = _get_label_weights(dataloader)
+ return torch.nn.BCEWithLogitsLoss(pos_weight=weights)
diff --git a/src/mednet/libs/classification/models/pasa.py b/src/mednet/libs/classification/models/pasa.py
index 35953ddfd14377ce9a88267a154fbe99a6b8f9b2..e2e47010ed431fc8436591c264bb7911531d5f52 100644
--- a/src/mednet/libs/classification/models/pasa.py
+++ b/src/mednet/libs/classification/models/pasa.py
@@ -13,6 +13,7 @@ import torch.utils.data
import torchvision.transforms
from mednet.libs.common.data.typing import TransformSequence
from mednet.libs.common.models.model import Model
+
from .separate import separate
from .transforms import Grayscale, SquareCenterPad
@@ -202,7 +203,7 @@ class Pasa(Model):
def training_step(self, batch, _):
images = batch[0]
- labels = batch[1]["label"]
+ labels = batch[1]["target"]
# Increase label dimension if too low
# Allows single and multiclass usage
@@ -216,7 +217,7 @@ class Pasa(Model):
def validation_step(self, batch, batch_idx, dataloader_idx=0):
images = batch[0]
- labels = batch[1]["label"]
+ labels = batch[1]["target"]
# Increase label dimension if too low
# Allows single and multiclass usage
diff --git a/src/mednet/libs/classification/models/separate.py b/src/mednet/libs/classification/models/separate.py
index 5d6065a867ac2bd104d9495a757756a65d2ae6d7..9b575e8ee1a39258b2437e1c968b303aff7b028b 100644
--- a/src/mednet/libs/classification/models/separate.py
+++ b/src/mednet/libs/classification/models/separate.py
@@ -28,7 +28,7 @@ def _as_predictions(
A list of typed predictions that can be saved to disk.
"""
- return [(v[1]["name"], v[1]["label"].item(), v[0].item()) for v in samples]
+ return [(v[1]["name"], v[1]["target"].item(), v[0].item()) for v in samples]
def separate(batch: Sample) -> list[BinaryPrediction | MultiClassPrediction]:
diff --git a/src/mednet/libs/common/data/datamodule.py b/src/mednet/libs/common/data/datamodule.py
index 30fc26bd9ce5b5817e4b45db7d31321c3ad5cffe..32e40ca0eaf919e501089eddbfda1768a5a450eb 100644
--- a/src/mednet/libs/common/data/datamodule.py
+++ b/src/mednet/libs/common/data/datamodule.py
@@ -112,7 +112,7 @@ class _DelayedLoadingDataset(Dataset):
An iterable containing the raw dataset samples representing one of the
database split datasets.
loader
- An object instance that can load samples and labels from storage.
+ An object instance that can load samples from storage.
transforms
A set of transforms that should be applied on-the-fly for this dataset,
to fit the output of the raw-data-loader to the model of interest.
@@ -137,17 +137,6 @@ class _DelayedLoadingDataset(Dataset):
sample_size_mb = _sample_size_bytes(first_sample) / (1024.0 * 1024.0)
logger.info(f"Estimated sample size: {sample_size_mb:.1f} Mb")
- def labels(self) -> list[int | list[int]]:
- """Return the integer labels for all samples in the dataset.
-
- Returns
- -------
- list[int | list[int]]
- The integer labels for all samples in the dataset.
- """
-
- return [self.loader.label(k) for k in self.raw_dataset]
-
def __getitem__(self, key: int) -> Sample:
tensor, metadata = self.loader.sample(self.raw_dataset[key])
return self.transform(tensor), transform_tvtensors(
@@ -206,7 +195,7 @@ class _CachedDataset(Dataset):
An iterable containing the raw dataset samples representing one of the
database split datasets.
loader
- An object instance that can load samples and labels from storage.
+ An object instance that can load samples and targets from storage.
parallel
Use multiprocessing for data loading: if set to -1 (default), disables
multiprocessing data loading. Set to 0 to enable as many data loading
@@ -255,16 +244,16 @@ class _CachedDataset(Dataset):
f"{sample_size_mb:.1f} / {(len(self.data)*sample_size_mb):.1f} Mb",
)
- def labels(self) -> list[int | list[int]]:
- """Return the integer labels for all samples in the dataset.
+ def targets(self) -> list[int | list[int]]:
+ """Return the integer targets for all samples in the dataset.
Returns
-------
list[int | list[int]]
- The integer labels for all samples in the dataset.
+ The integer targets for all samples in the dataset.
"""
- return [k[1]["label"] for k in self.data]
+ return [k[1]["target"] for k in self.data]
def __getitem__(self, key: int) -> Sample:
return self.data[key]
@@ -294,16 +283,16 @@ class _ConcatDataset(Dataset):
for j in range(len(datasets[i]))
]
- def labels(self) -> list[int | list[int]]:
- """Return the integer labels for all samples in the dataset.
+ def targets(self) -> list[int | list[int]]:
+ """Return the integer targets for all samples in the dataset.
Returns
-------
list[int | list[int]]
- The integer labels for all samples in the dataset.
+ The integer targets for all samples in the dataset.
"""
- return list(itertools.chain(*[k.labels() for k in self._datasets]))
+ return list(itertools.chain(*[k.targets() for k in self._datasets]))
def __getitem__(self, key: int) -> Sample:
i, j = self._indices[key]
@@ -317,6 +306,145 @@ class _ConcatDataset(Dataset):
yield from dataset
+def _make_balanced_random_sampler(
+ dataset: Dataset,
+ target: str = "target",
+) -> torch.utils.data.WeightedRandomSampler:
+ """Generate a pytorch sampler that samples according to class
+ probabilities.
+
+ This function takes as input a torch Dataset, and computes the weights to
+ balance each class in the dataset, and the datasets themselves if one
+ passes a :py:class:`torch.utils.data.ConcatDataset`.
+
+ In this implementation, we balance **both** class and dataset-origin
+ probabilities, what you expect for a truly *equitable* random sampler.
+
+ Take this example for illustration:
+
+ * Dataset 1: N = 10 samples, 9 samples with target=0, 1 sample with target=1
+ * Dataset 2: N = 6 samples, 3 samples with target=0, 3 samples with target=1
+
+ So:
+
+ | Dataset | Target | Samples | Weight | Normalised weight |
+ +---------+--------+---------+--------+-------------------+
+ | 1 | 0 | 9 | 1/9 | 1/36 |
+ | 1 | 1 | 1 | 1/1 | 1/4 |
+ | 2 | 0 | 3 | 1/3 | 1/12 |
+ | 2 | 1 | 3 | 1/3 | 1/12 |
+
+ Legend:
+
+ * Weight: the weights computed by this method
+ * Normalised weight: the weight per sample used by the random sampler,
+ after normalising the weights by the sum of all weights in the
+ concatenated dataset, such that the sum of all normalized weights times
+ the number of samples is 1.
+
+ The properties of this algorithm are as follows:
+
+ 1. The probability of picking a sample from any target is the same (0.5 in
+ this case). To verify this, notice that the probability of picking a
+ sample with ``target=0`` is :math:`1/4 x 1 + 1/12 x 3 = 0.5`.
+ 2. The probability of picking a sample with ``target=0`` from Dataset 2 is
+ 3 times higher than those from Dataset 1. As there are 3 times fewer
+ samples in Dataset 2 with ``target=0``, this makes choosing samples from
+ Dataset 1 proportionally less likely.
+ 3. The probability of picking a sample with ``target=1`` from Dataset 2 is
+ 3 times lower than those from Dataset 1. As there are 3 times fewer
+ samples in Dataset 1 with ``target=1``, this makes choosing samples from
+ Dataset 2 proportionally less likely.
+
+ This function assumes targets are stored on a dictionary entry named
+ ``target`` inside the metadata information for the
+ :py:data:`.typing.Sample`, and that its value is an integer.
+
+ We then instantiate a pytorch sampler using the inverse probabilities (the
+ more samples in a class, the less likely it becomes to be sampled.
+
+ Parameters
+ ----------
+ dataset
+ An instance of torch Dataset.
+ :py:class:`torch.utils.data.ConcatDataset` are supported.
+ target
+ The name of a metadata key pointing to an integer property that allows
+ balancing the dataset.
+
+ Returns
+ -------
+ A sampler, to be used in a dataloader equipped with the same dataset
+ used to calculate the relative sample weights.
+
+ Raises
+ ------
+ RuntimeError
+ If requested to balance a dataset (single, not-concatenated) without an
+ existing target.
+ """
+
+ def _calculate_weights(targets: list[int]) -> list[float]:
+ counts = collections.Counter(targets)
+ weights = {k: 1.0 / v for k, v in counts.items()}
+ return [weights[k] for k in targets]
+
+ if isinstance(dataset, torch.utils.data.ConcatDataset):
+ # There are two possible cases: targets/no-targets
+ metadata_example = dataset.datasets[0][0][1]
+ if target in metadata_example and isinstance(
+ metadata_example[target],
+ int,
+ ):
+ # there are integer targets, let's balance with those
+ logger.info(
+ f"Balancing sample selection probabilities **and** "
+ f"concatenated-datasets using metadata targets `{target}`",
+ )
+ targets = [
+ k
+ for ds in dataset.datasets
+ for k in typing.cast(Dataset, ds).targets()
+ ]
+ weights = _calculate_weights(targets) # type: ignore
+ else:
+ logger.warning(
+ f"Balancing samples **and** concatenated-datasets "
+ f"by using dataset totals as `{target}: int` is not true",
+ )
+ weights = [
+ k
+ for ds in dataset.datasets
+ for k in len(typing.cast(typing.Sized, ds))
+ * [1.0 / len(typing.cast(typing.Sized, ds))]
+ ]
+
+ pass
+
+ else:
+ metadata_example = dataset[0][1]
+ if target in metadata_example and isinstance(
+ metadata_example[target],
+ int,
+ ):
+ logger.info(
+ f"Balancing samples from dataset using metadata "
+ f"targets `{target}`",
+ )
+ weights = _calculate_weights(dataset.targets()) # type: ignore
+ else:
+ raise RuntimeError(
+ f"Cannot balance samples with multiple class targets "
+ f"({target}: list[int]) or without metadata targets `{target}`",
+ )
+
+ return torch.utils.data.WeightedRandomSampler(
+ weights,
+ len(weights),
+ replacement=True,
+ )
+
+
class ConcatDataModule(lightning.LightningDataModule):
"""A conveninent DataModule with dictionary split loading, mini- batching,
parallelisation and caching, all in one.
@@ -800,7 +928,7 @@ class CachingDataModule(ConcatDataModule):
not influence any early stop criteria during training, and are just
monitored beyond the ``validation`` dataset.
raw_data_loader
- An object instance that can load samples and labels from storage.
+ An object instance that can load samples from storage.
**kwargs
List of named parameters matching those of
:py:class:`ConcatDataModule`, other than ``splits``.
diff --git a/src/mednet/libs/common/data/split.py b/src/mednet/libs/common/data/split.py
index 64c23fed4a8811ed788d2f4edb0ecab1c43b308d..14d0ac084533d0e79d327e23ec66d4a4b863db8c 100644
--- a/src/mednet/libs/common/data/split.py
+++ b/src/mednet/libs/common/data/split.py
@@ -121,7 +121,7 @@ def check_database_split_loading(
name of a dataset in the split. Each value is a (potentially complex)
object that represents a single sample.
loader
- A loader object that knows how to handle full-samples or just labels.
+ A loader object that knows how to handle full-samples.
limit
Maximum number of samples to check (in each split/dataset
combination) in this dataset. If set to zero, then check
diff --git a/src/mednet/libs/common/data/typing.py b/src/mednet/libs/common/data/typing.py
index 48cce9e780dd1be756d5f7b85947feda2b7dc0be..521c8026f57dcc797bae31c239a3357a123f835c 100644
--- a/src/mednet/libs/common/data/typing.py
+++ b/src/mednet/libs/common/data/typing.py
@@ -17,12 +17,12 @@ First parameter
Second parameter
A dictionary containing a named set of meta-data. One the most common is
- the ``label`` entry.
+ the ``target`` entry.
"""
class RawDataLoader:
- """A loader object can load samples and labels from storage."""
+ """A loader object can load samples from storage."""
def sample(self, _: typing.Any) -> Sample:
"""Load whole samples from media.
@@ -77,9 +77,9 @@ class Dataset(torch.utils.data.Dataset[Sample], typing.Iterable, typing.Sized):
provide a dunder len method.
"""
- def labels(self) -> list[int | list[int]]:
- """Return the integer labels for all samples in the dataset."""
- raise NotImplementedError("You must implement the `labels()` method")
+ def targets(self) -> list[int | list[int]]:
+ """Return the integer targets for all samples in the dataset."""
+ raise NotImplementedError("You must implement the `targets()` method")
DataLoader: typing.TypeAlias = torch.utils.data.DataLoader[Sample]
diff --git a/src/mednet/libs/segmentation/config/data/drive/datamodule.py b/src/mednet/libs/segmentation/config/data/drive/datamodule.py
index 2ef7d1cbd97dd0361ad7d00221b016c3e4d77ab1..893cbbb8682fa248cc5eaf642ed404ad25d804d6 100644
--- a/src/mednet/libs/segmentation/config/data/drive/datamodule.py
+++ b/src/mednet/libs/segmentation/config/data/drive/datamodule.py
@@ -42,9 +42,8 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
Parameters
----------
sample
- A tuple containing the path suffix, within the dataset root folder,
- where to find the image to be loaded, and an integer, representing the
- sample label.
+ A tuple containing path suffixes to the sample image, target, and mask
+ to be loaded, within the dataset root folder.
Returns
-------
@@ -55,7 +54,7 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
mode="RGB"
)
tensor = tv_tensors.Image(to_tensor(image))
- label = tv_tensors.Image(
+ target = tv_tensors.Image(
to_tensor(
PIL.Image.open(Path(self.datadir) / str(sample[1])).convert(
mode="1", dither=None
@@ -70,7 +69,7 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
)
)
- return tensor, dict(label=label, mask=mask, name=sample[0]) # type: ignore[arg-type]
+ return tensor, dict(target=target, mask=mask, name=sample[0]) # type: ignore[arg-type]
def make_split(basename: str) -> DatabaseSplit: