diff --git a/src/ptbench/configs/models/pasa.py b/src/ptbench/configs/models/pasa.py
index 47324199ff6daf3475d96e5999063621493b985b..49ee76dbd002a9fbd13e99d052eef223c17a19ac 100644
--- a/src/ptbench/configs/models/pasa.py
+++ b/src/ptbench/configs/models/pasa.py
@@ -13,22 +13,25 @@ Reference: [PASA-2019]_
from torch import empty
from torch.nn import BCEWithLogitsLoss
+from torch.optim import Adam
-from ...data.transforms import ElasticDeformation
from ...models.pasa import PASA
-# config
-optimizer_configs = {"lr": 8e-5}
-
# optimizer
-optimizer = "Adam"
+optimizer = Adam
+optimizer_configs = {"lr": 8e-5}
# criterion
criterion = BCEWithLogitsLoss(pos_weight=empty(1))
criterion_valid = BCEWithLogitsLoss(pos_weight=empty(1))
+from ...data.transforms import ElasticDeformation
+
augmentation_transforms = [ElasticDeformation(p=0.8)]
+# from torchvision.transforms.v2 import ElasticTransform, InterpolationMode
+# augmentation_transforms = [ElasticTransform(alpha=1000.0, sigma=30.0, interpolation=InterpolationMode.NEAREST)]
+
# model
model = PASA(
criterion,
diff --git a/src/ptbench/data/datamodule.py b/src/ptbench/data/datamodule.py
index 8c297823fb649d6f750c2799416504b335b24f60..fc9883d2f89c008fe7fb6c52ed62dea8664df17b 100644
--- a/src/ptbench/data/datamodule.py
+++ b/src/ptbench/data/datamodule.py
@@ -13,6 +13,8 @@ import torch
import torch.utils.data
import torchvision.transforms
+from tqdm import tqdm
+
logger = logging.getLogger(__name__)
@@ -150,8 +152,13 @@ class _CachedDataset(torch.utils.data.Dataset):
typing.Callable[[torch.Tensor], torch.Tensor]
] = [],
):
- self.transform = torchvision.transforms.Compose(*transforms)
- self.data = [raw_data_loader(k) for k in split]
+ # Cannot unpack empty list
+ if len(transforms) > 0:
+ self.transform = torchvision.transforms.Compose([*transforms])
+ else:
+ self.transform = torchvision.transforms.Compose([])
+
+ self.data = [raw_data_loader(k) for k in tqdm(split)]
def __getitem__(self, key: int) -> tuple[torch.Tensor, typing.Mapping]:
tensor, metadata = self.data[key]
@@ -446,6 +453,7 @@ class CachingDataModule(lightning.LightningDataModule):
logger.info(f"Dataset {name} is already setup. Not reloading it.")
return
if self.cache_samples:
+ logger.info(f"Caching {name} dataset")
self._datasets[name] = _CachedDataset(
self.database_split[name],
self.raw_data_loader,
diff --git a/src/ptbench/data/dataset.py b/src/ptbench/data/dataset.py
index af8a736454608961535caee8cddb432a42199410..3529ee7f0a14c0fc03a01f9d02348cf09a43cb20 100644
--- a/src/ptbench/data/dataset.py
+++ b/src/ptbench/data/dataset.py
@@ -10,11 +10,11 @@ import torch.utils.data
logger = logging.getLogger(__name__)
-def _get_positive_weights(dataset):
+def _get_positive_weights(dataloader):
"""Compute the positive weights of each class of the dataset to balance the
BCEWithLogitsLoss criterion.
- This function takes as input a :py:class:`torch.utils.data.dataset.Dataset`
+ This function takes as input a :py:class:`torch.utils.data.DataLoader`
and computes the positive weights of each class to use them to have
a balanced loss.
@@ -22,9 +22,8 @@ def _get_positive_weights(dataset):
Parameters
----------
- dataset : torch.utils.data.dataset.Dataset
- An instance of torch.utils.data.dataset.Dataset
- ConcatDataset are supported
+ dataloader : :py:class:`torch.utils.data.DataLoader`
+ A DataLoader from which to compute the positive weights. Must contain a 'label' key in the metadata returned by __getitem__().
Returns
@@ -35,14 +34,8 @@ def _get_positive_weights(dataset):
"""
targets = []
- if isinstance(dataset, torch.utils.data.ConcatDataset):
- for ds in dataset.datasets:
- for s in ds._samples:
- targets.append(s["label"])
-
- else:
- for s in dataset._samples:
- targets.append(s["label"])
+ for batch in dataloader:
+ targets.extend(batch[1]["label"])
targets = torch.tensor(targets)
@@ -71,33 +64,3 @@ def _get_positive_weights(dataset):
)
return positive_weights
-
-
-def reweight_BCEWithLogitsLoss(datamodule, model, criterion, criterion_valid):
- from torch.nn import BCEWithLogitsLoss
-
- datamodule.prepare_data()
- datamodule.setup(stage="fit")
-
- train_dataset = datamodule.train_dataset
- validation_dataset = datamodule.validation_dataset
-
- # Redefine a weighted criterion if possible
- if isinstance(criterion, torch.nn.BCEWithLogitsLoss):
- positive_weights = _get_positive_weights(train_dataset)
- model.hparams.criterion = BCEWithLogitsLoss(pos_weight=positive_weights)
- else:
- logger.warning("Weighted criterion not supported")
-
- if validation_dataset is not None:
- # Redefine a weighted valid criterion if possible
- if (
- isinstance(criterion_valid, torch.nn.BCEWithLogitsLoss)
- or criterion_valid is None
- ):
- positive_weights = _get_positive_weights(validation_dataset)
- model.hparams.criterion_valid = BCEWithLogitsLoss(
- pos_weight=positive_weights
- )
- else:
- logger.warning("Weighted valid criterion not supported")
diff --git a/src/ptbench/engine/callbacks.py b/src/ptbench/engine/callbacks.py
index d0ac43f98e21b8ce6803797d6a1fde38c6302660..580cc26fd24efc6ebb98fbcbb02d51348c6c11ec 100644
--- a/src/ptbench/engine/callbacks.py
+++ b/src/ptbench/engine/callbacks.py
@@ -94,9 +94,7 @@ class LoggingCallback(Callback):
self.log("total_time", current_time)
self.log("eta", eta_seconds)
self.log("loss", numpy.average(self.training_loss))
- self.log(
- "learning_rate", pl_module.hparams["optimizer_configs"]["lr"]
- )
+ self.log("learning_rate", pl_module.optimizer_configs["lr"])
self.log("validation_loss", numpy.sum(self.validation_loss))
if len(self.extra_validation_loss) > 0:
diff --git a/src/ptbench/engine/trainer.py b/src/ptbench/engine/trainer.py
index 6b156f86822209445a709c0af99b8b1eb14c55dc..ecf29153bc56c84185a905edc97867b0730a0a57 100644
--- a/src/ptbench/engine/trainer.py
+++ b/src/ptbench/engine/trainer.py
@@ -51,20 +51,24 @@ def save_model_summary(
Returns
-------
- r
+ summary:
The model summary in a text format.
- n
+ total_parameters:
The number of parameters of the model.
"""
summary_path = os.path.join(output_folder, "model_summary.txt")
logger.info(f"Saving model summary at {summary_path}...")
with open(summary_path, "w") as f:
- summary = lightning.pytorch.callbacks.ModelSummary(model, max_depth=-1)
+ summary = lightning.pytorch.utilities.model_summary.ModelSummary(
+ model, max_depth=-1
+ )
f.write(str(summary))
return (
summary,
- lightning.pytorch.callbacks.ModelSummary(model).total_parameters,
+ lightning.pytorch.utilities.model_summary.ModelSummary(
+ model
+ ).total_parameters,
)
diff --git a/src/ptbench/models/pasa.py b/src/ptbench/models/pasa.py
index 76327670d047900f10b8a94d90a7bc95a44fe2a0..e93b4e61f3bd5c9a0259e7d21728362f8e26999a 100644
--- a/src/ptbench/models/pasa.py
+++ b/src/ptbench/models/pasa.py
@@ -9,6 +9,7 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
+import torchvision.transforms
logger = logging.getLogger(__name__)
@@ -31,7 +32,15 @@ class PASA(pl.LightningModule):
self.name = "pasa"
- self.augmentation_transforms = augmentation_transforms
+ self.augmentation_transforms = torchvision.transforms.Compose(
+ augmentation_transforms
+ )
+
+ self.criterion = criterion
+ self.criterion_valid = criterion_valid
+
+ self.optimizer = optimizer
+ self.optimizer_configs = optimizer_configs
self.normalizer = None
@@ -137,7 +146,7 @@ class PASA(pl.LightningModule):
Parameters
----------
- dataloader:
+ dataloader: :py:class:`torch.utils.data.DataLoader`
A torch Dataloader from which to compute the mean and std
"""
from .normalizer import make_z_normalizer
@@ -148,6 +157,35 @@ class PASA(pl.LightningModule):
)
self.normalizer = make_z_normalizer(dataloader)
+ def set_bce_loss_weights(self, datamodule):
+ """Reweights loss weights if BCEWithLogitsLoss is used.
+
+ Parameters
+ ----------
+
+ datamodule:
+ A datamodule implementing train_dataloader() and val_dataloader()
+ """
+ from ..data.dataset import _get_positive_weights
+
+ if isinstance(self.criterion, torch.nn.BCEWithLogitsLoss):
+ logger.info("Reweighting BCEWithLogitsLoss training criterion.")
+ train_positive_weights = _get_positive_weights(
+ datamodule.train_dataloader()
+ )
+ self.criterion = torch.nn.BCEWithLogitsLoss(
+ pos_weight=train_positive_weights
+ )
+
+ if isinstance(self.criterion_valid, torch.nn.BCEWithLogitsLoss):
+ logger.info("Reweighting BCEWithLogitsLoss validation criterion.")
+ validation_positive_weights = _get_positive_weights(
+ datamodule.val_dataloader()["validation"]
+ )
+ self.criterion_valid = torch.nn.BCEWithLogitsLoss(
+ pos_weight=validation_positive_weights
+ )
+
def training_step(self, batch, _):
images = batch[0]
labels = batch[1]["label"]
@@ -158,12 +196,11 @@ class PASA(pl.LightningModule):
labels = torch.reshape(labels, (labels.shape[0], 1))
# Forward pass on the network
- augmented_images = self.augmentation_transforms(images)
+ augmented_images = [self.augmentation_transforms(img) for img in images]
+ augmented_images = torch.unsqueeze(torch.cat(augmented_images, 0), 1)
outputs = self(augmented_images)
- # Manually move criterion to selected device, since not part of the model.
- self.hparams.criterion = self.hparams.criterion.to(self.device)
- training_loss = self.hparams.criterion(outputs, labels.double())
+ training_loss = self.criterion(outputs, labels.double())
return {"loss": training_loss}
@@ -179,11 +216,7 @@ class PASA(pl.LightningModule):
# data forwarding on the existing network
outputs = self(images)
- # Manually move criterion to selected device, since not part of the model.
- self.hparams.criterion_valid = self.hparams.criterion_valid.to(
- self.device
- )
- validation_loss = self.hparams.criterion_valid(outputs, labels.double())
+ validation_loss = self.criterion_valid(outputs, labels.double())
if dataloader_idx == 0:
return {"validation_loss": validation_loss}
@@ -233,9 +266,5 @@ class PASA(pl.LightningModule):
# raise NotImplementedError
def configure_optimizers(self):
- # Dynamically instantiates the optimizer given the configs
- optimizer = getattr(torch.optim, self.hparams.optimizer)(
- self.parameters(), **self.hparams.optimizer_configs
- )
-
+ optimizer = self.optimizer(self.parameters(), **self.optimizer_configs)
return optimizer